bioRxiv2021
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 x 10-5) 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. 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. Author SummaryIn untangling the genetic contribution to observed phenotype differences, situations can arise where causative variants might be tagged by haplotypes and not in linkage disequilibrium with individual SNPs. This scenario is likely for relatively newly arisen and rarer variants. Here, we propose a regional heritability method, SNHap-RHM, that jointly fits haplotype-based and SNP-based genomic relationship matrices (GRMs) to capture genomic regions harbouring rare variants that the SNP-based GRMs might miss. By analysing ~20,000 Scottish individuals, we show by simulation that the two GRMs are very specific to the type of variant effects they can capture; - the haplotype-based GRMs specifically target haplotype effects which are mostly missed by SNP-based GRMs and vice versa. Applying the method to height and major depressive disorder led to the uncovering of regions in the genome that harbour genes associated with those traits. These results are uniquely important because first they confirm that effects tagged by haplotypes may be missed by conventional SNP-based methods. Secondly, our method, SNHap-RHM, presents an exciting new opportunity to analyse complex traits by allowing the joint mapping of genomic regions tagged by either SNPs or haplotypes, potentially leading to the recovery of some of the "missing" heritability.