GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved]
Classifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially rel...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2023-04-01
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| Series: | F1000Research |
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| Online Access: | https://f1000research.com/articles/11-589/v2 |
| _version_ | 1797828233748545536 |
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| author | Pavel Nikonorov Dmitry Kolobkov Hui Wang Ruslan Vakhitov Vitalina Chamberlain-Evans Dmitriy Osipenko Mikhail Kosaretskiy Egor Kosaretskiy Alexander Tischenko Alexander Medvedev Andrew Ponomarev Mikhail Lebedev |
| author_facet | Pavel Nikonorov Dmitry Kolobkov Hui Wang Ruslan Vakhitov Vitalina Chamberlain-Evans Dmitriy Osipenko Mikhail Kosaretskiy Egor Kosaretskiy Alexander Tischenko Alexander Medvedev Andrew Ponomarev Mikhail Lebedev |
| author_sort | Pavel Nikonorov |
| collection | DOAJ |
| description | Classifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially relevant with recent increases in large-cohort studies. Accurate relationship classification is also required for genetic linkage analysis to identify disease-associated loci. Additionally, DNA relatives matching service is one of the leading drivers for the direct-to-consumer genetic testing market. Despite the availability of scientific and research information on the methods for determining kinship and the accessibility of relevant tools, the assembly of the pipeline, which stably operates on a real-world genotypic data, requires significant research and development resources. Currently, there is no open source end-to-end solution for relatedness detection in genomic data, that is fast, reliable and accurate for both close and distant degrees of kinship, combines all the necessary processing steps to work on a real data, and is ready for production integration. To address this, we developed GRAPE: Genomic RelAtedness detection PipelinE. It combines data preprocessing, identity-by-descent (IBD) segments detection, and accurate relationship estimation. The project uses software development best practices, as well as Global Alliance for Genomics and Health (GA4GH) standards and tools. Pipeline efficiency is demonstrated on both simulated and real-world datasets. GRAPE is available from: https://github.com/genxnetwork/grape. |
| first_indexed | 2024-04-09T13:01:20Z |
| format | Article |
| id | doaj.art-0fe1fca846e740938b643ca4e0d9ffc9 |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-04-09T13:01:20Z |
| publishDate | 2023-04-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-0fe1fca846e740938b643ca4e0d9ffc92023-05-13T00:00:01ZengF1000 Research LtdF1000Research2046-14022023-04-0111145655GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved]Pavel Nikonorov0https://orcid.org/0000-0002-8471-2069Dmitry Kolobkov1Hui Wang2https://orcid.org/0000-0003-4043-5060Ruslan Vakhitov3https://orcid.org/0000-0001-6001-2271Vitalina Chamberlain-Evans4Dmitriy Osipenko5Mikhail Kosaretskiy6https://orcid.org/0000-0003-2059-9121Egor Kosaretskiy7Alexander Tischenko8Alexander Medvedev9Andrew Ponomarev10Mikhail Lebedev11GENXT, Hinxton, UKGENXT, Hinxton, UKGENXT, Hinxton, UKGENXT, Hinxton, UKGENXT, Hinxton, UKAtlas Biomed Group Ltd, London, UKAtlas Biomed Group Ltd, London, UKGENXT, Hinxton, UKGENXT, Hinxton, UKSkolkovo Institute of Science and Technology, Moscow, Russian FederationGENXT, Hinxton, UKGENXT, Hinxton, UKClassifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially relevant with recent increases in large-cohort studies. Accurate relationship classification is also required for genetic linkage analysis to identify disease-associated loci. Additionally, DNA relatives matching service is one of the leading drivers for the direct-to-consumer genetic testing market. Despite the availability of scientific and research information on the methods for determining kinship and the accessibility of relevant tools, the assembly of the pipeline, which stably operates on a real-world genotypic data, requires significant research and development resources. Currently, there is no open source end-to-end solution for relatedness detection in genomic data, that is fast, reliable and accurate for both close and distant degrees of kinship, combines all the necessary processing steps to work on a real data, and is ready for production integration. To address this, we developed GRAPE: Genomic RelAtedness detection PipelinE. It combines data preprocessing, identity-by-descent (IBD) segments detection, and accurate relationship estimation. The project uses software development best practices, as well as Global Alliance for Genomics and Health (GA4GH) standards and tools. Pipeline efficiency is demonstrated on both simulated and real-world datasets. GRAPE is available from: https://github.com/genxnetwork/grape.https://f1000research.com/articles/11-589/v2kinship and relationship estimation identity-by-descent snakemake workflow bioinformatics pipeline phasing and imputation sequencing dataeng |
| spellingShingle | Pavel Nikonorov Dmitry Kolobkov Hui Wang Ruslan Vakhitov Vitalina Chamberlain-Evans Dmitriy Osipenko Mikhail Kosaretskiy Egor Kosaretskiy Alexander Tischenko Alexander Medvedev Andrew Ponomarev Mikhail Lebedev GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] F1000Research kinship and relationship estimation identity-by-descent snakemake workflow bioinformatics pipeline phasing and imputation sequencing data eng |
| title | GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] |
| title_full | GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] |
| title_fullStr | GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] |
| title_full_unstemmed | GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] |
| title_short | GRAPE: genomic relatedness detection pipeline [version 2; peer review: 2 approved] |
| title_sort | grape genomic relatedness detection pipeline version 2 peer review 2 approved |
| topic | kinship and relationship estimation identity-by-descent snakemake workflow bioinformatics pipeline phasing and imputation sequencing data eng |
| url | https://f1000research.com/articles/11-589/v2 |
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