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Association testing by DNA pooling: an effective initial screen.

ABSTRACT: With an ever-increasing resource of validated single-nucleotide polymorphisms (SNPs), the limiting factors in genome-wide association analysis have become genotyping capacity and the availability of DNA. We provide a proof of concept of the use of pooled DNA as a means of efficiently screening SNPs and prioritizing them for further study. This approach reduces the final number of SNPs that undergo full, sample-by-sample genotyping as well as the quantity of DNA used overall. We have examined 15 SNPs in the cholesteryl ester transfer protein (CETP) gene, a gene previously demonstrated to be associated with serum high-density lipoprotein cholesterol levels. The SNPs were amplified in two pools of DNA derived from groups of individuals with extremely high and extremely low serum high-density lipoprotein cholesterol levels, respectively. P values <0.05 were obtained for 14 SNPs, supporting the described association. Genotyping of the individual samples showed that the average margin of error in frequency estimate was approximately 4% when pools were used. These findings clearly demonstrate the potential of pooling techniques and their associated technologies as an initial screen in the search for genetic associations.

SUBMITTER: Bansal A

PROVIDER: S-EPMC139236 | biostudies-literature | 2002 Dec

REPOSITORIES: biostudies-literature

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Association testing by DNA pooling: an effective initial screen.

Bansal Aruna A van den Boom Dirk D Kammerer Stefan S Honisch Christiane C Adam Gail G Cantor Charles R CR Kleyn Patrick P Braun Andi A

Proceedings of the National Academy of Sciences of the United States of America 20021210 26

With an ever-increasing resource of validated single-nucleotide polymorphisms (SNPs), the limiting factors in genome-wide association analysis have become genotyping capacity and the availability of DNA. We provide a proof of concept of the use of pooled DNA as a means of efficiently screening SNPs and prioritizing them for further study. This approach reduces the final number of SNPs that undergo full, sample-by-sample genotyping as well as the quantity of DNA used overall. We have examined 15 ...[more]

PMID: 12475937

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Project description:Late-onset Alzheimer's disease (LOAD) is an age related neurodegenerative disease with a high prevalence that places major demands on healthcare resources in societies with increasingly aged populations. The only extensively replicable genetic risk factor for LOAD is the apolipoprotein E gene. In order to identify additional genetic risk loci we have conducted a genome-wide association (GWA) study in a large LOAD case - control sample, reducing costs through the use of DNA pooling.DNA samples were collected from 1,082 individuals with LOAD and 1,239 control subjects. Age at onset ranged from 60 to 95 and Controls were matched for age (mean = 76.53 years, SD = 33), gender and ethnicity. Equimolar amounts of each DNA sample were added to either a case or control pool. The pools were genotyped using Illumina HumanHap300 and Illumina Sentrix HumanHap240S arrays testing 561,494 SNPs. 114 of our best hit SNPs from the pooling data were identified and then individually genotyped in the case - control sample used to construct the pools.Highly significant association with LOAD was observed at the APOE locus confirming the validity of the pooled genotyping approach.For 109 SNPs outside the APOE locus, we obtained uncorrected p-values </= 0.05 for 74 after individual genotyping. To further test these associations, we added control data from 1400 subjects from the 1958 Birth Cohort with the evidence for association increasing to 3.4 x 10-6 for our strongest finding, rs727153.rs727153 lies 13 kb from the start of transcription of lecithin retinol acyltransferase (phosphatidylcholine - retinol O-acyltransferase, LRAT). Five of seven tag SNPs chosen to cover LRAT showed significant association with LOAD with a SNP in intron 2 of LRAT, showing greatest evidence of association (rs201825, p-value = 6.1 x 10-7).We have validated the pooling method for GWA studies by both identifying the APOE locus and by observing a strong enrichment for significantly associated SNPs. We provide evidence for LRAT as a novel candidate gene for LOAD. LRAT plays a prominent role in the Vitamin A cascade, a system that has been previously implicated in LOAD.

Project description:Until recently, genome-wide association studies (GWAS) have been restricted to research groups with the budget necessary to genotype hundreds, if not thousands, of samples. Replacing individual genotyping with genotyping of DNA pools in Phase I of a GWAS has proven successful, and dramatically altered the financial feasibility of this approach. When conducting a pool-based GWAS, how well SNP allele frequency is estimated from a DNA pool will influence a study's power to detect associations. Here we address how to control the variance in allele frequency estimation when DNAs are pooled, and how to plan and conduct the most efficient well-powered pool-based GWAS.By examining the variation in allele frequency estimation on SNP arrays between and within DNA pools we determine how array variance [var(e(array))] and pool-construction variance [var(e(construction))] contribute to the total variance of allele frequency estimation. This information is useful in deciding whether replicate arrays or replicate pools are most useful in reducing variance. Our analysis is based on 27 DNA pools ranging in size from 74 to 446 individual samples, genotyped on a collective total of 128 Illumina beadarrays: 24 1M-Single, 32 1M-Duo, and 72 660-Quad.For all three Illumina SNP array types our estimates of var(e(array)) were similar, between 3-4 × 10-4 for normalized data. Var(e(construction)) accounted for between 20-40% of pooling variance across 27 pools in normalized data.We conclude that relative to var(e(array)), var(e(construction)) is of less importance in reducing the variance in allele frequency estimation from DNA pools; however, our data suggests that on average it may be more important than previously thought. We have prepared a simple online tool, PoolingPlanner (available at http://www.kchew.ca/PoolingPlanner/), which calculates the effective sample size (ESS) of a DNA pool given a range of replicate array values. ESS can be used in a power calculator to perform pool-adjusted calculations. This allows one to quickly calculate the loss of power associated with a pooling experiment to make an informed decision on whether a pool-based GWAS is worth pursuing.

Dataset Information

Association testing by DNA pooling: an effective initial screen.

Publications

Association testing by DNA pooling: an effective initial screen.

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