Dataset Information

A method to address differential bias in genotyping in large-scale association studies.

ABSTRACT: In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: improvements to the basic clustering methods for identifying genotypes from fluorescence intensities, and use of "fuzzy" calls in association tests in order to make appropriate allowance for call uncertainty. We find that the main improvement is a modification of the calling algorithm that links the clustering of cases and controls while allowing for different DNA sourcing. We also find that, in the presence of different DNA sourcing, biases associated with missing data can increase the false-positive rate. Therefore, we propose the use of "fuzzy" calls to deal with uncertain genotypes that would otherwise be labeled as missing.

SUBMITTER: Plagnol V

PROVIDER: S-EPMC1868951 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A method to address differential bias in genotyping in large-scale association studies.

Plagnol Vincent V Cooper Jason D JD Todd John A JA Clayton David G DG

PLoS genetics 20070405 5

In a previous paper we have shown that, when DNA samples for cases and controls are prepared in different laboratories prior to high-throughput genotyping, scoring inaccuracies can lead to differential misclassification and, consequently, to increased false-positive rates. Different DNA sourcing is often unavoidable in large-scale disease association studies of multiple case and control sets. Here, we describe methodological improvements to minimise such biases. These fall into two categories: i ...[more]

PMID: 17511519

Similar Datasets

Project description:BACKGROUND: High-throughput sequencing technologies offer new perspectives for biomedical, agronomical and evolutionary research. Promising progresses now concern the application of these technologies to large-scale studies of genetic variation. Such studies require the genotyping of high numbers of samples. This is theoretically possible using 454 pyrosequencing, which generates billions of base pairs of sequence data. However several challenges arise: first in the attribution of each read produced to its original sample, and second, in bioinformatic analyses to distinguish true from artifactual sequence variation. This pilot study proposes a new application for the 454 GS FLX platform, allowing the individual genotyping of thousands of samples in one run. A probabilistic model has been developed to demonstrate the reliability of this method. RESULTS: DNA amplicons from 1,710 rodent samples were individually barcoded using a combination of tags located in forward and reverse primers. Amplicons consisted in 222 bp fragments corresponding to DRB exon 2, a highly polymorphic gene in mammals. A total of 221,789 reads were obtained, of which 153,349 were finally assigned to original samples. Rules based on a probabilistic model and a four-step procedure, were developed to validate sequences and provide a confidence level for each genotype. The method gave promising results, with the genotyping of DRB exon 2 sequences for 1,407 samples from 24 different rodent species and the sequencing of 392 variants in one half of a 454 run. Using replicates, we estimated that the reproducibility of genotyping reached 95%. CONCLUSIONS: This new approach is a promising alternative to classical methods involving electrophoresis-based techniques for variant separation and cloning-sequencing for sequence determination. The 454 system is less costly and time consuming and may enhance the reliability of genotypes obtained when high numbers of samples are studied. It opens up new perspectives for the study of evolutionary and functional genetics of highly polymorphic genes like major histocompatibility complex genes in vertebrates or loci regulating self-compatibility in plants. Important applications in biomedical research will include the detection of individual variation in disease susceptibility. Similarly, agronomy will benefit from this approach, through the study of genes implicated in productivity or disease susceptibility traits.

Dataset Information

A method to address differential bias in genotyping in large-scale association studies.

Publications

A method to address differential bias in genotyping in large-scale association studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets