Genetic Diversity, Population Structure and Marker Trait Associations for Seed Quality Traits in Cotton (Gossypium Hirsutum)

Overview

Journal J Genet

Specialty Genetics

Date 2015 Apr 8

PMID 25846880

Citations 11

Authors

Ashok Badigannavar

Gerald O Myers

Affiliations

Soon will be listed here.

Abstract

Cottonseed contains 16% seed oil and 23% seed protein by weight. High levels of palmitic acid provides a degree of stability to the oil, while the presence of bound gossypol in proteins considerably changes their properties, including their biological value. This study uses genetic principles to identify genomic regions associated with seed oil, protein and fibre content in upland cotton cultivars. Cotton association mapping panel representing the US germplasm were genotyped using amplified fragment length polymorphism markers, yielding 234 polymorphic DNA fragments. Phenotypic analysis showed high genetic variability for the seed traits, seed oil range from 6.47-25.16%, protein from 1.85-28.45% and fibre content from 15.88-37.12%. There were negative correlations between seed oil and protein content.With reference to genetic diversity, the average estimate of FST was 8.852 indicating a low level of genetic differentiation among subpopulations. The AMOVA test revealed that variation was 94% within and 6% among subpopulations. Bayesian population structure identified five subpopulations and was in agreement with their geographical distribution. Among the mixed models analysed, mixed linear model (MLM) identified 21 quantitative trait loci for lint percentage and seed quality traits, such as seed protein and oil. Establishing genetic diversity, population structure and marker trait associations for the seed quality traits could be valuable in understanding the genetic relationships and their utilization in breeding programmes.

Citing Articles

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops.

Chen J, Hu Y, Zhao T, Huang C, Chen J, He L BMC Biol. 2024; 22(1):110.

PMID: 38735918 PMC: 11089805. DOI: 10.1186/s12915-024-01909-x.

Genomic and co-expression network analyses reveal candidate genes for oil accumulation based on an introgression population in Upland cotton (Gossypium hirsutum).

Ma J, Jia B, Bian Y, Pei W, Song J, Wu M Theor Appl Genet. 2024; 137(1):23.

PMID: 38231256 DOI: 10.1007/s00122-023-04527-3.

Genome-wide association study of fiber yield-related traits uncovers the novel genomic regions and candidate genes in Indian upland cotton ( L.).

Joshi B, Singh S, Tiwari G, Kumar H, Manikanda Boopathi N, Jaiswal S Front Plant Sci. 2023; 14:1252746.

PMID: 37941674 PMC: 10630025. DOI: 10.3389/fpls.2023.1252746.

Lint percentage and boll weight QTLs in three excellent upland cotton (Gossypium hirsutum): ZR014121, CCRI60, and EZ60.

Niu H, Kuang M, Huang L, Shang H, Yuan Y, Ge Q BMC Plant Biol. 2023; 23(1):179.

PMID: 37020180 PMC: 10074700. DOI: 10.1186/s12870-023-04147-5.

Inheritance, QTLs, and Candidate Genes of Lint Percentage in Upland Cotton.

Niu H, Ge Q, Shang H, Yuan Y Front Genet. 2022; 13:855574.

PMID: 35450216 PMC: 9016478. DOI: 10.3389/fgene.2022.855574.

References

Pritchard J, Stephens M, Donnelly P . Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2):945-59. PMC: 1461096. DOI: 10.1093/genetics/155.2.945. View

Zhao J, Becker H, Zhang D, Zhang Y, Ecke W . Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield. Theor Appl Genet. 2006; 113(1):33-8. DOI: 10.1007/s00122-006-0267-5. View

Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M . AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 1995; 23(21):4407-14. PMC: 307397. DOI: 10.1093/nar/23.21.4407. View

Rosenberg N, Pritchard J, Weber J, Cann H, Kidd K, Zhivotovsky L . Genetic structure of human populations. Science. 2002; 298(5602):2381-5. DOI: 10.1126/science.1078311. View

Gupta V, Mukhopadhyay A, Arumugam N, Sodhi Y, Pental D, Pradhan A . Molecular tagging of erucic acid trait in oilseed mustard (Brassica juncea) by QTL mapping and single nucleotide polymorphisms in FAE1 gene. Theor Appl Genet. 2003; 108(4):743-9. DOI: 10.1007/s00122-003-1481-z. View

Bradbury P, Zhang Z, Kroon D, Casstevens T, Ramdoss Y, Buckler E . TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007; 23(19):2633-5. DOI: 10.1093/bioinformatics/btm308. View

Wright S . The genetical structure of populations. Ann Eugen. 2014; 15(4):323-54. DOI: 10.1111/j.1469-1809.1949.tb02451.x. View

Peakall R, Smouse P . GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics. 2012; 28(19):2537-9. PMC: 3463245. DOI: 10.1093/bioinformatics/bts460. View

Yu J, Zhang K, Li S, Yu S, Zhai H, Wu M . Mapping quantitative trait loci for lint yield and fiber quality across environments in a Gossypium hirsutum × Gossypium barbadense backcross inbred line population. Theor Appl Genet. 2012; 126(1):275-87. DOI: 10.1007/s00122-012-1980-x. View

10.

Dani R, Kohel R . Maternal effects and generation mean analysis of seed-oil content in cotton (Gossypium hirsutum L.). Theor Appl Genet. 2013; 77(4):569-75. DOI: 10.1007/BF00274282. View

11.

Hu X, Sullivan-Gilbert M, Gupta M, Thompson S . Mapping of the loci controlling oleic and linolenic acid contents and development of fad2 and fad3 allele-specific markers in canola (Brassica napus L.). Theor Appl Genet. 2006; 113(3):497-507. DOI: 10.1007/s00122-006-0315-1. View

12.

Abdurakhmonov I, Kohel R, Yu J, Pepper A, Abdullaev A, Kushanov F . Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm. Genomics. 2008; 92(6):478-87. DOI: 10.1016/j.ygeno.2008.07.013. View

13.

Flint-Garcia S, Thuillet A, Yu J, Pressoir G, Romero S, Mitchell S . Maize association population: a high-resolution platform for quantitative trait locus dissection. Plant J. 2005; 44(6):1054-64. DOI: 10.1111/j.1365-313X.2005.02591.x. View

14.

Nei M, Li W . Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci U S A. 1979; 76(10):5269-73. PMC: 413122. DOI: 10.1073/pnas.76.10.5269. View

15.

Evanno G, Regnaut S, Goudet J . Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611-20. DOI: 10.1111/j.1365-294X.2005.02553.x. View

16.

Excoffier L, Laval G, Schneider S . Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online. 2009; 1:47-50. PMC: 2658868. View

17.

Singh M, Singh T, Chahal G . Genetic analysis of some seed quality characters in upland cotton (Gossypium hirsutum L.). Theor Appl Genet. 2013; 71(1):126-8. DOI: 10.1007/BF00278264. View

18.

Gupta P, Rustgi S, Kulwal P . Linkage disequilibrium and association studies in higher plants: present status and future prospects. Plant Mol Biol. 2005; 57(4):461-85. DOI: 10.1007/s11103-005-0257-z. View

19.

Yu J, Pressoir G, Briggs W, Vroh Bi I, Yamasaki M, Doebley J . A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2005; 38(2):203-8. DOI: 10.1038/ng1702. View

20.

Kumar S, Tamura K, Nei M . MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment. Brief Bioinform. 2004; 5(2):150-63. DOI: 10.1093/bib/5.2.150. View