We work at the intersection of
biology + computer science + statistics + applied mathematics
The Krishnan lab develops and applies genomics/computational approaches to find mechanistic explanations for how our genome relates to health and disease.
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:
Gain more nuanced and accurate insights into the genes and networks underlying physiology, complex diseases, and clinical phenotypes, and
Use these insights to mechanistically link an individual's genomic profiles to a precise assessment of her/his physiological/clinical traits, risks, and outcomes.
[* Joint primary author] [† Corresponding author]
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]
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]
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.
Computational genomics and biomedical data science
Applied statistical and machine learning
Integrative analysis of large-scale genomics / biomedical data
Genome-wide molecular interaction networks
Age-specificity & sex differences in health and disease
Cross-species models for human traits and diseases
Genetic heterogeneity of complex disease and precision medicine
Dept. Biochemistry and Molecular Biology at MSU