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These GWAS have occurred on the background of genotyping arrays populated by common single nucleotide polymorphisms (SNPs), deployed in various cohorts that have coalesced to form large international consortia. As a result, a list of genetic loci that influence type 2 diabetes and quantitative glycemic traits has begun to accumulate. Over 100 type 2 diabetes-associated loci have been identified, in addition to others involved in determining quantitative glycemic traits, such as insulin resistance. However, no variant that is widely shared across populations has been found to have a stronger effect than the rs7903146 SNP in TCF7L2, which itself has only a modest effect (odds ratio ~1.4). 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<div class="pre-content"><div><div class="bk_prnt"><p class="small">NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.</p><p>Cowie CC, Casagrande SS, Menke A, et al., editors. Diabetes in America. 3rd edition. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018 Aug. </p></div><div class="bk_msg_box bk_bttm_mrgn clearfix bk_noprnt"><div class="iconblock clearfix"><a class="img_link icnblk_img" title="Table of Contents Page" href="/books/n/diaonline/"><img class="source-thumb" src="/corehtml/pmc/pmcgifs/bookshelf/thumbs/th-diaonline-lrg.png" alt="Cover" height="100px" width="80px" /></a><div class="icnblk_cntnt"><ul class="messages"><li class="info icon"><span class="icon"><a href="/books/n/diaonline/">Diabetes in America</a></span></li></ul></div></div></div><div class="iconblock clearfix whole_rhythm no_top_margin bk_noprnt"><a class="img_link icnblk_img" title="Table of Contents Page" href="/books/n/dia3ed/"><img class="source-thumb" src="/corehtml/pmc/pmcgifs/bookshelf/thumbs/th-dia3ed-lrg.png" alt="Cover of Diabetes in America" height="100px" width="80px" /></a><div class="icnblk_cntnt eight_col"><h2>Diabetes in America. 3rd edition.</h2><a data-jig="ncbitoggler" href="#__NBK567998_dtls__">Show details</a><div style="display:none" class="ui-widget" id="__NBK567998_dtls__"><div>Cowie CC, Casagrande SS, Menke A, et al., editors.</div><div>Bethesda (MD): <a href="https://www.niddk.nih.gov/" ref="pagearea=page-banner&amp;targetsite=external&amp;targetcat=link&amp;targettype=publisher">National Institute of Diabetes and Digestive and Kidney Diseases (US)</a>; 2018 Aug.</div></div><div class="half_rhythm"><ul class="inline_list"><li style="margin-right:1em"><a class="bk_cntns" href="/books/n/dia3ed/">Contents</a></li><li style="margin-left:1em"><a href="https://www.niddk.nih.gov/about-niddk/strategic-plans-reports/diabetes-in-america-3rd-edition" ref="pagearea=body&amp;targetsite=external&amp;targetcat=link&amp;targettype=publisher">Original Version at NIDDK</a></li></ul></div></div><div class="icnblk_cntnt two_col"><div class="pagination bk_noprnt"><a class="active page_link prev" href="/books/n/dia3ed/ch13/" title="Previous page in this title">&lt; Prev</a><a class="active page_link next" href="/books/n/dia3ed/ch15/" title="Next page in this title">Next &gt;</a></div></div></div></div></div>
<div class="main-content lit-style" itemscope="itemscope" itemtype="http://schema.org/CreativeWork"><div class="meta-content fm-sec"><h1 id="_NBK567998_"><span class="label">CHAPTER 14</span><span class="title" itemprop="name">Genetics of Type 2 Diabetes</span></h1><p class="contrib-group"><span itemprop="author">Jose C. Florez</span>, MD, PhD, <span itemprop="author">Miriam S. Udler</span>, MD, PhD, and <span itemprop="author">Robert L. Hanson</span>, MD, MPH.</p><a data-jig="ncbitoggler" href="#__NBK567998_ai__" style="border:0;text-decoration:none">Author Information and Affiliations</a><div style="display:none" class="ui-widget" id="__NBK567998_ai__"><p class="contrib-group"><h4>Authors</h4><span itemprop="author">Jose C. Florez</span>, MD, PhD,<sup>1</sup> <span itemprop="author">Miriam S. Udler</span>, MD, PhD,<sup>2</sup> and <span itemprop="author">Robert L. Hanson</span>, MD, MPH<sup>3</sup>.</p><h4>Affiliations</h4><div class="affiliation"><sup>1</sup> Dr. Jose C. Florez is Chief of the Diabetes Unit and an investigator in the Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, and Co-Director of the Program in Metabolism and Institute Member in the Broad Institute, Cambridge, MA, and Associate Professor in the Department of Medicine, Harvard Medical School, Boston, MA</div><div class="affiliation"><sup>2</sup> Dr. Miriam S. Udler is Clinical Fellow in the Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, and Postdoctoral Fellow in the Programs in Metabolism and Medical &#x00026; Population Genetics, Broad Institute, Cambridge, MA, and Research Fellow in the Department of Medicine, Harvard Medical School, Boston, MA</div><div class="affiliation"><sup>3</sup> Dr. Robert L. Hanson is Clinical Investigator and Head, Genetic Epidemiology and Statistics Unit in the Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ</div></div></div><div class="jig-ncbiinpagenav body-content whole_rhythm" data-jigconfig="allHeadingLevels: ['h2'],smoothScroll: false" itemprop="text"><div id="ch14.sum"><h2 id="_ch14_sum_">Summary</h2><p>Type 2 diabetes is thought to result from a combination of environmental, behavioral, and genetic factors, with the heritability of type 2 diabetes estimated to be in the range of 25% to 72% based on family and twin studies. Since early 2007, genome-wide association studies (GWAS) have led to an explosion of data for the genetics of type 2 diabetes and related traits. These GWAS have occurred on the background of genotyping arrays populated by common single nucleotide polymorphisms (SNPs), deployed in various cohorts that have coalesced to form large international consortia. As a result, a list of genetic loci that influence type 2 diabetes and quantitative glycemic traits has begun to accumulate. Over 100 type 2 diabetes-associated loci have been identified, in addition to others involved in determining quantitative glycemic traits, such as insulin resistance. However, no variant that is widely shared across populations has been found to have a stronger effect than the <a href="/snp/?term=7903146" class="bk_tag" ref="pagearea=body&amp;targetsite=entrez&amp;targetcat=term&amp;targettype=snp">rs7903146</a> SNP in <i>TCF7L2</i>, which itself has only a modest effect (odds ratio ~1.4). Nonetheless, GWAS findings have illustrated novel pathways, pointed toward fundamental biology, drawn attention to the role of beta cell dysfunction in type 2 diabetes, confirmed prior epidemiologic observations, and provided possible targets for pharmacotherapy and pharmacogenetic clinical trials.</p><p>On the other hand, the causal variants have only been identified for a handful of these loci, a substantial proportion of the heritability of these phenotypes remains unexplained, and this has tempered expectations with regard to their use in clinical prediction. Together, the approximately 100 loci associated with type 2 diabetes thus far explain ~10%&#x02013;15% of the genetic predisposition to the disease. Limitations of early GWAS included insufficient sample sizes to detect small effects, a nearly exclusive focus on populations of European descent, an imperfect capture of uncommon genetic variants, an incomplete ascertainment of alternate (non-SNP) forms of genetic variation, and the lack of exploration of additional genetic models.</p><p>As the community embraces complementary approaches that include systematic fine-mapping, custom-made replication, denser genotyping arrays, platforms that focus on functional variation, next-generation sequencing techniques, systems biology approaches, and expansion to non-European populations, the coming years will witness exponential growth in the understanding of the genetic architecture of metabolic phenotypes. Whether these findings prove useful in disease prediction or therapeutic decision-making must be tested in rigorously designed clinical trials.</p></div><div id="ch14.s1"><h2 id="_ch14_s1_">Type 2 Diabetes as a Genetic Disease</h2><p>The explosive parallel growth in the prevalence of the related metabolic disorders of obesity and type 2 diabetes in much of the developed and developing worlds over the past few decades is almost certainly driven by environmental and behavioral factors, since genetic components do not change in an appreciable manner over such a short time period. However, several lines of evidence suggest that variation in DNA sequence does contribute to type 2 diabetes risk. First, twin studies have shown that concordance for type 2 diabetes is greater for monozygotic twins (who share 100% of their DNA sequence) than for dizygotic twins (who, like siblings, share approximately 50% of their DNA sequence) (<a class="bk_pop" href="#ch14.ref1">1</a>,<a class="bk_pop" href="#ch14.ref2">2</a>,<a class="bk_pop" href="#ch14.ref3">3</a>,<a class="bk_pop" href="#ch14.ref4">4</a>,<a class="bk_pop" href="#ch14.ref5">5</a>). Second, the incidence of diabetes is much higher in certain racial/ethnic groups, despite an environment that is relatively comparable to that of neighboring populations (<a class="bk_pop" href="#ch14.ref6">6</a>,<a class="bk_pop" href="#ch14.ref7">7</a>,<a class="bk_pop" href="#ch14.ref8">8</a>). Third, family history is an independent risk factor for the development of diabetes in population studies (<a class="bk_pop" href="#ch14.ref9">9</a>,<a class="bk_pop" href="#ch14.ref10">10</a>). And fourth, rare familial forms of diabetes, caused by mutations in single genes (hence, termed monogenic or Mendelian), prove that single base pair changes in the coding regions of key genes, which lead to alterations in protein sequence and function, are sufficient to cause hyperglycemia in the diabetic range (<a class="bk_pop" href="#ch14.ref11">11</a>,<a class="bk_pop" href="#ch14.ref12">12</a>). Consistent with this notion, the heritability of type 2 diabetes estimated in a set of Scandinavian families ranges from 25% to 69% (<a class="bk_pop" href="#ch14.ref13">13</a>), and a large international meta-analysis of twin studies has reported a heritability estimate as high as 72% (<a class="bk_pop" href="#ch14.ref14">14</a>).</p><p>Taken together, these observations illustrate that rapid changes in the global epidemiology of type 2 diabetes are likely caused by environmental and behavioral factors overlaid on a background of genetic predisposition. This genetic predisposition may vary across populations, in some measure due to their divergent genetic history and unequal selection pressures in specific geographic regions. Thus, it is well known and described elsewhere in this volume (see <a href="/books/n/dia3ed/ch13/">Chapter 13</a>
<i>Risk Factors for Type 2 Diabetes</i>) that the risk of type 2 diabetes differs in the various ethnic groups that compose the U.S. population, and the presumption is that some of these differences are genetic in nature (<a class="bk_pop" href="#ch14.ref15">15</a>).</p><p>Why is genetic exploration relevant? Regardless of whether genetic predictors become useful markers of disease onset or progression in clinical practice, the identification of genetic variants associated with type 2 diabetes illuminates pathogenic mechanisms from which therapeutic windows may emerge. Because germline genetic variation always predates the onset of disease, the arrow of time establishes a causal relationship that is not evident with other biologic associations. Thus, the genetic approach has a unique opportunity to shed light on the pathophysiology of diabetes in its various manifestations, helping unravel its clinical heterogeneity and potentially refine therapeutic strategies.</p></div><div id="ch14.s2"><h2 id="_ch14_s2_">Discovery of Type 2 Diabetes Genes</h2><p>Before the sequencing of the human genome was accomplished, genetic mapping was dependent on the generation of anonymous genetic markers and their anchoring on specific locations in the genome. This task, first achieved with restriction fragment length polymorphisms and then with other markers, such as microsatellites or sequence tag sites, enabled the introduction of whole-genome linkage analysis and positional cloning, which proved extremely useful in the identification of genetic mutations that cause monogenic disease. The linkage approach, which depends on the cosegregation of a causal mutation with the anonymous marker along the lines of inheritance in pedigrees composed of affected and unaffected members, is particularly useful for traits where disease-causing alleles are highly penetrant: that is, the presence of the genetic variant virtually always co-occurs with disease, and its absence co-occurs with absence of the disease. As such, in the diabetes field, linkage analysis facilitated the discovery of the genes that underlie the various types of monogenic diabetes, such as maturity-onset diabetes of the young (MODY) (<a class="bk_pop" href="#ch14.ref12">12</a>,<a class="bk_pop" href="#ch14.ref16">16</a>) or neonatal diabetes (<a class="bk_pop" href="#ch14.ref17">17</a>,<a class="bk_pop" href="#ch14.ref18">18</a>,<a class="bk_pop" href="#ch14.ref19">19</a>); these are described in detail in <a href="/books/n/dia3ed/ch7/">Chapter 7</a>
<i>Monogenic Forms of Diabetes</i>.</p><p>In complex diseases, where the phenotype presumably arises as a combination of several genetic variants and their interaction with the environment, successful linkage analysis is considerably more difficult. Though it succeeded in demonstrating the strong influence of the human leukocyte antigen (HLA) region on type 1 diabetes (<a class="bk_pop" href="#ch14.ref20">20</a>,<a class="bk_pop" href="#ch14.ref21">21</a>), by and large, linkage analysis did not yield reproducible positive results for type 2 diabetes. This is because in type 2 diabetes there is not a single genetic locus that exerts a very strong effect in the general population or even in individual family pedigrees. Thus, the effect of genetic variation is <i>probabilistic</i> rather than <i>deterministic</i>; a substantial proportion of people with some risk variants may be disease-free, whereas others who carry protective alleles may instead have type 2 diabetes, due to a constellation of other factors. In such situations, the amount of information provided by meioses within families, on which the power of linkage analysis depends, is greatly reduced, and the number of families required can be inordinately large.</p><p>To demonstrate the effect of genetic variation on human phenotypes, an alternative approach was needed: association testing, which simply asks whether a specific allele is significantly overrepresented in diabetes cases compared to controls without diabetes, and which, with large sample size, has greater statistical power to detect a common variant of weak effect. Its major limitation&#x02014;prior to 2005&#x02014;was that only a handful of variants could be tested at a time, which required some prior biologic knowledge on the existence of such variants and the role of a given gene in diabetes pathophysiology. Although multiple genetic associations were described before 2005, only two of these stood the test of time, both at variants that change the amino acid sequence in genes that encode antihyperglycemic drug targets: the p.Pro12Ala polymorphism in the peroxisome proliferator-activated receptor gamma 2 (encoded by <i>PPARG</i>) (<a class="bk_pop" href="#ch14.ref22">22</a>) and the p.Glu23Lys polymorphism in the islet ATP-dependent potassium channel Kir6.2 (encoded by <i>KCNJ11</i>) (<a class="bk_pop" href="#ch14.ref22">22</a>,<a class="bk_pop" href="#ch14.ref23">23</a>). A third locus, a noncoding variant in the transcription factor 7-like 2 gene (<i>TCF7L2</i>), was discovered by large-scale association testing in areas of suggestive linkage (<a class="bk_pop" href="#ch14.ref24">24</a>). The common intronic <a href="/snp/?term=7903146" class="bk_tag" ref="pagearea=body&amp;targetsite=entrez&amp;targetcat=term&amp;targettype=snp">rs7903146</a> polymorphism had the strongest statistical association (though with a modest odds ratio ~1.4) and the most widespread effect on type 2 diabetes risk (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig1/?report=objectonly" target="object" rid-figpopup="figch14fig1" rid-ob="figobch14fig1">Figure 14.1</a>) (<a class="bk_pop" href="#ch14.ref25">25</a>,<a class="bk_pop" href="#ch14.ref26">26</a>), albeit with an interesting exception in some Native American populations (<a class="bk_pop" href="#ch14.ref27">27</a>).</p><p>The panorama changed dramatically with the advent of genome-wide association studies (GWAS) (<a class="bk_pop" href="#ch14.ref28">28</a>). Several factors coalesced to enable the conduct of GWAS: the discovery of millions of single nucleotide polymorphisms (SNPs) and their deposition in public databases; the manufacturing of genotyping arrays that could simultaneously query hundreds of thousands of SNPs with great precision; the understanding of an underlying correlation structure between SNPs, driven by the finite number of recombination events in human history, which reduced the complexity of the variation to be interrogated; the recognition that the scientific imperative of reproducibility required the acceptance of strict statistical thresholds that accounted for the universe of possible hypotheses in the human genome; and the corollary of such awareness, that for these very small p-values to be achieved, very large sample sizes had to be assembled through international collaboration. Thus, for the first time, most of the common variants in the human genome (i.e., those with a minor allele frequency &#x0003e;5%) could be tested in one fell swoop.</p><div class="iconblock whole_rhythm clearfix ten_col fig" id="figch14fig1" co-legend-rid="figlgndch14fig1"><a href="/books/NBK567998/figure/ch14.fig1/?report=objectonly" target="object" title="FIGURE 14.1" class="img_link icnblk_img figpopup" rid-figpopup="figch14fig1" rid-ob="figobch14fig1"><img class="small-thumb" src="/books/NBK567998/bin/ch14f1.gif" src-large="/books/NBK567998/bin/ch14f1.jpg" alt="Bar graph showing the discovery of genes associated with type 2 diabetes increased from 3 before 2007 to several dozen in the G W A S era between 2007 and 2014" /></a><div class="icnblk_cntnt" id="figlgndch14fig1"><h4 id="ch14.fig1"><a href="/books/NBK567998/figure/ch14.fig1/?report=objectonly" target="object" rid-ob="figobch14fig1">FIGURE 14.1</a></h4><p class="float-caption no_bottom_margin">Chronological Listing of Type 2 Diabetes-Associated Genes, Plotted by Year of Definitive Publication and Approximate Effect Size. Genes identified via the candidate gene approach are shown in green (*), genes identified via agnostic genome-wide association <a href="/books/NBK567998/figure/ch14.fig1/?report=objectonly" target="object" rid-ob="figobch14fig1">(more...)</a></p></div></div><p>Several independent GWAS (<a class="bk_pop" href="#ch14.ref29">29</a>,<a class="bk_pop" href="#ch14.ref30">30</a>,<a class="bk_pop" href="#ch14.ref31">31</a>,<a class="bk_pop" href="#ch14.ref32">32</a>,<a class="bk_pop" href="#ch14.ref33">33</a>) and the growing scientific exchange that led to successive meta-analyses of ever-increasing size (<a class="bk_pop" href="#ch14.ref34">34</a>,<a class="bk_pop" href="#ch14.ref35">35</a>) soon produced a plethora of robust associations, such that the landscape of type 2 diabetes-associated variants grew from three prior to the GWAS era to several dozen in just a few years (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig1/?report=objectonly" target="object" rid-figpopup="figch14fig1" rid-ob="figobch14fig1">Figure 14.1</a>, <a class="figpopup" href="/books/NBK567998/table/ch14.tab1/?report=objectonly" target="object" rid-figpopup="figch14tab1" rid-ob="figobch14tab1">Table 14.1</a>) (<a class="bk_pop" href="#ch14.ref26">26</a>,<a class="bk_pop" href="#ch14.ref36">36</a>,<a class="bk_pop" href="#ch14.ref37">37</a>). This list has been complemented by the implementation of similar approaches in the discovery of genetic determinants of quantitative glycemic traits (<a class="figpopup" href="/books/NBK567998/table/ch14.tab2/?report=objectonly" target="object" rid-figpopup="figch14tab2" rid-ob="figobch14tab2">Table 14.2</a>) (<a class="bk_pop" href="#ch14.ref37">37</a>,<a class="bk_pop" href="#ch14.ref38">38</a>,<a class="bk_pop" href="#ch14.ref39">39</a>,<a class="bk_pop" href="#ch14.ref40">40</a>,<a class="bk_pop" href="#ch14.ref41">41</a>,<a class="bk_pop" href="#ch14.ref42">42</a>,<a class="bk_pop" href="#ch14.ref43">43</a>,<a class="bk_pop" href="#ch14.ref44">44</a>,<a class="bk_pop" href="#ch14.ref45">45</a>), the extension of GWAS to non-European populations (<a class="bk_pop" href="#ch14.ref46">46</a>,<a class="bk_pop" href="#ch14.ref47">47</a>,<a class="bk_pop" href="#ch14.ref48">48</a>,<a class="bk_pop" href="#ch14.ref49">49</a>,<a class="bk_pop" href="#ch14.ref50">50</a>,<a class="bk_pop" href="#ch14.ref51">51</a>,<a class="bk_pop" href="#ch14.ref52">52</a>,<a class="bk_pop" href="#ch14.ref53">53</a>,<a class="bk_pop" href="#ch14.ref54">54</a>,<a class="bk_pop" href="#ch14.ref55">55</a>,<a class="bk_pop" href="#ch14.ref56">56</a>,<a class="bk_pop" href="#ch14.ref57">57</a>,<a class="bk_pop" href="#ch14.ref58">58</a>,<a class="bk_pop" href="#ch14.ref59">59</a>,<a class="bk_pop" href="#ch14.ref60">60</a>,<a class="bk_pop" href="#ch14.ref61">61</a>,<a class="bk_pop" href="#ch14.ref62">62</a>,<a class="bk_pop" href="#ch14.ref63">63</a>,<a class="bk_pop" href="#ch14.ref64">64</a>), trans-ethnic meta-analyses of many of these studies (<a class="bk_pop" href="#ch14.ref56">56</a>,<a class="bk_pop" href="#ch14.ref65">65</a>), and the deployment of custom-made arrays that allow for the rapid and efficient genotyping of top signals across thousands of additional samples (<a class="bk_pop" href="#ch14.ref66">66</a>,<a class="bk_pop" href="#ch14.ref67">67</a>).</p><p>A particularly illustrative example of a combination of these approaches has been furnished by Moltke <i>et al</i>. (<a class="bk_pop" href="#ch14.ref68">68</a>). On studying the population isolate of Greenland, they selected a custom-made array, the Metabochip (<a class="bk_pop" href="#ch14.ref69">69</a>), and focused on quantitative glycemic traits. They followed-up an original signal in <i>TBC1D4</i> by sequencing the exons of this gene and identified a nonsense p.Arg684Ter variant of Inuit ancestry that is common in the Greenlandic population (frequency 17%) and is associated with 2-hour glucose and insulin levels. Stop codon homozygotes harbor a tenfold increased risk of type 2 diabetes compared to wildtype allele carriers. Definitive identification of the implicated protein allowed for functional studies: the stop codon induces lower protein levels of TBC1D4 in human skeletal muscle, causing reduced numbers of the glucose transporter GLUT4 and decreased insulin-stimulated glucose uptake, leading to postprandial hyperglycemia and impaired glucose tolerance.</p><div class="iconblock whole_rhythm clearfix ten_col table-wrap" id="figch14tab1"><a href="/books/NBK567998/table/ch14.tab1/?report=objectonly" target="object" title="TABLE 14.1" class="img_link icnblk_img figpopup" rid-figpopup="figch14tab1" rid-ob="figobch14tab1"><img class="small-thumb" src="/books/NBK567998/table/ch14.tab1/?report=thumb" src-large="/books/NBK567998/table/ch14.tab1/?report=previmg" alt="TABLE 14.1. Genetic Loci Associated With Type 2 Diabetes at Genome-Wide Levels of Statistical Significance (p&#x0003c;5&#x000d7;10&#x02212;8)." /></a><div class="icnblk_cntnt"><h4 id="ch14.tab1"><a href="/books/NBK567998/table/ch14.tab1/?report=objectonly" target="object" rid-ob="figobch14tab1">TABLE 14.1</a></h4><p class="float-caption no_bottom_margin">Genetic Loci Associated With Type 2 Diabetes at Genome-Wide Levels of Statistical Significance (p&#x0003c;5&#x000d7;10<sup>&#x02212;8</sup>). </p></div></div><div class="iconblock whole_rhythm clearfix ten_col table-wrap" id="figch14tab2"><a href="/books/NBK567998/table/ch14.tab2/?report=objectonly" target="object" title="TABLE 14.2" class="img_link icnblk_img figpopup" rid-figpopup="figch14tab2" rid-ob="figobch14tab2"><img class="small-thumb" src="/books/NBK567998/table/ch14.tab2/?report=thumb" src-large="/books/NBK567998/table/ch14.tab2/?report=previmg" alt="TABLE 14.2. Genetic Variants Associated With Quantitative Glycemic Traits at Genome-Wide Levels of Statistical Significance (p&#x0003c;5&#x000d7;10&#x02212;8)." /></a><div class="icnblk_cntnt"><h4 id="ch14.tab2"><a href="/books/NBK567998/table/ch14.tab2/?report=objectonly" target="object" rid-ob="figobch14tab2">TABLE 14.2</a></h4><p class="float-caption no_bottom_margin">Genetic Variants Associated With Quantitative Glycemic Traits at Genome-Wide Levels of Statistical Significance (p&#x0003c;5&#x000d7;10<sup>&#x02212;8</sup>). </p></div></div><p>However, many of these GWAS only captured common variants, because imputation of ungenotyped variants depended on available reference panels from resources such as the HapMap (<a class="bk_pop" href="#ch14.ref70">70</a>). The introduction of massive parallel sequencing techniques and the concomitant dramatic drop in cost allowed for efficient, high-fidelity sequencing of thousands of samples. This enabled three major developments: first, denser reference panels could be developed for more accurate imputation of less common variants (<a class="bk_pop" href="#ch14.ref71">71</a>); second, targeted genotyping arrays that included less common but likely functional variation (e.g., coding variants) could be designed; and third, the allelic spectrum captured in case-control or quantitative trait studies could be expanded into less common frequencies, so that population genetics by which a rare variant may rise to prominence in a specific ethnic group could be exploited.</p><p>Arrays containing exome content deployed in large populations have identified coding variants in established or novel genes associated with type 2 diabetes or related quantitative traits (<a class="bk_pop" href="#ch14.ref72">72</a>,<a class="bk_pop" href="#ch14.ref73">73</a>,<a class="bk_pop" href="#ch14.ref74">74</a>). By detecting a robust association signal in the coding region of a specific gene (e.g., <i>SGSM2</i> and proinsulin levels) (<a class="bk_pop" href="#ch14.ref72">72</a>), these studies serve to advance the candidacy of said gene as the causal locus, from within the various possibilities under a noncoding GWAS association peak (<a class="bk_pop" href="#ch14.ref42">42</a>). Beyond genotyping, sequencing of whole exomes in Europeans (<a class="bk_pop" href="#ch14.ref75">75</a>) and Mexicans (<a class="bk_pop" href="#ch14.ref76">76</a>) has also yielded novel associations, and the extensive genetic and pedigree data available in the Icelandic population have allowed whole-genome sequences in 2,630 Icelanders to be extrapolated to a sample size of 11,114 type 2 diabetes cases and 267,140 controls for additional discovery (<a class="bk_pop" href="#ch14.ref77">77</a>). Finally, whole-genome sequencing in 2,657 European individuals with and without diabetes and whole-exome sequencing in 12,940 individuals from five ancestry groups have begun to shed light on the genetic architecture of type 2 diabetes in a more systematic fashion, in terms of plausible effect sizes, observed allelic frequencies, and the potential number of causal variants (<a class="bk_pop" href="#ch14.ref78">78</a>). All in all, these studies support a model in which type 2 diabetes is caused by hundreds or thousands of loci of modest effects, with no major role for low-frequency variants of strong effects in disease predisposition.</p></div><div id="ch14.s3"><h2 id="_ch14_s3_">Insights Gained</h2><p>The tremendous success of GWAS and their follow-up for type 2 diabetes and other human phenotypes have resulted in a number of insights into the genetic architecture of type 2 diabetes.</p><p><i>Although the functional variants at most type 2 diabetes-associated loci are not yet known, most associated loci are located near genes that were previously unsuspected to play a role in type 2 diabetes pathophysiology</i>. This observation highlights the complexity of the disease phenotype and the power of agnostic approaches in unearthing new knowledge. Conversely, it brings to the forefront the constraints imposed by prior knowledge on scientific inquiry and points to the inadequacy of prior candidate gene selection efforts, as most &#x0201c;logical&#x0201d; candidate genes did not yield significant associations. This observation does not necessarily minimize the role of such biologic candidates on glucose homeostasis; rather, it may indicate natural selection&#x02019;s little tolerance for functional variation in those key genes.</p><p><i>Noncoding variation can affect human phenotypes</i>. The SNPs with the strongest associations are often found in introns, regulatory regions, or intergenic segments, i.e., they do not change the amino acid sequence of the encoded proteins. Furthermore, for the most part, no obvious missense SNP has been identified in coding regions for which the associated SNP was a proxy and, thus, might have explained the association signal. The human genome is rife with regulatory sequences that influence the timing, location, and level of expression of genes (<a class="bk_pop" href="#ch14.ref79">79</a>), and these are thought to have a substantial impact on human biology.</p><p><i>Most genetic determinants of type 2 diabetes have modest effects</i>. No common variant that is widely shared across populations has been found to have a stronger effect than the <a href="/snp/?term=7903146" class="bk_tag" ref="pagearea=body&amp;targetsite=entrez&amp;targetcat=term&amp;targettype=snp">rs7903146</a> SNP in <i>TCF7L2</i>. The handful of variants with stronger effects (e.g., <i>TBC1D4</i> p.Arg684Ter in Greenland, odds ratio ~10 (<a class="bk_pop" href="#ch14.ref68">68</a>), or <i>HNF1A</i> p.Glu508Lys in Mexico, odds ratio ~5 (<a class="bk_pop" href="#ch14.ref76">76</a>)) are either rare or unique to specific populations. Moreover, pioneering whole-exome and whole-genome sequencing experiments in thousands of samples across multiple ethnic groups have failed to unveil a plethora of rare variant associations, and they have not provided support for the hypothesis that common variant association signals are undergirded by rare variants of strong effects (<a class="bk_pop" href="#ch14.ref78">78</a>,<a class="bk_pop" href="#ch14.ref80">80</a>). Thus, the genetic architecture of type 2 diabetes appears to involve hundreds of variants with modest effects (<a class="bk_pop" href="#ch14.ref66">66</a>,<a class="bk_pop" href="#ch14.ref78">78</a>,<a class="bk_pop" href="#ch14.ref81">81</a>). While rare variants might be found that have stronger effects in specific families or population groups, they are most likely to be private, as shared rare variants of strong effects should have been found by linkage. A corollary of this observation is that any single variant is unlikely to have significant predictive power in the individual, and even when many variants are combined into a genotype risk score (GRS), predictive power is poor. Together, the approximately 100 loci associated with type 2 diabetes thus far explain ~10%&#x02013;15% of the familial aggregation of the disease, or 5.7% of the variance in type 2 diabetes susceptibility (<a class="bk_pop" href="#ch14.ref66">66</a>).</p><p><i>The majority of genetic variants that influence type 2 diabetes risk affect beta cell function</i> (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig2/?report=objectonly" target="object" rid-figpopup="figch14fig2" rid-ob="figobch14fig2">Figure 14.2</a>) (<a class="bk_pop" href="#ch14.ref35">35</a>,<a class="bk_pop" href="#ch14.ref82">82</a>,<a class="bk_pop" href="#ch14.ref83">83</a>). Human studies have shown that most of the identified variants (whether causal or tagging a causal variant) are associated with impaired beta cell function, directly or indirectly (<a class="bk_pop" href="#ch14.ref83">83</a>). Insulin secretion appears to be more heritable than insulin resistance (<a class="bk_pop" href="#ch14.ref39">39</a>,<a class="bk_pop" href="#ch14.ref84">84</a>), confirming the pathogenic hypothesis put forth by early geneticists, by which a mostly environmental insult causing insulin resistance is overlaid on a mostly genetic predisposition to beta cell dysfunction.</p><p><i>The genetic architecture of beta cell function and insulin action seem to differ</i> (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig3/?report=objectonly" target="object" rid-figpopup="figch14fig3" rid-ob="figobch14fig3">Figure 14.3</a>) (<a class="bk_pop" href="#ch14.ref39">39</a>). As mentioned, measures of estimating beta cell function in humans are more amenable to genetic approaches (i.e., they have a higher likelihood of yielding significant findings) than measures of insulin sensitivity (<a class="bk_pop" href="#ch14.ref39">39</a>,<a class="bk_pop" href="#ch14.ref84">84</a>). Incorporating adiposity as a modulator of insulin resistance (<a class="bk_pop" href="#ch14.ref43">43</a>) or focusing genetic investigation on more sophisticated and refined measures of insulin sensitivity (<a class="bk_pop" href="#ch14.ref45">45</a>) has yielded additional loci; nevertheless, the majority of type 2 diabetes-associated loci for which a physiologic mechanism has been determined seem to influence beta cell function (<a class="bk_pop" href="#ch14.ref35">35</a>,<a class="bk_pop" href="#ch14.ref83">83</a>). Insulin resistance might be influenced by fewer loci, less frequent variants or those with more modest effects, or a stronger environmental component.</p><div class="iconblock whole_rhythm clearfix ten_col fig" id="figch14fig2" co-legend-rid="figlgndch14fig2"><a href="/books/NBK567998/figure/ch14.fig2/?report=objectonly" target="object" title="FIGURE 14.2" class="img_link icnblk_img figpopup" rid-figpopup="figch14fig2" rid-ob="figobch14fig2"><img class="small-thumb" src="/books/NBK567998/bin/ch14f2.gif" src-large="/books/NBK567998/bin/ch14f2.jpg" alt="Scatter plot showing that genetic variants linked to type 2 diabetes risk more often affect beta cell function than insulin resistance" /></a><div class="icnblk_cntnt" id="figlgndch14fig2"><h4 id="ch14.fig2"><a href="/books/NBK567998/figure/ch14.fig2/?report=objectonly" target="object" rid-ob="figobch14fig2">FIGURE 14.2</a></h4><p class="float-caption no_bottom_margin">Two-Dimensional Plot of Type 2 Diabetes-Associated Loci Placed in Relationship to Beta Cell Function and Insulin Resistance. Fasting measures of beta cell function (HOMA-B, X axis) and insulin resistance (HOMA-IR, Y axis) were obtained by homeostasis <a href="/books/NBK567998/figure/ch14.fig2/?report=objectonly" target="object" rid-ob="figobch14fig2">(more...)</a></p></div></div><div class="iconblock whole_rhythm clearfix ten_col fig" id="figch14fig3" co-legend-rid="figlgndch14fig3"><a href="/books/NBK567998/figure/ch14.fig3/?report=objectonly" target="object" title="FIGURE 14.3" class="img_link icnblk_img figpopup" rid-figpopup="figch14fig3" rid-ob="figobch14fig3"><img class="small-thumb" src="/books/NBK567998/bin/ch14f3.gif" src-large="/books/NBK567998/bin/ch14f3.jpg" alt="Quantile-quantile graph showing that measures of beta cell function have a higher likelihood of yielding significant findings than measures of insulin sensitivity" /></a><div class="icnblk_cntnt" id="figlgndch14fig3"><h4 id="ch14.fig3"><a href="/books/NBK567998/figure/ch14.fig3/?report=objectonly" target="object" rid-ob="figobch14fig3">FIGURE 14.3</a></h4><p class="float-caption no_bottom_margin">Quantile-Quantile Plots for Genome-Wide Association Studies of Beta Cell Function and Insulin Resistance. Homeostasis model assessments of beta cell function (HOMA-B) and insulin resistance (HOMA-IR) were generated for the Meta-analyses of Glucose and <a href="/books/NBK567998/figure/ch14.fig3/?report=objectonly" target="object" rid-ob="figobch14fig3">(more...)</a></p></div></div><p><i>The genes that elevate fasting glucose in normal individuals are not necessarily the same genes that cause type 2 diabetes</i>. While a simple model would predict that any locus that raises fasting glucose should raise risk of type 2 diabetes, the exploration of genetic determinants of glucose homeostasis in nondiabetic individuals has yielded a number of variants that do both (i.e., raise fasting glucose and increase type 2 diabetes risk), but also a non-trivial number that raise fasting glucose without appreciably increasing risk of type 2 diabetes. This observation has led physiologists to consider not just the magnitude of the glucose increase, but the manner in which this happens, as relevant to the emergence of disease (<a class="bk_pop" href="#ch14.ref39">39</a>). A simple elevation of the glucose set point, for example, may not necessarily lead to hyperglycemia in the diabetes range, if beta cell function is otherwise intact; however, an alteration that leads to progressive beta cell deterioration would cause diabetes in the future.</p><p><i>Genetic studies support prior epidemiologic observations</i>. A GWAS for fasting glucose yielded significant associations near two circadian genes (<i>MTNR1B</i> and <i>CRY2</i>) (<a class="bk_pop" href="#ch14.ref39">39</a>). A growing literature implicates circadian dysregulation, through epidemiologic reports (<a class="bk_pop" href="#ch14.ref85">85</a>), animal studies (<a class="bk_pop" href="#ch14.ref86">86</a>,<a class="bk_pop" href="#ch14.ref87">87</a>), and human perturbation experiments (<a class="bk_pop" href="#ch14.ref88">88</a>), in metabolically deleterious phenotypes. Thus, a GWAS for glycemia provides a potential genetic link between the two systems. Similarly, a SNP in <i>ADCY5</i> has been associated with fasting glucose, type 2 diabetes, and low birth weight, once again corroborating the known relationship between being born small for gestational age and future risk of obesity and diabetes (<a class="bk_pop" href="#ch14.ref89">89</a>). In addition, SNPs in <i>FTO</i> and <i>MC4R</i> contribute to obesity, insulin resistance, and type 2 diabetes, thus connecting various components of the metabolic syndrome (<a class="bk_pop" href="#ch14.ref67">67</a>,<a class="bk_pop" href="#ch14.ref90">90</a>).</p><div class="iconblock whole_rhythm clearfix ten_col fig" id="figch14fig4" co-legend-rid="figlgndch14fig4"><a href="/books/NBK567998/figure/ch14.fig4/?report=objectonly" target="object" title="FIGURE 14.4" class="img_link icnblk_img figpopup" rid-figpopup="figch14fig4" rid-ob="figobch14fig4"><img class="small-thumb" src="/books/NBK567998/bin/ch14f4.gif" src-large="/books/NBK567998/bin/ch14f4.jpg" alt="Graph showing genetic risk scores developed in people of European descent are associated with fasting glucose and type 2 diabetes when applied to people of non-European descent" /></a><div class="icnblk_cntnt" id="figlgndch14fig4"><h4 id="ch14.fig4"><a href="/books/NBK567998/figure/ch14.fig4/?report=objectonly" target="object" rid-ob="figobch14fig4">FIGURE 14.4</a></h4><p class="float-caption no_bottom_margin">Loci Initially Identified in Populations of European Descent Appear to Have Similar Effects in Other Racial/Ethnic Groups. Genetic risk scores constructed from variants associated with fasting glucose by the Meta-analyses of Glucose and Insulin-related <a href="/books/NBK567998/figure/ch14.fig4/?report=objectonly" target="object" rid-ob="figobch14fig4">(more...)</a></p></div></div><p><i>Most common risk variants are shared across ethnic groups</i>. Although GWAS of comparable size and power have not yet been performed in non-European populations, when investigators have tried to ascertain whether common variants that influence these traits in people of European descent also do so in individuals of other continental ancestries, by and large, they have found similar effects; though, some loci do show heterogeneity. Once allele frequency differences and altered haplotype structures are taken into account, analogous patterns of association emerge in African American, Hispanic, Asian, or Native American populations (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig4/?report=objectonly" target="object" rid-figpopup="figch14fig4" rid-ob="figobch14fig4">Figure 14.4</a>) (<a class="bk_pop" href="#ch14.ref56">56</a>,<a class="bk_pop" href="#ch14.ref91">91</a>,<a class="bk_pop" href="#ch14.ref92">92</a>,<a class="bk_pop" href="#ch14.ref93">93</a>).</p><p><i>Genetic information does not add much beyond clinical variables for type 2 diabetes prediction</i>. Commonly ascertained clinical variables are fairly precise at capturing future risk of type 2 diabetes (<a class="bk_pop" href="#ch14.ref10">10</a>); thus, adding the set of common variants known to date (which only explains a minor fraction of the genetic predisposition to type 2 diabetes) does not seem to improve predictive accuracy at the individual level or the ability to discriminate between risk strata in a clinically meaningful way (<a class="bk_pop" href="#ch14.ref94">94</a>,<a class="bk_pop" href="#ch14.ref95">95</a>,<a class="bk_pop" href="#ch14.ref96">96</a>). Prediction is slightly improved in younger individuals, in whom clinical risk factors are not yet fully manifest.</p><p><i>An intensive lifestyle intervention is effective in people with the highest burden of known risk alleles</i>. The Diabetes Prevention Program showed that an intensive lifestyle intervention, consisting of dietary and physical activity components, is effective even in the quartile of participants who carry the highest load of known risk variants (<a class="figpopup" href="/books/NBK567998/figure/ch14.fig5/?report=objectonly" target="object" rid-figpopup="figch14fig5" rid-ob="figobch14fig5">Figure 14.5</a>) (<a class="bk_pop" href="#ch14.ref97">97</a>).</p><p><i>Genetic variation may affect drug response</i>. Genetic information may eventually be used to guide medication choices in type 2 diabetes. Though this is the standard of care for monogenic diabetes with examples in both MODY and neonatal diabetes (<a class="bk_pop" href="#ch14.ref98">98</a>,<a class="bk_pop" href="#ch14.ref99">99</a>), the potential of genetically guided therapy is yet to be realized in common type 2 diabetes. A polymorphism in a metformin transporter may affect glycemic response to metformin (<a class="bk_pop" href="#ch14.ref100">100</a>,<a class="bk_pop" href="#ch14.ref101">101</a>,<a class="bk_pop" href="#ch14.ref102">102</a>), and a GWAS for metformin response in people with type 2 diabetes has identified a polymorphism near the <i>ATM</i> gene that influences metformin response in several independent cohorts (<a class="bk_pop" href="#ch14.ref103">103</a>,<a class="bk_pop" href="#ch14.ref104">104</a>), although it does not seem to exert the same effect for diabetes prevention in people with prediabetes (<a class="bk_pop" href="#ch14.ref105">105</a>). Some sulfonylureas are metabolized by the cytochrome P450 enzyme CYP2C9, and patients with loss-of-function variants in this gene are at increased risk of sulfonylurea-related hypoglycemia (<a class="bk_pop" href="#ch14.ref106">106</a>). These early pharmacogenomic lessons suggest that genes relevant to drug response may be the same as those that increase risk for type 2 diabetes, or they may be different.</p><div class="iconblock whole_rhythm clearfix ten_col fig" id="figch14fig5" co-legend-rid="figlgndch14fig5"><a href="/books/NBK567998/figure/ch14.fig5/?report=objectonly" target="object" title="FIGURE 14.5" class="img_link icnblk_img figpopup" rid-figpopup="figch14fig5" rid-ob="figobch14fig5"><img class="small-thumb" src="/books/NBK567998/bin/ch14f5.gif" src-large="/books/NBK567998/bin/ch14f5.jpg" alt="Bar graph showing that an intensive lifestyle intervention reduced the incidence rate of diabetes consistently among people in each quartile of genetic risk score" /></a><div class="icnblk_cntnt" id="figlgndch14fig5"><h4 id="ch14.fig5"><a href="/books/NBK567998/figure/ch14.fig5/?report=objectonly" target="object" rid-ob="figobch14fig5">FIGURE 14.5</a></h4><p class="float-caption no_bottom_margin">An Intensive Lifestyle Intervention, as Deployed in the Diabetes Prevention Program, is Effective Regardless of Genetic Risk Score for Type 2 Diabetes. Diabetes Prevention Program participants were stratified by quartile of genetic risk constructed by <a href="/books/NBK567998/figure/ch14.fig5/?report=objectonly" target="object" rid-ob="figobch14fig5">(more...)</a></p></div></div></div><div id="ch14.s4"><h2 id="_ch14_s4_">Limitations of Current Approaches (and Their Solutions)</h2><p>Despite the overwhelming success of GWAS strategies in advancing knowledge of the genetic determinants of type 2 diabetes, a number of limitations must be recognized. These limitations have been identified by the research community and are being addressed to complement gaps in understanding.</p><p><i>GWAS were initially undertaken only in populations of European descent</i>. A large swath of genetic variation is unique to other populations, especially those of African descent, due to the bottleneck introduced when a limited subset of human ancestors migrated out of Africa. In addition, genetic variants have been identified that, though present in Europeans, are much more common in other populations, thereby acquiring greater statistical power to detect modest effects. As described above, GWAS efforts in East Asian (<a class="bk_pop" href="#ch14.ref46">46</a>,<a class="bk_pop" href="#ch14.ref47">47</a>,<a class="bk_pop" href="#ch14.ref48">48</a>,<a class="bk_pop" href="#ch14.ref49">49</a>,<a class="bk_pop" href="#ch14.ref50">50</a>,<a class="bk_pop" href="#ch14.ref55">55</a>,<a class="bk_pop" href="#ch14.ref57">57</a>,<a class="bk_pop" href="#ch14.ref60">60</a>,<a class="bk_pop" href="#ch14.ref62">62</a>), South Asian (<a class="bk_pop" href="#ch14.ref51">51</a>,<a class="bk_pop" href="#ch14.ref58">58</a>,<a class="bk_pop" href="#ch14.ref59">59</a>), African American (<a class="bk_pop" href="#ch14.ref54">54</a>,<a class="bk_pop" href="#ch14.ref63">63</a>), Hispanic (<a class="bk_pop" href="#ch14.ref52">52</a>,<a class="bk_pop" href="#ch14.ref53">53</a>,<a class="bk_pop" href="#ch14.ref61">61</a>), and Native American (<a class="bk_pop" href="#ch14.ref64">64</a>) cohorts have already yielded novel genome-wide significant findings, many of which are also seen in Europeans. As larger consortia and trans-ethnic meta-analyses are undertaken, more novel findings are expected to come to light.</p><p><i>GWAS of large cohorts suffer from relatively crude phenotyping</i>. Because sample size is paramount to achieve adequate statistical power, GWAS are typically carried out in very large population cohorts where only limited phenotyping is feasible. The estimates of beta cell function or insulin sensitivity derived from simple measures, such as fasting glucose or insulin (<a class="bk_pop" href="#ch14.ref107">107</a>) (as opposed to those obtained from more labor-intensive and costly dynamic testing), are relatively imprecise. However, some of the participating cohorts do have more sophisticated phenotyping: while the participants have contributed their simple traits to the meta-analytic efforts, the investigators are also able to form subconsortia where additional physiologic inquiry can be carried out. This type of analysis has been performed for proinsulin levels adjusted for fasting insulin (<a class="bk_pop" href="#ch14.ref42">42</a>), various dynamic measures of insulin secretion (<a class="bk_pop" href="#ch14.ref44">44</a>), and insulin sensitivity derived from clamp studies (<a class="bk_pop" href="#ch14.ref45">45</a>), and it is underway for insulin sensitivity derived from oral glucose tolerance tests (<a class="bk_pop" href="#ch14.ref108">108</a>) and insulin clearance (<a class="bk_pop" href="#ch14.ref109">109</a>,<a class="bk_pop" href="#ch14.ref110">110</a>).</p><p><i>GWAS only capture common variants</i>. Due to the composition of genotyping arrays and statistical issues around rare observations, most GWAS to date have concentrated solely on common variation (i.e., minor allele frequency &#x0003e;5%). The introduction of next-generation DNA sequencing technologies has allowed for a downward expansion in the characterization of shared uncommon variation, as sequencing &#x0003e;1,000 individuals from multiple ethnic groups in the 1000 Genomes Project (<a class="bk_pop" href="#ch14.ref71">71</a>) has produced a catalog of uncommon variants that can be captured by their correlation to previously known common variants. Imputation to these 1000 Genomes panels is expanding the subset of testable variants in extant meta-analyses. The pioneering whole-exome (<a class="bk_pop" href="#ch14.ref75">75</a>,<a class="bk_pop" href="#ch14.ref76">76</a>) and whole-genome (<a class="bk_pop" href="#ch14.ref77">77</a>) sequencing studies described above are beginning to yield fruit, particularly when expanded to larger multi-ethnic samples (<a class="bk_pop" href="#ch14.ref78">78</a>). Nevertheless, under a model that the genetic architecture of type 2 diabetes is composed of several thousand variants of modest effects (<a class="bk_pop" href="#ch14.ref66">66</a>,<a class="bk_pop" href="#ch14.ref81">81</a>), even sequencing experiments require sample sizes of upwards of 25,000 cases to detect true associations, as either effect size or allele frequency is limiting (<a class="bk_pop" href="#ch14.ref111">111</a>).</p><p><i>Most analyses have not taken into account interactions with the environment</i>. Type 2 diabetes results from complex interactions among multiple genetic and environmental factors, and the rapid rise of its prevalence cannot be attributed to genetics. Because most GWAS are designed to detect loci that have main effects on type 2 diabetes risk, regardless of the environmental context, many variants whose impact varies according to an environmental parameter might be missed. This is due in part to the imprecision inherent to environmental measures and the noise introduced by a single cross-sectional environmental exposure; in contrast, genotyping methods are extremely accurate, and the genetic exposure is uniform across an individual&#x02019;s lifetime. Nevertheless, analytical methods are being developed that allow for the joint inquiry of main gene effects and gene &#x000d7; environment interactions (<a class="bk_pop" href="#ch14.ref112">112</a>), and these methods have already been deployed to identify loci for insulin sensitivity (<a class="bk_pop" href="#ch14.ref43">43</a>) and for the regulation of body mass index (<a class="bk_pop" href="#ch14.ref113">113</a>).</p><p><i>Analyses of predictive properties have only included the SNPs meeting genome-wide significance</i>. Analytical methods have been developed that utilize information from the entire genome (<a class="bk_pop" href="#ch14.ref114">114</a>,<a class="bk_pop" href="#ch14.ref115">115</a>), and these methods may provide better predictive properties than models that use only the established SNPs that have met genome-wide significance. The clinical utility of such approaches remains to be determined.</p><p><i>Most analyses are simple additive tests for association and do not explore more complex modes of inheritance</i>. There are good statistical reasons to primarily base GWAS experiments on the additive genetic model, in which the presence of two copies of the risk allele in an individual essentially doubles the risk associated with a single allele. This may not always be the case: in the two extreme examples, under a dominant model, two copies of the risk allele will not add any further risk to that conferred by a single copy, and under a recessive model, the risk will not be made manifest unless both copies are present. While the majority of type 2 diabetes-associated SNPs have been found to exert their action via an additive model, this is expected as their discovery took place precisely under such a model. A comprehensive examination of alternative modes of inheritance is needed in the existing GWAS datasets, which now are reaching adequate sizes to compensate for the smaller number of homozygous minor allele carriers and for the penalty incurred by additional statistical testing. Similarly, tests of gene &#x000d7; gene interactions (epistasis) and accounting for divergent effects depending on parental line of inheritance, where that information is available (<a class="bk_pop" href="#ch14.ref116">116</a>), are likely to yield additional loci.</p><p><i>GWAS only identify correlated SNPs but do not yield the causal variant</i>. Because GWAS leverage the correlative structure of the human genome to identify associations, they simply point to regions of the genome in which certain alleles are overrepresented in cases versus controls. Identifying which of the SNPs present in that segment is the actual molecular cause of the disease phenotype requires fine-mapping and functional studies. Fine-mapping can be performed by sequencing and saturation genotyping of all variants in the associated segment, retesting them and their haplotypes (linear arrangements of SNPs in a chromosome) for association with the phenotype. While custom-made arrays have been designed to achieve this task in type 2 diabetes and related metabolic traits (<a class="bk_pop" href="#ch14.ref69">69</a>), the conditional statistical testing that is required to distinguish the strength of the association of one variant from another&#x02019;s is laborious and requires inordinate sample sizes. Oftentimes, functional experiments are needed to establish which of the equally associated variants is pathogenic; these efforts have succeeded in some cases, for instance, in identifying the p.Ser1369Ala polymorphism in the sulfonylurea receptor gene <i>ABCC8</i> as the likely culprit for gliclazide response, rather than its tightly linked nearby polymorphism p.Glu23Lys in <i>KCNJ11</i> (<a class="bk_pop" href="#ch14.ref117">117</a>).</p><p><i>The road from association to function is arduous</i>. As intimated in the previous point, a genetic association does not necessarily yield insights into molecular function. While this may be relatively easier to establish for coding missense variants that change amino acid sequence (because appropriate protein-based experiments can be designed), only a handful of type 2 diabetes associations fulfill this description (<i>KCNJ11</i> p.Glu23Lys/<i>ABCC8</i> p.Ser1369Ala, <i>SLC30A8</i> p.Arg235Trp, and <i>GCKR</i> p.Pro446Leu). For many other variants that fall in regulatory regions, a different sort of investigation must be designed (<a class="bk_pop" href="#ch14.ref118">118</a>). The publication of a comprehensive catalog of functional regulatory elements in the human genome and availability of public tissue expression data linked to genotype data should facilitate this task enormously (<a class="bk_pop" href="#ch14.ref79">79</a>,<a class="bk_pop" href="#ch14.ref119">119</a>). The ability to perform genome editing (e.g., with CRISPR/CAS9) in informative experimental systems, such as induced pluripotent stem cells, should also greatly expand the ability to investigate function.</p><p>A pioneering illustration of how such an effort can yield fruits was achieved for <i>TCF7L2</i>: fine-mapping had established <a href="/snp/?term=7903146" class="bk_tag" ref="pagearea=body&amp;targetsite=entrez&amp;targetcat=term&amp;targettype=snp">rs7903146</a> as the likely culprit SNP, and physiologic studies had placed its pathogenic activity squarely in the beta cell (<a class="bk_pop" href="#ch14.ref120">120</a>,<a class="bk_pop" href="#ch14.ref121">121</a>); an intelligent integration of these data with areas of open chromatin in beta cells, combined with expression experiments, led to the determination that <a href="/snp/?term=7903146" class="bk_tag" ref="pagearea=body&amp;targetsite=entrez&amp;targetcat=term&amp;targettype=snp">rs7903146</a> affects an enhancer element that regulates <i>TCF7L2</i> expression (<a class="bk_pop" href="#ch14.ref122">122</a>). In another example, <i>KLF14</i> had been associated with type 2 diabetes, but the risk allele appeared to cause insulin resistance (<a class="bk_pop" href="#ch14.ref35">35</a>); by combining the GWAS finding with expression datasets in adipose tissue, the associated SNP was discovered to influence expression of <i>KLF14</i>, which itself is a master regulator of adipose gene expression and thereby responsible for multiple metabolic phenotypes (<a class="bk_pop" href="#ch14.ref123">123</a>). Analogous experiments have clarified the role of SNPs around the glucose-6-phosphatase catalytic subunit 2 gene (<i>G6PC2</i>) (<a class="bk_pop" href="#ch14.ref124">124</a>). A fuller description of successful association-to-function efforts in type 2 diabetes is available elsewhere (<a class="bk_pop" href="#ch14.ref125">125</a>).</p></div><div id="ch14.s5"><h2 id="_ch14_s5_">The Future of Research on the Genetics of Type 2 Diabetes</h2><p>Given the rapid progress achieved in genetic discovery in type 2 diabetes and the multipronged approach deployed to overcome experimental limitations, there is great hope that the pace will be maintained and a substantial part of the genetic architecture of type 2 diabetes will be elucidated in the coming years. If this vision is realized, a number of conceptual advances can be expected.</p><p><i>The nosology of disease will be refined</i>. Type 2 diabetes, diagnosed solely on the basis of the final common pathway of hyperglycemia, is likely a heterogeneous syndrome that can be caused by a variety of processes (<a class="bk_pop" href="#ch14.ref126">126</a>). Genetic etiologies have already helped classify the various forms of MODY and neonatal diabetes, and an analogous exercise could take place in type 2 diabetes. The categorization of the disease into subtypes based on genetic determinants of physiology, prognosis, or predisposition to complications should help stratify the patient population into groups for which therapeutic or surveillance decisions might be better tailored.</p><p><i>Novel pathways will be identified</i>. Unsuspected biology is already being uncovered via genetic discovery. With a greater number of genetic loci at hand, pathways or systems (e.g., cell proliferation) can be identified, some of which may be amenable to the development of new therapeutics.</p><p><i>Genetic discovery may identify drug targets</i>. As mentioned above, among the initial type 2 diabetes genetic associations were coding variants for <i>PPARG</i>, the gene that encodes the target of thiazolidinediones (<a class="bk_pop" href="#ch14.ref22">22</a>), and <i>KCNJ11/ABCC8</i>, the genes that encode the targets for sulfonylureas (<a class="bk_pop" href="#ch14.ref23">23</a>,<a class="bk_pop" href="#ch14.ref127">127</a>). More recent studies have identified the target for glucagon-like peptide 1 receptor agonists as another type 2 diabetes-associated gene (<a class="bk_pop" href="#ch14.ref73">73</a>,<a class="bk_pop" href="#ch14.ref74">74</a>). This proof of principle for established type 2 diabetes drug targets indicates that among the many type 2 diabetes-associated loci, there might be other genes for which a suitable drug might be found or developed. Aggregating all of the genetic data in humans and model systems, as well as ancillary associations that might point to off-target effects, will be essential if the genomic revolution is to catalyze new drug discovery.</p><p><i>Stratification of patients may allow for better targeting of public health or clinical trial interventions</i>. Some preventive or therapeutic measures may be too expensive to deploy in the population at large, or they may be futile in specific subgroups. Genetic characterization may help identify the groups of people more likely to benefit from particular public health strategies. Similarly, the efficiency of clinical trials may be enhanced by enrolling participants who are more likely to reach the desired endpoints or benefit from the agents being tested.</p><p><i>Genetics may facilitate the implementation of precision or personalized medicine</i>. Though it is not yet clear that genetic information will be powerful enough to apply therapeutic decisions at the individual level, it may help do so for specific subgroups. For example, genetic approaches may unveil who is more likely to develop a particular diabetic complication. For such an approach to be feasible, researchers envision that in the not too distant future, any individual who joins a public or private health care system would be genotyped or sequenced for the full list of actionable genetic variants (e.g., those that modify risk of common diseases or response to available medications), such that his/her information is available in the electronic medical record. When the time comes to make specific screening or therapeutic decisions, genetic information filtered through appropriate decision support tools would automatically guide the practitioner into the course of action most appropriate to the person and situation at hand.</p></div><div id="ch14.s6"><h2 id="_ch14_s6_">Conclusion</h2><p>In sum, the genetics of type 2 diabetes is in a steep discovery curve. Progress has been uneven, however, with most efforts focused on common variants and populations of European descent. The rapid and continuing progress in genotyping and sequencing technologies, with a concomitant improvement in affordability, the growing understanding of the human genome, and the ongoing development of analytical tools and methods present an optimistic perspective on the future. Whether this newfound knowledge will translate into improved patient care depends on the ability to design and execute genetically based and outcomes-driven clinical trials.</p></div><div id="ch14.s7"><h2 id="_ch14_s7_">List of Abbreviations</h2><dl><dt id="ch14_abb_DL1_DI1">GWAS</dt><dd><p>genome-wide association study</p></dd><dt id="ch14_abb_DL1_DI2">MODY</dt><dd><p>maturity-onset diabetes of the young</p></dd><dt id="ch14_abb_DL1_DI3">PPARG</dt><dd><p>peroxisome proliferator-activated receptor gamma</p></dd><dt id="ch14_abb_DL1_DI4">SNP</dt><dd><p>single nucleotide polymorphism</p></dd><dt id="ch14_abb_DL1_DI5">TCF7L2</dt><dd><p>transcription factor 7-like 2</p></dd></dl></div><div id="ch14.rl.r1"><h2 id="_ch14_rl_r1_">References</h2><dl class="temp-labeled-list"><dt>1.</dt><dd><div class="bk_ref" id="ch14.ref1">Barnett
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42:139&#x02013;145, 1999 [<a href="https://pubmed.ncbi.nlm.nih.gov/10064092" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 10064092</span></a>]</div></dd><dt>6.</dt><dd><div class="bk_ref" id="ch14.ref6">Bennett
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CS, Franklin
CS, Ganser
M, Gieger
C, Grarup
N, Green
T, Griffin
S, Groves
CJ, Guiducci
C, Hadjadj
S, Hassanali
N, Herder
C, Isomaa
B, Jackson
AU, Johnson
PR, Jorgensen
T, Kao
WH, Klopp
N, Kong
A, Kraft
P, Kuusisto
J, Lauritzen
T, Li
M, Lieverse
A, Lindgren
CM, Lyssenko
V, Marre
M, Meitinger
T, Midthjell
K, Morken
MA, Narisu
N, Nilsson
P, Owen
KR, Payne
F, Perry
JR, Petersen
AK, Platou
C, Proenca
C, Prokopenko
I, Rathmann
W, Rayner
NW, Robertson
NR, Rocheleau
G, Roden
M, Sampson
MJ, Saxena
R, Shields
BM, Shrader
P, Sigurdsson
G, Sparso
T, Strassburger
K, Stringham
HM, Sun
Q, Swift
AJ, Thorand
B, Tichet
J, Tuomi
T, van Dam
RM, van Haeften
TW, van Herpt
T, van Vliet-Ostaptchouk
JV, Walters
GB, Weedon
MN, Wijmenga
C, Witteman
J, Bergman
RN, Cauchi
S, Collins
FS, Gloyn
AL, Gyllensten
U, Hansen
T, Hide
WA, Hitman
GA, Hofman
A, Hunter
DJ, Hveem
K, Laakso
M, Mohlke
KL, Morris
AD, Palmer
CN, Pramstaller
PP, Rudan
I, Sijbrands
E, Stein
LD, Tuomilehto
J, Uitterlinden
A, Walker
M, Wareham
NJ, Watanabe
RM, Abecasis
GR, Boehm
BO, Campbell
H, Daly
MJ, Hattersley
AT, Hu
FB, Meigs
JB, Pankow
JS, Pedersen
O, Wichmann
HE, Barroso
I, Florez
JC, Frayling
TM, Groop
L, Sladek
R, Thorsteinsdottir
U, Wilson
JF, Illig
T, Froguel
P, van Duijn
CM, Stefansson
K, Altshuler
D, Boehnke
M, McCarthy
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MI: Genomics, type 2 diabetes, and obesity. <em>N Engl J Med</em>
363:2339&#x02013;2350, 2010 [<a href="https://pubmed.ncbi.nlm.nih.gov/21142536" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 21142536</span></a>]</div></dd><dt>37.</dt><dd><div class="bk_ref" id="ch14.ref37">Mohlke
KL, Boehnke
M: Recent advances in understanding the genetic architecture of type 2 diabetes. <em>Human Mol Genet</em>
24:R85&#x02013;R92, 2015 [<a href="/pmc/articles/PMC4572004/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC4572004</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/26160912" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 26160912</span></a>]</div></dd><dt>38.</dt><dd><div class="bk_ref" id="ch14.ref38">Prokopenko
I, Langenberg
C, Florez
JC, Saxena
R, Soranzo
N, Thorleifsson
G, Loos
RJ, Manning
AK, Jackson
AU, Aulchenko
Y, Potter
SC, Erdos
MR, Sanna
S, Hottenga
JJ, Wheeler
E, Kaakinen
M, Lyssenko
V, Chen
WM, Ahmadi
K, Beckmann
JS, Bergman
RN, Bochud
M, Bonnycastle
LL, Buchanan
TA, Cao
A, Cervino
A, Coin
L, Collins
FS, Crisponi
L, de Geus
EJ, Dehghan
A, Deloukas
P, Doney
AS, Elliott
P, Freimer
N, Gateva
V, Herder
C, Hofman
A, Hughes
TE, Hunt
S, Illig
T, Inouye
M, Isomaa
B, Johnson
T, Kong
A, Krestyaninova
M, Kuusisto
J, Laakso
M, Lim
N, Lindblad
U, Lindgren
CM, McCann
OT, Mohlke
KL, Morris
AD, Naitza
S, Orru
M, Palmer
CN, Pouta
A, Randall
J, Rathmann
W, Saramies
J, Scheet
P, Scott
LJ, Scuteri
A, Sharp
S, Sijbrands
E, Smit
JH, Song
K, Steinthorsdottir
V, Stringham
HM, Tuomi
T, Tuomilehto
J, Uitterlinden
AG, Voight
BF, Waterworth
D, Wichmann
HE, Willemsen
G, Witteman
JC, Yuan
X, Zhao
JH, Zeggini
E, Schlessinger
D, Sandhu
M, Boomsma
DI, Uda
M, Spector
TD, Penninx
BW, Altshuler
D, Vollenweider
P, Jarvelin
MR, Lakatta
E, Waeber
G, Fox
CS, Peltonen
L, Groop
LC, Mooser
V, Cupples
LA, Thorsteinsdottir
U, Boehnke
M, Barroso
I, Van Duijn
C, Dupuis
J, Watanabe
RM, Stefansson
K, McCarthy
MI, Wareham
NJ, Meigs
JB, Abecasis
GR: Variants in MTNR1B influence fasting glucose levels. <em>Nat Genet</em>
41:77&#x02013;81, 2009 [<a href="/pmc/articles/PMC2682768/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC2682768</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/19060907" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 19060907</span></a>]</div></dd><dt>39.</dt><dd><div class="bk_ref" id="ch14.ref39">Dupuis
J, Langenberg
C, Prokopenko
I, Saxena
R, Soranzo
N, Jackson
AU, Wheeler
E, Glazer
NL, Bouatia-Naji
N, Gloyn
AL, Lindgren
CM, Magi
R, Morris
AP, Randall
J, Johnson
T, Elliott
P, Rybin
D, Thorleifsson
G, Steinthorsdottir
V, Henneman
P, Grallert
H, Dehghan
A, Hottenga
JJ, Franklin
CS, Navarro
P, Song
K, Goel
A, Perry
JR, Egan
JM, Lajunen
T, Grarup
N, Sparso
T, Doney
A, Voight
BF, Stringham
HM, Li
M, Kanoni
S, Shrader
P, Cavalcanti-Proenca
C, Kumari
M, Qi
L, Timpson
NJ, Gieger
C, Zabena
C, Rocheleau
G, Ingelsson
E, An
P, O&#x02019;Connell
J, Luan
J, Elliott
A, McCarroll
SA, Payne
F, Roccasecca
RM, Pattou
F, Sethupathy
P, Ardlie
K, Ariyurek
Y, Balkau
B, Barter
P, Beilby
JP, Ben-Shlomo
Y, Benediktsson
R, Bennett
AJ, Bergmann
S, Bochud
M, Boerwinkle
E, Bonnefond
A, Bonnycastle
LL, Borch-Johnsen
K, Bottcher
Y, Brunner
E, Bumpstead
SJ, Charpentier
G, Chen
YD, Chines
P, Clarke
R, Coin
LJ, Cooper
MN, Cornelis
M, Crawford
G, Crisponi
L, Day
IN, de Geus
EJ, Delplanque
J, Dina
C, Erdos
MR, Fedson
AC, Fischer-Rosinsky
A, Forouhi
NG, Fox
CS, Frants
R, Franzosi
MG, Galan
P, Goodarzi
MO, Graessler
J, Groves
CJ, Grundy
S, Gwilliam
R, Gyllensten
U, Hadjadj
S, Hallmans
G, Hammond
N, Han
X, Hartikainen
AL, Hassanali
N, Hayward
C, Heath
SC, Hercberg
S, Herder
C, Hicks
AA, Hillman
DR, Hingorani
AD, Hofman
A, Hui
J, Hung
J, Isomaa
B, Johnson
PR, Jorgensen
T, Jula
A, Kaakinen
M, Kaprio
J, Kesaniemi
YA, Kivimaki
M, Knight
B, Koskinen
S, Kovacs
P, Kyvik
KO, Lathrop
GM, Lawlor
DA, Le Bacquer
O, Lecoeur
C, Li
Y, Lyssenko
V, Mahley
R, Mangino
M, Manning
AK, Martinez-Larrad
MT, McAteer
JB, McCulloch
LJ, McPherson
R, Meisinger
C, Melzer
D, Meyre
D, Mitchell
BD, Morken
MA, Mukherjee
S, Naitza
S, Narisu
N, Neville
MJ, Oostra
BA, Orru
M, Pakyz
R, Palmer
CN, Paolisso
G, Pattaro
C, Pearson
D, Peden
JF, Pedersen
NL, Perola
M, Pfeiffer
AF, Pichler
I, Polasek
O, Posthuma
D, Potter
SC, Pouta
A, Province
MA, Psaty
BM, Rathmann
W, Rayner
NW, Rice
K, Ripatti
S, Rivadeneira
F, Roden
M, Rolandsson
O, Sandbaek
A, Sandhu
M, Sanna
S, Sayer
AA, Scheet
P, Scott
LJ, Seedorf
U, Sharp
SJ, Shields
B, Sigurethsson
G, Sijbrands
EJ, Silveira
A, Simpson
L, Singleton
A, Smith
NL, Sovio
U, Swift
A, Syddall
H, Syvanen
AC, Tanaka
T, Thorand
B, Tichet
J, Tonjes
A, Tuomi
T, Uitterlinden
AG, van Dijk
KW, van Hoek
M, Varma
D, Visvikis-Siest
S, Vitart
V, Vogelzangs
N, Waeber
G, Wagner
PJ, Walley
A, Walters
GB, Ward
KL, Watkins
H, Weedon
MN, Wild
SH, Willemsen
G, Witteman
JC, Yarnell
JW, Zeggini
E, Zelenika
D, Zethelius
B, Zhai
G, Zhao
JH, Zillikens
MC; DIAGRAM Consortium; GIANT Consortium; Global BPgen Consortium, Borecki
IB, Loos
RJ, Meneton
P, Magnusson
PK, Nathan
DM, Williams
GH, Hattersley
AT, Silander
K, Salomaa
V, Smith
GD, Bornstein
SR, Schwarz
P, Spranger
J, Karpe
F, Shuldiner
AR, Cooper
C, Dedoussis
GV, Serrano-Rios
M, Morris
AD, Lind
L, Palmer
LJ, Hu
FB, Franks
PW, Ebrahim
S, Marmot
M, Kao
WH, Pankow
JS, Sampson
MJ, Kuusisto
J, Laakso
M, Hansen
T, Pedersen
O, Pramstaller
PP, Wichmann
HE, Illig
T, Rudan
I, Wright
AF, Stumvoll
M, Campbell
H, Wilson
JF; Anders Hamsten on behalf of Procardis Consortium; MAGIC Investigators, Bergman
RN, Buchanan
TA, Collins
FS, Mohlke
KL, Tuomilehto
J, Valle
TT, Altshuler
D, Rotter
JI, Siscovick
DS, Penninx
BW, Boomsma
DI, Deloukas
P, Spector
TD, Frayling
TM, Ferrucci
L, Kong
A, Thorsteinsdottir
U, Stefansson
K, van Duijn
CM, Aulchenko
YS, Cao
A, Scuteri
A, Schlessinger
D, Uda
M, Ruokonen
A, Jarvelin
MR, Waterworth
DM, Vollenweider
P, Peltonen
L, Mooser
V, Abecasis
GR, Wareham
NJ, Sladek
R, Froguel
P, Watanabe
RM, Meigs
JB, Groop
L, Boehnke
M, McCarthy
MI, Florez
JC, Barroso
I: New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. <em>Nat Genet</em>
42:105&#x02013;116, 2010 [<a href="/pmc/articles/PMC3018764/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC3018764</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/20081858" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 20081858</span></a>]</div></dd><dt>40.</dt><dd><div class="bk_ref" id="ch14.ref40">Saxena
R, Hivert
MF, Langenberg
C, Tanaka
T, Pankow
JS, Vollenweider
P, Lyssenko
V, Bouatia-Naji
N, Dupuis
J, Jackson
AU, Kao
WH, Li
M, Glazer
NL, Manning
AK, Luan
J, Stringham
HM, Prokopenko
I, Johnson
T, Grarup
N, Boesgaard
TW, Lecoeur
C, Shrader
P, O&#x02019;Connell
J, Ingelsson
E, Couper
DJ, Rice
K, Song
K, Andreasen
CH, Dina
C, Kottgen
A, Le Bacquer
O, Pattou
F, Taneera
J, Steinthorsdottir
V, Rybin
D, Ardlie
K, Sampson
M, Qi
L, van Hoek
M, Weedon
MN, Aulchenko
YS, Voight
BF, Grallert
H, Balkau
B, Bergman
RN, Bielinski
SJ, Bonnefond
A, Bonnycastle
LL, Borch-Johnsen
K, Bottcher
Y, Brunner
E, Buchanan
TA, Bumpstead
SJ, Cavalcanti-Proenca
C, Charpentier
G, Chen
YD, Chines
PS, Collins
FS, Cornelis
M, Crawford
GJ, Delplanque
J, Doney
A, Egan
JM, Erdos
MR, Firmann
M, Forouhi
NG, Fox
CS, Goodarzi
MO, Graessler
J, Hingorani
A, Isomaa
B, Jorgensen
T, Kivimaki
M, Kovacs
P, Krohn
K, Kumari
M, Lauritzen
T, Levy-Marchal
C, Mayor
V, McAteer
JB, Meyre
D, Mitchell
BD, Mohlke
KL, Morken
MA, Narisu
N, Palmer
CN, Pakyz
R, Pascoe
L, Payne
F, Pearson
D, Rathmann
W, Sandbaek
A, Sayer
AA, Scott
LJ, Sharp
SJ, Sijbrands
E, Singleton
A, Siscovick
DS, Smith
NL, Sparso
T, Swift
AJ, Syddall
H, Thorleifsson
G, Tonjes
A, Tuomi
T, Tuomilehto
J, Valle
TT, Waeber
G, Walley
A, Waterworth
DM, Zeggini
E, Zhao
JH; GIANT Consortium; MAGIC Investigators, Illig
T, Wichmann
HE, Wilson
JF, van Duijn
C, Hu
FB, Morris
AD, Frayling
TM, Hattersley
AT, Thorsteinsdottir
U, Stefansson
K, Nilsson
P, Syvanen
AC, Shuldiner
AR, Walker
M, Bornstein
SR, Schwarz
P, Williams
GH, Nathan
DM, Kuusisto
J, Laakso
M, Cooper
C, Marmot
M, Ferrucci
L, Mooser
V, Stumvoll
M, Loos
RJ, Altshuler
D, Psaty
BM, Rotter
JI, Boerwinkle
E, Hansen
T, Pedersen
O, Florez
JC, McCarthy
MI, Boehnke
M, Barroso
I, Sladek
R, Froguel
P, Meigs
JB, Groop
L, Wareham
NJ, Watanabe
RM: Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. <em>Nat Genet</em>
42:142&#x02013;148, 2010 [<a href="/pmc/articles/PMC2922003/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC2922003</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/20081857" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 20081857</span></a>]</div></dd><dt>41.</dt><dd><div class="bk_ref" id="ch14.ref41">Soranzo
N, Sanna
S, Wheeler
E, Gieger
C, Radke
D, Dupuis
J, Bouatia-Naji
N, Langenberg
C, Prokopenko
I, Stolerman
E, Sandhu
MS, Heeney
MM, Devaney
JM, Reilly
MP, Ricketts
SL, Stewart
AF, Voight
BF, Willenborg
C, Wright
B, Altshuler
D, Arking
D, Balkau
B, Barnes
D, Boerwinkle
E, Bohm
B, Bonnefond
A, Bonnycastle
LL, Boomsma
DI, Bornstein
SR, Bottcher
Y, Bumpstead
S, Burnett-Miller
MS, Campbell
H, Cao
A, Chambers
J, Clark
R, Collins
FS, Coresh
J, de Geus
EJ, Dei
M, Deloukas
P, Doring
A, Egan
JM, Elosua
R, Ferrucci
L, Forouhi
N, Fox
CS, Franklin
C, Franzosi
MG, Gallina
S, Goel
A, Graessler
J, Grallert
H, Greinacher
A, Hadley
D, Hall
A, Hamsten
A, Hayward
C, Heath
S, Herder
C, Homuth
G, Hottenga
JJ, Hunter-Merrill
R, Illig
T, Jackson
AU, Jula
A, Kleber
M, Knouff
CW, Kong
A, Kooner
J, Kottgen
A, Kovacs
P, Krohn
K, Kuhnel
B, Kuusisto
J, Laakso
M, Lathrop
M, Lecoeur
C, Li
M, Loos
RJ, Luan
J, Lyssenko
V, Magi
R, Magnusson
PK, Malarstig
A, Mangino
M, Martinez-Larrad
MT, Marz
W, McArdle
WL, McPherson
R, Meisinger
C, Meitinger
T, Melander
O, Mohlke
KL, Mooser
VE, Morken
MA, Narisu
N, Nathan
DM, Nauck
M, O&#x02019;Donnell
C, Oexle
K, Olla
N, Pankow
JS, Payne
F, Peden
JF, Pedersen
NL, Peltonen
L, Perola
M, Polasek
O, Porcu
E, Rader
DJ, Rathmann
W, Ripatti
S, Rocheleau
G, Roden
M, Rudan
I, Salomaa
V, Saxena
R, Schlessinger
D, Schunkert
H, Schwarz
P, Seedorf
U, Selvin
E, Serrano-Rios
M, Shrader
P, Silveira
A, Siscovick
D, Song
K, Spector
TD, Stefansson
K, Steinthorsdottir
V, Strachan
DP, Strawbridge
R, Stumvoll
M, Surakka
I, Swift
AJ, Tanaka
T, Teumer
A, Thorleifsson
G, Thorsteinsdottir
U, Tonjes
A, Usala
G, Vitart
V, Volzke
H, Wallaschofski
H, Waterworth
DM, Watkins
H, Wichmann
HE, Wild
SH, Willemsen
G, Williams
GH, Wilson
JF, Winkelmann
J, Wright
AF; WTCCC, Zabena
C, Zhao
JH, Epstein
SE, Erdmann
J, Hakonarson
HH, Kathiresan
S, Khaw
KT, Roberts
R, Samani
NJ, Fleming
MD, Sladek
R, Abecasis
G, Boehnke
M, Froguel
P, Groop
L, McCarthy
MI, Kao
WH, Florez
JC, Uda
M, Wareham
NJ, Barroso
I, Meigs
JB: Common variants at 10 genomic loci influence hemoglobin A1(C) levels via glycemic and nonglycemic pathways. <em>Diabetes</em>
59:3229&#x02013;3239, 2010 [<a href="/pmc/articles/PMC2992787/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC2992787</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/20858683" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 20858683</span></a>]</div></dd><dt>42.</dt><dd><div class="bk_ref" id="ch14.ref42">Strawbridge
RJ, Dupuis
J, Prokopenko
I, Barker
A, Ahlqvist
E, Rybin
D, Petrie
JR, Travers
ME, Bouatia-Naji
N, Dimas
AS, Nica
A, Wheeler
E, Chen
H, Voight
BF, Taneera
J, Kanoni
S, Peden
JF, Turrini
F, Gustafsson
S, Zabena
C, Almgren
P, Barker
DJ, Barnes
D, Dennison
EM, Eriksson
JG, Eriksson
P, Eury
E, Folkersen
L, Fox
CS, Frayling
TM, Goel
A, Gu
HF, Horikoshi
M, Isomaa
B, Jackson
AU, Jameson
KA, Kajantie
E, Kerr-Conte
J, Kuulasmaa
T, Kuusisto
J, Loos
RJ, Luan
J, Makrilakis
K, Manning
AK, Martinez-Larrad
MT, Narisu
N, Nastase Mannila
M, Ohrvik
J, Osmond
C, Pascoe
L, Payne
F, Sayer
AA, Sennblad
B, Silveira
A, Stancakova
A, Stirrups
K, Swift
AJ, Syvanen
AC, Tuomi
T, van &#x02019;t Hooft
FM, Walker
M, Weedon
MN, Xie
W, Zethelius
B; DIAGRAM Consortium; GIANT Consortium; MuTHER Consortium; CARDIoGRAM Consortium; C4D Consortium, Ongen
H, Malarstig
A, Hopewell
JC, Saleheen
D, Chambers
J, Parish
S, Danesh
J, Kooner
J, Ostenson
CG, Lind
L, Cooper
CC, Serrano-Rios
M, Ferrannini
E, Forsen
TJ, Clarke
R, Franzosi
MG, Seedorf
U, Watkins
H, Froguel
P, Johnson
P, Deloukas
P, Collins
FS, Laakso
M, Dermitzakis
ET, Boehnke
M, McCarthy
MI, Wareham
NJ, Groop
L, Pattou
F, Gloyn
AL, Dedoussis
GV, Lyssenko
V, Meigs
JB, Barroso
I, Watanabe
RM, Ingelsson
E, Langenberg
C, Hamsten
A, Florez
JC: Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. <em>Diabetes</em>
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AK, Hivert
MF, Scott
RA, Grimsby
JL, Bouatia-Naji
N, Chen
H, Rybin
D, Liu
CT, Bielak
LF, Prokopenko
I, Amin
N, Barnes
D, Cadby
G, Hottenga
JJ, Ingelsson
E, Jackson
AU, Johnson
T, Kanoni
S, Ladenvall
C, Lagou
V, Lahti
J, Lecoeur
C, Liu
Y, Martinez-Larrad
MT, Montasser
ME, Navarro
P, Perry
JR, Rasmussen-Torvik
LJ, Salo
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L, Venkatesh
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K, Tam
CH, Cai
Q, Zhao
Q, Jee
S, Takeuchi
F, Go
MJ, Ong
RT, Ohkubo
T, Kim
YJ, Zhang
R, Yamauchi
T, So
WY, Long
J, Gu
D, Lee
NR, Kim
S, Katsuya
T, Oh
JH, Liu
J, Umemura
S, Kim
YJ, Jiang
F, Maeda
S, Chan
JC, Lu
W, Hixson
JE, Adair
LS, Jung
KJ, Nabika
T, Bae
JB, Lee
MH, Seielstad
M, Young
TL, Teo
YY, Kita
Y, Takashima
N, Osawa
H, Lee
SH, Shin
MH, Shin
DH, Choi
BY, Shi
J, Gao
YT, Xiang
YB, Zheng
W, Kato
N, Yoon
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JC, Borch-Johnsen
K, Hartikainen
AL, Ruokonen
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B, Tauber
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41:89&#x02013;94, 2009 [<a href="https://pubmed.ncbi.nlm.nih.gov/19060909" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 19060909</span></a>]</div></dd><dt>142.</dt><dd><div class="bk_ref" id="ch14.ref142">Palmer
ND, Goodarzi
MO, Langefeld
CD, Wang
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KD, Fingerlin
TE, Norris
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TA, Xiang
AH, Haritunians
T, Ziegler
JT, Williams
AH, Stefanovski
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AW, Henkin
LF, Bergman
RN, Gao
X, Gauderman
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R, Hanis
CL, Cox
NJ, Highland
HM, Below
JE, Williams
AL, Burtt
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CA, Huerta-Chagoya
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64:1853&#x02013;1866, 2015 [<a href="/pmc/articles/PMC4407862/" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pmc">PMC free article<span class="bk_prnt">: PMC4407862</span></a>] [<a href="https://pubmed.ncbi.nlm.nih.gov/25524916" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 25524916</span></a>]</div></dd><dt>143.</dt><dd><div class="bk_ref" id="ch14.ref143">Go
MJ, Hwang
JY, Kim
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BG, Cho
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YS, Lee
JY: New susceptibility loci in MYL2, C12orf51 and OAS1 associated with 1-h plasma glucose as predisposing risk factors for type 2 diabetes in the Korean population. <em>J Hum Genet</em>
58:362&#x02013;365, 2013 [<a href="https://pubmed.ncbi.nlm.nih.gov/23575436" ref="pagearea=cite-ref&amp;targetsite=entrez&amp;targetcat=link&amp;targettype=pubmed">PubMed<span class="bk_prnt">: 23575436</span></a>]</div></dd></dl></div><div><dl class="temp-labeled-list small"><dt></dt><dd><div id="ch14.fn1"><p class="no_top_margin"><b>DUALITY OF INTEREST</b></p><p>Drs. Florez, Udler, and Hanson reported no conflicts of interest.</p></div></dd><dt></dt><dd><div><p class="no_top_margin"><b>ACKNOWLEDGMENTS/FUNDING</b> Dr. Florez was supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (DK072041, DK088214.A1, DK105554, DK105154) and the National Institute of General Medical Sciences (GM117163) and has received consulting honoraria from Merck and Boehringer-Ingelheim. Dr. Udler was supported by a training grant from the National Institute of Diabetes and Digestive and Kidney Diseases (DK007028). Dr. Hanson was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases.</p></div></dd></dl></div><div id="bk_toc_contnr"></div></div></div>
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<div xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"></div><div class="portlet"><div class="portlet_head"><div class="portlet_title"><h3><span>Views</span></h3></div><a name="Shutter" sid="1" href="#" class="portlet_shutter" title="Show/hide content" remembercollapsed="true" pgsec_name="PDF_download" id="Shutter"></a></div><div class="portlet_content"><ul xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="simple-list"><li><a href="/books/NBK567998/?report=reader">PubReader</a></li><li><a href="/books/NBK567998/?report=printable">Print View</a></li><li><a data-jig="ncbidialog" href="#_ncbi_dlg_citbx_NBK567998" data-jigconfig="width:400,modal:true">Cite this Page</a><div id="_ncbi_dlg_citbx_NBK567998" style="display:none" title="Cite this Page"><div class="bk_tt">Florez JC, Udler MS, Hanson RL. Genetics of Type 2 Diabetes. In: Cowie CC, Casagrande SS, Menke A, et al., editors. Diabetes in America. 3rd edition. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018 Aug. CHAPTER 14.<span class="bk_cite_avail"></span></div></div></li><li><a href="/books/NBK567998/pdf/Bookshelf_NBK567998.pdf">PDF version of this page</a> (1.2M)</li></ul></div></div><div class="portlet"><div class="portlet_head"><div class="portlet_title"><h3><span>In this Page</span></h3></div><a name="Shutter" sid="1" href="#" class="portlet_shutter" title="Show/hide content" remembercollapsed="true" pgsec_name="page-toc" id="Shutter"></a></div><div class="portlet_content"><ul xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="simple-list"><li><a href="#ch14.sum" ref="log$=inpage&amp;link_id=inpage">Summary</a></li><li><a href="#ch14.s1" ref="log$=inpage&amp;link_id=inpage">Type 2 Diabetes as a Genetic Disease</a></li><li><a href="#ch14.s2" ref="log$=inpage&amp;link_id=inpage">Discovery of Type 2 Diabetes Genes</a></li><li><a href="#ch14.s3" ref="log$=inpage&amp;link_id=inpage">Insights Gained</a></li><li><a href="#ch14.s4" ref="log$=inpage&amp;link_id=inpage">Limitations of Current Approaches (and Their Solutions)</a></li><li><a href="#ch14.s5" ref="log$=inpage&amp;link_id=inpage">The Future of Research on the Genetics of Type 2 Diabetes</a></li><li><a href="#ch14.s6" ref="log$=inpage&amp;link_id=inpage">Conclusion</a></li><li><a href="#ch14.s7" ref="log$=inpage&amp;link_id=inpage">List of Abbreviations</a></li><li><a href="#ch14.rl.r1" ref="log$=inpage&amp;link_id=inpage">References</a></li></ul></div></div><div class="portlet"><div class="portlet_head"><div class="portlet_title"><h3><span>Similar articles in PubMed</span></h3></div><a name="Shutter" sid="1" href="#" class="portlet_shutter" title="Show/hide content" remembercollapsed="true" pgsec_name="PBooksDiscovery_RA" id="Shutter"></a></div><div class="portlet_content"><ul><li class="brieflinkpopper two_line"><a class="brieflinkpopperctrl" href="/pubmed/38117926" ref="ordinalpos=1&amp;linkpos=1&amp;log$=relatedreviews&amp;logdbfrom=pubmed"><span xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="invert">Review</span> Genetics of Type 2 Diabetes.</a><span class="source">[Diabetes in America. 2023]</span><div class="brieflinkpop offscreen_noflow"><span xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="invert">Review</span> Genetics of Type 2 Diabetes.<div class="brieflinkpopdesc"><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="author">Kreienkamp RJ, Voight BF, Gloyn AL, Udler MS. </em><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="cit">Diabetes in America. 2023</em></div></div></li><li class="brieflinkpopper two_line"><a class="brieflinkpopperctrl" href="/pubmed/21091714" ref="ordinalpos=1&amp;linkpos=2&amp;log$=relatedreviews&amp;logdbfrom=pubmed"><span xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="invert">Review</span> The genetics of type 2 diabetes: what have we learned from GWAS?</a><span class="source">[Ann N Y Acad Sci. 2010]</span><div class="brieflinkpop offscreen_noflow"><span xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="invert">Review</span> The genetics of type 2 diabetes: what have we learned from GWAS?<div class="brieflinkpopdesc"><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="author">Billings LK, Florez JC. </em><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="cit">Ann N Y Acad Sci. 2010 Nov; 1212:59-77. </em></div></div></li><li class="brieflinkpopper two_line"><a class="brieflinkpopperctrl" href="/pubmed/23592221" ref="ordinalpos=1&amp;linkpos=3&amp;log$=relatedarticles&amp;logdbfrom=pubmed">Shared genetic factors for age at natural menopause in Iranian and European women.</a><span class="source">[Hum Reprod. 2013]</span><div class="brieflinkpop offscreen_noflow">Shared genetic factors for age at natural menopause in Iranian and European women.<div class="brieflinkpopdesc"><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="author">Rahmani M, Earp MA, Ramezani Tehrani F, Ataee M, Wu J, Treml M, Nudischer R, P-Behnami S, ReproGen Consortium, Perry JR, et al. </em><em xmlns:np="http://ncbi.gov/portal/XSLT/namespace" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="cit">Hum Reprod. 2013 Jul; 28(7):1987-94. 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