Prevalence of sexual dimorphism in mammalian phenotypic traits

Natasha A Karp,Robert E Braun,Jeremy Mason,Stephen A Murray,Ann M Flenniken,Natalja Kurbatova,Judith E Mank,Hiroshi Masuya,Helen Parkinson,Henrik Westerberg,Xiang Gao,Yoav Benjamini,Richard Mott ,Christopher J Lelliott,Terrence F Meehan,Anneliese O Speak,Martin Hrabě De Angelis ,Karen L Svenson,Colin Mckerlie,Helmut Fuchs,John R Seavitt,Elissa J Chesler,Simon Greenaway,David B West ,Ruth Heller,Monica J Justice,Shigeharu Wakana,Lynette Bower,Tania Sorg,Suhan Guo ,Yann Hérault ,Sara Wells,Mary E Dickinson,Ramiro Ramirez-Solis,Arthur L Beaudet,Ann Marie Mallon ,Luís Santos ,Steve Brown ,Kevin C K Lloyd ,Damian Smedley,Karen P Steel,Shay Yaacoby,Jacqueline K White

doi:10.1038/ncomms15475

Abstract

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans.

Highlights

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies
We first assessed the contribution of sex using linear modelling to determine how often sex contributed to the variation in the phenotype in wildtype mice for an individual data set (Supplementary Fig. 1a,b)
Our analysis revealed that 9.9% of data sets from categorical traits (54/545 data sets) were significantly influenced by sex at a 5% false discovery rate (FDR) (Fig. 1a)

Summary

Results

Sex as a biological variable within an experiment. We first assessed the contribution of sex using linear modelling to determine how often sex contributed to the variation in the phenotype in wildtype mice (control data) for an individual data set (a phenotypic test/trait at an individual phenotyping centre) (Supplementary Fig. 1a,b). Our analysis revealed that 9.9% of data sets from categorical traits (54/545 data sets) were significantly influenced by sex at a 5% false discovery rate (FDR) (Fig. 1a) Many of these cases included phenotypes that would not a priori be assumed to be sexually dimorphic (SD). Distinct from those implemented on the IMPC portal, were used to assess sexual dimorphism and control the false positive rate For this analysis, we used data collected from 2,186 mutant mouse lines, first assessing whether genotype significantly influenced phenotype, and if significant whether the effect was modified by sex.

Methods

Discussion

B2 B3 MB R Workflow