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HypothesisThe prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population.MethodsWe evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort.ResultsWe show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4×10−9) near ORC5. A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.ConclusionsOur results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.

BackgroundCharacterization of genetic variation that influences response to glucose-lowering medications is instrumental to precision medicine for treatment of type 2 diabetes (T2D). SUGAR-MGH examined the acute response to two anti-diabetes medications in order to understand the functional relevance of known T2D- and glycaemic trait-associated genetic loci.Methods1,000 participants at risk for T2D from diverse ancestries underwent sequential glipizide and metformin challenges. A genome-wide association study was performed using the Illumina Multi-Ethnic Genotyping Array. Imputation was performed with the TOPMed reference panel. Multiple linear regression using an additive model tested for association between variants and primary endpoints of drug response. We evaluated the influence of 804 unique T2D and glycaemic trait-associated variants on SUGAR-MGH outcomes and performed colocalization analyses to identify shared genetic signals.FindingsFive genome-wide significant variants were associated with metformin or glipizide response. The strongest association was between an African ancestry-specific variant (minor allele frequency=0·026) at rs149403252 and lower fasting glucose following metformin, adjusted for baseline glucose (p=1·9×10−9), with a 0·94 mmol/L larger decrease in fasting glucose after metformin. We identified associations between T2D-associated variants and glycaemic response, including the T2D-protective C allele of rs703972 near ZMIZ1 and increased levels of active GLP-1 (p=1·6×10−5), supporting the role of alterations in incretin levels in T2D pathophysiology.InterpretationWe present a well phenotyped, densely genotyped, multi-ancestry resource to study gene-drug interactions, uncover novel variation associated with response to common anti-diabetes medications, and provide insight into mechanisms of action of T2D-related variation.FundingUS National Institutes of Health.

Gene-environment interactions (GEIs) represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. GEIs often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci (vQTLs) can be prioritized in a two-stage GEI detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We performed genome-wide vQTL analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10−9). Most vQTLs were concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicated (p < 0.05) in the Women’s Genome Health Study (N = 23,294). Next, we tested each vQTL for interaction across 2,380 exposures, identifying 846 significant GEIs (p < 2.4×10−7). Specific examples demonstrated interaction of triglyceride-associated variants with distinct body mass-versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of vQTLs and GEIs is publicly available in an online portal.

Polygenic scores (PS), constructed from the combined effects of many genetic variants1, have been shown to predict risk or treatment strategies for certain common diseases2–6. As most PS to date are based on common variants7, the benefit of adding rare variation to PS remains largely unknown and methodically challenging. We developed and validated a method for constructing a rare variant PS and applied it to a previously identified clinical scenario, in which genetic variants modify the hemoglobin A1C (HbA1C) threshold recommended for type 2 diabetes (T2D) diagnosis6, 8–10. The resultant rare variant PS is highly polygenic (21,293 variants across 144 genes), depends on ultra-rare variants (72.7% of variants observed in p=1.51×10-6). A model combining the rare variant PS with a previously published common variant PS6 is expected to identify 4.9M misdiagnosed T2D cases in the USA, nearly 1.5-fold more than the common variant PS alone. These results provide a method for constructing complex trait PS from rare variants and suggest that rare variants will augment common variants in precision medicine approaches for common disease.

SUMMARYGenetic variation at the SLC16A11 locus contributes to the disproportionate impact of type 2 diabetes (T2D) on Latino populations. We recently demonstrated that T2D risk variants reduce SLC16A11 liver expression and function of MCT11, the monocarboxylate transporter encoded by the SLC16A11 gene. Here, we show that SLC16A11 expression within the liver is primarily localized to the low oxygen pericentral region, and that T2D risk variants disrupt oxygen-regulated SLC16A11 expression in human hepatocytes. Under physiologic oxygen conditions, MCT11 deficiency alters hepatocyte glucose metabolism, resulting in elevated intracellular lactate and a metabolic shift toward triacylglycerol accumulation. We also demonstrate an impact of Mct11 deficiency on glucose and lipid metabolism in Slc16a11 knockout mice, which display physiological changes that are observed in individuals with T2D. Our findings provide mechanistic insight into how SLC16A11 disruption impacts hepatic energy metabolism and T2D risk, and highlight MCT11-mediated regulation of lactate levels as a potential therapeutic target.

BackgroundDiabetes is the leading cause of kidney disease, and heritability studies demonstrate a substantial, yet poorly understood, contribution of genetics to kidney complications in people with diabetes.MethodsWe performed genome-wide association study (GWAS) meta-analyses using ten different phenotypic definitions of diabetic kidney disease (DKD), including nearly 27,000 individuals with diabetes, and integrated the results with various kidney omics datasets.ResultsThe meta-analysis identified a novel low frequency intronic variant (rs72831309) in the TENM2 gene encoding teneurin transmembrane protein 2 associated with a lower risk of the combined chronic kidney disease (CKD; eGFR2) and DKD (microalbuminuria or worse) phenotype (“CKD-DKD”, odds ratio 2.08, p=9.8×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A, and MFF, p−6). Integration of GWAS data with human glomerular and tubular expression data in a transcriptome-wide association study demonstrated higher tubular AKIRIN2 gene expression in DKD versus non-DKD controls (p=1.1×10−6). The lead SNPs within the DCLK1, AKIRIN2, SNX30 and three other gene regions significantly alterated the methylation at this region in kidneys (p−11). Expression of target genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes. For example, tubular TENM2 expression positively correlated with eGFR (p=2.3×10−9) and negatively with tubulointerstitial fibrosis (p=4.7×10−9), tubular DCLK1 expression positively correlated with fibrosis (p=1.6×10−12), and SNX30 level positively correlated with eGFR (p=7.6×10−13) and negatively with fibrosis (p−16).ConclusionsGWAS meta-analysis and integration with renal omics data points to novel genes contributing to pathogenesis of DKD.

SummaryA primary obstacle in translating genetics and genomics data into therapeutic strategies is elucidating the cellular programs affected by genetic variants and genes associated with human diseases. Broadly applicable high-throughput, unbiased assays offer a path to rapidly characterize gene and variant function and thus illuminate disease mechanisms. Here, we report LipocyteProfiler, an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types. We apply LipocyteProfiler to adipocytes and hepatocytes and demonstrate its ability to survey diverse cellular mechanisms by generating rich context-, and process-specific morphological and cellular profiles. We then use LipocyteProfiler to identify known and novel cellular programs altered by polygenic risk of metabolic disease, including insulin resistance, waist-to-hip ratio and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse phenotypic profiling in lipid-storing cells, and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

ObjectiveMost genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered.Research Design and MethodsWe conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We then used two additional cohorts, FinnGen and a Danish cohort, for replication. For the most significant recessive signal, we conducted a phenome-wide association study across hundreds of traits to make inferences about the pathophysiology underlying the increased risk seen in homozygous carriers.ResultsWe identified 51 loci associated with type 2 diabetes, including five variants with recessive effects undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk. We replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1×10−16) and a stronger effect in men than in women (interaction P=7×10−7). Colocalization analysis linked this signal to reduced expression of the nearby PELO gene, and the signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides.ConclusionsOur results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite notable anthropometric variability with ancestry proportions, and a high burden of growth stunting and overweight/obesity in Hispanic/Latino populations. This address this knowledge gap, we analyzed densely-imputed genetic data in a sample of Hispanic/Latino adults, to identify and fine-map common genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (Stage 1, n=59,769) and validated our findings in 9 additional studies (HISLA Stage 2, n=9,336). We conducted a trans-ethnic GWAS with summary statistics from HISLA Stage 1 and existing consortia of European and African ancestries. In our HISLA Stage 1+2 analyses, we discovered one novel BMI locus, as well two novel BMI signals and another novel height signal, each within established anthropometric loci. In our trans-ethnic meta- analysis, we identified three additional novel BMI loci, one novel height locus, and one novel WHRadjBMI locus. We also identified three secondary signals for BMI, 28 for height, and two for WHRadjBMI. We replicated >60 established anthropometric loci in Hispanic/Latino populations at genome-wide significance—representing up to 30% of previously-reported index SNP anthropometric associations. Trans-ethnic meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our novel findings demonstrate that future studies may also benefit from leveraging differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification.

Homeostatic control of blood glucose requires different physiological responses in the fasting and post-prandial states. We reasoned that glucose measurements under non-standardised conditions (random glucose; RG) may capture diverse glucoregulatory processes more effectively than previous genome-wide association studies (GWAS) of fasting glycaemia or after standardised glucose loads. Through GWAS meta-analysis of RG in 493,036 individuals without diabetes of diverse ethnicities we identified 128 associated loci represented by 162 distinct signals, including 14 with sex-dimorphic effects, 9 discovered through trans-ethnic analysis, and 70 novel signals for glycaemic traits. Novel RG loci were particularly enriched in expression in the ileum and colon, indicating a prominent role for the gastrointestinal tract in the control of blood glucose. Functional studies and molecular dynamics simulations of coding variants of GLP1R, a well-established type 2 diabetes treatment target, provided a genetic framework for optimal selection of GLP-1R agonist therapy. We also provided new evidence from Mendelian randomisation that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Thus, our approach based on RG GWAS provided wide-ranging insights into the biology of glucose regulation, diabetes complications and the potential for treatment stratification.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome and exome arrays, resulting in over 100 associated variants. We extended this work with a high-coverage whole genome sequencing (WGS) analysis from fifteen cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. More than 23,000 non-diabetic individuals from five self-reported race/ethnicities (African, Asian, European, Hispanic and Samoan) were included for each trait. We analyzed 60M variants in race/ethnicity-specific and pooled single variant and rare variant aggregate tests. Twenty-two variants across sixteen gene regions were found significantly associated with FG or FI, eight of which were rare (Minor Allele Frequency, MAFG6PC2locus we identified a distinct FG signal at rare variant rs2232326 (MAF=0.01) after conditioning on known common variants. Functional annotations show rs2232326 to be disruptive and likely damaging while being weakly transcribed in islets. A pair of FG-associated variants were identified near theSLC30A8locus. These variants, one of which was rare (MAF=0.001) and Asian race/ethnicity-specific, were shown to be in islet-specific active enhancer regions. Other associated regions include rare variants nearROBO1andPTPRT, and common variants nearMTNR1B, GCK, GCKR, FOXA2, APOB, TCF7L2, andADCY5. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions down to a minor allele count of 20, creating a foundation for future sequencing-based investigation of glycemic traits.

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards developing precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large sample size of the UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ∼340,000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (p < 5×10−8): two involved dietary patterns (meat pattern with rs147678157 and a fruit &vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534, and milk type [dairy vs. other] with 4:131148078_TAGAA_T). All of these were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked any genetic main effect that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) were identified as interacting with carbohydrate-containing food groups. Some of these interactions showed nominal replication in non-European ancestry UKB subsets, as well as association using alternative measures of glycemia (fasting glucose and follow-up HbA1c measurements). Our results highlight relevant GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency 2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

BackgroundType 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers.MethodsGenome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires.ResultsSevere insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P−9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10×10−8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3×10−9).ConclusionsNew diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis.Research in contextEvidence before this studyIn March 2018 we suggested a novel subclassification of diabetes into five subtypes. This classification was based on clustering using clinical parameters commonly measured at diabetes diagnosis (age at diabetes onset, HbA1c, bodymass index, presence of GAD autoantibodies and HOMA2 indices for insulin resistance and secretion). These subtypes differed with respect to clinical characteristics, disease progression and risk of complications, but it remained unclear to what extent these subtypes have different underlying pathologies. In our original publication we analysed a small set of genetic risk variants for diabetes and found differential associations between subtypes, suggesting potential aetiological differences.Added value of this studyIn this study we have conducted a full genome analysis of the original ANDIS cohort, including genome-wide association studies and polygenic risk score analysis with replication in an independent cohort. We have also compared heritability and prevalence of having a family history of diabetes in the subtypes.Implications of all the available evidenceWe demonstrate that stratification into subtypes facilitates identification of genetic risk loci and that the aetiology of the subtypes is at least partially distinct. These results are especially important for the future study and treatment of individuals belonging to the severe insulin-resistant diabetes (SIRD) subtype, whose pathogenesis appears to differ substantially from that of traditional T2D.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier will develop the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we applied clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias displayed effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers averaged below 60% in both studies for all conditions except monogenic diabetes. We assessed additional epidemiologic and genetic factors contributing to risk prediction, demonstrating that inclusion of common polygenic variation significantly improved biomarker estimation for two monogenic dyslipidemias.

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-8), including 237 attaining a more stringent trans-ancestry threshold (p-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.

Glycaemic traits are used to diagnose and monitor type 2 diabetes, and cardiometabolic health. To date, most genetic studies of glycaemic traits have focused on individuals of European ancestry. Here, we aggregated genome-wide association studies in up to 281,416 individuals without diabetes (30% non-European ancestry) with fasting glucose, 2h-glucose post-challenge, glycated haemoglobin, and fasting insulin data. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% with no significant evidence of between-ancestry heterogeneity. Analyses restricted to European ancestry individuals with equivalent sample size would have led to 24 fewer new loci. Compared to single-ancestry, 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 understanding of diabetes pathophysiology by use of trans-ancestry studies for improved power and resolution.

Unhealthy dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We performed genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identified 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations were only identified using dietary patterns. Mendelian randomization suggests a Western vs. prudent dietary pattern is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes.

Dietary intake, a major contributor to the global obesity epidemic1–5, is a complex phenotype partially affected by innate physiological processes.6–11 However, previous genome-wide association studies (GWAS) have only implicated a few loci in variability of dietary composition.12–14 Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium, and identify 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in β1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants yields three main genetic clusters with distinct associations with obesity and type 2 diabetes (T2D). Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.

The increasing size and diversity of genome-wide association studies provide an exciting opportunity to study how the genetics of complex traits vary among diverse populations. Here, we introduce covariate-adjusted LD score regression (cov-LDSC), a method to accurately estimate genetic heritability and its enrichment in both homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the GWAS samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates by 10% − 60% in admixed populations; in contrast, cov-LDSC is robust to all simulation parameters. We apply cov-LDSC to genotyping data from approximately 170,000 Latino, 47,000 African American and 135,000 European individuals. We estimate and detect heritability enrichment in three quantitative and five dichotomous phenotypes respectively, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals. Our results show that most traits have high concordance of and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of . We observe consistent patterns of tissue-specific heritability enrichment across populations; for example, in the limbic system for BMI, the per-standardized-annotation effect size τ* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in Latino, African American and European populations respectively. Our results demonstrate that our approach is a powerful way to analyze genetic data for complex traits from underrepresented populations.Author summaryAdmixed populations such as African Americans and Hispanic Americans bear a disproportionately high burden of disease but remain underrepresented in current genetic studies. It is important to extend current methodological advancements for understanding the genetic basis of complex traits in homogeneous populations to individuals with admixed genetic backgrounds. Here, we develop a computationally efficient method to answer two specific questions. First, does genetic variation contribute to the same amount of phenotypic variation (heritability) across diverse populations? Second, are the genetic mechanisms shared among different populations? To answer these questions, we use our novel method to conduct the first comprehensive heritability-based analysis of a large number of admixed individuals. We show that there is a high degree of concordance in total heritability and tissue-specific enrichment between different ancestral groups. However, traits such as age at menarche show a noticeable differences among populations. Our work provides a powerful way to analyze genetic data in admixed populations and may contribute to the applicability of genomic medicine to admixed population groups.

Diabetic kidney disease (DKD) is a heritable but poorly understood complication of diabetes. To identify genetic variants predisposing to DKD, we performed genome-wide association analyses in 19,406 individuals with type 1 diabetes (T1D) using a spectrum of DKD definitions basedon albuminuria and renal function. We identified 16 genome-wide significant loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain(COL4A3)gene, which encodes a major structural component of the glomerular basement membrane (GBM) implicated in heritable nephropathies. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of DKD, including albuminuria and end-stage renal disease. Three other loci are in or near genes with known or suggestive involvement in DKD(BMP7)or renal biology (COLEC11andDDR1). The 16 DKD-associated loci provide novel insights into the pathogenesis of DKD, identifying potential biological targets for prevention and treatment.

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1×10−3) and candidate genes from knockout mice (p=5.2×10−3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching ppwww.type2diabetesgenetics.org.