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| Home > Publications Database > GenoTools: an open-source Python package for efficient genotype data quality control and analysis. > print |
| 001 | 275875 | ||
| 005 | 20250126000505.0 | ||
| 024 | 7 | _ | |a 10.1093/g3journal/jkae268 |2 doi |
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| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Vitale, Dan |0 0000-0002-0637-3671 |b 0 |
| 245 | _ | _ | |a GenoTools: an open-source Python package for efficient genotype data quality control and analysis. |
| 260 | _ | _ | |a Pittsburgh, PA |c 2025 |b Genetics Soc. of America |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a Uniform Manifold Approximation and Projection (UMAP) |2 Other |
| 650 | _ | 7 | |a ancestry |2 Other |
| 650 | _ | 7 | |a genotype |2 Other |
| 650 | _ | 7 | |a principal component analysis (PCA) |2 Other |
| 650 | _ | 7 | |a python |2 Other |
| 650 | _ | 2 | |a Software |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Genotype |2 MeSH |
| 650 | _ | 2 | |a Genome-Wide Association Study: methods |2 MeSH |
| 650 | _ | 2 | |a Quality Control |2 MeSH |
| 650 | _ | 2 | |a Alzheimer Disease: genetics |2 MeSH |
| 650 | _ | 2 | |a Parkinson Disease: genetics |2 MeSH |
| 650 | _ | 2 | |a Genetics, Population |2 MeSH |
| 650 | _ | 2 | |a Computational Biology: methods |2 MeSH |
| 700 | 1 | _ | |a Koretsky, Mathew J |b 1 |
| 700 | 1 | _ | |a Kuznetsov, Nicole |b 2 |
| 700 | 1 | _ | |a Hong, Samantha |0 0009-0001-8968-6461 |b 3 |
| 700 | 1 | _ | |a Martin, Jessica |0 0009-0000-0830-9289 |b 4 |
| 700 | 1 | _ | |a James, Mikayla |b 5 |
| 700 | 1 | _ | |a Makarious, Mary B |b 6 |
| 700 | 1 | _ | |a Leonard, Hampton |b 7 |
| 700 | 1 | _ | |a Iwaki, Hirotaka |b 8 |
| 700 | 1 | _ | |a Faghri, Faraz |0 0000-0001-5744-8728 |b 9 |
| 700 | 1 | _ | |a Blauwendraat, Cornelis |0 P:(DE-2719)2810837 |b 10 |
| 700 | 1 | _ | |a Singleton, Andrew B |b 11 |
| 700 | 1 | _ | |a Song, Yeajin |b 12 |
| 700 | 1 | _ | |a Levine, Kristin |b 13 |
| 700 | 1 | _ | |a Kumar-Sreelatha, Ashwin Ashok |b 14 |
| 700 | 1 | _ | |a Fang, Zih-Hua |0 P:(DE-2719)9001362 |b 15 |u dzne |
| 700 | 1 | _ | |a Nalls, Mike |b 16 |
| 773 | _ | _ | |a 10.1093/g3journal/jkae268 |g Vol. 15, no. 1, p. jkae268 |0 PERI:(DE-600)2629978-1 |n 1 |p jkae268 |t G3: Genes, genomes, genetics |v 15 |y 2025 |x 2160-1836 |
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