Prediction of disease and phenotype associations from genome-wide association studies

Background

Genome wide association studies (GWAS) have proven useful as a method for identifying genetic variations associated with diseases. In this study, we analyzed GWAS data for 61 diseases and phenotypes to elucidate common associations based on single nucleotide polymorphisms (SNP). The study was an expansion on a previous study on identifying disease associations via data from a single GWAS on seven diseases.

Methodology/Principal Findings

Adjustments to the originally reported study included expansion of the SNP dataset using Linkage Disequilibrium (LD) and refinement of the four levels of analysis to encompass SNP, SNP block, gene, and pathway level comparisons. A pair-wise comparison between diseases and phenotypes was performed at each level and the Jaccard similarity index was used to measure the degree of association between two diseases/phenotypes. Disease relatedness networks (DRNs) were used to visualize our results. We saw predominant relatedness between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis for the first three levels of analysis. Expected relatedness was also seen between lipid- and blood-related traits.

Conclusions/Significance

The predominant associations between Multiple Sclerosis, type 1 diabetes, and rheumatoid arthritis can be validated by clinical studies. The diseases have been proposed to share a systemic inflammation phenotype that can result in progression of additional diseases in patients with one of these three diseases. We also noticed unexpected relationships between metabolic and neurological diseases at the pathway comparison level. The less significant relationships found between diseases require a more detailed literature review to determine validity of the predictions. The results from this study serve as a first step towards a better understanding of seemingly unrelated diseases and phenotypes with similar symptoms or modes of treatment.     

Evaluating diabetes and hypertension disease causality using mouse phenotypes

Background  

Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used.     

R-Gada: a fast and flexible pipeline for copy number analysis in association studies

Background  

Genome-wide association studies (GWAS) using Copy Number Variation (CNV) are becoming a central focus of genetic research. CNVs have successfully provided target genome regions for some disease conditions where simple genetic variation (i.e., SNPs) has previously failed to provide a clear association.     

SNP-based pathway enrichment analysis for genome-wide association studies

Background  

Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.     

Copy number polymorphisms and anticancer pharmacogenomics

Background  

Recent studies have investigated the contribution of copy number variants (CNVs) to disease susceptibility in a multitude of complex disorders, including systemic lupus erythematosus, Crohn's disease, and various neurodevelopmental disorders. Relatively few CNV studies, however, have been conducted on pharmacologic phenotypes even though these structural variants are likely to play an important role. We developed a genome-wide method to identify CNVs that contribute to heterogeneity in drug response, focusing on drugs that are widely used in anticancer treatment regimens.     

Partitioning of copy-number genotypes in pedigrees

Background  

Copy number variations (CNVs) and polymorphisms (CNPs) have only recently gained the genetic community's attention. Conservative estimates have shown that CNVs and CNPs might affect more than 10% of the genome and that they may be at least as important as single nucleotide polymorphisms in assessing human variability. Widely used tools for CNP analysis have been implemented in Birdsuite and PLINK for the purpose of conducting genetic association studies based on the unpartitioned total number of CNP copies provided by the intensities from Affymetrix's Genome-Wide Human SNP Array. Here, we are interested in partitioning copy number variations and polymorphisms in extended pedigrees for the purpose of linkage analysis on familial data.     

A study of CNVs as trait-associated polymorphisms and as expression quantitative trait loci

We conducted a comprehensive study of copy number variants (CNVs) well-tagged by SNPs (r(2)≥ 0.8) by analyzing their effect on gene expression and their association with disease susceptibility and other complex human traits. We tested whether these CNVs were more likely to be functional than frequency-matched SNPs as trait-associated loci or as expression quantitative trait loci (eQTLs) influencing phenotype by altering gene regulation. Our study found that CNV-tagging SNPs are significantly enriched for cis eQTLs; furthermore, we observed that trait associations from the NHGRI catalog show an overrepresentation of SNPs tagging CNVs relative to frequency-matched SNPs. We found that these SNPs tagging CNVs are more likely to affect multiple expression traits than frequency-matched variants. Given these findings on the functional relevance of CNVs, we created an online resource of expression-associated CNVs (eCNVs) using the most comprehensive population-based map of CNVs to inform future studies of complex traits. Although previous studies of common CNVs that can be typed on existing platforms and/or interrogated by SNPs in genome-wide association studies concluded that such CNVs appear unlikely to have a major role in the genetic basis of several complex diseases examined, our findings indicate that it would be premature to dismiss the possibility that even common CNVs may contribute to complex phenotypes and at least some common diseases.     

A genome-wide association study on copy-number variation identifies a 11q11 loss as a candidate susceptibility variant for colorectal cancer

Colorectal cancer (CRC) is a complex disease, and therefore its development is determined by the combination of both environmental factors and genetic variants. Although genome-wide association studies (GWAS) of SNP variation have conveniently identified 20 genetic variants so far, a significant proportion of the observed heritability is yet to be explained. Common copy-number variants (CNVs) are one of the most important genomic sources of variability, and hence a potential source to explain part of this missing genetic fraction. Therefore, we have performed a GWAS on CNVs to explore the relationship between common structural variation and CRC development. Phase 1 of the GWAS consisted of 881 cases and 667 controls from a Spanish cohort. Copy-number status was validated by quantitative PCR for each of those common CNVs potentially associated with CRC in phase I. Subsequently, SNPs were chosen as proxies for the validated CNVs for phase II replication (1,342 Spanish cases and 1,874 Spanish controls). Four common CNVs were found to be associated with CRC and were further replicated in Phase II. Finally, we found that SNP rs1944682, tagging a 11q11 CNV, was nominally associated with CRC susceptibility (p value = 0.039; OR = 1.122). This locus has been previously related to extreme obesity phenotypes, which could suggest a relationship between body weight and CRC susceptibility.     

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.     

Causal graph-based analysis of genome-wide association data in rheumatoid arthritis

Background

GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.

Results

Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.

Conclusions

Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.

Reviewers

This article was reviewed by Prof. Anthony Almudevar, Dr. Eugene V. Koonin, and Prof. Marianthi Markatou.     

Pathway-based analysis using reduced gene subsets in genome-wide association studies

Background  

Single Nucleotide Polymorphism (SNP) analysis only captures a small proportion of associated genetic variants in Genome-Wide Association Studies (GWAS) partly due to small marginal effects. Pathway level analysis incorporating prior biological information offers another way to analyze GWAS's of complex diseases, and promises to reveal the mechanisms leading to complex diseases. Biologically defined pathways are typically comprised of numerous genes. If only a subset of genes in the pathways is associated with disease then a joint analysis including all individual genes would result in a loss of power. To address this issue, we propose a pathway-based method that allows us to test for joint effects by using a pre-selected gene subset. In the proposed approach, each gene is considered as the basic unit, which reduces the number of genetic variants considered and hence reduces the degrees of freedom in the joint analysis. The proposed approach also can be used to investigate the joint effect of several genes in a candidate gene study.     

High resolution discovery and confirmation of copy number variants in 90 Yoruba Nigerians

Background  

Copy number variants (CNVs) account for a large proportion of genetic variation in the genome. The initial discoveries of long (> 100 kb) CNVs in normal healthy individuals were made on BAC arrays and low resolution oligonucleotide arrays. Subsequent studies that used higher resolution microarrays and SNP genotyping arrays detected the presence of large numbers of CNVs that are < 100 kb, with median lengths of approximately 10 kb. More recently, whole genome sequencing of individuals has revealed an abundance of shorter CNVs with lengths < 1 kb.     

Global copy number analyses by next generation sequencing provide insight into pig genome variation

Background

Copy number variations (CNVs) confer significant effects on genetic innovation and phenotypic variation. Previous CNV studies in swine seldom focused on in-depth characterization of global CNVs.

Results

Using whole-genome assembly comparison (WGAC) and whole-genome shotgun sequence detection (WSSD) approaches by next generation sequencing (NGS), we probed formation signatures of both segmental duplications (SDs) and individualized CNVs in an integrated fashion, building the finest resolution CNV and SD maps of pigs so far. We obtained copy number estimates of all protein-coding genes with copy number variation carried by individuals, and further confirmed two genes with high copy numbers in Meishan pigs through an enlarged population. We determined genome-wide CNV hotspots, which were significantly enriched in SD regions, suggesting evolution of CNV hotspots may be affected by ancestral SDs. Through systematically enrichment analyses based on simulations and bioinformatics analyses, we revealed CNV-related genes undergo a different selective constraint from those CNV-unrelated regions, and CNVs may be associated with or affect pig health and production performance under recent selection.

Conclusions

Our studies lay out one way for characterization of CNVs in the pig genome, provide insight into the pig genome variation and prompt CNV mechanisms studies when using pigs as biomedical models for human diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-593) contains supplementary material, which is available to authorized users.     

DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases

Background  

Domains are basic units of proteins, and thus exploring associations between protein domains and human inherited diseases will greatly improve our understanding of the pathogenesis of human complex diseases and further benefit the medical prevention, diagnosis and treatment of these diseases. Within a given domain-domain interaction network, we make the assumption that similarities of disease phenotypes can be explained using proximities of domains associated with such diseases. Based on this assumption, we propose a Bayesian regression approach named "domainRBF" (domain Rank with Bayes Factor) to prioritize candidate domains for human complex diseases.     

Tools for efficient epistasis detection in genome-wide association study

Background  

Genome-wide association study (GWAS) aims to find genetic factors underlying complex phenotypic traits, for which epistasis or gene-gene interaction detection is often preferred over single-locus approach. However, the computational burden has been a major hurdle to apply epistasis test in the genome-wide scale due to a large number of single nucleotide polymorphism (SNP) pairs to be tested.     

Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.     

CNstream: A method for the identification and genotyping of copy number polymorphisms using Illumina microarrays

Background  

Understanding the genetic basis of disease risk in depth requires an exhaustive knowledge of the types of genetic variation. Very recently, Copy Number Variants (CNVs) have received much attention because of their potential implication in common disease susceptibility. Copy Number Polymorphisms (CNPs) are of interest as they segregate at an appreciable frequency in the general population (i.e. > 1%) and are potentially implicated in the genetic basis of common diseases.     

SCtool:识别复杂性状和疾病间遗传关联的工具

识别复杂性状和疾病间遗传关联可以提供有用的病因学见解,并有助于确定可能的因果关系的优先级。尽管已有很多工具可以实现复杂性状和疾病间遗传关联,但是某些工具代码可读性差、并且不同工具基于不同的计算机语言、工具间的串联性较差。因此,本研究基于全基因组关联研究(GWAS)数据,提出了SCtool,一个开源、跨平台和用户友好的软件工具。SCtool整合了ldsc, TwosampleMR和MR-BMA三种软件,其主要功能是基于GWAS汇总水平的数据,识别复杂性状和疾病、复杂性状和复杂性状以及疾病与疾病间的遗传相关性并探究其间潜在的因果关联。最后,使用SCtool揭示了全身性铁状态(铁蛋白,血清铁,转铁蛋白,转铁蛋白饱和度)与表观遗传时钟GrimAge之间的遗传关联。