Analysis of genome-wide association study data using the protein knowledge base

Background

Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.

Results

Here we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.

Conclusions

Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.     

Selecting SNPs for association studies based on population frequencies: a novel interactive tool and its application to polygenic diseases

Common complex polygenic diseases as autoimmune diseases have not been completely understood on a molecular level. While many genes are known to be involved in the pathways responsible for the phenotype, explicit causes for the susceptibility of the disease remain to be elucidated. The susceptibility to disease is thought to be the result of genetic epistatic interactions between common polymorphic genes. This polymorphism is mostly caused by single nucleotide polymorphisms (SNPs). Human subpopulations are known to differ in the susceptibility to the diseases and generally in the distribution of single nucleotide polymorphisms. The here presented approach retrieves SNPs with the most divergent frequencies for selected human subpopulations to help defining properties for the experimental verification of SNPs within defined regions. A web-accessible program implementing this approach was evaluated for multiple sclerosis (MS), a common human polygenic disease. A link to a summary of data from "The SNP Consortium" (TSC) with sex-dependencies of SNPs is available. Associations of SNPs to genes, genetic markers and chromosomal loci are retrieved from the Ensembl project. This tool is recommended to be used in conjunction with microarray analyses or marker association studies that link genes or chromosomal loci to particular diseases.     

Network Analysis of Differential Expression for the Identification of Disease-Causing Genes

Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.     

Gene prioritization through genomic data fusion

The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.     

Genome-wide multilocus analysis for immune-mediated complex diseases

In postgenomic era, searching and identification of disease genes associated with complex diseases are still one of the great challenge for dissecting human complex diseases. To improve the disease gene localization for complex diseases, a group of closely immune-mediated disease loci were overlapped on each chromosome based on previously reported genome-wide scanning data. Interestingly, five overlapping chromosomal regions (1q21, 2q33, 5q31.1-q33.1, 6p21, and 11q13) were identified by co-localizing disease loci for the following diseases: diabetes, asthma, atopic dermatitis, osteoporosis, and inflammatory bowel disease. The development of specific disease was associated with different combinations of disease loci among five overlapped chromosomal regions. Therefore, the analysis of multiple genetic loci should be considered to determine the effects of multiple genes responsible for complex diseases resulting from the influence of multiple genes.     

Common denominator procedure: a novel approach to gene-expression data mining for identification of phenotype-specific genes

MOTIVATION: We have established a novel data mining procedure for the identification of genes associated with pre-defined phenotypes and/or molecular pathways. Based on the observation that these genes are frequently expressed in the same place or in close proximity at about the same time, we have devised an approach termed Common Denominator Procedure. One unusual feature of this approach is that the specificity and probability to identify genes linked to the desired phenotype/pathway increase with greater diversity of the input data. RESULT: To show the feasibility of our approach, the Cancer Genome Anatomy Project expression data combined with a defined set of angiogenic factors was used to identify additional and novel angiogenesis-associated genes. A multitude of these additional genes were known to be associated with angiogenesis according to published data, verifying our approach. For some of the remaining candidate genes, application of a high-throughput functional genomics platform (XantoScreen) provided further experimental evidence for association with angiogenesis.     

Prediction of human disease genes by human-mouse conserved coexpression analysis

Background

Even in the post-genomic era, the identification of candidate genes within loci associated with human genetic diseases is a very demanding task, because the critical region may typically contain hundreds of positional candidates. Since genes implicated in similar phenotypes tend to share very similar expression profiles, high throughput gene expression data may represent a very important resource to identify the best candidates for sequencing. However, so far, gene coexpression has not been used very successfully to prioritize positional candidates.

Methodology/Principal Findings

We show that it is possible to reliably identify disease-relevant relationships among genes from massive microarray datasets by concentrating only on genes sharing similar expression profiles in both human and mouse. Moreover, we show systematically that the integration of human-mouse conserved coexpression with a phenotype similarity map allows the efficient identification of disease genes in large genomic regions. Finally, using this approach on 850 OMIM loci characterized by an unknown molecular basis, we propose high-probability candidates for 81 genetic diseases.

Conclusion

Our results demonstrate that conserved coexpression, even at the human-mouse phylogenetic distance, represents a very strong criterion to predict disease-relevant relationships among human genes.     

Using zebrafish to study the complex genetics of glaucoma

The overall goal of this review is to highlight the power of zebrafish as a model system for studying complex diseases which involve multiple genetic loci. We are interested in identifying and characterizing genes implicated in the blinding condition of glaucoma. Glaucoma is a complex disease that often involves multiple genetic loci. Most disease causing and modifying genes for glaucoma remain unidentified. However, several genes that regulate various aspects of ocular development have been shown to associate with glaucoma. With zebrafish, forward and reverse genetic approaches can be combined in order to identify critical genetic interactions required for normal and pathological events in the development and maintenance of the eye.     

Genetic relatedness of the Streptococcus pneumoniae capsular biosynthetic loci

Streptococcus pneumoniae (the pneumococcus) produces 1 of 91 capsular polysaccharides (CPS) that define the serotype. The cps loci of 88 pneumococcal serotypes whose CPS is synthesized by the Wzy-dependent pathway were compared with each other and with additional streptococcal polysaccharide biosynthetic loci and were clustered according to the proportion of shared homology groups (HGs), weighted for the sequence similarities between the genes encoding the shared HGs. The cps loci of the 88 pneumococcal serotypes were distributed into eight major clusters and 21 subclusters. All serotypes within the same serogroup fell into the same major cluster, but in six cases, serotypes within the same serogroup were in different subclusters and, conversely, nine subclusters included completely different serotypes. The closely related cps loci within a subcluster were compared to the known CPS structures to relate gene content to structure. The Streptococcus oralis and Streptococcus mitis polysaccharide biosynthetic loci clustered within the pneumococcal cps loci and were in a subcluster that also included the cps locus of pneumococcal serotype 21, whereas the Streptococcus agalactiae cps loci formed a single cluster that was not closely related to any of the pneumococcal cps clusters.     

A critical analysis of the combined usage of protein localization prediction methods: Increasing the number of independent data sets can reduce the accuracy of predicted mitochondrial localization

In the absence of a comprehensive experimentally derived mitochondrial proteome, several bioinformatic approaches have been developed to aid the identification of novel mitochondrial disease genes within mapped nuclear genetic loci. Often, many classifiers are combined to increase the sensitivity and specificity of the predictions. Here we show that the greatest sensitivity and specificity are obtained by using a combination of seven carefully selected classifiers. We also show that increasing the number of independent prediction methods can paradoxically decrease the accuracy of predicting mitochondrial localization. This approach will help to accelerate the identification of new mitochondrial disease genes by providing a principled way for the selection for combination of appropriate prediction methods of mitochondrial localization of proteins.     

牛疾病相关基因的DNA变异及遗传控制

牛基因组中一些重要基因的DNA突变通过改变基因的表达和蛋白质功能来影响机体对疾病的抗性或易感性。控制牛疾病的DNA变异主要分为单基因座及多基因座两类。导致疾病的单基因座类型亦称因果突变,其遗传基础较简单,突变一般位于基因编码区或非编码区,多为单碱基或少数几个碱基的突变,这些突变导致氨基酸的错义突变、翻译提前终止或部分外显子缺失等。相比而言,多基因相关疾病的遗传基础较为复杂,遗传-病原体-环境间的互作是导致这类复杂疾病的主要原因。文章综述了由单基因座和多基因座遗传变异所控制的牛主要疾病的研究和应用现状,以及在牛育种及生产中为降低这些疾病的发生所采用的遗传控制策略。     

Multifactor-dimensionality reduction versus family-based association tests in detecting susceptibility loci in discordant sib-pair studies

Complex diseases are generally thought to be under the influence of multiple, and possibly interacting, genes. Many association methods have been developed to identify susceptibility genes assuming a single-gene disease model, referred to as single-locus methods. Multilocus methods consider joint effects of multiple genes and environmental factors. One commonly used method for family-based association analysis is implemented in FBAT. The multifactor-dimensionality reduction method (MDR) is a multilocus method, which identifies multiple genetic loci associated with the occurrence of complex disease. Many studies of late onset complex diseases employ a discordant sib pairs design. We compared the FBAT and MDR in their ability to detect susceptibility loci using a discordant sib-pair dataset generated from the simulated data made available to participants in the Genetic Analysis Workshop 14. Using FBAT, we were able to identify the effect of one susceptibility locus. However, the finding was not statistically significant. We were not able to detect any of the interactions using this method. This is probably because the FBAT test is designed to find loci with major effects, not interactions. Using MDR, the best result we obtained identified two interactions. However, neither of these reached a level of statistical significance. This is mainly due to the heterogeneity of the disease trait and noise in the data.     

Neural networks for modeling gene-gene interactions in association studies

Background

The monogenic disease osteogenesis imperfecta (OI) is due to single mutations in either of the collagen genes ColA1 or ColA2, but within the same family a given mutation is accompanied by a wide range of disease severity. Although this phenotypic variability implies the existence of modifier gene variants, genome wide scanning of DNA from OI patients has not been reported. Promising genome wide marker-independent physical methods for identifying disease-related loci have lacked robustness for widespread applicability. Therefore we sought to improve these methods and demonstrate their performance to identify known and novel loci relevant to OI.

Results

We have improved methods for enriching regions of identity-by-descent (IBD) shared between related, afflicted individuals. The extent of enrichment exceeds 10- to 50-fold for some loci. The efficiency of the new process is shown by confirmation of the identification of the Col1A2 locus in osteogenesis imperfecta patients from Amish families. Moreover the analysis revealed additional candidate linkage loci that may harbour modifier genes for OI; a locus on chromosome 1q includes COX-2, a gene implicated in osteogenesis.

Conclusion

Technology for physical enrichment of IBD loci is now robust and applicable for finding genes for monogenic diseases and genes for complex diseases. The data support the further investigation of genetic loci other than collagen gene loci to identify genes affecting the clinical expression of osteogenesis imperfecta. The discrimination of IBD mapping will be enhanced when the IBD enrichment procedure is coupled with deep resequencing.     

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.     

Exploiting Protein-Protein Interaction Networks for Genome-Wide Disease-Gene Prioritization

Complex genetic disorders often involve products of multiple genes acting cooperatively. Hence, the pathophenotype is the outcome of the perturbations in the underlying pathways, where gene products cooperate through various mechanisms such as protein-protein interactions. Pinpointing the decisive elements of such disease pathways is still challenging. Over the last years, computational approaches exploiting interaction network topology have been successfully applied to prioritize individual genes involved in diseases. Although linkage intervals provide a list of disease-gene candidates, recent genome-wide studies demonstrate that genes not associated with any known linkage interval may also contribute to the disease phenotype. Network based prioritization methods help highlighting such associations. Still, there is a need for robust methods that capture the interplay among disease-associated genes mediated by the topology of the network. Here, we propose a genome-wide network-based prioritization framework named GUILD. This framework implements four network-based disease-gene prioritization algorithms. We analyze the performance of these algorithms in dozens of disease phenotypes. The algorithms in GUILD are compared to state-of-the-art network topology based algorithms for prioritization of genes. As a proof of principle, we investigate top-ranking genes in Alzheimer''s disease (AD), diabetes and AIDS using disease-gene associations from various sources. We show that GUILD is able to significantly highlight disease-gene associations that are not used a priori. Our findings suggest that GUILD helps to identify genes implicated in the pathology of human disorders independent of the loci associated with the disorders.     

Integrated Enrichment Analysis of Variants and Pathways in Genome-Wide Association Studies Indicates Central Role for IL-2 Signaling Genes in Type 1 Diabetes,and Cytokine Signaling Genes in Crohn's Disease

Pathway analyses of genome-wide association studies aggregate information over sets of related genes, such as genes in common pathways, to identify gene sets that are enriched for variants associated with disease. We develop a model-based approach to pathway analysis, and apply this approach to data from the Wellcome Trust Case Control Consortium (WTCCC) studies. Our method offers several benefits over existing approaches. First, our method not only interrogates pathways for enrichment of disease associations, but also estimates the level of enrichment, which yields a coherent way to promote variants in enriched pathways, enhancing discovery of genes underlying disease. Second, our approach allows for multiple enriched pathways, a feature that leads to novel findings in two diseases where the major histocompatibility complex (MHC) is a major determinant of disease susceptibility. Third, by modeling disease as the combined effect of multiple markers, our method automatically accounts for linkage disequilibrium among variants. Interrogation of pathways from eight pathway databases yields strong support for enriched pathways, indicating links between Crohn''s disease (CD) and cytokine-driven networks that modulate immune responses; between rheumatoid arthritis (RA) and “Measles” pathway genes involved in immune responses triggered by measles infection; and between type 1 diabetes (T1D) and IL2-mediated signaling genes. Prioritizing variants in these enriched pathways yields many additional putative disease associations compared to analyses without enrichment. For CD and RA, 7 of 8 additional non-MHC associations are corroborated by other studies, providing validation for our approach. For T1D, prioritization of IL-2 signaling genes yields strong evidence for 7 additional non-MHC candidate disease loci, as well as suggestive evidence for several more. Of the 7 strongest associations, 4 are validated by other studies, and 3 (near IL-2 signaling genes RAF1, MAPK14, and FYN) constitute novel putative T1D loci for further study.     

Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls

Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits.