Abstract
Understanding the population structure and linkage disequilibrium in an association panel can effectively avoid spurious associations and improve the accuracy in association mapping. In this study, one hundred and fifty eight elite cotton (Gossypium hirsutum L.) germplasm from all over the world, which were genotyped with 212 whole genome-wide marker loci and phenotyped with an disease nursery and greenhouse screening method, were assayed for population structure, linkage disequilibrium, and association mapping of Verticillium wilt resistance. A total of 480 alleles ranging from 2 to 4 per locus were identified from all collections. Model-based analysis identified two groups (G1 and G2) and seven subgroups (G1a–c, G2a–d), and differentiation analysis showed that subgroup having a single origin or pedigree was apt to differentiate with those having a mixed origin. Only 8.12% linked marker pairs showed significant LD (P<0.001) in this association panel. The LD level for linked markers is significantly higher than that for unlinked markers, suggesting that physical linkage strongly influences LD in this panel, and LD level was elevated when the panel was classified into groups and subgroups. The LD decay analysis for several chromosomes showed that different chromosomes showed a notable change in LD decay distances for the same gene pool. Based on the disease nursery and greenhouse environment, 42 marker loci associated with Verticillium wilt resistance were identified through association mapping, which widely were distributed among 15 chromosomes. Among which 10 marker loci were found to be consistent with previously identified QTLs and 32 were new unreported marker loci, and QTL clusters for Verticillium wilt resistanc on Chr.16 were also proved in our study, which was consistent with the strong linkage in this chromosome. Our results would contribute to association mapping and supply the marker candidates for marker-assisted selection of Verticillium wilt resistance in cotton.
Highlights
Cotton is an important economic crop worldwide, which provides the most important natural fiber for the textile industry
Based on linkage disequilibrium (LD) analyses both in the whole genome level and at the individual chromosome level, the LD level was elevated when the panel was classified into groups and subgroups (Table 5 and Table 6), implying that variable extents of LD are expected within the different genetic groups and highlight the fact that different marker densities will be required if association studies are planned in the different genetic groups
Unlike previous study in bi-parental populations [11], which showed that 41QTLs related to Verticillium wilt resistance intensively distributed on chromosomes D9(Chr.23) and D7(Chr.16), our study identified 42 associations widely distributed on 15 chromosomes(Table 8).This implied that association mapping can locate many QTLs over the entire genome since the mapping population includes a large number of diversified entries of germplasm, while conventional QTL mapping based on bi-parental populations only identified fewer QTLs which be located in a limited area in the genome where the two parents differ, causing QTL clustering [11]
Summary
Cotton is an important economic crop worldwide, which provides the most important natural fiber for the textile industry. Fiber quality and disease resistance is the most important objectives in cotton breeding programs worldwide. The development of molecular quantitative genetics has made it possible to map the quantitative trait loci (QTLs) for yield, fiber quality and disease resistance, facilitating the application of marker-assisted selection (MAS) for genetic improvement. Numerous QTLs for yield, fiber quality and disease resistance were identified [2,3,4,5,6,7,8,9,10,11] In all these studies, the QTL mapping had been performed in segregating populations derived from biparental crosses. Due to limited recombination events, it is difficult for biparental segregating populations to detect closely linked markers for marker-assisted selection. What’s more, association mapping has been used to identify causal polymorphism within a gene that is responsible for the phenotypic variations [14]
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