University of Michigan Center for Statistical 


Global Lipids Genetics Consortium Results

Result Files

Ancestry-specific GWAS summary statistics for HDL-C, LDL-C, nonHDL-C, TC and TG (90 files plus README)

Trans-ancestry GWAS summary statistics for HDL-C, LDL-C, nonHDL-C, TC and C (20 files plus README)

LDL-C polygenic score weights for ALL, Admixed African, East Asian, European, Hispanic and South Asian ancestries (12 files plus README)

Trans-ancestry credible sets for HDL-C, LDL-C, nonHDL-C, TC and TG (5 files plus README)

Noncoding variant prioritization at lipid loci

ChrX GWAS summary statistics for HDL-C, LDL-C, nonHDL-C, TC and TG (180 files)

Sex-specific GWAS summary statistics for HDL-C, LDL-C, nonHDL-C, TC and TG (20 files)


Graham et al. (2021) The power of genetic diversity in genome-wide association studies of lipids. In press at Nature.

Kanoni et al. medRxiv (2021) Implicating genes, pleiotropy and sexual dimorphism at blood lipid loci through multiancestry meta-analysis.

Ramdas et al. bioRxiv (2021) A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.


The Global Lipids Genetics Consortium aggregated GWAS results from 1,654,960 individuals from 201 primary studies representing five genetic ancestry groups: Admixed African or African (AdmAFR, N=99.4k, 6.0% of sample), East Asian (EAS, N=146.5k, 8.9%), European (EUR, N=1.32m, 79.8%), Hispanic (HIS, N=48.1k, 2.9%), and South Asian (SAS, N=41.0k, 2.5%). We performed GWAS for five blood lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), total cholesterol (TC), and non-high-density lipoprotein cholesterol (nonHDL-C). We performed meta-analysis within each ancestry using RAREMETAL. For the meta-analysis of all ancestries we performed meta-analysis of single cohort files using MR-MEGA to account for heterogeneity in variant effect sizes on lipids between ancestry groups. For the all ancestry analysis, fixed-effects meta-analysis was carried out with METAL to estimate effect sizes and se. The meta-analysis summary statistics include all studies (i.e. UK biobank is included in the meta-analysis).

Additionally, we generated LDL-C polygenic scores from the meta-analyses. We evaluated the potential of polygenic scores to predict elevated LDL-C, a major causal risk factor of CAD, in diverse ancestry groups. We created three non-overlapping datasets to separately: i) perform ancestry-specific or trans-ancestry GWAS to estimate variant effect sizes, ii) optimize risk score parameters, and iii) evaluate the utility of the resulting scores. For each ancestry-specific or trans-ancestry GWAS we created multiple polygenic score weights -- either genome-wide with PRS-CS or using pruning and thresholding to select independent variants. We tested each score in the optimizing dataset, which was matched for ancestry to the GWAS (AdmAFR, EAS, EUR, SAS, ALL from UK biobank or HIS from Michigan Genomics Initiative, MGI). The top-performing score from each GWAS was selected: PRS-CS for East Asian ancestry, European ancestry, and European ancestry 2010 scores from a previous GLGC GWAS, and an optimized pruning and threshold-based score for all others. We provide the optimized P&T and the PRS-CS score weights for each ancestry group and for the trans-ancestry analysis (note: UK biobank was excluded from the meta-analysis used to generate the weights with the exception of the trans-ancestry risk score, which included UK Biobank South Asian ancestry individuals (these individuals were excluded from the testing set) due to an initial focus on comparing predictions among European and African ancestry individuals).

Please e-mail Cristen Willer with comments or suggestions.


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