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Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations.

ABSTRACT: Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.

SUBMITTER: Wierbowski SD

PROVIDER: S-EPMC6510504 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations.

Wierbowski Shayne D SD Fragoza Robert R Liang Siqi S Yu Haiyuan H

Current opinion in systems biology 20180917

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fail ...[more]

PMID: 31086831

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Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations.

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Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations.

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