Abstract：
UNASSIGNED: The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer\'s disease (AD).
UNASSIGNED: To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner.
UNASSIGNED: Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer\'s Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.
UNASSIGNED: Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).
UNASSIGNED: We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
UNASSIGNED: To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner.
UNASSIGNED: Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer\'s Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome.
UNASSIGNED: Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002).
UNASSIGNED: We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6.
摘要：
■在疾病关联研究中，尽管X染色体包含数千个基因，这些基因可以提供对阿尔茨海默病（AD）风险中众所周知的性别差异的见解。
■对X染色体基因的表达进行建模，并以性别分层的方式评估其对AD风险的影响。
■使用弹性网，我们在一组175个全血样本和126个大脑皮层样本中评估了多种建模策略，全基因组测序和RNA-seq数据。顺式调节窗内的SNP(MAF>0.05)用于训练每个基因的组织特异性模型。我们将两种组织中的最佳模型应用于阿尔茨海默病遗传学联盟（ADGC）研究的荟萃分析中的性别分层汇总统计，以鉴定X染色体上的AD相关基因。
■跨不同的模型参数，样本性别，和组织类型，我们模拟了217个基因的表达（95个基因在血液中，135个基因在大脑皮层中）。平均模型R2为0.12(范围从0.03到0.34)。我们还比较了X染色体上的性别分层和性别组合模型。我们进一步研究了逃避X染色体失活（XCI）的基因，以确定它们的遗传调控模式是否不同。我们发现了10个与AD相关的基因，p<0.05，只有女性大脑皮层中的ARMCX6（p=0.008）在调整多次测试后接近显着性阈值（α=0.002）。
■我们优化了X染色体基因的表达预测，将这些模型应用于性别分层ADGWAS汇总统计，并确定了一个推定的AD风险基因，ARMCX6.
■对X染色体基因的表达进行建模，并以性别分层的方式评估其对AD风险的影响。
■使用弹性网，我们在一组175个全血样本和126个大脑皮层样本中评估了多种建模策略，全基因组测序和RNA-seq数据。顺式调节窗内的SNP(MAF>0.05)用于训练每个基因的组织特异性模型。我们将两种组织中的最佳模型应用于阿尔茨海默病遗传学联盟（ADGC）研究的荟萃分析中的性别分层汇总统计，以鉴定X染色体上的AD相关基因。
■跨不同的模型参数，样本性别，和组织类型，我们模拟了217个基因的表达（95个基因在血液中，135个基因在大脑皮层中）。平均模型R2为0.12(范围从0.03到0.34)。我们还比较了X染色体上的性别分层和性别组合模型。我们进一步研究了逃避X染色体失活（XCI）的基因，以确定它们的遗传调控模式是否不同。我们发现了10个与AD相关的基因，p<0.05，只有女性大脑皮层中的ARMCX6（p=0.008）在调整多次测试后接近显着性阈值（α=0.002）。
■我们优化了X染色体基因的表达预测，将这些模型应用于性别分层ADGWAS汇总统计，并确定了一个推定的AD风险基因，ARMCX6.
