Favorable QTL alleles for yield and its components identified by association mapping in Chinese upland cotton cultivars

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Abstract

Linkage disequilibrium based association mapping is a powerful tool for dissecting the genetic basis underlying complex traits. In this study, an association mapping panel consisting of 356 representative Upland cotton cultivars was constructed, evaluated in three environments and genotyped using 381 SSRs to detect molecular markers associated with lint yield and its components. The results showed that abundant phenotypic and moderate genetic diversities existed within this germplasm panel. The population could be divided into two subpopulations, and weak relatedness was detected between pair-wise accessions. LD decayed to the background (r2 = 0.1182, P≤0.01), r2 = 0.1 and r2 = 0.2 level within 12-13 cM, 17-18 cM and 3-4 cM, respectively, providing the potential for association mapping of agronomically important traits in Chinese Upland cotton. A total of 55 marker-trait associations were detected between 26 SSRs and seven lint yield traits, based on a mixed linear model (MLM) and Bonferroni correction (P≤0.05/145, -log10P≥3.46). Of which 41 could be detected in more than one environment and 17 markers were simultaneously associated with two or more traits. Many associations were consistent with QTLs identified by linkage mapping in previous reports. Phenotypic values of alleles of each loci in 41 stably detected associations were compared, and 23 favorable alleles were identified. Population frequency of each favorable allele in historically released cultivar groups was also evaluated. The QTLs detected in this study will be helpful in further understanding the genetic basis of lint yield and its components, and the favorable alleles may facilitate future high-yield breeding by genomic selection in Upland cotton. © 2013 Mei et al.

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APA

Mei, H., Zhu, X., & Zhang, T. (2013). Favorable QTL alleles for yield and its components identified by association mapping in Chinese upland cotton cultivars. PLoS ONE, 8(12). https://doi.org/10.1371/journal.pone.0082193

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