Bayesian regional heritability mapping for genomic-wide associations across multiple chromosomal regions

Regional heritability mapping (RHM) employs mixed-model and single-genomic region approaches for Genome-Wide Association Studies (GWAS). Although it utilizes marker groups and possesses a high detection power for identifying markers associated with target phenotypes, the significant linkage disequilibrium (LD) among markers limits the RHM's capacity to estimate the effects of one genomic region simultaneously. This constraint may hinder its capacity to detect complex associations or relationships among various genomic regions. Within a Bayesian framework, RHM can operate with multiple structured covariance matrices and can incorporate several genomic regions into a single model, effectively utilizing the LD present in these regions. In this study, our objectives were: (i) to propose the simultaneous estimation of multiple genomic region effects using a Bayesian model; (ii) to compare the efficiency of this simultaneous estimation with that of the single-region estimation using simulated data, focusing on the detection of significant genomic regions for phenotypes characterized by diverse genetic architectures; and (iii) to demonstrate the applicability of these models in breeding programs, particularly applying them to rice data. The results indicated that the simultaneous estimation of genomic region effects using the Bayesian approach offered greater detection power for more complex traits in simulated data. In the rice dataset, the simultaneous estimation method identified more regions than those previously reported in the literature, as well as newly uncovered genomic regions that deserve further investigation in post-GWAS analyses. This methodology holds promise for exploring and applying new genomic regions associated with target traits.

Keywords:
Oryza sativa; molecular markers; detection power; false positives; simulation