Sexually antagonistic (SA) genetic variation—in which alleles favored in one sex are disfavored in the other—is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches  for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research—including F_ST and F_IS statistics, genome‐wide association studies, pedigree analyses, reciprocal transplant studies, and evolve‐and‐resequence experiments—to evaluate methods for identifying SA genes and genome‐wide signals of SA genetic variation. We begin by developing theoretical models for between‐sex F_ST and F_IS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex‐specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of F_STand F_IS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work.

In this study, we compare male female Fst of published datasets on a SNP per SNP basis to a null expected distribution. 

This re-analyis include three datasets :

• 1. Dutoit et al. 2018 using 95,974 sites sequenced for 47 males and 47 females. All the sites are in coding sequences (no missing data).Minor allele frequency(i.e. MAF)>0.05
• 2. Flanagan and Jones 2017 using 44,773 sites RAD-Seq generated SNPs, 57 females and 167 males. Up to 50% missing data. Number of sequenced alleles saved for each sex at each site. MAF>0.05
• 3. 1000 Human genomes. 1233 males and 1271 females. 7,477 sites. All the sites are in coding sequences. MAF>0.05

Everything is in Markdown format. The same folder is available at https://github.com/ldutoit/male_female_fst.