Zhang Q, Sidorenko J, Couvy-Duchesne B, Marioni RE, Wright MJ, Goate AM, Marcora E, Huang KL, Porter T, Laws SM, Australian Imaging Biomarkers and Lifestyle (AIBL) Study, Sachdev PS, Mather KA, Armstrong NJ, Thalamuthu A, Brodaty H, Yengo L, Yang J, Wray NR, McRae AF, Visscher PM. Risk prediction of late-onset Alzheimer's disease implies an oligogenic architecture. Nat Commun. 2020 Sep 23;11(1):4799. PubMed.
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Cardiff University
Cardiff University
UK Dementia Research Institute@UCL and VIB@KuLeuven
Zhang and colleagues suggest that late-onset Alzheimer’s disease is oligogenic (~100 genes), whereas our earlier publications suggest that LOAD is polygenic (thousands of genes, Escott-Price et al., 2015). There is no fundamental reason why Alzheimer’s disease would have a genetic architecture different from other neurodevelopmental or neurodegenerative disorders, or other polygenic traits like height.
The cohorts on which the Zhang et al. study is based are not ideal. The authors include fewer than 700 cases who have a diagnosis of AD, and combine these with “cases” based upon the self-reported impression of offspring that their parent had dementia (i.e., dementia by proxy). The accuracy of these impressions is suspect, but assuming that 80 percent of parents had dementia, only 60 percent of these are likely to have had AD. This will introduce significant noise into the dataset, resulting in between 40-50 percent of cases having a different form of dementia or no dementia at all.
In addition, the heritability of the simulated dataset for “AD cases” is set at 9 percent (in addition to APOE), whereas earlier studies in which all AD cases were diagnosed have heritability of around 24-53 percent. Thus, both the power and precision of the study by Zhang and colleagues are relatively diminished.
Moreover, this diagnostic imprecision may specifically limit the detection of variants of small effect, which are the basis of the polygenic architecture of AD as discovered previously. It is noteworthy that previous findings, which have consistently supported a polygenic architecture for AD (all references below), include up to 40,000 diagnosed AD cases, have significantly higher heritability, and predict AD with much greater precision.
Of minor consideration, the authors might want to correct the assertion that the polygenic architecture of AD was due to the inclusion of young controls. The paper of Escott-Price et al. (2015), to which they refer, both removed young controls and stratified by age, which actually showed a similar pattern of prediction irrespective of age.
An intermediate omnigenic AD disease model, also used for the understanding of other complex polygenic diseases such as schizophrenia or traits such as height (Liu et al., 2019), where the genetic contributions to AD are partitioned into direct effects from core genes and indirect effects from peripheral genes, provides a good concept to understand how this would work. Most heritability is likely driven by weak trans-eQTL SNPs, whose effects are mediated through peripheral genes to impact the expression of core genes.
This omnigenic disease model may well explain the controversy: 1) the core genes (most significant) are found to be predictive in the majority of datasets even from different ancestry or environmental background; and 2) the peripheral genes, whose significance differs according to their epigenetic influence on gene expression in different environments (e.g., Australian geographical, cultural and viral environment differ from North European one), are predictive in the North European samples and less predictive otherwise (Sierksma et al., 2020). APOE4 is a notorious exception as it is linked strongly to AD in the Caucasian population while not in population of African ancestry.
—Rebecca Sims is a co-author of this comment.
References:
Escott-Price V, Sims R, Bannister C, Harold D, Vronskaya M, Majounie E, Badarinarayan N, GERAD/PERADES, IGAP consortia, Morgan K, Passmore P, Holmes C, Powell J, Brayne C, Gill M, Mead S, Goate A, Cruchaga C, Lambert JC, van Duijn C, Maier W, Ramirez A, Holmans P, Jones L, Hardy J, Seshadri S, Schellenberg GD, Amouyel P, Williams J. Common polygenic variation enhances risk prediction for Alzheimer's disease. Brain. 2015 Dec;138(Pt 12):3673-84. Epub 2015 Oct 21 PubMed.
Liu X, Li YI, Pritchard JK. Trans Effects on Gene Expression Can Drive Omnigenic Inheritance. Cell. 2019 May 2;177(4):1022-1034.e6. PubMed.
Lee SH, Harold D, Nyholt DR, , Goddard ME, Zondervan KT, Williams J, Montgomery GW, Wray NR, Visscher PM. Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis. Hum Mol Genet. 2013 Feb 15;22(4):832-41. PubMed.
Sierksma A, Escott-Price V, De Strooper B. Translating genetic risk of Alzheimer's disease into mechanistic insight and drug targets. Science. 2020 Oct 2;370(6512):61-66. PubMed.
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