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Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

Lana S. Martin, Eleazar Eskin
doi: https://doi.org/10.1101/092106
Lana S. Martin
1Department of Computer Science, University of California, Los Angeles, CA, USA
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Eleazar Eskin
1Department of Computer Science, University of California, Los Angeles, CA, USA
2Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
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Abstract

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to effectively test for association while correcting for population structure is a computational and statistical challenge. Our review motivates the problem of population structure in association studies using laboratory mouse strains and how it can cause false positives associations. We then motivate mixed models in the context of unmodeled factors.

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Posted December 07, 2016.
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Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
Lana S. Martin, Eleazar Eskin
bioRxiv 092106; doi: https://doi.org/10.1101/092106
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Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
Lana S. Martin, Eleazar Eskin
bioRxiv 092106; doi: https://doi.org/10.1101/092106

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