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.

Public Health Relevance

The National Institute of Aging Late Onset Alzheimer?s Disease Family Based Study is the largest collection of suchfamilies worldwide. Virtually every major genetic study of Alzheimer?s disease has included family members and controls from this cohort. The availability of well characterized individuals with biological samples and genetic data will greatly advance research efforts to identify and understand both causal and protective genetic variation.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
3U24AG056270-02S1
Application #
9812732
Study Section
Program Officer
Yao, Alison Q
Project Start
2017-08-01
Project End
2022-05-31
Budget Start
2019-03-01
Budget End
2019-05-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Neurology
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Raghavan, Neha S; Brickman, Adam M; Andrews, Howard et al. (2018) Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease. Ann Clin Transl Neurol 5:832-842
Fernández, Maria Victoria; Kim, Jong Hun; Budde, John P et al. (2017) Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease. PLoS Genet 13:e1007045
Kunkle, Brian W; Vardarajan, Badri N; Naj, Adam C et al. (2017) Early-Onset Alzheimer Disease and Candidate Risk Genes Involved in Endolysosomal Transport. JAMA Neurol 74:1113-1122
Fernández, Maria Victoria; Black, Kathleen; Carrell, David et al. (2016) SORL1 variants across Alzheimer's disease European American cohorts. Eur J Hum Genet 24:1828-1830
Herold, C; Hooli, B V; Mullin, K et al. (2016) Family-based association analyses of imputed genotypes reveal genome-wide significant association of Alzheimer's disease with OSBPL6, PTPRG, and PDCL3. Mol Psychiatry 21:1608-1612
Jakobsdottir, Johanna; van der Lee, Sven J; Bis, Joshua C et al. (2016) Rare Functional Variant in TM2D3 is Associated with Late-Onset Alzheimer's Disease. PLoS Genet 12:e1006327