A HapMap harvest of insights into the genetics of common disease

Genome-wide association study of blood pressure and hypertension

Blood pressure is a major cardiovascular disease risk factor. To date, few variants associated with interindividual blood pressure variation have been identified and replicated. Here we report results of a genome-wide association study of systolic (SBP) and diastolic (DBP) blood pressure and hypertension in the CHARGE Consortium (n = 29,136), identifying 13 SNPs for SBP, 20 for DBP and 10 for hypertension at P < 4 × 10(-7). The top ten loci for SBP and DBP were incorporated into a risk score; mean BP and prevalence of hypertension increased in relation to the number of risk alleles carried. When ten CHARGE SNPs for each trait were included in a joint meta-analysis with the Global BPgen Consortium (n = 34,433), four CHARGE loci attained genome-wide significance (P < 5 × 10(-8)) for SBP (ATP2B1, CYP17A1, PLEKHA7, SH2B3), six for DBP (ATP2B1, CACNB2, CSK-ULK3, SH2B3, TBX3-TBX5, ULK4) and one for hypertension (ATP2B1). Identifying genes associated with blood pressure advances our understanding of blood pressure regulation and highlights potential drug targets for the prevention or treatment of hypertension.

Genomewide association studies: history, rationale, and prospects for psychiatric disorders

OBJECTIVE

The authors conducted a review of the history and empirical basis of genomewide association studies (GWAS), the rationale for GWAS of psychiatric disorders, results to date, limitations, and plans for GWAS meta-analyses.

METHOD

A literature review was carried out, power and other issues discussed, and planned studies assessed.

RESULTS

Most of the genomic DNA sequence differences between any two people are common (frequency >5%) single nucleotide polymorphisms (SNPs). Because of localized patterns of correlation (linkage disequilibrium), 500,000 to 1,000,000 of these SNPs can test the hypothesis that one or more common variants explain part of the genetic risk for a disease. GWAS technologies can also detect some of the copy number variants (deletions and duplications) in the genome. Systematic study of rare variants will require large-scale resequencing analyses. GWAS methods have detected a remarkable number of robust genetic associations for dozens of common diseases and traits, leading to new pathophysiological hypotheses, although only small proportions of genetic variance have been explained thus far and therapeutic applications will require substantial further effort. Study design issues, power, and limitations are discussed. For psychiatric disorders, there are initial significant findings for common SNPs and for rare copy number variants, and many other studies are in progress.

CONCLUSIONS

GWAS of large samples have detected associations of common SNPs and of rare copy number variants with psychiatric disorders. More findings are likely, since larger GWAS samples detect larger numbers of common susceptibility variants, with smaller effects. The Psychiatric GWAS Consortium is conducting GWAS meta-analyses for schizophrenia, bipolar disorder, major depressive disorder, autism, and attention deficit hyperactivity disorder. Based on results for other diseases, larger samples will be required. The contribution of GWAS will depend on the true genetic architecture of each disorder.

Genome-wide association analysis identifies PDE4D as an asthma-susceptibility gene

Asthma, a chronic airway disease with known heritability, affects more than 300 million people around the world. A genome-wide association (GWA) study of asthma with 359 cases from the Childhood Asthma Management Program (CAMP) and 846 genetically matched controls from the Illumina ICONdb public resource was performed. The strongest region of association seen was on chromosome 5q12 in PDE4D. The phosphodiesterase 4D, cAMP-specific (phosphodiesterase E3 dunce homolog, Drosophila) gene (PDE4D) is a regulator of airway smooth-muscle contractility, and PDE4 inhibitors have been developed as medications for asthma. Allelic p values for top SNPs in this region were 4.3 x 10(-07) for rs1588265 and 9.7 x 10(-07) for rs1544791. Replications were investigated in ten independent populations with different ethnicities, study designs, and definitions of asthma. In seven white and Hispanic replication populations, two PDE4D SNPs had significant results with p values less than 0.05, and five had results in the same direction as the original population but had p values greater than 0.05. Combined p values for 18,891 white and Hispanic individuals (4,342 cases) in our replication populations were 4.1 x 10(-04) for rs1588265 and 9.2 x 10(-04) for rs1544791. In three black replication populations, which had different linkage disequilibrium patterns than the other populations, original findings were not replicated. Further study of PDE4D variants might lead to improved understanding of the role of PDE4D in asthma pathophysiology and the efficacy of PDE4 inhibitor medications.

Psychosis genetics: modeling the relationship between schizophrenia, bipolar disorder, and mixed (or "schizoaffective") psychoses

As a result of improving technologies and greatly increased sample sizes, the last 2 years has seen unprecedented advances in identification of specific genetic risk factors for psychiatric phenotypes. Strong genetic associations have been reported at common polymorphisms within ANK3 and CACNA1C in bipolar disorder and ZNF804A in schizophrenia and a relatively specific association between common variation in GABA(A) receptor genes and cases with features of both bipolar disorder and schizophrenia. Further, the occurrence of rare copy number variants (CNVs) has been shown to be increased in schizophrenia compared with controls. These emerging data provide a powerful resource for exploring the relationship between psychiatric phenotypes and can, and should, be used to inform conceptualization, classification, and diagnosis in psychiatry. It is already clear that, in general, genetic associations are not specific to one of the traditional diagnostic categories. For example, variation at ZNF804A is associated with risk of both bipolar disorder and schizophrenia, and some rare CNVs are associated with risk of autism and epilepsy as well as schizophrenia. These data are not consistent with a simple dichotomous model of functional psychosis and indicate the urgent need for moves toward approaches that (a) better represent the range of phenotypic variation seen in the clinical population and (b) reflect the underlying biological variation that gives rise to the phenotypes. We consider the implications for models of psychosis and the importance of recognizing and studying illness that has prominent affective and psychotic features. We conclude that if psychiatry is to translate the opportunities offered by new research methodologies, we must finally abandon a 19th-century dichotomy and move to a classificatory approach that is worthy of the 21st century.

The genetic signatures of noncoding RNAs

The majority of the genome in animals and plants is transcribed in a developmentally regulated manner to produce large numbers of non-protein-coding RNAs (ncRNAs), whose incidence increases with developmental complexity. There is growing evidence that these transcripts are functional, particularly in the regulation of epigenetic processes, leading to the suggestion that they compose a hitherto hidden layer of genomic programming in humans and other complex organisms. However, to date, very few have been identified in genetic screens. Here I show that this is explicable by an historic emphasis, both phenotypically and technically, on mutations in protein-coding sequences, and by presumptions about the nature of regulatory mutations. Most variations in regulatory sequences produce relatively subtle phenotypic changes, in contrast to mutations in protein-coding sequences that frequently cause catastrophic component failure. Until recently, most mapping projects have focused on protein-coding sequences, and the limited number of identified regulatory mutations have been interpreted as affecting conventional cis-acting promoter and enhancer elements, although these regions are often themselves transcribed. Moreover, ncRNA-directed regulatory circuits underpin most, if not all, complex genetic phenomena in eukaryotes, including RNA interference-related processes such as transcriptional and post-transcriptional gene silencing, position effect variegation, hybrid dysgenesis, chromosome dosage compensation, parental imprinting and allelic exclusion, paramutation, and possibly transvection and transinduction. The next frontier is the identification and functional characterization of the myriad sequence variations that influence quantitative traits, disease susceptibility, and other complex characteristics, which are being shown by genome-wide association studies to lie mostly in noncoding, presumably regulatory, regions. There is every possibility that many of these variations will alter the interactions between regulatory RNAs and their targets, a prospect that should be borne in mind in future functional analyses.

Genome-wide association studies in ADHD

Attention-deficit/hyperactivity disorder, ADHD, is a common and highly heritable neuropsychiatric disorder that is seen in children and adults. Although heritability is estimated at around 76%, it has been hard to find genes underlying the disorder. ADHD is a multifactorial disorder, in which many genes, all with a small effect, are thought to cause the disorder in the presence of unfavorable environmental conditions. Whole genome linkage analyses have not yet lead to the identification of genes for ADHD, and results of candidate gene-based association studies have been able to explain only a tiny part of the genetic contribution to disease, either. A novel way of performing hypothesis-free analysis of the genome suitable for the identification of disease risk genes of considerably smaller effect is the genome-wide association study (GWAS). So far, five GWAS have been performed on the diagnosis of ADHD and related phenotypes. Four of these are based on a sample set of 958 parent-child trio's collected as part of the International Multicentre ADHD Genetics (IMAGE) study and genotyped with funds from the Genetic Association Information Network (GAIN). The other is a pooled GWAS including adult patients with ADHD and controls. None of the papers reports any associations that are formally genome-wide significant after correction for multiple testing. There is also very limited overlap between studies, apart from an association with CDH13, which is reported in three of the studies. Little evidence supports an important role for the 'classic' ADHD genes, with possible exceptions for SLC9A9, NOS1 and CNR1. There is extensive overlap with findings from other psychiatric disorders. Though not genome-wide significant, findings from the individual studies converge to paint an interesting picture: whereas little evidence-as yet-points to a direct involvement of neurotransmitters (at least the classic dopaminergic, noradrenergic and serotonergic pathways) or regulators of neurotransmission, some suggestions are found for involvement of 'new' neurotransmission and cell-cell communication systems. A potential involvement of potassium channel subunits and regulators warrants further investigation. More basic processes also seem involved in ADHD, like cell division, adhesion (especially via cadherin and integrin systems), neuronal migration, and neuronal plasticity, as well as related transcription, cell polarity and extracellular matrix regulation, and cytoskeletal remodeling processes. In conclusion, the GWAS performed so far in ADHD, though far from conclusive, provide a first glimpse at genes for the disorder. Many more (much larger studies) will be needed. For this, collaboration between researchers as well as standardized protocols for phenotyping and DNA-collection will become increasingly important.

Fine mapping and functional analysis of a common variant in MSMB on chromosome 10q11.2 associated with prostate cancer susceptibility

Two recent genome-wide association studies have independently identified a prostate cancer susceptibility locus on chromosome 10q11.2. The most significant single-nucleotide polymorphism (SNP) marker reported, rs10993994, is 57 bp centromeric of the first exon of the MSMB gene, which encodes beta-microseminoprotein (prostatic secretory protein 94). In this study, a fine-mapping analysis using HapMap SNPs was conducted across a approximately 65-kb region (chr10: 51168330-51234020) flanking rs10993994 with 13 tag SNPs in 6,118 prostate cancer cases and 6,105 controls of European origin from the Cancer Genetic Markers of Susceptibility (CGEMS) project. rs10993994 remained the most strongly associated marker with prostate cancer risk [P = 8.8 x 10(-18); heterozygous odds ratio (OR) = 1.20, 95% confidence interval (CI): 1.11-1.30; homozygous OR = 1.64, 95% CI: 1.47-1.86 for the adjusted genotype test with 2 df]. In follow-up functional analyses, the T variant of rs10993994 significantly affected expression of in vitro luciferase reporter constructs. In electrophoretic mobility shift assays, the C allele of rs10993994 preferentially binds to the CREB transcription factor. Analysis of tumor cell lines with a CC or CT genotype revealed a high level of MSMB gene expression compared with cell lines with a TT genotype. These findings were specific to the alleles of rs10993994 and were not observed for other SNPs determined by sequence analysis of the proximal promoter. Together, our mapping study and functional analyses implicate regulation of expression of MSMB as a plausible mechanism accounting for the association identified at this locus. Further investigation is warranted to determine whether rs10993994 alone or in combination with additional variants contributes to prostate cancer susceptibility.

Beyond odds ratios--communicating disease risk based on genetic profiles

The brisk discovery of novel inherited disease markers by genome-wide association (GWA) studies has raised expectations for predicting disease risk by analysing multiple common alleles. However, the statistics used during the discovery phase of research (such as odds ratios or p values for association) are not the most appropriate measures for evaluating the predictive value of genetic profiles. We argue that other measures--such as sensitivity, specificity, and positive and negative predictive values--are more useful when proposing a genetic profile for risk prediction.

Schizophrenia genetics: advancing on two fronts

Recent studies have supported the hypothesis that the high heritability of schizophrenia reflects a combination of relatively common alleles of small effect and some rare alleles with relatively large effects. Genome-wide association studies have identified at least one common allele of small effect at ZNF804a, which encodes a putative zinc finger binding protein, as well as possible roles for other loci. The genome-wide studies of at least one class of relatively uncommon variant, submicroscopic chromosomal abnormalities often referred to as copy number variations (CNVs), suggest that these confer high risk of schizophrenia. There is evidence both for an increased burden of CNVs in schizophrenia and that risk is conferred by specific large deletions at 1q21.1 and at 15q13.2 and by deletions of NRXN1 which encodes the synaptic scaffolding protein neurexin 1.

The genetics of reading disability

Genetic factors contribute substantially to the development of reading disability (RD). Family linkage studies have implicated many chromosomal regions containing RD susceptibility genes, of which putative loci at 1p34-p36 (DYX8), 2p (DYX3), 6p21.3 (DYX2), and 15q21 (DYX1) have been frequently replicated, whereas those at 3p12-q12 (DYX5), 6q13-q16 (DYX4), 11p15 (DYX7), 18p11 (DYX6), and Xq27 (DYX9) have less evidence. Association studies of positional candidate genes have implicated DCDC2 and KIAA0319 in DYX2, as well as C2ORF3 and MRPL19 (DYX3), whereas DYX1C1/EKN1 (DYX1) and ROBO1 (DYX5) were found to be disrupted by rare translocation breakpoints in reading-disabled individuals. Four of the candidate genes (DYX1C1, KIAA0319, DCDC2, and ROBO1) appear to function in neuronal migration and guidance, suggesting the importance of early neurodevelopmental processes in RD. Future studies to help us understand the function of these and other RD candidate genes promise to yield enormous insight into the neurobiologic mechanisms underlying the pathophysiology of this disorder.

Genetics and personal genomics for personalized breast cancer surgery: progress and challenges in research and clinical practice

BACKGROUND

The age of personal genomics is here. A flood of translational research discoveries may influence also surgeon oncologist. Breast-conserving surgery (BCS) is standard care in early breast cancer. Classic clinicopathologic factors are suboptimal to predict risk of ipsilateral breast cancer (IBC) recurrence and/or contralateral breast cancer (CBC). Human genetic variation may be involved in local failures.

OBJECTIVE

To describe the potential clinical utility of genetics, personal genomics, and epigenetics to identify IBC/CBC high-risk patients who might benefit from aggressive surgery (bilateral mastectomy).

DATA SOURCES AND SYNTHESIS

PubMed (MEDLINE) was searched (January 1990 to November 2008).

RESULTS

Even following current guidelines, IBC/CBC as isolated first event in a long-term aspect after treatment suggests a serious problem. Preclinical and clinical data reveal that at highest risk of IBC/CBC are patients with inherited BRCA1/2 mutations who benefited from bilateral mastectomy. Local failure risk prediction is currently unfeasible among familial non-BRCA1/2 (BRCA-test negative) and sporadic (no family history) breast cancer. Genome-wide association studies have already identified novel risk alleles with a series of tumor-initiating single-nucleotide polymorphisms (SNPs). Some of these variants and other novel SNPs and copy-number variants (CNVs) may also be relevant for local failures (IBC/CBC).

CONCLUSIONS

Beyond established risk factors, genetic testing allows identification of high-risk patients (BRCA mutation carriers) who may benefit from bilateral mastectomy rather than BCS. Human genetic variation (SNPs/CNVs) and DNA methylation may be relevant for local failures assessment. Technological revolution has opened a new avenue but multiple challenges should be overcome to integrate SNPs/CNVs as markers for IBC/CBC risk-stratification-based personalized surgery.

Common variants at ten loci modulate the QT interval duration in the QTSCD Study

The QT interval, a measure of cardiac repolarization, predisposes to ventricular arrhythmias and sudden cardiac death (SCD) when prolonged or shortened. A common variant in NOS1AP is known to influence repolarization. We analyze genome-wide data from five population-based cohorts (ARIC, KORA, SardiNIA, GenNOVA and HNR) with a total of 15,842 individuals of European ancestry, to confirm the NOS1AP association and identify nine additional loci at P < 5 x 10(-8). Four loci map near the monogenic long-QT syndrome genes KCNQ1, KCNH2, SCN5A and KCNJ2. Two other loci include ATP1B1 and PLN, genes with established electrophysiological function, whereas three map to RNF207, near LITAF and within NDRG4-GINS3-SETD6-CNOT1, respectively, all of which have not previously been implicated in cardiac electrophysiology. These results, together with an accompanying paper from the QTGEN consortium, identify new candidate genes for ventricular arrhythmias and SCD.

A genome wide survey of SNP variation reveals the genetic structure of sheep breeds

The genetic structure of sheep reflects their domestication and subsequent formation into discrete breeds. Understanding genetic structure is essential for achieving genetic improvement through genome-wide association studies, genomic selection and the dissection of quantitative traits. After identifying the first genome-wide set of SNP for sheep, we report on levels of genetic variability both within and between a diverse sample of ovine populations. Then, using cluster analysis and the partitioning of genetic variation, we demonstrate sheep are characterised by weak phylogeographic structure, overlapping genetic similarity and generally low differentiation which is consistent with their short evolutionary history. The degree of population substructure was, however, sufficient to cluster individuals based on geographic origin and known breed history. Specifically, African and Asian populations clustered separately from breeds of European origin sampled from Australia, New Zealand, Europe and North America. Furthermore, we demonstrate the presence of stratification within some, but not all, ovine breeds. The results emphasize that careful documentation of genetic structure will be an essential prerequisite when mapping the genetic basis of complex traits. Furthermore, the identification of a subset of SNP able to assign individuals into broad groupings demonstrates even a small panel of markers may be suitable for applications such as traceability.

MicroRNA polymorphisms: a giant leap towards personalized medicine

"An individual's genetic inheritance of microRNA polymorphisms associated with disease progression, prognosis and treatment holds the key to create safer and more personalized drugs and can be a giant leap towards personalized medicine."

Genome-wide association study of smoking initiation and current smoking

For the identification of genes associated with smoking initiation and current smoking, genome-wide association analyses were carried out in 3497 subjects. Significant genes that replicated in three independent samples (n = 405, 5810, and 1648) were visualized into a biologically meaningful network showing cellular location and direct interaction of their proteins. Several interesting groups of proteins stood out, including glutamate receptors (e.g., GRIN2B, GRIN2A, GRIK2, GRM8), proteins involved in tyrosine kinase receptor signaling (e.g., NTRK2, GRB14), transporters (e.g., SLC1A2, SLC9A9) and cell-adhesion molecules (e.g., CDH23). We conclude that a network-based genome-wide association approach can identify genes influencing smoking behavior.

Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk

Genome-wide association (GWAS) analyses have identified susceptibility loci for many diseases, but most risk for any complex disorder remains unattributed. There is therefore scope for complementary approaches to these data sets. Gene-wide approaches potentially offer additional insights. They might identify association to genes through multiple signals. Also, by providing support for genes rather than single nucleotide polymorphisms (SNPs), they offer an additional opportunity to compare the results across data sets. We have undertaken gene-wide analysis of two GWAS data sets: schizophrenia and bipolar disorder. We performed two forms of analysis, one based on the smallest P-value per gene, the other on a truncated product of P method. For each data set and at a range of statistical thresholds, we observed significantly more SNPs within genes (P(min) for excess<0.001) showing evidence for association than expected whereas this was not true for extragenic SNPs (P(min) for excess>0.1). At a range of thresholds of significance, we also observed substantially more associated genes than expected (P(min) for excess in schizophrenia=1.8 x 10(-8), in bipolar=2.4 x 10(-6)). Moreover, an excess of genes showed evidence for association across disorders. Among those genes surpassing thresholds highly enriched for true association, we observed evidence for association to genes reported in other GWAS data sets (CACNA1C) or to closely related family members of those genes including CSF2RB, CACNA1B and DGKI. Our analyses show that association signals are enriched in and around genes, large numbers of genes contribute to both disorders and gene-wide analyses offer useful complementary approaches to more standard methods.

Control Selection Options for Genome-Wide Association Studies in Cohorts

Investigators planning studies within cohorts have many options for choosing an efficient sampling design for genome-wide association and other molecular epidemiology studies. Consideration of person-year and proportional hazards analyses of full cohorts may add further insight into ramifications of different designs. Empirical evidence from genome-wide association studies can supplement intuition and simulations in comparing properties of various case-control designs within cohorts. Additional theoretical and empirical work, justification of sampling choice in publications, and consideration of context and scientific aims can improve designs and, thereby, increase the scientific value and cost effectiveness of future studies. (Cancer Epidemiol Biomarkers Prev 2009;18(3):695–7)

Genome-based prediction of common diseases: methodological considerations for future research

The translation of emerging genomic knowledge into public health and clinical care is one of the major challenges for the coming decades. At the moment, genome-based prediction of common diseases, such as type 2 diabetes, coronary heart disease and cancer, is still not informative. Our understanding of the genetic basis of multifactorial diseases is improving, but the currently identified susceptibility variants contribute only marginally to the development of disease. At the same time, an increasing number of companies are offering personalized lifestyle and health recommendations on the basis of individual genetic profiles. This discrepancy between the limited predictive value and the commercial availability of genetic profiles highlights the need for a critical appraisal of the usefulness of genome-based applications in clinical and public health care. Anticipating the discovery of a large number of genetic variants in the near future, we need to prepare a framework for the design and analysis of studies aiming to evaluate the clinical validity and utility of genetic tests. In this article, we review recent studies on the predictive value of genetic profiling from a methodological perspective and address issues around the choice of the study population, the construction of genetic profiles, the measurement of the predictive value, calibration and validation of prediction models, and assessment of clinical utility. Careful consideration of these issues will contribute to the knowledge base that is needed to identify useful genome-based applications for implementation in clinical and public health practice.

Recent advances in genetic predisposition to clinical acute lung injury

It has been well established that acute lung injury (ALI), and the more severe presentation of acute respiratory distress syndrome (ARDS), constitute complex traits characterized by a multigenic and multifactorial etiology. Identification and validation of genetic variants contributing to disease susceptibility and severity has been hampered by the profound heterogeneity of the clinical phenotype and the role of environmental factors, which includes treatment, on outcome. The critical nature of ALI and ARDS, compounded by the impact of phenotypic heterogeneity, has rendered the amassing of sufficiently powered studies especially challenging. Nevertheless, progress has been made in the identification of genetic variants in select candidate genes, which has enhanced our understanding of the specific pathways involved in disease manifestation. Identification of novel candidate genes for which genetic association studies have confirmed a role in disease has been greatly aided by the powerful tool of high-throughput expression profiling. This article will review these studies to date, summarizing candidate genes associated with ALI and ARDS, acknowledging those that have been replicated in independent populations, with a special focus on the specific pathways for which candidate genes identified so far can be clustered.

Symposium on 'The challenge of translating nutrition research into public health nutrition'. Session 2: Personalised nutrition. Genetic variation and disease risk: new advances

Variations in human DNA, most frequently single-nucleotide polymorphisms (SNPs), can have functional consequences ranging from severe to none. Variations in outcome (phenotype) can be compared, from cystic fibrosis through haemochromatosis to general familial risks in, for example, colo-rectal cancer (CRC). Cystic fibrosis and haemochromatosis have severe phenotypes with high penetrance, with signs and symptoms always or mostly present; thus, they have been easy to identify from family studies. However, the familial risks that are known to contribute markedly to CRC are unknown. The sequencing of the human genome has now made possible the identification of these and other disease variants. Knowing the DNA sequence in an idealised individual adds little unless variants that increase (or decrease) disease risk from the norm can be identified. Such variants can be expected to be very common in the general population, but have low penetrance and only change risk to a limited extent. Many patients will not have the risk variant and many 'normal' patients will have the risk variant. Thus, very large case-control cohorts are essential. These case-control cohorts can be analysed at three different levels: (1) individual SNPs; (2) individual genes; (3) genome-wide analysis (GWA). Level 1 looks for case-control differences for specific SNPs. Alternatively, new technology can be applied to examine a range of SNPs within a gene to track differences in its regulation as well as in function. Finally, the whole genome with >or=0.5x10(6) SNPs could be marked. The first two approaches involve selecting 'candidate' SNPs or genes, while GWA looks for any variation in the genome that is enriched in the cases. All three approaches carry the certainty that significant associations will be found by statistical chance, for which correction must be made. This latter issue is helped by large numbers and by independent replication cohorts.

Meta-analysis in genome-wide association studies

The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.

Detecting shared pathogenesis from the shared genetics of immune-related diseases

Recent genetic studies have revealed shared immunological mechanisms in several immune-related disorders that further our understanding of the development and concomitance of these diseases. Our Review focuses on these shared aspects, using the novel findings of recently performed genome-wide association studies and non-synonymous SNP scans as a starting point. We discuss how identifying new genes that are associated with more than one autoimmune or chronic inflammatory disorder could explain the genetic basis of the shared pathogenesis of immune-related diseases. This analysis helps to highlight the key molecular pathways that are involved in these disorders and the potential roles of novel genes in immune-related diseases.

Prediction of cardiovascular disease outcomes and established cardiovascular risk factors by genome-wide association markers

BACKGROUND

Genome-wide association (GWA) platforms have yielded a rapidly increasing number of new genetic markers. The ability of these markers to improve prediction of clinically important outcomes is debated.

METHODS AND RESULTS

A systematic review was performed of GWA-derived markers associated with cardiovascular outcomes or other phenotypes that represent common established risk factors for cardiovascular outcomes. Sources of information included the National Human Genome Research Institute catalog of published GWA studies, and perusal of the eligible GWA articles, meta-analyses on the respective associations, and articles on the incremental predictive performance of common variants in the GWA era. A total of 95 eligible associations were retrieved from the National Human Genome Research Institute catalogue of published GWA studies as of September 2008. Of those 36 have statistical support of P<10(-7). In depth evaluation of the respective articles shows 28 independent associations with such statistical support, pertaining to coronary artery disease, myocardial infarction, atrial fibrillation/flutter, prolongation of QT interval, as well as type 2 diabetes, body mass index, high-density lipoprotein levels, low-density lipoprotein levels, and nicotine dependence. Between-study heterogeneity is not taken into account usually, but it seems common and it would pose a challenge to generalizability across different populations for these markers. Still limited data are available in non-white populations. Effect sizes are small and may be even smaller in subsequent replications and meta-analysis. Population attributable fractions are substantial, given the large frequency of the risk alleles. However, individualized risk measures are typically very small (proportion of variance explained <1% per marker). When used in conjunction with traditional predictors, improvement in overall prediction (eg, area under the curve) or risk reclassification is limited, and subject to methodological caveats.

CONCLUSIONS

Despite very promising signals in terms of statistical significance, evidence for improvement in cardiovascular prediction by currently available markers derived from GWA studies is sparse. Clinical use of such markers currently would be premature.

An open access database of genome-wide association results

BACKGROUND

The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results.

METHODS

We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS.

RESULTS

Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10(-14)), a finding which was not perturbed by a sensitivity analysis.

CONCLUSION

We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.

Systems pharmacology and genome medicine: a future perspective

Genome medicine uses genomic information in the diagnosis of disease and in prescribing treatment. This transdisciplinary field brings together knowledge on the relationships between genetics, pathophysiology and pharmacology. Systems pharmacology aims to understand the actions and adverse effects of drugs by considering targets in the context of the biological networks in which they exist. Genome medicine forms the base on which systems pharmacology can develop. Experimental and computational approaches enable systems pharmacology to obtain holistic, mechanistic information on disease networks and drug responses, and to identify new drug targets and specific drug combinations. Network analyses of interactions involved in pathophysiology and drug response across various scales of organization, from molecular to organismal, will allow the integration of the systems-level understanding of drug action with genome medicine. The interface of the two fields will enable drug discovery for personalized medicine. Here we provide a perspective on the questions and approaches that drive the development of these new interrelated fields.

An open access database of genome-wide association results

BACKGROUND

The number of genome-wide association studies (GWAS) is growing rapidly leading to the discovery and replication of many new disease loci. Combining results from multiple GWAS datasets may potentially strengthen previous conclusions and suggest new disease loci, pathways or pleiotropic genes. However, no database or centralized resource currently exists that contains anywhere near the full scope of GWAS results.

METHODS

We collected available results from 118 GWAS articles into a database of 56,411 significant SNP-phenotype associations and accompanying information, making this database freely available here. In doing so, we met and describe here a number of challenges to creating an open access database of GWAS results. Through preliminary analyses and characterization of available GWAS, we demonstrate the potential to gain new insights by querying a database across GWAS.

RESULTS

Using a genomic bin-based density analysis to search for highly associated regions of the genome, positive control loci (e.g., MHC loci) were detected with high sensitivity. Likewise, an analysis of highly repeated SNPs across GWAS identified replicated loci (e.g., APOE, LPL). At the same time we identified novel, highly suggestive loci for a variety of traits that did not meet genome-wide significant thresholds in prior analyses, in some cases with strong support from the primary medical genetics literature (SLC16A7, CSMD1, OAS1), suggesting these genes merit further study. Additional adjustment for linkage disequilibrium within most regions with a high density of GWAS associations did not materially alter our findings. Having a centralized database with standardized gene annotation also allowed us to examine the representation of functional gene categories (gene ontologies) containing one or more associations among top GWAS results. Genes relating to cell adhesion functions were highly over-represented among significant associations (p < 4.6 x 10(-14)), a finding which was not perturbed by a sensitivity analysis.

CONCLUSION

We provide access to a full gene-annotated GWAS database which could be used for further querying, analyses or integration with other genomic information. We make a number of general observations. Of reported associated SNPs, 40% lie within the boundaries of a RefSeq gene and 68% are within 60 kb of one, indicating a bias toward gene-centricity in the findings. We found considerable heterogeneity in information available from GWAS suggesting the wider community could benefit from standardization and centralization of results reporting.

Is there still a need for candidate gene approaches in the era of genome-wide association studies?

Most genetic variants associated with complex diseases in humans are believed to have a small impact on risk. With traditional candidate gene/pathway approaches several associations with disease risk could be identified. However, now that genome-wide association studies are feasible, the question arises if there is still a need for these approaches. By using HapMap data, we evaluated to which extent commercially available microarrays cover, through linkage disequilibrium, all currently known genes and biological processes in different populations. Furthermore, we estimated the power to detect an association with any specific SNP. Our study shows that coverage of individual genes and pathways by current commercial genotyping platforms is satisfactory for the vast majority of RefSeq gene regions. However, depending on the gene or the population, there may still be a need for candidate gene approaches, especially when looking at polymorphisms with low allele frequencies.

Emerging paradigms in cancer genetics: some important findings from high-density single nucleotide polymorphism array studies

High-density single nucleotide polymorphism (SNP) mapping arrays have identified chromosomal features whose importance to cancer predisposition and progression is not yet clearly defined. Of interest is that the genomes of normal somatic cells (reflecting the combined parental germ-line contributions) often contain long homozygous stretches. These chromosomal segments may be explained by the common ancestry of the individual's parents and thus may also be called autozygous. Several studies link consanguinity to higher rates of cancer, suggesting that autozygosity (a genomic consequence of consanguinity) may be a factor in cancer predisposition. SNP array analysis has also identified chromosomal regions of somatic uniparental disomy (UPD) in cancer genomes. These are chromosomal segments characterized by loss of heterozygosity (LOH) and a normal copy number (two) but which are not autozygous in the germ-line or normal somatic cell genome. In this review, we will also discuss a model [cancer gene activity model (CGAM)] that may explain how autozygosity influences cancer predisposition. CGAM can also explain how the occurrence of certain chromosomal aberrations (copy number gain, LOH, and somatic UPDs) during carcinogenesis may be dependent on the germ-line genotypes of important cancer-related genes (oncogenes and tumor suppressors) found in those chromosomal regions.

Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies--challenges and opportunities

The recent success of genome-wide association studies in finding susceptibility genes for many common diseases presents tremendous opportunities for epidemiologic studies of environmental risk factors. Analysis of gene-environment interactions, included in only a small fraction of epidemiologic studies until now, will begin to accelerate as investigators integrate analyses of genome-wide variation and environmental factors. Nevertheless, considerable methodological challenges are involved in the design and analysis of gene-environment interaction studies. The authors review these issues in the context of evolving methods for assessing interactions and discuss how the current agnostic approach to interrogating the human genome for genetic risk factors could be extended into a similar approach to gene-environment-wide interaction studies of disease occurrence in human populations.

Gene-gene interactions in the NOD mouse model of type 1 diabetes

Human genome wide association studies (GWAS) have recently identified at least four new, non-MHC-linked candidate genes or gene regions causing type one diabetes (T1D), highlighting the need for functional models to investigate how susceptibility alleles at multiple common genes interact to mediate disease. Progress in localizing genes in congenic strains of the nonobese diabetic (NOD) mouse has allowed the reproducible testing of gene functions and gene-gene interactions that can be reflected biologically as intrapathway interactions, for example, IL-2 and its receptor CD25, pathway-pathway interactions such as two signaling pathways within a cell, or cell-cell interactions. Recent studies have identified likely causal genes in two congenic intervals associated with T1D, Idd3, and Idd5, and have documented the occurrence of gene-gene interactions, including "genetic masking", involving the genes encoding the critical immune molecules IL-2 and CTLA-4. The demonstration of gene-gene interactions in congenic mouse models of T1D has major implications for the understanding of human T1D since such biological interactions are highly likely to exist for human T1D genes. Although it is difficult to detect most gene-gene interactions in a population in which susceptibility and protective alleles at many loci are randomly segregating, their existence as revealed in congenic mice reinforces the hypothesis that T1D alleles can have strong biological effects and that such genes highlight pathways to consider as targets for immune intervention.

Genetics of bipolar disorder: successful start to a long journey

Family and twin studies attest to the importance of genetic factors influencing susceptibility to bipolar disorder and to its genetic and phenotypic complexity. Although linkage and candidate gene association studies have repeatedly implicated some chromosome regions and certain genes, they have not produced the level of unambiguous support required to confirm the involvement of any specific gene or sequence variant in the pathogenesis of bipolar disorder. However, strong associations have recently been reported in meta-analyses of genome-wide association studies and the systematic study of structural variation is ongoing. These findings indicate that the study of large, phenotypically well-characterized samples will make an important contribution to delineating the etiology and pathogenesis of bipolar disorder and thereby pave the way for major improvements in clinical management.

New findings from genetic association studies of schizophrenia

In the past 20 years, association studies of schizophrenia have evolved from analyses in lesser than 100 subjects of one or two markers in candidate genes to systematic analyses of association at a genome-wide level in samples of thousands of subjects. During this process, much of the emergent literature has been difficult to interpret and definitive findings that have met with universal acceptance have been elusive, largely because studies have been underpowered for such conclusions to be drawn. Nevertheless, in the course of the past few years, a few promising candidate genes have been reported for which the evidence is positive across multiple studies, and more recently, genome-wide association studies have yielded findings of a compelling nature. It is clear that genetic studies in schizophrenia have borne fruit, a process that can be expected to accelerate in the next few years, and that these findings are providing new avenues for research into the pathophysiology of this poorly understood disorder.

Genome-based prediction of common diseases: advances and prospects

Common diseases such as type 2 diabetes and coronary heart disease result from a complex interplay of genetic and environmental factors. Recent developments in genomics research have boosted progress in the discovery of susceptibility genes and fueled expectations about opportunities of genetic profiling for personalizing medicine. Personalized medicine requires a test that fairly accurately predicts disease risk, particularly when interventions are invasive, expensive or have major side effects. Recent studies on the prediction of common diseases based on multiple genetic variants alone or in addition to traditional disease risk factors showed limited predictive value so far, but all have investigated only a limited number of susceptibility variants. New gene discoveries from genome-wide association studies will certainly further improve the prediction of common diseases, but the question is whether this improvement is sufficient to enable personalized medicine. In this paper, we argue that new gene discoveries may not evidently improve the prediction of common diseases to a degree that it will change the management of individuals at increased risk. Substantial improvements may only be expected if we manage to understand the complete causal mechanisms of common diseases to a similar extent as we understand those of monogenic disorders. Genomics research will contribute to this understanding, but it is likely that the complexity of complex diseases may ultimately limit the opportunities for accurate prediction of disease in asymptomatic individuals as unraveling their complete causal pathways may be impossible.