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Abstract Background There is large individual variation in both clinical presentation and progression between Parkinson’s disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods Replicating across three large Parkinson’s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson’s study (n = 1807) and Parkinson’s Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors (“phenotypic axes”). Results When applied to disease severity at diagnosis, the most influential of three phenotypic axes “Axis 1” was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer’s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer’s disease genetic risk, and in contrast to Parkinson’s disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer’s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson’s disease genetics did not. Conclusions We identify universal axes of Parkinson’s disease phenotypic variation which reveal that Parkinson’s patients with high concomitant genetic risk for Alzheimer’s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.

Tissue damage and turnover lead to the release of DNA in the blood and can be used to monitor changes in tissue state. Here, the authors developed a tool to accurately estimate the proportion of cell types contributing to cell-free DNA in the blood, with an application to pregnant women and ALS patients.

Abstract The observation that disease-associated genetic variants typically reside outside of exons has inspired widespread investigation into the genetic basis of transcriptional regulation. While associations between the mRNA abundance of a gene and its proximal SNPs (cis-eQTLs) are now readily identified, identification of high-quality distal associations (trans-eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-positive signals. To address these issues, we develop GBAT, a powerful gene-based pipeline that allows robust detection of high-quality trans-gene regulation signal.

Background: Vitamin D deficiency has been associated with multiple diseases, but the causal relevance and underlying processes are not fully understood. Elucidating the mechanisms of action of drug treatments in humans is challenging, but application of functional genomic approaches in randomized trials may afford an opportunity to systematically assess molecular responses. Methods: In the Biochemical Efficacy and Safety Trial of Vitamin D (BEST-D), a double-blind, placebo-controlled, dose-finding, randomized clinical trial, 305 community-dwelling individuals aged over 65 years were randomly allocated to treatment with vitamin D3 4000 IU, 2000 IU or placebo daily for 12 months. Genome-wide genotypes at baseline, and transcriptome and plasma levels of cytokines (IFN-γ, IL-10, IL-8, IL-6 and TNF-α) at baseline and after 12 months, were measured. The trial had >90% power to detect 1.2-fold changes in gene expression. Findings: Allocation to vitamin D for 12-months was associated with 2-fold higher plasma levels of 25-hydroxy-vitamin D (25[OH]D, 4000 IU regimen), but had no significant effect on whole-blood gene expression (FDR