Evidence map›Paper›PMID 28334355›Full record

ArticleBioinformatics (Oxford, England)2017

A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests.

Maggie Haitian Wang, Haoyi Weng, Rui Sun, Jack Lee, William Ka Kei Wu, Ka Chun Chong, Benny Chung-Ying Zee

Abstract read
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Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Maggie Haitian WangDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.
Haoyi WengDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.
Rui SunDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.
Jack LeeDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.
William Ka Kei WuDepartment of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR.
Ka Chun ChongDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.
Benny Chung-Ying ZeeDivision of Biostatistics and Centre for Clinical Research and Biostatistics, JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong SAR.

Funding

GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

motivationIncreasing amounts of whole exome or genome sequencing data present the challenge of analysing rare variants with extremely small minor allele frequencies. Various statistical tests have been proposed, which are specifically configured to increase power for rare variants by conducting the test within a certain bin, such as a gene or a pathway. However, a gene may contain from several to thousands of markers, and not all of them are related to the phenotype. Combining functional and non-functional variants in an arbitrary genomic region could impair the testing power.

resultsWe propose a Zoom-Focus algorithm (ZFA) to locate the optimal testing region within a given genomic region. It can be applied as a wrapper function in existing rare variant association tests to increase testing power. The algorithm consists of two steps. In the first step, Zooming, a given genomic region is partitioned by an order of two, and the best partition is located. In the second step, Focusing, the boundaries of the zoomed region are refined. Simulation studies showed that ZFA substantially increased the statistical power of rare variants' tests, including the SKAT, SKAT-O, burden test and the W-test. The algorithm was applied on real exome sequencing data of hypertensive disorder, and identified biologically relevant genetic markers to metabolic disorders that were undetectable by a gene-based method. The proposed algorithm is an efficient and powerful tool to enhance the power of association study for whole exome or genome sequencing data. AVAILABILITY AND IMPLEMENTATION: The ZFA software is available at: http://www2.ccrb.cuhk.edu.hk/statgene/software.html. CONTACT: maggiew@cuhk.edu.hk or bzee@cuhk.edu.hk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genetic VariationSoftwareAlgorithmsComputer SimulationExomeGene FrequencyGenetic Association StudiesGenetic MarkersGenetic Predisposition to DiseaseGenomicsHumansHypertensionSequence Analysis, DNAGenetic Markers

Identifiers

PMID28334355
PMCPMC5860107

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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.