Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height.

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare ( < 0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P < 6 × 10 - 10 after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7 cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5 cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

Rare variant contribution to the heritability of coronary artery disease.

Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Genetic associations with neural reward responsivity to food cues in children.

Objective: To test associations of candidate obesity-related single nucleotide polymorphisms (SNPs) and obesity polygenic risk scores (PRS) with neural reward reactivity to food cues. Methods: After consuming a pre-load meal, 9-12-year-old children completed a functional magnetic resonance imaging (fMRI) paradigm with exposure to food and non-food commercials. Genetic exposures included FTO rs9939609, MC4R rs571312, and a pediatric-specific obesity PRS. A targeted region-of-interest (ROI) analysis for 7 bilateral reward regions and a whole-brain analysis were conducted. Independent associations between each genetic factor and reward responsivity to food cues in each ROI were evaluated using linear models. Results: Analyses included 151 children (M = 10.9 years). Each FTO rs9939609 obesity risk allele was related to a higher food-cue-related response in the right lateral hypothalamus after controlling for covariates including the current BMI Z-score (p < 0.01), however, the association did not remain significant after applying the multiple testing correction. MC4R rs571312 and the PRS were not related to heightened food-cue-related reward responsivity in any examined regions. The whole-brain analysis did not identify additional regions of food-cue-related response related to the examined genetic factors. Conclusion: Children genetically at risk for obesity, as indicated by the FTO genotype, may be predisposed to higher food-cue-related reward responsivity in the lateral hypothalamus in the sated state, which, in turn, could contribute to overconsumption. Clinical trial registration: https://clinicaltrials.gov/study/NCT03766191, identifier NCT03766191.

Association between plausible genetic factors and weight loss from GLP1-RA and bariatric surgery: a multi-ancestry study in 10 960 individuals from 9 biobanks.

Obesity is a significant public health concern. GLP-1 receptor agonists (GLP1-RA), predominantly in use as a type 2 diabetes treatment, are a promising pharmacological approach for weight loss, while bariatric surgery (BS) remains a durable, but invasive, intervention. Despite observed heterogeneity in weight loss effects, the genetic effects on weight loss from GLP1-RA and BS have not been extensively explored in large sample sizes, and most studies have focused on differences in race and ethnicity, rather than genetic ancestry. We studied whether genetic factors, previously shown to affect body weight, impact weight loss due to GLP1-RA therapy or BS in 10,960 individuals from 9 multi-ancestry biobank studies in 6 countries. The average weight change between 6 and 12 months from therapy initiation was -3.93% for GLP1-RA users, with marginal differences across genetic ancestries. For BS patients the weight change between 6 and 48 months from the operation was -21.17%. There were no significant associations between weight loss due to GLP1-RA and polygenic scores for BMI or type 2 diabetes or specific missense variants in the GLP1R, PCSK1 and APOE genes, after multiple-testing correction. However, a higher polygenic score for BMI was significantly linked to lower weight loss after BS (+0.7% for 1 standard deviation change in the polygenic score, P = 1.24×10-4). In contrast, higher weight at baseline was associated with greater weight loss. Our findings suggest that existing polygenic scores related to weight and type 2 diabetes and missense variants in the drug target gene do not have a large impact on GLP1-RA effectiveness. Our results also confirm the effectiveness of these treatments across all major continental ancestry groups considered.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.

AIMS/HYPOTHESIS: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European. METHODS: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations. RESULTS: We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. CONCLUSIONS/INTERPRETATION: Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. DATA AVAILABILITY: The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

Genetic associations with consumption of palatable foods in the absence of hunger in response to food cues in children.

OBJECTIVE: The objective of this study is to evaluate obesity-related genetic factors in relation to excess consumption and assess if food cues modify associations. METHODS: Children (9-12 years) completed a randomized crossover experiment. During two visits, children ate a preload and then snacks ad libitum while watching television, embedded with food or non-food advertisements to assess eating in the absence of hunger (EAH). Primary exposures were obesity-associated genotypes, FTO rs9939609 and MC4R rs571312, and a paediatric-specific polygenic risk score (PRS). Outcomes included consumption of all snacks (total EAH) and gummy candy only (gummy candy EAH). Linear mixed-effects models tested whether genetic exposures related to EAH outcomes. We tested for effect modification by food cues using multiplicative interaction terms. RESULTS: Among 177 children, each FTO risk allele was associated with a 30% increase in gummy candy EAH (p = 0.025) in adjusted models. Food cue exposure exacerbated associations between the FTO variant with gummy candy EAH (p = 0.046). No statistically significant associations were found between MC4R and EAH. CONCLUSION: The results suggest children with the FTO rs9939609 risk allele may be predisposed to excess consumption of candy and that this association may be exacerbated by food cues.

On the pathogenesis of obesity: causal models and missing pieces of the puzzle.

Application of the physical laws of energy and mass conservation at the whole-body level is not necessarily informative about causal mechanisms of weight gain and the development of obesity. The energy balance model (EBM) and the carbohydrate-insulin model (CIM) are two plausible theories, among several others, attempting to explain why obesity develops within an overall common physiological framework of regulation of human energy metabolism. These models have been used to explain the pathogenesis of obesity in individuals as well as the dramatic increases in the prevalence of obesity worldwide over the past half century. Here, we summarize outcomes of a recent workshop in Copenhagen that brought together obesity experts from around the world to discuss causal models of obesity pathogenesis. These discussions helped to operationally define commonly used terms; delineate the structure of each model, particularly focussing on areas of overlap and divergence; challenge ideas about the importance of purported causal factors for weight gain; and brainstorm on the key scientific questions that need to be answered. We hope that more experimental research in nutrition and other related fields, and more testing of the models and their predictions will pave the way and provide more answers about the pathogenesis of obesity than those currently available.

Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE checklist.

Precision medicine should aspire to reduce error and improve accuracy in medical and health recommendations by comparison with contemporary practice, while maintaining safety and cost-effectiveness. The etiology, clinical manifestation and prognosis of diseases such as obesity, diabetes, cardiovascular disease, kidney disease and fatty liver disease are heterogeneous. Without standardized reporting, this heterogeneity, combined with the diversity of research tools used in precision medicine studies, makes comparisons across studies and implementation of the findings challenging. Specific recommendations for reporting precision medicine research do not currently exist. The BePRECISE (Better Precision-data Reporting of Evidence from Clinical Intervention Studies & Epidemiology) consortium, comprising 23 experts in precision medicine, cardiometabolic diseases, statistics, editorial and lived experience, conducted a scoping review and participated in a modified Delphi and nominal group technique process to develop guidelines for reporting precision medicine research. The BePRECISE checklist comprises 23 items organized into 5 sections that align with typical sections of a scientific publication. A specific section about health equity serves to encourage precision medicine research to be inclusive of individuals and communities that are traditionally under-represented in clinical research and/or underserved by health systems. Adoption of BePRECISE by investigators, reviewers and editors will facilitate and accelerate equitable clinical implementation of precision medicine.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA

An integrative framework to prioritize genes in more than 500 loci associated with body mass index.

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

Meta-Analysis of Genome-Wide Association Studies Reveals Genetic Mechanisms of Supraventricular Arrhythmias.

BACKGROUND: Substantial data support a heritable basis for supraventricular tachycardias, but the genetic determinants and molecular mechanisms of these arrhythmias are poorly understood. We sought to identify genetic loci associated with atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular accessory pathways or atrioventricular reciprocating tachycardia (AVAPs/AVRT). METHODS: We performed multiancestry meta-analyses of genome-wide association studies to identify genetic loci for AVNRT (4 studies) and AVAP/AVRT (7 studies). We assessed evidence supporting the potential causal effects of candidate genes by analyzing relations between associated variants and cardiac gene expression, performing transcriptome-wide analyses, and examining prior genome-wide association studies. RESULTS: Analyses comprised 2384 AVNRT cases and 106 489 referents, and 2811 AVAP/AVRT cases and 1,483 093 referents. We identified 2 significant loci for AVNRT, which implicate NKX2-5 and TTN as disease susceptibility genes. A transcriptome-wide association analysis supported an association between reduced predicted cardiac expression of NKX2-5 and AVNRT. We identified 3 significant loci for AVAP/AVRT, which implicate SCN5A, SCN10A, and TTN/CCDC141. Variant associations at several loci have been previously reported for cardiac phenotypes, including atrial fibrillation, stroke, Brugada syndrome, and electrocardiographic intervals. CONCLUSIONS: Our findings highlight gene regions associated with ion channel function (AVAP/AVRT), as well as cardiac development and the sarcomere (AVAP/AVRT and AVNRT) as important potential effectors of supraventricular tachycardia susceptibility.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Genomic and phenotypic correlates of mosaic loss of chromosome Y in blood.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer's disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole genome sequencing of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European American (EA) ancestry group compared to those of Hispanic American (HA), African American (AA), and East Asian (EAS) ancestry. Further, we identified two genes ( CFHR1 and LRP6 ) that harbor multiple rare, putatively deleterious variants associated with mLOY susceptibility, show that subsets of human hematopoietic stem cells are enriched for activity of mLOY susceptibility variants, and that certain alleles on chromosome Y are more likely to be lost than others.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations.

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Genetic underpinnings of fasting and oral glucose-stimulated based insulin sensitivity indices.

CONTEXT: Insulin sensitivity (IS) is an important factor in type 2 diabetes (T2D) and can be estimated by many different indices. OBJECTIVE: We aimed to compare the genetic components underlying IS indices obtained from fasting and oral glucose-stimulated plasma glucose and serum insulin levels. METHODS: We computed 21 IS indices, classified as fasting, OGTT0,120 and OGTT0,30,120 indices, using fasting and oral glucose tolerance test (OGTT) data in two cohorts. We used data from a family cohort (n=313) to estimate the heritability and the genetic and phenotypic correlations of IS indices. The population cohort, Inter99 (n=5,343), was used to test for associations between IS indices and 426 genetic variants known to be associated with T2D. RESULTS: Heritability estimates of IS indices ranged between 19% and 38%. Fasting and OGTT0,30,120 indices had high genetic (ρG) and phenotypic (ρP) pairwise correlations (ρG and ρP: 0.88 to 1) The OGTT0,120 indices displayed a wide range of pairwise correlations (ρG: 0.17-1.00 and ρP: 0.13-0.97). We identified statistically significant associations between IS indices and established T2D-associated variants. The PPARG rs11709077 was associated only with fasting indices, and PIK3R rs4976033 only with OGTT0,30,120 indices. The variants in FAM63A/MINDY1, GCK, C2CD4A/B, and FTO loci were associated only with OGTT0,120 indices. CONCLUSION: Even though the IS indices mostly share a common genetic background, notable differences emerged between OGTT0,120 indices. The fasting and OGTT based indices have distinct associations with T2D risk variants. This work provides a basis for future large-scale genetic investigations into the differences between IS indices.