Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases

Perry, John; Voight, Benjamin F.; Yengo, Loïc; Amin, Najaf; Dupuis, Josée; Ganser, Martha; Grallert, Harald; Navarro, Pau; Li, Man; Qi, Lü; Steinthórsdóttir, Valgerður; Scott, Robert A.; Almgren, Peter; Arking, Dan E.; Aulchenko, Yurii S.; Balkau, Beverley; Benediktsson, Rafn; Bergman, Richard N.; Boerwinkle, Eric; Bonnycastle, Lori L.; Burtt, Noél P.; Campbell, Harry; Charpentier, G.; Collins, Francis S.; Gieger, Christian; Green, Todd; Hadjadj, Samy; Hattersley, Andrew T.; Herder, Christian; Hofman, Albert; Johnson, Andrew D.; Köttgen, Anna; Kraft, Peter; Labrune, Yann; Langenberg, Claudia; Manning, Alisa K.; Mohlke, Karen L.; Morris, Andrew P.; Oostra, Ben A.; Pankow, James S.; Petersen, Ann Kristin; Pramstaller, Peter P.; Prokopenko, Inga; Rathmann, Wolfgang; Rayner, W; Roden, Michael; Rudan, Igor; Rybin, Denis; Scott, Laura J.; Sigurðsson, Gunnar; Sladek, Robert; Þorleifsson, Guðmar; Þorsteinsdóttir, Unnur; Tuomilehto, Jaakko; Uitterlinden, André G.; Vivequin, Sidonie; Weedon, Michael N.; Wright, Alan F.; Hu, Frank B.; Illig, Thomas; Kao, Linda; Meigs, James B.; Wilson, James F.; Stefánsson, Kári; Duijn, Cornelia M. van; Altschuler, David M.; Morris, Andrew D.; Boehnke, Michael; McCarthy, Mark I.; Froguel, Philippe; Palmer, Colin N.A.; Wareham, Nicholas J.; Groop, Leif; Frayling, Timothy M.; Cauchi, Stéphane

doi:10.1371/journal.pgen.1002741

Cited by 195 publications

(147 citation statements)

References 49 publications

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“…A third possible reason is genetics. Some single nucleotide polymorphism (SNP) variants are more strongly associated with type 2 diabetes in lean compared with obese subsets [65]. Among such SNPs, variants at TCF7L2 and CDKAL1 have also been associated with increased risk of cancer in people with diabetes [66], potentially explaining the higher mortality risk for low BMI values.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear association of BMI with all-cause and cardiovascular mortality in type 2 diabetes mellitus: a systematic review and meta-analysis of 414,587 participants in prospective studies

et al. 2016

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Aims/hypothesis The relationship between BMI and mortality has been extensively investigated in the general population; however, it is less clear in people with type 2 diabetes. We aimed to assess the association of BMI with all-cause and cardiovascular mortality in individuals with type 2 diabetes mellitus. Methods We searched electronic databases up to 1 March 2016 for prospective studies reporting associations for three or more BMI groups with all-cause and cardiovascular mortality in individuals with type 2 diabetes mellitus. Study-specific associations between BMI and the most-adjusted RR were estimated using restricted cubic splines and a generalised least squares method before pooling study estimates with a multivariate random-effects meta-analysis. Results We included 21 studies including 24 cohorts, 414,587 participants, 61,889 all-cause and 4470 cardiovascular incident deaths; follow-up ranged from 2.7 to 15.9 years. There was a strong nonlinear relationship between BMI and all-cause mortality in both men and women, with the lowest estimated risk from 31-35 kg/m 2 and 28-31 kg/m 2 (p value for nonlinearity <0.001) respectively. The risk of mortality at higher BMI values increased significantly only in women, whilst lower values were associated with higher mortality in both sexes. Limited data for cardiovascular mortality were available, with a possible inverse linear association with BMI (higher risk for BMI <27 kg/m 2 ). Conclusions/interpretation In type 2 diabetes, BMI is nonlinearly associated with all-cause mortality with lowest risk in the overweight group in both men and women. Further research is needed to clarify the relationship with cardiovascular mortality and assess causality and sex differences.

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Section: Discussionmentioning

confidence: 99%

Nonlinear association of BMI with all-cause and cardiovascular mortality in type 2 diabetes mellitus: a systematic review and meta-analysis of 414,587 participants in prospective studies

et al. 2016

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“…18,19 Similarly, individuals with type 2 diabetes with higher body mass index may have decreased genetic susceptibility to the disease. 18,20 Analysis of subgroups of cases in GWAS data may therefore be valuable to identify further associations. Such analyses have been performed, 13,15,20,21 but this has generally been carried out without consideration of the relative power of these analyses, and the conditions under which such analyses are advantageous are not well understood.…”

Section: Introductionmentioning

confidence: 99%

Homogeneous case subgroups increase power in genetic association studies

2014

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Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect.

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“…24 Among the remaining top 20 SNVs, chromosome 16 SNVs (rs8059849, rs9931529, rs13332434, rs9783765) are near FTO, a gene known for its association with both BMI and T2D. [24][25][26][27][28][29] The SNV rs10894188 (chromosome 11) is near MTNR1B, a gene known to be associated with both T2D and obesity-related traits; 26 rs12097783 (chromosome 1) is near previously identified BMI gene SEC16B; [29][30][31][32] rs11145958 (chromosome 9) is near GPSM1, a T2D-associated gene; 33 5 SNVs on chromosome 1 are near NOTCH2 25 and ADAM30, 25 two genes known for SNVs associated with T2D; rs17863929 (chromosome 4) is approximately 3 Mb away from IL2, 34 a gene known for SNVs in the intron region associated with type-1 diabetes.…”

Section: Application To the Fhsmentioning

confidence: 99%

Joint association analysis of a binary and a quantitative trait in family samples

2016

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In recent years, improved genotyping and sequencing technologies have enabled the discovery of new loci associated with various diseases or traits. For instance, by testing the association with each single-nucleotide variant (SNV) separately, genomewide association studies (GWAS) have achieved tremendous success in identifying SNVs associated with specific traits. However, little is known about the common genetic basis of multiple traits owing to lack of efficient methods. With the use of extended quasi-likelihood, a Wald test has been proposed to perform a bivariate analysis of a continuous and a binary trait in unrelated samples. However, owing to its low computational efficiency, it has not been implemented in real applications to large-scale genetic studies. In this paper, we propose an efficient bivariate robust score test for two traits, one continuous and one binary, based on extended generalized estimating equations. Our approach is applicable to both family-based and unrelated study designs and can be extended to test the association of multiple traits. Our simulation studies demonstrate the type-I error rate of our approach is well controlled in all minor allele frequency (MAF) scenarios, with MAF ranging from 1 to 30%, and the method is more powerful in certain MAF scenarios than univariate testing with correction for multiple testing. Because of the computational advantage of score tests, our approach is readily applicable to GWAS or sequencing studies. Finally, we present a real application to uncover genetic variants associated with body mass index and type-2 diabetes in the Framingham Heart Study. European Journal of Human Genetics (2017) 25, 130-136; doi:10.1038/ejhg.2016; published online 26 October 2016 INTRODUCTIONIn recent years, univariate association test has been implemented as the predominant statistical method in genetic epidemiology and has yielded fruitful results in many applications. For example, univariate association tests have led to tremendous success in the discovery of disease susceptibility loci when applied to genome-wide association studies (GWAS) for various diseases. However, for the genetic association testing of multiple and often correlated traits, univariate association testing combined with multiple testing correction has usually been implemented owing to the ease of computation. Other variations include MultiPhen 1 and Yang's combination of univariate association tests. 2 However, none of these approaches are as powerful or efficient as a joint multivariate test with each trait treated as a dependent variable in discovering genetic loci associated with all traits under study. 1,3,4 For example, in the case of two continuous traits assumed to be normally distributed, a joint test can be derived as a simple extension of a univariate normal test. However, if one of the two traits is a discrete trait, for example, a binary trait, deriving such a test becomes challenging, and it further complicates in family samples. One reason is that there is no exact closed form of the...

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Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases

Cited by 195 publications

References 49 publications

Nonlinear association of BMI with all-cause and cardiovascular mortality in type 2 diabetes mellitus: a systematic review and meta-analysis of 414,587 participants in prospective studies

Nonlinear association of BMI with all-cause and cardiovascular mortality in type 2 diabetes mellitus: a systematic review and meta-analysis of 414,587 participants in prospective studies

Homogeneous case subgroups increase power in genetic association studies

Joint association analysis of a binary and a quantitative trait in family samples

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