Abstract
We describe a statistical approach to predict gender-labeling errors in candidate-gene association studies, when Y-chromosome markers have not been included in the genotyping set. The approach adds value to methods that consider only the heterozygosity of X-chromosome SNPs, by incorporating available information about the intensity of X-chromosome SNPs in candidate genes relative to autosomal SNPs from the same individual. To our knowledge, no published methods formalize a framework in which heterozygosity and relative intensity are simultaneously taken into account. Our method offers the advantage that, in the genotyping set, no additional space is required beyond that already assigned to X-chromosome SNPs in the candidate genes. We also show how the predictions can be used in a two-phase sampling design to estimate the gender-labeling error rates for an entire study, at a fraction of the cost of a conventional design.
Highlights
This work proposes a cost–effective approach to predict sample gender-labeling errors and estimate gender-labeling error rates in candidate gene case–control studies, when Y-chromosome data are unavailable but genotypes and intensities are available for SNPs in candidate genes. (By the “gender labeling” of a genotyped sample, we mean the self-reported gender of the study subject associated with that sample.) As long as the genotyping data set contains SNPs in candidate genes on the X-chromosome, the approach requires no extra space for additional gender-prediction SNPs.For SNP microarray and genome-wide association data that include SNPs on the X- and Y-chromosomes, sample sexing can be determined by heterozygosity of the X-chromosome SNPs and the presence of Y-chromosome SNPs
We have described a statistical approach to predict genderlabeling errors and estimate gender-labeling error rates in candidate-gene association studies when Y-chromosome data are unavailable, but some X-chromosome SNPs are in the genotyping set
In the prediction step of our approach, we identify potential gender-labeling errors by using the genotypes of the X-chromosome SNPs and their intensities, normalized to the intensities of the autosomal SNP genotypes of the same sample
Summary
This work proposes a cost–effective approach to predict sample gender-labeling errors and estimate gender-labeling error rates in candidate gene case–control studies, when Y-chromosome data are unavailable but genotypes and intensities are available for SNPs in candidate genes. (By the “gender labeling” of a genotyped sample, we mean the self-reported gender of the study subject associated with that sample.) As long as the genotyping data set contains SNPs in candidate genes on the X-chromosome, the approach requires no extra space for additional gender-prediction SNPs.For SNP microarray and genome-wide association data that include SNPs on the X- and Y-chromosomes, sample sexing can be determined by heterozygosity of the X-chromosome SNPs and the presence of Y-chromosome SNPs. Current methods for sex determination for forensic and other laboratory purposes rely on genotyping small differences between the two genes (Graham, 2006), such as a 6-bp deletion in AMELX (Sullivan et al, 1993). This method requires that each sample be tested by PCR. Alternative high-throughput methods use single nucleotide differences between the AMELX and AMELY genes to determine sex (Tzvetkov et al, 2010) While these assays do not require special laboratory equipment, they all require laborintensive laboratory work. By increasing the feasibility and cost–effectiveness of quality assurance in laboratory handling procedures, it can play a role in any integrated laboratory system for candidate-gene association studies
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