We assembled an ancestrally diverse collection of genome-wide association studies of type 2 diabetes (T2D) in 180,834 cases and 1,159,055 controls (48.9% non-European descent). We identified 277 loci at genome-wide significance (p<5x10-8), including 237 attaining a more stringent trans-ancestry threshold (p<5x10-9), which were delineated to 338 distinct association signals. Trans-ancestry meta-regression offered substantial enhancements to fine-mapping, with 58.6% of associations more precisely localised due to population diversity, and 54.4% of signals resolved to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying foundations for functional investigations. Trans-ancestry genetic risk scores enhanced transferability across diverse populations, providing a step towards more effective clinical translation to improve global health.

Human DNA varies across geographic regions, with most variation observed so far reflecting distant ancestry differences. Here, we investigate the geographic clustering of genetic variants that influence complex traits and disease risk in a sample of ~450,000 individuals from Great Britain. Out of 30 traits analyzed, 16 show significant geographic clustering at the genetic level after controlling for ancestry, likely reflecting recent migration driven by socio-economic status (SES). Alleles associated with educational attainment (EA) show most clustering, with EA-decreasing alleles clustering in lower SES areas such as coal mining areas. Individuals that leave coal mining areas carry more EA-increasing alleles on average than the rest of Great Britain. In addition, we leveraged the geographic clustering of complex trait variation to further disentangle regional differences in socio-economic and cultural outcomes through genome-wide association studies on publicly available regional measures, namely coal mining, religiousness, 1970/2015 general election outcomes, and Brexit referendum results. of reasons. They may be driven by the search for specific neighborhood, housing, and inhabitant characteristics, and/or socio-economic factors (e.g., education or job-related considerations), 9 such as the mass migrations from rural to industrial areas during the industrialization. 10 These geographic movements may coincide with regional clustering of heritable social outcomes such as socio-economic status and major group ideologies (e.g., religion 11 and political preference 12 ).Understanding what drives the geographic distribution of genome-wide complex trait variation is important for a variety of reasons. Studying regional differences of genetic variants associated with complex traits that reflect education, wealth, growth, health, and disease, may help understand why those traits are unevenly distributed across Great Britain. Besides the known regional differences in income and SES, significant regional differences have been reported for mental 13 and physical 14 health problems. Regional differences in wealth and health are likely linked to each other, [15][16][17] and have been shown to be partly driven by migration. 14,18 If genome-wide complex trait variation is geographically clustered, this should also be taken into account in certain genetically-informative study designs. Mendelian randomization for example uses genetic variants as instrumental variables to identify causality, under the assumption that the genetic instrument is not associated with confounders that influence the two traits under investigation. 19 Geographic clustering of genetic complex trait variation could introduce geneenvironment correlations that violate this assumption. 20 Such gene-environment correlations could also introduce bias in heritability estimates in twin and family studies, 21 and could affect signals from genomewide association studies (GWASs). Furthermore, studying the genetics of migration and geographically clustered cultural...

Sleep is an essential human function but its regulation is poorly understood. Identifying genetic variants associated with quality, quantity and timing of sleep will provide biological insights into the regulation of sleep and potential links with disease. Using accelerometer data from 85,670 individuals in the UK Biobank, we performed a genome-wide association study of 8 accelerometer-derived sleep traits. We identified 47 genetic associations across the sleep traits (P<5x10 -8 ) and replicated our findings in 5,819 individuals from 3 independent studies. These included 10 novel associations for sleep duration and 26 for sleep quality. Most newly identified variants were associated with a single sleep trait, but variants previously associated with restless legs syndrome were observed to be associated with multiple sleep traits. As a group, sleep quality loci were enriched for serotonin processing genes and all sleep traits were enriched for cerebellar-expressed genes. These findings provide new biological insights into sleep characteristics.

word summaryFibroblast Growth Factor 21 (FGF21) is a hormone that induces weight loss in model organisms. These findings have led to trials in humans of FGF21 analogues with some showing weight loss and lipid lowering effects. Recent genetic studies have shown that a common allele in the FGF21 gene alters the balance of macronutrients consumed but there was little evidence of an effect on metabolic traits. We studied a common FGF21 allele (A:rs838133) in 451,099 people from the UK Biobank study. We replicated the association between the A allele and higher percentage carbohydrate intake. We then showed that this allele is more strongly associated with body fat distribution, with less fat in the lower body, and higher blood pressure, than it is with BMI, where there is only nominal evidence of an effect. These human phenotypes of naturally occurring variation in the FGF21 gene will inform decisions about FGF21's therapeutic potential. IntroductionFGF21 is a hormone secreted primarily by the liver whose multiple functions include signalling to the paraventricular nucleus of the hypothalamus to suppress sugar and alcohol intake [1,2], stimulating insulin-independent glucose uptake by adipocytes [3] and acting as an insulin sensitizer [4]. These features and several other lines of evidence have prompted the development of FGF21 based therapies as potential treatments for obesity and type 2 diabetes, with consistent effects on triglyceride lowering, some effects on weight loss but little effect on glucose tolerance [5,6]. An early trial showed lipid lowering effects in people with type 2 diabetes and obesity but there was only suggestive evidence for effects on weight and glucose tolerance [7]. A recent study suggested that FGF21 analogues may alter not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/214700 doi: bioRxiv preprint first posted online Nov. 6, 2017; blood pressure in humans [8], although changes in blood pressure were not observed in a previous trial [9]. Pre-clinical evidence of FGF21's potential role in metabolism includes resistance to diet induced obesity in mice overexpressing FGF21 [3] and improved glucose tolerance in obese mice through administration of recombinant FGF21 [3]. Subsequent studies have confirmed these findings in mice [10] and shown similar effects in non human primates, including improvement of glucose tolerance and slight weight loss in diabetic rhesus monkeys [11], but other studies are less conclusive [5].Recent studies have shown that FGF21 affects the balance of macronutrients consumed. Studies in mice and non human primates show that genetically and pharmacologically raising FGF21 levels suppresses sugar and alcohol intake [1,2]. Three human genetic studies have shown that the minor A allele at rs838133 (A/G, Minor Allele Frequency=44.7%), which results in a synonymous change to the first exon of FGF21, is associated with higher carbohydrate and lower...

Susceptibility to obesity in today's environment has a strong genetic component.Lower socioeconomic position (SEP) is associated with a higher risk of obesity but it is not known if it accentuates genetic susceptibility to obesity. We aimed to use up to 120,000 individuals from the UK Biobank study to test the hypothesis that measures of socioeconomic position accentuate genetic susceptibility to obesity. We used the Townsend deprivation index (TDI) as the main measure of socioeconomic position, and a 69-variant genetic risk score (GRS) as a measure of genetic susceptibility to obesity. We also tested the hypothesis that interactions between BMI genetics and socioeconomic position would result in evidence of interaction with individual measures of the obesogenic environment and behaviours that correlate strongly with socioeconomic position, even if they have no obesogenic role. These measures included self-reported TV watching, diet and physical activity, and an objective measure of activity derived from accelerometers. We performed several negative control tests, including a simulated environment correlated with BMI but not TDI, and sun protection use. We found evidence of gene-environment interactions with TDI (Pinteraction=3x10 -10 ) such that, within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMIraising alleles was associated with approximately 2.9 kg extra weight. We also observed evidence of interaction between sun protection use and BMI genetics, suggesting that residual confounding may result in evidence of non-causal interactions. Our findings provide evidence that relative social deprivation best captures aspects of the obesogenic environment that accentuate the genetic predisposition to obesity in the UK.

Aims/Hypothesis Higher maternal BMI during pregnancy results in higher offspring birth weight, but it is not known whether this is solely the result of adverse metabolic consequences of higher maternal adiposity, such as maternal insulin resistance and fetal exposure to higher glucose levels, or whether there is any effect of raised adiposity through non-metabolic (e.g. mechanical) factors. We aimed to use genetic variants known to predispose to higher adiposity coupled with a favourable metabolic profile, in a Mendelian Randomisation (MR) study comparing the effect of maternal "metabolically favourable adiposity" on offspring birth weight with the effect of maternal general adiposity (as indexed by BMI). Methods To test the causal effects of maternal metabolically favourable adiposity or general adiposity on offspring birth weight, we performed two sample MR. We used variants identified in large genetic association studies as associated with either higher adiposity and a favourable metabolic profile, or higher BMI (N = 442,278 and N = 322,154 for metabolically favourable adiposity and BMI, respectively). We then used data from the same variants in a large genetic study of maternal genotype and offspring birth weight independent of fetal genetic effects (N = 406,063 with maternal and/or fetal genotype effect estimates). We used several sensitivity analyses to test the reliability of the results. As secondary analyses, we used data from four cohorts (total N = 9,323 mother-child pairs) to test the effects of maternal metabolically favourable adiposity or BMI on maternal gestational glucose, anthropometric components of birth weight and cord-blood biomarkers. Results Higher maternal adiposity with a favourable metabolic profile was associated with lower offspring birth weight (-94 (95% CI: -150 to -38) grams per 1 SD (6.5%) higher maternal metabolically favourable adiposity). By contrast, higher maternal BMI was associated with higher offspring birth weight (35 (95% CI: 16 to 53) grams per 1 SD (4 kg/m2) higher maternal BMI). Sensitivity analyses were broadly consistent with the main results. There was evidence of outlier SNPs for both exposures and their removal slightly strengthened the metabolically favourable adiposity estimate and made no difference to the BMI estimate. Our secondary analyses found evidence to suggest that maternal metabolically favourable adiposity decreases pregnancy fasting glucose levels whilst maternal BMI increases them. The effects on neonatal anthropometric traits were consistent with the overall effect on birth weight, but the smaller sample sizes for these analyses meant the effects were imprecisely estimated. We also found evidence to suggest that maternal metabolically favourable adiposity decreases cord-blood leptin whilst maternal BMI increases it. Conclusions/Interpretation Our results show that higher adiposity in mothers does not necessarily lead to higher offspring birth weight. Higher maternal adiposity can lead to lower offspring birth weight if accompanied by a favourable metabolic profile.

Women with X chromosome aneuploidy such as 45,X (Turner syndrome) or 47,XXX (Triple X syndrome) present with a range of characteristics including differences in stature, an increased risk of cardiovascular disease and premature ovarian insufficiency. Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, biased ascertainment of cases may mean that the penetrance of phenotypes is overestimated. We aimed to characterise the prevalence and phenotypic consequences of X chromosome aneuploidy in a large population of older adults. We detected 30 women with 45,X, 186 with mosaic 45,X/46,XX and 110 with 47,XXX in 245,203 women from UK Biobank, using SNP array data. The phenotypic features of women with full aneuploidy (whether 45,X or 47,XXX) were similar to those previously reported.Consistent with the recognised Turner syndrome phenotype, those with 45,X were 17.2cmshorter than controls and 53% did not go through menarche. Similarly, the phenotype of women with 47,XXX included increased height (on average 5.3cm taller than controls, P = 1 . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/177659 doi: bioRxiv preprint first posted online Aug. 18, 2017; x 10 -18 ), earlier menopause age (on average 5.12 years earlier than controls, P = 1.2 x 10 -14 )and a lower fluid intelligence (on average 24% lower than controls, P = 3.7 x 10 -8 ). In contrast, women with 45,X/46,XX mosaicism had a very mild phenotype; were not as short, had a normal reproductive lifespan and birth rate, with no reported cardiovascular complications. This study characterises X chromosome aneuploidy phenotypes in an adult population-based sample of older individuals and suggests that clinical management of women with a 45,X/46,XX mosaic karyotype should be minimal, particularly those identified incidentally.

Genome-wide association studies (GWASs) have been successful in discovering replicable SNP-trait associations for many quantitative traits and common diseases in humans. Typically the effect sizes of SNP alleles are very small and this has led to large genome-wide association meta-analyses (GWAMA) to maximize statistical power. A trend towards ever-larger GWAMA is likely to continue, yet dealing with summary statistics from hundreds of cohorts increases logistical and quality control problems, including unknown sample overlap, and these can lead to both false positive and false negative findings. In this study we propose a new set of metrics and visualization tools for GWAMA, using summary statistics from cohort-level GWASs. We proposed a pair of methods in examining the concordance between demographic information and summary statistics. In method I, we use the population genetics Fststatistic to verify the genetic origin of each cohort and their geographic location, and demonstrate using GWAMA data from the GIANT Consortium that geographic locations of cohorts can be recovered and outlier cohorts can be detected. In method II, we conduct principal component analysis based on reported allele frequencies, and is able to recover the ancestral information for each cohort. In addition, we propose a new statistic that uses the reported allelic effect sizes and their standard errors to identify significant sample overlap or heterogeneity between pairs of cohorts. Finally, to quantify unknown sample overlap across all pairs of cohorts we propose a method that uses randomly generated genetic predictors that does not require the sharing of individual-level genotype data and does not breach individual privacy.