Dataset Information

Genome-Wide Association Mapping of QTL Underlying Groat Protein Content of a Diverse Panel of Oat Accessions.

ABSTRACT: Groat protein content (GPC) is a key quality trait attribute in oat. Understanding the variation of GPC in oat germplasms and identifying genomic regions associated with GPC are essential for improving this trait. In this study, the GPC of 174 diverse oat accessions was evaluated in three field trials. The results showed a wide variation in GPC, ranging from 6.97% to 22.24% in this panel. Hulless oats displayed a significantly higher GPC compared to hulled oats across all environments. A GWAS analysis was performed based on 38,313 high-quality SNPs, which detected 27 non-redundant QTLs with 41 SNPs significantly associated with GPC. Two QTLs on chromosome 6C (QTL16) and 4D (QTL11) were consistently detected in multiple environments, with QTL16 being the most significant and explaining the highest proportion of the phenotypical variation in all tested environments except in CZ20. Haplotype analysis showed that the favorable haplotypes for GPC are more prevalent in hulless oats. These findings provide a foundation for future efforts to incorporate favorable alleles into new cultivars through introgression, fine mapping, and cloning of promising QTLs.

SUBMITTER: Yan H

PROVIDER: S-EPMC10053717 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Genome-Wide Association Mapping of QTL Underlying Groat Protein Content of a Diverse Panel of Oat Accessions.

Yan Honghai H Zhang Haixu H Zhou Pingping P Ren Changzhong C Peng Yuanying Y

International journal of molecular sciences 20230315 6

Groat protein content (GPC) is a key quality trait attribute in oat. Understanding the variation of GPC in oat germplasms and identifying genomic regions associated with GPC are essential for improving this trait. In this study, the GPC of 174 diverse oat accessions was evaluated in three field trials. The results showed a wide variation in GPC, ranging from 6.97% to 22.24% in this panel. Hulless oats displayed a significantly higher GPC compared to hulled oats across all environments. A GWAS an ...[more]

PMID: 36982656

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Project description:Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.

Dataset Information

Genome-Wide Association Mapping of QTL Underlying Groat Protein Content of a Diverse Panel of Oat Accessions.

Publications

Genome-Wide Association Mapping of QTL Underlying Groat Protein Content of a Diverse Panel of Oat Accessions.

Similar Datasets

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