Supplemental Material for Funkhouser et al., 2020

Supplemental Methods contain additional method details not in manuscript. Figure S1 contains LD decay statistics. Figure S2 contains simulation results for the discovery of any observed causal variant (whether they affect males, females, or both). Figure S3 contains simulation results for the discovery of regions containing masked causal variants (whether they affect males, females, or both). Figure S4 compares local Bayesian regression to single-marker regression for the discovery of gene-by-sex interactions. Figure S5 shows eQTL enrichment as a function of the number of gene-by-sex signals selected, and compares local Bayesian regression to single marker regression. Table S1 contains within-sex phenotype statistics. Table S2 contains a sample of per-replicate simulation results when causal variants are observed. Table S3 contains a sample of per-replicate simulation results when causal variants are masked. Table S4 contains all gene-by-sex interactions discovered using sex-specific window variances.

补充方法部分收录了正文中未涵盖的额外方法细节。图S1包含连锁不平衡（Linkage Disequilibrium, LD）衰减统计结果。图S2包含任意观测到的因果变异（无论其影响雄性、雌性或两性）的识别模拟结果。图S3包含对包含被掩盖因果变异（无论其影响雄性、雌性或两性）的区域进行识别的模拟结果。图S4对比了局部贝叶斯回归（local Bayesian regression）与单标记回归在基因-性别交互作用识别中的应用效果。图S5展示了表达数量性状位点（expression Quantitative Trait Locus, eQTL）富集程度随所选基因-性别信号数量的变化情况，并对比了局部贝叶斯回归与单标记回归的性能。表S1包含性别特异性表型统计量。表S2收录了因果变异可被观测时的单次重复模拟结果示例。表S3收录了因果变异被掩盖时的单次重复模拟结果示例。表S4收录了所有通过性别特异性窗口方差识别得到的基因-性别交互作用。