Rapid Mining of Candidate Genes for Verticillium Wilt Resistance in Cotton Based on BSA-Seq Analysis

Abstract

Cotton is a globally important cash crop. Verticillium wilt (VW) is commonly known as “cancer” of cotton and causes serious loss of yield and fiber quality in cotton production around the world. Here, we performed a BSA-seq analysis using an F2:3 segregation population to identify the candidate loci involved in VW resistance. Two QTLs (qvw-D05-1 and qvw-D05-2) related to VW resistance in cotton were identified using two resistant/susceptible bulks from the F2 segregation population constructed by crossing the resistant cultivar ZZM2 with the susceptible cultivar J11. A total of 30stop-lost SNPs and 42 stop-gained SNPs, which included 17 genes, were screened in the qvw-D05-2 region by SnpEff analysis. Further analysis of the transcriptome data and qRT-PCR revealed that the expression level of Ghir_D05G037630 (designated as GhDRP) varied significantly at certain time points after infection with V. dahliae. The virus-induced gene silencing of GhDRP resulted in higher susceptibility of the plants to V. dahliae than the control, suggesting that GhDRP is involved in the resistance to V. dahlia infection. This study provides a method for rapid mining of quantitative trait loci and screening of candidate genes, as well as enriches the genomic information and gene resources for the molecular breeding of disease resistance in cotton.

An aldehyde dehydrogenase gene, GhALDH7B4_A06, positively regulates fiber strength in upland cotton (Gossypium hirsutum L.)

“…The combination of BSA and sequencing is effective for rapidly mapping major QTLs and is widely used in various crops (Takagi et al, 2013;Illa-Berenguer et al, 2015;Chen et al, 2021). In cotton, this approach has been applied to studies on important traits such as fiber quality, boll weight, agronomic traits, and disease resistance (Zhang et al, 2016;Cui et al, 2021;Ma et al, 2022;Jia et al, 2023;Zhang et al, 2024). In our study, an F 2 population was used for SLAF-BSA-seq, and four QTLs associated with FS were mapped to three chromosomes.…”

Two decades of association mapping: Insights on disease resistance in major crops

“…Extreme phenotypes can be used to determine QTLs and screen candidate genes quickly. Cui et al (2021) conducted an experiment on XP-GWAS and 145 trait-associated variants for kernel row number traits were identified in maize at a false discovery rate of 0.05. These identified associations are somewhat less than the number obtained (260) by the conventional GWAS approach, but this lacunae is counter balanced by a considerable reduction in the cost of genotyping (Yang et al, 2015).…”

“…Extreme phenotypes can be used to determine QTLs and screen candidate genes quickly. Cui et al. (2021) conducted an experiment on XP‐GWAS and 145 trait-associated variants for kernel row number traits were identified in maize at a false discovery rate of 0.05.…”

Identification, Analysis, and Confirmation of Seed Storability-Related Loci in Dongxiang Wild Rice (Oryza rufipogon Griff.)

“…On the other hand, bulked segregant analysis via whole genome sequencing (BSA-seq) is a fast and powerful tool to accurately identify QTLs associated with complex traits [17]. BSA-seq has been widely used in various plants, such as rice [18], cucumbers [19], and cotton [20]. However, there is limited research on the identification of elite QTLs from wild rice by BSA-seq.…”