Showing papers by "Jose C. Florez published in 2021"
TL;DR: This paper 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.
Abstract: 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.
178 citations
TL;DR: In this paper, the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses were evaluated.
Abstract: Background Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses. Methods and findings We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10−8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10−5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10−5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null. Conclusions In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.
87 citations
TL;DR: In this paper, the authors assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses.
Abstract: Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
69 citations
TL;DR: In this article, the authors used genome-wide association and genetic risk score (GRS) analysis to compare the underlying genetic drivers of Type 2 diabetes and found that the subtypes have partially distinct genetic backgrounds indicating etiological differences.
Abstract: Type 2 diabetes has been reproducibly clustered into five subtypes with different disease progression and risk of complications; however, etiological differences are unknown. We used genome-wide association and genetic risk score (GRS) analysis to compare the underlying genetic drivers. Individuals from the Swedish ANDIS (All New Diabetics In Scania) study were compared to individuals without diabetes; the Finnish DIREVA (Diabetes register in Vasa) and Botnia studies were used for replication. We show that subtypes differ with regard to family history of diabetes and association with GRS for diabetes-related traits. The severe insulin-resistant subtype was uniquely associated with GRS for fasting insulin but not with variants in the TCF7L2 locus or GRS reflecting insulin secretion. Further, an SNP (rs10824307) near LRMDA was uniquely associated with mild obesity-related diabetes. Therefore, we conclude that the subtypes have partially distinct genetic backgrounds indicating etiological differences. Genome-wide association and genetic risk score analyses highlight differences in genetic architecture across five subtypes of diabetes.
52 citations
TL;DR: Guidance as discussed by the authors improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association and identify 94 genome-wide associated loci, including 26 previously unreported.
Abstract: Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.
35 citations
University of California, San Francisco1, Harvard University2, University of Vermont3, University of Oklahoma4, Case Western Reserve University5, University of Colorado Denver6, Children's Hospital of Philadelphia7, Janssen Pharmaceutica8, Wake Forest University9, Massachusetts Institute of Technology10, Boston Children's Hospital11, Centers for Disease Control and Prevention12, Colorado School of Public Health13
TL;DR: In this paper, a multiethnic collaboration of three studies (TODAY, SEARCH, and T2D-GENES) with 3,006 youth case subjects with Type 2 diabetes (mean age 15.1 ± 2.9 years) and 6,061 diabetes-free adult control subjects (mean average 54.2 ± 12.4 years).
Abstract: The prevalence of type 2 diabetes in youth has increased substantially, yet the genetic underpinnings remain largely unexplored. To identify genetic variants predisposing to youth-onset type 2 diabetes, we formed ProDiGY, a multiethnic collaboration of three studies (TODAY, SEARCH, and T2D-GENES) with 3,006 youth case subjects with type 2 diabetes (mean age 15.1 ± 2.9 years) and 6,061 diabetes-free adult control subjects (mean age 54.2 ± 12.4 years). After stratifying by principal component-clustered ethnicity, we performed association analyses on ∼10 million imputed variants using a generalized linear mixed model incorporating a genetic relationship matrix to account for population structure and adjusting for sex. We identified seven genome-wide significant loci, including the novel locus rs10992863 in PHF2 (P = 3.2 × 10-8; odds ratio [OR] = 1.23). Known loci identified in our analysis include rs7903146 in TCF7L2 (P = 8.0 × 10-20; OR 1.58), rs72982988 near MC4R (P = 4.4 × 10-14; OR 1.53), rs200893788 in CDC123 (P = 1.1 × 10-12; OR 1.32), rs2237892 in KCNQ1 (P = 4.8 × 10-11; OR 1.59), rs937589119 in IGF2BP2 (P = 3.1 × 10-9; OR 1.34), and rs113748381 in SLC16A11 (P = 4.1 × 10-8; OR 1.04). Secondary analysis with 856 diabetes-free youth control subjects uncovered an additional locus in CPEB2 (P = 3.2 × 10-8; OR 2.1) and consistent direction of effect for diabetes risk. In conclusion, we identified both known and novel loci in the first genome-wide association study of youth-onset type 2 diabetes.
26 citations
TL;DR: In this article, a covariate-adjusted linkage disequilibrium score regression (cov-LDSC) was proposed to estimate SNP-heritability and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates.
Abstract: It is important to study the genetics of complex traits in diverse populations. Here, we introduce covariate-adjusted linkage disequilibrium (LD) score regression (cov-LDSC), a method to estimate SNP-heritability (${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}})$ and its enrichment in homogenous and admixed populations with summary statistics and in-sample LD estimates. In-sample LD can be estimated from a subset of the genome-wide association studies samples, allowing our method to be applied efficiently to very large cohorts. In simulations, we show that unadjusted LDSC underestimates ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ by 10-60% in admixed populations; in contrast, cov-LDSC is robustly accurate. We apply cov-LDSC to genotyping data from 8124 individuals, mostly of admixed ancestry, from the Slim Initiative in Genomic Medicine for the Americas study, and to approximately 161 000 Latino-ancestry individuals, 47 000 African American-ancestry individuals and 135 000 European-ancestry individuals, as classified by 23andMe. We estimate ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and detect heritability enrichment in three quantitative and five dichotomous phenotypes, making this, to our knowledge, the most comprehensive heritability-based analysis of admixed individuals to date. Most traits have high concordance of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$ and consistent tissue-specific heritability enrichment among different populations. However, for age at menarche, we observe population-specific heritability estimates of ${\boldsymbol{h}}_{\boldsymbol{g}}^{\mathbf{2}}$. 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 $ \tau $* is 0.16 ± 0.04, 0.28 ± 0.11 and 0.18 ± 0.03 in the Latino-, African American- and European-ancestry populations, respectively. Our approach is a powerful way to analyze genetic data for complex traits from admixed populations.
26 citations
TL;DR: In this article, the differences in blood-derived DNA methylation patterns between individuals with Type 1 diabetes mellitus (T1DM) and individuals with long-duration T1DM but no evidence of kidney disease were assessed.
Abstract: A subset of individuals with type 1 diabetes mellitus (T1DM) are predisposed to developing diabetic kidney disease (DKD), the most common cause globally of end-stage kidney disease (ESKD). Emerging evidence suggests epigenetic changes in DNA methylation may have a causal role in both T1DM and DKD. The aim of this exploratory investigation was to assess differences in blood-derived DNA methylation patterns between individuals with T1DM-ESKD and individuals with long-duration T1DM but no evidence of kidney disease upon repeated testing to identify potential blood-based biomarkers. Blood-derived DNA from individuals (107 cases, 253 controls and 14 experimental controls) were bisulphite treated before DNA methylation patterns from both groups were generated and analysed using Illumina’s Infinium MethylationEPIC BeadChip arrays (n = 862,927 sites). Differentially methylated CpG sites (dmCpGs) were identified (false discovery rate adjusted p ≤ × 10–8 and fold change ± 2) by comparing methylation levels between ESKD cases and T1DM controls at single site resolution. Gene annotation and functionality was investigated to enrich and rank methylated regions associated with ESKD in T1DM. Top-ranked genes within which several dmCpGs were located and supported by functional data with methylation look-ups in other cohorts include: AFF3, ARID5B, CUX1, ELMO1, FKBP5, HDAC4, ITGAL, LY9, PIM1, RUNX3, SEPTIN9 and UPF3A. Top-ranked enrichment pathways included pathways in cancer, TGF-β signalling and Th17 cell differentiation. Epigenetic alterations provide a dynamic link between an individual’s genetic background and their environmental exposures. This robust evaluation of DNA methylation in carefully phenotyped individuals has identified biomarkers associated with ESKD, revealing several genes and implicated key pathways associated with ESKD in individuals with T1DM.
24 citations
03 Mar 2021
TL;DR: A more complete molecular description of Type 2 diabetes is a major undertaking, requiring a multidisciplinary effort, including new strategies for patient sampling, phenotyping, genotyping and genetic analysis.
Abstract: 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 rs7903146 SNP in TCF7L2, 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.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%–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.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.
22 citations
Broad Institute1, Harvard University2, Boston University3, University of Copenhagen4, Brigham and Women's Hospital5, University of Virginia6, Beth Israel Deaconess Medical Center7, National Institutes of Health8, Merck & Co.9, Statens Serum Institut10, University of Manchester11, Manchester Academic Health Science Centre12
TL;DR: Merino et al. as discussed by the authors presented a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.
Abstract: Dietary intake is a major contributor to the global obesity epidemic and represents a complex behavioural phenotype that is partially affected by innate biological differences. Here, we present a multivariate genome-wide association analysis of overall variation in dietary intake to account for the correlation between dietary carbohydrate, fat and protein in 282,271 participants of European ancestry from the UK Biobank (n = 191,157) and Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 91,114), and identify 26 distinct genome-wide significant loci. Dietary intake signals map exclusively to specific brain regions and are enriched for genes expressed in specialized subtypes of GABAergic, dopaminergic and glutamatergic neurons. We identified two main clusters of genetic variants for overall variation in dietary intake that were differently associated with obesity and coronary artery disease. These results enhance the biological understanding of interindividual differences in dietary intake by highlighting neural mechanisms, supporting functional follow-up experiments and possibly providing new avenues for the prevention and treatment of prevalent complex metabolic diseases. In a multivariate genetic analysis including 282,271 adults, Merino et al. identified 26 genomic regions associated with carbohydrate, protein and fat intake. The identified loci implicate brain regions and neuronal subtypes in influencing eating behaviour.
17 citations
University of Maryland, Baltimore1, University of California, San Francisco2, Saint Louis University3, Harvard University4, Children's Hospital of Philadelphia5, University of Oklahoma Health Sciences Center6, Baylor College of Medicine7, Centers for Disease Control and Prevention8, Kaiser Permanente9, University of Washington10, New York University11, Broad Institute12, Boston Children's Hospital13, Anschutz Medical Campus14
TL;DR: In this paper, the prevalence of maturity-onset diabetes of the young (MODY) was assessed in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes.
Abstract: OBJECTIVE Maturity-onset diabetes of the young (MODY) is frequently misdiagnosed as type 1 or type 2 diabetes. Correct diagnosis may result in a change in clinical treatment and impacts prediction of complications and familial risk. In this study, we aimed to assess the prevalence of MODY in multiethnic youth under age 20 years with a clinical diagnosis of type 2 diabetes. RESEARCH DESIGN AND METHODS We evaluated whole-exome sequence data of youth with a clinical diagnosis of type 2 diabetes. We considered participants to have MODY if they carried a MODY gene variant classified as likely pathogenic (LP) or pathogenic (P) according to current guidelines. RESULTS Of 3,333 participants, 93 (2.8%) carried an LP/P variant in HNF4A (16 participants), GCK (23), HNF1A (44), PDX1 (5), INS (4), and CEL (1). Compared with those with no LP/P variants, youth with MODY had a younger age at diagnosis (12.9 ± 2.5 vs. 13.6 ± 2.3 years, P = 0.002) and lower fasting C-peptide levels (3.0 ± 1.7 vs. 4.7 ± 3.5 ng/mL, P CONCLUSIONS By comprehensively sequencing the coding regions of all MODY genes, we identified MODY in 2.8% of youth with clinically diagnosed type 2 diabetes; importantly, in 89% (n = 83) the specific diagnosis would have changed clinical management. No clinical criterion reliably separated the two groups. New tools are needed to find ideal criteria for selection of individuals for genetic testing.
TL;DR: It is suggested that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.
Abstract: There is a limited understanding of how genetic loci associated with glycemic traits and type 2 diabetes (T2D) influence the response to antidiabetic medications. Polygenic scores provide increasing power to detect patterns of disease predisposition that might benefit from a targeted pharmacologic intervention. In the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), we constructed weighted polygenic scores using known genome-wide significant associations for T2D, fasting glucose, and fasting insulin, comprising 65, 43, and 13 single nucleotide polymorphisms, respectively. Multiple linear regression tested for associations between scores and glycemic traits as well as pharmacodynamic end points, adjusting for age, sex, race, and BMI. A higher T2D score was nominally associated with a shorter time to insulin peak, greater glucose area over the curve, shorter time to glucose trough, and steeper slope to glucose trough after glipizide. In replication, a higher T2D score was associated with a greater 1-year hemoglobin A1c reduction to sulfonylureas in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study (P = 0.02). Our findings suggest that individuals with a higher genetic burden for T2D experience a greater acute and sustained response to sulfonylureas.
TL;DR: In this article, the authors used Mendelian randomisation to test for the causality of diet in urate levels and found that diet has a relatively minor role in determining serum urate level and hyperuricaemia.
Abstract: Prevention of hyperuricaemia (HU) is critical to the prevention of gout. Understanding causal relationships and relative contributions of various risk factors to hyperuricemia is therefore important in the prevention of gout. Here, we use attributable fraction to compare the relative contribution of genetic, dietary, urate-lowering therapy (ULT) and other exposures to HU. We use Mendelian randomisation to test for the causality of diet in urate levels. Four European-ancestry sample sets, three from the general population (n = 419,060) and one of people with gout (n = 6781) were derived from the Database of Genotypes and Phenotypes (ARIC, FHS, CARDIA, CHS) and UK Biobank. Dichotomised exposures to diet, genetic risk variants, BMI, alcohol, diuretic treatment, sex and age were used to calculate adjusted population and average attributable fractions (PAF/AAF) for HU (≥0.42 mmol/L [≥7 mg/dL]). Exposure to ULT was also assessed in the gout cohort. Two sample Mendelian randomisation was done in the UK Biobank using dietary pattern-associated genetic variants as exposure and serum urate levels as outcome. Adherence to dietary recommendations, BMI (< 25 kg/m2), and absence of the SLC2A9 rs12498742 urate-raising allele produced PAFs for HU of 20 to 24%, 59 to 69%, and 57 to 64%, respectively, in the three non-gout cohorts. In the gout cohort, diet, BMI, SLC2A9 rs12498742 and ULT PAFs for HU were 12%, 49%, 48%, and 63%, respectively. Mendelian randomisation demonstrated weak causal effects of four dietary habits on serum urate levels (e.g. preferentially drinking skim milk increased urate, β = 0.047 mmol/L, P = 3.78 × 10−8). These effects were mediated by BMI, and they were not significant (P ≥ 0.06) in multivariable models assessing the BMI-independent effect of diet on urate. Diet has a relatively minor role in determining serum urate levels and HU. In gout, the use of ULT was the largest attributable fraction tested for HU.
TL;DR: This study aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering and identified a cluster that was strongly associated withGDM as well as associated with higher infant birth weight.
Abstract: Hundreds of common genetic variants acting through distinguishable physiologic pathways influence the risk of type 2 diabetes (T2D). It is unknown to what extent the physiology underlying gestational diabetes mellitus (GDM) is distinct from that underlying T2D. In this study of >5,000 pregnant women from three cohorts, we aimed to identify physiologically related groups of maternal variants associated with GDM using two complementary approaches that were based on Bayesian nonnegative matrix factorization (bNMF) clustering. First, we tested five bNMF clusters of maternal T2D-associated variants grouped on the basis of physiology outside of pregnancy for association with GDM. We found that cluster polygenic scores representing genetic determinants of reduced β-cell function and abnormal hepatic lipid metabolism were associated with GDM; these clusters were not associated with infant birth weight. Second, we derived bNMF clusters of maternal variants on the basis of pregnancy physiology and tested these clusters for association with GDM. We identified a cluster that was strongly associated with GDM as well as associated with higher infant birth weight. The effect size for this cluster’s association with GDM appeared greater than that for T2D. Our findings imply that the genetic and physiologic pathways that lead to GDM differ, at least in part, from those that lead to T2D.
TL;DR: In this paper, additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) were tested.
Abstract: Aim: To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes. Materials and methods: We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up. Results: A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals. Conclusions: Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes. (Less)
TL;DR: LipocyteProfiler as mentioned in this paper is an unbiased high-throughput, high-content microscopy assay that is amenable to large-scale morphological and cellular profiling of lipid-accumulating cell types.
Abstract: A 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.
TL;DR: In this paper, a large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, were leveraged to conduct genome-wide interaction studies in ~ 340 000 European-ancestry participants to identify novel gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) for type 2 diabetes.
Abstract: 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 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 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). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects 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) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.
TL;DR: In this article, gene-based association testing of blood lipid levels with rare (minor allele frequency 170,000 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 was performed.
Abstract: 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 frequency 170,000 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.
Wellcome Trust Sanger Institute1, Katholieke Universiteit Leuven2, University of Oxford3, Imperial College London4, University of Queensland5, Erasmus University Rotterdam6, University of Surrey7, University of Copenhagen8, University of Ferrara9, University of Tartu10, Coventry University11, Lund University12, Amgen13, University of Ioannina14, University of South Australia15, University of Cambridge16, Maastricht University17, Queen Mary University of London18, Boston University19, University of Michigan20, New England Biolabs21, Vanderbilt University22, University of Ulm23, Norwegian University of Science and Technology24, University of Tromsø25, University of North Carolina at Chapel Hill26, Technische Universität München27, Nanyang Technological University28, University of Exeter29, National Institute for Health and Welfare30, University of Helsinki31, King Abdulaziz University32, Broad Institute33, Harvard University34, Karolinska Institutet35, Karolinska University Hospital36, QIMR Berghofer Medical Research Institute37, National Institute for Health Research38, University of Iceland39, Vanderbilt University Medical Center40, university of lille41, Monash University42, University Medical Center Groningen43, University of Essex44, Russian Academy of Sciences45
TL;DR: In this paper, a meta-analysis of random glucose measurements in 493,036 individuals without diabetes of diverse ethnicities was conducted to identify 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.
Abstract: 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.
TL;DR: In this article, a non-invasive method to predict fetal glucokinase (GCK) genotype using cell-free DNA in three pregnant women carrying an inactivating GCK variant was proposed.
Abstract: Context Persons with monogenic diabetes due to inactivating glucokinase (GCK) variants typically do not require treatment, except potentially during pregnancy. In pregnancy, fetal GCK genotype determines whether treatment is indicated, but non-invasive methods are not clinically available. Objective To develop a method to determine fetal GCK genotype non-invasively using maternal cell-free fetal DNA. Design, and main outcome measure This was a proof-of-concept study which used information from 1) massive parallel sequencing of maternal plasma cell-free DNA, 2) direct haplotype sequences of maternal genomic DNA, and 3) paternal genotype to estimate relative haplotype dosage of the pathogenic variant-linked haplotype. Statistical testing of variant inheritance was performed using a sequential probability ratio test (SPRT). Patients and setting Three pregnant women with causal GCK variant. Results In each of the three cases, plasma cell-free DNA was extracted once between gestational weeks 24 and 36. The fetal fraction of cell-free DNA ranged between 21.8 - 23.0%. Paternal homozygous alleles that were identical to the maternal GCK variant-linked allele were not overrepresented in the cell-free DNA. Paternal homozygous alleles that were identical to maternal wild-type-linked allele were significantly overrepresented. Based on the SPRT, we predicted that all three cases did not inherit the GCK variant. Postnatal infant genotyping confirmed our prediction in each case. Conclusions We have successfully implemented a non-invasive method to predict fetal GCK genotype using cell-free DNA in three pregnant women carrying an inactivating GCK variant. This method could guide tailoring of hyperglycemia treatment in pregnancies of women with GCK monogenic diabetes.
TL;DR: In this article, the authors developed and validated a novel 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) diagnosis.
Abstract: Polygenic scores (PS), constructed from the combined effects of many genetic variants, have been shown to predict risk or treatment strategies for certain common diseases. As most PS to date are based on common variants, the benefit of adding rare variation to PS remains largely unknown and methodically challenging. We developed and validated a novel 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) diagnosis. 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
TL;DR: The original version of this article contained an error in Fig 2, in which panels a and b were inadvertently swapped This has now been corrected in the PDF and HTML versions of the Article.
Abstract: The original version of this Article contained an error in Fig 2, in which panels a and b were inadvertently swapped This has now been corrected in the PDF and HTML versions of the Article
TL;DR: Clinical important genetic effects at genomewide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins are identified, which will be important when prescribing glucose-lowering drugs.
Abstract: Background: Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However there exists significant variability in glycaemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction.
Methods: As an initiative of the Metformin Genetics Plus (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortia, 5,485 white European adults with type 2 diabetes treated with sulfonylurea were recruited from 6 referral centres in Europe and North America. We first estimated heritability using generalized restricted maximum likelihood (REML) and then undertook GWAS of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy in each cohort using linear regression followed by meta-analysis. These results were supported by cis-eQTLs and functional validation in cellular models.
Findings: After establishing that sulfonylurea response is heritable (37±11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction. The C-allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), p=2.4×10−8) lower HbA1c reduction. Similarly, the C-allele was associated with higher glucose trough levels (β=1.61, p=0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). The C-allele of rs10770791, near SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater HbA1c reduction (p=4.8×10 −8 ). In 1,183 human liver samples, the C-allele at rs10770791 is a cis-eQTL for reduced SLCO1B1 expression (p=1.61×10−7) which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use, sulfonylurea response and SCLO1B1 genotype was observed (p=0.001). In statin non-users (but not users), C-allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48±0.12% (5.2±1.26 mmol/mol)), equivalent to initiating a DPP4 inhibitor.
Interpretation: We have identified clinically important genetic effects at genomewide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important when prescribing glucose-lowering drugs.
Funding Statement: Innovative Medicines Initiative, the National Institutes of Health, the Geisinger
Health Plan Quality Pilot Fund.
Declaration of Interests: ERP has received honoraria for speaking from Lilly and Sanofi. JCF has received honoraria for speaking at scientific conferences from Novo, and for consulting from Goldfinch Bio. All other authors declare no competing interest.
Ethics Approval Statement: This study was approved by respective research ethics review boards and participants provided written informed consent.
Journal Article•
University of Molise1, Yale University2, Harvard University3, University of Vermont4, Cincinnati Children's Hospital Medical Center5, George Washington University6, Baylor College of Medicine7, University of Colorado Denver8, University of Washington9, Massachusetts Institute of Technology10, Boston Children's Hospital11, University of California, San Francisco12
TL;DR: In this article, a genome-wide association study was conducted to evaluate the genetic determinants of lipid traits in youth with Type 2 diabetes through a genome wide association study, which identified a novel association between a deletion on chromosome 3 (3:67817380_AT/A_Deletion:RP11-81N13.1) and triglyceride levels at genomewide level of significance with each risk allele increasing triglycerides by 20%.
Abstract: Context Dyslipidemia is highly prevalent in youth with type 2 diabetes (T2D), yet the pathogenic components of dyslipidemia in youth with T2D are poorly understood. Objective To evaluate the genetic determinants of lipid traits in youth with T2D through a genome-wide association study. Design Participants and Main Outcome Measures We genotyped 206 928 variants and imputed 17 642 824 variants in 1076 youth (mean age 15.0 ± 2.48 years) with T2D from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) and SEARCH for Diabetes in Youth (SEARCH) studies as part of the Progress in Diabetes Genetics in Youth (ProDiGY) consortium. We performed association testing for triglyceride and low-density lipoprotein cholesterol and high-density lipoprotein cholesterol (HDL-c) concentrations adjusted for the genetic relationship matrix within each substudy followed by meta-analyses for each trait. Results We identified a novel association between a deletion on chromosome 3 (3:67817380_AT/A_Deletion:RP11-81N13.1) and triglyceride levels at genome-wide level of significance (P = 2.3 × 10-8) with each risk allele increasing triglycerides by 20%. We also identified a genome-wide significant signal at rs247617 (P = 5.1 × 10-9) between HERFUD1 and CETP associated with HDL-c, with carriers of 1 copy of the risk allele having twice higher HDL-c. Conclusions Our genetic analyses of lipid traits in youth with T2D have identified 1 novel and 1 previously known locus. Additional studies are needed to further characterize the genetic architecture of dyslipidemia in youth with T2D.
TL;DR: This article showed that SLC16A11 expression within the liver is primarily localized to the low oxygen pericentral region, and that T2D risk variants disrupt oxygen-regulated SLC 16A11 gene expression in human hepatocytes.
Abstract: Genetic 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.
University of Helsinki1, Boston Children's Hospital2, Harvard University3, Broad Institute4, University of Pennsylvania5, Lund University6, University of Oxford7, University of Dundee8, Queen's University Belfast9, University of California, San Diego10, University College Dublin11, National Institutes of Health12, Belfast City Hospital13
TL;DR: In this article, the authors 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.
Abstract: 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; eGFR<60 ml/min/1.73 m2) and DKD (microalbuminuria or worse) phenotype ("CKD-DKD", odds ratio 2.08, p=9.8x10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A, and MFF, p<2.7x10-6). 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=1.1x10-6). The lead SNPs within the DCLK1, AKIRIN2, SNX30 and three other gene regions significantly alterated the methylation at this region in kidneys (p<2.2x10-11). Expression of target genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes. For example, tubular TENM2 expression positively correlated with eGFR (p=2.3x10-9) and negatively with tubulointerstitial fibrosis (p=4.7x10-9), tubular DCLK1 expression positively correlated with fibrosis (p=1.6x10-12), and SNX30 level positively correlated with eGFR (p=7.6x10-13) and negatively with fibrosis (p<2x10-16). ConclusionsGWAS meta-analysis and integration with renal omics data points to novel genes contributing to pathogenesis of DKD.
TL;DR: The authors 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 < 4.5x10-9).
Abstract: 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 < 4.5x10-9). 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 Womens Genome Health Study (N = 23,294). Next, we tested each vQTL for interaction across 2,380 exposures, identifying 846 significant GEIs (p < 2.4x10-7). 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.
TL;DR: This paper conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes and identified 51 loci associated with Type 2 diabetes, including five variants with recessive effects undetected by prior additive analyses.
Abstract: Objective: Most 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 Methods: We 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.
Results: We 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.
Conclusions: Our 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.
University of North Carolina at Chapel Hill1, Pennsylvania State University2, University of California, Los Angeles3, Vanderbilt University Medical Center4, Open University5, Wake Forest University6, Children's Hospital of Philadelphia7, University of São Paulo8, University of Southern California9, Vertex Pharmaceuticals10, University of Copenhagen11, Icahn School of Medicine at Mount Sinai12, Hasso Plattner Institute13, University of Antioquia14, Universidade Federal do Rio Grande do Sul15, National Autonomous University of Mexico16, Cayetano Heredia University17, University of Tarapacá18, Case Western Reserve University19, University of Washington20, Albert Einstein College of Medicine21, National Institutes of Health22, University of Michigan23, Ohio State University24, University of California, Berkeley25, Los Angeles Biomedical Research Institute26, Cedars-Sinai Medical Center27, The Ohio State University Wexner Medical Center28, Beckman Research Institute29, New York Medical College30, Kaiser Permanente31, Mexican Social Security Institute32, Universidade Federal de Pelotas33, University of Texas at Austin34, University of California, Davis35, Stanford University36, Massachusetts Institute of Technology37, Harvard University38, University of California, San Diego39, University of Texas Health Science Center at Houston40, Baylor College of Medicine41, United States Department of Agriculture42, Texas Biomedical Research Institute43, Obesity Society44, Brigham and Women's Hospital45, University of Toronto46
TL;DR: In this article, the authors 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).
Abstract: 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.