GenoTools: an open-source Python package for efficient genotype data quality control and analysis
GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control, and genome-wide association studies capabilities into efficient pipelines. By tracking samples, variants, and quality-specific measures throughout fully customizable pipelines, users can easily manage genetics data for large and small studies. GenoTools' "Ancestry" module renders highly accurate predictions, allowing for high-quality ancestry-specific studies, and enables custom ancestry model training and serialization specified to the user's genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIH's Center for Alzheimer's and Related Dementias and the Global Parkinson's Genetics Program, GenoTools was used to process and analyze the UK Biobank and major Alzheimer's disease and Parkinson's disease datasets with over 400,000 genotypes from arrays and 5,000 whole genome sequencing samples and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous quality control, and conducted genetic ancestry-specific genome-wide association studies to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotyping and sequencing (whole genome sequencing and exome) data with reproducible and scalable ancestry, quality control, and genome-wide association studies pipelines.
Authors
- Ashwin Ashok Kumar Sreelatha (ORCID: https://orcid.org/0000-0001-7113-9278)
- Kristin Levine (ORCID: https://orcid.org/0000-0002-5702-0980)
- Zih‐Hua Fang (ORCID: https://orcid.org/0000-0002-0225-8772)
- Samantha Hong (ORCID: https://orcid.org/0009-0001-8968-6461)
- Hampton L. Leonard (ORCID: https://orcid.org/0000-0003-2390-8110)
- Mathew J. Koretsky (ORCID: https://orcid.org/0000-0003-4341-3991)
- Mary B. Makarious (ORCID: https://orcid.org/0000-0002-7978-1051)
- Faraz Faghri (ORCID: https://orcid.org/0000-0001-5744-8728)
- Cornelis Blauwendraat (ORCID: https://orcid.org/0000-0001-9358-8111)
- Dan Vitale (ORCID: https://orcid.org/0000-0002-0637-3671)
- Hirotaka Iwaki (ORCID: https://orcid.org/0000-0002-8982-7885)
- M R James (ORCID: https://orcid.org/0000-0002-2472-4957)
- Yeajin Song
- Jessica Martin (ORCID: https://orcid.org/0009-0000-0830-9289)
- Nicole Kuznetsov (ORCID: https://orcid.org/0009-0005-9847-0282)
- Andrew B. Singleton
- Mike A. Nalls
Institutions
- National Institutes of Health (US)
- King's College London (GB)
- German Center for Neurodegenerative Diseases (DE)
- Data Tecnica International (United States) (US)
- National Institute of Neurological Disorders and Stroke (US)
- All Russian Research Institute of Hydrometeorological information World Data Center (RU)
- National Institute on Aging (US)
- National Hospital for Neurology and Neurosurgery (GB)
- University College London (GB)
- University of Tübingen (DE)
Publication Details
- Journal
- G3 Genes Genomes Genetics
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1093/g3journal/jkae268
- Citations
- 32
- Primary Topic
- RNA modifications and cancer
- Type
- article
- Field-Weighted Citation Impact
- 7.48
Funders
- U.S. Department of Health and Human Services
- National Institutes of Health