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

Millions of single nucleotide polymorphisms have been identified as a result of the human genome project and the rapid advance of high throughput genotyping technology. Genetic association studies, such as recent genome-wide association studies (GWAS), have provided a springboard for exploring the contribution of inherited genetic variation and gene/environment interactions in relation to disease. Given the capacity of such studies to produce a plethora of information that may then be described in a number of publications, selecting possible disease susceptibility genes and identifying related modifiable risk factors is a major challenge. A Web-based application for finding evidence of such relationships is key to the development of follow-up studies and evidence for translational research. We developed a Web-based application that selects and prioritizes potential disease-related genes by using a highly curated and updated literature database of genetic association studies. The application, called Gene Prospector, also provides a comprehensive set of links to additional data sources. We compared Gene Prospector results for the query "Parkinson" with a list of 13 leading candidate genes (Top Results) from a curated, specialty database for genetic associations with Parkinson disease (PDGene). Nine of the thirteen leading candidate genes from PDGene were in the top 10th percentile of the ranked list from Gene Prospector. In fact, Gene Prospector included more published genetic association studies for the 13 leading candidate genes than PDGene did. Gene Prospector provides an online gateway for searching for evidence about human genes in relation to diseases, other phenotypes, and risk factors, and provides links to published literature and other online data sources. Gene Prospector can be accessed via http://www.hugenavigator.net/HuGENavigator/geneProspectorStartPage.do
 .

/pdf/gene-prospector-an-evidence-gateway-for-evaluating-potential-4alkr0pe07.pdf

Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases

Inferences regarding causes of birth defects in humans are often based on results of case-control studies conducted after birth To address bias in these studies caused by potential differential recall of past exposures between case and control mothers, many investigators have advocated the use of affected controls (babies with birth defects other than the one of interest) To evaluate whether the use of affected controls is warranted for a wide range of scenarios, we analyzed data from a population-based case-control study of birth defects in Atlanta, in which there were 4,918 babies with serious defects ascertained in the first year of life and 3,029 babies without defects We compared the magnitude of the odds ratios for 10 specific defects--risk factor associations between normal and affected controls These associations included demographic factors (eg, advanced maternal age and Down syndrome), chronic maternal illnesses (eg, diabetes and cardiac defects), chronic exposures (eg, multivitamins and neural tube defects), and acute exposures (eg, flu and neural tube defects) In all instances, the use of affected controls did not change etiologic inferences derived from using normal controls and there were only moderate changes in odds ratios On the basis of theoretical considerations, we show that recall bias can lead to spurious inferences only under extreme conditions We conclude that concerns about recall bias are overrated in birth defects studies and that the use of normal controls is acceptable unless evidence of substantial recall bias exists

On the use of affected controls to address recall bias in case‐control studies of birth defects

Applications of the Case-Control Method in Genetic Epidemiology

Contribution of different HFE genotypes to iron overload disease: a pooled analysis.

There is accumulating evidence that periconceptional multivitamin use may prevent the occurrence of some birth defects other than neural tube defects. Using data from the population-based Atlanta Birth Defects Case-Control Study, we investigated the possible association between periconceptional multivitamin use and the occurrence of limb deficiency. We examined the periconceptional use of multivitamins among mothers of 117 babies with nonsyndromic limb deficiency who were liveborn or stillborn to residents of metropolitan Atlanta from 1968 to 1980 and among mothers of 3,029 control babies born without birth defects who were randomly selected through birth certificates. We found that children whose mothers were periconceptional multivitamin users had a lower risk of having a limb deficiency [odds ratio (OR) = 0.47; 95% confidence interval (CI) = 0.23-0.97]. This protective effect, however, was mostly seen for transverse limb deficiency (OR = 0.30; 95% CI = 0.07-1.32) and not for longitudinal deficiency (including preaxial and postaxial deficiencies; OR = 1.03; 95% CI = 0.17-4.30). Adjustment for potential confounding factors did not change these findings. We found a trend of decreasing risk for all transverse limb deficiencies with earlier vitamin use. These data indicate that mothers' periconceptional multivitamin use may reduce the risk for some types of limb deficiency among their offspring. In addition, because we did not find the protective effect for all types of limb deficiency, the data may also indicate causal heterogeneity of limb deficiencies.

Muin J. Khoury

Papers

Gene Prospector: An evidence gateway for evaluating potential susceptibility genes and interacting risk factors for human diseases

On the use of affected controls to address recall bias in case‐control studies of birth defects

Applications of the Case-Control Method in Genetic Epidemiology

Contribution of different HFE genotypes to iron overload disease: a pooled analysis.

Does periconceptional multivitamin use reduce the risk for limb deficiency in offspring