genetic variation in bitter taste perception ......salty taste is thought to help maintain sodium...

GENETIC VARIATION IN BITTER TASTE PERCEPTION,

FOOD PREFERENCE AND DIETARY INTAKE

by

Christine Rose Asik

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

© Copyright by Christine Rose Asik (2010)

ii

GENETIC VARIATION IN BITTER TASTE PERCEPTION, FOOD

PREFERENCE AND DIETARY INTAKE

Christine Rose Asik

Master of Science

Graduate Department of Nutritional Sciences

University of Toronto

2010

Abstract

The role of variation in the TAS2R50 bitter taste receptor gene is unknown, but may influence

taste perception and dietary habits. Individuals (n=1171) aged 20 to 29, from the Toronto

Nutrigenomics and Health Study, completed a food preference checklist and a semi-quantitative

food frequency questionnaire to assess their preference and intake of potentially bitter foods and

beverages. DNA was isolated from blood and genotyped for 3 polymorphisms in the TAS2R50

gene (rs2900554 A>C; rs10772397 A>G; rs1376251 A>G). Taste intensity was examined using

taste strips infused with 3µg of naringin. The rs2900554 SNP was associated with naringin taste

intensity, grapefruit preference and grapefruit intake in females. Homozygotes for the C allele

reported the highest frequency of experiencing a high naringin taste intensity, disliking grapefruit

and not consuming grapefruit. The rs10772397 and rs1376251 SNPs were associated with

disliking grapefruit. These results suggest that naringin may be a ligand for the T2R50 receptor.

iii

ACKNOWLEDGEMENTS

I would like to thank my supervisor Dr. Ahmed El-Sohemy for believing in my abilities

as a graduate student and researcher. You are an outstanding mentor. Thank you for your

unconditional guidance and support, and for providing an environment that encouraged scientific

exploration and personal growth. Without you none of this would be possible.

I am also deeply grateful for the guidance and feedback I received from my advisory

committee, Dr. Anthony Hanley and Dr. Elena Comelli. You are truly caring and patient

teachers. Dr. Anthony Hanley, thank you for helping me develop the critical thought needed in

research. Furthermore, thank you to the Bazinet lab and Archer lab for their feedback on my

research presentations.

I feel blessed to have had the opportunity to work alongside the intelligent, kind and hard

working individuals in Dr. El-Sohemy‟s lab. Leah Cahill and Karen Eny, you are exceptional

researchers and mentors. Thank you for the great amount of guidance and support you provided

me with my thesis. Sarah Herd and Joanne Brathwaite, thank you for sharing with me your

knowledge in genetics. Erica Day-Tasevski, Francesca Garofalo and Cristina Cuda, I am so

grateful I got to begin graduate school with all of you. Thank you for all the encouragement,

advice and friendship this year. Hyeon-Joo Lee, thank you greatly for the much needed lab

support and Daiva Nielsen, thank you for coordinating the Toronto Nutrigenomics and Health

Study. Darren Brenner, thank you for your advice in statistics. Bibiana Garcia Bailo, Laura

DaCosta and Andre Dias thank you for your friendship and support. It has been a joy working

with all of you.

I would like to extend a heartfelt thank you to my father and mother, Dr. and Mrs. Masis

and Elizabeth Asik, for always believing in me and encouraging me to try my hardest in all my

endeavors. I would like to further thank my mother for spending countless hours proof-reading

my thesis. I am grateful to have such a wonderful and supportive family.

iv

TABLE OF CONTENTS

Abstract ........................................................................................................................................... ii

Acknowledgements ........................................................................................................................ iii

Table of contents ............................................................................................................................ iv

Lists of tables ................................................................................................................................ vii

Lists of figures ............................................................................................................................... ix

Lists of abbreviations ....................................................................................................................... x

CHAPTER 1: INTRODUCTION .................................................................................................... 1

CHAPTER 2: LITERATURE REVIEW ......................................................................................... 4

2. 1 Taste .......................................................................................................................................... 5

2. 1. 1 Bitter compounds ............................................................................................................. 6

2. 1. 2 Bitter taste perception, dietary habits and health ............................................................. 6

2. 2 Bitter taste assessment .............................................................................................................. 8

2. 3 Food preference assessment .................................................................................................... 10

2. 4 Dietary intake assessment ....................................................................................................... 11

2. 5 Taste anatomy ......................................................................................................................... 12

2. 5. 1 T2R Bitter taste receptor family ..................................................................................... 13

2. 5. 2 Taste genotype – phenotype association ........................................................................ 16

2. 5. 3 TAS2R50 ......................................................................................................................... 16

2. 6 Grapefruit and naringin ........................................................................................................... 18

v

2. 7 Summary ................................................................................................................................. 19

CHAPTER 3: RATIONALE, HYPOTHESIS AND OBJECTIVES ............................................. 21

CHAPTER 4: MATERIALS AND METHODS ........................................................................... 23

4.1 Participants ............................................................................................................................... 24

4.2 Study protocol .......................................................................................................................... 25

4.3 Bitter taste assessment ............................................................................................................. 26

4.4 Food preference assessment ..................................................................................................... 26

4.5 Dietary intake assessment ........................................................................................................ 27

4.6 Anthropometrics ...................................................................................................................... 27

4.7 Physical activity questionnaire ................................................................................................ 28

4.8 Genotyping ............................................................................................................................... 28

4.9 Statistical analyses ................................................................................................................... 29

CHAPTER 5: RESULTS ............................................................................................................... 33

5.1 Genotype frequency, allele frequency and Hardy Weinberg Equilibrium ............................. 34

5.2 Linkage disequilibrium ............................................................................................................ 34

5.3 Subject Characteristics ............................................................................................................. 35

5.4 TAS2R50 genotype and food preference .................................................................................. 35

5.5 TAS2R50 genotype and dietary intake ..................................................................................... 38

5.6 TAS2R50 genotype and taste intensity ..................................................................................... 40

vi

5.7 Difference in food preferences between Caucasians and East Asians ..................................... 41

CHAPTER 6: DISCUSSION ......................................................................................................... 68

6. 1 Limitations .............................................................................................................................. 74

6. 2 Future direction ....................................................................................................................... 78

6. 3 Implications ............................................................................................................................. 79

6. 4 Conclusions ............................................................................................................................. 80

REFERENCES .............................................................................................................................. 81

vii

LIST OF TABLES

Table 1: Taste receptor genes and their ligands ............................................................................. 15

Table 2: Variable type and definition ............................................................................................ 32

Table 3: Genotype frequency, allele frequency and Hardy Weinberg Equilibrium value by

TAS2R50 genotype in the total population and in Caucasians and East Asians and other

ethnocultural groups ....................................................................................................................... 42

Table 4: Subject characteristics for East Asians and Caucasians .................................................. 45

Table 5: The frequency and odds of disliking grapefruit and grapefruit juice by TAS2R50

genotype in Caucasians and Asians ............................................................................................... 46

Table 6: The frequency and odds of disliking vegetables by TAS2R50 genotype in Caucasians

and East Asians .............................................................................................................................. 47

Table 7: The frequency and odds of disliking soy products by TAS2R50 genotypes in Caucasians

and East Asians .............................................................................................................................. 52

Table 8: The frequency and odds of disliking cocoa containing products by TAS2R50 genotype in

Caucasians and East Asians ........................................................................................................... 53

Table 9: The frequency and odds of disliking alcoholic beverages by TAS2R50 genotype in


Table 10: The frequency and odds of disliking caffeinated beverages by TAS2R50 genotype in


viii

Table 11: The frequency of disliking grapefruit by rs2900554 SNP and rs1376251 SNP genotype

combinations in Caucasians and East Asians ................................................................................ 56

Table 12: The frequency and odds of not consuming grapefruit in the past month by TAS2R50

genotype in Caucasians and East Asians ....................................................................................... 57

Table 13: The frequency and odds of not consuming grapefruit juice in the past month by

TAS2R50 genotype in Caucasians and East Asians ....................................................................... 58

Table 14: The frequency and odds of not consuming raw spinach, cooked spinach or Swish

chard, and kale, mustard, collard or turnip greens in the past month by TAS2R50 genotype in


Table 15: The frequency and odds of not consuming soymilk and chocolate in the past month by

TAS2R50 genotype in Caucasians and East Asians ....................................................................... 60

Table 16: Frequency and odds of high naringin or PTC intensity by TAS2R50 genotype in


Table 17: The frequency and odds of disliking fruits and vegetables by ethnocultural group ...... 62

Table 18: The frequency and odds of disliking soy products by ethnocultural group ................... 64

Table 19: The frequency and odds of disliking cocoa-containing products by ethnocultural group

........................................................................................................................................................ 65

Table 20: The frequency and odds of disliking alcoholic beverages by ethnocultural group ....... 66

Table 21: The frequency and odds of disliking caffeinated beverages by ethnocultural group .... 67

ix

LIST OF FIGURES

Figure 1: Scatter plot of TAS2R50 genotype for the rs2900553 SNP ............................................ 43

Figure 2: Linkage disequilibrium between TAS2R50 SNPs in the total population and in

Caucasians, East Asians and other ethnocultural groups ............................................................... 44

x

LIST OF ABBREVIATIONS

A adenine

AVI alanine, valine, isoleucine

A49P alanine 49 proline

BMI body mass index

C cytosine

CI confidence interval

CVD cardiovascular disease

CYP3A4 cytochrome P450 3A4

DNA Deoxyribonucleic acid

FFQ food frequency questionnaire

FPC food preference checklist

GHLQ general health and lifestyle questionnaire

gLMS generalized Linear Magnitude Scale

GPCR G-protein coupled receptor

HEK human embryonic kidney cells

HR hazard ratio

HWE Hardy Weinberg Equilibrium

I236V isoleucine 236 valine

LDL low density lipoprotein

MET metabolic equivalent of task

MI myocardial infarction

OR odds ratio

PAQ physical activity questionnaire

xi

PAV proline, alanine, valine

PROP 6-n-proplythiouracil

PTC phenylthiocarbamide

SNP single nucleotide polymorphism

T2R taste receptor, type 2, receptor

TAS2R taste receptor, type 2, gene

T2R50 taste receptor, type 2, member 50, receptor

TAS2R50 taste receptor, type 2, member 50, gene

TRC taste receptor cell

TNH Toronto Nutrigenomics and Health Study

UTR untranslated region

V26A valine 26 alanine

1

Chapter 1

Introduction

2

Food selection is influenced by a number of physiological, environmental, economic

and sociocultural factors1, however taste may be the primary determinate of food selection

2.

Bitter taste is thought to act as a warning sensor for noxious compounds3. Foods that are

perceived as extremely bitter are considered unpalatable and avoided3,4

. However, several

nutritious foods, such as some fruits and vegetables, contain bitter compounds and many of

these bitter compounds are phytonutrients5. Diets rich in phytonutrients have been associated

with a lower risk of heart disease and cancer6,7

. Individuals who perceive bitter compounds as

more intense may avoid the consumption of bitter foods and compromise their health.

Bitter taste is a variable trait both within and between populations8-10

. Variability in

bitter taste is influenced by genetic variation in the T2R bitter taste receptor gene family, which

mediate bitter taste perception11,12

. A large amount of variation exists within the 25 functional

bitter taste receptor genes, however very little is known about its affect on bitter taste

perception, dietary habits and health13

. Furthermore, more than half of the 25 functional bitter

taste receptor genes have not been deorphaned13

, limiting the advancement in the understanding

of the mode of bitter taste perception.

Recent studies have associated a adenine to guanine (rs1376251, A/G) single

nucleotide polymorphism (SNP) in the taste receptor, type 2, member 50 (TAS2R50) gene with

myocardial infarction (MI) risk14,15

. Genetic variation in the TAS2R50 gene could lead to MI by

influencing bitter taste perception and in turn food preferences and intake14

. However, to our

knowledge no studies have examined the functional or behavioral significance of genetic

variation in the TAS2R50 gene. Naringin, the primary bitter constituent of grapefruit has been

shown to have cardioprotective properties16

. It is possible that the bitter taste of phytonutrients,

such as naringin, are influenced by genetic variation in the TAS2R50 gene, leading to altered

dietary intake and increased MI risk. Since cardiovascular disease (CVD) is the leading cause of

3

hospitalization in Canada17,18

, with MI accounting for the majority of CVD related deaths in

200217

, research is needed to elucidate the affects of genetic variation in the TAS2R50 gene on

taste perception, food preference and dietary intake.

4

Chapter Two

Literature Review

5

2.1 TASTE

The gustatory system regulates taste perception or gustation. Taste is categorized

into five taste modalities, which are sweet, bitter, sour, salty and umami (or savory) 4,12,19

.

Emergent evidence suggests that fat may be the sixth taste modality 20

. Taste perception helps

individuals evaluate the nutrient content of food and discriminate between safe and harmful

foods 4,12,19

. In humans, taste contributes to the overall enjoyment of a meal.

It is thought that the role of sweet taste is to identify calorie dense foods, while the role

of umami taste is to identify protein rich foods 12,19

. Salty taste is thought to help maintain

sodium and other mineral levels within the body4. Sour taste may serve to warn against the

ingestion of spoiled food or unripened fruit21

. While bitter taste is thought to warn against the

ingestion of toxic or poisonous compounds12,19

.

The assumption that bitter taste is a warning sensory for noxious compounds is due in

part by the large number of naturally occurring noxious compounds that taste bitter5. Many of

these compounds are produced by plants as natural defense mechanisms, such as toxic

glycosides or alkaloids22

. Natural physiological responses to bitter compounds also help

validate this hypothesis. The rejection of bitter taste is thought to be innate4. Young children and

nonhuman primates find bitter compounds aversive and react with stereotypic rejection

responses 3,23

. Pregnant women have a higher sensitivity to bitter taste in their first trimester,

possibly warning against the ingestion of toxic compounds during a critical period of fetal

development24

. Furthermore, the threshold detection levels of bitter tastants are considerably

lower than the other taste modalities ensuring even low levels of potentially harmful compounds

are identified and avoided25

.

6

2. 1. 1 Bitter compounds

A vast number of structurally diverse compounds elicit bitter taste. Bitter compounds

can be found in a large number of chemical groups, such as amino acids and peptides, fatty

acids, amines, amides, sulfimides, ureas and thioureas, alkaloids, glycosides, carbamides, esters,

lactones, phenolic compounds, terpenes, diterpenes, triterpens, crown ethers, metal ions, etc.4,5

.

Bitter taste is thought to help warn against the ingestion of noxious compounds however, many

bitter compounds have been shown to be beneficial to health5.

Bitter tasting phytonutrients such as phenols, flavonoids, isoflavones, terpenes and

glucosinolates found in plants have been shown to have potential antioxidant and anti-cancer

properties5,16,26,27

. Furthermore, many bitter compounds are found in nutritious foods28

including: isothiocyanates resulting from the breakdown of glucosinolate in cruciferous

vegetables, such as broccoli, watercress, cabbage, cauliflower, bok choy, arugula, radish and

kale; caroteniods found in spinach, carrots and tomatoes; limoniods and naringin found in citrus

fruit; phenol flavonoids found in tea, berries, wine, citrus fruit, endive, cranberries, onion and

kale; isoflavones found in miso, soymilk, soy nut, tofu and licorice; phenolic acids found in tea,

berries, orange, grapefruit, grape juice and coffee; and polyphenols found in wine 4,27

. Other

common foods and beverages found to elicit bitter taste responses are sharp cheeses29

and beer

28. Individuals who perceive bitter compounds as more intense may avoid the consumption of

bitter foods and affect their nutritional or health status.

2. 1. 2 Bitter taste perception, dietary habits and health

Bitter taste perception is a variable trait both within and between populations8-10,30

. The

best known examples of variation in bitter taste perception are those of phenylthiocarbaminde

(PTC) and 6-n-proplythiouracil (PROP). PTC and PROP are structurally similar compounds

7

containing a thiourea group (N-C=S) that is responsible for their bitter taste3. About 25% of

individuals worldwide are unable to taste PTC and PROP, with the prevalence of “nontasters”

varying between ethnocultural groups31

. It has been reported that 30% of North American

Caucasians, 3% of West Africans, 6 to 23% of Chinese Asians and 40% of Indians are unable to

taste PTC and PROP28,32,33

. Tasters of PTC and PROP can be further subdivided into two groups

based on their perceived sensitivity to the bitter taste of PTC or PROP, “medium tasters” and

“supertasters”10

.

Variations in bitter taste intensity have been associated with differences in food

preference and intake in multiple populations. Sensitivity to PROP taste has been associated

with lower preferences for a variety of bitter foods such as cruciferous vegetables34

, Brussels

sprouts, cabbage, spinach, coffee 35

, asparagus, kale36

, grapefruit juice9, and beer

37.

Furthermore, dietary intake of raw watercress, cooked turnip 38

, olives, cucumber and broccoli 30

have been shown to be less in PROP taster individuals within certain populations. Although

several studies examining bitter taste sensitivity have found significant associations between

PTC/PROP bitter taste sensitivity and food preference and/or intake, these studies have been

inconsistent or non-significant warranting further investigation in the association between bitter

taste intensity and food acceptance and selection 39-42

. Most bitter taste research is focused on

PTC/PROP sensitivity and little is known about how variation in sensitivity to other bitter

tastants affects food preferences and dietary intake.

Variation in bitter taste intensity may influence disease risk, however studies

examining bitter taste sensitivity and disease risk are limited. PROP sensitivity was found to be

positively associated with number of colon polyps, a risk factor for colon cancer, in men

undergoing endoscopy8. In that study, men who were sensitive to PROP consumed the lowest

number of vegetable servings/day, suggesting that PROP sensitivity is related to vegetable

8

intake and colon cancer risk8. Cardiovascular disease

39 or lipid profile

41 have not been found to

be associated with bitter taste intensity. However, differences in bitter taste intensity have been

associated with body weight, which is a risk factor for cardiovascular disease43

. PROP nontaster

women were found to have a higher body mass index, percent body weight and triceps skinfold

thickness compared to supertasters of PROP in a group of overweight women43

. This may be

because PTC/PROP supertasters generally have a higher taste acuity, perceiving sucrose44

,

sourness45

, saltiness46

and oral irritants47

as more intense and having a greater ability to

differentiate between high and low fat foods, than PTC/PROP nontasters. It has been suggested

that this higher taste acuity may promote a lower intake of all foods3. However, studies

examining the association between BMI and PROP sensitivity in different populations have not

always found comparable or significant associations40,48,49

. To our knowledge, all studies

examining the association between health and bitter taste intensity have focused on PTC/PROP

sensitivity. Further studies are needed to investigate if sensitivity to other bitter tastants affects

disease risk.

2. 2 BITTER TASTE ASSESSMENT

One of the challenges in assessing bitter taste perception is that one cannot know for

certain the perceived bitter taste intensity or sensitivity experienced by another individual,

however many methods have been devised to help estimate an individual‟s bitter taste

experience. Bitter taste assessment methods can be divided into two categories, threshold

determination and suprathreshold assessment3. Threshold determination is a process used to

determine the lowest concentration of a bitter compound that an individual can detect3.

Suprathreshold assessment is the measurement of an individual‟s perceived bitter taste intensity

of a bitter compound above threshold detection levels3.

9

Traditionally, PTC/PROP threshold detection levels were used to categorize individuals

into two groups, tasters of PTC/PROP and non-tasters, based on the distribution of PTC/PROP

threshold levels50,51

. However, when suprathreshold assessment of PTC/PROP were examined it

became clear that threshold assessment of PTC/PROP did not adequately explain all variation in

the bitter taste perception of these compounds50

. It was found that PTC/PROP “taster”

individuals who had similar threshold detection levels varied in their perception of bitter taste

intensity when suprathreshold levels of PTC/PROP were assessed, which helped separate tasters

into “medium tasters” and “supertasters”51

. Since this discovery suprathreshold assessment has

been the favored method of bitter taste perception assessment.

Suprathreshold assessment is the measurement of an individual‟s perceived bitter taste

intensity of a bitter compound above threshold detection levels3. A large diversity of

suprathreshold assessment methods exist. Suprathreshold assessment methods track an

individual‟s perceived taste intensity over several concentrations of a bitter compound or

estimate the intensity of a bitter compound at a single concentration3. During these tests the

bitter compound may be presented as a solute in water or infused on filter papers which are

placed on the tongue3. The filter paper method of bitter tastant delivery is simple, cost effective

and suited for large epidemiological studies52,53

. Recently, a method of impregnating filter

papers has been devised that delivers a constant concentration of bitter tastant across filter

papers53

. Furthermore, the filter paper method has been shown to be a reliable and valid way to

assess taste intensity and identify PTC/PROP taster status, when compared to the three-solution

test, which is considered the standard53

. Taken together, the suprathreshold assessment method

of bitter taste perception using the filter paper method of tastant delivery may be the most

efficient and cost effective method of bitter taste assessment in large epidemiological studies.

10

2. 3 FOOD PREFERENCE ASSESSMENT

Food preference assessment is used to evaluate liking or disliking of a food54

. Taste

perception is generally assumed to predict food preference, however this assumption is not

always correct54

. Numerous factors influence food preference such as sensory attributes of food

(taste, texture, odor and appearance)55

as well as an individual‟s age56

, gender57

, ethnocultural

group58

, BMI54

and health consciousness54

.

There are two methods of food preference assessment. Food preference can be assessed

using taste tests of real food in a controlled laboratory environment or using a questionnaire54

.

Food preference questionnaires contain a list of foods and ask subjects to rate their liking or

disliking of the specific food on a hedonic scale. Food preference questionnaires have been

criticized because they rely on a subject‟s memory of a past sensory experience and may reflect

a subject‟s attitude towards the food rather than the taste of the food59

. However, a food

preference questionnaire is the ideal method of food preference assessment in large

epidemiologic studies because of its ease of use, low cost and ability to assess the preference of

a large number of foods.

The most widely used scale to assess food preference is the 9-point hedonic scale60

.

This 9-point scale is an ordinal scale ranging from 1= dislike extremely to 9=like extremely with

a neutral midpoint of 5=neither like nor dislike61

. A considerable amount of research was

invested into identifying the appropriate number of categories, verbal category descriptors and

scale midpoint, in order to achieve an assessment tool that was easy, quick to use, sensitive,

reliable and uniformly understood61

. The 9-point hedonic scale has been criticized because it

does not exhibit ratio properties, may reduce variability in responses due to the ceiling effect

and may not be uniformly understood due to the use of verbal descriptors60

. However, the

degree of variability in response, reliability of the scale, ease of use and ability to discriminate

11

preference between samples when using the 9-point hedonic scale was comparable and

occasionally superior to other food preference scales (line, scanner and magnitude estimation)62

.

Furthermore, similar food preference results are obtained with the use of the 9-point hedonic

scale and the labeled affective magnitude scale60

. The labeled affective magnitude scale is a

category scale with possible ratio properties and extreme anchors that may avoid limitations due

to the ceiling effect60

. These results indicate that the 9-point scale may be the most appropriate

scale for subject‟s to record food preference responses.

2. 4 DIETARY INTAKE ASSESSMENT

Dietary intake assessment is used to measure actual food consumption or estimate

habitual consumption over a specific duration of time. Food selection is a complex behavior that

is influenced by physiological, environmental, economic and sociocultural factors1. Taste

perception and food preferences are thought to have a strong influence on food selection54

. A

linear relationship is thought to exist between taste perception, food preferences and dietary

intake, however few studies have examined this relationship in the same population54

.

There are several tools used to assess diet including diet histories, diet records, 24-hour

recalls and food frequency questionnaires (FFQ). A diet history does not follow a set template,

but is used to obtain information about an individual‟s past dietary habits63

. A food record

requires trained individuals to record all foods and beverages consumed as they are consumed,

usually over a series of 3 to 5 days, with at least one weekend day64

. This method may require

preparation methods, eating times and brand names to be reported as well as food and beverage

portions to be measured or estimated63

. A 24-hour recall is administered by a trained interviewer

who asks subjects to recall all foods and beverages consumed and their portion sizes,

preparation methods and brand names, within a 24 hour period64

. If this method is used to

12

estimate usual consumption over a study period, multiple 24-hour recalls must be administered

since daily diet is highly variable64

. The FFQ measures usual eating habits by requiring

individuals to record the frequency of consumption of foods and beverages from a list, over a

specific period of time (usually a month or year)63,64

.

FFQs have been criticized because they collect less detailed information on foods and

beverages, portion sizes and preparation methods, and require the use of memory65

. However,

FFQs are beneficial because they can be self-administered and processed quickly through

optical scanning techniques, which can significantly reduce time and costs63

. Respondent burden

has been reported to be lower for FFQs than multiple dietary records or 24-hour dietary

recalls63

. FFQs have been validated by comparison to 3 day food records or multiple 24-hour

recalls and correlation coefficients for food and nutrients between these methods have been

found to range between 0.4 and 0.7 63

. These benefits make the FFQ ideal for assessing habitual

dietary intake in large epidemiological studies.

2. 5 TASTE ANATOMY

Taste is mediated by taste receptors that are located on the surface of taste receptor

cells (TRCs). TRCs cluster together in groups of approximately 60 -100 to form onion shaped

taste buds12,13

. Taste buds are embedded predominantly in the endothelial surface of the tongue,

and to a lesser extent soft palate, pharynx, larynx, and epiglottis 12,13,19,66

. TRCs interact with

molecules in the oral cavity by way of microvilli which are located in a depression called the

taste pore within a taste bud4,19

. It is thought that each TRC detects a single taste modality67-70

Taste buds are distributed within specialized folds and protrusions on the tongue called

papillae. There are three types of papillae on the tongue, which are called circumvallate, foliate

and fungiform papillae4. There are about 3 to 18 circumvallate papillae distributed in a v-shape

13

at the root of the tongue that contain about 250 taste buds each4,19,71

. Foliate papillae are made

up of about 2 to 19 alternating ridges and crevices containing about 120 taste bud pre ridge that

are present on the back edges of the tongue4,19,71

. There are about 200-300 fungiform papillae

containing a total of approximately 1 to 3 taste buds each distributed across the tongue, with the

highest density at the tip of the tongue4,19,71

. Based on the differences in papillae location on the

tongue, a „taste map‟ of the tongue had been proposed for the five taste modalities4,19

. However,

these „taste maps‟ have been dated since research has shown that all five taste modalities can be

perceived in all areas of the tongue19,69,70,72

and variation in threshold sensitivity to tastes do not

vary to a great extent across the tongue4.

2. 5. 1 T2R Bitter taste receptor family

Bitter taste perception is mediated by about 25 functional bitter taste receptor genes

located on chromosomes 5, 7 and 12 that make up the T2R bitter taste receptor family 19

. The

T2R genes encode G protein coupled receptors (GPCR), that consist of about 300 amino acids,

that create a protein with seven transmembrane domains and a short extracellular N-terminus 12

.

The T2R receptor family is expressed on TRCs of the circumvallate and foliate papillae and to a

lesser extent fungiform papillae of the tongue 12

. The T2R receptor family is also found in TRCs

of the epiglottis and palate12

. Non-gustatory cells of the digestive 73

and respiratory tract 74

have

been found to express T2Rs creating the possibility that all T2R genes do not function as taste

receptors. However, a recent study detected the mRNA of all the functioning T2R genes in

human circumvallate papillae using reverse transcriptase-PCR analysis and in situ hybridization

methods, validating a possible gustatory function for all T2R genes 75

.

The pattern of expression of T2R genes within bitter TRCs is unclear. Previous studies

have examined if each bitter TRC is the same, expressing all taste receptor genes, or not. The

14

prevailing model is one in which bitter TRCs are heterogeneous, each expressing a subset of

bitter taste receptors 23

. Evidence for this model was identified by Behrens et al. using in situ

hybridization experiments that found that the level of expression of T2R genes and number of

TRCs expressing T2R genes differed75

.

It is not understood how approximately 25 taste receptor genes identify and transmit

sensory information from thousands of structurally diverse bitter compounds13

. Only a few taste

receptor proteins have had ligands identified (Figure 1), leaving a majority of the taste receptor

proteins orphaned13

. A few bitter taste receptors seem to identify bitter molecules with structural

similarities13

. For example, TAS2R16 and TAS2R38 bitter taste receptors identify bitter

molecules with β-D-glycopyranoside and thiourea moieties, respectively13

. However, other taste

receptors, such as TAS2R7, TAS2R14 and TAS2R46, seem to identify a broad range of

structurally diverse bitter compounds13

. In order to fully understand how sensory information is

coded by bitter taste receptors, further research is needed to deorphanize all remaining taste

receptors13

.

Bitter taste is perceived when bitter agonists in the oral cavity come into contact with

the microvilli of TRCs, which activate bitter taste receptors12,19,66

. An activated bitter taste

receptor activates α-gustducin and other G proteins which release their βγ subunits causing

phospholipase C β2 to increase intracellular levels of inositol 1,2,5, triphosphate13,23

. This leads

to a release of intracellular calcium stores that stimulate the transient receptor potential channel

5 causing a change in membrane potential and release of adenosine triphosphate13,76

. These

changes cause sensory information to be sent to the gustatory cortex of the brain by way of

afferent facial (VII), glossopharyngeal (IX) and vagus (X) cranial nerves 12

.

15

Table 1: Taste receptor genes and their ligands

Taste Receptor

Ligand

TAS2R4 Denatonium, high concentrations of 6-n-propylthiouracil

77

TAS2R7 Strychnine, quinacrine, chloroquine, papaverine78

TAS2R8 Saccharin79

TAS2R10 strychnine80

TAS2R14

Aristolochic acid78

, 1-naphthoic acid, picrotoxinin, (-)-α-thujone,

1,8 – naphthalaldehydic acid, 1-nitronaphthalene, picrotin,

piperonylic acid, sodium benzoate81

TAS2R16

β-D-glucopyranosides moieties (Salicin, phenyl- β-D-

glucopyranosides, helicon, arbutin, 2-ntiro-phenyl- β-D-

glucopyranosides, methyl- β-D-glucopyranosides, amygdalin,

esulin)80

TAS2R38 Phenylthiocarbamide, Propylthiouracil82

TAS2R43, TAS2R44 Saccharin, acesulfame K, aristolochic acid83

TAS2R46

Sesquiterpene lactones (absinthin, arborescin, arglabin,

artemorin, peroxy-artemorin, cnicin, costunolide, crispolide,

cynaropicrin, epizaluzannin C, germacradien-6,11-dihydroxy-

8,12-olide, grosheimin, herbolide D, herbolide D acetate, nobilin,

parthenin, parthenolide, prcrotin, pcirotoxnin, santamarine,

sinternin, speciformin acetate, tatridin A, tatridin A acetate,

tatridin D, taurin, dihydro-taurin, umbellifolide, vulgarolide,

zaluzannin D), Diterpenoids (andrographolide, cascarillin,

marrubiin, teuflavin, teuflavoside, teumarin), brucine,

chloramphenicol, denatonium benzoate, strychnine, strychnine-N-

oxide, sucrose octacetate84

TAS2R47 denatonium, 6-nitro-saccharin79

16

2. 5. 2 Taste genotype-phenotype association

The majority of studies on bitter taste perception have focused on the TAS2R38 gene.

The TAS2R38 gene is a bitter taste receptor gene that belongs to the T2R receptor family and is

located on chromosome 7q3485

. This gene consists of one single 1002-bp long exon that

transcribes a 333-amino acid protein that forms a 7-transmemebrane G-protein coupled receptor

with short N and C terminal domains and is expressed on the surface of taste receptor cells 11,86

.

PTC/PROP sensitivity has been used as a marker for genetic variation, however with

the discovery of the T2R gene family the phenotype of PTC/PROP sensitivity was attributed to

genetic variation in the TAS2R38 gene11

. It has been suggested that three single nucleotide

polymorphisms (SNPs) in the TAS2R38 gene can explain 50 to 85% of the variability in PTC

and to a lesser extent PROP sensitivity3,11,28,87

. The three SNPs result in proline/ alanine,

alanine/valine and valine/ isoleucine amino acid substitutions at position 49, 262 and 296 in the

taste receptor protein, respectively, and result from cytosine to guanine, cytosine to thymine and

guanine to adenine substitutions in the taste receptor gene, respectively 3,11,28

. The most common

haplotypes of the TAS2R38 gene, named in the order of the three amino acid substitutions

(A49P, V262A and I236V), are AVI and PAV, which have a frequency of 47% and 49% in a

European sample 3,11,28

. The remaining 4% are considered rare haplotypes11

. PAV homozygotes

exhibit the greatest sensitivity to PTC and PROP, AVI homozygotes exhibit the least sensitivity,

while PAV/AVI heterozygotes exhibit an intermediate sensitivity, corresponding to the

nontaster, medium taster and supertaster phenotypes3,11,28,82

.

2. 5. 3 TAS2R50

The TAS2R50 gene is a bitter taste receptor gene that belongs to the T2R receptor

family and is located on chromosome 12p13.2 88

. This gene contains one exonic region that

17

transcribes a 299-amino acid protein that forms a 7-transmembrane G-protein coupled receptor

with short N terminal domain expressed on the surface of TRCs 88

.

A polymorphism in the TAS2R50 gene has been associated with risk of myocardial

infarction (MI)14

. A three-stage genome-wide association study was undertaken to determine if

11,053 SNPs in 9,891 genes were associated with MI14

. This three-stage genome wide

association study included two case-control studies to identify possible SNPs associated with

MI and a third study to test the hypothesis that the genes identified in the first two studies are

associated with MI. It was found that a SNP (rs1376251, A/G) in the TAS2R50 gene was

associated with MI 14

. Individuals with the (GG) genotype were 59% more likely to have MI

compared to individuals with the (AA) genotype (p=0.007) 14

. When risk factors were adjusted,

a similar trend was seen (OR 1.40, p=0.06) 14

. The association between MI and the TAS2R50

gene (rs1376251, A/G) was further validated in a subsequent study using a population-based

prospective study of 4522 Caucasian individuals 65years or older, however a weaker association

was seen (HR 1.14, p=0.04) 15

. No association was found between MI and the TAS2R50 gene in

a population with familial hypercholesterolemia 89

.

Variation in the TAS2R50 gene could lead to MI by influencing dietary intake, however

no studies have examined this association. The adenine to guanine polymorphism that is

associated with MI (rs1376251) is located within the exonic region of the TAS2R50 gene and

causes a cysteine to tyrosine amino acid change at position 203 in the taste receptor protein85

.

This SNP may affect the structure of the taste receptor protein causing a change in ligand

binding potential and as a result taste perception. The frequency of the ancestral allele (G) has

been found to be 65%, 29% and 96% in a European, Han Chinese and sub-Saharan African

population, respectively85

. Shiffman et al.14

explained that the association between MI and the

TAS2R50 gene may not be due to the A/G SNP (rs1376251) because TAS2R50 is inherited in a

18

~500 kb haplotype block that contains 13 taste-receptor homologs and three genes that encode

proline-rich proteins, warranting further investigation into the TAS2R50 gene14

.

Other SNPs may have a potential to influence the TAS2R50 taste receptor and in turn

bitter taste perception. The (rs10772397) SNP is a A/G substitution in the coding region of the

TAS2R50 gene and produces a silent mutation at position 259 in the taste receptor protein 85

.

This can affect mRNA stability and TAS2R50 taste receptor production. The frequency of the

ancestral A allele in a European, Han Chinese and sub-Saharan African population is 61%, 73%

and 20%85

. An A/C SNP (rs2900554) located 3859 base pairs downstream of the TAS2R50 gene

can also affect bitter taste perception through post translational modification of the T2R50

protein. The frequency of the ancestral A allele in a European, Han Chinese and West African

population is 49%, 78% and 64%85

.

2. 6 GRAPEFRUIT AND NARINGIN

Naringin (4‟,5,7-trihydroxy-flavanone-7-rhamnoglucoside) is a bitter flavonoid found

in grapefruit that is primarily responsible for its characteristic bitter taste90

. Naringin is a

glycoside that is made up of the flavanone moiety naringenin and disaccharide β-

neohesperidose90

. There is about 400 to 700 mg/l of naringin in grapefruit juice and this amount

varies depending on the maturity of the fruit, fraction of fruit and cultivar9,90,91

.

Naringin has been shown to have several cardio-protective properties such as anti-

inflammatory, cholesterol lowering92

, antiatherogenic93

and antioxidant94

as well as anticancer

properties95,96

, and hepatoprotective effects97,98

. Furthermore, naringenin the metabolic

breakdown product of naringin, has been shown to improve insulin sensitivity and glucose

tolerance by increasing hepatic fatty acid oxidation, decreasing hepatic cholesterol synthesis and

preventing lipogenesis in LDL null mice fed a high fat diet99

. Grapefruit juice has been shown to

19

interact with over 50 drugs leading to typically an increase in circulating drug levels, by

inhibiting metabolism and/or affecting intestinal absorption of certain drugs100,101

. It is thought

that naringin plays a role in this interaction by inhibiting drug transport by P-glycoprotein102,103

and metabolism by CYP3A4104

. Bitter taste perception of naringin may affect an individual‟s

grapefruit and grapefruit juice consumption and influence their health.

The bitter taste perception of naringin varies between individuals. Threshold detection

levels of naringin in water ranges from 1.5 mg/ l to 50 mg/l105

. Significant variation is also seen

with naringin suprathreshold intensity ratings9. Drewnowski et. al. examined if naringin taste

intensity and grapefruit preference varied between PROP taster status9. Naringin taste intensity

was not found to vary significantly with PROP taster status9. PROP taster status was only

weakly negatively correlated with naringin preference and grapefruit juice preference9. These

results allude to the possibility that naringin and PROP are detected by different taste receptors

and warrant further investigation.

2. 7 SUMMARY

Bitter taste is a variable trait that influences food preferences and dietary intake3. The

majority of the research that examines the affect of variation in bitter taste, on food preferences

and dietary habits, has used PTC or PROP sensitivity as a marker of genetic variation in bitter

taste. However, a considerable amount of genetic variation exists within the T2R bitter taste

receptor gene family88

and little is known about how this variation affects the expression and

function of the T2R bitter taste receptors, as well as bitter taste perception and behavioral

responses to foods. It is possible that certain variants of the T2R gene family increase bitter

sensitivity causing individuals to dislike and avoid the consumption of bitter foods. The

avoidance of bitter foods over time can be detrimental to health since many nutritious foods,

20

such as certain fruits and vegetables, contain bitter tastants, and many of these bitter tastants are

phytonutrients5. Research on how variation in the T2R gene family affects bitter taste perception

and dietary habits are important since diets high in fruits and vegetables, as well as

phytonutrients, have been thought to be protective against certain chronic diseases, such as

cancers6 and cardiovascular disease

7.

Recently, a genome-wide association study found that an adenine to guanine SNP

(rs1376251) in the TAS2R50 gene was associated with MI risk in three separate populations14

. It

is unlikely that the rs1376251 SNP directly affect MI risk. Rather, it is possible that the

rs1376251 SNP alters bitter taste perception causing certain individuals to undertake dietary

habits that may modify their risk of cardiovascular disease and MI14

. Since, cardiovascular

disease (CVD) is the leading cause of hospitalization and mortality in Canada17,18

with MI

accounting for the majority of CVD related deaths in 200217

, it is important to identify

modifiable risk factors associated with both CVD and MI. Understanding how taste may be a

barrier to healthy eating can help improve health promotion programs and create programs

targeted to bitter sensitive individuals.

To our knowledge, the T2R50 taste receptor has not been deorphaned. In order to

understand how the rs1376251 SNP may affect MI risk, research is needed to elucidate the

function of the T2R50 bitter taste receptor, as well as identify the functional and behavioral role

of SNPs in and around the TAS2R50 bitter taste receptor gene. The aim of this thesis will be to

examine how genetic variation in the TAS2R50 gene region affects bitter taste perception, food

preference and dietary intake.

21

Chapter Three

Rationale, Hypothesis and Objectives

22

Rationale: The majority of studies examining the association between genetic variation in bitter

taste and food selection have used PROP or PTC sensitivity as a marker for genetic variation.

Studies that have examined the genetic variation of bitter taste using SNPs in taste receptor

genes have predominantly focused on the TAS2R38 gene, which has been shown to be

responsible for detecting PTC/PROP31

. A study examining genetic variation in the TAS2R50

bitter taste receptor gene will help advance knowledge and understanding of bitter taste

perception. A polymorphism (rs1376251 A/G) in the TAS2R50 gene has been associated with

MI risk14,15

, and it has been hypothesized that this association may be due to a difference in

dietary intake caused by variations in taste perception. However, this association has not yet

been examined. Naringin, the primary bitter compound in grapefruit90

, has been shown to have

potential cardioprotective properties92-94

and affect drug metabolism102-104

. Genetic variation in

the TAS2R50 gene may affect the bitter taste perception of naringin and other bitter tastants,

which can affect food selection and health. Research is needed to examine the association

between polymorphisms in the TAS2R50 gene region and bitter taste intensity, food preferences

and dietary intake.

Hypothesis: Genetic polymorphisms of the TAS2R50 gene region are associated with

differences in bitter taste perception, food preferences and dietary intake.

Objectives: To determine if polymorphisms in the TAS2R50 gene region (rs2900554 A/C ,

rs10772397 A/G and rs1376251 A/G) are associated with differences in naringin and PTC taste

intensity perception, food preferences, dietary intake.

23

Chapter Four

Materials and Methods

24

4.1 Participants

Participants are young men (n=358) and women (n=825) aged 20-29 years from the

ongoing Toronto Nutrigenomics and Health (TNH) study which is a cross sectional study with

an overall goal of investigating gene-diet interactions on biomarkers of chronic disease and

genetic determinants of food intake behaviors. A younger population is ideal for examining taste

and food preferences because taste diminishes with age106

. Participants were recruited from the

University of Toronto through campus newspaper, internet postings, and class room

announcements. Individuals who were unable to communicate in English were excluded from

this study since questionnaires were available only in English. Additionally, women who were

pregnant or breastfeeding were excluded because changes in taste, metabolism and dietary

habits occur with these life stages24

.

For the purpose of this study subjects with type 2 diabetes (n=1), inflammatory bowel

disease (n=2) and cancer (n=2) during study completion were excluded, since presence or

treatment of these diseases my affect taste perception and/or dietary habits. Subjects with

missing data for any of the variables of interest were excluded (n=6). One subject with

unreliable food preference checklist and food frequency questionnaire results was excluded.

Leaving a population of 1171 individuals (men=352, women=819) free of chronic disease.

Subjects were placed into ethnocultural groups based on self-reported ethnicities. There were

560 Caucasian (European, Hispanic or Middle-Eastern), 404 East Asian (Chinese, Japanese,

Korean, Vietnamese or Filipino), 120 South Asian (Indian, Pakistani and Sri Lankan) and 87

individuals grouped as “other” (African descent, First Nations or individuals of two or more

ethnocultures). Participants allergic or intolerant to alcohol (n=1), fruits (n=15) or vegetables

(n=10) were excluded when assessing food preferences and dietary intake since

allergies/intolerances to these food groups may affect preference or intake responses of many

25

bitter foods. Individuals (n=87) who reported consuming less than 800 kcal, women who

reported greater than 3500 kcal and men who reported greater than 4000 kcal on the food

frequency questionnaire (FFQ) were excluded when assessing dietary intake. These cut-points

were based on cut-points established by Walter Willet in order to avoid calorie over or under

reporters and individuals with disordered eating habits or unusual dietary needs64

.

4.2 Study protocol

Potential subjects were screened for age by phone or email and individuals who met the

study requirements were recruited. Subjects were required to visit the study office on two

separate occasions. On the first visit written consent was obtained, anthropometric

measurements were taken (height, weight, waist circumference and blood pressure) and naringin

taste intensity was assessed. Subjects were instructed on how to complete four questionnaires: a

general health and lifestyle questionnaire (GHLQ), a physical activity questionnaire (PAQ), a

food frequency questionnaire (FFQ) and a food preference checklist (FPC). The GHLQ and

PAQ were completed in the study office while the FFQ and the FPC were completed away from

the office. Subjects were given a requisition for a 12 hour fasting venous blood sample to be

drawn at a MDS laboratory (LifeLabs, Toronto) at 180 Bloor Street West between 8:30 and

10:30am, within 10 days of their first visit to the study office. Subjects were scheduled for a

second visit to the study office after blood samples were received from MDS laboratory. During

the second visit subjects returned completed FFQs and FPCs and they were reviewed for

completeness. Additionally, PTC taste intensity was assessed. Subjects received a $20

honorarium upon study completion.

26

4.3 Bitter taste assessment

Naringin and PTC infused taste strips were used to rank subjects‟ perceived bitter taste

intensity on a 9-level scale. Taste strips were infused with a solution containing 3 µg of naringin

or PTC. The intensity of the bitter taste perception of naringin and PTC were determined using a

standardized control/filter paper protocol (Precision Labs Inc, Cottenwood, AZ). Subjects were

asked to rinse their mouth with bottled spring water before the filter papers were administered.

Subjects placed a control strip with no bitter substance on the anterior medial surface of their

tongue until completely moistened (5-10 seconds). Next, subjects placed the bitter taste strip on

the anterior medial surface of their tongue until completely moistened (5-10 seconds). Subjects

were then asked to circle the number corresponding to their perceived bitterness of the taste strip

on a 9-point numbered scale ranging from “1=not at all bitter” to “9=extremely bitter” with a

central point labeled “5=moderately bitter”. This scale is a variant of the Natick 9-point

scale50,107

. Naringin and PTC taste intensity testing were done on two separate visits.

4.4 Food preference assessment

Subjects completed a 63-item food preference checklist to assess their preference of 25

potentially bitter foods and beverages. This food preference checklist was modeled after a 171-

item food preference checklist that is widely used to assess food preference in North

America35,41,108

. The number of food items was reduced to 63 commonly consumed fruits (12),

vegetables (21), beverages (11), breads (4), and other miscellaneous food items (9) to minimize

participant burden. The food preference checklist asked subjects to rank their liking/disliking of

common foods on an anchored 9-point hedonic scale ranging from dislike extremely to like

27

extremely. For each food, subjects were able to indicate if they never tried a food or would not

try the food.

4.5 Dietary intake assessment

A comprehensive, self-administered, semi-quantitative FFQ was used to assess habitual

diet during the month prior to study participation. This FFQ has been modified from the Willett

FFQ109,110

to improve dietary assessment of whole grains, fruits and vegetables, glycemic index,

and caffeine by the addition of questions on the consumption of 6 fruits, 7 vegetables, 6

cereals/breads, 4 beverages and 3 miscellaneous food items. The Willett FFQ has been

extensively used and validated for use in North America65

. The FFQ has 24 pages and contains

questions on the consumption of 189 food items and 12 vitamin/mineral supplements. For most

foods, 9 possible responses are given from never to 4 or more servings per day. Subjects were

given verbal instructions on how to complete the FFQ and examples of standard portion sizes

were shown to participants to help improve the accuracy of their food intake responses.

Completed FFQs were sent to Harvard University for dietary intake analysis. The nutrient

database that is used is based on the United States Department of Agriculture‟s Nutrient

Database for Standard Reference111

which is the source of 86% of non-zero nutrient data in

Health Canada‟s Canadian Nutrient Files112

. Average daily energy intake was calculated by

dividing monthly energy intake by 30 days.

4.6 Anthropometrics

Height (to the nearest 0.1cm) was measured using a wall-mounted stadiometer (model

Seca 206, Seca Corporation, Hanover, MD, USA) and body weight (to the nearest 0.1 kg) was

28

measured using a digital scale (model Bellissima 841, Seca Corporation, Hanover, MD, USA).

Body mass index (BMI) was calculated (kg/m2). Waist circumference was measured twice to the

nearest 0.1cm and averaged. Resting blood pressure was measured twice, one minute apart,

using the OMRON IntelliSense Blood Pressure Monitor (Model HEM-907XL, OMRON

Healthcare, Vernon Hills, Illinois, USA) and measurements were averaged.

4.7 Physical activity questionnaire

Metabolic equivalent of task (MET) can be used to express the estimated energy costs

of a specific task113

. One MET is approximately equal to 1 kcal/ kg/ hour sitting at rest113

. A

physical activity questionnaire was used to measure the number of hours spent sleeping (0.9

METs) or involved in sitting or reclining activities (1.0 METs), light activities (2.3 MET),

moderate activities (3.6 MET), and intense activities (7.5 MET) during a usual weekday and a

weekend day in the month prior to study participation. This questionnaire was taken from a

portion of the questionnaire created by Paffenbarger et. al., which is used to measure physical

activity to assess health effects in a healthy free-living population114

. Examples of physical

activities were provided and were grouped according to their intensities in metabolic equivalents

(MET). MET-hours per week was calculated by multiplying the time spent on each activity by

the intensity of the activity in METs and then multiplying the product by the frequency the

activity is preformed per week (5 for weekdays and 2 for weekends).

4.8 Genotyping

Approximately 44 ml of venous blood was collected from each participant at MDS

Laboratories (Toronto, Canada) in yellow citric acid-trisodium citrate-dextrose (ACD) treated

tubes (Becton Dickinson Vacutainer Tubes, Franklin Lakes, NJ). The blood samples were kept

29

at room temperature and delivered to the University of Toronto on the day of blood collection.

DNA was isolated from leukocytes in whole blood using the GenomicPrep™ Blood DNA

Isolation Kit (Amersham Pharmacia Biotech Inc, Piscataway, NJ).

Real time PCR was used to genotype the rs2900554, rs10772397 and rs1376251 SNPs

in the TAS2R50 gene region using TaqMan allelic discrimination assays, C___1326589,

C___1326594, C___12107274, respectively, from Applied Biosystems (Foster City, CA). A 10

µl reaction containing 0.25µl of TaqMan assay, 5.0 µl of TaqMan Mastermix, 2.75 µl of

distilled water and 2.0 µl DNA was used to genotype a SNP for each sample. Allelic

discrimination was performed using an ABI 7000 Sequence Detection System and PCR

conditions were set at 95οC for 15 minutes, followed by 40 cycles of 95

οC for 15 seconds and

60οC for 1 minute. 10% of genotyped results were replicated randomly for each SNP.

4.9 Statistical analysis

Statistical analyses were conducted using SAS version 9.1 (SAS Institute, Cary, NC,

USA) and a double sided p<0.05 was considered significant. Statistical analyses were performed

in Caucasians and Asians separately due to differences in allele frequencies and linkage

disequilibrium between ethnocultural groups. A gene-sex interaction was examined for bitter

taste, food preference and dietary intake analyses since women may be more sensitive to bitter

tastes3. Definitions of all variables used in the statistical analysis are provided in table 2.

Differences in population characteristics between TAS2R50 genotypes were assessed

using Pearson‟s test for categorical variables and analysis of variance (ANOVA) with

Tukey‟s post hoc test for continuous variables. Since bitter taste is generally disliked, food

preference responses (9 categories) were grouped into binary variables, disliking (category 1-3)

and neutral/liking (categories 4-9), to allow for the assessment of differences in the odds of

30

strongly disliking foods between TAS2R50 genotypes. Individuals who reported that they “never

tried” or “would not try” a specific food in the FPC were excluded from the analysis of food

preference by TAS2R50 genotype for the specific food in which they reported “never tried” or

“would not try”. Pearson‟s and binary logistic regression was used to assess if the odds of

strongly disliking 25 foods differed between TAS2R50 genotypes. Binary logistic regression was

adjusted for age, sex, BMI, physical activity and smoking status. BMI was log transformed

because it was not normally distributed. A variable was considered a confounding variable for

all food preference models (TAS2R50 genotype → disliking foods) if it changed the beta

estimate of the unadjusted logistic regression model by 10%, for any of the 25 food preference

variables, in order to standardize food preference models.

Dietary intake was grouped into a binary variable, did not consume in the past month

(category 1) and consumed in the past month (categories 2-9 or 2-10). Only foods that had a

significant difference in food preference between TAS2R50 genotypes were used to assess if an

association exists between dietary intake and TAS2R50 genotype using Pearson‟s 2 and binary

logistic regression. Age, sex, BMI, physical activity, smoking status, calorie intake and season

were considered confounding variables because they changed the beta estimate of the

unadjusted logistic regression model by10%, for any of the dietary intake variables, in order to

standardize logistic regression models. Since the objective of this study is to determine if

differences in taste affect food consumption, individuals who reported that they never tried a

food on the FPC were excluded from the dietary intake analysis of the specific food.

Naringin and PTC taste intensity ratings were grouped into a binary variable, high

intensity (7-9) and medium/low intensity (1-6). Pearson‟s and binary logistic regression was

used to assess if the odds of reporting a high taste intensity rating differed between TAS2R50

genotypes. Age, sex and smoking status were thought to confound the relationship between

31

TAS2R50 genotype and taste intensity if they changed the beta estimate of the unadjusted

regression model by 10%. Age and sex were found to confound the relationship between

TAS2R50 genotype and taste intensity of naringin and PTC.

Haploview Software115

was used to assess hardy Weinberg Equilibrium and linkage

disequilibrium between the three SNPs.

32

Table 2: Variable type and definition

Variable

Category Variable

Variable

Type

Variable Definition

Subject

Characteristics/

Potential

Confounding

Variables

Age

Continuous

Years

BMI

Continuous

Kg/m2

Physical activity

Continuous

MET×hours/week

Calorie intake

Continuous

Kcal/day

Sex

Categorical

Male and Female

Ethnocultural group

Categorical

Caucasian (European, Middle Eastern, Hispanic), East

Asian (Chinese, Japanese, Korean), South Asian

(Indian, Pakistani, Sri Lankan) and Other (African

descent, Aboriginal and mixed ethnocultural group)

Smoking status

Categorical

Smoker (at least 1 cigarette per day for 1 month or

longer) and Non-smoker

Season

Categorical

Spring (March, April, May), Summer (June, July,

August), Fall (September, October, November),

Winter (December, January, February)

Food Preference

Assessment

Grapefruit, Grapefruit Juice,

Asparagus, Broccoli, Brussels

sprouts, Cabbage, Cauliflower,

Endives, kale, Onion raw,

Parsley, Radicchio, Radish,

Rapini, Spinach, Soymilk, Tofu,

Dark Chocolate, Bitter-

Sweet/Semi-Sweet Chocolate,

Red Wine, White Wine, Beer,

Coffee, Green tea, Black Tea

Categorical

Dislike (1=dislike extremely, 2=dislike very much,

3=dislike moderately) and Neutral/Like (4=dislike

slightly, 5= neither like nor dislike, 6= like slightly, 7=

like moderately, 8= like very much, 9= like extremely)

Dietary Intake

Assessment

Foods that had a significant

association between TAS2R50

genotype and food preference

Categorical

Did not consume (Never) and Consumed (less than

once per month, 1-3 times per month, once per week,

2-4 times per week, 5-6 times per week, once per day,

2-3 servings per day, 4 or more servings per day) in

the past month

Bitter Taste

Intensity

Assessment

Naringin or PTC taste intensity

Categorical

High intensity (7-9)

Medium/Low intensity (1-6)

TAS2R50

Genotypes

rs2900554 A/C Categorical CC, AC and AA

rs10772397 A/G

Categorical GG, AG and AA

rs1376251 A/G Categorical GG, AG and AA

33

Chapter 5

Results

34

5.1 Genotype frequency, allele frequency and Hardy Weinberg Equilibrium

Genotype frequencies, allele frequencies and HWE for the three TAS2R50 SNPs are

presented in table 3 for the total population and stratified by Caucasian, East Asian and other

ethnocultural groups. The genotype frequencies and allele frequencies differed between

ethnocultural groups, particularly for the rs2900554 SNP and the rs1376251 SNP. In the East

Asian population allele frequencies for the rs10772397 SNP and the rs1376251 SNP were not in

Hardy-Weinberg equilibrium. However, the potential for genotyping error is unlikely since the

genotype and allele frequencies of the three SNPs are similar to the genotype and allele

frequencies reported in other Caucasian and Asian populations85

. Furthermore, real-time PCR

results produced clear genotype calls for all three SNPs (Figure 1) and 10% of samples were

randomly replicated producing identical genotype results. All subjects were genotyped

successfully for the three SNPs.

5.2 Linkage disequilibrium

Figure 2 shows linkage disequilibrium (D‟ and r2) between the three SNPs in the total

population and stratified by Caucasian, East Asians and other ethnocultural groups. When

linkage disequilibrium was measured using the D‟ statistic, SNPs rs2900554 and rs1376251, as

well as SNPs rs10772397 and rs1376251 are in high linkage disequilibrium in all samples.

However, the linkage disequilibrium between the rs2900554 SNP and the rs10772397 SNP

differs between ethnocultural groups with the SNPs being 98% in linkage disequilibrium in East

Asians, 23% in linkage disequilibrium in Caucasians and 34% in linkage disequilibrium in the

other ethnocultural group.

The correlations (r2) between the three SNPs are also different between ethnocultural

groups. A moderate correlation exists between the rs2900554 and rs1376251 SNPs in

35

Caucasians while the rs2900554 and rs1376251 SNPs, and the rs10772397 and the rs1376251

SNPs were not highly correlated. In contrast, the three TAS2R50 SNPs were highly correlated in

East Asians. Since the allele frequency and linkage disequilibrium (D‟ and r2) between the three

TAS2R50 SNPs varied highly between ethnocultural groups all subsequent analyses were

stratified by Caucasians and East Asians.

5.3 Subject Characteristics

Subject characteristics are shown in table 4. In Caucasians, a significant difference in

age was seen between genotypes of the rs2900554 SNP and was controlled for in all subsequent

multivariate analysis.

5.4 TAS2R50 genotype and food preference

The frequency and odds of disliking foods are presented in tables 5 to 10. Multivariate

analyses were adjusted for sex, BMI (kg/m2), physical activity (MET.hrs/wk), and smoking

status. The modifying effect of sex on the association between TAS2R50 genotypes and food

preference could not be examined for most food preference variables due to the limited number

of males who reported disliking foods.

The frequency and odds of disliking grapefruit and grapefruit juice is shown in table 5.

In Caucasians, genetic variation in the TAS2R50 gene region was significantly associated with

grapefruit disliking in the rs2900554 SNP and the rs1376251 SNP for individuals homozygous

for the C and G alleles, respectively. Heterozygous individuals had an intermediate odds of

grapefruit disliking, compared to individuals homozygous for the A allele, however only

individuals heterozygous for the rs2900554 SNP had an odds ratio that reached significance.

The rs10772397 SNP was only weakly associated with grapefruit disliking in Caucasians. In

36

contrast, all three TAS2R50 SNPs were associated with grapefruit disliking in East Asians. This

is because the rs10772397 SNP is almost in 100% linkage disequilibrium (r2) with the

rs1376251 SNP and is therefore a marker of this SNP. East Asians homozygous for the minor

alleles in all three SNPs disliked grapefruit significantly more than East Asians homozygous for

the A allele in all three SNPs. Heterozygous East Asians showed intermediate odds of disliking

grapefruit compared to East Asians homozygous for the A allele, however the odds of disliking

grapefruit did not reach significance. The rs2900554 SNP had the greatest influence on

grapefruit disliking in both Caucasians and Asians. After adjusting for sex, BMI, physical

activity and smoking status the odds ratio (95% confidence interval) of grapefruit disliking in

Caucasians and East Asians homozygous for the C allele was 6.6 (3.0, 14.3) and 5.9 (2.2, 15.6),

respectively, compared to individuals homozygous for the A allele. This SNP was also

significantly associated with grapefruit juice disliking in both Caucasians and East Asians with

individuals homozygous for the C allele having a two and three fold higher adjusted odd of

disliking grapefruit juice respectively, compared to individuals homozygous for the A allele.

The frequency and odds of disliking vegetables is shown in table 6 for Caucasians and

East Asians. East Asians homozygous for the C allele in the rs2900554 SNP had significantly

higher odds of radish disliking compared to individuals homozygous for the A allele, however

this association was not seen in Caucasians. A greater proportion of Caucasians with the GG

genotype for the rs10772397 SNP disliked kale compared to individuals homozygous for the A

allele. In the East Asian population individuals heterozygous for the rs2900554 SNP and G

allele carriers for the rs10772397 SNP and the rs1376251 SNP significantly disliked kale greater

than individuals homozygous for the A alleles in all three SNPs. A greater proportion of East

Asians homozygous for the C allele in the rs2900554 SNP and G alleles in the rs10772397 SNP

and the rs1376251 SNP disliked spinach significantly more than individuals homozygous for the

37

A allele for all three SNPs, however this trend was not seen in Caucasians. Interpretation of kale

and spinach preference in the East Asian population must be cautioned due to the limited

number of individuals who reported disliking these foods. No other significant association was

seen between the TAS2R50 genotype and disliking of vegetables.

Table 7 shows the frequency and odds of disliking soy products by TAS2R50 genotype.

A borderline significant association was seen between the rs1376251 SNP and soymilk

preference in Caucasians, with heterozygotes for the rs1376251 SNP disliking soymilk less than

individuals homozygous for the A allele. A greater proportion of Caucasians with a C allele for

the rs2900554 SNP disliked bitter-sweet/semi-sweet chocolate more than individuals

homozygous for the A allele (Table 8). Too few East Asians reported disliking soymilk and

semi-sweet/bitter-sweet chocolate to examine this association in the East Asian population.

There were no significant associations between TAS2R50 genotype and preference for alcoholic

(Table 9) or caffeinated beverages (Table 10).

Cross tabular analyses of the three TAS2R50 SNPs by food preference was Bonferonni

corrected for 25 potentially bitter foods and beverages. Preference for grapefruit was the only

food that remained significantly associated with the genetic variation in the TAS2R50 gene

region, with the rs2900554 SNP and rs1376251 SNP in Caucasians and the rs2900554 SNP in

East Asians having p-values below 0.002.

The combined effect of the rs2900554 SNP and the rs1376251 SNP on the frequency of

grapefruit disliking was analyzed since both SNPs were strongly associated with grapefruit

disliking in Caucasians and East Asians. Table 11 shows all possible genotype combinations for

the rs2900554 SNP and the rs1376251 SNP and the frequency of individuals in the Caucasian

and East Asian population who dislike grapefruit by the rs2900554 SNP and the rs1376251 SNP

genotype combinations. Individuals with the CC/GG genotype were more likely to report

38

disliking grapefruit than all other genotype combinations, followed by genotypes GT/CC and

GT/CT. The frequency of individuals who reported disliking grapefruit reduced substantially in

the absence of a C allele at the rs2900554 SNP, suggesting the rs2900554 SNP may be driving

the association between genetic variation in the rs1376251 SNP and grapefruit disliking through

linkage disequilibrium.

5.5 TAS2R50 genotype and dietary intake

Since grapefruit preference showed a strong association with TAS2R50 genotype, the

association between TAS2R50 genotype and both grapefruit (1/2 fruit serving) (Table 12) and

grapefruit juice (100% juice, 1/2 cup serving) (Table 13) consumption was assessed in

Caucasians and East Asians. Sex was found to modify the association between the rs2900554

SNP and grapefruit intake (p=0.05) and the rs10772397 SNP and grapefruit juice intake

(p=0.04) in Caucasians, so all analyses for grapefruit and grapefruit juice intake were stratified

by sex. No significant association was seen between genetic variation in the rs2900554 SNP and

grapefruit consumption in Caucasians and East Asians. However, when the analysis was

stratified by sex a significant association was seen between the rs2900554 SNP and grapefruit

consumption in Caucasian females. Compared to individuals homozygous for the A allele, the

odds ratio (95% confidence interval) for individuals who did not consume grapefruit in the past

month was 2.1 (1.2, 4.0) for females homozygous for the C allele. East Asian females had a

similar trend in grapefruit consumption for the rs2900554 SNP, with individuals homozygous

for the C allele having the highest odds of never consuming grapefruit, however this result did

not reach significance. Genetic variation in the rs2900554 SNP was not associated with

grapefruit consumption in Caucasian or East Asian males.

39

A borderline significant association was seen with the rs1376251 SNP and grapefruit

consumption, with Caucasian heterozygote individuals having a lower odds of not consuming

grapefruit in the past month than individuals homozygous for the A allele. When the Caucasian

population was stratified by sex this association was pronounced in the male population. A

similar trend in grapefruit consumption for the rs1376251 SNP was seen in East Asian males,

however this trend did not reach significance. There was no association between the rs1376251

SNP and grapefruit consumption in Caucasian and East Asian females. Genetic variation in the

rs10772397 SNP was not associated with grapefruit consumption in any of the populations.

Furthermore, grapefruit juice consumption was not significantly associated with TAS2R50

genotype in any of the three SNPs in Caucasians and East Asians and this association remained

non-significant when the populations were stratified by sex. However, the trend in grapefruit

juice consumption for the rs2900554 SNP in Caucasian females was similar to that of grapefruit

consumption in the same population.

The association between genetic variation in the TAS2R50 gene region and the

consumption of spinach (raw in salad, 1 cup serving or cooked with Swiss chard, ½ cup

serving), kale (or mustard, collard or turnip greens, ½ cup serving) (table 14), soymilk (8 oz.

glass) and chocolate (candy bar or packet) (table 15) were assessed because preference for these

foods were found to be associated with genetic variation in the TAS2R50 gene. Sex did not

modify the association between genetic variation in the TAS2R50 gene region and consumption

of spinach, kale, soymilk or chocolate. Genetic variation in the rs2900554 SNP was associated

with cooked spinach and Swiss chard consumption, with 70% more East Asian heterozygotes

reporting to have not consumed cook spinach and Swiss chard in the past month compared to

individuals homozygous for the A allele. This association was not seen in Caucasians, or in the

40

rs10772397 and rs1376251 SNPs for any populations. Genetic variation in the TAS2R50 gene

region was not associated with raw spinach, kale, soymilk or chocolate consumption.

5.6 TAS2R50 genotype and taste intensity

The difference in naringin taste intensity was assessed in a subpopulation of 211

individuals because an association was found between genetic variation in the TAS2R50 gene

region and grapefruit preference and intake. The frequency and odds of experiencing a high

naringin and PTC taste intensity between TAS2R50 genotypes is shown in table 16. Sex did not

modify the association between TAS2R50 genotype and naringin or PTC taste intensity for any

of the TAS2R50 SNPs assessed.

Genetic variation in the rs2900554 SNP was found to be significantly associated with

naringin taste intensity in Caucasians. Caucasians homozygous for the C allele had a 5.4 (1.5,

9.3) fold higher odds (95% confidence interval) of experiencing a high naringin taste intensity

compared to individuals homozygous for the A allele. A similar trend was seen in East Asians

with a higher proportion of C allele carriers reporting a high naringin taste intensity, however

this did not reach significance due most likely to the low number of East Asians with the CC

genotype. Naringin taste intensity was not associated with genetic variation in the rs10772397

SNP or the rs1376251 SNP. In Caucasians, a significant association was seen between genetic

variation in the rs10772397 and rs1376251 SNPs, and PTC taste intensity with heterozygous

individuals experiencing a high PTC taste intensity 60% less than individuals homozygous for

the A allele. This trend was not seen in East Asians. No significant association was seen

between genetic variation in the rs2900554 SNP and PTC taste intensity.

41

5.7 Difference in food preferences between Caucasians and Asians

Tables 17 to 21 present the frequency and odds of disliking foods by ethnocultural

groups. The reason for examining if food preference differs between ethnocultural groups was to

determine if differences in allele frequencies for the three TAS2R50 SNPs between ethnocultural

groups can help predict food preferences between groups. The allele frequencies for the

rs2900554 SNP and the rs1376251 SNP differ greatly between Caucasians and East Asians,

with Caucasians having a higher C and G allele frequency, respectively. Since the C allele for

the rs2900554 SNP and the G allele for the rs1376251 SNP have been shown to be associated

with a higher disliking of grapefruit and other bitter foods, Caucasians may be more likely to

dislike these foods than East Asians. As expected, East Asians were found to be significantly

less likely to dislike grapefruit and grapefruit juice compared to Caucasians (table 17). East

Asians were also found to dislike cauliflower, turnip, radish (table 17), coffee, black tea (table

21) and soymilk (table 18) more than Caucasians. Broccoli, rapini (table 17), green tea (table

21) and tofu (table 18) were disliked less by East Asians than Caucasians, however these results

need to be interpreted with caution since fewer than 5 Asians disliked these foods. Parsley (table

17), beer and white wine (table 20) were disliked more by East Asians than Caucasians.

42

Table 3: Genotype frequency, allele frequency and Hardy Weinberg Equilibrium by TAS2R50

genotype in the total population and in Caucasians and East Asians and other ethnocultural

groups

Genotype Frequency

n (%)

Allele

Frequency (%)

HWE

rs2900554 CC AC AA Total C A p-value

Total Population 180 (15) 492 (42) 499 (43) 1171 (100) 0.36 0.64 0.002

Caucasian 131 (23) 268 (48) 161 (29) 560 (100) 0.47 0.53 0.4

East Asian 24 (6) 136 (34) 244 (60) 404 (100) 0.23 0.77 0.5

Others 25 (12) 88 (43) 94 (45) 207 (100) 0.33 0.67 0.6

rs10772397 GG AG AA Total G A p-value

Total Population 147 (12) 510 (44) 514 (44) 1171 (100) 0.34 0.66 0.3

Caucasian 84 (15) 264 (47) 212 (38) 560 (100) 0.39 0.61 1.0

East Asian 38 (9) 142 (35) 224 (56) 404 (100) 0.27 0.73 0.04

Others 25 (12) 104 (50) 78 (38) 207 (100) 0.37 0.63 0.4

rs1376251 GG AG AA Total G A p-value

Total Population 320 (27) 499 (42) 352 (30) 1171 (100) 0.49 0.51 5.6×10-7

Caucasian 227 (41) 253 (45) 80 (14) 560 (100) 0.63 0.37 0.5

East Asian 38 (10) 143 (35) 223 (56) 404 (100) 0.27 0.73 0.05

Others 55 (26) 103 (50) 49 (24) 207 (100) 0.51 0.49 1.0

Subjects were classified based on self-identified ethnocultural group. HWE was assessed using

Haploview software.

43

Figure 1: Scatter plot of TAS2R50 genotype for the rs2900554 SNP. Ninety-two individuals are represented in this scatter plot

which shows real-time PCR results of relative fluorescence given off by TaqMan allelic discrimination assay C_1326589. Grey

squares represent negative controls. Blue diamonds, green triangles and red diamonds represent individuals with AA, AC and CC

genotypes for the rs2900554 SNP.

C allele fluorescence signal

A a

llel

e fl

uore

scen

ce s

ign

al

44

TOTAL POPULATION CAUCASIANS

EAST ASIANS OTHERS

Linkage

Disequilibrium

D‟

Linkage

Disequilibrium

D‟

Linkage

Disequilibrium

D‟

Linkage

Disequilibrium

D‟

Correlation

Coefficient

r2

Correlation

Coefficient

r2

Correlation

Coefficient

r2

Correlation

Coefficient

r2

Figure 2: Linkage disequilibrium between TAS2R50 SNPs in the total population and

in Caucasians, East Asians and other ethnocultural groups. Squares indicate linkage

disequilibrium (D‟ or r2 values x 100%) between SNPs with a darker shade indicating

stronger linkage disequilibrium.

45

Table 4: Subject Characteristics for East Asians and Caucasians

rs2900554 rs10772397 rs1376251

CC AC AA p GG AG AA p GG AG AA p

Caucasians

n (%) 131 (23) 268 (48) 161 (29) 84 (15) 264 (47) 212 (38) 227 (41) 253 (45) 80 (14)

Age (y) 23.6±0.2a

23.1±0.2 22.7±0.2b

0.006 23.0±0.3 22.9±0.2 23.4±0.2 0.1 23.2±0.2 23.1±0.2 22.7±0.3 0.4

Female (%) 94 (72) 183 (68) 115 (71) 0.7 58 (69) 181 (69) 153 (72) 0.7 161 (71) 177 (70) 54 (68) 0.9

BMI ( kg/m2 )* 22.9±4.7 22.6±3.8 22.3±4.0 0.7 22.8±4.8 22.6±4.0 22.4±4.0 0.5 22.8±4.3 22.5 ±3.9 22.5±3.8 1.0

Non-Smoker (%) 120 (92) 245 (91) 143 (89) 0.6 74 (88) 244 (92) 190 (90) 0.4 209 (92) 229 (91) 70 (88) 0.5

Physical Activity

(MET.hrs/week)

12.4±0.2 12.7±0.1 12.5±0.2 0.5 12.5±0.3 12.4±0.1 12.7±0.2 0.3 12.6±0.2 12.5±0.2 12. 5±0.3 1.0

Calorie (kcal/day) 1945±56 2039±39 2003±51 0.4 1985±71 2007±40 2013±44 0.9 1957±43 2024±40 2089±72 0.2

East Asians

n (%) 24 (6) 136 (34) 244 (60) 38 (9) 142 (35) 224 (56) 38 (10) 143 (35) 223 (55)

Age (y) 21.9±0.4 22.2±0.2 21.9±0.1 0.5 21.7±0.3 22.1±0.2 22.0±0.1 0.6 21.7±0.3 22.1±0.2 22.0±0.1 0.6

Female (%) 20 (83) 96 (71) 178 (73) 0.4 29 (76) 103 (73) 162 (72) 0.9 29 (76) 104 (73) 161 (72) 0.9

BMI ( kg/m2 )* 20.6±4.6 21.4±3.9 21.2±3.3 0.9 21.2±3.7 21.3±3.9 21.3±3.3 0.9 21.2±3.7 21.2±3.9 21.3±3.3 0.9

Non-Smoker (%) 23 (96) 132 (97) 232 (95) 0.7 37 (97) 136 (96) 214 (96) 0.9 37 (97) 137 (96) 213 (96) 0.9

Physical Activity

(MET.hrs/week)

12.0±0.5 11.9±0.1 11.5±0.1 0.1 11.9±0.4 11.7±0.2 11.6±0.2 0.6 11.9±0.4 11.8±0.2 11.5±0.2 0.5

Calorie (kcal/day)

1717±135 1844±56 1863±42 0.6 1798±106 1832±55 1867±43 0.8 1798±106 1830±55 1869±43 0.8

Values shown are mean ± SE for normally distributed continuous variables, *median ± quartile range for continuous variables that are not normally distributed

and n (%) for categorical variables. Differences between TAS2R50 groups were compared using an analysis of variance for normally distributed continuous

variables, Kruskal-Wallis test for continuous variables that were not normally distributed and a Pearson chi-square test for categorical variables. Means with

different letters are significantly different following a Tukey correction (P <0.05). When assessing calorie intake between genotypes, individuals who consumed

less than 800 kcal/day and more than 3500 kcal/day for women or 4000 kcal/day for men were excluded leaving n=530 Caucasians and n=365 East Asians.

46

Table 5: The frequency and odds of disliking grapefruit and grapefruit juice by TAS2R50

genotype in Caucasians and East Asians

Caucasian East Asian

Food Dislike Like Unadjusted Adjusted* Dislike Like Unadjusted Adjusted*

n (%) OR (95% CI) n (%) OR (95% CI)

Grapefruit n=539 n=386

rs2900554

AA 9 (6) 146 (94) 1 1 21 (9) 214 (91) 1 1

AC 46 (18) 204 (82) 3.6 (1.7 ,7.5) 3.6 (1.7, 7.6) 18 (13) 111 (87) 1.6 (0.8, 3.1) 1.6 (0.8, 3.2)

CC 38 (29) 91 (71) 6.7 (3.1, 14.7) 6.6 (3.0,14.3) 8 (35) 15 (65) 5.4 (2.1, 14.3) 5.9 (2.2, 15.6)

rs10772397

AA 27 (13) 177 (87) 1 1 21 (10) 195 (90) 1 1

AG 52 (21) 201 (79) 1.7 (1.0, 2.8) 1.7 (1.0, 2.9) 16 (12) 119 (88) 1.2(0.6,2.5) 1.3 (0.6 ,2.6)

GG 14 (17) 68 (83) 1.4 (0.7, 2.7) 1.4 (0.7, 2.8) 9 (26) 26 (74) 3.2 (1.3, 7.8) 3.5 (1.4, 8.5)

rs1376251

AA 6 (8) 70 (92) 1 1 21 (10) 194 (90) 1 1

AG 33 (14) 208 (86) 1.9 (0.7, 4.6) 1.9 (0.7, 4.6) 16 (12) 120 (88) 1.2 (0.6, 2.5) 1.3 (0.6, 2.5)

GG 54 (24) 168 (76) 3.8 (1.5, 9.1) 3.8 (1.5, 9.2) 9 (26) 26 (74) 3.2 (1.3, 7.7) 3.5 (1.4, 8.5)

Grapefruit

Juice

n=535

n=374

rs2900554

AA 28 (18) 124 (82) 1 1 27 (12) 202 (88) 1 1

AC 53 (21) 203 (79) 1.2 (0.7, 1.9) 1.2 (0.7, 2.0) 14 (11) 110 (89) 1.0 (0.5, 2.0) 0.9 (0.4, 1.8)

CC 43 (34) 84 (66) 2.3 (1.3, 3.9) 2.3 (1.3, 4.1) 6 (29) 15 (71) 3.0 (1.1, 8.4) 3.0 (1.1, 8.8)

rs10772397

AA 42 (21) 162 (79) 1 1 27 (13) 182 (87) 1 1

AG 65 (26) 186 (74) 1.4 (0.9, 2.1) 1.3 (0.9, 2.1) 12 (9) 120 (91) 0.7 (0.3, 1.4) 0.7 (0.3, 1.4)

GG 17 (21) 63 (79) 1.0 (0.6, 2.0) 1.0 (0.5, 1.9) 8 (24) 25 (76) 2.2 (0.9, 5.3) 2.4 (0.9, 5.9)

rs1376251

AA 15 (20) 61 (80) 1 1 27 (13) 181 (87) 1 1

AG 49 (20) 191 (80) 1.0 (0.6, 2.0) 1.1 (0.6, 2.1) 12 (9) 121 (91) 0.7 (0.3, 1.4) 0.7 (0.3, 1.3)

GG 60 (27) 159 (73) 1.5 (0.8, 2.9) 1.6 (0.8, 3.0) 8 (24) 25 (76) 2.2 (0.9, 5.2) 2.3 (0.9, 5.8)

* Logistic regression model adjusted for age (years), sex, BMI (kg/m2), physical activity

(MET.hrs/wk), smoking status.

47

Table 6: The frequency and odds of disliking vegetables by TAS2R50 genotype in Caucasians

and East Asians



n (%) OR (95% CI) n (%) OR (95% CI)

Asparagus

n=528

n=372

rs2900554

AA 8 (5) 140 (95) 1 1 9 (4) 216 (96) 1 1

AC 21 (8) 231 (92) 1.6 (0.7, 3.7) 1.7 (0.7, 4.0) 9 (6) 116 (93) 1.7 (0.6, 4.4) 1.6 (0.6, 4.3)

CC 8 (6) 120 (94) 1.2 (0.4, 3.2) 1.4 (0.5, 3.8) 2 (9) 21 (91) 2.3 (0.5, 11.3) 2.2 (0.5, 11.1)

rs10772397

AA 14 (7) 185 (93) 1 1 8 (4) 200 (96) 1 1

AG 18 (7) 229 (93) 1.0 (0.5, 2.1) 1.0 (0.5, 2.0) 8 (6) 120 (94) 1.7 (0.6, 4.6) 1.6 (0.6, 4.5)

GG 5 (6) 77 (94) 0.9 (0.3, 2.5) 0.8 (0.3, 2.3) 3 (8) 33 (92) 2.3 (0.6, 9.0) 2.2 (0.6, 8.9)

rs1376251

AA 6 (9) 64 (91) 1 1 8 (4) 199 (96) 1 1

AG 14 (6) 223 (94) 0.7 (0.3, 1.8) 0.7 (0.3, 2.0) 8 (6) 121 (94) 1.7 (0.6, 4.5) 1.6 (0.6, 4.4)

GG 17 (8) 204 (92) 0.9 (0.3, 2.4) 1.0 (0.4, 2.7) 3 (8) 33 (92) 2.3 (0.6, 9.0) 2.2 (0.6, 8.9)

Broccoli

n=547

n=391

rs2900554

AA 3 (2) 154 (98) 1 1 3 (1) 233 (99)

AC 14 (5) 246 (95) 2.9 (0.8, 10.3) 3.0 (0.8, 10.6) 1 (1) 131 (99) n/a n/a

CC 5 (4) 125 (96) 2.1 (0.5, 8.8) 2.0 (0.5, 8.6) 0 (0) 23 (100)

rs10772397

AA 9 (4) 198 (96) 1 1 3 (1) 214 (99)

AG 9 (3) 248 (97) 0.8 (0.3, 2.1) 0.8 (0.3, 2.1) 1 (1) 137 (99) n/a n/a

GG 4 (5) 79 (95) 1.1 (0.3, 3.7) 1.0 (0.3, 3.5) 0 (0) 36 (100)

rs1376251

AA 2 (3) 76 (97) 1 1 3 (1) 213 (99)

AG 12 (5) 233 (95) 2.0 (0.4, 8.9) 2.0 (0.4, 9.3) 1 (1) 138 (99) n/a n/a

GG 8 (4) 216 (96) 1.4 (0.3, 6.8) 1.5 (0.3, 7.0) 0 (0) 36 (100)

Brussels

Sprouts

n=486

n=321

rs2900554

AA 25 (18) 111 (82) 1 1 22 (11) 171 (89) 1 1

AC 50 (22) 180 (78) 1.2 (0.7, 2.1) 1.2 (0.7, 2.1) 16 (13) 97 (87) 1.1 (0.6, 2.3) 1.1 (0.5, 2.2)

CC 31 (26) 89 (74) 1.6 (0.9, 2.8) 1.5 (0.8, 2.7) 2 (12) 15 (88) 1.0 (0.2, 4.8) 0.9 (0.2, 4.5)

rs10772397

AA 44 (24) 139 (76) 1 1 21 (12) 156 (88) 1 1

AG 45 (20) 182 (80) 0.8 (0.5, 1.3) 0.8 (0.5, 1.3) 15 (13) 101 (86) 1.1 (0.5, 2.5) 1.1 (0.5, 2.2)

GG 17 (22) 59 (78) 0.9 (0.5, 1.7) 0.9 (0.5, 1.7) 2 (7) 26 (93) 0.6 (0.1, 2.6) 0.5 (0.1, 2.5)

rs1376251

AA 14 (21) 53 (79) 1 1 21 (12) 156 (88) 1 1

AG 42 (20) 170 (80) 1.0 (0.5, 1.8) 0.9 (0.5, 1.8) 15 (13) 101 (87) 1.1 (0.5, 2.2) 1.1 (0.5, 2.2)

GG 50 (24) 157 (76) 1.2 (0.6, 2.4) 1.2 (0.6, 2.3) 2 (7) 26 (93) 0.6 (0.1, 2.6)

0.5 (0.1, 2.5)



n/a = Odds ratio is not estimable due to one or more empty cells.

48

Table 6 continued: The frequency and odds of disliking vegetables by TAS2R50 genotype in

Caucasians and East Asians



n (%) OR (95% CI) n (%) OR (95% CI)

Cabbage

n=523

n=369

rs2900554

AA 22 (15) 127 (85) 1 1 33 (15) 191 (85) 1 1

AC 40 (16) 207 (84) 1.1 (0.6, 2.0) 1.2 (0.7, 2.0) 15 (11) 109 (87) 0.7 (0.4, 1.5) 0.8 (0.4, 1.6)

CC 22 (17) 105 (83) 1.2 (0.6, 2.3) 1.2 (0.6, 2.4) 4 (18) 18 (82) 1.3 (0.4, 4.1) 1.3 (0.4, 4.1)

rs10772397

AA 31 (15) 170 (85) 1 1 32 (16) 174 (84) 1 1

AG 44 (18) 196 (82) 1.2 (0.7, 2.0) 1.2 (0.7, 2.1) 15 (12) 114 (88) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)

GG 9 (11) 73 (89) 0.7 (0.3, 1.5) 0.6 (0.3, 1.4) 4 (12) 30 (88) 0.7 (0.2, 2.2) 0.7 (0.2, 2.2)

rs1376251

AA 9 (12) 66 (88) 1 1 32 (16) 173 (84) 1 1

AG 39 (17) 191 (83) 1.5 (0.7, 3.3) 1.6 (0.7, 3.5) 15 (12) 115 (88) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)

GG 36 (17) 182 (83) 1.5 (0.7, 3.2) 1.6 (0.7, 3.5) 4 (12) 30 (88) 0.7 (0.2, 2.2) 0.7 (0.2, 2.2)

Cauliflower

n=539

n=382

rs2900554

AA 10 (6) 145 (94) 1 1 5 (2) 227 (98) 1 1

AC 22 (9) 234 (91) 1.4 (0.6, 3.0) 1.4 (0.6, 3.1) 5 (4) 123 (96) 1.8 (0.5, 6.5) 2.1 (0.6, 7.9)

CC 11 (9) 117(91) 1.4 (0.6, 3.3) 1.3 (0.5, 3.3) 1 (5) 21 (95) 2.2 (0.2, 19.4) 2.0 (0.2, 18.9)

rs10772397

AA 18 (9) 188 (91) 1 1 4 (2) 210 (98) 1 1

AG 16 (6) 235 (94) 0.7 (0.3, 1.4) 0.7 (0.4, 1.4) 6 (5) 127 (95) 2.5 (0.7, 9.0) 2.6 (0.7, 9.8)

GG 9 (11) 73(89) 1.3 (0.6, 3.0) 1.2 (0.5, 2.9) 1 (3) 34 (97) 1.5 (0.2, 14.2) 1.5 (0.2, 15.0)

rs1376251

AA 5 (6) 72 (94) 1 1 4 (2) 209 (98) 1 1

AG 18 (7) 223 (93) 1.2 (0.4, 3.2) 1.2 (0.4, 3.4) 6 (4) 128 (96) 2.5 (0.7, 8.9) 2.5 (0.7, 9.7)

GG 20 (9) 201 (91) 1.4 (0.5, 4.0) 1.5 (0.5, 4.2) 1 (3) 34 (97) 1.5 (0.2, 14.2) 1.5 (0.2, 15.0)

Endives

n=290

n=174

rs2900554

AA 7 (9) 63 (91) 1 1 8 (7) 100 (93)

AC 8 (6) 130 (94) 0.6 (0.2, 1.7) 0.6 (0.2, 1.7) 2 (3) 57 (97) n/a n/a

CC 7 (9) 70 (91) 1.0 (0.3, 2.9) 0.9 (0.3, 2.7) 0 (0) 7 (100)

rs10772397

AA 11 (10) 96 (90) 6 (6) 92 (94)

AG 11 (8) 128 (92) n/a n/a 4 (7) 55 (93) n/a n/a

GG 0 (0) 44 (100) 0 (0) 17 (100)

rs1376251

AA 3 (8) 36 (92) 1 1 6 (6) 92 (94)

AG 10 (8) 109 (92) 1.1 (0.3, 4.2) 1.1 (0.3, 4.3) 4 (7) 55 (93) n/a n/a

GG 9 (7) 123 (93) 0.9 (0.2, 3.4) 0.8 (0.2, 3.3) 0 (0) 17 (100)




49

Table 6 continued: The frequency and odds of disliking vegetables by TAS2R50 genotype in

Caucasians and East Asian



n (%) OR (95% CI) n (%) OR (95% CI)

Kale

n=294

n=215

rs2900554

AA 6 (7) 75 (93) 1 1 2 (2) 131 (98) 1 1

AC 11 (8) 135 (92) 1.0 (0.4, 2.8) 1.0 (0.3, 2.8) 6 (8) 66 (92) 6.0 (1.2 , 30.3) 6.4 (1.2, 34.4)

CC 9 (13) 58 (87) 1.9 (0.7, 5.8) 1.9 (0.6, 5.8) 1 (10) 9 (90) 7.3 (0.6, 88.1) 10.3 (0.7, 145.3)

rs10772397

AA 8 (7) 108 (93) 1 1 1 (1) 122 (99) 1 1

AG 10 (8) 121 (92) 1.1 (0.4, 3.0) 1.3 (0.5, 3.4) 6 (8) 67 (92) 10.9 (1.3, 92.6) 12.4 (1.4, 110.5)

GG 8 (17) 39 (83) 2.8 (0.97, 7.9) 3.1 (1.1, 9.2) 2 (11) 17 (89) 14.4 (1.3, 167) 20.8 (1.6, 277.9)

rs1376251

AA 2 (5) 38 (95) 1 1 1 (1) 122 (99) 1 1

AG 8 (6) 123 (94) 1.2 (0.3, 6.1) 1.2 (0.2, 5.8) 6 (8) 67 (92) 10.9 (1.3, 92.6) 12.4 (1.4, 110.5)

GG 16 (13) 107 (87) 2.8 (0.6, 12.9) 2.9(0.6,13.4) 2 (11) 17 (89) 14.4 (1.2, 167) 20.4 (1.6, 269.9)

Onion Raw

n=540

n=389

rs2900554

AA 34 (22) 122 (78) 1 1 56 (24) 180 (76) 1 1

AC 56 (22) 198 (78) 1.0 (0.6, 1.6) 1.1 (0.6, 1.7) 28 (22) 102 (78) 0.9 (0.5, 1.5) 0.9 (0.5, 1.5)

CC 18 (14) 112 (86) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1) 7 (30) 16 (70) 1.4 (0.6, 3.6) 1.4 (0.5, 3.5)

rs10772397

AA 45 (22) 161 (78) 1 1 50 (23) 167 (77) 1 1

AG 50 (20) 202 (80) 0.9 (0.6, 1.4) 0.9 (0.5, 1.4) 30 (22) 107 (78) 0.9 (0.6, 1.6) 0.9 (0.6, 1.6)

GG 13 (16) 69 (84) 0.7 (0.3, 1.3) 0.6 (0.3, 1.3) 11 (31) 24 (69) 1.5 (0.7, 3.3) 1.5 (0.7, 3.3)

rs1376251

AA 18 (23) 60 (77) 1 1 50 (23) 166 (77) 1 1

AG 50 (21) 189 (79) 0.9 (0.5, 1.6) 0.9 (0.5, 1.7) 30 (22) 108 (78) 0.9 (0.6, 1.5) 0.9 (0.6, 1.6)

GG 40 (18) 183 (82) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4) 11 (31) 24 (69) 1.5 (0.7, 3.3) 1.5 (0.7, 3.2)

Parsley

n=535

n=375

rs2900554

AA 7 (5) 147 (95) 1 1 18 (8) 206 (90) 1 1

AC 10 (4) 243 (96) 0.9 (0.3, 2.3) 0.9 (0.3, 2.5) 16 (12) 113 (88) 1.6 (0.8, 3.3) 1.7 (0.8, 3.5)

CC 6 (5) 122 (95) 1.0 (0.4, 3.2) 1.1 (0.4, 3.4) 1 (95) 21 (95) 0.6 (0.1, 4.5) 0.6 (0.1, 4.5)

rs10772397

AA 13 (6) 191 (94) 1 1 16 (8) 191 (92) 1 1

AG 9 (4) 242 (96) 0.6 (0.2, 1.3) 0.5 (0.2, 1.3) 16 (12) 117 (88) 1.6 (0.8, 3.4) 1.7 (0.8, 3.5)

GG 1 (1) 79 (99) 0.2 (0.02, 1.5) 0.2(0.02,1.4) 3 (9) 32 (91) 1.1 (0.3, 4.1) 1.2 (0.3, 4.3)

rs1376251

AA 4 (5) 73 (95) 1 1 16 (8) 190 (92) 1 1

AG 12 (5) 227 (95) 1.0 (0.3, 3.1) 1.0 (0.3, 3.2) 16 (12) 118 (88) 1.6 (0.8, 3.3) 1.7 (0.8, 3.4)

GG 7 (3) 212 (97) 0.6 (0.2, 2.1) 0.6 (0.2, 2.2) 3 (9) 32 (91) 1.1 (0.3, 4.0) 1.2 (0.3, 4.3)



50

Table 6 continued: Frequency and odds of disliking vegetables by TAS2R50 genotype in




n (%) OR (95% CI) n (%) OR (95% CI)

Radicchio

n=346

n=199

rs2900554

AA 11 (12) 80 (88) 1 1 5 (4) 118 (96) 1 1

AC 13 (8) 154 (92) 0.6 (0.3, 1.4) 0.6 (0.3, 1.4) 5 (7) 65 (93) 1.9 (0.5, 6.7) 1.9 (0.5, 7.3)

CC 9 (10) 79 (90) 0.8 (0.3, 2.1) 0.8 (0.3, 2.0) 1 (13) 7 (87) 3.4 (0.4, 32.9) 3.7 (0.3, 42.6)

rs10772397

AA 14 (11) 112 (89) 1 1 5 (5) 106 (95) 1 1

AG 12 (7) 150 (93) 0.6 (0.3, 1.4) 0.7 (0.3, 1.5) 5 (7) 65 (93) 1.6 (0.5, 5.9) 1.6 (0.4, 6.1)

GG 7 (12) 51 (88) 1.1 (0.4, 2.9) 1.1 (0.4, 3.0) 1 (6) 17 (94) 1.3 (0.1, 11.3) 1.3 (0.1, 13.2)

rs1376251

AA 3 (7) 40 (93) 1 1 5 (5) 106 (95) 1 1

AG 13 (9) 137 (91) 1.3 (0.3, 4.7) 1.2 (0.3, 4.6) 5 (7) 65 (63) 1.6 (0.5, 5.8) 1.6 (0.4, 6.1)

GG 17 (11) 136 (89) 1.7 (0.5, 6.0) 1.6 (0.5, 5.9) 1 (6) 17 (94) 1.3 (0.1, 11.3) 1.3 (0.1, 13.2)

Radish

n=517

n=365

rs2900554

AA 33 (23) 113 (77) 1 1 18 (8) 204 (92) 1 1

AC 39 (16) 208 (84) 0.6 (0.4, 1.1) 0.6 (0.4, 1.1) 15 (12) 108 (88) 1.6 (0.8, 3.2) 1.7 (0.8, 3.6)

CC 25 (20) 99 (80) 0.9 (0.5, 1.6) 0.9 (0.5, 1.6) 5 (23) 17 (77) 3.3 (1.1, 10.1) 3.6 (1.2, 11.4)

rs10772397

AA 35 (18) 161 (82) 1 1 18 (9) 187 (91) 1 1

AG 47 (19) 195 (81) 1.1 (0.7, 1.8) 1.1 (0.7, 1.8) 14 (11) 111 (89) 1.3 (0.6, 2.7) 1.4 (0.6, 2.9)

GG 15 (19) 64 (81) 1.1 (0.6, 2.1) 1.1 (0.6, 2.1) 5 (14) 30 (86) 1.7 (0.6, 5.0) 2.0 (0.7, 5.8)

rs1376251

AA 13 (18) 58 (82) 1 1 18 (9) 186 (91) 1 1

AG 42 (18) 189 (82) 1.0 (0.5, 2.0) 1.0 (0.5, 2.0) 15 (11) 112 (89) 1.3 (0.6, 2.7) 1.3 (0.6, 2.8)

GG 42 (20) 173 (80) 1.1 (0.5, 2.2) 1.1 (0.5, 2.1) 5 (14) 30 (86) 1.7 (0.6, 5.0) 2.0 (0.7, 5.8)

Rapini

n=215

n=130

rs2900554

AA 6 (13) 40 (87) 1 1 0 (0) 76 (100)

AC 14 (13) 98 (88) 1.0 (0.3, 2.7) 1.0 (0.4, 2.9) 2 (4) 44 (96) n/a n/a

CC 6 (11) 51 (89) 0.8 (0.2, 2.6) 0.9 (0.3, 3.2) 0 (0) 8 (100)

rs10772397

AA 9 (11) 74 (89) 1 1 0 (0) 67 (100)

AG 10 (11) 81 (89) 1.0 (0.4, 2.6) 0.9 (0.3, 1.5) 2 (4) 46 (96) n/a n/a

GG 7 (17) 34 (83) 1.7 (0.6, 4.9) 1.6 (0.5, 5.0) 0 (0) 15 (100)

rs1376251

AA 3 (12) 23 (88) 1 1 0 (0) 67 (100)

AG 10 (11) 80 (89) 1.0 (0.2, 3.8) 1.0 (0.2, 4.0) 2 (4) 46 (96) n/a n/a

GG 13 (13) 86 (87) 1.2 (0.3, 4.4) 1.3 (0.3, 5.0) 0 (0) 15 (100)




51

Table 6 continued: Frequency and odds of disliking vegetables by TAS2R50 genotype in




n (%) OR (95% CI) n (%) OR (95% CI)

Spinach

n=548

n=390

rs2900554

AA 5 (3) 152 (97) 1 1 3 (1) 232 (99)

AC 13 (5) 248 (95) 1.6 (0.6, 4.6) 1.6 (0.6, 4.8) 5 (4) 127 (96) 3.0 (0.7, 13.0) 3.3 (0.8, 14.6)

CC 8 (6) 122 (94) 2.0 (0.6, 6.3) 2.3 (0.7, 7.2) 2 (9) 21 (91) 7.4 (1.2, 46.6) 9.9 (1.4, 69.3)

rs10772397

AA 9 (4) 198 (96) 1 1 3 (1) 213 (99) 1 1

AG 14 (5) 244 (95) 1.3 (0.5, 3.0) 1.2 (0.5, 2.8) 4 (3) 134 (97) 2.1 (0.5, 9.6) 2.2 (0.5, 10.4)

GG 3 (4) 80 (96) 0.8 (0.2, 3.1) 0.8 (0.2, 3.0) 3 (8) 33 (92) 6.5 (1.3, 33.3) 7.0 (1.3, 39.3)

rs1376251

AA 3 (4) 75 (96) 1 1 3 (1) 212 (99) 1 1

AG 10 (4) 236 (96) 1.1 (0.3, 4.0) 1.1 (0.3, 4.1) 4 (3) 135 (97) 2.1 (0.5, 9.5) 2.3 (0.5, 10.4)

GG 13 (6) 211 (94) 1.5 (0.4, 5.6) 1.7 (0.5, 6.0) 3 (8) 33 (92) 6.4 (1.2, 33.2) 7.0 (1.3, 39.3)



52

Table 7: The frequency and odds of disliking soy products by TAS2R50 genotype in Caucasians

and East Asians



n (%) OR (95% CI) n (%) OR (95% CI)

Soymilk

n=454

n=385

rs2900554

AA 33 (26) 96 (74) 1 1 8 (3) 223 (97)

AC 48 (22) 171 (78) 0.8 (0.5, 1.4) 0.9 (0.5, 1.5) 10 (8) 121 (92) n/a n/a

CC 26 (25) 80 (75) 1.0 (0.5, 1.7) 1.0 (0.6, 1.9) 0 (0) 23 (100)

rs10772397

AA 44 (26) 127 (74) 1 1 8 (4) 203 (96)

AG 48 (23) 162 (77) 0.9 (0.5, 1.4) 0.8 (0.5, 1.3) 10 (7) 128 (93) n/a n/a

GG 15 (21) 58 (79) 0.8 (0.4, 1.5) 0.7 (0.4, 1.4) 0 (0) 36 (100)

rs1376251

AA 20 (33) 41 (67) 1 1 8 (4) 202 (96)

AG 39 (19) 165 (81) 0.5 (0.3, 0.9) 0.5 (0.3,0.95) 10 (7) 129 (93) n/a n/a

GG 48 (25) 141 (75) 0.7 (0.4, 1.3) 0.7 (0.4, 1.4) 0 (0) 36 (100)

Tofu

n=502

n=391

rs2900554

AA 28 (20) 112 (80) 1 1 2 (1) 234 (99)

AC 35 (15) 205 (85) 0.7 (0.4, 1.2) 0.7 (0.4, 1.3) 0 (0) 132

(100)

n/a n/a

CC 13 (11) 109 (89) 0.5 (0.2, 1.0) 0.5 (0.3, 1.1) 0 (0) 23 (100)

rs10772397

AA 27 (14) 160 (86) 1 1 2 (1) 215 (99)

AG 35 (15) 202 (85) 1.0 (0.6, 1.8) 1.0 (0.6, 1.7) 0 (0) 141

(100)

n/a n/a

GG 14 (18) 64 (82) 1.3 (0.6, 2.6) 1.2 (0.6, 2.4) 0 (0) 36 (100)

rs1376251

AA 12 (18) 53 (82) 1 1 2 (1) 213 (99)

AG 33 (15) 194 (85) 0.8 (0.4, 1.6) 0.8 (0.4, 1.7) 0 (0) 140

(100)

n/a n/a

GG 31 (15) 179 (85) 0.8 (0.4, 1.6) 0.9 (0.4, 1.8) 0 (0) 36 (100)




53

Table 8: Frequency and odds of disliking cocoa-containing products by TAS2R50 genotype in




n (%) OR (95% CI) n (%) OR (95% CI)

Dark

Chocolate

n=548

n=393

rs2900554

AA 8 (5) 149 (95) 1 1 17 (7) 221 (93) 1 1

AC 20 (8) 241 (92) 1.6 (0.7, 3.6) 1.6 (0.7, 3.7) 12 (8) 121 (92) 1.2 (0.5, 2.6) 1.2 (0.5, 2.6)

CC 10 (8) 120 (92) 1.6 (0.6, 4.1) 1.6 (0.6, 4.3) 2 (9) 21 (91) 1.2 (0.3, 5.8) 1.3 (0.3, 6.1)

rs10772397

AA 16 (8) 91 (92) 1 1 15 (7) 203 (93) 1 1

AG 16 (6) 242 (94) 0.8 (0.4, 1.6) 0.8 (0.4, 1.6) 12 (9) 127 (91) 1.3 (0.6, 2.8) 1.3 (0.6, 2.8)

GG 6 (7) 77 (93) 0.9 (0.4, 2.5) 0.9 (0.3, 2.4) 3 (8) 33 (92) 1.2 (0.3, 4.5) 1.4 (0.4, 5.2)

rs1376251

AA 3 (4) 75 (96) 1 1 15 (7) 202 (93) 1 1

AG 19 (8) 227 (92) 2.1 (0.6, 7.2) 2.1 (0.6, 7.3) 12 (9) 128 (91) 1.3 (0.6, 2.8) 1.3 (0.6, 2.0)

GG 16 (7) 208 (93) 1.9 (0.6, 6.8) 2.0 (0.6, 6.9) 3 (8) 33 (92) 1.2 (0.3, 4.5) 1.4 (0.4, 5.2)

Bitter-Sweet

or Semi-

Sweet

Chocolate

n=544

n=390

rs2900554

AA 3 (2) 153 (98) 1 1 9 (4) 228 (96) 1 1

AC 16 (6) 244 (94) 3.3 (1.0, 11.7) 3.5 (1.0, 12.4) 8 (6) 122 (94) 1.7 (0.6, 4.4) 1.5 (0.5, 4.0)

CC 8 (6) 120 (94) 3.4 (0.9, 13.1) 3.8 (1.0, 14.8) 1 (4) 22 (96) 1.2 (0.1, 9.5) 1.1 (0.1, 9.8)

rs10772397

AA 10 (5) 196 (95) 1 1 9 (4) 209 (96) 1 1

AG 12 (5) 243 (95) 1.0 (0.4, 2.3) 1.0 (0.4, 2.3) 8 (6) 128 (94) 1.5 (0.6, 3.9) 1.4 (0.5, 3.7)

GG 5 (6) 78 (94) 1.3 (0.4, 3.8) 1.2 (0.4, 3.6) 1 (3) 35 (97) 0.7 (0.08, 5.4) 0.7 (0.08,

5.8)

rs1376251

AA 1 (1) 76 (99) 1 1 9 (4) 208 (96) 1 1

AG 15 (6) 230 (94) 5.0 (0.6, 38.1) 5.3 (0.7, 40.6) 8 (6) 129 (94) 1.4 (0.5, 3.8) 1.3 (0.5, 3.6)

GG 11 (5) 211 (95) 4.0 (0.5, 31.2) 4.3 (0.5, 33.9) 1 (3) 35 (97) 0.7 (0.08, 5.4) 0.7

(0.08,5.9)



54

Table 9: The frequency and odds of disliking alcoholic beverages by TAS2R50 genotype in




n (%) OR (95% CI) n (%) OR (95% CI)

Red Wine

n=528

n=369

rs2900554

AA 27 (18) 121 (82) 1 1 40 (18) 183 (82) 1 1

AC 27 (11) 226 (89) 0.5 (0.3, 1.0) 0.5 (0.3, 1.0) 25 (19) 100 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 2.0)

CC 23 (18) 104 (82) 1.0 (0.6, 1.8) 1.0 (0.6, 1.9) 6 (27) 16 (73) 1.7 (0.6, 4.7) 1.7 (0.6, 4.6)

rs10772397

AA 28 (14) 170 (86) 1 1 37 (18) 167 (82) 1 1

AG 38 (15) 212 (85) 1.1 (0.6, 1.9) 1.0 (0.6, 1.8) 25 (19) 105 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 2.0)

GG 11 (14) 69 (86) 1.0 (0.5, 2.1) 1.0 (0.5, 2.0) 8 (23) 27 (77) 1.3 (0.6, 3.2) 1.3 (0.6, 3.2)

rs1376251

AA 12 (17) 59 (83) 1 1 37 (18) 166 (82) 1 1

AG 30 (13) 210 (87) 0.7 (0.3, 1.5) 0.7 (0.3, 1.4) 25 (19) 106 (81) 1.1 (0.6, 1.9) 1.1 (0.6, 1.9)

GG 35 (16) 182 (84) 1.0 (0.5, 1.9) 0.9 (0.5, 1.9) 8 (23) 27 (77) 1.3 (0.6, 3.2) 1.3 (0.6, 3.2)

White Wine

n=523

n=353

rs2900554

AA 18 (12) 128 (88) 1 1 37 (17) 176 (83) 1 1

AC 24 (10) 226 (90) 0.8 (0.4, 1.4) 0.8 (0.4, 1.5) 18 (14) 102 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)

CC 16 (13) 111 (87) 1.0 (0.5, 2.1) 1.0 (0.5, 2.1) 3 (14) 18 (86) 0.8 (0.2, 2.8) 0.8 (0.2, 2.8)

rs10772397

AA 22 (11) 174 (89) 1 1 33 (17) 161 (83) 1 1

AG 28 (11) 219 (89) 1.0 (0.6, 1.8) 0.9 (0.5, 1.7) 19 (14) 107 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)

GG 8 (10) 72 (90) 0.9 (0.4 , 2.1) 0.8 (0.4, 2.0) 6 (18) 28 (82) 1.1 (0.4, 2.7) 1.1 (0.4, 2.8)

rs1376251

AA 9 (13) 62 (87) 1 1 33 (17) 160 (83) 1 1

AG 24 (10) 211 (90) 0.8 (0.3, 1.8) 0.8 (0.3, 1.8) 18 (14) 108 (86) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5)

GG 25 (12) 192 (88) 0.9 (0.4, 2.0) 0.9 (0.4, 2.0) 6 (18) 28 (82) 1.0 (0.4, 2.7) 1.1 (0.4, 2.8)

Beer

n=530

n=374

rs2900554

AA 38 (26) 109 (74) 1 1 79 (35) 147 (65) 1 1

AC 64 (25) 192 (75) 1.0 (0.6, 1.5) 1.0 (0.6, 1.6) 41 (32) 85 (68) 0.9 (0.6, 1.4) 0.9 (0.6, 1.5)

CC 32 (25) 95 (75) 1.0 (0.6, 1.7) 1.0 (0.5, 1.7) 5 (22) 18 (78) 0.5 (0.2, 1.5) 0.5 (0.2, 1.3)

rs10772397

AA 46 (23) 154 (77) 1 1 70 (34) 137 (66) 1 1

AG 73 (29) 177 (71) 1.4 (0.9, 2.1) 1.4 (0.9, 2.2) 44 (34) 87 (66) 1.0 (0.6, 1.6) 1.0 (0.6, 1.6)

GG 15 (19) 65 (81) 0.8 (0.4, 1.5) 0.8 (0.4, 1.6) 10 (28) 26 (72) 0.8 (0.3, 1.7) 0.7 (0.3, 1.6)

rs1376251

AA 20 (28) 52 (72) 1 1 70 (34) 136 (66) 1 1

AG 57 (24) 183 (76) 0.8 (0.5, 1.5) 0.7 (0.4, 1.4) 44 (33) 88 (67) 1.0 (0.6, 1.5) 1.0 (0.6, 1.6)

GG 57 (26) 161 (74) 0.9 (0.5, 1.7) 0.8 (0.4, 1.5) 10 (28) 26 (72) 0.7 (0.3, 1.6) 0.7 (0.3, 1.6)



55

Table 10: The frequency and odds of disliking caffeinated beverages by TAS2R50 genotype in




n (%) OR (95% CI) n (%) OR (95% CI)

Coffee

n=541

n=390

rs2900554

AA 34 (22) 123 (78) 1 1 40 (17) 196 (83) 1 1

AC 52 (20) 204 (80) 0.9 (0.6, 1.5) 0.9 (0.6, 1.5) 14 (11) 119 (89) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1)

CC 31 (24) 97 (76) 1.2 (0.7, 2.0) 1.3 (0.7, 2.2) 1 (4) 22 (96) 0.2 (0.03,

1.7)

0.2 (0.03, 1.7)

rs10772397

AA 42 (20) 164 (80) 1 1 36 (17) 180 (83) 1 1

AG 61 (24) 191 (76) 1.3 (0.8, 2.0) 1.1 (0.7, 1.8) 18 (13) 120 (87) 0.8 (0.4, 1.4) 0.7 (0.4, 1.4)

GG 14 (17) 69 (83) 0.8 (0.4, 1.5) 0.8 (0.4, 1.5) 1 (3) 35 (97) 0.1(0.01,1.1) 0.2 (0.02, 1.2)

rs1376251

AA 21 (27) 57 (73) 1 1 36 (17) 179 (83) 1 1

AG 44 (18) 199 (82) 0.6 (0.3, 1.1) 0.6 (0.3, 1.1) 18 (13) 121 (87) 0.7 (0.4, 1.4) 0.7 (0.4, 1.4)

GG 52 (24 ) 168 (76) 0.8 (0.5, 1.5) 0.8 (0.5, 1.5) 1 (3) 35 (97) 0.1(0.02,1.1) 0.2 (0.02, 1.2)

Green tea

n=525

n=395

rs2900554

AA 12 (8) 136 (92) 1 1 1 (1) 236 (99)

AC 19 (8) 234 (92) 0.9 (0.4, 2.0) 0.9 (0.4, 2.0) 0 (0) 132 (100) n/a n/a

CC 10 (8) 114 (92) 1.0 (0.4, 2.4) 1.0 (0.4, 2.5) 0 (0) 23 (100)

rs10772397

AA 16 (8) 182 (92) 1 1 0 (0) 217 (100)

AG 20 (8) 226 (92) 1.0 (0.5, 2.0) 0.9 (0.5, 1.9) 1 (1) 138 (99) n/a n/a

GG 5 (6) 76 (94) 0.8 (0.3, 2.1) 0.7 (0.3, 2.1) 0 (0) 36 (100)

rs1376251

AA 5 (7) 68 (93) 1 1 0 (0) 216 (100)

AG 17 (7) 220 (93) 1.1 (0.4, 3.0) 1.1 (0.4, 3.0) 1(1) 139 (99) n/a n/a

GG 19 (9) 196 (91) 1.3 (0.5, 3.7) 1.4 (0.5, 3.9) 0 (0) 36 (100)

Black Tea

n=500

n=362

rs2900554

AA 12 (8) 131 (92) 1 1 4 (2) 215 (98)

AC 22 (9) 215 (91) 1.1 (0.5, 2.3) 1.1 (0.5, 2.4) 5 (4) 116 (96) n/a n/a

CC 9 (8) 111 (92) 0.9 (0.4, 2.2) 0.9 (0.4, 2.3) 0 (0) 22 (100)

rs10772397

AA 13 (7) 181 (93) 1 1 3 (2) 196 (98)

AG 23 (10) 206 (90) 1.6 (0.8, 3.2) 1.4 (0.7, 3.0) 6 (5) 123 (95) n/a n/a

GG 7 (9) 70 (91) 1.4 (0.5, 3.6) 1.4 (0.5, 3.7) 0 (0) 34 (100)

rs1376251

AA 5 (7) 63 (93) 1 1 3 (2) 195 (98)

AG 21 (9) 207 (91) 1.3 (0.5, 3.5) 1.3 (0.5, 3.6) 6 (5) 124 (95) n/a n/a

GG 17 (8) 187 (92) 1.2 (0.4, 3.2) 1.2 (0.4, 3.4) 0 (0) 34 (100)




56

Table 11: Frequency of disliking grapefruit by rs2900554 SNP and rs1376251 SNP genotype

combinations in Caucasians and East Asians


Combination rs2900554 rs1376251 Dislike Neutral/ Like Dislike Neutral/ Like

n (%) n (%)

1 CC GG 38 (29) 91 (71) 8 (35) 15 (65)

2 CC AG - - - -

4 CC AA - - - -

3 AC GG 16 (20) 66 (80) 1 (9) 10 (91)

5 AC AG 30 (17) 143 (83) 16 (14) 100 (86)

7 AC AA - - 0 (0) 1 (100)

6 AA GG 0 (0) 11 (100) 0 (0) 1 (100)

8 AA AG 3 (4) 65 (96) 0 (0) 20 (100)

9 AA AA 6 (8) 70 (92) 21 (10) 193 (90)

Genotype combinations that were not possessed by any subjects are indicated by -.

57

Table 12: The frequency and odds not consuming grapefruit in the past month by TAS2R50

genotype in Caucasians and East Asians


Food No Yes Unadjusted Adjusted* No Yes Unadjusted Adjusted*

n (%) OR (95% CI) n (%) OR (95% CI)

rs2900554 n=509 n=350

AA 76 (52) 69 (48) 1 1 144 (68) 69 (32) 1 1

AC 138 (57) 103 (43) 1.2 (0.8, 1.8) 1.2 (0.8, 1.9) 73 (62) 44 (38) 0.8 (0.5, 1.3) 0.8 (0.5, 1.3)

CC 79 (64) 44 (36) 1.6 (1.0, 2.7) 1.5 (0.9, 2.6) 17 (85) 3 (15) 2.7 (0.8,9.6) 2.6 (0.7, 9.5)

Male n=149 n=92

AA 28 (67) 14 (33) 1 1 42 (75) 14 (25)

AC 48 (64) 27 (36) 0.9 (0.4, 2.0) 0.9 (0.4, 2.2) 20 (61) 13 (39) n/a n/a

CC 19 (59) 13 (41) 0.7 (0.3, 1.9) 0.9 (0.3, 2.4) 3 (100) 0 (0)

Female n=360 n=258

AA 48 (47) 55 (53) 1 1 102 (65) 55 (35) 1 1

AC 90 (54) 76 (46) 1.4 (0.8, 2.2) 1.4 (0.8, 2.5) 53 (63) 31 (37) 0.9 (0.5, 1.6) 0.9 (0.5, 1.7)

CC 60 (66) 31 (34) 2.2 (1.2, 4.0) 2.1 (1.2, 4.0) 14 (82) 3 (18) 2.5 (0.7, 9.1) 2.1 (0.6, 8.1)

rs10772397 n=509 n=350

AA 115 (59) 80 (41) 1 1 137 (69) 61 (31) 1 1

AG 136 (57) 102 (43) 0.9 (0.6, 1.4) 0.9 (0.6, 1.3) 75 (62) 46 (38) 0.7 (0.5, 1.2) 0.7 (0.5, 1.2)

GG 42 (55) 34 (45) 0.9 (0.5, 1.5) 0.8 (0.5, 1.4) 22 (71) 9 (29) 1.1 (0.5, 2.5) 1.2 (0.5, 2.8)

Male n=149 n=92

AA 39 (70) 17 (30) 1 1 41 (75) 14 (25) 1 1

AG 45 (63) 27 (37) 0.7 (0.4, 1.5) 0.7 (0.3, 1.6) 19 (63) 11 (37) 0.6 (0.2, 1.5) 0.6 (0.2, 1.5)

GG 11 (52) 10 (48) 0.5 (0.2, 1.3) 0.7 (0.2, 1.9) 5 (71) 2 (29) 0.9 (0.2, 4.9) 1.0 (0.2, 6.6)

Female n=360 n=257

AA 76 (55) 63 (45) 1 1 96 (67) 47 (33) 1 1

AG 91 (55) 75 (45) 1.0 (0.6, 1.6) 0.9 (0.6, 1.5) 55 (61) 35 (39) 0.8 (0.5, 1.4) 0.8 (0.4, 1.4)

GG 31 (56) 24 (44) 1.1 (0.6, 2.0) 1.1 (0.6, 2.1) 17 (71) 7 (29) 1.2 (0.5, 3.1) 1.1 (0.4, 3.0)

rs1376251 n=509 n=350

AA 45 (63) 26 (34) 1 1 136 (69) 61 (31) 1 1

AG 119 (52) 109 (48) 0.6 (0.4, 1.1) 0.5 (0.3,0.95) 76 (62) 46 (38) 0.7 (0.5, 1.2) 0.8 (0.5, 1.2)

GG 129 (61) 81 (39) 0.9 (0.5, 1.6) 0.8 (0.5, 1.5) 22 (71) 9 (29) 1.1 (0.5, 2.5) 1.2 (0.5, 2.8)

Male n=149 n=92

AA 20 (83) 4 (17) 1 1 41 (75) 14 (25) 1 1

AG 39 (57) 30 (43) 0.3 (0.08, 0.8) 0.2 (0.07,0.8) 19 (63) 11 (37) 0.6 (0.2, 1.5) 0.6 (0.2, 1.5)

GG 36 (64) 20 (36) 0.4 (0.1, 1.2) 0.4 (0.1, 1.4) 5 (71) 2 (29) 0.9 (0.2, 4.9) 1.0 (0.2, 6.6)

Female n=360 n=258

AA 25 (53) 22 (47) 1 1 95 (67) 47 (33) 1 1

GA 80 (50) 79 (50) 0.9 (0.5, 1.7) 0.7 (0.4, 1.4) 57 (62) 35 (38) 0.8 (0.5, 1.4) 0.8 (0.5, 1.5)

GG 93 (60) 61 (40) 1.3 (0.7, 2.6) 1.2 (0.6, 2.4) 17 (71) 7 (29) 1.2 (0.5, 3.1) 1.1 (0.4, 3.1)

*Logistic regression model adjusted for age, sex, BMI (kg/m2), physical activity (MET.hrs/wk),

smoking status, caloric intake (kcal/day) and season.


58

Table 13: The frequency and odds of not consuming grapefruit juice in the past month by

TAS2R50 genotype in Caucasians and East Asians



n (%) OR (95% CI) n (%) OR (95% CI)

rs2900554 n=505 n=341

AA 109 (77) 33 (23) 1 1 166 (80) 41 (20) 1 1

AC 188 (78) 54 (22) 1.1 (0.6, 1.7) 1.0 (0.6, 1.7) 88 (77) 27 (23) 0.8 (0.5, 1.4) 0.8 (0.5, 1.4)

CC 99 (82) 22 (18) 1.4 (0.8, 2.5) 1.2 (0.6, 2.2) 16 (83) 3 (16) 1.3 (0.4, 4.7) 1.1 (0.3, 4.2)

Male n=150 n=92

AA 34 (83) 7 (17) 1 1 43 (75) 14 (25)

AC 58 (75) 19 (25) 0.6 (0.2, 1.7) 0.6 (0.2, 1.6) 22 (69) 10 (31) n/a n/a

CC 27 (84) 5 (16) 1.1 (0.3, 3.9) 1.1 (0.3, 4.3) 3 (100) 0 (0)

Female n=355 n=249

AA 75 (74) 26 (26) 1 1 123 (82) 27 (18) 1 1

AC 130 (79) 35 (21) 1.3 (0.7, 2.3) 1.3 (0.7, 2.3) 66 (80) 17 (20) 0.9 (0.5, 1.8) 0.9 (0.4, 1.3)

CC 72 (81) 17 (19) 1.5 (0.7, 2.9) 1.3 (0.6, 2.6) 13 (81) 3 (19) 1.0 (0.3, 3.6) 0.8 (0.2, 3.0)

rs10772397 n=505 n=341

AA 150 (77) 45 (23) 1 1 154 (81) 37 (19) 1 1

AG 188 (80) 48 (20) 1.2 (0.7, 1.9) 1.1 (0.7, 1.8) 92 (77) 28 (23) 0.8 (0.5, 1.4) 0.8 (0.4, 1.3)

GG 58 (78) 16 (22) 1.1 (0.6, 2.1) 1.0 (0.5, 2.0) 24 (80) 6 (20) 1.0 (0.4, 2.5) 0.9 (0.3, 2.5)

Male n=150 n=92

AA 49 (87) 7 (13) 1 1 41 (75) 14 (25) 1 1

AG 54 (74) 19 (26) 0.4 (0.2, 1.1) 0.4 (0.1,1.0) 21 (70) 9 (30) 0.8 (0.3, 2.1) 0.8 (0.3, 2.4)

GG 16 (76) 5 (24) 0.5 (0.1, 1.6) 0.5 (0.1, 1.8) 6 (86) 1 (14) 2.1 (0.2, 18.5) 1.8(0.2,19.3)

Female n=355 n=249

AA 101 (73) 38 (27) 1 1 113 (83) 23 (17) 1 1

AG 134 (82) 29 (18) 1.7 (1.0, 3.0) 1.7 (0.9,2.9) 71 (79) 19 (21) 0.8 (0.4, 1.5) 0.7 (0.3, 1.4)

GG 42 (79) 11 (21) 1.4 (0.7, 3.1) 1.5 (0.7, 3.2) 18 (78) 5 (22) 0.7 (0.3, 2.2) 0.7 (0.2, 2.0)

rs1376251 n=505 n=341

AA 56 (79) 15 (21) 1 1 153 (81) 37 (19) 1 1

AG 175 (77) 52 (23) 0.9 (0.5, 1.7) 0.8 (0.4, 1.6) 93 (77) 28 (23) 0.8 (0.5, 1.4) 0.8 (0.4, 1.4)

GG 165 (80) 42 (20) 1.1 (0.5, 2.0) 0.9 (0.5, 1.8) 24 (80) 6 (20) 1.0 (0.4, 2.5) 0.9 (0.3, 2.5)

Male n=150 n=92

AA 22 (92) 2 (8) 1 1 41 (75) 14 (25) 1 1

AG 53 (76) 17 (24) 0.3 (0.06, 1.3) 0.2 (0.04, 1.1) 21 (70) 9 (30) 0.8 (0.3, 2.1) 0.8 (0.3, 2.4)

GG 44 (79) 12 (21) 0.3 (0.07, 1.6) 0.3 (0.06,1.5) 6 (86) 1 (14) 2.1 (0.2, 18.5) 1.8(0.2,19.3)

Female n=355 n=249

AA 34 (72) 13 (28) 1 1 112 (83) 23 (17) 1 1

AG 122 (78) 35 (22) 1.3 (0.6, 2.8) 1.1 (0.5, 2.5) 72 (79) 19 (21) 0.8 (0.4, 1.5) 0.7 (0.4, 1.4)

GG 121 (80) 30 (20) 1.5 (0.7, 3.3) 1.3 (0.6, 2.8) 18 (78) 5 (23) 0.7 (0.3, 2.2) 0.7 (0.2, 2.1)




59

Table 14: The frequency and odds of not consuming raw spinach, cooked spinach or Swiss

chard, and kale, mustard, collars, or turnip greens in the past month by TAS2R50 genotypes in




n (%) OR (95% CI) n (%) OR (95% CI)

Spinach,

Raw in Salad

n=518

n=354

rs2900554

AA 51 (35) 96 (65) 1 1 109 (51) 105 (49) 1 1

AC 77 (31) 170 (69) 0.9 (0.6, 1.3) 0.8 (0.5, 1.3) 62 (52) 58 (48) 1.0 (0.7, 1.6) 1.1 (0.7, 1.7)

CC 44 (35) 80 (65) 1.0 (0.6, 1.7) 1.0 (0.6, 1.6) 9 (45) 11 (55) 0.8 (0.3, 2.0) 0.7 (0.3, 1.8)

rs10772397

AA 56 (28) 142 (72) 1 1 100 (51) 98 (49) 1 1

AG 88 (36) 155 (64) 1.4 (1.0, 2.2) 1.4 (0.9, 2.1) 64 (52) 60 (48) 1.1 (0.7, 1.6) 1.1 (0.7, 1.7)

GG 28 (36) 49 (64) 1.5 (0.8, 2.5) 1.4 (0.8, 2.5) 16 (50) 16 (50) 1.0 (0.5, 2.1) 0.9 (0.4, 2.0)

rs1376251

AA 24 (33) 49 (67) 1 1 99 (50) 98 (50) 1 1

AG 77 (33) 156 (67) 1.0 (0.6, 1.8) 1.0 (0.5, 1.7) 65 (52) 60 (48) 1.1 (0.7, 1.7) 1.1 (0.7, 1.8)

GG 71 (33) 141 (67) 1.0 (0.6, 1.8) 1.0 (0.6, 1.8) 16 (50) 16 (50) 1.0 (0.5, 2.1) 0.9 (0.4, 2.0)

Spinach or

Swiss Chard,

Cooked

n=518

n=354

rs2900554

AA 50 (34) 97 (66) 1 1 55 (26) 159 (74) 1 1

AC 90 (36) 157 (64) 1.1 (0.7, 1.7) 1.1 (0.7, 1.6) 42 (35) 78 (65 ) 1.6 (0.98, 2.6) 1.7 (1.0, 2.8)

CC 43 (35) 81 (65) 1.0 (0.6, 1.7) 0.9 (0.6, 1.6) 6 (30) 14 (70) 1.3 (0.5, 3.5) 1.2 (0.4, 3.3)

rs10772397

AA 66 (33) 132 (67) 1 1 53 (27) 145 (73) 1 1

AG 90 (37) 153 (63) 1.2 (0.8, 1.7) 1.1 (0.8, 1.7) 39 (31) 85 (69) 1.2 (0.8, 2.1) 1.4 (0.8, 2.3)

GG 27 (35) 50 (65) 1.1 (0.6, 1.9) 1.1 (0.6, 1.9) 11 (34) 21 (66) 1.4 (0.7, 3.2) 1.4 (0.6, 3.1)

rs1376251

AA 24 (33) 49 (67) 1 1 53 (27) 144 (73) 1 1

AG 79 (34) 154 (66) 1.0 (0.6, 1.8) 0.9 (0.5, 1.6) 39 (31) 86 (69) 1.2 (0.8, 2.0) 1.3 (0.8, 2.2)

GG 80 (38) 132 (62) 1.2 (0.7, 2.2) 1.1 (0.6, 1.9) 11 (34) 21 (66) 1.4 (0.6, 3.2) 1.3 (0.6, 3.1)

Kale,

mustard,

collard, or

turnip greens

n=284

n=354

rs2900554 62 (50) 61 (50) 1 1

AA 46 (59) 32 (41) 1 1 37 (55) 30 (45) 1.2 (0.7, 2.2) 1.2 (0.6, 2.3)

AC 71 (50) 71 (50) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 2 (25) 6 (75) 0.3 (0.06, 1.7) 0.3(0.05,1.4)

CC 43 (67) 21 (33) 1.4 (0.7, 2.8) 1.4 (0.7, 3.0)

rs10772397

AA 58 (51) 55 (49) 1 1 61 (53) 54 (47) 1 1

AG 73 (58) 53 (42) 1.3 (0.8, 2.2) 1.4 (0.8, 2.4) 34 (52) 32 (48) 0.9 (0.5, 1.7) 0.9 (0.5, 0.7)

GG 29 (64) 16 (36) 1.7 (0.8, 3.5) 1.9 (0.9, 3.9) 6 (35) 11 (65) 0.5 (0.2, 1.4) 0.4 (0.1, 1.4)

rs1376251

AA 21 (55) 17 (45) 1 1 61 (53) 54 (47) 1 1

AG 64 (50) 65 (50) 0.8 (0.4, 1.7) 0.8 (0.4, 1.5) 34 (52) 32 (48) 0.9 (0.5, 1.7) 0.9 (0.5, 1.7)

GG 75 (64) 42 (36) 1.5 (0.7, 3.0) 1.5 (0.7, 3.2) 6 (35) 11 (65) 0.5 (0.2, 1.4) 0.5 (0.2, 1.1)



60

Table 15: The frequency and odds of not consuming soymilk and chocolate in the past month by

TAS2R50 genotypes in Caucasians and East Asians



n (%) OR (95% CI) n (%) OR (95% CI)

Soymilk

n=437

n=350

rs2900554

AA 75 (61) 47 (39) 1 1 93 (44) 118 (56) 1 1

AC 130 (62) 79 (38) 1.0 (0.7, 1.6) 1.1 (0.7, 1.8) 51 (43) 68 (57) 1.0 (0.6, 1.5) 0.9 (0.6, 1.5)

CC 68 (64) 38 (36) 1.1 90.7, 1.9) 1.2 (0.7, 2.1) 6 (30) 14 (70) 0.5 (0.2, 1.5) 0.5 (0.2, 1.4)

rs10772397

AA 105 (63) 61 (67) 1 1 88 (45) 107 (55) 1 1

AG 125 (61) 79 (39) 0.9 (0.6, 1.4) 0.9 (0.6, 1.3) 53 (43) 70 (57) 0.9 (0.6, 1.5) 0.9 (0.6, 1.4)

GG 43 (64) 24 (36) 1.0 (0.6, 1.9) 1.0 (0.5, 1.8) 9 (28) 23 (72) 0.5 (0.2, 1.1) 0.5 (0.2, 1.1)

rs1376251

AA 37 (63) 22 (37) 1 1 88 (45) 106 (55) 1 1

AG 120 (62) 74 (38) 1.0 (0.5, 1.8) 1.0 (0.5, 1.8) 53 (43) 71 (57) 0.9 (0.6, 1.4) 0.9 (0.6, 1.4)

GG 116 (63) 68 (37) 1.0 (0.6, 1.9) 1.1 (0.6, 2.0) 9 (28) 23 (72) 0.5 (0.2, 1.1) 0.5 (0.2, 1.1)

Chocolate

candy bar or

packet

n=518

n=356

rs2900554

AA 31 (21) 116 (79) 1 1 36 (17) 180 (83) 1 1

AC 39 (16) 208 (84) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 24 (20) 96 (80) 1.3 (0.7, 2.2) 1.2 (0.7, 2.2)

CC 21 (17) 103 (83) 0.8 (0.4, 1.4) 0.8 (0.4,1 .5) 1 (5) 19 (95) 0.3 (0.03, 2.0) 0.3 (0.03, 2.0)

rs10772397

AA 30 (15) 168 (85) 1 1 34 (17) 166 (83) 1 1

AG 45 (19) 198 (81) 1.3 (0.8, 2.1) 1.3 (0.8, 2.2) 25 (20) 99 (80) 1.2 (0.7, 2.2) 1.3 (0.7, 2.3)

GG 16 (21) 61 (79) 1.5 (0.8, 2.9) 1.6 (0.8, 3.1) 2 (6) 30 (94) 0.3 (0.07, 1.4) 0.3 (0.07, 1.4)

rs1376251

AA 16 (22) 57 (78) 1 1 34 (17) 165 (83) 1 1

AG 41 (18) 192 (82) 0.8 (0.4, 1.5) 0.8 90.4, 1.5) 25 (20) 100 (80) 1.2 (0.7, 2.2) 1.2 (0.7, 2.3)

GG 34 (16) 178 (84) 0.7 (0.4, 1.3) 0.7 (0.3, 1 .3) 2 (6) 30 (94) 0.3 (0.07, 1.4) 0.3 (0.07, 1.3)



61

Table 16: Frequency and odds of high naringin or PTC taste intensity by TAS2R50 genotype in



High Medium/

Low

Unadjusted Adjusted* High Medium/

Low

Unadjusted Adjusted*

n (%) OR (95% CI) n (%) OR (95% CI)

Naringin

Taste

Intensity

n=108

n=103

rs2900554

AA 6 (20) 24 (80) 1 1 14 (24) 45 (76) 1 1

AC 14 (25) 41 (75) 1.4 (0.5, 4.0) 1.5 (0.5, 4.8) 15 (39) 23 (61) 2.1 (0.9, 5.1) 2.2 (0.9, 5.4)

CC 13 (57) 10 (43) 5.2 (1.5, 7.6) 5.4 (1.5, 9.3) 2 (33) 4 (67) 1.6 (0.3, 9.7) 1.5 (0.2, 8.8)

rs10772397

AA 16 (31) 36 (69) 1 1 14 (26) 39 (74) 1 1

AG 13 (32) 28 (68) 1.1 (0.4, 2.5) 1.1 (0.4, 2.8) 15 (38) 25 (62) 1.7 (0.7, 4.1) 1.7 (0.7, 4.1)

GG 4 (27) 11 (73) 0.8 (0.2, 3.0) 0.8 (0.2, 3.1) 2 (20) 8 (80) 0.7 (0.1, 3.7) 0.7 (0.1, 3.6)

rs1376251

AA 4 (21) 15 (79) 1 1 14 (26) 39 (74) 1 1

AG 14 (30) 33 (70) 1.6 (0.5, 5.7) 1.4 (0.4, 5.2) 15 (38) 25 (62) 1.7 (0.7, 4.0) 1.7 (0.7, 4.1)

GG 15 (36) 27 (64) 2.1 (0.6, 7.4) 2.0 (0.5, 7.4) 2 (20) 8 (80) 0.7 (0.1, 3.7) 0.7 (0.1, 3.6)

PTC Taste

Intensity

n= 560

n=404

rs2900554

AA 54 (34) 107 (66) 1 1 111 (45) 133 (55) 1 1

AC 80 (30) 188 (70) 0.8 (0.6, 1.3) 0.9 (0.6, 1.3) 69 (51) 67 (49) 1.2 (0.8, 1.9) 1.3 (0.8, 2.0)

CC 45 (34) 86 (66) 1.0 (0.6, 1.7) 1.1 (0.6, 1.7) 9 (38) 15 (62) 0.7 (0.3, 1.7) 0.7 (0.3, 1.6)

rs10772397

AA 80 (38) 132 (62) 1 1 103 (46 ) 120 (54) 1 1

AG 68 (26) 196 (74) 0.6 (0.4, 0.9) 0.6 (0.4, 0.9) 72 (50) 71 (50) 1.2 (0.8, 1.8) 1.2 (0.8, 1.8)

GG 31 (37) 53 (63) 1.0 (0.6, 1.6) 1.0 (0.6, 1.6) 14 (37) 24 (63) 0.7 (0.3, 1.4) 0.6 (0.3, 1.3)

rs1376251

AA 33 (41) 47 (59) 1 1 103 (46) 120 (54) 1 1

AG 71 (28) 182 (72) 0.6 (0.3, 0.9) 0.6 (0.3, 0.9) 72 (50) 71 (50) 1.2 (0.8, 1.8) 1.2 (0.8, 1.8)

GG 75 (33) 152 (67) 0.7 (0.4, 1.2) 0.7 (0.4, 1.2) 14 (37) 24 (63) 0.7 (0.3, 1.4) 0.6 (0.3, 1.3)

*Binary logistic regression adjusted for age and sex.

62

Table 17: Frequency and odds of disliking fruits and vegetables by ethnocultural group

*Binary logistic regression adjusted for age, sex, BMI (kg/m2), physical activity level


Food N Dislike Like Unadjusted Adjusted*

n (%) OR (95% CI)

Grapefruit

925

Caucasian 93 (17) 446 (83) 1 1

Asian 46 (12) 340 (88) 0.7 (0.4, 0.95) 0.6 (0.4, 0.94)

Grapefruit Juice 909

Caucasian 124 (23) 411 (77) 1 1

Asian 47 (13) 327 (87) 0.5 (0.3, 0.7) 0.5 (0.3, 0.7)

Asparagus 900

Caucasian 37 (7) 491 (93) 1 1

Asian 19 (5) 353 (95) 0.7 (0.4, 1.3) 0.7 (0.4, 1.3)

Broccoli 938

Caucasian 22 (4) 525 (96) 1 1

Asian 4 (1) 387 (99) 0.3 (0.08, 0.7) 0.3 (0.08, 0.7)

Brussels Sprouts 807

Caucasian 106 (22) 380 (78) 1 1

Asian 38 (12) 283 (88) 0.5 (0.3, 0.7) 0.5 (0.4, 0.8)

Cauliflower 921

Caucasian 43 (8) 496 (92) 1 1

Asian 11 (3) 371 (97) 0.3 (0.2, 0.7) 0.3 (0.2, 0.6)

Endives 464

Caucasian 22 (8) 268 (92) 1 1

Asian 10 (6) 164 (94) 0.7 (0.3, 1.6) 0.8 (0.3, 1.7)

Kale

509

Caucasian 26 (9) 268 (91) 1 1

Asian 9 (4) 206 (96) 0.5 (0.2, 1.0) 0.5 (0.2, 1.2)

Onion Raw 929

Caucasian 108 (20) 432 (80) 1 1

Asian 91 (23) 298 (77) 1.2 (0.9, 1.7) 1.1 (0.8, 1.6)

Parsley 910

Caucasian 23 (4) 512 (96) 1 1

Asian 35 (9) 340 (91) 2.3 (1.3, 4.0) 2.1 (1.2, 3.8)

63

Table 17 continued: Frequency and odds of disliking fruits and vegetables by ethnocultural

group




n (%) OR (95% CI)

Radicchio 501

Caucasian 33 (10) 313 (90) 1 1

Asian 11 (6) 188 (94) 0.6 (0.3, 1.1) 0.6 (0.3, 1.4)

Radish 882

Caucasian 97 (19) 420 (81) 1 1

Asian 37 (10) 328 (90) 0.5 (0.3, 0.7) 0.5 (0.3, 0.8)

Rapini 345

Caucasian 26 (12) 189 (88) 1 1

Asian 2 (2) 128 (98) 0.1 (0.03, 0.5) 0.09 (0.02, 0.4)

Spinach 938

Caucasian 26 (5) 522 (95) 1 1

Asian 10 (3) 380 (97) 0.5 (0.3, 1.1) 0.5 (0.2, 1.0)

Turnip 775

Caucasian 84 (18) 379 (82) 1 1

Asian 28 (9) 284 (91) 0.5 (0.3, 0.7) 0.4 (0.3, 0.7)

64

Table 18: Frequency and odds of disliking soy products by ethnocultural group




n (%) OR (95% CI)

Soymilk

839

Caucasian 107 (24) 347 (76) 1 1

Asian 18 (5) 367 (95) 0.2 (0.1, 0.3) 0.2 (0.09, 0.3)

Tofu 893

Caucasian 76 (15) 426 (85) 1 1

Asian 2 (1) 389 (99) 0.03 (0.007,0.1) 0.03(0.007,0.1)

65

Table 19: Frequency and odds of disliking cocoa-containing products by ethnocultural group




n (%) OR (95% CI)

Dark Chocolate

941

Caucasian 38 (6) 510 (93) 1 1

Asian

30 (8) 363 (92) 1.1 (0.7, 1.8) 1.3 (0.6, 2.3)

Bitter-Sweet or

Semi-Sweet

Chocolate

934

Caucasian 27 (5) 517 (95) 1 1

Asian 18 (5) 372 (95) 0.9 (0.5, 1.7) 1.2 (0.6, 2.3)

66

Table 20: Frequency and odds of disliking alcoholic beverages by ethnocultural group



Food N Dislike Like Unadjusted Adjusted

n (%) OR (95% CI)

Beer

904

Caucasian 134 (25) 396 (75) 1 1

Asian 124 (33) 250 (67) 1.5 (1.1, 2.0) 1.3 (0.96, 1.8)

Red Wine 987

Caucasian 77 (15) 451 (85) 1 1

Asians 70 (19) 299 (81) 1.4 (1.0, 2.0) 1.3 (0.9, 1.8)

White Wine 876

Caucasian 58 (11) 465 (89) 1 1

Asian 57 (16) 296 (84) 1.5 (1.0, 2.3) 1.5 (1.0, 2.3)

67

Table 21: Frequency and odds of disliking caffeinated beverages by ethnocultural group




n (%) OR (95% CI)

Coffee

931

Caucasian 117 (22) 424 (78) 1 1

Asian 55 (14) 335 (86) 0.6 (0.4, 0.9) 0.5 (0.4, 0.8)

Green Tea 917

Caucasian 41 (8) 484 (92) 1 1

Asian 1 (1) 391 (99) 0.03 (0.004, 0.2) 0.02 (0.003 ,0.2)

Black Tea 862

Caucasian 43 (9) 457 (91) 1 1

Asian 9 (2) 353 (98) 0.3 (0.1, 0.6) 0.2 (0.1, 0.5)

68

Chapter 6

Discussion

69

The results of this study demonstrate that genetic variation in the genomic region of the

TAS2R50 gene is associated with differences in bitter taste perception, food preferences and

dietary intake in a young Caucasian and East Asian population. The main finding of this study

was that genetic variation in the TAS2R50 gene region was found to be associated with naringin

taste intensity in Caucasians, grapefruit and grapefruit juice preference in Caucasians and East

Asians, and grapefruit intake in Caucasian females, with the rs2900554 SNP accounting for the

association. A greater proportion of individuals homozygous for the C allele for the rs2900554

SNP reported a high naringin taste intensity, disliking of grapefruit and grapefruit juice and not

consuming grapefruit in the past month compared to individuals homozygous for the A allele.

These results indicate that the T2R50 taste receptor may be associated with naringin sensing.

Food preference analyses revealed that the genetic variation in the TAS2R50 gene

region was also associated with preference of spinach, kale, radish, soymilk and bitter-

sweet/semi-sweet chocolate. Associations seen between genetic variation in the TAS2R50 gene

region and food preferences were limited to one ethnocultural group for these foods, except for

kale. This was unlike the association between grapefruit/grapefruit juice preference and

TAS2R50 genotype, which was found to be significant in both East Asians and Caucasians, thus

strengthening this finding. The differences in associations between ethnocultural groups for

TAS2R50 genotype and food preference may be due to cultural differences in food practices or

differences in linkage disequilibrium between SNPs. However, a possibility exists that some

food preference associations are false positives due to multiple comparisons between TAS2R50

genotype and food preferences. After a Bonferroni correction of food preference results for 25

potentially bitter foods or beverages (p<0.002), only grapefruit preference remained

significantly associated with the TAS2R50 gene region. Furthermore, a limited number of

70

individuals reported disliking spinach, kale, radish, soymilk and bitter-sweet/semi-sweet

chocolate to adequately assess the significance of the association between TAS2R50 genotype

and food preference in one or both ethnocultural groups.

Food preference analyses revealed multiple bitter tastants may be ligands of the T2R50

taste receptor. To our knowledge, no study has attempted to deorphanize the T2R50 taste

receptor. Since we found that genetic variation in the TAS2R50 gene region was associated with

the preference of grapefruit, grapefruit juice, kale, spinach, radish, soymilk, and bitter-

sweet/semi-sweet chocolate, it is possible that bitter tastants within these foods bind to the

T2R50 taste receptor. Naringin, a bitter compound in grapefruit, has the greatest potential of

being a ligand for the T2R50 taste receptor, since naringin taste intensity, grapefruit preference

and grapefruit consumption were all associated with the genetic variation in the TAS2R50 gene

region.

It is not known which bitter compounds in kale, spinach, radish, soymilk and bitter-

sweet/semi-sweet chocolate are driving the association between genetic variation in the

TAS2R50 gene region and preference for these foods. It is possible that these foods contain

naringin or structurally similar bitter compounds, however, structural similarity to naringin is

not a requirement for T2R receptor activation. In vitro cell studies have found that several bitter

taste receptors are broadly tuned, detecting a variety of structurally diverse compounds78,84

. Kale

and radishes are part of the cruciferous vegetable family and contain glucosinolates which are

responsible for their characteristic bitter taste38

. Like PTC/PROP, glucosinolates contain

thiocyanate moieties that may be decoded by the same taste receptors3. Spinach and kale are

high in carotenoids such as lutein and β-carotene116

, soymilk contains isoflavones such as

genistein and daidzein5, and bitter-sweet/semi-sweet chocolate contain theobromine, caffeine, l-

71

leucine and catechin flavonoids117

. All these compounds have been reported to elicit a bitter

taste in humans, and may be responsible for their association with genetic variation in the

TAS2R50 gene region.

Research has shown women to be more sensitive to bitter tastes than men50,118,119

. A

greater proportion of females have been found to be PROP supertasters and have been shown to

have a higher density of fungiform papillae and a greater number of taste pores per taste

papillae50,118

. Our study found a significant association between the rs2900554 SNP and

grapefruit consumption in females but not males, with females homozygous for the C allele not

consuming grapefruit in the past month more than females homozygous for the A allele. This

suggests that the bitter taste of naringin may influence the consumption of grapefruit to a greater

extent in women than it does in men. It is possible that among C allele carriers for the

rs2900554 SNP, women are more sensitive to the bitter taste of naringin and thus avoid the

consumption of grapefruit.

Bitter taste intensity analyses revealed that genetic variation in the TAS2R50 gene

region was associated with both naringin and PTC taste intensity. The trend in naringin taste

intensity by TAS2R50 genotype followed the trend in grapefruit and grapefruit juice preference

as well as grapefruit consumption in females for the rs2900554 SNP, thus strengthening the

possibility that this SNP is associated with naringin sensing. Furthermore, this SNP was not

associated with PTC taste intensity.

The rs10772397 SNP and the rs1376251 SNP were found to be associated with PTC

taste intensity in Caucasians, with heterozygotes reporting a significantly lower odds of

experiencing a high PTC taste intensity compared to individuals homozygous for the A allele.

However, this trend was not seen in the East Asian population. Furthermore, food preference

72

analyses of glucosinolate containing foods did not follow a similar trend in association with

TAS2R50 genotype as PTC taste intensity did. Previous research does not support an

association between PTC taste intensity and the TAS2R50 gene region, or any loci on

chromosome 12 where the TAS2R50 gene region is situated. Variation in the TAS2R38 gene,

located on chromosome 7, has been thought to account for up to 85% of PTC threshold

sensitivity87

. Furthermore, loci on chromosome 5120

and 16121

have also been associated with

PTC/PROP sensitivity in genome-wide scans. While an in vitro cell study found that human

embryonic kidney (HEK) 293 cells transfected with the T2R4 taste receptor, located on

chromosome 7, was activated by high levels of PTC77

.

Previous work has found an association between genetic variation in the TAS2R50

gene and myocardial infarction risk (MI). A three stage genome-wide association study14

and a

population-based prospective study15

found an association between the rs1376251 SNP and MI

risk, with G allele carriers experiencing an increased risk of MI compared to individuals

homozygous for the A allele. It was suggested that the rs1376251 SNP may lead to MI by

influencing bitter taste perception and in turn dietary choices14,15

. Our study found that G allele

carriers for the rs1376251 SNP disliked grapefruit significantly more than individuals

homozygous for the A allele in both Caucasians and East Asians, however this observation was

not related to a significant reduction in grapefruit consumption by G allele carriers. The dislike

of kale and spinach were also found to be associated with the rs1376251 SNP in East Asians,

with G allele carriers disliking kale and spinach at a greater frequency than individuals

homozygous for the A allele. Differences in the consumption of kale and spinach were however

not observed, perhaps due to limitations of the FFQ (discussed in section 6.1). Although a clear

trend in dietary consumption by rs1376251 genotype was not observed, our study still shows

73

that grapefruit, kale and spinach preference differ by rs1376251 genotypes. Difference in the

consumption of these types of foods may influence one‟s risk of CVD and MI. Studies

examining food consumption over time in relation to CVD endpoints found that the

consumption of citrus fruit and green leafy or cruciferous vegetables are particularly protective

against CVD122,123

.

Dietary intake is a complex behavior that is affected by physiological, environmental,

economic and sociocultural factors1. A relationship is thought to exist between taste perception,

food preferences and dietary intake, however this relationship is often assessed indirectly,

through multiple studies on different populations54

. A strength of our study is that it assesses

bitter taste perception, food preferences and dietary intake in one large, healthy, multi-

ethnocultural population. We found that genetic variation in the TAS2R50 gene region

influenced bitter taste intensity, food preferences and dietary intake, specifically naringin taste

intensity, grapefruit preference and grapefruit intake. Individuals who had the CC genotype for

the rs2900554 SNP were more likely to experience a high naringin taste intensity and dislike

grapefruit. In females, these individuals were also more likely to never consume grapefruit.

Barriers to grapefruit and bitter food consumption are important to health promotion and disease

prevention. Naringin, the major flavonoid that provides grapefruit with its characteristic bitter

taste, is an antioxidant and has been shown to have the potential to be protective against diseases

such as CVD92-94

and cancer95,96

. The preference of kale, spinach, radish, soymilk and bitter-

sweet/semi-sweet chocolate were associated with genetic variation in the TAS2R50 gene. These

foods have also been shown to contain phytonutrients and consumption of these foods may offer

additional health benefits5.

74

6.1 Limitations

Consideration must be given to potential limitations in the design of this study.

Subjects reported bitter taste intensity on an anchored 9-point category scale which has been

criticized because it may reduce, obscure or reverse variability in responses due to the ceiling

effect limiting response range and use of verbal descriptors that may not be uniformly

understood60,107

. Since there was no definite trend between PTC taste intensity and preference or

intake for glucosinolate containing vegetables by TAS2R50 genotype, it is possible that

misclassification of PTC taste intensity resulted with the use of the 9-point category rating scale.

However, since naringin taste intensity was found to vary by TAS2R50 genotype in the same

direction as grapefruit preference and intake, it is unlikely that severe misclassification in

naringin taste intensity resulted. It is possible that with the use of the generalized Linear

Magnitude Scale (gLMS), which is considered the gold standard50

, further variability in naringin

taste intensity response will be seen between TAS2R50 genotype groups. The gLMS is a scale

with seven descriptive adjectives ranging from “nothing” (or zero) to “strongest imaginable

sensation of any kind”, spaced empirically allowing the scale to have ratio properties50,124

.

Furthermore, the gLMS scale is also thought to avoid bias caused by the ceiling effect and the

use of descriptive adjectives50,107

.

The Toronto Nutrigenomics and Health (TNH) study uses a 196-item, self-

administered, semi-quantitative FFQ, modified from the Willet FFQ109,110

, to assess habitual

consumption over a one month period. Although the Willet FFQ has been extensively validated

for use in North America65

and possesses multiple benefits for use in large epidemiological

studies64

, it may not have been the best method to assess intake of bitter foods in our study. Our

63-item food preference checklist identified preference for grapefruit, spinach, kale, radish,

75

soymilk and bitter-sweet/semi-sweet chocolate as being associated with genetic variation in the

TAS2R50 gene region. Our intent was to then assess if the consumption of these foods were also

associated with TAS2R50 genotype, however the FFQ used to assess habitual consumption in

the TNH study did not measure radish or bitter-sweet/semi-sweet chocolate consumption. A

question that assessed the consumption of chocolate candy bars or packets was used as a

substitute to assess bitter-sweet/semi-sweet chocolate, however chocolate candy bars and

packets may differ significantly in theobromine, caffeine, l-leucine and catechin flavonoids

levels, which are responsible for the bitter taste in bitter-sweet/semi-sweet chocolate117

.

Furthermore, questions assessing kale and cooked spinach consumption grouped these foods

with nutritiously similar foods and therefore direct assessment of these foods was not possible64

.

Finally, assessment of dietary intake over the past month may not take into account seasonal

variation in food intake64

. However, a cross-tabular analysis of grapefruit (p=0.4), grapefruit

juice (p=0.4), kale, spinach (raw p=0.6 and cooked p=0.4), chocolate (p=0.9) and soymilk (0.04)

consumption by season reveals that the consumption of most foods do not vary greatly by

season in our population (data not shown).

Despite the FFQ‟s limitations, a satisfactory analysis of grapefruit and grapefruit juice

intake may have been accomplished. This is because the FFQ used separate questions to assess

consumption of grapefruit and grapefruit juice. Furthermore, the association between grapefruit

consumption in Caucasian females and genetic variation in the TAS2R50 gene region mirrored

the associations between TAS2R50 genotype and grapefruit preference as well as naringin taste

intensity, alluding to the possibility of adequate assessment in this population. Additional

studies examining the association between genetic variation in the TAS2R50 gene region and

76

dietary intake may benefit from using multiple diet records, collected seasonally, over a one

year period, to assess dietary intake of bitter foods.

Our research demonstrated that three SNPs in the TAS2R50 gene region are associated

with, bitter taste intensity, food preferences and dietary intake, however, the functional

significance of these three SNPs is unknown. The rs2900554 SNP is located 3859 base pairs

downstream of the TAS2R50 gene and may be located in the 3‟UTR region of the gene85

.

Genetic variation in the 3‟UTR region of a gene can affect mRNA folding and translational

efficiency. Since a greater proportion of G allele carriers for the rs2900554 SNP reported having

a high naringin taste intensity, disliking grapefruit and never consuming grapefruit, it is possible

that these individuals translate and express a greater number of T2R50 taste receptors. The

rs1376251 and the rs10772397 SNPs are located within the exonic region of the TAS2R50

gene85

. The rs1376251 SNP causes a cysteine to tyrosine amino acid change at position 203 in

the taste receptor protein, which may affect bitter taste perception by changing the structure of

the bitter taste receptor protein85

. While the rs10772397 SNP is a synonymous substitution that

may affect bitter taste perception by influencing the mRNA stability and T2R50 translation85

.

Although a possible genetic function exists for the association between genetic

variation in the TAS2R50 gene region and differences in bitter taste perception, food preference

and dietary intake, there is still a possibility that this association is due to linkage

disequilibrium. Haploview software115

was used to assess the CEPH (Utah residents with

ancestry from northern and western Europe) population from the International HapMap Project

(release 22) and it was found that the TAS2R50 gene was located within a ~ 229 kb haplotype

block that contains eight bitter taste receptor genes and 6 genes that encode proline-rich

protein125

. Since there is high linkage disequilibrium within the haplotype block a possibility

77

exists that the three SNPs examined in our study are not functionally significant, but are in

linkage disequilbrium with functionally significant SNPs in other genes within the haplotype

block. A pair-wise tag SNP analysis (r2

≥ 0.8) of the CEPH population (release 22) revealed that

the rs2900554 and rs1376251 SNPs were markers of genetic variation at other SNPs located in

coding or regulatory regions of additional bitter taste receptor genes125

. The rs2900554 SNP

was found to be strongly linked to a SNP (rs10772420)125

in the coding region of the TAS2R48

gene that causes a missense mutation (Cys299Arg) in the taste receptor protein85

. To our

knowledge no study has attempted to deorphan this receptor. The rs10845293 and rs7134036

SNPs, found in the 5‟ region of the TAS2R44 gene, were also found to be captured by the

rs2900554 SNP125

. The T2R44 receptor has been associated with the sensing of saccharine,

acesulfamce K and aristolochic acid79,83

, however to our knowledge no study has tested naringin

as a possible ligand to the T2R44 receptor. The rs10772420, rs10845293 and rs7134036 SNPs

were also strongly linked to the rs2900554 SNP in a Japanese population in Tokyo and a Han

Chinese population in Beijing (release 22)125

. The possibility that these SNPs are driving the

association between the TAS2R50 gene region and differences in naringin taste perception,

grapefruit preference and grapefruit intake cannot be ruled out in our study.

The rs1376251 SNP was found to be strongly linked (r2

≥ 0.8) to 3 SNPs in the coding

region of the TAS2R49 gene125

, causing missense mutations85

, as well as 6 SNPs in the 5‟ region

of the TAS2R49 gene125

. One SNP in the 5‟ region of the TAS2R48 gene was also strongly

linked to the rs2900554 SNP125

. To our knowledge, no study has identified ligands associated

with the TAS2R48 or TAS2R49 genes. The rs10772397 SNP was not found to capture the

variation of any other SNP within the haplotype block that the TAS2R50 gene is located, in the

CEPH population125

. Additional studies are needed to determine if linkage disequilibrium is

78

responsible for the association between the rs2900554 and rs1376251 SNPs, and bitter taste

intensity, food preferences and dietary intake.

6.2 Future Direction

Deorphanizing the T2R50 bitter taste receptor would help address issues relating to the

possible function of the T2R50 taste receptor. Heterologous expression studies of TAS2R-

cDNA in HEK 293 cells have been used previously to deorphanize T2Rs with success77,80

.

Numerous bitter tastants can be exposed to HEK 293 cells expressing T2R50 taste receptors and

activation of the T2R50 receptor can be monitored through calcium imaging analysis77,80

.

Furthermore, the response of common variants of the T2R50 protein, to bitter tastants, could be

assessed in order to determine if genetic variation in the TAS2R50 gene affects T2R50 receptor

function.

Although studies are needed to directly identify the function of the T2R50 taste

receptor and its variants, additional studies are needed to determine to what extent these variants

influence human sensory perception and dietary behaviors. To our knowledge, the present study

is the first to examine if genetic variation in the TAS2R50 gene region is associated with bitter

taste perception, food preferences and dietary intake. Further studies may examine if genetic

variants in the TAS2R50 gene region affect the perception of additional bitter tastants, as well as

the preference and consumption of additional bitter foods. Our study examined if genetic

variants in the TAS2R50 gene were associated with bitter taste intensity, however bitter taste

thresholds may also be important in explaining how genetic variation in the TAS2R50 gene

affects bitter perception. Furthermore, additional SNPs exist within the TAS2R50 gene and their

effects have yet to be determined.

79

6.3 Implications

Research on how genetic variation in bitter taste influences taste perception, food

preferences and dietary intake in humans is important because it is necessary to explain the

behavioral significance of common variation in the T2R gene family. Results of this study

demonstrate that common variants in the TAS2R50 gene region are associated with differences

in bitter taste perception, food preferences and dietary intake, specifically naringin taste

intensity, grapefruit preference and grapefruit intake. Preferences for other bitter foods were also

associated with genetic variation in the TAS2R50 gene region, however to a lesser extent than

grapefruit preference. These results suggest possible ligands of the T2R50 taste receptor.

Moreover, these results help further the understanding of the association between the TAS2R50

gene and MI risk by establishing a possible explanation for the association between the

rs1376251 SNP and MI risk.

Little is known about how genetic variation in the T2R gene family affects bitter taste

perception. Research in this area may help identify bitter sensitive populations who are at risk of

avoiding the consumption of bitter tasting foods with putative health benefits. Understanding

how bitter taste may be a barrier to a healthy diet can help in the creation of health promotion

programs targeted to bitter sensitive populations. Many phytonutrients have been shown to have

health benefits and are thus sought after in foods5. However, phytonutrients may cause a bitter

taste and are thus removed, or masked using flavorants such as sugar or salt, in order to improve

palatability5. Improving the understanding of how genetic variation affects taste receptor

function and bitter taste perception can help the food industry create novel methods of masking

or modulating bitter tastes that do not compromise the nutritional value of the food.

80

6.4 Conclusion

Bitter taste perception is a variable trait that is influenced by genetic variation in the

T2R bitter taste receptor gene family11

. This study assessed the association between genetic

variation in the genomic region of the TAS2R50 bitter taste receptor gene and differences in

bitter taste perception, food preference and dietary intake. Genetic variation in the TAS2R50

gene region was found to be associated with bitter taste intensity, food preferences and dietary

intake. A higher frequency of C allele carriers for the rs2900554 gene reported experiencing a

high naringin taste intensity, disliking grapefruit and not consuming grapefruit in the past

month, compared to individuals with the AA genotype. This suggests that naringin may be a

ligand of the T2R50 bitter taste receptor. In order to understand the function of the TAS2R50

bitter taste receptor gene and its variants, additional studies should be undertaken to deorphan

the T2R50 taste receptor.

81

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