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1.
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. 相似文献2.
Genome-wide association study (GWAS) aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1) An introduction to the background of GWAS. (2) The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3) The limitations of current approaches and future directions.
What to Learn in This Chapter
- The background of Genome-wide association study (GWAS).
- The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods.
- The limitations of current approaches and future directions.
This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.相似文献
3.
Background
Recently introduced pathway-based approach is promising and advantageous to improve the efficiency of analyzing genome-wide association scan (GWAS) data to identify disease variants by jointly considering variants of the genes that belong to the same biological pathway. However, the current available pathway-based approaches for analyzing GWAS have limited power and efficiency. 相似文献4.
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. 相似文献5.
Background
With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.Results
Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.Conclusion
The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.6.
Background
Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes.Methodology
We develop a general model for testing high-order epistatic interactions for a complex disease in a case-control study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components.Conclusions
Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis. 相似文献7.
Xia Jiang Richard E Neapolitan M Michael Barmada Shyam Visweswaran 《BMC bioinformatics》2011,12(1):89
Background
Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL. 相似文献8.
Lukas Forer Sebastian Schönherr Hansi Weissensteiner Florian Haider Thomas Kluckner Christian Gieger Heinz-Erich Wichmann Günther Specht Florian Kronenberg Anita Kloss-Brandstätter 《BMC bioinformatics》2010,11(1):318
Background
Genome-wide association studies (GWAS) based on single nucleotide polymorphisms (SNPs) revolutionized our perception of the genetic regulation of complex traits and diseases. Copy number variations (CNVs) promise to shed additional light on the genetic basis of monogenic as well as complex diseases and phenotypes. Indeed, the number of detected associations between CNVs and certain phenotypes are constantly increasing. However, while several software packages support the determination of CNVs from SNP chip data, the downstream statistical inference of CNV-phenotype associations is still subject to complicated and inefficient in-house solutions, thus strongly limiting the performance of GWAS based on CNVs. 相似文献9.
Hong Yu Jialiang Huang Nan Qiao Christopher D Green Jing-Dong J Han 《BMC systems biology》2010,4(1):97
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. 相似文献10.
Background
One common goal of a case/control genome wide association study (GWAS) is to find SNPs associated with a disease. Traditionally, the first step in such studies is to assign a genotype to each SNP in each subject, based on a statistic summarizing fluorescence measurements. When the distributions of the summary statistics are not well separated by genotype, the act of genotype assignment can lead to more potential problems than acknowledged by the literature. 相似文献11.
Ioanna Marinou Simon H Till David J Moore Anthony G Wilson 《Arthritis research & therapy》2008,10(4):R80
Introduction
A feature of rheumatoid arthritis (RA) is an imbalance between proinflammatory and anti-inflammatory cytokines. Several recent studies have implicated polymorphism in the IL-4 signalling pathway in the development of erosive RA. The aim of the present study was to investigate the role of polymorphism in the IL-4, IL-4Rα and IL-13 genes in RA, including an examination of epistasis. 相似文献12.
Marian Suarez-Gestal Eva Perez-Pampin Manuel Calaza Juan J Gomez-Reino Antonio Gonzalez 《Arthritis research & therapy》2010,12(2):R72
Introduction
We aimed to replicate the strong associations that a recent genome wide association study (GWAS) has found between 16 single nucleotide polymorphisms (SNPs) and response to anti-tumour necrosis factor (TNF) treatment in 89 patients with rheumatoid arthritis (RA). This study is very important because, according to published simulations, associations as strong as the reported ones will mean that these SNPs could be used as predictors of response at the individual level. 相似文献13.
Shengjie Yang Yiyuan Liu Ning Jiang Jing Chen Lindsey Leach Zewei Luo Minghui Wang 《BMC genomics》2014,15(1)
Background
While the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits.Results
Heritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed.Conclusions
We utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users. 相似文献14.
Eun-Young Hwang Qijian Song Gaofeng Jia James E Specht David L Hyten Jose Costa Perry B Cregan 《BMC genomics》2014,15(1):1-12
Background
Association analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content.Results
A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r 2 ) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil.Conclusions
This research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s). 相似文献15.
《PloS one》2010,5(11)
Background
Late Onset Alzheimer''s disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes.Methodology
We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset.Principal Findings
We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD.Significance
Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer''s disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches. 相似文献16.
Background
Emerging studies demonstrate that single nucleotide polymorphisms (SNPs) resided in the microRNA recognition element seed sites (MRESSs) in 3′UTR of mRNAs are putative biomarkers for human diseases and cancers. However, exhaustively experimental validation for the causality of MRESS SNPs is impractical. Therefore bioinformatics have been introduced to predict causal MRESS SNPs. Genome-wide association study (GWAS) provides a way to detect susceptibility of millions of SNPs simultaneously by taking linkage disequilibrium (LD) into account, but the multiple-testing corrections implemented to suppress false positive rate always sacrificed the sensitivity. In our study, we proposed a method to identify candidate causal MRESS SNPs from 12 GWAS datasets without performing multiple-testing corrections. Alternatively, we used biological context to ensure credibility of the selected SNPs.Results
In 11 out of the 12 GWAS datasets, MRESS SNPs were over-represented in SNPs with p-value ≤ 0.05 (odds ratio (OR) ranged from 1.1 to 2.4). Moreover, host genes of susceptible MRESS SNPs in each of the 11 GWAS dataset shared biological context with reported causal genes. There were 286 MRESS SNPs identified by our method, while only 13 SNPs were identified by multiple-testing corrections with a given threshold of 1 × 10−5, which is a common cutoff used in GWAS. 27 out of the 286 candidate SNPs have been reported to be deleterious while only 2 out of 13 multiple-testing corrected SNPs were documented in PubMed. MicroRNA-mRNA interactions affected by the 286 candidate SNPs were likely to present negatively correlated expression. These SNPs introduced greater alternation of binding free energy than other MRESS SNPs, especially when grouping by haplotypes (4210 vs. 4105 cal/mol by mean, 9781 vs. 8521 cal/mol by mean, respectively).Conclusions
MRESS SNPs are promising disease biomarkers in multiple GWAS datasets. The method of integrating GWAS p-value and biological context is stable and effective for selecting candidate causal MRESS SNPs, it reduces the loss of sensitivity compared to multiple-testing corrections. The 286 candidate causal MRESS SNPs provide researchers a credible source to initialize their design of experimental validations in the future.Electronic supplementary material
The online version of this article (doi:10.1186/1471-2164-15-669) contains supplementary material, which is available to authorized users. 相似文献17.
Amber Dahlin Augusto Litonjua John J. Lima Mayumi Tamari Michiaki Kubo Charles G. Irvin Stephen P. Peters Kelan G. Tantisira 《PloS one》2015,10(6)
Background
Genome-wide association study (GWAS) is a powerful tool to identify novel pharmacogenetic single nucleotide polymorphisms (SNPs). Leukotriene receptor antagonists (LTRAs) are a major class of asthma medications, and genetic factors contribute to variable responses to these drugs. We used GWAS to identify novel SNPs associated with the response to the LTRA, montelukast, in asthmatics.Methods
Using genome-wide genotype and phenotypic data available from American Lung Association - Asthma Clinical Research Center (ALA-ACRC) cohorts, we evaluated 8-week change in FEV1 related to montelukast administration in a discovery population of 133 asthmatics. The top 200 SNPs from the discovery GWAS were then tested in 184 additional samples from two independent cohorts.Results
Twenty-eight SNP associations from the discovery GWAS were replicated. Of these, rs6475448 achieved genome-wide significance (combined P = 1.97 x 10-09), and subjects from all four studies who were homozygous for rs6475448 showed increased ΔFEV1 from baseline in response to montelukast.Conclusions
Through GWAS, we identified a novel pharmacogenomic locus related to improved montelukast response in asthmatics. 相似文献18.
Helen M Wallace 《Theoretical biology & medical modelling》2006,3(1):35-24
Background
The potential public health benefits of targeting environmental interventions by genotype depend on the environmental and genetic contributions to the variance of common diseases, and the magnitude of any gene-environment interaction. In the absence of prior knowledge of all risk factors, twin, family and environmental data may help to define the potential limits of these benefits in a given population. However, a general methodology to analyze twin data is required because of the potential importance of gene-gene interactions (epistasis), gene-environment interactions, and conditions that break the 'equal environments' assumption for monozygotic and dizygotic twins. 相似文献19.
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