Connecting biobanks - adding value in the genetics of complex traits

The Australian Twin Collections

Biobank

Nick MartinQueensland Institute of Medical Research

Brisbane

MRC CAiTE Symposium Bristol

January 12, 2011

My brief…• how biobanks can be beneficial for

researchers • what’s happening and what is

accomplished • some results of projects I’m involved in

How beneficial biobanks can be  for research[ers] (1)

1 page of authors and affiliations!

2 pages of authors and affiliations !

How beneficial biobanks can be  for research[ers] (2)

• Founded 1978• Voluntary enrolment – schools, media, etc• ~30,000 pairs enrolled (~15% of all pairs)• Two adult cohorts studied

• 1893-1964 (5967 pairs), 1965-1971 (4629 pairs)• Typical of population wrt psychiatric symptoms,

personality, social class & education (females)• Males slightly more educated and middle class

• New cohort of ~8000 pairs (born 1972-85)

Australian Twin Registry

1980 1990 2000

Cohort 1

Cohort 2

Siblings

Parents

1985 1995

N12 5375

N12 6014

N23, A, D 3808 p / 576 s

N12, A, D 3051 p / 468 s

DSM-IIIR MD, PD 2456 p / 771 s

N12, A, D 1279 p / 558 s

N12, A, D 2270 p / 518 s

765

N23, CIDI 1172

N23, CIDI 404

N23, CIDI 894

Timetable of Questionnaires and Interviews

Quantitative phenotypes related to disease risk:• Metabolic / cardiovascular risks

Biochemical test resultsLipidsGlucose, insulinUrate, CRP, ferritinLiver enzymes GGT, ALT, AST, BCHE

• Personality, depression, anxiety, cognition, MRI, taste, smell

• Addictions (alcohol, nicotine, cannabis, opioids, gambling)

• Melanoma; endometriosis; asthma; migraine; twinning

QIMR GenEpi core interests

• Biochemical phenotypes N ≈ 19,000 adultsN ≈ 2,500

adolescents• GWAS N ≈ 20,000

Data (Twins and families)

ENGAGE participation• Meta-analysis of lipids, urate, alcohol, liver function

tests, glucose• Meta-analysis of iron markers, transferrin isoforms

Queensland Twin Registry

Adolescent twins + sibs

12yrs 14yrs 16yrs

Sun exposure -Sun protective behaviour -Mole counts and locations -Melanoma family history -Mosquito bite susceptibility -Mouth ulcers -Sociodemographic Variables Eye, hair and skin colour Personality (JEPQ, NEO) Acne Height, weight Blood pressure Fingerprints, handprints

Phenotypes measured on teenage twins included; - no information

12yrs 14yrs 16yrs

Photoaging (skin mould) Visual acuity AutoRefractometry (myopia) ENT (grommets, T&A) Asthma, eczema Laterality (hand, eye, foot) Hand preference (peg board) Binocular rivalry (bipolar) - Computer Use - - Reading Ability (CCRT) - - Cognitive Ability (IQ – MAB) - - Information Processing (IT) - - Working Memory (DRT) - - ERPs (DRT) - - EEG (power, coherence) - - Academic achievement (QCST) - - Taste (PTC, bitter, sweet) Smell (BSIT, NatGeo) - - Psychiatric signs (SPHERE) Relationships - - Leisure activity - -

12yrs 14yrs 16yrs

Haemoglobin Red blood cell count Packed cell volume Mean corpuscular volume Platelet count White blood cell count Neutrophils Monocytes Eosinophils Basophils Total lymphocytes CD3+ T-cells CD4+ helper T-cells CD8+ cytotoxic T-cells CD19+ B cells CD56+ natural killer cells CD4+/CD8+ T-cell ratio Blood groups (ABO, MNS, Rh) - -

Blood phenotypes

12yrs 14yrs 16yrs

Cholesterol, HDL, LDL Triglyceride Apolipoproteins A1,A2.B,E Lp(a) Glucose, Insulin Ca, PO4 Creatinine Urea, Uric acid Alkaline phosphatase Albumin, Bilirubin AST, ALT, GGT Fe, Ferritin, Transferrin Heavy metals (Pb, As etc)

Serum biochemistry

Population 21 millionArea 7.7 million km2

Preparing Labmailers

Biobottle Box Incoming Blood Samples

Receipting the blood sample

Preparing FTA cards

External blood collection: Labmailer Process

Samples are collected in the following tubes:

2 x EDTA 1 x SERUM 1 x ACD 1 x PAX 1 x BUCCAL

4 x Red Blood Cells

4 x Plasma

2 x Buffy Coats

4 x Serum

Stored in Freezer for later RNA work

The 2 x EDTA & 1 x SERUM tubes are centrifuged at 3000rpm for 10mins and then the fractions are collected. All fractions & 1 x Buffy Coat are stored in the -80oC freezers

MNC Processing Buccal Extraction

1 x Buffy Coat Extraction

Standard blood collection and processing

(10ml EDTA blood collection)

Average DNA Yield per buffy coat

Mean = 171.291Std. Dev = 68.5431N = 3,554

Genetic EpidemiologyFrozen sample inventoryFraction Number of SamplesPlasma 128,012Buffy Coats 101,333Red Blood Cells 130,668Serum 97,677Buccals 5,591FO Plasma 7,815FO BC 7,387FO RBC 7,500Total 485,983

Genetic EpidemiologyDNA sample inventory

Fraction Number of SamplesDNA Dilutions at 50ng/µl 44,926DNA Stocks 50,719DNA Other 16,443Total 112,088

Study Subjects N Platform Site Funding

CVD Risk Adult MZ ff 923 Illumina 317k Helsinki EU

Migraine+ Nic Adult twins 1,234 Illumina 610k deCode NHMRC

Alcohol (1) Adult twins 2,736 Illumina 370k deCode NIH

Alcohol (2) Adult sibships 4477 Illumina 370k CIDR NIH

Depression Adult cases (1,257) Affy 6.0 TGen GAIN

Endometriosis Adult cases 2,383 Illumina 660k deCode Wellcome

Adolescent Twin families 4,556 Illumina 610k deCode NHMRC+

Asthma/Angst Twin families 1,766 Illumina 610k Brown NHMRC

TOTAL 19,257

GWAS studies at QIMR

Australia’s changing ethic composition

NHGRI GWA Catalogwww.genome.gov/GWAStudies

Published Genome-Wide Associations through 6/2010

904 published GWA at p<5x10-8 for 165 traits

• Genetic risks for complex traits are modest• A genetic risk (OR) of 1.3 (2% variance) is large• Most genetic risks are in the 1.1 to 1.2 range or

less (<1% variance)• This is true for most complex diseases (e.g.

alcoholism, schizophrenia, bipolar disorder, lung cancer) and traits (height, BMI, lipids)

BUT not always………….(use your Biobank !)

(Most) genetic effects are modest

• a waste product of the normal breakdown of red blood cells

• excreted from the body after being conjugated with glucuronic acid ~ UGT (Uridine Diphosphate Glucuronyltransferase) enzyme

• a diagnostic marker of liver and blood disorders• acts as an antioxidant: an increase in bilirubin

levels is associated with a reduced risk of cardiovascular diseases

Serum Bilirubin

rs2070959

Bilirubin in adolescents

Measure Allele Effect (b) Se R2 P Value

Age 12 A -0.58 0.04 21% 3E-59Age 14 A -0.71 0.05 23% 1E-50Age 16 A -0.97 0.06 29% 4E-65Age 18 A -0.72 0.09 24% 5E-15Mean A -0.76 0.03 28% 2.1E-115

– What genes affect iron status (e.g. serum iron, transferin, saturation, ferritin), and the risk of either deficiency or overload in general population?

Genetics of Iron Status

HFEP = 5E-38

HFEP = 1E-73

TMPRSS6P = 7E-27

TFP = 3E-104

HFEP = 8E-83

TMPRSS6P = 2E-27

HFEP = 4E-12

ZNF521 (Zinc Finger Protein 521)P = 4E-08

Serum iron

Transferrin

Tf saturation

Ferritin

GWAS (N = 8942)

ENGAGE meta-analysis to find more iron metabolism genes

Large effects of TF and HFE variants

Measures TF Mutation (rs3811647) HFE mutation (rs1800562)

Effect % var p Effect % var p

Iron -.01±.10 SD 0 .81 .66±.10 SD 10 3.5 x 10-11

Transferrin .46±.06 SD 13 3 x 10-15 -.68±.10 SD 9 1.1 x 10-10

Saturation -.17±.06 SD 2 .002 .80±.10 SD 13 4.3 x 10-15

Ferritin -.13±.06 SD 1 .03 .44±.11 4 4.5 x 10-5

• Enzyme found in plasma• Rare variants in BCHE

extensively studied because of pharmacogenetic effects

• Evidence of involvement with T2DM, CVD, Alzheimer disease (questionable)

Correlations ≥ 0.25 for:BMIBlood pressureApoBApoETotal cholesterolTriglyceridesGGT

+ significant but smaller correlations for ALT, AST, HDL-C, LDL-C, urate.

Butyrylcholinesterase (BCHE)

GWAS Meta-Analysis (3 studies, total N = 8781)

Cholinesterase

Before and After Adjustment for the BCHE K Variant –many other variants contributing…….

QQ Plots

All SNPs with p ≤ 0.001 (Total 5662, of which 2003 mapped to 440 genes)

Ingenuity Pathway Analysis on all butyrlcholinesterase GWAS data

CD4+/ CD8+ ratio h2 = 0.84 (0.79–0.87)

Not only blood variables show large SNP effects...

λ = 1.00008

Hair curliness – straight, wavy, curly

P = 10-31

Other peaks

GWAS for curliness in

three independent Australian Cohorts

~6% variance

GWAS for hair curliness

Trichohyalin is expressed in hair root sheaths

Heterogeneity of gene effects by age, and

sex...and environment?

Several significant hits in the combined data, but not the expected one on Chr. 22

Heterogeneity between adult and adolescent results at this locus!

?

Liver function: gamma glutamyl transferase (GGT)

Multiple SNPs show

heterogeneity between adult and adolescent results for GGT

Melanocytic naevi (common moles)

The largest risk factor for melanoma

IRF4 MTAP

Note inverse association signals for MTAP and IRF4 with flat and raised nevi

QIMR GWAS for total, flat and raised nevi

American Journal of Human Genetics 87, 6–16, 2010

Mole count: Interaction of IRF4 genotype with age

• 4 point rating (none to severe)• 3 sites – face, chest, back

• at age 12 and 14• at age 16 face only

• How to combine these 7 measures ?• Lots of missingness• Item response modelling in WinBUGS enables

Bayesian estimation of liability, allowing for twin relatedness and adjusting for age, sex

Teenage acne

Joint

F + M

Females

Males

GWAS for Acne – different genes for males and females ?

• Is sensitivity to the environment a function of genotype?

• For MZ twins |twin1 – twin2| is a pure measure of e

• does |twin1 – twin2| vary systematically between genotypes?

• A direct test of G x E

Gene – environment interaction

Systematic GWA search for GxE using MZ twins

• 1800 MZ female pairs aged 30-70 from AU, UK, NL, DK, SE

• GWAS using Illumina 317k array• Focus on CVD risk factors (lipids), but

other phenotypes as well (including depression)

GenomEUtwin

Genome-wide association scan of MZ pair mean levels of HDL

cholesterol

1800 MZ female pairs from GenomEUtwin

A gene for environmental sensitivity on Chr 16 ?

GWAS of MZ pair |differences| of HDL cholesterol

- expression and epigenetic data

Adding value to your Biobank (1)

Study Design

980 Individuals

Full FamiliesParents +

Offspring (MZ / DZ / Sib)

MZ and DZ twin pairs

PAX

MZ, DZ and Sib

~2/3 of samples

~1/3 of samples

PAX PAX

• Gene expression

profiles for ~980 individuals

• Individuals from 3 ‘family’ groups

• Only PAX gene expression generated

• expression levels generated using Illumina HumanHT-12 v4.0 chips

Expression levels can be correlated with all other phenotypes

eQTL Study

Study Design

980 Individuals

Full FamiliesParents +

Offspring (MZ / DZ / Sib)

MZ and DZ twin pairs

Methylation

MZ, DZ and Sib

~2/3 of samples

~1/3 of samples

Methylation Methylation

• From the sample

individuals as the full expression study

• Whole genome methylation levels determined

• Using Illumina methylation 450k chips

Methylation levels can be correlated with expression…and with MZ discordance !

Methylation levels

- widespread methylation differences

Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus.

Javierre BM et al. Genome Res. 2010 20: 170-179, 2010

MZ pairs discordant for SLE

- keep adding new phenotypes !

Adding value to your Biobank (2)

Associated with:

testosterone exposureaggression

ADHDhomosexuality

fertilityothersMultivariate Genetic Analyses of the 2D:4D Ratio: Examining the Effects of Hand and

Measurement Technique in Data from 757 Twin Families.Sarah E. Medland and John C. LoehlinTwin Research and Human Genetics 11: 335–341, 2008

Ratio of 2nd to 4th finger length

LIN28B SNP associated with:

2D:4D ratioAge of menarche

MenopauseHeight

A Variant in LIN28B Is Associated with 2D:4DFinger-Length Ratio, a Putative RetrospectiveBiomarker of Prenatal Testosterone ExposureSarah E. Medland…. David M. Evans Am J Human Genetics 86, 519–525, 2010

Large consortia…..

Brisbane Adolescent Twin database - (>700 scanned) Data acquisition: 4 Tesla Bruker Medspec scanner –

CMR, UQ MRI DTI (HARDI) fMRI (n-back) resting-fMRI

Processing and analysis: MRI - UCLA DTI (HARDI) -UCLA fMRI (n-back) - UQ resting-fMRI – UQ + NYU

Twin Imaging Study (TIMS)

http://enigma.loni.ucla.edu/

- sequencing !

Adding value to your Biobank (3)

Whole-genome sequencingWhy?Discover novel, rare variants with potential relevance for disease, including CNVs.These can then be imputed/genotyped and tested for association in large cohorts.

Pilot study: first look at data14 cases + 1 control (including trio) sequenced with deep coverage using HiSeq.Cases with strong family history, severe disease and other co-morbid phenotypes.

~97% concordance of sequence with KGP imputation (610k)

Twins and their families for the participation

John Whitfield, Peter Visscher, David Duffy, Grant Montgomery, Dale Nyholt

Dixie Statham, Ann Eldridge, Marlene Grace, Anjali Henders and Megan Campbell,

Leanne Wallace for the data collection and sample processing.

Allan McRae, Manuel Ferreira, Brian McEvoy, Scott Gordon, Sarah Medland, Gu Zhu,

Beben Benyamin, Rita Middelberg, Margie Wright for helping with data & analysis

Harry Beeby and David Smyth for IT support

Collaborators: Netherlands Twin Registry: Gonneke Willemsen, Jouke-Jan Hottenga, Eco

de Geus, Brenda Penninx, Dorret Boomsma UK Twin Registry: Tim Spector, Mangimo Massimo ALSPAC Study: David Evans, George Davey Smith Sanger Institute / U Helsinki: Aarno Palotie, Leena Peltonen University of Queensland: Ian Frazer, Rick Sturm, Greig de Zubicaray Washington University, St. Louis: Andrew Heath, Pam Madden

Acknowledgements