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 < 1 × 10−5) for MDD. These significant regions have genes mapped to within 400 kb of them. The genes mapped for height have been reported to be associated with height in humans. Similarly, 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.

Genetic architecture and polygenic risk score prediction of degenerative suspensory ligament desmitis (DSLD) in the Peruvian Horse

“…Regional variance analysis enhances the power to detect QTLs by effectively capturing the combined contribution of multiple marker effects within a specific region. This approach enables the identification of genetic variants that may have modest effects individually but collectively contribute to the trait’s variation as well as rare variants whose effects are difficult to capture because of lack of statistical power ( Oppong et al, 2022 ). Consequently, there is a benefit to be gained in terms of improving heritability estimates and uncovering genetic variants involved in the control of traits by fitting genome-wide analytical models that adequately capture the combined effects of rare genetic variants ( Shirali et al, 2016 ).…”

The big challenge for livestock genomics is to make sequence data pay

“…We need some methods to create windows or blocks, that are often arbitrary. Proposals to better deal with this includes defining windows based on recombination hotspots (Oppong et al, 2022) or haplotype block methods that create overlapping segments (Pook et al, 2019).…”

The big challenge for livestock genomics is to make sequence data pay

“…We need some methods to create windows or blocks, that are often arbitrary. Proposals to better deal with this includes defining windows based on recombination hotspots (Oppong et al, 2022), haplotype block methods that create overlapping segments (Pook et al, 2019), and haplotype clustering methods (Browning & Browning, 2007).…”