Selective sweeps for mutations increasing height impede identification of causative mutations for fertility and other...

Fertility, growth and body composition are key drivers of profitability in beef cattle. With the aim of identifying causative mutations underpinning variation in these traits, we integrated multi-trait genome-wide association analysis (M-GWAS) in a cohort of 28,351 multibreed beef cattle with imputed whole genome sequence (WGS) data, with expression quantitative trait loci (eQTL) summary statistics from 489 indicine cattle using the same WGS variants. An additional aim was to provide insights into the biological basis for the association between growth, metabolism, and reproductive development. First, we conducted M-GWAS for live weight, hip height, body condition score and heifer puberty at approximately 600 days. Subsequently, focusing on a 2 Mb region around the lead GWAS SNP we identified the top eQTL in each region. Through iterative conditional analysis, we successively integrated these variants into individual single trait GWAS and further analysed expression and trait information using conditional and joint GWAS analysis. This iterative process continued until no additional significant SNPs emerged from the M-GWAS.

Fifteen candidate genes were identified, including IRAK3, HELB, HMGA2, LAP3, FAM184B, LCORL, PPM1K, ABCG2, MED28, PLAG1, BPNT2, UBXN2B, CTNNA2, SNRPN, and SNURF. When we investigated the number of eQTL in blood associated with these genes, IRAK3, HELB, PPM1K, ABCG2, MED28, BPNT2, and UBXN2B were associated with a single eQTL, while ABCG2 was clearly associated with two eQTLs (Bonferroni corrected P < 1 × 10^-10). However, the identification of potential QTLs in these regions was impeded by extensive localised linkage disequilibrium. Analysis of extended haplotype homozygosity in the regions revealed this extended linkage disequilibrium was likely the result of recent strong selection, in most cases for the allele increasing height (Chi-square P = 0.000967).

This observation sheds some light on why it has been so difficult to identify mutations affecting fertility, and other traits that are pleiotropic with height, in cattle.