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Binary diagnosis of coronary artery disease does not preserve the complexity of disease or quantify its severity or its associated risk with death; hence, a quantitative marker of coronary artery disease is warranted. We evaluated a quantitative marker of coronary artery disease derived from probabilities of a machine learning model. In this cohort study, we developed and validated a coronary artery disease-predictive machine learning model using 95 935 electronic health records and assessed its probabilities as in-silico scores for coronary artery disease (ISCAD; range 0 [lowest probability] to 1 [highest probability]) in participants in two longitudinal biobank cohorts. We measured the association of ISCAD with clinical outcomes—namely, coronary artery stenosis, obstructive coronary artery disease, multivessel coronary artery disease, all-cause death, and coronary artery disease sequelae. Among 95 935 participants, 35 749 were from the BioMe Biobank (median age 61 years [IQR 18]; 14 599 [41%] were male and 21 150 [59%] were female; 5130 [14%] were with diagnosed coronary artery disease) and 60 186 were from the UK Biobank (median age 62 [15] years; 25 031 [42%] male and 35 155 [58%] female; 8128 [14%] with diagnosed coronary artery disease). The model predicted coronary artery disease with an area under the receiver operating characteristic curve of 0·95 (95% CI 0·94–0·95; sensitivity of 0·94 [0·94–0·95] and specificity of 0·82 [0·81–0·83]) and 0·93 (0·92–0·93; sensitivity of 0·90 [0·89–0·90] and specificity of 0·88 [0·87–0·88]) in the BioMe validation and holdout sets, respectively, and 0·91 (0·91–0·91; sensitivity of 0·84 [0·83–0·84] and specificity of 0·83 [0·82–0·83]) in the UK Biobank external test set. ISCAD captured coronary artery disease risk from known risk factors, pooled cohort equations, and polygenic risk scores. Coronary artery stenosis increased quantitatively with ascending ISCAD quartiles (increase per quartile of 12 percentage points), including risk of obstructive coronary artery disease, multivessel coronary artery disease, and stenosis of major coronary arteries. Hazard ratios (HRs) and prevalence of all-cause death increased stepwise over ISCAD deciles (decile 1: HR 1·0 [95% CI 1·0–1·0], 0·2% prevalence; decile 6: 11 [3·9–31], 3·1% prevalence; and decile 10: 56 [20–158], 11% prevalence). A similar trend was observed for recurrent myocardial infarction. 12 (46%) undiagnosed individuals with high ISCAD (≥0·9) had clinical evidence of coronary artery disease according to the 2014 American College of Cardiology/American Heart Association Task Force guidelines. Electronic health record-based machine learning was used to generate an in-silico marker for coronary artery disease that can non-invasively quantify atherosclerosis and risk of death on a continuous spectrum, and identify underdiagnosed individuals. National Institutes of Health.

Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome—not just genome-wide-significant loci—to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n  = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant–trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures. PolyFun is a computationally scalable framework for functionally informed fine-mapping that makes full use of genome-wide data. It prioritizes more variants than previous methods when applied to 49 complex traits from UK Biobank.

Causality between plasma triglyceride (TG) levels and atherosclerotic cardiovascular disease (ASCVD) risk remains controversial despite more than four decades of study and two recent landmark trials, STRENGTH, and REDUCE-IT. Further unclear is the association between TG levels and non-atherosclerotic diseases across organ systems. Here, we conducted a phenome-wide, two-sample Mendelian randomization (MR) analysis using inverse-variance weighted (IVW) regression to systematically infer the causal effects of plasma TG levels on 2600 disease traits in the European ancestry population of UK Biobank. For replication, we externally tested 221 nominally significant associations (p<0.05) in an independent cohort from FinnGen. To account for potential horizontal pleiotropy and the influence of invalid instrumental variables, we performed sensitivity analyses using MR-Egger regression, weighted median estimator, and MR-PRESSO. Finally, we used multivariable MR (MVMR) controlling for correlated lipid fractions to distinguish the independent effect of plasma TG levels. Our results identified seven disease traits reaching Bonferroni-corrected significance in both the discovery (p<1.92 × 10 ) and replication analyses (p<2.26 × 10 ), suggesting a causal relationship between plasma TG levels and ASCVDs, including coronary artery disease (OR 1.33, 95% CI 1.24-1.43, p=2.47 × 10 ). We also identified 12 disease traits that were Bonferroni-significant in the discovery or replication analysis and at least nominally significant in the other analysis (p<0.05), identifying plasma TG levels as a novel potential risk factor for nine non-ASCVD diseases, including uterine leiomyoma (OR 1.19, 95% CI 1.10-1.29, p=1.17 × 10 ). Taking a phenome-wide, two-sample MR approach, we identified causal associations between plasma TG levels and 19 disease traits across organ systems. Our findings suggest unrealized drug repurposing opportunities or adverse effects related to approved and emerging TG-lowering agents, as well as mechanistic insights for future studies. RD is supported by the National Institute of General Medical Sciences of the National Institutes of Health (NIH) (R35-GM124836) and the National Heart, Lung, and Blood Institute of the NIH (R01-HL139865 and R01-HL155915).

Background Advances in antiretroviral therapies have greatly improved the survival of people living with human immunodeficiency virus (HIV) infection (PLWH); yet, PLWH have a higher risk of cardiovascular disease than those without HIV. While numerous genetic loci have been linked to cardiometabolic risk in the general population, genetic predictors of the excessive risk in PLWH are largely unknown. Methods We screened for common and HIV-specific genetic variants associated with variation in lipid levels in 6284 PLWH (3095 European Americans [EA] and 3189 African Americans [AA]), from the Centers for AIDS Research Network of Integrated Clinical Systems cohort. Genetic hits found exclusively in the PLWH cohort were tested for association with other traits. We then assessed the predictive value of a series of polygenic risk scores (PRS) recapitulating the genetic burden for lipid levels, type 2 diabetes (T2D), and myocardial infarction (MI) in EA and AA PLWH. Results We confirmed the impact of previously reported lipid-related susceptibility loci in PLWH. Furthermore, we identified PLWH-specific variants in genes involved in immune cell regulation and previously linked to HIV control, body composition, smoking, and alcohol consumption. Moreover, PLWH at the top of European-based PRS for T2D distribution demonstrated a > 2-fold increased risk of T2D compared to the remaining 95% in EA PLWH but to a much lesser degree in AA. Importantly, while PRS for MI was not predictive of MI risk in AA PLWH, multiethnic PRS significantly improved risk stratification for T2D and MI. Conclusions Our findings suggest that genetic loci involved in the regulation of the immune system and predisposition to risky behaviors contribute to dyslipidemia in the presence of HIV infection. Moreover, we demonstrate the utility of the European-based and multiethnic PRS for stratification of PLWH at a high risk of cardiometabolic diseases who may benefit from preventive therapies.

Despite considerable progress on pathogenicity scores prioritizing variants for Mendelian disease, little is known about the utility of these scores for common disease. Here, we assess the informativeness of Mendelian disease-derived pathogenicity scores for common disease and improve upon existing scores. We first apply stratified linkage disequilibrium (LD) score regression to evaluate published pathogenicity scores across 41 common diseases and complex traits (average N = 320K). Several of the resulting annotations are informative for common disease, even after conditioning on a broad set of functional annotations. We then improve upon published pathogenicity scores by developing AnnotBoost, a machine learning framework to impute and denoise pathogenicity scores using a broad set of functional annotations. AnnotBoost substantially increases the informativeness for common disease of both previously uninformative and previously informative pathogenicity scores, implying that Mendelian and common disease variants share similar properties. The boosted scores also produce improvements in heritability model fit and in classifying disease-associated, fine-mapped SNPs. Our boosted scores may improve fine-mapping and candidate gene discovery for common disease. Pathogenicity scores are instrumental in prioritizing variants for Mendelian disease, yet their application to common disease is largely unexplored. Here, the authors assess the utility of pathogenicity scores for 41 complex traits and develop a framework to improve their informativeness for common disease.

Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Genetic architecture of cardiometabolic risks in people living with HIV [RAW_REF_TEXT] Haoxiang Cheng1 na1, [/RAW_REF_TEXT] [RAW_REF_TEXT] Anshuman Sewda1,2 na1, [/RAW_REF_TEXT] [RAW_REF_TEXT] Carla Marquez-Luna3, [/RAW_REF_TEXT] [RAW_REF_TEXT] Sierra R. White1, [/RAW_REF_TEXT] [RAW_REF_TEXT] Bridget M. Whitney4, [/RAW_REF_TEXT] [RAW_REF_TEXT] Jessica Williams-Nguyen4, [/RAW_REF_TEXT] [RAW_REF_TEXT] Robin M. Nance1,5, [/RAW_REF_TEXT] [RAW_REF_TEXT] Won Jun Lee1, [/RAW_REF_TEXT] [RAW_REF_TEXT] Mari M. Kitahata5,6, [/RAW_REF_TEXT] [RAW_REF_TEXT] Michael S. Saag7, [/RAW_REF_TEXT] [RAW_REF_TEXT] Amanda Willig7, [/RAW_REF_TEXT] [RAW_REF_TEXT] Joseph J. Eron8, [/RAW_REF_TEXT] [RAW_REF_TEXT] W. Christopher Mathews9, [/RAW_REF_TEXT] [RAW_REF_TEXT] Peter W. Hunt10, [/RAW_REF_TEXT] [RAW_REF_TEXT] Richard D. Moore11,12, [/RAW_REF_TEXT] [RAW_REF_TEXT] Allison Webel13, [/RAW_REF_TEXT] [RAW_REF_TEXT] Kenneth H. Mayer14, [/RAW_REF_TEXT] [RAW_REF_TEXT] Joseph A. Delaney4, [/RAW_REF_TEXT] [RAW_REF_TEXT] Paul K. Crane5, [/RAW_REF_TEXT] [RAW_REF_TEXT] Heidi M. Crane5,6, [/RAW_REF_TEXT] [RAW_REF_TEXT] Ke Hao1 na2 & [/RAW_REF_TEXT] [RAW_REF_TEXT] Inga Peter 1 na2 [/RAW_REF_TEXT] BMC Medicine volume 19, Article number: 114 (2021) Cite this article [RAW_REF_TEXT] 102 Accesses [/RAW_REF_TEXT] [RAW_REF_TEXT] Metrics details [/RAW_REF_TEXT] The Original Article was published on 28 October 2020 Correction to: BMC Med 18, 288 (2020) https://doi.org/10.1186/s12916-020-01762-z The original article [1] contained an error whereby first author, Haoxiang Cheng’s name was displayed incorrectly. Genetic architecture of cardiometabolic risks in people living with HIV [RAW_REF_TEXT] Haoxiang Cheng1 na1, Anshuman Sewda1,2 na1, Carla Marquez-Luna3, Sierra R. White1, Bridget M. Whitney4, Jessica Williams-Nguyen4, Robin M. Nance1,5, Won Jun Lee1, Mari M. Kitahata5,6, Michael S. Saag7, Amanda Willig7, Joseph J. Eron8, W. Christopher Mathews9, Peter W. Hunt10, Richard D. Moore11,12, Allison Webel13, Kenneth H. Mayer14, Joseph A. Delaney4, Paul K. Crane5, Heidi M. Crane5,6, Ke Hao1 na2 & Inga Peter 1 na2 [/RAW_REF_TEXT] BMC Medicine volume 19, Article number: 114 (2021) Cite this article [RAW_REF_TEXT] 102 Accesses Metrics details

Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R 2 = 0.144; highest R 2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts ( N = 1107 K) increased prediction R 2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits. Incorporating functional information has shown promise for improving polygenic risk prediction of complex traits. Here, the authors describe polygenic prediction method LDpred-funct, and demonstrate its utility across 21 heritable traits in the UK Biobank.

Studies have shown that drug targets with human genetic support are more likely to succeed in clinical trials. Hence, a tool integrating genetic evidence to prioritize drug target genes is beneficial for drug discovery. We built a genetic priority score (GPS) by integrating eight genetic features with drug indications from the Open Targets and SIDER databases. The top 0.83%, 0.28% and 0.19% of the GPS conferred a 5.3-, 9.9- and 11.0-fold increased effect of having an indication, respectively. In addition, we observed that targets in the top 0.28% of the score were 1.7-, 3.7- and 8.8-fold more likely to advance from phase I to phases II, III and IV, respectively. Complementary to the GPS, we incorporated the direction of genetic effect and drug mechanism into a directional version of the score called the GPS with direction of effect. We applied our method to 19,365 protein-coding genes and 399 drug indications and made all results available through a web portal. A human genetics-informed drug prioritization tool, genetic priority score (GPS), combines genetic features and drug datasets. GPS-supported indications are more likely to progress through clinical trials, suggesting the utility of this score for target prioritization.

Coronary artery disease (CAD) exists on a spectrum of disease represented by a combination of risk factors and pathogenic processes. An in silico score for CAD built using machine learning and clinical data in electronic health records captures disease progression, severity and underdiagnosis on this spectrum and could enhance genetic discovery efforts for CAD. Here we tested associations of rare and ultrarare coding variants with the in silico score for CAD in the UK Biobank, All of Us Research Program and Bio Me Biobank. We identified associations in 17 genes; of these, 14 show at least moderate levels of prior genetic, biological and/or clinical support for CAD. We also observed an excess of ultrarare coding variants in 321 aggregated CAD genes, suggesting more ultrarare variant associations await discovery. These results expand our understanding of the genetic etiology of CAD and illustrate how digital markers can enhance genetic association investigations for complex diseases. A machine learning-based, continuous in silico coronary artery disease (CAD) score built using electronic health record data is applied to rare variant association analysis of CAD, implicating novel candidate genes and biological mechanisms.

[...]we analyzed genotypes imputed with the Haplotype Reference Consortium (HRC), a more comprehensive SNP set that produced higher likelihoods for each model analyzed (Fig. 1b). In summary, the baseline-LD model attained higher likelihoods than the LDAK model; the S-LDSC method produced functional enrichment estimates nearly identical to those produced by the gold-standard S-LDSC + LDAK method (which was unbiased in simulations under both baseline-LD and LDAK models) in empirical analyses of 16 UK Biobank traits; and the lower enrichment estimates for LDAK (and SumHer) could potentially be explained by the assignment of zero weights to most SNPs. Accurate estimation of components of heritability relies on accurate modeling of genetic architectures, and we anticipate that new models and corresponding methods will continue to improve current knowledge. Steven Gaza!©1,2*, Carla Marquez-Luna2,3, Hilary K. Finucane©2,4* and Alkes L. Price©12,3,4* 1epartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 2Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. 3Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 4These authors jointly supervised this work: