The trans-ancestral genomic architecture of glycemic traits.

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Estimating narrow-sense heritability using family data from admixed populations.

Estimating total narrow-sense heritability in admixed populations remains an open question. In this work, we used extensive simulations to evaluate existing linear mixed-model frameworks for estimating total narrow-sense heritability in two population-based cohorts from Greenland, and compared the results with data from unadmixed individuals from Denmark. When our analysis focused on Greenlandic sib pairs, and under the assumption that shared environment among siblings has a negligible effect, the model with two relationship matrices, one capturing identity by descent and one capturing identity by state, returned heritability estimates close to the true simulated value, while using each of the two matrices alone led to downward biases. When phenotypes correlated with ancestry, heritability estimates were inflated. Based on these observations, we propose a PCA-based adjustment that recovers the true simulated heritability. We use this knowledge to estimate the heritability of ten quantitative traits from the two Greenlandic cohorts, and report differences such as lower heritability for height in Greenlanders compared with Europeans. In conclusion, narrow-sense heritability in admixed populations is best estimated when using a mixture of genetic relationship matrices on individuals with at least one first-degree relative included in the sample.

Human pancreatic islet three-dimensional chromatin architecture provides insights into the genetics of type 2 diabetes.

Genetic studies promise to provide insight into the molecular mechanisms underlying type 2 diabetes (T2D). Variants associated with T2D are often located in tissue-specific enhancer clusters or super-enhancers. So far, such domains have been defined through clustering of enhancers in linear genome maps rather than in three-dimensional (3D) space. Furthermore, their target genes are often unknown. We have created promoter capture Hi-C maps in human pancreatic islets. This linked diabetes-associated enhancers to their target genes, often located hundreds of kilobases away. It also revealed >1,300 groups of islet enhancers, super-enhancers and active promoters that form 3D hubs, some of which show coordinated glucose-dependent activity. We demonstrate that genetic variation in hubs impacts insulin secretion heritability, and show that hub annotations can be used for polygenic scores that predict T2D risk driven by islet regulatory variants. Human islet 3D chromatin architecture, therefore, provides a framework for interpretation of T2D genome-wide association study (GWAS) signals.

Author Correction: Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

The original version of this Article contained an error in Fig. 2. In panel a, the two legend items "rare" and "common" were inadvertently swapped. This has been corrected in both the PDF and HTML versions of the Article.

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens that are independent of established common variant signals (ADIPOQ and adiponectin, P = 4.2 × 10-8; APOC3 and triglyceride levels, P = 1.5 × 10-26), and identify replicating evidence for a burden associated with triglyceride levels in FAM189B (P = 2.2 × 10-8), indicating a role for this gene in lipid metabolism.

Prospective Studies Exploring the Possible Impact of an ID3 Polymorphism on Changes in Obesity Measures.

OBJECTIVE: Changes in fat mass depend on adipogenesis and angiogenesis, mechanisms regulated by the inhibitor of differentiation-3 (ID3). Id3 knockout mice showed attenuated increases in BMI and visceral fat mass. We hypothesized that the ID3 missense variant (rs11574-A) would lead to an attenuated increase over time in fat mass, BMI, waist circumference (WC), and waist-hip ratio (WHR) in humans. METHODS: The genotyped study populations included the Obesity Research Group - Genetics (ORGGEN) cohort, a cohort of men with obesity (N = 716) and of randomly selected men (N = 826) from the Danish draft register who were examined at mean ages of 20 and 46 years, and the Inter99 (N = 6,116) and Health2006 (N = 2,761) cohorts, two population-based samples of middle-aged people, followed up after 5 years. RESULTS: In meta-analyses of all data, no association was found between rs11574-A and changes in BMI, WC, WHR, or fat mass. We found an association between rs11574-A and cross-sectional BMI (N = 10,359, ß: -0.16 kg/m2 per allele, 95% CI: -0.30 to -0.01, P = 0.033) and fat mass (N = 4,188, ß: -0.52 kg/m2 per allele, 95% CI: -1.03 to -0.01, P = 0.046). CONCLUSIONS: No consistent impact of the genetic variant on changes in fat mass, BMI, or fat distribution was found in three Danish cohorts.

Genetic determinants of glycated hemoglobin levels in the Greenlandic Inuit population.

We previously showed that a common genetic variant leads to a remarkably increased risk of type 2 diabetes (T2D) in the small and historically isolated Greenlandic population. Motivated by this, we aimed at discovering novel genetic determinants for glycated hemoglobin (HbA1C) and at estimating the effect of known HbA1C-associated loci in the Greenlandic population. We analyzed genotype data from 4049 Greenlanders generated using the Illumina Cardio-Metabochip. We performed the discovery association analysis by an additive linear mixed model. To estimate the effect of known HbA1C-associated loci, we modeled the effect in the European and Inuit ancestry proportions of the Greenlandic genome (EAPGG and IAPGG, respectively). After correcting for multiple testing, we found no novel significant associations. When we investigated loci known to associate with HbA1C levels, we found that the lead variant in the GCK locus associated significantly with HbA1C levels in the IAPGG ([Formula: see text]). Furthermore, for 10 of 15 known HbA1C loci, the effects in IAPGG were similar to the previously reported effects. Interestingly, the ANK1 locus showed a statistically significant ancestral population differential effect, with opposing directions of effect in the two ancestral populations. In conclusion, we found only 1 of the 15 known HbA1C loci to be significantly associated with HbA1C levels in the IAPGG and that two-thirds of the loci showed similar effects in Inuit as previously found in European and East Asian populations. Our results shed light on the genetic effects across ethnicities.

Comparative Analyses of QTLs Influencing Obesity and Metabolic Phenotypes in Pigs and Humans.

The pig is a well-known animal model used to investigate genetic and mechanistic aspects of human disease biology. They are particularly useful in the context of obesity and metabolic diseases because other widely used models (e.g. mice) do not completely recapitulate key pathophysiological features associated with these diseases in humans. Therefore, we established a F2 pig resource population (n = 564) designed to elucidate the genetics underlying obesity and metabolic phenotypes. Segregation of obesity traits was ensured by using breeds highly divergent with respect to obesity traits in the parental generation. Several obesity and metabolic phenotypes were recorded (n = 35) from birth to slaughter (242 ± 48 days), including body composition determined at about two months of age (63 ± 10 days) via dual-energy x-ray absorptiometry (DXA) scanning. All pigs were genotyped using Illumina Porcine 60k SNP Beadchip and a combined linkage disequilibrium-linkage analysis was used to identify genome-wide significant associations for collected phenotypes. We identified 229 QTLs which associated with adiposity- and metabolic phenotypes at genome-wide significant levels. Subsequently comparative analyses were performed to identify the extent of overlap between previously identified QTLs in both humans and pigs. The combined analysis of a large number of obesity phenotypes has provided insight in the genetic architecture of the molecular mechanisms underlying these traits indicating that QTLs underlying similar phenotypes are clustered in the genome. Our analyses have further confirmed that genetic heterogeneity is an inherent characteristic of obesity traits most likely caused by segregation or fixation of different variants of the individual components belonging to cellular pathways in different populations. Several important genes previously associated to obesity in human studies, along with novel genes were identified. Altogether, this study provides novel insight that may further the current understanding of the molecular mechanisms underlying human obesity.