GWAS on family history of Alzheimer's disease.
Riccardo E MarioniSarah E HarrisQian ZhangAllan F McRaeSaskia P HagenaarsW David HillGail DaviesCraig W RitchieCatharine R GaleJohn M StarrAlison Mary GoateDavid J PorteousJian YangKathryn L EvansIan J DearyNaomi R WrayPeter M VisscherPublished in: Translational psychiatry (2018)
Alzheimer's disease (AD) is a public health priority for the 21st century. Risk reduction currently revolves around lifestyle changes with much research trying to elucidate the biological underpinnings. We show that self-report of parental history of Alzheimer's dementia for case ascertainment in a genome-wide association study of 314,278 participants from UK Biobank (27,696 maternal cases, 14,338 paternal cases) is a valid proxy for an AD genetic study. After meta-analysing with published consortium data (n = 74,046 with 25,580 cases across the discovery and replication analyses), three new AD-associated loci (P < 5 × 10-8) are identified. These contain genes relevant for AD and neurodegeneration: ADAM10, BCKDK/KAT8 and ACE. Novel gene-based loci include drug targets such as VKORC1 (warfarin dose). We report evidence that the association of SNPs in the TOMM40 gene with AD is potentially mediated by both gene expression and DNA methylation in the prefrontal cortex. However, it is likely that multiple variants are affecting the trait and gene methylation/expression. Our discovered loci may help to elucidate the biological mechanisms underlying AD and, as they contain genes that are drug targets for other diseases and disorders, warrant further exploration for potential precision medicine applications.
Keyphrases
- genome wide
- dna methylation
- copy number
- gene expression
- genome wide association study
- public health
- cognitive decline
- prefrontal cortex
- mild cognitive impairment
- atrial fibrillation
- cardiovascular disease
- metabolic syndrome
- physical activity
- systematic review
- venous thromboembolism
- randomized controlled trial
- direct oral anticoagulants
- weight loss
- machine learning
- pregnant women
- global health
- angiotensin ii
- weight gain