Abstract
Improving the salt-tolerance of direct-seeding rice at the seed germination stage is a major goal of breeders. Efficiently identifying salt tolerance loci will help researchers develop effective rice breeding strategies. In this study, six multi-locus genome-wide association studies (GWAS) methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were applied to identify quantitative trait nucleotides (QTNs) for the salt tolerance traits of 478 rice accessions with 162,529 SNPs at the seed germination stage. Among the 371 QTNs detected by the six methods, 56 were identified by at least three methods. Among these 56 QTNs, 12, 6, 7, 4, 13, 12, and 12 were found to be associated with SSI-GI, SSI-VI, SSI-MGT, SSI-IR-24h, SSI-IR-48h, SSI-GR-5d, and SSI-GR-10d, respectively. Additionally, 66 candidate genes were identified in the vicinity of the 56 QTNs, and two of these genes (LOC_Os01g45760 and LOC_Os10g04860) are involved in auxin biosynthesis according to the enriched GO terms and KEGG pathways. This information will be useful for identifying the genes responsible for rice salt tolerance. A comparison of the six methods revealed that ISIS EM-BLASSO identified the most co-detected QTNs and performed best, with the smallest residual errors and highest computing speed, followed by FASTmrMLM, pLARmEB, mrMLM, pKWmEB, and FASTmrEMMA. Although multi-locus GWAS methods are superior to single-locus GWAS methods, their utility for identifying QTNs may be enhanced by adding a bin analysis to the models or by developing a hybrid method that merges the results from different methods.
Highlights
A genome-wide association studies (GWAS) represents a powerful option for the genetic characterization of quantitative traits, and has been widely used for analyzing agronomic traits related to plants
The statistical power of quantitative trait nucleotide (QTN) detection improves after controlling the polygenic background, most of the small effects associated with complex traits are still not captured by single-locus genomewide association studies (GWAS) methods
All salt tolerance-related traits were measured for plants treated with 60 mM NaCl or water as follows: imbibition rate (IR) was calculated as IR = (W2 − W1)/W1 × 1000 at 24 and 48 h after starting the incubation, where W1 represents the dry seed weight and W2 represents the imbibed seed weight; germination rate (GR) was calculated as GR = Nt/N0 × 100% at days 5 and 10, where Nt is the number of germinated seeds at day t and N0 is the total number of seeds; germination index (GI) was calculated as GI = (Gt/Tt), where Gt is the accumulated number of germinated seeds at day t and Tt is the time; mean germination time (MGT) was calculated as MGT =
Summary
A genome-wide association studies (GWAS) represents a powerful option for the genetic characterization of quantitative traits, and has been widely used for analyzing agronomic traits related to plants. Many penalized multilocus GWAS methods have been developed, including the least absolute shrinkage and selection operator (LASSO), empirical Bayes LASSO, and adaptive mixed LASSO (Yi and Xu, 2008; Cho et al, 2009, 2010; Wu et al, 2009; Ayers and Cordell, 2010; Wang et al, 2010; Giglio and Brown, 2018). These methods can minimize some marker effects to zero when the number of single nucleotide polymorphisms (SNPs) is not much larger than the sample size. The mrMLM package solves the problem associated with co-factor selection in the multi-locus GWAS model when there are many markers
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