NDLI: A comprehensive evaluation of collapsing methods using simulated and real data: excellent annotation of functionality and large sample sizes required

Content Provider	PubMed Central
Author	Dering, Carmen König, Inke R. Ramsey, Laura B. Relling, Mary V. Yang, Wenjian Ziegler, Andreas
Copyright Year	2014
Abstract	The advent of next generation sequencing (NGS) technologies enabled the investigation of the rare variant-common disease hypothesis in unrelated individuals, even on the genome-wide level. Analysis of this hypothesis requires tailored statistical methods as single marker tests fail on rare variants. An entire class of statistical methods collapses rare variants from a genomic region of interest (ROI), thereby aggregating rare variants. In an extensive simulation study using data from the Genetic Analysis Workshop 17 we compared the performance of 15 collapsing methods by means of a variety of pre-defined ROIs regarding minor allele frequency thresholds and functionality. Findings of the simulation study were additionally confirmed by a real data set investigating the association between methotrexate clearance and the SLCO1B1 gene in patients with acute lymphoblastic leukemia. Our analyses showed substantially inflated type I error levels for many of the proposed collapsing methods. Only four approaches yielded valid type I errors in all considered scenarios. None of the statistical tests was able to detect true associations over a substantial proportion of replicates in the simulated data. Detailed annotation of functionality of variants is crucial to detect true associations. These findings were confirmed in the analysis of the real data. Recent theoretical work showed that large power is achieved in gene-based analyses only if large sample sizes are available and a substantial proportion of causing rare variants is present in the gene-based analysis. Many of the investigated statistical approaches use permutation requiring high computational cost. There is a clear need for valid, powerful and fast to calculate test statistics for studies investigating rare variants.
Related Links	http://dx.doi.org/10.3389/fgene.2014.00323
Starting Page	323
File Format	PDF
ISSN	16648021
e-ISSN	16648021
Journal	Frontiers in Genetics
Volume Number	5
Language	English
Publisher	Frontiers Media S.A.
Publisher Date	2014-09-01
Access Restriction	Open
Rights Holder	Frontiers Media S.A.
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Genetics Molecular Medicine Genetics (clinical)

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