Limits...
Fine mapping and RNA-Seq unravels candidate genes for a major QTL controlling multiple fiber quality traits at the T1 region in upland cotton.

Liu D, Zhang J, Liu X, Wang W, Liu D, Teng Z, Fang X, Tan Z, Tang S, Yang J, Zhong J, Zhang Z - BMC Genomics (2016)

Bottom Line: The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 50.0 % (LOD = 194.3) and 30.1 % (LOD = 100.4) of phenotypic variation with additive effects of 2.78, -0.43, 2.92 and 1.90 units for fiber length, micronaire, strength and uniformity, respectively.This study mapped a major QTL influencing four fiber quality traits to a 0.28-cM interval and identified three candidate genes by RNA-Seq and RT-PCR analysis.Integration of fine mapping and RNA-Seq is a powerful strategy to uncover candidates for QTL in large genomes.

View Article: PubMed Central - PubMed

Affiliation: Engineering Research Center of South Upland Agriculture, Ministry of Education, Southwest University, 400716, Chongqing, People's Republic of China.

ABSTRACT

Background: Improving fiber quality is a major challenge in cotton breeding, since the molecular basis of fiber quality traits is poorly understood. Fine mapping and candidate gene prediction of quantitative trait loci (QTL) controlling cotton fiber quality traits can help to elucidate the molecular basis of fiber quality. In our previous studies, one major QTL controlling multiple fiber quality traits was identified near the T1 locus on chromosome 6 in Upland cotton.

Results: To finely map this major QTL, the F2 population with 6975 individuals was established from a cross between Yumian 1 and a recombinant inbred line (RIL118) selected from a recombinant inbred line population (T586 × Yumian 1). The QTL was mapped to a 0.28-cM interval between markers HAU2119 and SWU2302. The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 50.0 % (LOD = 194.3) and 30.1 % (LOD = 100.4) of phenotypic variation with additive effects of 2.78, -0.43, 2.92 and 1.90 units for fiber length, micronaire, strength and uniformity, respectively. The QTL region corresponded to a 2.7-Mb interval on chromosome 10 in the G. raimondii genome sequence and a 5.3-Mb interval on chromosome A06 in G. hirsutum. The fiber of Yumian 1 was much longer than that of RIL118 from 3 DPA to 7 DPA. RNA-Seq of ovules at 0 DPA and fibers at 5 DPA from Yumian 1 and RIL118 showed four genes in the QTL region of the G. raimondii genome to be extremely differentially expressed. RT-PCR analysis showed three genes in the QTL region of the G. hirsutum genome to behave similarly.

Conclusions: This study mapped a major QTL influencing four fiber quality traits to a 0.28-cM interval and identified three candidate genes by RNA-Seq and RT-PCR analysis. Integration of fine mapping and RNA-Seq is a powerful strategy to uncover candidates for QTL in large genomes.

No MeSH data available.


Related in: MedlinePlus

Genetic map of the QTL region and trace of the log probabilities for the four fiber quality traits. The genetic map and QTL identification come from 1440 F2 plants in 2011. The genetic distances between adjacent markers are shown to the left of chromosomes
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Fig3: Genetic map of the QTL region and trace of the log probabilities for the four fiber quality traits. The genetic map and QTL identification come from 1440 F2 plants in 2011. The genetic distances between adjacent markers are shown to the left of chromosomes

Mentions: To further define the QTL controlling fiber quality traits, all 26 markers in the confidence interval between MUCS114 and MUSB0165 were employed to genotype the 1434 F2 plants in 2011. The confidence interval for the position of the QTL from the peak LOD to less than 1, the QTL controlling fiber quality traits was mapped within a 0.35-cM interval between MUCS114 and SWU2302, and co-segregated with T1 and 17 SSR markers (Fig. 3 and Table 2). The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 30.1 % (LOD = 100.4), and 50.0 % (LOD = 194.3) of total phenotypic variation, with additive effects of 2.65, −0.41, 1.83 and 2.91 for FL, FM, FU and FS, respectively.Fig. 3


Fine mapping and RNA-Seq unravels candidate genes for a major QTL controlling multiple fiber quality traits at the T1 region in upland cotton.

Liu D, Zhang J, Liu X, Wang W, Liu D, Teng Z, Fang X, Tan Z, Tang S, Yang J, Zhong J, Zhang Z - BMC Genomics (2016)

Genetic map of the QTL region and trace of the log probabilities for the four fiber quality traits. The genetic map and QTL identification come from 1440 F2 plants in 2011. The genetic distances between adjacent markers are shown to the left of chromosomes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4837631&req=5

Fig3: Genetic map of the QTL region and trace of the log probabilities for the four fiber quality traits. The genetic map and QTL identification come from 1440 F2 plants in 2011. The genetic distances between adjacent markers are shown to the left of chromosomes
Mentions: To further define the QTL controlling fiber quality traits, all 26 markers in the confidence interval between MUCS114 and MUSB0165 were employed to genotype the 1434 F2 plants in 2011. The confidence interval for the position of the QTL from the peak LOD to less than 1, the QTL controlling fiber quality traits was mapped within a 0.35-cM interval between MUCS114 and SWU2302, and co-segregated with T1 and 17 SSR markers (Fig. 3 and Table 2). The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 30.1 % (LOD = 100.4), and 50.0 % (LOD = 194.3) of total phenotypic variation, with additive effects of 2.65, −0.41, 1.83 and 2.91 for FL, FM, FU and FS, respectively.Fig. 3

Bottom Line: The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 50.0 % (LOD = 194.3) and 30.1 % (LOD = 100.4) of phenotypic variation with additive effects of 2.78, -0.43, 2.92 and 1.90 units for fiber length, micronaire, strength and uniformity, respectively.This study mapped a major QTL influencing four fiber quality traits to a 0.28-cM interval and identified three candidate genes by RNA-Seq and RT-PCR analysis.Integration of fine mapping and RNA-Seq is a powerful strategy to uncover candidates for QTL in large genomes.

View Article: PubMed Central - PubMed

Affiliation: Engineering Research Center of South Upland Agriculture, Ministry of Education, Southwest University, 400716, Chongqing, People's Republic of China.

ABSTRACT

Background: Improving fiber quality is a major challenge in cotton breeding, since the molecular basis of fiber quality traits is poorly understood. Fine mapping and candidate gene prediction of quantitative trait loci (QTL) controlling cotton fiber quality traits can help to elucidate the molecular basis of fiber quality. In our previous studies, one major QTL controlling multiple fiber quality traits was identified near the T1 locus on chromosome 6 in Upland cotton.

Results: To finely map this major QTL, the F2 population with 6975 individuals was established from a cross between Yumian 1 and a recombinant inbred line (RIL118) selected from a recombinant inbred line population (T586 × Yumian 1). The QTL was mapped to a 0.28-cM interval between markers HAU2119 and SWU2302. The QTL explained 54.7 % (LOD = 222.3), 40.5 % (LOD = 145.0), 50.0 % (LOD = 194.3) and 30.1 % (LOD = 100.4) of phenotypic variation with additive effects of 2.78, -0.43, 2.92 and 1.90 units for fiber length, micronaire, strength and uniformity, respectively. The QTL region corresponded to a 2.7-Mb interval on chromosome 10 in the G. raimondii genome sequence and a 5.3-Mb interval on chromosome A06 in G. hirsutum. The fiber of Yumian 1 was much longer than that of RIL118 from 3 DPA to 7 DPA. RNA-Seq of ovules at 0 DPA and fibers at 5 DPA from Yumian 1 and RIL118 showed four genes in the QTL region of the G. raimondii genome to be extremely differentially expressed. RT-PCR analysis showed three genes in the QTL region of the G. hirsutum genome to behave similarly.

Conclusions: This study mapped a major QTL influencing four fiber quality traits to a 0.28-cM interval and identified three candidate genes by RNA-Seq and RT-PCR analysis. Integration of fine mapping and RNA-Seq is a powerful strategy to uncover candidates for QTL in large genomes.

No MeSH data available.


Related in: MedlinePlus