SNP-bin linkage analysis and genome-wide association study of plant height in soybean
Jiajing Wang A , Bo Hu A , Shanshan Huang B , Xiping Hu B , Mahfishan Siyal A , Chang Yang A , Hengxing Zhao A , Tao Yang A , Haoran Li A , Yongqin Hou A , Cuiqiao Liu A , Xu Sun A , Raja Rameez Veesar A , Wen-Xia Li A and Hailong Ning
A *
+ Author Affiliations
- Author Affiliations
A Key Laboratory of Soybean Biology, Ministry of Education, Key Laboratory of Soybean Biology and Breeding/Genetics, Ministry of Agriculture, Northeast Agricultural University, Harbin 150030, China.
B Key Laboratory of Crop Biotechnology Breeding of the Ministry of Agriculture, Beidahuang Kenfeng Seed Co. Ltd, Harbin, China.
Crop & Pasture Science 73(3) 222-237 https://doi.org/10.1071/CP21128
Submitted: 25 February 2021 Accepted: 1 October 2021 Published: 25 January 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
As the major source of edible protein and oil, the global demand for soybean (Glycine max (L.) Merr.) is increasing. Plant height is closely related to yield; therefore, understanding the genetic basis of plant height will help to improve soybean plant type and increase seed yield. In this study, quantitative trait loci (QTLs) and nucleotides (QTNs) for soybean plant height were detected by linkage analysis and association analysis. A high-density map containing 2225 bin markers was constructed by using 108 342 SNPs of a recombinant inbred line population (named RIL3613) of 120 lines for linkage analysis. In total, 39 QTLs were detected, including 16 QTLs that were repeatedly detected in multiple environments. Association analysis was performed by using 63 306 SNPs from a germplasm population of 455 natural soybean accessions. In total, 62 QTNs were detected, and 26 QTNs were repeatedly detected by multiple methods. Fourteen QTNs were distributed in the intervals of six multiple-environment QTLs by comparing the results of association analysis and linkage analysis. With pathway analysis, six candidate genes were identified as being associated with plant height. These results contribute to analysis of the genetic basis of plant height and will promote marker-assisted selection for breeding ideal plant shape.
Keywords: Genes, GWAS, linkage analysis, plant height, QTL, QTN, SNP-bin marker, soybean.
References
Adewusi OF, Odiyi AC, Akinyele BO (2017) Identification of genomic region governing yield related characters in soybean, Glycine max (L.) Merrill using SNP markers. Journal of Advances in Biology & Biotechnology 15, 1–9.| Identification of genomic region governing yield related characters in soybean, Glycine max (L.) Merrill using SNP markers.Crossref | GoogleScholarGoogle Scholar |
Akhter M, Sneller CH (1996) Yield and yield components of early maturing soybean genotypes in the mid-south. Crop Science 36, 877–882.
| Yield and yield components of early maturing soybean genotypes in the mid-south.Crossref | GoogleScholarGoogle Scholar |
Bansal R, Mittapelly P, Cassone BJ, Mamidala P, Redinbaugh MG, Michel A (2015) Recommended reference genes for quantitative pcr analysis in soybean have variable stabilities during diverse biotic stresses. PLoS ONE 10, e0134890
| Recommended reference genes for quantitative pcr analysis in soybean have variable stabilities during diverse biotic stresses.Crossref | GoogleScholarGoogle Scholar | 26244340PubMed |
Belamkar V, Farmer AD, Weeks NT, Kalberer SR, Blackmon WJ, Cannon SB (2016) Genomics-assisted characterization of a breeding collection of Apios americana, an edible tuberous legume. Scientific Reports 6, 34908
| Genomics-assisted characterization of a breeding collection of Apios americana, an edible tuberous legume.Crossref | GoogleScholarGoogle Scholar | 27721469PubMed |
Cao Y, Li S, Wang Z, Chang F, Kong J, Gai J, Zhao T (2017) Identification of major quantitative trait loci for seed oil content in soybeans by combining linkage and genome-wide association mapping. Frontiers in Plant Science 8, 1222
| Identification of major quantitative trait loci for seed oil content in soybeans by combining linkage and genome-wide association mapping.Crossref | GoogleScholarGoogle Scholar | 28747922PubMed |
Cao Y, Li S, Chen G, Wang Y, Bhat JA, Karikari B, Kong J, Gai J, Zhao T (2019) Deciphering the genetic architecture of plant height in soybean using two RIL populations sharing a common M8206 parent. Plants 8, 373
| Deciphering the genetic architecture of plant height in soybean using two RIL populations sharing a common M8206 parent.Crossref | GoogleScholarGoogle Scholar |
Chang F, Guo C, Sun F, Zhang J, Wang Z, Kong J, He Q, Sharmin RA, Zhao T (2018) Genome-wide association studies for dynamic plant height and number of nodes on the main stem in summer sowing soybeans. Frontiers in Plant Science 9, 1184
| Genome-wide association studies for dynamic plant height and number of nodes on the main stem in summer sowing soybeans.Crossref | GoogleScholarGoogle Scholar | 30177936PubMed |
Chapman A, Pantalone VR, Ustun A, Allen FL, Landau-Ellis D, Trigiano RN, Gresshoff PM (2003) Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population. Euphytica 129, 387–393.
| Quantitative trait loci for agronomic and seed quality traits in an F2 and F4:6 soybean population.Crossref | GoogleScholarGoogle Scholar |
Eskandari M, Cober ER, Rajcan I (2013) Genetic control of soybean seed oil. II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield. Theoretical and Applied Genetics 126, 1677–1687.
| Genetic control of soybean seed oil. II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield.Crossref | GoogleScholarGoogle Scholar | 23536049PubMed |
Fang Y, Liu S, Dong Q, Zhang K, Tian Z, Li X, Li W, Qi Z, Wang Y, Tian X, Song J, Wang J, Yang C, Jiang S, Li W-X, Ning H (2020) Linkage analysis and multi-locus genome-wide association studies identify QTNs controlling soybean plant height. Frontiers in Plant Science 11, 9
| Linkage analysis and multi-locus genome-wide association studies identify QTNs controlling soybean plant height.Crossref | GoogleScholarGoogle Scholar | 32117360PubMed |
Fulton TM, Beck-Bunn T, Emmatty D, Eshed Y, Lopez J, Petiard V, Uhlig J, Zamir D, Tanksley SD (1997) QTL analysis of an advanced backcross of Lycopersicon peruvianum to the cultivated tomato and comparisons with QTLs found in other wild species. Theoretical and Applied Genetics 95, 881–894.
| QTL analysis of an advanced backcross of Lycopersicon peruvianum to the cultivated tomato and comparisons with QTLs found in other wild species.Crossref | GoogleScholarGoogle Scholar |
Gai J, Wang Y, Wu X, Chen S (2007) A comparative study on segregation analysis and QTL mapping of quantitative traits in plants: with a case in soybean. Frontiers of Agriculture in China 1, 1–7.
| A comparative study on segregation analysis and QTL mapping of quantitative traits in plants: with a case in soybean.Crossref | GoogleScholarGoogle Scholar |
Gholizadeh F, Mirzaghaderi G (2020) Genome-wide analysis of the polyamine oxidase gene family in wheat (Triticum aestivum L.) reveals involvement in temperature stress response. PLoS ONE 15, e0236226
| Genome-wide analysis of the polyamine oxidase gene family in wheat (Triticum aestivum L.) reveals involvement in temperature stress response.Crossref | GoogleScholarGoogle Scholar | 32866160PubMed |
Grzechowiak M, Sliwiak J, Jaskolski M, Ruszkowski M (2020) Structural studies of glutamate dehydrogenase (isoform 1) from Arabidopsis thaliana, an important enzyme at the branch-point between carbon and nitrogen metabolism. Frontiers in Plant Science 11, 754
| Structural studies of glutamate dehydrogenase (isoform 1) from Arabidopsis thaliana, an important enzyme at the branch-point between carbon and nitrogen metabolism.Crossref | GoogleScholarGoogle Scholar | 32655590PubMed |
Guzman PS, Diers BW, Neece DJ, St. Martin SK, LeRoy AR, Grau CR, Hughes TJ, Nelson RL (2007) QTL associated with yield in three backcross-derived populations of soybean. Crop Science 47, 111–122.
| QTL associated with yield in three backcross-derived populations of soybean.Crossref | GoogleScholarGoogle Scholar |
Jing Y, Zhao X, Wang J, Lian M, Teng W, Qiu L, Han Y, Li W (2019) Identification of loci and candidate genes for plant height in soybean (Glycine max) via genome-wide association study. Plant Breeding 138, 721–732.
| Identification of loci and candidate genes for plant height in soybean (Glycine max) via genome-wide association study.Crossref | GoogleScholarGoogle Scholar |
Josie J, Alcivar A, Rainho J, Kassem MA (2007) Genomic regions containing QTL for plant height, internodes length, and flower color in soybean [Glycine max (L.) Merr]. BIOS 78, 119–126.
| Genomic regions containing QTL for plant height, internodes length, and flower color in soybean [Glycine max (L.) Merr].Crossref | GoogleScholarGoogle Scholar |
Kabelka EA, Diers BW, Fehr WR, LeRoy AR, Baianu IC, You T, Neece DJ, Nelson RL (2004) Putative alleles for increased yield from soybean plant introductions. Crop Science 44, 784–791.
| Putative alleles for increased yield from soybean plant introductions.Crossref | GoogleScholarGoogle Scholar |
Kang NY, Cho C, Kim J (2013) Inducible expression of Arabidopsis response regulator 22 (ARR22), a type-C ARR, in transgenic Arabidopsis enhances drought and freezing tolerance. PLoS ONE 8, e79248
| Inducible expression of Arabidopsis response regulator 22 (ARR22), a type-C ARR, in transgenic Arabidopsis enhances drought and freezing tolerance.Crossref | GoogleScholarGoogle Scholar | 24244460PubMed |
Karikari B, Chen S, Xiao Y, Chang F, Zhou Y, Kong J, Bhat JA, Zhao T (2019) Utilization of interspecific high-density genetic map of RIL population for the QTL detection and candidate gene mining for 100-seed weight in soybean. Frontiers in Plant Science 10, 1001
| Utilization of interspecific high-density genetic map of RIL population for the QTL detection and candidate gene mining for 100-seed weight in soybean.Crossref | GoogleScholarGoogle Scholar | 31552060PubMed |
Keim P, Schupp JM, Travis SE, Clayton K, Zhu T, Shi L, Ferreira A, Webb DM (1997) A high-density soybean genetic map based on AFLP markers. Crop Science 37, 537–543.
| A high-density soybean genetic map based on AFLP markers.Crossref | GoogleScholarGoogle Scholar |
Lander ES, Botstein D. (1994) Mapping mendelian factors underlying quantitative traits using RFLP linkage map. Genetics 136, 185–199.
| Mapping mendelian factors underlying quantitative traits using RFLP linkage map.Crossref | GoogleScholarGoogle Scholar |
Lee SH, Bailey MA, Mian MA, Shipe ER, Ashley DA, Parrott WA, Hussey RS, Boerma HR (1996) Identification of quantitative trait loci for plant height, lodging, and maturity in a soybean population segregating for growth habit. Theoretical and Applied Genetics 92, 516–523.
| Identification of quantitative trait loci for plant height, lodging, and maturity in a soybean population segregating for growth habit.Crossref | GoogleScholarGoogle Scholar | 24166318PubMed |
Lee S, Jun TH, Michel AP, Rouf Mian MR (2015) SNP markers linked to QTL conditioning plant height, lodging, and maturity in soybean. Euphytica 203, 521–532.
| SNP markers linked to QTL conditioning plant height, lodging, and maturity in soybean.Crossref | GoogleScholarGoogle Scholar |
Lee S, Van K, Sung M, Nelson R, LaMantia J, McHale LK, Mian MAR (2019) Genome-wide association study of seed protein, oil and amino acid contents in soybean from maturity groups I to IV. Theoretical and Applied Genetics 132, 1639–1659.
| Crossref |
Li N, Wang L, Zhang W, Takechi K, Takano H, Lin X (2014) Overexpression of UDP-glucose pyrophosphorylase from Larix gmelinii enhances vegetative growth in transgenic Arabidopsis thaliana. Plant Cell Reports 33, 779–791.
| Overexpression of UDP-glucose pyrophosphorylase from Larix gmelinii enhances vegetative growth in transgenic Arabidopsis thaliana.Crossref | GoogleScholarGoogle Scholar | 24408396PubMed |
Li Y-H, Reif JC, Hong H-L, Li H-H, Liu Z-X, Ma Y-S, Li J, Tian Y, Li Y-F, Li W-B (2018) Genome-wide association mapping of QTL underlying seed oil and protein contents of a diverse panel of soybean accessions. Plant Science 266, 95–101.
| Genome-wide association mapping of QTL underlying seed oil and protein contents of a diverse panel of soybean accessions.Crossref | GoogleScholarGoogle Scholar | 29241572PubMed |
Li X, Zhang K, Sun X, Huang S, Wang J, Yang C, Siyal M, Wang C, Guo C, Hu X, Li W-X, Ning H (2020) Detection of QTL and QTN and candidate genes for oil content in soybean using a combination of four-way-RIL and germplasm populations. The Crop Journal 8, 802–811.
| Detection of QTL and QTN and candidate genes for oil content in soybean using a combination of four-way-RIL and germplasm populations.Crossref | GoogleScholarGoogle Scholar |
Liu W, Kim MY, Van K, Lee Y-H, Li H, Liu X, Lee S-H (2011) QTL identification of yield-related traits and their association with flowering and maturity in soybean. Journal of Crop Science and Biotechnology 14, 65–70.
| QTL identification of yield-related traits and their association with flowering and maturity in soybean.Crossref | GoogleScholarGoogle Scholar |
Lü H-Y, Li H-W, Fan R, Li H-Y, Yin J-Y, Zhang J-J, Zhang D (2016) Genome-wide association study of dynamic developmental plant height in soybean. Canadian Journal of Plant Science 97, 308–315.
| Genome-wide association study of dynamic developmental plant height in soybean.Crossref | GoogleScholarGoogle Scholar |
Lukowitz W, Nickle TC, Meinke DW, Last RL, Conklin PL, Somerville CR (2001) Arabidopsis cyt1 mutants are deficient in a mannose-1-phosphate guanylyltransferase and point to a requirement of N-linked glycosylation for cellulose biosynthesis. Proceedings of the National Academy of Sciences of the United States of America 98, 2262–2267.
| Arabidopsis cyt1 mutants are deficient in a mannose-1-phosphate guanylyltransferase and point to a requirement of N-linked glycosylation for cellulose biosynthesis.Crossref | GoogleScholarGoogle Scholar | 11226227PubMed |
Mansur LM, Orf JH, Lark KG (1993) Determining the linkage of quantitative trait loci to RFLP markers using extreme phenotypes of recombinant inbreds of soybean (Glycine max L. Merr.). Theoretical and Applied Genetics 86, 914–918.
| Determining the linkage of quantitative trait loci to RFLP markers using extreme phenotypes of recombinant inbreds of soybean (Glycine max L. Merr.).Crossref | GoogleScholarGoogle Scholar | 24193997PubMed |
McCouch SR, Cho YG, Yano M, Paul E, Blinstrub M, Morishima H, Kinoshita T (1997) Report on QTL nomenclature. Rice Genetics Newsletter 14, 11–13.
Mian MAR, Ashley DA, Vencill WK, Boerma HR (1998) QTLs conditioning early growth in a soybean population segregating for growth habit. Theoretical and Applied Genetics 97, 1210–1216.
| QTLs conditioning early growth in a soybean population segregating for growth habit.Crossref | GoogleScholarGoogle Scholar |
Ning H, Yuan J, Dong Q, Li W, Xue H, Wang Y, Tian Y, Li W-X (2018) Identification of QTLs related to the vertical distribution and seed-set of pod number in soybean [Glycine max (L.) Merri]. PLoS ONE 13, e0195830
| Identification of QTLs related to the vertical distribution and seed-set of pod number in soybean [Glycine max (L.) Merri].Crossref | GoogleScholarGoogle Scholar | 29664958PubMed |
Orf JH, Chase K, Jarvik T, Mansur LM, Cregan PB, Adler FR, Lark KG (1999) Genetics of soybean agronomic traits. I. Comparison of three related recombinant inbred populations. Crop Science 39, 1642–1651.
| Genetics of soybean agronomic traits. I. Comparison of three related recombinant inbred populations.Crossref | GoogleScholarGoogle Scholar |
Palomeque L, Li-Jun L, Li W, Hedges B, Cober ER, Rajcan I (2009) QTL in mega-environments. II. Agronomic trait QTL co-localized with seed yield QTL detected in a population derived from a cross of high-yielding adapted × high-yielding exotic soybean lines. Theoretical and Applied Genetics 119, 429–436.
| QTL in mega-environments. II. Agronomic trait QTL co-localized with seed yield QTL detected in a population derived from a cross of high-yielding adapted × high-yielding exotic soybean lines.Crossref | GoogleScholarGoogle Scholar | 19462149PubMed |
Pathan SM, Vuong T, Clark K, Lee JD, Shannon JG, Roberts CA, Ellersieck MR, Burton JW, Cregan PB, Hyten DL, Nguyen HT, Sleper DA (2013) Genetic mapping and confirmation of quantitative trait loci for seed protein and oil contents and seed weight in soybean. Crop Science 53, 765–774.
| Genetic mapping and confirmation of quantitative trait loci for seed protein and oil contents and seed weight in soybean.Crossref | GoogleScholarGoogle Scholar |
Patil G, Vuong TD, Kale S, Valliyodan B, Deshmukh R, Zhu C, Wu X, Bai Y, Yungbluth D, Lu F, Kumpatla S, Shannon JG, Varshney RK, Nguyen HT (2018) Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping. Plant Biotechnology Journal 16, 1939–1953.
| Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping.Crossref | GoogleScholarGoogle Scholar | 29618164PubMed |
Priolli RHG, Campos JB, Stabellini NS, Pinheiro JB, Vello NA (2015) Association mapping of oil content and fatty acid components in soybean. Euphytica 203, 83–96.
| Association mapping of oil content and fatty acid components in soybean.Crossref | GoogleScholarGoogle Scholar |
Qi Z, Song J, Zhang K, Liu S, Tian X, Wang Y, Fang Y, Li X, Wang J, Yang C, Jiang S, Sun X, Tian Z, Li W, Ning H (2020) Identification of QTNs controlling 100-seed weight in soybean using multilocus genome-wide association studies. Frontiers in Genetics 11, 689
| Identification of QTNs controlling 100-seed weight in soybean using multilocus genome-wide association studies.Crossref | GoogleScholarGoogle Scholar | 32765581PubMed |
Ren W-L, Wen Y-J, Dunwell JM, Zhang Y-M (2018) pKWmEB: integration of Kruskal–Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study. Heredity 120, 208–218.
| pKWmEB: integration of Kruskal–Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study.Crossref | GoogleScholarGoogle Scholar | 29234158PubMed |
Rossi ME, Orf JH, Liu L-J, Dong Z, Rajcan I (2013) Genetic basis of soybean adaptation to North American vs. Asian mega-environments in two independent populations from Canadian × Chinese crosses. Theoretical and Applied Genetics 126, 1809–1823.
| Genetic basis of soybean adaptation to North American vs. Asian mega-environments in two independent populations from Canadian × Chinese crosses.Crossref | GoogleScholarGoogle Scholar | 23595202PubMed |
Sasi S, Venkatesh J, Daneshi RF, Gururani MA (2018) Photosystem II extrinsic proteins and their putative role in abiotic stress tolerance in higher plants. Plants 7, 100
| Photosystem II extrinsic proteins and their putative role in abiotic stress tolerance in higher plants.Crossref | GoogleScholarGoogle Scholar |
Sebolt AM, Shoemaker RC, Diers BW (2000) Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean. Crop Science 40, 1438–1444.
| Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean.Crossref | GoogleScholarGoogle Scholar |
Seo JH, Kim KS, Ko JM, Choi MS, Kang BK, Kwon SW, Jun TH (2019) Quantitative trait locus analysis for soybean (Glycine max) seed protein and oil concentrations using selected breeding populations. Plant Breeding 138, 95–104.
| Quantitative trait locus analysis for soybean (Glycine max) seed protein and oil concentrations using selected breeding populations.Crossref | GoogleScholarGoogle Scholar |
Sonah H, O’Donoughue L, Cober E, Rajcan I, Belzile F (2015) Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean. Plant Biotechnology Journal 13, 211–221.
| Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean.Crossref | GoogleScholarGoogle Scholar | 25213593PubMed |
Specht JE, Chase K, Macrander M, Graef GL, Chung J, Markwell JP, Germann M, Orf JH, Lark KG (2001) Soybean response to water. a QTL analysis of drought tolerance. Crop Science 41, 493–509.
| Soybean response to water. a QTL analysis of drought tolerance.Crossref | GoogleScholarGoogle Scholar |
Sun D, Li W, Zhang Z, Chen Q, Ning H, Qiu L, Sun G (2006) Quantitative trait loci analysis for the developmental behavior of soybean (Glycine max L. Merr.). Theoretical and Applied Genetics 112, 665–673.
| Quantitative trait loci analysis for the developmental behavior of soybean (Glycine max L. Merr.).Crossref | GoogleScholarGoogle Scholar | 16365761PubMed |
Tamba CL, Zhang Y-M (2018) A fast mrMLM algorithm for multi-locus genome-wide association studies. biorxiv
| A fast mrMLM algorithm for multi-locus genome-wide association studies.Crossref | GoogleScholarGoogle Scholar |
Tamba CL, Ni Y-L, Zhang Y-M (2017) Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies. PLoS Computational Biology 13, e1005357
| Iterative sure independence screening EM-Bayesian LASSO algorithm for multi-locus genome-wide association studies.Crossref | GoogleScholarGoogle Scholar | 28141824PubMed |
Tang KQ (2020) Molecular mechanism of GmLIM1 gene regulation of soybean plant height. PhD dissertation, University of Chinese Academy of Sciences, China.
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. Nature Genetics 43, 159–162.
| Genome-wide association study of leaf architecture in the maize nested association mapping population.Crossref | GoogleScholarGoogle Scholar | 21217756PubMed |
Wan X, Mo A, Liu S, Yang L, Li L (2011) Constitutive expression of a peanut ubiquitin-conjugating enzyme gene in Arabidopsis confers improved water-stress tolerance through regulation of stress-responsive gene expression. Journal of Bioscience and Bioengineering 111, 478–484.
| Constitutive expression of a peanut ubiquitin-conjugating enzyme gene in Arabidopsis confers improved water-stress tolerance through regulation of stress-responsive gene expression.Crossref | GoogleScholarGoogle Scholar | 21193345PubMed |
Wang J-K (2009) Inclusive composite interval mapping of quantitative trait genes. Acta Agronomica Sinica 35, 239–245.
| Inclusive composite interval mapping of quantitative trait genes.Crossref | GoogleScholarGoogle Scholar |
Wang CS (2020) Function study on soybean GmGAI gene regulating flowering time via gibberellin pathway. Masters dissertation, Northeast Agricultural University, China.
Wang D, Graef GL, Procopiuk AM, Diers BW (2004) Identification of putative QTL that underlie yield in interspecific soybean backcross populations. Theoretical and Applied Genetics 108, 458–467.
| Identification of putative QTL that underlie yield in interspecific soybean backcross populations.Crossref | GoogleScholarGoogle Scholar | 14504749PubMed |
Wang S-B, Feng J-Y, Ren W-L, Huang B, Zhou L, Wen Y-J, Zhang J, Dunwell JM, Xu S, Zhang Y-M (2016) Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Scientific Reports 6, 19444
| Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology.Crossref | GoogleScholarGoogle Scholar | 26787347PubMed |
Wen Y-J, Zhang Y-W, Zhang J, Feng J-Y, Dunwell JM, Zhang Y-M (2019) An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2. Briefings in Bioinformatics 20, 1913–1924.
| An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2.Crossref | GoogleScholarGoogle Scholar | 30032279PubMed |
Xia Z, Tsubokura Y, Hoshi M, Hanawa M, Yano C, Okamura K, Ahmed TA, Anai T, Watanabe S, Hayashi M, Kawai T, Hossain KG, Masaki H, Asai K, Yamanaka N, Kubo N, Kadowaki K-I, Nagamura Y, Yano M, Sasaki T, Harada K (2007) An integrated high-density linkage map of soybean with RFLP, SSR, STS, and AFLP markers using a single F2 population. DNA Research 14, 257–269.
| An integrated high-density linkage map of soybean with RFLP, SSR, STS, and AFLP markers using a single F2 population.Crossref | GoogleScholarGoogle Scholar | 18192280PubMed |
Xia T, Xiao D, Liu D, Chai W, Gong Q, Wang NN (2012) Heterologous expression of ATG8c from soybean confers tolerance to nitrogen deficiency and increases yield in Arabidopsis. PLoS ONE 7, e37217
| Heterologous expression of ATG8c from soybean confers tolerance to nitrogen deficiency and increases yield in Arabidopsis.Crossref | GoogleScholarGoogle Scholar | 22629371PubMed |
Yang G, Zhai H, Wu H-Y, Zhang X-Z, Lü S-X, Wang Y-Y, Li Y-Q, Hu B, Wang L, Wen Z-X, Wang D-C, Wang S-D, Kyuya H, Xia Z-J, Xie F-T (2017) QTL effects and epistatic interaction for flowering time and branch number in a soybean mapping population of Japanese × Chinese cultivars. Journal of Integrative Agriculture 16, 1900–1912.
| QTL effects and epistatic interaction for flowering time and branch number in a soybean mapping population of Japanese × Chinese cultivars.Crossref | GoogleScholarGoogle Scholar |
Yu J, Buckler ES (2006) Genetic association mapping and genome organization of maize. Current Opinion in Biotechnology 17, 155–160.
| Genetic association mapping and genome organization of maize.Crossref | GoogleScholarGoogle Scholar | 16504497PubMed |
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. Nature Genetics 38, 203–208.
| A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.Crossref | GoogleScholarGoogle Scholar | 16380716PubMed |
Zhang J, Feng J-Y, Ni Y-L, Wen Y-J, Niu Y, Tamba CL, Yue C, Song Q, Zhang Y-M (2017) pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies. Heredity 118, 517–524.
| pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.Crossref | GoogleScholarGoogle Scholar | 28295030PubMed |
Zhang YW, Tamba CL, Wen YJ, Li P, Ren WL, Ni YL, Gao J, Zhang YM (2020) mrMLM v4.0. An R platform for multi-locus genome-wide association studies. Genomics, Proteomics & Bioinformatics 18, 481–487.
| mrMLM v4.0. An R platform for multi-locus genome-wide association studies.Crossref | GoogleScholarGoogle Scholar |
