Dataset Information

Risk prediction for complex diseases: application to Parkinson disease.

ABSTRACT: The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease.A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk prediction. Risk allele scores were calculated as the weighted count of the minor alleles. Five models were constructed. Discriminatory capability was evaluated using the area under the curve.Both family history and genetic risk scores increased risk for Parkinson disease. Although the fullest model, which included both family history and genetic risk information, resulted in the highest area under the curve, there were no significant differences between models using family history alone and those using genetic information alone.Adding genome-wide association study-derived genotypes, family history information, or both to standard demographic risk factors for Parkinson disease resulted in an improvement in discriminatory capacity. In the full model, the contributions of genotype data and family history information to discriminatory capacity were similar, and both were statistically significant. This suggests that there is limited overlap between genetic risk factors identified through genome-wide association study and unmeasured susceptibility variants captured by family history. Our results are similar to those of studies of other complex diseases and indicate that genetic risk prediction for Parkinson disease requires identification of additional genetic risk factors and/or better methods for risk prediction in order to achieve a degree of risk prediction that is clinically useful.Genet Med 2013:15(5):361-367.

SUBMITTER: Hall TO

PROVIDER: S-EPMC3687522 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk prediction for complex diseases: application to Parkinson disease.

Hall Taryn O TO Wan Jia Y JY Mata Ignacio F IF Kerr Kathleen F KF Snapinn Katherine W KW Samii Ali A Roberts John W JW Agarwal Pinky P Zabetian Cyrus P CP Edwards Karen L KL

Genetics in medicine : official journal of the American College of Medical Genetics 20121206 5

<h4>Purpose</h4>The aim of this study was to evaluate the risk of Parkinson disease using clinical and demographic data alone and when combined with information from genes associated with Parkinson disease.<h4>Methods</h4>A total of 1,967 participants in the dbGAP NeuroGenetics Research Consortium data set were included. Single-nucleotide polymorphisms associated with Parkinson disease at a genome-wide significance level in previous genome-wide association studies were included in risk predictio ...[more]

PMID: 23222663

Similar Datasets

Project description:MRI has been used to develop biomarkers for movement disorders such as Parkinson disease (PD) and other neurodegenerative disorders with parkinsonism such as progressive supranuclear palsy and multiple system atrophy. One of these imaging biomarkers is neuromelanin (NM), whose integrity can be assessed from its contrast and volume. NM is found mainly in certain brain stem structures, namely, the substantia nigra pars compacta (SNpc), the ventral tegmental area, and the locus coeruleus. Another major biomarker is brain iron, which often increases in concert with NM degeneration. These biomarkers have the potential to improve diagnostic certainty in differentiating between PD and other neurodegenerative disorders similar to PD, as well as provide a better understanding of pathophysiology. Mapping NM in vivo has clinical importance for gauging the premotor phase of PD when there is a greater than 50% loss of dopaminergic SNpc melanized neurons. As a metal ion chelator, NM can absorb iron. When NM is released from neurons, it deposits iron into the intracellular tissues of the SNpc; the result is iron that can be imaged and measured using quantitative susceptibility mapping. An increase of iron also leads to the disappearance of the nigrosome-1 sign, another neuroimage biomarker for PD. Therefore, mapping NM and iron changes in the SNpc are a practical means for improving early diagnosis of PD and in monitoring disease progression. In this review, we discuss the functions and location of NM, how NM-MRI is performed, the automatic mapping of NM and iron content, how NM-related imaging biomarkers can be used to enhance PD diagnosis and differentiate it from other neurodegenerative disorders, and potential advances in NM imaging methods. With major advances currently evolving for rapid imaging and artificial intelligence, NM-related biomarkers are likely to have increasingly important roles for enhancing diagnostic capabilities in PD. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Project description:ImportanceRecent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes.ObjectivesTo test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach.Design, setting, and participantsUsing the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017.Main outcomes and measuresThe primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases.ResultsGenome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK, HLA-DRB5, LRRK2, and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA, LRRK2, MAPT, TRIM10, and SETD1A in PD. Among the novel genes discovered, WNT3, KANSL1, CRHR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes.Conclusions and relevanceThe study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents.

Project description:ImportanceLoneliness is associated with morbidity and mortality, including higher risk of neurodegenerative diseases. To our knowledge, no study has examined whether the association between loneliness and detrimental outcomes extends to Parkinson disease (PD).ObjectiveTo assess whether loneliness is associated with risk of incident PD and whether the association is independent of other risk factors or modified by age, sex, and genetic vulnerability.Design, setting, and participantsThis prospective cohort study included a population-based sample of UK Biobank participants aged 38 to 73 years with loneliness data and without a diagnosis of PD at baseline who were first assessed from March 13, 2006, to October 1, 2010, and followed up to October 9, 2021.ExposureFeeling lonely and covariates that are known risk factors for or prodromal features of PD.Main outcome and measureIncident PD was ascertained through UK National Health Service health records.ResultsOf 491 603 participants (mean [SD] age, 56.54 [8.09] years; 54.4% female), 2822 developed PD during the 15-year follow-up. Individuals who reported being lonely had a higher risk of PD (hazard ratio [HR], 1.37; 95% CI, 1.25-1.51), an association that remained after accounting for demographic factors, socioeconomic status, social isolation, PD polygenetic risk score, smoking, physical activity, body mass index, diabetes, hypertension, stroke, myocardial infarction, depression, and ever seeing a psychiatrist (fully adjusted model: HR 1.25; 95% CI, 1.12-1.39). The association between loneliness and incident PD was not moderated by sex (HR for interaction, 0.98; 95% CI, 95% CI, 0.81-1.18), age (HR for interaction, 0.99; 95% CI, 0.98-1.01), or polygenic risk score (HR for interaction, 0.93; 95% CI, 0.85-1.02). Contrary to expectations for a prodromal syndrome, when stratified by time, loneliness was not associated with risk for incident PD during the first 5 years (HR, 1.15; 95% CI, 0.91-1.45) but was associated with PD risk during the subsequent 10 years (HR, 1.32; 95% CI, 1.19-1.46).Conclusions and relevanceThis large cohort study found that loneliness was associated with risk of incident PD across demographic groups and independent of depression and other prominent risk factors and genetic risk. The findings add to the evidence that loneliness is a substantial psychosocial determinant of health.

Project description:Few epidemiologic studies have evaluated the effects of air pollution on the risk of Parkinson disease (PD).We investigated the associations of long-term residential concentrations of ambient particulate matter (PM) < 10 ?m in diameter (PM10) and < 2.5 ?m in diameter (PM2.5) and nitrogen dioxide (NO2) in relation to PD risk.Our nested case-control analysis included 1,556 self-reported physician-diagnosed PD cases identified between 1995 and 2006 and 3,313 controls frequency-matched on age, sex, and race. We geocoded home addresses reported in 1995-1996 and estimated the average ambient concentrations of PM10, PM2.5, and NO2 using a national fine-scale geostatistical model incorporating roadway information and other geographic covariates. Air pollutant exposures were analyzed as both quintiles and continuous variables, adjusting for matching variables and potential confounders.We observed no statistically significant overall association between PM or NO2 exposures and PD risk. However, in preplanned subgroup analyses, a higher risk of PD was associated with higher exposure to PM10 (ORQ5 vs. Q1 = 1.65; 95% CI: 1.11, 2.45; p-trend = 0.02) among women, and with higher exposure to PM2.5 (ORQ5 vs. Q1 = 1.29; 95% CI: 0.94, 1.76; p-trend = 0.04) among never smokers. In post hoc analyses among female never smokers, both PM2.5 (ORQ5 vs. Q1 = 1.79; 95% CI: 1.01, 3.17; p-trend = 0.05) and PM10 (ORQ5 vs. Q1 = 2.34; 95% CI: 1.29, 4.26; p-trend = 0.01) showed positive associations with PD risk. Analyses based on continuous exposure variables generally showed similar but nonsignificant associations.Overall, we found limited evidence for an association between exposures to ambient PM10, PM2.5, or NO2 and PD risk. The suggestive evidence that exposures to PM2.5 and PM10 may increase PD risk among female never smokers warrants further investigation. Citation: Liu R, Young MT, Chen JC, Kaufman JD, Chen H. 2016. Ambient air pollution exposures and risk of Parkinson disease. Environ Health Perspect 124:1759-1765;?http://dx.doi.org/10.1289/EHP135.

Dataset Information

Risk prediction for complex diseases: application to Parkinson disease.

Publications

Risk prediction for complex diseases: application to Parkinson disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets