Ancestry-specific associations identified in genome-wide combined-phenotype study of red blood cell traits emphasize benefits of diversity in genomics

Abstract

Background: Quantitative 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. Results: We 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 multiethnic 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.

The adaptive architecture is shaped by population ancestry and not by selection regime

“…This expectation nicely conforms with empirical data, mostly from QTL studies, which find heterogeneous sets of contributing loci among different populations (Adeyemo et al 2009;Wu et al 2013;Al Olama et al 2014;Li and Keating 2014;Conte et al 2015;Horikoshi et al 2018;Takata et al 2019;Wojcik et al 2019;Zan and Carlborg 2019;Hodonsky et al 2020). In the case of adaptation to a new trait optimum, genetically differentiated populations will adapt by frequency changes of different sets of loci.…”

“…Various studies, mainly using QTL mapping and GWAS, have identified different loci contributing to the same trait in diverged populations (Adeyemo et al 2009;Wu et al 2013;Al Olama et al 2014;Li and Keating 2014;Conte et al 2015;Horikoshi et al 2018;Takata et al 2019;Wojcik et al 2019;Zan and Carlborg 2019;Hodonsky et al 2020;Kemppainen et al 2020). A recent experimental evolution study using D. subobscura populations with different genetic background also observed very little overlap in the genomic regions responding to a common selection regime (Seabra et al 2018).…”

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

“…Here, we conducted large-scale TWAS meta-analyses for 4 blood cell traits in AA and HL populations, using a larger whole-blood eQTL reference panel (DGN) in European ancestry populations and smaller but ancestry-matched reference panels from MESA monocytes. To our knowledge, this is the largest genetic discovery effort yet conducted in these populations for blood cell indices, exceeding available sample sizes from recent trans-ethnic blood cell traits GWAS ([ 38 ], n = 15,171 African ancestry, n = 9368 Hispanic/Latino), recent analyses of red blood cells in the Population Architecture using Genomics and Epidemiology [PAGE] studies ([ 54 ], n = 16,258 African American, n = 20,784 Hispanic/Latino), and our own recent TOPMed imputed GWAS analyses ([ 55 ], n = 21,513 African ancestry and 21,689 Hispanic/Latino participants). These previous efforts all included some but not all of the cohorts included here.…”

The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime

“…Haplotype blocks are identified and characterized by SNPs with a correlated allele frequency change after multiple generations of experimental evolution [6]. [27][28][29][30][31][32][33][34][35][36]. In the case of adaptation to a new trait optimum, genetically differentiated populations will adapt by frequency changes of different sets of loci.…”