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CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies.

ABSTRACT: PURPOSE:Increases in throughput and affordability of genotyping products have led to large sample sizes in genetic studies, increasing the likelihood that incidental genetic findings may occur. We set out to survey potential notifiable variants on arrays used in genome-wide association studies and in direct-to-consumer genetic services. METHODS:We used multiple bioinformatics strategies to identify, and map variants tested for genetic disorders in > or = 2 CLIA-approved laboratories (based on the GeneTests database). We subsequently surveyed 18 commercial single nucleotide polymorphism arrays and HapMap for these variants. RESULTS:Of 1,362 genes tested according to GeneTests, we identified 298 specific targeted mutations measured in more than or equal to two laboratories, encompassing 56 disorders. Only 88 of 298 mutations could be identified as known single nucleotide polymorphisms in genomic databases. We found 18 of 88 single nucleotide polymorphisms present in HapMap or on commercial single nucleotide polymorphism arrays. Homozygotes for rare alleles of some variants were identified in the Framingham Heart Study, an active genome-wide association studies cohort (n = 8,410). CONCLUSIONS:Variants in genes including APOE, F5, HFE, CYP21A2, MEFV, SPINK1, BTD, GALT, and G6PD were found on single nucleotide polymorphism arrays or in the HapMap. Some of these variants may warrant further review to determine their likelihood to trigger incidental findings in the course of genome-wide association studies or direct-to-consumer testing.

SUBMITTER: Johnson AD

PROVIDER: S-EPMC3056880 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies.

Johnson Andrew D AD Bhimavarapu Anupama A Benjamin Emelia J EJ Fox Caroline C Levy Daniel D Jarvik Gail P GP O'Donnell Christopher J CJ

Genetics in medicine : official journal of the American College of Medical Genetics 20100601 6

<h4>Purpose</h4>Increases in throughput and affordability of genotyping products have led to large sample sizes in genetic studies, increasing the likelihood that incidental genetic findings may occur. We set out to survey potential notifiable variants on arrays used in genome-wide association studies and in direct-to-consumer genetic services.<h4>Methods</h4>We used multiple bioinformatics strategies to identify, and map variants tested for genetic disorders in > or = 2 CLIA-approved laboratori ...[more]

PMID: 20556870

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Project description:BackgroundAdmixture mapping is a powerful approach for identifying genetic variants involved in human disease that exploits the unique genomic structure in recently admixed populations. To use existing published panels of ancestry-informative markers (AIMs) for admixture mapping, markers have to be genotyped de novo for each admixed study sample and samples representing the ancestral parental populations. The increased availability of dense marker data on commercial chips has made it feasible to develop panels wherein the markers need not be predetermined.ResultsWe developed two panels of AIMs (approximately 2,000 markers each) based on the Affymetrix Genome-Wide Human SNP Array 6.0 for admixture mapping with African American samples. These two AIM panels had good map power that was higher than that of a denser panel of approximately 20,000 random markers as well as other published panels of AIMs. As a test case, we applied the panels in an admixture mapping study of hypertension in African Americans in the Washington, D.C. metropolitan area.ConclusionsDeveloping marker panels for admixture mapping from existing genome-wide genotype data offers two major advantages: (1) no de novo genotyping needs to be done, thereby saving costs, and (2) markers can be filtered for various quality measures and replacement markers (to minimize gaps) can be selected at no additional cost. Panels of carefully selected AIMs have two major advantages over panels of random markers: (1) the map power from sparser panels of AIMs is higher than that of approximately 10-fold denser panels of random markers, and (2) clusters can be labeled based on information from the parental populations. With current technology, chip-based genome-wide genotyping is less expensive than genotyping approximately 20,000 random markers. The major advantage of using random markers is the absence of ascertainment effects resulting from the process of selecting markers. The ability to develop marker panels informative for ancestry from SNP chip genotype data provides a fresh opportunity to conduct admixture mapping for disease genes in admixed populations when genome-wide association data exist or are planned.

Project description:To facilitate whole-genome association studies (WGAS), several high-density SNP genotyping arrays have been developed. Genetic coverage and statistical power are the primary benchmark metrics in evaluating the performance of SNP arrays. Ideally, such evaluations would be done on a SNP set and a cohort of individuals that are both independently sampled from the original SNPs and individuals used in developing the arrays. Without utilization of an independent test set, previous estimates of genetic coverage and statistical power may be subject to an overfitting bias. Additionally, the SNP arrays' statistical power in WGAS has not been systematically assessed on real traits. One robust setting for doing so is to evaluate statistical power on thousands of traits measured from a single set of individuals. In this study, 359 newly sampled Americans of European descent were genotyped using both Affymetrix 500K (Affx500K) and Illumina 650Y (Ilmn650K) SNP arrays. From these data, we were able to obtain estimates of genetic coverage, which are robust to overfitting, by constructing an independent test set from among these genotypes and individuals. Furthermore, we collected liver tissue RNA from the participants and profiled these samples on a comprehensive gene expression microarray. The RNA levels were used as a large-scale set of quantitative traits to calibrate the relative statistical power of the commercial arrays. Our genetic coverage estimates are lower than previous reports, providing evidence that previous estimates may be inflated due to overfitting. The Ilmn650K platform showed reasonable power (50% or greater) to detect SNPs associated with quantitative traits when the signal-to-noise ratio (SNR) is greater than or equal to 0.5 and the causal SNP's minor allele frequency (MAF) is greater than or equal to 20% (N = 359). In testing each of the more than 40,000 gene expression traits for association to each of the SNPs on the Ilmn650K and Affx500K arrays, we found that the Ilmn650K yielded 15% times more discoveries than the Affx500K at the same false discovery rate (FDR) level.

Dataset Information

CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies.

Publications

CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies.

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