Genome-wide association study and genomic selection for plant height, maturity, seed weight, and yield in soybean

Ravelombola, Waltram; Qin, Jun; Shi, Ainong; Wang, Fengmin; Yan, Feng; Yue, Meng; Yang, Chen; Zhang, Mengchen

doi:10.21203/rs.2.20026/v1

Cited by 1 publication

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References 60 publications

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“…Genomic selection has been evaluated for a range of crops, including sugar beet ( Beta vulgaris L.) (Würschum et al., 2013), oat ( Avena sativa L.) (Asoro et al., 2011), rye ( Secale cereale L.) (Wang et al., 2014), rapeseed ( Brassica napus L.) (Würschum et al., 2014; Zou et al., 2016), barley ( Hordeum vulgare L.) (Schmidt et al., 2016; Thorwarth et al., 2017), maize ( Zea mays L.) (Zhao et al., 2012; Pace et al., 2015; Li et al., 2019), triticale (× Triticosecale Wittmack) (Würschum et al., 2017), and wheat ( Triticum aestivum L.) (He et al., 2016; Huang et al., 2016; Lozada et al., 2020). For soybean, comparably few studies are available, although the approach has the potential to assist breeding for disease resistance (Bao et al., 2014; Wen et al., 2018; Ravelombola et al., 2019), abiotic stress resistance (Jähne et al., 2019), and yield or yield‐related agronomic traits (Jarquín et al., 2014; Ma et al., 2016; Zhang et al., 2016; Duhnen et al., 2017; Matei et al., 2018; Đorđević et al., 2019; Stewart‐Brown et al., 2019; Ravelombola et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Training set design in genomic prediction with multiple biparental families

et al. 2021

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Genomic selection is a powerful tool to reduce the cycle length and enhance the genetic gain of complex traits in plant breeding. However, questions remain about the optimum design and composition of the training set. In this study, we used 944 soybean [Glycine max (L.) Merr.] recombinant inbred lines from eight families derived through a partial-diallel mating design among five parental lines. The cross-validated prediction accuracies for the six traits seed yield, 1,000-seed weight, protein yield, plant height, protein content, and oil content were high, ranging from 0.79 to 0.87. We investigated among-family predictions, making use of the special mating design with different degrees of relatedness among families. Generally, the prediction accuracy decreased from full-sibs to half-sib families to unrelated families. However, half-sib and unrelated families also showed substantial variation in their prediction accuracy for a given family, which appeared to be caused at least in part by the shared segregation of quantitative trait loci in both the training and prediction sets. Combining several half-sib families in composite training sets generally led to an increase in the prediction accuracy compared with the best family alone. The prediction accuracy increased with the size of the training set, but for comparable prediction accuracy, substantially more half-sibs were required than full-sibs. Collectively, our results highlight the potential of genomic selection for soybean breeding and, in a broader context, illustrate the importance of the targeted design of the training set. INTRODUCTIONGenomic selection was first suggested for animal breeding and has been shown to enhance the rate of genetic improvement Meuwissen et al., 2001). Genomic selection has also emerged as a valuable tool for plant breeding.

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