About

Anshul Kundaje is an Assistant Professor of Genetics and of Computer Science at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His research focuses on the application of artificial intelligence and computational methods to medicine and imaging, contributing to the advancement of healthcare through innovative data-driven approaches. His work involves integrating genetics, computer science, and AI to develop new tools and insights in biomedical research and clinical practice.

Research topics

• Biology
• Genetics
• Computational biology
• Medicine
• Computer Science
• Bioinformatics
• Data Mining
• Machine Learning
• Pathology
• Cancer research
• Cell biology
• Artificial Intelligence
• Engineering
• Psychology
• Neuroscience
• Evolutionary biology
• Anatomy
• Geography
• World Wide Web
• Ecology
• Psychiatry
• Cartography
• Mathematics

Selected publications

• Multiomics and deep learning dissect regulatory syntax in human development

  Nature · 2026-04-08 · 1 citations

  articleOpen accessCorresponding

  Abstract Transcription factors establish cell identity during development by binding regulatory DNA in a sequence-specific manner, often promoting local chromatin accessibility and regulating gene expression 1 . Mapping accessible chromatin offers critical insights into transcriptional control, but available datasets for human development are restricted to bulk tissue, single organs or single modalities 2 . Here we present the Human Development Multiomic Atlas, a single-cell atlas of chromatin accessibility and gene expression from 817,740 fetal cells across 12 organs, spanning 203 cell types and more than 1 million candidate cis -regulatory elements, many of which exhibit organ-specific in vivo enhancer activity. Deep learning models trained to predict accessibility from local DNA sequence unravel a comprehensive lexicon of motifs that influence accessibility, including composite motifs exhibiting distinct syntactic constraints that are predicted to mediate transcription factor cooperativity. We identify ‘hard’ syntactic rules requiring precise motif spacing and orientation, ‘soft’ rules allowing flexible motif arrangements, and ubiquitous motifs inhibiting accessibility. Model-based interpretation of genetic variants reveals that disruption of motifs with positive and negative effects is associated with concordant effects on gene expression. Our work delineates how motif syntax governs cell-type-specific chromatin accessibility and provides a foundational resource for decoding cis -regulatory logic and interpreting genetic variation during human development.

  PublisherDOI

• An expanded registry of candidate cis-regulatory elements

  Nature · 2026-01-07 · 17 citations

  articleOpen access

  Abstract Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression 1 . Previously, the ENCODE consortium mapped biochemical signals across hundreds of cell types and tissues and integrated these data to develop a registry containing 0.9 million human and 300,000 mouse candidate cis -regulatory elements (cCREs) annotated with potential functions 2 . Here we have expanded the registry to include 2.37 million human and 967,000 mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays such as STARR-seq 3 , massively parallel reporter assay 4 , CRISPR perturbation 5,6 and transgenic mouse assays 7 have profiled more than 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer and silencer roles in different cellular contexts. Integrating the registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by the identification of KLF1 as a novel causal gene for red blood cell traits. This expanded registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

  PublisherDOI

• TGF-β Pathway–Based Polygenic Risk Score Modifies the Association between Red Meat Intake and Colorectal Cancer Risk: Application of a Novel Pathway-Based PRS Method

  Cancer Epidemiology Biomarkers & Prevention · 2026-04-01 · 1 citations

  article

  BACKGROUND: Red and/or processed meat are established colorectal cancer (CRC) risk factors. Genome-wide association studies (GWAS) have reported over 200 variants associated with CRC risk. We used functional annotation data to identify subsets of variants within known pathways to construct pathway-based Polygenic Risk Scores (pPRS) to assess interactions with meat intake. METHODS: A pooled sample of 30,812 cases and 40,504 CRC controls from 27 studies were analyzed. Quantiles for red and processed meat intake were constructed. 204 GWAS variants were annotated to genes with AnnoQ and assessed for overrepresentation in PANTHER-reported pathways. pPRS's were constructed from significantly overrepresented pathways. Covariate-adjusted logistic regression models evaluated interactions between pPRS and red or processed meat intake in relation to CRC risk. RESULTS: A total of 30 variants were overrepresented in four pathways: Presenilin-Alzheimer disease, Cadherin/WNT-signaling, Gonadotropin-releasing hormone receptor, and TGF-β signaling. We found a significant interaction between TGF-β-pPRS and red meat intake (ORint = 0.95; 95% CI = 0.92-0.98; p = 0.003). When variants in the TGF-β pathway were assessed, we observed significant interactions of red meat with rs2337113 (intron SMAD7 gene, Chr18), and rs2208603 (intergenic region BMP5, Chr6) (p = 0.0005 & 0.036, respectively). There was no evidence of pPRS x red meat interactions for other pathways or with processed meat Conclusions:This pathway-based interaction analysis revealed a statistically significant interaction between variants in the TGF-β pathway and red meat consumption that impacts CRC risk. IMPACT: These findings shed light into the possible mechanistic link between red meat consumption and CRC risk.

  PublisherDOI

• Table S11 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This file includes the association parameters (OR [95% CI]) for the imputed gene expression levels of genes associated with lead SNPs.</p>

  PublisherDOI

• Figure S2 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This figure depicts the quantile-quantile plots for the standard 1-df interaction tests.</p>

  PublisherOA PDFDOI

• Figure S1 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This figure depicts the flowchart of the two-step interaction test.</p>

  PublisherOA PDFDOI

• Table S8 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This file includes the studies included in the analysis stratifing individuals by tumour features.</p>

  PublisherDOI

• Supplementary Figure from Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region

  2025-11-26

  articleOpen access

  Supplementary Figure from Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region

  PublisherDOI

• Table S6 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This file includes the association parameters (OR [95% CI]) for the identified SNPs with significant interaction term for smoking intensity by genotypes.</p>

  PublisherDOI

• Table S2 from Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response

  2025-11-26

  articleOpen access

  <p>This table includes the overall sample description stratified by colorectal cancer (CRC) status and smoking status.</p>

  PublisherDOI

Recent grants

• NIH Grant DP2GM12348501

  NIH · $2.4M · 2021

• Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code

  NIH · $3.2M · 2021–2026

• SOFTWARE FOR LARGE-SCALE INFERENCE OF THE GENETICS OF LIFESTYLE MEASURES, BIOMARKERS, AND COMMON AND RARE DISEASES

  NIH · $2.0M · 2018–2023

• Learning Regulatory Drivers of Chromatin and Expression Dynamics during Nuclear Reprogramming

  NIH · $2.1M · 2017–2020

Frequent coauthors

• Jenny Chang‐Claude

  117 shared

• Loı̈c Le Marchand

  Cancer Center of Hawaii

  113 shared

• William J. Greenleaf

  107 shared

• Vı́ctor Moreno

  Universitat de Barcelona

  99 shared

• Hermann Brenner

  TU Bergakademie Freiberg

  93 shared

• Andrew T. Chan

  Brigham and Women's Hospital

  92 shared

• Joshua W. K. Ho

  University of Hong Kong

  91 shared

• M Snyder

  85 shared

Education

• Ph.D., Genetics and Computer Science

  Stanford University

  2013

• M.S., Computer Science

  Stanford University

  2009

• B.S., Computer Science

  University of Pennsylvania

  2007