Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals

Andrey Ziyatdinov, Miquel Vázquez-Santiago, Helena Brunel, Angel Martinez-Perez, Hugues Aschard, Jose Manuel Soria
doi: https://doi.org/10.1101/139816
Andrey Ziyatdinov
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: ziyatdinov@hsph.harvard.edu
Miquel Vázquez-Santiago
2Unitat de Genòmica de Malalties Complexes, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
3Unitat d’Hemostàsia i Trombosi, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Helena Brunel
2Unitat de Genòmica de Malalties Complexes, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Angel Martinez-Perez
2Unitat de Genòmica de Malalties Complexes, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hugues Aschard
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
4Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jose Manuel Soria
2Unitat de Genòmica de Malalties Complexes, Institut d’Investigació Biomèdica Sant Pau (IIB-Sant Pau), Barcelona, Spain
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted August 31, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals
Andrey Ziyatdinov, Miquel Vázquez-Santiago, Helena Brunel, Angel Martinez-Perez, Hugues Aschard, Jose Manuel Soria
bioRxiv 139816; doi: https://doi.org/10.1101/139816
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals
Andrey Ziyatdinov, Miquel Vázquez-Santiago, Helena Brunel, Angel Martinez-Perez, Hugues Aschard, Jose Manuel Soria
bioRxiv 139816; doi: https://doi.org/10.1101/139816

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Genetics
  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3586)
  • Biochemistry (7545)
  • Bioengineering (5495)
  • Bioinformatics (20732)
  • Biophysics (10294)
  • Cancer Biology (7951)
  • Cell Biology (11611)
  • Clinical Trials (138)
  • Developmental Biology (6586)
  • Ecology (10168)
  • Epidemiology (2065)
  • Evolutionary Biology (13580)
  • Genetics (9521)
  • Genomics (12817)
  • Immunology (7906)
  • Microbiology (19503)
  • Molecular Biology (7641)
  • Neuroscience (41982)
  • Paleontology (307)
  • Pathology (1254)
  • Pharmacology and Toxicology (2192)
  • Physiology (3259)
  • Plant Biology (7025)
  • Scientific Communication and Education (1294)
  • Synthetic Biology (1947)
  • Systems Biology (5419)
  • Zoology (1113)