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

国家老龄晚发性阿尔茨海默病家族研究(NIA-LOAD FBS) 开始于2003年，开启了临床和生物资源更大程度的合作和共享的趋势 研究人员。到目前为止，总共招募了1,454个多重迟发性AD(LOAD)家庭，其中8,543个 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名管制员。全基因组 已经在5428个个体上产生了SNP阵列，在1278个个体上进行了外显子芯片基因分型， 1,484个外显子组测序，928个家系成员和对照的全基因组测序。TO的转化率 NIA-Load FBS中未受影响亲属的负荷率是预期的三倍 年龄相仿的个人(见第67号参考书目)。所有这些数据已于#年公之于众 NIAGADS和DBGaP。NIA-Load FBS在阿尔茨海默病遗传学中的应用广泛，高达79 支持这一说法的出版物(参考书目)。 NIA-Load FBS提供了一个极好的机会来提高我们对临床和 遗传变异对老年人的生物学影响。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负荷的诊断。 我们已经开始招募更多的家庭成员，特别强调子孙后代。 我们已经能够储存来自家庭成员的脑组织，创造了最大的脑部集合之一 用于家庭负担的组织。我们现在将扩大生物采样，以包括RNA和外周血液 选定家族中的单个核细胞。 随着更多的基因和变异被识别出来，NIA LOAD家族研究的成员将再次发挥作用 在我们探索时，一个核心作用是：这些风险和保护性变体对疾病风险的影响是什么？是不是 基因变异具有很强的渗透性？在后代中发展负荷的风险是什么？有没有可能 变种是否用于将患者分成特定的亚型进行临床试验？家庭数据是否可以 用于识别疾病风险、发病年龄或进展的新生物标记物？ NIA-Load FBS数据集是唯一可以解决这些临床和生物学问题的数据集，因为 其规模庞大，确定标准严格，临床评估标准化，对 特定的突变。我们的努力使基因数据的共享变得容易和无缝，从而 更多的研究人员获取作为NIA-Load FBS的一部分收集的数据和样本进行研究 学习。这是迄今为止世界上可用的最大载荷族集合。几乎所有的专业 阿尔茨海默病的遗传学研究包括来自NIA-Load FBS数据库的患者和对照组。这个 密集的表型和遗传数据的可用性也将定位NIA-Load FBS以确定 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.