Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping

Hong, Lan; Chen, Meng; Flowers, Jessica B.; Yandell, Brian S.; Stapleton, Donnie S.; Mata, Christine; Mui, Eric Ton-Keen; Flowers, Matthew T.; Schueler, Kathryn L.; Manly, Kenneth F.; Williams, Robert W.; Kendziorski, Christina; Attie, Alan D.

doi:10.1371/journal.pgen.0020006

Cited by 121 publications

(108 citation statements)

References 51 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…Here we present two applications of HESS to: (i) mouse gene expression data published in Lan et al (2006) that is commonly used as a benchmark data set for detection of eQTL (Chun and Keles x 2009) and eQTL hotspots (Kendziorski et al 2006;Jia and Xu 2007) and (ii) human monocytes expression data set recently analyzed for disease susceptibility by Zeller et al (2010).…”

Section: Real Case Studiesmentioning

confidence: 99%

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Bottolo

Petretto

Blankenberg³

et al. 2011

Genetics

View full text Add to dashboard Cite

High-throughput genomics allows genome-wide quantification of gene expression levels in tissues and cell types and, when combined with sequence variation data, permits the identification of genetic control points of expression (expression QTL or eQTL). Clusters of eQTL influenced by single genetic polymorphisms can inform on hotspots of regulation of pathways and networks, although very few hotspots have been robustly detected, replicated, or experimentally verified. Here we present a novel modeling strategy to estimate the propensity of a genetic marker to influence several expression traits at the same time, based on a hierarchical formulation of related regressions. We implement this hierarchical regression model in a Bayesian framework using a stochastic search algorithm, HESS, that efficiently probes sparse subsets of genetic markers in a high-dimensional data matrix to identify hotspots and to pinpoint the individual genetic effects (eQTL). Simulating complex regulatory scenarios, we demonstrate that our method outperforms current state-of-the-art approaches, in particular when the number of transcripts is large. We also illustrate the applicability of HESS to diverse real-case data sets, in mouse and human genetic settings, and show that it provides new insights into regulatory hotspots that were not detected by conventional methods. The results suggest that the combination of our modeling strategy and algorithmic implementation provides significant advantages for the identification of functional eQTL hotspots, revealing key regulators underlying pathways.T HE current focus of biological research has turned to high-throughput genomics, which encompasses largescale data generation and a variety of integrated approaches that combine two or more -omics of data sets. An important example of integrative genomics analysis is the investigation of the genetic regulation of transcription, also called expression quantitative trait locus (eQTL) or "genetical genomics" studies (Cookson et al. 2009;Majewski and Pastinen 2011). A typical eQTL analysis follows a natural structure of parallel regressions between the large set of q responses (i.e., expression phenotypes), and that of p explanatory variables (i.e., genetic markers, often single nucleotide polymorphism, SNPs), where p is typically much larger than the number of observations n.From a statistical point of view, the size and the complex multidimensional structure of eQTL data sets pose a significant challenge. Not only does one wish to detect the set of important genetic control points for each response (expression phenotype), including cis-and trans-acting control for the same transcript, but, ideally, one would wish to exploit the dependence between multiple expression phenotypes. This will facilitate the discovery of key regulatory markers, so-called hotspots (Breitling et al. 2008), i.e., genetic loci or single polymorphisms that influence a large number of transcripts. Identification of hotspots can inform on network and pathways, which are likel...

show abstract

Section: Real Case Studiesmentioning

confidence: 99%

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Bottolo

Petretto

Blankenberg³

et al. 2011

Genetics

View full text Add to dashboard Cite

show abstract

“…Early approaches to a posteriori network construction 18,30 use functional annotations to construct putative regulatory networks from the eQTL associations. More recent work (e.g., Aten et al 31 and Kang et al 32 ) construct probabilistic causal networks by inferring regulatory relationships among transcripts from expression probe correlation and the strength of SNP-probe associations.…”

Section: Associating Variation With Regulationmentioning

confidence: 99%

Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis

Martini

Nair

2012

Methods in Molecular Biology

View full text Add to dashboard Cite

A current challenge in interpretation of Genome Wide Association studies (GWAS) is to establish the mechanistic links between the measured genotype and observed phenotype. The integration of gene expression with disease GWAS is emerging as an important strategy for deciphering these regulatory mechanisms. For renal disease, the availability of both tissue-and disease-specific expression data makes the strategy a compelling option. In this review, three approaches of integrating Single Nucleotide Polymorphism (SNP) genotypes with transcriptional regulation are discussed: (1) interpreting the functional role of transcripts affected by a SNP, (2) identifying the mechanistic role of non-coding SNPs in regulation, and (3) identifying regulatory candidate SNPs with expression associations. Combining these strategies in an integrative manner should allow the discovery of more extensive regulatory information. Linking genetics to systems biology more directly promises the opportunity to explain how genetic variants contribute to disease in a truly holistic manner.

show abstract

“…Yet, coinciding QTLs not necessarily represent the same causal gene because effects of closely linked genes are difficult to distinguish from true pleiotropic effects of a single gene. Without further experimentation genetic interactions can be predicted computationally by comparing QTL profiles and correlation analyses [66]. However, the accuracy of constructed networks can benefit tremendously from the integration of additional information like gene ontology [29,67], sequence data [68] and related quantitative trait data for end traits including metabolites and plant performance [35,69].…”

Section: Regulatory Network Constructionmentioning

confidence: 99%

Quantitative genetics in the age of omics

Keurentjes

Koornneef

Vreugdenhil³

2008

Current Opinion in Plant Biology

View full text Add to dashboard Cite

The use of natural variation in the genetic dissection of quantitative traits has a long-standing tradition. Recent advances in high-throughput technologies for the quantification of biological molecules have shifted the focus in quantitative genetics from single traits to comprehensive largescale analyses. So-called omic technologies now enable geneticists to take a look in the black box that translates genetic information into biological function. These processes include transcriptional and (post) translational regulation as well as metabolic signaling pathways. The progress made in analytical and statistical techniques now allows the construction of regulatory networks that integrate the different levels of the biological information flow from gene-to-function. IntroductionFor most organisms, including plants, variation between individuals of the same species is observed in nature, which can partly be explained by genetic differences [1]. Natural variation among different genotypes (accessions, varieties, etc.) can be classified as qualitative or quantitative. Qualitative traits are characterized by distinct phenotypic classes and are often a result of differences at single genes. Such traits can relatively easily be dissected genetically because of their clear segregation pattern in the progeny of crosses. Quantitative traits, on the contrary, often display a more continuous variation in phenotypes because of a multiplicity of genes involved and a relatively large effect of environmental factors on the expression of the trait. Recombination of genes results in a large number of phenotypic classes, which cannot unambiguously be associated with genotypic classes [2] because various genes can contribute positively or negatively to a quantifiable trait. The complexity of quantitative traits is further enhanced by the presence of epistatic interactions and interactions between genes and the environment [3 ].Quantitative natural variation controls adaptive strategies of organisms to cope with biotic and abiotic influences and its understanding can provide insight into ecological mechanisms and the evolutionary history of plants [4]. Moreover, it is the basis of variation for many agronomic traits [5]. Arabidopsis thaliana has proven to be a very efficient model plant because of a number of biological properties and available genetic resources that make genetic and molecular analyses very efficient [6]. These advantages also make A. thaliana very suitable for the genetic analysis of natural variation [7]. Because of the growing impact of large-scale molecular detection techniques (collectively nicknamed 'omics' technologies) in the dissection of complex traits, we aim to present a brief overview of the key technological advances and some of the recent findings in the field with emphasis on A. thaliana. Genetic analysis of natural variation in quantitative traitsDespite the complexity in genetic regulation of quantitative traits much progress has been made over the past decades in dissecting these traits using mo...

show abstract

Combined Expression Trait Correlations and Expression Quantitative Trait Locus Mapping

Cited by 121 publications

References 51 publications

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Bayesian Detection of Expression Quantitative Trait Loci Hot Spots

Linking Variants from Genome-Wide Association Analysis to Function via Transcriptional Network Analysis

Quantitative genetics in the age of omics

Contact Info

Product

Resources

About