Arjun Krishnan is leading a research group that develops computational approaches to study the genetic basis of biomedical phenomena relevant to human health and disease. The Krishnan Lab is primarily interested in bridging the gap between large-scale genomic/clinical data and actionable biological insights using statistical and machine learning approaches.
He joined the faculty of Michigan State University in January 2017. Krishnan received his Ph.D. in 2010 from Virginia Tech, and continued briefly as a postdoctoral researcher. There, working with Prof. Andy Pereira, he developed computational genomic methods to reconstruct the gene-regulatory programs in both model and crop plants. In 2011, he began his postdoctoral research in the Lewis-Sigler Institute for Integrative Genomics at Princeton University with Prof. Olga Troyanskaya. There, he developed integrative data-driven approaches to study tissue-specificity in the function of human genes and their association with complex diseases.
Areas of Expertise (12)
Genome-wide molecular networks
Cross-species Models for Human Disease
Age-specificity and Sexual-dimorphism in Health & Disease
Virginia Tech: PhD, Genetics, Bioinformatics, and Computational Biology 2010
Anna University: BTech, Biotechnology 2006
New Early Career Award Trains World-Class Postdocs, Advances Wheels of Innovation
Hickey will be working with a team that includes Ralston, who studies how molecules instruct stem cell behavior, Jin He, assistant professor in BMB who studies brains cells and brain cancer, David Arnosti, professor in BMB who studies fundamental problems in early embryo genesis, and Arjun Krishnan, assistant professor in BMB and the Department of Computational Math, Science and Engineering who applies computational, data-driven approaches to the study of genomes.
New Computational Methods May Lead to Healthier Future
Work in the lab of Arjun Krishnan (seated), a CMSE assistant professor with a joint appointment in BMB (also a Global Impact researcher), focuses on how genomics relates to human health and disease. His approach involves using existing large datasets that represent decades of experimental work by hundreds of researchers across the world who have made their data publicly available.
Journal Articles (4)
Arjun Krishnan, Jaclyn N. Taroni, Casey S. Greene
2016 Integrative networks combine multiple layers of biological data into a model of how genes work together to carry out cellular processes. Such networks become more valuable as they become more context-specific, for example, by capturing how genes work together in a certain tissue or cell type. We discuss the applications of these networks to the study of human disease.
Arjun Krishnan, Ran Zhang, Victoria Yao, Chandra L Theesfeld, Aaron K Wong, Alicja Tadych, Natalia Volfovsky, Alan Packer, Alex Lash & Olga G Troyanskaya
2016 Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic basis. Yet, only a small fraction of potentially causal genes—about 65 genes out of an estimated several hundred—are known with strong genetic evidence from sequencing studies. We developed a complementary machine-learning approach based on a human brain-specific gene network to present a genome-wide prediction of autism risk genes, including hundreds of candidates for which there is minimal or no prior genetic evidence. Our approach was validated in a large independent case–control sequencing study. Leveraging these genome-wide predictions and the brain-specific network, we demonstrated that the large set of ASD genes converges on a smaller number of key pathways and developmental stages of the brain. Finally, we identified likely pathogenic genes within frequent autism-associated copy-number variants and proposed genes and pathways that are likely mediators of ASD across multiple copy-number variants. All predictions and functional insights are available at http://asd.princeton.edu.
Casey S Greene, Arjun Krishnan, Aaron K Wong, Emanuela Ricciotti, Rene A Zelaya, Daniel S Himmelstein, Ran Zhang, Boris M Hartmann, Elena Zaslavsky, Stuart C Sealfon, Daniel I Chasman, Garret A FitzGerald, Kara Dolinski, Tilo Grosser & Olga G Troyanskaya
2015 Tissue and cell-type identity lie at the core of human physiology and disease. Understanding the genetic underpinnings of complex tissues and individual cell lineages is crucial for developing improved diagnostics and therapeutics. We present genome-wide functional interaction networks for 144 human tissues and cell types developed using a data-driven Bayesian methodology that integrates thousands of diverse experiments spanning tissue and disease states. Tissue-specific networks predict lineage-specific responses to perturbation, identify the changing functional roles of genes across tissues and illuminate relationships among diseases. We introduce NetWAS, which combines genes with nominally significant genome-wide association study (GWAS) P values and tissue-specific networks to identify disease-gene associations more accurately than GWAS alone. Our webserver, GIANT, provides an interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than a hundred human tissues and cell types.
Madana M. R. Ambavaram, Supratim Basu, Arjun Krishnan, Venkategowda Ramegowda, Utlwang Batlang, Lutfor Rahman, Niranjan Baisakh & Andy Pereira
2014 Plants capture solar energy and atmospheric carbon dioxide (CO2) through photosynthesis, which is the primary component of crop yield, and needs to be increased considerably to meet the growing global demand for food. Environmental stresses, which are increasing with climate change, adversely affect photosynthetic carbon metabolism (PCM) and limit yield of cereals such as rice (Oryza sativa) that feeds half the world. To study the regulation of photosynthesis, we developed a rice gene regulatory network and identified a transcription factor HYR (HIGHER YIELD RICE) associated with PCM, which on expression in rice enhances photosynthesis under multiple environmental conditions, determining a morpho-physiological programme leading to higher grain yield under normal, drought and high-temperature stress conditions. We show HYR is a master regulator, directly activating photosynthesis genes, cascades of transcription factors and other downstream genes involved in PCM and yield stability under drought and high-temperature environmental stress conditions.