A HapMap harvest of insights into the genetics of common disease

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies

Introduction

The eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR) systems for advancing discovery in genome science. eMERGE also includes a special emphasis on the ethical, legal and social issues related to these endeavors.

Organization

The five sites are supported by an Administrative Coordinating Center. Setting of network goals is initiated by working groups: (1) Genomics, (2) Informatics, and (3) Consent & Community Consultation, which also includes active participation by investigators outside the eMERGE funded sites, and (4) Return of Results Oversight Committee. The Steering Committee, comprised of site PIs and representatives and NHGRI staff, meet three times per year, once per year with the External Scientific Panel.

Current progress

The primary site-specific phenotypes for which samples have undergone genome-wide association study (GWAS) genotyping are cataract and HDL, dementia, electrocardiographic QRS duration, peripheral arterial disease, and type 2 diabetes. A GWAS is also being undertaken for resistant hypertension in ≈2,000 additional samples identified across the network sites, to be added to data available for samples already genotyped. Funded by ARRA supplements, secondary phenotypes have been added at all sites to leverage the genotyping data, and hypothyroidism is being analyzed as a cross-network phenotype. Results are being posted in dbGaP. Other key eMERGE activities include evaluation of the issues associated with cross-site deployment of common algorithms to identify cases and controls in EMRs, data privacy of genomic and clinically-derived data, developing approaches for large-scale meta-analysis of GWAS data across five sites, and a community consultation and consent initiative at each site.

Future activities

Plans are underway to expand the network in diversity of populations and incorporation of GWAS findings into clinical care.

Summary

By combining advanced clinical informatics, genome science, and community consultation, eMERGE represents a first step in the development of data-driven approaches to incorporate genomic information into routine healthcare delivery.

An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies

MOTIVATION

In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models.

RESULTS

The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS.

AVAILABILITY

The software code for mixed models analysis is freely available at http://biostat.mc.vanderbilt.edu/LilyWang.

Neural markers of genetic vulnerability to drug addiction

This chapter will summarize genetics findings derived from various strategies and highlight important neural markers (or correlates) in some specific and extensively studied genes. Most studies highlighted here focus on alcohol and nicotine dependence (AD and ND, respectively). AD and ND are among the most prevalent addictive disorders worldwide, are among the best studied, and are also associated globally with the largest socioeconomic impact.We describe different mechanisms through which genes can have an impact on the addictive behaviors, distinguishing between the genes that inscribe the proteins affecting the metabolism of the addictive substance (e.g., ADH/ALDH for alcohol or CYP2A6 for nicotine) and genes that code for the brain transmitter systems, such as genes involved in cerebral neurotransmission thought to be involved in addiction (e.g., brain reward system, mood regulation, opioid system). Strategies include linkage analyses, association studies, whole genome association studies as well as intermediate/endophenotype studies. Moreover, some important findings derived from animal studies and from neuroimaging studies are highlighted. In conclusion, we provide the reader with an overview of most important studies related to AD and ND and give an outlook how these findings may become useful and beneficial in the future.

Consumer perceptions of direct-to-consumer personalized genomic risk assessments

PURPOSE

To evaluate consumer perceptions of direct-to-consumer personalized genomic risk assessments and assess the extent to which consumer characteristics may be associated with attitudes toward testing.

METHODS

Adult participants aged 18-85 years of age purchased a personalized genomic risk test at a subsidized rate and were administered a web-based health assessment that included questions regarding perceptions and attitudes toward undergoing testing.

RESULTS

Assessments were obtained for 3640 individual study participants, and 49.7% expressed overall concerns about undergoing testing. Logistic regression analysis revealed that women were more likely to express concerns (odds ratio [OR] = 1.20, 95% confidence interval [CI]: 1.04 -1.39), as were individuals employed by a health care organization (OR = 1.23, 95% CI: 1.04 -1.46). Further, younger age (OR = 0.97, 95% CI: 0.96-0.98), higher education (OR = 1.09, 95% CI: 1.04 -1.14), and higher trait anxiety (OR = 1.28, 95% CI: 1.20-1.37) were also significantly associated with expressing concerns related to testing. Attitudes regarding disclosure of genetic risk for a nonpreventable disease were also assessed. None of the individuals in our sample indicated that they would definitely not want to know their risk, and a total of 82.4% indicated that they would want to know.

CONCLUSION

Among individuals who undergo direct-to-consumer genetic testing, approximately half still express concerns about the process/experience. Further, given that concerns vary among different subgroups of consumers, if the clinical validity and utility of these tests are demonstrated, tailored genetic education and counseling services may be of benefit.

Genetic association and gene-environment interaction: a new method for overcoming the lack of exposure information in controls

The use of a reference control panel in genome-wide association studies is an interesting solution to the problem of how to reduce costs. In such designs, data on relevant environmental factors are usually collected only in cases, making it more difficult to deal with potential gene-environment interactions when testing for genetic association. However, under certain circumstances, neglecting an existing interaction with the environment may be detrimental in terms of statistical power to detect the genetic factor. In this paper, the authors propose a novel method based on a multinomial logistic regression model to overcome the lack of environmental exposure information in controls, by contrasting both exposed and unexposed cases with the control sample. For each case group, a genetic effect-size parameter is estimated, and the genetic association and the gene-environment interaction are tested jointly. The authors evaluate the performance of this method through asymptotic computations and simulations of cases and population controls under different models. In the presence of a gene-environment interaction, this approach outperforms other available methods that test for genetic association and gene-environment interaction either separately or jointly. Interestingly, it even has better power than the joint test requiring full knowledge of the environmental information in both cases and controls.

Genetics of psychiatric disorders methods: molecular approaches

The practice of psychiatry has long suffered from the limited information available on the biological basis of mental disorders. This limitation is now coming to an end. Advances in DNA analysis technologies and in our understanding of the human genome, together with our new knowledge of the properties of the genome and significant efforts toward generating large patient and control sample collections, have paved the way for successful genome-wide association studies. As a result, reports now appear in the literature every week identifying new genes for complex disorders. Next-generation sequencing methods, combined with the results of association and perhaps linkage studies, will help us uncover missing heritability factors, achieve a better understanding of the genetic aspects of psychiatric disease, and devise the best strategies for incorporating genetics in the service of patients.

A powerful hybrid approach to select top single-nucleotide polymorphisms for genome-wide association study

BACKGROUND

Genome-wide association (GWA) study has recently become a powerful approach for detecting genetic variants for common diseases without prior knowledge of the variant's location or function. Generally, in GWA studies, the most significant single-nucleotide polymorphisms (SNPs) associated with top-ranked p values are selected in stage one, with follow-up in stage two. The value of selecting SNPs based on statistically significant p values is obvious. However, when minor allele frequencies (MAFs) are relatively low, less-significant p values can still correspond to higher odds ratios (ORs), which might be more useful for prediction of disease status. Therefore, if SNPs are selected using an approach based only on significant p values, some important genetic variants might be missed. We proposed a hybrid approach for selecting candidate SNPs from the discovery stage of GWA study, based on both p values and ORs, and conducted a simulation study to demonstrate the performance of our approach.

RESULTS

The simulation results showed that our hybrid ranking approach was more powerful than the existing ranked p value approach for identifying relatively less-common SNPs. Meanwhile, the type I error probabilities of the hybrid approach is well-controlled at the end of the second stage of the two-stage GWA study.

CONCLUSIONS

In GWA studies, SNPs should be considered for inclusion based not only on ranked p values but also on ranked ORs.

Technological issues and experimental design of gene association studies

Genome-wide association studies (GWAS), in which thousands of single-nucleotide polymorphisms (SNPs) spanning the genome are genotyped in individuals who are phenotypically well characterized, -currently represent the most popular strategy for identifying gene regions associated with common -diseases and related quantitative traits. Improvements in technology and throughput capability, development of powerful statistical tools, and more widespread acceptance of pooling-based genotyping approaches have led to greater utilization of GWAS in human genetics research. However, important considerations for optimal experimental design, including selection of the most appropriate genotyping platform, can enhance the utility of the approach even further. This chapter reviews experimental and technological issues that may affect the success of GWAS findings and proposes strategies for developing the most comprehensive, logical, and cost-effective approaches for genotyping given the population of interest.

Incorporating genetics into your studies: a guide for social scientists

There has been a surge of interest in recent years in incorporating genetic components into on-going longitudinal, developmental studies and related psychological studies. While this represents an exciting new direction in developmental science, much of the research on genetic topics in developmental science does not reflect the most current practice in genetics. This is likely due, in part, to the rapidly changing landscape of the field of genetics, and the difficulty this presents for developmental scientists who are trying to learn this new area. In this review, we present an overview of the paradigm shifts that have occurred in genetics and we introduce the reader to basic genetic methodologies. We present our view of the current stage of research ongoing at the intersection of genetics and social science, and we provide recommendations for how we could do better. We also address a number of issues that social scientists face as they integrate genetics into their projects, including choice of a study design (candidate gene versus genome-wide association versus sequencing), different methods of DNA collection, and special considerations involved in the analysis of genotypic data. Through this review, we hope to equip social scientists with a deeper understanding of the many considerations that go into genetics research, in an effort to foster more meaningful cross-disciplinary initiatives.

Gene-environment interaction in psychological traits and disorders

There has been an explosion of interest in studying gene-environment interactions (GxE) as they relate to the development of psychopathology. In this article, I review different methodologies to study gene-environment interaction, providing an overview of methods from animal and human studies and illustrations of gene-environment interactions detected using these various methodologies. Gene-environment interaction studies that examine genetic influences as modeled latently (e.g., from family, twin, and adoption studies) are covered, as well as studies of measured genotypes. Importantly, the explosion of interest in gene-environment interactions has raised a number of challenges, including difficulties with differentiating various types of interactions, power, and the scaling of environmental measures, which have profound implications for detecting gene-environment interactions. Taking research on gene-environment interactions to the next level will necessitate close collaborations between psychologists and geneticists so that each field can take advantage of the knowledge base of the other.

Laser microdissection for gene expression profiling

Microarray-based gene expression profiling is revolutionizing biomedical research by allowing expression profiles of thousands of genes to be interrogated in a single experiment. In cancer research, the use of laser microdissection (LM) to isolate RNA from tissues provides the ability to accurately identify molecular profiles from different cell types that comprise the tumor and its surrounding microenvironment. Because RNA is an unstable molecule, the quality of RNA extracted from tissues can be affected by sample preparation and processing. Thus, special protocols have been developed to isolate research-quality RNA after LM. This chapter provides detailed descriptions of protocols used to generate micro-array data from high-quality frozen breast tissue specimens, as well as challenges associated with formalin-fixed paraffin-embedded specimens.

Gene × environment interaction models in psychiatric genetics

Gene-environment (G × E) interaction research is an emerging area in psychiatry, with the number of G × E studies growing rapidly in the past two decades. This article aims to give a comprehensive introduction to the field, with an emphasis on central theoretical and practical problems that are worth considering before conducting a G × E interaction study. On the theoretical side, we discuss two fundamental, but controversial questions about (1) the validity of statistical models for biological interaction and (2) the utility of G × E research for psychiatric genetics. On the practical side, we focus on study characteristics that potentially influence the outcome of G × E interaction studies and discuss strengths and pitfalls of different study designs, including recent approaches like Genome-Environment Wide Interaction Studies (GEWIS). Finally, we discuss recent developments in G × E interaction research on the most heavily investigated example in psychiatric genetics, the interaction between a serotonin transporter gene promoter variant (5-HTTLPR) and stress on depression.

An Interdisciplinary Approach to Studying Gene-Environment Interactions: From Twin Studies to Gene Identification and Back

There has been a surge of interest in studying gene-environment interaction; however, research in this area faces a number of challenges. Interdisciplinary collaborations are critical at this juncture. This article reviews studies that illustrate how findings across different literatures can be synthesized to characterize how genetic and environmental influences impact developmental pathways. Developmental scientists are poised to make important contributions to studying gene-environment interaction. However, for this potential to be realized developmental-genetic studies must incorporate the most recent advances in genetics, and bridge the current schism that exists between genetic research being conducted in the fields of psychology and genetics.

Planning a genome-wide association study: points to consider

It is well established that genetic diversity combined with specific environmental exposures contributes to disease susceptibility. However, it has turned out to be challenging to isolate the genes underlying the genetic component conferring susceptibility to most complex disorders. Traditional candidate gene and family-based linkage studies, which dominated gene discovery efforts for many years, were largely unsuccessful in unraveling the genetics of these traits due to the relatively limited information gained. Within the last 5 years, new advances in high-throughput methods have allowed for large volumes of single nucleotide polymorphisms (SNPs) throughout the genome to be genotyped across large and comprehensively phenotyped patient cohorts. Unlike previous approaches, these 'genome-wide association studies' (GWAS) have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with hundreds of novel disease-associated variants being largely replicated by independent groups. This review provides an overview of these recent breakthroughs in the context of the pitfalls and challenges related to designing and carrying out a successful GWAS.

A knowledge-based weighting framework to boost the power of genome-wide association studies

BACKGROUND

We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances.

METHODOLOGY/PRINCIPAL FINDINGS

We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance.

CONCLUSIONS/SIGNIFICANCE

With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases.

Knowledge gaining by human genetic studies on tuberculosis susceptibility

Tuberculosis (TB) is a serious health issue in the developing world. Lack of knowledge on the etiological mechanisms of TB hinders the development of effective strategies for the treatment or prevention of TB disease. Human genetic study is an indispensable approach to understand the molecular basis of common diseases. Numerous efforts were made to screen the human genome for TB susceptibility by linkage mapping. A large number of candidate-based association studies of TB were conducted to examine the association of predicted functional DNA variations in candidate genes. Recently, the first genome-wide association study (GWAS) on TB was reported. The GWAS is a proof-of-principle evidence that justifies the genetic approach to understand TB. Further hypothesis-free efforts on TB research may renovate the traditional idea of TB genetic susceptibility as none of the candidate genes with important roles in containing Mycobacterium tuberculosis (MTB) infection was identified of association with active TB, whereas the TB-associated loci in the GWAS harbors no gene with function in MTB infection.

Analysing biological pathways in genome-wide association studies

Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

The Genetic Basis of Spondyloarthritis: SPARTAN/IGAS 2009

A joint meeting was held in July 2009 in Houston, Texas, of members of the Spondyloarthritis Research and Therapy Network (SPARTAN), founded in 2003 to promote research, education, and treatment of ankylosing spondylitis (AS) and related forms of spondyloarthritis (SpA), and members of International Genetics of AS (IGAS), founded in 2003 to encourage and coordinate studies internationally in the genetics of AS. The general topic was the genetic basis of SpA, with presentations on the future of human genetic studies; microbes, SpA, and innate immunity; susceptibility of AS to the major histocompatibility complex (MHC) and non-MHC; and individual discussions of the genetics of psoriasis and psoriatic arthritis, uveitis, inflammatory bowel disease, and enteropathic arthritis. Summaries of those discussions are presented.

Genome-wide association studies and genetic risk assessment of liver diseases

Genetic tests can help clinicians to diagnose rare monogenic liver diseases. For most common liver diseases, however, multiple gene variants that have small to moderate individual phenotypic effects contribute to the overall risk of disease. An individual's level of risk depends on interactions between environmental factors and a wide range of modifier genes, which are yet to be identified systematically. The latest genome-wide association studies in large cohorts of patients with gallstones, fatty liver disease, viral hepatitis, chronic cholestatic liver diseases or drug-induced liver injury have provided new insights into the pathophysiology of these illnesses and have suggested the contribution of previously unsuspected pathogenic pathways. Studies in mouse models have identified further susceptibility genes for several complex liver diseases. As a result, in the future polygenic risk scores might help to define subgroups of patients at risk of developing liver diseases who would benefit from preventative measures and/or personalized therapy. Now that whole-genome sequencing is possible, comprehensive strategies for integrating genomic data and counseling of patients need to be developed.

Estimating the total number of susceptibility variants underlying complex diseases from genome-wide association studies

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the "winner's curse" effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winner's curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohn's disease) and estimated that hundreds to nearly a thousand variants underlie these traits.

Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease

BACKGROUND

There is significant interest in leveraging the electronic medical record (EMR) to conduct genome-wide association studies (GWAS).

METHODS

A biorepository of DNA and plasma was created by recruiting patients referred for non-invasive lower extremity arterial evaluation or stress ECG. Peripheral arterial disease (PAD) was defined as a resting/post-exercise ankle-brachial index (ABI) less than or equal to 0.9, a history of lower extremity revascularization, or having poorly compressible leg arteries. Controls were patients without evidence of PAD. Demographic data and laboratory values were extracted from the EMR. Medication use and smoking status were established by natural language processing of clinical notes. Other risk factors and comorbidities were ascertained based on ICD-9-CM codes, medication use and laboratory data.

RESULTS

Of 1802 patients with an abnormal ABI, 115 had non-atherosclerotic vascular disease such as vasculitis, Buerger's disease, trauma and embolism (phenocopies) based on ICD-9-CM diagnosis codes and were excluded. The PAD cases (66+/-11 years, 64% men) were older than controls (61+/-8 years, 60% men) but had similar geographical distribution and ethnic composition. Among PAD cases, 1444 (85.6%) had an abnormal ABI, 233 (13.8%) had poorly compressible arteries and 10 (0.6%) had a history of lower extremity revascularization. In a random sample of 95 cases and 100 controls, risk factors and comorbidities ascertained from EMR-based algorithms had good concordance compared with manual record review; the precision ranged from 67% to 100% and recall from 84% to 100%.

CONCLUSION

This study demonstrates use of the EMR to ascertain phenocopies, phenotype heterogeneity and relevant covariates to enable a GWAS of PAD. Biorepositories linked to EMR may provide a relatively efficient means of conducting GWAS.

Laboratory mouse models for the human genome-wide associations

The agnostic screening performed by genome-wide association studies (GWAS) has uncovered associations for previously unsuspected genes. Knowledge about the functional role of these genes is crucial and laboratory mouse models can provide such information. Here, we describe a systematic juxtaposition of human GWAS-discovered loci versus mouse models in order to appreciate the availability of mouse models data, to gain biological insights for the role of these genes and to explore the extent of concordance between these two lines of evidence. We perused publicly available data (NHGRI database for human associations and Mouse Genome Informatics database for mouse models) and employed two alternative approaches for cross-species comparisons, phenotype- and gene-centric. A total of 293 single gene-phenotype human associations (262 unique genes and 69 unique phenotypes) were evaluated. In the phenotype-centric approach, we identified all mouse models and related ortholog genes for the 51 human phenotypes with a comparable phenotype in mice. A total of 27 ortholog genes were found to be associated with the same phenotype in humans and mice, a concordance that was significantly larger than expected by chance (p<0.001). In the gene-centric approach, we were able to locate at least 1 knockout model for 60% of the 262 genes. The knockouts for 35% of these orthologs displayed pre- or post-natal lethality. For the remaining non-lethal orthologs, the same organ system was involved in mice and humans in 71% of the cases (p<0.001). Our project highlights the wealth of available information from mouse models for human GWAS, catalogues extensive information on plausible physiologic implications for many genes, provides hypothesis-generating findings for additional GWAS analyses and documents that the concordance between human and mouse genetic association is larger than expected by chance and can be informative.

Statistical analysis strategies for association studies involving rare variants

The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.

Analyze multivariate phenotypes in genetic association studies by combining univariate association tests

Multivariate phenotypes are frequently encountered in genome-wide association studies (GWAS). Such phenotypes contain more information than univariate phenotypes, but how to best exploit the information to increase the chance of detecting genetic variant of pleiotropic effect is not always clear. Moreover, when multivariate phenotypes contain a mixture of quantitative and qualitative measures, limited methods are applicable. In this paper, we first evaluated the approach originally proposed by O'Brien and by Wei and Johnson that combines the univariate test statistics and then we proposed two extensions to that approach. The original and proposed approaches are applicable to a multivariate phenotype containing any type of components including continuous, categorical and survival phenotypes, and applicable to samples consisting of families or unrelated samples. Simulation results suggested that all methods had valid type I error rates. Our extensions had a better power than O'Brien's method with heterogeneous means among univariate test statistics, but were less powerful than O'Brien's with homogeneous means among individual test statistics. All approaches have shown considerable increase in power compared to testing each component of a multivariate phenotype individually in some cases. We apply all the methods to GWAS of serum uric acid levels and gout with 550,000 single nucleotide polymorphisms in the Framingham Heart Study.

Introduction to genetic epidemiology

Genetic epidemiology is of topical and increasingly practical relevance. The subject attempts to answer 2 questions: (1) is there a genetic component to a disease, and (2) what genes are involved? This article summarizes genetic epidemiologic methods, describing family- and population-based methods used to locate and identify genes and the advantages and disadvantages of each approach. Health care professionals are faced with more and more genetic information, both from interested patients and from the media, and understanding the principles underlying genetic studies allows such information to be placed in context.

Advances in the genetics of endometriosis

Endometriosis is a gynecological disease characterized by implantation of endometrial tissue outside of the uterus. Early familial aggregation and twin studies noted a higher risk of endometriosis among relatives. Studies on the roles of the environment, genetics and aberrant regulation in the endometrium and endometriotic lesions of women with endometriosis suggest that endometriosis arises from the interplay between genetic variants and environmental factors. Elucidating the hereditary component has proven difficult because multiple genes seem to produce a susceptibility to developing endometriosis. Molecular techniques, including linkage and genome-wide analysis, have identified candidate genes located near known loci related to development and regulation of the female reproductive tract. As new candidate genes are discovered and hereditary pathways identified using technologies such as genome-wide analysis, the possibility of prevention and treatment becomes more tangible for millions of women affected by endometriosis. Here, we discuss the advances of genetic research in endometriosis and describe technologies that have contributed to the current understanding of the genetic variability in endometriosis, variability that includes regulatory polymorphisms in key genes.

Being more realistic about the public health impact of genomic medicine

Wayne Hall and colleagues discuss the limitations of genomic risk prediction for population-level preventive health care.

Statistical significance in genetic association studies

Clinical & Investigative Medicine (CIM) is receiving an increasing number of reports of candidate-based association studies. The track record of such studies in the past has been poor: numerous genetic associations reported from candidate gene studies have not been replicated in later studies. The rise of the genome-wide association study (GWAS) is changing this situation. A well-designed GWAS may identify a number of candidate loci without bias by screening the whole human genome. Validating and fine-mapping the candidate loci from GWAS are required to clarify the genetic mechanisms. Thus, a candidate-based association study has become a well-directed effort, instead of searching for a needle in a haystack. In the post-GWAS era, exponential growth of candidate-based genetic association studies is expected. A pressing issue accompanying this new trend is the assessment of the validity of an association study. In this editorial, we illustrate the major cause of false positive association from random sampling bias by an empirical example, and emphasize the application of the probability theory in assessing the validity of a genetic association study.

Consumers' views of direct-to-consumer genetic information

In this report, we describe the evolution and types of genetic information provided directly to consumers, discuss potential advantages and disadvantages of these products, and review research evaluating consumer responses to direct-to-consumer (DTC) genetic testing. The available evidence to date has focused on predictive tests and does not suggest that individuals, health care providers, or health care systems have been harmed by a DTC provision of genetic information. An understanding of consumer responses to susceptibility tests has lagged behind. The Multiplex Initiative is presented as a case study of research to understand consumers' responses to DTC susceptibility tests. Three priority areas are recommended for accelerated research activities to inform public policy regarding DTC genetic information: (a) exploring consumer's long-term responses to DTC genetic testing on a comprehensive set of outcomes, (b) evaluating optimal services to support decision making about genetic testing, and (c) evaluating best practices in promoting genetic competencies among health providers.

Genomics of long-range regulatory elements

Transcriptional regulation of gene expression plays a significant role in establishing the diversity of human cell types and biological functions from a common set of genes. The components of regulatory control in the human genome include cis-acting elements that act across immense genomic distances to influence the spatial and temporal distribution of gene expression. Here we review the established categories of distant-acting regulatory elements, discussing the classical and contemporary evidence of their regulatory potential and clinical importance. Current efforts to identify regulatory sequences throughout the genome and elucidate their biological significance depend heavily on advances in sequence conservation-based analyses and on increasingly large-scale efforts applying transgenic technologies in model organisms. We discuss the advantages and limitations of sequence conservation as a predictor of regulatory function and present complementary emerging technologies now being applied to annotate regulatory elements in vertebrate genomes.

Human behavioral informatics in genetic studies of neuropsychiatric disease: multivariate profile-based analysis

While genome-wide association (GWA) studies have yielded notable findings with regard to the identification of risk variants in diseases such as obesity and diabetes, similar studies of schizophrenia - and neuropsychiatric diseases in general - have failed to produce strong findings. One, plausible explanation for this relates to phenotypic heterogeneity and what may be inherent imprecision associated with diagnostic categories in neuropsychiatric disorders. In this review we discuss a general approach to addressing the problem of heterogeneity that draws on concepts in behavioral informatics and the use of multivariable behavioral profiles in genetic studies of neuropsychiatric disease. The use of behavioral profiles as phenotypes eliminates the need for categorizing individuals with different 'subtypes' of a disease into one group and provides a way to investigate genetic susceptibility to different neuropsychiatric disorders that share similar clinical characteristics, such as schizophrenia and bipolar disorder. Further, behavioral profiles are a direct, quantitative representation of the emotional, personality, and neurocognitive functioning of the individuals being studied, and as such, the use of these profiles may provide increased statistical power to detect genetic associations and linkages. We describe and discuss four general data analysis approaches that can be used to analyze and integrate multivariate behavioral profile data and high-dimensional genomic data. Ultimately, we propose that behavioral profile-based phenotypes provide a meaningful alternative to the use of single measures, such as diagnostic category, in genetic association studies of neuropsychiatric disease.

Genetic and molecular functional characterization of variants within TNFSF13B, a positional candidate preeclampsia susceptibility gene on 13q

BACKGROUND

Preeclampsia is a serious pregnancy complication, demonstrating a complex pattern of inheritance. The elucidation of genetic liability to preeclampsia remains a major challenge in obstetric medicine. We have adopted a positional cloning approach to identify maternal genetic components, with linkages previously demonstrated to chromosomes 2q, 5q and 13q in an Australian/New Zealand familial cohort. The current study aimed to identify potential functional and structural variants in the positional candidate gene TNFSF13B under the 13q linkage peak and assess their association status with maternal preeclampsia genetic susceptibility.

METHODOLOGY/PRINCIPAL FINDINGS

The proximal promoter and coding regions of the positional candidate gene TNFSF13B residing within the 13q linkage region was sequenced using 48 proband or founder individuals from Australian/New Zealand families. Ten sequence variants (nine SNPs and one single base insertion) were identified and seven SNPs were successfully genotyped in the total Australian/New Zealand family cohort (74 families/480 individuals). Borderline association to preeclampsia (p = 0.0153) was observed for three rare SNPs (rs16972194, rs16972197 and rs56124946) in strong linkage disequilibrium with each other. Functional evaluation by electrophoretic mobility shift assays showed differential nuclear factor binding to the minor allele of the rs16972194 SNP, residing upstream of the translation start site, making this a putative functional variant. The observed genetic associations were not replicated in a Norwegian case/control cohort (The Nord-Trøndelag Health Study (HUNT2), 851 preeclamptic and 1,440 non-preeclamptic women).

CONCLUSION/SIGNIFICANCE

TNFSF13B has previously been suggested to contribute to the normal immunological adaption crucial for a successful pregnancy. Our observations support TNFSF13B as a potential novel preeclampsia susceptibility gene. We discuss a possible role for TNFSF13B in preeclampsia pathogenesis, and propose the rs16972194 variant as a candidate for further functional evaluation.

CLIA-tested genetic variants on commercial SNP arrays: potential for incidental findings in genome-wide association studies

PURPOSE

Increases in throughput and affordability of genotyping products have led to large sample sizes in genetic studies, increasing the likelihood that incidental genetic findings may occur. We set out to survey potential notifiable variants on arrays used in genome-wide association studies and in direct-to-consumer genetic services.

METHODS

We used multiple bioinformatics strategies to identify, and map variants tested for genetic disorders in > or = 2 CLIA-approved laboratories (based on the GeneTests database). We subsequently surveyed 18 commercial single nucleotide polymorphism arrays and HapMap for these variants.

RESULTS

Of 1,362 genes tested according to GeneTests, we identified 298 specific targeted mutations measured in more than or equal to two laboratories, encompassing 56 disorders. Only 88 of 298 mutations could be identified as known single nucleotide polymorphisms in genomic databases. We found 18 of 88 single nucleotide polymorphisms present in HapMap or on commercial single nucleotide polymorphism arrays. Homozygotes for rare alleles of some variants were identified in the Framingham Heart Study, an active genome-wide association studies cohort (n = 8,410).

CONCLUSIONS

Variants in genes including APOE, F5, HFE, CYP21A2, MEFV, SPINK1, BTD, GALT, and G6PD were found on single nucleotide polymorphism arrays or in the HapMap. Some of these variants may warrant further review to determine their likelihood to trigger incidental findings in the course of genome-wide association studies or direct-to-consumer testing.

From GWAS to the clinic: risk factors for intracranial aneurysms

Subarachnoid hemorrhage (SAH) from a ruptured intracranial aneurysm is a devastating subset of stroke, occurring in relatively young people (mean age around 50 years) of whom around a third die within the initial weeks after the bleed. Environmental and genetic risk factors both have a role in SAH. A recent genome-wide association study of intracranial aneurysms in Finnish, Dutch and Japanese cohorts totaling 5,891 cases and 14,181 controls identified three new loci strongly associated with intracranial aneurysms on chromosomes 18q11.2 and 10q24.32, and replicated two previously found loci on chromosomes 8q11.23-q12.1 and 9p21.3. However, these five intracranial aneurysm risk loci identified so far explain only up to 5% of the familial risk of intracranial aneurysms, which makes genetic risk prediction tests currently unfeasible for intracranial aneurysms. New approaches, including identification of causal variants, rare variants and copy number variants, such as insertions and deletions, may improve genetic risk prediction for SAH and intracranial aneurysms. This may lead to diagnostic tools for identifying individuals at increased risk for aneurysm formation and rupture of aneurysms. In this way, genetic diagnostic tools will identify the people who will benefit most from screening by imaging studies for aneurysms and those who are most likely to benefit from preventive treatment of incidentally discovered aneurysms.

Quantitative analyses link modulation of sonic hedgehog signaling to continuous variation in facial growth and shape

Variation is an intrinsic feature of biological systems, yet developmental biology does not frequently address population-level phenomena. Sonic hedgehog (SHH) signaling activity in the vertebrate forebrain and face is thought to contribute to continuous variation in the morphology of the upper jaw, but despite its potential explanatory power, this idea has never been quantitatively assessed. Here, we test this hypothesis with an experimental design that is explicitly focused on the generation and measurement of variation in multivariate shape, tissue growth, cellular behavior and gene expression. We show that the majority of upper jaw shape variation can be explained by progressive changes in the spatial organization and mitotic activity of midfacial growth zones controlled by SHH signaling. In addition, nonlinearity between our treatment doses and phenotypic outcomes suggests that threshold effects in SHH signaling may play a role in variability in midfacial malformations such as holoprosencephaly (HPE). Together, these results provide novel insight into the generation of facial morphology, and demonstrate the value of quantifying variation for our understanding of development and disease.

Model-based multifactor dimensionality reduction for detecting epistasis in case-control data in the presence of noise

Analyzing the combined effects of genes and/or environmental factors on the development of complex diseases is a great challenge from both the statistical and computational perspective, even using a relatively small number of genetic and nongenetic exposures. Several data-mining methods have been proposed for interaction analysis, among them, the Multifactor Dimensionality Reduction Method (MDR) has proven its utility in a variety of theoretical and practical settings. Model-Based Multifactor Dimensionality Reduction (MB-MDR), a relatively new MDR-based technique that is able to unify the best of both nonparametric and parametric worlds, was developed to address some of the remaining concerns that go along with an MDR analysis. These include the restriction to univariate, dichotomous traits, the absence of flexible ways to adjust for lower order effects and important confounders, and the difficulty in highlighting epistatic effects when too many multilocus genotype cells are pooled into two new genotype groups. We investigate the empirical power of MB-MDR to detect gene-gene interactions in the absence of any noise and in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Power is generally higher for MB-MDR than for MDR, in particular in the presence of genetic heterogeneity, phenocopy, or low minor allele frequencies.

Using genetics to predict the natural history of asthma?

Clinical practice reminds us that there is considerable variability in the course of asthma over time. Treatment of patients with asthma would be considerably improved if one could accurately predict the likely course of disease over the life course. Recently, with the advent of the era of genome-wide association studies, there has been a monumental shift in our understanding of the genetic factors that underlie inherited susceptibility to asthma. Genes have been identified that modulate many aspects of the natural history of asthma, such as susceptibility to atopy, altered lung development, and susceptibility to more severe disease. Heritability studies have even suggested a role for genetic factors in remission of asthma. However, although the discovery of novel genetic factors underlying disease susceptibility has undoubtedly improved our understanding of disease pathogenesis, whether these advances have improved the ability to predict the natural history in individual patients is questionable, and the application of genetic testing to clinical practice remains some way off.

Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies

BACKGROUND

Vast sample sizes are often essential in the quest to disentangle the complex interplay of the genetic, lifestyle, environmental and social factors that determine the aetiology and progression of chronic diseases. The pooling of information between studies is therefore of central importance to contemporary bioscience. However, there are many technical, ethico-legal and scientific challenges to be overcome if an effective, valid, pooled analysis is to be achieved. Perhaps most critically, any data that are to be analysed in this way must be adequately 'harmonized'. This implies that the collection and recording of information and data must be done in a manner that is sufficiently similar in the different studies to allow valid synthesis to take place.

METHODS

This conceptual article describes the origins, purpose and scientific foundations of the DataSHaPER (DataSchema and Harmonization Platform for Epidemiological Research; http://www.datashaper.org), which has been created by a multidisciplinary consortium of experts that was pulled together and coordinated by three international organizations: P³G (Public Population Project in Genomics), PHOEBE (Promoting Harmonization of Epidemiological Biobanks in Europe) and CPT (Canadian Partnership for Tomorrow Project).

RESULTS

The DataSHaPER provides a flexible, structured approach to the harmonization and pooling of information between studies. Its two primary components, the 'DataSchema' and 'Harmonization Platforms', together support the preparation of effective data-collection protocols and provide a central reference to facilitate harmonization. The DataSHaPER supports both 'prospective' and 'retrospective' harmonization.

CONCLUSION

It is hoped that this article will encourage readers to investigate the project further: the more the research groups and studies are actively involved, the more effective the DataSHaPER programme will ultimately be.

Quality control and quality assurance in genotypic data for genome-wide association studies

Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies (GWAS). This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium test P-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis to SNP selection. The methods are illustrated with examples from the "Gene Environment Association Studies" (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of GWAS.

Direct-to-Consumer Genetic and Genomic Testing: Preparing Nurse Practitioners for Genomic Healthcare

Rapidly emerging technologies make it possible for consumers to acquire information that is intended to explain their inherited susceptibility to disease and facilitate tailored healthcare services through direct-to-consumer (DTC) marketing of personal genetic (PG) and personal genomic (PGM) testing. However, the health benefits and risks associated with these technologies are largely unknown. Consumers will turn to their healthcare providers, including nurse practitioners, to interpret test results and seek guidance on how to use these test results for medical decision-making. Nurse practitioners will need to constantly update their practice skills in response to advances in genomic technology that create new expectations among patients and lead to substantial changes in healthcare delivery.

Clair D, Sigurdsson E, Petursson H, Rujescu D, Melle I, Steen VM, Djurovic S, Andreassen OA. Gene variants associated with schizophrenia in a Norwegian genome-wide study are replicated in a large European cohort

We have performed a genome-wide association study (GWAS) of schizophrenia in a Norwegian discovery sample of 201 cases and 305 controls (TOP study) with a focused replication analysis in a larger European sample of 2663 cases and 13,780 control subjects (SGENE-plus study). Firstly, the discovery sample was genotyped with Affymetrix Genome-Wide Human SNP Array 6.0 and 572,888 markers were tested for schizophrenia association. No SNPs in the discovery sample attained genome-wide significance (P<8.7 x 10(-8)). Secondly, based on the GWAS data, we selected 1000 markers with the lowest P values in the discovery TOP sample, and tested these (or HapMap-based surrogates) for association in the replication sample. Sixteen loci were associated with schizophrenia (nominal P value<0.05 and concurring OR) in the replication sample. As a next step, we performed a combined analysis of the findings from these two studies, and the strongest evidence for association with schizophrenia was provided for markers rs7045881 on 9p21, rs433598 on 16p12 and rs10761482 on 10q21. The markers are located in PLAA, ACSM1 and ANK3, respectively. PLAA has not previously been described as a susceptibility gene, but 9p21 is implied as a schizophrenia linkage region. ACSM1 has been identified as a susceptibility gene in a previous schizophrenia GWAS study. The association of ANK3 with schizophrenia is intriguing in light of recent associations of ANK3 with bipolar disorder, thereby supporting the hypothesis of an overlap in genetic susceptibility between these psychopathological entities.

Molecular diagnostics in transplantation

The past few decades are characterized by an explosive evolution of genetics and molecular cell biology. Advances in chemistry and engineering have enabled increased data throughput, permitting the study of complete sets of molecules with increasing speed and accuracy using techniques such as genomics, transcriptomics, proteomics, and metabolomics. Prediction of long-term outcomes in transplantation is hampered by the absence of sufficiently robust biomarkers and a lack of adequate insight into the mechanisms of acute and chronic alloimmune injury and the adaptive mechanisms of immunological quiescence that may support transplantation tolerance. Here, we discuss some of the great opportunities that molecular diagnostic tools have to offer both basic scientists and translational researchers for bench-to-bedside clinical application in transplantation medicine, with special focus on genomics and genome-wide association studies, epigenetics (DNA methylation and histone modifications), gene expression studies and transcriptomics (including microRNA and small interfering RNA studies), proteomics and peptidomics, antibodyomics, metabolomics, chemical genomics and functional imaging with nanoparticles. We address the challenges and opportunities associated with the newer high-throughput sequencing technologies, especially in the field of bioinformatics and biostatistics, and demonstrate the importance of integrative approaches. Although this Review focuses on transplantation research and clinical transplantation, the concepts addressed are valid for all translational research.