Dataset Information

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.).

ABSTRACT: Oilseed rape (Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.

SUBMITTER: Luo X

PROVIDER: S-EPMC5318398 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (<i>Brassica napus</i> L.).

Luo Xiang X Ding Yi Y Zhang Linzhong L Yue Yao Y Snyder John H JH Ma Chaozhi C Zhu Jun J

Frontiers in genetics 20170221

Oilseed rape (<i>Brassica napus</i>) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicte ...[more]

PMID: 28270831

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Project description:Genomic selection (GS) is a modern breeding approach where genome-wide single-nucleotide polymorphism (SNP) marker profiles are simultaneously used to estimate performance of untested genotypes. In this study, the potential of genomic selection methods to predict testcross performance for hybrid canola breeding was applied for various agronomic traits based on genome-wide marker profiles. A total of 475 genetically diverse spring-type canola pollinator lines were genotyped at 24,403 single-copy, genome-wide SNP loci. In parallel, the 950 F1 testcross combinations between the pollinators and two representative testers were evaluated for a number of important agronomic traits including seedling emergence, days to flowering, lodging, oil yield and seed yield along with essential seed quality characters including seed oil content and seed glucosinolate content. A ridge-regression best linear unbiased prediction (RR-BLUP) model was applied in combination with 500 cross-validations for each trait to predict testcross performance, both across the whole population as well as within individual subpopulations or clusters, based solely on SNP profiles. Subpopulations were determined using multidimensional scaling and K-means clustering. Genomic prediction accuracy across the whole population was highest for seed oil content (0.81) followed by oil yield (0.75) and lowest for seedling emergence (0.29). For seed yieId, seed glucosinolate, lodging resistance and days to onset of flowering (DTF), prediction accuracies were 0.45, 0.61, 0.39 and 0.56, respectively. Prediction accuracies could be increased for some traits by treating subpopulations separately; a strategy which only led to moderate improvements for some traits with low heritability, like seedling emergence. No useful or consistent increase in accuracy was obtained by inclusion of a population substructure covariate in the model. Testcross performance prediction using genome-wide SNP markers shows considerable potential for pre-selection of promising hybrid combinations prior to resource-intensive field testing over multiple locations and years.

Project description:Genetic improvement of seed yield per plant (SY) is one of the major objectives in Brassica napus breeding programme. SY, being a complex quantitative trait is directly and indirectly influenced by yield-component traits such as siliqua length (SL), number of seeds per siliqua (NSS), and thousand seed weight (TSW). Therefore, concurrent improvement in SL, NSS and TSW can lead to higher SY in B. napus. This study was conducted to identify significant SNPs and putative candidate genes governing SY and its component traits (SL, NSS, TSW). All these traits were evaluated in a diverse set of 200 genotypes representing diversity from wide geographical locations. Of these, a set of 125 genotypes were chosen based on pedigree diversity and multi-location trait variation for genotyping by sequencing (GBS). Best linear unbiased predictors (BLUPs) of all the traits were used for genome-wide association study (GWAS) with 85,126 SNPs obtained from GBS. A total of 16, 18, 27 and 18 SNPs were found to be significantly associated for SL, NSS, TSW and SY respectively. Based on linkage disequilibrium decay analysis, 150 kb genomic region flanking the SNP was used for the identification of underlying candidate genes for each test trait. Important candidate genes involved in phytohormone signaling (WAT1, OSR1, ARR8, CKX1, REM7, REM9, BG1) and seed storage proteins (Cruciferin) were found to have significant influence on seed weight and yield. Genes involved in sexual reproduction and fertilization (PERK7, PERK13, PRK3, GATA15, NFD6) were found to determine the number of seeds per siliqua. Several genes found in this study namely ATS3A, CKX1, SPL2, SPL6, SPL9, WAT1 showed pleiotropic effect with yield component traits. Significant SNPs and putative candidate genes identified for SL, NSS, TSW and SY could be used in marker-assisted breeding for improvement of crop yield in B. napus. Genotypes identified with high SL, NSS, TSW and SY could serve as donors in crop improvement programs in B. napus.Supplementary informationThe online version contains supplementary material available at 10.1007/s12298-021-01060-9.

Project description:BackgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.ResultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019-2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY.ConclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

Dataset Information

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.).

Publications

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (<i>Brassica napus</i> L.).

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