Mendelian randomization analyses clarify the effects of height on cardiovascular diseases.

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by cardiovascular, anthropometric, lung function, and lifestyle-related risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

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.

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program.

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

Digital Footprints of Obesity Treatment: GLP-1 Receptor Agonists and the Health Equity Divide.

Our research investigates the societal implications of access to glucagon-like peptide-1 (GLP-1) agonists, particularly in light of recent clinical trials demonstrating the efficacy of semaglutide in reducing cardiovascular mortality. A decade-long analysis of Google Trends indicates a significant increase in searches for GLP-1 agonists, primarily in North America. This trend contrasts with the global prevalence of obesity. Given the high cost of GLP-1 agonists, a critical question arises: Will this disparity in medication accessibility exacerbate the global health equity gap in obesity treatment? This viewpoint explores strategies to address the health equity gap exacerbated by this emerging medication. Because GLP-1 agonists hold the potential to become a cornerstone in obesity treatment, ensuring equitable access is a pressing public health concern.

Design and Pilot Results from Million Veteran Program Return Of Actionable Genetic Results (MVP-ROAR) Study.

BACKGROUND: As a mega-biobank linked to a national healthcare system, the Million Veteran Program (MVP) can directly improve the health care of participants. To determine the feasibility and outcomes of returning medically actionable genetic results to MVP participants, the program launched the MVP Return Of Actionable Results (MVP-ROAR) Study, with familial hypercholesterolemia (FH) as an exemplar actionable condition. METHODS: The MVP-ROAR Study consists of a completed single-arm pilot phase and an ongoing randomized clinical trial (RCT), in which MVP participants are recontacted and invited to receive clinical confirmatory gene sequencing testing and a telegenetic counseling intervention. The primary outcome of the RCT is 6-month change in low-density lipoprotein cholesterol (LDL-C) between participants receiving results at baseline and those receiving results after 6 months. RESULTS: The pilot developed processes to identify and recontact participants nationally with probable pathogenic variants in low-density lipoprotein receptor (LDLR) on the MVP genotype array, invite them to clinical confirmatory gene sequencing, and deliver a telegenetic counseling intervention. Among participants in the pilot phase, 8 (100%) had active statin prescriptions after 6 months. Results were shared with 16 first-degree family members. Six-month ΔLDL-C (low-density lipoprotein cholesterol) after the genetic counseling intervention was -37 mg/dL (95% CI: -12 to -61; p=0.03). The ongoing RCT will determine between-arm differences in this primary outcome. CONCLUSION: While underscoring the importance of clinical confirmation of research results, the pilot phase of the MVP-ROAR Study marks a turning point in MVP and demonstrates the feasibility of returning genetic results to participants and their providers. The ongoing RCT will contribute to understanding how such a program might improve patient health care and outcomes.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers.

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Development and utility of a clinical research informatics application for participant recruitment and workflow management for a return of results pilot trial in familial hypercholesterolemia in the Million Veteran Program.

Objective: The development of clinical research informatics tools and workflow processes associated with re-engaging biobank participants has become necessary as genomic repositories increasingly consider the return of actionable research results. Materials and Methods: Here we describe the development and utility of an informatics application for participant recruitment and enrollment management for the Veterans Affairs Million Veteran Program Return Of Actionable Results Study, a randomized controlled pilot trial returning individual genetic results associated with familial hypercholesterolemia. Results: The application is developed in Python-Flask and was placed into production in November 2021. The application includes modules for chart review, medication reconciliation, participant contact and biospecimen logging, survey recording, randomization, and documentation of genetic counseling and result disclosure. Three primary users, a genetic counselor and two research coordinators, and 326 Veteran participants have been integrated into the system as of February 23, 2023. The application has successfully handled 3367 task requests involving greater than 95 000 structured data points. Specifically, application users have recorded 326 chart reviews, 867 recruitment telephone calls, 158 telephone-based surveys, and 61 return of results genetic counseling sessions, among other available study tasks. Conclusion: The development of usable, customizable, and secure informatics tools will become increasingly important as large genomic repositories begin to return research results at scale. Our work provides a proof-of-concept for developing and using such tools to aid in managing the return of results process within a national biobank.

A functional genomic framework to elucidate novel causal non-alcoholic fatty liver disease genes.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for NAFLD have been hampered by the relative paucity of human data from gold-standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using NAFLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined GWAS of NAFLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for NAFLD. Approach & Results: We used the UK Biobank to explore the associations of our novel NAFLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study NAFLD genes in vitro using CRISPRi. Our data identify VKORC1, TNKS, LYPLAL1 and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of NAFLD. Conclusions: Complementary genetic and genomic approaches are useful for the identification of NAFLD genes. Our data supports VKORC1 as a bona fide NAFLD gene. We have established a functional genomic framework to study at scale putative novel NAFLD genes from human genetic association studies.

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 (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) 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 PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African 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 ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.

Diet Quality and Epigenetic Aging in the Women's Health Initiative.

BACKGROUND: Higher diet quality scores are associated with a lower risk for many chronic diseases and all-cause mortality; however, it is unclear if diet quality is associated with aging biology. OBJECTIVE: This study aimed to examine the association between diet quality and a measure of biological aging known as epigenetic aging. DESIGN: A cross-sectional data analysis was used to examine the association between three diet quality scores based on self-reported food frequency questionnaire data and five measures of epigenetic aging based on DNA methylation (DNAm) data from peripheral blood. PARTICIPANTS/SETTING: This study included 4,500 postmenopausal women recruited from multiple sites across the United States (1993-98), aged 50 to 79 years, with food frequency questionnaire and DNAm data available from the Women's Health Initiative baseline visit. MAIN OUTCOME MEASURES: Five established epigenetic aging measures were generated from HumanMethylation450 Beadchip DNAm data, including AgeAccelHannum, AgeAccelHorvath, AgeAccelPheno, AgeAccelGrim, and DunedinPACE. STATISTICAL ANALYSES PERFORMED: Linear mixed models were used to test for associations between three diet quality scores (Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores) and epigenetic aging measures, adjusted for age, race and ethnicity, education, tobacco smoking, physical activity, Women's Health Initiative substudy from which DNAm data were obtained, and DNAm-based estimates of leukocyte proportions. RESULTS: Healthy Eating Index, Dietary Approaches to Stop Hypertension, and alternate Mediterranean diet scores were all inversely associated with AgeAccelPheno, AgeAccelGrim, and DunedinPACE (P < 0.05), with the largest effects with DunedinPACE. A one standard deviation increment in diet quality scores was associated with a decrement (ß ± SE) in DunedinPACE z score of -0.097 ± 0.014 (P = 9.70 x 10-13) for Healthy Eating Index, -0.107 ± 0.014 (P = 1.53 x 10-14) for Dietary Approaches to Stop Hypertension, and -0.068 ± 0.013 (P = 2.31 x 10-07) for the alternate Mediterranean diet. CONCLUSIONS: In postmenopausal women, diet quality scores were inversely associated with DNAm-based measures of biological aging, particularly DunedinPACE.

Cardiorespiratory fitness and risk of heart failure with preserved ejection fraction.

AIMS: Preventive strategies for heart failure with preserved ejection fraction (HFpEF) include pharmacotherapies and lifestyle modifications. However, the association between cardiorespiratory fitness (CRF) assessed objectively by a standardized exercise treadmill test (ETT) and the risk of HFpEF has not been evaluated. Thus, we evaluated the association between CRF and HFpEF incidence. METHODS AND RESULTS: We assessed CRF in US Veterans (624 551 men; mean age 61.2 ± 9.7 years and 43 179 women; mean age 55.0 ± 8.9 years) by a standardized ETT performed between 1999 and 2020 across US Veterans Affairs Medical Centers. All had no evidence of heart failure or myocardial infarction prior to completion of the ETT. We assigned participants to one of five age- and gender-specific CRF categories (quintiles) based on peak metabolic equivalents (METs) achieved during the ETT and four categories based on CRF changes in those with two ETT evaluations (n = 139 434) ≥1.0 year apart. During a median follow-up of 10.1 years (interquartile range 6.0-14.3 years), providing 6 879 229 person-years, there were 16 493 HFpEF events with an average annual rate of 2.4 events per 1000 person-years. The adjusted risk of HFpEF decreased across CRF categories as CRF increased, independent of comorbidities. For fit individuals (≥10.5 METs) the hazard ratio (HR) was 0.48 (95% confidence interval [CI] 0.46-0.51) compared with least fit (≤4.9 METs; referent). Being unfit carried the highest risk (HR 2.88, 95% CI 2.67-3.11) of any other comorbidity. The risk of unfit individuals who became fit was 37% lower (HR 0.63, 95% CI 0.57-0.71), compared to those who remained unfit. CONCLUSIONS: Higher CRF levels are independently associated with lower HFpEF in a dose-response manner. Changes in CRF reflected proportional changes in HFpEF risk, suggesting that the HFpEF risk was modulated by CRF.

Mendelian randomization analyses suggest a causal role for circulating GIP and IL-1RA levels in homeostatic model assessment-derived measures of ß-cell function and insulin sensitivity in Africans without type 2 diabetes.

BACKGROUND: In vitro and in vivo studies have shown that certain cytokines and hormones may play a role in the development and progression of type 2 diabetes (T2D). However, studies on their role in T2D in humans are scarce. We evaluated associations between 11 circulating cytokines and hormones with T2D among a population of sub-Saharan Africans and tested for causal relationships using Mendelian randomization (MR) analyses. METHODS: We used logistic regression analysis adjusted for age, sex, body mass index, and recruitment country to regress levels of 11 cytokines and hormones (adipsin, leptin, visfatin, PAI-1, GIP, GLP-1, ghrelin, resistin, IL-6, IL-10, IL-1RA) on T2D among Ghanaians, Nigerians, and Kenyans from the Africa America Diabetes Mellitus study including 2276 individuals with T2D and 2790 non-T2D individuals. Similar linear regression models were fitted with homeostatic modelling assessments of insulin sensitivity (HOMA-S) and ß-cell function (HOMA-B) as dependent variables among non-T2D individuals (n = 2790). We used 35 genetic variants previously associated with at least one of these 11 cytokines and hormones among non-T2D individuals as instrumental variables in univariable and multivariable MR analyses. Statistical significance was set at 0.0045 (0.05/11 cytokines and hormones). RESULTS: Circulating GIP and IL-1RA levels were associated with T2D. Nine of the 11 cytokines and hormones (exceptions GLP-1 and IL-6) were associated with HOMA-S, HOMA-B, or both among non-T2D individuals. Two-stage least squares MR analysis provided evidence for a causal effect of GIP and IL-RA on HOMA-S and HOMA-B in multivariable analyses (GIP ~ HOMA-S ß = - 0.67, P-value = 1.88 × 10-6 and HOMA-B ß = 0.59, P-value = 1.88 × 10-5; IL-1RA ~ HOMA-S ß = - 0.51, P-value = 8.49 × 10-5 and HOMA-B ß = 0.48, P-value = 5.71 × 10-4). IL-RA was partly mediated via BMI (30-34%), but GIP was not. Inverse variance weighted MR analysis provided evidence for a causal effect of adipsin on T2D (multivariable OR = 1.83, P-value = 9.79 × 10-6), though these associations were not consistent in all sensitivity analyses. CONCLUSIONS: The findings of this comprehensive MR analysis indicate that circulating GIP and IL-1RA levels are causal for reduced insulin sensitivity and increased ß-cell function. GIP's effect being independent of BMI suggests that circulating levels of GIP could be a promising early biomarker for T2D risk. Our MR analyses do not provide conclusive evidence for a causal role of other circulating cytokines in T2D among sub-Saharan Africans.

CYP2C19 Polymorphisms and Clinical Outcomes Following Percutaneous Coronary Intervention (PCI) in the Million Veterans Program.

Background: CYP2C19 loss-of-function (LOF) alleles decrease the antiplatelet effect of clopidogrel following percutaneous coronary intervention (PCI) in patients presenting with acute coronary syndrome (ACS). The impact of genotype in stable ischemic heart disease (SIHD) is unclear. Objectives: Determine the association of CYP2C19 genotype with major adverse cardiac events (MACE) after PCI for ACS or SIHD. Methods: Million Veterans Program (MVP) participants age <65 years with a PCI documented in the VA Clinical Assessment, Reporting and Tracking (CART) Program between 1/1/2009 to 9/30/2017, treated with clopidogrel were included. Time to MACE defined as the composite of all-cause death, stroke or myocardial infarction within 12 months following PCI. Results: Among 4,461 Veterans (mean age 59.1 ± 5.1 years, 18% Black); 44% had ACS, 56% had SIHD and 29% carried a CYP2C19 LOF allele. 301 patients (6.7%) experienced MACE while being treated with clopidogrel, 155 (7.9%) in the ACS group and 146 (5.9%) in the SIHD group. Overall, MACE was not significantly different between LOF carriers vs. noncarriers (adjusted hazard ratio [HR] 1.18, confidence interval [95%CI] 0.97-1.45, p=0.096). Among patients presenting with ACS, MACE risk in LOF carriers versus non-carriers was numerically higher (HR 1.30, 95%CI 0.98-1.73, p=0.067). There was no difference in MACE risk in patients with SIHD (HR 1.09, 95%CI 0.82-1.44; p=0.565). Conclusions: CYP2C19 LOF carriers presenting with ACS treated with clopidogrel following PCI experienced a numerically greater elevated risk of MACE events. CYP2C19 LOF genotype is not associated with MACE among patients presenting with SIHD.

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.

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.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program.

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P<4.6×10-11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

Systems Age: A single blood methylation test to quantify aging heterogeneity across 11 physiological systems.

Individuals, organs, tissues, and cells age in diverse ways throughout the lifespan. Epigenetic clocks attempt to quantify differential aging between individuals, but they typically summarize aging as a single measure, ignoring within-person heterogeneity. Our aim was to develop novel systems-based methylation clocks that, when assessed in blood, capture aging in distinct physiological systems. We combined supervised and unsupervised machine learning methods to link DNA methylation, system-specific clinical chemistry and functional measures, and mortality risk. This yielded a panel of 11 system-specific scores- Heart, Lung, Kidney, Liver, Brain, Immune, Inflammatory, Blood, Musculoskeletal, Hormone, and Metabolic. Each system score predicted a wide variety of outcomes, aging phenotypes, and conditions specific to the respective system, and often did so more strongly than existing epigenetic clocks that report single global measures. We also combined the system scores into a composite Systems Age clock that is predictive of aging across physiological systems in an unbiased manner. Finally, we showed that the system scores clustered individuals into unique aging subtypes that had different patterns of age-related disease and decline. Overall, our biological systems based epigenetic framework captures aging in multiple physiological systems using a single blood draw and assay and may inform the development of more personalized clinical approaches for improving age-related quality of life.

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.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

AIMS/HYPOTHESIS: The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. METHODS: We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). RESULTS: A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. CONCLUSIONS/INTERPRETATION: A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.