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Polygenic scores via penalized regression on summary statistics

View ORCID ProfileTimothy Shin Heng Mak, View ORCID ProfileRobert Milan Porsch, View ORCID ProfileShing Wan Choi, Xueya Zhou, View ORCID ProfilePak Chung Sham
doi: https://doi.org/10.1101/058214
Timothy Shin Heng Mak
1Centre for Genomic Sciences, University of Hong Kong
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Robert Milan Porsch
2Department of Psychiatry, University of Hong Kong
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Shing Wan Choi
2Department of Psychiatry, University of Hong Kong
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Xueya Zhou
2Department of Psychiatry, University of Hong Kong
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Pak Chung Sham
1Centre for Genomic Sciences, University of Hong Kong
2Department of Psychiatry, University of Hong Kong
3State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong
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Abstract

Polygenic scores (PGS) summarize the genetic contribution of a person’s genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating polygenic scores have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can make use of LD information available elsewhere to supplement such analyses. To answer this question we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and p-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred.

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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-ND 4.0 International license.
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Posted March 22, 2017.
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Polygenic scores via penalized regression on summary statistics
Timothy Shin Heng Mak, Robert Milan Porsch, Shing Wan Choi, Xueya Zhou, Pak Chung Sham
bioRxiv 058214; doi: https://doi.org/10.1101/058214
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Polygenic scores via penalized regression on summary statistics
Timothy Shin Heng Mak, Robert Milan Porsch, Shing Wan Choi, Xueya Zhou, Pak Chung Sham
bioRxiv 058214; doi: https://doi.org/10.1101/058214

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