The National Institute on Aging (NIA) Late Onset of Alzheimer's Disease (LOAD) Family-Based Study (FBS)

美国国家老龄化研究所晚发性阿尔茨海默病家族研究 (NIA-LOAD FBS) 始于2003年，开启了临床和生物资源更大合作与共享的趋势 研究人员。迄今为止，共招募了 1,454 个多发性迟发性 AD (LOAD) 家庭，其中 8,543 名 对家庭成员进行临床评估并采集 DNA 样本。我们还招募了 1,030 名控制人员。全基因组 已对 5,428 名个体生成了 SNP 阵列，对 1,278 名个体进行了外显子组芯片基因分型，全部 对 1,484 名家庭成员和对照进行了外显子组测序和 928 名家庭成员和对照的全基因组测序。的转化率为 NIA-LOAD FBS 中未受影响亲属的 LOAD 比预期的高出三倍 年龄相仿的个体（参见#67 参考书目）。所有这些数据均已置于公共领域 NIAGADS 和 dbGaP。 NIA-LOAD FBS 广泛应用于阿尔茨海默病遗传学，具有 79 个高水平 支持这一主张的出版物（参考书目）。 NIA-LOAD FBS 提供了一个绝佳的机会来提高我们对临床和临床的理解。 老年人遗传变异的生物学影响。表型信息在这些中不断更新 家属通过定期的认知评估和死亡时的尸检来确认LOAD的诊断。 我们已经开始招募更多的家庭成员，特别是后代。 我们已经能够储存家庭成员的脑组织，创造了最大的脑组织收藏之一 用于家庭负荷的组织。我们现在将扩大生物采样范围，包括 RNA 和外周血 选定家族中的单核细胞。 随着其他基因和变异的确定，NIA LOAD 家族研究的成员将再次发挥作用 我们探讨的核心角色是：这些风险和保护性变异对疾病风险有什么影响？是 遗传变异的渗透率高吗？后代发生 LOAD 的风险有多大？能否存在 变体可用于将患者分层为临床试验的特定亚型？家庭资料可以吗 用于识别疾病风险、发病年龄、发病或进展的新型生物标志物？ NIA-LOAD FBS 数据集具有独特的能力来解决这些临床和生物学问题，因为 规模大、认定标准严格、临床评估标准化、不受限制 特定的突变。我们的努力使遗传数据的共享变得简单、无缝，从而使 更多研究人员可以获得 NIA-LOAD FBS 收集的数据和样本进行研究 研究。这是迄今为止世界上最大的 LOAD 系列集合。几乎每个专业 阿尔茨海默病的基因研究包括来自 NIA-LOAD FBS 数据集的患者和对照。的 密集表型和遗传数据的可用性也将使 NIA-LOAD FBS 能够确定 目前正在进行的全基因组和全外显子组测序项目中发现的变异的影响。

The National Institute of Aging Late Onset Alzheimer’s Disease Family Based Study (NIA-LOAD FBS) began in 2003, starting a trend of greater cooperation and sharing of clinical and biological resources among researchers. To date, a total of 1,454 multiplex late onset AD (LOAD) families have been recruited with 8,543 family members clinically assessed and DNA sampled. We have also recruited 1,030 controls. Genome-wide SNP arrays have been generated on 5,428 individuals, exome chip genotyping on 1,278 individuals, whole exome sequencing in 1,484 and whole genome in 928 family members and controls. The conversion rate of to LOAD among unaffected relatives in the NIA-LOAD FBS is three-fold higher than would be expected among individuals of similar age (see #67 Bibliography). All of these data have been placed in the public domain in NIAGADS and dbGaP. The NIA-LOAD FBS is widely used in Alzheimer disease genetics with 79 high level publications to support this claim (Bibliography). The NIA-LOAD FBS provides an excellent opportunity to improve our understanding of the clinical and biological impact of genetic variation in the elderly. Phenotypic information is continually updated in these families by regular cognitive evaluations and autopsy at the time of death to confirm the diagnosis of LOAD. We have begun to recruit additional family members with a particular emphasis on the offspring generation. We have been able to bank brain tissue from family members creating one of the largest collections of brain tissues for familial LOAD. We will now expand biological sampling to include RNA and peripheral blood mononuclear cells in selected families. As additional genes and variants are identified, the members of the NIA LOAD Family Study will again play a central role as we explore: What is the impact of these risk and protective variants on disease risk? Are the genetic variants highly penetrant? What is the risk of developing LOAD in offspring? Can the presence of variants be used for stratification of patients into specific subtypes for clinical trials? Can the family data be used to identify novel biomarkers of disease risk, age at onset onset or progression? The NIA-LOAD FBS dataset is uniquely poised to address these clinical and biological questions because of its large size, rigorous ascertainment criteria, standardized clinical assessment and lack of restriction to specific mutations. Our efforts have made it easy and seamless for the genetic data to be shared, allowing even more researchers to obtain the data and samples collected as part of the NIA-LOAD FBS for research studies. This is by far the largest collection of LOAD families available in the world. Virtually every major genetic study of Alzheimer’s disease has included patients and controls from the NIA-LOAD FBS dataset. The availability of dense phenotypic and genetic data will also position the NIA-LOAD FBS in to determine the impact of variants identified in whole genome and whole exome sequencing projects currently underway.