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Accuracy of across-environment genome-wide prediction in maize nested association mapping populations.

ABSTRACT: Most of previous empirical studies with genome-wide prediction were focused on within-environment prediction based on a single-environment (SE) model. In this study, we evaluated accuracy improvements of across-environment prediction by using genetic and residual covariance across correlated environments. Predictions with a multienvironment (ME) model were evaluated for two corn polygenic leaf structure traits, leaf length and leaf width, based on within-population (WP) and across-population (AP) experiments using a large maize nested association mapping data set consisting of 25 populations of recombinant inbred-lines. To make our study more applicable to plant breeding, two cross-validation schemes were used by evaluating accuracies of (CV1) predicting unobserved phenotypes of untested lines and (CV2) predicting unobserved phenotypes of lines that have been evaluated in some environments but not others. We concluded that (1) genome-wide prediction provided greater prediction accuracies than traditional quantitative trait loci-based prediction in both WP and AP and provided more advantages over quantitative trait loci -based prediction for WP than for AP. (2) Prediction accuracy with ME was significantly greater than that attained by SE in CV1 and CV2, and gains with ME over SE were greater in CV2 than in CV1. These gains were also greater in WP than in AP in both CV1 and CV2. (3) Gains with ME over SE attributed to genetic correlation between environments, with little effect from residual correlation. Impacts of marker density on predictions also were investigated in this study.

SUBMITTER: Guo Z

PROVIDER: S-EPMC3564986 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHeat tolerance is becoming increasingly important where maize is grown under spring season in India which coincide with grain filling stage of crop resulting in tassel blast, reduced pollen viability, pollination failure and barren ears that causes devastating yield losses. So, there is need to identify the genomic regions associated with heat tolerance component traits which could be further employed in maize breeding program.ResultsAn association mapping panel, consisting of 662 doubled haploid (DH) lines, was evaluated for yield contributing traits under normal and natural heat stress conditions. Genome wide association studies (GWAS) carried out using 187,000 SNPs and 130 SNPs significantly associated for grain yield (GY), days to 50% anthesis (AD), days to 50% silking (SD), anthesis-silking interval (ASI), plant height (PH), ear height (EH) and ear position (EPO) were identified under normal conditions. A total of 46 SNPs strongly associated with GY, ASI, EH and EPO were detected under heat stress conditions. Fifteen of the SNPs was found to have common association with more than one trait such as two SNPs viz. S10_1,905,273 and S10_1,905,274 showed colocalization with GY, PH and EH whereas S10_7,132,845 SNP associated with GY, AD and SD under normal conditions. No such colocalization of SNP markers with multiple traits was observed under heat stress conditions. Haplotypes trend regression analysis revealed 122 and 85 haplotype blocks, out of which, 20 and 6 haplotype blocks were associated with more than one trait under normal and heat stress conditions, respectively. Based on SNP association and haplotype mapping, nine and seven candidate genes were identified respectively, which belongs to different gene models having different biological functions in stress biology.ConclusionsThe present study identified significant SNPs and haplotype blocks associated with yield contributing traits that help in selection of donor lines with favorable alleles for multiple traits. These results provided insights of genetics of heat stress tolerance. The genomic regions detected in the present study need further validation before being applied in the breeding pipelines.

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Accuracy of across-environment genome-wide prediction in maize nested association mapping populations.

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