Dataset Information

Genome-wide association study for morphological traits and resistance to Peryonella pinodes in the USDA pea single plant plus collection.

ABSTRACT: Peas (Pisum sativum) are the second most cultivated pulse crop in the world. They can serve as human food, fodder, and cover crop. The most serious foliar disease of pea cultivars worldwide is Ascochyta blight, which can be caused by several pathogens. Of these, Peyronella pinodes is the most aggressive and prevalent worldwide. Several traits, including resistance to Peyronella pinodes, stem diameter, internode length between nodes 2-3 and 5-6, and area of 7th leaf, were measured in 269 entries of the pea single plant plus collection. The heritability (H2) of the morphological traits was relatively high, while disease resistance had low heritability. Using 53,196 single-nucleotide polymorphism markers to perform a genome-wide association study to identify genomic loci associated with variation in all the traits measured, we identified 27 trait-locus associations, 5 of which were associated with more than 1 trait.

SUBMITTER: Martins LB

PROVIDER: S-EPMC9434253 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Genome-wide association study for morphological traits and resistance to Peryonella pinodes in the USDA pea single plant plus collection.

Martins Lais B LB Balint-Kurti Peter P Reberg-Horton S Chris SC

G3 (Bethesda, Md.) 20220801 9

Peas (Pisum sativum) are the second most cultivated pulse crop in the world. They can serve as human food, fodder, and cover crop. The most serious foliar disease of pea cultivars worldwide is Ascochyta blight, which can be caused by several pathogens. Of these, Peyronella pinodes is the most aggressive and prevalent worldwide. Several traits, including resistance to Peyronella pinodes, stem diameter, internode length between nodes 2-3 and 5-6, and area of 7th leaf, were measured in 269 entries ...[more]

PMID: 35792880

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Project description:Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

Project description:BackgroundRice is an important human staple food vulnerable to heavy metal contamination leading to serious concerns. High yield with low heavy metal contamination is a common but highly challenging goal for rice breeders worldwide due to lack of genetic knowledge and markers.ResultsTo identify candidate QTLs and develop molecular markers for rice yield and heavy metal content, a total of 191 accessions from the USDA Rice mini-core collection with over 3.2 million SNPs were employed to investigate the QTLs. Sixteen ionomic and thirteen agronomic traits were analyzed utilizing two univariate (GLM and MLM) and two multivariate (MLMM and FarmCPU) GWAS methods. 106, 47, and 97 QTLs were identified for ionomics flooded, ionomics unflooded, and agronomic traits, respectively, with the criterium of p-value < 1.53 × 10- 8, which was determined by the Bonferroni correction for p-value of 0.05. While 49 (~ 20%) of the 250 QTLs were coinciding with previously reported QTLs/genes, about 201 (~ 80%) were new. In addition, several new candidate genes involved in ionomic and agronomic traits control were identified by analyzing the DNA sequence, gene expression, and the homologs of the QTL regions. Our results further showed that each of the four GWAS methods can identify unique as well as common QTLs, suggesting that using multiple GWAS methods can complement each other in QTL identification, especially by combining univariate and multivariate methods.ConclusionsWhile 49 previously reported QTLs/genes were rediscovered, over 200 new QTLs for ionomic and agronomic traits were found in the rice genome. Moreover, multiple new candidate genes for agronomic and ionomic traits were identified. This research provides novel insights into the genetic basis of both ionomic and agronomic variations in rice, establishing the foundation for marker development in breeding and further investigation on reducing heavy-metal contamination and improving crop yields. Finally, the comparative analysis of the GWAS methods showed that each method has unique features and different methods can complement each other.

Dataset Information

Genome-wide association study for morphological traits and resistance to Peryonella pinodes in the USDA pea single plant plus collection.

Publications

Genome-wide association study for morphological traits and resistance to Peryonella pinodes in the USDA pea single plant plus collection.

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