G3 Genes Genomes GeneticsGenomicsPeer-reviewed journal article
GenoTools: An Open-Source Python Package for Efficient Genotype Data Quality Control and Analysis
Dan VitaleiD, Mathew J. KoretskyiD, Nicole KuznetsoviD, Samantha HongiD, Jessica MartiniD, Mikayla James, Mary B. MakariousiD, Hampton L. LeonardiD, Hirotaka IwakiiD, Faraz FaghriiD, Cornelis BlauwendraatiD, Andrew SingletoniD, Yeajin Song, Kristin S. LevineiD, Ashwin Ashok Kumar SreelathaiD, Zih‐Hua FangiD, Mike A. NallsiD
Abstract
GenoTools, a Python package, streamlines population genetics research by integrating ancestry estimation, quality control (QC), and genome-wide association studies (GWAS) 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 users genotyping or sequencing platform. As the genotype processing engine that powers several large initiatives, including the NIHs Center for Alzheimers and Related Dementias (CARD) and the Global Parkinsons Genetics Program (GP2). GenoTools was used to process and analyze the UK Biobank and major Alzheimers Disease (AD) and Parkinsons Disease (PD) datasets with over 400,000 genotypes from arrays and 5000 sequences and has led to novel discoveries in diverse populations. It has provided replicable ancestry predictions, implemented rigorous QC, and conducted genetic ancestry-specific GWAS to identify systematic errors or biases through a single command. GenoTools is a customizable tool that enables users to efficiently analyze and scale genotype data with reproducible and scalable ancestry, QC, and GWAS pipelines.
- assay: whole-genome sequencing
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: GWAS / association mapping
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 67
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