{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Karp NA"],"funding":["European Research Council","NHGRI NIH HHS","Wellcome Trust"],"pagination":["491-501"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5289831"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["205(2)"],"pubmed_abstract":["Biological research frequently involves the study of phenotyping data. Many of these studies focus on rare event categorical data, and functional genomics studies typically study the presence or absence of an abnormal phenotype. With the growing interest in the role of sex, there is a need to assess the phenotype for sexual dimorphism. The identification of abnormal phenotypes for downstream research is challenged by the small sample size, the rare event nature, and the multiple testing problem, as many variables are monitored simultaneously. Here, we develop a statistical pipeline to assess statistical and biological significance while managing the multiple testing problem. We propose a two-step pipeline to initially assess for a treatment effect, in our case example genotype, and then te"],"journal":["Genetics"],"pubmed_title":["Improving the Identification of Phenotypic Abnormalities and Sexual Dimorphism in Mice When Studying Rare Event Categorical Characteristics."],"pmcid":["PMC5289831"],"funding_grant_id":["U54 HG006370","294519","WT098051"],"pubmed_authors":["White JK","Benjamini Y","Heller R","Karp NA","Yaacoby S"],"additional_accession":[]},"is_claimable":false,"name":"Improving the Identification of Phenotypic Abnormalities and Sexual Dimorphism in Mice When Studying Rare Event Categorical Characteristics.","description":"Biological research frequently involves the study of phenotyping data. Many of these studies focus on rare event categorical data, and functional genomics studies typically study the presence or absence of an abnormal phenotype. With the growing interest in the role of sex, there is a need to assess the phenotype for sexual dimorphism. The identification of abnormal phenotypes for downstream research is challenged by the small sample size, the rare event nature, and the multiple testing problem, as many variables are monitored simultaneously. Here, we develop a statistical pipeline to assess statistical and biological significance while managing the multiple testing problem. We propose a two-step pipeline to initially assess for a treatment effect, in our case example genotype, and then te","dates":{"release":"2017-01-01T00:00:00Z","publication":"2017 Feb","modification":"2026-05-02T07:02:56.403Z","creation":"2019-03-27T02:35:37Z"},"accession":"S-EPMC5289831","cross_references":{"pubmed":["27932544"],"doi":["10.1534/genetics.116.195388"]}}