The power of genetic diversity in genome-wide association studies of lipids

Abstract

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use¹. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels², heart disease remains the leading cause of death worldwide³. Genome-wideassociation 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 that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns²⁴. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine²⁵, we anticipate that increased diversity of participants will lead to more accurate and equitable²⁶ application of polygenic scores in clinical practice.

Extended Data Figure 1:. Effect sizes of identified index variants from trans-ancestry meta-analysis

Index variants associated with a) HDL cholesterol, b) LDL cholesterol, c) triglycerides, d) nonHDL cholesterol and e) total cholesterol include both common variants of small to moderate effect and low frequency variants of moderate to large effect.

Extended Data Figure 2:. Comparison of the number of index variants by sample size

a) Comparison of the number of index variants reaching genome-wide significance (p < 5x10⁻⁸) from meta-analysis of LDL-C in each ancestry group. A meta-analysis of five random subsets of European cohorts selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals is also shown. b) Comparison of chi-squared values from meta-analysis of LDL-C for each possible combination of ancestry groups (without genomic-control correction) for variants with minor allele frequency (MAF) ≥ 5%. The colored lines indicate a linear regression model of all meta-analyses for a specific ancestry (eg. all analyses including European individuals). c) Comparison of chi-squared values from meta-analysis of LDL-C for variants with MAF ≤ 5%. d) Comparison of chi-squared valued for variants with MAF ≥ 5% for LDL-C without genomic-control correction in a meta-analysis of all European cohorts as well as five subsets selected to reach sample sizes of approximately 100,000, 200,000, 400,000, 600,000, or 800,000 individuals.

Extended Data Figure 3:. Effect sizes by ancestry for unique index variants from ancestry-specific meta-analysis

Comparison of effect sizes and standard errors for variants reaching genome-wide significance (p-value < 5x10⁻⁸ as given by RAREMETAL) in both ancestry groups. Variants with discordant directions of effect between ancestries are labeled by chromosome and position (build 37). Association results for all index variants are given in Supplementary Table 3. The red line depicts an equivalent European ancestry and non-European ancestry effect size while the black line depicts a linear regression model. R²=0.93

Extended Data Figure 4:. Comparison of credible set size

The number of variants in the 99% credible sets for each association signal are compared between a) Admixed African ancestry and trans-ancestry analysis and b) European ancestry and trans-ancestry analysis

Extended Data Figure 5:. Overview of LDL-C polygenic score generation and validation

Polygenic scores were calculated separately in each ancestry group or in all ancestries combined using either pruning and thresholding or PRS-CS. The polygenic scores were then taken forward for testing in ancestry-matched participants followed by validation in independent data sets.

Extended Data Figure 6:. Optimal polygenic score threshold by ancestry group for either PRS-CS or pruning and thresholding based LDL-C polygenic scores

Adjusted R² estimated upon testing in UK Biobank ancestry-matched participants (not included in GWAS summary statistics).

1. Admixed African, East Asian and South Asian ancestry polygenic scores

2. European and trans-ancestry polygenic scores

3. European ancestry (GLGC 2010) and trans-ancestry polygenic scores

4. All polygenic scores across all thresholds used for score construction

5. Comparison of adjusted R² across ancestry groups relative to the maximum for covariates alone, polygenic scores from PRS-CS or polygenic scores from pruning and thresholding

Extended Data Figure 7:. Comparison of PRS performance by admixture quartile

We divided the testing cohorts into quartiles by proportion of African ancestry and estimated the performance of the PRS separately within each quartile in a) the Michigan Genomics Initiative (N = 1,341) and b) in the Million Veteran Program (N = 18,251). Error bars represent 95% confidence intervals.

Extended Data Figure 8:. Improvement in PRS performance in African Americans when starting with ancestry-mismatched European ancestry scores by updating weights, updating variant lists, or updating both variants and weights to be ancestry-matched.

By comparison to the gold-standard performance of the trans-ancestry-derived PRS in African Americans (adjusted R² = 0.12), a European ancestry derived score capture only 47% of the variance explained by the trans-ancestry PRS. When LD and association information from the target population is used to optimize the list of variants for inclusion in the PRS, but with ancestry-mismatched weights from European ancestry GWAS, the variance explained reaches 71% of the gold standard. If the PRS variant list selected in European ancestry individuals were genotyped in the target population, and PRS weights were updated using a GWAS from the target population, the variance explained reached 87% of the gold standard. Finally, deriving both the marker list and weights from the target population (single-ancestry GWAS) explained 94% of the variance relative to the gold-standard trans-ancestry PRS.

Figure 1:. Comparison of identified loci across ancestry groups

a) Allele frequency distribution and b) effect sizes of Admixed African ancestry index variants in non-African ancestry populations. c) Allele frequency distribution and d) effect sizes of European ancestry index variants in non-European ancestry populations. Boxplots depict the median value as the center, first and third quartiles as box boundaries and whiskers extending 1.5 times the inter-quartile range, with points beyond this region shown individually. Sample sizes for each ancestry are provided in Table 1. The mean effect size of Admixed African ancestry identified index variants is larger than from European ancestry analysis, reflecting the difference in power to detect an association within each group as a result of the >10-fold difference in sample size. e) Number of loci identified within each ancestry group, normalized to a constant sample size of 100,000 individuals and averaged across lipid traits. At currently available sample sizes, trans-ancestry and European ancestry analyses identify a lower proportion of loci relative to the number of individuals than analyses of other ancestry groups. However, the larger sample size of European or trans-ancestry analyses leads to a greater relative proportion of novel loci and a higher proportion of loci significant only in European ancestry analyses. f) Proportion of index variants identified from each ancestry-specific meta-analysis that would be well-powered to detect an association of the same effect size but with ancestry-specific frequencies in the other ancestry groups. Dark blue regions indicate variants likely to be detected at an equivalent sample size only in the original ancestry group (i.e. ancestry-specific). Additional comparisons of allele frequencies and effect sizes across ancestries are provided in Supplementary Figure 3.

Figure 2:. Inclusion of multiple ancestries drives improved fine-mapping

a) Association of the DMTN intron variant rs900776 with LDL-C or b) DMTN expression. The region spanned by the 99% credible sets are shown in the center box. The LDL-C association signal significantly colocalizes with the GTEx eQTL signal of DMTN in liver. c) The LD patterns for variants in the European ancestry 99% credible set differ greatly between African and European ancestry individuals in 1000 Genomes. The lead variant has a posterior probability of 0.86 in Admixed African, 0.51 in European, and >0.99 in the trans-ancestry analysis.

Figure 3:. Trans-ancestry LDL-C PRS show similar performance across ancestry groups

a) Polygenic scores generated from trans-ancestry meta-analysis show equivalent or better performance across most ancestry groups relative to ancestry-specific PRS within each cohort, whereas European ancestry-specific scores show less transferability. Adjusted R² is calculated with the risk score as a predictor of LDL-C in a linear model with covariates. AFR: African, AFRAMR: African American, ASN: Asian American b) Trans-ancestry scores derived from equal proportions of each ancestry group predict LDL-C better for African Americans in MGI than predominantly European ancestry scores at constant sample size. Error bars depict 95% confidence intervals. Sample sizes for each cohort are provided in Supplementary Table 16.