Noah Zaitlen, PhD

Assistant Professor
Department of Medicine
Lung Biology Center
+1 415 502-2027

We develop statistical and computational tools to discover the genetic basis of complex phenotypes. We are especially interested in human disease, variation in drug/treatment response, and disease outcomes. Current projects primarily focus on incorporating environmental context into medical genetics. These include developing novel techniques to partition the proportion of phenotype driven by genetic and environmental factors in world-wide populations (Nature versus Nurture), and improving our power to identify disease causing mutations by leveraging gene-expression, meta-genomic, and clinical data such as smoking status, BMI, and age.

Primary Thematic Area: 
Human Genetics
Secondary Thematic Area: 
Research Summary: 
Development of statistical methods for uncovering the genetic basis of human disease



Heritable natural variation of an anxiety-like behavior in larval zebrafish.

Journal of neurogenetics

Wagle M, Nguyen J, Lee S, Zaitlen N, Guo S

GLINT: a user-friendly toolset for the analysis of high-throughput DNA-methylation array data.

Bioinformatics (Oxford, England)

Rahmani E, Yedidim R, Shenhav L, Schweiger R, Weissbrod O, Zaitlen N, Halperin E

Correction: Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

PLoS genetics

Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA, Hendren RL, Traglia M, Lavillaureix A, Zaitlen N, Oldham MC, Levitt P, Nelson S, Amaral DG, Hertz-Picciotto I, Fallin MD, Weiss LA

Cis-eQTL-based trans-ethnic meta-analysis reveals novel genes associated with breast cancer risk.

PLoS genetics

Hoffman JD, Graff RE, Emami NC, Tai CG, Passarelli MN, Hu D, Huntsman S, Hadley D, Leong L, Majumdar A, Zaitlen N, Ziv E, Witte JS

Correcting for cell-type heterogeneity in DNA methylation: a comprehensive evaluation.

Nature methods

Rahmani E, Zaitlen N, Baran Y, Eng C, Hu D, Galanter J, Oh S, Burchard EG, Eskin E, Zou J, Halperin E