Gestational Diabetes During and After Pregnancy

13. Genetics of Gestational Diabetes Mellitus and Type 2 Diabetes

Richard M. Watanabe 

(1)

Department of Preventive Medicine, Keck School of Medicine University of Southern California, 1540 Alcazar Street, CHP-220, Los Angeles, CA 90089-9011, USA

Richard M. Watanabe

Email: rwatanab@usc.edu

Abstract

Identifying genes underlying complex diseases hold the promise of new drug targets, improved interventions, and the advent of so-called “personalized medicine.” For almost 2 decades, investigators have attempted to identify genes underlying gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM), but until recently were mostly unsuccessful. Improvements in genetic information and technology changed the landscape of complex disease genetics. Prior to 2006, only 3 genes were accepted as bonafide T2DM susceptibility genes and there were none for GDM. Currently, approximately 21 genes underlying susceptibility to T2DM and related traits have been identified. However, our knowledge of the genetics underlying GDM has lagged. This chapter reviews the current state of knowledge as to the genetics of both GDM and T2DM, and related traits. The potential relationship between genes underlying these 2 forms of diabetes is discussed and some cautionary notes regarding interpretation of this wealth of genetic knowledge are offered.

13.1 Introduction

Positional cloning is the process of identifying disease susceptibility genes through various gene mapping techniques when little is known about the genes underlying the disease. The success of positional cloning in single-gene diseases led to the possibility that the same process could be used to identify genes underlying complex diseases like diabetes.1,2

However, years before any of the major efforts to positionally clone diabetes genes began, a book entitled “The Genetics of Diabetes Mellitus” appeared with a lead chapter prophetically entitled “Diabetes Mellitus – A Geneticist’s Nightmare”.3 This chapter, written by James V. Neel, considered the father of human genetics, warned of the complexity surrounding diabetes, small gene effects, confounding due to environmental factors, and interactions among genes and environment, and the challenge that would be faced in uncovering the genetic architecture of diabetes.

Scientific and technological advances led to an explosion of discoveries regarding genes underlying type 2 diabetes mellitus (T2DM) and related traits. Unfortunately, knowledge regarding the genetic basis for gestational diabetes mellitus (GDM) lags behind that of T2DM. In this chapter, we will summarize the general state of knowledge regarding the genetic basis for T2DM and whether there is a unique genetic basis underlying GDM.

13.2 Genetics of T2DM

13.2.1 State of Knowledge Prior to 2007

Over 25 genome-wide linkage scans for T2DM4 and countless candidate gene studies had been performed between 1976 and 2006. There were numerous successes related to autosomal dominant forms of diabetes,5 altered forms of insulin,69 and rare forms of diabetes like maternally inherited diabetes and deafness.10 However, by 2006 peroxisome proliferator-activated receptor γ (PPARG11,12), potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11 13), and transcription factor 7-like 2 (TCF7L214) were the only “accepted” T2DM susceptibility genes. Other genes, such as calpain 10 (CAPN10 15), protein tyrosine phosphatase 1B (PTPN116), and hepatocyte nuclear factor-4α (HNF4A1719) showed evidence of association with T2DM, but most of these were not consistently replicated across study populations, raising some question as to their validity as T2DM susceptibility genes. Unfortunately, the inability to identify diabetes genes simply reflected the nightmare originally described by Dr. Neel and the reality that the technology and methods of the time would not allow for identification of small gene effects.

In 1996, Risch and Merikangas suggested that complex disease susceptibility genes could be identified by genotyping a number of single nucleotide polymorphisms (SNP) in each gene across the human genome20 and performing association analysis. However, the ability to genotype the requisite number of SNPs in a sufficiently large sample was not feasible at that time. Also, many recognized that the framework proposed by Risch and Merikangas was gene-centric and did not take into account the possibility of regulatory elements outside the coding region of genes or intronic SNPs that might result in alternative splice forms of the gene.

However, three milestones altered the genomics landscape. The Human Genome Project (http://genome.ucsc.edu), an effort to decode our DNA, released the first draft of the human genome in 2000.21 The HapMap Project (http://www.hapmap.org), an effort to identify genetic differences across human populations, has to-date identified over six million common SNPs across the human genome.22,23 Finally, improved technology now allows for genotyping of hundreds of thousands of SNPs in thousands of individuals in a span of weeks.

13.2.2 State of Knowledge After 2006: Genome-Wide Association

Results from the first genome-wide association (GWA) study for T2DM appeared in February 200724 and was based on ∼400,000 SNPs genotyped in approximately 2,800 T2DM cases and controls with promising associations in this first stage followed-up in a larger sample of ∼5,500 cases and controls. This 2-stage approach was employed to reduce study cost, while maintaining statistical power close to what would have been achieved if all subjects had been genotyped for all SNPs.25 The analysis identified five regions of the genome showing evidence of association with T2DM that survived stringent statistical criteria that accounted for the large number of tests performed (Table 13.1). Among the loci identified was TCF7L2, which acted as a positive control in this study and provided added confidence these loci were indeed T2DM susceptibility genes. Another was a nonsynonymous variant in the solute carrier family 30 (zinc transporter), member 8 (SLC30A8) encoding the zinc transporter regulating zinc concentration in insulin secretory vesicles of the β-cell. There were two regions of extended linkage disequilibrium harboring multiple genes. One region included hematopoietically expressed homeobox (HHEX), insulin-degrading enzyme (IDE), and kinesin family member 11 (KIF11), all of which could be logically tied to the biology underlying T2DM. The second region included exostoses (multiple) 2 (EXT2) and aristaless-like homeobox 4 (ALX4), which although less obvious candidates, nonetheless have plausible biologic ties to T2DM. Interestingly, the final SNP was in a region on chromosome 11 that did not harbor any known human genes; a so-called “gene desert,” which raised interesting questions regarding human genome architecture. This study demonstrated that large-scale anonymous interrogation of the human genome by association could indeed identify susceptibility genes for a complex disease.

Table 13.1

Current type 2 diabetes susceptibility genes and their reported effect sizes

Chromosome

SNP

Gene region

Description

Reported odds ratio

References

1

rs10923931

NOTCH1

Intronic

1.13

97

2

rs7578597

THADA

Nonsynonymous

1.15

97

3

rs4607103

ADAMTS9

Upstream from the gene

1.09

97

3

rs4402960

IGF2BP2

Intronic

1.14

2628

3

rs1801282

PPARG

Nonsynonymous

0.79

12

4

rs10010131

WFS1

Intronic

0.90

98

6

rs7754840

CDKAL1

Intronic

1.12

2628

7

rs864745

JAZF1

Intronic

1.10

97

8

rs13266634

SLC30A8

Nonsynonymous

1.18

24

9

rs10811661

CDKN2A/2B

Upstream from the genes

1.20

2628

10

rs12779790

CDC123-CAMK1D

Intergenic

1.11

97

10

rs7903146

TCF7L2

Intronic

1.37

14

10

rs1111875

HHEX-IDE-KIF11

Downstream from the genes

1.19

24

11

rs7480010

- - -

Intergenic

1.14

24

11

rs5219

KCNJ11

Nonsynonymous

1.23

13

11

rs3740878

EXT2

Intronic

1.26

24

11

rs2237892

KCNQ1

Intronic

1.49

99,100

11

rs10830963

MTNR1B

Intronic

1.09

41

12

rs7961581

TSPAN8-LGR5

Intergenic

1.09

97

16

rs8050136

FTO

Intronic

1.17

2628

17

rs757210

HNF1B

Intronic

1.13

101

This initial success was immediately followed in April 2007 by a series of three GWA studies that collaborated to improve the power of their individual studies by performing a meta-analysis.2628 These groups replicated some of the findings by the original GWA study, such as TCF7L2SLC30A8, the HHEX region, and even the intragenic region on chromosome 11, but also identified additional new loci (Table 13.1). This meta-analysis approach has become the current standard for GWA studies and as of this writing, 21 T2DM susceptibility genes have been identified (Table 13.1).

13.2.3 T2DM-Related Quantitative Traits

A general rule of thumb in statistics is that analysis of a continuous variable will be statistically more powerful than an analysis that categorizes the continuous variable. For a variety of reasons, this maxim may not fully extend to the analysis of disease-related quantitative traits. For example, as one progresses toward disease both the disease process and disease treatment could significantly alter one tail of the trait distribution. However, in principle the analysis of such traits could provide additional insights into the genetic architecture underlying a complex disease. Many groups have begun to apply GWA to search for genes underlying variation in disease-related quantitative traits, hypothesizing that a gene regulating variability in a trait may also be a disease susceptibility variant. To date, genes underlying height,2930lipids,31,32 anthropometrics,3337 and fasting glucose3841 have been identified.

One obvious trait to examine with respect to diabetes is fasting glucose. Among the first loci to be identified as regulating fasting glucose concentrations was a promoter variant in glucokinase (GCK) first reported in the mid to late 1990s42,43 and subsequently confirmed in a large meta-analysis in 2005 by Weedon et al.44,45 This promoter variant appears to contribute to mild hyperglycemia and does not contribute to risk for T2DM, consistent with observations regarding coding variants in GCK and MODY2.46 47 Interestingly, this variant also appears to be associated with birth weight,44,45 suggesting implications for fetal development. Recent results from the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) have shown that variation in GCK is associated with both fasting glucose levels and risk for type 2 diabetes (Dupuis et al., Nat Genet. 2010;42:105-116 (PMID 20081858).

A recent GWA meta-analysis of fasting glucose reported association with glucose-6-phosphatase, catalytic unit 2 (G6PC23848), which was replicated in a separate study.39 This association was interesting in that G6PC2appeared to only be associated with fasting glucose and showed no evidence of association with T2DM.38,39 Most recently, a large GWA-based meta-analysis by the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) reported that melatonin receptor-1B (MTNR1B) was associated with fasting glucose,41 but in contrast to G6PC2, also showed evidence for association with T2DM.41 These observations were independently reported by Bouatia-Naji et al.40 Overall, these results suggest that there may be genes that only contribute to the day-to-day regulation of fasting glucose levels, while others may also contribute to susceptibility to T2DM.

MAGIC also demonstrated that MTNR1B was associated with insulin secretion and β-cell function.49 These results demonstrate that while there may be an initial association between a genetic variant and specific quantitative trait, the primary effect of the gene may be elsewhere in the biologic pathway. Therefore, it is important to examine the association with other traits to best characterize the specific biology underlying the gene. A second example is the fat and obesity associated (FTO) gene, which was initially identified as being associated with T2DM,2628 but has subsequently been shown to be primarily associated with body mass index and body fat.33,50

It is also noteworthy that like T2DM susceptibility genes, genetic variants underlying quantitative traits typically have very small effect sizes. For example, G6PC2 and MTNR1B each independently have a per allele effect of ∼0.07 mM, accounting for a very small proportion of the variation in fasting glucose.3841 The fact that only a small proportion of the variation in these traits is accounted for by genes suggests additional loci for this and other traits.

13.3 Genetics of GDM

13.3.1 Heritability of GDM

Despite successful identification of genes associated with T2DM and T2DM-related traits, surprisingly there has been relatively little research in the area of GDM genetics. This may partly reflect the fact that the study of GDM genetics is fraught with difficulties. An essential first step in genetics research has been the determination of evidence for a genetic basis for the disease, typically achieved through assessment of heritability or familial clustering. However, performing such studies in a prospective fashion is hindered by, among other things, identification of multiple GDM cases in a single family. Similarly, retrospective studies are hampered by the evolution of the clinical definition of GDM and the fact that definitions differ (Chap. 1.2).5155 Furthermore, screening for GDM in the United States has not been routine until the 1990s, leading to possible ascertainment bias. There are also difficulties in ascertaining sufficient numbers of GDM cases, given its relatively low prevalence. Finally, many existing cohort studies typically lack DNA samples and resampling for DNA is either not possible or a Herculean task.

No published studies have estimated familiality with GDM. There has been only one unpublished attempt to estimate familiality of GDM. Williams and colleagues used the state-wide medical record system in the state of Washington to link sisters diagnosed with GDM and estimated the sibling GDM risk ratio to be 1.75 (Michelle Williams, personal communication), suggesting some evidence for a genetic basis for GDM. Although the risk ratio of 1.75 is likely an underestimate of the true sibling risk ratio, it does provide some evidence for familiality of GDM and suggests the genetic basis for GDM may fall within the range of T2DM.56

There have been studies examining whether GDM clusters with type 1 diabetes mellitus or T2DM. Dorner et al showed that offspring with type 1 diabetes whose mothers had GDM showed increased familial aggregation of diabetes on the maternal side compared with the paternal side.57 There is also evidence for clustering of impaired glucose tolerance and T2DM in parents of a woman with GDM58 and evidence for higher prevalence of T2DM specifically in mothers of women with GDM.59 Kim and colleagues examined the association between family history of diabetes and history of GDM in the National Health and Nutrition Examination Survey and also found that sibling history of diabetes increased the odds of GDM (Catherine Kim, personal communication). Thus, there exists evidence of a link between both autoimmune and nonautoimmune forms of diabetes and GDM. The fact that GDM clusters with both autoimmune and nonautoimmune forms of diabetes suggests a potential overlap in genetic susceptibility among these various forms of the disease.

13.3.2 Candidate Gene Studies

Candidate genes related to both autoimmune and nonautoimmune forms of GDM have been assessed in a variety of cohorts6069 and candidate genes in GDM are also reviewed by Robitaille and Grant.70 It appears that many of the candidate gene associations reported with modest evidence of association are likely to represent under-powered studies. However, the reader is cautioned to carefully evaluate results for genetic studies based on the sample collected and the analyses performed. The fact that a study does not achieve a certain, somewhat arbitrary, level of statistical significance, or is not of a sample size approaching that of a GWA should not be grounds for outright dismissal. For example, gene association studies are essentially signal-to-noise ratio problems. While one solution to this problem is to increase sample size to rise above the “noise,” another approach is selective sample collection to reduce the “noise.” Both can yield equally valid results.

One candidate gene of particular interest for GDM is GCK.627174 Although the frequency of GCK variants among GDM patients is low,71 the important contribution of GCK variation to risk for GDM can be observed in MODY2 families, where a large proportion of female members present with GDM.72 Saker et al speculated that because GCK variants typically result in subclinical hyperglycemia,47,75 the frequency of GCK variants may be higher and only detectable upon pregnancy.72 Their speculation was supported by Ellard et al who estimated that the prevalence of GCK variants may be as high as 80% in a small subset of women selected using stringent criteria.74

The BetaGene study is unique among studies examining candidate genes for GDM in that it is studying Mexican American families of probands with or without a diagnosis of GDM and has focused on T2DM-related quantitative traits, rather than GDM per se.7678 Another unique aspect of BetaGene is the examination of whether the association between a gene and trait is modified by adiposity. For example, the study found that TCF7L2 was associated with both GDM and with insulin secretion and β-cell function.76 However, the association between TCF7L2 and insulin secretion and β-cell function differed by the subject’s level of body fat. Similar observations were made for the association between insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) and insulin sensitivity.78 These are among the first observations to suggest that the effect of genes may be modified by other factors, like adiposity. The BetaGene investigators have also examined gene–gene interactions,77 another critical component of the genetic architecture of complex diseases.

13.3.3 Genes for GDM?

The question of GDM genes is closely tied to the debate surrounding whether GDM represents a unique disease state, or whether pregnancy results in metabolic derangements revealing individuals already on a trajectory towards T2DM. If T2DM susceptibility variants are also associated with GDM, from a purely genetic perspective it would be difficult to argue a unique genetic predisposition for GDM. This does not take into account the possibility of unique environmental exposures related to pregnancy that may interact with genes to alter disease risk or may affect pregnancy outcomes.

One group has recently assessed a subset of T2DM susceptibility loci for association with GDM.79 They selected a case–control set of 238 women with a history of GDM and 2,446 normal glucose tolerant women from the population-based Inter99 cohort80 and eleven T2DM susceptibility loci were tested for association with GDM. Among the 11 T2DM genes tested, all but 1 showed estimated odds ratios >1.0 and among these loci 4 (TCF7L2CDKAL1TCF2, and FTO) showed nominal evidence for association with GDM. While not all 11 loci were associated with T2DM, a highly significant additive effect of all 11 loci on risk for GDM (OR = 1.18; p = 3.2 × 10−6) was found, suggesting that the genetic basis for GDM and T2DM may, in fact, be the same.

13.4 What Does the Future Hold?

The new GWA era means it is now likely possible to identify large GDM case–control samples and perform GWA studies to identify susceptibility genes for GDM. The unanswered question of whether GDM per se has a genetic basis may also be directly addressed using the GWA framework, by incorporating carefully selected samples of T2DM cases as a secondary contrast group into the study design. Also, it should be possible to begin to understand how gene variants in the mother and fetus interact in terms of pregnancy outcomes, an area that has received very little attention.8182

Should GDM have unique genetic susceptibility loci, this information could be critical in developing new interventional strategies to not just treat GDM, but also prevent progression to T2DM, which appears to occur in a large proportion of GDM cases.83,84

Three important cautionary notes arise. First, one should not directly correlate the relatively small odds ratios associated with these gene variants in terms of risk for T2DM or GDM and their relative importance in terms of disease biology. A common misconception is that with such small odds ratios, these gene variants are not likely to be playing a large role in disease pathogenesis and are therefore clinically irrelevant. However, one cannot deny the important role PPARG plays in terms of T2DM treatment8587 and possibly prevention8890 via thiazolidinedione therapy. Similarly, KCNJ11 is another important therapeutic target of the sulfonylurea class of medications.87,9193 Yet, the odds ratio for T2DM risk associated with PPARG is ∼1.2 and for KCNJ11 is ∼1.1.

Second, it is insufficient to simply know that a genetic variant is associated with a disease or disease-related trait. The relative role played by any gene in terms of disease pathogenesis requires additional molecular and physiologic study. This is exemplified by the role of PPARG and KCNJ11 in terms of diabetes therapy and the interactions with adiposity observed in the BetaGene study.

Finally, knowledge of the genes underlying disease will not be useful in predicting future disease until the biologic effect of genes is well understood. Several studies have already attempted to determine if gene variants can be used to “predict” T2DM9496 or GDM.79 Each of these analyses shows that gene variants alone do not predict T2DM nor do they significantly improve predictability over traditional clinical predictors. Rather, genetic information should be leveraged to improve clinical care through development of new pharmacologic agents or interventional strategies.

Our knowledge of the genetics underlying both T2DM and GDM will continue to improve over the next several years. At the time of this writing, I am aware of at least an additional eight genes related to T2DM or T2DM-related traits that will soon be appearing in the literature. “Next generation” sequencing methods allow for sequencing of the genome of a single individual in a relatively short time and at relatively low cost and will likely introduce yet another wave of genetic discoveries. Already this technology is being applied in the 1,000 Genomes Project (http://www.1000genomes.org), which has set a goal to completely sequence the genomes of 2,000 individuals selected from around the world.

Pharmacogenetics is likely to be an area that will significantly impact clinical care as we learn more about the relationship between genetic variation and drug responses. Also, genetic findings are raising interest in specific areas of diabetes research. For example, the MTNR1B findings have increased interest in studies of circadian rhythms and sleep disorders and their relation to diabetes and obesity; an area many clinicians have not considered a part of their interventional arsenal. Our own BetaGene study is now looking at the possible effect of genetic variation on longitudinal change in T2DM-related traits, based on our finding of interactions with adiposity.76,78 In the end, as we learn more about the intricacies of the genetic architecture underlying GDM and T2DM, it is easy to envision a time, in the not too distant future, when genetic information will be used for so-called “personalized medicine.”

Acknowledgments

I would like to thank my colleagues on the FUSION and BetaGene studies and in MAGIC, who have made many of the contributions discussed in this chapter. RMW was or is supported as principle investigator or coinvestigator on grants from the American Diabetes Association (05-RA-140), National Institutes of Health (DK69922, DK62370, and DK61628), and Merck & Co.

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