No retraction, correction or concern found when last checked on Oct 30, 2026.
lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals
Andrey Ziyatdinov, Miquel Vázquez-Santiago, Helena Brunel and 3 more
BMC Bioinformatics · 2018 · CC BY
Abstract
Background. Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software.
Results. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project.
Conclusions. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl.
Electronic supplementary material. The online version of this article (10.1186/s12859-018-2057-x) contains supplementary material, which is available to authorized users.
References 21
- 1.
Lynch M, Walsh B, et al. Genetics and analysis of quantitative traits, vol 1. 1998.
Not in our library
- 2.
Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Human Genet. 1998;62(5):1198-211.
Not in our library10.1086/301844
- 3.
Kang HM, Zaitlen NA, Wade CM, Kirby A, Heckerman D, Daly MJ, Eskin E. Efficient control of population structure in model organism association mapping. Genetics. 2008;178(3):1709-23.
Not in our library10.1534/genetics.107.080101
- 4.
Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL. Advantages and pitfalls in the application of mixed-model association methods. Nat Genet. 2014;46(2):100-6.
Not in our library10.1038/ng.2876
- 5.
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011;8(10):833-7.
Not in our library10.1038/nmeth.1681
- 6.
Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821-4.
Not in our library10.1038/ng.2310
- 7.
Blangero J, Diego VP, Dyer TD, Almeida M, Peralta J, Kent Jr JW, Williams JT, Almasy L, Göring HHH. A kernel of truth: statistical advances in polygenic variance component models for complex human pedigrees. Adv Genet. 2013;81:1.
Not in our library10.1016/b978-0-12-407677-8.00001-4
- 8.
Gilmour AR, Gogel BJ, Cullis BR, Thompson R, Butler D, et al. ASReml user guide release 3.0. 2009.
Not in our library
- 9.
Loh P-R, Bhatia G, Gusev A, Finucane HK, Bulik-Sullivan BK, Pollack SJ, de Candia TR, Lee SH, Wray NR, Kendler KS, et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat Genet. 2015;47(12):1385.
Not in our library10.1038/ng.3431
- 10.
- 11.
Blangero J. Statistical genetic approaches to human adaptability. Hum Biol. 2009;81(5):523-46.
Not in our library10.3378/027.081.0603
- 12.
Perdry H, Dandine-Roulland C. Gaston: Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models. 2017. https://CRAN.R-project.org/package=gaston. R package version 1.5.
Not in our library
- 13.
Vazquez AI, Bates DM, Rosa GJM, Gianola D, Weigel KA. Technical note: an r package for fitting generalized linear mixed models in animal breeding. J Anim Sci. 2010;88(2):497-504.
Not in our library10.2527/jas.2009-1952
- 14.
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1):1-48.
Not in our library10.18637/jss.v067.i01
- 15.
Scheipl F, Greven S, Kuechenhoff H. Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Comput Stat Data Anal. 2008;52(7):3283-99.
Not in our library10.1016/j.csda.2007.10.022
- 16.
Kuznetsova A, Bruun Brockhoff P, Haubo Bojesen Christensen R. lmerTest: Tests in Linear Mixed Effects Models. 2016. https://CRAN.R-project.org/package=lmerTest. R package version 2.0-33.
Not in our library
- 17.
Harville DA, Callanan TP. Computational aspects of likelihood-based inference for variance components. In: Advances in statistical methods for genetic improvement of livestock. Springer: 1990. p. 136–76.
Not in our library
- 18.
Weihong Tang, Schwienbacher C, Lopez LM, Ben-Shlomo Y, Oudot-Mellakh T, Johnson AD, Samani NJ, Basu S, Gögele M, Davies G, et al. Genetic associations for activated partial thromboplastin time and prothrombin time, their gene expression profiles, and risk of coronary artery disease. Am J Hum Genet. 2012;91(1):152-62.
Not in our library10.1016/j.ajhg.2012.05.009
- 19.
Andrey Ziyatdinov HelenaBrunel. Alexandre Perera, and Jose Manuel Soria. solarius: an R interface to SOLAR for variance component analysis in pedigrees. Bioinformatics. 2016;32(12):1901-2.
Not in our library10.1093/bioinformatics/btw080
- 20.
Therneau TM. coxme: Mixed Effects Cox Models. 2015. https://CRAN.R-project.org/package=coxme. R package version 2.2-5.
Not in our library
- 21.
Github. https://github.com/lme4/lme4. Last accessed 27 Jan 2017.
Not in our library
Cited by papers we hold 3
- Mexican Biobank advances population and medical genomics of diverse ancestries · 2023No notice
- Identification of circulating metabolites associated with wooden breast and white striping. · 2022Not checked
- Stool and Ruminal Microbiome Components Associated With Methane Emission and Feed Efficiency in Nelore Beef Cattle · 2022Not checked
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.