Evidence for colocalisation of eQTL and disease causal variants can suggest causal genes and cells for these genetic associations. Several studies that have investigated the genetic regulation of gene expression have shown that disease-associated variants are over-represented amongst expression quantitative trait loci (eQTL) variants. The genes and cells that mediate genetic associations identified through genome-wide association studies (GWAS) are only partially understood. Our method is generic and can potentially be incorporated in any of the available imputation methods as an add-on. We evaluated our method across a large set of populations and found that our choice of reference data set considerably improves the accuracy of imputation, especially for regions with low LD and for populations without a reference population available as well as for admixed populations such as the Hispanic population. Furthermore, because our approach treats each region in the genome separately, our method is suitable for the imputation of recently admixed populations. This allows the flexibility to account for the diversity within populations, as well as across populations. In contrast to the current paradigm of imputation methods, our method assigns a different reference dataset for each sample in the studied population, and for each region in the genome. Here we present a Coalescent-based method that addresses this issue. In many instances there is no reference population that exactly matches the studied population, and a natural question arises as to how to choose the reference population for the imputation. The LD structure is normally learned from a dense genotype map of a reference population that matches the studied population. The imputation procedure uses the linkage disequilibrium (LD) structure in the population to infer the genotype of an unobserved single nucleotide polymorphism. An important component in the analysis of genome-wide association studies involves the imputation of genotypes that have not been measured directly in the studied samples.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |