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Quantifying polygenic effects in genome-wide association studies using generalized estimating equations

Julian Hecker, Dmitry Prokopenko, Christoph Lange, Heide Löhlein Fier
doi: https://doi.org/10.1101/032854
Julian Hecker
1Institute of Genomic Mathematics, Bonn, Germany
2Insitute of Human Genetics, Bonn, Germany
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Dmitry Prokopenko
1Institute of Genomic Mathematics, Bonn, Germany
2Insitute of Human Genetics, Bonn, Germany
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Christoph Lange
1Institute of Genomic Mathematics, Bonn, Germany
3Harvard School of Public Health, Boston, USA
4Brigham and Women’s Hospital, Boston, USA.
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Heide Löhlein Fier
1Institute of Genomic Mathematics, Bonn, Germany
2Insitute of Human Genetics, Bonn, Germany
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1 Abstract

Recently, LD Score regression1 has been proposed as a computationally fast method to contrast confounding biases with polygenicity and to quantify their contribution to the inflation of test statistics in GWAS.

In this communication, we extend the LD Score regression approach by applying the generalized estimation equations (GEE) framework, which is capable of incorporating more external information from reference panels about the correlation structure of test statistics. We apply our GEE approach and LD Score regression to simulated and real data to compare their performance.

We show that our proposed methodology obtains more efficient estimates while preserving the robustness and desired properties of LD Score regression.

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Posted November 25, 2015.
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Quantifying polygenic effects in genome-wide association studies using generalized estimating equations
Julian Hecker, Dmitry Prokopenko, Christoph Lange, Heide Löhlein Fier
bioRxiv 032854; doi: https://doi.org/10.1101/032854
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Quantifying polygenic effects in genome-wide association studies using generalized estimating equations
Julian Hecker, Dmitry Prokopenko, Christoph Lange, Heide Löhlein Fier
bioRxiv 032854; doi: https://doi.org/10.1101/032854

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