GAPscreener: an automatic tool for screening human genetic association literature in PubMed using the support vector machine technique.

BACKGROUND: Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies. RESULTS: The data source for this research was the HuGE Navigator, formerly known as the HuGE Pub Lit database. Weighted SVM feature selection based on a keyword list obtained by the two-way z score method demonstrated the best screening performance, achieving 97.5% recall, 98.3% specificity and 31.9% precision in performance testing. Compared with the traditional screening process based on a complex PubMed query, the SVM tool reduced by about 90% the number of abstracts requiring individual review by the database curator. The tool also ascertained 47 articles that were missed by the traditional literature screening process during the 4-week test period. We examined the literature on genetic associations with preterm birth as an example. Compared with the traditional, manual process, the GAPscreener both reduced effort and improved accuracy. CONCLUSION: GAPscreener is the first free SVM-based application available for screening the human genetic association literature in PubMed with high recall and specificity. The user-friendly graphical user interface makes this a practical, stand-alone application. The software can be downloaded at no charge.

HuGE Watch: tracking trends and patterns of published studies of genetic association and human genome epidemiology in near-real time.

HuGE Watch is a web-based application for tracking the evolution of published studies on genetic association and human genome epidemiology in near-real time. The application allows users to display temporal trends and spatial distributions as line charts and google maps, providing a quick overview of progress in the field. http://www.hugenavigator.net/HuGENavigator/startPageWatch.do

Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment.

BACKGROUND: Several thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies. METHODS: Articles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001-2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001-2003. RESULTS: During both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample. CONCLUSION: We conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

A systematic review of cancer GWAS and candidate gene meta-analyses reveals limited overlap but similar effect sizes.

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.

Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies.

In the field of cancer, genetic association studies are among the most active and well-funded research areas, and have produced hundreds of genetic associations, especially in the genome-wide association studies (GWAS) era. Knowledge synthesis of these discoveries is the first critical step in translating the rapidly emerging data from cancer genetic association research into potential applications for clinical practice. To facilitate the effort of translational research on cancer genetics, we have developed a continually updated database named Cancer Genome-wide Association and Meta Analyses database that contains key descriptive characteristics of each genetic association extracted from published GWAS and meta-analyses relevant to cancer risk. Here we describe the design and development of this tool with the aim of aiding the cancer research community to quickly obtain the current updated status in cancer genetic association studies.

Tracking the epidemiology of human genes in the literature: the HuGE Published Literature database.

Completion of the human genome sequence has inspired a new wave of epidemiologic studies on the prevalence of gene variants and their associations with diseases in human populations. In 2001, the Human Genome Epidemiology (HuGE) Network launched the HuGE Published Literature database (HuGE Pub Lit), a searchable, online knowledge base of published, population-based epidemiologic studies of human genes. The database contains links to PubMed articles and can be searched by gene, disease, interacting factor, type of study design or analysis, or any combination of terms in these categories. The search output contains a link to each identified article, along with a table summarizing key features of the reported study. As of September 6, 2005, some 17,665 articles were indexed in the database. Most described gene-disease associations (86%); fewer evaluated gene-gene or gene-environment interactions (17%), the prevalence of gene variants (10%), or genetic tests (3%). Although not comprehensive, this database is a unique tool for epidemiologic researchers and others concerned with the role of genetic variation in population health. Here, the authors provide an overview of the database and its characteristics and uses.