Literature DB >> 14748014

Testing for association with a case-parents design in the presence of genotyping errors.

Richard W Morris1, Norman L Kaplan.   

Abstract

Genotyping errors can create a problem for the analysis of case-parents data because some families will exhibit genotypes that are inconsistent with Mendelian inheritance. The problem with correcting Mendelian inconsistent genotype errors by regenotyping or removing families in which they occur is that the remaining unidentified genotype errors can produce excess type I (false positive) error for some family-based tests for association. We address this problem by developing a likelihood ratio test (LRT) for association in a case-parents design that incorporates nuisance parameters for a general genotype error model. We extend the likelihood approach for a single SNP to include short haplotypes consisting of 2 or 3 SNPs. The extension to haplotypes is based on assumptions of random mating, multiplicative penetrances, and at most a single genotype error per family. For a single SNP, we found, using Monte Carlo simulation, that type I error rate can be controlled for a number of genotype error models at different error rates. Simulation results suggest the same is true for 2 and 3 SNPs. In all cases, power declined with increasing genotyping error rates. In the absence of genotyping errors, power was similar whether nuisance parameters for genotype error were included in the LRT or not. The LRT developed here does not require prior specification of a particular model for genotype errors and it can be readily computed using the EM algorithm. Consequently, this test may be generally useful as a test of association with case-parents data in which Mendelian inconsistent families are observed. Published 2004 Wiley-Liss, Inc.

Mesh:

Substances:

Year:  2004        PMID: 14748014     DOI: 10.1002/gepi.10297

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  12 in total

1.  Family-based association tests using genotype data with uncertainty.

Authors:  Zhaoxia Yu
Journal:  Biostatistics       Date:  2011-12-08       Impact factor: 5.899

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.  Simultaneously correcting for population stratification and for genotyping error in case-control association studies.

Authors:  K F Cheng; W J Lin
Journal:  Am J Hum Genet       Date:  2007-08-22       Impact factor: 11.025

4.  Testing for genetic association with constrained models using triads.

Authors:  J F Troendle; K F Yu; J L Mills
Journal:  Ann Hum Genet       Date:  2009-01-28       Impact factor: 1.670

5.  Power comparisons between similarity-based multilocus association methods, logistic regression, and score tests for haplotypes.

Authors:  Wan-Yu Lin; Daniel J Schaid
Journal:  Genet Epidemiol       Date:  2009-04       Impact factor: 2.135

6.  Assessing the utility of whole-genome amplified serum DNA for array-based high throughput genotyping.

Authors:  Kristine L Bucasas; Gagan A Pandya; Sonal Pradhan; Robert D Fleischmann; Scott N Peterson; John W Belmont
Journal:  BMC Genet       Date:  2009-12-18       Impact factor: 2.797

7.  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

8.  Estimating the single nucleotide polymorphism genotype misclassification from routine double measurements in a large epidemiologic sample.

Authors:  Iris M Heid; Claudia Lamina; Helmut Küchenhoff; Guido Fischer; Norman Klopp; Melanie Kolz; Harald Grallert; Caren Vollmert; Stefanie Wagner; Cornelia Huth; Julia Müller; Martina Müller; Steven C Hunt; Annette Peters; Bernhard Paulweber; H-Erich Wichmann; Florian Kronenberg; Thomas Illig
Journal:  Am J Epidemiol       Date:  2008-09-12       Impact factor: 4.897

9.  A transmission disequilibrium test for general pedigrees that is robust to the presence of random genotyping errors and any number of untyped parents.

Authors:  Derek Gordon; Chad Haynes; Christopher Johnnidis; Shailendra B Patel; Anne M Bowcock; Jürg Ott
Journal:  Eur J Hum Genet       Date:  2004-09       Impact factor: 4.246

10.  Precision and type I error rate in the presence of genotype errors and missing parental data: a comparison between the original transmission disequilibrium test (TDT) and TDTae statistics.

Authors:  Sandra Barral; Chad Haynes; Mark A Levenstien; Derek Gordon
Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

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