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Examining two sets of introgression lines across multiple environments reveals background-independent and stably expressed quantitative trait loci of fiber quality in cotton.

ABSTRACT: KEY MESSAGE:Background-independent (BI) and stably expressed (SE) quantitative trait loci (QTLs) were identified using two sets of introgression lines across multiple environments. Genetic background more greatly affected fiber quality traits than environmental factors. Sixty-one SE-QTLs, including two BI-QTLs, were novel and 48 SE-QTLs, including seven BI-QTLs, were previously reported. Cotton fiber quality traits are controlled by QTLs and are susceptible to environmental influence. Fiber quality improvement is an essential goal in cotton breeding but is hindered by limited knowledge of the genetic basis of fiber quality traits. In this study, two sets of introgression lines of Gossypium hirsutum?×?G. barbadense were used to dissect the QTL stability of three fiber quality traits (fiber length, strength and micronaire) across environments using 551 simple sequence repeat markers selected from our high-density genetic map. A total of 76 and 120 QTLs were detected in the CCRI36 and CCRI45 backgrounds, respectively. Nine BI-QTLs were found, and 78 (41.71%) of the detected QTLs were reported previously. Thirty-nine and 79 QTLs were SE-QTLs in at least two environments in the CCRI36 and CCRI45 backgrounds, respectively. Forty-eight SE-QTLs, including seven BI-QTLs, were confirmed in previous reports, and 61 SE-QTLs, including two BI-QTLs, were considered novel. These results indicate that genetic background more strongly impacts on fiber quality traits than environmental factors. Twenty-three clusters with BI- and/or SE-QTLs were identified, 19 of which harbored favorable alleles from G. barbadense for two or three fiber quality traits. This study is the first report using two sets of introgression lines to identify fiber quality QTLs across environments in cotton, providing insights into the effect of genetic backgrounds and environments on the QTL expression of fiber quality and important information for the genetic basis underlying fiber quality traits toward QTL cloning and molecular breeding.

SUBMITTER: Shi Y

PROVIDER: S-EPMC7311500 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Examining two sets of introgression lines across multiple environments reveals background-independent and stably expressed quantitative trait loci of fiber quality in cotton.

Shi Yuzhen Y Liu Aiying A Li Junwen J Zhang Jinfa J Li Shaoqi S Zhang Jinfeng J Ma Liujun L He Rui R Song Weiwu W Guo Lixue L Lu Quanwei Q Xiang Xianghui X Gong Wankui W Gong Juwu J Ge Qun Q Shang Haihong H Deng Xiaoying X Pan Jingtao J Yuan Youlu Y

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 20200317 7

<h4>Key message</h4>Background-independent (BI) and stably expressed (SE) quantitative trait loci (QTLs) were identified using two sets of introgression lines across multiple environments. Genetic background more greatly affected fiber quality traits than environmental factors. Sixty-one SE-QTLs, including two BI-QTLs, were novel and 48 SE-QTLs, including seven BI-QTLs, were previously reported. Cotton fiber quality traits are controlled by QTLs and are susceptible to environmental influence. Fi ...[more]

PMID: 32185421

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Project description:A new mixed-model method was developed for mapping quantitative trait loci (QTL) by incorporating multiple polygenic covariance structures. First, we used genome-wide markers to calculate six different kinship matrices. We then partitioned the total genetic variance into six variance components, one corresponding to each kinship matrix, including the additive, dominance, additive × additive, dominance × dominance, additive × dominance, and dominance × additive variances. The six different kinship matrices along with the six estimated polygenic variances were used to control the genetic background of a QTL mapping model. Simulation studies showed that incorporating epistatic polygenic covariance structure can improve QTL mapping resolution. The method was applied to yield component traits of rice. We analyzed four traits (yield, tiller number, grain number, and grain weight) using 278 immortal F2 crosses (crosses between recombinant inbred lines) and 1619 markers. We found that the relative importance of each type of genetic variance varies across different traits. The total genetic variance of yield is contributed by additive × additive (18%), dominance × dominance (14%), additive × dominance (48%), and dominance × additive (15%) variances. Tiller number is contributed by additive (17%), additive × additive (22%), and dominance × additive (43%) variances. Grain number is mainly contributed by additive (42%), additive × additive (19%), and additive × dominance (31%) variances. Grain weight is almost exclusively contributed by the additive (73%) variance plus a small contribution from the additive × additive (10%) variance. Using the estimated genetic variance components to capture the polygenic covariance structure, we detected 39 effects for yield, 39 effects for tiller number, 24 for grain number, and 15 for grain weight. The new method can be directly applied to polygenic-effect-adjusted genome-wide association studies (GWAS) in human and other species.

Project description:BACKGROUND: Upland cotton (Gossypium hirsutum L.) accounts for about 95% of world cotton production. Improving Upland cotton cultivars has been the focus of world-wide cotton breeding programs. Negative correlation between yield and fiber quality is an obstacle for cotton improvement. Random-mating provides a potential methodology to break this correlation. The suite of fiber quality traits that affect the yarn quality includes the length, strength, maturity, fineness, elongation, uniformity and color. Identification of stable fiber quantitative trait loci (QTL) in Upland cotton is essential in order to improve cotton cultivars with superior quality using marker-assisted selection (MAS) strategy. RESULTS: Using 11 diverse Upland cotton cultivars as parents, a random-mated recombinant inbred (RI) population consisting of 550 RI lines was developed after 6 cycles of random-mating and 6 generations of self-pollination. The 550 RILs were planted in triplicates for two years in Mississippi State, MS, USA to obtain fiber quality data. After screening 15538 simple sequence repeat (SSR) markers, 2132 were polymorphic among the 11 parents. One thousand five hundred eighty-two markers covering 83% of cotton genome were used to genotype 275 RILs (Set 1). The marker-trait associations were analyzed using the software program TASSEL. At p < 0.01, 131 fiber QTLs and 37 QTL clusters were identified. These QTLs were responsible for the combined phenotypic variance ranging from 62.3% for short fiber content to 82.8% for elongation. The other 275 RILs (Set 2) were analyzed using a subset of 270 SSR markers, and the QTLs were confirmed. Two major QTL clusters were observed on chromosomes 7 and 16. Comparison of these 131 QTLs with the previously published QTLs indicated that 77 were identified before, and 54 appeared novel. CONCLUSIONS: The 11 parents used in this study represent a diverse genetic pool of the US cultivated cotton, and 10 of them were elite commercial cultivars. The fiber QTLs, especially QTL clusters reported herein can be readily implemented in a cotton breeding program to improve fiber quality via MAS strategy. The consensus QTL regions warrant further investigation to better understand the genetics and molecular mechanisms underlying fiber development.

Project description:BackgroundNumerous quantitative trait loci (QTLs) and candidate genes associated with yield-related traits have been identified in cotton by genome-wide association study (GWAS) analysis. However, most of the phenotypic data were from a single or few environments, and the stable loci remained to be validated under multiple field environments.ResultsHere, 242 upland cotton accessions collected from different origins were continuously investigated for phenotypic data of four main yield components, including boll weight (BW) and lint percentage (LP) under 13 field environments, and boll number per plant (BN) and seed index (SI) under 11 environments. Correlation analysis revealed a positive correlation between BN and LP, BW and SI, while SI had a negative correlation with LP and BN. Genetic analysis indicated that LP had the highest heritability estimates of 94.97%, followed by 92.08% for SI, 86.09% for BW, and 72.92% for BN, indicating LP and SI were more suitable traits for genetic improvement. Based on 56,010 high-quality single nucleotide polymorphisms (SNPs) and GWAS analysis, a total of 95 non-redundant QTLs were identified, including 12 of BN, 23 of BW, 45 of LP, and 33 of SI, respectively. Of them, 10 pairs of homologous QTLs were detected between A and D sub-genomes. We also found that 15 co-located QTLs with more than two traits and 12 high-confidence QTLs were detected under more than six environments, respectively. Further, two NET genes (GH_A08G0716 and GH_A08G0783), located in a novel QTL hotspot (qtl24, qtl25 and qlt26) were predominately expressed in early fiber development stages, exhibited significant correlation with LP and SI. The GH_A07G1389 in the stable qtl19 region encoded a tetratricopeptide repeat (TPR)-like superfamily protein and was a homologous gene involved in short fiber mutant ligon lintless-y (Liy), implying important roles in cotton yield.ConclusionsThe present study provides a foundation for understanding the regulatory mechanisms of yield components and may enhance yield improvement through molecular breeding in cotton.

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Examining two sets of introgression lines across multiple environments reveals background-independent and stably expressed quantitative trait loci of fiber quality in cotton.

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Examining two sets of introgression lines across multiple environments reveals background-independent and stably expressed quantitative trait loci of fiber quality in cotton.

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