Decoding the impact of sex differences on Alzheimer's disease risk

解读性别差异对阿尔茨海默病风险的影响 阿尔茨海默病(AD)的分子基础和遗传结构仍不明确。解决这些问题 男女之间存在的差异使问题变得更加复杂 AD的患病率、发病、进展和共病，表明一些促成AD的遗传变异是 特定的性别。到目前为止，混合性别全基因组关联研究已经将100多个基因座与阿尔茨海默病联系起来。 这些基因座解释了大部分人群归因性风险，但只解释了一小部分遗传性，而且没有区别 在男人和女人之间。这种遗传力差距不太可能仅仅通过按性别划分研究来改善， 然而，由于对大约一半的患者进行单独分析将不那么有力。相反，为了 为每个性别设计有效的监测、筛查、预防和分层方案， 迫切需要更灵敏和准确的方法，能够计算遗传变异和 广告风险是分开的，特别是在男性和女性中。为此，我们提出了一种综合计算方法 这一方法将通过对候选基因的实验和翻译研究来验证。而不是寻求 对于单个变种，我们转而关注基因及其编码区。为了增加我们的力量 研究中，我们开发了一种基于进化的无偏见连续评分，用于衡量任何编码的功能影响 变量，从0(中性)到1(完全丧失功能)。使用此评分系统增加了对 人类变异大量的氨基酸突变实验已经由进化进行了数十亿次 根据其系统发育分化的背景，每个突变都与一个功能读数相关联。 有了这个分数，我们现在建议识别在阿尔茨海默病中携带显著更有影响力的编码变体的基因 女性，或AD男性，与性别匹配的对照组进行比较。比较了这种方法在统计能力方面的收益 已经在初步数据中证明了这一点。在目标1中，我们建议发现性别特异的AD基因 对1000多名阿尔茨海默病测序项目(ADSP)男性和1400名ADSP女性进行了研究；在AIM 2寻找APOE的性别特异性修饰因子。目标3将包括对通过计算得出的候选人进行验证 人脑组织和脑脊液中的基因及其在AD中的实验特征 动物模型(小鼠和果蝇)。总而言之，这种新颖的、集成的计算 方法和多模型系统验证实验将产生新的生物标记物，改善性别特异性 AD状态的风险分层，并揭示了男女之间疾病机制的差异 突出每个特定的潜在治疗目标。

Decoding the Impact of Sex Differences on Alzheimer’s Disease Risk The molecular basis and genetic architecture of Alzheimer’s Disease (AD) remain poorly defined. Solving these problems is further complicated by the differences that exist between men and women with respect to the prevalence, onset, progression and comorbidities of AD, suggesting that some contributing genetic variants are sex-specific. So far, mixed-gender Genome-wide association studies (GWAS) have linked over 100 loci with AD. These loci explain much of the population-attributable risk but just a fraction of heritability, and with no distinction between men and women. It is unlikely that this heritability gap would improve just by splitting studies by sex, however, since separate analyses on about half as many patients would be less powerful. Rather, in order to design effective surveillance, screening, preventive, and stratification programs tailored to each sex, there is a critical need for more sensitive and accurate methods, able to compute the link between genetic variants and AD risk separately and specifically in men and women. To do so, we propose an integrative computational approach that will be validated by experimental and translational studies of candidate genes. Rather than seek individual variants, we focus instead on genes and their coding regions. In order to increase the power of our studies, we developed an unbiased evolution-based continuous score for the functional impact of any coding variant, from 0 (neutral) to 1 (complete loss of function). Using this scoring system adds to the usual analyses of human variants a vast number of amino acid mutation experiments already performed by evolution over billions of years, with each mutation being tied to a functional readout based on the context of its phylogenetic divergence. With this score, we now propose to identify genes that carry significantly more impactful coding variants in AD women, or AD men, compared to sex-matched controls. The gain in statistical power of this approach compared to GWAS has been demonstrated in preliminary data. In Aim 1 we propose to discover sex-specific AD genes on more than 1000 Alzheimer’s Disease Sequencing Project (ADSP) men and 1400 ADSP women; and in Aim 2 to discover sex-specific modifiers of APOE. Aim 3 will include validation of computationally-derived candidate genes in human brain tissue and cerebrospinal fluid (CSF), and thorough experimental characterization in AD animal-models (mouse and Drosophila). Together, this combination of novel, integrative computational approaches and multi-model systems validation experiments will yield new biomarkers that improve sex-specific risk stratification of AD status and reveal differences in disease mechanisms between women and men that highlight potential therapeutic targets specific to each.