Even without access to the large publicly available databases, consistently conducting and reporting analysis on sex in all genetic association experiments can be beneficial to science in a number of ways. The first and most obvious benefit is the possibility of detecting a genome-wide significant result that depends on sex, even while lacking a large enough sample size to reach genome-wide significance in a combined male/female dataset. As mentioned above, stratifying analyses by sex can result in increased power to detect significant genetic signals if the signals are small (or null) in one sex and larger in the other, or if large signals exist with opposite effects across sex [35]. Further, a difference in variance (not means) between sexes is lost by a standard, fixed effect regression term for sex; therefore conducting analyses stratified by sex will improve power [40]. Finally, estimates of genetic associations with non-significant (genome-wide) p values can be useful to other researchers who use meta-analytical techniques to synthesize data across a large number of studies. By reporting the estimates of association stratified by sex, they become usable in meta-analyses, allowing for even more information to be gleaned over time.
