Abstract
The study of complex traits and their genetic underpinnings is crucial for understanding the evolutionary processes and mechanisms that shape natural populations. Regional heritability mapping (RHM) is a method for estimating the heritability of genomic segments that may contain both common and rare variants affecting a complex trait. This research is important because it advances our ability to detect genetic loci that contribute to phenotypic variation, even those that might be missed by traditional methods such as genome-wide association studies (GWAS). Here, we compare three RHM methods: SNP-RHM, which uses genomic relationship matrices (GRMs) based on SNP genotypes; Hap-RHM, which utilizes GRMs based on haplotypes; and SNHap-RHM, which integrates both SNP-based and haplotype-based GRMs jointly. These methods were applied to data from a wild population of sheep, focusing on the analysis of eleven polygenic traits. The results were compared with findings from previous GWAS to assess how RHM performed at identifying both known and novel associated loci. We found that while the inclusion of the regional matrix did not account for significant variation in all regions associated with trait variation as identified by GWAS, it did uncover several regions that were not previously linked to trait variation. This suggests that RHM methods can provide additional insights into the genetic architecture of complex traits, highlighting regions of the genome that may be overlooked by GWAS alone. This study underscores the importance of using complementary approaches to fully understand the genetic basis of complex traits in natural populations.