Abstract
Genetic architecture of branch traits has large influences on the morphological structure, photosynthetic capacity, planting density, and yield of Upland cotton (Gossypium hirsutum L.). This research aims to reveal the genetic effects of six branch traits, including bottom fruit branch node number (BFBNN), bottom fruit branch length (BFBL), middle fruit branch node number (MFBNN), middle fruit branch length (MFBL), upper fruit branch node number (UFBNN), and upper fruit branch length (UFBL). Association mapping was conducted for these traits of 39 lines and their 178 F1 hybrids in three environments. There were 20 highly significant Quantitative Trait SSRs (QTSs) detected by mixed linear model approach analyzing a full genetic model with genetic effects of additive, dominance, epistasis and their environment interaction. The phenotypic variation explained by genetic effects ranged from 32.64 ~ 91.61%, suggesting these branch traits largely influenced by genetic factors.
Highlights
Gossypium hirsutum is one of the commercially grown species of cotton
Since association mapping has been used in many researches related to complex disease and agronomic traits, it becomes an effective way to dissect the genetic basis of complex traits
Association mapping is a statistically powerful method, which is utilized to dissect the genetic architecture of complex traits with high resolution
Summary
Gossypium hirsutum is one of the commercially grown species of cotton. Thousands of analyses have been carried out to find what factors are important in cotton growth. Previous studies have revealed that branch traits were related to plant density, canopy structure, and photosynthetic capacity, influencing fiber quality and yield [1,2]. Association mapping aims to discover quantitative trait loci (QTLs) by evaluating the marker-trait associates, which influences the strength of linkage disequilibrium between genotypes and phenotypes across a population [3]. Since association mapping has been used in many researches related to complex disease and agronomic traits, it becomes an effective way to dissect the genetic basis of complex traits. Association mapping has three advantages: no need to construct mapping population; detect multiple loci at one time; high resolution
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