Quantification of rare allelic variants from pooled genomic DNA

Druley, Todd E.; Vallania, Francesco; Wegner, Daniel; Varley, Katherine E.; Knowles, Olivia L; Bonds, Jacqueline A.; Robison, Sarah; Doniger, Scott; Hamvas, Aaron; Cole, F. Sessions; Fay, Justin C.; Mitra, Robi D.

doi:10.1038/nmeth.1307

Cited by 134 publications

(157 citation statements)

References 9 publications

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“…Several groups have developed SNP calling methods based on pooled sequencing data [7,9-12]. Out et al [7] modeled the number of sequencing errors as a Poisson random variable and identified SNPs by comparing the number of minor alleles within the reads with the Poisson distribution.…”

Section: Introductionmentioning

confidence: 99%

“…For rare variants with minor allele frequencies similar to or lower than the sequencing error rate, this approach could miss many true variants if the pool size is relatively large. Druley et al [9] developed a SNP identification method, SNPSeeker, that can be applied to large pools by using control sequences without SNPs. In many studies, control sequences may not be available, making this approach impractical.…”

Section: Introductionmentioning

confidence: 99%

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A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

Chen

Sun

2013

BMC Genomics

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BackgroundGenome-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.ResultsA 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.ConclusionsThe 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

Chen

Sun

2013

BMC Genomics

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“…They enable direct sequencing of mixed samples, such as virus populations 6,7 , bacterial communities 8 , tumours [9][10][11] and pooled samples 12,13 , and the reconstruction of their genomic composition. However, single-nucleotide errors resulting from target enrichment, library preparation and base calling are frequent on all current sequencing platforms 5 , and they are difficult to separate from true lowfrequency single-nucleotide variants (SNVs).…”

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confidence: 99%

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

et al. 2012

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According to the clonal evolution model, tumour growth is driven by competing subclones in somatically evolving cancer cell populations, which gives rise to genetically heterogeneous tumours. Here we present a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, called deepsnV, for detecting and quantifying subclonal single-nucleotide variants in mixed populations. We show in a rigorous experimental assessment that our approach is capable of detecting variants with frequencies as low as 1/10,000 alleles. In selected genomic loci of the TP53 and VHL genes isolated from matched tumour and normal samples of four renal cell carcinoma patients, we detect 24 variants at allele frequencies ranging from 0.0002 to 0.34. moreover, we demonstrate how the allele frequencies of known single-nucleotide polymorphisms can be exploited to detect loss of heterozygosity. our findings demonstrate that genomic diversity is common in renal cell carcinomas and provide quantitative evidence for the clonal evolution model.

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“…Strelka [13] explicitly models mixtures of tumor and normal cells and can also call small indels. Also, several tools exist to accurately detect SNVs in pooled data [14-17], even mutations of low abundance. Apart from analyzing single SNVs, also haplotype inference and assembly has been addressed [18-20].…”

Section: Introductionmentioning

confidence: 99%

Unraveling overlapping deletions by agglomerative clustering

Wittler

2013

BMC Genomics

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BackgroundStructural variations in human genomes, such as deletions, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Methods like paired-end mapping allow to simultaneously analyze data from several samples in order to, e.g., distinguish tumor from patient specific variations. However, it has been shown that, especially in this setting, there is a need to explicitly take overlapping deletions into consideration. Existing tools have only minor capabilities to call overlapping deletions, unable to unravel complex signals to obtain consistent predictions.ResultWe present a first approach specifically designed to cluster short-read paired-end data into possibly overlapping deletion predictions. The method does not make any assumptions on the composition of the data, such as the number of samples, heterogeneity, polyploidy, etc. Taking paired ends mapped to a reference genome as input, it iteratively merges mappings to clusters based on a similarity score that takes both the putative location and size of a deletion into account.ConclusionWe demonstrate that agglomerative clustering is suitable to predict deletions. Analyzing real data from three samples of a cancer patient, we found putatively overlapping deletions and observed that, as a side-effect, erroneous mappings are mostly identified as singleton clusters. An evaluation on simulated data shows, compared to other methods which can output overlapping clusters, high accuracy in separating overlapping from single deletions.

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Quantification of rare allelic variants from pooled genomic DNA

Cited by 134 publications

References 9 publications

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

Unraveling overlapping deletions by agglomerative clustering

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