Elevated blood lipid levels are heritable risk factors of cardiovascular disease with varying prevalence worldwide due to differing dietary patterns and medication use 1 . Despite advances in prevention and treatment, particularly through the lowering of low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wide association studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants contributing to lipid level variation in other ancestry groups due to differences in allele frequencies, effect

SummaryWith the increasing availability of biobank-scale datasets that incorporate both genomic data and electronic health records, many associations between genetic variants and phenotypes of interest have been discovered. Polygenic risk scores (PRS), which are being widely explored in precision medicine, use the results of association studies to predict the genetic component of disease risk by accumulating risk alleles weighted by their effect sizes. However, limited studies have thoroughly investigated best practices for PRS in global populations across different diseases. In this study, we utilize data from the Global-Biobank Meta-analysis Initiative (GBMI), which consists of individuals from diverse ancestries and across continents, to explore methodological considerations and PRS prediction performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRS using heuristic (pruning and thresholding, P+T) and Bayesian (PRS-CS) methods. We found that the genetic architecture, such as SNP-based heritability and polygenicity, varied greatly among endpoints. For both PRS construction methods, using a European ancestry LD reference panel resulted in comparable or higher prediction accuracy compared to several other non-European based panels; this is largely attributable to European descent populations still comprising the majority of GBMI participants. PRS-CS overall outperformed the classic P+T method, especially for endpoints with higher SNP-based heritability. For example, substantial improvements are observed in East-Asian ancestry (EAS) using PRS-CS compared to P+T for heart failure (HF) and chronic obstructive pulmonary disease (COPD). Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma which has known variation in disease prevalence across global populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using the GBMI and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Identifying individuals at high risk of heart failure during precursor stages could allow for earlier initiation of treatments to modify disease progression. We performed a GWAS meta-analysis to generate a heart failure (HF) polygenic risk score (PRS) then tested the association with phenotypic subtypes (reduced ejection fraction [HFrEF] and preserved ejection fraction [HFpEF]) to evaluate the value of polygenic risk prediction. Results from the European-ancestry analysis showed that an ancestry-matched PRS, calculated from GBMI meta-analysis outperformed the previous HF GWAS (HERMES), yielding an adjusted odds ratio (aOR) of 2.27 (95% CI: 2.05-2.51; p: 1.76x10-56) from GBMI compared to 1.30 (95% CI: 1.18-1.44; p: 1.42x10-7) from HERMES, and 1.49 (95% CI: 1.33-1.66; p: 8.38x10-13) compared to 1.17 (95% CI: 1.05-1.31; p: 0.004) for HFrEF and HFpEF, respectively. Next, we evaluated the performance differences between ancestry-matched and multi-ancestry PRS in the African American cohort. The GBMI multi-ancestry GWAS-based PRS had a significant aOR of 1.49 (p: 0.006). Findings suggest that a PRS for heart failure derived from the GBMI multi-ancestry study is useful in predicting HFrEF, but less powerful in predicting HFpEF in an independent cohort. The difficulty in predicting HFpEF could result from the GBMI HF phenotype, preferencing HFrEF over HFpEF, and/or greater genetic heterogeneity in the HFpEF phenotype.

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SummaryGenomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWAS) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (Nmean = 1,329,242). First, we evaluated the overlap enrichment between disease risk genes and the drug-target genes of the disease-relevant medication categories. An omnibus approach integrating the four gene prioritization tools yielded twice the enrichment in the disease-relevant medication categories compared with any single tool, and identified drugs with approved indications for asthma, gout, and venous thromboembolism. Second, we performed an endophenotype Mendelian randomization analysis using protein quantitative trait loci as instrumental variables. After the application of quality controls, including a colocalization analysis, significant causal relationships were estimated for 18 protein–disease pairs, including MAP2K inhibitors for heart failure. Third, we conducted an in silico screening for negative correlations between genetically determined disease case–control gene expression profiles and compound-regulated ones. Significant negative correlations were observed for 31 compound–disease pairs, including a histone deacetylase inhibitor for asthma. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analysis.