Parkinson's disease (PD) is a neurodegenerative disorder which affects the quality of life of patient and their family. Sleep disorders appear in 80-90% of PD patients and have a great impact on the PD well-being. We examined the relationship of patients' sleep quality and depression on burden, mood, quality of life, and quality of sleep of their caregivers. A multicenter, regional (Veneto), observational, cross-sectional study that included 55 patient-caregiver pairs was conducted. Patients were assessed using Parkinson's Disease Sleep Scale (PDSS) and Epworth Sleepiness Scale (ESS) for sleep disorders, Beck Depression Inventory (BDI) as a measure of depression, and Parkinson's Disease Questionnaire (PDQ-39) as a measure of quality of life. Caregivers were evaluated by the Caregiver Burden Inventory (CBI) a measure of burden, BDI, SF-36 Health Survey as measures of HRQoL, and Medical Outcomes Study-Sleep Scale (MOS-SS) for quality of sleep. CBI, HRQoL, MOS-SS, and BDI scores displayed no association with patients' age, cognition (Mini Mental State Examination (MMSE) and Frontal Assessment Battery (FAB)), disease duration, and Hoehn and Yahr (H&Y), and UPDRS III scales whereas were significantly correlated with patients' quality of sleep, depression, and quality life. CBI and HRQoL were also associated respectively with patients' ESS and L-dopa daily dose. This study underscores the presence of a significant relationship between patient and caregiver quality of life. Interestingly, sleep quality and depression rather than motor disability best predicted caregivers' well-being.

Biological plausibility and other prior information could help select genome-wide association (GWA) findings for further follow-up, but there is no consensus on which types of knowledge should be considered or how to weight them. We used experts’ opinions and empirical evidence to estimate the relative importance of 15 types of information at the single nucleotide polymorphism (SNP) and gene levels. Opinions were elicited from ten experts using a two-round Delphi survey. Empirical evidence was obtained by comparing the frequency of each type of characteristic in SNPs established as being associated with seven disease traits through GWA meta-analysis and independent replication, with the corresponding frequency in a randomly selected set of SNPs. SNP and gene characteristics were retrieved using a specially developed bioinformatics tool. Both the expert and the empirical evidence rated previous association in a meta-analysis or more than one study as conferring the highest relative probability of true association, while previous association in a single study ranked much lower. High relative probabilities were also observed for location in a functional protein domain, while location in a region evolutionarily conserved in vertebrates was ranked high by the data but not by the experts. Our empirical evidence did not support the importance attributed by the experts to whether the gene encodes a protein in a pathway or shows interactions relevant to the trait. Our findings provide insight into the selection and weighting of different types of knowledge in SNP or gene prioritization, and point to areas requiring further research.

Prioritization is the process whereby a set of possible candidate genes or SNPs is ranked so that the most promising can be taken forward into further studies. In a genome-wide association study, prioritization is usually based on the p-values alone, but researchers sometimes take account of external annotation information about the SNPs such as whether the SNP lies close to a good candidate gene. Using external information in this way is inherently subjective and is often not formalized, making the analysis difficult to reproduce. Building on previous work that has identified fourteen important types of external information, we present an approximate Bayesian analysis that produces an estimate of the probability of association. The calculation combines four sources of information: the genome-wide data, SNP information derived from bioinformatics databases, empirical SNP weights, and the researchers’ subjective prior opinions. The calculation is fast enough that it can be applied to millions of SNPS and although it does rely on subjective judgments, those judgments are made explicit so that the final SNP selection can be reproduced. We show that the resulting probability of association is intuitively more appealing than the p-value because it is easier to interpret and it makes allowance for the power of the study. We illustrate the use of the probability of association for SNP prioritization by applying it to a meta-analysis of kidney function genome-wide association studies and demonstrate that SNP selection performs better using the probability of association compared with p-values alone.

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