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© 2020 by the Krishnan Lab

RESEARCH

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The Krishnan lab develops and applies genomics/computational approaches to find mechanistic explanations for how our genome relates to health and disease.​

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We use statistics and machine learning to analyze large-scale data and build genome-scale predictive models about the genetic basis of biomedical phenomena.

 

We work in synergy with experimental and clinical researchers to motivate our methods and put our computational models/predictions to test in human and model systems.​

 

Our overarching goal is two-fold:
1) gain more nuanced and accurate insights into the genes and networks underlying physiology, complex diseases, and clinical phenotypes, and
2) use these insights to mechanistically link an individual's genomic profiles to a precise assessment of her/his physiological/clinical traits, risks, and outcomes.

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Select Publications

[* Joint primary author] [† Corresponding author]

  1. Understanding multi-cellular function and disease with human tissue-specific gene interaction networks.
    Greene CS*, Krishnan A*, Wong AK*, Ricciotti E, Zelaya R, Himmelstein D, Chasman D, Fitzgerald G, Dolinski K, Grosser T, Troyanskaya OG.
    Nature Genetics (2015) 47:569-576. doi:10.1038/ng.3259
    [pubmed] [commentary: nature biotechnology]
    [web-interface: GIANT]
     
  2. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder.
    Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG.
    Nature Neuroscience (2016) 19:1454-1462. doi:10.1038/nn.4353
    [bioRxiv] [pubmed] [commentary: science]
    [press: princeton | simons foundation | popular science | researchgate]
    [web-interface: ASD]
     
  3. Integrative networks illuminate biological factors underlying gene-disease associations.
    Krishnan A†, Taroni JN, Greene CS.
    Current Genetic Medicine Reports (2016) 4:155-162. doi:10.1007/s40142-016-0102-5
    [bioRxiv] [journal]

  • Genomics & computational biology

  • Applied statistical/machine learning

  • Integrative analysis of large-scale genomics and biomedical data

  • Genome-wide molecular networks

  • Age-specificity and sexual dimorphism in health and disease

  • Cross-species models for human traits and diseases

  • Genetic heterogeneity of complex disease and precision medicine

Funding

Collaborators

Ingo Braasch, Michigan State U.

Keith English, Michigan State U.

Julia Ganz, Michigan State U.

Santhosh Girirajan, Penn State U.

Kelly Klump, Michigan State U.

Rick Leach, Michigan State U.

Adam Moeser, Michigan State U.

Andy Pereira, U. Arkansas

Dhandapany Perundurai, OHSU

Aaditya Rangan, New York U.

Olga Troyanskaya, Princeton U.