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Second‐generation antipsychotics (SGA) are widely used for treating psychiatric disorders; however, their use is associated with an increased risk of metabolic syndrome (MetS). To identify common genetic associations of SGA‐induced metabolic syndrome (SGA‐MetS), we conducted a genome‐wide association study (GWAS) in a diverse patient population within the BioVU and BioMe electronic health records (EHRs)‐linked biobanks. Additionally, we performed Mendelian Randomization (MR) analysis to investigate the association between the individual metabolic parameters comprising MetS (body mass index [BMI], fasting glucose, blood pressure, HDL, and triglycerides) and SGA‐MetS. The meta‐analysis of European ancestry GWAS from BioVU and BioMe (N = 9248) identified a genome‐wide signal (rs61900075, β = −0.27, SE = 0.05, p = 1.6 × 10−8) on chromosome 11. Multiple associated variants met the suggestive level of association (p ≤ 10−5) in the PELO‐ITGA1 locus on chromosome 5 and were associated among the Hispanic Ancestry within BioMe. The meta‐analysis of the African Ancestry patients of BioVU and BioMe (N = 2018) identified multiple genome‐wide signals that were functionally mapped to NPPC‐DIS3L2 in chromosome 2. Finally, the inverse‐variance weighted average MR (BMI: OR = 1.2, 95% CI: 1.1–1.4, p = 0.002) showed that genetically predicted, higher BMI was associated with an increased risk of SGA‐MetS. Similar results were seen in the sensitivity analyses using the weighted median and Egger MR. This study identified novel variants for SGA‐MetS and suggested a role of BMI in increasing the risk of SGA‐MetS. The findings highlight the value of EHR biobanks for identifying the genetics underlying SGA‐MetS. The associations in chromosome 2 and 5 will need further replication.

Background The branched chain amino acids (BCAA) leucine, isoleucine, and valine are essential nutrients that have been associated with diabetes, cancers, and cardiovascular diseases. Observational studies suggest that BCAAs exert homogeneous phenotypic effects, but these findings are inconsistent with results from experimental human and animal studies. Methods Hypothesizing that inconsistencies between observational and experimental BCAA studies reflect bias from shared lifestyle and genetic factors in observational studies, we used data from the UK Biobank and applied multivariable Mendelian randomization causal inference methods designed to address these biases. Results In n  = 97,469 participants of European ancestry (mean age = 56.7 years; 54.1% female), we estimate distinct and often opposing total causal effects for each BCAA. For example, of the 117 phenotypes with evidence of a statistically significant total causal effect for at least one BCAA, almost half (44%, n  = 52) are associated with only one BCAA. These 52 associations include total causal effects of valine on diabetic eye disease [odds ratio = 1.51, 95% confidence interval (CI) = 1.31, 1.76], valine on albuminuria (odds ratio = 1.14, 95% CI = 1.08, 1.20), and isoleucine on angina (odds ratio = 1.17, 95% CI = 1.31, 1.76). Conclusions Our results suggest that the observational literature provides a flawed picture of BCAA phenotypic effects that is inconsistent with experimental studies and could mislead efforts developing novel therapeutics. More broadly, these findings motivate the development and application of causal inference approaches that enable ‘omics studies conducted in observational settings to account for the biasing effects of shared genetic and lifestyle factors. Plain language summary The three branched chain amino acids (BCAAs) leucine, isoleucine, and valine are important building blocks of muscle proteins that are obtained from the diet. Many studies in human populations have examined whether BCAAs affect health and disease. These human studies report results that are inconsistent with results from highly controlled animal studies. Because interest in the therapeutic targeting of BCAAs is growing, we wanted to better understand these discrepancies. Briefly, we used data from a large database that captured many diseases (e.g., cardiovascular disease, cancers, and respiratory disease) and new statistical methods. Our results showed that discrepancies between human studies and animal studies may reflect errors in the ways human studies were designed and conducted. As a result, these human studies may provide a flawed picture of BCAA effects that could mislead efforts developing novel therapeutics. Avery et al. utilize data from the UK Biobank in a multivariable Mendelian randomization study to evaluate the effects of branched chain amino acids (BCAAs) on human disease. Their findings suggest observational studies of BCAA phenotypic effects are prone to substantial error, potentially misleading efforts to develop novel therapeutics.

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 cohorts with 88,316 MD cases and 902,757 controls to previously reported data. This analysis used a range of measures to define MD and included samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latin American participants (32%). The multi-ancestry GWAS identified 53 significantly associated novel loci. For loci from GWAS in European ancestry samples, fewer than expected were transferable to other ancestry groups. Fine mapping benefited from additional sample diversity. A transcriptome-wide association study identified 205 significantly associated novel genes. These findings suggest that, for MD, increasing ancestral and global diversity in genetic studies may be particularly important to ensure discovery of core genes and inform about transferability of findings. Multi-ancestry genome-wide association meta-analysis of major depression identifies new risk loci, assesses the transferability of risk loci across ancestry groups, and improves fine-mapping resolution and prioritization of candidate effector genes.

Foroughmand et al. (1) have recently reported association between coronary artery disease (CAD) and two well-known single nucleotide polymorphisms (SNPs) on chromosome 9p21.3 in subjects from South-West Iran. We doubt the validity of their findings. Genotyping was done using ARMS-PCR for rs1333049 and rs10757274 in their study. When we first looked at the genotype frequencies, we observed a substantial excess of heterozygote subjects for both SNPs. Specifically, the relative excess of heterozygosity (REH) (2), a measure for the strength of deviation from Hardy-Weinberg equilibrium (HWE), was approximately 137% for rs1333049 in controls (REH=2.3688, Table 1). In contrast, we did not observe any deviation from HWE in our own studies (3, 4). We additionally conducted a short literature search to identify other studies from Asia, which reported genotype frequencies in controls for rs1333049. These studies are summarized in table 1. None of these studies shows a deviation from HWE in their control groups (all P>0.05). In summary, only the recent study by Foroughmand and colleagues (1) shows a marked deviation from HWE in controls with this deviation observed for both reported SNPs. Possible reasons for deviations from HWE have been summarized, e.g., in Ziegler et al. (2). The most likely cause for such a strong deviation from HWE is genotyping errors, especially because genotyping by ARMS-PCR plus gel electrophoresis is prone to such errors. However, REH could also be caused by population specifics, which has been discussed by Namipashaki et al. (5). In any case, we (2) and others (5) recommend the investigation of HWE in population-based genetic association studies to improve quality and reliability of the research results.

Despite the dramatic underrepresentation of non-European populations in human genetics studies, researchers continue to exclude participants of non-European ancestry, as well as variants rare in European populations, even when these data are available. This practice perpetuates existing research disparities and can lead to important and large effect size associations being missed. Here, we conducted genome-wide association studies (GWAS) of 31 serum and urine biomarker quantitative traits in African (n = 9354), East Asian (n = 2559), and South Asian (n = 9823) ancestry UK Biobank (UKBB) participants. We adjusted for all known GWAS catalog variants for each trait, as well as novel signals identified in a recent European ancestry-focused analysis of UKBB participants. We identify 7 novel signals in African ancestry and 2 novel signals in South Asian ancestry participants (p < 1.61E−10). Many of these signals are highly plausible, including a cis pQTL for the gene encoding gamma-glutamyl transferase and PIEZO1 and G6PD variants with impacts on HbA1c through likely erythrocytic mechanisms. This work illustrates the importance of using the genetic data we already have in diverse populations, with novel discoveries possible in even modest sample sizes.

Aims/hypothesis We examined the contribution of rare HNF1A variants to type 2 diabetes risk and age of diagnosis, and the extent to which their impact is affected by overall genetic susceptibility, across three ancestry groups. Methods Using exome sequencing data of 160,615 individuals of the UK Biobank and 18,797 individuals of the Bio Me Biobank, we identified 746 carriers of rare functional HNF1A variants (minor allele frequency ≤1%), of which 507 carry variants in the functional domains. We calculated polygenic risk scores (PRSs) based on genome-wide association study summary statistics for type 2 diabetes, and examined the association of HNF1A variants and PRS with risk of type 2 diabetes and age of diagnosis. We also tested whether the PRS affects the association between HNF1A variants and type 2 diabetes risk by including an interaction term. Results Rare HNF1A variants that are predicted to impair protein function are associated with increased risk of type 2 diabetes in individuals of European ancestry (OR 1.46, p =0.049), particularly when the variants are located in the functional domains (OR 1.89, p =0.002). No association was observed for individuals of African ancestry (OR 1.10, p =0.60) or Hispanic-Latino ancestry (OR 1.00, p =1.00). Rare functional HNF1A variants were associated with an earlier age at diagnosis in the Hispanic-Latino population (β=−5.0 years, p =0.03), and this association was marginally more pronounced for variants in the functional domains (β=−5.59 years, p =0.03). No associations were observed for other ancestries (African ancestry β=−2.7 years, p =0.13; European ancestry β=−3.5 years, p =0.20). A higher PRS was associated with increased odds of type 2 diabetes in all ancestries (OR 1.61–2.11, p <10 −5 ) and an earlier age at diagnosis in individuals of African ancestry (β=−1.4 years, p =3.7 × 10 −6 ) and Hispanic-Latino ancestry (β=−2.4 years, p <2 × 10 −16 ). Furthermore, a higher PRS exacerbated the effect of the functional HNF1A variants on type 2 diabetes in the European ancestry population ( p interaction =0.037). Conclusions/interpretation We show that rare functional HNF1A variants, in particular those located in the functional domains, increase the risk of type 2 diabetes, at least among individuals of European ancestry. Their effect is even more pronounced in individuals with a high polygenic susceptibility. Our analyses highlight the importance of the location of functional variants within a gene and an individual’s overall polygenic susceptibility, and emphasise the need for more genetic data in non-European populations. Graphical abstract

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS , ADM17 , PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis . A method that allows for the detection of mosaic chromosomal alterations from blood whole-genome sequencing data highlights ancestry-specific differences in the distribution of common and rare germline susceptibility variants.

Severe obesity (SevO) is a primary driver of cardiovascular diseases (CVD), cardiometabolic diseases (CMD) and several cancers, with a disproportionate impact on marginalized populations. SevO is an understudied global health disease, limiting knowledge about its mechanisms and impacts. In genome-wide association study (GWAS) meta-analyses of the tail end of the BMI distribution (≥95 th percentile BMI) and two SevO phenotypes [Obesity Class III BMI ≥ 40 kg/m 2 and Obesity Class IV BMI ≥ 50 kg/m 2 ] in 159,359 individuals across eleven ancestrally diverse population-based studies followed by replication in 480,897 individuals across six ancestrally diverse studies, we identified and replicated three novel signals in known loci of BMI [ TENM2 , PLCL2 , ZNF184 ], associated with SevO traits. We confirmed a large overlap in the genetic architecture of continuous BMI and severe obesity phenotypes, suggesting little genetic heterogeneity in common variants, between obesity subgroups. Systematic analyses combining functional mapping, polygenic risk scores (PRS), phenome wide association studies (PheWAS) and environmental risk factors further reinforce shared downstream comorbidities associated with continuous measures of BMI and the importance of known lifestyle factors in interaction with genetic predisposition to SevO. Our study expands the number of SevO signals, demonstrates a strong overlap in the genetic architecture of SevO and BMI and reveals a remarkable impact of SevO on the clinical phenome, affording new opportunities for clinical prevention and mechanistic insights.

IntroductionThe independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations.MethodsHere, we examined how ancestrally diverse studies could clarify Lp(a)’s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations.ResultsFourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a ‘rule out’ for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%–99.9%) across PRS thresholds (80th–99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10−6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects.ConclusionsOur results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence). Combining 32 genome-wide association studies with high-density imputation provides a comprehensive view of the genetic contribution to type 2 diabetes in individuals of European ancestry with respect to locus discovery, causal-variant resolution, and mechanistic insight.