Abstract

BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments.ResultsHere, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield.ConclusionsThe present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

Highlights

  • Numerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis

  • Phenotypic variation of the four yield‐related traits We analyzed the phenotypic data of four yield-related traits boll number (BN), boll weight (BW), lint percentage (LP), and seed index (SI) in multiple field environments to evaluate the phenotypic variation in the natural population of 242 upland cotton accessions (Additional file 1 Table S1)

  • boll number per plant (BN) exhibited the largest coefficient of variation (CV), ranging from 13.13% to 24.26%, while LP showed the smallest CV ranging from 7.19% to 10.53%

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Summary

Introduction

Numerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. It is of great significance to explore and pyramid the elite quantitative trait loci (QTLs)/genes related to yield components for improving cotton yield through molecular breeding. Based on the reference genome sequence, a large number of QTLs and candidate genes associated with yield-related traits were identified by genome-wide association study (GWAS) analysis [12,13,14,15]. Multi-environment and multi-locus GWAS coupled with improved experimental design and associated methods may increase efficiency to mine QTLs/genes related to fiber yield traits, which is still challenging in cotton breeding

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