{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Bocher O"],"funding":["Laboratory of Excellence on Medical Genomics","Initiative excellence of the university of Bordeaux","Inserm","Familjen Erling-Perssons Stiftelse","Stockholms Läns Landsting"],"pagination":["e1009923"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9518893"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(9)"],"pubmed_abstract":["Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: \"RAVA-FI"],"journal":["PLoS genetics"],"pubmed_title":["Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score."],"pmcid":["PMC9518893"],"funding_grant_id":["EPIDEMIOM-VTE","GOLD Cross Cutting Program","ANR-10-LABX-0013","SLL 2017-0842"],"pubmed_authors":["Oglobinsky MS","Bocher O","Deleuze JF","Perdry H","Ludwig TE","Genin E","Odeberg J","Suryakant S","Marenne G","Morange PE","Tregouet DA"],"additional_accession":[]},"is_claimable":false,"name":"Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score.","description":"Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: \"RAVA-FI","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Sep","modification":"2026-05-28T03:34:17.084Z","creation":"2025-02-19T02:24:49.928Z"},"accession":"S-EPMC9518893","cross_references":{"pubmed":["36112662"],"doi":["10.1371/journal.pgen.1009923"]}}