R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental crosses. It is implemented as an add-on package for the freely available and widely used statistical language/software R (see the R project homepage). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression. R/qtl is distributed as source code for unix or compiled code for Windows or Mac. R/qtl is released under the GNU General Public License. To download the software, you must agree to the terms in that download.

References in zbMATH (referenced in 13 articles )

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  1. Han Yu; Janhavi Moharil; Rachael Hageman Blair: BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks (2020) not zbMATH
  2. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  3. Pariya Behrouzi, Ernst C. Wit: netgwas: An R Package for Network-Based Genome-Wide Association Studies (2017) arXiv
  4. Dou, Xiaoling; Kuriki, Satoshi; Maeno, Akiteru; Takada, Toyoyuki; Shiroishi, Toshihiko: Influence analysis in quantitative trait loci detection (2014)
  5. Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata: Detection of epistatic effects with logic regression and a classical linear regression model (2014)
  6. Sun, Wei; Li, Lexin: Multiple loci mapping via model-free variable selection (2012)
  7. Andrey A. Shabalin: Matrix eQTL: Ultra fast eQTL analysis via large matrix operations (2011) arXiv
  8. Huang, Hanwen; Zhou, Haibo; Cheng, Fuxia; Hoeschele, Ina; Zou, Fei: Gaussian process based Bayesian semiparametric quantitative trait loci interval mapping (2010)
  9. Neto, Elias Chaibub; Keller, Mark P.; Attie, Alan D.; Yandell, Brian S.: Causal graphical models in systems genetics: a unified framework for joint inference of causal network and genetic architecture for correlated phenotypes (2010)
  10. Sun, Wei; Wright, Fred A.: A geometric interpretation of the permutation (p)-value and its application in eQTL studies (2010)
  11. Hu, Cheng-Cheng; Ye, Xiu-Zi; Zhang, Yin; Yu, Rong-Dong; Yang, Jian; Zhu, Jun: 3d graphical visualization of the genetic architectures underlying complex traits in multiple environments (2007)
  12. Kendziorski, C. M.; Chen, M.; Yuan, M.; Lan, H.; Attie, A. D.: Statistical methods for expression quantitative trait loci (eQTL) mapping (2006)
  13. Liu, Mengling; Lu, Wenbin; Shao, Yongzhao: Interval mapping of quantitative trait loci for time-to-event data with the proportional hazards mixture cure model (2006)