Dataset Information

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 which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.

SUBMITTER: Bian Y

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Bian Y Y Holland J B JB

Heredity 20170215 6

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 indiv ...[more]

PMID: 28198815

Similar Datasets

Project description:In sub-Saharan Africa, one of the major challenges to smallholder farmers is soil with low fertility and inability to apply nitrogen fertilizer externally due to the cost. Development of maize hybrids, which perform better in nitrogen depleted soils, is one of the promising solutions. However, breeding maize for nitrogen use efficiency (NUE) is hindered by expensive phenotypic evaluations and trait complexity under low N stress. Genome-wide association study (GWAS) and genomic prediction (GP) are promising tools to circumvent this interference. Here, we evaluated a mapping panel in diverse environments both under optimum and low N management. The objective of this study was to identify SNPs significantly associated with grain yield (GY) and other traits through GWAS and assess the potential of GP under low N and optimum conditions. Testcross progenies of 411 inbred lines were planted under optimum and low N conditions in several locations in Africa and Latin America. In all locations, low N fields were previously depleted over several seasons, and no N fertilizer was applied throughout the growing season. All inbred lines were genotyped with genotyping by sequencing. Genotypic and GxE interaction variances were significant, and heritability estimates were moderate to high for all traits under both optimum and low N conditions. Genome-wide LD decay at r 2 = 0.2 and r 2 = 0.34 were 0.24 and 0.19 Mbp, respectively. Chromosome-specific LD decays ranged from 0.13 to 0.34 Mbps with an average of 0.22 Mbp at r 2 = 0.2. GWAS analyses revealed 38 and 45 significant SNPs under optimum and low N conditions, respectively. Out of these 83 significant SNPs, 3 SNPs on chromosomes 1, 2, and 6 were associated either with different traits or the same trait under different management conditions, suggesting pleiotropic effects of genes. A total of 136 putative candidate genes were associated with the significant SNPs, of which seven SNPs were linked with four known genes. Prediction accuracies were moderate to high for all traits under both optimum and low N conditions. These results can be used as useful resources for further applications to develop hybrids or lines with better performance under low N conditions.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets