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STATISTICAL CORRECTION OF THE WINNER’S CURSE EXPLAINS REPLICATION VARIABILITY IN QUANTITATIVE TRAIT GENOME-WIDE ASSOCIATION STUDIES

Autoři: Palmer Cameron1,2, Pe’er Itsik2

Autoři - působiště: 1Department of Systems Biology, Columbia University Medical Center, New York, New York, United States of America, 2Department of Computer Science, Columbia University, New York, New York, United States of America

Článek: Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies. PLoS Genet 13(7): e1006916. doi:10.1371/journal.pgen.1006916
Kategorie: Research Article
Počet zobrazení článku: 6x

Specializace: genetika reprodukční medicína
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Statistical correction of the Winner’s Curse explains replication variability in quantitative trait genome-wide association studies

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Summary

The majority of associations between common genetic variation and human traits come from genome-wide association studies, which have analyzed millions of single-nucleotide polymorphisms in millions of samples. These kinds of studies pose serious statistical challenges to discovering new associations. Finite resources restrict the number of candidate associations that can brought forward into validation samples, introducing the need for a significance threshold. This threshold creates a phenomenon called the Winner’s Curse, in which candidate associations close to the discovery threshold are more likely to have biased overestimates of the variant’s true association in the sampled population. We survey all human quantitative trait association studies that validated at least one signal. We find the majority of these studies do not publish sufficient information to actually support their claims of replication. For studies that did, we computationally correct the Winner’s Curse and evaluate replication performance. While all variants combined replicate significantly less than expected, we find that the subset of studies that (1) perform both discovery and replication in samples of the same ancestry; and (2) report accurate per-variant sample sizes, replicate as expected. This study provides strong, rigorous evidence for the broad reliability of genome-wide association studies. We furthermore provide a model for more efficient selection of variants as candidates for replication, as selecting variants using cursed discovery data enriches for variants with little real evidence for trait association.

 

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