Showing papers by "Marta Gwinn published in 2014"
TL;DR: It is suggested that meta-analyses of well-conducted candidate gene studies may continue to add to the understanding of the genetic associations in the post-GWAS era.
Abstract: Candidate gene and genome-wide association studies (GWAS) represent two complementary approaches to uncovering genetic contributions to common diseases. We systematically reviewed the contributions of these approaches to our knowledge of genetic associations with cancer risk by analyzing the data in the Cancer Genome-wide Association and Meta Analyses database (Cancer GAMAdb). The database catalogs studies published since January 1, 2000, by study and cancer type. In all, we found that meta-analyses and pooled analyses of candidate genes reported 349 statistically significant associations and GWAS reported 269, for a total of 577 unique associations. Only 41 (7.1%) associations were reported in both candidate gene meta-analyses and GWAS, usually with similar effect sizes. When considering only noteworthy associations (defined as those with false-positive report probabilities ≤0.2) and accounting for indirect overlap, we found 202 associations, with 27 of those appearing in both meta-analyses and GWAS. Our findings suggest that meta-analyses of well-conducted candidate gene studies may continue to add to our understanding of the genetic associations in the post-GWAS era.
60 citations
TL;DR: A horizon‐scanning method is developed that assigns genomic applications to tiers defined by availability of synthesized evidence, and an application of the method is illustrated to pharmacogenomics tests.
Abstract: As evidence accumulates on the use of genomic tests and other health-related applications of genomic technologies, decision makers may increasingly seek support in identifying which applications have sufficiently robust evidence to suggest they might be considered for action. As an interim working process to provide such support, we developed a horizon-scanning method that assigns genomic applications to tiers defined by availability of synthesized evidence. We illustrate an application of the method to pharmacogenomics tests.
53 citations
TL;DR: A method for horizon scanning and 1 year data on translational research beyond bench to bedside to assess the validity, utility, implementation, and outcomes of such applications are provided.
28 citations
TL;DR: Each state’s program was different because of the need to adapt to existing culture, infrastructure, and resources, yet all were able to enhance their chronic disease prevention programs with the use of family health history, a low-tech “genomic tool.”
Abstract: State health departments in Michigan, Minnesota, Oregon, and Utah explored the use of genomic information, including family health history, in chronic disease prevention programs. To support these explorations, the Office of Public Health Genomics at the Centers for Disease Control and Prevention provided cooperative agreement funds from 2003 through 2008. The 4 states’ chronic disease programs identified advocates, formed partnerships, and assessed public data; they integrated genomics into existing state plans for genetics and chronic disease prevention; they developed projects focused on prevention of asthma, cancer, cardiovascular disease, diabetes, and other chronic conditions; and they created educational curricula and materials for health workers, policymakers, and the public. Each state’s program was different because of the need to adapt to existing culture, infrastructure, and resources, yet all were able to enhance their chronic disease prevention programs with the use of family health history, a low-tech “genomic tool.” Additional states are drawing on the experience of these 4 states to develop their own approaches.
16 citations
TL;DR: Meta-analysis emerged as an important tool for assessing gene-disease associations, both for neutralizing reports of spurious associations and for revealing subtle associations in genetic association studies.
Abstract: Even before the human genome was sequenced, Khoury and Dorman published an editorial in the American Journal of Epidemiology (AJE) calling for a population-based approach to health-related discoveries that stemmed from the Human Genome Project (1). The approach was termed “human genome epidemiology” (HuGE for short) and led to the formation of an informal collaboration (HuGENet) (2) to explore the systematic use of epidemiologic methods to investigate the role of genetic variation in health and disease. That inaugural editorial outlined the goals of human genome epidemiology, which ranged from estimating the population prevalence of gene variants to evaluating genetic tests and services. In 2000, the AJE proposed a schema for publishing HuGE reviews, which were envisioned as systematic summaries of population-based data on gene-disease associations for use by researchers, health officials, and policymakers (3). Each review would address a particular combination of 1 or more genetic variants and health outcomes, summarizing the available data on genotype prevalence, gene-disease associations, and gene-environment interactions and commenting on its implications for population health. The AJE offered to consider HuGE reviews for publication, and 10 more journals followed suit. From 2000 through 2013, the AJE published 65 HuGE reviews, and 58 more reviews appeared in other journals. Together, these HuGE reviews assessed the relationships of variants of 195 genes with specific outcomes, including inherited disorders (e.g., sickle-cell anemia), common diseases (e.g., coronary artery disease), several cancers, birth defects, and other conditions. The surge in genetic association studies during the last decade has been well documented in online databases, including the HuGE Navigator and the GWAS Catalog, which captures genome-wide association studies (GWAS) (4, 5). The number of genetic association studies published annually grew from 2,514 in 2001 to 10,940 in 2013 (http://www. hugenavigator.net). From the beginning, it was clear that many reported genetic associations with common diseases were spurious; furthermore, even consistently replicated associations had mostly small effects (6). Meta-analysis thus emerged as an important tool for assessing gene-disease associations, both for neutralizing reports of spurious associations and for revealing subtle associations. In 2009, HuGENet authors recommended reporting the results of primary genetic association studies in sufficient detail to allow their evaluation for quality and inclusion in systematic reviews (7, 8). Updated HuGE review guidelines recommended the use of meta-analysis to arrive at summary estimates of association, as well as a heuristic to help gauge their reliability (the “Venice criteria”) (9). In 2009, Minelli et al. (10) published a systematic review of meta-analyses of published genetic association studies. They found that general methodological problems—such as failure to document search strategy, account for publication bias, or test for heterogeneity—were common, although no more common than among meta-analyses in other fields. However, methodological considerations specific to genetics—such as Hardy-Weinberg equilibrium, genotype frequency, choice of genetic model, and population stratification—were often poorly addressed or completely ignored. The authors concluded with a set of practical recommendations for the conduct and reporting of such meta-analyses. Although meta-analyses of genetic associations account for only a small proportion of publications on gene-disease associations, they have proliferated rapidly from only 29 in 2001 to 1,606 in 2013. During that time, the genetic association research strategy shifted focus from candidate gene studies that evaluated 1 or a few polymorphisms to GWAS for the discoveryof new candidate genes; recent years have seen an increase in studies of rare variants in both common and
10 citations