Identification of disease causing loci using an array-based genotyping approach on pooled DNA

Craig, David W.; Huentelman, Matthew J.; Hu-Lince, Diane; Zismann, Victoria; Kruer, Michael C.; Lee, Anne M.; Puffenberger, Erik G.; Pearson, J. M.; Stephan, Dietrich A.

doi:10.1186/1471-2164-6-138

Cited by 64 publications

(64 citation statements)

References 20 publications

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“…Most pooling-based GWA studies use, like us in the current study, a two-stage design in which the most promising SNPs identified in the screening stage are followed up by individual genotyping. The effectiveness of this method has been successfully demonstrated by the identification of previously published as well as novel susceptibility loci [8,9,10,11,12]. …”

Section: Discussionmentioning

confidence: 89%

“…They are an excellent but, unfortunately, expensive approach to identify genes and pathways involved in complex diseases. Pooling DNA in the first stage of a GWA study substantially reduces costs, and has been shown to be an efficient method to select candidate susceptibility loci for follow-up by individual genotyping [7,8,9,10,11,12,13]. To identify novel pancreatic cancer susceptibility loci, we conducted a two-stage GWA study using the Affymetrix® Genome-Wide Human SNP Array 6.0 (>900,000 single-nucleotide polymorphisms, SNPs) and DNA pooling in the screening stage.…”

Section: Introductionmentioning

confidence: 99%

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Pooling-Based Genome-Wide Association Study Implicates Gamma-Glutamyltransferase 1 (GGT1) Gene in Pancreatic Carcinogenesis

et al. 2010

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Background/Aims: Knowledge regarding genetic factors that influence pancreatic cancer risk is currently limited. To identify novel pancreatic cancer susceptibility loci, we conducted a two-stage genome-wide association study. Methods: The Affymetrix® Genome-Wide Human SNP Array 6.0 and DNA pooling were used in the screening stage. Twenty-six single-nucleotide polymorphisms (SNPs) were selected for follow-up. These 26 lead SNPs and additionally selected tagSNPs for the regions around the lead SNPs were evaluated by individual genotyping of the pooling population and an independent validation population. Results: Of the lead SNPs, the strongest association was found with rs4820599 located in the γ-glutamyltransferase 1 (GGT1) gene. This SNP was significantly associated with pancreatic cancer risk in the validation population and the combined dataset (p_allele-based = 0.019 and p_allele-based = 0.003, respectively). Statistically significant associations were also observed with two GGT1 tagSNPs: rs2017869 and rs8135987. Lead SNP rs4820599 is in high linkage disequilibrium (LD; pairwise r²: 0.69) and tagSNP rs2017869 is in strong LD (pairwise r²: 0.96) with SNP rs5751901, which has been reported to be associated with increased GGT1 serum levels. GGT is expressed in the pancreas and plays a key role in glutathione metabolism. Conclusion: Our results suggest that common variation in the GGT1 gene may affect the risk of pancreatic cancer.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Pooling-Based Genome-Wide Association Study Implicates Gamma-Glutamyltransferase 1 (GGT1) Gene in Pancreatic Carcinogenesis

et al. 2010

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“… a Correction methods used include those based on (1) Hoogendoorn et al [6], (2) Meaburn et al [7], (3) Craig et al [8] plus Meaburn et al [7], and (4) no adjustment. b Mean of the absolute values for the differences in allele frequencies between DNA pools and individual samples. c In total, 108 SNPs were compared for allele frequency differences as estimated for DNA pools and individual DNA samples. However, only 105 SNPs were compared for the correction method (3) (Craig et al [8] plus Meaburn et al [7]) because 3 SNPs were filtered out.…”

Section: Resultsmentioning

confidence: 99%

“…However, only 105 SNPs were compared for the correction method (3) (Craig et al [8] plus Meaburn et al [7]) because 3 SNPs were filtered out.…”

Section: Resultsmentioning

confidence: 99%

UPDG: U tilities package for data analysis of P ooled D NA G WAS

Yap

Yip

2012

BMC Genet

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BackgroundDespite being a well-established strategy for cost reduction in disease gene mapping, pooled DNA association study is much less popular than the individual DNA approach. This situation is especially true for pooled DNA genomewide association study (GWAS), for which very few computer resources have been developed for its data analysis. This motivates the development of UPDG (Utilities package for data analysis of Pooled DNA GWAS).ResultsUPDG represents a generalized framework for data analysis of pooled DNA GWAS with the integration of Unix/Linux shell operations, Perl programs and R scripts. With the input of raw intensity data from GWAS, UPDG performs the following tasks in a stepwise manner: raw data manipulation, correction for allelic preferential amplification, normalization, nested analysis of variance for genetic association testing, and summarization of analysis results. Detailed instructions, procedures and commands are provided in the comprehensive user manual describing the whole process from preliminary preparation of software installation to final outcome acquisition. An example dataset (input files and sample output files) is also included in the package so that users can easily familiarize themselves with the data file formats, working procedures and expected output. Therefore, UPDG is especially useful for users with some computer knowledge, but without a sophisticated programming background.ConclusionsUPDG provides a free, simple and platform-independent one-stop service to scientists working on pooled DNA GWAS data analysis, but with less advanced programming knowledge. It is our vision and mission to reduce the hindrance for performing data analysis of pooled DNA GWAS through our contribution of UPDG. More importantly, we hope to promote the popularity of pooled DNA GWAS, which is a very useful research strategy.

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“…Therefore, if RAS values generated using pooled DNA samples from one group (e. g. patients) shall be compared to averaged SNP-specific call values generated by individual genotyping of a different group (e. g. healthy control individuals), a correction step has to be included to compensate for this hybridization-specific bias [19]. One option is provided by the so called k-correction [8,12,14,20], which corrects the RAS value for heterozygote calls. The correction factor c is specific for each probe set and has to be determined empirically by individual array-based genotyping of heterozygous individuals.…”

Section: Methodsmentioning

confidence: 99%

Comparison of genotyping using pooled DNA samples (allelotyping) and individual genotyping using the affymetrix genome-wide human SNP array 6.0

et al. 2013

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BackgroundGenome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared.ResultsRegardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case–control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively.ConclusionsCorrection of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ≤1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0.

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Identification of disease causing loci using an array-based genotyping approach on pooled DNA

Cited by 64 publications

References 20 publications

Pooling-Based Genome-Wide Association Study Implicates Gamma-Glutamyltransferase 1 (GGT1) Gene in Pancreatic Carcinogenesis

Pooling-Based Genome-Wide Association Study Implicates Gamma-Glutamyltransferase 1 (GGT1) Gene in Pancreatic Carcinogenesis

UPDG: U tilities package for data analysis of P ooled D NA G WAS

Comparison of genotyping using pooled DNA samples (allelotyping) and individual genotyping using the affymetrix genome-wide human SNP array 6.0

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