{"title":"Combining whole genome sequencing and non-adaptive group testing for large-scale ethnicity screens.","authors":"Elior Avraham, Noam Shental","doi":"10.1186/s12859-025-06192-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Estimating an individual's ethnicity from genetic data is crucial for analyzing disease association studies, making informed medical decisions, conducting forensic investigations, and tracing genealogical ancestry.</p><p><strong>Results: </strong>This work combines non-adaptive group testing using the mathematical field of compressed sensing and standard short-read sequencing to allow an up to 4-fold increase in the number of samples in large-scale ethnicity estimates. The method requires no prior knowledge regarding the tested individuals and provides almost identical results compared to testing each individual independently. Our results are based on simulated data, and on simulations based on experimental data from the 1000 Genomes Project and the Human Genome Diversity Project.</p><p><strong>Conclusions: </strong>Our computational approach aims to reduce the costs of large-scale ancestry testing by up to 4-fold in many real-life scenarios while not compromising accuracy. We hope this method will allow more efficient large-scale ethnicity screenings.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"192"},"PeriodicalIF":3.3000,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-025-06192-3","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Estimating an individual's ethnicity from genetic data is crucial for analyzing disease association studies, making informed medical decisions, conducting forensic investigations, and tracing genealogical ancestry.
Results: This work combines non-adaptive group testing using the mathematical field of compressed sensing and standard short-read sequencing to allow an up to 4-fold increase in the number of samples in large-scale ethnicity estimates. The method requires no prior knowledge regarding the tested individuals and provides almost identical results compared to testing each individual independently. Our results are based on simulated data, and on simulations based on experimental data from the 1000 Genomes Project and the Human Genome Diversity Project.
Conclusions: Our computational approach aims to reduce the costs of large-scale ancestry testing by up to 4-fold in many real-life scenarios while not compromising accuracy. We hope this method will allow more efficient large-scale ethnicity screenings.
期刊介绍：
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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