Dataset Information

Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping.

ABSTRACT: Several linkage studies suggest that chromosome 5q31-32 might contain risk loci for schizophrenia (SZ). We wanted to identify susceptibility genes for schizophrenia within this region.We saturated the interval between markers D5S666 and D5S436 with 90 polymorphic microsatellite markers and genotyped two sets of DNA pools consisting of 300 SZ patients of Bulgarian origin and their 600 parents. Positive associations were followed-up with SNP genotyping.Nominally significant evidence for association (p < 0.05) was found for seven markers (D5S0023i, IL9, RH60252, 5Q3133_33, D5S2017, D5S1481, D5S0711i) which were then individually genotyped in the trios. The predicted associations were confirmed for two of the markers: D5S2017, localised in the SPRY4-FGF1 locus (p = 0.004) and IL9, localized within the IL9 gene (p = 0.014). Fine mapping was performed using single nucleotide polymorphisms (SNPs) around D5S2017 and IL9. In each region four SNPs were chosen and individually genotyped in our full sample of 615 SZ trios. Two SNPs showed significant evidence for association: rs7715300 (p = 0.001) and rs6897690 (p = 0.032). Rs7715300 is localised between the TGFBI and SMAD5 genes and rs6897690 is within the SPRY4 gene.Our screening of 5q31-32 implicates three potential candidate genes for SZ: SMAD5, TGFBI and SPRY4.

SUBMITTER: Zaharieva I

PROVIDER: S-EPMC2268687 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping.

Zaharieva Irina I Georgieva Lyudmila L Nikolov Ivan I Kirov George G Owen Michael J MJ O'Donovan Michael C MC Toncheva Draga D

BMC psychiatry 20080225

<h4>Background</h4>Several linkage studies suggest that chromosome 5q31-32 might contain risk loci for schizophrenia (SZ). We wanted to identify susceptibility genes for schizophrenia within this region.<h4>Methods</h4>We saturated the interval between markers D5S666 and D5S436 with 90 polymorphic microsatellite markers and genotyped two sets of DNA pools consisting of 300 SZ patients of Bulgarian origin and their 600 parents. Positive associations were followed-up with SNP genotyping.<h4>Result ...[more]

PMID: 18298822

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Project description:BACKGROUND: Genome-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. RESULTS: Regardless 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. CONCLUSIONS: Correction 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.

Project description:BackgroundPooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.ResultsWe report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.ConclusionOur results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.

Dataset Information

Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping.

Publications

Association study in the 5q31-32 linkage region for schizophrenia using pooled DNA genotyping.

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