A HapMap harvest of insights into the genetics of common disease

Inter-chromosomal level of genome organization and longevity-related phenotypes in humans

Studies focusing on unraveling the genetic origin of health span in humans assume that polygenic, aging-related phenotypes are inherited through Mendelian mechanisms of inheritance of individual genes. We use the Framingham Heart Study (FHS) data to examine whether non-Mendelian mechanisms of inheritance can drive linkage of loci on non-homologous chromosomes and whether such mechanisms can be relevant to longevity-related phenotypes. We report on genome-wide inter-chromosomal linkage disequilibrium (LD) and on chromosome-wide intra-chromosomal LD and show that these are real phenomena in the FHS data. Genetic analysis of inheritance in families based on Mendelian segregation reveals that the alleles of single nucleotide polymorphisms (SNPs) in LD at loci on non-homologous chromosomes are inherited as a complex resembling haplotypes of a genetic unit. This result implies that the inter-chromosomal LD is likely caused by non-random assortment of non-homologous chromosomes during meiosis. The risk allele haplotypes can be subject to dominant-negative selection primary through the mechanisms of non-Mendelian inheritance. They can go to extinction within two human generations. The set of SNPs in inter-chromosomal LD (N=68) is nearly threefold enriched, with high significance (p=1.6 × 10(-9)), on non-synonymous coding variants (N=28) compared to the entire qualified set of the studied SNPs. Genes for the tightly linked SNPs are involved in fundamental biological processes in an organism. Survival analyses show that the revealed non-genetic linkage is associated with heritable complex phenotype of premature death. Our results suggest the presence of inter-chromosomal level of functional organization in the human genome and highlight a challenging problem of genomics of human health and aging.

Genomics of human health and aging

Despite notable progress of the candidate-gene and genome-wide association studies (GWAS), understanding the role of genes contributing to human health and lifespan is still very limited. We use the Framingham Heart Study to elucidate if recognizing the role of evolution and systemic processes in an aging organism could advance such studies. We combine throughput methods of GWAS with more detail methods typical for candidate-gene analyses and show that both lifespan and ages at onset of CVD and cancer can be controlled by the same allelic variants. The risk allele carriers are at highly significant risk of premature death (e.g., RR=2.9, p=5.0 × 10(-66)), onset of CVD (e.g., RR=1.6, p=4.6 × 10(-17)), and onset of cancer (e.g., RR=1.6, p=1.5 × 10(-6)). The mechanism mediating the revealed genetic associations is likely associated with biological aging. These aging-related phenotypes are associated with a complex network which includes, in this study, 62 correlated SNPs even so these SNPs can be on non-homologous chromosomes. A striking result is three-fold, highly significant (p=3.6 × 10(-10)) enrichment of non-synonymous SNPs (N=27) in this network compared to the entire qualified set of the studied SNPs. Functional significance of this network is strengthened by involvement of genes for these SNPs in fundamental biological processes related to aging (e.g., response to stimuli, protein degradation, apoptosis) and by connections of these genes with neurological (20 genes) and cardio-vascular (nine genes) processes and tumorigenesis (10 genes). These results document challenging role of gene networks in regulating human health and aging and call for broadening focus on genomics of such phenotypes.

Evaluating rare variants in complex disorders using next-generation sequencing

Determining the genetic architecture of liability for complex neuropsychiatric disorders like autism spectrum disorders and schizophrenia poses a tremendous challenge for contemporary biomedical research. Here we discuss how genetic studies first tested, and rejected, the hypothesis that common variants with large effects account for the prevalence of these disorders. We then explore how the discovery of structural variation has contributed to our understanding of the etiology of these disorders. The rise of fast and inexpensive oligonucleotide sequencing and methods of targeted enrichment and their influence on the search for rare genetic variation contributing to complex neuropsychiatric disorders is the next focus of our article. Finally, we consider the technical challenges and future prospects for the use of next-generation sequencing to reveal the complex genetic architecture of complex neuropsychiatric disorders in both research and the clinical settings.

Endothelial NO synthase gene polymorphisms and risk of ischemic stroke in Asian population: a meta-analysis

Background

The association between polymorphism 4b/a, T-786C and G894T in endothelial NO synthase gene (eNOS) and ischemic stroke (IS) remains controversial in Asian. A meta-analysis was performed to better clarify the association between eNOS gene and IS risk.

Methods

Based on the search of PubMed, Web of Science (ISI), CNKI (National Knowledge Infrastructure), Wan Fang Med Online and CBM (Chinese Biology Medical Literature Database) databases, all eligible case-control or cohort studies were identified. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) from fixed and random effect models were calculated. Heterogeneity among studies was evaluated using the I². Meta-regression was used to explore the potential sources of between-study heterogeneity. Begg's test was used to estimate publication bias.

Results

Our study included 27 articles, contained 28 independent case–control studies, involved a total of 3,742 cases and 3,691 controls about 4b/a, 1,800 cases and 1,751 controls about T-786C and 2,747 cases and 2,872 controls about G894T. A significant association of 4a allele with increased risk of IS was found in dominant (FEM: OR = 1.498, 95% CI = 1.329–1.689), recessive (FEM: OR = 2.132, 95% CI = 1.383–3.286) and codominant (REM: OR = 1.456, 95% CI = 1.235–1.716) models. For T-786C and G894T, there were significant associations with dominant and codominant genetic models, but not with recessive genetic model.

Conclusions

The meta-analysis indicated that eNOS gene 4b/a, T-786C, G894T polymorphism might be associated with IS.

SNPflow: a lightweight application for the processing, storing and automatic quality checking of genotyping assays

Single nucleotide polymorphisms (SNPs) play a prominent role in modern genetics. Current genotyping technologies such as Sequenom iPLEX, ABI TaqMan and KBioscience KASPar made the genotyping of huge SNP sets in large populations straightforward and allow the generation of hundreds of thousands of genotypes even in medium sized labs. While data generation is straightforward, the subsequent data conversion, storage and quality control steps are time-consuming, error-prone and require extensive bioinformatic support. In order to ease this tedious process, we developed SNPflow. SNPflow is a lightweight, intuitive and easily deployable application, which processes genotype data from Sequenom MassARRAY (iPLEX) and ABI 7900HT (TaqMan, KASPar) systems and is extendible to other genotyping methods as well. SNPflow automatically converts the raw output files to ready-to-use genotype lists, calculates all standard quality control values such as call rate, expected and real amount of replicates, minor allele frequency, absolute number of discordant replicates, discordance rate and the p-value of the HWE test, checks the plausibility of the observed genotype frequencies by comparing them to HapMap/1000-Genomes, provides a module for the processing of SNPs, which allow sex determination for DNA quality control purposes and, finally, stores all data in a relational database. SNPflow runs on all common operating systems and comes as both stand-alone version and multi-user version for laboratory-wide use. The software, a user manual, screenshots and a screencast illustrating the main features are available at http://genepi-snpflow.i-med.ac.at.

A new way to protect privacy in large-scale genome-wide association studies

Motivation: Increased availability of various genotyping techniques has initiated a race for finding genetic markers that can be used in diagnostics and personalized medicine. Although many genetic risk factors are known, key causes of common diseases with complex heritage patterns are still unknown. Identification of such complex traits requires a targeted study over a large collection of data. Ideally, such studies bring together data from many biobanks. However, data aggregation on such a large scale raises many privacy issues.

Results: We show how to conduct such studies without violating privacy of individual donors and without leaking the data to third parties. The presented solution has provable security guarantees.

Contact:jaak.vilo@ut.ee

Supplementary information:Supplementary data are available at Bioinformatics online.

Anticipating the arrival of low-penetrance genetic testing to primary care medicine

Primary prevention is a pillar of primary care medicine. Furthermore, the identification of commonly occurring genetic mutations that confer only modest increases in disease risk (i.e., low-penetrance mutations or LPMs) is expanding our conception of how genetic testing supports prevention goals. To date, most predictive genetic testing has focused on identifying the minority of patients who carry mutations that significantly increase their risk for developing future disease (i.e., high-penetrance mutations or HPMs). Genetic tests for LPMs are more similar in structure and purpose to commonly used biomarker tests like lipid testing than to HPM testing. In the primary care setting, LPM testing will likely be presented to patients as one part of a multifactorial risk assessment that contains only a small amount of genetics-specific information. Consequently, preparing primary care clinicians for the anticipated use of LPM genetic tests will not require development of a completely new skill set but rather a re-conceptualization of both genetic testing and biomarker evaluation for primary prevention.

Genome-wide association study meta-analysis of European and Asian-ancestry samples identifies three novel loci associated with bipolar disorder

Meta-analyses of bipolar disorder (BD) genome-wide association studies (GWAS) have identified several genome-wide significant signals in European-ancestry samples, but so far account for little of the inherited risk. We performed a meta-analysis of ∼750,000 high-quality genetic markers on a combined sample of ∼14,000 subjects of European and Asian-ancestry (phase I). The most significant findings were further tested in an extended sample of ∼17,700 cases and controls (phase II). The results suggest novel association findings near the genes TRANK1 (LBA1), LMAN2L and PTGFR. In phase I, the most significant single nucleotide polymorphism (SNP), rs9834970 near TRANK1, was significant at the P=2.4 × 10(-11) level, with no heterogeneity. Supportive evidence for prior association findings near ANK3 and a locus on chromosome 3p21.1 was also observed. The phase II results were similar, although the heterogeneity test became significant for several SNPs. On the basis of these results and other established risk loci, we used the method developed by Park et al. to estimate the number, and the effect size distribution, of BD risk loci that could still be found by GWAS methods. We estimate that >63,000 case-control samples would be needed to identify the ∼105 BD risk loci discoverable by GWAS, and that these will together explain <6% of the inherited risk. These results support previous GWAS findings and identify three new candidate genes for BD. Further studies are needed to replicate these findings and may potentially lead to identification of functional variants. Sample size will remain a limiting factor in the discovery of common alleles associated with BD.

Assessment of systematic effects of methodological characteristics on candidate genetic associations

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case-control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes

AIMS/HYPOTHESIS

Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes.

METHODS

The study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m(2) and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case-control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans.

RESULTS

Exome sequencing identified 70,182 polymorphisms with MAF >1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 × 10(-14)), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10(-11)) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10(-10)).

CONCLUSIONS/INTERPRETATION

We applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.

Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze the combined effect of a SNP set grouped per pathway or gene region. We used this approach to study the joint effect on human longevity of genetic variation in two candidate pathways, the insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway and the telomere maintenance (TM) pathway. For the analyses, we used genotyped GWAS data of 403 unrelated nonagenarians from long-lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. We analyzed 1,021 SNPs in 68 IIS pathway genes and 88 SNPs in 13 TM pathway genes using four self-contained pathway tests (PLINK set-based test, Global test, GRASS and SNP ratio test). Although we observed small differences between the results of the different pathway tests, they showed consistent significant association of the IIS and TM pathway SNP sets with longevity. Analysis of gene SNP sets from these pathways indicates that the association of the IIS pathway is scattered over several genes (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK, SGK2, and YWHAG), while the association of the TM pathway seems to be mainly determined by one gene (POT1). In conclusion, this study shows that genetic variation in genes involved in the IIS and TM pathways is associated with human longevity.

[Endophenotypes: the molecular biology point of view]

Endophenotypes are proposed for a better understanding of the molecular substrate underlying psychiatric disorders vulnerability. In this review, we discuss key points of the definition of endophenotypes from the molecular biology point of view. First, we examine the concept of heritability of endophenotype, which does not directly explain the molecular mechanisms responsible for the studied disorder Indeed, we discuss the necessity to better decipher the functional role of polymorphisms associated to endophenotypes, especially if those endophenotypes would be assigned a clinical and biological value. The complexity of endophenotypes definition and use in psychiatric research is also illustrated by the complexity of the human genome organization and gene networks as well as by the gene x environment interactions and also the possible existence of phenocopies.

Implementing genomic medicine in the clinic: the future is here

Although the potential for genomics to contribute to clinical care has long been anticipated, the pace of defining the risks and benefits of incorporating genomic findings into medical practice has been relatively slow. Several institutions have recently begun genomic medicine programs, encountering many of the same obstacles and developing the same solutions, often independently. Recognizing that successful early experiences can inform subsequent efforts, the National Human Genome Research Institute brought together a number of these groups to describe their ongoing projects and challenges, identify common infrastructure and research needs, and outline an implementation framework for investigating and introducing similar programs elsewhere. Chief among the challenges were limited evidence and consensus on which genomic variants were medically relevant; lack of reimbursement for genomically driven interventions; and burden to patients and clinicians of assaying, reporting, intervening, and following up genomic findings. Key infrastructure needs included an openly accessible knowledge base capturing sequence variants and their phenotypic associations and a framework for defining and cataloging clinically actionable variants. Multiple institutions are actively engaged in using genomic information in clinical care. Much of this work is being done in isolation and would benefit from more structured collaboration and sharing of best practices.

Genet Med 2013:15(4):258–267

Prospects of personalized medicine in cardiovascular diseases

Cardiovascular diseases remain the dominant cause of death worldwide. In the last decades, the remarkable advances in human genetic and genomic research, plus the now common use of genome-wide association studies, have led to the identification of numerous genetic variants associated with specific cardiovascular traits and diseases. Although the clinical applications are limited because the genetic risk of common cardiovascular disease is still unexplained, and the mechanisms of action of the genetic factor(s) are not known, these research advances have, in turn, widely opened the concept of personalized medicine. In this paper, the status and prospects of personalized medicine for cardiovascular disease will be presented. This will be followed by a discussion of issues regarding the implementation of personalized medicine.

DNA replication timing and higher-order nuclear organization determine single-nucleotide substitution patterns in cancer genomes

Single-nucleotide substitutions are a defining characteristic of cancer genomes. Many single-nucleotide substitutions in cancer genomes arise because of errors in DNA replication, which is spatio-temporally stratified. Here we propose that DNA replication patterns help shape the mutational landscapes of normal and cancer genomes. Using data on five fully sequenced cancer types and two personal genomes, we determined that the frequency of intergenic single-nucleotide substitution is significantly higher in late DNA replication timing regions, even after controlling for a number of genomic features. Furthermore, some substitution signatures are more frequent in certain DNA replication timing zones. Finally, integrating data on higher-order nuclear organization, we found that genomic regions in close spatial proximity to late-replicating domains display similar mutation spectra as the late-replicating regions themselves. These data suggest that DNA replication timing together with higher-order genomic organization contribute to the patterns of single-nucleotide substitution in normal and cancer genomes.

Investigation of single nucleotide polymorphisms and biological pathways associated with response to TNFα inhibitors in patients with rheumatoid arthritis

OBJECTIVE

Recently, two genome-wide association studies identified single nucleotide polymorphisms (SNPs) significantly associated with the treatment response to tumor necrosis factor α (TNFα) inhibitors in patients with rheumatoid arthritis (RA). We aimed to replicate these results and identify SNPs and the possible biological pathways associated with the treatment response to TNFα inhibitors.

METHODS

TNFα-naive patients with RA, who had available DNA and initiated TNFα inhibitor therapy between 1999 and 2008, were identified in the DANBIO registry and genotyped using the Illumina HumanHap550K Duo array. The associations between SNPs and changes in the absolute and the relative Disease Activity Score, and European League Against Rheumatism good versus no response after 14 weeks of treatment were tested. SNP data were combined with two independent cohorts in a meta-analysis. A gene-set enrichment analysis (GSEA) was carried out to identify the biological pathways associated with the treatment response.

RESULTS

After genotyping and quality control, 486 450 SNPs were analyzed in 196 Danish patients with moderate to severe RA treated with infliximab (n=142), etanercept (n=12), and adalimumab (n=42). None of the previously identified SNPs were confirmed in our dataset or in meta-analyses of available studies. Other potential SNPs were identified, but none achieved genome-wide significance. A GSEA identified the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways to have a potential influence on the treatment response.

CONCLUSION

In a genome-wide association study of 196 genetically homogenous Danish patients with RA and in a meta-analysis, we found no SNPs associated with treatment response to TNFα inhibitors. A GSEA suggested that the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways may be associated with treatment response.

iLOCi: a SNP interaction prioritization technique for detecting epistasis in genome-wide association studies

Background

Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).

Results

In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.

Conclusion

iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.

An exponential combination procedure for set-based association tests in sequencing studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives-that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk-a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.

[Important genodermatoses for the practitioner]

Health or disease is a result of the genetic constellation and environmental influences. The phenotype of monogenic diseases is highly influenced by one single mutation. According to the WHO more than 10,000 monogenic diseases exist while for 1,000 diseases a molecular genetic test is available. Genodermatoses are well-documented and characterized; the most important data base for the diagnosis is the Online Mendelian Inheritance of Men data base, which can be searched in Google with the keyword "OMIM". Here genetic diseases are categorized and clinically described. We present our own epidemiologic data from the Department of Dermatology, University Hospital Basel, concerning genodermatoses. Our results show that the most common genodermatoses seen in the daily practice are porokeratoses, ichthyoses, Darier disease, neurofibromatosis and epidermolysis bullosa. They account for 91% of all genodermatoses seen in a hospital-based dermatology department of Dermatology.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

Epistasis and immunity: the role of genetic interactions in autoimmune diseases

Autoimmune disorders are a complex and varied group of diseases that are caused by breakdown of self-tolerance. The aetiology of autoimmunity is multi-factorial, with both environmental triggers and genetically determined risk factors. In recent years, it has been increasingly recognized that genetic risk factors do not act in isolation, but rather the combination of individual additive effects, gene-gene interactions and gene-environment interactions determine overall risk of autoimmunity. The importance of gene-gene interactions, or epistasis, has been recently brought into focus, with research demonstrating that many autoimmune diseases, including rheumatic arthritis, autoimmune glomerulonephritis, systemic lupus erythematosus and multiple sclerosis, are influenced by epistatic interactions. This review sets out to examine the basic mechanisms of epistasis, how epistasis influences the immune system and the role of epistasis in two major autoimmune conditions, systemic lupus erythematosus and multiple sclerosis.

Genome-wide association studies in mice

Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.

Genotype imputation in a coalescent model with infinitely-many-sites mutation

Empirical studies have identified population-genetic factors as important determinants of the properties of genotype-imputation accuracy in imputation-based disease association studies. Here, we develop a simple coalescent model of three sequences that we use to explore the theoretical basis for the influence of these factors on genotype-imputation accuracy, under the assumption of infinitely-many-sites mutation. Employing a demographic model in which two populations diverged at a given time in the past, we derive the approximate expectation and variance of imputation accuracy in a study sequence sampled from one of the two populations, choosing between two reference sequences, one sampled from the same population as the study sequence and the other sampled from the other population. We show that, under this model, imputation accuracy-as measured by the proportion of polymorphic sites that are imputed correctly in the study sequence-increases in expectation with the mutation rate, the proportion of the markers in a chromosomal region that are genotyped, and the time to divergence between the study and reference populations. Each of these effects derives largely from an increase in information available for determining the reference sequence that is genetically most similar to the sequence targeted for imputation. We analyze as a function of divergence time the expected gain in imputation accuracy in the target using a reference sequence from the same population as the target rather than from the other population. Together with a growing body of empirical investigations of genotype imputation in diverse human populations, our modeling framework lays a foundation for extending imputation techniques to novel populations that have not yet been extensively examined.

'Location, Location, Location': a spatial approach for rare variant analysis and an application to a study on non-syndromic cleft lip with or without cleft palate

Motivation: For the analysis of rare variants in sequence data, numerous approaches have been suggested. Fixed and flexible threshold approaches collapse the rare variant information of a genomic region into a test statistic with reduced dimensionality. Alternatively, the rare variant information can be combined in statistical frameworks that are based on suitable regression models, machine learning, etc. Although the existing approaches provide powerful tests that can incorporate information on allele frequencies and prior biological knowledge, differences in the spatial clustering of rare variants between cases and controls cannot be incorporated. Based on the assumption that deleterious variants and protective variants cluster or occur in different parts of the genomic region of interest, we propose a testing strategy for rare variants that builds on spatial cluster methodology and that guides the identification of the biological relevant segments of the region. Our approach does not require any assumption about the directions of the genetic effects.

Results: In simulation studies, we assess the power of the clustering approach and compare it with existing methodology. Our simulation results suggest that the clustering approach for rare variants is well powered, even in situations that are ideal for standard methods. The efficiency of our spatial clustering approach is not affected by the presence of rare variants that have opposite effect size directions. An application to a sequencing study for non-syndromic cleft lip with or without cleft palate (NSCL/P) demonstrates its practical relevance. The proposed testing strategy is applied to a genomic region on chromosome 15q13.3 that was implicated in NSCL/P etiology in a previous genome-wide association study, and its results are compared with standard approaches.

Availability: Source code and documentation for the implementation in R will be provided online. Currently, the R-implementation only supports genotype data. We currently are working on an extension for VCF files.

Contact:heide.fier@googlemail.com

A two-SNP IL-6 promoter haplotype is associated with increased lung cancer risk

BACKGROUND

Aberrant expression of interleukin-6 (IL-6) may play an important role in lung carcinogenesis. Whether IL-6 promoter haplotypes are associated with lung cancer risk and their functions have not yet been studied. We tested the hypothesis that single-nucleotide polymorphism (SNP) and/or haplotypes of IL-6 promoter are associated with risk of lung cancer.

METHODS

Two functional IL-6 promoter SNPs (-6331T>C and -572C>G) were genotyped in the discovery group including 622 patients and 614 controls, and the results were replicated in an independent validation group including 615 patients and 638 controls. Luciferase reporter gene assays were conducted to examine the function of IL-6 promoter haplotypes.

RESULTS

None of the functional IL-6 promoter SNPs were associated with lung cancer risk in either study. However, a two-SNP CC (-6331C and -572C) IL-6 promoter haplotype was significantly more common among cases than among controls in both groups (P = 0.031 and P = 0.035, respectively), indicating that this haplotype is associated with increased lung cancer risk {adjusted odds ratio [OR], 1.56 [95 % confidence interval (95 % CI), 1.04-2.34] and 1.51 [95 % CI, 1.03-2.22], respectively}. Combined analysis of both studies showed a strong association of this two-SNP haplotype with increased lung cancer risk (adjusted OR, 1.53; 95 % CI, 1.16-2.03; P = 0.003). Comparably, luciferase reporter assays of A549 lung cancer cell lines transfected with the CC haplotype revealed that the two-SNP haplotype had significantly higher IL-6 transcriptional activity compared with cells transfected with the common haplotype.

CONCLUSIONS

This is the first evidence of identifying an IL-6 promoter haplotype (CC) associated with increased risk of lung cancer.

The role of large pedigrees in an era of high-throughput sequencing

Rare variation is the current frontier in human genetics. The large pedigree design is practical, efficient, and well-suited for investigating rare variation. In large pedigrees, specific rare variants that co-segregate with a trait will occur in sufficient numbers so that effects can be measured, and evidence for association can be evaluated, by making use of methods that fully use the pedigree information. Evidence from linkage analysis can focus investigation, both reducing the multiple testing burden and expanding the variants that can be evaluated and followed up, as recent studies have shown. The large pedigree design requires only a small fraction of the sample size needed to identify rare variants of interest in population-based designs, and many highly suitable, well-understood, and available statistical and computational tools already exist. Samples consisting of large pedigrees with existing rich phenotype and genome scan data should be prime candidates for high-throughput sequencing in the search of the determinants of complex traits.

Personalized medicine in cardiovascular diseases

Personalized medicine is a novel medical model with all decisions and practices being tailored to individual patients in whatever ways possible. In the era of genomics, personalized medicine combines the genetic information for additional benefit in preventive and therapeutic strategies. Personalized medicine may allow the physician to provide a better therapy for patients in terms of efficiency, safety and treatment length to reduce the associated costs. There was a remarkable growth in scientific publication on personalized medicine within the past few years in the cardiovascular field. However, so far, only very few cardiologists in the USA are incorporating personalized medicine into clinical treatment. We review the concepts, strengths, limitations and challenges of personalized medicine with a particular focus on cardiovascular diseases (CVDs). There are many challenges from both scientific and policy perspectives to personalized medicine, which can overcome them by comprehensive concept and understanding, clinical application, and evidence based practices. Individualized medicine serves a pivotal role in the evolution of national and global healthcare reform, especially, in the CVDs fields. Ultimately, personalized medicine will affect the entire landscape of health care system in the near future.

Informatics and clinical genome sequencing: opening the black box

Adoption of whole-genome sequencing as a routine biomedical tool is dependent not only on the availability of new high-throughput sequencing technologies, but also on the concomitant development of methods and tools for data collection, analysis, and interpretation. It would also be enormously facilitated by the development of decision support systems for clinicians and consideration of how such information can best be incorporated into care pathways. Here we present an overview of the data analysis and interpretation pipeline, the wider informatics needs, and some of the relevant ethical and legal issues.

Technical reproducibility of genotyping SNP arrays used in genome-wide association studies

During the last several years, high-density genotyping SNP arrays have facilitated genome-wide association studies (GWAS) that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. Moreover, discordance observed in results between independent GWAS indicates the potential for Type I and II errors. High reliability of genotyping technology is needed to have confidence in using SNP data and interpreting GWAS results. Therefore, reproducibility of two widely genotyping technology platforms from Affymetrix and Illumina was assessed by analyzing four technical replicates from each of the six individuals in five laboratories. Genotype concordance of 99.40% to 99.87% within a laboratory for the sample platform, 98.59% to 99.86% across laboratories for the same platform, and 98.80% across genotyping platforms was observed. Moreover, arrays with low quality data were detected when comparing genotyping data from technical replicates, but they could not be detected according to venders' quality control (QC) suggestions. Our results demonstrated the technical reliability of currently available genotyping platforms but also indicated the importance of incorporating some technical replicates for genotyping QC in order to improve the reliability of GWAS results. The impact of discordant genotypes on association analysis results was simulated and could explain, at least in part, the irreproducibility of some GWAS findings when the effect size (i.e. the odds ratio) and the minor allele frequencies are low.

Age of onset and effect size in genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) have identified many susceptibility loci for complex traits, but have not identified the majority of the genetic contribution to common diseases. We explored whether the magnitude of associations detected in GWAS and, therefore, the likelihood of detecting a significant association for a given sample size, is generally greater for childhood-onset traits (e.g., birth defects) than for traits with onset in adulthood.

METHODS

Data were obtained from the National Human Genome Research Institute Catalog of Published GWAS. Traits were categorized as having an average age of onset in childhood (<18 years, n = 15 traits), early adulthood (18-54 years, n = 32 traits), or late adulthood (≥55 years, n = 31 traits). The relationship between age of onset category and the magnitude of significant associations detected by GWAS was assessed using logistic regression.

RESULTS

Associations characterized by an odds ratio (OR) ≥ 1.5 were significantly more common for GWAS of childhood traits than for late adulthood-onset traits after adjustment for several covariates (adjusted OR, 2.55; 95% confidence interval, 1.37-4.73). Results in subgroup analyses using more stringent inclusion criteria (based on sample size, effect size, p value threshold for inclusion, and novel variant-trait associations) were similar.

CONCLUSIONS

These findings suggest that, on average, marker-trait associations detected in GWAS for traits with young onset may have a larger magnitude of effect than those for traits with adult onset. Therefore, GWAS for young-onset traits, such as birth defects, may be more likely than those for adult-onset traits to identify major genetic risk factors. Birth Defects Research (Part A) 2012. © 2012 Wiley Periodicals, Inc.

Cyclin D3 coordinates the cell cycle during differentiation to regulate erythrocyte size and number

Genome-wide association studies (GWASs) have identified a genetic variant of moderate effect size at 6p21.1 associated with erythrocyte traits in humans. We show that this variant affects an erythroid-specific enhancer of CCND3. A Ccnd3 knockout mouse phenocopies these erythroid phenotypes, with a dramatic increase in erythrocyte size and a concomitant decrease in erythrocyte number. By examining human and mouse primary erythroid cells, we demonstrate that the CCND3 gene product cyclin D3 regulates the number of cell divisions that erythroid precursors undergo during terminal differentiation, thereby controlling erythrocyte size and number. We illustrate how cell type-specific specialization can occur for general cell cycle components-a finding resulting from the biological follow-up of unbiased human genetic studies.

Complement factor H genotypes impact risk of age-related macular degeneration by interaction with oxidized phospholipids

The rs1061170T/C variant encoding the Y402H change in complement factor H (CFH) has been identified by genome-wide association studies as being significantly associated with age-related macular degeneration (AMD). However, the precise mechanism by which this CFH variant impacts the risk of AMD remains largely unknown. Oxidative stress plays an important role in many aging diseases, including cardiovascular disease and AMD. A large amount of oxidized phospholipids (oxPLs) are generated in the eye because of sunlight exposure and high oxygen content. OxPLs bind to the retinal pigment epithelium and macrophages and strongly activate downstream inflammatory cascades. We hypothesize that CFH may impact the risk of AMD by modulating oxidative stress. Here we demonstrate that CFH binds to oxPLs. The CFH 402Y variant of the protective rs1061170 genotype binds oxPLs with a higher affinity and exhibits a stronger inhibitory effect on the binding of oxPLs to retinal pigment epithelium and macrophages. In addition, plasma from non-AMD subjects with the protective genotype has a lower level of systemic oxidative stress measured by oxPLs per apolipoprotein B (oxPLs/apoB). We also show that oxPL stimulation increases expression of genes involved in macrophage infiltration, inflammation, and neovascularization in the eye. OxPLs colocalize with CFH in drusen in the human AMD eye. Subretinal injection of oxPLs induces choroidal neovascularization in mice. In addition, we show that the CFH risk allele confers higher complement activation and cell lysis activity. Together, these findings suggest that CFH influences AMD risk by modulating oxidative stress, inflammation, and abnormal angiogenesis.

Insights into the genetic architecture of diabetic nephropathy

Diabetic nephropathy (DN) is a devastating complication of type 1 and type 2 diabetes and leads to increased morbidity and premature mortality. Susceptibility to DN has an inherent genetic basis as evidenced by familial aggregation and ethnic-specific prevalence rates. Progress in identifying the underlying genetic architecture has been arduous with the realization that a single locus of large effect does not exist, unlike in predisposition to non-diabetic nephropathy in individuals with African ancestry. Numerous risk variants have been identified, each with a nominal effect, and they collectively contribute to disease. These results have identified loci targeting novel pathways for disease susceptibility. With continued technological advances and development of new analytic methods, additional genetic variants and mechanisms (e.g., epigenetic variation) will be identified and help to elucidate the pathogenesis of DN. These advances will lead to early detection and development of novel therapeutic strategies to decrease the incidence of disease.

Are polymorphisms in oestrogen receptors genes associated with lipid levels in response to hormone therapy?

BACKGROUND

Polymorphisms in the oestrogen receptor 1 (ESR1) and oestrogen receptor 2 (ESR2) genes are associated with intermediate or endpoint markers of cardiovascular disease and with the efficacy of postmenopausal hormone therapy (HT). Contradictory findings have been described in the past and the role of these genetics variants remains unclear.

METHODS

A cross-sectional study was carried out with 266 postmenopausal women, of whom 115 received oral HT (HT+) and 151 did not receive any HT (HT-). We analysed three single-nucleotide polymorphisms (SNPs) in ESR1 (rs1801132, rs7757956 and rs2813544) and two in ESR2 (rs3020450 and rs7154455) and derived haplotypes with three additional polymorphisms that had been previously investigated by our group (ESR1 rs2234693 and ESR2 rs1256049 and rs4986938).

RESULTS

The ESR1 rs2813544 polymorphism was associated with low-density lipoprotein cholesterol (LDL-C) in HT+ postmenopausal women (p = 0.044; pC = 0.388), while one ESR2 gene haplotype was associated with total cholesterol (T-chol) (p = 0.015; pC = 0.090) and LDL-C in HT+ postmenopausal women (p = 0.021; pC = 0.126).

CONCLUSION

Our findings suggest that, in HT+ postmenopausal women, the rs2813544 polymorphism may influence LDL-C levels and, as previously described, ESR2 rs1256049 is associated with T-chol and LDL-C. No previous study has investigated the association of this SNP set with lipoprotein levels in women while taking into account the hormonal status of the patients.

Evidence for a genetic interaction in allergy-related responsiveness to vitamin D deficiency

BACKGROUND

The hormonal form of vitamin D affects both adaptive and innate immune functions involved in the development of allergies. Certain genotypes have been seen to alter the association between vitamin D deficiency (VDD) and the risk of food sensitization in children.

METHODS

We examined 27 functional single nucleotide polymorphisms (SNPs) in/near selected candidate genes for association with total immunoglobulin E (IgE) and effect modification by 25-hydroxyvitamin D in the 1958 British birth cohort (aged 45 years, n = 4921). A cut-off value of 50 nmol/L was used to define VDD.

RESULTS

Four SNPs (in FCER1A, IL13, and CYP24A1) and three SNPs (in IL4 and CYP24A1) were associated with total IgE and specific IgE, respectively, after correction for multiple testing. As in a previous study, MS4A2 (rs512555, P(interaction) = 0.04) and IL4 (rs2243250, P(interaction) = 0.02), and their composite score (P(interaction) = 0.009) modified the association between VDD and allergy-related outcome. Vitamin D deficiency was associated with higher total IgE only in the carriers of the 'C' allele (IL4), which is present in 86% of white Europeans, while only 26% of Chinese and <20% of some African populations are carriers.

CONCLUSIONS

Our study on white European adults was consistent with a previous study on children from largely non-white ethnic groups, suggesting that IL4 and MS4A2 genotypes modify the association between VDD and allergy risk. The risk allele in IL4 is present in nearly 90% of white Europeans, while less than a quarter are carriers in some other populations, highlighting the need to consider possible ethnic differences in allergy-related responsiveness to VDD.

Utility-preserving transaction data anonymization with low information loss

Transaction data record various information about individuals, including their purchases and diagnoses, and are increasingly published to support large-scale and low-cost studies in domains such as marketing and medicine. However, the dissemination of transaction data may lead to privacy breaches, as it allows an attacker to link an individual's record to their identity. Approaches that anonymize data by eliminating certain values in an individual's record or by replacing them with more general values have been proposed recently, but they often produce data of limited usefulness. This is because these approaches adopt value transformation strategies that do not guarantee data utility in intended applications and objective measures that may lead to excessive data distortion. In this paper, we propose a novel approach for anonymizing data in a way that satisfies data publishers' utility requirements and incurs low information loss. To achieve this, we introduce an accurate information loss measure and an effective anonymization algorithm that explores a large part of the problem space. An extensive experimental study, using click-stream and medical data, demonstrates that our approach permits many times more accurate query answering than the state-of-the-art methods, while it is comparable to them in terms of efficiency.

No association between catechol-O-methyltransferase polymorphisms and neurotic disorders among mainland Chinese university students

This study investigates the genetic association between catechol-O-methyltransferase (COMT) gene polymorphisms and neurotic disorders. Data were derived from a case-control association study of 255 undergraduates affected by neurotic disorders and 269 matched healthy undergraduate controls. The polymorphisms of eight tag single nucleotide polymorphisms (SNPs) on the COMT gene were tested using polymerase chain reaction (PCR)-based Ligase Detection Reaction (PCR-LDR). The eight tag SNPs on the COMT gene assessed were not associated with neurotic disorders. Our finding suggests that the COMT gene may not be a susceptibility gene for neurotic disorders.

Inflammatory priming predisposes mice to age-related retinal degeneration

Disruption of cellular processes affected by multiple genes and accumulation of numerous insults throughout life dictate the progression of age-related disorders, but their complex etiology is poorly understood. Postmitotic neurons, such as photoreceptor cells in the retina and epithelial cells in the adjacent retinal pigmented epithelium, are especially susceptible to cellular senescence, which contributes to age-related retinal degeneration (ARD). The multigenic and complex etiology of ARD in humans is reflected by the relative paucity of effective compounds for its early prevention and treatment. To understand the genetic differences that drive ARD pathogenesis, we studied A/J mice, which develop ARD more pronounced than that in other inbred mouse models. Although our investigation of consomic strains failed to identify a chromosome associated with the observed retinal deterioration, pathway analysis of RNA-Seq data from young mice prior to retinal pathological changes revealed that increased vulnerability to ARD in A/J mice was due to initially high levels of inflammatory factors and low levels of homeostatic neuroprotective factors. The genetic signatures of an uncompensated preinflammatory state and ARD progression identified here aid in understanding the susceptible genetic loci that underlie pathogenic mechanisms of age-associated disorders, including several human blinding diseases.

Sample size and statistical power calculation in genetic association studies

A sample size with sufficient statistical power is critical to the success of genetic association studies to detect causal genes of human complex diseases. Genome-wide association studies require much larger sample sizes to achieve an adequate statistical power. We estimated the statistical power with increasing numbers of markers analyzed and compared the sample sizes that were required in case-control studies and case-parent studies. We computed the effective sample size and statistical power using Genetic Power Calculator. An analysis using a larger number of markers requires a larger sample size. Testing a single-nucleotide polymorphism (SNP) marker requires 248 cases, while testing 500,000 SNPs and 1 million markers requires 1,206 cases and 1,255 cases, respectively, under the assumption of an odds ratio of 2, 5% disease prevalence, 5% minor allele frequency, complete linkage disequilibrium (LD), 1:1 case/control ratio, and a 5% error rate in an allelic test. Under a dominant model, a smaller sample size is required to achieve 80% power than other genetic models. We found that a much lower sample size was required with a strong effect size, common SNP, and increased LD. In addition, studying a common disease in a case-control study of a 1:4 case-control ratio is one way to achieve higher statistical power. We also found that case-parent studies require more samples than case-control studies. Although we have not covered all plausible cases in study design, the estimates of sample size and statistical power computed under various assumptions in this study may be useful to determine the sample size in designing a population-based genetic association study.

Rare genetic variants and treatment response: sample size and analysis issues

Incorporating information about common genetic variants may help improve the design and analysis of clinical trials. For example, if genes impact response to treatment, one can pregenotype potential participants to screen out genetically determined nonresponders and substantially reduce the sample size and duration of a trial. Genetic associations with response to treatment are generally much larger than those observed for development of common diseases, as highlighted here by findings from genome-wide association studies. With the development and decreasing cost of next generation sequencing, more extensive genetic information - including rare variants - is becoming available on individuals treated with drugs and other therapies. We can use this information to evaluate whether rare variants impact treatment response. The sparseness of rare variants, however, raises issues of how the resulting data should be best analyzed. As shown here, simply evaluating the association between each rare variant and treatment response one-at-a-time will require enormous sample sizes. Combining the rare variants together can substantially reduce the required sample sizes, but require a number of assumptions about the similarity among the rare variants' effects on treatment response. We have developed an empirical approach for aggregating and analyzing rare variants that limit such assumptions and work well under a range of scenarios. Such analyses provide a valuable opportunity to more fully decipher the genomic basis of response to treatment.

Human longevity and common variations in the LMNA gene: a meta-analysis

A mutation in the LMNA gene is responsible for the most dramatic form of premature aging, Hutchinson-Gilford progeria syndrome (HGPS). Several recent studies have suggested that protein products of this gene might have a role in normal physiological cellular senescence. To explore further LMNA's possible role in normal aging, we genotyped 16 SNPs over a span of 75.4 kb of the LMNA gene on a sample of long-lived individuals (LLI) (US Caucasians with age ≥ 95 years, N=873) and genetically matched younger controls (N=443). We tested all common nonredundant haplotypes (frequency ≥ 0.05) based on subgroups of these 16 SNPs for association with longevity. The most significant haplotype, based on four SNPs, remained significant after adjustment for multiple testing (OR=1.56, P=2.5 × 10(-5) , multiple-testing-adjusted P=0.0045). To attempt to replicate these results, we genotyped 3619 subjects from four independent samples of LLI and control subjects from (i) the New England Centenarian Study (NECS) (N=738), (ii) the Southern Italian Centenarian Study (SICS) (N=905), (iii) France (N=1103), and (iv) the Einstein Ashkenazi Longevity Study (N= 702). We replicated the association with the most significant haplotype from our initial analysis in the NECS sample (OR=1.60, P=0.0023), but not in the other three samples (P > 0.15). In a meta-analysis combining all five samples, the best haplotype remained significantly associated with longevity after adjustment for multiple testing in the initial and follow-up samples (OR=1.18, P=7.5 × 10(-4) , multiple-testing-adjusted P=0.037). These results suggest that LMNA variants may play a role in human lifespan.

Early determinants of obesity: genetic, epigenetic, and in utero influences

There is an emerging body of work indicating that genes, epigenetics, and the in utero environment can impact whether or not a child is obese. While certain genes have been identified that increase one's risk for becoming obese, other factors such as excess gestational weight gain, gestational diabetes mellitus, and smoking can also influence this risk. Understanding these influences can help to inform which behaviors and exposures should be targeted if we are to decrease the prevalence of obesity. By helping parents and young children change certain behaviors and exposures during critical time periods, we may be able to alter or modify one's genetic predisposition. However, further research is needed to determine which efforts are effective at decreasing the incidence of obesity and to develop new methods of prevention. In this paper, we will discuss how genes, epigenetics, and in utero influences affect the development of obesity. We will then discuss current efforts to alter these influences and suggest future directions for this work.

Meta-analysis of genetic association studies under heterogeneity

In multi-cohort genetic association studies or meta-analysis, associations of genetic variants with complex traits across cohorts may be heterogeneous because of genuine genetic diversity or differential biases or errors. To detect the associations of genes with heterogeneous associations across cohorts, new global fixed-effect (FE) and random-effects (RE) meta-analytic methods have been recently proposed. These global methods had improved power over both traditional FE and RE methods under heterogeneity in limited simulation scenarios and data application, but their usefulness in a wide range of practical situations is not clear. We assessed the performance of these methods for both binary and quantitative traits in extensive simulations and applied them to a multi-cohort association study. We found that these new approaches have higher power to detect mostly the very small to small associations of common genetic variants when associations are highly heterogeneous across cohorts. They worked well when both the underlying and assumed genetic models are either multiplicative or dominant. But, they offered no clear advantage for less common variants unless heterogeneity was substantial. In conclusion, these new meta-analytic methods can be used to detect the association of genetic variants with high heterogeneity, which can then be subjected to further exploration, in multi-cohort association studies and meta-analyses.

Fixing the broken system of genetic locus symbols: Parkinson disease and dystonia as examples

Originally, locus symbols (e.g., DYT1) were introduced to specify chromosomal regions that had been linked to a familial disorder with a yet unknown gene. Symbols were systematically assigned in a numerical series to designate mapped loci for a specific phenotype or group of phenotypes. Since the system of designating and using locus symbols was originally established, both our knowledge and our techniques of gene discovery have evolved substantially. The current system has problems that are sources of confusion, perpetuate misinformation, and misrepresent the system as a useful reference tool for a list of inherited disorders of a particular phenotypic class. These include erroneously assigned loci, duplicated loci, missing symbols, missing loci, unconfirmed loci in a consecutively numbered system, combining causative genes and risk factor genes in the same list, and discordance between phenotype and list assignment. In this article, we describe these problems and their impact, and propose solutions. The system could be significantly improved by creating distinct lists for clinical and research purposes, creating more informative locus symbols, distinguishing disease-causing mutations from risk factors, raising the threshold of evidence prior to assigning a locus symbol, paying strict attention to the predominant phenotype when assigning symbols lists, and having a formal system for reviewing and continually revising the list that includes input from both clinical and genetics experts.

New routes for transgenesis of the mouse

Transgenesis refers to the molecular genetic techniques for directing specific insertions, deletions and point mutations in the genome of germ cells in order to create genetically modified organisms (GMO). Genetic modification is becoming more practicable, efficient and predictable with the development and use of a variety of cell and molecular biology tools and DNA sequencing technologies. A collection of plasmidial and viral vectors, cell-type specific promoters, positive and negative selectable markers, reporter genes, drug-inducible Cre-loxP and Flp/FRT recombinase systems are available which ensure efficient transgenesis in the mouse. The technologies for the insertion and removal of genes by homologous-directed recombination in embryonic stem cells (ES) and generation of targeted gain- and loss-of function alleles have allowed the creation of thousands of mouse models of a variety of diseases. The engineered zinc finger nucleases (ZFNs) and small hairpin RNA-expressing constructs are novel tools with useful properties for gene knockout free of ES manipulation. In this review we briefly outline the different approaches and technologies for transgenesis as well as their advantages and disadvantages. We also present an overview on how the novel integrative mouse and human genomic databases and bioinformatics approaches have been used to understand genotype-phenotype relationships of hundreds of mutated and candidate disease genes in mouse models. The updating and continued improvements of the genomic technologies will eventually help us to unraveling the biological and pathological processes in such a way that they can be translated more efficiently from mouse to human and vise-versa.

Human genome-wide association and mouse knockout approaches identify platelet supervillin as an inhibitor of thrombus formation under shear stress

BACKGROUND

High shear force critically regulates platelet adhesion and thrombus formation during ischemic vascular events. To identify genetic factors that influence platelet thrombus formation under high shear stress, we performed a genome-wide association study and confirmatory experiments in human and animal platelets.

METHODS AND RESULTS

Closure times in the shear-dependent platelet function analyzer (PFA)-100 were measured on healthy, nondiabetic European Americans (n=125) and blacks (n=116). A genome-wide association (P<5×10(-8)) was identified with 2 single-nucleotide polymorphisms within the SVIL gene (chromosome 10p11.23) in African Americans but not European Americans. Microarray analyses of human platelet RNA demonstrated the presence of SVIL isoform 1 (supervillin) but not muscle-specific isoforms 2 and 3 (archvillin, SmAV). SVIL mRNA levels were associated with SVIL genotypes (P≤0.02) and were inversely correlated with PFA-100 closure times (P<0.04) and platelet volume (P<0.02). Leukocyte-depleted platelets contained abundant levels of the ≈205-kDa supervillin polypeptide. To assess functionality, mice lacking platelet supervillin were generated and back-crossed onto a C57BL/6 background. Compared with controls, murine platelets lacking supervillin were larger by flow cytometry and confocal microscopy and exhibited enhanced platelet thrombus formation under high-shear but not low-shear conditions.

CONCLUSIONS

We show for the first time that (1) platelets contain supervillin; (2) platelet thrombus formation in the PFA-100 is associated with human SVIL variants and low SVIL expression; and (3) murine platelets lacking supervillin exhibit enhanced platelet thrombus formation at high shear stress. These data are consistent with an inhibitory role for supervillin in platelet adhesion and arterial thrombosis.

Data sharing in neuroimaging research

Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging.