Predicting chronic renal insufficiency in idiopathic membranous glomerulonephritis

Predicting chronic renal insufficiency in idiopathic membranous glomerulonephritis. We developed an approach in quantifying the risk of developing chronic renal insufficiency (CRI) based on a cohort of 184 patients with idiopathic membranous glomerulonephritis (IMGN), prospectively followed by the Toronto Glomerulonephritis Registry between 1974 and 1988. After a mean follow-up period of 5.8 years, 26% of patients developed CRI (defined as persistent reduction of creatinine clearance (CCr) ≤ 60 ml/min/1.73m2 for ≥12 months). We found that when compared to the baseline probability of the unselected patients, the severity of proteinuria at kidney biopsy added only marginally to the prediction of CRI. We introduced a special test condition: persistent proteinuria (PP) (that is, duration of proteinuria, g/day, above different cut-off levels). We examined the positive predictive value (PPV) and sensitivity (SEN) of 15 arbitrarily chosen levels of PP (that is, proteinuria ≥4, 6 or 8 g/day persisting for ≥6, 9, 12, 18 or 24 months) to select levels with optimal predictive characteristics. We found that PP ≥ 8 g/day for ≥six months was a simple and useful predictor of CRI with a PPV and SEN of 66%. To further improve our prediction, we tested the following parameters: age, sex, initial SCr and CCr, proteinuria, serum albumin, hypertension, rate of change of CCr over time, and therapy (steroids ± immunosuppressive drugs) in a multivariate analysis. Proteinuria, initial CCr, and rate of change of CCr were most important in predicting CRI. Fifteen models were then developed by including each patient's CCr at the start of PP and its rate of change during the time period selected. Two models based on PP ≥ 4 g/day for ≥18 months, or ≥6 g/day for ≥9 months significantly improved the PPV's for CRI from those based on the same levels of PP alone. Using these test conditions, we can improve the prediction of CRI from a baseline probability of 26% in unselected patients to a range of 55 to 86% in the “high-risk” patients (with SEN > 60%). Application of these predictive strategies in IMGN will be useful in managing the individual patients and in selecting patients for clinical trials by limiting the exposure of potentially toxic therapy to the “high-risk” patients

Integrative Analysis of Gene Expression Data Including an Assessment of Pathway Enrichment for Predicting Prostate Cancer

Background: Microarray technology has been previously used to identify genes that are differentially expressed between tumour and normal samples in a single study, as well as in syntheses involving multiple studies. When integrating results from several Affymetrix microarray datasets, previous studies summarized probeset-level data, which may potentially lead to a loss of information available at the probe-level. In this paper, we present an approach for integrating results across studies while taking probe-level data into account. Additionally, we follow a new direction in the analysis of microarray expression data, namely to focus on the variation of expression phenotypes in predefined gene sets, such as pathways. This targeted approach can be helpful for revealing information that is not easily visible from the changes in the individual genes. Results: We used a recently developed method to integrate Affymetrix expression data across studies. The idea is based on a probe-level based test statistic developed for testing for differentially expressed genes in individual studies. We incorporated this test statistic into a classic random-effects model for integrating data across studies. Subsequently, we used a gene set enrichment test to evaluate the significance of enriched biological pathways in the differentially expressed genes identified from the integrative analysis. We compared statistical and biological significance of the prognostic gene expression signatures and pathways identified in the probe-level model (PLM) with those in the probeset-level model (PSLM). Our integrative analysis of Affymetrix microarray data from 110 prostate cancer samples obtained from three studies reveals thousands of genes significantly correlated with tumour cell differentiation. The bioinformatics analysis, mapping these genes to the publicly available KEGG database, reveals evidence that tumour cell differentiation is significantly associated with many biological pathways. In particular, we observed that by integrating information from the insulin signalling pathway into our prediction model, we achieved better prediction of prostate cancer. Conclusions: Our data integration methodology provides an efficient way to identify biologically sound and statistically significant pathways from gene expression data. The significant gene expression phenotypes identified in our study have the potential to characterize complex genetic alterations in prostate cancer

Using the ratio of means as the effect size measure in combining results of microarray experiments

<p>Abstract</p> <p>Background</p> <p>Development of efficient analytic methodologies for combining microarray results is a major challenge in gene expression analysis. The widely used effect size models are thought to provide an efficient modeling framework for this purpose, where the measures of association for each study and each gene are combined, weighted by the standard errors. A significant disadvantage of this strategy is that the quality of different data sets may be highly variable, but this information is usually neglected during the integration. Moreover, it is widely known that the estimated standard deviations are probably unstable in the commonly used effect size measures (such as standardized mean difference) when sample sizes in each group are small.</p> <p>Results</p> <p>We propose a re-parameterization of the traditional mean difference based effect measure by using the log ratio of means as an effect size measure for each gene in each study. The estimated effect sizes for all studies were then combined under two modeling frameworks: the quality-unweighted random effects models and the quality-weighted random effects models. We defined the quality measure as a function of the detection p-value, which indicates whether a transcript is reliably detected or not on the Affymetrix gene chip. The new effect size measure is evaluated and compared under the quality-weighted and quality-unweighted data integration frameworks using simulated data sets, and also in several data sets of prostate cancer patients and controls. We focus on identifying differentially expressed biomarkers for prediction of cancer outcomes.</p> <p>Conclusion</p> <p>Our results show that the proposed effect size measure (log ratio of means) has better power to identify differentially expressed genes, and that the detected genes have better performance in predicting cancer outcomes than the commonly used effect size measure, the standardized mean difference (SMD), under both quality-weighted and quality-unweighted data integration frameworks. The new effect size measure and the quality-weighted microarray data integration framework provide efficient ways to combine microarray results.</p

Linkage and association analysis in pedigrees from different populations

Using the Genetic Analysis Workshop 14 simulated datasets we carried out nonparametric linkage analyses and applied a log-linear method for analysis of case-parent-triad data with stratification on parental mating type. We proposed and applied a random effect modelling approach to explore the impact of population heterogeneity on tests of association between genetic markers and disease status. The estimated genetic effect may appear to be strongly significant in one population but nonsignificant in another population, leading to confusion about interpretation. However, when results are interpreted in the light of a random effects model, both studies may be making similar statements about a genetic effect that varies depending on environment and background

Development of risk prediction models for depression combining genetic and early life risk factors

BackgroundBoth genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated.MethodsLeveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression.ResultsIn the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14–1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05–1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively.ConclusionWe have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression

Pathway analysis for genetic association studies: to do, or not to do? That is the question

In Genetic Analysis Workshop 18 data, we used a 3-stage approach to explore the benefits of pathway analysis in improving a model to predict 2 diastolic blood pressure phenotypes as a function of genetic variation. At stage 1, gene-based tests of association in family data of approximately 800 individuals found over 600 genes associated at p<0.05 for each phenotype. At stage 2, networks and enriched pathways were estimated with Cytoscape for genes from stage 1, separately for the 2 phenotypes, then examining network overlap. This overlap identified 4 enriched pathways, and 3 of these pathways appear to interact, and are likely candidates for playing a role in hypertension. At stage 3, using 157 maximally unrelated individuals, partial least squares regression was used to find associations between diastolic blood pressure and single-nucleotide polymorphisms in genes highlighted by the pathway analyses. However, we saw no improvement in the adjusted cross-validated R(2). Although our pathway-motivated regressions did not improve prediction of diastolic blood pressure, merging gene networks did identify several plausible pathways for hypertension

Identifying cis- and trans-acting single-nucleotide polymorphisms controlling lymphocyte gene expression in humans

Assuming multiple loci play a role in regulating the expression level of a single phenotype, we propose a new approach to identify cis- and trans-acting loci that regulate gene expression. Using the Problem 1 data set made available for Genetic Analysis Workshop 15 (GAW15), we identified many expression phenotypes that have significant evidence of association and linkage to one or more chromosomal regions. In particular, six of ten phenotypes that we found to be regulated by cis- and trans-acting loci were also mapped by a previous analysis of these data in which a total of 27 phenotypes were identified with expression levels regulated by cis-acting determinants. However, in general, the p-values associated with these regulators identified in our study were larger than in their studies, since we had also identified other factors regulating expression. In fact, we found that most of the gene expression phenotypes are influenced by at least one trans-acting locus. Our study also shows that much of the observable heritability in the phenotypes could be explained by simple single-nucleotide polymorphism associations; residual heritability was reduced and the remaining heritability may represent complex regulation systems with interactions or noise

A genome scan for parent-of-origin linkage effects in alcoholism

BACKGROUND: Alcoholism is a complex disease in which genomic imprinting may play an important role in its susceptibility. OBJECTIVE: To conduct a genome-wide search for loci that may have strong parent-of-origin linkage effects in alcoholism; to compare the linkage results between the microsatellites and the two single-nucleotide polymorphism (SNP) platforms. METHODS: Nonparametric linkage analyses were performed using ALLEGRO with the three sets of markers provided by the Genetic Analysis Workshop 14 for the Collaborative Study on the Genetics of Alcoholism Problem 1 data. Both sex-averaged and sex-specific genetic maps were used. We also provided a valid statistical test to determine whether the parental allele sharing differed significantly. RESULTS: Significant maternal linkage effects (paternal imprinting) were observed on chromosome 12 using either the microsatellite markers or the two SNP panels. The two SNP sets did not improve the linkage signals compared to the results from the microsatellite markers on chromosome 12. Possible paternal linkage effects (maternal imprinting) on chromosome 7 and maternal linkage effects (paternal imprinting) on chromosome 10 were found using the two SNP panels. CONCLUSION: For diseases which may have parent-of-origin effects, linkage analysis looking at parental sharing separately may reduce locus heterogeneity and increase the ability to identify that which can not be identified with usual linkage analysis