Neurol Genet 2018 Dec 21;4(6):e286. Epub 2018 Nov 21.
John P. Hussman Institute for Human Genomics (G.W.B., J.J., K.H.-N., B.K., E.R.M., F.R., M.A.P.-V.), University of Miami, Miller School of Medicine; Dr. John T. Macdonald Foundation Department of Human Genetics (G.W.B., E.R.M., M.A.P.-V.), University of Miami, Miller School of Medicine, FL; The Taub Institute for Research on Alzheimer's Disease and the Aging Brain (B.V., S.B., C.R., R.M.), Columbia University; The Gertrude H. Segievsky Center (B.V., S.B., C.R., R.M.), Columbia University, New York Presbyterian Hospital; Division of Medical Genetics (E. Blue, E.W.), Department of Medicine, University of Washington, Seattle; Institute for Computational Biology (W.B., J.L.H.), Case Western Reserve University, Cleveland, OH; Department of Neurology (A.D., S.S., L.A.F.), Boston University School of Medicine; Department of Biostatistics (A.D., S.S., L.A.F.), Boston University School of Medicine, MA; School of Medicine (A.N., G.S.), University of Pennsylvania, Philadelphia; Department of Biostatistics (T.T., E.W.), University of Washington, Seattle; Erasmus Medical University (C.D.), Rotterdam, The Netherlands; Icahn School of Medicine at Mount Sinai (A.G.), New York, NY; Department of Medicine (L.A.F.), Boston University School of Medicine, MA; and University of Texas (E. Boerwinkle), Houston.
Objective: To identify genetic variation influencing late-onset Alzheimer disease (LOAD), we used a large data set of non-Hispanic white (NHW) extended families multiply-affected by LOAD by performing whole genome sequencing (WGS).
Methods: As part of the Alzheimer Disease Sequencing Project, WGS data were generated for 197 NHW participants from 42 families (affected individuals and unaffected, elderly relatives). A two-pronged approach was taken. First, variants were prioritized using heterogeneity logarithm of the odds (HLOD) and family-specific LOD scores as well as annotations based on function, frequency, and segregation with disease. Second, known Alzheimer disease (AD) candidate genes were assessed for rare variation using a family-based association test.
Results: We identified 41 rare, predicted-damaging variants that segregated with disease in the families that contributed to the HLOD or family-specific LOD regions. These included a variant in nitric oxide synthase 1 adaptor protein that segregates with disease in a family with 7 individuals with AD, as well as variants in , and . Rare-variant association identified 2 LOAD candidate genes associated with disease in these families: (-values = 0.001) and (-value = 0.009). These genes still showed association while controlling for common index variants, indicating the rare-variant signal is distinct from common variation that initially identified the genes as candidates.
Conclusions: We identified multiple genes with putative damaging rare variants that segregate with disease in multiplex AD families and showed that rare variation may influence AD risk at AD candidate genes. These results identify novel AD candidate genes and show a role for rare variation in LOAD etiology, even at genes previously identified by common variation.