Data Sharing Uncovers Five New Risk Genes For Alzheimer’s

Summary: A new study of the genetic data from over 94,000 people with Alzheimer’s has identified five new risk genes, as well as confirmed 20 other known genes in the disease.

Source: NIH/NIA.

Analysis of genetic data from more than 94,000 individuals has revealed five new risk genes for Alzheimer’s disease, and confirmed 20 known others. An international team of researchers also reports for the first time that mutations in genes specific to tau, a hallmark protein of Alzheimer’s disease, may play an earlier role in the development of the disease than originally thought. These new findings support developing evidence that groups of genes associated with specific biological processes, such as cell trafficking, lipid transport, inflammation and the immune response, are “genetic hubs” that are an important part of the disease process. The study, which was funded in part by the National Institute on Aging (NIA) and other components of the National Institutes of Health, follows results from 2013. It will be published online February 28, 2019 in the journal Nature Genetics.

“This continuing collaborative research into the genetic underpinnings of Alzheimer’s is allowing us to dig deeper into the complexities of this devastating disease,” said Richard J. Hodes, M.D., director of the NIA. “The size of this study provides additional clarity on the genes to prioritize as we continue to better understand and target ways to treat and prevent Alzheimer’s.”

The researchers, members of the International Genomic Alzheimer’s Project (IGAP), analyzed both rare and common gene variants in 94,437 individuals with late onset Alzheimer’s disease, the most common form of dementia in older adults. IGAP is made up of four consortia in the United States and Europe that have been working together since 2011 on genome-wide association studies (GWAS) involving thousands of DNA samples and shared datasets. GWAS are aimed at detecting variations in the genome that are associated with Alzheimer’s. Understanding genetic variants is helping researchers define the molecular mechanisms that influence disease onset and progression.

In addition to confirming the known association of 20 genes with risk of Alzheimer’s and identifying five additional Alzheimer’s-associated genes, these genes were analyzed to see what cellular pathways might be implicated in the disease process. The pathway analysis implicated the immune system, lipid metabolism and amyloid precursor protein (APP) metabolism. Mutations in the APP gene have been shown to be directly related to early onset Alzheimer’s. The present study, done in late onset Alzheimer’s subjects, suggests that variants affecting APP and amyloid beta protein processing are associated with both early-onset autosomal dominant Alzheimer’s and with late onset Alzheimer’s. In addition, for the first time, the study implicated a genetic link to tau binding proteins. Taken together, data suggest that therapies developed by studying subjects with early-onset disease could also be applied to the late-onset form of Alzheimer’s.

The research was led by an international team of experts including Brian Kunkle, Ph.D. and Margaret Pericak-Vance, Ph.D., from the Miller School of Medicine’s John P. Hussman Institute for Human Genomics at the University of Miami, and Benjamin Grenier-Boley, Ph.D. and Jean-Charles Lambert, Ph.D., from INSERM, Lille, France.

Once the functions of the five genes newly associated with Alzheimer’s–IQCK, ACE, ADAM10, ADAMTS1 and WWOX–are understood and examined in conjunction with the functions of the 20 known genes, researchers will be in a better position to identify where the genetic hubs of Alzheimer’s are clustering. Armed with these findings, researchers can look more deeply into these genetic hubs to reveal disease mechanisms and potential drug targets.

A key to these discoveries was the sample size, the largest to date for this kind of Alzheimer’s study. A large sample is especially important to find rare genes that might be involved with a disease.

graph

Newly identified (red) and known (blue) genes linked to Alzheimer’s disease spike in this table plotting results from genome-wide association analysis of 94,437 individuals with late onset Alzheimer’s. NeuroscienceNews.com image is credited to Kunkle et al and Nature Genetics.

“Having more and more samples in GWAS data sets is like adding more and more pixels to a photograph–it helps researchers see details that they otherwise wouldn’t and helps them decide where to focus further study,” explained Marilyn Miller, Ph.D., director of the Genetics of Alzheimer’s Disease program in the Division of Neuroscience at NIA. “If the genes only appear in one out of ten thousand people, you need to find several samples containing those genes for results to be statistically significant.”

Miller also emphasized the collaborative resources that made these discoveries possible. In addition to IGAP, the study resulted from open data sharing and coordination among the Alzheimer’s Disease Research Centers (funded under a variety of awards), the National Alzheimer’s Coordinating Center, the NIA Genetics of Alzheimer’s Disease Data Storage Site, the National Cell Repository for Alzheimer’s Disease, Alzheimer’s Disease Genetics Consortium, the CHARGE Consortium, and the Late-onset Alzheimer’s Disease Family Study.

About this neuroscience research article

Funding: The research was funded by multiple NIH grants, including AG032984, AG036528, AG21886, AG041689, AG016976, AG049505 and AG056270 from NIA. Other NIH institutes involved were the National Heart, Lung and Blood Institute, National Human Genome Research Institute, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, National Institute of Diabetes and Digestive and Kidney Disease, and National Institute of Neurological Disorders and Stroke.

Source: Joe Ballintfy – NIH/NIA
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is credited to Kunkle et al and Nature Genetics.
Original Research: Abstract for “Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing” by Kunkle et al in Nature Genetics. Published February 28 2019.
doi:10.1038/s41588-019-0358-2

Cite This NeuroscienceNews.com Article
NIH/NIA “Data Sharing Uncovers Five New Risk Genes For Alzheimer’s.” NeuroscienceNews. NeuroscienceNews, 28 February 2019.
<http://neurosciencenews.com/alzheimers-risk-genes-10834/>.
NIH/NIA (2019, February 28). Data Sharing Uncovers Five New Risk Genes For Alzheimer’s. NeuroscienceNews. Retrieved February 28, 2019 from http://neurosciencenews.com/alzheimers-risk-genes-10834/
NIH/NIA “Data Sharing Uncovers Five New Risk Genes For Alzheimer’s.” http://neurosciencenews.com/alzheimers-risk-genes-10834/ (accessed February 28, 2019).

Abstract

Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing

Risk for late-onset Alzheimer’s disease (LOAD), the most prevalent dementia, is partially driven by genetics. To identify LOAD risk loci, we performed a large genome-wide association meta-analysis of clinically diagnosed LOAD (94,437 individuals). We confirm 20 previous LOAD risk loci and identify five new genome-wide loci (IQCK, ACE, ADAM10, ADAMTS1, and WWOX), two of which (ADAM10, ACE) were identified in a recent genome-wide association (GWAS)-by-familial-proxy of Alzheimer’s or dementia. Fine-mapping of the human leukocyte antigen (HLA) region confirms the neurological and immune-mediated disease haplotype HLA-DR15 as a risk factor for LOAD. Pathway analysis implicates immunity, lipid metabolism, tau binding proteins, and amyloid precursor protein (APP) metabolism, showing that genetic variants affecting APP and Aβ processing are associated not only with early-onset autosomal dominant Alzheimer’s disease but also with LOAD. Analyses of risk genes and pathways show enrichment for rare variants (P = 1.32 × 10−7), indicating that additional rare variants remain to be identified. We also identify important genetic correlations between LOAD and traits such as family history of dementia and education.

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