the impact of genetic variation on type 2 diabetes
TRANSCRIPT
U N I V E R S I T Y O F C O P E N H A G E N F A C U L T Y O F S C I E N C E
PhD Thesis Jihua Sun
The Impact of Genetic Variation on Type 2 Diabetes - insights into rare variant susceptibility
Supervisors: Karsten Kristiansen, Torben Hansen, Jun Wang, Anette P. Gjesing
Submitted: May 2019
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Name of department: Department of Biology
Author(s): Jihua Sun Title and subtitle: The Impact of Genetic Variation on Type 2 Diabetes -
insights into rare variant susceptibility
Topic description: The overall aim of this thesis is to explore the underlying genetic
mechanisms of type 2 diabetes and related metabolic traits, with focus on
the risk of type 2 diabetes among carriers of rare variants in GLIS3 and
PPP1R3B – two well-established genes for type 2 diabetes.
Supervisors: Karsten Kristiansen, Torben Hansen, Jun Wang, Anette P. Gjesing Submitted: May 2019
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Preface
This PhD thesis started in 2015 as a collaboration between the Department of Biology, University of
Copenhagen and BGI-Shenzhen (BGI). The studies included have been conducted at the Novo
Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen and BGI from
2015-2018 under the supervision of Prof. Karsten Kristiansen, Prof. Torben Hansen, Prof. Jun Wang
and Dr. Anette P. Gjesing.
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TABLE OF CONTENTS
ACKNOWLEDGEMENTS .............................................................................................................................................. 5 ABSTRACT ................................................................................................................................................................. 6 DANSK RESUMÉ ........................................................................................................................................................ 7 LIST OF ARTICLES ....................................................................................................................................................... 9 AIM AND HYPOTHESIS OF THE THESIS ..................................................................................................................... 10 ABBREVIATIONS ...................................................................................................................................................... 11 1.0 INTRODUCTION ................................................................................................................................................. 12
1.1 DIABETES MELLITUS ..................................................................................................................................................... 12 1.1.1 What is diabetes mellitus ............................................................................................................................... 12 1.1.2 Epidemic of diabetes ...................................................................................................................................... 13 1.1.3 Type 1 diabetes .............................................................................................................................................. 14 1.1.4 Type 2 diabetes .............................................................................................................................................. 14 1.1.5 Gestational diabetes mellitus ........................................................................................................................ 16 1.1.6 Monogenic forms of diabetes ........................................................................................................................ 17
1.2 GENETIC ASPECTS OF T2D ............................................................................................................................................ 18 1.2.1 Genetic pathology of T2D .............................................................................................................................. 18 1.2.2 Methods for exploring the genetic aspects of T2D ........................................................................................ 18 1.2.3 Genetic studies of T2D in different ethnic groups .......................................................................................... 21
1.3 RARE VARIANTS’ SUSCEPTIBILITY TO T2D ......................................................................................................................... 21 1.4 STUDIES ON GLIS3 AND PPP1R3B ................................................................................................................................ 23
1.4.1 Roles of GLIS3 in diabetes .............................................................................................................................. 23 1.4.2 Roles of PPP1R3B in diabetes ......................................................................................................................... 27 1.4.3 Participants in this PhD study ........................................................................................................................ 28
2.0 SUMMARY OF PHD STUDY ................................................................................................................................. 29 2.1 RESULT AND DISCUSSION OF PAPER I: SEQUENCING REVEALS PROTECTIVE AND PATHOGENIC EFFECTS ON DEVELOPMENT OF DIABETES OF RARE GLIS3 VARIANTS ...................................................................................................................................................... 29
2.1.1 Result of Paper I ............................................................................................................................................. 29 2.1.2 Discussion of paper I ...................................................................................................................................... 33
2.2 RESULT AND DISCUSSION OF PAPER II: INCREASED FREQUENCY OF RARE MISSENSE PPP1R3B VARIANTS AMONG DANISH PATIENTS WITH TYPE 2 DIABETES ....................................................................................................................................................... 34
2.2.1 Result of paper II ............................................................................................................................................ 34 2.2.2 Discussion of paper II ..................................................................................................................................... 36
3.0 SUMMARY AND CONCLUSIONS OF THE TWO PAPERS ........................................................................................ 37 4.0 FUTURE PERSPECTIVES ...................................................................................................................................... 38 5.0 CONCLUDING REMARKS .................................................................................................................................... 40 6.0 REFERENCES ...................................................................................................................................................... 41 7.0 APPENDIX .......................................................................................................................................................... 51
7.1 MANUSCRIPT OF PAPER I ............................................................................................................................................. 51 7.2 MANUSCRIPT OF PAPER II ............................................................................................................................................ 65
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Acknowledgements
I would like to thank everyone who has supported me during my PhD project.
First of all, I would like to express my sincere gratitude to my supervisors, Professor Karsten
Kristiansen, Professor Torben Hansen, Professor Jun Wang and Dr. Anette P. Gjesing for giving me
the opportunity to study this exciting research area, as well as giving me guidance on conducting
good academic research. Without my supervisors’ support I could not have finished this PhD project.
Further, I would like to thank all my colleagues at the Section of Metabolic Genetics for creating such
a good and supportive academic environment. I have learned a lot from the group. I would particularly
like to thank Dr. Anette P. Gjesing for her patience in supervising me step by step in project.
I also thank my colleagues in BGI for preparing next generation sequencing and providing valuable
discussions and instructions, especially Matt Poulter, Kristin Lin, Rita Machado, Qiang Zhang, Zhuo
Liu, Tony Zhu, Nick Luo, Qiuyan Wu and Shufan Hu from BGI-Europe.
In addition, I would like to thank all my coauthors for all their effort on this project.
Finally, a special thanks to my family. They have given me the power to meet challenges and to cope
with difficulties. Without their encouragement, I would not have been able to complete my PhD.
Jihua Sun
Copenhagen
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Abstract Type 2 diabetes (T2D) is a common form of diabetes and it is one of the main threats to human health
in an industrial society [1]. The prevalence and incidence of T2D have been increasing rapidly
worldwide. The etiology and pathogenesis of T2D include both genetic and environmental factors.
In an attempt to investigate the underlying genetic pathogenesis of T2D, a total of 243 genetic loci,
most of which are common variants, have been reported in the past decade [2]. However, as with
most polygenic diseases, the discovered genes only account for a minority of the inherited risk
(heritability) of T2D (< 20%). The gap of the heritability explained by common genetic variants and
the heritability identified in familial studies have been termed as ‘missing heritability’ [3]. We are
still in the process of fully dissecting the complexity of T2D. Until recently, the majority of genetic
studies has been performed using array-based genotyping and genome wide association studies
(GWAS), focusing on the effect of common variants. However, with the development of next
generation sequencing (NGS), scientists can directly test associations between rare variants and
complex diseases. The NGS heralds a new era of investigating the impact of genetic variation on T2D.
This thesis provides an overview of the development of genetic aspects of diabetes, with emphasis
on T2D. The etiology and pathogenesis of T2D are introduced. The current status of identification
and association of genetic variants is discussed and related to the results obtained in my PhD project.
Also, the methodological improvements in investigations of genetic variants made in the last decade
are discussed, especially focusing on the development of NGS.
The overall aim of the thesis is to explore the underlying genetic mechanisms of T2D and related
metabolic traits, with focus on risk contributions of rare variants in two well-established T2D
candidate genes -GLIS3 and PPP1R3B. Common variants in both GLIS3 and PPP1R3B have been
associated with several traits related to T2D as well as other forms of diabetes. In this PhD project,
we applied deep sequencing on the coding regions of GLIS3 and PPP1R3B in a well-characterized
Danish population which allowed us to explore the effect of rare variants. These investigations show
that rare GLIS3 variants appear to have both protective and pathogenic effects on T2D. Also, rare
variants present in PPP1R3B affect glycogen synthesis and lipid metabolism in the development of
T2D.
In conclusion, the studies presented in this thesis provide an important contribution to the
understanding of the role played by rare variants in the development of diabetes and underlying
pathogenesis.
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Dansk Resumé Type 2 diabetes (T2D) er en meget udbredt form for diabetes og en af de største trusler mod
sundheden i et industrielt samfund [1]. Prævalensen og hyppigheden af T2D har været kraftigt
stigende i hele verdenen. Ætiologien og patogenesen af T2D inkluderer både genetiske og
miljømæssige faktorer.
I et forsøg på at undersøge den underliggende genetiske T2D arvelighed, er i alt 243 genetiske loci,
hvoraf de fleste er almindelige varianter, blevet rapporteret i det sidste årti [2]. Som ved de fleste
polygenetiske sygdomme, står de identificerede gener i midlertidigt kun for en minoritet af den
arvelige risiko (arvelighed) for T2D (<20%). Forskellen i arvelighed forklaret af almindelige
genetiske varianter og arvelighed identificeret i familiære studier er blevet betegnet som ’manglende
arvelighed’ [3]. Vi er stadig i gang med at fuldt dissekere og forstå kompleksiteten af T2D. De fleste
genetiske studier har indtil for nyligt været baseret på ‘array-based genotypning fulgt af genome wide
association studies’ (GWAS), og har været fokuseret på effekten af almindeligt forekommende
genetiske variationer. Med udviklingen af ‘next generation sequencing’ (NGS) er det blevet muligt
for forskere direkte at teste associationer mellem sjældne genetiske variationer og komplekse
sygdomme. NGS er blevet starten på en ny æra med at undersøge påvirkningen af genetiske T2D
risiko variationer.
Denne afhandling giver et overblik over udviklingen af forståelsen for genetiske aspekter i diabetes
med fokus på T2D. T2Ds ætiologi og patogenese bliver introduceret. Det nuværende status i
identifikation og association af genetiske varianter er diskuteret og relateret til de resultater, jeg har
opnået i mit PhD-projekt. Derudover er metodiske forbedringer i undersøgelsen af genetiske
variationer i det sidste årti diskuteret, særligt udviklingen af NGS.
Det overordnede mål med denne afhandling er at udforske de underliggende genetiske mekanismer
ved T2D og relaterede metaboliske træk, med fokus på risikoen ved sjældne varianter i to
veletablerede T2D kandidat gener – GLIS3 og PPP1R3B. Almindelige varianter af både GLIS3 og
PPP1R3B er begge associeret med adskillige træk relateret til T2D og andre typer diabetes. Vi har i
dette PhD -projekt anvendt ‘deep sequencing’ på kodende regioner af GLIS3 og PPP1R3B i en
velkarakteriseret Dansk population, hvilket har muliggjort at udforske effekten af sjældne genetiske
variationer. Disse undersøgelser viser at sjældne GLIS3 variationer ser ud til at have både en
beskyttende og patogen effekt på T2D. Sjældne variationer til stede i PPP1R3B har indflydelse på
glykogensyntesen og lipidmetabolismen i udviklingen af T2D.
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Undersøgelserne præsenteret i denne afhandling giver et vigtigt bidrag til forståelsen af den rolle
sjældne genetiske variationer spiller i udviklingen af diabetes og sygdommens underliggende
patogenese.
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List of articles Scientific papers contributing to this thesis
This thesis is based on two manuscripts. One is under submission and another has been published in
PLoS One.
Paper I: Jihua Sun, Christian Theil Have, Mette Hollensted, Niels Grarup, Allan Linneberg, Oluf Pedersen, Jens Steen Nielsen, Jørgen Rungby, Cramer Christensen, Ivan Brandslund, Karsten Kristiansen, Wang Jun, Torben Hansen, and Anette P. Gjesing. Sequencing reveals protective and pathogenic effects on development of diabetes of rare GLIS3 variants. Submitted to PLoS One, 2019. Paper II: Robina Khan Niazi, Jihua Sun, Christian Theil Have, Mette Hollensted, Allan Linneberg, Oluf Pedersen, Jens Steen Nielsen, Jørgen Rungby, Anders Albrechten, Niels Grarup，Torben Hansen and Anette Prior Gjesing. Increased frequency of rare missense PPP1R3B variants among Danish patients with type 2 diabetes. PLoS One.14(1): e0210114. 01.2019.
Publications not included in this thesis
Fangming Yang, Jihua Sun, Huainian Luo, Yuxiang Lin, Mo Han, Hongcheng Zhou, Yang Li, Bing Chen, Huijue Jia, Karsten Kristiansen, Huanzi Zhong. Comparison of fecal DNA extraction methods for metagenomic sequencing (co first author, manuscript). Tang, A., Y. Huang, Z. Li, S. Wan, L. Mou, G. Yin, N. Li, J. Xie, Y. Xia, X. Li, L. Luo, J. Zhang, S. Chen, S. Wu, J. Sun, X. Sun, Z. Jiang, J. Chen, Y. Li, J. Wang, J. Wang, Z. Cai, and Y. Gui. "Analysis of a Four Generation Family Reveals the Widespread Sequence-Dependent Maintenance of Allelic DNA Methylation in Somatic and Germ Cells." Sci Rep 6 (Jan 13, 2016): 19260. Poulsen, J. B., F. Lescai, J. Grove, M. Baekvad-Hansen, M. Christiansen, C. M. Hagen, J. Maller, C. Stevens, S. Li, Q. Li, J. J.Sun, Wang, M. Nordentoft, T. M. Werge, P. B. Mortensen, A. D. Borglum, M. Daly, D. M. Hougaard, J. Bybjerg-Grauholm, and M. V. Hollegaard. "High-Quality Exome Sequencing of Whole-Genome Amplified Neonatal Dried Blood Spot DNA." PLoS One 11, no. 4 (Apr 18, 2016): e0153253. Gjesing, A. P., G. Rui, J. Lauenborg, C. T. Have, M. Hollensted, E. Andersson, N. Grarup, J. Sun, S. Quan, I. Brandslund, P. Damm, O. Pedersen, J. Wang, and T. Hansen. "High Prevalence of Diabetes-Predisposing Variants in Mody Genes among Danish Women with Gestational Diabetes Mellitus." J Endocr Soc 1, no. 6 (Jun 1, 2017): 681-90. Maretty, L., J. M. Jensen, B. Petersen, J. A. Sibbesen, S. Liu, P. Villesen, L. Skov, K. Belling, C. Theil Have, J. M. G. Izarzugaza, M. Grosjean, J. Bork-Jensen, J. Grove, T. D. Als, S. Huang, Y. Chang, R. Xu, W. Ye, J. Rao, X. Guo, J. Sun, H. Cao, C. Ye, J. van Beusekom, T. Espeseth, E. Flindt, R. M. Friborg, A. E. Halager, S. Le Hellard, C. M. Hultman, F. Lescai, S. Li, O. Lund, P. Longren, T. Mailund, M. L. Matey-Hernandez, O. Mors, C. N. S. Pedersen, T. Sicheritz-Ponten, P. Sullivan, A. Syed, D. Westergaard, R. Yadav, N. Li, X. Xu, T. Hansen, A. Krogh, L. Bolund, T. I. A. Sorensen, O. Pedersen, R. Gupta, S. Rasmussen, S. Besenbacher, A. D. Borglum, J. Wang, H. Eiberg, K. Kristiansen, S. Brunak, and M. H. Schierup. "Sequencing and De Novo Assembly of 150 Genomes from Denmark as a Population Reference." Nature 548, no. 7665 (Aug 3, 2017): 87-91.
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Aim and hypothesis of the thesis
In my PhD project, the overall aim was to explore the effects of rare variants in relation to T2D and
related metabolic traits as a mean to understand the underlying genetic mechanisms of diabetes
susceptibility. More specifically, the objective of the thesis was to apply high-throughput target region
sequencing of the coding regions of two well-established T2D candidate genes GLIS3 and PPP1R3B
to elucidate the pathological effect of rare variants on the development of T2D.
Hypothesis：
I Rare and low-frequency variants explain part of the genetic susceptibility to T2D.
II Next generation sequencing in deeply phenotyped participants is an efficient way to study the
genetic susceptibility of T2D.
III Deep sequencing of two candidate genes GLIS3 and PPP1R3B in a well phenotyped Danish
population can reveal influence of rare variants on the development of T2D.
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Abbreviations
BMI, Body Mass Index; cPAS,combinatorial Probe-Anchor Synthesis; CI, Confidence Interval; CRP,
C-Reactive Protein. GADA, Glutamic Acid Decarboxylase Antibody; GDM, Gestational Diabetes
Mellitus; GWAS, Genome-Wide Association Study; IFG, Impaired Fasting Glycemia; IGT, Impaired
Glucose Tolerance; MPS, Massively Parallel Sequencing; MODY, Maturity Onset Diabetes of the
Young; NGS, Next Generation Sequencing; NIPT, Non-Invasive Prenatal Test; NDM, Neonatal
Diabetes Mellitus; NIDDM, Non-Insulin Dependent Diabetes Mellitus; OHA, Oral Hyperglycemic
Agent. OR, Odds Ratio; SIGMA, Slim Initiative in Genomic Medicine for the Americas；T1D, Type
1 Diabetes; T2D, Type 2 Diabetes；s-LDLc, serum LDL-cholesterol；VLDL-C, Very Low Density
Lipoprotein-Cholesterol; WES, Whole-Exome Sequencing; WGS, Whole-Genome Sequencing;
WGBS, Whole-Genome Bisulfite Sequencing;
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1.0 Introduction
1.1 Diabetes mellitus
1.1.1 What is diabetes mellitus
Diabetes mellitus is a serious complex disease which is defined by the level of blood glucose [4]. An
elevated level of blood glucose might not present any symptoms in itself, however, over time, it may
lead to the development micro- and macro-vascular complications such as cardiovascular co-
morbidities, kidney disease, limb amputation and blindness [5, 6].
Diabetes can be diagnosed based on a measure of fasting blood glucose and on long-term levels of
blood glucose (HbA1C proportion) [7]. HbA1C refers to the fraction of glycated hemoglobin A1c
and is the quantification of beta-N-1-deoxy fructosyl component of hemoglobin in blood [8]. Once a
glucose molecule binds to hemoglobin in the red blood cells, hemoglobin continues to remain
glycated for the remainder of its life-span (120 days) [8]. Thus, HbA1C is a measure of the long term
(approx. 3 months) level of glycaemia and can be used for diagnostic purposes [9]. However, a more
detailed method for measuring the level of glycaemia is a 2-hour Oral Glucose Tolerance
Test (OGTT), which also allows for the detection of Impaired Fasting Hyperglycemia (IFG) and
Impaired Glucose Tolerance (IGT). These (IFG and IGT) are two different initial states that
predispose to diabetes, thus also referred to as prediabetes, where the blood glucose concentration is
lower than the diagnostic of diabetes, but higher than normal fasting blood glucose levels (Table 1).
Table 1. Diagnosis criteria of Impaired Fasting Glycemia (IFG), Impaired Glucose Tolerance (IGT) and Diabetes
Mellitus (DM).
Normal IFG IGT DM
Fasting 2 hour
during
OGTT
Fasting Fasting 2 hour
during
OGTT
Fasting 2 hour
during
OGTT
Venous plasma glucose concentration (mmol/L)
< 6.1
< 7.8
6.1-<7.0
<7.0
7.8-<11.1
≥ 7.0
≥ 11.1
Mechanism for regulation of blood glucose
Islets of Langerhans cells account for about 2% of the pancreas [10]. It contains α and β cells which
secrete glucagon and insulin respectively [10]. The β cells secrete insulin in situations of higher blood
glucose levels. However, the α cells then secrete glucagon in times of lower blood glucose levels [11].
Insulin and glucagon are important hormones in regulating the levels of blood glucose from
hyperglycemia or hypoglycemia [11, 12]. After eating, the carbohydrates are broken down into
glucose and the blood sugar levels increase, which is the signal to the β islet cells in the pancreas to
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start releasing insulin into the bloodstream. The secreted insulin then enhances glucose uptake from
the bloodstream by cells, such as cells of liver, skeletal muscle and the adipose tissues [11]. However,
in diabetes the patient’s blood glucose is no longer kept at the correct level, the underlying reasons
for this lack of glycaemic control is different in different forms of diabetes. The underlying cause of
type 1 diabetes (T1D) is the lack of a sufficient production of insulin due to β cell death. Whereas for
T2D the cause can be both the inability to respond to insulin, produce insulin or a combination of the
two（Figure 1）[4, 11-14].
In addition to T1D and T2D, there are also several other forms of diabetes, including monogenic
forms of diabetes such as Neonatal Diabetes Mellitus (NDM）and Maturity Onset Diabetes of the
Young (MODY) as well as Gestational Diabetes Mellitus (GDM), which is diagnosed when diabetes
is encountered for the first time during pregnancy.
Figure 1. An overview of T1D and T2D. In normal physiological state (Normal), insulin is secreted from the pancreas which signals to cells to absorb glucose from the bloodstream. In T1D patients, little or no insulin is produced by pancreas. In T2D, either the pancreas cannot produce enough insulin to maintain a normal glucose level or the cells resist the effects of insulin. Reprinted from Sargis, 2015 [15].
1.1.2 Epidemic of diabetes
Diabetes is a global epidemic. The prevalence of diabetes has rapidly increased worldwide over the
past few decades [16, 17]. According to statistics in 2017, about 425 million people worldwide have
diabetes [18]. A total of 1.6 million deaths were directly caused by diabetes in 2015. Over 80% of
diabetes deaths occur in developing countries [17]. The International Diabetes Federation has
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estimated that the number of diabetes patients may reach 629 million by 2045 [18]. It is hard to
estimate the prevalence of T1D and T2D separately, as most of the studies have not investigated them
independently [1]. But one estimate shows that in developed countries approximately 87%-91% of
all diabetes cases are T2D and 7%-12% patients are estimated to have T1D [1]. In addition, 1%-3%
are estimated to have other types of diabetes [1].
1.1.3 Type 1 diabetes
T1D is characterized by loss of β cells in the pancreas and consequently loss of insulin production
due to autoimmunity, leading to insulin deficiency. Insulin replacement therapy is required to keep
blood glucose levels under control. T1D is distinguished from T2D by testing for lower levels of C-
peptides at the time of diagnosis [19] and the presence of autoantibodies [20] such as the
65kDa isoform of glutamic acid decarboxylase (GAD65), islet cells (ICA), islet antigen-2 (IA-2A),
zinc transporter autoantibodies (ZnT8) [21, 22]. T1D is the result of pancreatic β cells being destroyed
by a β cell specific autoimmune process. The causes of the specific autoimmune destructive process
of β cells in T1D are not fully explained, but genetic and environmental effects have been implicated
[23, 24]. Most of T1D occurs in children and adolescents. The distribution of T1D varies from region
to region and country to country. Europe, North America and the Caribbean have the largest number
of children and adolescents with T1D [1]. The highest recorded incidence in children below 15 and
20 years was seen in Finland [1, 25] and the lowest incidence in Papua New Guinea and Venezuela
[26].
1.1.4 Type 2 diabetes
T2D is characterized by impaired insulin secretion from β cells coupled with insulin resistance in
target tissues such as the liver, muscles and adipose tissue [27, 28].
Insulin secretion
Normally, under healthy insulin secretion, when the bloodstream glucose level is higher, the glucose
is transported into β cells by GLUT glucose transporter. This drives oxidative phosphorylation and
ATP production, which closes the KATP channels (ATP-sensitive), and the plasma membrane
becomes depolarized. These triggers electrical activity in the β cell and the release of calcium through
the voltage-gated calcium channels, which results in release of insulin. However, under impaired
insulin secretion, the genetic mutation blocks or slows down the whole metabolic pathway which
leads to decreased insulin secretion or βcell death（Figure 2）[29].
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Figure 2. Healthy and impaired insulin secretion metabolic pathway. In healthy insulin secretion (left): Glucose transfer into β cell by the GLUT transporter. KATP channels were closed by the effect of ATP, which leads to plasma membrane depolarization and triggers β cell electrical activity with an influx of calcium. Which in turn to stimulate insulin release. Oppositely, in impaired insulin secretion condition (right): Genetic variants such as mutation on protein GLUT, GCK, GLP-1R (red bolts), or environmental stimuli affect the insulin-secretion pathways, which block or slow down whole metabolic pathways, finally leading to inhibiting insulin secretion. Reprinted from Langenberg 2018. [14].
Insulin resistance
Insulin resistance is a condition, where the cells in peripheral tissues do not respond appropriate to
insulin, and thus, glucose cannot be transported from the bloodstream into target cells in an efficient
manner [30]. To compensate for the insulin resistance, more insulin is produced by β cells to manage
the blood glucose at a normal level. However, when the β cells are not able to increase insulin
production, the glucose levels increase and diabetes may develop. Obesity and a sedentary lifestyle
are the main lifestyle risk factors for developing insulin resistance [31]. Insulin resistance is often the
earliest abnormality used to detect the development of T2D. However, diabetes does not become
apparent until the β cells cannot secrete sufficient insulin to compensate for the level of insulin
resistance [32, 33].
Abnormalities of insulin secretion and insulin resistance often coexist in the same individual [34]
without a clear distinction between which defect is the primary. However, the link between insulin
secretion and insulin resistance is strong, if changes happen at one, the adaptation will be in the other
[35].
Risk factors for T2D
Age, obesity, low physical activity, family history of diabetes and GDM are among the most
important risk factors for T2D [36]. The risk is 2-4 times higher for individuals over 45 years of age
[37] to develop T2D. The obesity epidemic is strongly associated with a higher prevalence of T2D
[31] and more than 70% of T2D patients are overweight or obese [38].There is also 2-4 times higher
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risk of developing T2D if one parent or sibling has diabetes [39]. GDM is a significantly increasing
risk factor of developing diabetes, as 20-50% women with GDM develop T2D later in life [40, 41].
Co-morbidities
Non-traumatic blindness and kidney failure are the two major diseases caused by T2D.And
Cardiovascular disease is also 2-4 times more likely to appear with T2D patients [1]. The debilitating
disorder occurs in more than 50 percent of patients with diabetes. About half of patients with T2D
develop diabetic peripheral neuropathy, which is one of the most common forms of nerve damage [1,
42]. In addition, patients with T2D have approximately 20 times increased risk of lower limb
amputations due to micro and macrovascular complications [43]. Other complications of T2D
include acanthosis nigricans, sexual dysfunction and frequent infections [5].
Treatment of T2D
Healthy diet, exercise and good lifestyle management at earlier stages can prevent the progression of
T2D to a certain extent [44]. However, with the development of T2D, most patients need medication
such as oral hypoglycemic agents (OHA) and will need insulin therapy in later stages of. There are
two types of OHA agents. One works by improving the sensitivity of body tissues to insulin, such as
Metformin and Thiazolidinediones [45]. Another works by stimulating the pancreas to secrete more
insulin, such as Sulfonylureas and Meglitinides [45].
In recent years, incretin therapies have been developed and used as T2D medication, such as
dipeptidyl peptidase-4 (DPP-4) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs).
They work by increasing the body incretin inhibitor (DPP-4) level to increase the insulin secretion,
decreasing gastric emptying and thereby decrease blood glucose levels. Incretin therapy can improve
glycaemic control with low complication risks and contribute to reduce blood pressure,
cardiovascular risks and improvement of β cell function [46, 47]. Sodium-glucose co-transporter 2
(SGLT2) inhibitors are the newest diabetes agents on the market [48]. They work by reducing glucose
absorption by the kidneys. Instead, the glucose is excreted through the urine [49].
1.1.5 Gestational diabetes mellitus
GDM is characterized by elevated blood sugar levels detected in pregnancy. It is estimated that 21.3
million (16.2%) of women with live births had hyperglycemia in pregnancy in 2017. As many as
86.4% of those cases were due to GDM [1]. Many maternal and fetal co-morbidities have been linked
with GDM. GDM increases the risk of developing T2D for both mother and offspring later in life. In
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addition, GDM has also been linked with development of cardiovascular disease and metabolic
syndrome [50].
During pregnancy, lots of hormones are produced by placenta to help the fetal develop, some of which
can block the function of insulin. The hormones such as cortisol and oestrogen have strong
diabetogenic effects [51], gradually leading to the insulin resistance. Genetics might be a key factor
affecting the development of GDM [1, 52].
1.1.6 Monogenic forms of diabetes
Monogenic diabetes is a group of relatively rare disorders, resulting from a single or couple of gene
variants. It represents a form of early onset and non-autoimmune diabetes [53]. Monogenic diabetes
is primarily caused by genetics rather than the combined effect of genetic susceptibility and
environmental factors seen in T1D and T2D [4, 53]. It is estimated that about 1-5% of diabetes has a
monogenic cause [1]. More than 20 genes have been associated with monogenic diabetes so far. [54,
55]. Maturity-onset diabetes of the young (MODY) and neonatal diabetes mellitus (NDM) are the
two main forms of monogenic diabetes. MODY is a young-onset, rare, familial, genetically inherited
and clinically heterogeneous subtype of diabetes [56]. Endogenous partly preserved insulin secretion,
no obesity, no insulin resistance and no pancreatic β cell autoimmunity are also major features of
MODY [56]. Due to the genetic cause of MODY, most MODY patients have other members of the
family with diabetes [57]. MODY is estimated to represent 0.6-2% of all diabetes [58], and usually
first occurs in adolescence or early adulthood before 25 years of age. So far, a total of 14 genes
(HNF4α, GCK, HNF1α, PDX/IPF1, HNF-1β, NEUROD1, KLF11, CEL, PAX4, INS, BLK, ABCC8,
KCNJ11, APPL1) have been identified as MODY genes, in which single mutations lead to the
development of diabetes [55]. Mutations in HNF4α, HNF1α and GCK account for about 90% of all
known MODY genetic causes [59, 60]. However, lack of an accurate and affordable testing method
to identify genetic diabetic background of patient and family limits the diagnostic application to
identify monogenic diabetes. If a patient is diagnosed with clinical MODY (Clinical diagnosis: onset
of hyperglycaemia before the age of 25, non-insulin dependent, and dominant autosomal inheritance),
but the underlying genetic abnormality is still unknown, the status is called MODYX [61]. With the
emerging of next-generation sequencing (NGS) as one of the most promising and effective tools to
identify novel gene mutation, MODY testing has become more feasible [62-65].
NDM occurs in one out of 100,000 to 500,000 live births [1],much rarer than MODY. It usually
occurs in newborns and young infants and is normally diagnosed before the age of 12 months. Cases
that happen in the first 6 months of birth almost always have a genetic cause [54]. For almost half of
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NDM newborns, the disease will be lifelong [66]. For the other NDM cases, the condition is transient
and may disappear, yet reappear later in life [66].
1.2 Genetic aspects of T2D
T2D is an etiologically heterogeneous disease [6].Both genetic and environmental factors have an
effect on the pathogenesis of T2D, as well as the interactions between them [67]. Multiple pieces of
evidence show that T2D has strong genetic determinants: 1) monozygotic twins have a higher
concordance rate of T2D than dizygotic twins [68] [69], 2) diabetes presents a pattern of family
clustering and the risk of offspring having a greater risk of developing diabetes (if one parent has
T2D the chance is around 40%, and almost 70% if both parents are affected by T2D) [70],and 3) the
prevalence of diabetes is very different in different ethnicities living in a similar environment [71].
1.2.1 Genetic pathology of T2D
To date, almost 250 genetic variants have been identified that contribute to the risk of T2D [2] . There
are two main hypothesis on genetic pathology of T2D, one is ‘common disease, common variant’ and
another is ‘common disease, rare variant’ [72]. In the ‘common disease, common variant’ theory,it
is hypothesized that common variants (MAF >5%) with small effect size and low penetrance can
cause the disease [72]. On the oter side, according to the ‘common disease, rare variant’ view, rare
variants (MAF <1%) with large effect sizes and high penetrance, might be the dominant cause of the
disease [72]. In previous genetic research, most of the T2D genetic studies focus on the common
variant [33]. However,most of the identified susceptability loci have very small effect sizes and
account for only a fraction of the apparent heritability and a majority of them are located outside of
the coding regions [14, 33, 73]. Rare variants with larger effects have been suggested to explain more
of the ‘missing heritability’, however this have yet to be uncovered [74, 75]. Furthermore, the
inheritance model and risks of T2D differ across different ethnicities in a similar environment, which
supports that there is a more complex genetic architecture underlying the pathology of T2D [76, 77].
1.2.2 Methods for exploring the genetic aspects of T2D
Four main methods have been successfully applied to investigate the genetics of T2D. Genetic
Linkage Analysis (GLA), Candidate Gene Association Studies (CGAS), Genome-Wide Association
Studies (GWAS) and high-throughput Next Generation Sequencing (NGS).
Studies of common variants: GLA, CGAS and GWAS
GLA
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GLA is a powerful tool to identify a chromosomal location containing disease genes. By using linkage
relationship (linkage disequilibrium) of target gene and a known gene, the GLA method can identify
the target gene location on the chromosome [78]. It usually is applied in family studies (parents,
offspring and sibling pairs) to identify the location of associated variant(s) [79, 80].
GLA has been successfully used to identify causal genes for monogenic diabetes such as MODY [81].
Beside MODY genes, TCF7L2 and CAPN10 have also been identified by GLA [82, 83]. The risk
TCF7L2 variant is still regarded as the most influential common T2D variant to date (OR=1.46) [84].
Although many new genetic tools have been developed, GLA is still an indispensable basic method
to identify disease-related regions and genes. However, GLA is mostly suitable for monogenic disease
studies. It is often influenced by other factors when used on polygenic diseases, such as multiple
pathogenic loci, interaction of genetic and environment, epistasis and minor effects of the variants
[85].
CGAS
CGAS is a hypothesis-driven method where candidate genes are selected based on prior knowledge
such as biological function, position or a potential role of a particular gene in relation to a specific
phenotype [86]. It is often applied in unrelated individuals [87]. Small effects of common variants in
individuals are hard to detect by the linkage approach, which has led to CGAS becoming widely used
[86]. One disadvantage of CGAS is lack of ability to identify novel genes [86]. Two genes PPARG
and KCNJ11 were found to be associated with T2D by CGAS and have been verified in multiple
studies [88-90].
GWAS
GWAS are large-scale hypothesis-free studies that involve rapid scanning of genetic variants(SNPs
on genotyping array)across the entire human genome to identify novel genetic associations with a
particular trait [91] (the GWAS means the genotyping array based technology combined genome
wide associations study in this thesis, as opposed to NGS study). The International HapMap consortia
[92, 93] and the 1000 genomes projects [94] have provided complete genetic variation reference
resources which has made the GWAS possible to implement. The development of the SNP array
technology makes GWAS effective and affordable to use in large cohort studies. GWAS is a powerful
tool to identify common genetic variants of predisposition to T2D in the past decade [95]. It
investigates the entire genome (number of SNPs vary from 0.2M to more than 200M) and has been
proved very suitable to identify genetic variants in cohort studies. The first GWAS paper reported the
discovery of four novel T2D loci including the TCF7L2 in 2007 [96]. To date, approximately 250
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significant susceptibility loci for T2D have been identified [2]. The majority of them have been found
in recent years based on meta-analyses [97]. The meta-analysis of GWAS data has proved that large
sample sizes are necessary to identify the small effects of susceptible SNPs [2]. However, GWAS’
capability is based on array content, which did not contain potential rare variant sites. The current
GWAS genotyping arrays are desgined based on HapMap and the 1000 genome project dataset, which
focus on the common SNPs (MAF>1%) [94, 98, 99]. Thus the previous GWAS did not test rare
variants for an association with the trait directly [95]. Furthermore, GWAS cannot detect novel
mutations beyond the HapMap and 1000 genome database. In addition, GWAS usually ignore the
effect of rare variants during data analysis, for example, the analysis strategy often focuses on the
typed traits which are more connected to the common variants [100]. Although the meta-analysis can
assemble extremely large samples at population level, the GWAS method still lacks the imputation
to investigate rare variants [101].
Rare variants studies based on: NGS technology
In 1977, Sanger and colleagues invented the chain termination sequencing method, which was a
milestone in molecular biology research history [102]. Applied Biosystems company (ABI) designed
the first auto fluorescence sequencer in the middle of the 1980s [103], which moved life science
research into a new era of automated sequencing. The first human genome map was completed in
2001 based on the improved Sanger sequencing method [104, 105]. However, with the rapid
development of molecular biology, a high-throughput, faster and low-cost sequencing technology
was required by scientists, and Next Generation Sequencing (NGS) technology emerged to fulfill all
of these needs [106].
The first NGS system Genome Sequencer 20 (GS20) appeared in 2005, developed by 454 Life
Sciences company based on pyrosequencing technology [107]. It enables sequencing over millions
of DNA fragments in parallel at a cost of less than 1/1000 of traditional Sanger sequencing [108].
Following this, Solexa introduced its NGS system Genome Analyzer (GA) in 2006. GA was
developed based on sequencing by synthesis (SBS) and 3’blocked reversible terminator technology
[106]. In 2007, Illumina acquired Solexa and commercialized the GA. The latest Illumina
NovaSeq6000 platform can generate more than 6T of data in two days. BGI released a new desktop
sequencing system BGISEQ-500 based on Combinatorial Probe-Anchor Synthesis (cPAS)
technology in 2015 [109]. It was updated to MGISEQ-2000 in 2017 and MGISEQ-T7 in 2018. This
brings down the cost of personal genome sequencing (30x,100Gb data) to 600USD. The competition
between the companies has had a positive effect in making sequencing more feasible for research.
Sequencing throughput doubled in less than a year, with accuracy increasing and costs reduced. Mini-
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type sequencers are now widely used in small laboratories and NGS clinical application in hospitals
is developing at high speed.
1.2.3 Genetic studies of T2D in different ethnic groups
Originally, the majority of T2D studies were performed on individuals of European descent [14]. But
in recent years, more studies have been conducted among Asians [110-114], African Americans
[115], American Indians [116] and Mexicans or Latin Americans [117]. The trans-ethnic approach
enables us to identify population-specific variants, such as GRK5 in East Asians [118, 119]. Moreover,
the large-scale meta-analyses with multi-ethnics have shown a significant directional consistency of
allelic associations for most genetic variants across diverse ethnicities [120, 121].
1.3 Rare variants’ susceptibility to T2D
In recent years, research on rare mutations has focused on exploring the missing heritability of
complex disease [122-124]. With the affordability of NGS sequencing, many rare variants have been
identified and linked with complex traits [125], such as Alzheimer’s disease [126], inflammatory
bowel disease [127] and prostate cancer [128]. Rare variants also play an important role in T2D
development, but there are few examples and limited knowledge about the mechanisms underlying
the development of T2D so far [74, 75]. A large part of the genetic predisposition to T2D is still
undiscovered. Only approximately 20% of observed T2D heritability can be explained by the
identified genetic risk mutations to date [129, 130].
In spite of the linkage and GWAS approach focused on common variants [95], evolutionary theory
supports the idea that rare variants play a key role in etiology of complex disease [131, 132]. It has
been suggested that the missing heritability is likely partly caused by rare variants, which may explain
the remaining genetic susceptibility to common diseases [72, 133, 134].
Thanks to the development of sequencing technology, more disease-associated rare variants have
been identified [135, 136]. In particular, the target capture combined with deep resequencing makes
it possible to identify rare variants associating with disease traits in the absence of pedigrees [63, 137,
138]. The target capture resequencing method is a cost-effective alternative which limits the
sequencing to a region of interest. Selecting a candidate-gene panel for specific phenotype disease
makes it easy and affordable to apply it on big cohort studies and facilitate the identification of de
novo and rare variants in certain genes associated with disease [52, 139]. In addition, it can detect de
novo and rare mutations with higher sensitivity and accuracy based on deep coverage on the gene
sites (>100x) than common WGS which normally have data below 30x [64, 140].
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With the sequencing cost continuously decreasing, more studies have applied sequencing to explore
the effect of rare variants on T2D directly [141, 142]. In 2012, a large-scale resequencing of two
exons of MTNR1B was performed in total 7,632 Europeans including 2,186 T2D patients [143]. A
total of 36 rare variants (MAF <0.1%) were identified and associated with T2D with risk OR=3.31,
which is more than a two-fold increase compared to the common variants risk (OR of ~1.10-1.15)
detected by GWAS [144, 145].
In 2013, a deep whole exome sequencing (average 200x) study was performed on 1,000 Danish T2D
patients and 1,000 control individuals. Perhaps due to the limited sample size, the study only detected
significant rare variants associated with T2D in a limited number of cluster genes (below 20 genes).
This indicates the association of rare variants might be scattered across many genes [3].
In 2014, a resequencing project performed WGS on 2,630 Icelanders (average 22.9x), followed by
genotyping of 11,114 Icelandic T2D patients and 267,140 Danish and Iranian control individuals,
identified four previously unreported variants associated with protective and deleterious effect of T2D
[146]. It included one variant in CCND2 significantly associating with reduced risk of T2D (OR=
0.53). The WGS data first identified 34.2 million variants, subsequently the GWAS array was
designed based on those detected variants, followed up by genotyping on large cases and control
individuals. The project showed that WGS combined with genotyping is a good method for rare
variants study. The strategy is easy for big cohorts and can improve the possibility of detecting the
rare variants with smaller effect size [146].
In 2016, Fuchsberger et al performed WGS (average ~5x) on 2,657 European individuals (1,326 cases
and 1,331controls), and WES（average ~82x）on 12,940 participants, including 6,504 T2D patients
and 6,436 controls to investigate the risk of low frequency variants on the development of T2D [130].
However, perhaps due to lack of coverage of WGS data, a number of rare variants were not identified.
The total WES data was big, but it was on five different ethnic genetics. Thus, the above meant that
the power of the study to detect associations of rare variants with T2D was not as big as expected.
This may also be the reason why the project did not find significantly low-frequency variants that
contribute to T2D heritability.
Overall, sequencing is a good method for rare variants association studies. Using NGS data to identify
the variants in a particular population or ethnic group, followed up by using a customized genotyping
array, is the most used strategy [147]. However, the sequencing coverage and size of cohort are the
two important factors which most affect the result. Rare variants’ studies show that rare variants have
high effect sizes, which might partly explain why the missing heritability has yet to be found in
different ethnic groups. Despite the results of studies on the contribution of rare variants to the
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development of T2D, the underlying mechanism of these rare variants also needs to be investigated
[148].
1.4 Studies on GLIS3 and PPP1R3B
To investigate susceptibility mechanisms of rare variants underlying T2D, throughout my PhD study,
I have been working especially with two studies of rare variants on GLIS3 and PPP1R3B separately.
In these two studies, using target gene capture combined with NGS technology, rare missense variants
on two genes among 2,930 newly-diagnosed T2D Danish patients and 5,726 non-diabetic controls,
as well as 54 MODYX individuals were detected. The association of the detected rare variants with
T2D, MODY and measures of related glucose metabolism parameters was investigated as well.
GLIS3 and PPP1R3B were selected based on their biological function, which indicates that they may
be associated with the development of T2D [149, 150].
Common variants in GLIS3 have been associated with multiple types of diabetes [151, 152]. Previous
reports have identified an association of two common variants in GLIS3 with T2D (rs7034200
(OR=1.16, p = 3.49×10−3) [153] and rs7041847 (OR=1.03, p= 0.3889)) [154]. Common GLIS3
mutations are also associated with several kinds of T2D-related metabolic traits, such as fasting
glucose homeostasis [153, 155] and insulin clearance [156]. For PPP1R3B, the region 8p23.1 has
shown linkage with T2D and MODY in different populations [61, 157, 158].
Common variants in gene PPP1R3B - rs4240624 and rs4841132 have been identified as being
associated with decreased levels of fasting glucose [159, 160] and variant rs4841132 has also been
associated with increased levels of fasting insulin [160]. In addition, variants in PPP1R3B -rs9987289,
rs2972146, rs2126259 have been detected to be associated with lipid profiles (HDL, LDL and total
cholesterol) [161, 162] and variant rs9987289 has been identified associated with CRP levels as well
[163] .
Common variants of GLIS3 and PPP1R3B have been related to the development of T2D. However,
the association of rare variants in PPP1R3B with diabetes has not been investigated, and only low-
frequency (1% >MAF >0.1%) GLIS3 variants’ association with T1D in Japanese population have
been studied [135]. Therefore, in order to analyze the risks of rare variants in GLIS3 and PPP1R3B
with T2D, we applied target region deep sequencing on GLIS3 and PPP1R3B in a well characterized
Danish population of both T2D patients and participants without diabetes.
1.4.1 Roles of GLIS3 in diabetes
The functional relationship between GLIS3 and diabetes and diabetic metabolic characteristics has
been widely studied in recent years [152, 164].
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The full length of the human GLIS3 gene is 524,266 base pairs (bp). It has different alternatively
spliced transcripts which encode different protein isoforms. Two main transcripts (NM_001042413
and NM_152629) of the human GLIS3 gene are encoding isoform a (930aa) and isoform b (775aa)
(Figure 3) [165]. In mice, the GLIS3 gene (NM_175459) size is 7524 base pairs (bp), containing 11
exons, which encodes a 935 amino acid protein (Figure 3). GLIS3 is predominantly expressed in the
pancreas, thyroid and kidney in both human and adult mice [166, 167]. It is strongly expressed from
early stages of β cell neogenesis in the pancreas [166].
The protein GLIS3 is a member of Krüppel-like zinc finger protein subfamily [152]. It was first
reported in 2003 and characterized as a novel transcription factor, which functions both as an activator
and repressor of transcription [167]. The GLIS3 contains five highly conserved C2H2-type zinc finger
domains (ZFD) in both humans and mice, which interacts with GLIS-binding sites (GLISBS) of the
target gene in combination with co-activators or co-repressors to regulate the gene transcription
(Figure 3) [152, 168, 169]. GLIS3 plays important role in multiple biological processes, including
being the key regulator of pancreatic cell production and maturation [170] and also the expression of
insulin gene (Figure 3) [169].
Figure 3. Gene and protein structure of GLIS3. (A) In mice. Part of exon 4 encodes the ZFD, which is the same in humans. Transactivation domain (TAD) is located at the C-terminus. Suppressor Fused homolog (SUFU) inhibit Cullin3 (CUL3) combined with GLIS3 through the VYGHF motif. CUL3 and ITCH promotes GLIS3 proteasomal degradation. (B) In humans. Gene GLIS3 has two major isoforms which are denoted a and b and they share 9 exons. Isoforms a and b encode 930 aa and 775aa protein, respectively. Modified from Wen and Yang 2017 [165].
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T1D and T2D are two genetically distinct diseases [53]. GLIS3 is one of the few genes in which
variation has been linked with both T1D and T2D [152, 165]. In addition, common variants in GLIS3
have also been found to be associated with neonatal diabetes [155, 166] and development of
gestational diabetes [97] (Figure 4).
The pathogenic consequence of GLIS3 variants on neonatal diabetes is related to the function of
GLIS3 as a key regulator of islet differentiation in pancreas [165]. The protein Neurogenin 3 (NGN3)
is a crucial factor for pancreatic endocrine cell differentiation [171]. GLIS3 binds to the promoter of
NGN3 to stimulate NGN3 transcription [172]. In mice, GLIS3 has also been identified as interacting
with HNF6 and FOXA2, two positive regulators of NGN3, which synergistically transactivate NGN3
expression to further regulate islet differentiation [172].
GLIS3 controls insulin gene transcription and insulin secretion
GLIS3 has been identified as being involved in the transcription of the insulin2 gene (INS2) and
GLIS3 expression exerts a stimulating effect on the Ins2 promoter in mice [149].Insulin transcription
is mediated via the binding of GLIS3 to the two conserved Glis enhancer elements (GlisBS) in the
proximal INS2 promoter initiating insulin transcription [149, 173]. It has been further verified that
GLIS3 regulates insulin gene expression by combining with the C-terminal binding protein
(CBP/p300) as a scaffold for the formation of a larger transcriptional regulatory complex, including
PDX1, NeuroD1 and MAFA at the insulin promoter [169] (Figure 4). Additionally, when comparing
Glis3−/− mice to wild-type controls it appears that GLIS3 also effects insulin secretion in βcells by
regulating glucose transporter 2 (Glut2) mRNA expression and ATP-binding cassette transporter sub-
family C member 8 (Abcc8) [174].
GLIS3 and insulin clearance
It has been suggested that GLIS3 is not only involved in insulin production but also in the clearance
of insulin. For example, the SNP rs2380949 in GLIS3 was found to be associated with insulin
clearance in Hispanic Americans [156]. Lower insulin clearance has been identified as being linked
with the incidence of T2D [175], thus, GLIS3 may effect T2D development both through regulation
of insulin production and clearance.
GLIS3 involved inβcell proliferation, mass expansion and apoptosis
Compensatoryβcell proliferation and βcell mass expansion are the two primary mechanisms in
response to insulin resistance. GLIS3 appears to be involved in both of them. It regulatesβcell
26
proliferation in response to insulin resistance by controlling the transcription of cyclin D2 (Ccnd2) in
mice under high-fat diet condition（Figure 4） [176]. Heterozygous Glis3-mutant (Glis3+/−) mice
failed in expanding theirβcell mass in response to high-fat diet as compared to Glis3+/+ controls[165].
This shows the GLIS3 involvement in regulating miceβcell mass expansion under high-fat diet
conditions. However, the mechanism of this needs to be further identified. Despite regulating the
production of βcell, GLIS3 also can induce β cell apoptosis via aggravating expression of the pro-
apoptotic variant BimS (An isoform of protein Bim) (Figure 4) [177].
Overall, GLIS3 is strongly expressed in pancreatic cells in both mice and humans. The protein GLIS3
regulates insulin gene expression and also affects insulin clearance. It can also controlβcell
proliferation and mass expansion in case of insulin resistance. Thus, the involvement of GLIS3 in
these many aspects of βcell function may explain why the variants in GLIS3 have been involved in
most forms of diabetes.
Figure 4. GLIS3 related to diabetes. (A) In T1D/T2D/NDM: GLIS3 induced β cell apoptosis and effects insulin secretion. GLIS3 can interact with PDX1, MAFA and NEUROD1 to regulate the transcription of insulin. (B) In NDM and T2D, GLIS3 work together with HNF6 and FOXA2 to control the islet differentiation via transactivating NGN3 [172]. (C) In T2D: GLIS3 induced β cell proliferation via heighten Ccnd2 mRNA transcription [176]. Reprinted from Wen and Yang 2017[165].
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1.4.2 Roles of PPP1R3B in diabetes
Full length of human gene PPP1R3B (protein phosphatase 1 regulatory subunit 3B) is 15,389bp.
PPP1R3B is located on the short arm of chromosome 8 (8p23.1) and is alternatively spliced, which
results in multiple transcripts that encode the catalytic regulatory subunit of protein phosphatase
1(PP1). Protein PPP1R3B also called hepatic glycogen-binding subunit GL, most commonly found
in liver and detectable in human skeletal muscle and heart tissues [178]. As a regulatory subunit of
PP1, the main function of PPP1R3B is promoting glycogen synthesis and inactivating glycogen
metabolism in conditions of high glucose [179, 180]. It has three distinct functional domains for
binding of PP1, glycogen and phosphorylase to target the PP1 to glycogen synthase compound [181].
Having a reduced glycogen synthesis is a clinical characteristic of T2D [182]. PPP1R3B is one of the
key regulators of glycogen synthase where it increases the activity of glycogen synthesis [180, 183].
Common variants in PPP1R3B have been identified as being associated with decreased rates of
glycogen synthesis and fasting plasma glucose levels [159, 184], and also linked with increasing of
fasting serum insulin levels [160]. In addition, SNPs in PPP1R3B have also been found to be
associated with lower plasma lipid levels (including HDL-C, TC and LDL-C) in individuals of
European ancestry, and experimentally in mouse model [161]. Also elevated levels of very low
density lipoprotein-cholesterol (VLDL-C) [185] and C-reactive protein (CRP) levels have been
detected in carriers of PPP1R3B variants individuals in European ancestry [163] whereas variants in
PPP1R3B are associated with lower plasma lipid level and CPR in the Chinese population as well
[186].
The region 8p23 is among the regions which have been involved in T2D [157] and MODY [61].
However, the association between region 8p23 and MODY has not been further verified, more
evidence needs to be acquired [151].
Linkage with T2D was found on 8p23 in Japanese (peak at 15 cM) [158] and Australian (peak at 32
cM) families [187]. There is also a wide peak across the whole 8p region (8p21.3-22 and 8q24.21-
24.23) which has been linked to T2D in English and Irish sib pairs [188]. Beside common variants in
PPP1R3B that have been reported as being associated with T2D and related metabolic traits, the
mutations in another PP1 regulatory subunit (PPP1R3A) has also been linked with insulin resistance
and the development of T2D in different ethnicities, including individuals of European ancestry and
Mayan populations [189, 190].
Together, these facts indicate that the region where PPP1R3B is located is involved in T2D and likely
in MODY. In this study, we investigated whether PPP13R3B rare variants could be involved in the
development of T2D and MODY.
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1.4.3 Participants in this PhD study
The participants in the present PhD study for both the GLIS3 and PPP1R3B papers were recruited
through different cohorts including newly diagnosed T2D patients (n=2,930) from the DD2 cohort
[191]. The non-diabetic individuals, including 1,157 prediabetic and 4,569 glucose tolerant
individuals were from the Inter99 study [192, 193].
The detailed characteristics of individuals of the GLIS3 and PPP1R3B study are listed below, as well
as the description of the DD2 and Inter99 studies. The studies were approved by the regional ethics
committees and conducted according to the Declaration of Helsinki.
Individuals included in paper I（GLIS3 paper）
The 2,930 newly-diagnosed T2D patients were recruited from the DD2-cohort [191]. 5,726 non-
diabetic individuals were recruited as control from the Inter99 study [192, 193]. These controls
comprised 1,157 prediabetic individuals and 4,569 normal glucose tolerant individuals (NGT)
classified in accordance with the WHO 1999 criteria using a 2-hour OGTT. 53 MODYX probands
were recruited from the outpatient clinic at Steno Diabetes Center, Copenhagen. In addition, 206 T2D
patients with GADA-positive were recruited from the DD2-cohort (n=85) and Vejle Hospital,
Denmark (n=121).
Individuals included in paper II （PPP1R3B paper）
The 2,930 newly diagnosed T2D patients and 5,726 controls without diabetes were recruited from the
DD2 cohort and Inter99 study respectively. 54 MODYX patients including a trio of probands were
recruited from the outpatient clinic (Steno Diabetes Center) as well.
Inter99 study
Inter99 is a large population-based study of cardiovascular disease prevention [192]. The project
recruited 6,784 individuals between 30-60 years old from the Centre for Prevention and Health at
Glostrup, Denmark (ClinicalTrials.gov ID NCT00289237) [194]. A 75g standardized OGTT test was
performed in individuals who did not get their glucose tolerance status. Plasma glucose, lipid, HbA1c,
C-peptide and insulin levels were also measured. Subsequently, the 6,094 Danish participants with
DNA available were classified according to their glucose tolerance status based on the performed 2-
hour OGTT, including NGT (n= 4,525), IFG (n= 504), IGT (n= 693), screen-detected T2D (n= 253),
or previously diagnosed T2D (n= 119) based on the WHO 1999 criteria [192, 193]. However, in this
PhD project, we only recruited individuals without diabetes from the Inter99 study.
29
DD2-cohort
DD2, Danish Centre for Strategic Research in T2D, is an ongoing nationwide population study, which
has run since 2010 [191]. The project enrolls and tracks the development of T2D in individuals in
Denmark. The goal of DD2 is to find the right treatment for T2D and set up a large T2D database that
can serve as a platform for T2D studies [191]. It includes genotype and clinical phenotype data of
T2D studies, and also includes personalized T2D treatment data, such as pharmaco-epidemiological
and long-term follow-up on predictors of T2D complications and prognosis studies [191]. A total of
8,385 T2D patients had been recruited as of September 2018 (https://dd2.nu).
2.0 SUMMARY OF PhD STUDY
2.1 Result and discussion of paper I: Sequencing reveals protective and pathogenic effects on development of diabetes of rare GLIS3 variants
2.1.1 Result of Paper I
The aim of the GLIS3 study was to evaluate the effect of rare GLIS3 variants on the development of
T2D among a well-studied Danish population. We recruited T2D patients from the ongoing DD2
study and controls without diabetes from the Inter99 study. The T2D patients included 206 GADA-
positive individuals, and the controls without diabetes included IGT, IFG and NGT individuals. In
addition, we recruited 54 MODYX individuals to investigate if the rare mutation in GLIS3 co-
segregated with MODY. Each participant was sequenced, and we examined the effect of rare
mutations in GLIS3 on T2D and on related metabolic traits.
In summary, we found a total of 87 missense mutations in the coding region of GLIS3, including three
common variants with MAF>1%, and six low-frequency variants having a MAF between 0.1% to
1%.
The association between three common variants and glucose metabolism traits, including plasma
glucose, insulin secretion and insulin resistance among T2D patients and controls were investigated.
However, no significant enrichment of carriers was found among T2D patients for the three common
variants. Nor was any significant difference in measures of βcell function or plasma glucose levels
found. In total, 78 rare mutations were distributed among T2D patients including GADA-positive
ones, non-diabetic controls and MODYX individuals. In general, the prevalence of rare mutations
(MAF<0.1%) in T2D patients was 2.39%, which was higher than among controls (1.75%). Yet this
elevation was not detected in the GADA-positive diabetes patients (1.46%)（Table1）.
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Two common variants (p.G313A and p.D512E) and one rare variant (p.V916L) were detected in
MODYX. However, both the common and rare variants were also found among T2D and glucose
tolerant individuals (Table 2), thus, neither of these variants is considered associated with the
development of MODY.
From the overall investigation of the effect of rare mutations on the metabolic characteristics in T2D
patients, T2D with GADA-positive, MODYX individuals and controls without diabetes, we observed
a significantly increased level of HbA1c among carriers of rare GLIS3 variants with p value of 0.02
in newly diagnosed diabetes patients and p value of 0.004 among 206 T2D patients with GADA-
positive.
In addition, it is worth noting that we detected a mutation (p.I28V) that may protect against T2D
development. The mutation p.I28V has a MAF of only 0.03% in T2D patients compared to 0.2%
among non-diabetic individuals (p=0.08). It suggests that this mutation has a protective function
against the development of T2D. This variant is also associated with a lower level of fasting plasma
glucose among non-diabetic individuals.
Table 1. Distribution of rare mutations in GLIS3 among T2D patients and individuals without diabetes, with and
without p. I28V.
Controls without diabetes (n=5,726)
T2D patients (n=2,930)
Controls versus T2D patients: Fishers exact, OR (95% CI), p-value
Carriers of GLIS3 rare variants (with p.I28V) 100 70 -
Occurrence (%) 1.75% 2.39% 1.37 (1.01-1.88); p=0.04 Carriers of GLIS3 rare variants
(without p.I28V) 90 69 -
Occurrence 1.57% 2.35% 1.51 (1.10-2.07); p=0.01
Table 2. Identified GLIS3 missense variants among non-diabetic individuals, patients with T2D, MODYX and patients
with GADA-positive diabetes.
Variants Rs-number Position
MAF
Gnomad
(All)
CADD-
score
MAF
our
cohort
T2D
patients
(het/ho)
Non-
diabetic
(het/ho)
GADA-
positive
(het/ho)
MODYX
(het/ho)
Common variants MAF > 1%
p.G313A rs35154632 4118540 0.0081 25.6 0.01 69/1 114/0 3/0 2/0
p.P456Q rs6415788 4118111 0.66 0.622 0.57 1381/493 2744/ 995 102/27 35/0
p.D512E rs148199056 4117942 0.017 12.13 0.021 126/1 224/7 11/0 2/0
Low frequency variants 1% < MAF > 0.1%
p.P282A rs143051164 4118634 2.0*10-3 8.40 4.0*10-3 18/0 47/0 2/0 0/0
31
p.S298Y rs148572278 4118585 2.0*10-3 25.6 2.0*10-3 11/0 23/0 1/0 0/0
p.P364S rs143056249 4118388 1.4*10-3 0.001 1.0*10-3 5/0 15/0 1/0 0/0
p.Q397H rs138497710 4118287 2.1*10-3 20.5 4.0*10-3 22/0 46/0 0/0 0/0
p.H400R rs376031632 4118279 8.0*10-4 18.11 2.0*10-3 9/0 32/0 1/0 0/0
p.E515D rs72687988 4117933 2.9*10-3 24.2 2.0*10-3 9/0 23/0 1/0 0/0
Total low frequency 74/0 186/0 0/0
Rare variants MAF < 0.1%
p.C6G rs767988875 4286410 1.8*10-5 19.3 1.3*10-4 1/0 2/0 0/0 0/0
p.H11Y rs773069755 4286395 1.1*10-4 22.3 4.4*10-5 0/0 1/0 0/0 0/0
p.T13I NA 4286388 - 21.9 4.4*10-5 1/0 0/0 0/0 0/0
p.S24N NA 4286355 - 17.6 4.4*10-5 0/0 1/0 0/0 0/0
p.I28V rs113754532 4286344 2.5*10-4 14.4 4.9*10-4 1/0 10/0 0/0 0/0
p.R32Q rs375834888 4286331 2.8*10-5 23.7 8.9*10-5 1/0 1/0 0/0 0/0
p.G36R rs199788224 4286320 1.2*10-4 30.0 2.2*10-4 2/0 2/0 0/0 0/0
p.S53R NA 4286267 4.1*10-6 23.0 4.4*10-5 1/0 0/0 0/0 0/0
p.L54F NA 4286266 - 13.3 4.4*10-5 0/0 1/0 0/0 0/0
p.S77G rs775442287 4286197 4.1*10-6 24.3 4.4*10-5 1/0 0/0 0/0 0/0
p.R78H rs200195201 4286193 2.9*10-5 19.2 2.2*10-4 1/0 2/0 0/0 0/0
p.L84F rs200986848 4286174 2.1*10-4 24.6 1.8*10-4 1/0 2/0 0/0 0/0
p.P86A rs368959854 4286170 2.4*10-5 15.2 2.2*10-4 1/0 4/0 0/0 0/0
p.P96L rs143425492 4286139 1.9*10-4 8.0 3.6*10-4 2/0 1/0 2/0 0/0
p.S109P rs753973362 4286101 2.2*10-5 4.9 4.4*10-5 1/0 0/0 0/0 0/0
p.G120R rs200701636 4286068 1.8*10-5 26.2 4.4*10-5 1/0 0/0 0/0 0/0
p.I141T rs202165554 4125908 2.2*10-4 25.4 5.3*10-4 3/0 8/0 0/0 0/0
p.Q170E rs779077595 4125822 4.1*10-5 24.6 3.6*10-4 2/0 4/0 0/0 0/0
p.A174S NA 4125810 3.2*10-5 23.8 4.4*10-5 0/0 1/0 0/0 0/0
p.N187Y NA 4125771 - 25.7 4.4*10-5 0/0 1/0 0/0 0/0
p.R232K NA 4118783 - 11.0 8.9*10-5 0/0 1/0 0/0 0/0
p.V265F rs143192828 4118685 2.8*10-4 19.6 4.4*10-5 0/0 1/0 0/0 0/0
p.A329V rs776646791 4118492 4.1*10-6 28.6 1.3*10-4 0/0 1/0 0/0 0/0
p.I331L NA 4118487 4.1*10-6 16.4 4.4*10-5 1/0 0/0 0/0 0/0
p.S357N rs374752356 4118408 1.7*10-5 21.7 4.4*10-5 1/0 0/0 0/0 0/0
p.P364R rs772125440 4118387 7.6*10-5 14.8 1.3*10-4 0/0 1/0 0/0 0/0
p.P364L rs772125440 4118387 3.2*10-5 10.0 4.4*10-5 0/0 1/0 0/0 0/0
p.L373V rs200263979 4118361 1.2*10-3 6.1 4.9*10-4 3/0 7/0 0/0 0/0
p.P376S rs76342955 4118352 2.5*10-3 0.001 8.9*10-5 0/0 1/0 0/0 0/0
p.G385D rs200959196 4118324 2.0*10-4 0.003 4.4*10-5 1/0 0/0 0/0 0/0
32
p.L398M NA 4118286 - 24.7 4.4*10-5 0/0 1/0 0/0 0/0
p.P405L rs764072714 4118264 5.9*10-5 11.9 4.4*10-5 0/0 1/0 0/0 0/0
p.G406C rs75462592 4118262 2.8*10-3 23.7 1.3*10-4 2/0 0/0 0/0 0/0
p.N409K rs534295783 4118251 7.2*10-5 14.8 4.4*10-5 1/0 0/0 0/0 0/0
p.P420L rs768040059 4118219 5.3*10-6 0.06 4.4*10-5 0/0 1/0 0/0 0/0
p.T440A rs80161424 4118160 1.0*10-3 0.001 4.4*10-5 1/0 0/0 0/0 0/0
p.P445R NA 4118144 NA 0.01 1.8*10-4 3/0 1/0 0/0 0/0
p.L473F rs369088290 4118061 9.4*10-5 22.6 1.8*10-4 1/0 1/0 0/0 0/0
p.P475L rs769236310 4118054 1.4*10-5 0.003 4.4*10-5 0/0 1/0 0/0 0/0
p.P475T NA 4118055 - 0.002 4.4*10-5 1/0 0/0 0/0 0/0
p.D490V rs371985224 4118009 1.1*10-5 24.0 4.4*10-5 1/0 0/0 0/0 0/0
p.M493T NA 4118000 4.1*10-6 0.02 4.4*10-5 1/0 0/0 0/0 0/0
p.I505V rs369355792 4117965 1.6*10-5 15.8 1.3*10-4 1/0 0/0 0/0 0/0
p.G540S rs759183029 4117860 7.2*10-6 29.5 2.7*10-4 2/0 2/0 0/0 0/0
p.A607T NA 3937081 - 32.0 2.2*10-4 2/0 2/0 0/0 0/0
p.P626H NA 3932466 - 27.8 8.9*10-5 0/0 2/0 0/0 0/0
p.R663W NA 3898832 8.1*10-6 35.0 4.4*10-5 0/0 1/0 0/0 0/0
p.P684L rs542599450 3898768 3.6*10-5 22.7 1.8*10-4 3/0 0/0 0/0 0/0
p.A693T rs568262538 3898742 2.5*10-5 9.3 3.1*10-4 3/0 4/0 0/0 0/0
p.V697M rs148816140 3898730 7.8*10-4 11.3 4.4*10-5 1/0 0/0 0/0 0/0
p.R699H rs149840771 3898723 2.5*10-4 15.8 2.2*10-4 0/0 3/0 0/0 0/0
p.P703S rs200705602 3898712 2.8*10-5 9.2 4.4*10-5 0/0 0/0 1/0 0/0
p.I712N NA 3879589 - 23.4 4.4*10-5 0/0 1/0 0/0 0/0
p.F713L rs201347665 3879585 3.3*10-5 29.3 1.3*10-4 0/0 1/0 0/0 0/0
p.S714C rs139924264 3879583 1.4*10-5 18.7 4.4*10-5 1/0 0/0 0/0 0/0
p.N716K NA 3879576 4.1*10-6 16.7 4.4*10-5 0/0 1/0 0/0 0/0
p.N716S rs756574107 3879577 8.1*10-6 6.9 2.7*10-4 3/0 2/0 0/0 0/0
p.G726E rs764370927 3879547 1.2*10-5 26.0 1.3*10-4 2/0 0/0 0/0 0/0
p.Q753R rs750346762 3879466 4.5*10-5 17.2 4.4*10-5 0/0 1/0 0/0 0/0
p.H775Q rs780948928 3856157 4.1*10-6 15.4 4.4*10-5 1/0 0/0 0/0 0/0
p.R791T NA 3856110 - 23.2 4.4*10-5 1/0 0/0 0/0 0/0
p.T792A rs867985925 3856108 4.1*10-6 1.5 4.4*10-5 1/0 0/0 0/0 0/0
p.H824Y NA 3856012 - 26.4 4.4*10-5 0/0 1/0 0/0 0/0
p.K832E rs779354091 3829472 1.5*10-4 23.2 4.4*10-5 1/0 0/0 0/0 0/0
p.D840N rs201704428 3829448 8.3*10-5 25.5 3.1*10-4 2/0 2/0 0/0 0/0
p.I844T rs193061752 3829435 4.3*10-5 0.003 1.8*10-4 2/0 2/0 0/0 0/0
p.P846L rs199505727 3829429 2.8*10-4 22.9 4.4*10-5 0/0 1/0 0/0 0/0
33
p.D859G NA 3829390 - 25.2 4.4*10-5 1/0 0/0 0/0 0/0
p.F871L NA 3829353 - 2.5 2.2*10-4 1/0 3/0 0/0 0/0
p.S892C rs749999750 3828391 8.2*10-6 16.1 4.4*10-5 0/0 1/0 0/0 0/0
p.S894P NA 3828385 4.1*10-6 23.1 4.4*10-5 0/0 1/0 0/0 0/0
p.L896F rs76094493 3828379 1. 1*10-2 22.4 4.4*10-4 1/0 3/0 0/0 0/0
p.F897C NA 3828375 8.2*10-6 25.2 4.4*10-5 1/0 0/0 0/0 0/0
p.R902H rs772126214 3828360 3.7*10-5 23.6 4.4*10-5 1/0 0/0 0/0 0/0
p.G904R rs150310830 3828355 2.1*10-4 24.6 4.4*10-5 0/0 1/0 0/0 0/0
p.V916L rs151140581 3828319 - 20.7 6.7*10-4 3/0 5/0 0/0 1/0
p.V916M rs151140581 3828319 1.4*10-4 23.6 8.9*10-5 1/0 1/0 0/0 0/0
p.R918H rs147357710 3828312 1.4*10-4 27.4 4.4*10-5 0/0 1/0 0/0 0/0
p.S924A rs781124953 3828295 4.1*10-6 28.0 4.4*10-5 0/0 1/0 0/0 0/0
Total rare
carriers 70/0# 100/0## 3/0 1/0
# there are two individuals carrying two mutations. ## there are three carriers carrying two mutations. Het: heterozygotes; Ho: homozygotes.
2.1.2 Discussion of paper I
Common mutations in GLIS3 have been well documented being associated with T2D [165, 195]. In
this study, we focused on the association of rare GLIS3 mutations and the development of T2D and
related metabolic characteristics.
In addition to three common variants and six low-frequency mutations, we found a total of 78 rare
missense mutations in GLIS3. As expected, the prevalence of rare GLIS3 missense variants is slightly
higher in T2D patients compared to the controls. We also tested the effect of rare variants on glucose
metabolism. The result shows the level of HbA1c was elevated, and the p-value (0.02 of newly T2D
patients and 0.004 of T2D with GADA-positive) was significant when compared with mutation
carriers to no-carrier of T2D individuals. This is consistent with the increase of prevalence of rare
GLIS3 missense variants in T2D patients.
Despite the increased level of HbA1c, we did not observe any effect of rare GLIS3 mutations on C
peptide and insulin levels. This leaves us with the question of whether the increased level of HbA1c
in carriers of T2D patients is associated with the decrease of insulin levels. HbA1c is a form of
glycated hemoglobin that has been used to identify the three-month average level of plasma glucose.
The longer the hyperglycemia in blood, the more glucose binds to hemoglobin in red blood cells and
results in higher glycated hemoglobin. If the HbA1c increase is not from the decrease of insulin, it
might be from other aspects of insulin secretion, such as timeliness and persistence of the insulin
secretion. However, the mechanism behind it is not clear. Above all, it is hard to determine if the high
level of HbA1c is associated with a long-term high glycaemic condition.
34
The protein GLIS3 has a significant role in regulating insulin gene (INS2) expression [149] and
insulin clearance [175], and also acts as the key regulator of the development of the pancreas [167],
β cell proliferation and mass expansion as well [176]. This makes us wonder why rare mutations of
GLIS3 did not appear to associate with insulin secretion in our data. We reviewed the previous GLIS3-
related studies and found that the majority of findings on GLIS3 were based on mice models [169,
177, 196]. There might be some inconsistencies in the effects of GLIS3 function between humans and
mice. Furthermore, there might be some compensating metabolic mechanisms for the effects of
GLIS3 mutations in humans.
Another point of attention is the rare mutation of p.I28V. It appears to be associated with preventing
the rise of glucose levels. The mutation of p.I28V only had one carrier among 2930 T2D patients
(0.03 %), but 9 carriers in controls (0.16 %). In addition, individuals without diabetes whom carried
p.I28V had a significant reduction in level of fasting plasma glucose. These results suggest that the
rare mutation of p.I28V might have a protective function on T2D development. T1D and T2D are
two genetically and pathogenetically distinct diseases. However, p.A908V, a low-frequency mutation
in GLIS3, has also been found to be potentially protective in relation to the development of T1D in a
Japanese population, with a 0.7% frequency in 863 controls and no p.A908V detected in 706 patients,
with OR=0.046, p=8.21x10-4 [135]. The likely protective effects of rare mutation p.I28V in GLIS3
have revealed a more important and comprehensive role of GLIS3, which may not only have a
deleterious effect in the pathogenesis of T2D.
2.2 Result and discussion of Paper II: Increased frequency of rare missense PPP1R3B variants among Danish patients with type 2 diabetes
2.2.1 Result of paper II
The aim of this study was to identify whether rare missense variants in PPP1R3B increase the risk of
T2D, MODY or affect related glucose metabolism parameters. Here we examined T2D patients from
the DD2 project and controls without diabetes from the Inter99 study. In addition, 54 MODYX
patients were recruited from the outpatient clinic.
In total we detected 23 missense variants in 396 carriers of T2D and MODYX patients, and controls
without diabetes. Among them, eight of the carriers carried two variants. Out of the 23 missense
mutations, 21 were rare, having a MAF <0.1% and two were common variants with MAF>1%.
35
The rare missense mutations with a MAF <0.1% were analysed to investigate the risk of rare
PPP1R3B missense mutations on T2D, and this was done by investigating the effects of rare missense
variants in T2D patients compared to the controls without diabetes.
The rare missense mutation carriers in T2D patients (0.58%) significantly increased compared with
individuals without diabetes (0.31%) with a risk on T2D development of OR=2.57, p=0.02 (age and
sex adjusted) (Table 3).
Table 3. Prevalence of rare missense PPP1R3B variants in T2D patients and percipients without diabetes.
NGT IFG/IGT T2D patients
NGT versus T2D patients
NGT+ IFG/IGT versus T2D patients
Non-carriers (n) 4,557 1,151 2,913 - - Carriers (n) 12 6 17 - -
Prevalence (%) (95% confidence interval)
0.26 (0.15-0.46)
0.52 (0.24-1.13)
0.58 (0.36-0.93) - -
Fishers exact OR (95% CI): p-value - - - 2.22 (1.06-4.65)
p=0.03 1.85 (0.95-3.60)
p=0.07 Logistic regression
OR (95% CI): p-value - - - 3.07(1.24- 7.74), p =0.02
2.57 (1.14- 5.79), p =0.02
Kbac (kernel-based adaptive
cluster) - - - p=0.04 p=0.04
The four heterozygous mutations of PPP1R3B (p.S41R, p.G48E, p.G218E and p.R263W) found in
MODYX patients were further investigated. The mutations p.S41R and p.G48E have a MAF of 1.7
and 3.3% respectively, both classified as common mutations and therefore unlikely to be causal
variations of MODY. Pathogenicity of the remaining two mutations p.G218E and p.R263W were
analysed by using the Combined Annotation Dependent Depletion (CADD) score system, a score
over 10 and 20 means pathogenicity in the top 10 and 1 percentile of all variants [197]. The variants
p.G218E and p.R263W have CADD scores of 24.3 and 33 respectively. The prevalence of mutation
p.G218E was 0.4% in South Asians [198]. Thus, it is not likely to be a pathogenic mutation of MODY.
To further detect whether the variant p.R263W is associated with the MODY, we sequenced an
additional three family members with diabetes and genotyped an additional eight family members
without diabetes. The result showed both family members with and without diabetes carried the
p.R263W variant, which does not support the variant p.R263W being a causal variant of MODY.
The prevalence of coding non-synonymous variants in PPP1R3B were investigated by http://www.
type2diabetesgenetics.org. However, there were 47 carriers in 5,023 T2D patients in contrast to 40
carriers in 4,977 individuals without diabetes. Thus, the enrichment of variants in PPP1R3B was not
significantly different between the diabetics and the controls.
In the data on glucose metabolism, HbA1c was slightly elevated among carriers of glucose tolerant
with p=0.02. Similar increases in measures of birth weight were also found in control carriers without
36
diabetes (p=0.04) (table 4). However, in T2D patients, the waist-hip ratio was slightly lower among
rare variant carriers (p=0.03). In addition, level of serum LDL-cholesterol (s-LDLc) was significantly
elevated in carriers of T2D, with p=0.006 (Table 5).
Table 4, Significant differences between rare PPP1R3B missense variants (MAF <0.1%) carriers and non-carriers in glucose tolerant individuals.
(Data is presented as median and interquartile range. Traits were all q-transformed).
Table 5, Significant trait differences between rare missense PPP1R3B variants (MAF <0.1%) carriers and non-carriers in T2D patients.
(Data is presented as median and interquartile range. Traits were all q-transformed).
2.2.2 Discussion of paper II
In this study, the association of rare PPP1R3B missense variants and the development of T2D and
MODY were investigated. In general, a total 23 of missense variants of PPP1R3B were detected,
with only two of them having a MAF over 0.1%. The two common variants (MAF> 0.1%) p.S41R
and p.G48E found in this study have also been detected in a previous GWAS study with much greater
statistical significance [199] and thus, these were not further investigated. The rare missense variants
of PPP1R3B were associated with an increased risk of T2D. However, four missense variants
detected in MODYX patients are not likely associated with the pathogenesis of MODY.
As the regulatory subunit of PP1, PPP1R3B can increase the activity of PP1, which is an important
enzyme in glycogenesis to activate glycogen synthase. PP1 can inactivate glycogen phosphorylase to
limit the enzyme in glycogenolysis as well [183]. This can explain why we observed rare PPP1R3B
variants associated with the risk of developing T2D and higher levels of HbA1c in this study. Mehta
et al found hepatic glycogen synthase severely impaired and glycogen storage decreased in specific
liver PPP1R3B gene knockout mice [180], which supports our result.
PP1 has the function of inactivating glycogen phosphorylase to limit glycogenolysis, which
contributes to avoiding hyperglycemia in the body. Therefore, PP1 has the potential to be a novel
anti-diabetic target [200, 201].
Trait Non-carriers (n= 4,557) Carriers (n=12) p-value (sex and age
adjusted) HbA1c (%) 5.80 (5.50- 6.00) 6.05 (5.98- 6.30) 0.02
Birth-weight (g) 3,400 (3050- 3750) 3,750 (3400- 4200) 0.04
Trait Non-carriers (n=2,913) Carriers (n=17) p-value (sex and age adjusted)
Waist-hip ratio (cm) 0.98 (0.92-1.03) 0.97 (0.94-1.03) 0.03
s-LDLc (mmol/L) 2.20 (1.80-2.90) 3.40 (2.60-3.90) 0.006
37
PPP1R3B is located on the 8p23.1. The 8p23 is one of the few regions that has been identified as
being involved in T2D [157] and likely in MODY [61, 151]. Linkage with T2D has been found at
8P23 in Japanese families (peak at 15 cM) [158] and Australian families (32 cM) [187]. However, in
our data, four missense mutations in PPP1R3B detected in MODYX patients were not linked with
the pathogenicity of monogenic diabetes. In addition to PPP1R3B in the region of 8p23.1, there are
also other interesting genes in the same region, such as GAT4, which encodes a zinc finger, and by
interacting with HNF1A (hepatocyte nuclear factor 1 homeobox A) [202] and the tyrosine kinase
gene BLK that is associated with MODY and alcohol consumption impacts on blood pressure [203,
204]. These genes might contribute to the effect on the pathogenicity of monogenic diabetes.
In this study, lower s-LDLc was detected in PPP1R3B variants carriers in T2D patients. The s-LDLc
has been reported in patients with T2D previously [205]. Similarly, the rs9987289 variant in
PPP1R3B effect the plasma HDL-cholesterol, LDL-cholesterol and total cholesterol [161]. The
variant rs4240624 in PPP1R3B has also been connected to histologic non-alcoholic fatty liver disease
(NAFLD) [159]. This study as well as previous reports show that rare PPP1R3B variants may affect
glycemic and lipid metabolism.
3.0 SUMMARY AND CONCLUSIONS OF THE TWO PAPERS
During my PhD study, there has been tremendous progress in genetic-association research combined
with a rapid development of NGS technology, which has had a great impact on the genetic studies
[63, 190]. I am therefore very fortunate to be involved in this rare-variant susceptibility study using
NGS during my PhD.
In this PhD study, we investigated the connection between rare variants in two candidate genes and
T2D and MODYX through target region sequencing. The investigation focused on the rare variants
and the association of the rare mutations with the onset of diabetes, with the levels of HbA1C and
lipid metabolism. The study intended to reveal the relevant mechanisms of rare variants in the
pathogenesis of T2D, and the results presented in this thesis contribute to our knowledge on rare
variants in genetics of diabetes research, and our understanding of the pathogenesis of T2D. Our
results demonstrate that:
• Rare missense variants in GLIS3 associated with elevated levels of HbA1C and increased risk
of developing T2D.
38
• The p.I28V variant in GLIS3 associated with reduced level of fasting plasma glucose and may
be protective against T2D.
• Rare GLIS3 missense variants identified appear to affect measures of glucose in both
deleterious and protection directions. However, this was without any measurable effect on
insulin secretion.
• Rare missense variants in GLIS3 and PPP1R3B are not likely linked to the development of
MODYX.
• Rare PPP1R3B missense variants increase the risk of T2D development. The mechanism
behind it might involve lipid metabolism and regulating PP1 to alter glycogen synthase.
4.0 FUTURE PERSPECTIVES
T2D is a multifactorial and polygenic disease. The pathogenesis of T2D includes genetic, epigenetic
and environmental factors, and the interaction between them. Although at least 70 genes and almost
250 genome loci have been identified as being associated with T2D, only a limited amount of the
estimated heritability for T2D (< 20%) can be explained so far [2, 206]. Clarifying the functional link
between related variants and phenotypic traits remains a huge challenge.
From genetic variants, and trans-omics to precision medicine
To date, only a small part of the heritability of T2D can be explained by the investigated common
variants [14, 95]. Common genetic variants which give a modest risk increase of T2D and are not
suited for use as prediction targets [137]. Also, the strategy of combining the association variants
together as a form of genetic risk score (GRS) does not successfully predict T2D [207-209]. In light
of this, the trend for genetic risk factors of T2D is moving from common variants to rare variations
[208]. Future studies will give more emphasis to rare variants, which may reveal more important high
effect size genetic susceptibly elements. In this personal health genome era, with the advent of much
higher throughput sequencing, the genetic research on pathogenic mechanisms of common disease
will move from GB to the TB level per individual in population studies [210]. Furthermore, meta-
analyses by combining big cohort data sets have improved the power of rare and novel susceptibility
loci discovery [2]. These developments possibly make a new understanding of the composite genetic
architecture, especially in rare variants studies.
Besides genetic factors, the systemic data from other trans-omics such as epigenomics,
transcriptomics, proteomics, metabolomics and metagenomics will bring more contributions to the
39
understanding of genetic determinants in the progression of metabolic diseases such as T2D [211].
The improved power and big data from genomics and trans-omics will definitely elucidate the
functional impact of more genetic variants. However, how to clearly evaluate the link between those
trans-omics and disease is a great challenge for genetic studies.
The development of technological, computational and collaborative advances continues to reveal new
genetic T2D risk factors. Based on the increasing knowledge of the molecular genetic profile of T2D,
there are high hopes for personalized treatment of T2D in future. However, to date, despite the
abundance of T2D genetic association studies, their influence on clinical practice are still limited
[208]. The successful translation from research to clinical application on monogenic diabetes suggests
that knowledge on subsets of disease and genetically homogenous groups can improve the accuracy
of individualized treatment. Monogenic diabetes is a good precision medicine model and lessons from
monogenic diabetes can be translated well to T2D [55]. For instance, to increase the accuracy of the
treatment, it is necessary to create subsets of the monogenic and pathophysiologically specific
subgroups based on range of genetic profiles, which can be applied to individualized treatment. On
the other hand, it is also important to identify the heterogeneous group to whom the individualized
genetic treatment should not be given.
Effect of Epigenetic and Genetic-Epigenetic Interaction on T2D
The epigenetic mediating environmental factors and disease might be potentially important
pathogenic mechanisms in T2D [212]. Furthermore, the interactions between genetic and epigenetic
factors can also influence the pathogenesis of T2D [213]. For instance, a high-fat diet has been
reported to be linked with the increase of DNA methylation level in humans [214]. Additionally, a
connection between DNA methylation level and elevated HbA1c and reduced mRNA expression was
found for the pancreatic duodenal homeobox 1 gene (PDX-1) in human pancreatic islets [215].
(Figure 4). In a study of T2D meta-analysis, Xue et al first identified the putative T2D functional
genes by GWAS, then combined analysis of those genes with the DNA methylation data. The project
successfully revealed the regulatory mechanism of three associated genes, which affect T2D through
epigenetic regulation to modify gene expression [129].
However, in spite of a few identified cases, the association of the epigenetic modification and
pathogenesis of T2D is still poorly understood. More research and data are needed to explore the role
of epigenetic factors in T2D development and its complications. Whole-genome bisulfite sequencing
(WGBS) has made it possible to analyze the DNA methylation in a genome-wide manner and gives
genome-wide insight into the impact of DNA methylation on the pathogenesis of T2D [216]. As more
40
data on epigenetics and genetic-epigenetic interaction is acquired and analyzed, the mechanism
behind their effects will become clearer.
Figure 5. Environmental factors can alter the DNA methylation. The DNA methylation alternation genes have impacted on the insulin and insulin resistance, which contribute to the development of T2D. Reprinted from Zhou and Sun 2018 [217].
5.0 Concluding remarks
In this thesis, I have described previous and ongoing efforts to identify the rare genetic variants which
confer susceptible to and is associated with risk of T2D development. The committed efforts of
scientists and rapid development of NGS technology have both greatly increased our understanding
of the genetic architecture of T2D in the last few years. The role of rare variants in monogenic diseases,
such as MODY, has been firmly established [218]. But our understanding of the role of rare variants,
which tend to have larger effects on T2D, is still limited. Despite this, with access to ever increasing
amounts of NGS data and new technologies, there will be many more opportunities for using genetics
combined with trans-omics approaches in the coming years. This will bring the possibility of
investigating new associated rare mutations, and it will reveal further details on the mechanisms in
dysmetabolic pathways, which will help us better understand the pathogenic mechanisms behind T2D
in a genetic perspective.
Epigenetic factors Age, Obesity, environmental factors, high-fat diet, exercise, palmitate, et al.
Alteration of DNA methylation
PPARGC1A,PGX1,INS,et al. NDUFB,OXPHOX,IGFBP1,IGFBP7, et al.
Reduced insulin secretion Increased insulin resistance
T2D Development
41
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7.0 APPENDIX
7.1 Manuscript of Paper I
Sequencing reveals protective and pathogenic effects on development of diabetes of rare GLIS3 variants Short title: Variants in GLIS3 and diabetes Jihua Sun1,2, Christian Theil Have3, Mette Hollensted3, Niels Grarup3, Allan Linneberg4,5,6, Oluf Pedersen3, Jens Steen Nielsen7, Jørgen Rungby8, Cramer Christensen9, Ivan Brandslund10,11, Karsten Kristiansen12,13, Wang Jun14, Torben Hansen3, and Anette P. Gjesing3* Affiliations 1Biology Department, University of Copenhagen, Copenhagen, Denmark 2BGI-Europe, Copenhagen, Denmark 3Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 4Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark 5Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark 6Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 7Odense University Hospital, Odense, Denmark 8Bispebjerg Hospital, University of Copenhagen, DenmarkLaboratory of Genomics and 9Department of Internal Medicine and Endocrinology, SLB, Hospital Lillebaelt, Kabbeltoft 25, DK-7100, Vejle, Denmark 10Department of Clinical Biochemistry, Hospital Lillebaelt, Kabbeltoft 25, DK-7100, Vejle, Denmark 11Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark 12Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark 13BGI-Research, Shenzhen, China 14iCarbonX, Shenzhen, China * Corresponding author Email: [email protected] (APG) Abbreviations: BMI, body mass index; CI, confidence interval; GADA, glutamic acid
decarboxylase antibody; GDM, gestational diabetes mellitus; hsCRP, high-sensitivity CRP; IFG,
impaired fasting glycemia; IGT, impaired glucose tolerance; MAF, minor allele frequency.
MODY, maturity- onset diabetes of the young; OHA, oral hyperglycemic agent. T2D, type 2
diabetes.
52
Abstract
Background
Based on the association of common GLIS3 variants with various forms of diabetes and the biological
role of GLIS3 in beta-cells, we sequenced GLIS3 in non-diabetic and diabetic Danes to investigate
the effect of rare coding variants on glucose metabolism.
Methods
We sequenced 53 patients with maturity-onset diabetes of the young (MODY), 5,726 non-diabetic
participants, 2,930 patients with newly diagnosed type 2 diabetes and 206 patients with glutamic acid
decarboxylase antibody (GADA) -positive diabetes.
Results
In total we identified 86 rare (minor allele frequency < 0.1%) missense variants. None was considered
causal for the presence of MODY. Among patients with type 2 diabetes, we observed a higher
prevalence of rare GLIS3 variants (2.5%) compared to non-diabetic individuals (1.8%) (odds ratio of
1.37 (interquartile range:1.01-1.88, p=0.04)). A significantly increased HbA1c was found among
patients with type 2 diabetes and with GADA-positive diabetes carrying rare GLIS3 variants
compared to non-carriers of rare GLIS3 variants with diabetes (p=0.02 and p=0.004, respectively).
One variant (p.I28V) was found to have a minor allele frequency of only 0.03% among patients with
type 2 diabetes compared to 0.2% among non-diabetic individuals suggesting a protective function
(odds ratio of 0.20 (interquartile range: 0.005-1.4, p=0.1)), an effect which was supported by
publically available data. This variant was also associated with a lower level of fasting plasma glucose
among non-diabetic individuals (p=0.046).
Conclusion
Rare missense variants in GLIS3 associates with increased levels of HbA1c and increased risk of
developing type 2 diabetes. In contrast, the rare p.I28V variant associated with reduced level of
fasting plasma glucose and may be protective against type 2 diabetes.
Introduction
GLIS3 is encoding a member of the Krüppel-like zinc finger protein subfamily GLI-similar 3 which
is a transcription factor playing a critical role both as a repressor and activator of transcription (1).
Human GLIS3 exist in two isoforms: Isoform A which is the longest including a total of 930 amino
acids, and isoform B which is 155 amino acids shorter at the N-terminus (2). Isoform A is highly
expressed in the pancreas, kidney, and thyroid, whereas the smaller isoform B is strongly expressed
in the heart, liver, kidney, and skeletal muscle (3). The transcriptional regulation by GLIS3 is
53
mediated through an interaction between GLIS-binding sites (GLISBS) in the regulatory region of
target genes and zinc finger domains in GLIS3 (1, 4).
Globally Glis3 deficient mice have a decreased beta-cell mass and develop neonatal diabetes in
addition to hypothyroidism, and cystic kidney disease (5). In addition, Glis3 missense variants
identified in Goto-Kakizaki rats have been found to increase basal production of insulin but reduce
the glucose stimulated insulin secretion in INS1-cells (6).
Also in humans, mutations in GLIS3 are a rare cause of neonatal diabetes as well as congenital
hypothyroidism and in some cases in combination with polycystic kidney disease, glaucoma, and
hepatic fibrosis (3, 7). It has been found for the neonatal diabetes genes INS, KCNJ11 and ABCC8
that certain variants also can give rise to the monogenic form of diabetes called maturity onset
diabetes of the young (MODY) (8-10). This may also be the case for GLIS3.
Type 1 diabetes (T1D) and T2D are two genetically distinct diseases, yet GLIS3 is one of the few
genes in which common variants have been found to associate with both forms of diabetes (11, 12).
In addition, common GLIS3 variants also associate with development of gestational diabetes (13),
with increased level of fasting glucose (11) and with altered insulin clearance (14). Thus, GLIS3
appear to be implicated in various diabetes forms.
This pathogenic effect of GLIS3 variants may be related to GLIS3 being instrumental for an optimal
transcriptional activation of Ins2 in conjunction with the transcriptions factors Pdx1, NeuroD1, and
MafA (15). This has been further verified by the observation that mutations in the GLISBS of the
insulin promoter are responsible for the development of neonatal diabetes in some patients (15).
However, GLIS3 has also been shown in INS1-cells to stimulate the transcription of genes of
importance for apoptosis such as Atg7 and Atg4a (6) as well as the pro-apoptotic BH3-only protein
Bim (16).
In humans common and low frequency GLIS3 variants (MAF > 0.1%) have previously been
investigated in relation to diabetes (11). Thus, we sequenced the coding region of GLIS3 using next-
generation sequencing (NGS) in order to investigate the effect of rare GLIS3 variants in a well
characterised Danish population. The sequencing was completed in 53 MODY patients with an
unknown genetic etiology (MODYX), 5,726 non-diabetic individuals, 2,930 patients newly
diagnosed with T2D and 206 patients with GADA-positive diabetes with the aim to investigate 1) if
GLIS3 variants is involved in MODYX, 2) if GLIS3 variants increase the risk of T2D development
and 3) if GLIS3 variants affect measures of glucose metabolism.
54
Materials and Methods
Population
Individuals in the present study included: 1) 53 MODYX probands recruited from the outpatient clinic
at Steno Diabetes Center, Copenhagen, Denmark, 2) 5,726 non-diabetic individuals recruited from
the Danish population-based Inter99 study (17, 18) including 1,157 prediabetic individuals (having
impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT)) and 4,569 glucose tolerant
individuals (NGT) classified according the WHO 1999 criteria using a 2-hour oral glucose tolerance
test (OGTT) (19); 3) 2,930 patients with newly-diagnosed T2D recruited through the DD2-cohort
(20); and 4) 206 patients with GADA-positive diabetes recruited from the DD2-cohort (n=85) and at
Hospital Lillebaelt, Denmark (n=121). A clinical description of included individuals can be found in
Table 1.
Table 1: Clinical description of participants.
Data presented as mean (SD).
Patients with newly diagnosed T2D were GADA-negative and had a fasting serum C-peptide
concentration > 150 pmol/l within 1.5 years from diabetes diagnosis. GADA-positive patients were
selected based on a GADA>30 IU/ml.
Prior to participation, written informed consents were obtained from all participants. The study design
was in accordance with the ethical scientific principles of the Helsinki Declaration II and approved
by The Scientific Ethics Committee of the Capital Region of Denmark (Inter99: KA-98155) and by
the Danish National Ethical Committee on Health Research (DD2: S-20100082).
Anthropometrics measurements
Body weight, height, waist, and hip circumference were measured with light indoor clothes and
without shoes. Body mass index (BMI) was defined as body mass divided by body height in units of
Trait Glucose tolerant
(Inter99, n = 4,569)
Pre-diabetes
(Inter99, n = 1,157)
T2D patients
(n = 2,930)
GADA-positive
diabetes patients
(n =206)
MODYX
probands
(n =53)
Sex (n) 2,113/2,456 700/457 1,678/1,177 108/95 25/ 28
Age (mean, SD) 45.2 (7.87) 48.8 (7.33) 60.2 (11.1) 49.3 (12.6) 22.4 (14.1)
BMI (kg/m2) 25.5 (4.14) 28.1 (4.97) 31.4 (6.29) 27.2 (5.67) 23.72 (6.16)
Fasting plasma glucose
(mmol/l) 5.31 (0.40) 6.00 (0.52) 7.49 (1.73) 10.0 (4.41) 8.47 (2.94)
Fasting serum- C-peptide
(pmol/l) 542.1 (217.2) 731.9 (315.3) 1243 (551.6) 433 (607.1) 503.8 (287.1)
Fasting serum triglyceride
(mmol/L) 1.18 (0.94) 1.71 (2.19) 1.98 (1.43) 1.17 (0.69) 1.75 (0.75)
Fasting serum total
cholesterol (mmol/L) 5.43 (1.03) 5.83 (1.14) 4.52 (1.08) 4.50 (0.90) 4.81 (0.78)
55
kg/m2.
Metabolic measurements
Inter99 participants and participants recruited at Hospital Lillebaelt, Denmark: The blood samples
were drawn after a 12h fast for measures of glycated hemoglobin (HbA1c), plasma glucose, serum
insulin, serum C-peptide, total cholesterol and serum triglyceride (17, 21). In addition, individuals
without known diabetes underwent a standard 75g OGTT with samples drawn at 30, and 120 minutes
measured for plasma glucose, serum insulin and C-peptide. Serum insulin levels (excluding des-31,32
and intact proinsulin) were measured using the AutoDELFIA insulin kit (Perkin-Elmer, Wallac,
Turku, Finland). Plasma glucose was analysed using a glucose oxidase method (Granutest; Merck,
Darmstadt, Germany)(18). Concentrations of serum triglycerides and total cholesterol were analysed
using enzymatic colorimetric methods (GPO-PAP and CHOD-PAP, Roche Molecular Biochemicals,
Germany). HbA1c was measured using ion-exchange high performance liquid chromatography
(normal reference range: 4.1-6.4%) (22).
Participants from the DD2-cohort: Measures of BMI, and routine laboratory measurements such as
fasting plasma glucose, fasting serum C-peptide and GADA were extracted from the Danish Diabetes
Database for Adults (23).
Estimates of glucose metabolism
Oral glucose-stimulated insulin response was measured as the insulinogenic index. Homeostatic
model assessment of insulin resistance (HOMA-IR) index measured as previous reported (24).
Targeted resequencing
A customized oligonucleotide probe was designed including all coding regions of GLIS3 as
previously described (25). Genomic DNA was extracted from human leucocyte nuclei. Target
regions were captured and subsequently underwent library construction. The captured DNA libraries
were sequenced using the Illumina HiSeq2000 as paired-end 90 bp reads (following the
manufacturer’s standard cluster generation and sequencing protocols). GLIS3 was covered with a
minimum depth of over 20X and a mean depth of the target region of 166X. Qualified reads were
aligned to the reference of human genome (UCSC hg19) using the Burrows-Wheeler Aligner tool
(http://bio-bwa.sourceforge.net), and single-nucleotide polymorphisms and indels were identified
using the Genome Analysis Toolkit (https://www.broadinstitute.org/gatk/). The variants were
annotated using Annovar (26) with variants annotated according to transcript NM_001042413.
56
The linkage disequilibrium (LD) structure between presently identified and previously investigated
variants was calculated using LDlink (27).
Statistical Analysis
The statistical differences in carrier-frequency between cases and controls were calculated using chi-
squared, as well as a kernel-based adaptive cluster (KBAC) test (28). However, due to the low number
of carriers of each group, the statistical difference between cases and control for variant p.I28V was
calculated using a fisher’s exact test. Quantitative trait analyses were performed using linear
regression using additive genetic models with adjustment for age and sex. Traits were all q-
transformed prior to analysis. Statistical analyses were performed using RStudio software version R-
3.4.1 (version 3.2.3; R Foundation for Statistical Computing, Boston, MA, USA) except KBAC
which was performed using rvtests (29). A p-value < 0.05 was considered statistically significant.
Results
The coding regions of GLIS3 were sequenced in 53 MODYX patients, 2,930 patients with T2D, 5,724
non-diabetic individuals and 206 patients with GADA-positive diabetes. A total 88 missense variants
were identified of which three were common variants (MAF >1%), six were low frequency variants
(MAF between 1% and 0.1%) and the vast majority (n=79) were rare (MAF<0.1%) (S1 table).
Three variants were found among the patients having MODYX. These included the common variants
p.G313A and p.D512E and the rare variant p.V916L. However, the rare variant p.V916L was also
found among three patients with T2D and five glucose tolerant individuals (S1 table), thus, none of
these variants can be considered causal for the development of MODY.
Among the remaining patients with T2D and participants without diabetes, we found three common
variants (p.G313A, p.P456Q, p.D512E) and six low frequency variants (p.P282A, p.S298Y, p.P364S,
p.Q397H, p.H400R and p.E515D) which have previously been investigated in a large GWAS study
conducted among more than 150,000 individuals (30). Thus, we did not further investigate the effect
of neither common nor low frequency variants but focussed on the previously un-investigated effect
of rare variants in GLIS3.
A total of 79 rare variants were found among 100 non-diabetic individuals including three individuals
carrying two variants and among 70 patients with T2D of whom two individuals are carrying two
variants. Thus, the prevalence of rare GLIS3 variants was 2.4% among patients with T2D compared
to 1.7% among non-diabetic individuals revealing a slight enrichment of rare GLIS3 variants in
patients having T2D with odds ratio (OR) of 1.37 (IQR: 1.01-1.85, p=0.04, Table 2).
57
Table 2: The prevalence of rare (MAF<0.1%) GLIS3 missense mutations among non-diabetic individuals (IGT+IFG),
patients with T2D and patients with GADA-positive diabetes.
Non-diabetic
(n=5,726)
T2Dpatients
(n=2,930)
Non-diabeticversusT2Dpatients:
CarriersofGLIS3variants 100 70
Fishers exact
OR
(95% CI),
p-value
Kbac
Non-carriersofGLIS3variants 5,626 2,860
1.37
(1.01-1.88)p=0.04
p=0.02
OR (Odds ratios) and p-values are adjusted for age and sex. CI: 95% confidence interval.
The effect of GLIS3 mutations on glucose metabolism was further explored and in patients with T2D,
levels of HbA1c were significantly elevated to 7.20% (SD:1.69) among carriers of rare GLIS3
variants compared to 6.80% (SD:1.23) among non-carriers, p=0.02 (Table 3). As variants in GLIS3
also have been found to associate with T1D, we investigated the effect of rare GLIS3 variants in
among patients classified as T2D, yet being GADA-positive (n=206). Three carriers of GLIS3
variants were found with two carriers of the p.P96L and one carrier of p.P703S (S1 table). Among
these, a significantly higher level of HbA1c (11.37%, SD: 2.99) compared to non-carriers (7.62%,
SD: 1.40), p=0.004 was also found (Table 3).
Table 3. Quantitative analysis of rare GLIS3 variants in 5,726 non-diabetic individuals, 2,930 patients with T2D and
206 patients with GADA-positive diabetes. Trait Non-carriers Carriers p-value
Non-diabetic individuals (n=5,726)
n (men/women) 2,763/2,863 50/50 NA
Age (years) 45.9 (7.61) 47.5 (7.11) 0.1
BMI (kg/m2) 26.0 (4.43) 26.5 (4.75) 0.5
Waist/hip ratio 0.85 (0.09) 0.86 (0.09) 0.6
Glycated hemoglobin (HbA1c %) 5.79 (0.40) 5.83 (0.42) 0.7
Glycated hemoglobin (HbA1c mmol/mol) 39.8 (4.35) 40.3 (4.61) 0.7
HOMA-IR 1.67 (1.14) 1.83 (1.22) 0.1
Fasting plasma glucose (mmol/l) 5.45 (0.51) 5.47 (0.51) 0.9
30-min plasma glucose (mmol/l) 8.56 (1.70) 8.60 (1.78) 0.8
2-h plasma glucose (mmol/l) 5.94 (1.53) 5.90(1.45) 0.7
0-min serum C-peptide (pmol/l.min) 579.8 (251.1) 616.6 (302.3) 0.3
30-min serum C-peptide (pmol/l.min) 2001 (712.0) 2076 (884.6) 0.5
2-h serum C-peptide (pmol/l.min) 2253 (975.5) 2311 (1030) 0.7
Fasting serum insulin (pmol/l) 40.8 (26.4) 44.3 (27.6) 0.5
30-min serum insulin (pmol/l) 290.6 (180.9) 302.5 (215.7) 0.09
2-h serum insulin (pmol/l) 203.8 (193.1) 226.2 (248.9) 0.5
Insulinogenic Index 29.7 (19.3) 30.8 (22.3) 0.5
T2D patients
n (men/women) 1638/1151 40/26 NA
Age (years) 60.7 (11.6) 60.2 (10.2) 0.5
58
BMI (kg/m2) 31.4 (6.29) 31.1 (5.52) 0.9
W/H ratio 0.98 (0.094) 0.97 (0.076) 0.6
Glycated hemoglobin (HbA1c %) 6.80 (1.23) 7.20 (1.66) 0.02
Glycated hemoglobin (HbA1c mmol/mol) 50.8 (13.5) 55.2 (18.1) 0.02
Onset (years) 59.3 (11.65) 59.1 (10.2) 0.9
Fasting plasma glucose (mmol/l) 7.49 (1.74) 7.24 (1.57) 0.3
Fasting serum C-peptide (pmol/l.min) 1246 (553.6) 1116 (441.7) 0.1
GADA-positive diabetes patients
n (men/women) 106/94 2/1 NA
Age (years) 47.2 (11.8) 31.9 (4.37) 0.1
BMI (kg/m2) 27.2 (5.66) 24.4 (6.71) 0.3
W/H ratio 0.92 (0.082) 0.87 (0.097) 0.3
Glycated hemoglobin (HbA1c %) 7.62 (1.40) 11.37 (2.99) 0.004
Glycated hemoglobin (mmol/mol) 59.8 (15.3) 100.8 (32.7) 0.004
Onset (years) 36.4 (16.8) 29.7 (9.72) 0.4
Fasting plasma glucose (mmol/l) 9.88 (4.36) 16.7 (1.05) 0.07
Fasting serum C-peptide (pmol/l.min) 436.2 (610.9) 201.7 (177.3) 0.7
Data presented as mean (SD). P-values are adjusted for sex and age.
In contrast to the overall prevalence of rare variants, one of the identified rare variants (p.I28V) was
far more frequent among non-diabetic individuals compared to patients with T2D with ten carriers
among non-diabetic individuals and only one carrier with T2D. These non-diabetic carriers had a
slightly lower level of fasting plasma glucose compared to non-diabetic individuals not carrying this
variant (p=0.047, S2 table). The prevalence of this variant was further investigated in a larger
publically available dataset where we observed an excess of five carriers among non-diabetic
individuals (n=9,335) in contrast to only one carriers among patients with T2D (n=9,121) (31) which
combined with our data disclose a combined OR of 0.17 (IQR: 0.038-0.72), p=0.006. The publically
available online data also identifies a significantly lower level of fasting glucose among carriers of
this variant (beta=-0.21, p=0.02) (31), an analysis which is performed in more than 75,000 individuals.
Discussion
We investigated the effect of rare missense variants within GLIS3 on risk of T2D and on measures of
glucose homeostasis. Since amino acids 1-156 are only present in isoform A, 17 of the identified
variants are only affecting this longer transcript. However, the longer isoform A is expressed in the
pancreas, thus, we assume that all of the identified variants may have potential effect on glucose
metabolism.
A slightly elevated prevalence of rare GLIS3 missense variants was observed among patients with
T2D compared to non-diabetic individuals. Supporting this observation, levels of HbA1c were also
elevated among patients with T2D carrying rare GLIS3 missense variants compared to patients not
carrying such variants.
59
Despite the known function of GLIS3 in beta-cell function, we did not observe any effect of GLIS3
mutations neither on circulating levels of C-peptide nor levels of insulin. Thus, we were unable to
determine the cause of elevated HbA1c measuring the long-term glycaemic control. Our observations
are well in line with GWAS, finding that common GLIS3 variants are associated with T2D and
elevated levels of fasting glucose, however, no other measures of glucose homeostasis have been
found to be affected by common GLIS3 variants.
It is puzzling that GLIS3 variants, do not appear to affect insulin secretion in human studies as GLIS3
has been found to be: 1) directly implemented in the transcription of INS (15), 2) been found to be
involved in pancreas maturation (7), 3) been found to be involved in beta-cell apoptosis (32) and 4)
the physiological consequence of GLIS3 mutations results in diabetes. Yet, direct effects of the
mutations on the protein and on beta-cell function are still undisclosed.
One of the identified variants (p.I28V) was less prevalent among patients with T2D and associated
with reduced levels of fasting plasma glucose both in our data and in online available data. This
suggests a protective effect of this variant on the development of hyperglycaemia. This potential
protective variant is only present in Europeans (MAF = 0.1%) and to a smaller extend in the Hispanic
population (MAF = 0.02%), indicating that this is mutation is a de novo mutations arisen in Europeans.
In a Japanese study, a GLIS3 variant (p.A908V) protecting against T1D was found among
approximately 3,000 Japanese patients with T1D and control individuals (33), indicating that variants
in GLIS3 may not only have a deleterious effects on diabetes.
Due to the central role of GLIS3 in glucose metabolism, GLIS3 may be considered a potential
treatment target and two studies have found compounds able to significantly diminish the apoptotic
effect of GLIS3. These compounds may be developed further into anti-diabetic treatments in the
future (16, 32).
In conclusion, we find that rare missense variants in GLIS3 may increase risk of diabetes and
elevate levels of plasma glucose. However, we also find a rare missense variant which likely
protects against the development of diabetes. Thus, variants in GLIS3 may explain a fraction of
heterogeneity in susceptibility to T2D.
60
Acknowledgements
The authors would like to thank the Exome Aggregation Consortium and the groups that provided
exome variant data for comparison. A full list of contributing groups can be found at
http://exac.broadinstitute.org/about. From Novo Nordisk Foundation Center for Basic Metabolic
Research, University of Copenhagen, Denmark, we wish to thank A. Forman, T. H. Lorentzen and G.
J. Klavsen for laboratory assistance, P. Sandbeck for data management, and T. F. Toldsted and
Camilla Verdich for grant management.
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Supporting information S1 table: Identified GLIS3 missense variants among non-diabetic individuals, patients with T2D and patients with
GADA-positive diabetes.
Variants Rs-number Position MAF Gnomad (All)
CADD-score
MAF our cohort
T2D patients (het/ho)
Non-diabetic (het/ho)
GADA-positive (het/ho)
MODYX (het/ho)
Common variants MAF > 1% p.G313A rs35154632 4118540 0.0081 25.6 0.01 69/1 114/0 3/0 2/0 p.P456Q rs6415788 4118111 0.66 0.622 0.57 1381/493 2744/ 995 102/27 35/0 p.D512E rs148199056 4117942 0.017 12.13 0.021 126/1 224/7 11/0 2/0
Low frequency variants 1% < MAF > 0.1%
p.P282A rs143051164 4118634 2.0*10-3 8.40 4.0*10-3 18/0 47/0 2/0 0/0 p.S298Y rs148572278 4118585 2.0*10-3 25.6 2.0*10-3 11/0 23/0 1/0 0/0 p.P364S rs143056249 4118388 1.4*10-3 0.001 1.0*10-3 5/0 15/0 1/0 0/0 p.Q397H rs138497710 4118287 2.1*10-3 20.5 4.0*10-3 22/0 46/0 0/0 0/0 p.H400R rs376031632 4118279 8.0*10-4 18.11 2.0*10-3 9/0 32/0 1/0 0/0 p.E515D rs72687988 4117933 2.9*10-3 24.2 2.0*10-3 9/0 23/0 1/0 0/0 Total low frequency 74/0 186/0 0/0 Rare variants MAF < 0.1% p.C6G rs767988875 4286410 1.8*10-5 19.3 1.3*10-4 1/0 2/0 0/0 0/0 p.H11Y rs773069755 4286395 1.1*10-4 22.3 4.4*10-5 0/0 1/0 0/0 0/0
63
p.T13I NA 4286388 - 21.9 4.4*10-5 1/0 0/0 0/0 0/0 p.S24N NA 4286355 - 17.6 4.4*10-5 0/0 1/0 0/0 0/0 p.I28V rs113754532 4286344 2.5*10-4 14.4 4.9*10-4 1/0 10/0 0/0 0/0
p.R32Q rs375834888 4286331 2.8*10-5 23.7 8.9*10-5 1/0 1/0 0/0 0/0
p.G36R rs199788224 4286320 1.2*10-4 30.0 2.2*10-4 2/0 2/0 0/0 0/0 p.S53R NA 4286267 4.1*10-6 23.0 4.4*10-5 1/0 0/0 0/0 0/0 p.L54F NA 4286266 - 13.3 4.4*10-5 0/0 1/0 0/0 0/0 p.S77G rs775442287 4286197 4.1*10-6 24.3 4.4*10-5 1/0 0/0 0/0 0/0 p.R78H rs200195201 4286193 2.9*10-5 19.2 2.2*10-4 1/0 2/0 0/0 0/0 p.L84F rs200986848 4286174 2.1*10-4 24.6 1.8*10-4 1/0 2/0 0/0 0/0 p.P86A rs368959854 4286170 2.4*10-5 15.2 2.2*10-4 1/0 4/0 0/0 0/0 p.P96L rs143425492 4286139 1.9*10-4 8.0 3.6*10-4 2/0 1/0 2/0 0/0 p.S109P rs753973362 4286101 2.2*10-5 4.9 4.4*10-5 1/0 0/0 0/0 0/0 p.G120R rs200701636 4286068 1.8*10-5 26.2 4.4*10-5 1/0 0/0 0/0 0/0 p.I141T rs202165554 4125908 2.2*10-4 25.4 5.3*10-4 3/0 8/0 0/0 0/0 p.Q170E rs779077595 4125822 4.1*10-5 24.6 3.6*10-4 2/0 4/0 0/0 0/0 p.A174S NA 4125810 3.2*10-5 23.8 4.4*10-5 0/0 1/0 0/0 0/0 p.N187Y NA 4125771 - 25.7 4.4*10-5 0/0 1/0 0/0 0/0 p.R232K NA 4118783 - 11.0 8.9*10-5 0/0 1/0 0/0 0/0 p.V265F rs143192828 4118685 2.8*10-4 19.6 4.4*10-5 0/0 1/0 0/0 0/0 p.A329V rs776646791 4118492 4.1*10-6 28.6 1.3*10-4 0/0 1/0 0/0 0/0 p.I331L NA 4118487 4.1*10-6 16.4 4.4*10-5 1/0 0/0 0/0 0/0 p.S357N rs374752356 4118408 1.7*10-5 21.7 4.4*10-5 1/0 0/0 0/0 0/0 p.P364R rs772125440 4118387 7.6*10-5 14.8 1.3*10-4 0/0 1/0 0/0 0/0 p.P364L rs772125440 4118387 3.2*10-5 10.0 4.4*10-5 0/0 1/0 0/0 0/0 p.L373V rs200263979 4118361 1.2*10-3 6.1 4.9*10-4 3/0 7/0 0/0 0/0 p.P376S rs76342955 4118352 2.5*10-3 0.001 8.9*10-5 0/0 1/0 0/0 0/0
p.G385D rs200959196 4118324 2.0*10-4 0.003 4.4*10-5 1/0 0/0 0/0 0/0
p.L398M NA 4118286 - 24.7 4.4*10-5 0/0 1/0 0/0 0/0 p.P405L rs764072714 4118264 5.9*10-5 11.9 4.4*10-5 0/0 1/0 0/0 0/0 p.G406C rs75462592 4118262 2.8*10-3 23.7 1.3*10-4 2/0 0/0 0/0 0/0 p.N409K rs534295783 4118251 7.2*10-5 14.8 4.4*10-5 1/0 0/0 0/0 0/0 p.P420L rs768040059 4118219 5.3*10-6 0.06 4.4*10-5 0/0 1/0 0/0 0/0
p.T440A rs80161424 4118160 1.0*10-3 0.001 4.4*10-5 1/0 0/0 0/0 0/0
p.P445R NA 4118144 NA 0.01 1.8*10-4 3/0 1/0 0/0 0/0
p.L473F rs369088290 4118061 9.4*10-5 22.6 1.8*10-4 1/0 1/0 0/0 0/0
p.P475L rs769236310 4118054 1.4*10-5 0.003 4.4*10-5 0/0 1/0 0/0 0/0
p.P475T NA 4118055 - 0.002 4.4*10-5 1/0 0/0 0/0 0/0 p.D490V rs371985224 4118009 1.1*10-5 24.0 4.4*10-5 1/0 0/0 0/0 0/0 p.M493T NA 4118000 4.1*10-6 0.02 4.4*10-5 1/0 0/0 0/0 0/0 p.I505V rs369355792 4117965 1.6*10-5 15.8 1.3*10-4 1/0 0/0 0/0 0/0 p.G540S rs759183029 4117860 7.2*10-6 29.5 2.7*10-4 2/0 2/0 0/0 0/0 p.A607T NA 3937081 - 32.0 2.2*10-4 2/0 2/0 0/0 0/0 p.P626H NA 3932466 - 27.8 8.9*10-5 0/0 2/0 0/0 0/0 p.R663W NA 3898832 8.1*10-6 35.0 4.4*10-5 0/0 1/0 0/0 0/0 p.P684L rs542599450 3898768 3.6*10-5 22.7 1.8*10-4 3/0 0/0 0/0 0/0 p.A693T rs568262538 3898742 2.5*10-5 9.3 3.1*10-4 3/0 4/0 0/0 0/0
p.V697M rs148816140 3898730 7.8*10-4 11.3 4.4*10-5 1/0 0/0 0/0 0/0
p.R699H rs149840771 3898723 2.5*10-4 15.8 2.2*10-4 0/0 3/0 0/0 0/0 p.P703S rs200705602 3898712 2.8*10-5 9.2 4.4*10-5 0/0 0/0 1/0 0/0 p.I712N NA 3879589 - 23.4 4.4*10-5 0/0 1/0 0/0 0/0 p.F713L rs201347665 3879585 3.3*10-5 29.3 1.3*10-4 0/0 1/0 0/0 0/0
p.S714C rs139924264 3879583 1.4*10-5 18.7 4.4*10-5 1/0 0/0 0/0 0/0
p.N716K NA 3879576 4.1*10-6 16.7 4.4*10-5 0/0 1/0 0/0 0/0
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p.N716S rs756574107 3879577 8.1*10-6 6.9 2.7*10-4 3/0 2/0 0/0 0/0 p.G726E rs764370927 3879547 1.2*10-5 26.0 1.3*10-4 2/0 0/0 0/0 0/0 p.Q753R rs750346762 3879466 4.5*10-5 17.2 4.4*10-5 0/0 1/0 0/0 0/0 p.H775Q rs780948928 3856157 4.1*10-6 15.4 4.4*10-5 1/0 0/0 0/0 0/0 p.R791T NA 3856110 - 23.2 4.4*10-5 1/0 0/0 0/0 0/0 p.T792A rs867985925 3856108 4.1*10-6 1.5 4.4*10-5 1/0 0/0 0/0 0/0 p.H824Y NA 3856012 - 26.4 4.4*10-5 0/0 1/0 0/0 0/0 p.K832E rs779354091 3829472 1.5*10-4 23.2 4.4*10-5 1/0 0/0 0/0 0/0 p.D840N rs201704428 3829448 8.3*10-5 25.5 3.1*10-4 2/0 2/0 0/0 0/0 p.I844T rs193061752 3829435 4.3*10-5 0.003 1.8*10-4 2/0 2/0 0/0 0/0 p.P846L rs199505727 3829429 2.8*10-4 22.9 4.4*10-5 0/0 1/0 0/0 0/0 p.D859G NA 3829390 - 25.2 4.4*10-5 1/0 0/0 0/0 0/0 p.F871L NA 3829353 - 2.5 2.2*10-4 1/0 3/0 0/0 0/0 p.S892C rs749999750 3828391 8.2*10-6 16.1 4.4*10-5 0/0 1/0 0/0 0/0 p.S894P NA 3828385 4.1*10-6 23.1 4.4*10-5 0/0 1/0 0/0 0/0 p.L896F rs76094493 3828379 1. 1*10-2 22.4 4.4*10-4 1/0 3/0 0/0 0/0 p.F897C NA 3828375 8.2*10-6 25.2 4.4*10-5 1/0 0/0 0/0 0/0 p.R902H rs772126214 3828360 3.7*10-5 23.6 4.4*10-5 1/0 0/0 0/0 0/0 p.G904R rs150310830 3828355 2.1*10-4 24.6 4.4*10-5 0/0 1/0 0/0 0/0 p.V916L rs151140581 3828319 - 20.7 6.7*10-4 3/0 5/0 0/0 1/0 p.V916M rs151140581 3828319 1.4*10-4 23.6 8.9*10-5 1/0 1/0 0/0 0/0
p.R918H rs147357710 3828312 1.4*10-4 27.4 4.4*10-5 0/0 1/0 0/0 0/0
p.S924A rs781124953 3828295 4.1*10-6 28.0 4.4*10-5 0/0 1/0 0/0 0/0 Total rare carriers
70/0# 100/0## 3/0 1/0
# there are two individuals carrying two mutations. ## there are three carriers carrying two mutations. Het: heterozygotes; Ho: homozygotes S2 table: The effect of the p.I28V variants among non-diabetic participants.
Trait Non-carriers of p.I28V* (n=5,716)
Carriers of p.I28V (n=10) P-value
Non-diabetic individuals (n=5,726) n (men/women) 2,809/2,907 4/6 NA Age (years) 45.9 (7.90) 46.7 (7.58) 0.5 BMI (kg/m2) 26.0 (4.43) 26.9 (6.16) 0.2 Waist/hip ratio 0.85 (0.09) 0.86 (0.09) 0.5 Glycated hemoglobin (HbA1c %) 5.79 (0.40) 5.81 (0.42) 1.0 HOMA-IR 1.67 (1.14) 1.52 (0.71) 0.8 Fasting plasma glucose (mmol/l) 5.45 (0.51) 5.17 (0.34) 0.05 (0.046) 30-min glucose (mmol/l) 8.56 (1.70) 8.38 (1.02) 0.8 2-h glucose (mmol/l) 5.94 (1.53) 6.28 (1.57) 0.6 Fasting serum insulin (pmol/l) 40.8 (26.5) 39.5 (18.4) 0.4 30-min Insulin (pmol/l) 290.9 (181.6) 243.4 (99.1) 1.0 2-h Insulin (pmol/l) 204.0 (194.1) 250 (23.4) 0.9 0-min C-peptide (pmol/l.min) 580.5 (252.2) 555.1 (187.4) 0.6 30-min C-peptide (pmol/l.min) 2002 (715.6) 1992 (637.3) 0.9 2-h C-peptide (pmol/l.min) 2254 (976.6) 2332 (885.3) 1.0 Insulinogenic Index 29.7 (19.3) 25.2 (14.5) 0.3
*all non-diabetic individuals not carrying the p.I28V variant, however, including individuals carrying other GLIS3 variants.
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7.2 Manuscript of Paper II Increased frequency of rare missense PPP1R3B variants among Danish patients with type 2 diabetes. Robina Khan Niazi, Jihua Sun, Christian Theil Have, Mette Hollensted, Allan Linneberg, Oluf Pedersen, Jens Steen Nielsen, Jørgen Rungby, Anders Albrechten, Niels Grarup，Torben Hansen and Anette Prior Gjesing. Increased frequency of rare missense PPP1R3B variants among Danish patients with type 2 diabetes. PLoS One.14(1): e0210114. 01.2019.
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