Literature DB >> 16646805

Increasing power for tests of genetic association in the presence of phenotype and/or genotype error by use of double-sampling.

Derek Gordon1, Yaning Yang, Chad Haynes, Stephen J Finch, Nancy R Mendell, Abraham M Brown, Vahram Haroutunian.   

Abstract

Phenotype and/or genotype misclassification can: significantly increase type II error probabilities for genetic case/control association, causing decrease in statistical power; and produce inaccurate estimates of population frequency parameters. We present a method, the likelihood ratio test allowing for errors (LRTae) that incorporates double-sample information for phenotypes and/or genotypes on a sub-sample of cases/controls. Population frequency parameters and misclassification probabilities are determined using a double-sample procedure as implemented in the Expectation-Maximization (EM) method. We perform null simulations assuming a SNP marker or a 4-allele (multi-allele) marker locus. To compare our method with the standard method that makes no adjustment for errors (LRTstd), we perform power simulations using a 2/k factorial design with high and low settings of: case/control samples, phenotype/genotype costs, double-sampled phenotypes/genotypes costs, phenotype/genotype error, and proportions of double-sampled individuals. All power simulations are performed fixing equal costs for the LRTstd and LRTae methods. We also consider case/control ApoE genotype data for an actual Alzheimer's study. The LRTae method maintains correct type I error proportions for all null simulations and all significance level thresholds (10%, 5%, 1%). LRTae average estimates of population frequencies and misclassification probabilities are equal to the true values, with variances of 10e-7 to 10e-8. For power simulations, the median power difference LRTae-LRTstd at the 5% significance level is 0.06 for multi-allele data and 0.01 for SNP data. For the ApoE data example, the LRTae and LRTstd p-values are 5.8 x 10e-5 and 1.6 x 10e-3, respectively. The increase in significance is due to adjustment in the LRTae for misclassification of the most commonly reported risk allele. We have developed freely available software that performs our LRTae statistic.

Entities:  

Year:  2004        PMID: 16646805     DOI: 10.2202/1544-6115.1085

Source DB:  PubMed          Journal:  Stat Appl Genet Mol Biol        ISSN: 1544-6115


  25 in total

Review 1.  Recent developments in genomewide association scans: a workshop summary and review.

Authors:  Duncan C Thomas; Robert W Haile; David Duggan
Journal:  Am J Hum Genet       Date:  2005-08-01       Impact factor: 11.025

Review 2.  Factors affecting statistical power in the detection of genetic association.

Authors:  Derek Gordon; Stephen J Finch
Journal:  J Clin Invest       Date:  2005-06       Impact factor: 14.808

3.  Optimal two-stage design for case-control association analysis incorporating genotyping errors.

Authors:  Y Zuo; G Zou; J Wang; H Zhao; H Liang
Journal:  Ann Hum Genet       Date:  2008-01-23       Impact factor: 1.670

4.  Incorporating duplicate genotype data into linear trend tests of genetic association: methods and cost-effectiveness.

Authors:  Bryce Borchers; Marshall Brown; Brian McLellan; Airat Bekmetjev; Nathan L Tintle
Journal:  Stat Appl Genet Mol Biol       Date:  2009-05-05

5.  Detecting and estimating contamination of human DNA samples in sequencing and array-based genotype data.

Authors:  Goo Jun; Matthew Flickinger; Kurt N Hetrick; Jane M Romm; Kimberly F Doheny; Gonçalo R Abecasis; Michael Boehnke; Hyun Min Kang
Journal:  Am J Hum Genet       Date:  2012-10-25       Impact factor: 11.025

Review 6.  Informatics and machine learning to define the phenotype.

Authors:  Anna Okula Basile; Marylyn DeRiggi Ritchie
Journal:  Expert Rev Mol Diagn       Date:  2018-02-16       Impact factor: 5.225

7.  Genotyping error detection in samples of unrelated individuals without replicate genotyping.

Authors:  Nianjun Liu; Dabao Zhang; Hongyu Zhao
Journal:  Hum Hered       Date:  2008-12-15       Impact factor: 0.444

Review 8.  Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies.

Authors:  Duncan Thomas
Journal:  Annu Rev Public Health       Date:  2010       Impact factor: 21.981

9.  Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

Authors:  Wonkuk Kim; Douglas Londono; Lisheng Zhou; Jinchuan Xing; Alejandro Q Nato; Anthony Musolf; Tara C Matise; Stephen J Finch; Derek Gordon
Journal:  Hum Hered       Date:  2013-04-11       Impact factor: 0.444

10.  Detecting new neurodegenerative disease genes: does phenotype accuracy limit the horizon?

Authors:  David C Samuels; David J Burn; Patrick F Chinnery
Journal:  Trends Genet       Date:  2009-10-12       Impact factor: 11.639

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