Online genetic databases informing human genome epidemiology

Registered access: a 'Triple-A' approach

We propose a standard model for a novel data access tier – registered access – to facilitate access to data that cannot be published in open access archives owing to ethical and legal risk. Based on an analysis of applicable research ethics and other legal and administrative frameworks, we discuss the general characteristics of this Registered Access Model, which would comprise a three-stage approval process: Authentication, Attestation and Authorization. We are piloting registered access with the Demonstration Projects of the Global Alliance for Genomics and Health for which it may provide a suitable mechanism for access to certain data types and to different types of data users.

A literature search tool for intelligent extraction of disease-associated genes

OBJECTIVE

To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods.

METHODS

We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article.

RESULTS

We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder-gene link are more extensive and accurate than other general purpose gene-to-disorder association databases.

CONCLUSIONS

We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene-disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

Toward modernizing the systematic review pipeline in genetics: efficient updating via data mining

PURPOSE

The aim of this study was to demonstrate that modern data mining tools can be used as one step in reducing the labor necessary to produce and maintain systematic reviews.

METHODS

We used four continuously updated, manually curated resources that summarize MEDLINE-indexed articles in entire fields using systematic review methods (PDGene, AlzGene, and SzGene for genetic determinants of Parkinson disease, Alzheimer disease, and schizophrenia, respectively; and the Tufts Cost-Effectiveness Analysis (CEA) Registry for cost-effectiveness analyses). In each data set, we trained a classification model on citations screened up until 2009. We then evaluated the ability of the model to classify citations published in 2010 as "relevant" or "irrelevant" using human screening as the gold standard.

RESULTS

Classification models did not miss any of the 104, 65, and 179 eligible citations in PDGene, AlzGene, and SzGene, respectively, and missed only 1 of 79 in the CEA Registry (100% sensitivity for the first three and 99% for the fourth). The respective specificities were 90, 93, 90, and 73%. Had the semiautomated system been used in 2010, a human would have needed to read only 605/5,616 citations to update the PDGene registry (11%) and 555/7,298 (8%), 717/5,381 (13%), and 334/1,015 (33%) for the other three databases.

CONCLUSION

Data mining methodologies can reduce the burden of updating systematic reviews, without missing more papers than humans.

The Epilepsy Genetic Association Database (epiGAD): analysis of 165 genetic association studies, 1996-2008

We have created the Epilepsy Genetic Association Database (epiGAD, http://www.epigad.org, an online database of epilepsy genetic association studies. A systematic search using several search engines identified 165 studies. Herein we analyze the types of studies available, the sample sizes used, and the strength of the findings. Common questions examined were susceptibility to idiopathic generalized epilepsy, focal epilepsy, or febrile seizures, and pharmacogenomic approaches to drug-resistant epilepsy. Sample sizes were generally small; 80% of studies had 200 or fewer cases, although more recent studies published from 2005-2008 incorporated slightly larger sample sizes. No association was judged as "strong" using current criteria for assessing genetic associations--this is probably due to inadequate sample sizes. Sample sizes need to increase, either by research collaboration or via systematic reviews and meta-analyses. We believe epiGAD will facilitate future meta-analyses.

Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies

BACKGROUND

Studies of the genetic basis of drug response could help clarify mechanisms of drug action/metabolism, and facilitate development of genotype-based predictive tests of efficacy or toxicity (pharmacogenetics).

OBJECTIVES

We conducted a systematic review and field synopsis of pharmacogenetic studies to quantify the scope and quality of available evidence in this field in order to inform future research.

DATA SOURCES

Original research articles were identified in Medline, reference lists from 24 meta-analyses/systematic reviews/review articles and U.S. Food and Drug Administration website of approved pharmacogenetic tests. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTION CRITERIA: We included any study in which either intended or adverse response to drug therapy was examined in relation to genetic variation in the germline or cancer cells in humans.

STUDY APPRAISAL AND SYNTHESIS METHODS

Study characteristics and data reported in abstracts were recorded. We further analysed full text from a random 10% subset of articles spanning the different subclasses of study.

RESULTS

From 102,264 Medline hits and 1,641 articles from other sources, we identified 1,668 primary research articles (1987 to 2007, inclusive). A high proportion of remaining articles were reviews/commentaries (ratio of reviews to primary research approximately 25 ratio 1). The majority of studies (81.8%) were set in Europe and North America focussing on cancer, cardiovascular disease and neurology/psychiatry. There was predominantly a candidate gene approach using common alleles, which despite small sample sizes (median 93 [IQR 40-222]) with no trend to an increase over time, generated a high proportion (74.5%) of nominally significant (p<0.05) reported associations suggesting the possibility of significance-chasing bias. Despite 136 examples of gene/drug interventions being the subject of >or=4 studies, only 31 meta-analyses were identified. The majority (69.4%) of end-points were continuous and likely surrogate rather than hard (binary) clinical end-points.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The high expectation but limited translation of pharmacogenetic research thus far may be explained by the preponderance of reviews over primary research, small sample sizes, a mainly candidate gene approach, surrogate markers, an excess of nominally positive to truly positive associations and paucity of meta-analyses. Recommendations based on these findings should inform future study design to help realise the goal of personalised medicines. SYSTEMATIC REVIEW REGISTRATION NUMBER: Not Registered.

Genetic influences on blood lipids and cardiovascular disease risk: tools for primary prevention

Genetic polymorphism in human populations is part of the evolutionary process that results from the interaction between the environment and the human genome. Recent changes in diet have upset this equilibrium, potentially influencing the risk of most common morbidities such as cardiovascular diseases, obesity, diabetes, and cancer. Reduction of these conditions is a major public health concern, and such a reduction could be achieved by improving our ability to detect disease predisposition early in life and by providing more personalized behavioral recommendations for successful primary prevention. In terms of cardiovascular diseases, polymorphisms at multiple genes have been associated with differential effects in terms of lipid metabolism; however, the connection with cardiovascular disease has been more elusive, and considerable heterogeneity exists among studies regarding the predictive value of genetic markers. This may be because of experimental limitations, the intrinsic complexity of the phenotypes, and the aforementioned interactions with environmental factors. The integration of genetic and environmental complexity into current and future research will drive the field toward the implementation of clinical tools aimed at providing dietary advice optimized for the individual's genome. This may imply that dietary changes are implemented early in life to gain maximum benefit. However, it is important to highlight that most reported studies have focused on adult populations and to extrapolate these findings to children and adolescents may not be justified until proper studies have been carried out in these populations and until the ethical and legal issues associated with this new field are adequately addressed.

Systematic reviews of genetic association studies. Human Genome Epidemiology Network

Gurdeep S. Sagoo and colleagues describe key components of the methodology for undertaking systematic reviews and meta-analyses of genetic association studies.

Network integration and graph analysis in mammalian molecular systems biology

Abstraction of intracellular biomolecular interactions into networks is useful for data integration and graph analysis. Network analysis tools facilitate predictions of novel functions for proteins, prediction of functional interactions and identification of intracellular modules. These efforts are linked with drug and phenotype data to accelerate drug-target and biomarker discovery. This review highlights the currently available varieties of mammalian biomolecular networks, and surveys methods and tools to construct, compare, integrate, visualise and analyse such networks.