Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Kennedy, Justin; Măndoiu, Ion; Paşaniuc, Bogdan

doi:10.1007/978-3-540-74126-8_8

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…Our LD-PAC method is different from the LD-based QC methods [Kennedy et al, 2008;Scheet and Stephens, 2008]. Besides the fact that these methods are QC filters for the preassociation stage whereas our method is a PAC filter for the postassociation stage, these methods can be thought of as an extension of imputation methods [Kang et al, 2010;Marchini et al, 2007;Scheet and Stephens, 2006;Zaitlen et al, 2005], whereas our method is a formalization of the intuitive idea that has been typically performed manually.…”

Section: Discussionmentioning

confidence: 96%

Postassociation cleaning using linkage disequilibrium information

2010

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In genetic association studies, quality control (QC) filters are applied to remove potentially problematic markers before the markers are tested for statistical associations. However, spurious associations can still occur after QC. We introduce Post-Association Cleaning (PAC) approach that can complement QC by capturing spurious associations using the information in the post-association results. Specifically, we propose a PAC filter based on the linkage disequilibrium (LD) information. The intuition is that if the association is caused by a true genetic effect, neighboring markers in LD should show comparably significant P-values. If not, it may be evidence of spurious association. Previous studies have applied the same idea but only manually without a formal statistical framework. Our proposed method LD-PAC provides a systematic framework to quantitatively measure the evidence of spurious associations based on the likelihood ratio. Simulations show that LD-PAC can detect spurious associations with high detection rate (84%). In addition to detecting spurious associations, our method can also be used to "rescue" candidate associations from the supposedly unclean data such as the markers excluded by QC. Although the additional associations must be treated with care, they can suggest interesting regions. The application of our method to the Wellcome Trust Case Control Consortium (WTCCC) data led to the discovery of an additional candidate association for type 1 diabetes among the QC-excluded markers. This locus turns out to be in a region recently identified as significant by a meta-analysis performed after the WTCCC study was published.

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Section: Discussionmentioning

confidence: 96%

Postassociation cleaning using linkage disequilibrium information

2010

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“…To find the most likely missing genotype, we need to model population haplotype frequencies, and we do so using a Markov model with a similar structure to those recently used by Kimmel and Shamir (2005;), Kennedy et al, (2008), and Browning (2006). The basic structure of this LD model is that of a left-to-right directed graph, with M disjoint sets of nodes corresponding to the M loci.…”

Section: Missing Genotypesmentioning

confidence: 99%

“…The edges carry the population frequency of transition from a haplotype in one position to a haplotype in the next position, meaning that every haplotype in the EALPS population corresponds to a path in the graph. Training of the model according to population samples can be done either with the Baum-Welch algorithm for HMMs, like in Kennedy et al (2008), or in the constructive approach described in Browning (2006). In our implementation, we used the BEAGLE genetic analysis software package (version 3.3.2) to build the LD model.…”

Section: Missing Genotypesmentioning

confidence: 99%

eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data

Eskin

Hormozdiari

Conde

et al. 2013

Lecture Notes in Computer Science

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The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species.

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“…There are many automated algorithms used to determine the characteristic of genomic profiles, Justin K. et al use Hidden Markov Models (HMM) algorithm to demonstrate that highly accurate SNP genotypes can infer from very low coverage shotgun [18]. Cooper et al use SNP conditional mixture modelling (SCIMM) by applied a mixture-likelihood clustering method within the R statistical package to identify deletions of copy number changes [19].…”

Section: (Ijacsa) International Journal Of Advanced Computer Science Andmentioning

confidence: 99%

Novel Altered Region for Biomarker Discovery in Hepatocellular Carcinoma (HCC) Using Whole Genome SNP Array

Hashem¹,

Mabrouk²,

Eldeib³

2016

ijacsa

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Abstract-cancer represents one of the greatest medical causes of mortality. The majority of Hepatocellular carcinoma arises from the accumulation of genetic abnormalities, and possibly induced by exterior etiological factors especially HCV and HBV infections. There is a need for new tools to analysis the large sum of data to present relevant genetic changes that may be critical for both understanding how cancers develop and determining how they could ultimately be treated. Gene expression profiling may lead to new biomarkers that may help develop diagnostic accuracy for detecting Hepatocellular carcinoma. In this work, statistical technique (discrete stationary wavelet transform) for detection of copy number alternations to analysis high-density single-nucleotide polymorphism array of 30 cell lines on specific chromosomes, which are frequently detected in Hepatocellular carcinoma have been proposed. The results demonstrate the feasibility of whole-genome fine mapping of copy number alternations via high-density single-nucleotide polymorphism genotyping, Results revealed that a novel altered chromosomal region is discovered; region amplification (4q22.1) have been detected in 22 out of 30-Hepatocellular carcinoma cell lines (73%). This region strike, AFF1 and DSPP, tumor suppressor genes. This finding has not previously reported to be involved in liver carcinogenesis; it can be used to discover a new HCC biomarker, which helps in a better understanding of hepatocellular carcinoma.

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Genotype Error Detection Using Hidden Markov Models of Haplotype Diversity

Cited by 3 publications

References 19 publications

Postassociation cleaning using linkage disequilibrium information

Postassociation cleaning using linkage disequilibrium information

eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data

Novel Altered Region for Biomarker Discovery in Hepatocellular Carcinoma (HCC) Using Whole Genome SNP Array

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