Association mapping for seed cotton yield, its contributing and fiber quality traits in Upland Cotton (G. hirsutum L.) germplasm lines.
Jun 16, 2019
Breeding & Genetic improvement Network coordination Network-MedMiddleEast Breeding-Asia Fiber Quality
Suresh handi and I.S.Katageri*
*Professor and Head,
Institute of Agribiotechnology, University of Agricultural Sciences, Dharwad, Karnataka, INDIA, email@example.com
Determination of the genetic basis of complex quantitative traits has been one of the major scientific challenges in the process of crop improvement. To assist in this effort, an increasing number of genomic and genetic resources are today exploitable, including genome sequences, germplasm collections and public databases of genomic information. The availability of these resources, the recent advances in high-throughput genomic platforms and the increasing interest in exploring natural genetic diversity, make association mapping an appealing and affordable approach to identify genes responsible for quantitative variation of complex traits. Association mapping requires high-density oligonucleotide arrays to efficiently identify SNPs distributed across the genome at a density that accurately reflects genome wide LD structure and haplotype diversity. For Cotton, a high-density infinium array (63K SNP array) was recently built (Hulse-Kemp et al., 2015), with 63058 SNPs developed from different species which resulted in suitability for genome wide association analysis.
Association or linkage disequilibrium (LD) mapping revolutionized genetic mapping in humans, and is increasingly used to examine in plant genetics; it is an efficient way of determining the genetic basis of complex traits. In the present study, association mapping was examined with the use 201 germplasm of G. hirsutum lines evaluated for yield, yield components and fiber quality traits. Results from fastSTRCUTURE identified 12 subgroups in the population. The critical value of R2 was set to 0.243 was taken as a threshold to claim LD between two loci. About 3.13 % marker pairs showed significant high LD (R2=1) and about 82.72 percent pairs of loci were in linkage equilibrium with R2 values less than 0.3. Mixed linear model accounting for population structure and kinship has identified 349 significant marker trait associations for yield, yield components and fiber quality traits effectively controlling false positives reported in GLM (642 markers). More number of markers showing significant association were situated on D genome indicates than ‘A’ genome indicates detection of diverse SNP markers than ‘D’ genome or this may also because of the dense marker coverage in the D genome. The phenotypic variation explained by makers in this study was smaller suggesting minor QTLs or polygenic nature of these traits.
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