A framework for pathway knowledge driven prioritization in genome-wide association studies.
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ABSTRACT:
SUBMITTER: Biswas S
PROVIDER: S-EPMC7116354 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. CONCLUSIONS/SIGNIFICANCE: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases.
| S-EPMC3013112 | biostudies-literature
Project description:Recently, Steen et al proposed a novel two-stage approach for family-based genome-wide association studies. In the first stage, a test based on between-family information is used to rank SNPs according to their P-values or conditional power of the test. In the second stage, the R most promising SNPs are tested using a family-based association test. We call this two-stage approach top R method. Ionita-Laza et al proposed an exponential weighting method within a two-stage framework. In the second stage of this approach, instead of testing top R SNPs, it tests all SNPs and weights the P-values of association test according to the information of the first stage. However, both of the top R and exponential weighting methods only use the information from the first stage to rank SNPs. It seems that the two methods do not use information from the first stage efficiently. Furthermore, it may be unreasonable for the exponential weighting method to use the same weight for all SNPs within a group when only one or a few SNPs are related with a disease. In this article, we propose a data-driven weighting scheme within a two-stage framework. In this method, we use the information from the first stage to determine a SNP-specific weight for each SNP. We use simulation studies to evaluate the performance of our method. The simulation results showed that our proposed method is consistently more powerful than the top R method and the exponential weighting method, regardless of the LD structure, population structure, and family structure.
| S-EPMC2858789 | biostudies-other
Project description:Although Genome Wide Association Studies (GWAS) have identified many susceptibility loci for common diseases, they only explain a small portion of heritability. It is challenging to identify the remaining disease loci because their association signals are likely weak and difficult to identify among millions of candidates. One potentially useful direction to increase statistical power is to incorporate functional genomics information, especially gene expression networks, to prioritize GWAS signals. Most current methods utilizing network information to prioritize disease genes are based on the 'guilt by association' principle, in which networks are treated as static, and disease-associated genes are assumed to locate closer with each other than random pairs in the network. In contrast, we propose a novel 'guilt by rewiring' principle. Studying the dynamics of gene networks between controls and patients, this principle assumes that disease genes more likely undergo rewiring in patients, whereas most of the network remains unaffected in disease condition. To demonstrate this principle, we consider the changes of co-expression networks in Crohn's disease patients and controls, and how network dynamics reveals information on disease associations. Our results demonstrate that network rewiring is abundant in the immune system, and disease-associated genes are more likely to be rewired in patients. To integrate this network rewiring feature and GWAS signals, we propose to use the Markov random field framework to integrate network information to prioritize genes. Applications in Crohn's disease and Parkinson's disease show that this framework leads to more replicable results, and implicates potentially disease-associated pathways.
| S-EPMC3990172 | biostudies-literature
Project description:BackgroundGenome-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).ResultsIn 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.ConclusioniLOCi 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.
| S-EPMC3521387 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have emerged as a powerful tool to identify loci that affect drug response or susceptibility to adverse drug reactions. However, current GWAS based on a simple analysis of associations between genotype and phenotype ignores the biochemical reactions of drug response, thus limiting the scope of inference about its genetic architecture. To facilitate the inference of GWAS in pharmacogenomics, we sought to undertake the mathematical integration of the pharmacodynamic (PD) process of drug reactions through computational models. By estimating and testing the genetic control of PD and pharmacokinetic (PK) parameters, this mechanistic approach does not only enhance the biological and clinical relevance of significant genetic associations, but also improve the statistical power and robustness of gene detection. This report discusses the general principle and development of PDs-based GWAS, highlights the practical use of this approach in addressing various pharmacogenomic problems, and suggests that this approach will be an important method to study the genetic architecture of drug responses or reactions.
| S-EPMC4029408 | biostudies-literature
Project description:Methods to impute missing data are routinely used to increase power in genome-wide association studies. There are two broad classes of imputation methods. The first class imputes genotypes at the untyped variants, given those at the typed variants, and then performs a statistical test of association at the imputed variants. The second class, summary statistic imputation (SSI), directly imputes association statistics at the untyped variants, given the association statistics observed at the typed variants. The second class is appealing as it tends to be computationally efficient while only requiring the summary statistics from a study, while the former class requires access to individual-level data that can be difficult to obtain. The statistical properties of these two classes of imputation methods have not been fully understood. In this study, we show that the two classes of imputation methods yield association statistics with similar distributions for sufficiently large sample sizes. Using this relationship, we can understand the effect of the imputation method on power. We show that a commonly used approach to SSI that we term SSI with variance reweighting generally leads to a loss in power. On the contrary, our proposed method for SSI that does not perform variance reweighting fully accounts for imputation uncertainty, while achieving better power.
| S-EPMC7081249 | biostudies-literature
Project description:For analyzing complex trait association with sequencing data, most current studies test aggregated effects of variants in a gene or genomic region. Although gene-based tests have insufficient power even for moderately sized samples, pathway-based analyses combine information across multiple genes in biological pathways and may offer additional insight. However, most existing pathway association methods are originally designed for genome-wide association studies, and are not comprehensively evaluated for sequencing data. Moreover, region-based rare variant association methods, although potentially applicable to pathway-based analysis by extending their region definition to gene sets, have never been rigorously tested. In the context of exome-based studies, we use simulated and real datasets to evaluate pathway-based association tests. Our simulation strategy adopts a genome-wide genetic model that distributes total genetic effects hierarchically into pathways, genes, and individual variants, allowing the evaluation of pathway-based methods with realistic quantifiable assumptions on the underlying genetic architectures. The results show that, although no single pathway-based association method offers superior performance in all simulated scenarios, a modification of Gene Set Enrichment Analysis approach using statistics from single-marker tests without gene-level collapsing (weighted Kolmogrov-Smirnov [WKS]-Variant method) is consistently powerful. Interestingly, directly applying rare variant association tests (e.g., sequence kernel association test) to pathway analysis offers a similar power, but its results are sensitive to assumptions of genetic architecture. We applied pathway association analysis to an exome-sequencing data of the chronic obstructive pulmonary disease, and found that the WKS-Variant method confirms associated genes previously published.
| S-EPMC3856324 | biostudies-literature
Project description: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. RESULTS: We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. CONCLUSIONS: The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.
| S-EPMC3102637 | biostudies-literature
Project description:BackgroundThe use of biological annotation such as genes and pathways in the analysis of gene expression data has aided the identification of genes for follow-up studies and suggested functional information to uncharacterized genes. Several studies have applied similar methods to genome wide association studies and identified a number of disease related pathways. However, many questions remain on how to best approach this problem, such as whether there is a need to obtain a score to summarize association evidence at the gene level, and whether a pathway, dominated by just a few highly significant genes, is of interest.MethodsWe evaluated the performance of two pathway-based methods (Random Set, and Binomial approximation to the hypergeometric test) based on their applications to three data sets of Crohn's disease. We consider both the disease status as a phenotype as well as the residuals after conditioning on IL23R, a known Crohn's related gene, as a phenotype.ResultsOur results show that Random Set method has the most power to identify disease related pathways. We confirm previously reported disease related pathways and provide evidence for IL-2 Receptor Beta Chain in T cell Activation and IL-9 signaling as Crohn's disease associated pathways.ConclusionsOur results highlight the need to apply powerful gene score methods prior to pathway enrichment tests, and that controlling for genes that attain genome wide significance enable further biological insight.
| S-EPMC2908056 | biostudies-literature
Project description:Comprehensive analyses of results from genome-wide association studies (GWAS) have demonstrated that complex disease/trait-associated loci are enriched in gene regulatory regions of the genome. The search for causal regulatory variation has focused primarily on transcriptional elements, such as promoters and enhancers. microRNAs (miRNAs) are now widely appreciated as critical posttranscriptional regulators of gene expression and are thought to impart stability to biological systems. Naturally occurring genetic variation in the miRNA regulome is likely an important contributor to phenotypic variation in the human population. However, the extent to which polymorphic miRNA-mediated gene regulation underlies GWAS signals remains unclear. In this study, we have developed the most comprehensive bioinformatic analysis pipeline to date for cataloging and prioritizing variants in the miRNA regulome as functional candidates in GWAS. We highlight specific findings, including a variant in the promoter of the miRNA let-7 that may contribute to human height variation. We also provide a discussion of how our approach can be expanded in the future. Overall, we believe that the results of this study will be valuable for researchers interested in determining whether GWAS signals implicate the miRNA regulome in their disease/trait of interest.
| S-EPMC3807557 | biostudies-literature