Regression Analyses

Variables included in the models of the final steps of binary logistic regression analysis (forward LR method) in two datasets presorted according to phenotypes in male knockout mice (Dataset M) and men (Dataset H). For the variables included, see Methods and Data Sources. For the inference of false discovery rates, see Greither et al. (2020) at Privacy, Funding and Citation.

Testis-Expressed Genes Sorted Based on Mice (Dataset M)

VariableBS.E.WaldExp(B)False discovery rate
dN/dS-2.0910.61611.5300.1240.012
RNA level relative to brain0.1640.02835.3841.1780.000
RNA level relative to heart0.1740.02547.7841.1900.000
number of neighbours in the PPI network which are male fertility candidates0.2220.06711.1111.2490.012

 

Testis-Expressed Genes Sorted Based on Humans (Dataset H)

VariableBS.E.WaldExp(B)False discovery rate
betweenness centrality174.43361.5938.0205.694E+750.040
RNA level relative to brain0.1700.05210.7041.1850.012
RNA level relative to heart0.2550.05125.3731.2900.000
number of neighbours in the PPI network which are male fertility candidates0.8090.24311.1402.2470.012

Abbreviations used in above Tables: B, logit value; Exp(B), odds ratio; PPI, protein-protein interaction; S.E., standard error