A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

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.

Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Lipid levels are important markers for the development of cardio-metabolic diseases. Although hundreds of associated loci have been identified through genetic association studies, the contribution of genetic factors to variation in lipids is not fully understood, particularly in U.S. minority groups. We performed genome-wide association analyses for four lipid traits in over 45,000 ancestrally diverse participants from the Population Architecture using Genomics and Epidemiology (PAGE) Study, followed by a meta-analysis with several European ancestry studies. We identified nine novel lipid loci, five of which showed evidence of replication in independent studies. Furthermore, we discovered one novel gene in a PrediXcan analysis, minority-specific independent signals at eight previously reported loci, and potential functional variants at two known loci through fine-mapping. Systematic examination of known lipid loci revealed smaller effect estimates in African American and Hispanic ancestry populations than those in Europeans, and better performance of polygenic risk scores based on minority-specific effect estimates. Our findings provide new insight into the genetic architecture of lipid traits and highlight the importance of conducting genetic studies in diverse populations in the era of precision medicine.

Genome-wide effects of social status on DNA methylation in the brain of a cichlid fish, Astatotilapia burtoni.

BACKGROUND: Successful social behavior requires real-time integration of information about the environment, internal physiology, and past experience. The molecular substrates of this integration are poorly understood, but likely modulate neural plasticity and gene regulation. In the cichlid fish species Astatotilapia burtoni, male social status can shift rapidly depending on the environment, causing fast behavioral modifications and a cascade of changes in gene transcription, the brain, and the reproductive system. These changes can be permanent but are also reversible, implying the involvement of a robust but flexible mechanism that regulates plasticity based on internal and external conditions. One candidate mechanism is DNA methylation, which has been linked to social behavior in many species, including A. burtoni. But, the extent of its effects after A. burtoni social change were previously unknown. RESULTS: We performed the first genome-wide search for DNA methylation patterns associated with social status in the brains of male A. burtoni, identifying hundreds of Differentially Methylated genomic Regions (DMRs) in dominant versus non-dominant fish. Most DMRs were inside genes supporting neural development, synapse function, and other processes relevant to neural plasticity, and DMRs could affect gene expression in multiple ways. DMR genes were more likely to be transcription factors, have a duplicate elsewhere in the genome, have an anti-sense lncRNA, and have more splice variants than other genes. Dozens of genes had multiple DMRs that were often seemingly positioned to regulate specific splice variants. CONCLUSIONS: Our results revealed genome-wide effects of A. burtoni social status on DNA methylation in the brain and strongly suggest a role for methylation in modulating plasticity across multiple biological levels. They also suggest many novel hypotheses to address in mechanistic follow-up studies, and will be a rich resource for identifying the relationships between behavioral, neural, and transcriptional plasticity in the context of social status.

Hormonal regulation of social ascent and temporal patterns of behavior in an African cichlid.

For many species, social rank determines which individuals perform certain social behaviors and when. Higher ranking or dominant (DOM) individuals maintain status through aggressive interactions and perform courtship behaviors while non-dominant (ND) individuals do not. In some species ND individuals ascend (ASC) in social rank when the opportunity arises. Many important questions related to the mechanistic basis of social ascent remain to be answered. We probed whether androgen signaling regulates social ascent in male Astatotilapia burtoni, an African cichlid whose social hierarchy can be readily controlled in the laboratory. As expected, androgen receptor (AR) antagonism abolished reproductive behavior during social ascent. However, we discovered multiple AR- and status-dependent temporal behavioral patterns that typify social ascent and dominance. AR antagonism in ASC males increased the time between successive behaviors compared to DOM males. Socially ascending males, independent of AR activation, were more likely than DOM males to follow aggressive displays with another aggressive display. Further analyses revealed differences in the sequencing of aggressive and courtship behaviors, wherein DOM males were more likely than ASC males to follow male-directed aggression with courtship displays. Strikingly, this difference was driven mostly by ASC males taking longer to transition from aggression to courtship, suggesting ASC males can perform certain DOM-typical temporal behavioral patterns. Our results indicate androgen signaling is necessary for social ascent and hormonal signaling and social experience may shape the full suite of DOM-typical behavioral patterns.

Brains over brawn: experience overcomes a size disadvantage in fish social hierarchies.

Life experiences can alter cognitive abilities and subsequent behavior. Here we asked whether differences in experience could affect social status. In hierarchical animal societies, high-ranking males that typically win aggressive encounters gain territories and hence access to mates. To understand the relative contributions of social experience and physical environment on status, we used a highly territorial African cichlid fish species, Astatotilapia burtoni, that lives in a dynamic lek-like social hierarchy. Astatotilapia burtoni males are either dominant or submissive and can switch status rapidly depending on the local environment. Although dominant males are innately aggressive, we wondered whether they modulated their aggression based on experience. We hypothesized that as males mature they might hone their fighting tactics based on observation of other males fighting. We compared males of different ages and sizes in distinctly different physical environments and subsequently tested their fighting skills. We found that a size difference previously thought negligible (<10% body length) gave a significant advantage to the larger opponent. In contrast, we found no evidence that increasing environmental complexity affected status outcomes. Surprisingly, we found that males only a few days older than their opponents had a significant advantage during territorial disputes so that being older compensated for the disadvantage of being smaller. Moreover, the slightly older winners exploited a consistent fighting strategy, starting with lower levels of aggression on the first day that significantly increased on the second day, a pattern absent in younger winners. These data suggest that experience is an advantage during fights for status, and that social learning provides more relevant experience than the physical complexity of the territory.

Increased BMI associated with decreased breastfeeding initiation in Million Veteran Program participants.

Background: Breastfeeding has been associated with maternal and infant health benefits but has been inversely associated with body mass index (BMI) prepartum. Breastfeeding and BMI are both linked to socioeconomic factors. Methods: Data from parous female participants with available breastfeeding information from the Million Veteran Program cohort was included. BMI at enrollment and earliest BMI available were extracted, and polygenic scores (PGS) for BMI were calculated. We modeled breastfeeding for one month or more as a function of BMI at enrollment; earliest BMI where available pre-pregnancy; and PGS for BMI. We conducted Mendelian randomization for breastfeeding initiation using PGS as an instrumental variable. Results: A higher BMI predicted a lower likelihood of breastfeeding for one month or more in all analyses. A +5 kg/m 2 BMI pre-pregnancy was associated with a 24% reduced odds of breastfeeding, and a +5 kg/m 2 genetically predicted BMI was associated with a 17% reduced odds of breastfeeding. Conclusions: BMI predicts a lower likelihood of breastfeeding for one month or longer. Given the high success of breastfeeding initiation regardless of BMI in supportive environments as well as potential health benefits, patients with elevated BMI may benefit from additional postpartum breastfeeding support.

Genetically predicted lipoprotein(a) associates with coronary artery plaque severity independent of low-density lipoprotein cholesterol.

AIMS: Elevated Lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease, but the mechanisms of risk are debated. Studies have found inconsistent associations between Lp(a) and measurements of atherosclerosis. We aimed to assess the relationship between Lp(a), low-density lipoprotein cholesterol (LDL-C) and coronary artery plaque severity. METHODS: The study population consisted of participants of the Million Veteran Program who have undergone an invasive angiogram. The primary exposure was genetically predicted Lp(a), estimated by a polygenic score. Genetically predicted LDL-C was also assessed for comparison. The primary outcome was coronary artery plaque severity, categorized as normal, non-obstructive disease, 1-vessel disease, 2-vessel disease, and 3-vessel or left main disease. RESULTS: Among 18,927 adults of genetically inferred European ancestry and 4,039 adults of genetically inferred African ancestry, we observed consistent associations between genetically predicted Lp(a) and obstructive coronary plaque, with effect sizes trending upward for increasingly severe categories of disease. Associations were independent of risk factors, clinically measured LDL-C and genetically predicted LDL-C. However, we did not find strong or consistent evidence for an association between genetically predicted Lp(a) and risk for non-obstructive plaque. CONCLUSIONS: Genetically predicted Lp(a) is positively associated with coronary plaque severity independent of LDL-C, consistent with Lp(a) promoting atherogenesis. However, the effects of Lp(a) may be greater for progression of plaque to obstructive disease than for the initial development of non-obstructive plaque. A limitation of this study is that Lp(a) was estimated using genetic markers and could not be directly assayed, nor could apo(a) isoform size.

This study assessed the association between genetic propensity towards higher lipoprotein(a) [Lp(a)] in the blood and the severity of coronary artery plaque seen on clinical angiograms, independent of other factors, including low-density lipoprotein cholesterol (LDL-C). The study was conducted in a large U.S. population using data from the Million Veteran Program. Genetically predicted high Lp(a) was associated with obstructive coronary plaque, but it was not associated with non-obstructive coronary plaque. This association was independent of LDL-C, and the association was greater for more severe forms of disease.The mechanisms of association between Lp(a) and cardiovascular events are debated. Prior studies have shown that Lp(a) does not associate with early markers of atherosclerosis. Our analyses support the idea that Lp(a) plays less of a role in early plaque initiation but plays a significant role in the progression of plaque towards more severe disease, independent of LDL-C.

Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort.

Aims/hypothesis: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits. Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO2max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R2) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Δ) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM. Results: Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R2 of truncal fat and VO2max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Δ AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM. Conclusions/Interpretation: Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort.

Background: While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD. Method: Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Δ AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis. Result: The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Δ AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Δ AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD. Conclusion: A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

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.

Distinct neurogenomic states in basal ganglia subregions relate differently to singing behavior in songbirds.

Both avian and mammalian basal ganglia are involved in voluntary motor control. In birds, such movements include hopping, perching and flying. Two organizational features that distinguish the songbird basal ganglia are that striatal and pallidal neurons are intermingled, and that neurons dedicated to vocal-motor function are clustered together in a dense cell group known as area X that sits within the surrounding striato-pallidum. This specification allowed us to perform molecular profiling of two striato-pallidal subregions, comparing transcriptional patterns in tissue dedicated to vocal-motor function (area X) to those in tissue that contains similar cell types but supports non-vocal behaviors: the striato-pallidum ventral to area X (VSP), our focus here. Since any behavior is likely underpinned by the coordinated actions of many molecules, we constructed gene co-expression networks from microarray data to study large-scale transcriptional patterns in both subregions. Our goal was to investigate any relationship between VSP network structure and singing and identify gene co-expression groups, or modules, found in the VSP but not area X. We observed mild, but surprising, relationships between VSP modules and song spectral features, and found a group of four VSP modules that were highly specific to the region. These modules were unrelated to singing, but were composed of genes involved in many of the same biological processes as those we previously observed in area X-specific singing-related modules. The VSP-specific modules were also enriched for processes disrupted in Parkinson's and Huntington's Diseases. Our results suggest that the activation/inhibition of a single pathway is not sufficient to functionally specify area X versus the VSP and support the notion that molecular processes are not in and of themselves specialized for behavior. Instead, unique interactions between molecular pathways create functional specificity in particular brain regions during distinct behavioral states.

CXCL12 regulates coronary artery dominance in diverse populations and links development to disease.

Mammalian cardiac muscle is supplied with blood by right and left coronary arteries that form branches covering both ventricles of the heart. Whether branches of the right or left coronary arteries wrap around to the inferior side of the left ventricle is variable in humans and termed right or left dominance. Coronary dominance is likely a heritable trait, but its genetic architecture has never been explored. Here, we present the first large-scale multi-ancestry genome-wide association study of dominance in 61,043 participants of the VA Million Veteran Program, including over 10,300 Africans and 4,400 Admixed Americans. Dominance was moderately heritable with ten loci reaching genome wide significance. The most significant mapped to the chemokine CXCL12 in both Europeans and Africans. Whole-organ imaging of human fetal hearts revealed that dominance is established during development in locations where CXCL12 is expressed. In mice, dominance involved the septal coronary artery, and its patterning was altered with Cxcl12 deficiency. Finally, we linked human dominance patterns with coronary artery disease through colocalization, genome-wide genetic correlation and Mendelian Randomization analyses. Together, our data supports CXCL12 as a primary determinant of coronary artery dominance in humans of diverse backgrounds and suggests that developmental patterning of arteries may influence one's susceptibility to ischemic heart disease.

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification.

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification.

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

Coronary Artery Disease Risk of Familial Hypercholesterolemia Genetic Variants Independent of Clinically Observed Longitudinal Cholesterol Exposure.

BACKGROUND: Familial hypercholesterolemia (FH) genetic variants confer risk for coronary artery disease independent of LDL-C (low-density lipoprotein cholesterol) when considering a single measurement. In real clinical settings, longitudinal LDL-C data are often available through the electronic health record. It is unknown whether genetic testing for FH variants provides additional risk-stratifying information once longitudinal LDL-C is considered. METHODS: We used the extensive electronic health record data available through the Million Veteran Program to conduct a nested case-control study. The primary outcome was coronary artery disease, derived from electronic health record codes for acute myocardial infarction and coronary revascularization. Incidence density sampling was used to match case/control exposure windows, defined by the date of the first LDL-C measurement to the date of the first coronary artery disease code of the index case. Adjustments for the first, maximum, or mean LDL-C were analyzed. FH variants in LDLR, APOB, and PCSK9 (Proprotein convertase subtilisin/kexin type 9) were assessed by custom genotype array. RESULTS: In a cohort of 23 091 predominantly prevalent cases at enrollment and 230 910 matched controls, FH variant carriers had an increased risk for coronary artery disease (odds ratio [OR], 1.53 [95% CI, 1.24-1.89]). Adjusting for mean LDL-C led to the greatest attenuation of the risk estimate, but significant risk remained (odds ratio, 1.33 [95% CI, 1.08-1.64]). The degree of attenuation was not affected by the number and the spread of LDL-C measures available. CONCLUSIONS: The risk associated with carrying an FH variant cannot be fully captured by the LDL-C data available in the electronic health record, even when considering multiple LDL-C measurements spanning more than a decade.

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

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.

Genetic Loci Associated With COVID-19 Positivity and Hospitalization in White, Black, and Hispanic Veterans of the VA Million Veteran Program.

SARS-CoV-2 has caused symptomatic COVID-19 and widespread death across the globe. We sought to determine genetic variants contributing to COVID-19 susceptibility and hospitalization in a large biobank linked to a national United States health system. We identified 19,168 (3.7%) lab-confirmed COVID-19 cases among Million Veteran Program participants between March 1, 2020, and February 2, 2021, including 11,778 Whites, 4,893 Blacks, and 2,497 Hispanics. A multi-population genome-wide association study (GWAS) for COVID-19 outcomes identified four independent genetic variants (rs8176719, rs73062389, rs60870724, and rs73910904) contributing to COVID-19 positivity, including one novel locus found exclusively among Hispanics. We replicated eight of nine previously reported genetic associations at an alpha of 0.05 in at least one population-specific or the multi-population meta-analysis for one of the four MVP COVID-19 outcomes. We used rs8176719 and three additional variants to accurately infer ABO blood types. We found that A, AB, and B blood types were associated with testing positive for COVID-19 compared with O blood type with the highest risk for the A blood group. We did not observe any genome-wide significant associations for COVID-19 severity outcomes among those testing positive. Our study replicates prior GWAS findings associated with testing positive for COVID-19 among mostly White samples and extends findings at three loci to Black and Hispanic individuals. We also report a new locus among Hispanics requiring further investigation. These findings may aid in the identification of novel therapeutic agents to decrease the morbidity and mortality of COVID-19 across all major ancestral populations.