Abstract
The limited proportion of complex trait variance identified in genome-wide association studies may reflect the limited power of single SNP analyses to detect either rare causative alleles or those of small effect. Motivated by studies that demonstrate that loci contributing to trait variation may contain a number of different alleles, we have developed an analytical approach termed Regional Genomic Relationship Mapping that, like linkage-based family methods, integrates variance contributed by founder gametes within a pedigree. This approach takes advantage of very distant (and unrecorded) relationships, and this greatly increases the power of the method, compared with traditional pedigree-based linkage analyses. By integrating variance contributed by founder gametes in the population, our approach provides an estimate of the Regional Heritability attributable to a small genomic region (e.g. 100 SNP window covering ca. 1 Mb of DNA in a 300000 SNP GWAS) and has the power to detect regions containing multiple alleles that individually contribute too little variance to be detectable by GWAS as well as regions with single common GWAS-detectable SNPs. We use genome-wide SNP array data to obtain both a genome-wide relationship matrix and regional relationship (“identity by state" or IBS) matrices for sequential regions across the genome. We then estimate a heritability for each region sequentially in our genome-wide scan. We demonstrate by simulation and with real data that, when compared to traditional (“individual SNP") GWAS, our method uncovers new loci that explain additional trait variation. We analysed data from three Southern European populations and from Orkney for exemplar traits – serum uric acid concentration and height. We show that regional heritability estimates are correlated with results from genome-wide association analysis but can capture more of the genetic variance segregating in the population and identify additional trait loci.
Highlights
Despite the success of genome wide association studies (GWAS) in detecting new loci associated with complex traits, for most traits only a relatively low proportion of the total genetic variation has been localised
To explore the power of the regional approach compared to the standard single SNP GWAS we analysed the real data for each of a moderate heritability and a high heritability trait in which an additional 2.5% additive genetic variance was simulated for each genomic region in turn
The simulated data were analysed with the single SNP GWAS approach or via the regional heritability approach with windows of 50 adjacent markers
Summary
Despite the success of genome wide association studies (GWAS) in detecting new loci associated with complex traits, for most traits only a relatively low proportion of the total genetic variation has been localised. A variety of genetic mechanisms may contribute to this missing heritability [1], a significant component is likely to be the lack of power of GWAS to detect rare causative alleles that individually generate little SNP-associated variation but which collectively may contribute a substantial fraction of the heritability [2]. To circumvent the drawbacks of single-SNP association analyses requires approaches that are more efficient at capturing the variance of individual rare causative alleles and are capable of accumulating the variance over all alleles at a locus. Appropriate linkage based analyses have some advantages over association analyses for detection of multiple rare variants at a locus because they integrate variance contributed by founder gametes within a pedigree, making no assumption about the association of individual gametes with particular causative alleles. Pedigree-based linkage analyses lack power to detect true effects in the small nuclear families that typify a human population [3], power to detect effects in extended pedigrees is substantially greater [4]
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