Analysis of Stop-Gain and Frameshift Variants in Human Innate Immunity Genes

Loss-of-function variants in innate immunity genes are associated with Mendelian disorders in the form of primary immunodeficiencies. Recent resequencing projects report that stop-gains and frameshifts are collectively prevalent in humans and could be responsible for some of the inter-individual variability in innate immune response. Current computational approaches evaluating loss-of-function in genes carrying these variants rely on gene-level characteristics such as evolutionary conservation and functional redundancy across the genome. However, innate immunity genes represent a particular case because they are more likely to be under positive selection and duplicated. To create a ranking of severity that would be applicable to innate immunity genes we evaluated 17,764 stop-gain and 13,915 frameshift variants from the NHLBI Exome Sequencing Project and 1,000 Genomes Project. Sequence-based features such as loss of functional domains, isoform-specific truncation and nonsense-mediated decay were found to correlate with variant allele frequency and validated with gene expression data. We integrated these features in a Bayesian classification scheme and benchmarked its use in predicting pathogenic variants against Online Mendelian Inheritance in Man (OMIM) disease stop-gains and frameshifts. The classification scheme was applied in the assessment of 335 stop-gains and 236 frameshifts affecting 227 interferon-stimulated genes. The sequence-based score ranks variants in innate immunity genes according to their potential to cause disease, and complements existing gene-based pathogenicity scores. Specifically, the sequence-based score improves measurement of functional gene impairment, discriminates across different variants in a given gene and appears particularly useful for analysis of less conserved genes.     

Data integration workflow for search of disease driving genes and genetic variants

Comprehensive characterization of a gene's impact on phenotypes requires knowledge of the context of the gene. To address this issue we introduce a systematic data integration method Candidate Genes and SNPs (CANGES) that links SNP and linkage disequilibrium data to pathway- and protein-protein interaction information. It can be used as a knowledge discovery tool for the search of disease associated causative variants from genome-wide studies as well as to generate new hypotheses on synergistically functioning genes. We demonstrate the utility of CANGES by integrating pathway and protein-protein interaction data to identify putative functional variants for (i) the p53 gene and (ii) three glioblastoma multiforme (GBM) associated risk genes. For the GBM case, we further integrate the CANGES results with clinical and genome-wide data for 209 GBM patients and identify genes having effects on GBM patient survival. Our results show that selecting a focused set of genes can result in information beyond the traditional genome-wide association approaches. Taken together, holistic approach to identify possible interacting genes and SNPs with CANGES provides a means to rapidly identify networks for any set of genes and generate novel hypotheses. CANGES is available in http://csbi.ltdk.helsinki.fi/CANGES/     

PIPS: pathogenicity island prediction software

The adaptability of pathogenic bacteria to hosts is influenced by the genomic plasticity of the bacteria, which can be increased by such mechanisms as horizontal gene transfer. Pathogenicity islands play a major role in this type of gene transfer because they are large, horizontally acquired regions that harbor clusters of virulence genes that mediate the adhesion, colonization, invasion, immune system evasion, and toxigenic properties of the acceptor organism. Currently, pathogenicity islands are mainly identified in silico based on various characteristic features: (1) deviations in codon usage, G+C content or dinucleotide frequency and (2) insertion sequences and/or tRNA genetic flanking regions together with transposase coding genes. Several computational techniques for identifying pathogenicity islands exist. However, most of these techniques are only directed at the detection of horizontally transferred genes and/or the absence of certain genomic regions of the pathogenic bacterium in closely related non-pathogenic species. Here, we present a novel software suite designed for the prediction of pathogenicity islands (pathogenicity island prediction software, or PIPS). In contrast to other existing tools, our approach is capable of utilizing multiple features for pathogenicity island detection in an integrative manner. We show that PIPS provides better accuracy than other available software packages. As an example, we used PIPS to study the veterinary pathogen Corynebacterium pseudotuberculosis, in which we identified seven putative pathogenicity islands.     

Integrative Omics Analysis of Rheumatoid Arthritis Identifies Non-Obvious Therapeutic Targets

Identifying novel therapeutic targets for the treatment of disease is challenging. To this end, we developed a genome-wide approach of candidate gene prioritization. We independently collocated sets of genes that were implicated in rheumatoid arthritis (RA) pathogenicity through three genome-wide assays: (i) genome-wide association studies (GWAS), (ii) differentially expression in RA fibroblast-like synoviocytes (FLS), and (iii) differentially methylation in RA FLS. Integrated analysis of these complementary data sets identified a significant enrichment of multi-evidence genes (MEGs) within pathways relating to RA pathogenicity. One MEG is Engulfment and Cell Motility Protein-1 (ELMO1), a gene not previously considered as a therapeutic target in RA FLS. We demonstrated in RA FLS that ELMO1 is: (i) expressed, (ii) promotes cell migration and invasion, and (iii) regulates Rac1 activity. Thus, we created links between ELMO1 and RA pathogenicity, which in turn validates ELMO1 as a potential RA therapeutic target. This study illustrated the power of MEG-based approaches for therapeutic target identification.     

Experimental designs for reliable detection of linkage disequilibrium in unstructured random population association studies

A method is given for design of experiments to detect associations (linkage disequilibrium) in a random population between a marker and a quantitative trait locus (QTL), or gene, with a given strength of evidence, as defined by the Bayes factor. Using a version of the Bayes factor that can be linked to the value of an F-statistic with an existing deterministic power calculation makes it possible to rapidly evaluate a comprehensive range of scenarios, demonstrating the feasibility, or otherwise, of detecting genes of small effect. The Bayes factor is advocated for use in determining optimal strategies for selecting candidate genes for further testing or applications. The prospects for fine-scale mapping of QTL are reevaluated in this framework. We show that large sample sizes are needed to detect small-effect genes with a respectable-sized Bayes factor, and to have good power to detect a QTL allele at low frequency it is necessary to have a marker with similar allele frequency near the gene.     

Incorporating regulatory interactions into gene-set analyses for GWAS data: A controlled analysis with the MAGMA tool

To date, genome-wide association studies have identified thousands of statistically-significant associations between genetic variants, and phenotypes related to a myriad of traits and diseases. A key goal for human-genetics research is to translate these associations into functional mechanisms. Popular gene-set analysis tools, like MAGMA, map variants to genes they might affect, and then integrate genome-wide association study data (that is, variant-level associations for a phenotype) to score genes for association with a phenotype. Gene scores are subsequently used in competitive gene-set analyses to identify biological processes that are enriched for phenotype association. By default, variants are mapped to genes in their proximity. However, many variants that affect phenotypes are thought to act at regulatory elements, which can be hundreds of kilobases away from their target genes. Thus, we explored the idea of augmenting a proximity-based mapping scheme with publicly-available datasets of regulatory interactions. We used MAGMA to analyze genome-wide association study data for ten different phenotypes, and evaluated the effects of augmentation by comparing numbers, and identities, of genes and gene sets detected as statistically significant between mappings. We detected several pitfalls and confounders of such “augmented analyses”, and introduced ways to control for them. Using these controls, we demonstrated that augmentation with datasets of regulatory interactions only occasionally strengthened the enrichment for phenotype association amongst (biologically-relevant) gene sets for different phenotypes. Still, in such cases, genes and regulatory elements responsible for the improvement could be pinpointed. For instance, using brain regulatory-interactions for augmentation, we were able to implicate two acetylcholine receptor subunits involved in post-synaptic chemical transmission, namely CHRNB2 and CHRNE, in schizophrenia. Collectively, our study presents a critical approach for integrating regulatory interactions into gene-set analyses for genome-wide association study data, by introducing various controls to distinguish genuine results from spurious discoveries.     

Performance of Polygenic Scores for Predicting Phobic Anxiety

Context

Anxiety disorders are common, with a lifetime prevalence of 20% in the U.S., and are responsible for substantial burdens of disability, missed work days and health care utilization. To date, no causal genetic variants have been identified for anxiety, anxiety disorders, or related traits.

Objective

To investigate whether a phobic anxiety symptom score was associated with 3 alternative polygenic risk scores, derived from external genome-wide association studies of anxiety, an internally estimated agnostic polygenic score, or previously identified candidate genes.

Design

Longitudinal follow-up study. Using linear and logistic regression we investigated whether phobic anxiety was associated with polygenic risk scores derived from internal, leave-one out genome-wide association studies, from 31 candidate genes, and from out-of-sample genome-wide association weights previously shown to predict depression and anxiety in another cohort.

Setting and Participants

Study participants (n = 11,127) were individuals from the Nurses'' Health Study and Health Professionals Follow-up Study.

Main Outcome Measure

Anxiety symptoms were assessed via the 8-item phobic anxiety scale of the Crown Crisp Index at two time points, from which a continuous phenotype score was derived.

Results

We found no genome-wide significant associations with phobic anxiety. Phobic anxiety was also not associated with a polygenic risk score derived from the genome-wide association study beta weights using liberal p-value thresholds; with a previously published genome-wide polygenic score; or with a candidate gene risk score based on 31 genes previously hypothesized to predict anxiety.

Conclusion

There is a substantial gap between twin-study heritability estimates of anxiety disorders ranging between 20–40% and heritability explained by genome-wide association results. New approaches such as improved genome imputations, application of gene expression and biological pathways information, and incorporating social or environmental modifiers of genetic risks may be necessary to identify significant genetic predictors of anxiety.     

Genome-wide linkage of febrile seizures and epilepsy to the FEB4 locus at 5q14.3-q23.1 and no MASS1 mutation

Febrile seizures (FS) represent the most common seizure disorder in childhood and contribution of a genetic predisposition has been clearly proven. In some families FS is associated with a wide variety of afebrile seizures. Generalized epilepsy with febrile seizures plus (GEFS+) is a familial epilepsy syndrome with a spectrum of phenotypes including FS, atypical febrile seizures (FS+) and afebrile generalized and partial seizures. Mutations in the genes SCN1B, SCN1A and GABRG2 were identified in GEFS+ families. GEFS+ is genetically heterogeneous and mutations in these three genes were detected in only a minority of the families. We performed a 10 cM density genome-wide scan in a multigenerational family with febrile seizures and epilepsy and obtained a maximal multipoint LOD score of 3.12 with markers on chromosome 5q14.3-q23.1. Fine mapping and segregation analysis defined a genetic interval of ≈33 cM between D5S2103 and D5S1975. This candidate region overlapped with a previously reported locus for febrile seizures (FEB4) in the Japanese population, in which MASS1 was proposed as disease gene. Mutation analysis of the exons and exon–intron boundaries of MASS1 in our family did not reveal a disease causing mutation. Our linkage data confirm for the first time that a locus on chromosome 5q14-q23 plays a role in idiopathic epilepsies. However, our mutation data is negative and do not support a role for MASS1 suggesting that another gene within or near the FEB4 locus might exist.     

Chain functions and scoring functions in genetic networks

One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks.     

Comparing phylogeny and the predicted pathogenicity of protein variations reveals equal purifying selection across the global human mtDNA diversity

We used detailed phylogenetic trees for human mtDNA, combined with pathogenicity predictions for each amino acid change, to evaluate selection on mtDNA-encoded protein variants. Protein variants with high pathogenicity scores were significantly rarer in the older branches of the tree. Variants that have formed and survived multiple times in the human phylogenetics tree had significantly lower pathogenicity scores than those that only appear once in the tree. We compared the distribution of pathogenicity scores observed on the human phylogenetic tree to the distribution of all possible protein variations to define a measure of the effect of selection on these protein variations. The measured effect of selection increased exponentially with increasing pathogenicity score. We found no measurable difference in this measure of purifying selection in mtDNA across the global population, represented by the macrohaplogroups L, M, and N. We provide a list of all possible single amino acid variations for the human mtDNA-encoded proteins with their predicted pathogenicity scores and our measured selection effect as a tool for assessing novel protein variations that are often reported in patients with mitochondrial disease of unknown origin or for assessing somatic mutations acquired through aging or detected in tumors.     

A glutamic acid-rich protein identified in Verticillium dahliae from an insertional mutagenesis affects microsclerotial formation and pathogenicity

Verticillium dahliae Kleb. is a phytopathogenic fungus that causes wilt disease in a wide range of crops, including cotton. The life cycle of V. dahliae includes three vegetative phases: parasitic, saprophytic and dormant. The dormant microsclerotia are the primary infectious propagules, which germinate when they are stimulated by root exudates. In this study, we report the first application of Agrobacterium tumefaciens-mediated transformation (ATMT) for construction of insertional mutants from a virulent defoliating isolate of V. dahliae (V592). Changes in morphology, especially a lack of melanized microsclerotia or pigmentation traits, were observed in mutants. Together with the established laboratory unimpaired root dip-inoculation approach, we found insertional mutants to be affected in their pathogenicities in cotton. One of the genes tagged in a pathogenicity mutant encoded a glutamic acid-rich protein (VdGARP1), which shared no significant similarity to any known annotated gene. The vdgarp1 mutant showed vigorous mycelium growth with a significant delay in melanized microsclerotial formation. The expression of VdGARP1 in the wild type V529 was organ-specific and differentially regulated by different stress agencies and conditions, in addition to being stimulated by cotton root extract in liquid culture medium. Under extreme infertile nutrient conditions, VdGARP1 was not necessary for melanized microsclerotial formation. Taken together, our data suggest that VdGARP1 plays an important role in sensing infertile nutrient conditions in infected cells to promote a transfer from saprophytic to dormant microsclerotia for long-term survival. Overall, our findings indicate that insertional mutagenesis by ATMT is a valuable tool for the genome-wide analysis of gene function and identification of pathogenicity genes in this important cotton pathogen.     

The Stigma of Migraine

Background

People who have a disease often experience stigma, a socially and culturally embedded process through which individuals experience stereotyping, devaluation, and discrimination. Stigma has great impact on quality of life, behavior, and life chances. We do not know whether or not migraine is stigmatizing.

Methods

We studied 123 episodic migraine patients, 123 chronic migraine patients, and 62 epilepsy patients in a clinical setting to investigate the extent to which stigma attaches to migraine, using epilepsy as a comparison. We used the stigma scale for chronic illness, a 24-item questionnaire suitable for studying chronic neurologic diseases, and various disease impact measures.

Results

Patients with chronic migraine had higher scores (54.0±20.2) on the stigma scale for chronic illness than either episodic migraine (41.7±14.8) or epilepsy patients (44.6±16.3) (p<0.001). Subjects with migraine reported greater inability to work than epilepsy subjects. Stigma correlated most strongly with the mental component score of the short form of the medical outcomes health survey (SF-12), then with ability to work and migraine disability score for chronic and episodic migraine and the Liverpool impact on epilepsy scale for epilepsy. Analysis of covariance showed adjusted scores for the stigma scale for chronic illness were similar for chronic migraine (49.3; 95% confidence interval, 46.2 to 52.4) and epilepsy (46.5; 95% confidence interval, 41.6 to 51.6), and lower for episodic migraine (43.7; 95% confidence interval, 40.9 to 46.6). Ability to work was the strongest predictor of stigma as measured by the stigma scale for chronic illness.

Conclusion

In our model, adjusted stigma was similar for chronic migraine and epilepsy, which were greater than for episodic migraine. Stigma correlated most strongly with inability to work, and was greater for chronic migraine than epilepsy or episodic migraine because chronic migraine patients had less ability to work.     

Genome-wide microarray expression analysis of CD4+ T Cells from nonobese diabetic congenic mice identifies Cd55 (Daf1) and Acadl as candidate genes for type 1 diabetes

NOD.Idd3/5 congenic mice have insulin-dependent diabetes (Idd) regions on chromosomes 1 (Idd5) and 3 (Idd3) derived from the nondiabetic strains B10 and B6, respectively. NOD.Idd3/5 mice are almost completely protected from type 1 diabetes (T1D) but the genes within Idd3 and Idd5 responsible for the disease-altering phenotype have been only partially characterized. To test the hypothesis that candidate Idd genes can be identified by differential gene expression between activated CD4+ T cells from the diabetes-susceptible NOD strain and the diabetes-resistant NOD.Idd3/5 congenic strain, genome-wide microarray expression analysis was performed using an empirical Bayes method. Remarkably, 16 of the 20 most differentially expressed genes were located in the introgressed regions on chromosomes 1 and 3, validating our initial hypothesis. The two genes with the greatest differential RNA expression on chromosome 1 were those encoding decay-accelerating factor (DAF, also known as CD55) and acyl-coenzyme A dehydrogenase, long chain, which are located in the Idd5.4 and Idd5.3 regions, respectively. Neither gene has been implicated previously in the pathogenesis of T1D. In the case of DAF, differential expression of mRNA was extended to the protein level; NOD CD4+ T cells expressed higher levels of cell surface DAF compared with NOD.Idd3/5 CD4+ T cells following activation with anti-CD3 and -CD28. DAF up-regulation was IL-4 dependent and blocked under Th1 conditions. These results validate the approach of using congenic mice together with genome-wide analysis of tissue-specific gene expression to identify novel candidate genes in T1D.     

Hierarchical Bayes models for cDNA microarray gene expression

cDNA microarrays are used in many contexts to compare mRNA levels between samples of cells. Microarray experiments typically give us expression measurements on 1000-20 000 genes, but with few replicates for each gene. Traditional methods using means and standard deviations to detect differential expression are not satisfactory in this context. A handful of alternative statistics have been developed, including several empirical Bayes methods. In the present paper we present two full hierarchical Bayes models for detecting gene expression, of which one (D) describes our microarray data very well. We also compare the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. The proposed models are compared to existing empirical Bayes models in a simulation study and for a set of data (Yuen et al., 2002), where 27 genes have been categorized by quantitative real-time PCR. It turns out that the existing empirical Bayes methods have at least as good performance as the full Bayes ones.     

Substrate-Specific Gene Expression in Batrachochytrium dendrobatidis,the Chytrid Pathogen of Amphibians

Determining the mechanisms of host-pathogen interaction is critical for understanding and mitigating infectious disease. Mechanisms of fungal pathogenicity are of particular interest given the recent outbreaks of fungal diseases in wildlife populations. Our study focuses on Batrachochytrium dendrobatidis (Bd), the chytrid pathogen responsible for amphibian declines around the world. Previous studies have hypothesized a role for several specific families of secreted proteases as pathogenicity factors in Bd, but the expression of these genes has only been evaluated in laboratory growth conditions. Here we conduct a genome-wide study of Bd gene expression under two different nutrient conditions. We compare Bd gene expression profiles in standard laboratory growth media and in pulverized host tissue (i.e., frog skin). A large proportion of genes in the Bd genome show increased expression when grown in host tissue, indicating the importance of studying pathogens on host substrate. A number of gene classes show particularly high levels of expression in host tissue, including three families of secreted proteases (metallo-, serine- and aspartyl-proteases), adhesion genes, lipase-3 encoding genes, and a group of phylogenetically unusual crinkler-like effectors. We discuss the roles of these different genes as putative pathogenicity factors and discuss what they can teach us about Bd’s metabolic targets, host invasion, and pathogenesis.     

Evidence for linkage of adolescent-onset idiopathic generalized epilepsies to chromosome 8-and genetic heterogeneity

Several loci and candidate genes for epilepsies or epileptic syndromes map or have been suggested to map to chromosome 8. We investigated families with adolescent-onset idiopathic generalized epilepsy (IGE), for linkage to markers spanning chromosome 8. The IGEs that we studied included juvenile myoclonic epilepsy (JME), epilepsy with only generalized tonic-clonic seizures occurring either randomly during the day (random grand mal) or on awakening (awakening grand mal), and juvenile absence epilepsy (JAE). We looked for a gene common to all these IGEs, but we also investigated linkage to specific subforms of IGE. We found evidence for linkage to chromosome 8 in adolescent-onset IGE families in which JME was not present. The maximum multipoint LOD score was 3.24 when family members with IGE or generalized spike-and-waves (SW) were considered affected. The LOD score remained very similar (3.18) when clinically normal family members with SW were not considered to be affected. Families with either pure grand mal epilepsy or absence epilepsy contributed equally to the positive LOD score. The area where the LOD score reaches the maximum encompasses the location of the gene for the beta3-subunit of the nicotinic acetylcholine receptor (CHRNB3), thus making this gene a possible candidate for these specific forms of adolescent-onset IGE. The data excluded linkage of JME to this region. These results indicate genetic heterogeneity within IGE and provide no evidence, on chromosome 8, for a gene common to all IGEs.     

Discovering Functional Modules across Diverse Maize Transcriptomes Using COB,the Co-Expression Browser

Tools that provide improved ability to relate genotype to phenotype have the potential to accelerate breeding for desired traits and to improve our understanding of the molecular variants that underlie phenotypes. The availability of large-scale gene expression profiles in maize provides an opportunity to advance our understanding of complex traits in this agronomically important species. We built co-expression networks based on genome-wide expression data from a variety of maize accessions as well as an atlas of different tissues and developmental stages. We demonstrate that these networks reveal clusters of genes that are enriched for known biological function and contain extensive structure which has yet to be characterized. Furthermore, we found that co-expression networks derived from developmental or tissue atlases as compared to expression variation across diverse accessions capture unique functions. To provide convenient access to these networks, we developed a public, web-based Co-expression Browser (COB), which enables interactive queries of the genome-wide networks. We illustrate the utility of this system through two specific use cases: one in which gene-centric queries are used to provide functional context for previously characterized metabolic pathways, and a second where lists of genes produced by mapping studies are further resolved and validated using co-expression networks.