Detection of quantitative trait loci in Bos indicus and Bos taurus cattle using genome-wide association studies

Abstract

BackgroundThe apparent effect of a single nucleotide polymorphism (SNP) on phenotype depends on the linkage disequilibrium (LD) between the SNP and a quantitative trait locus (QTL). However, the phase of LD between a SNP and a QTL may differ between Bos indicus and Bos taurus because they diverged at least one hundred thousand years ago. Here, we test the hypothesis that the apparent effect of a SNP on a quantitative trait depends on whether the SNP allele is inherited from a Bos taurus or Bos indicus ancestor.MethodsPhenotype data on one or more traits and SNP genotype data for 10 181 cattle from Bos taurus, Bos indicus and composite breeds were used. All animals had genotypes for 729 068 SNPs (real or imputed). Chromosome segments were classified as originating from B. indicus or B. taurus on the basis of the haplotype of SNP alleles they contained. Consequently, SNP alleles were classified according to their sub-species origin. Three models were used for the association study: (1) conventional GWAS (genome-wide association study), fitting a single SNP effect regardless of subspecies origin, (2) interaction GWAS, fitting an interaction between SNP and subspecies-origin, and (3) best variable GWAS, fitting the most significant combination of SNP and sub-species origin.ResultsFitting an interaction between SNP and subspecies origin resulted in more significant SNPs (i.e. more power) than a conventional GWAS. Thus, the effect of a SNP depends on the subspecies that the allele originates from. Also, most QTL segregated in only one subspecies, suggesting that many mutations that affect the traits studied occurred after divergence of the subspecies or the mutation became fixed or was lost in one of the subspecies.ConclusionsThe results imply that GWAS and genomic selection could gain power by distinguishing SNP alleles based on their subspecies origin, and that only few QTL segregate in both B. indicus and B. taurus cattle. Thus, the QTL that segregate in current populations likely resulted from mutations that occurred in one of the subspecies and can have both positive and negative effects on the traits. There was no evidence that selection has increased the frequency of alleles that increase body weight.

A genomewide association mapping study using ultrasound‐scanned information identifies potential genomic regions and candidate genes affecting carcass traits in Nellore cattle

“…In an association study in Australian cattle, the authors identified an important region in the Chr14 associated with RFT, especially for markers close to 20 Mb (Bolormaa et al 2011). Another study also detected a QTL on Chr14 using SNPs associated with BFT and RFT in cattle B. indicus and in cattle B. taurus (Bolormaa et al 2013). For RFT, we highlight the RARRES1 and VEPH1 as candidate genes.…”

“…Another study also detected a QTL on Chr14 using SNPs associated with BFT and RFT in cattle B. indicus and in cattle B. taurus (Bolormaa et al . ).…”

How old are quantitative trait loci and how widely do they segregate?

“…However, Bolormaa et al . () found that QTL rarely segregate in B. taurus and Bos indicus which indicates that they are seldom 100 000 generations old. These factors suggest that the mutations underlying across‐breed QTL are relatively old, but not as old as the B. taurus/B.…”

“…The finding that at least some QTL variants which segregate across B. taurus breeds is supported by Saatchi et al (2014) who found QTL in multiple beef breeds. However, Bolormaa et al (2013a) found that QTL rarely segregate in B. taurus and Bos indicus which indicates that they are seldom 100 000 generations old. These factors suggest that the mutations underlying across-breed QTL are relatively old, but not as old as the B. taurus/B.…”

Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

“…These positive and overall high estimates of correlation show that the heritability has indeed limited the prediction accuracies, among other factors. In fact, some authors have reported superior accuracies of genomic breeding values in scenarios involving higher heritable traits as well as larger numbers of phenotypic records and genotyped animals (Bolormaa et al 2013a,b; Neves et al ; Fernandes Júnior et al ).…”