Abstract
BackgroundGenetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. To improve our understanding of these mechanisms, we conducted a weighted single-step genome-wide association study (WssGWAS) and then integrated the results with large-scale transcriptome data for five wool traits and one growth trait in Merino sheep: mean fibre diameter (MFD), coefficient of variation of the fibre diameter (CVFD), crimp number (CN), mean staple length (MSL), greasy fleece weight (GFW), and live weight (LW).ResultsOur dataset comprised 7135 individuals with phenotype data, among which 1217 had high-density (HD) genotype data (n = 372,534). The genotypes of 707 of these animals were imputed from the Illumina Ovine single nucleotide polymorphism (SNP) 54 BeadChip to the HD Array. The heritability of these traits ranged from 0.05 (CVFD) to 0.36 (MFD), and between-trait genetic correlations ranged from − 0.44 (CN vs. LW) to 0.77 (GFW vs. LW). By integrating the GWAS signals with RNA-seq data from 500 samples (representing 87 tissue types from 16 animals), we detected tissues that were relevant to each of the six traits, e.g. liver, muscle and the gastrointestinal (GI) tract were the most relevant tissues for LW, and leukocytes and macrophages were the most relevant cells for CN. For the six traits, 54 quantitative trait loci (QTL) were identified covering 81 candidate genes on 21 ovine autosomes. Multiple candidate genes showed strong tissue-specific expression, e.g. BNC1 (associated with MFD) and CHRNB1 (LW) were specifically expressed in skin and muscle, respectively. By conducting phenome-wide association studies (PheWAS) in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with MFD in sheep and with hair colour in humans, and CHRNB1 was significantly associated with LW in sheep and with body mass index in humans.ConclusionsOur findings provide novel insights into the biological and genetic mechanisms underlying wool and growth traits, and thus will contribute to the genetic improvement and gene mapping of complex traits in sheep.
Highlights
Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive
Zhao et al Genet Sel Evol (2021) 53:56 in humans, we found that orthologues of several of these candidate genes were significantly (FDR < 0.05) associated with similar traits in humans, e.g. BNC1 was significantly associated with mean fibre diameter (MFD) in sheep and with hair colour in humans, and CHRNB1 was significantly associated with live weight (LW) in sheep and with body mass index in humans
The objectives of our study were: (1) to estimate the genetic parameters for five wool traits; and one growth trait in a dual-purpose Merino sheep population (n = 7135); (2) to identify tissues and genes that are associated with these traits by integrating the results of a weighted single-step genome-wide association study (WssGWAS) with data from 500 RNA-seq samples of 87 tissues; and (3) to explore whether the results from human studies can help validate and explain the findings in this sheep study via a phenome-wide association study (PheWAS)
Summary
Genetic improvement of wool and growth traits is a major goal in the sheep industry, but their underlying genetic architecture remains elusive. Megdiche et al [5] found that genomic regions under positive selection in Merino and other Merino-derived breeds were significantly associated with wool traits These analyses were limited by the number of animals for which both genotypes and phenotypes were available. Compared with the classical single-marker GWAS, WssGWAS allows the simultaneous use of all data, including those from individuals with phenotype but without genotype data, by using a scaled and properly augmented relationship matrix ( H matrix) This efficient approach for identifying genes or quantitative trait loci (QTL) that underlie complex traits in animals has recently emerged [14,15,16]. WssGWAS might be able to provide more accurate estimates of genetic parameters than the classical GWAS, thereby leading to an increased power of QTL detection [18,19,20]
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