title: Improving the Identification of Phenotypic Abnormalities and Sexual Dimorphism in Mice When Studying Rare Event Categorical Characteristics.
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 test for an interaction with sex. We compare multiple statistical methods and use simulations to investigate the control of the type-one error rate and power. To maximize the power while addressing the multiple testing issue, we implement filters to remove data sets where the hypotheses to be tested cannot achieve significance. A motivating case study utilizing a large scale high-throughput mouse phenotyping data set from the Wellcome Trust Sanger Institute Mouse Genetics Project, where the treatment is a gene ablation, demonstrates the benefits of the new pipeline on the downstream biological calls.
authors.affiliation: Mouse Informatics Group, Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, United Kingdom natasha.karp@astrazeneca.com.
authors.forename: Natasha A
authors.identifier: 0000-0002-8404-2907
authors.initials: NA
authors.lastname: Karp
authors.affiliation: Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Israel.
authors.forename: Ruth
authors.initials: R
authors.lastname: Heller
authors.affiliation: Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Israel.
authors.forename: Shay
authors.initials: S
authors.lastname: Yaacoby
authors.affiliation: Mouse Genetics Project, Wellcome Trust Sanger Institute, Cambridge, CB10 1SA, United Kingdom.
authors.forename: Jacqueline K
authors.initials: JK
authors.lastname: White
authors.affiliation: Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Israel.|The Sagol School of Neuroscience, Tel Aviv University, 69978 Israel.
authors.forename: Yoav
authors.initials: Y
authors.lastname: Benjamini
mesh_terms: D000818:Animals; D005260:Female; D005838:Genotype; D008297:Male; D051379:Mice; D008957:Models, Genetic; D010641:Phenotype; D011336:Probability; D012727:Sex Characteristics
keywords: gene–phenotype map; mouse models; multiple testing; rare events; sexual dimorphism