Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

http://ibgwww.colorado.edu/cdrom2009/ScriptsA/purcell/GWAS/boulder_09_plink_intro.pdf

PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study's generalisability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control, and cross-sectional studies. We convened a 2-day workshop in September 2004, with methodologists, researchers, and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results, and discussion sections of articles. 18 items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the Web sites of PLoS Medicine, Annals of Internal Medicine, and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

/pdf/the-strengthening-the-reporting-of-observational-studies-in-1a60tcw2sx.pdf

The Strengthening the Reporting of Observational Studies in Epidemiology [STROBE] statement: guidelines for reporting observational studies

Much biomedical research is observational. The reporting of such research is often inadequate, which hampers the assessment of its strengths and weaknesses and of a study’s generalizability. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Initiative developed recommendations on what should be included in an accurate and complete report of an observational study. We defined the scope of the recommendations to cover three main study designs: cohort, case-control and cross-sectional studies. We convened a two-day workshop, in September 2004, with methodologists, researchers and journal editors to draft a checklist of items. This list was subsequently revised during several meetings of the coordinating group and in e-mail discussions with the larger group of STROBE contributors, taking into account empirical evidence and methodological considerations. The workshop and the subsequent iterative process of consultation and revision resulted in a checklist of 22 items (the STROBE Statement) that relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to all three study designs and four are specific for cohort, case-control, or cross-sectional studies. A detailed Explanation and Elaboration document is published separately and is freely available on the web sites of PLoS Medicine, Annals of Internal Medicine and Epidemiology. We hope that the STROBE Statement will contribute to improving the quality of reporting of observational studies.

/pdf/the-strengthening-the-reporting-of-observational-studies-in-5fvhasotyl.pdf

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

14 – Genetic Disease

Since the completion of the Human Genome Project, progress toward translating genomic research discoveries to address population health issues has been limited. Several meetings of social and behavioral scientists have outlined priority research areas where advancement of translational research could increase population health benefits of genomic discoveries. In this review, we track the pace of progress, study size and design, and focus of genomics translational research from 2012 to 2018 and its concordance with five social and behavioral science recommended priorities. We conducted a review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Guidelines for Scoping Reviews. Steps involved completing a search in five databases and a hand search of bibliographies of relevant literature. Our search (from 2012 to 2018) yielded 4,538 unique studies; 117 were included in the final analyses. Two coders extracted data including items from the PICOTS framework. Analysis included descriptive statistics to help identify trends in pace, study size and design, and translational priority area. Among the 117 studies included in our final sample, nearly half focused on genomics applications that have evidence to support translation or implementation into practice (Centers for Disease Control and Prevention Tier 1 applications). Common study designs were cross-sectional (40.2%) and qualitative (24.8%), with average sample sizes of 716 across all studies. Most often, studies addressed public understanding of genetics and genomics (33.3%), risk communication (29.1%), and intervention development and testing of interventions to promote behavior change (19.7%). The number of studies that address social and behavioral science priority areas is extremely limited and the pace of this research continues to lag behind basic science advances. Much of the research identified in this review is descriptive and related to public understanding, risk communication, and intervention development and testing of interventions to promote behavior change. The field has been slow to develop and evaluate public health-friendly interventions and test implementation approaches that could enable health benefits and equitable access to genomic discoveries. As the completion of the human genome approaches its 20th anniversary, full engagement of transdisciplinary efforts to address translation challenges will be required to close this gap.

/pdf/a-scoping-review-of-social-and-behavioral-science-research-1c7pd4oc2v.pdf

A scoping review of social and behavioral science research to translate genomic discoveries into population health impact

Javitt et al. [1]propose a blueprint for a mandated genetic test registry to support informed health care decision making. According to the authors, the core aim of the registry is ‘to promote transparency, which includes disclosure of both what is known and what is not known.’ They propose that for each test, the registry should include not only a description of the test and its intended use, but also information about its analytic and clinical validity, clinical utility, and proficiency testing.

We agree that reporting data on validity and utility is an essential element of quality assurance in genetic testing. Nevertheless, whether reporting to a genetic test registry is mandated or not, there is a fundamental challenge in implementation of such a registry, particularly with regard to the sources, standards and quality of submitted data and their systematic evaluation. As acknowledged by the authors, laboratories performing genetic tests may have only some of the data needed to evaluate validity and utility of tests. In particular, information on clinical validity and utility will have to be brought together from multiple sources, including basic research, clinical trials, and epidemiological and clinical studies. The available information is frequently fragmented, sometimes contradictory, or simply not available. Thus, a comprehensive assessment of clinical validity and utility requires knowledge synthesis in the form of systematic evidence reviews from multiple sources. Most laboratories are currently not equipped to conduct such analyses.

We believe that a remedy for this problem is a process of systematic knowledge synthesis followed by evidence-based recommendations by an independent group. The Centers for Disease Control and Prevention (CDC) has explored the feasibility and methods for such a process by 2 synergistic initiatives. The first initiative, the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) project was launched in 2004 [2]. The main goal of EGAPP is to establish a rigorous, systematic, evidence-based process for evaluating genetic tests that are in transition from research to clinical and public health practice. An independent, non-federal EGAPP working group developed methods for reviewing evidence on emerging complex genetic tests by modifying existing approaches used by professional organizations, advisory committees, independent task forces (e.g., U.S. Preventive Services Task Force and CDC's Task Force on Community Preventive Services) and international health technology assessment groups. The EGAPP working group also developed processes for identifying, prioritizing and selecting topics, performing evidence reviews, and developing recommendations for practice based on the evidence. As of July 2009, EGAPP has published their methods [3], 5 evidence reports and 4 recommendations, with others still in the pipeline [4].

The second initiative is the Genomic Applications in Practice and Prevention Network (GAPPNet), launched in 2009 [5]. GAPPNet is a stakeholder-driven enterprise with representation from academia, government, health care, public health, industry, and the community. The network aims to promote systematic review of research findings, support for translation research, and diffusion of high quality information on genomic applications in practice and prevention. One of GAPPNet's goals is the development of an online knowledge base on the clinical validity and utility of genomic applications [6]. When systematic reviews have been conducted by EGAPP or other health technology assessment groups, the knowledge base will provide links to these reviews. For selected genomic applications lacking comprehensive reviews, brief systematic summaries of available evidence will be provided. When no information is available, this will also be noted, which will stimulate research to fill these knowledge gaps. The GAPPNet knowledge base is expected to provide an initial point of reference for what we know and what we don't know, as well as a source for updated information as it is compiled by GAPPNet members from published and unpublished applied research.

Clearly, establishing a genetic test registry is a worthwhile goal; however, the blueprint by Javitt et al. describes a structure without the necessary utilities: that is, the connections to a growing grid of translational research and evidence-based knowledge syntheses that will provide the essential multifaceted data on validity and utility. Creating a registry with credible information requires more than mandating submission of data by laboratories. The methods and products of EGAPP, together with the goals and upcoming activities of GAPPNet, could easily be integrated into the implementation of the genetic test registry to enhance the utility of the registry to providers, consumers, researchers, and policy makers.

/pdf/a-genetic-test-registry-bringing-credible-and-actionable-131lof0bry.pdf

A genetic test registry: bringing credible and actionable data together.

In the 1950s, case-control studies of smoking and lung cancer established a paradigm for epidemiologic studies of risk factors for chronic diseases. Since then, thousands of case-control studies have examined possible associations of countless risk factors with numerous diseases, rarely finding associations as strong or consistent as that of smoking with lung cancer. Recently, researchers have applied advances in molecular genetics to conduct candidate gene and genome-wide association studies of lung cancer. Skeptics among both epidemiologists and geneticists have argued that genomic research adds little value when most cases of disease can be attributed to a preventable exposure; however, well-conducted studies of gene-environment interactions that draw on data from more than 50 years of research in toxicology, pathophysiology, and behavioral science offer important models for the development of more comprehensive approaches to understanding the etiology of chronic diseases.

/pdf/invited-commentary-genes-environment-and-hybrid-vigor-2z1le5h01w.pdf

Invited Commentary: Genes, Environment, and Hybrid Vigor

Exposure misclassification is a well-known problem in epidemiologic studies and can considerably dilute relative risk (RR) measures toward unity. A vivid illustration of such misclassification occurs in familial aggregation studies, where "exposure" is defined as a specified genetic relationship to an affected individual (case) or to an unaffected individual (control). Even in the simplest single-gene model, only a fraction of relatives of cases have the disease genotype. Also, if the trait is not fully penetrant, a proportion of relatives of controls can have the genotype. Thus the familial RR measures are subject to misclassification bias if they are interpreted as the relative effect of a susceptible genotype. The effects of this form of "exposure" (or more properly genotype) misclassification on familial RR measures were quantified and applied empirically to known disease-genetic trait associations. Expected RRs in first-, second-, and third-degree relatives were generated and plotted for different genotype RRs, disease, and allele frequencies. In general, familial RRs are substantially lower than genotypic RRs. Even in the case of strong associations such as HLA-B27 and ankylosing spondylitis (RR = 100), RR measures in first-, second-, and third-degree relatives are only 6.0, 3.5, and 2.2, respectively. Such strong misclassification effects may result in considerable reduction of statistical power in family studies. It is suggested that etiologic studies of disease explore directly the role of measurable genetic traits in epidemiologic studies in populations as well as in families.

Muin J. Khoury

Papers

14 – Genetic Disease

A scoping review of social and behavioral science research to translate genomic discoveries into population health impact

A genetic test registry: bringing credible and actionable data together.

Invited Commentary: Genes, Environment, and Hybrid Vigor

Illustration of the effects of genotype misclassification on the measurement of familial aggregation in epidemiologic studies.