The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–73.

Article
PubMed Central
Google Scholar

Cao J, Schneeberger K, Ossowski S, Günther T, Bender S, Fitz J, et al. Whole-genome sequencing of multiple Arabidopsis thaliana populations. Nat Genet. 2011;43:956–63.

Article
CAS
PubMed
Google Scholar

Stange M, Utz HF, Schrag TA, Melchinger AE, Würschum T. High-density genotyping: an overkill for QTL mapping? Lessons learned from a case study in maize and simulations. Theor Appl Genet. 2013;126:2563–74.

Article
CAS
PubMed
Google Scholar

Mackay TFC, Stone EA, Ayroles JF. The genetics of quantitative traits: challenges and prospects. Nat Rev Genet. 2009;10:565–77.

Article
CAS
PubMed
Google Scholar

Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, et al. The collaborative cross, a community resource for the genetic analysis of complex traits. Nat Genet. 2004;36:1133–7.

Article
CAS
PubMed
Google Scholar

Kover PX, Valdar W, Trakalo J, Scarcelli N, Ehrenreich IM, Purugganan MD, et al. A multiparent advanced generation inter-cross to fine-map quantitative traits in Arabidopsis thaliana. PLoS Genet. 2009;5, e1000551.

Article
PubMed Central
PubMed
Google Scholar

Huang BE, George AW, Forrest KL, Kilian A, Hayden MJ, Morell MK, et al. A multiparent advanced generation inter-cross population for genetic analysis in wheat. Plant Biotechnol J. 2012;10:826–39.

Article
CAS
PubMed
Google Scholar

Mackay IJ, Bansept-Basler P, Barber T, Bentley AR, Cockram J, Gosman N, et al. An eight-parent multiparent advanced generation inter-cross population for winter-sown wheat: creation, properties, and validation. G3 (Bethesda). 2014;4:1603–10.

Article
Google Scholar

McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q, et al. Genetic properties of the maize nested association mapping population. Science. 2009;325:737–40.

Article
CAS
PubMed
Google Scholar

Lehermeier C, Krämer N, Bauer E, Bauland C, Camisan C, Campo L, et al. Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics. 2014;198:3–16.

Article
PubMed Central
PubMed
Google Scholar

Churchill GA, Gatti DM, Munger SC, Svenson KL. The diversity outbred mouse population. Mamm Genome. 2012;23:713–8.

Article
PubMed Central
PubMed
Google Scholar

Gatti DM, Svenson KL, Shabalin A, Wu L-Y, Valdar W, Simecek P, et al. Quantitative trait locus mapping methods for diversity outbred mice. G3 (Bethesda). 2014;4:1623–33.

Article
Google Scholar

Rat Genome Sequencing and Mapping Consortium. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat Genet. 2013;45:767–75.

Article
Google Scholar

Collaborative Cross Consortium. The genome architecture of the collaborative cross mouse genetic reference population. Genetics. 2012;190:389–401.

Article
PubMed Central
Google Scholar

Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, et al. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nat Genet. 2006;38:879–87.

Article
CAS
PubMed
Google Scholar

Klasen JR, Piepho H-P, Stich B. QTL detection power of multi-parental RIL populations in Arabidopsis thaliana. Heredity. 2012;108:626–32.

Article
PubMed Central
CAS
PubMed
Google Scholar

Chesler EJ, Lu L, Wang J, Williams RW, Manly KF. WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nat Neurosci. 2004;7:485–6.

Article
CAS
PubMed
Google Scholar

Alvarez Prado S, López CG, Senior ML, Borrás L. The genetic architecture of maize (Zea mays L.) kernel weight determination. G3 (Bethesda). 2014;4:1611–21.

Article
Google Scholar

Rincent R, Nicolas S, Bouchet S, Altmann T, Brunel D, Revilla P, et al. Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor Appl Genet. 2014;127:2313–31.

Article
CAS
PubMed
Google Scholar

Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, et al. A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS One. 2011;6, e28334.

Article
PubMed Central
CAS
PubMed
Google Scholar

Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326:1112–5.

Article
CAS
PubMed
Google Scholar

Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.

Article
CAS
PubMed
Google Scholar

Broman KW. The genomes of recombinant inbred lines. Genetics. 2005;169:1133–46.

Article
PubMed Central
CAS
PubMed
Google Scholar

Hung H-Y, Shannon LM, Tian F, Bradbury PJ, Chen C, Flint-Garcia SA, et al. ZmCCT and the genetic basis of day-length adaptation underlying the postdomestication spread of maize. Proc Natl Acad Sci U S A. 2012;109:E1913–21.

Article
PubMed Central
CAS
PubMed
Google Scholar

Yu J, Holland JB, McMullen MD, Buckler ES. Genetic design and statistical power of nested association mapping in maize. Genetics. 2008;178:539–51.

Article
PubMed Central
PubMed
Google Scholar

Valdar W, Flint J, Mott R. Simulating the collaborative cross: power of quantitative trait loci detection and mapping resolution in large sets of recombinant inbred strains of mice. Genetics. 2006;172:1783–97.

Article
PubMed Central
CAS
PubMed
Google Scholar

Aylor DL, Valdar W, Foulds-Mathes W, Buus RJ, Verdugo RA, Baric RS, et al. Genetic analysis of complex traits in the emerging collaborative cross. Genome Res. 2011;21:1213–22.

Article
PubMed Central
CAS
PubMed
Google Scholar

Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–7.

Article
CAS
PubMed
Google Scholar

Proost S, Van Bel M, Vaneechoutte D, Van de Peer Y, Inzé D, Mueller-Roeber B, et al. PLAZA 3.0: an access point for plant comparative genomics. Nucleic Acids Res. 2015;43:D974–81.

Article
PubMed Central
PubMed
Google Scholar

Shen Q, Uknes SJ, Ho TH. Hormone response complex in a novel abscisic acid and cycloheximide-inducible barley gene. J Biol Chem. 1993;268:23652–60.

CAS
PubMed
Google Scholar

Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, et al. The genetic architecture of maize flowering time. Science. 2009;325:714–8.

Article
CAS
PubMed
Google Scholar

Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, et al. Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics. 2004;168:2169–85.

Article
PubMed Central
CAS
PubMed
Google Scholar

Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, et al. Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proc Natl Acad Sci. 2007;104:11376–81.

Article
PubMed Central
CAS
PubMed
Google Scholar

Vlăduţu C, McLaughlin J, Phillips RL. Fine mapping and characterization of linked quantitative trait loci involved in the transition of the maize apical meristem from vegetative to generative structures. Genetics. 1999;153:993–1007.

PubMed Central
PubMed
Google Scholar

Meng X, Muszynski MG, Danilevskaya ON. The FT-like ZCN8 gene functions as a floral activator and is involved in photoperiod sensitivity in maize. Plant Cell Online. 2011;23:942–60.

Article
CAS
Google Scholar

Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, et al. Adaptation of maize to temperate climates: mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus. PLoS One. 2013;8, e71377.

Article
PubMed Central
CAS
PubMed
Google Scholar

Romay MC, Millard MJ, Glaubitz JC, Peiffer JA, Swarts KL, Casstevens TM, et al. Comprehensive genotyping of the USA national maize inbred seed bank. Genome Biol. 2013;14:R55.

Article
PubMed Central
PubMed
Google Scholar

Li L, Eichten SR, Shimizu R, Petsch K, Yeh C-T, Wu W, et al. Genome-wide discovery and characterization of maize long non-coding RNAs. Genome Biol. 2014;15:R40.

Article
PubMed Central
PubMed
Google Scholar

Lai Z, Schluttenhofer CM, Bhide K, Shreve J, Thimmapuram J, Lee SY, et al. MED18 interaction with distinct transcription factors regulates multiple plant functions. Nat Commun. 2014;5.

Eichten SR, Foerster JM, de Leon N, Kai Y, Yeh C-T, Liu S, et al. B73-Mo17 near-isogenic lines demonstrate ispersed structural variation in maize. Plant Physiol. 2011;156:1679–90.

Article
PubMed Central
CAS
PubMed
Google Scholar

Frascaroli E, Schrag TA, Melchinger AE. Genetic diversity analysis of elite European maize (Zea mays L.) inbred lines using AFLP, SSR, and SNP markers reveals ascertainment bias for a subset of SNPs. Theor Appl Genet. 2013;126:133–41.

Article
PubMed
Google Scholar

Nelson OE. Non-reciprocal cross-sterility in maize. Genetics. 1952;37:101–24.

PubMed Central
CAS
PubMed
Google Scholar

Hallauer AR, Russel WA, Lamkey KR. Corn breeding. In: Sprague GF, Dudley JW, editors. Corn and corn improvement. Madison, WI: American Society of Agronomy; 1988. p. 463–563.

Google Scholar

Ogut F, Bian Y, Bradbury PJ, Holland JB. Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population. Heredity. 2015;114:552–63.

Article
PubMed Central
CAS
PubMed
Google Scholar

Ajmone-Marsan P, Monfredini G, Ludwig WF, Melchinger AE, Franceschini P, Pagnotto G, et al. In an elite cross of maize a major quantitative trait locus controls one-fourth of the genetic variation for grain yield. Theor Appl Genet. 1995;90:415–24.

CAS
Google Scholar

Bertin P, Gallais A. Genetic variation for nitrogen use efficiency in a set of recombinant inbred lines II-QTL detection and coincidences. Maydica. 2001;46:53–68.

Google Scholar

Li X, Liu X, Li M, Zhang S. Identification of quantitative trait loci for anthesis-silking interval and yield components under drought stress in Maize. Acta Bot Sin. 2003;45:852–7.

Google Scholar

Coque M, Gallais A. Genomic regions involved in response to grain yield selection at high and low nitrogen fertilization in maize. Theor Appl Genet. 2006;112:1205–20.

Article
CAS
PubMed
Google Scholar

Le-Deaux JR, Graham GI, Stuber CW. Stability of QTLs involved in heterosis in maize when mapped under several stress conditions. Maydica. 2006;51:151.

Google Scholar

Frascaroli E, Cane MA, Landi P, Pea G, Gianfranceschi L, Villa M, et al. Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines. Genetics. 2007;176:625–44.

Article
PubMed Central
CAS
PubMed
Google Scholar

Frascaroli E, Canè MA, Pè ME, Pea G, Morgante M, Landi P. QTL detection in maize testcross progenies as affected by related and unrelated testers. Theor Appl Genet Theor Angew Genet. 2009;118:993–1004.

Article
Google Scholar

Beló A, Beatty MK, Hondred D, Fengler KA, Li B, Rafalski A. Allelic genome structural variations in maize detected by array comparative genome hybridization. Theor Appl Genet Theor Angew Genet. 2010;120:355–67.

Article
Google Scholar

Springer NM, Ying K, Fu Y, Ji T, Yeh C-T, Jia Y, et al. Maize inbreds exhibit high levels of copy number variation (CNV) and presence/absence variation (PAV) in genome content. PLoS Genet. 2009;5, e1000734.

Article
PubMed Central
PubMed
Google Scholar

Cong B, Barrero LS, Tanksley SD. Regulatory change in YABBY-like transcription factor led to evolution of extreme fruit size during tomato domestication. Nat Genet. 2008;40:800–4.

Article
CAS
PubMed
Google Scholar

Chen C-NN, Chen H-R, Yeh S-Y, Vittore G, Ho T-HD. Autophagy is enhanced and floral development is impaired in AtHVA22d RNA interference Arabidopsis. Plant Physiol. 2009;149:1679–89.

Article
PubMed Central
CAS
PubMed
Google Scholar

Hobbs DH, Flintham JE, Hills MJ. Genetic control of storage Oil synthesis in seeds of Arabidopsis. Plant Physiol. 2004;136:3341–9.

Article
PubMed Central
CAS
PubMed
Google Scholar

Chardon F, Hourcade D, Combes V, Charcosset A. Mapping of a spontaneous mutation for early flowering time in maize highlights contrasting allelic series at two-linked QTL on chromosome 8. Theor Appl Genet. 2005;112:1–11.

Article
CAS
PubMed
Google Scholar

Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, Price AH, et al. Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun. 2011;2:467.

Article
PubMed Central
PubMed
Google Scholar

Atwell S, Huang YS, Vilhjálmsson BJ, Willems G, Horton M, Li Y, et al. Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines. Nature. 2010;465:627–31.

Article
PubMed Central
CAS
PubMed
Google Scholar

Castelletti S, Tuberosa R, Pindo M, Salvi S. A MITE transposon insertion is associated with differential methylation at the maize flowering time QTL Vgt1. G3 (Bethesda). 2014;4:805–12.

Article
CAS
Google Scholar

Wang KC, Chang HY. Molecular mechanisms of long noncoding RNAs. Mol Cell. 2011;43:904–14.

Article
PubMed Central
CAS
PubMed
Google Scholar

Ietswaart R, Wu Z, Dean C. Flowering time control: another window to the connection between antisense RNA and chromatin. Trends Genet. 2012;28:445–53.

Article
CAS
PubMed
Google Scholar

Swiezewski S, Liu F, Magusin A, Dean C. Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target. Nature. 2009;462:799–802.

Article
CAS
PubMed
Google Scholar

Zheng Z, Guan H, Leal F, Grey PH, Oppenheimer DG. Mediator Subunit18 controls flowering time and floral organ identity in Arabidopsis. PLoS One. 2013;8, e53924.

Article
PubMed Central
CAS
PubMed
Google Scholar

Li J, Chory J. A putative leucine-rich repeat receptor kinase involved in brassinosteroid signal transduction. Cell. 1997;90:929–38.

Article
CAS
PubMed
Google Scholar

Domagalska MA, Schomburg FM, Amasino RM, Vierstra RD, Nagy F, Davis SJ. Attenuation of brassinosteroid signaling enhances FLC expression and delays flowering. Development. 2007;134:2841–50.

Article
CAS
PubMed
Google Scholar

Stokes D, Fraser F, Morgan C, O’Neill CM, Dreos R, Magusin A, et al. An association transcriptomics approach to the prediction of hybrid performance. Mol Breed. 2010;26:91–106.

Article
CAS
Google Scholar

Thiemann A, Fu J, Schrag TA, Melchinger AE, Frisch M, Scholten S. Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L. Theor Appl Genet. 2010;120:401–13.

Article
CAS
PubMed
Google Scholar

Harper AL, Trick M, Higgins J, Fraser F, Clissold L, Wells R, et al. Associative transcriptomics of traits in the polyploid crop species Brassica napus. Nat Biotechnol. 2012;30:798–802.

Article
CAS
PubMed
Google Scholar

Cheng R, Parker CC, Abney M, Palmer AA. Practical considerations regarding the use of genotype and pedigree data to model relatedness in the context of genome-wide association studies. G3 (Bethesda). 2013;3:1861–7.

Article
Google Scholar

Zhou X, Carbonetto P, Stephens M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 2013;9, e1003264.

Article
PubMed Central
CAS
PubMed
Google Scholar

Liang Z, Zhang K, Chen K, Gao C. Targeted Mutagenesis in Zea mays using TALENs and the CRISPR/Cas System. J Genet Genomics. 2014;41:63–8.

Article
CAS
PubMed
Google Scholar

Yuan Z, Zou F, Liu Y. Bayesian multiple quantitative trait loci mapping for recombinant inbred intercrosses. Genetics. 2011;188:189–95.

Article
PubMed Central
CAS
PubMed
Google Scholar

Liu K, Goodman M, Muse S, Smith JS, Buckler E, Doebley J. Genetic structure and diversity among maize inbred lines as inferred from DNA microsatellites. Genetics. 2003;165:2117–28.

PubMed Central
CAS
PubMed
Google Scholar

Cochran WG, Cox GM. Experimental Designs. 2nd ed. New York: Wiley; 1992.

Google Scholar

Wald A. Tests of statistical hypotheses concerning several parameters when the number of observations is large. Trans Am Math Soc. 1943;54:426–82.

Article
Google Scholar

Piepho H-P. Data transformation in statistical analysis of field trials with changing treatment variance. Agron J. 2009;101:865.

Article
Google Scholar

Hallauer AR, Carena MJ, Filho JBM. Quantitative genetics in maize breeding. New York: Springer; 2010.

Google Scholar

R: The R Project for Statistical Computing. Available at: www.R-project.org.

Fraley C, Raftery AE. MCLUST: software for model-based cluster analysis. J Classif. 1999;297–306.

Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25:1754–60.

Article
PubMed Central
CAS
PubMed
Google Scholar

Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. 2011;6, e19379.

Article
PubMed Central
CAS
PubMed
Google Scholar

Ensemble Genomes. Available at: ftp://ftp.ensemblgenomes.org/.

Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2011;39(Database issue):D19–21.

Article
PubMed Central
CAS
PubMed
Google Scholar

Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.

Article
Google Scholar

Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

Article
PubMed Central
PubMed
Google Scholar

McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.

Article
PubMed Central
CAS
PubMed
Google Scholar

Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy‐Moonshine A, et al. From FastQ data to high‐confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinforma. 2013;11:11.10.

Google Scholar

DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–8.

Article
PubMed Central
CAS
PubMed
Google Scholar

Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5, e1000529.

Article
PubMed Central
PubMed
Google Scholar

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

Article
PubMed Central
CAS
PubMed
Google Scholar

Rymen B, Fiorani F, Kartal F, Vandepoele K, Inzé D, Beemster GTS. Cold nights impair leaf growth and cell cycle progression in maize through transcriptional changes of cell cycle genes. Plant Physiol. 2007;143:1429–38.

Article
PubMed Central
CAS
PubMed
Google Scholar

Clauw P, Coppens F, Beuf KD, Dhondt S, Daele TV, Maleux K, et al. Leaf responses to mild drought stress in natural variants of Arabidopsis. Plant Physiol. 2015;167:800–16.

Article
PubMed Central
CAS
PubMed
Google Scholar

FASTX-Toolkit. Available at: http://hannonlab.cshl.edu/fastx_toolkit/.

Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics. 2010;26:873–81.

Article
PubMed Central
CAS
PubMed
Google Scholar

Anders S, Pyl PT, Huber W. HTSeq - a python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9.

Article
PubMed Central
PubMed
Google Scholar

Henning C. Fpc: Flexible Procedures for Clustering. 2010. Available at: http://www.homepages.ucl.ac.uk/~ucakche/.

Google Scholar

Dabney A, Storey JD, Warnes GR. Qvalue: Q-value estimation for false discovery rate control. Available at: citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.367.3331&rep=rep1&type=pdf.

Clayton D. snpStats: SnpMatrix and XSnpMatrix classes and methods. 2013. Available at: www.bioconductor.org/packages//2.13/bioc/manuals/snpStats/man/snpStats.pdf.

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinforma Oxf Engl. 2012;28:3326–8.

Article
CAS
Google Scholar

Jombart T, Ahmed I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. 2011;27:3070–1.

Article
PubMed Central
CAS
PubMed
Google Scholar

Paradis E, Claude J, Strimmer K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004;20:289–90.

Article
CAS
PubMed
Google Scholar

Shin J-H, Blay S, McNeney B, Graham J. LDheatmap: an R function for graphical display of pairwise linkage disequilibria between single nucleotide polymorphisms. J Stat Softw. 2006;16:3.

Article
Google Scholar

Churchill GA. Stochastic models for heterogeneous DNA sequences. Bull Math Biol. 1989;51:79–94.

Article
CAS
PubMed
Google Scholar

Rabiner LR. A tutorial on hidden Markov models and selected applications in speech recognition. Proceed IEEE. 1989;77:257–86.

Article
Google Scholar

Cheng R, Abney M, Palmer AA, Skol AD. QTLRel: an R package for genome-wide association studies in which relatedness is a concern. BMC Genet. 2011;12:66.

Article
PubMed Central
PubMed
Google Scholar

Gramene. Available at: http://www.gramene.org.