Evidence map›Paper›PMID 27605192›Full record

ArticleMicroarrays (Basel, Switzerland)2013

Kernel-Based Aggregation of Marker-Level Genetic Association Tests Involving Copy-Number Variation.

Yinglei Li, Patrick Breheny

Abstract read
In one paragraph

Article in Microarrays (Basel, Switzerland), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yinglei LiDepartment of Statistics, University of Kentucky, 311 MDS, 725 Rose Street, Lexington, KY 40536, USA. yinglei.li@uky.edu.
Patrick BrehenyDepartment of Biostatistics, University of Iowa, N336 CPHB, 105 River Street, Iowa City, IA 52242, USA. patrick-breheny@uiowa.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this pooling is unclear. In this article, we propose a kernel-based method for aggregation of marker-level tests and explore several aspects of its implementation. In addition, we explore some of the theoretical aspects of marker-level test aggregation, proposing a permutation-based approach that preserves the family-wise error rate of the testing procedure, while demonstrating that several simpler alternatives fail to do so. The empirical power of the approach is studied in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine and compares favorably with several competing approaches.

Indexed as

CNV association studyfamily-wise error ratekernel methodsmultiple testing

Identifiers

PMID27605192
PMCPMC5003460

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.