Optimising the identification of causal variants across varying genetic architectures in crops.

ABSTRACT: Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real-world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade-offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes fo

SUBMITTER: Miao C 

PROVIDER: S-EPMC6587547 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature