Genome wide association analysis in a mouse advanced intercross line
Natalia M. Gonzales,
Jungkyun Seo,
Ana I. Hernandez Cordero,
Celine L. St. Pierre,
Jennifer S. Gregory,
Margaret G. Distler,
Mark Abney,
Stefan Canzar,
Arimantas Lionikas and
Abraham A. Palmer ()
Additional contact information
Natalia M. Gonzales: University of Chicago
Jungkyun Seo: Duke University
Ana I. Hernandez Cordero: University of Aberdeen
Celine L. St. Pierre: Washington University School of Medicine
Jennifer S. Gregory: University of Aberdeen
Margaret G. Distler: University of California Los Angeles
Mark Abney: University of Chicago
Stefan Canzar: Ludwig-Maximilians-Universität München
Arimantas Lionikas: University of Aberdeen
Abraham A. Palmer: University of California San Diego
Nature Communications, 2018, vol. 9, issue 1, 1-12
Abstract:
Abstract The LG/J x SM/J advanced intercross line of mice (LG x SM AIL) is a multigenerational outbred population. High minor allele frequencies, a simple genetic background, and the fully sequenced LG and SM genomes make it a powerful population for genome-wide association studies. Here we use 1,063 AIL mice to identify 126 significant associations for 50 traits relevant to human health and disease. We also identify thousands of cis- and trans-eQTLs in the hippocampus, striatum, and prefrontal cortex of ~200 mice. We replicate an association between locomotor activity and Csmd1, which we identified in an earlier generation of this AIL, and show that Csmd1 mutant mice recapitulate the locomotor phenotype. Our results demonstrate the utility of the LG x SM AIL as a mapping population, identify numerous novel associations, and shed light on the genetic architecture of mammalian behavior.
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.nature.com/articles/s41467-018-07642-8 Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07642-8
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-018-07642-8
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().