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1.
The quest for genetic risk factors for Crohn's disease in the post-GWAS era
Karin Fransen, Mitja Mitrovič, Cleo C van Diemen, Rinse K. Weersma, 2011, review article

Abstract: Multiple genome-wide association studies (GWASs) and two large scale meta-analyses have been performed for Crohn's disease and have identified 71 susceptibility loci. These findings have contributed greatly to our current understanding of the disease pathogenesis. Yet, these loci only explain approximately 23% of the disease heritability. One of the future challenges inthis post-GWAS era is to identify potential sources of the remaining heritability. Such sources may include common variants with limited effect size, rare variants with higher effect sizes, structural variations, or even more complicated mechanisms such as epistatic, gene-environment and epigeneticinteractions. Here, we outline potential sources of this hidden heritability, focusing on Crohn's disease and the currently available data. Wealso discuss future strategies to determine more about the heritability; these strategies include expanding current GWAS, fine-mapping, whole genome sequencing or exome sequencing, and using family-based approaches. Despite thecurrent limitations, such strategies may help to transfer research achievements into clinical practice and guide the improvement of preventive and therapeutic measures.
Keywords: genetic risk factors, Crohn’s disease
Published: 05.06.2012; Views: 1093; Downloads: 69
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2.
Insights into population health management through disease diagnoses networks
Keith Feldman, Gregor Štiglic, Dipanwita Dasgupta, Mark Kricheff, Zoran Obradović, Nitesh Chawla, 2016, original scientific article

Abstract: The increasing availability of electronic health care records has provided remarkable progress in the field of population health. In particular the identification of disease risk factors has flourished under the surge of available data. Researchers can now access patient data across a broad range of demographics and geographic locations. Utilizing this Big healthcare data researchers have been able to empirically identify specific high-risk conditions found within differing populations. However to date the majority of studies approached the issue from the top down, focusing on the prevalence of specific diseases within a population. Through our work we demonstrate the power of addressing this issue bottom-up by identifying specifically which diseases are higher-risk for a specific population. In this work we demonstrate that network-based analysis can present a foundation to identify pairs of diagnoses that differentiate across population segments. We provide a case study highlighting differences between high and low income individuals in the United States. This work is particularly valuable when addressing population health management within resource-constrained environments such as community health programs where it can be used to provide insight and resource planning into targeted care for the population served.
Keywords: population screening, risk factors, network analysis
Published: 23.06.2017; Views: 460; Downloads: 200
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