Dataset Information

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model.

ABSTRACT: BACKGROUND: High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals. RESULTS: This method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r2 = 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way. CONCLUSION: We conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.

SUBMITTER: Yin BC

PROVIDER: S-EPMC2640397 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model.

Yin Bin-Cheng BC Li Honghua H Ye Bang-Ce BC

BMC genomics 20081216

<h4>Background</h4>High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analy ...[more]

PMID: 19087310

Similar Datasets

Project description:BackgroundDNA extracted from historical samples is an important resource for understanding genetic consequences of anthropogenic influences and long-term environmental change. However, such samples generally yield DNA of a lower amount and quality, and the extent to which DNA degradation affects SNP genotyping success and allele frequency estimation is not well understood. We conducted high density SNP genotyping and allele frequency estimation in both individual DNA samples and pooled DNA samples extracted from dried Atlantic salmon (Salmo salar) scales stored at room temperature for up to 35 years, and assessed genotyping success, repeatability and accuracy of allele frequency estimation using a high density SNP genotyping array.ResultsIn individual DNA samples, genotyping success and repeatability was very high (> 0.973 and > 0.998, respectively) in samples stored for up to 35 years; both increased with the proportion of DNA of fragment size > 1000 bp. In pooled DNA samples, allele frequency estimation was highly repeatable (Repeatability = 0.986) and highly correlated with empirical allele frequency measures (Mean Adjusted R2 = 0.991); allele frequency could be accurately estimated in > 95% of pooled DNA samples with a reference group of at least 30 individuals. SNPs located in polyploid regions of the genome were more sensitive to DNA degradation: older samples had lower genotyping success at these loci, and a larger reference panel of individuals was required to accurately estimate allele frequencies.ConclusionsSNP genotyping was highly successful in degraded DNA samples, paving the way for the use of degraded samples in SNP genotyping projects. DNA pooling provides the potential for large scale population genetic studies with fewer assays, provided enough reference individuals are also genotyped and DNA quality is properly assessed beforehand. We provide recommendations for future studies intending to conduct high-throughput SNP genotyping and allele frequency estimation in historical samples.

Project description:BackgroundThe central role of the somatotrophic axis in animal post-natal growth, development and fertility is well established. Therefore, the identification of genetic variants affecting quantitative traits within this axis is an attractive goal. However, large sample numbers are a pre-requisite for the identification of genetic variants underlying complex traits and although technologies are improving rapidly, high-throughput sequencing of large numbers of complete individual genomes remains prohibitively expensive. Therefore using a pooled DNA approach coupled with target enrichment and high-throughput sequencing, the aim of this study was to identify polymorphisms and estimate allele frequency differences across 83 candidate genes of the somatotrophic axis, in 150 Holstein-Friesian dairy bulls divided into two groups divergent for genetic merit for fertility.ResultsIn total, 4,135 SNPs and 893 indels were identified during the resequencing of the 83 candidate genes. Nineteen percent (n = 952) of variants were located within 5' and 3' UTRs. Seventy-two percent (n = 3,612) were intronic and 9% (n = 464) were exonic, including 65 indels and 236 SNPs resulting in non-synonymous substitutions (NSS). Significant (P < 0.01) mean allele frequency differentials between the low and high fertility groups were observed for 720 SNPs (58 NSS). Allele frequencies for 43 of the SNPs were also determined by genotyping the 150 individual animals (Sequenom® MassARRAY). No significant differences (P > 0.1) were observed between the two methods for any of the 43 SNPs across both pools (i.e., 86 tests in total).ConclusionsThe results of the current study support previous findings of the use of DNA sample pooling and high-throughput sequencing as a viable strategy for polymorphism discovery and allele frequency estimation. Using this approach we have characterised the genetic variation within genes of the somatotrophic axis and related pathways, central to mammalian post-natal growth and development and subsequent lactogenesis and fertility. We have identified a large number of variants segregating at significantly different frequencies between cattle groups divergent for calving interval plausibly harbouring causative variants contributing to heritable variation. To our knowledge, this is the first report describing sequencing of targeted genomic regions in any livestock species using groups with divergent phenotypes for an economically important trait.

Project description:Genome-wide association studies (GWAS) have identified many common polymorphisms associated with complex traits. However, these associated common variants explain only a small fraction of the phenotypic variances, leaving a substantial portion of genetic heritability unexplained. As a result, searches for "missing" heritability are drawing increasing attention, particularly for rare variant studies that often require a large sample size and, thus, extensive sequencing effort. Although the development of next generation sequencing (NGS) technologies has made it possible to sequence a large number of reads economically and efficiently, it is still often cost prohibitive to sequence thousands of individuals that are generally required for association studies. A more efficient and cost-effective design would involve pooling the genetic materials of multiple individuals together and then sequencing the pools, instead of the individuals. This pooled sequencing approach has improved the plausibility of association studies for rare variants, while, at the same time, posed a great challenge to the pooled sequencing data analysis, essentially because individual sample identity is lost, and NGS sequencing errors could be hard to distinguish from low frequency alleles.A unified approach for estimating minor allele frequency, SNP calling and association studies based on pooled sequencing data using an expectation maximization (EM) algorithm is developed in this paper. This approach makes it possible to study the effects of minor allele frequency, sequencing error rate, number of pools, number of individuals in each pool, and the sequencing depth on the estimation accuracy of minor allele frequencies. We show that the naive method of estimating minor allele frequencies by taking the fraction of observed minor alleles can be significantly biased, especially for rare variants. In contrast, our EM approach can give an unbiased estimate of the minor allele frequency under all scenarios studied in this paper. A SNP calling approach, EM-SNP, for pooled sequencing data based on the EM algorithm is then developed and compared with another recent SNP calling method, SNVer. We show that EM-SNP outperforms SNVer in terms of the fraction of db-SNPs among the called SNPs, as well as transition/transversion (Ti/Tv) ratio. Finally, the EM approach is used to study the association between variants and type I diabetes.The EM-based approach for the analysis of pooled sequencing data can accurately estimate minor allele frequencies, call SNPs, and find associations between variants and complex traits. This approach is especially useful for studies involving rare variants.

Project description:BackgroundBoth common and rare mitochondrial DNA (mtDNA) variants may contribute to genetic susceptibility to some complex human diseases. Understanding of the role of mtDNA variants will provide valuable insights into the etiology of these diseases. However, to date, there have not been any large-scale, genome-wide association studies of complete mtDNA variants and disease risk. One reason for this might be the substantial cost of sequencing the large number of samples required for genetic epidemiology studies. Next-generation sequencing of pooled mtDNA samples will dramatically reduce the cost of such studies and may represent an appealing approach for large-scale genetic epidemiology studies. However, the performance of the different designs of sequencing pooled mtDNA has not been evaluated.MethodsWe examined the approach of sequencing pooled mtDNA of multiple individuals for estimating allele frequency using the Illumina genome analyzer (GA) II sequencing system. In this study the pool included mtDNA samples of 20 subjects that had been sequenced previously using Sanger sequencing. Each pool was replicated once to assess variation of the sequencing error between pools. To reduce such variation, barcoding was used for sequencing different pools in the same lane of the flow cell. To evaluate the effect of different pooling strategies pooling was done at both the pre- and post-PCR amplification step.ResultsThe sequencing error rate was close to that expected based on the Phred score. When only reads with Phred ≥ 20 were considered, the average error rate was about 0.3%. However, there was significant variation of the base-calling errors for different types of bases or at different loci. Using the results of the Sanger sequencing as the standard, the sensitivity of single nucleotide polymorphism detection with post-PCR pooling (about 99%) was higher than that of the pre-PCR pooling (about 82%), while the two approaches had similar specificity (about 99%). Among a total of 298 variants in the sample, the allele frequencies of 293 variants (98%) were correctly estimated with post-PCR pooling, the correlation between the estimated and the true allele frequencies being >0.99, while only 206 allele frequencies (69%) were correctly estimated in the pre-PCR pooling, the correlation being 0.89.ConclusionSequencing of mtDNA pooled after PCR amplification is a viable tool for screening mitochondrial variants potentially related to human diseases.

Dataset Information

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model.

Publications

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets