Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association
| العنوان: | Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association |
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| المؤلفون: | Michael O'Connell, Alden Green, Jason Westra, Jaron Arbet, Nathan L. Tintle, Alessandra M. Valcarcel, Kelsey Grinde |
| المصدر: | Frontiers in Genetics, Vol 8 (2017) Frontiers in Genetics |
| بيانات النشر: | Frontiers Media S.A., 2017. |
| سنة النشر: | 2017 |
| مصطلحات موضوعية: | 0301 basic medicine, Mean squared error, lcsh:QH426-470, Computer science, Inference, Locus (genetics), computer.software_genre, Statistical power, 03 medical and health sciences, winner's curse, Winner's curse, Post-hoc analysis, Statistics, Genetics, Gene, burden test, Genetics (clinical), Original Research, SKAT, Genetic architecture, lcsh:Genetics, 030104 developmental biology, Molecular Medicine, next-generation sequencing, Data mining, computer, case-control |
| الوصف: | To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as “winner's curse.” We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10−6) and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures. |
| اللغة: | English |
| تدمد: | 1664-8021 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92b5e2353a4d365b6b0f8d61451b6e18 http://journal.frontiersin.org/article/10.3389/fgene.2017.00117/full |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....92b5e2353a4d365b6b0f8d61451b6e18 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 16648021 |
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