Enhancing genomic prediction with genome-wide association studies in multiparental maize populations.

ABSTRACT: Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in wh

SUBMITTER: Bian Y 

PROVIDER: S-EPMC5436027 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature