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Dissection of the genetic variation and candidate genes of lint percentage by a genome-wide association study in upland cotton

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Abstract

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A genome-wide associated study identified six novel QTLs for lint percentage. Two candidate genes underlying this trait were also detected.

Abstract

Increasing lint percentage (LP) is a core goal of cotton breeding. To better understand the genetic basis of LP, a genome-wide association study (GWAS) was conducted using 276 upland cotton accessions planted in multiple environments and genotyped with a CottonSNP63K array. After filtering, 10,660 high-quality single-nucleotide polymorphisms (SNPs) were retained. Population structure, principal component and neighbor-joining phylogenetic tree analyses divided the accessions into two subpopulations. These results along with linkage disequilibrium decay indicated accessions were not highly structured and exhibited weak relatedness. GWAS uncovered 23 polymorphic SNPs and 15 QTLs significantly associated with LP, with six new QTLs identified. Two candidate genes, Gh_D05G0313 and Gh_D05G1124, both contained one significant SNP, highly expressed during ovule and fiber development stages, implying that the two genes may act as the most promising regulators of LP. Furthermore, the phenotypic value of LP was found to be positively correlated with the number of favorable SNP alleles. These favorable alleles for LP identified in the study may be useful for improving lint yield.

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References

  • Abdurakhmonov IY, Buriev ZT, Saha S, Pepper AE, Musaev JA, Almatov A, Shermatov SE, Kushanov FN, Mavlonov GT, Reddy UK (2007) Microsatellite markers associated with lint percentage trait in cotton, Gossypium hirsutum. Euphytica 156:141–156

    Article  CAS  Google Scholar 

  • Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265

    Article  CAS  PubMed  Google Scholar 

  • Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixedeffects models using lme4. J Stat Softw 67(1):1–48

    Article  Google Scholar 

  • Cai C, Zhu G, Zhang T, Guo W (2017) High-density 80K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis. BMC Genom 18:654

    Article  CAS  Google Scholar 

  • Cavanagh C, Morell M, Mackay I, Powell W (2008) From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants. Curr Opin Plant Biol 11:215–221

    Article  CAS  PubMed  Google Scholar 

  • Chen ZJ, Scheffler BE, Dennis E, Triplett BA, Zhang T, Guo W, Chen X, Stelly DM, Rabinowicz PD, Town CD, Arioli T, Brubaker C, Cantrell RG, Lacape JM, Ulloa M, Chee P, Gingle AR, Haigler CH, Percy R, Saha S, Wilkins T, Wright RJ, Van Deynze A, Zhu Y, Yu S, Abdurakhmonov I, Katageri I, Kumar PA, Mehboob Ur R, Zafar Y, Yu JZ, Kohel RJ, Wendel JF, Paterson AH (2007) Toward sequencing cotton (Gossypium) genomes. Plant Physiol 145:1303–1310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dong H, Zhao H, Li S, Han Z, Hu G, Liu C, Yang G, Wang G, Xie W, Xing Y (2018) Genome-wide association studies reveal that members of bHLH subfamily 16 share a conserved function in regulating flag leaf angle in rice (Oryza sativa). PLoS Genet 14:e1007323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Earl DA, Vonholdt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361

    Article  Google Scholar 

  • Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    Article  CAS  PubMed  Google Scholar 

  • Fang L, Wang Q, Hu Y, Jia Y, Chen J, Liu B, Zhang Z, Guan X, Chen S, Zhou B, Mei G, Sun J, Pan Z, He S, Xiao S, Shi W, Gong W, Liu J, Ma J, Cai C, Zhu X, Guo W, Du X, Zhang T (2017) Genomic analyses in cotton identify signatures of selection and loci associated with fiber quality and yield traits. Nat Genet 49:1089–1098

    Article  CAS  PubMed  Google Scholar 

  • Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967

    Article  CAS  PubMed  Google Scholar 

  • Huang C, Nie X, Shen C, You C, Li W, Zhao W, Zhang X, Lin Z (2017) Population structure and genetic basis of the agronomic traits of upland cotton in China revealed by a genome-wide association study using high-density SNPs. Plant Biotechnol J 15:1374–1386

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang J, Li J, Zhou J, Wang L, Yang S, Hurst LD, Li WH, Tian D (2018) Identifying a large number of high-yield genes in rice by pedigree analysis, whole-genome sequencing, and CRISPR-Cas9 gene knockout. Proc Natl Acad Sci USA 115:E7559–e7567

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hulse-Kemp AM, Lemm J, Plieske J, Ashrafi H, Buyyarapu R, Fang DD, Frelichowski J, Giband M, Hague S, Hinze LL, Kochan KJ, Riggs PK, Scheffler JA, Udall JA, Ulloa M, Wang SS, Zhu QH, Bag SK, Bhardwaj A, Burke JJ, Byers RL, Claverie M, Gore MA, Harker DB, Islam MS, Jenkins JN, Jones DC, Lacape JM, Llewellyn DJ, Percy RG, Pepper AE, Poland JA, Mohan Rai K, Sawant SV, Singh SK, Spriggs A, Taylor JM, Wang F, Yourstone SM, Zheng X, Lawley CT, Ganal MW, Van Deynze A, Wilson IW, Stelly DM (2015) Development of a 63K SNP array for cotton and high-density mapping of intraspecific and interspecific populations of Gossypium spp. G3 (Bethesda) 5:1187–1209

    Article  Google Scholar 

  • Immenkamp M (2006) Correlation and path coefficient analysis for earliness and yield traits in cotton (G. hirsutum L.). Asian J Plant Sci 5:27–36

    Google Scholar 

  • Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806

    Article  CAS  PubMed  Google Scholar 

  • Jamshed M, Jia F, Gong J, Palanga KK, Shi Y, Li J, Shang H, Liu A, Chen T, Zhang Z, Cai J, Ge Q, Liu Z, Lu Q, Deng X, Tan Y, Or Rashid H, Sarfraz Z, Hassan M, Gong W, Yuan Y (2016) Identification of stable quantitative trait loci (QTLs) for fiber quality traits across multiple environments in Gossypium hirsutum recombinant inbred line population. BMC Genom 17:197

    Article  CAS  Google Scholar 

  • Li H, Peng Z, Yang X, Wang W, Fu J, Wang J, Han Y, Chai Y, Guo T, Yang N, Liu J, Warburton ML, Cheng Y, Hao X, Zhang P, Zhao J, Liu Y, Wang G, Li J, Yan J (2013) Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels. Nat Genet 45:43–50

    Article  CAS  PubMed  Google Scholar 

  • Li F, Fan G, Wang K, Sun F, Yuan Y, Song G, Li Q, Ma Z, Lu C, Zou C, Chen W, Liang X, Shang H, Liu W, Shi C, Xiao G, Gou C, Ye W, Xu X, Zhang X, Wei H, Li Z, Zhang G, Wang J, Liu K, Kohel RJ, Percy RG, Yu JZ, Zhu YX, Wang J, Yu S (2014) Genome sequence of the cultivated cotton Gossypium arboreum. Nat Genet 46:567–572

    Article  CAS  PubMed  Google Scholar 

  • Li C, Dong Y, Zhao T, Li L, Li C, Yu E, Mei L, Daud MK, He Q, Chen J, Zhu S (2016) Genome-wide SNP linkage mapping and QTL analysis for fiber quality and yield traits in the upland cotton recombinant inbred lines population. Front Plant Sci 7:1356

    PubMed  PubMed Central  Google Scholar 

  • Li T, Ma X, Li N, Zhou L, Liu Z, Han H, Gui Y, Bao Y, Chen J, Dai X (2017) Genome-wide association study discovered candidate genes of Verticillium wilt resistance in upland cotton (Gossypium hirsutum L.). Plant Biotechnol J 15:1520–1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li C, Wang Y, Ai N, Li Y, Song J (2018) A genome-wide association study of early-maturation traits in upland cotton based on the CottonSNP80K array. J Integr Plant Biol 60:970–985

    Article  CAS  PubMed  Google Scholar 

  • Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, Gore MA, Buckler ES, Zhang Z (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28:2397–2399

    Article  CAS  PubMed  Google Scholar 

  • Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129

    Article  CAS  PubMed  Google Scholar 

  • Liu X, Teng Z, Wang J, Wu T, Zhang Z, Deng X, Fang X, Tan Z, Ali I, Liu D, Zhang J, Liu D, Liu F, Zhang Z (2017) Enriching an intraspecific genetic map and identifying QTL for fiber quality and yield component traits across multiple environments in upland cotton (Gossypium hirsutum L.). Mol Genet Genom 292:1281–1306

    Article  CAS  Google Scholar 

  • Lu Q, Zhang M, Niu X, Wang S, Xu Q, Feng Y, Wang C, Deng H, Yuan X, Yu H, Wang Y, Wei X (2015) Genetic variation and association mapping for 12 agronomic traits in indica rice. BMC Genom 16:1067

    Article  CAS  Google Scholar 

  • Ma Z, He S, Wang X, Sun J, Zhang Y, Zhang G, Wu L, Li Z, Liu Z, Sun G, Yan Y, Jia Y, Yang J, Pan Z, Gu Q, Li X, Sun Z, Dai P, Liu Z, Gong W, Wu J, Wang M, Liu H, Feng K, Ke H, Wang J, Lan H, Wang G, Peng J, Wang N, Wang L, Pang B, Peng Z, Li R, Tian S, Du X (2018) Resequencing a core collection of upland cotton identifies genomic variation and loci influencing fiber quality and yield. Nat Genet 50:803–813

    Article  CAS  PubMed  Google Scholar 

  • Mezmouk S, Dubreuil P, Bosio M, Decousset L, Charcosset A, Praud S, Mangin B (2011) Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels. Theor Appl Genet 122:1149–1160

    Article  PubMed  PubMed Central  Google Scholar 

  • Mitchell-Olds T (2010) Complex-trait analysis in plants. Genome Biol 11:1–3

    Article  Google Scholar 

  • Nei M (1972) Genetic distance between populations. Am Nat 106:283–292

    Article  Google Scholar 

  • Nie X, Huang C, You C, Li W, Zhao W, Shen C, Zhang B, Wang H, Yan Z, Dai B, Wang M, Zhang X, Lin Z (2016) Genome-wide SSR-based association mapping for fiber quality in nation-wide upland cotton inbreed cultivars in China. BMC Genom 17:352

    Article  CAS  Google Scholar 

  • Paterson AH, Wendel JF, Gundlach H, Guo H, Jenkins J, Jin D, Llewellyn D, Showmaker KC, Shu S, Udall J, Yoo MJ, Byers R, Chen W, Doron-Faigenboim A, Duke MV, Gong L, Grimwood J, Grover C, Grupp K, Hu G, Lee TH, Li J, Lin L, Liu T, Marler BS, Page JT, Roberts AW, Romanel E, Sanders WS, Szadkowski E, Tan X, Tang H, Xu C, Wang J, Wang Z, Zhang D, Zhang L, Ashrafi H, Bedon F, Bowers JE, Brubaker CL, Chee PW, Das S, Gingle AR, Haigler CH, Harker D, Hoffmann LV, Hovav R, Jones DC, Lemke C, Mansoor S, ur Rahman M, Rainville LN, Rambani A, Reddy UK, Rong JK, Saranga Y, Scheffler BE, Scheffler JA, Stelly DM, Triplett BA, Van Deynze A, Vaslin MF, Waghmare VN, Walford SA, Wright RJ, Zaki EA, Zhang T, Dennis ES, Mayer KF, Peterson DG, Rokhsar DS, Wang X, Schmutz J (2012) Repeated polyploidization of Gossypium genomes and the evolution of spinnable cotton fibres. Nature 492:423–427

    Article  CAS  PubMed  Google Scholar 

  • Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Qin H, Chen M, Yi X, Bie S, Zhang C, Zhang Y, Lan J, Meng Y, Yuan Y, Jiao C (2015) Identification of associated SSR markers for yield component and fiber quality traits based on frame map and upland cotton collections. PLoS ONE 10:e0118073

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reinisch AJ, Dong JM, Brubaker CL, Stelly DM, Wendel JF, Paterson AH (1994) A detailed RFLP map of cotton, Gossypium hirsutum × Gossypium barbadense: chromosome organization and evolution in a disomic polyploid genome. Genetics 138:829–847

    CAS  PubMed  PubMed Central  Google Scholar 

  • Rong J, Abbey C, Bowers JE, Brubaker CL, Chang C, Chee PW, Delmonte TA, Ding X, Garza JJ, Marler BS, Park CH, Pierce GJ, Rainey KM, Rastogi VK, Schulze SR, Trolinder NL, Wendel JF, Wilkins TA, Williams-Coplin TD, Wing RA, Wright RJ, Zhao X, Zhu L, Paterson AH (2004) A 3347-locus genetic recombination map of sequence-tagged sites reveals features of genome organization, transmission and evolution of cotton (Gossypium). Genetics 166:389–417

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Said JI, Knapka JA, Song M, Zhang J (2015a) Cotton QTLdb: a cotton QTL database for QTL analysis, visualization, and comparison between Gossypium hirsutum and G. hirsutum × G. barbadense populations. Mol Genet Genom 290:1615–1625

    Article  CAS  Google Scholar 

  • Said JI, Song M, Wang H, Lin Z, Zhang X, Fang DD, Zhang J (2015b) A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum and interspecific G. hirsutum × G. barbadense populations. Mol Genet Genom 290:1003–1025

    Article  CAS  Google Scholar 

  • Saidou AA, Thuillet AC, Couderc M, Mariac C, Vigouroux Y (2014) Association studies including genotype by environment interactions: prospects and limits. BMC Genet 15:3

    Article  PubMed  PubMed Central  Google Scholar 

  • Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 3:1101–1108

    Article  CAS  PubMed  Google Scholar 

  • Su J, Fan S, Li L, Wei H, Wang C, Wang H, Song M, Zhang C, Gu L, Zhao S, Mao G, Wang C, Pang C, Yu S (2016) Detection of favorable QTL alleles and candidate genes for lint percentage by GWAS in Chinese upland cotton. Front Plant Sci 7:1576

    PubMed  PubMed Central  Google Scholar 

  • Su J, Li L, Zhang C, Wang C, Gu L, Wang H, Wei H, Liu Q, Huang L, Yu S (2018) Genome-wide association study identified genetic variations and candidate genes for plant architecture component traits in Chinese upland cotton. Theor Appl Genet 131:1299–1314

    Article  CAS  PubMed  Google Scholar 

  • Sun C, Zhang F, Yan X, Zhang X, Dong Z, Cui D, Chen F (2017a) Genome-wide association study for 13 agronomic traits reveals distribution of superior alleles in bread wheat from the Yellow and Huai Valley of China. Plant Biotechnol J 15:953–969

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sun Z, Wang X, Liu Z, Gu Q, Zhang Y, Li Z, Ke H, Yang J, Wu J, Wu L, Zhang G, Zhang C, Ma Z (2017b) Genome-wide association study discovered genetic variation and candidate genes of fibre quality traits in Gossypium hirsutum L. Plant Biotechnol J 15:982–996

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sun Z, Wang X, Liu Z, Gu Q, Zhang Y, Li Z, Ke H, Yang J, Wu J, Wu L, Zhang G, Zhang C, Ma Z (2018) A genome-wide association study uncovers novel genomic regions and candidate genes of yield-related traits in upland cotton. Theor Appl Genet 131:2413–2425

    Article  CAS  PubMed  Google Scholar 

  • Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30:2725–2729

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Team RDC (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Computing 14:12–21

    Google Scholar 

  • Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162

    Article  CAS  PubMed  Google Scholar 

  • Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Turner SD (2014) qqman: an R package for visualizing GWAS results using Q–Q and manhattan plots. Biorxiv. https://doi.org/10.1101/005165

    Article  Google Scholar 

  • VanRaden PM (2008) Efficient methods to compute genomic predictions. J Dairy Sci 91:4414–4423

    Article  CAS  PubMed  Google Scholar 

  • Wang C, Zhang T, Guo W (2013) The mutant gene negatively affects many aspects of fiber quality traits and lint percentage in cotton. Crop Sci 53:27–37

    Article  CAS  Google Scholar 

  • Wang H, Huang C, Guo H, Li X, Zhao W, Dai B, Yan Z, Lin Z (2015) QTL mapping for fiber and yield traits in upland cotton under multiple environments. PLoS ONE 10:e0130742

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang M, Tu L, Lin M, Lin Z, Wang P, Yang Q, Ye Z, Shen C, Li J, Zhang L, Zhou X, Nie X, Li Z, Guo K, Ma Y, Huang C, Jin S, Zhu L, Yang X, Min L, Yuan D, Zhang Q, Lindsey K, Zhang X (2017) Asymmetric subgenome selection and cis-regulatory divergence during cotton domestication. Nat Genet 49:579–587

    Article  CAS  PubMed  Google Scholar 

  • Wang B, Wu Z, Li Z, Zhang Q, Hu J, Xiao Y, Cai D, Wu J, King GJ, Li H, Liu K (2018a) Dissection of the genetic architecture of three seed-quality traits and consequences for breeding in Brassica napus. Plant Biotechnol J 16:1336–1348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang YY, Li YQ, Wu HY, Hu B, Zheng JJ, Zhai H, Lv SX, Liu XL, Chen X, Qiu HM, Yang J, Zong CM, Han DZ, Wen ZX, Wang DC, Xia ZJ (2018b) Genotyping of soybean cultivars with medium-density array reveals the population structure and QTNs underlying maturity and seed traits. Front Plant Sci 9:610

    Article  PubMed  PubMed Central  Google Scholar 

  • Wei L, Jian H, Lu K, Filardo F, Yin N, Liu L, Qu C, Li W, Du H, Li J (2016) Genome-wide association analysis and differential expression analysis of resistance to Sclerotinia stem rot in Brassica napus. Plant Biotechnol J 14:1368–1380

    Article  CAS  PubMed  Google Scholar 

  • Wen Z, Tan R, Zhang S, Collins PJ, Yuan J, Du W, Gu C, Ou S, Song Q, An YC, Boyse JF, Chilvers MI, Wang D (2018) Integrating GWAS and gene expression data for functional characterization of resistance to white mould in soya bean. Plant Biotechnol J 16:1825–1835

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yu J, Pressoir G, Briggs WH, Vroh Bi I, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  • Zhang JF, Stewart JM (2000) Economical and rapid method for extracting cotton genomic DNA. J Cotton Sci 4:193–201

    CAS  Google Scholar 

  • Zhang HB, Li Y, Wang B, Chee PW (2008) Recent advances in cotton genomics. Int J Plant Genom 2008:742304

    Google Scholar 

  • Zhang T, Hu Y, Jiang W, Fang L, Guan X, Chen J, Zhang J, Saski CA, Scheffler BE, Stelly DM, Hulse-Kemp AM, Wan Q, Liu B, Liu C, Wang S, Pan M, Wang Y, Wang D, Ye W, Chang L, Zhang W, Song Q, Kirkbride RC, Chen X, Dennis E, Llewellyn DJ, Peterson DG, Thaxton P, Jones DC, Wang Q, Xu X, Zhang H, Wu H, Zhou L, Mei G, Chen S, Tian Y, Xiang D, Li X, Ding J, Zuo Q, Tao L, Liu Y, Li J, Lin Y, Hui Y, Cao Z, Cai C, Zhu X, Jiang Z, Zhou B, Guo W, Li R, Chen ZJ (2015) Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement. Nat Biotechnol 33:531–537

    Article  CAS  PubMed  Google Scholar 

  • Zhao Z, Zhang H, Fu Z, Chen H, Lin Y, Yan P, Li W, Xie H, Guo Z, Zhang X, Tang J (2018) Genetic-based dissection of arsenic accumulation in maize using a genome-wide association analysis method. Plant Biotechnol J 16:1085–1093

    Article  CAS  PubMed  Google Scholar 

  • Zheng XM, Gong T, Ou HL, Xue D, Qiao W, Wang J, Liu S, Yang Q, Olsen KM (2018) Genome-wide association study of rice grain width variation. Genome 61:233–240

    Article  CAS  PubMed  Google Scholar 

  • Zhou Z, Jiang Y, Wang Z, Gou Z, Lyu J, Li W, Yu Y, Shu L, Zhao Y, Ma Y, Fang C, Shen Y, Liu T, Li C, Li Q, Wu M, Wang M, Wu Y, Dong Y, Wan W, Wang X, Ding Z, Gao Y, Xiang H, Zhu B, Lee SH, Wang W, Tian Z (2015) Resequencing 302 wild and cultivated accessions identifies genes related to domestication and improvement in soybean. Nat Biotechnol 33:408–414

    Article  CAS  PubMed  Google Scholar 

  • Zhou Q, Han D, Mason AS, Zhou C, Zheng W, Li Y, Wu C, Fu D, Huang Y (2018) Earliness traits in rapeseed (Brassica napus): SNP loci and candidate genes identified by genome-wide association analysis. DNA Res 25:229–244

    Article  CAS  PubMed  Google Scholar 

  • Zhu C, Gore M, Buckler ES, Yu J (2008) Status and prospects of association mapping in plants. Plant Genome 1:5–20

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported by the National Key R&D Program for Crop Breeding (2016YFD0100306), the Key Project of Science and Technology of Henan Province of China (182102110306), and the Natural Science Foundation of Henan Province of China (152300410010).

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Figure S1

Frequency distribution of phenotypic variation of LP across multiple environments and correlation coefficients among different locations in 276 accessions. ** indicates a significant difference at a threshold of p = 0.01. Figure S2 Results of relative kinship analysis of 276 upland cotton accessions. Figure S3 Distribution of kinship coefficient (K) values in 276 upland cotton accessions. Figure S4 Manhattan plots of the results of a genome-wide association study for lint percentage (LP) in multiple environments using the mixed linear model (MLM). Figure S5 Heatmap of expressions of genes situated in all QTL regions identified by this study. Gene expression levels were calculated from log2 (FPKM) values, and the color bar denotes gene transcript abundances: red for high and green for low. R, S, L and DPA represent root, stem, leaf and days post-anthesis, respectively. Figure S6 GWAS results for lint percentage and identification of a candidate gene on chromosome Dt05. (a) Local Manhattan plot for the candidate region on Dt05. The purple dot represents the peak SNP i08888Gh. Red dotted lines indicate the candidate region. (b) LD block analysis of SNPs in this region. The degree of linkage is represented by the value of r2. (c) Gene structure of Gh_D05G0313 and a non-synonymous SNP within it. Purple rectangles and black lines indicate exons and introns, respectively. Ref and Alt stand for reference and alternate, respectively. (d) Box plots of LP based on the allele of SNP i08888Gh. The significance of differences was analyzed by a two-sided Wilcoxon test. (e) Tissue-specific expression profiles of Gh_D05G0313. Expression of Gh_D05G0313 was investigated in ovule (0, 10, 20 and 30 DPA) and fiber (10, 20 and 30 DPA) developmental stages by qRT-PCR. GhHis3 was used as an internal control. Error bars indicate the standard deviation of three technical replicates (DOCX 2014 kb)

Table S1

Detailed information on 276 upland cotton accessions. Table S2 Variance analysis of lint percentage (LP) across multiple environments in the association population. Table S3 Genome-wide association loci of lint percentage (LP) in all environments. Table S4 Information on QTLs detected in this study and those co-localized with QTLs identified in previous studies. Table S5 Information on candidate genes located in QTL regions. Table S6 Primer sequences for qRT-PCR (XLSX 62 kb)

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Song, C., Li, W., Pei, X. et al. Dissection of the genetic variation and candidate genes of lint percentage by a genome-wide association study in upland cotton. Theor Appl Genet 132, 1991–2002 (2019). https://doi.org/10.1007/s00122-019-03333-0

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