GRAPE: genomic relatedness detection pipeline.
Alexander Medvedev1,2, Mikhail Lebedev2, Andrew Ponomarev2
1Skolkovo Institute of Science and Technology, Moscow, Russian Federation.
F1000Research
|May 24, 2023
Summary
We developed GRAPE, a Genomic RelAtedness detection PipelinE, to accurately classify genetic relatedness. This open-source tool addresses the need for a fast, reliable solution for genomic data analysis in research and commercial applications.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Accurate relatedness classification is crucial for genomic studies like Genome-Wide Association Studies (GWAS) and genetic linkage analysis.
- Unrecognized population structure can lead to false positives in GWAS, impacting research validity.
- The direct-to-consumer genetic testing market relies on accurate DNA relative matching services.
Purpose of the Study:
- To develop an open-source, end-to-end pipeline for detecting genomic relatedness.
- To provide a fast, reliable, and accurate solution for both close and distant kinship.
- To integrate necessary processing steps for real-world genotypic data and production readiness.
Main Methods:
- Developed GRAPE (Genomic RelAtedness detection PipelinE), an integrated pipeline.
- Incorporated data preprocessing, Identity-by-Descent (IBD) segment detection, and relationship estimation.
- Utilized software development best practices and Global Alliance for Genomics and Health (GA4GH) standards.
Main Results:
- Demonstrated pipeline efficiency on both simulated and real-world genomic datasets.
- GRAPE provides a comprehensive solution for genomic relatedness detection.
- The pipeline is ready for production integration.
Conclusions:
- GRAPE addresses the current gap for an open-source, production-ready relatedness detection pipeline.
- The tool enhances the accuracy and reliability of genomic data analysis.
- GRAPE facilitates advancements in genetic research and commercial applications.
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