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Journal Articles Nature Communications Year : 2020

Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Julia Sidorenko
Riccardo E Marioni
  • Function : Author
Margaret J Wright
Alison M Goate
Edoardo Marcora
Kuan-Lin Huang
Tenielle Porter
  • Function : Author
Simon M Laws
Colin L Masters
  • Function : Author
Ashley I Bush
  • Function : Author
Christopher Fowler
  • Function : Author
David Darby
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Kelly Pertile
  • Function : Author
Carolina Restrepo
  • Function : Author
Blaine Roberts
  • Function : Author
Jo Robertson
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Rebecca Rumble
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Tim Ryan
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Steven Collins
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Christine Thai
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Brett Trounson
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Kate Lennon
  • Function : Author
Qiao-Xin Li
  • Function : Author
Fernanda Yevenes Ugarte
  • Function : Author
Irene Volitakis
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Michael Vovos
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Rob Williams
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Jenalle Baker
  • Function : Author
Alyce Russell
  • Function : Author
Madeline Peretti
  • Function : Author
Lidija Milicic
  • Function : Author
Lucy Lim
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Mark Rodrigues
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Kevin Taddei
  • Function : Author
Tania Taddei
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Eugene Hone
  • Function : Author
Florence Lim
  • Function : Author
Shane Fernandez
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Stephanie Rainey-Smith
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Steve Pedrini
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Ralph Martins
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James Doecke
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Pierrick Bourgeat
  • Function : Author
Jurgen Fripp
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Simon Gibson
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Hugo Leroux
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David Hanson
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Vincent Dore
  • Function : Author
Ping Zhang
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Samantha Burnham
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Christopher C Rowe
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Victor L Villemagne
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Paul Yates
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Sveltana Bozin Pejoska
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Gareth Jones
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David Ames
  • Function : Author
Elizabeth Cyarto
  • Function : Author
Nicola Lautenschlager
  • Function : Author
Kevin Barnham
  • Function : Author
Lesley Cheng
  • Function : Author
Andy Hill
  • Function : Author
Neil Killeen
  • Function : Author
Paul Maruff
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Brendan Silbert
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Belinda Brown
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Harmid Sohrabi
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Greg Savage
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Michael Vacher
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Perminder S Sachdev
Karen A Mather
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Nicola J Armstrong
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Anbupalam Thalamuthu
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Henry Brodaty
Jian Yang
Naomi R Wray
Peter M Visscher
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  • PersonId : 1060516

Abstract

Abstract Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P -threshold ( P optimal ) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
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Origin : Publication funded by an institution
Licence : CC BY - Attribution

Dates and versions

hal-04520268 , version 1 (25-03-2024)

Identifiers

Cite

Qian Zhang, Julia Sidorenko, Baptiste Couvy-Duchesne, Riccardo E Marioni, Margaret J Wright, et al.. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nature Communications, 2020, 11 (1), pp.4799. ⟨10.1038/s41467-020-18534-1⟩. ⟨hal-04520268⟩
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