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Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline.

ABSTRACT: Soybean is a primary meal protein for human consumption, poultry, and livestock feed. In this study, quantitative trait locus (QTL) controlling protein content was explored via genome-wide association studies (GWAS) and linkage mapping approaches based on 284 soybean accessions and 180 recombinant inbred lines (RILs), respectively, which were evaluated for protein content for 4 years. A total of 22 single nucleotide polymorphisms (SNPs) associated with protein content were detected using mixed linear model (MLM) and general linear model (GLM) methods in Tassel and 5 QTLs using Bayesian interval mapping (IM), single-trait multiple interval mapping (SMIM), single-trait composite interval mapping maximum likelihood estimation (SMLE), and single marker regression (SMR) models in Q-Gene and IciMapping. Major QTLs were detected on chromosomes 6 and 20 in both populations. The new QTL genomic region on chromosome 6 (Chr6_18844283-19315351) included 7 candidate genes and the Hap.X ^AA at the Chr6_19172961 position was associated with high protein content. Genomic selection (GS) of protein content was performed using Bayesian Lasso (BL) and ridge regression best linear unbiased prediction (rrBULP) based on all the SNPs and the SNPs significantly associated with protein content resulted from GWAS. The results showed that BL and rrBLUP performed similarly; GS accuracy was dependent on the SNP set and training population size. GS efficiency was higher for the SNPs derived from GWAS than random SNPs and reached a plateau when the number of markers was >2,000. The SNP markers identified in this study and other information were essential in establishing an efficient marker-assisted selection (MAS) and GS pipelines for improving soybean protein content.

SUBMITTER: Qin J

PROVIDER: S-EPMC9244705 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Soybean seeds are primary sources of natural tocopherols used by the food and pharmaceutical industries, owing to their beneficial impacts on human health. Selection for higher tocopherol contents in seeds along with other desirable traits is an important goal in soybean breeding. In order to identify the genomic loci and candidate genes controlling tocopherol content in soybean seeds, the bulked-segregant analysis technique was performed using a natural population of soybean consisting of 1525 accessions. We constructed the bulked-segregant analysis based on 98 soybean accessions that showed extreme phenotypic variation for the target trait, consisting of 49 accessions with extremely-high and 49 accessions with extremely-low tocopherol content. A total of 144 variant sites and 109 predicted genes related to tocopherol content were identified, in which a total of 83 genes were annotated by the gene ontology functions. Furthermore, 13 enriched terms (p < 0.05) were detected, with four of them found to be highly enriched: response to lipid, response to abscisic acid, transition metal ion transmembrane transporter activity, and double-stranded DNA binding. Especially, six candidate genes were detected at 41.8−41.9 Mb genomic hotspots on chromosome 5 based on ANNOtate VARiation analysis. Among the genes, only Glyma.05G243400 carried a non-synonymous mutation that encodes a “translation elongation factor EF1A or initiation factor IF2gamma family protein” was identified. The haplotype analysis confirmed that Glyma.05G243400 exhibited highly significant variations in terms of tocopherol content across multiple experimental locations, suggesting that it can be the key candidate gene regulating soybean seed tocopherols. The present findings provide novel gene resources related to seed tocopherols for further validation by genome editing, functional characterization, and genetic improvement targeting enhanced tocopherol composition in soybean molecular breeding.

Project description:In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10-30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.

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Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline.

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