Ryan S Dhindsa, M.D., Ph.D.
Assistant Professor
Positions
- Assistant Professor
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Pathology & Immunology
Baylor College of Medicine
Houston, US
- Principal Investigator
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Jan and Dan Duncan Neurological Research Institute
- Faculty Member
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Department of Pathology
Division of Genomic Medicine
Texas Children's Hospital
Addresses
- 1250 MOURSUND STREET (Office)
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6th floor, Room N0650.28
Houston, TX, 77030
United States
ryan.dhindsa@bcm.edu
https://www.dhindsalab.com
Professional Statement
The Dhindsa Lab is focused on advancing precision medicine by integrating human genomics and other omics, stem cell models, and computational biology.
Our research focuses on three primary areas:
(1) Population-level omics. We perform very large genetic sequencing studies to understand the genetic contribution to various human diseases. We have uncovered new genetic associations and therapeutic targets for several diseases, including epilepsy, diabetes, idiopathic pulmonary fibrosis, and others. We also integrate other -omic modalities into our genomics studies, including transcriptomics, metabolomics, and proteomics, to gain insight into the pathophysiology of disease-associated variants.
(2) Human stem cell models. CRISPR-based functional genomics platform in human iPSC-derived neurons to identify convergent mechanisms in intellectual disability, autism spectrum disorder, and epilepsy. We employ a variety of functional approaches, including single-cell RNA-sequencing, chromatin profiling, and electrophysiology assays.
(3) Computational biology & machine learning. We use population genetics and machine learning to improve the clinical interpretation of genetic variation and accelerate genetic discoveries. We are particularly interested in using machine learning and artificial intelligence to identify regions of the human genome most likely to be associated with disease when mutated.
Our research focuses on three primary areas:
(1) Population-level omics. We perform very large genetic sequencing studies to understand the genetic contribution to various human diseases. We have uncovered new genetic associations and therapeutic targets for several diseases, including epilepsy, diabetes, idiopathic pulmonary fibrosis, and others. We also integrate other -omic modalities into our genomics studies, including transcriptomics, metabolomics, and proteomics, to gain insight into the pathophysiology of disease-associated variants.
(2) Human stem cell models. CRISPR-based functional genomics platform in human iPSC-derived neurons to identify convergent mechanisms in intellectual disability, autism spectrum disorder, and epilepsy. We employ a variety of functional approaches, including single-cell RNA-sequencing, chromatin profiling, and electrophysiology assays.
(3) Computational biology & machine learning. We use population genetics and machine learning to improve the clinical interpretation of genetic variation and accelerate genetic discoveries. We are particularly interested in using machine learning and artificial intelligence to identify regions of the human genome most likely to be associated with disease when mutated.
Selected Publications
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Ryan S Dhindsa, Anthony W Zoghbi, Daniel K Krizay, Chirag Vasavda, David B Goldstein. " A Transcriptome-Based Drug Discovery Paradigm for Neurodevelopmental Disorders " Annals of Neurology. 2021 Feb ; 89 (2) : 199-211.
Pubmed PMID: 33159466. -
Ryan S Dhindsa, Shelton S Bradrick, Xiaodi Yao, Erin L Heinzen, Slave Petrovski, Brian J Krueger, Michael R Johnson, Wayne N Frankel, Steven Petrou, Rebecca M Boumil, David B Goldstein. " Epileptic encephalopathy-causing mutations in DNM1 impair synaptic vesicle endocytosis " Neurology Genetics. 2015 Jun 1; 1 (1)
Pubmed PMID: 27066543. -
Ryan S Dhindsa, Johan Mattsson, Abhishek Nag, et al.. " Identification of a missense variant in SPDL1 associated with idiopathic pulmonary fibrosis " Commun Biol.. 2021 Mar 23; 4 (1) : 1-8.
Pubmed PMID: 33758299. -
Ryan S Dhindsa, Brett R Copeland, Anthony M Mustoe, David B Goldstein. " Natural selection shapes codon usage in the human genome " The American Journal of Human Genetics. 2020 Jul 2; 107 (1) : 83-95.
Pubmed PMID: 32516569.
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