Person: Ablorh, Akweley
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Ablorh
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Akweley
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Ablorh, Akweley
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Publication Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities(BioMed Central, 2016) Lindström, Sara; Ablorh, Akweley; Chapman, Brad; Gusev, Alexander; Chen, Gary; Turman, Constance; Eliassen, A; Price, Alkes; Henderson, Brian E.; Le Marchand, Loic; Hofmann, Oliver; Haiman, Christopher A.; Kraft, PhillipBackground: 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. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.Publication Meta-Analysis of a Multi-Ethnic, Breast Cancer Case-Control Targeted Sequencing Study(2015-05-01) Ablorh, Akweley; Kraft, Peter; Price, Alkes; Eliassen, A. HeatherBreast cancer, the most commonly diagnosed cancer in American women, is a heritable disease with nearly one hundred known genetic risk factors. Using next generation sequencing, we explored the contribution of genetics at 12 GWAS-identified loci to breast cancer susceptibility in a multi-ethnic breast cancer case-control study. Methods: The study population consists of 4,611 breast cancer cases and controls (2,316 cases and 2,295 controls) from four mutually exclusive ethnicities: African, Latina, Japanese, or European American.We conducted rare variant association testing between sequenced genotypes and simulated phenotypes to compare the performance of several approaches for assessing rare variant associations across multiple ethnicities and the statistical performance of different ethnic sampling fractions, including single-ethnicity studies and studies that sample up to four ethnicities. Findings from simulation of causal rare variant penetrance models were then applied to a non-synonymous protein-coding rare variant association study of breast cancer. Next, we applied variance partitioning methods to determine what proportion of breast cancer heritability is explained by rare and common, coding and non-coding, and the complete set of sequenced genetic variants. Results: Variance component-based tests were better powered in several scenarios. Multi-ethnic studies were well powered, with inclusion of African Americans providing the largest gains in statistical power. Rare variation in several genes was nominally associated (alpha=0.05) with breast cancer risk. Common variants explained a significant amount of breast cancer heritability (5%; SE=2%). Total breast cancer heritability from all sequenced SNVs from all 12 loci was approximately 11% (S.E.=4%), a substantial portion of breast cancer heritability which ranges from 27% to 32% in European familial studies. Conclusion: Our findings suggest that association studies between rare variants and complex disease should consider including subjects from multiple ethnicities, with preference given to genetically diverse groups. We demonstrate practices with the potential to uncover and localize gene-based associations using gene-based rare variant association testing at 12 GWAS-identified breast cancer susceptibility loci. We also present strong evidence that just this subset of previously-identified loci explains a substantial portion of heritability which suggests that all GWAS-identified loci may explain more breast cancer heritability than the 17% previously reported.Publication Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators(Public Library of Science (PLoS), 2016) Valeri, Linda; Patterson-Lomba, Oscar; Gurmu, Yared; Ablorh, Akweley; Bobb, Jennifer; Townes, Will; Harling, GuyBackground 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.