Analysts were asked to run linear regression on AFB and a transformed AFS variable . Since age at first sexual intercourse tends to have a markedly non - normal distribution ( see Fig S1 ) , we asked the analyst for a within - sex inverse rank normal transformation before running statistical models . Analysts were asked to include birth year of the respondent ( represented by birth year -1900/10 ) , its square and cubic to control for non - linear birth cohort effects . For those with family - based data , we suggested controlling for non - independence of family members or only include one family member in the analyses . We furthermore asked studies with family data to run a pooled GWAS on both sexes . Combined analyses that included both men and women also needed to include interactions of birth year and its polynomials with sex . In general , we asked to include top principal components to control for population stratification and cohort specific covariates if appropriate . Some cohorts only used birth year and not its polynomials because of multi - collinearity issues / convergence of the GWA analysis . Omission of these nonlinear birth year effects is unlikely to lead to biased inferences , since genotypes are not usually considered to be truly associated with birth year . However , inferences might be less accurate ( i.e. , have larger standard errors ) , since omission of nonlinear birth year effects can lead to larger residual variation .