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)是识别在衰老表型中重要的遗传变异的一种成本效益和统计功能强大的方法。初步数据表明，衰老的数量性状可以用来识别重要的基因变异。这项研究建立在最近的成功基础上，并补充了NIH基于疾病的WGA研究计划。该研究设计通过建立复制并与其他NIA和NIH资助的研究建立强大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可用。这一新资源使BLSA和INCHIANTI的研究能够参与美国和欧洲的许多合作项目，旨在了解遗传变异性对与衰老相关的表型的贡献。 人类基因组中有超过1000万种常见的变异，其中一小部分可能会对生物途径、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，有效地识别了所有常见变异的90%以上。使用群体特定参考的归因策略还允许产生多达3800万个SNP，从而更好地覆盖基因分型芯片上未显示的基因座。然后，这些标记可以在统计上与相关的表型相联系，以执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，这些基因或基因模式的变化与随着年龄增长而出现的功能状态和生活质量的差异有关。衰老过程受一系列生物途径的控制，这些途径对整个身体系统有不同的影响，而不仅仅限于特定的疾病。准确识别哪些基因变异与早发性、早发性或潜在衰老表型的某些方面相关，将为人类衰老过程提供重要的见解，并为预防、治疗和预后测试开辟新的领域。 美国国立卫生研究院在BLSA和INCHIANTI衰老研究中投入了大量资源来非常高质量地测量衰老表型，这些研究对关键表型的测量具有可比性，包括：胰岛素抵抗、肌肉力量、步速、骨密度、虚弱、睾酮下降和其他衰老的量化指标，包括认知和听力损失等。这些表型具有明显的临床和公共卫生重要性，已被证明在识别基因变异方面是有效的。这两项研究都有关于关键通路上的中间标志物或内源性表型的信息，包括一大套炎症标志物。至关重要的是，这些队列有几波观测，可以更准确地跨波估计生物年龄，并计算衰减率。通过检查生物变量的纵向轨迹和使用特定年龄的危重疾病(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，并研究在早期生活中可以检测到的预测老年生活质量的因素。此外，这两个队列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套标准的亲子标记来标记所有BLSA样本来进一步增强。 一旦从统计上确定了基因变异，后续的实验室工作将确定生物效应的特征。BLSA和INCHIANTI的研究已经收集了样本，用于后续工作中的RNA表达研究。还收集了用于蛋白质组学的其他样本，如血清。因此，这些队列为发现、复制和表征活性变异体的生物学效应提供了持续和广泛的资源。 目标： 1.确定与确定的定量衰老表型相关的多态，包括循环蛋白、体能、认知功能、肌肉力量或骨骼减少、骨质疏松症和胰岛素抵抗，采取措施排除假阳性关联 2.提供持续的资源，对照其他测量的表型进行初步评估，包括在这些队列中的几波后续行动中的最终结果和变化率测量 具体目标： 1.使用NIA神经遗传学实验室的550K Illumina平台，对约1200名BLSA和1200名INCHIANTI参与者进行全基因组基因分型，并对不同的参考基因组进行归属。 2.找出所有统计上与身体表现、认知功能和其他选定的衰老表型(横截面和纵向)相关的SNP，预计数百个明显的关联将是假阳性。 3.尝试复制在NIA支持的独立研究样本中的两个队列中已经发现的SNP关联，期望有相当多的SNP关联将无法复制，而那些重复两次的关联将表明重要和真实的关联。 4.开发和维护关于WGA的生物信息学资源，该资源将用于其他测量的表型的初步研究，并还用于测量文献中确定的特定疾病的基因变异的衰老效应。 5.在国家统计研究所内发展统计遗传专门知识，并与其他小组建立强有力的合作，重点是在其他研究中遗传对年龄相关特征的贡献。

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 study builds on recent successes and supplements NIH disease-based WGA study programs. The study design minimizes false positive associations by building in replication and creating strong collaborations with other NIA- and NIH-funded studies that either have performed 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 will 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 which have comparable measures of key phenotypes, including: insulin resistance, muscle strength, gait speed, bone density, frailty, 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 have collected samples to be used in RNA expression studies in follow-up work. Other samples, such as serum, for proteomics have also been 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 1200 BLSA participants and 1200 InCHIANTI participants, using the 550K Illumina platform in the NIA Laboratory of Neurogenetics and imputation against different reference genome. 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.