Meta-Analysis of Rare Variant Association Tests in Multiethnic Populations.

Several methods have been proposed to increase power in rare variant association testing by aggregating information from individual rare variants (MAF < 0.005). However, how to best combine rare variants across multiple ethnicities and the relative performance of designs using different ethnic sampling fractions remains unknown. In this study, we compare the performance of several statistical approaches for assessing rare variant associations across multiple ethnicities. We also explore how different ethnic sampling fractions perform, including single-ethnicity studies and studies that sample up to four ethnicities. We conducted simulations based on targeted sequencing data from 4,611 women in four ethnicities (African, European, Japanese American, and Latina). As with single-ethnicity studies, burden tests had greater power when all causal rare variants were deleterious, and variance component-based tests had greater power when some causal rare variants were deleterious and some were protective. Multiethnic studies had greater power than single-ethnicity studies at many loci, with inclusion of African Americans providing the largest impact. On average, studies including African Americans had as much as 20% greater power than equivalently sized studies without African Americans. This suggests that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups.

Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities.

BACKGROUND: Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. METHODS: Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. RESULTS: After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. CONCLUSIONS: This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs.

Investigating Transitional Care to Decrease Post-pancreatectomy 30-Day Hospital Readmissions for Dehydration or Failure to Thrive.

BACKGROUND: Current literature emphasizes post-operative complications as a leading cause of post-pancreatectomy readmissions. Transitional care factors associated with potentially preventable conditions such as dehydration and failure to thrive (FTT) may play a significant role in readmission after pancreatectomy and have not been studied. METHODS: Thirty-one post-pancreatectomy patients, who were readmitted for dehydration or FTT between 2009 and 2014, were compared to 141 nonreadmitted patients. Medical record review and a questionnaire-based survey, specifically designed to assess transitional care, were used to identify predictors of readmissions for dehydration or FTT. Logistic regression models were used to evaluate outcomes. RESULTS: On multivariable analysis, the strongest predictors of readmission for dehydration and FTT were the patient's lower educational level (P = 0.0233), the absence of family during the delivery of discharge instructions (P = 0.0098), episodic intermittent nausea at discharge (P = 0.0019), uncertainty about quantity, quality, or frequency of fluid intake (P = 0.0137), and the inability or failure to adhere to the clinician's instructions in the outpatient setting (P = 0.0048). CONCLUSION: Transitional-care-related factors are found to be associated with post-pancreatectomy readmission for dehydration and FTT. Using these results to identify high-risk patients and implement focused preventive measures combining efficient communication and optimal inpatient and outpatient management could potentially decrease readmission rates.

Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators.

BACKGROUND: The recent Ebola virus disease (EVD) outbreak in West Africa has spread wider than any previous human EVD epidemic. While individual-level risk factors that contribute to the spread of EVD have been studied, the population-level attributes of subnational regions associated with outbreak severity have not yet been considered. METHODS: To investigate the area-level predictors of EVD dynamics, we integrated time series data on cumulative reported cases of EVD from the World Health Organization and covariate data from the Demographic and Health Surveys. We first estimated the early growth rates of epidemics in each second-level administrative district (ADM2) in Guinea, Sierra Leone and Liberia using exponential, logistic and polynomial growth models. We then evaluated how these growth rates, as well as epidemic size within ADM2s, were ecologically associated with several demographic and socio-economic characteristics of the ADM2, using bivariate correlations and multivariable regression models. RESULTS: The polynomial growth model appeared to best fit the ADM2 epidemic curves, displaying the lowest residual standard error. Each outcome was associated with various regional characteristics in bivariate models, however in stepwise multivariable models only mean education levels were consistently associated with a worse local epidemic. DISCUSSION: By combining two common methods-estimation of epidemic parameters using mathematical models, and estimation of associations using ecological regression models-we identified some factors predicting rapid and severe EVD epidemics in West African subnational regions. While care should be taken interpreting such results as anything more than correlational, we suggest that our approach of using data sources that were publicly available in advance of the epidemic or in real-time provides an analytic framework that may assist countries in understanding the dynamics of future outbreaks as they occur.