Downloads

1. Kearsey MJ, Farquhar AG. QTL analysis in plants: where are we now? Heredity 1998;8(2):137–42. 2. Mackay TF, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and prospects. Nat Rev Genet 2009;10(8): 565–77. 3. Buckler ES, Holland JB, Bradbury PJ, et al. The genetic architecture of maize flowering time. Science 2009;325(5941):714–18. 4. Kroymann J, Mitchell-Olds T. Epistasis and balanced polymorphism influencing complex trait variation. Nature 2005; 435(7038):95–8. 5. Heffner EL, Sorrells ME, Jannink JL. Genomic selection for crop improvement. Crop Sci 2009;49(1):1–12. 6. Zhang YM, Xu S. Advanced statistical methods for detecting multiple quantitative trait loci. Recent Res Dev Genet Breed 2005a;2:1–23. 7. Gibson G, Weir B. The quantitative genetics of transcription. Trends Genet 2005;21(11):616–23. 8. Gilad Y, Rifkin SA, Pritchard JK. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet 2008;24(8):408–15. 9. Chan EK, Rowe HC, Hansen BG, et al. The complex genetic architecture of the metabolome. PLoS Genet 2010;6(11): e1001198. 10.Park E, Guo J, Shen S, et al. Population and allelic variation of A-to-I RNA editing in human transcriptomes. Genome Biol 2017;18(1):143. 11.Rand AC, Jain M, Eizenga JM, et al. Mapping DNA methylation with high-throughput nanopore sequencing. Nat Methods 2017;14(4):411–13. 12.Jansen RC. Interval mapping of multiple quantitative trait loci. Genetics 1993;135(1):205–11. 13.Zeng ZB. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci USA 1993;90(23):10972–6. 14.Kao CH, Zeng ZB, Teasdale RD. Multiple interval mapping for quantitative trait loci. Genetics 1999;152(3):1203–16. 15.Li H, Ye G, Wang J. A modified algorithm for the improvement of composite interval mapping. Genetics 2007;175(1): 361–74. 16.Zhang L, Li H, Li Z, et al. Interactions between markers can be caused by the dominance effect of quantitative trait loci. Genetics 2008;180(2):1177–90. 17. Yi N, George V, Allison DB. Stochastic search variable selection for mapping multiple quantitative trait loci. Genetics 2003;164(3):1129–38. 18.Xu S. Estimating polygenic effects using markers of the entire genome. Genetics 2003;163(2):789–801. 19.Xu S. An empirical Bayes method for estimating epistatic effects of quantitative trait loci. Biometrics 2007;63(2):513–21. 20.Zhang YM, Xu S. A penalized maximum likelihood method for estimating epistatic effects of QTL. Heredity 2005;95(1): 96–104. 21.Hoggart CJ, Whittaker JC, De Iorio M, Balding DJ. Simultaneous analysis of all SNPs in genome-wide and resequencing association studies. PLoS Genet 2008;4(7): e1000130. 22. Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Statist Soc Ser B 1996;58(1):267–88. 23.Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc 2001;96(456): 1348–60. 24.Xu S. An expectation-maximization algorithm for the Lasso estimation of quantitative trait locus effects. Heredity 2010; 105(5):483–94. 25.Bernardo R. Genome wide markers as cofactors for precision mapping of quantitative trait loci. Theor Appl Genet 2013; 126(4):999–1009. 26.Xu S. Mapping quantitative trait loci by controlling polygenic background effects. Genetics 2013;195(4):1209–22. 27.Wang S-B, Wen Y-J, Ren W-L, et al. Mapping small-effect and linked quantitative trait loci for complex traits in backcross or DH populations via a multi-locus GWAS methodology. Sci Rep 2016;6:29951. 28.Goddard ME, Wray NR, Verbyla K, et al. Estimating effects and making predictions from genome-wide marker data. Stat Sci 2009;24(4):517–29. 29.Wang SB, Feng JY, Ren WL, et al. Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology. Sci Rep 2016;6:19444. 30.Zhang Z, Ersoz E, Lai CQ, et al. Mixed linear model approach adapted for genome-wide association studies. Nat Genet 2010; 42(4):355–60. 31.Wen YJ, Zhang H, Ni YL, et al. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Brief Bioinform 2017, DOI: 10.1093/bib/bbw145. 32.Zhou G, Chen Y, Yao W, et al. Genetic composition of yield heterosis in an elite rice hybrid. Proc Natl Acad Sci USA 2012; 109(39):15847–52. 33.Fan C, Xing Y, Mao H, et al. GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein. Theor Appl Genet 2006;112(6):1164–71. 34. Liu J, Chen J, Zheng X, et al. GW5 acts in the brassinosteroid signalling pathway to regulate grain width and weight in rice. Nat Plants 2017;3:17043. 35.Xue W, Xing Y, Weng X, et al. Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet 2008;40(6):761–7. 36.Ramegowda V, Basu S, Krishnan A, et al. Rice GROWTH UNDER DROUGHT KINASE is required for drought tolerance and grain yield under normal and drought stress conditions. Plant Physiol 2014;166(3):1634–45. 37.Ashikari M, Sakakibara H, Lin S, et al. Cytokinin oxidase regulates rice grain production. Science 2005;309(5735): 741–5. 38.Zou X, Qin Z, Zhang C, et al. Over-expression of an S-domain receptor-like kinase extracellular domain improves panicle architecture and grain yield in rice. J Exp Bot 2015;66(22): 7197–209. 39.Huo X,Wu S, Zhu Z, et al. NOG1 increases grain production in rice. Nat Commun 2017;8(1):1497. 40.Song XJ, Huang W, Shi M, et al. A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet 2007;39(5):623–30. 41. Li X, Sun L, Tan L, et al. TH1, a DUF640 domain-like gene controls lemma and palea development in rice. Plant Mol Biol 2012;78(4–5):351–9. 42.Wang E, Wang J, Zhu X, et al. Control of rice grain-filling and yield by a gene with a potential signature of domestication. Nat Genet 2008;40(11):1370–4. 43. Ishikawa S, Maekawa M, Arite T, et al. Suppression of tiller bud activity in tillering dwarf mutants of rice. Plant Cell Physiol 2005;46(1):79–86. 44.Song XJ, Kuroha T, Ayano M, et al. Rare allele of a previously unidentified histone H4 acetyltransferase enhances grain weight, yield, and plant biomass in rice. Proc Natl Acad Sci USA 2015;112(1):76–81. 45.Ikeda-Kawakatsu K, Maekawa M, Izawa T, et al. Aberrant Panicle Organization 2/RFL, the rice ortholog of Arabidopsis LEAFY, suppresses the transition from inflorescence meristem to floral meristem through interaction with APO1. Plant J 2012;69(1):168–80. 46.Tan L, Li X, Liu F, et al. Control of a key transition from prostrate to erect growth in rice domestication. Nat Genet 2008; 40(11):1360–4. 47.Zhao L, Tan L, Zhu Z, et al. PAY1 improves plant architecture and enhances grain yield in rice. Plant J 2015;83(3): 528–36. 48.Wang L, Xu Y, Zhang C, et al. OsLIC, a novel CCCH-type zinc finger protein with transcription activation, mediates rice architecture via brassinosteroids signaling. PLoS One 2008; 3(10):e3521. 49.Yu B, Lin Z, Li H, et al. TAC1, a major quantitative trait locus controlling tiller angle in rice. Plant J 2007;52(5):891–8. 50. Jiao Y, Wang Y, Xue D, et al. Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet 2010;42(6):541–4. 51.Wang H, Zhang YM, Li X, et al. Bayesian shrinkage estimation of quantitative trait loci parameters. Genetics 2005;170(1):465–80. 52.Broman KW, Sen S. A Guide to QTL Mapping with R/Qtl. New York, NY: Springer Science þ Business Media, LLC, 2009. 53.Yu H, Xie W, Wang J, et al. Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers. PLoS One 2011;6(3): e17595. 54.Wei J, Xu S. A random-model approach to QTL mapping in multiparent advanced generation intercross (MAGIC) populations. Genetics 2016;202(2):471–86. 55.Zou H. The adaptive lasso and its oracle properties. J Am Statist Assoc 2006;101(476):1418–29. 56.Kra¨mer N, Scha¨ fer J, Boulesteix AL. Regularized estimation of large-scale gene regulatory networks using Gaussian graphical models. BMC Bioinformatics 2009;10(1):384.