Abstract
Improved fiber yield is considered a constant goal of upland cotton (Gossypium hirsutum) breeding worldwide, but the understanding of the genetic basis controlling yield-related traits remains limited. Dissecting the genetic architecture of complex traits is an ongoing challenge for geneticists. Two complementary approaches for genetic mapping, linkage mapping and association mapping have led to successful dissection of complex traits in many crop species. Both of these methods detect quantitative trait loci (QTL) by identifying marker–trait associations, and the only fundamental difference between them is that between mapping populations, which directly determine mapping resolution and power. Nowadays, the availability of genomic tools and resources is leading to a new revolution of plant breeding, as they facilitate the study of the genotype and its relationship with the phenotype, in particular for complex traits. Next Generation Sequencing (NGS) technologies are allowing the mass sequencing of genomes and transcriptomes, which is producing a vast array of genomic information with the development of high-throughput genotyping, phenotyping will be a major challenge for genetic mapping studies. We believe that high-quality phenotyping and appropriate experimental design coupled with new statistical models will accelerate progress in dissecting the genetic architecture of complex traits.
Highlights
Cotton is a crop of immense importance as being a dominant source of fiber and oil from cottonseed all over the world [1]
Fiber quality enhancement through genetics is the ultimate objective of breeding strategy in cotton
Cotton scientists have been involved in fiber quality improvement for a long time due to the increase in demand for multiple products from cotton
Summary
Cotton is a crop of immense importance as being a dominant source of fiber and oil from cottonseed all over the world [1]. The hereditary variation present among the gene pool land races can be exploited by applying the mapping based on linkage disequilibrium. It will speed up the cotton breeding through identification of markers among trait of interest and ensure molecular breeding. Association mapping relies upon the magnitude of different pair of genes for population analysis This mapping shows powerful connection between required character and a genetic marker while nonrandom combination between two quantitative trait loci or markers manifests linkage disequilibrium [8]. Population based polygenic characters mapping for desired traits became a widely used technique thanks to the innovations in omics and availability of advanced bioinformatic tools for analyzing genetic variations [17]. The ultimate benefits of this technique includes the ability to work with a large number of loci, producibilty of highly saturated maps, its speed and its low cost [18]
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