Dataset Information

TAPPA: topological analysis of pathway phenotype association.

ABSTRACT: Extracting biological insight from microarray data is important but challenging. Here we describe TAPPA, a java-based tool, for identification of phenotype-associated genetic pathways utilizing the pathway topological measures. This is achieved by first calculating a Pathway Connectivity Index (PCI) for each pathway, followed by evaluating its correlation to the phenotypic variation. Our PCI definition not only efficiently captures the contributions from genes that show subtle but consistent changes in expression, but also naturally overweighs the hub genes that interact with a large number of other genes in the pathway. TAPPA also allows evaluation of sub-modules within a pathway and their association to phenotypes.

SUBMITTER: Gao S

PROVIDER: S-EPMC2473868 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature

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TAPPA: topological analysis of pathway phenotype association.

Gao Shouguo S Wang Xujing X

Bioinformatics (Oxford, England) 20070921 22

Extracting biological insight from microarray data is important but challenging. Here we describe TAPPA, a java-based tool, for identification of phenotype-associated genetic pathways utilizing the pathway topological measures. This is achieved by first calculating a Pathway Connectivity Index (PCI) for each pathway, followed by evaluating its correlation to the phenotypic variation. Our PCI definition not only efficiently captures the contributions from genes that show subtle but consistent cha ...[more]

PMID: 17890270

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Project description:BackgroundTraumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge.Methods and findingsThe Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2).ConclusionsTDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients.Trial registrationClinicalTrials.gov Identifier NCT01565551.

Project description:With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms.

Dataset Information

TAPPA: topological analysis of pathway phenotype association.

Publications

TAPPA: topological analysis of pathway phenotype association.

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