Abstract
Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry. However, the understanding of genetic components and their genome-wide interactions controlling fiber quality remains fragmentary. Here, we sequenced a multiple-parent advanced-generation inter-cross (MAGIC) population, consisting of 550 individuals created by inter-crossing 11 founders, and established a mosaic genome map through tracing the origin of haplotypes that share identity-by-descent (IBD). We performed two complementary GWAS methods—SNP-based GWAS (sGWAS) and IBD-based haplotype GWAS (hGWAS). A total of 25 sQTLs and 14 hQTLs related to cotton fiber quality were identified, of which 26 were novel QTLs. Two major QTLs detected by both GWAS methods were responsible for fiber strength and length. The gene Ghir_D11G020400 (GhZF14) encoding the MATE efflux family protein was identified as a novel candidate gene for fiber length. Beyond the additive QTLs, we detected prevalent epistatic interactions that contributed to the genetics of fiber quality, pinpointing another layer for trait variance. This study provides new targets for future molecular design breeding of superior fiber quality.
Highlights
Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry
Genotyping has been the main limitation of the genome-wide association study (GWAS) method for a long time, but in the past few years, advances in high-throughput sequencing and data processing have facilitated the use of this approach in model species, and in crops[7,8,9,10,11,12,13,14]
Beyond the additive quantitative trait locus (QTL) obtained, we found that epistasis was prevalent, and most epistatic pairs showed moderate effects, indicating that epistatic interactions were as important as additive effects
Summary
Cotton (Gossypium hirsutum L.) fiber is the most important resource of natural and renewable fiber for the textile industry. Genotyping has been the main limitation of the GWAS method for a long time, but in the past few years, advances in high-throughput sequencing and data processing have facilitated the use of this approach in model species, and in crops[7,8,9,10,11,12,13,14]. This approach is limited by the presence of rare alleles and confounding population structure[15]. Studies have shown that IBD-based GWAS is complementary to conventional single-variant-based association mapping and is superior in the identification of QTL with allelic series or small effects[15,16]
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