Author:
Nabais Marta F., ,Laws Simon M.,Lin Tian,Vallerga Costanza L.,Armstrong Nicola J.,Blair Ian P.,Kwok John B.,Mather Karen A.,Mellick George D.,Sachdev Perminder S.,Wallace Leanne,Henders Anjali K.,Zwamborn Ramona A. J.,Hop Paul J.,Lunnon Katie,Pishva Ehsan,Roubroeks Janou A. Y.,Soininen Hilkka,Tsolaki Magda,Mecocci Patrizia,Lovestone Simon,Kłoszewska Iwona,Vellas Bruno,Furlong Sarah,Garton Fleur C.,Henderson Robert D.,Mathers Susan,McCombe Pamela A.,Needham Merrilee,Ngo Shyuan T.,Nicholson Garth,Pamphlett Roger,Rowe Dominic B.,Steyn Frederik J.,Williams Kelly L.,Anderson Tim J.,Bentley Steven R.,Dalrymple-Alford John,Fowder Javed,Gratten Jacob,Halliday Glenda,Hickie Ian B.,Kennedy Martin,Lewis Simon J. G.,Montgomery Grant W.,Pearson John,Pitcher Toni L.,Silburn Peter,Zhang Futao,Visscher Peter M.,Yang Jian,Stevenson Anna J.,Hillary Robert F.,Marioni Riccardo E.,Harris Sarah E.,Deary Ian J.,Jones Ashley R.,Shatunov Aleksey,Iacoangeli Alfredo,van Rheenen Wouter,van den Berg Leonard H.,Shaw Pamela J.,Shaw Cristopher E.,Morrison Karen E.,Al-Chalabi Ammar,Veldink Jan H.,Hannon Eilis,Mill Jonathan,Wray Naomi R.,McRae Allan F.,
Abstract
Abstract
Background
People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease.
Results
We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights.
Conclusions
We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.
Publisher
Springer Science and Business Media LLC