Dataset Information

Statistical adjustment of genotyping error in a case-control study of childhood leukaemia.

ABSTRACT:

Background

Genotyping has become more cost-effective and less invasive with the use of buccal cell sampling. However, low or fragmented DNA yields from buccal cells collected using FTA cards often requires additional whole genome amplification to produce sufficient DNA for genotyping. In our case-control study of childhood leukaemia, discordance was found between genotypes derived from blood and whole genome amplified FTA buccal DNA samples. We aimed to develop a user-friendly method to correct for this genotype misclassification, as existing methods were not suitable for use in our study.

Methods

Discordance between the results of blood and buccal-derived DNA was assessed in childhood leukaemia cases who had both blood and FTA buccal samples. A method based on applying misclassification probabilities to measured data and combining results using multiple imputations, was devised to correct for error in the genotypes of control subjects, for whom only buccal samples were available, to minimize bias in the odds ratios in the case-control analysis.

Results

Application of the correction method to synthetic datasets showed it was effective in producing correct odds ratios from data with known misclassification. Moreover, when applied to each of six bi-allelic loci, correction altered the odds ratios in the logically anticipated manner given the degree and direction of the misclassification revealed by the investigations in cases. The precision of the effect estimates decreased with decreasing size of the misclassification data set.

Conclusions

Bias arising from differential genotype misclassification can be reduced by correcting results using this method whenever data on concordance of genotyping results with those from a different and probably better DNA source are available.

SUBMITTER: Cooper MN

PROVIDER: S-EPMC3514307 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Statistical adjustment of genotyping error in a case-control study of childhood leukaemia.

Cooper Matthew N MN de Klerk Nicholas H NH Greenop Kathryn R KR Jamieson Sarra E SE Anderson Denise D van Bockxmeer Frank M FM Armstrong Bruce K BK Milne Elizabeth E

BMC medical research methodology 20120913

<h4>Background</h4>Genotyping has become more cost-effective and less invasive with the use of buccal cell sampling. However, low or fragmented DNA yields from buccal cells collected using FTA cards often requires additional whole genome amplification to produce sufficient DNA for genotyping. In our case-control study of childhood leukaemia, discordance was found between genotypes derived from blood and whole genome amplified FTA buccal DNA samples. We aimed to develop a user-friendly method to ...[more]

PMID: 22970889

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Project description:Main purpose To evaluate the feasibility of a measurement-based assessment of benzene exposure in case-control studies of paediatric cancer; Additional aims To identify the sources of exposure variability; to assess the performance of two benzene biomarkers; to verify the occurrence of participation bias; to check whether exposures to benzene and to 50 Hz magnetic fields were correlated, and might exert reciprocal confounding effects. Pilot case-control study of childhood leukaemia and exposure to benzene assessed by repeated seasonal weekly measurements in breathing zone air samples and outside the children's dwellings, with concurrent determinations of cotinine, t-t-muconic acid (MA) and sulfo-phenylmercapturic acid (S-PMA) in urine. 108 cases and 194 controls were eligible for inclusion. Full-participation was obtained from 46 cases and 60 controls, with low dropout rates before four repeats (11% and 17%); an additional 23 cases and 80 controls allowed the collection of outdoor air samples only. The average benzene concentration in personal and outdoor air samples was 3 μg/m(3) (SD 1.45) and 2.7 μg/m(3) (SD 1.41), respectively. Personal exposure was strongly influenced by outdoor benzene concentrations, higher in the cold seasons than in warm seasons, and not affected by gender, age, area of residence or caseness. Urinary excretion of S-PMA and personal benzene exposure were well correlated. Outdoor benzene levels were lower among participant controls compared with non-participants, but did not differ between participant and non-participant cases; the direction of the bias was found to depend on the cut-point chosen to distinguish exposed and unexposed. Exposures to benzene and extremely low-frequency magnetic fields were positively correlated. Repeated individual measurements are needed to account for the seasonal variability in benzene exposure, and they have the additional advantage of increasing the study power. Measurement-based assessment of benzene exposure in studies of childhood leukaemia appears feasible, although it is financially and logistically demanding.

Project description:BackgroundIn epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis.MethodsUsing a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed.ResultsThe proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed.ConclusionsIn individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

Dataset Information

Statistical adjustment of genotyping error in a case-control study of childhood leukaemia.

Background

Methods

Results

Conclusions

Publications

Statistical adjustment of genotyping error in a case-control study of childhood leukaemia.

Similar Datasets

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