The Aging Genome Association Study "AGE-GAIN"

巴尔的摩老龄化纵向研究和InCHIANTI队列中的全基因组关联（WGA）是一种具有成本效益和统计学功能的方法，用于识别衰老表型中重要的遗传变异。 初步数据表明，衰老的数量性状可用于识别重要的基因变异。这项提案建立在最近的成功和补充NIH疾病为基础的WGA研究计划。拟议的研究设计将通过建立复制和与其他NIA和NIH资助的研究建立强有力的合作来最大限度地减少假阳性关联，这些研究要么进行了GWAS，要么具有特定的表型和DNA。这一新资源使BLSA和InCHIANTI研究能够参与美国和欧洲的许多合作项目，旨在了解遗传变异对衰老相关表型的贡献。 人类基因组中有超过1000万种常见变异，其中一小部分可能对生物途径、疾病和衰老产生重大影响。随着成本的下降，现在对整个基因组中精心选择的550K标记进行基因分型是具有成本效益的，有效地识别了90%以上的常见变异。使用群体特异性参考的插补策略还允许产生高达3800万个SNP，从而允许更好地覆盖基因分型芯片上未呈现的基因座。然后，这些标记物可以在统计学上与相关表型相关联，以进行全基因组或全基因组关联研究（WGA）。 特别关注的是识别基因或基因模式，其变异与功能状态和衰老生活质量的差异相关。衰老过程受一系列生物途径的控制，这些生物途径对身体系统有不同的影响，而不限于特定的疾病。准确识别哪些基因变异与潜在衰老表型的早发、速率或方面相关，将为人类衰老过程提供重要见解，并为预防、治疗和预后测试开辟新领域。 NIH在BLSA和InChianti老化研究中投入了大量资源用于非常高质量的老化表型测量。BLSA和InCHIANTI在关键表型方面具有可比性，包括：胰岛素抵抗、肌肉力量、步态速度、骨密度、睾酮下降和其他衰老的数量性状，包括认知和听力损失等。这些表型具有明确的临床和公共卫生重要性，并已被证明可有效识别遗传变异。 这两项研究都有关于关键途径的中间标志物或内表型的信息，包括大量的炎症标志物。至关重要的是，这些队列有几波观察，可以更准确地划分生物年龄，并计算下降率。 通过检查生物变量的纵向轨迹，并使用年龄特定的危重疾病（心血管疾病，痴呆症，骨折等）发病率，作为结果，我们可以开始了解疾病风险遗传倾向的本质，并研究在生命早期可检测到的预测老年生活质量的因素。此外，这两个群体的家庭结构也得到了很好的描述。 在BLSA中，这种结构将通过使用一组标准的亲子鉴定标记物标记所有BLSA样品来进一步增强。 一旦在统计学上确定了遗传变异，后续的实验室工作将描述生物学效应。BLSA和InCHIANTI研究将收集样本，用于后续工作中的RNA表达研究。 也可以收集用于蛋白质组学的其他样品，例如血清。 因此，这些队列为发现、复制和表征活性变体的生物学效应提供了持续和广泛的资源。 目的： 1.确定与定义的定量衰老表型相关的多态性，包括循环蛋白、体能、认知功能、肌肉力量或肌肉减少症、骨质疏松症和胰岛素抵抗，采取措施排除假阳性关联 2.为针对其他测量表型的初始评估提供持续资源，包括这些队列中几波随访的最终结局和变化率测量 具体目标： 1.使用NIA神经遗传学实验室的550K Illumina平台，对约1350名BLSA参与者和1300名InCHIANTI参与者进行全基因组基因分型 2.识别与身体表现、认知功能和其他选定的衰老表型（横截面和纵向）统计相关的所有SNP，预计数百个明显的关联将是假阳性。 3.尝试在独立的NIA支持的研究样本中复制两个队列中已经发现的SNP关联，期望大量SNP将无法复制，而那些复制两次的SNP将表明重要和真实的关联。 4.开发和维护WGA的生物信息学资源，用于其他测量表型的初步研究，并测量文献中确定的特定疾病的基因变异的衰老效应。 5.在NIA内发展统计遗传学专业知识，并与其他研究小组建立强有力的合作，重点是对其他研究中年龄相关性状的遗传贡献。

Whole genome association (WGA) in the Baltimore Longitudinal Study of Aging and the InCHIANTI cohorts is a cost-effective and statistically powerful method for identifying genetic variations of importance in aging phenotypes. Preliminary data show that quantitative traits for aging can be used to identify important gene variants. This proposal builds on recent successes and supplements NIH disease-based WGA study programs. The proposed study design will minimize false positive associations by building in replication and by creating a strong collaboration with other NIA- and NIH-funded studies that either have perfomed GWAS or have specific phenotypes and DNA available. This new resource has enabled the BLSA and InCHIANTI studies to participate in many collaborative projects in the United States and Europe aimed at understanding the contribution of genetic variability to aging-relevant phenotypes. There are over 10 million common variants in the human genome, a small proportion of which may have significant effects on biological pathways, disease and aging. With falling costs, it is now cost effective to genotype 550K carefully chosen markers across the genome, effectively identifying over 90% of all common variations. Imputation strategies using population specific references has also allowed generation of up to 38 million SNPs allowing better coverage of loci not represented on the genotyping chips. These markers can then be linked statistically to relevant phenotypes to perform genome wide or whole genome association studies (WGA). The particular focus is to identify genes or patterns of genes whose variation is associated with differences in functional status and quality of life with aging. The aging process is governed by a range of biological pathways which have diverse effects across body systems, not limited to specific diseases. Identifying exactly which gene variants are associated with early onset, rates or aspects of underlying aging phenotypes would provide vital insights into the aging process in humans and open up new areas for prevention, treatment and prognostic testing. The NIH has invested substantial resources in very high quality measurement of aging phenotypes in the BLSA and InCHIANTI aging studies. Both BLSA and InCHIANTI have comparable measures of key phenotypes, including: insulin resistance, muscle strength, gait speed, bone density, testosterone decline and other quantitative traits of aging including cognition and hearing loss, etc. These phenotypes have clear clinical and public health importance and have been shown to be effective in identifying genetic variations. Both studies have information on intermediate markers or endo-phenotypes on critical pathways, including a large set of inflammatory markers. Crucially, these cohorts have several waves of observations, allowing more accurate staging of biological age across waves, and calculation of rates of decline. By examining longitudinal trajectories of biological variables and by using the age-specific incidence of critical diseases (cardiovascular diseases, dementia, fractures etc.) as outcomes, we can begin to understand the nature of genetic propensity to disease risk and study factors detectable in early life that predict quality of life in old age. In addition, the familial structure of the two cohorts has been well characterized. In the BLSA, such structure will be further enhanced by labeling all the BLSA samples using a standard set of paternity markers. Once genetic variants are identified statistically, follow-up lab work will characterize the biological effects. The BLSA and InCHIANTI study will collect samples to be used in RNA expression studies in follow-up work. Other samples, such as serum, for proteomics may also be collected. These cohorts therefore provide continuing and broad-based resources for discovery, replication, and characterization of the biological effects of active variants. Objectives: 1. Identify polymorphisms associated with defined quantitative aging phenotypes, including circulating proteins, physical performance, cognitive function, muscle strength or sarcopenia, osteoporosis and insulin resistance, taking steps to exclude false positive associations 2. Provide a continuing resource for initial assessment against other measured phenotypes, including eventual outcomes and rates of change measures across several waves of follow-up in these cohorts Specific aims: 1. Undertake whole genome genotyping in about 1350 BLSA participants and 1300 InCHIANTI participants, using the 550K Illumina platform in the NIA Laboratory of Neurogenetics 2. Identify all SNPs statistically associated with physical performance, cognitive function and other selected aging phenotypes (both cross-sectional and longitudinal), expecting that several hundred apparent associations will be false positives. 3. Attempt to replicate SNP associations already found in two cohorts in independent NIA-supported study samples, with the expectation that a significant number will fail to replicate and those that are replicated twice will indicate important and true associations. 4. Develop and maintain a bioinformatics resource on this WGA, which will be used for initial study of other measured phenotypes, and also to measure the aging effects of gene variants identified in the literature for specific diseases. 5. Develop statistical genetic expertise within NIA and establish strong collaboration with other groups focusing on the genetic contribution to age-associated traits in other studies.