Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

BACKGROUND: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS: We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding and continuous shrinkage priors (polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176 988 individuals across 9 diverse cohorts. RESULTS: Multi-ancestry polygenic risk score for CHD developed using pruning and thresholding methods and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods outperformed ancestry-specific Polygenic risk score for CHD developed using pruning and thresholding methods and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, polygenic risk score for CHD developed using pruning and thresholding methods optimized using a multi-ancestry population and polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods optimized using a multi-ancestry population) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. Polygenic risk score for CHD developed using pruning and thresholding methods (PT) optimized using a multi-ancestry population demonstrated the strongest association with CHD in individuals of South Asian genetic ancestry and European genetic ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian genetic ancestry (1.56 [1.50-1.61]), Hispanic/Latino genetic ancestry (1.38 [1.24-1.54]), and African genetic ancestry (1.16 [1.11-1.21]). Polygenic risk score for CHD developed using ancestry-based continuous shrinkage methods optimized using a multi-ancestry population showed the strongest associations in South Asian genetic ancestry (2.67 [2.38-3.00]) and European genetic ancestry (1.65 [1.59-1.71]), lower in East Asian genetic ancestry (1.59 [1.54-1.64]), Hispanic/Latino genetic ancestry (1.51 [1.35-1.69]), and the lowest in African genetic ancestry (1.20 [1.15-1.26]). CONCLUSIONS: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African genetic ancestry. This highlights the need for larger Genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Genetic risk and likelihood of prostate cancer detection on first biopsy by ancestry.

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male veterans using 2-sided tests. Our analysis included 36 717 veterans (10 297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; P < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (odds ratio = 2.18, 95% confidence interval = 1.93 to 2.47), but the effect was stronger among men of European descent (odds ratio = 3.89, 95% confidence interval = 3.62 to 4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

Genetic architecture distinguishes tinnitus from hearing loss.

Tinnitus is a heritable, highly prevalent auditory disorder treated by multiple medical specialties. Previous GWAS indicated high genetic correlations between tinnitus and hearing loss, with little indication of differentiating signals. We present a GWAS meta-analysis, triple previous sample sizes, and expand to non-European ancestries. GWAS in 596,905 Million Veteran Program subjects identified 39 tinnitus loci, and identified genes related to neuronal synapses and cochlear structural support. Applying state-of-the-art analytic tools, we confirm a large number of shared variants, but also a distinct genetic architecture of tinnitus, with higher polygenicity and large proportion of variants not shared with hearing difficulty. Tissue-expression analysis for tinnitus infers broad enrichment across most brain tissues, in contrast to hearing difficulty. Finally, tinnitus is not only correlated with hearing loss, but also with a spectrum of psychiatric disorders, providing potential new avenues for treatment. This study establishes tinnitus as a distinct disorder separate from hearing difficulties.

A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization.

Background: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods: We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results: Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions: Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.

A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease.

Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.

Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy.

BACKGROUND: A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect. METHODS: We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization. RESULTS: Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; ß, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (ß, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007). CONCLUSIONS: Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

Oligogenic Architecture of Rare Noncoding Variants Distinguishes 4 Congenital Heart Disease Phenotypes.

BACKGROUND: Congenital heart disease (CHD) is highly heritable, but the power to identify inherited risk has been limited to analyses of common variants in small cohorts. METHODS: We performed reimputation of 4 CHD cohorts (n=55 342) to the TOPMed reference panel (freeze 5), permitting meta-analysis of 14 784 017 variants including 6 035 962 rare variants of high imputation quality as validated by whole genome sequencing. RESULTS: Meta-analysis identified 16 novel loci, including 12 rare variants, which displayed moderate or large effect sizes (median odds ratio, 3.02) for 4 separate CHD categories. Analyses of chromatin structure link 13 of the genome-wide significant loci to key genes in cardiac development; rs373447426 (minor allele frequency, 0.003 [odds ratio, 3.37 for Conotruncal heart disease]; P=1.49×10-8) is predicted to disrupt chromatin structure for 2 nearby genes BDH1 and DLG1 involved in Conotruncal development. A lead variant rs189203952 (minor allele frequency, 0.01 [odds ratio, 2.4 for left ventricular outflow tract obstruction]; P=1.46×10-8) is predicted to disrupt the binding sites of 4 transcription factors known to participate in cardiac development in the promoter of SPAG9. A tissue-specific model of chromatin conformation suggests that common variant rs78256848 (minor allele frequency, 0.11 [odds ratio, 1.4 for Conotruncal heart disease]; P=2.6×10-8) physically interacts with NCAM1 (PFDR=1.86×10-27), a neural adhesion molecule acting in cardiac development. Importantly, while each individual malformation displayed substantial heritability (observed h2 ranging from 0.26 for complex malformations to 0.37 for left ventricular outflow tract obstructive disease) the risk for different CHD malformations appeared to be separate, without genetic correlation measured by linkage disequilibrium score regression or regional colocalization. CONCLUSIONS: We describe a set of rare noncoding variants conferring significant risk for individual heart malformations which are linked to genes governing cardiac development. These results illustrate that the oligogenic basis of CHD and significant heritability may be linked to rare variants outside protein-coding regions conferring substantial risk for individual categories of cardiac malformation.

Relationship Between Ascending Thoracic Aortic Diameter and Blood Pressure: A Mendelian Randomization Study.

BACKGROUND: Observational studies identified elevated blood pressure (BP) as a strong risk factor for thoracic aortic dilation, and BP reduction is the primary medical intervention recommended to prevent progression of aortic aneurysms. However, although BP may impact aortic dilation, aortic size may also impact BP. The causal relationship between BP and thoracic aortic size has not been reliably established. METHODS: Genome-wide association studies summary statistics were obtained for BP and ascending thoracic aortic diameter (AscAoD). Causal effects of BP on AscAoD were estimated using 2-sample Mendelian randomization using a range of pleiotropy-robust methods. RESULTS: Genetically predicted increased systolic BP, diastolic BP, and mean arterial pressure all significantly associate with higher AscAoD (systolic BP: ß estimate, 0.0041 mm/mm Hg [95% CI, 0.0008-0.0074]; P=0.02, diastolic BP: ß estimate, 0.0272 mm/mm Hg [95% CI, 0.0224-0.0320]; P<0.001, and mean arterial pressure: ß estimate, 0.0168 mm/mm Hg [95% CI, 0.0130-0.0206]; P<0.001). Genetically predicted pulse pressure, meanwhile, had an inverse association with AscAoD (ß estimate, -0.0155 mm/mm Hg [95% CI, -0.0213 to -0.0096]; P<0.001). Multivariable Mendelian randomization analyses showed that genetically predicted increased mean arterial pressure and reduced pulse pressure were independently associated with AscAoD. Bidirectional Mendelian randomization demonstrated that genetically predicted AscAoD was inversely associated with pulse pressure (ß estimate, -2.0721 mm Hg/mm [95% CI, -3.1137 to -1.0306]; P<0.001) and systolic BP (ß estimate, -1.2878 mm Hg/mm [95% CI, -2.3533 to -0.2224]; P=0.02), while directly associated with diastolic BP (0.8203 mm Hg/mm [95% CI, 0.2735-1.3672]; P=0.004). CONCLUSIONS: BP likely contributes causally to ascending thoracic aortic dilation. Increased AscAoD likely contributes to lower systolic BP and pulse pressure, but not diastolic BP, consistent with the hemodynamic consequences of a reduced aortic diameter.

Genomics and phenomics of body mass index reveals a complex disease network.

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity.

Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic Architecture.

BACKGROUND: The risk of arterial diseases may be elevated among family members of individuals having multifocal fibromuscular dysplasia (FMD). We sought to investigate the risk of arterial diseases in families of individuals with FMD. METHODS: Family histories for 73 probands with FMD were obtained, which included an analysis of 463 total first-degree relatives focusing on FMD and related arterial disorders. A polygenic risk score for FMD (PRSFMD) was constructed from prior genome-wide association findings of 584 FMD cases and 7139 controls and evaluated for association with an abdominal aortic aneurysm (AAA) in a cohort of 9693 AAA cases and 294 049 controls. A previously published PRSAAA was also assessed among the FMD cases and controls. RESULTS: Of all first degree relatives of probands, 9.3% were diagnosed with FMD, aneurysms, and dissections. Aneurysmal disease occurred in 60.5% of affected relatives and 5.6% of all relatives. Among 227 female first-degree relatives of probands, 4.8% (11) had FMD, representing a relative risk (RR)FMD of 1.5 ([95% CI, 0.75-2.8]; P=0.19) compared with the estimated population prevalence of 3.3%, though not of statistical significance. Of all fathers of FMD probands, 11% had AAAs resulting in a RRAAA of 2.3 ([95% CI, 1.12-4.6]; P=0.014) compared with population estimates. The PRSFMD was found to be associated with an AAA (odds ratio, 1.03 [95% CI, 1.01-1.05]; P=2.6×10-3), and the PRSAAA was found to be associated with FMD (odds ratio, 1.53 [95% CI, 1.2-1.9]; P=9.0×10-5) as well. CONCLUSIONS: FMD and AAAs seem to be sex-dimorphic manifestations of a heritable arterial disease with a partially shared complex genetic architecture. Excess risk of having an AAA according to a family history of FMD may justify screening in family members of individuals having FMD.

Understanding the comorbidity between posttraumatic stress severity and coronary artery disease using genome-wide information and electronic health records.

The association between coronary artery disease (CAD) and posttraumatic stress disorder (PTSD) contributes to the high morbidity and mortality observed for these conditions. To understand the dynamics underlying PTSD-CAD comorbidity, we investigated large-scale genome-wide association (GWA) statistics from the Million Veteran Program (MVP), the UK Biobank (UKB), the Psychiatric Genomics Consortium, and the CARDIoGRAMplusC4D Consortium. We observed a genetic correlation of CAD with PTSD case-control and quantitative outcomes, ranging from 0.18 to 0.32. To investigate possible cause-effect relationships underlying these genetic correlations, we performed a two-sample Mendelian randomization (MR) analysis, observing a significant bidirectional relationship between CAD and PTSD symptom severity. Genetically-determined PCL-17 (PTSD 17-item Checklist) total score was associated with increased CAD risk (odds ratio = 1.04; 95% confidence interval, 95% CI = 1.01-1.06). Conversely, CAD genetic liability was associated with reduced PCL-17 total score (beta = -0.42; 95% CI = -0.04 to -0.81). Because of these opposite-direction associations, we conducted a pleiotropic meta-analysis to investigate loci with concordant vs. discordant effects on PCL-17 and CAD, observing that concordant-effect loci were enriched for molecular pathways related to platelet amyloid precursor protein (beta = 1.53, p = 2.97 × 10-7) and astrocyte activation regulation (beta = 1.51, p = 2.48 × 10-6) while discordant-effect loci were enriched for biological processes related to lipid metabolism (e.g., triglyceride-rich lipoprotein particle clearance, beta = 2.32, p = 1.61 × 10-10). To follow up these results, we leveraged MVP and UKB electronic health records (EHR) to assess longitudinal changes in the association between CAD and posttraumatic stress severity. This EHR-based analysis highlighted that earlier CAD diagnosis is associated with increased PCL-total score later in life, while lower PCL total score was associated with increased risk of a later CAD diagnosis (Mann-Kendall trend test: MVP tau = 0.932, p < 2 × 10-16; UKB tau = 0.376, p = 0.005). In conclusion, both our genetically-informed analyses and our EHR-based follow-up investigation highlighted a bidirectional relationship between PTSD and CAD where multiple pleiotropic mechanisms are likely to be involved.

Large-scale genome-wide association study of coronary artery disease in genetically diverse populations.

We report a genome-wide association study (GWAS) of coronary artery disease (CAD) incorporating nearly a quarter of a million cases, in which existing studies are integrated with data from cohorts of white, Black and Hispanic individuals from the Million Veteran Program. We document near equivalent heritability of CAD across multiple ancestral groups, identify 95 novel loci, including nine on the X chromosome, detect eight loci of genome-wide significance in Black and Hispanic individuals, and demonstrate that two common haplotypes at the 9p21 locus are responsible for risk stratification in all populations except those of African origin, in which these haplotypes are virtually absent. Moreover, in the largest GWAS for angiographically derived coronary atherosclerosis performed to date, we find 15 loci of genome-wide significance that robustly overlap with established loci for clinical CAD. Phenome-wide association analyses of novel loci and polygenic risk scores (PRSs) augment signals related to insulin resistance, extend pleiotropic associations of these loci to include smoking and family history, and precisely document the markedly reduced transferability of existing PRSs to Black individuals. Downstream integrative analyses reinforce the critical roles of vascular endothelial, fibroblast, and smooth muscle cells in CAD susceptibility, but also point to a shared biology between atherosclerosis and oncogenesis. This study highlights the value of diverse populations in further characterizing the genetic architecture of CAD.

Broad clinical manifestations of polygenic risk for coronary artery disease in the Women's Health Initiative.

Background: The genetic basis for coronary artery disease (CAD) risk is highly complex. Genome-wide polygenic risk scores (PRS) can help to quantify that risk, but the broader impacts of polygenic risk for CAD are not well characterized. Methods: We measured polygenic risk for CAD using the meta genomic risk score, a previously validated genome-wide PRS, in a subset of genotyped participants from the Women's Health Initiative and applied a phenome-wide association study framework to assess associations between the PRS and a broad range of blood biomarkers, clinical measurements, and health outcomes. Results: Polygenic risk for CAD is associated with a variety of biomarkers, clinical measurements, behaviors, and diagnoses related to traditional risk factors, as well as risk-enhancing factors. Analysis of adjudicated outcomes shows a graded association between atherosclerosis related outcomes, with the highest odds ratios being observed for the most severe manifestations of CAD. We find associations between increased polygenic risk for CAD and decreased risk for incident breast and lung cancer, with replication of the breast cancer finding in an external cohort. Genetic correlation and two-sample Mendelian randomization suggest that breast cancer association is likely due to horizontal pleiotropy, while the association with lung cancer may be causal. Conclusion: Polygenic risk for CAD has broad clinical manifestations, reflected in biomarkers, clinical measurements, behaviors, and diagnoses. Some of these associations may represent direct pathways between genetic risk and CAD while others may reflect pleiotropic effects independent of CAD risk.

Use of Polygenic Risk Scores for Coronary Heart Disease in Ancestrally Diverse Populations.

PURPOSE OF REVIEW: A polygenic risk score (PRS) is a measure of genetic liability to a disease and is typically normally distributed in a population. Individuals in the upper tail of this distribution often have relative risk equivalent to that of monogenic form of the disease. The majority of currently available PRSs for coronary heart disease (CHD) have been generated from cohorts of European ancestry (EUR) and vary in their applicability to other ancestry groups. In this report, we review the performance of PRSs for CHD across different ancestries and efforts to reduce variability in performance including novel population and statistical genetics approaches. RECENT FINDINGS: PRSs for CHD perform robustly in EUR populations but lag in performance in non-EUR groups, particularly individuals of African ancestry. Several large consortia have been established to enable genomic studies in diverse ancestry groups and develop methods to improve PRS performance in multi-ancestry contexts as well as admixed individuals. These include fine-mapping to ascertain causal variants, trans ancestry meta-analyses, and ancestry deconvolution in admixed individuals. PRSs are being used in the clinical setting but enthusiasm has been tempered by the variable performance in non-EUR ancestry groups. Increasing diversity in genomic association studies and continued innovation in methodological approaches are needed to improve PRS performance in non-EUR individuals for equitable implementation of genomic medicine.

A multi-population phenome-wide association study of genetically-predicted height in the Million Veteran Program.

BACKGROUND: Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. METHODS AND FINDINGS: Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. CONCLUSIONS: We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits.

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation.

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10-8). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10-4), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.