Citations

ABSTRACTNext‐generation sequencing technologies have afforded unprecedented characterization of low‐frequency and rare genetic variation. Due to low power for single‐variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel‐machine regression and adaptive testing methods for aggregative rare‐variant association testing have been demonstrated to be powerful approaches for pathway‐level analysis, although these methods tend to be computationally intensive at high‐variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare‐variant analysis using component gene‐level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family‐wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case‐control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open‐source R code for public use to facilitate easy application of our methods to existing rare‐variant analysis results.

Abstract Background: Human papillomavirus (HPV) vaccination rates in the United States remain low and lag behind other recommended adolescent vaccines. Studies evaluating the association of geographic and area-level characteristics with HPV vaccination rates provide a valuable resource for public health planning. Method: We used the Rochester Epidemiology Project data linkage system to ascertain HPV vaccination rates between 2010 and 2015 in a 7-county region of southern Minnesota. Geocoded individual patient data were spatially linked to socioeconomic data from the American Community Survey at the census block group level. Bayesian hierarchical logistic regression was used to model incident vaccination rates, adjusting for individual- and area-level sociodemographic characteristics, and geolocation. Geolocation was modeled as an approximated Gaussian field using a Stochastic Partial Differential Equations approach. All models were estimated using Integrated Nested Laplace Approximations. Results: In adjusted models, increasing age and female sex were associated with increased HPV vaccination. Lower socioeconomic status was associated with decreased rates of initiation [adjusted odds ratio (AOR); 95% confidence interval = 0.90 (0.86–0.95)], completion of the second dose [AOR = 0.88 (0.83–0.93)], and completion of the third dose [AOR = 0.85 (0.80–0.92)]. Geographic spatial analysis demonstrated increased odds of vaccination for the eastern region and in the greater Rochester metropolitan area, showing significant spatial variation not explained by individual level characteristics and ACS block group-level data. Conclusions: HPV vaccination rates varied geographically and by individual and geographically indexed sociodemographic characteristics. Impact: Identifying geographic regions with low HPV vaccination rates can help target clinical and community efforts to improve vaccination rates. Cancer Epidemiol Biomarkers Prev; 26(4); 533–40. ©2017 AACR. See all the articles in this CEBP Focus section, “Geospatial Approaches to Cancer Control and Population Sciences.”

AbstractPatients with light chain amyloidosis (AL) often have delayed diagnosis and present with significant symptomatology; this may result in decreased quality of life (QOL). We prospectively employ a “Hematology Patient Reported Symptom Screen” (HPRSS), which is three questions about fatigue, pain, and QOL, scored 0–10. The aim of this study is to better understand QOL and determine if HPRSS parameters predict for clinical outcomes. From 2009 to 2014, 302 newly diagnosed AL patients were included. Baseline median scores [interquartile range] for fatigue, pain, and QOL were 6 [3,7], 2 [0,5], 5 [3,8], respectively. Median overall survival was 53 months, with 102 (34%) deaths in the first year. There were significant differences in baseline HPRSS between those that lived longer than one year and early death patients in the domains of fatigue (5 [IQR 3, 7] vs. 7 [IQR 5, 8], P < 0.0001) and QOL (6 [IQR 4, 8] vs. 5 [IQR 3, 7], P = 0.006). On univariate analysis fatigue, QOL, physician‐reported performance status, autologous stem cell transplant (ASCT), and Mayo stage were prognostic for survival. On multivariate analysis Mayo stage, ASCT, and baseline fatigue remained independently prognostic. When analyses were restricted to the 125 patients with HPRSS measurements at 12 months, we found that over time QOL scores improved significantly 6 [IQR 3.5, 8] → 7 [IQR 5, 8] (P = 0.01). Asking AL patients to rate their fatigue and QOL has predictive value. Baseline patient reported fatigue is an independent prognostic factor for survival. Survival at one year was associated with significant improvement in QOL.