Polygenic score prediction captures nearly all common genetic risk for Alzheimer's disease

Valentina Escott-Price; Maryam Shoai; Richard Pither; Julie Williams; John Hardy

doi:10.1016/j.neurobiolaging.2016.07.018

Polygenic score prediction captures nearly all common genetic risk for Alzheimer's disease

Neurobiol Aging. 2017 Jan:49:214.e7-214.e11. doi: 10.1016/j.neurobiolaging.2016.07.018. Epub 2016 Aug 5.

Authors

Valentina Escott-Price¹, Maryam Shoai², Richard Pither³, Julie Williams⁴, John Hardy⁵

Affiliations

¹ Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK. Electronic address: EscottPriceV@cardiff.ac.uk.
² Institute of Neurology, University College London, London, UK.
³ Cytox Ltd, UK, Oxford, UK.
⁴ Institute of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics & Genomics, Cardiff University, Cardiff, UK.
⁵ Institute of Neurology, University College London, London, UK. Electronic address: j.hardy@ucl.ac.uk.

PMID: 27595457
DOI: 10.1016/j.neurobiolaging.2016.07.018

Abstract

We estimate the maximum prediction accuracy for the risk of Alzheimer's disease based on disease prevalence and heritability of liability. We demonstrate that the recently reported AUC values for predicting of Alzheimer's disease using polygenic scores reach about 90% of the estimated maximum accuracy that can be achieved by predictors of genetic risk based on genomic profiles.

Keywords: AUC; Heritability; Polygenic score.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Aged, 80 and over
Alzheimer Disease / epidemiology
Alzheimer Disease / genetics*
Area Under Curve*
Female
Forecasting
Genetic Association Studies*
Genetic Predisposition to Disease / genetics*
Humans
Male
Multifactorial Inheritance / genetics*
Prevalence
ROC Curve*
Risk

Abstract

Publication types

MeSH terms

Grants and funding