Publications

Preprints Under Review

An empirical Bayes framework for burden and dispersion association tests helps prioritize rare variants associated with Alzheimer's disease
Anjali Das, Chirag Lakhani, Vera M. Mazeeva, Towfique Raj, David A. Knowles
medRxiv, 2026
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Single-cell machine learning uncovers genetically anchored, cell-type specific programs of Alzheimer's disease
Adithya Madduri, Randall Ellis, Chirag Lakhani, David Bennett, Chirag J. Patel
medRxiv, 2026
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Machine learning-based prediction of cell-type resolved brain eQTLs enhances discovery of variants explaining Alzheimer's disease heritability
Chirag M. Lakhani, Giacomo Cavalca, Anjing Liu, Rohan Nidumbur, Ru Feng, Towfique Raj, Philip De Jager, The Alzheimer's Disease Functional Genomics Consortium, Gao Wang, David A. Knowles
medRxiv, 2025
Introduces scEEMS (single-cell Enhanced Expression Modifier Scores), a machine learning framework predicting cell-type-resolved brain eQTLs; scEEMS annotations identify variants explaining 15.3% of Alzheimer's disease heritability at 140-fold enrichment.
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Distributional genetic effects reveal context-dependent molecular regulation in human brain aging and Alzheimer's disease
Anjing Liu, Roulan Jiang, Ruixi Li, Xuewei Cao, Zining Qi, Ru Feng, Hao Sun, Masashi Fujita, Natacha Comandante-Lou, Chirag M. Lakhani, Jenny Empawi, David A. Knowles, Xiaoling Zhang, Kushal K. Dey, Philip De Jager, David Bennett, The Alzheimer's Disease Functional Genomics Consortium, Tianying Wang, Gao Wang
Research Square, 2025
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Mosaic chromosomal alterations in blood are associated with an increased risk of Alzheimer's disease
Tatsuhiko Naito, Kosei Hirata, Beomjin Jang, Chirag M. Lakhani, Alice Buonfiglioli, Wan-Ping Lee, Otto Valladares, Li-San Wang, Yukinori Okada, Hong-Hee Won, Eirini P. Papapetrou, Samuele G. Marro, David A. Knowles, Towfique Raj
medRxiv, 2025
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Integration of deep learning annotations with functional genomics improves identification of causal Alzheimer's disease variants
Chirag M. Lakhani, Jui-Shan T. Lin, Anjali Das, Tatsuhiko Naito, Towfique Raj, David A. Knowles
medRxiv, 2025
Integrates genomic deep learning annotations with functional genomics data in a functionally informed fine-mapping framework, improving identification of causal Alzheimer's disease variants.
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Geospatial analysis of individual and community-level socioeconomic factors impacting SARS-CoV-2 prevalence and outcomes
Sara J. Cromer, Chirag M. Lakhani, Deborah J. Wexler, Sherri-Ann M. Burnett-Bowie, Miriam Udler, Chirag J. Patel
medRxiv, 2020
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Peer-Reviewed Publications

Interpretable deep learning for functional fine-mapping of chromatin QTLs in Alzheimer's disease
Clara Yu, Chirag Lakhani, David A. Knowles
14th International Conference on Bioinformatics and Computational Biology (ICBCB), IEEE, pp. 85-89, 2026
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Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere
Anjali Das, Chirag Lakhani, Chloé Terwagne, Jui-Shan T. Lin, Tatsuhiko Naito, Towfique Raj, David A. Knowles
The American Journal of Human Genetics, vol. 112, no. 9, pp. 2138-2151, 2025
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LDmat: efficiently queryable compression of linkage disequilibrium matrices
Rockwell J. Weiner, Chirag Lakhani, David A. Knowles, Gamze Gürsoy
Bioinformatics, vol. 39, no. 2, pp. btad092, 2023
Paper Code Project Page
Association and interaction of genetics and area-level socioeconomic factors on the prevalence of type 2 diabetes and obesity
Sara J. Cromer, Chirag M. Lakhani, Josep M. Mercader, Timothy D. Majarian, Philip Schroeder, Joanne B. Cole, Jose C. Florez, Chirag J. Patel, Alisa K. Manning, Sherri-Ann M. Burnett-Bowie, et al.
Diabetes Care, vol. 46, no. 5, pp. 944-952, 2023
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A new method for estimating the probability of causal relationships from observational data: Application to the study of the short-term effects of air pollution on cardiovascular and respiratory disease
Bryan Andrews, Chirayu Wongchokprasitti, Shyam Visweswaran, Chirag M. Lakhani, Chirag J. Patel, Gregory F. Cooper
Artificial Intelligence in Medicine, vol. 139, pp. 102546, 2023
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The impact of socioeconomic status on subsequent neurological outcomes in multiple sclerosis
Devi Sai Sri Kavya Boorgu, Shruthi Venkatesh, Chirag M. Lakhani, Elizabeth Walker, Ines M. Aguerre, Claire Riley, Chirag J. Patel, Philip L. De Jager, Zongqi Xia
Multiple Sclerosis and Related Disorders, vol. 65, pp. 103994, 2022
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Identifying communities at risk for COVID-19-related burden across 500 US cities and within New York City: unsupervised learning of the coprevalence of health indicators
Andrew Deonarine, Genevieve Lyons, Chirag Lakhani, Walter De Brouwer
JMIR Public Health and Surveillance, vol. 7, no. 8, pp. e26604, 2021
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Comparisons of polyexposure, polygenic, and clinical risk scores in risk prediction of type 2 diabetes
Yixuan He, Chirag M. Lakhani, Danielle Rasooly, Arjun K. Manrai, Ioanna Tzoulaki, Chirag J. Patel
Diabetes Care, vol. 44, no. 4, pp. 935-943, 2021
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Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes
Chirag M. Lakhani, Braden T. Tierney, Arjun K. Manrai, Jian Yang, Peter M. Visscher, Chirag J. Patel
Nature Genetics, vol. 51, no. 2, pp. 327-334, 2019
Repurposed a health insurance claims dataset of 45 million individuals to conduct the largest twin study in the United States (56,396 twin pairs), estimating genetic and environmental contributions to 560 phenotypes.
Paper Code Project Page
Press coverage: Washington Post | The Verge | CNN | Axios | Harvard Magazine | NIEHS Environmental Factor

Earlier Work

From my doctoral training in mathematics.
The GIT compactification of quintic threefolds
Chirag Lakhani
arXiv preprint arXiv:1010.3803, 2010
From my doctoral training in algebraic geometry.
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