Regional heritability mapping and genome-wide association identify loci for rice traits

Although genome-wide association studies (GWAS) based on single-marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false-positive rates and the limited power to detect associations. Bayesian methods, which estimate marker effects simultaneously, proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM), on the other hand, determines the genome region (group of markers) associated with the phenotype, considers population structure and familial relatedness, and is more powerful to detect quantitative trait loci (QTL) and reduced false-positive rates than single-marker methodologies. A single-marker mixed model (SM-MM) Bayesian approach and RHM were used for 11 traits in 413 rice (Oryza sativa L.) accessions genotyped for 44,100 single-nucleotide polymorphism (SNP) markers. Using RHM in regions of 0.21 and 0.69 Mb, respectively, detected five and seven associated regions with 163 and 569 SNPs. Bayesian method with regions of 0.21 and 0.69 Mb detected regions for all traits, whereas SM-MM detected four single SNP-trait associations. For the 11 traits, RHM explained approximately 25-40 and 25-76% using genome regions of 0.21 and 0.69 Mb, respectively, and SM-MM using single markers explained 1-7% of the genomic heritability. Regional heritability mapping was more effective than SM-MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q-TARO and Gramene QTL databases.