Assessing the prospects of genome-wide association studies performed in inbred mice.

Mammalian Genome : Official Journal of the International Mammalian Genome Society
Wan-Lin SuEric E Schadt

Abstract

The remarkable success in mapping genes linked to a number of disease traits using genome-wide association studies (GWAS) in human cohorts has renewed interest in applying this same technique in model organisms such as inbred laboratory mice. Unlike humans, however, the limited genetic diversity in the ancestry of laboratory mice combined with selection pressure over the past decades have yielded an intricate population genetic structure that can complicate the results obtained from association studies. This problem is further exacerbated by the small number of strains typically used in such studies where multiple spurious associations arise as a result of random chance. We sought to empirically assess the viability of GWAS in inbred mice using hundreds of expression traits for which the true location of the expression quantitative trait locus was known a priori. We then measured transcript abundance levels for these expression traits in 16 classical and 3 wild-derived inbred strains and carried out a genome-wide association scan, demonstrating the low statistical power of such studies and empirically estimating the large extent to which allelic association of transcripts gives rise to spurious associations. We provide evidence...Continue Reading

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Citations

Sep 3, 2010·Genetica·Hong Li, Hongwen Deng
Sep 3, 2010·Proceedings of the National Academy of Sciences of the United States of America·Jennifer ListgartenDavid Heckerman
Nov 22, 2011·Nucleic Acids Research·Terry P MaddatuMolly A Bogue
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May 3, 2011·PloS One·Irena RadovanovicPhilippe Gros
Nov 29, 2011·Trends in Genetics : TIG·Ming ZhengGary Peltz
Feb 18, 2014·British Journal of Clinical Pharmacology·Yuan JiRichard M Weinshilboum
Jun 1, 2012·Current Opinion in Anaesthesiology·Ming ZhengGary Peltz
Dec 5, 2019·The Journal of the Acoustical Society of America·Royce E CliffordKevin K Ohlemiller

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