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

• 批准号: 10300802
• 负责人: Ismael Al-Ramahi
• 金额: $ 120.18万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10300802
• 关键词: Address; Affect; Algorithms; Alleles; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease risk; Amino Acids; Amyloid beta-Protein; Animal Disease Models; Apolipoprotein E; Attenuated; Autopsy; Benchmarking; Biological Markers; Biological Models; Brain; Candidate Disease Gene; Cerebrospinal Fluid; Code; Data; Disease; Disease stratification; Drosophila genus; Evolution; Female; Freezing; Gender; Genes; Genetic; Genetic Risk; Genetic Transcription; Heritability; Human; Human Genome; Individual; Light; Link; Machine Learning; Methods; Modeling; Molecular; Mus; Mutation; Neighborhoods; Outcome; Pathogenesis; Pathway interactions; Patients; Phenotype; Phylogenetic Analysis; Population Attributable Risks; Prevalence; Prevention; Preventive; Problem Solving; Proteins; Recording of previous events; Rest; Risk; Risk Assessment; Sample Size; Sampling; Sex Differences; Stratification; System; Testing; Therapeutic; Time; Validation; Variant; Woman; Work; anillin; base; brain cell; brain tissue; candidate validation; cohort; comorbidity; design; experimental study; genetic architecture; genetic variant; genome wide association study; genomic variation; improved; in vivo; insight; interest; laboratory experiment; loss of function; machine learning algorithm; male; marginalized population; men; mouse model; neuroinflammation; neurotoxicity; novel; novel strategies; programs; protective effect; risk stratification; risk variant; robot assistance; screening; sex; specific biomarkers; statistical and machine learning; stem; tau Proteins; therapeutic target; tool; translational study

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.

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然不清楚。解决这些 由于男女之间在性别平等方面存在的差异， AD的患病率、发病、进展和合并症，这表明一些起作用的遗传变异是 性别特异性到目前为止，混合性别全基因组关联研究（GWAS）已经将100多个基因座与AD联系起来。 这些基因座解释了大部分人群归因风险，但只是遗传力的一小部分，并且没有区别。 男人和女人之间。仅仅通过按性别分类研究不太可能改善这种遗传性差距， 然而，因为对大约一半的患者进行单独分析的效果会较差。相反，为了 设计有效的监测，筛查，预防和分层方案，为每个性别量身定制，有一个 迫切需要更敏感和准确的方法，能够计算遗传变异之间的联系， AD风险分别和具体在男性和女性。为此，我们提出了一个综合计算 该方法将通过候选基因的实验和翻译研究来验证。而不是寻求 我们关注的是基因及其编码区。为了增加我们的能量 研究中，我们开发了一个无偏的基于进化的连续评分，用于任何编码的功能影响 变异，从0（中性）到1（功能完全丧失）。使用这种评分系统增加了对 人类变异大量的氨基酸突变实验已经通过进化进行了数十亿次 每个突变都与基于其系统发育差异背景的功能读数有关。 有了这个分数，我们现在建议识别在AD中携带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相比。这种方法的统计功效的增益与 GWAS已经在初步数据中得到证实。在目标1中，我们提出发现性别特异性AD基因 超过1000名阿尔茨海默病测序项目（ADSP）男性和1400名ADSP女性; 2发现APOE的性别特异性修饰物。目标3将包括验证计算得出的候选人 人类脑组织和脑脊液（CSF）中的基因，以及AD中的彻底实验表征 动物模型（小鼠和果蝇）。总之，这种新颖的，综合的计算 方法和多模型系统验证实验将产生新的生物标志物， AD状态的风险分层，并揭示女性和男性之间疾病机制的差异， 突出了各自特异性潜在治疗靶点。