Estrogen-related and other disease diagnoses preceding Parkinson’s disease

PURPOSE: Estrogen exposure has been associated with the occurrence of Parkinson's disease (PD), as well as many other disorders, and yet the mechanisms underlying these relations are often unknown. While it is likely that estrogen exposure modifies the risk of various diseases through many different mechanisms, some estrogen-related disease processes might work in similar manners and result in association between the diseases. Indeed, the association between diseases need not be due only to estrogen-related factors, but due to similar disease processes from a variety of mechanisms. PATIENTS AND METHODS: All female Parkinson's disease cases between 1982 and 2007 (n = 12,093) were identified from the Danish National Registry of Patients, along with 10 controls matched by years of birth and enrollment. Conditional logistic regressions (CLR) were used to calculate risk of PD after diagnosis of the estrogen-related diseases, endometriosis and osteoporosis, conditioning on years of birth and enrollment. To identify novel associations between PD and any other preceding conditions, CLR was also used to calculate the odds ratios (ORs) for risk of PD for 202 different categories of preceding disease diagnoses. Empirical Bayes methods were used to identify the robust associations from the over 200 associations produced by this analysis. RESULTS: We found a positive association between osteoporosis and osteoporotic fractures and PD (OR = 1.18, 95% confidence interval [CI] of 1.08–1.28), while a lack of association was observed between endometriosis and PD (OR = 1.37, 95% CI 0.99–1.90). Using empirical Bayes analyses, 24 additional categories of diseases, likely unrelated to estrogen exposure, were also identified as potentially associated with PD. CONCLUSION: We identified several novel associations, which may provide insight into common causal mechanisms between the diseases or greater understanding of potential early preclinical signs of PD. In particular, the associations with several categories of mental disorders suggest that these may be early warning signs of PD onset or these diseases (or the causes of these diseases) may predispose to PD.US Public Health Service (R01 NS36711-09); Robert P. and Judith N. Goldberg Foundation; Aarhus University Hospital Department of Clinical Epidemiology's Research Foundatio

Two-stage approach for identifying single-nucleotide polymorphisms associated with rheumatoid arthritis using random forests and Bayesian networks

We used the simulated data set from Genetic Analysis Workshop 15 Problem 3 to assess a two-stage approach for identifying single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). In the first stage, we used random forests (RF) to screen large amounts of genetic data using the variable importance measure, which takes into account SNP interaction effects as well as main effects without requiring model specification. We used the simulated 9187 SNPs mimicking a 10 K SNP chip, along with covariates DR (the simulated DRB1 gentoype), smoking, and sex as input to the RF analyses with a training set consisting of 750 unrelated RA cases and 750 controls. We used an iterative RF screening procedure to identify a smaller set of variables for further analysis. In the second stage, we used the software program CaMML for producing Bayesian networks, and developed complex etiologic models for RA risk using the variables identified by our RF screening procedure. We evaluated the performance of this method using independent test data sets for up to 100 replicates

Pathway analysis following association study

Genome-wide association studies often emphasize single-nucleotide polymorphisms with the smallest p-values with less attention given to single-nucleotide polymorphisms not ranked near the top. We suggest that gene pathways contain valuable information that can enable identification of additional associations. We used gene set information to identify disease-related pathways using three methods: gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA). Association tests were performed for common single-nucleotide polymorphisms and aggregated rare variants with traits Q1 and Q4. These pathway methods were evaluated by type I error, power, and the ranking of the VEGF pathway, the gene set used in the simulation model. GSEA and IPA had high power for detecting the VEGF pathway for trait Q1 (91.2% and 93%, respectively). These two methods were conservative with deflated type I errors (0.0083 and 0.0072, respectively). The VEGF pathway ranked 1 or 2 in 123 of 200 replicates using IPA and ranked among the top 5 in 114 of 200 replicates for GSEA. The empirical enrichment method had lower power and higher type I error. Thus pathway analysis approaches may be useful in identifying biological pathways that influence disease outcomes

Genetic Correlates of Brain Aging on MRI and Cognitive Test Measures: A Genome-Wide Association and Linkage Analysis in the Framingham Study

BACKGROUND: Brain magnetic resonance imaging (MRI) and cognitive tests can identify heritable endophenotypes associated with an increased risk of developing stroke, dementia and Alzheimer's disease (AD). We conducted a genome-wide association (GWA) and linkage analysis exploring the genetic basis of these endophenotypes in a community-based sample. METHODS: A total of 705 stroke- and dementia-free Framingham participants (age 62 +9 yrs, 50% male) who underwent volumetric brain MRI and cognitive testing (1999–2002) were genotyped. We used linear models adjusting for first degree relationships via generalized estimating equations (GEE) and family based association tests (FBAT) in additive models to relate qualifying single nucleotide polymorphisms (SNPs, 70,987 autosomal on Affymetrix 100K Human Gene Chip with minor allele frequency ≥ 0.10, genotypic call rate ≥ 0.80, and Hardy-Weinberg equilibrium p-value ≥ 0.001) to multivariable-adjusted residuals of 9 MRI measures including total cerebral brain (TCBV), lobar, ventricular and white matter hyperintensity (WMH) volumes, and 6 cognitive factors/tests assessing verbal and visuospatial memory, visual scanning and motor speed, reading, abstract reasoning and naming. We determined multipoint identity-by-descent utilizing 10,592 informative SNPs and 613 short tandem repeats and used variance component analyses to compute LOD scores. RESULTS: The strongest gene-phenotype association in FBAT analyses was between SORL1 (rs1131497; p = 3.2 × 10-6) and abstract reasoning, and in GEE analyses between CDH4 (rs1970546; p = 3.7 × 10-8) and TCBV. SORL1 plays a role in amyloid precursor protein processing and has been associated with the risk of AD. Among the 50 strongest associations (25 each by GEE and FBAT) were other biologically interesting genes. Polymorphisms within 28 of 163 candidate genes for stroke, AD and memory impairment were associated with the endophenotypes studied at p < 0.001. We confirmed our previously reported linkage of WMH on chromosome 4 and describe linkage of reading performance to a marker on chromosome 18 (GATA11A06), previously linked to dyslexia (LOD scores = 2.2 and 5.1). CONCLUSION: Our results suggest that genes associated with clinical neurological disease also have detectable effects on subclinical phenotypes. These hypothesis generating data illustrate the use of an unbiased approach to discover novel pathways that may be involved in brain aging, and could be used to replicate observations made in other studies.National Institutes of Health National Center for Research Resources Shared Instrumentation grant (ISI0RR163736-01A1); National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195); National Institute of Aging (5R01-AG08122, 5R01-AG16495); National Institute of Neurological Disorders and Stroke (5R01-NS17950

Genetic analyses of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach

BACKGROUND: We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two were univariate approaches and used 1) baseline measure at exam one or 2) summary measures such as mean and slope from multiple exams. The third method was a multivariate approach that directly models multiple measurements on a subject. A variance components model (SOLAR) was employed in the univariate approaches. A mixed regression model with polynomials was employed in the multivariate approach and implemented in SAS/IML. RESULTS: Using the baseline measure at exam 1, we detected all baseline or slope genes contributing a substantial amount (0.08) of variance (LOD > 3). Compared to the baseline measure, the mean measures yielded slightly higher LOD at the slope genes, and a lower LOD at the baseline genes. The slope measure produced a somewhat lower LOD for the slope gene than did the mean measure. Descriptive information on the pattern of changes in gene effects with age was estimated for three linked loci by the third approach. CONCLUSION: We found simple univariate methods may be effective to detect genes affecting longitudinal phenotypes but may not fully reveal temporal trends in gene effects. The relative efficiency of the univariate methods to detect genes depends heavily on the underlying model. Compared with the univariate approaches, the multivariate approach provided more information on temporal trends in gene effects at the cost of more complicated modelling and more intense computations

Bone mineral density and the risk of incident dementia:A meta-analysis

Background: It is not known whether bone mineral density (BMD) measured at baseline or as the rate of decline prior to baseline (prior bone loss) is a stronger predictor of incident dementia or Alzheimer's disease (AD). Methods:We performed a meta-analysis of three longitudinal studies, the Framingham Heart Study (FHS), the Rotterdam Study (RS), and the Rush Memory and Aging Project (MAP), modeling the time to diagnosis of dementia as a function of BMD measures accounting for covariates. We included individuals with one or two BMD assessments, aged ≥60 years, and free of dementia at baseline with follow-up available. BMD was measured at the hip femoral neck using dual-energy X-ray absorptiometry (DXA), or at the heel calcaneus using quantitative ultrasound to calculate estimated BMD (eBMD). BMD at study baseline (“baseline BMD”) and annualized percentage change in BMD prior to baseline (“prior bone loss”) were included as continuous measures. The primary outcome was incident dementia diagnosis within 10 years of baseline, and incident AD was a secondary outcome. Baseline covariates included age, sex, body mass index, ApoE4 genotype, and education. Results: The combined sample size across all three studies was 4431 with 606 incident dementia diagnoses, 498 of which were AD. A meta-analysis of baseline BMD across three studies showed higher BMD to have a significant protective association with incident dementia with a hazard ratio of 0.47 (95% CI: 0.23–0.96; p = 0.038) per increase in g/cm2, or 0.91 (95% CI: 0.84–0.995) per standard deviation increase. We observed a significant association between prior bone loss and incident dementia with a hazard ratio of 1.30 (95% CI: 1.12–1.51; p &lt; 0.001) per percent increase in prior bone loss only in the FHS cohort. Conclusions: Baseline BMD but not prior bone loss was associated with incident dementia in a meta-analysis across three studies.</p

Associations of NINJ2 sequence variants with incident ischemic stroke in the Cohorts for Heart and Aging in Genomic Epidemiology (CHARGE) consortium

Background&lt;p&gt;&lt;/p&gt; Stroke, the leading neurologic cause of death and disability, has a substantial genetic component. We previously conducted a genome-wide association study (GWAS) in four prospective studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and demonstrated that sequence variants near the NINJ2 gene are associated with incident ischemic stroke. Here, we sought to fine-map functional variants in the region and evaluate the contribution of rare variants to ischemic stroke risk.&lt;p&gt;&lt;/p&gt; Methods and Results&lt;p&gt;&lt;/p&gt; We sequenced 196 kb around NINJ2 on chromosome 12p13 among 3,986 European ancestry participants, including 475 ischemic stroke cases, from the Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, and Framingham Heart Study. Meta-analyses of single-variant tests for 425 common variants (minor allele frequency [MAF] ≥ 1%) confirmed the original GWAS results and identified an independent intronic variant, rs34166160 (MAF = 0.012), most significantly associated with incident ischemic stroke (HR = 1.80, p = 0.0003). Aggregating 278 putatively-functional variants with MAF≤ 1% using count statistics, we observed a nominally statistically significant association, with the burden of rare NINJ2 variants contributing to decreased ischemic stroke incidence (HR = 0.81; p = 0.026).&lt;p&gt;&lt;/p&gt; Conclusion&lt;p&gt;&lt;/p&gt; Common and rare variants in the NINJ2 region were nominally associated with incident ischemic stroke among a subset of CHARGE participants. Allelic heterogeneity at this locus, caused by multiple rare, low frequency, and common variants with disparate effects on risk, may explain the difficulties in replicating the original GWAS results. Additional studies that take into account the complex allelic architecture at this locus are needed to confirm these findings

Identifying Rare Variants from Exome Scans: The GAW17 Experience

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the worksho

Identifying rare variants from exome scans: the GAW17 experience

Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop