Polygenic risk score in post-mortem diagnosed sporadic early onset Alzheimer’s disease

Chaudhary, Sultan and Patel, Tulsi and Barber, Imelda S. and Guetta-Baranes, Tamar and Brookes, Keeley and Chappell, Sally and Turton, James and Guerreiro, Rita and Bras, Jose and Hernandez, Dena and Singleton, Andrew and Hardy, John and Mann, David and Morgan, Kevin (2017) Polygenic risk score in post-mortem diagnosed sporadic early onset Alzheimer’s disease. Neurobiology of Aging . ISSN 1558-1497 (In Press)

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Abstract

Sporadic early onset Alzheimer’s disease (sEOAD) exhibits the symptoms of late onset Alzheimer’s disease (LOAD) but lacks the familial aspect of the early onset familial form. The genetics of Alzheimer’s disease (AD) identifies APOEε4 to be the greatest risk factor; however, it is a complex disease involving both environmental risk factors and multiple genetic loci. Polygenic risk scores (PRS) accumulate the total risk of a phenotype in an individual based on variants present in their genome. We determined whether sEOAD cases had a higher PRS compared to controls. A cohort of sEOAD cases were genotyped on the NeuroX array and PRS were generated using PRSice. The target dataset consisted of 408 sEOAD cases and 436 controls. The base dataset was collated by the IGAP consortium, with association data from 17,008 LOAD cases and 37,154 controls, which can be used for identifying sEOAD cases due to having shared phenotype. PRS were generated using all common SNPs between the base and target dataset, PRS were also generated using only SNPs within a 500kb region surrounding the APOE gene. Sex and number of APOE ε2 or ε4 alleles were used as variables for logistic regression and combined with PRS. The results show that PRS is higher on average in sEOAD cases than controls, although there is still overlap amongst the whole cohort. Predictive ability of identifying cases and controls using PRSice was calculated with 72.9% accuracy, greater than the APOE locus alone (65.2%). Predictive ability was further improved with logistic regression, identifying cases and controls with 75.5% accuracy.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Life Sciences > School of Molecular Medical Sciences > Human Genetics Research Group
Depositing User: Morgan, Kevin
Date Deposited: 03 Oct 2017 11:06
Last Modified: 14 Oct 2017 08:41
URI: http://eprints.nottingham.ac.uk/id/eprint/46892

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