Detecting epistatic interactions contributing to human gene expression using the CEPH family data.
Ontology highlight
ABSTRACT:
SUBMITTER: Li H
PROVIDER: S-EPMC2367573 | biostudies-literature | 2007
REPOSITORIES: biostudies-literature
Publications
Detecting epistatic interactions contributing to human gene expression using the CEPH family data.
Li Hua H Gao Guimin G Li Jian J Page Grier P GP Zhang Kui K
BMC proceedings 20071218
It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed metho ...[more]
Similar Datasets
Project description:BackgroundMultiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.ResultsWe have developed a new method, named SNPInterForest, for identifying epistatic interactions by extending an ensemble learning technique called random forest. Random forest is a predictive method that has been proposed for use in discovering single-nucleotide polymorphisms (SNPs), which are most predictive of the disease status in association studies. However, it is less sensitive to SNPs with little marginal effect. Furthermore, it does not natively exhibit information on interaction patterns of susceptibility SNPs. We extended the random forest framework to overcome the above limitations by means of (i) modifying the construction of the random forest and (ii) implementing a procedure for extracting interaction patterns from the constructed random forest. The performance of the proposed method was evaluated by simulated data under a wide spectrum of disease models. SNPInterForest performed very well in successfully identifying pure epistatic interactions with high precision and was still more than capable of concurrently identifying multiple interactions under the existence of genetic heterogeneity. It was also performed on real GWAS data of rheumatoid arthritis from the Wellcome Trust Case Control Consortium (WTCCC), and novel potential interactions were reported.ConclusionsSNPInterForest, offering an efficient means to detect epistatic interactions without statistical analyses, is promising for practical use as a way to reveal the epistatic interactions involved in common complex diseases.
| S-EPMC3260223 | biostudies-literature
Project description:Reduced activity of two genes in combination often has a more detrimental effect than expected. Such epistatic interactions not only occur when genes are mutated but also due to variation in gene expression, including among isogenic individuals in a controlled environment. We hypothesized that these 'epigenetic' epistatic interactions could place important constraints on the evolution of gene expression. Consistent with this, we show here that yeast genes with many epistatic interaction partners typically show low expression variation among isogenic individuals and low variation across different conditions. In addition, their expression tends to remain stable in response to the accumulation of mutations and only diverges slowly between strains and species. Yeast promoter architectures, the retention of gene duplicates, and the divergence of expression between humans and chimps are also consistent with selective pressure to reduce the likelihood of harmful epigenetic epistatic interactions. Based on these and previous analyses, we propose that the tight regulation of epistatic interaction network hubs makes an important contribution to the maintenance of a robust, 'canalized' phenotype. Moreover, that epigenetic epistatic interactions may contribute substantially to fitness defects when single genes are deleted.
| S-EPMC3588906 | biostudies-other
Project description:Differential gene expression testing is an analysis commonly applied to RNA-Seq data. These statistical tests identify genes that are significantly different across phenotypes. We extend this testing paradigm to multivariate gene interactions from a classification perspective with the goal to detect novel gene interactions for the phenotypes of interest. This is achieved through our novel computational framework comprised of a hierarchical statistical model of the RNA-Seq processing pipeline and the corresponding optimal Bayesian classifier. Through Markov Chain Monte Carlo sampling and Monte Carlo integration, we compute quantities where no analytical formulation exists. The performance is then illustrated on an expression dataset from a dietary intervention study where we identify gene pairs that have low classification error yet were not identified as differentially expressed. Additionally, we have released the software package to perform OBC classification on RNA-Seq data under an open source license and is available at http://bit.ly/obc_package.
| S-EPMC4818202 | biostudies-other
Project description:The widespread microarray technology capable of analyzing global gene expression at the level of transcription is expanding its application not only in medicine but also in studies on basic biology. This paper presents our analysis on microarray gene expression data in the CEPH Utah families focusing on the demographic characteristics such as age and sex on differential gene expression patterns. Our results show that the differential gene expression pattern between age groups is dominated by down-regulated transcriptional activities in the old subjects. Functional analysis on age-regulated genes identifies cell-cell signaling as an important functional category implicated in human aging. Sex-dependent gene expression is characterized by genes that may escape X-inactivation and, most interestingly, such a pattern is not affected by the aging process. Analysis on sibship correlation on gene expression revealed a large number of significant genes suggesting the importance of a genetic mechanism in regulating transcriptional activities. In addition, we observe an interesting pattern of sibship correlation on gene expression that increases exponentially with the mean of gene expression reflecting the enhanced genetic control over the functionally active genes.
| S-EPMC3622883 | biostudies-literature
Project description:Understanding the relationship between genetic variations and variations in complex and quantitative phenotypes remains an ongoing challenge. While Genome-wide association studies (GWAS) have become a vital tool for identifying single-locus associations, we lack methods for identifying epistatic interactions. In this article, we propose a novel method for higher-order epistasis detection using mixed effect conditional inference forest (epiMEIF). The proposed method is fitted on a group of single nucleotide polymorphisms (SNPs) potentially associated with the phenotype and the tree structure in the forest facilitates the identification of n-way interactions between the SNPs. Additional testing strategies further improve the robustness of the method. We demonstrate its ability to detect true n-way interactions via extensive simulations in both cross-sectional and longitudinal synthetic datasets. This is further illustrated in an application to reveal epistatic interactions from natural variations of cardiac traits in flies (Drosophila). Overall, the method provides a generalized way to identify higher-order interactions from any GWAS data, thereby greatly improving the detection of the genetic architecture underlying complex phenotypes.
| S-EPMC9639209 | biostudies-literature
Project description:AimsThe aim of the present study was to evaluate the performance of 'high'-'low' yielding pyramided lines (PLs), having the same combinations of qDTYs in Samba Mahsuri, MR219 and IR64-Sub1 genetic backgrounds, and to understand the genetic interactions among QTL and/with genetic background affecting grain yield.BackgroundEpistasis regulates the expression of traits governed by several major/minor genes/QTL. Multiple pyramided lines (PLs) with the same grain yield QTL (qDTYs) combinations but possessing grain yield variability under different levels of reproductive stage drought stress were identified in different rice genetic backgrounds at International Rice Research Institute (IRRI).ObjectivesThe objectives of the present study were to evaluate the performance pyramided lines (PLs) with drought QTL in the backgrounds of Samba Mahsuri, MR219 and IR64-Sub1 under reproductive stage drought stress (RS) and NS (non-stress) conditions, to understand the effect of epistatic interactions among qDTYs and with genetic background on GY under the differential level of stress and to identify the promising drought-tolerant lines with high yield under drought and higher background recovery in different genetic backgrounds.MethodsThe experiments were conducted in 2015 DS (dry season), 2015 WS (wet season) and 2017 DS at IRRI, Los Baños, Philippines, in a transplanted lowland ecosystem under lowland severe stress (LSS), lowland moderate stress (LMS) and lowland non-stress (LNS). The experiments were laid out in alpha lattice design with two replications.ResultsSeveral digenic interactions were found in different genetic backgrounds, 13 interactions in Samba Mahsuri, 11 in MR219 and 20 in IR64-Sub1 backgrounds. Among all digenic interactions, one QTL × QTL interaction, 17 QTL × background and 26 background × background interactions resulted in GY reduction in low yielding PLs in different genetic backgrounds under LSS or LMS. Negative interaction of qDTY3.1 , qDTY4.1 and qDTY9.1 with background markers and background × background interactions caused up to 15% GY reduction compared to the high yielding PLs under LMS in the Samba Mahsuri PLs. In MR219 PLs, the negative interaction of qDTY2.2 , qDTY3.2 , qDTY4.1 and qDTY12.1 with the background marker interval RM314-RM539, RM273-RM349 and RM445-RM346, RM473D-RM16, respectively resulted 52% GY reduction compared to the high yielding PLs under LSS. In IR64-Sub1 PLs, qDTY6.1 interacted with background loci at RM16-RM135, RM228-RM333, RM202-RM287 and RM415-RM558A marker interval under LSS and at RM475-RM525 marker interval under LMS, causing GY reduction to 58% compared to the high yielding PLs.ConclusionHigh yielding PLs in Samba Mahsuri (IR 99734:1-33-69-1-22-6), MR219 (IR 99784-156-87-2-4-1) and IR64-Sub1 (IR 102784:2-89-632-2-1-2) backgrounds without any negative interactions were identified. The identified selected promising PLs may be used as potential drought-tolerant donors or may be released as varieties for drought-prone ecosystems in different countries.
| S-EPMC8886627 | biostudies-literature
Project description:Despite the importance of gene-environment (GxE) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of GxE interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant GxE interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing GxE interactions are discussed.
| S-EPMC2924567 | biostudies-literature
Project description:Gene-environment interactions may provide a mechanism for targeting interventions to those individuals who would gain the most benefit from them. Searching for interactions agnostically on a genome-wide scale requires large sample sizes, often achieved through collaboration among multiple studies in a consortium. Family studies can contribute to consortia, but to do so they must account for correlation within families by using specialized analytic methods. In this paper, we investigate the performance of methods that account for within-family correlation, in the context of gene-environment interactions with binary exposures and quantitative outcomes. We simulate both cross-sectional and longitudinal measurements, and analyze the simulated data taking family structure into account, via generalized estimating equations (GEE) and linear mixed-effects models. With sufficient exposure prevalence and correct model specification, all methods perform well. However, when models are misspecified, mixed modeling approaches have seriously inflated type I error rates. GEE methods with robust variance estimates are less sensitive to model misspecification; however, when exposures are infrequent, GEE methods require modifications to preserve type I error rate. We illustrate the practical use of these methods by evaluating gene-drug interactions on fasting glucose levels in data from the Framingham Heart Study, a cohort that includes related individuals.
| S-EPMC5070904 | biostudies-other
Project description:With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.
| S-EPMC4539430 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.ResultsHere we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance.ConclusionsWe describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.
| S-EPMC2885376 | biostudies-literature