Literature DB >> 10597497

Power loss for multiallelic transmission/disequilibrium test when errors introduced: GAW11 simulated data.

D Gordon1, T C Matise, S C Heath, J Ott.   

Abstract

Many researchers are considering the use of transmission/disequilibrium tests (TDT) for trios of genotypes (father, mother, child) as a method for localizing genes associated with complex diseases. We evaluate the effect of random errors (allele changes) in trios on the power to detect linkage. For a marker in the simulated data set, one allele is associated with the fictitious disease in a certain subpopulation. For the data as given (no errors), our power to detect linkage using the multiallelic TDT (TDTmhet) is 68% (critical p-value set at 0.0001). We introduce errors into trios at various rates (1%, 5%, or 10%), remove only trios displaying mendelian inconsistencies, and recalculate power to detect linkage. Our principal finding is that there is power loss to detect linkage with the TDTmhet when errors are introduced. We observe power losses of 8%, 16%, and 48% for error rates of 1%, 5%, and 10%, respectively. To determine the source of the power loss, we perform Monte Carlo simulations. At the 1% and 5% rates, we conclude that power loss is due primarily to loss in sample size. At the 10% rate, we observe substantial power loss due to error introduction in addition to sample size reduction. We also determine, given a particular error rate, the probability that we detect errors if we use only mendelian consistency as a check. We find that the mean detection rates for the data sets with 1%, 5%, or 10% error rates are 58%, 60%, and 62%, respectively. As a result, the apparent error rate appears to be almost half the true error rate. Based on these results, we recommend that researchers maintain error rates below 5% when using the TDTmhet for linkage, use additional methods beyond mendelian consistency checks when searching for errors in their data, and modify sample size calculations when accounting for errors in their genotype data.

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Year:  1999        PMID: 10597497     DOI: 10.1002/gepi.1370170795

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


  10 in total

1.  Detection and integration of genotyping errors in statistical genetics.

Authors:  Eric Sobel; Jeanette C Papp; Kenneth Lange
Journal:  Am J Hum Genet       Date:  2002-01-08       Impact factor: 11.025

2.  A transmission/disequilibrium test that allows for genotyping errors in the analysis of single-nucleotide polymorphism data.

Authors:  D Gordon; S C Heath; X Liu; J Ott
Journal:  Am J Hum Genet       Date:  2001-07-05       Impact factor: 11.025

3.  The effect that genotyping errors have on the robustness of common linkage-disequilibrium measures.

Authors:  J M Akey; K Zhang; M Xiong; P Doris; L Jin
Journal:  Am J Hum Genet       Date:  2001-05-16       Impact factor: 11.025

4.  Disentangling fetal and maternal susceptibility for pre-eclampsia: a British multicenter candidate-gene study.

Authors: 
Journal:  Am J Hum Genet       Date:  2005-05-11       Impact factor: 11.025

5.  Detection of genotyping errors and pseudo-SNPs via deviations from Hardy-Weinberg equilibrium.

Authors:  Suzanne M Leal
Journal:  Genet Epidemiol       Date:  2005-11       Impact factor: 2.135

Review 6.  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

7.  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

8.  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

Review 9.  Evaluation of a susceptibility gene for schizophrenia: genotype based meta-analysis of RGS4 polymorphisms from thirteen independent samples.

Authors:  Michael E Talkowski; Howard Seltman; Anne S Bassett; Linda M Brzustowicz; Xiangning Chen; Kodavali V Chowdari; David A Collier; Quirino Cordeiro; Aiden P Corvin; Smita N Deshpande; Michael F Egan; Michael Gill; Kenneth S Kendler; George Kirov; Leonard L Heston; Pat Levitt; David A Lewis; Tao Li; Karoly Mirnics; Derek W Morris; Nadine Norton; Michael C O'Donovan; Michael J Owen; Christian Richard; Prachi Semwal; Janet L Sobell; David St Clair; Richard E Straub; B K Thelma; Homero Vallada; Daniel R Weinberger; Nigel M Williams; Joel Wood; Feng Zhang; Bernie Devlin; Vishwajit L Nimgaonkar
Journal:  Biol Psychiatry       Date:  2006-04-21       Impact factor: 13.382

10.  Assessing genuine parents-offspring trios for genetic association studies.

Authors:  Yik Y Teo; Andrew E Fry; Miguel A Sanjoaquin; Bonnie Pederson; Kerrin S Small; Kirk A Rockett; Dominic P Kwiatkowski; Taane G Clark
Journal:  Hum Hered       Date:  2008-10-17       Impact factor: 0.444

  10 in total

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