Author

Larry Baum

University of Hong KongORCID 0000-0002-5345-9355Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genetic & Genomic Medicine· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Research Focus:variant calling· 2 Research Focus:variant effect prediction· 2 Research Focus:GWAS / association mapping· 1 Assay:GWAS summary statistics· 1 Assay:whole-exome sequencing· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

Pak Chung ShamiD· 2 shared Rui YeiD· 2 shared Ahmad A. BeydouniD· 1 shared Alica M. GoldmaniD· 1 shared Alison AndersoniD· 1 shared Andrew J. ColeiD· 1 shared Annika B. FauconiD· 1 shared Antonio GambardellaiD· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Pak Chung ShamORCID

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3 papers

Recent·Most cited

Common and rare variant analyses implicate late-infancy cerebellar development and immune genes in ADHD

Yuanxin Zhong, Larry Baum, Justin D. Tubbs, Rui Ye, Lu Hua Chen, Tian T. Wu, Se‐Fong Hung, Chun-pan Tang, Tin-Ian Ho, Robert K. Moyzis, James M. Swanson, Chi‐Chiu Lee, Pak Chung Sham, Patrick W. L. Leung

This study identifies common and rare variant risk loci for ADHD in a Hong Kong population, implicating cerebellar development and immune genes.

Journal of Neurodevelopmental Disorders · Jun 20, 2025Peer-reviewedGenetic & Genomic Medicine

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes

Epi25 Collaborative, Siwei Chen, Bassel W Abou-Khalil, Zaid Afawi, Quratulain Zulfiqar Ali, Elisabetta Amadori, Alison Anderson, Joe Anderson, Danielle Molinari Andrade, Grazia Annesi, Mutluay Arslan, Pauls Auce, Melanie Bahlo, Mark D. Baker, Ganna Balagura, , Alica M. Goldman

This study identifies shared and distinct ultra-rare genetic risk factors for epilepsy subtypes using whole-exome sequencing of 20,979 patients and 33,444 controls.

Nature Neuroscience · Oct 1, 2024Peer-reviewedGenetic & Genomic Medicine

LFMD: detecting low-frequency mutations in high-depth genome sequencing data without molecular tags

Rui Ye, Xuehan Zhuang, Jie Ruan, Yanwei Qi, Yitai An, Jiaming Xu, Timothy Mak, Xiao Liu, Xiuqing Zhang, Huanming Yang, Xun Xu, Larry Baum, Chao Nie, Pak Chung Sham

LFMD improves low-frequency mutation detection in high-depth sequencing by combining duplex sequencing and bottleneck sequencing to enhance sensitivity and specificity.

bioRxiv · Nov 18, 2020 · PreprintBioinformatics