Abstract
BackgroundQuantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population.ResultsWe used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p < 5E-9), with lead SNPs at nine loci significantly associated with three or more RBC traits. A majority of the lead SNPs were common (MAF > 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP.ConclusionThis work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.
Highlights
Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported genome-wide association study (GWAS) loci
Complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/ Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations
Estimated partial correlations by RBC trait pair ranged from HCTMCHC to HCT-hemoglobin concentration (HGB) (ρ = 0.94, Fig. 1b)
Summary
Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. Developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. We leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Utilization of diverse study populations can improve identification of rare or ancestry-specific variants located in biological pathways that affect phenotypes in global populations and, when summary data are made publicly available, enable construction of broadly applicable polygenic risk scores [40]. Gaps between estimated heritability and the proportion of variance explained by GWAS findings suggest that additional associations remain to be identified, including rare variants and independent secondary associations at known loci that are both more likely to be ancestrally specific [12, 41, 42]. RBC traits exhibit modest to high correlation, and several dozen loci have been reported for two or more RBC traits, few studies have leveraged this shared genetic architecture to increase statistical power to map novel RBC trait loci [12, 20, 26, 43,44,45]
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