Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

Zhang, Dongni; Cui, Hongzhu; Korkin, Dmitry; Wu, Zheyang

doi:10.1186/s12919-016-0043-8

Cited by 3 publications

(12 citation statements)

References 29 publications

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“…Statistical methods employed by this group (see Table 1 ) to incorporate filters or informative priors are mostly based on regression models [ 27 , 30 , 33 , 36 , 37 ]; one is also based on counting methods [ 28 ]. Analyses of family data adjusted for familial dependence based on the kinship matrix.…”

Section: Methodsmentioning

confidence: 99%

“…A variety of freely available genetic databases and highly developed software tools support annotation of location and biological function of SNVs. In our group, SNV locations were obtained by ANNOVAR [ 28 , 36 ] or determined based on reference data, for example, from the Genome Reference Consortium [ 40 ] or the International Haplotype Map (HapMap) Consortium [ 41 ] [ 30 , 37 ]. Reference data were also used to determine linkage disequilibrium (LD) blocks [ 30 ] with Haploview [ 42 ].…”

Section: Methodsmentioning

confidence: 99%

“…Aggregation tests can prioritize SNVs by weighting minor allele dosages in the test statistic. Typical weights account for MAF, but may also incorporate putative functional relevance of SNVs [ 27 , 28 ]. Moreover, weights may be used to combine aggregation test statistics [ 21 , 29 , 30 ], and one may weight p values while controlling the false discovery rate (FDR) [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

“…Approaches described herein mostly analyzed imputed dosages to avoid missing genotypes and to maximize sample size. Zhang et al [ 28 ] analyzed the GAW19 sample of 1943 independent Hispanic subjects with whole exome sequence. This sample had been ascertained by T2D status.…”

Section: Introductionmentioning

confidence: 99%

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Filtering genetic variants and placing informative priors based on putative biological function

Friedrichs

Malzahn

Pugh³

et al. 2016

BMC Genet

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High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Filtering genetic variants and placing informative priors based on putative biological function

Friedrichs

Malzahn

Pugh³

et al. 2016

BMC Genet

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“…of -omics data have demonstrated considerable power in bioinformatics studies [28]. Integrating PPI networks with gene expression profiles to identify sets of genes that participate in a biological function is a successful example of the benefits of data integration [29] .…”

Section: Introductionmentioning

confidence: 99%

Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

Cui

Srinivasan

Korkin

2019

Preprint

Self Cite

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Progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, DIMSUM, which enables the integration of genome-wide association studies (GWAS), functional effects of mutations, and protein-protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for disease module analysis, facilitating discovery of new disease markers.Unlike the driver mutations, which induce the clonal expansion, the passenger mutations do not provide any functional advantage to the development of cancer cells [9,10]. Thus, distinguishing between the functional and non-functional mutations is usually the first step in genetics studies. Additionally, computational approaches for functional annotation are increasingly important, since many variants are not previously described in the literature [6,11]. There are a plethora of functional annotation tools for genetic variants [6]. Many tools focus on the annotation of single nucleotide variants (SNVs), the most common type of genetic variation, which is easier to capture and analyze. Recently, we have developed a new computational method, SNP-IN tool [12], which predicts the effects of non-synonymous SNVs on protein-protein interaction (PPI), provided the interaction's structure or structural model. The method leverages supervised and semi-supervised feature-based classifiers, including a new random forest self-learning protocol. The accurate and balanced performance of SNP-IN tool makes it apt for functional annotation of non-synonymous single nucleotide polymorphisms (nsSNPs). SNP-IN tool could also be helpful system-wide variation analysis to discover pathways shared by associated alleles and reveal disease-related biological process.The need of integrating GWAS studies and the functional impact of the disease-associated mutations with the systems data is supported by the increasing body of evidence that large-scal...

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Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

Cui

Srinivasan

Korkin

2019

Genes

View full text Add to dashboard Cite

Rapid progress in high-throughput -omics technologies moves us one step closer to the datacalypse in life sciences. In spite of the already generated volumes of data, our knowledge of the molecular mechanisms underlying complex genetic diseases remains limited. Increasing evidence shows that biological networks are essential, albeit not sufficient, for the better understanding of these mechanisms. The identification of disease-specific functional modules in the human interactome can provide a more focused insight into the mechanistic nature of the disease. However, carving a disease network module from the whole interactome is a difficult task. In this paper, we propose a computational framework, Discovering most IMpacted SUbnetworks in interactoMe (DIMSUM), which enables the integration of genome-wide association studies (GWAS) and functional effects of mutations into the protein–protein interaction (PPI) network to improve disease module detection. Specifically, our approach incorporates and propagates the functional impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on PPIs to implicate the genes that are most likely influenced by the disruptive mutations, and to identify the module with the greatest functional impact. Comparison against state-of-the-art seed-based module detection methods shows that our approach could yield modules that are biologically more relevant and have stronger association with the studied disease. We expect for our method to become a part of the common toolbox for the disease module analysis, facilitating the discovery of new disease markers.

show abstract

Incorporation of protein binding effects into likelihood ratio test for exome sequencing data

Cited by 3 publications

References 29 publications

Filtering genetic variants and placing informative priors based on putative biological function

Filtering genetic variants and placing informative priors based on putative biological function

Enriching human interactome with functional mutations to detect high-impact network modules underlying complex diseases

Enriching Human Interactome with Functional Mutations to Detect High-Impact Network Modules Underlying Complex Diseases

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