Abstract
BackgroundHere we present convergent methodologies using theoretical calculations, empirical assessment on in-house and publicly available datasets as well as in silico simulations, that validate a panel of SNPs for a variety of necessary tasks in human genetics disease research before resources are committed to larger-scale genotyping studies on those samples. While large-scale well-funded human genetic studies routinely have up to a million SNP genotypes, samples in a human genetics laboratory that are not yet part of such studies may be productively utilized in pilot projects or as part of targeted follow-up work though such smaller scale applications require at least some genome-wide genotype data for quality control purposes such as DNA “barcoding” to detect swaps or contamination issues, determining familial relationships between samples and correcting biases due to population effects such as population stratification in pilot studies.Principal FindingsEmpirical performance in classification of relative types for any two given DNA samples (e.g., full siblings, parental, etc) indicated that for outbred populations the panel performs sufficiently to classify relationship in extended families and therefore also for smaller structures such as trios and for twin zygosity testing. Additionally, familial relationships do not significantly diminish the (mean match) probability of sharing SNP genotypes in pedigrees, further indicating the uniqueness of the “barcode.” Simulation using these SNPs for an African American case-control disease association study demonstrated that population stratification, even in complex admixed samples, can be adequately corrected under a range of disease models using the SNP panel.ConclusionThe panel has been validated for use in a variety of human disease genetics research tasks including sample barcoding, relationship verification, population substructure detection and statistical correction. Given the ease of genotyping our specific assay contained herein, this panel represents a useful and economical panel for human geneticists.
Highlights
While microarray SNP genotype data are the standard genotype data in human genetics there will still be a financially motivated need for lower throughput genotyping to accomplish a range of tasks prior to larger investments
The mean match probabilities obtained with the 52 SNP panel for the Centre d"Etude du Polymorphisme Humain (CEPH) pedigrees (1.97610219) and HapMap samples (CEU 4.14610220; YRI 2.59610215; CHB 4.25610217; JPT 1.27610216) were in line with previous estimates [4,6]
Even though the CEPH pedigree sample contains related individuals, the mean match probability is still lower than any non-European population by 2 orders of magnitude
Summary
While microarray SNP genotype data are the standard genotype data in human genetics there will still be a financially motivated need for lower throughput genotyping to accomplish a range of tasks prior to larger investments. 3) Adjustment of case-control association sample sets to control for potential type I error when case and control samples may systematically differ in their genetic backgrounds based on population substructure [2]. While large-scale well-funded human genetic studies routinely have up to a million SNP genotypes, samples in a human genetics laboratory that are not yet part of such studies may be productively utilized in pilot projects or as part of targeted follow-up work though such smaller scale applications require at least some genome-wide genotype data for quality control purposes such as DNA ‘‘barcoding’’ to detect swaps or contamination issues, determining familial relationships between samples and correcting biases due to population effects such as population stratification in pilot studies
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