The genetic architecture of type 1 diabetes mellitus

Molecular and Cellular Endocrinology 477 (2018) 70–80

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Molecular and Cellular Endocrinology journal homepage: www.elsevier.com/locate/mce

The genetic architecture of type 1 diabetes mellitus a

a

Denis M. Nyaga , Mark H. Vickers , Craig Jefferies a b

a,b

a

, Jo K. Perry , Justin M. O'Sullivan

T a,∗

The Liggins Institute, The University of Auckland, New Zealand Starship Children's Health, Auckland, New Zealand

A R T I C LE I N FO

A B S T R A C T

Keywords: Type 1 diabetes Beta-cell autoimmunity Single nucleotide polymorphisms (SNPs) Genome-wide association studies (GWAS) Human leukocyte antigen (HLA) Genome organization

Type 1 diabetes mellitus (T1D) is a complex autoimmune disorder characterised by loss of the insulin-producing pancreatic beta cells in genetically predisposed individuals, ultimately resulting in insulin deficiency and hyperglycaemia. T1D is most common among children and young adults, and the incidence is on the rise across the world. The aetiology of T1D is hypothesized to involve genetic and environmental factors that result in the T-cell mediated destruction of pancreatic beta cells. There is a strong genetic risk to T1D; with genome-wide association studies (GWAS) identifying over 60 susceptibility regions within the human genome which are marked by single nucleotide polymorphisms (SNPs). Here, we review what is currently known about the genetics of T1D. We argue that advancing our understanding of the aetiology and pathogenesis of T1D will require the integration of genome biology (omics-data) with GWAS data, thereby making it possible to elucidate the putative gene regulatory networks modulated by disease-associated SNPs. This approach has a potential to revolutionize clinical management of T1D in an era of precision medicine.

1. Introduction Globally, diabetes affects more than 400 million people (World Health Organization, 2016), with Type 1 (insulin-dependent) diabetes (T1D) accounting for up to 10 percent of cases (American Diabetes Association, 2009). In the United States, T1D occurs at a rate of 15–30 cases per 100,000 children aged 0–14 years annually (International Diabetes Foundation, 2017; Maahs et al., 2010), with similar prevalence in Canada, Europe, Australia, and New Zealand (Fig. 1) (Derraik et al., 2012; International Diabetes Foundation, 2017; Maahs et al., 2010). By contrast, the estimated incidence rate of T1D among Asians, South Americans, and Africans is below 15 cases per 100,000 children (Fig. 1) (International Diabetes Foundation, 2017; Maahs et al., 2010). The global incidence of T1D has been rising by 3–5% per annum over the past two decades, with a notable increase in children below 10 years of age (Diamond Project, 2006; Patterson et al., 2009). The marked increase of T1D incidence cannot be solely attributed to genetic risk (Snouffer, 2018). In fact, disease discordance in monozygotic twins (30–70%) strongly suggests environmental factors contribute to the aetiology of T1D (Redondo et al., 2008). These contributions may manifest through epigenetic modification including altered DNA methylation (Cepek et al., 2016; Paul et al., 2016; Stefan et al., 2014), which has been reported to play a key role in the transcriptional regulation of gene expression, and in some part, contributes to the aetiology of T1D (Stefan et al., 2014). Other environmental

∗

Corresponding author. The Liggins Institute, The University of Auckland, New Zealand. E-mail address: [email protected] (J.M. O'Sullivan).

https://doi.org/10.1016/j.mce.2018.06.002 Received 13 February 2018; Received in revised form 14 May 2018; Accepted 6 June 2018

Available online 18 June 2018 0303-7207/ © 2018 Elsevier B.V. All rights reserved.

exposures attributable to the rising prevalence of T1D include diet (Hansen et al., 2006), gestational infections (Rešić Lindehammer et al., 2012), and viral infections (Lönnrot et al., 2000). As such, it is highly likely that these non-genetic triggers interact with susceptibility genes in genetically predisposed individuals to influence the development of T1D. The early signs of T1D onset begin with the detection of islet autoantibodies, which progresses to loss of pancreatic beta cells, and subsequent insulin deficiency and hyperglycaemia (Fig. 2) (Insel et al., 2015). The complex aetiology of T1D remains to be clearly defined; however, it is understood to be an autoimmune process that causes the destruction of the insulin-producing pancreatic beta cells over a period of several years culminating to clinical diabetes (Fig. 2). During clinical onset of T1D, the extent of beta cell loss is dependent on age (i.e. at 20 years of age, a loss of 40% beta cell mass is sufficient to cause clinical symptoms) (Klinke, 2008), with a detectable decrease in plasma Cpeptide concentration (a marker of pancreatic insulin production) (Wang et al., 2012). Circulating C-peptide concentration characterizes distinct pathophysiological T1D subtypes, with a rapid drop in circulating C-peptide concentration defining early-onset disease, while slower C-peptide loss indicates late-onset disease (Kühtreiber et al., 2017; Laugesen et al., 2015). Autoantibodies to islet cells are detected in T1D patients (Gepts, 1965; Verge et al., 1996; Wenzlau and Hutton, 2013; Willcox et al., 2009) and are widely understood to be markers of beta cell destruction

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Fig. 1. Global prevalence rates of T1D in children. Numbers are an estimate of new cases of T1D in children and adolescents (0–14 years) by geographical region per year (2017). Data obtained with permission from the IDF (International Diabetes Foundation) Diabetes Atlas (8TH Edition) (International Diabetes Foundation, 2017).

More than 60 susceptibility loci have been identified (Table 1). The greatest genetic risk (∼50%) for T1D is conferred by alterations to immune genes, especially those encoding the classical HLAs (OunissiBenkalha and Polychronakos, 2008). Other genetic loci (Table 1) are believed to influence population-level risk for T1D, although it is poorly understood how these non-HLA loci contribute to disease susceptibility (Ram et al., 2016a).

in the clinical onset of the disease (Wenzlau and Hutton, 2013). Autoantigens associated with autoimmune responses in T1D, including zinc transporter 8 (ZnT8), glutamic acid decarboxylase (GAD65), insulin, and islet antigen 2 (IA-2) have been defined (Han et al., 2013; Verge et al., 1996). Presentation of these autoantigens to cytotoxic T cells (CD8+) is performed by the major histocompatibility complexes class I and II (MHC I and II), which consequently destroys the antigen-presenting beta cells (Knip and Siljander, 2008). Overexpression of the human leukocyte antigen (HLA) class I has been observed with the presence of beta cell antigen-specific CD8+ T cells in pancreatic lesions of patients with either long-term and recentonset T1D (Coppieters et al., 2012; Itoh et al., 1993). Despite the apparent role for autoantibodies in T1D, a study of recent-onset T1D patients demonstrated that insulin secretion could be restored in islets cultured ex vivo from pancreatic biopsies (Krogvold et al., 2015). Moreover, a majority of adults with T1D, referred to as latent autoimmune diabetes in adulthood (LADA), do not initially require insulin administration despite detectable amounts of diabetes-associated autoantibodies (Mishra et al., 2017). LADA, which accounts for less than 10% of diabetes cases, presents as an intermediate feature between T1D and type 2 diabetes (T2D), with the autoantibodies considered to be non-pathological (Tuomi et al., 1993). Arguably, LADA should be defined as a distinct form of T1D, thereby making it easier to distinguish between young- and adult-onset autoimmune diabetes. Notably, there is a growing focus on reclassifying the different forms of diabetes which could potentially improve the clinical management of diabetes. (Ahlqvist et al., 2018).

2.1. Human leukocyte antigen (HLA) The association between T1D and the HLA complex was first demonstrated in 1973 following observation of an increased frequency of HL-W15 (HLA antigen) in T1D patients compared to controls (Singal and Blajchman, 1973). The gene encoding HL-W15 antigen is located within the HLA class I genetic locus (Singal and Blajchman, 1973). The hypothesized link between T1D and the HLA complex gained further support when a correlation between the HL-A8 and W15 alleles (now known as HLA-B62 (B*1501) and B8 (B*0801)), and a 2–3 fold risk of juvenile T1D onset was identified (Nerup et al., 1974). Interestingly, HLA-encoding genes were also found to be strongly associated with the pathogenesis of several additional autoimmune conditions that include multiple sclerosis (MS) (Naito et al., 1972), rheumatoid arthritis (RA) (Stastny, 1976), and celiac disease (CD) (Falchuk et al., 1972). As such, the HLA-encoding region is the most strongly associated T1D locus (Mychaleckyj et al., 2010). However, the molecular understanding of how HLA contributes to T1D remains unclear due the large number of distinctive HLA alleles and unusual frequencies that make the overall mechanism difficult to interpret (Sanchez-Mazas and Meyer, 2014). This has raised new questions, particularly with respect to the approximation of genetic distances, and other significant statistics in population genetics studies (Buhler and Sanchez-Mazas, 2011; SanchezMazas and Meyer, 2014). As such, improving our understanding of the basic biology of the HLA locus is an essential facet of research into the mechanisms and causes of T1D.

2. The genetics of type 1 diabetes There is a strong genetic risk to T1D. This is exemplified by (Redondo et al., 2001) who demonstrated a strong concordance of genetic inheritance (65%) and T1D susceptibility in monozygotic twin pairs. That is, when one sibling is afflicted, there is a high probability that the other twin will develop T1D by the age of 60 years. Additionally, autoantibody positivity and islet destruction was observed after a prospective long-term follow-up of monozygotic twins of patients with T1D, despite initial disease-discordance among the twins (Redondo et al., 2008). Genome-wide association studies (GWAS) have made a significant contribution to our current knowledge of the role(s) of genetic variation in population-level susceptibility to T1D (Mychaleckyj et al., 2010).

2.2. The physical structure of the HLA locus The HLA locus spans 4 mega base pairs (Mb) of chromosome 6p21, and encodes the major histocompatibility complex (MHC) molecules (Fig. 3). The HLA encoded glycoproteins interact with peptide antigen for recognition by T cell receptors (TCR). Their classically accepted function is to help the immune system differentiate between self and 71

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Fig. 2. The pathogenesis of Type 1 diabetes. (A) The pancreas contains both endocrine (the islet) and exocrine tissues. The islet is comprised of glucagon producing alpha (α) cells which make up approximately 20% of total islet cells; insulin-producing beta (β) cells (70%); and somatostatin-producing delta (δ) cells (< 10%). Other minor pancreatic endocrine cells (not shown) include gamma (γ) and epsilon (ε) cells. The beta cell regulates blood glucose by releasing insulin as a result of rising glucose levels. (B) T cells (CD4+ and CD8+) have a role in the pathogenesis of autoimmune T1D. Presentation of islet antigens by dendritic cells to islet antigen-specific T cells occurs, consequently leading to beta cell damage and death. Similarly, islet-targeting autoantibodies can also be produced by interactions between B cells and beta cell autoantigens. Arrows represent the potential immune interactions between immune cells during T1D onset. BCR, B-cell receptor; MHC, major histocompatibility complex; TCR, T-cell receptor; DC, dendritic cell. (Figures modified with permission from (Katsarou et al., 2017) and (MacDonald and Rorsman, 2006)).

into a heterodimer with the non-polymorphic beta 2-microglobulin (i.e. α3 and β2m) (Fig. 4) (Bjorkman and Parham, 1990). Other class I HLA genes encode non-classical MHC molecules like HLA-E, which is involved in the presentation of peptides to natural killer (NK) cells, and HLA-G, which protects the foetus from the maternal immune response (Bjorkman and Parham, 1990). The classical HLA class II genes (HLA-DP, HLA-DQ, and HLA-DR) encode cell surface glycoproteins that exist as heterodimers produced from two polypeptides (i.e. α2 and β2 chains) (Fig. 4). These glycoproteins are usually present exclusively on antigen presenting cells that include Langerhans cells, B cells, dendritic cells, macrophages, and activated T cells (Holling et al., 2004). Notably, several nucleated cells (e.g. keratinocytes, epithelial cells, fibroblasts) can be stimulated by interferon (IFN)-gamma to express class II MHC glycoproteins (Holling et al., 2004). Numerous distinct alleles of the HLA gene have been

non-self (Bjorkman and Parham, 1990). The MHC proteins are classified into HLA class I and class II molecules, with class I chains containing A, B, and C antigen, and class II chains having DR, DQ and DP antigens. Additionally, the HLA class III genes are mapped within the HLA region and encode for proteins that form part of the complement system (i.e. C2, C4a, C4b, and Bf). Notably, the HLA region spans 4 mega base pairs (Mb), with the class I genes located at the telomere proximal end of the locus, while the class II genes are more centromeric (Fig. 3). 2.3. HLA encoding genes The HLA class I gene cluster comprises approximately 20 genetic features including six expressed genes, non-processed pseudogenes, and an array of partial gene copies (Choo, 2007). The HLA class I genes (HLA-A, HLA-B, and HLA-C) encode polypeptide chains which combine 72

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Table 1 Polymorphisms in the human genome associated with type 1 diabetes (Adapted from (Ram et al., 2016b)). Gene/locus PTPN22

SNP rs2476601

Chromosome 1

Locus 1p13.2

P values 8.5E

−85

−7

PGM1 RGS1 IL10 CTLA4 IFIH1 Intergenic Intergenic, IL18RAP

rs2269241 rs2816316 rs3024505 rs231727 rs3747517 rs2165738 rs1534422

1 1 1 2 2 2 2

1p31.3 1q31.2 1q32.1 2q33.2 2q24.2 2p23.3 2p25.1

4.0E 3.1E−5 2.0E−9 2.1E−18 6.1E−9 4.0E−6 2.0E−6

SLC11A1 DQX1 AFF3

rs3731865 rs363609 rs9653442

2 2 2

2q35 2p13.1 2q11.2

1.6E−6 8.5E−6 5.0E−6

CCR5 Intergenic IL2

rs11711054 rs10517086 rs2069763

3 4 4

3p21.31 4p15.2 4q27

1.7E−5 5.0E−10 4.6E−6

IL7R HLA Intergenic TAGAP

rs6897932 rs9272346 rs924043 rs1738074

5 6 6 6

5p13.2 6p21.32 6q27 6q25.3

8.0E−6 6.0E−129 8.0E−9 7.6E−9

TNFAIP3 RNF5 C6orf173 BACH2 LOC729653 COBL SKAP2 IKZF1 GLIS3 GLIS3 RNLS PRKCQ IL2RA

rs10499194 rs9469089 rs9388489 rs11755527 rs1592410 rs4948088 rs7804356 rs10272724 rs7020673 rs10758593 rs10509540 rs947474 rs12251307

6 6 6 6 6 7 7 7 9 9 10 10 10

6q23.3 6p21.32 6q22.32 6q15 6p22.1 7p12.1 7p15.2 7p12.2 9p24.2 9p24.2 10q23.31 10p15.1 10p15.1

3.0E−4 3.7E−5 4.2E−13 5.0E−12 2.2E−8 4.4E−8 5.3E−9 4.8E−9 5.4E−12 3.0E−6 1.0E−28 1.2E−7 1.0E−13

INS

rs689

11

11p15.5

8.9E−195

SH2B3 ERBB3

rs17696736 rs2292239; rs1701704

12 12

12q24.12 12q13.2

CUX2 CD69 LMO7 DLK1-MEG3 Intergenic ZFP36L1 RASGRP1 CTSH IL27 PRM3; ORF; TNP2 CTRB1/2 UMOD CLEC16A SMARCE1 CCR7 GSDMB,ORMDL3 DNAH2 CD226 FHOD3 PTPN2 FUT2 PRKD2 Intergenic UBASH3A UBASH3A C1QTNF6 Intergenic

rs1265564 rs4763879 rs539514 rs941576 rs4900384 rs1465788 rs17574546 rs3825932 rs4788084 rs416603 rs7202877 rs12444268 rs12708716 rs7221109 rs7221109 rs2290400 rs16956936 rs763361 rs2644261 rs2542151 rs601338 rs425105 rs2281808 rs11203203 rs876498 rs229541 rs5753037

12 12 13 14 14 14 15 15 16 16 16 16 16 17 17 17 17 18 18 18 19 19 20 21 21 22 22

12q24.12 12p13.31 13q22.2 14q32.2 14q32.2 14q24.1 15q14 15q25.1 16p11.2 16p13.13 16q23.1 16p12.3 16p13.13 17q21.2 17q21.2 17q21.1 17p13.1 18q22.2 18q12.2 18p11.21 19q13.33 19q13.32 20p13 21q22.3 21q22.3 22q12.3 22q12.2

2.8E−27 2.0E−20 9.0E−10 1.0E−16 1.9E−11 6.0E−11 1.0E−10 4.0E−9 1.8E−12 1.3E−6 3.0E−15 5.2E−08 3.0E−6 3.1E−15 1.7E−7 2.2E−16 1.0E−9 1.0E−9 6.0E−13 5.3E−7 1.6E−8 5.9E−4 1.0E−14 5.1E−12 3.0E−11 1.0E−11 1.7E−9 7.1E−9 2.0E−8 3.0E−16

Reference Barrett et al. (Barrett et al., 2010) Noble et al. (Noble and Erlich, 2012) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Smyth et al. (Smyth et al., 2008) Yang et al. (Yang et al., 2011) Todd et al. (Todd et al., 2007) Bradfield et al. (Bradfield et al., 2011) Todd et al. (Todd et al., 2007) Bradfield et al. (Bradfield et al., 2011) Barrett et al. (Barrett et al., 2010) Bradfield et al. (Bradfield et al., 2011) Cooper et al. (Cooper et al., 2012) Onengut-Gumuscu et al. (Onengut-Gumuscu et al., 2015) Noble et al. (Noble and Erlich, 2012) Bradfield et al. (Bradfield et al., 2011) Bradfield et al. (Bradfield et al., 2011) Cooper et al. (Cooper et al., 2012) Bradfield et al. (Bradfield et al., 2011) Forbes et al. (Forbes et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Baschal et al. (Baschal et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Swafford et al. (Swafford et al., 2011) Barrett et al. (Barrett et al., 2010) Bradfield et al. (Bradfield et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Noble et al. (Noble and Erlich, 2012) Smyth et al. (Smyth et al., 2008) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Huang et al. (Huang et al., 2012) Barrett et al. (Barrett et al., 2010) Bradfield et al. (Bradfield et al., 2011) Bradfield et al. (Bradfield et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Bradfield et al. (Bradfield et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Cooper et al. (Cooper et al., 2012) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Copper et al. (Cooper et al., 2012) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Smyth et al. (Smyth et al., 2011) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010) Onengut-Gumuscu et al. (Onengut-Gumuscu et al., 2015) Barrett et al. (Barrett et al., 2010) Barrett et al. (Barrett et al., 2010)

The genetic polymorphism data (i.e. SNPs) has been associated with T1D using genome-wide association studies and meta-analyses (references as noted). SNP, single nucleotide polymorphism.

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Fig. 3. The structure and arrangement of the HLA locus (chromosome 6p21). Green bars, classical HLA genes; yellow bars, other T1D-associated genes located within the HLA locus; Double ended arrows approximate HLA class I, II and III boundaries. Modified with permission from (Noble and Erlich, 2012). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) Fig. 4. The major histocompatibility class I and II molecules showing the single-chain HLA class I molecule attachment to the transmembrane segment and its interaction with the β2-microglobulin. The position of the peptides illustrates the binding groove. The two HLA class II chains are formed by immunoglobulin-like domains α1, α2, β1, and β2, and are attached to the transmembrane segment.

The most frequently detected genetic variations in the HLA locus on chromosome 6p (Table 2) are observed predominantly in the HLA-DQ, -DR, and -DP alleles (Barrett et al., 2010; Erlich et al., 2008). The HLA DR/DQ region is a strongly linked T1D susceptibility locus, with the alleles reported to have a high likelihood of associating non-randomly (i.e. they are in close linkage disequilibrium (LD)) (Erlich et al., 2008). Notably, either or both the DR4/DQ8 or DR3/DQ2 haplotypes are detected in approximately 90% of all T1D phenotypes (Erlich et al., 2008; Smigoc Schweiger et al., 2016). The presence of the DQ8 (DQA1*0301, DQB1*0302) allele raises the odds ratio for T1D onset significantly to roughly 11 (Erlich et al., 2008), suggesting that T1D is 11 times more likely to develop in an individual with the allele than those without. Surprisingly, the DQB1*0602 allele of the variant DQ6 haplotype has a low odds ratio of 0.03, predominantly protecting individuals from T1D onset (Erlich et al., 2008; Todd et al., 1987). Although previous analyses of DP alleles and T1D have shown

documented (Horton et al., 2004), resulting in a myriad of MHC protein encoding variants. 3. HLA genetic variation and T1D autoimmunity There are many forms of population level genetic variation that can be identified by analyses of microsatellite loci and the quantitative evaluation of allelic frequencies (Witherspoon et al., 2007). SNPs are the most prevalent variants in the human genome, with an average occurrence of one in approximately 300 nucleotides which translates to over 10 million genomic SNPs (Huang et al., 2012). Most SNPs have no impact on development or health; however, GWAS data on human populations has revealed thousands of SNPs that associate with minor traits including height (Yang et al., 2010) and susceptibility to complex diseases such as T1D (http://www.gwascentral.org/studies) (Barrett et al., 2010; Bradfield et al., 2011; Cooper et al., 2012). 74

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(Noble et al., 2002). The HLA-B*39:06 allele, for instance, has the strongest risk of T1D susceptibility with an odds ratio of 10.31, while HLA-B*57:01 appears to be highly protective with an OR of 0.19 even after considering the LD with DQ and DR (Noble et al., 2010). Notably, Mikk et al. suggested that B*39:06 can significantly improve the prognosis of T1D disease, especially in patients with the DRB1*04:04DQA1*03:01-DQB1*03:02 class II haplotypes (Mikk et al., 2014). Therefore, it is important to account for LD when elucidating for genetic risk within the class I locus.

Table 2 The extent of genetic variation at the classical HLA loci. HLA gene

Number of polymorphic allelesa

A B C E F G DRA DRB1 DRB3 DRB4 DRB5 DQA1 DQB1 DPA1 DPB1

3913 4765 3510 25 22 54 7 2058 137 60 48 78 1079 45 828

3.1. HLA variations across populations Since the early 1970s, over 13,000 HLA alleles have been reported (Robinson et al., 2015) albeit approximately 90% of these alleles have been detected only once or twice (Mack et al., 2013). The class II HLA alleles are consistently observed to impact on T1D across different ethnic populations even allowing for significant variations in allele frequencies (Ikegami et al., 2008; Noble, 2015b). In Caucasian populations, the DR4-DQ8 (DRB1*04-DQB1*0302) and DR3-DQ2 (DRB1*0301-DQB1*0201) haplotypes are associated with a high risk of T1D (Mijovic et al., 1991). Notably, a combination of these haplotypes contributes to a genetic risk of 1:15 in the general populations (Noble et al., 1996). Among the African population, the DRB1*07:01DQA1*03:01-DQB1*02:02 haplotype is frequently detected among T1D patients, suggesting a role in disease predisposition (Mijovic et al., 1991; Noble et al., 2013). Haplotypes can predispose to T1D in one population while protecting against it in a second. For example, the DRB1*07:01DQA1*03:01-DQB1*02:02 haplotype has been found to protect individuals of European descent against T1D (Erlich et al., 2008). Similarly, DRB1*03:02-DQA1*04:01- DQB1*04:02 (a DR3 haplotype) is specific to the African population and is observed to confer protection (Howson et al., 2013). By contrast, the DRB1*03:01-DQA1*05:01DQB1*02:01 haplotype increases susceptibility to T1D onset in other ethnicities, suggesting opposing effects of alleles across populations (Noble et al., 2013). Moreover, the DRB1*04:03 haplotype which is detected more widely among Asian than European populations, confers protection in the Asian population (Gragert et al., 2013). Thus, the effects of HLA haplotype combinations are dependent on the overall genotype, even if the haplotype is common to multiple populations.

The most polymorphic HLA genes are HLA-A, HLA-B, HLA-C, HLADRB1, and HLA-DQB1. a Polymorphism numbers were retrieved from the IPD-IMGT/ HLA database release 3.28.0, 2017-04-13.

conflicting results, increasing evidence supports the role of DPB1 in modulating the DQ-DR associated risk in T1D onset (Cucca et al., 2001; Johansson et al., 2003b; Noble et al., 2000). A study involving 269 Caucasian families in America found that DPB1*0301 was associated with T1D among patients carrying the DQ2-DR3/DQ8-DR4 genotype (Noble et al., 2000). By contrast, a study performed on families from the United Kingdom and Sardinian by Coca et al. suggested that association of DPB1 alleles with T1D onset occurred in non-DQ8-DR4 haplotypes (Cucca et al., 2001). Finally, DPB1*0402 was observed to confer a level of protection on DQ8-DR4-negative haplotypes, suggesting a minimal role for DPB1 in influencing HLA-associated T1D pathogenesis (Cucca et al., 2001). The DPB1*0202 allele, found only in the susceptible DR3-B18 familial haplotype (18.2AH) among Caucasians, is consistently associated with high risk of T1D (Cucca et al., 2001; Johansson et al., 2003a). These reports also suggest a weak link between DPB1*0301 and DPB1*0202 with T1D, with observed protection in a DPB1*0402 background. However, Johansson et al. found that the alleles were probably markers of genes for other HLA-associated predisposing gene (s) and were indirectly associated T1D susceptibility (Johansson et al., 2003b). Despite this, it is worth noting that the DPB1 alleles increase genetic susceptibility to several other conditions including pauciarticular juvenile idiopathic arthritis (an autoimmune disease) and chronic beryllium disease (an inflammatory lung disease resulting from beryllium toxicity) (Begovich et al., 1989; Rosenman et al., 2011). Numerous studies have established T1D genetic risk associated with HLA class 1 (Noble et al., 2010, 2002; Valdes et al., 2005). The class I HLA gene products have critical roles in presenting antigens to T lymphocytes (CD8+) which initiates T lymphocyte-mediated cell killing (Bjorkman and Parham, 1990). As such, these gene products make a significant contribution to shaping and maintaining the T cell repertoire (Noble, 2015a). Importantly, T1D associated autoimmunity is a consequence of cytotoxic (CD8+) T cell killing of insulin-producing pancreatic beta cells leads to clinical onset (Itoh et al., 1993; Knip and Siljander, 2008). Therefore, it is possible that particular associations of peptides and class I HLA molecules can influence destruction of beta cells. Notably, the presence of the HLA-A*24 allele is associated with low residual islet function in patients with T1D, indicating that the A*24 allele facilitates complete beta cell destruction (Nakanishi et al., 1993). Studies by Valdes et al. have reported that HLA class I alleles associate with age-of-onset of T1D (Valdes et al., 2012, 1999). Several alleles in the HLA class I region (Table 2) appear to confer high risk, but this effect is modified when accounting for LD with class II haplotypes

4. Other loci within the HLA region associated with T1D The HLA class III locus contains genes involved in immunological responses that include the MIC-A, TNFA, and complement protein genes, and it is widely understood that these genes have a reproducible contribution in the autoimmune pathogenesis of T1D (White, 1989). For instance, deficiency of the C4 complement gene can increase the risk of T1D onset among DR3/DR4 individuals at a young age (Mason et al., 2014). Copy number variations within the C4A and C4B complement genes have been linked to the aetiology of autoimmune disorders including human systemic lupus erythematosus (SLE) (Yang et al., 2007). Studies in mice have demonstrated an association with TNF-α and T1D pathogenesis, revealing an important role of cytokines in disease onset (Koulmanda et al., 2012; Lee et al., 2005). Interestingly, associations of TNF-α gene variants at the promoter region, TNF-308A (rs1800629) and TNF-238A (rs361525), and T1D development have shown conflicting results in different ethnic populations (Bouqbis et al., 2003; Ilonen et al., 1992; Nishimura et al., 2003). Notably, associations between the TNF-308A allele (rs1800629) and T1D were not significant when effects of -HLA-alleles were considered in a stepwise regression model (Patente et al., 2015). Reports on different ethnic groups implicate MIC-A alleles, which encode a stress-induced antigen in the epithelium of the gut, in conferring either increased risk or protection to T1D pathogenesis (Bilbao 75

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potentially causes autoimmunity (Visperas and Vignali, 2016). Finally, deno novo activating mutations in the STAT3 transcription factor, which regulates cytokine production, have been reported to contribute to the development of early-onset T1D (Flanagan et al., 2014).

et al., 2006; Gambelunghe et al., 2000; Gupta et al., 2003; Kumar et al., 2012). Reports have associated MICA5 (a polymorphism in the MIC-A gene) with childhood-onset T1D (Gupta et al., 2003; Nikitina-Zake et al., 2004; Shtauvere-Brameus et al., 2002). In contrast to this finding, other studies have identified the MIC-A5.1 allele as a distinctive genetic marker for adult-onset T1D and suggests it contributes to disease protection (Gambelunghe et al., 2007; Kumar et al., 2012). Again this is consistent with a possible linkage disequilibria between MICA alleles and other genes within the HLA locus that cause the observed differences across ethnic populations (Nikitina-Zake et al., 2004).

5. Making sense of genome-wide association studies on T1D GWAS studies have contributed significantly to our current knowledge of the role(s) of genetic variation (SNPs) in population-level susceptibility to T1D (Ram et al., 2016a). However, GWAS analyses do not automatically determine the particular gene(s) in a specific locus that are mechanistically associated with disease pathogenesis, or elucidate the manner in which disease gene(s) interact (Zhong et al., 2010). The difficulty associated with ascribing functional impacts to SNPs is partly explained by the fact that most disease-associated SNPs identified by GWAS are intergenic and fall outside of genes. Therefore, association studies only provide low-level insights into the molecular mechanisms that underlie the pathogenesis of diseases which makes it difficult to understand disease mechanisms. Several research approaches have made progress towards understanding the functional relevance of disease-associated genetic variants (Bergholdt et al., 2012; Boyle et al., 2017). These advances are based on a growing awareness that the three-dimensional juxtaposition of DNA regions within nuclei means that genes can be regulated by regulatory elements that are located at some distance from the gene (Fig. 5) (Javierre et al., 2016; Kadauke and Blobel, 2009). As a result of this, disease associated SNPs have been shown to fall in gene regulatory elements (Chen and Tian, 2016; Fadason et al., 2017; Farh et al., 2014; Lee et al., 2014; Schierding et al., 2015). There have been several efforts to elucidate the effect of T1D-associated SNPs on the differential expression of genes (i.e. expression quantitative trait loci (eQTLs)), particularly in immune cell types (Newman et al., 2017; Ram and Morahan, 2017). Recent studies are focussing on integrating an understanding of the three-dimensional (3D) genome organization to examine the effects of disease-associated SNPs in deregulating gene expression (Javierre et al., 2016). In fact, Fadason et al. demonstrated that functionally relevant type 2 diabetesassociated SNPs are spatially linked with specific changes in the expression levels of genes within disease-associated tissues (Fadason et al., 2017). Similarly, a study demonstrated that integrating chromatin interactions with GWAS analyses is important in elucidating causal genes that modulate regulatory networks in autoimmune diseases (McGovern et al., 2016). As such, the spatial organization of DNA within the nucleus (Fig. 5) can impact gene regulation in a spatiotemporal manner that affects development (Won et al., 2016). Therefore, there is a strong possibility that functionally relevant intergenic T1D-associated SNPs (imputed by GWAS) disrupt gene regulatory networks in the pancreas and other cell types that contribute to the development of T1D.

4.1. Non-HLA genes associated with T1D There are variable number of tandem repeats within the 5′upstream region of the INS-susceptibility locus (11p15.5) known as INS-VNTR (Perez de Nanclares et al., 2003). The short VNTR alleles (class I) within this region have been observed to positively increase the genetic risk of T1D as they influence the expression of (pro)insulin in the thymus (an allele-specific mechanism) (Pugliese et al., 1997). Notably, increased insulin transcription levels and reduced T1D risk are promoted by long VNTR alleles (class III) consistent with the possibility that high insulin transcript may protect against T1D. (Bennett et al., 1995). In addition to the length of the VNTR alleles, studies also indicate that the INS-allele specific risk associated with T1D susceptibility is influenced by the consensus repeat sequence (Bennett et al., 1995; Perez de Nanclares et al., 2003; Pugliese et al., 1997). An association between the CTLA4 locus and the T1D phenotype was previously identified in different ethnic populations (Marron et al., 1997). Meta-analyses have confirmed the associations between CTLA4 and T1D, however, the association was observed to vary across different ethnic groups (Wang et al., 2014). These variants contribute to T1D development through deregulation of gene expression leading to dysregulation of immune response and subsequent autoimmune imbalance (Kavvoura and Ioannidis, 2005). Polymorphisms within PTPN22 (e.g. at nucleotide 1858 in codon 620) have also been associated with T1D (Bottini et al., 2004). This SNP directly hinders the formations of the LYP-Csk complex which causes hyper-reactivity in T cells and thus a destructive autoimmune aetiology (Bottini et al., 2004). Other PTPN22 polymorphisms (e.g. R620W due to rs2476601) are associated with rheumatoid arthritis (Begovich et al., 2004). Interleukin genes (mainly IL-4 and IL13, chromosome 5q31) have been implicated in T1D pathogenesis. Notably, T1D risk associated with the IL4/IL13 pathway was attributable to the haplotypes at the IL-4R region and specific genotype combinations at the IL4, IL13, and IL4R loci (Bugawan et al., 2003). Polymorphisms at the IL-4R loci (I50V IL4R SNP) have also been observed to be high-risk genetic markers for rheumatoid arthritis (Prots et al., 2006). There is a growing interest in the associations between the killer-cell immunoglobulin-like receptor (KIR) locus (mapped on chromosome 19q13) and T1D susceptibility, largely because HLA class I glycoproteins are KIR ligands (Campbell and Purdy, 2011; Noble, 2015a). The variant KIR allele, KIR2DL2, binds to HLA-C alleles and has been reported to positively influence T1D development (Mogami et al., 2007; Shastry et al., 2008). However, further analyses are needed to clearly annotate the reported risk of KIRHLA association with T1D. Transcription factors have also been reported to play a role in the development of T1D. The transcription factor, Autoimmune Regulator (AIRE) on chromosome 21, has been associated with T1D and the mechanism of this association may be through the differential expression of auto-peptides (Chentoufi and Polychronakos, 2002; Sabater et al., 2005). The AIRE protein is highly expressed in the thymus marrow. Therefore, the presence of AIRE mutations in T1D patients strongly suggests that AIRE affects the expression and presentation of diabetesassociated autoantigens (e.g. insulin) in the thymus (Sabater et al., 2005). Similarly, differential expression of FoxP3 (transcription factor on X chromosome) affects the cytolytic activity of T cells which

6. Conclusions and future directions There is a striking controversy regarding the role of autoantibodies in the aetiology of autoimmune T1D. For instance, some patients with T1D have a prevalence of other autoimmune disorders (Hughes et al., 2016). Interestingly, this apparent association is often regarded as an argument for T1D autoimmunity. However, such an association is not ubiquitous amongst T1D patients, rather only a small proportion of T1D patients exhibit symptoms consistent with very specific diseases: celiac disease; adrenal insufficiency; Grave's disease; Hashimoto's thyroiditis; and vitiligo (Hughes et al., 2016). Despite this, it is apparent that there is an identifiable pathological activation of the immune system in most T1D patients, as demonstrated by the detection of islet-targeting autoantibodies and islet antigen-specific T cells (Gepts, 1965; Wenzlau and Hutton, 2013). Notably, similar pathological activations are discernible in other diseases that involve cellular death, e.g. type 2 diabetes 76

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Fig. 5. DNA is organized in a non-random fashion in the nucleus. (A) Cartoon of the three-dimensional organization of chromatin within eukaryotic nuclei. Chromatin binding proteins (e.g. CTCF and cohesin), transcription factors, and chromatin modifiers organize stretches of DNA from distant genomic sites in close proximity to each other (e.g. (Gassler et al., 2017)), providing a mechanism through which co-regulated topological domains co-localize in three-dimensions. The chromatin loops within topological domains contact other domains and enable gene promoters to interact with regulatory elements from many kilobases away, or in some cases, on different chromosomes. (B) Mutations (such as SNPs) in one locus can affect the localisation or composition of the transcription factory which has a significant effect on the expression of genes associated with that particular factory.

(Brooks-Worrell et al., 2012), and further complicated by detectable amounts of diabetes-associated autoantibodies in LADA (Mishra et al., 2017). Therefore, the argument remains as to whether such immunological reactions are the fundamental drivers, or just epiphenomena in T1D pathogenesis. The strong association of T1D with the HLA region supports an autoimmune aetiology (Concannon et al., 2009). Nevertheless, this cannot be regarded as conclusive, since this loci is gene dense and encodes many non-immune associated genes which may influence beta cell death by other mechanisms. Therefore, population-level risk (GWAS data) represents an exciting opportunity for further research into T1D, targeted at understanding disease pathogenesis. Moreover, prospective studies including TEDDY (The Environmental Determinants of Diabetes in the Young), DAISY (The Diabetes Autoimmunity Study in the Young), BABYDIAB, and the TrialNet, continue to provide valuable information regarding the genetic predisposition and progression of T1D (Redondo et al., 2018). Translating this information into knowledge through the hypothesis driven integration of genomic and epigenomic approaches (i.e. 3D genome organization, targeted genome editing (CRISPR/Cas9), and functional eQTL analyses) is imperative to: 1) design predictive genetic risk models; 2) understand the contribution of environmental factors in T1D development; and 3) better diagnosis and treat young- and adult-onset T1D.

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