Genome-Wide Association Study Identifying Candidate Genes Influencing Important Agronomic Traits of Flax (Linum usitatissimum L.) Using SLAF-seq

Abstract

Flax (Linum usitatissimum L.) is an important cash crop, and its agronomic traits directly affect yield and quality. Molecular studies on flax remain inadequate because relatively few flax genes have been associated with agronomic traits or have been identified as having potential applications. To identify markers and candidate genes that can potentially be used for genetic improvement of crucial agronomic traits, we examined 224 specimens of core flax germplasm; specifically, phenotypic data for key traits, including plant height, technical length, number of branches, number of fruits, and 1000-grain weight were investigated under three environmental conditions before specific-locus amplified fragment sequencing (SLAF-seq) was employed to perform a genome-wide association study (GWAS) for these five agronomic traits. Subsequently, the results were used to screen single nucleotide polymorphism (SNP) loci and candidate genes that exhibited a significant correlation with the important agronomic traits. Our analyses identified a total of 42 SNP loci that showed significant correlations with the five important agronomic flax traits. Next, candidate genes were screened in the 10 kb zone of each of the 42 SNP loci. These SNP loci were then analyzed by a more stringent screening via co-identification using both a general linear model (GLM) and a mixed linear model (MLM) as well as co-occurrences in at least two of the three environments, whereby 15 final candidate genes were obtained. Based on these results, we determined that UGT and PL are candidate genes for plant height, GRAS and XTH are candidate genes for the number of branches, Contig1437 and LU0019C12 are candidate genes for the number of fruits, and PHO1 is a candidate gene for the 1000-seed weight. We propose that the identified SNP loci and corresponding candidate genes might serve as a biological basis for improving crucial agronomic flax traits.

Resequencing 200 Flax Cultivated Accessions Identifies Candidate Genes Related to Seed Size and Weight and Reveals Signatures of Artificial Selection

“…These genes control seed growth in the aspects of integument cell proliferation, embryo development, information exchange between seed coat, endosperm and embryo (Li and Li, 2016;Huang et al, 2017a). However, only SPX and EXS domain-containing protein (PHO1) has been reported as candidate genes for 1000-seed weight in flax (Xie et al, 2018a). The conservation of seed size regulators and pathway connectivity in different species may help us understand the seed development in flax.…”

“…193 SSR markers were used to perform association analysis on 124 flax samples using GLM and MLM models, 32 loci were found to be associated with yield-related traits (Deng, 2013). Xie et al performed genome-wide association studies (GWAS) on 5 yield-related traits of 224 flax samples using 34,932 SNPs based on GLM and MLM, and a total of 42 SNPs loci were identified and 15 candidate genes were obtained (Xie et al, 2018a). Subsequently, Xie et al (2018b) performed GWAS on 13 agronomic traits using the same data set, only 6 traits were associated with 16 SNPs.…”

Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

“…PCR primer sequences of AFLPs and SSRs were retrieved from the literature [15,[19][20][21]23,24]. For the SNPs named based on the scaffold sequences, their scaffold names and coordinates were collected directly from the publications [17,26]. For the SNPs identified without a reference [18], flanking sequences of the SNP markers were downloaded from the publication [18].…”

Mapping Quantitative Trait Loci onto Chromosome-scale Pseudomolecules in Flax

“…PCR primer sequences of AFLPs and SSRs were retrieved from the literature [15,[19][20][21]23,24]. For SNPs based on the scaffold sequences, scaffold names and coordinates of SNPs on scaffolds were collected directly from the publications [17,26]. For SNPs identified without a reference [18], flanking sequences of the SNP markers were downloaded from the publication [18].…”

“…These QTL were identified mainly using SSR or SNP markers ( Table 2). Most (200) of the QTL were mapped based on genetic maps [15,[18][19][20][21][22][23][24], scaffold sequences [17,25,26], or an early (hereafter pre-released) version of chromosome-scale pseudomolecules (PCPs) [27,28]; however, only 67 QTL for pasmo severity (PAS) were mapped on the most recent release of the chromosome-scale pseudomolecules (RCPs) ( Table 2) [14,29]. For comparison purposes, coordinates of the QTL based on the PCPs must be converted to the RCPs [29] because the two versions are slightly different.…”