Systematically Sifting Big Data to Identify Novel Causal Genes for Human Traits

Nicholas J Hand; Daniel J Rader

doi:10.1016/j.cmet.2020.03.013

Systematically Sifting Big Data to Identify Novel Causal Genes for Human Traits

Cell Metab. 2020 Apr 7;31(4):658-659. doi: 10.1016/j.cmet.2020.03.013.

Authors

Nicholas J Hand¹, Daniel J Rader²

Affiliations

¹ Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
² Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: rader@pennmedicine.upenn.edu.

PMID: 32268109
DOI: 10.1016/j.cmet.2020.03.013

Abstract

Widespread technological advances have propelled human genetics into a "big data" era, in which genome-wide data from extremely large cohorts can be integrated with other "-omics" datasets from humans and model systems. Li et al. (2020) demonstrate the power of applying multiple computational analyses to publicly available data to prioritize the study of genes with novel trait associations.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Comment

MeSH terms

Animals
Big Data
Cholesterol
Genome-Wide Association Study*
Human Genetics*
Humans
Mice
Phenotype

Substances

Cholesterol

Abstract

Publication types

MeSH terms

Substances

Grants and funding