Genome-wide association and epistatic interactions of flowering time in soybean cultivar

English version

Autoři: Kyoung Hyoun Kim ^aff001; Jae-Yoon Kim ^aff001; Won-Jun Lim ^aff001; Seongmun Jeong ^aff001; Ho-Yeon Lee ^aff001; Youngbum Cho ^aff001; Jung-Kyung Moon ^aff003; Namshin Kim ^aff001
Působiště autorů: Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Republic of Korea ^aff001; Department of Bioinformatics, KRIBB School of Bioscience, University of Science and Technology (UST), Daejeon, Republic of Korea ^aff002; National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea ^aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0228114

Souhrn

Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom^® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.

Klíčová slova:

Arabidopsis thaliana – Flowers – Gene regulation – Genome-wide association studies – Molecular genetics – Quantitative trait loci – Soybean – Structural genomics

Zdroje

1. Contreras-Soto RI, Mora F, de Oliveira MAR, Higashi W, Scapim CA, Schuster I. A genome-wide association study for agronomic traits in soybean using SNP markers and SNP-based haplotype analysis. PLoS One. 2017;12(2):e0171105. doi: 10.1371/journal.pone.0171105 28152092

2. Chen H, Xie W, He H, Yu H, Chen W, Li J, et al. A high-density SNP genotyping array for rice biology and molecular breeding. Mol Plant. 2014;7(3):541–53. doi: 10.1093/mp/sst135 24121292

3. Hu X, Ren J, Ren X, Huang S, Sabiel SA, Luo M, et al. Association of agronomic traits with SNP markers in durum wheat (Triticum turgidum L. durum (Desf.)). PLoS One. 2015;10(6):e0130854. doi: 10.1371/journal.pone.0130854 26110423

4. Navarro JAR, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, et al. A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet. 2017;49(3):476. doi: 10.1038/ng.3784 28166212

5. Nascimento M, Nascimento ACC, Silva FFe, Barili LD, do Vale NM, Carneiro JE, et al. Quantile regression for genome-wide association study of flowering time-related traits in common bean. PLoS One. 2018;13(1):e0190303. doi: 10.1371/journal.pone.0190303 29300788

6. Sales E, Viruel J, Domingo C, Marqués L. Genome wide association analysis of cold tolerance at germination in temperate japonica rice (Oryza sativa L.) varieties. PLoS One. 2017;12(8):e0183416. doi: 10.1371/journal.pone.0183416 28817683

7. Fan Y, Zhou G, Shabala S, Chen Z-H, Cai S, Li C, et al. Genome-wide association study reveals a new QTL for salinity tolerance in barley (Hordeum vulgare L.). Front Plant Sci. 2016;7:946. doi: 10.3389/fpls.2016.00946 27446173

8. Patil G, Do T, Vuong TD, Valliyodan B, Lee J-D, Chaudhary J, et al. Genomic-assisted haplotype analysis and the development of high-throughput SNP markers for salinity tolerance in soybean. Sci Rep. 2016;6:19199. doi: 10.1038/srep19199 26781337

9. Thabet SG, Moursi YS, Karam MA, Graner A, Alqudah AM. Genetic basis of drought tolerance during seed germination in barley. PLoS One. 2018;13(11):e0206682. doi: 10.1371/journal.pone.0206682 30388157

10. Kim S-M, Reinke RF. A novel resistance gene for bacterial blight in rice, Xa43(t) identified by GWAS, confirmed by QTL mapping using a bi-parental population. PLoS One. 2019;14(2):e0211775. doi: 10.1371/journal.pone.0211775 30753229

11. Chen G, Wang X, Hao J, Yan J, Ding J. Genome-wide association implicates candidate genes conferring resistance to maize rough dwarf disease in maize. PLoS One. 2015;10(11):e0142001. doi: 10.1371/journal.pone.0142001 26529245

12. Begum H, Spindel JE, Lalusin A, Borromeo T, Gregorio G, Hernandez J, et al. Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa). PLoS One. 2015;10(3):e0119873. doi: 10.1371/journal.pone.0119873 25785447

13. Zhang J, Song Q, Cregan PB, Nelson RL, Wang X, Wu J, et al. Genome-wide association study for flowering time, maturity dates and plant height in early maturing soybean (Glycine max) germplasm. BMC Genomics. 2015;16(1):217. doi: 10.1186/s12864-015-1441-4 25887991

14. Hoang GT, Gantet P, Nguyen KH, Phung NTP, Ha LT, Nguyen TT, et al. Genome-wide association mapping of leaf mass traits in a Vietnamese rice landrace panel. PLoS One. 2019;14(7). doi: 10.1371/journal.pone.0219274 31283792

15. Lu S, Zhang M, Zhang Z, Wang Z, Wu N, Song Y, et al. Screening and verification of genes associated with leaf angle and leaf orientation value in inbred maize lines. PLoS One. 2018;13(12):e0208386. doi: 10.1371/journal.pone.0208386 30532152

16. Zhao X, Dong H, Chang H, Zhao J, Teng W, Qiu L, et al. Genome wide association mapping and candidate gene analysis for hundred seed weight in soybean [Glycine max (L.) Merrill]. BMC Genomics. 2019;20(1):648. doi: 10.1186/s12864-019-6009-2 31412769

17. Sehgal D, Mondal S, Guzman C, Barrios GG, Franco C, Singh RP, et al. Validation of candidate gene-based markers and identification of novel loci for thousand-grain weight in spring bread wheat. Front Plant Sci. 2019;10:1189. doi: 10.3389/fpls.2019.01189 31616457

18. Battenfield SD, Sheridan JL, Silva LD, Miclaus KJ, Dreisigacker S, Wolfinger RD, et al. Breeding-assisted genomics: Applying meta-GWAS for milling and baking quality in CIMMYT wheat breeding program. PLoS One. 2018;13(11):e0204757. doi: 10.1371/journal.pone.0204757 30496187

19. Wang B, Li J. Understanding the molecular bases of agronomic trait improvement in rice. Plant Cell. 2019;31(7): 1416–7. doi: 10.1105/tpc.19.00343 31068452

20. Langer SM, Longin CFH, Würschum T. Flowering time control in European winter wheat. Front Plant Sci. 2014;5:537. doi: 10.3389/fpls.2014.00537 25346745

21. Mazaheri M, Heckwolf M, Vaillancourt B, Gage JL, Burdo B, Heckwolf S, et al. Genome-wide association analysis of stalk biomass and anatomical traits in maize. BMC Plant Biol. 2019;19(1):45. doi: 10.1186/s12870-019-1653-x 30704393

22. Alqudah AM, Sharma R, Pasam RK, Graner A, Kilian B, Schnurbusch T. Genetic dissection of photoperiod response based on GWAS of pre-anthesis phase duration in spring barley. PLoS One. 2014;9(11):e113120. doi: 10.1371/journal.pone.0113120 25420105

23. Huang X, Zhao Y, Li C, Wang A, Zhao Q, Li W, et al. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2012;44(1):32–9. doi: 10.1038/ng.1018 22138690

24. Ahsan A, Monir M, Meng X, Rahaman M, Chen H, Chen M. Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect. DNA Res. 2018;26(2):119–30. doi: 10.1093/dnares/dsy043 30590457

25. Koo CL, Liew MJ, Mohamad MS, Salleh AHM, Deris S, Ibrahim Z, et al. Software for detecting gene-gene interactions in genome wide association studies. Biotechnol Bioproc E. 2015;20(4):662–76. doi: 10.1007/s12257-015-0064-6

26. Moellers TC, Singh A, Zhang J, Brungardt J, Kabbage M, Mueller DS, et al. Main and epistatic loci studies in soybean for Sclerotinia sclerotiorum resistance reveal multiple modes of resistance in multi-environments. Sci Rep. 2017;7(1):3554. doi: 10.1038/s41598-017-03695-9 28620159

27. Zhang J, Singh A, Mueller DS, Singh AK. Genome-wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean. Plant J. 2015;84(6):1124–36. doi: 10.1111/tpj.13069 26561232

28. Mamidi S, Lee RK, Goos JR, McClean PE. Genome-wide association studies identifies seven major regions responsible for iron deficiency chlorosis in soybean (Glycine max). PLoS One. 2014;9(9):e107469. doi: 10.1371/journal.pone.0107469 25225893

29. Assefa T, Otyama PI, Brown AV, Kalberer SR, Kulkarni RS, Cannon SB. Genome-wide associations and epistatic interactions for internode number, plant height, seed weight and seed yield in soybean. BMC Genomics. 2019;20(1):527. doi: 10.1186/s12864-019-5907-7 31242867

30. He T, Hill CB, Angessa TT, Zhang X-Q, Chen K, Moody D, et al. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection. J Exp Bot. 2019;70(20):5603–16. doi: 10.1093/jxb/erz332 31504706

31. Bernard R. Two major genes for time of flowering and maturity in soybeans. Crop Sci. 1971;11(2):242–4. doi: 10.2135/cropsci1971.0011183X001100020022x

32. Buzzell R. Inheritance of a soybean flowering response to fluorescent-daylength conditions. Can J Genet Cytol. 1971;13(4):703–7. doi: 10.1139/g71-100

33. Buzzell R, Voldeng H. Inheritance of insensitivity to long daylength. Soyb Genet Newsl. 1980;7(1):13. doi: 10.1093/jhered/esp113

34. Saindon G, Voldeng H, Beversdorf W, Buzzell R. Genetic control of long daylength response in soybean. Crop Sci. 1989;29(6):1436–9. doi: 10.2135/cropsci1989.0011183X002900060021x

35. McBlain B, Bernard R. A new gene affecting the time of flowering and maturity in soybeans. J Hered. 1987;78(3):160–2. doi: 10.1093/oxfordjournals.jhered.a110349

36. Bonato ER, Vello NA. E6, a dominant gene conditioning early flowering and maturity in soybeans. Genet Mol Biol. 1999;22(2):229–32. doi: 10.1590/S1415-47571999000200016

37. Cober ER, Voldeng HD. A new soybean maturity and photoperiod-sensitivity locus linked to E1 and T. Crop Sci. 2001;41(3):698–701. doi: 10.2135/cropsci2001.413698x

38. Cober ER, Molnar SJ, Charette M, Voldeng HD. A new locus for early maturity in soybean. Crop Sci. 2010;50(2):524–7. doi: 10.2135/cropsci2009.04.0174

39. Kong F, Nan H, Cao D, Li Y, Wu F, Wang J, et al. A new dominant gene E9 conditions early flowering and maturity in soybean. Crop Sci. 2014;54(6):2529–35. doi: 10.2135/cropsci2014.03.0228

40. Zhao C, Takeshima R, Zhu J, Xu M, Sato M, Watanabe S, et al. A recessive allele for delayed flowering at the soybean maturity locus E9 is a leaky allele of FT2a, a FLOWERING LOCUS T ortholog. BMC Plant Biol. 2016;16(1):20. doi: 10.1186/s12870-016-0704-9 26786479

41. Samanfar B, Molnar SJ, Charette M, Schoenrock A, Dehne F, Golshani A, et al. Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean. Theor Appl Genet. 2017;130(2):377–90. doi: 10.1007/s00122-016-2819-7 27832313

42. Ray JD, Hinson K, Mankono J, Malo MF. Genetic control of a long-juvenile trait in soybean. Crop Sci. 1995;35(4):1001–6. doi: 10.2135/cropsci1995.0011183X003500040012x

43. Liu B, Watanabe S, Uchiyama T, Kong F, Kanazawa A, Xia Z, et al. The soybean stem growth habit gene Dt1 is an ortholog of Arabidopsis TERMINAL FLOWER1. Plant Physiol. 2010;153(1):198–210. doi: 10.1104/pp.109.150607 20219831

44. Tian Z, Wang X, Lee R, Li Y, Specht JE, Nelson RL, et al. Artificial selection for determinate growth habit in soybean. Proc Natl Acad Sci U S A. 2010;107(19):8563–8. doi: 10.1073/pnas.1000088107 20421496

45. Lee YG, Jeong N, Kim JH, Lee K, Kim KH, Pirani A, et al. Development, validation and genetic analysis of a large soybean SNP genotyping array. Plant J. 2015;81(4):625–36. doi: 10.1111/tpj.12755 25641104

46. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet. 2018;103(3):338–48. doi: 10.1016/j.ajhg.2018.07.015 30100085

47. Jeong S-C, Moon J-K, Park S-K, Kim M-S, Lee K, Lee SR, et al. Genetic diversity patterns and domestication origin of soybean. Theor Appl Genet. 2019:132(4):1179–93. doi: 10.1007/s00122-018-3271-7 30588539

48. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. doi: 10.1086/519795 17701901

49. Gascuel O. BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol Biol Evol. 1997;14(7):685–95. doi: 10.1093/oxfordjournals.molbev.a025808 9254330

50. Raj A, Stephens M, Pritchard JK. fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics. 2014;197(2):573–89. doi: 10.1534/genetics.114.164350 24700103

51. Kimura M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980;16(2):111–20. doi: 10.1007/bf01731581 7463489

52. Zhang C, Dong S-S, Xu J-Y, He W-M, Yang T-L. PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files. Bioinformatics. 2019;35(10):1786–8 doi: 10.1093/bioinformatics/bty875 30321304

53. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCFtools. Bioinformatics. 2011;27(15):2156–8. doi: 10.1093/bioinformatics/btr330 21653522

54. Lipka AE, Tian F, Wang Q, Peiffer J, Li M, Bradbury PJ, et al. GAPIT: genome association and prediction integrated tool. Bioinformatics. 2012;28(18):2397–9. doi: 10.1093/bioinformatics/bts444 22796960

55. Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92. doi: 10.4161/fly.19695 22728672

56. Jung C-H, Wong CE, Singh MB, Bhalla PL. Comparative genomic analysis of soybean flowering genes. PLoS One. 2012;7(6):e38250. doi: 10.1371/journal.pone.0038250 22679494

57. Tsubokura Y, Watanabe S, Xia Z, Kanamori H, Yamagata H, Kaga A, et al. Natural variation in the genes responsible for maturity loci E1, E2, E3 and E4 in soybean. Ann Bot. 2014;113(3):429–41. doi: 10.1093/aob/mct269 24284817

58. Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio—the analysis and navigation of molecular networks. Bioinformatics. 2003;19(16):2155–7. doi: 10.1093/bioinformatics/btg290 14594725

59. Watanabe S, Harada K, Abe J. Genetic and molecular bases of photoperiod responses of flowering in soybean. Breed Sci. 2012;61(5):531–43. doi: 10.1270/jsbbs.61.531 23136492

60. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1):421. doi: 10.1186/1471-2105-10-421 20003500

61. Wang J, Joshi T, Valliyodan B, Shi H, Liang Y, Nguyen HT, et al. A Bayesian model for detection of high-order interactions among genetic variants in genome-wide association studies. BMC Genomics. 2015;16(1):1011. doi: 10.1186/s12864-015-2217-6 26607428

62. Zhang J, Hou T, Wang W, Liu JS. Detecting and understanding combinatorial mutation patterns responsible for HIV drug resistance. Proc Natl Acad Sci U S A. 2010;107(4):1321–6. doi: 10.1073/pnas.0907304107 20080674

63. Stewart DW, Cober ER, Bernard RL. Modeling genetic effects on the photothermal response of soybean phenological development. Agron J. 2003;95(1):65–70.

64. Zhang X, Zhai H, Wang Y, Tian X, Zhang Y, Wu H, et al. Functional conservation and diversification of the soybean maturity gene E1 and its homologs in legumes. Sci Rep. 2016;6(1):29548. doi: 10.1038/srep29548 27405888

65. Watanabe S, Hideshima R, Xia Z, Tsubokura Y, Sato S, Nakamoto Y, et al. Map-based cloning of the gene associated with the soybean maturity locus E3. Genetics. 2009;182(4):1251–62. doi: 10.1534/genetics.108.098772 19474204

66. Watanabe S, Xia Z, Hideshima R, Tsubokura Y, Sato S, Yamanaka N, et al. A map-based cloning strategy employing a residual heterozygous line reveals that the GIGANTEA gene is involved in soybean maturity and flowering. Genetics. 2011;188(2):395–407. doi: 10.1534/genetics.110.125062 21406680

67. Liu B, Kanazawa A, Matsumura H, Takahashi R, Harada K, Abe J. Genetic redundancy in soybean photoresponses associated with duplication of the phytochrome A gene. Genetics. 2008;180(2):995–1007. doi: 10.1534/genetics.108.092742 18780733

68. Lee S-J, Lee BH, Jung J-H, Park SK, Song JT, Kim JH. GROWTH-REGULATING FACTOR and GRF-INTERACTING FACTOR specify meristematic cells of gynoecia and anthers. Plant Physiol. 2018;176(1):717–29. doi: 10.1104/pp.17.00960 29114079

69. Preston JC, Hileman L. Functional evolution in the plant SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) gene family. Front Plant Sci. 2013;4:80. doi: 10.3389/fpls.2013.00080 23577017

70. Xu D, Zhu D, Deng XW. The role of COP1 in repression of photoperiodic flowering. F1000Res. 2016;5. doi: 10.12688/f1000research.7346.1 26949521

71. Zeng X, Liu H, Du H, Wang S, Yang W, Chi Y, et al. Soybean MADS-box gene GmAGL1 promotes flowering via the photoperiod pathway. BMC Genomics. 2018;19(1):51. doi: 10.1186/s12864-017-4402-2 29338682

72. Jaudal M, Zhang L, Che C, Putterill J. Three Medicago MtFUL genes have distinct and overlapping expression patterns during vegetative and reproductive development and 35S:MtFULb accelerates flowering and causes a terminal flower phenotype in Arabidopsis. Front Genet. 2015;6:50. doi: 10.3389/fgene.2015.00050 25745430

73. Ren C, Zhang Z, Wang Y, Li S, Liang Z. Genome-wide identification and characterization of the NF-Y gene family in grape (vitis vinifera L.). BMC Genomics. 2016;17(1):605. doi: 10.1186/s12864-016-2989-3 27516172

74. Sánchez R, Kim MY, Calonje M, Moon Y-H, Sung ZR. Temporal and spatial requirement of EMF1 activity for Arabidopsis vegetative and reproductive development. Mol Plant. 2009;2(4):643–53. doi: 10.1093/mp/ssp004 19825645

75. Kim SY, Lee J, Eshed-Williams L, Zilberman D, Sung ZR. EMF1 and PRC2 cooperate to repress key regulators of Arabidopsis development. PLoS Genet. 2012;8(3):e1002512. doi: 10.1371/journal.pgen.1002512 22457632

76. Song YH, Estrada DA, Johnson RS, Kim SK, Lee SY, MacCoss MJ, et al. Distinct roles of FKF1, GIGANTEA, and ZEITLUPE proteins in the regulation of CONSTANS stability in Arabidopsis photoperiodic flowering. Proc Natl Acad Sci U S A. 2014;111(49):17672–7. doi: 10.1073/pnas.1415375111 25422419

77. Seaton DD, Smith RW, Song YH, MacGregor DR, Stewart K, Steel G, et al. Linked circadian outputs control elongation growth and flowering in response to photoperiod and temperature. Mol Syst Biol. 2015;11(1):776. doi: 10.15252/msb.20145766 25600997

78. Agliassa C, Narayana R, Bertea CM, Rodgers CT, Maffei ME. Reduction of the geomagnetic field delays Arabidopsis thaliana flowering time through downregulation of flowering-related genes. Bioelectromagnetics. 2018;39(5):361–74. doi: 10.1002/bem.22123 29709075

79. Cai X, Ballif J, Endo S, Davis E, Liang M, Chen D, et al. A putative CCAAT-binding transcription factor is a regulator of flowering timing in Arabidopsis. Plant Physiol. 2007;145(1):98–105. doi: 10.1104/pp.107.102079 17631525

80. Capovilla G, Schmid M, Posé D. Control of flowering by ambient temperature. J Exp Bot. 2015;66(1):59–69. doi: 10.1093/jxb/eru416 25326628

81. Kang H, Zhang C, An Z, Shen W-H, Zhu Y. AtINO80 and AtARP5 physically interact and play common as well as distinct roles in regulating plant growth and development. New Phytol. 2019;223(1):336–53. doi: 10.1111/nph.15780 30843208

82. Jiao Y, Yang H, Ma L, Sun N, Yu H, Liu T, et al. A genome-wide analysis of blue-light regulation of Arabidopsis transcription factor gene expression during seedling development. Plant Physiol. 2003;133(4):1480–93. doi: 10.1104/pp.103.029439 14605227

83. Helliwell CA, Robertson M, Finnegan EJ, Buzas DM, Dennis ES. Vernalization-repression of Arabidopsis FLC requires promoter sequences but not antisense transcripts. PLoS One. 2011;6(6):e21513. doi: 10.1371/journal.pone.0021513 21713009

84. Hohenstatt ML, Mikulski P, Komarynets O, Klose C, Kycia I, Jeltsch A, et al. PWWP-DOMAIN INTERACTOR OF POLYCOMBS1 interacts with Polycomb-group proteins and histones and regulates Arabidopsis flowering and development. Plant Cell. 2018;30(1):117–33. doi: 10.1105/tpc.17.00117 29330200

85. Berardini TZ, Reiser L, Li D, Mezheritsky Y, Muller R, Strait E, et al. The Arabidopsis information resource: Making and mining the “gold standard” annotated reference plant genome. Genesis. 2015;53(8):474–85. doi: 10.1002/dvg.22877 26201819

86. Cheng J-Z, Zhou Y-P, Lv T-X, Xie C-P, Tian C-E. Research progress on the autonomous flowering time pathway in Arabidopsis. Physiol Mol Biol Plants. 2017;23(3):477–85. doi: 10.1007/s12298-017-0458-3 28878488

87. Liu Q, Wang Q, Deng W, Wang X, Piao M, Cai D, et al. Molecular basis for blue light-dependent phosphorylation of Arabidopsis cryptochrome 2. Nat Commun. 2017;8(1):15234. doi: 10.1038/ncomms15234 28492234

88. Yu X, Liu H, Klejnot J, Lin C. The cryptochrome blue light receptors. Arabidopsis Book. 2010;2010(8):e0135. doi: 10.1199/tab.0135 21841916

89. Kim MY, Kang YJ, Lee T, Lee S-H. Divergence of flowering-related genes in three legume species. Plant Genome. 2013;6(3). doi: 10.3835/plantgenome2013.03.0008

90. Amasino RM, Michaels SD. The timing of flowering. Plant Physiol. 2010;154(2):516–20. doi: 10.1104/pp.110.161653 20921176

91. Boss PK, Bastow RM, Mylne JS, Dean C. Multiple pathways in the decision to flower: enabling, promoting, and resetting. Plant Cell. 2004;16(suppl 1):S18–S31. doi: 10.1105/tpc.015958 15037730

92. Xia Z, Watanabe S, Yamada T, Tsubokura Y, Nakashima H, Zhai H, et al. Positional cloning and characterization reveal the molecular basis for soybean maturity locus E1 that regulates photoperiodic flowering. Proc Natl Acad Sci U S A. 2012;109(32):E2155–E2164. doi: 10.1073/pnas.1117982109 22619331

93. Liu W, Jiang B, Ma L, Zhang S, Zhai H, Xu X, et al. Functional diversification of Flowering Locus T homologs in soybean: GmFT1a and GmFT2a/5a have opposite roles in controlling flowering and maturation. New Phytol. 2018;217(3):1335–45. doi: 10.1111/nph.14884 29120038

94. Jiang B, Nan H, Gao Y, Tang L, Yue Y, Lu S, et al. Allelic combinations of soybean maturity loci E1, E2, E3 and E4 result in diversity of maturity and adaptation to different latitudes. PLoS One. 2014;9(8):e106042. doi: 10.1371/journal.pone.0106042 25162675

95. Zhai H, Lü S, Liang S, Wu H, Zhang X, Liu B, et al. GmFT4, a homolog of FLOWERING LOCUS T, is positively regulated by E1 and functions as a flowering repressor in soybean. PLoS One. 2014;9(2):e89030. doi: 10.1371/journal.pone.0089030 24586488

96. Xu M, Xu Z, Liu B, Kong F, Tsubokura Y, Watanabe S, et al. Genetic variation in four maturity genes affects photoperiod insensitivity and PHYA-regulated post-flowering responses of soybean. BMC Plant Biol. 2013;13(1):91. doi: 10.1186/1471-2229-13-91 23799885

97. Tsubokura Y, Matsumura H, Xu M, Liu B, Nakashima H, Anai T, et al. Genetic variation in soybean at the maturity locus E4 is involved in adaptation to long days at high latitudes. Agron J. 2013;3(1):117–34. doi: 10.3390/agronomy3010117

98. Zhai H, Lü S, Wang Y, Chen X, Ren H, Yang J, et al. Allelic variations at four major maturity E genes and transcriptional abundance of the E1 gene are associated with flowering time and maturity of soybean cultivars. PLoS One. 2014;9(5):e97636. doi: 10.1371/journal.pone.0097636 24830458

99. Li Z, Nelson RL. Genetic diversity among soybean accessions from three countries measured by RAPDs. Crop Sci. 2001;41(4):1337–47. doi: 10.2135/cropsci2001.4141337x

100. Lee G-A, Choi Y-M, Yi J-Y, Chung J-W, Lee M-C, Ma K-H, et al. Genetic diversity and population structure of Korean soybean collection using 75 microsatellite markers. Korean J Crop Sci. 2014;59(4):492–7. doi: 10.7740/kjcs.2014.59.4.492

101. Sedivy EJ, Wu F, Hanzawa Y. Soybean domestication: the origin, genetic architecture and molecular bases. New Phytol. 2017;214(2):539–53. doi: 10.1111/nph.14418 28134435

102. Ritchie MD, Steen KV. The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation. Ann Transl Med. 2018;6(8):157. doi: 10.21037/atm.2018.04.05 29862246

103. Sun X, Lu Q, Mukherjee S, Crane PK, Elston R, Ritchie MD. Analysis pipeline for the epistasis search—statistical versus biological filtering. Front Genet. 2014;5:106. doi: 10.3389/fgene.2014.00106 24817878

104. Niel C, Sinoquet C, Dina C, Rocheleau G. A survey about methods dedicated to epistasis detection. Front Genet. 2015;6:285. doi: 10.3389/fgene.2015.00285 26442103

105. Yi N. Statistical analysis of genetic interactions. Genet Res. 2010;92(5–6):443–59. doi: 10.1017/S0016672310000595 21429274

106. Bernier G, Périlleux C. A physiological overview of the genetics of flowering time control. Plant Biotechnol J. 2005;3(1):3–16. doi: 10.1111/j.1467-7652.2004.00114.x 17168895

107. Rouse DT, Sheldon CC, Bagnall DJ, Peacock WJ, Dennis ES. FLC, a repressor of flowering, is regulated by genes in different inductive pathways. Plant J. 2002;29(2):183–91. doi: 10.1046/j.0960-7412.2001.01210.x 11851919

108. Henderson IR, Dean C. Control of Arabidopsis flowering: the chill before the bloom. Development. 2004;131(16):3829–38. doi: 10.1242/dev.01294 15289433

Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Souhrn

Klíčová slova:

Zdroje

PLOS One

Svět praktické medicíny 1/2024 (znalostní test z časopisu)

Koncepce osteologické péče pro gynekology a praktické lékaře

Sekvenční léčba schizofrenie

Hypertenze a hypercholesterolémie – synergický efekt léčby

Význam metforminu pro „udržitelnou“ terapii diabetu