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PLoS Genet. 2017 Nov 1;13(11):e1007045. doi: 10.1371/journal.pgen.1007045. eCollection 2017 Nov.

Analysis of neurodegenerative Mendelian genes in clinically diagnosed Alzheimer Disease.

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Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States of America.
Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, United States of America.
Department of Neurology, Dementia Center, Ilsan hospital, National Health Insurance Service, Goyang, South Korea.
Medical Scientist Training Program, Division of Biology and Biomedical sciences, School of Medicine, Washington University in Saint Louis, St. Louis, MO, United States of America.
Knight Alzheimer's Disease Research Center, Washington University School of Medicine, St. Louis, MO, United States of America.
Ronald M. Loeb Center for Alzheimer's disease, Dept of Neuroscience, Icahn School of Medicine at Mount Sinai, ICAHN 10-52, New York, NY, United States of America.


Alzheimer disease (AD), Frontotemporal lobar degeneration (FTD), Amyotrophic lateral sclerosis (ALS) and Parkinson disease (PD) have a certain degree of clinical, pathological and molecular overlap. Previous studies indicate that causative mutations in AD and FTD/ALS genes can be found in clinical familial AD. We examined the presence of causative and low frequency coding variants in the AD, FTD, ALS and PD Mendelian genes, in over 450 families with clinical history of AD and over 11,710 sporadic cases and cognitive normal participants from North America. Known pathogenic mutations were found in 1.05% of the sporadic cases, in 0.69% of the cognitively normal participants and in 4.22% of the families. A trend towards enrichment, albeit non-significant, was observed for most AD, FTD and PD genes. Only PSEN1 and PINK1 showed consistent association with AD cases when we used ExAC as the control population. These results suggest that current study designs may contain heterogeneity and contamination of the control population, and that current statistical methods for the discovery of novel genes with real pathogenic variants in complex late onset diseases may be inadequate or underpowered to identify genes carrying pathogenic mutations.

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