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

Venn diagram showing the number of differentially expressed genes in ovules and fibers. a b Differentially expressed genes between RIL118 and Yuan 1 on 0 DPA ovule and 5DPA fiber; c The expression of regulated genes were significantly different between two joint time points (P-value < 0.05 and fold change > 1)
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Fig6: Venn diagram showing the number of differentially expressed genes in ovules and fibers. a b Differentially expressed genes between RIL118 and Yuan 1 on 0 DPA ovule and 5DPA fiber; c The expression of regulated genes were significantly different between two joint time points (P-value < 0.05 and fold change > 1)

Mentions: The total number of mapped reads for all identified transcripts was used for differential expression analysis in DESeq with /log2 (Fold Change)/ > 1 and q value < 0.005. There were 1262 genes with significantly different expression levels between Yumian 1 and RIL118 at 0 DPA ovules, among which 1006 were up-regulated and 256 were down-regulated in Yumian 1 (Fig. 6a). There were 4436 genes with significantly different expression levels between Yumian 1 and RIL118 at 5 DPA fibers, among which 2315 were up-regulated and 2121 were down-regulated in Yumian 1 (Fig. 6b). Combining the two time-points of cotton fiber development, 457 significantly differentially expressed genes were observed with a consistent high-or-low variation between 0 DPA ovules and 5 DPA fibers (Fig. 6c). KEGG pathway-based analysis facilitated systematical study on complicated metabolic pathways and biological behaviors of functional molecules. Among differentially expressed genes comparing RIL118 and Yumian1, there were 107 and 122 pathways determined at the 0 DPA ovule and 5 DPA fiber, respectively. The most 20 pathways significantly enriched genes at two stages were showed in Additional file 9.Fig. 6


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)

Venn diagram showing the number of differentially expressed genes in ovules and fibers. a b Differentially expressed genes between RIL118 and Yuan 1 on 0 DPA ovule and 5DPA fiber; c The expression of regulated genes were significantly different between two joint time points (P-value < 0.05 and fold change > 1)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Venn diagram showing the number of differentially expressed genes in ovules and fibers. a b Differentially expressed genes between RIL118 and Yuan 1 on 0 DPA ovule and 5DPA fiber; c The expression of regulated genes were significantly different between two joint time points (P-value < 0.05 and fold change > 1)
Mentions: The total number of mapped reads for all identified transcripts was used for differential expression analysis in DESeq with /log2 (Fold Change)/ > 1 and q value < 0.005. There were 1262 genes with significantly different expression levels between Yumian 1 and RIL118 at 0 DPA ovules, among which 1006 were up-regulated and 256 were down-regulated in Yumian 1 (Fig. 6a). There were 4436 genes with significantly different expression levels between Yumian 1 and RIL118 at 5 DPA fibers, among which 2315 were up-regulated and 2121 were down-regulated in Yumian 1 (Fig. 6b). Combining the two time-points of cotton fiber development, 457 significantly differentially expressed genes were observed with a consistent high-or-low variation between 0 DPA ovules and 5 DPA fibers (Fig. 6c). KEGG pathway-based analysis facilitated systematical study on complicated metabolic pathways and biological behaviors of functional molecules. Among differentially expressed genes comparing RIL118 and Yumian1, there were 107 and 122 pathways determined at the 0 DPA ovule and 5 DPA fiber, respectively. The most 20 pathways significantly enriched genes at two stages were showed in Additional file 9.Fig. 6

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