Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM, Blaxter ML. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet. 2011;12:499–510.
Article
CAS
PubMed
Google Scholar
Martin JA, Wang Z. Next-generation transcriptome assembly. Nat Publ Gr. 2011;2:671–82.
Google Scholar
Hirsch CN, Buell CR. Tapping the Promise of Genomics in Species with Complex, Nonmodel Genomes. Annu Rev Plant Biol. 2013;64:89–110.
Article
CAS
PubMed
Google Scholar
Bedewitz MA, Góngora-Castillo E, Uebler JB, Gonzales-Vigil E, Wiegert-Rininger KE, Childs KL, Hamilton JP, Vaillancourt B, Yeo YS, Chappell J, DellaPenna D, Jones AD, Buell CR, Barry CS. A Root-Expressed L -Phenylalanine: 4-Hydroxyphenylpyruvate Aminotransferase Is Required for Tropane Alkaloid Biosynthesis in Atropa belladonna. Plant Cell. 2014;9:3745–62.
Article
Google Scholar
Zhong S, Fei Z, Chen Y, Zheng Y, Huang M, Vrebalov J, Mcquinn R, Gapper N, Liu B, Xiang J, Shao Y, Giovannoni JJ. Single-base resolution methylomes of tomato fruit development reveal epigenome modifications associated with ripening. Nat Biotechnol. 2013;31:154–9.
Article
CAS
PubMed
Google Scholar
Schneeberger K. Using next-generation sequencing to isolate mutant genes from forward genetic screens. Nat Publ Gr. 2014;15:662–76.
CAS
Google Scholar
Zhang G, Liu X, Quan Z, Cheng S, Xu X, Pan S, Xie M, Zeng P, Yue Z, Wang W, Tao Y, Bian C, Han C, Xia Q, Peng X, Cao R, Yang X, Zhan D, Hu J, Zhang Y, Li H, Li H, Li N, Wang J, Wang C, Wang R, Guo T, Cai Y, Liu C, Xiang H, Shi Q, Huang P, Chen Q, Li Y, Wang J, Zhao Z, Wang J. Genome sequence of foxtail millet (Setaria italica) provides insights into grass evolution and biofuel potential. Nat Biotechnol. 2012;30:549–54.
Article
CAS
PubMed
Google Scholar
Heslot N, Rutkoski J, Poland J, Jannink J-L, Sorrells ME. Impact of marker ascertainment bias on genomic selection accuracy and estimates of genetic diversity. PLoS ONE. 2013;8:e74612.
Article
CAS
PubMed
PubMed Central
Google Scholar
Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, Elshire RJ, Acharya CB, Mitchell SE, Flint-Garcia SA, McMullen MD, Holland JB, Buckler ES, Gardner CA. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14:R55.
Article
PubMed
PubMed Central
Google Scholar
Poland J, Endelman J, Dawson J, Rutkoski J, Wu S, Manes Y, Dreisigacker S, Crossa J, Sánchez-Villeda H, Sorrells M, Jannink J-L. Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing. Plant Genome J. 2012;5:103.
Article
CAS
Google Scholar
Poland JA, Brown PJ, Sorrells ME, Jannink J-L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE. 2012;7:e32253.
Article
CAS
PubMed
PubMed Central
Google Scholar
Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE. 2011;6:e19379.
Article
CAS
PubMed
PubMed Central
Google Scholar
Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: Using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol. 2010;34:816–34.
Article
PubMed
PubMed Central
Google Scholar
Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007;39:906–13.
Article
CAS
PubMed
Google Scholar
Scheet P, Stephens M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet. 2006;78:629–44.
Article
CAS
PubMed
PubMed Central
Google Scholar
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.
Article
CAS
PubMed
PubMed Central
Google Scholar
Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81:1084–97.
Article
CAS
PubMed
PubMed Central
Google Scholar
Browning SR. Missing data imputation and haplotype phase inference for genome-wide association studies. Hum Genet. 2008;124:439–50.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jannink J-L, Iwata H, Bhat PR, Chao S, Wenzl P, Muehlbauer GJ. Marker Imputation in Barley Association Studies. Plant Genome J. 2009;2:11.
Article
CAS
Google Scholar
Hao K, Chudin E, McElwee J, Schadt EE. Accuracy of genome-wide imputation of untyped markers and impacts on statistical power for association studies. BMC Genet. 2009;10:27.
Article
PubMed
PubMed Central
Google Scholar
Pei Y-F, Li J, Zhang L, Papasian CJ, Deng HW. Analyses and comparison of accuracy of different genotype imputation methods. PLoS ONE. 2008;3:e3551.
Article
PubMed
PubMed Central
Google Scholar
Iwata H, Jannink J-L. Marker genotype imputation in a low-marker-density panel with a high-marker-density reference panel. Accuracy evaluation in barley breeding lines. Crop Sci. 2010;50:1269.
Article
Google Scholar
Guan Y, Stephens M. Practical issues in imputation-based association mapping. PLoS Genet. 2008;4:e1000279.
Article
PubMed
PubMed Central
Google Scholar
Pasaniuc B, Rohland N, McLaren PJ, Garimella K, Zaitlen N, Li H, Gupta N, Neale BM, Daly MJ, Sklar P, Sullivan PF, Bergen S, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Purcell SM, Haas DW, Liang L, Sunyaev S, Patterson N, de Bakker PIW, Reich D, Price AL. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat Genet. 2012;44:631–5.
Article
CAS
PubMed
PubMed Central
Google Scholar
Almeida MAA, Oliveira PSL, Pereira TV, Krieger JE, Pereira AC. An empirical evaluation of imputation accuracy for association statistics reveals increased type-I error rates in genome-wide associations. BMC Genet. 2011;12:10.
Article
PubMed
PubMed Central
Google Scholar
Aulchenko YS, Struchalin MV, van Duijn CM. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics. 2010;11:134.
Article
PubMed
PubMed Central
Google Scholar
De Bakker PIW, Ferreira MAR, Jia X, Neale BM, Raychaudhuri S, Voight BF. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet. 2008;17:122–28.
Article
Google Scholar
Bernardo R. Breeding for quantitative traits in plants. 2nd ed. Minnesota: Stemma Press; 2010.
Google Scholar
Sibson R. SLINK: an optimally efficient algorithm for the single-link cluster method. Comput J. 1973;30–34.
Lande R, Thompson R. Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics. 1990;124:743–56.
CAS
PubMed
PubMed Central
Google Scholar
He S, Zhao Y, Mette MF, Bothe R, Ebmeyer E, Sharbel TF, Reif JC, Jiang Y. Prospects and limits of marker imputation in quantitative genetic studies in European elite wheat (Triticum aestivum L.). BMC Genomics. 2015;16:1–12.
Article
Google Scholar
Edae EA, Byrne PF, Haley SD, Lopes MS, Reynolds MP. Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes. Theor Appl Genet. 2014;127:791–807.
Article
CAS
PubMed
Google Scholar
Bennett D, Reynolds M, Mullan D, Izanloo A, Kuchel H, Langridge P, Schnurbusch T. Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments. Theor Appl Genet. 2012;125:1473–85.
Article
PubMed
Google Scholar
Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F. Multi-environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet. 2008;117:1077–91.
Article
PubMed
Google Scholar
Mayer KFX, Rogers J, el Dole J, Pozniak C, Eversole K, Feuillet C, Gill B, Friebe B, Lukaszewski AJ, Sourdille P, Endo TR, Kubalakova M, Ihalikova J, Dubska Z, Vrana J, Perkova R, Imkova H, Febrer M, Clissold L, McLay K, Singh K, Chhuneja P, Singh NK, Khurana J, Akhunov E, Choulet F, Alberti A, Barbe V, Wincker P, Kanamori H, et al. A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science. 2014;345(6194):1251788.
Article
Google Scholar
Gutiérrez L, Germán S, Pereyra S, Hayes PM, Pérez CA, Capettini F, Locatelli A, Berberian NM, Falconi EE, Estrada R, Fros D, Gonza V, Altamirano H, Huerta-Espino J, Neyra E, Orjeda G, Sandoval-Islas S, Singh R, Turkington K, Castro AJ. Multi-environment multi-QTL association mapping identifies disease resistance QTL in barley germplasm from Latin America. Theor Appl Genet. 2015;128:501–16.
Article
PubMed
Google Scholar
Close TJ, Bhat PR, Lonardi S, Wu Y, Rostoks N, Ramsay L, Druka A, Stein N, Svensson JT, Wanamaker S, Bozdag S, Roose ML, Moscou MJ, Chao S, Varshney RK, Sz P, Sato K, Hayes PM, Matthews DE, Kleinhofs A, Muehlbauer GJ, Deyoung J, Marshall DF, Madishetty K, Fenton RD, Condamine P, Graner A, Waugh R. Development and implementation of high-throughput SNP genotyping in barley. BMC Genomics. 2009;13:1–13.
Google Scholar
Szűcs P, Blake VC, Bhat PR, Chao S, Close TJ, Cuesta-Marcos A, Muehlbauer GJ, Ramsay L, Waugh R, Hayes PM. An Integrated Resource for Barley Linkage Map and Malting Quality QTL Alignment. Plant Genome J. 2009;2:134.
Article
Google Scholar
Lado B, Matus I, Rodríguez A, Inostroza L, Poland JA, Belzile F, del Pozo A, Quincke M, Castro M, von Zitzewitz J. Increased genomic prediction accuracy in wheat breeding through spatial adjustment of field trial data. G3 (Bethesda). 2013;3:2105–14.
Article
Google Scholar
Glaubitz JC, Casstevens TN, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE. 2014;9(2):e90346.
Article
PubMed
PubMed Central
Google Scholar
R Core Team: R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, https://www.r-project.org/ 2015.
Endelman JB. Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome J. 2011;4:250–5.
Article
Google Scholar
Yu J, Pressoir G, Briggs WH, Bi IV, Yamasaki M, Doebley JF, McMullen MD, Gaut BS, Nielsen DM, Holland JB, Kresovich S, Buckler ES. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 2006;38:203–8.
Article
CAS
PubMed
Google Scholar
Li J, Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity. 2005;95:221–27.
Article
CAS
PubMed
Google Scholar
Chengsong Z, Jianming Y. Nonmetric multidimensional scaling corrects for population structure in association mapping with different sample types. Genetics. 2009;182:875–88.
Article
Google Scholar