Joint regression analysis of multiple traits based on genetic relationships.

Motivation: Polygenic scores (PGSs) are widely available and employed in genomic data analyses for predicting and understanding genetic architectures. Existing approaches either require information on SNP level, do not infer clusters of traits sharing genetic characteristic, or do not have any immediate predictive properties. Results: Here, we present geneJAM, which is a novel clustering and estimation method using PGSs for inferring a genetic relationship among multiple, simultaneously measured and potentially correlated traits in a multivariate GWAS.Using graphical lasso, we estimate a sparse covariance matrix of the PGSs and obtain clusters of traits sharing genetic characteristics. We use the clusters to specify the structure of the error covariance matrix of a generalized least squares (GLS) model and use the feasible GLS estimator for estimating a linear regression model with a certain unknown degree of correlation between the residuals.The method suits many biology studies well with traits embedded in some genetic functioning groups and facilitates development of the PGS research. We compare the method with fully parametric techniques on simulated data and illustrate the utility of the methods by examining a heterogeneous stock mouse data set from the Wellcome Trust Centre for Human Genetics. We demonstrate that the method successfully identifies clusters of traits and increases precision, power, and computational efficiency. Availability and implementation: GeneJAM is implemented in R and available at: https://github.com/abuchardt/geneJAM.

Longitudinal symptom burden in adult patients with acute leukaemia participating in the PACE-AL randomised controlled exercise trial-an explorative analysis.

OBJECTIVE: Patients with acute leukaemia undergoing chemotherapy experience multiple symptoms that interfere with activities of daily living. Exercise-based interventions have been used to remedy disease and treatment-related symptoms in patients with cancer. We explored the impact of exercise and health counselling on symptom prevalence, severity and longitudinal patterns. METHODS: Explorative analysis of M.D. Anderson Symptom Inventory and Brief Fatigue Inventory completed weekly in a randomized controlled trial of patients with acute leukaemia undergoing consolidation chemotherapy. Seventy patients were consecutively recruited and randomly allocated to usual care (n = 36) or 12-week supervised exercise and health counselling (n = 34) at Copenhagen University Hospital, 2011-2014. RESULTS: There was no difference in symptom prevalence between groups, but we found a significant increase in symptom and fatigue severity in the intervention group during the study period. However, the symptom burden reduced significantly in both groups at 12 weeks. Longitudinally, the symptom cluster; 'drowsiness, fatigue, disturbed sleep, difficulty remembering' was significantly more severe in the intervention group. CONCLUSION: Intervention and control group participants had substantial symptom and fatigue burden during 12-week moderate exercise and health counselling in patients with acute leukaemia undergoing chemotherapy. A greater symptom burden was found in the intervention group during the 12 weeks, though reducing in both groups at 12 weeks. Studies are needed to examine the link between exercise and symptom severity.

Survival prognosis and variable selection: A case study for metastatic castrate resistant prostate cancer patients.

Survival prognosis is challenging, and accurate prediction of individual survival times is often very difficult. Better statistical methodology and more data can help improve the prognostic models, but it is important that methods and data usages are evaluated properly. The Prostate Cancer DREAM Challenge offered a framework for training and blinded validation of prognostic models using a large and rich dataset on patients diagnosed with metastatic castrate resistant prostate cancer. Using the Prostate Cancer DREAM Challenge data we investigated and compared an array of methods combining imputation techniques of missing values for prognostic variables with tree-based and lasso-based variable selection and model fitting methods. The benchmark metric used was integrated AUC (iAUC), and all methods were benchmarked using cross-validation on the training data as well as via the blinded validation. We found that survival forests without prior variable selection achieved the best overall performance (cv-iAUC = 0.70, validation-iACU = 0.78), while a generalized additive model was best among those methods that used explicit prior variable selection (cv-iAUC = 0.69, validation-iACU = 0.76). Our findings largely concurred with previous results in terms of the choice of important prognostic variables, though we did not find the level of prostate specific antigen to have prognostic value given the other variables included in the data.