SNP and Haplotype Regional Heritability Mapping (SNHap-RHM): joint mapping of common and rare variation affecting complex traits

Abstract

We describe a genome-wide analytical approach, SNP and Haplotype Regional Heritability Mapping (SNHap-RHM), that provides regional estimates of the heritability across locally defined regions in the genome. This approach utilises relationship matrices that are based on sharing of SNP and haplotype alleles at local haplotype blocks delimited by recombination boundaries in the genome. We implemented the approach on simulated data and show that the haplotype-based regional GRMs capture variation that is complementary to that captured by SNP-based regional GRMs, and thus justifying the fitting of the two GRMs jointly in a single analysis (SNHap-RHM). SNHap-RHM captures regions in the genome contributing to the phenotypic variation that existing genome-wide analysis methods may fail to capture. We further demonstrate that there are real benefits to be gained from this approach by applying it to real data from about 20,000 individuals from the Generation Scotland: Scottish Family Health Study. We analysed height and major depressive disorder (MDD). We identified seven genomic regions that are genome-wide significant for height, and three regions significant at a suggestive threshold (p-value <1x10^(-5) ) for MDD. These significant regions have genes mapped to within 400kb of them. The genes mapped for height have been reported to be associated with height in humans, whiles those mapped for MDD have been reported to be associated with major depressive disorder and other psychiatry phenotypes. The results show that SNHap-RHM presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of novel genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability.

Evaluating regional heritability mapping methods for identifying QTLs in a wild population of Soay sheep

“…where d ik is the diplotype code (coded as the number of copies of haplotype k ) for individual i and takes the values 0, 1, and 2, pk is the frequency of haplotype k and h is the number of haplotypes in the region (see Oppong et al 2021 for further information and examples).…”

“…To overcome the limitations of GWAS, especially when a trait is influenced by multiple independent effects and/or rare variants in a region, regional heritability mapping (RHM) methods have been developed (Nagamine et al 2012;Shirali et al 2018;Oppong et al 2021). RHM is a technique that estimates the heritability of a trait that is explained by a specific region of the genome.…”

“…In addition, haplotype effects may reflect the interaction effects of closely linked causal variants. The third method, SNHap-RHM, simultaneously fits two rGRMs: one SNP-based and one haplotype-based, and defines regions as haplotype blocks (Oppong et al 2021). This combines the advantages of both SNP-RHM and Hap-RHM to increase power to detect regions containing variants influencing the phenotype.…”