Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlation between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated, and yet we don9t fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations to develop a novel framework termed correlation scan. This framework was used to identify regions associated with the genetic correlations between male and female fertility traits across the bovine genome. The traits used were age at first corpus luteum (AGECL) and serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP (single nucleotide polymorphism) effects in a 100-SNPs sliding window in each chromosome to identify regions in the genome that either drive (i.e., SNP effects on the same direction) or antagonize (i.e., SNP effects in the opposite direction) the genetic correlations between traits. We used a permutation test to confirm which regions of the genome harboured significant correlations. Hence, this framework can also identify neutral genomic regions with no effect on the pairwise trait studied. About 40% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two population. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. Quantitative trait loci (QTL) and functional enrichment analysis revealed that many significant windows co-located with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to the chromosome X. These results suggest regions of the chromosome X for further investigation into the trade-offs between male and female fertility. Although the methodology was applied to cattle phenotypes, using high-density SNP genotypes, the general framework developed can be applied to any species or traits, and it can easily accommodate genome sequence data.