The Experts below are selected from a list of 101415 Experts worldwide ranked by ideXlab platform
Karl W. Broman - One of the best experts on this subject based on the ideXlab platform.
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the dissection of expression Quantitative Trait locus hotspots
Genetics, 2016Co-Authors: Jianan Tian, Mark P Keller, Aimee Teo Broman, Christina Kendziorski, Brian S Yandell, Alan D Attie, Karl W. BromanAbstract:Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression Quantitative Trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression Traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression Trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.
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mapping Quantitative Trait loci underlying function valued Traits using functional principal component analysis and multi Trait mapping
G3: Genes Genomes Genetics, 2016Co-Authors: Il Youp Kwak, Candace R Moore, Edgar P Spalding, Karl W. BromanAbstract:We previously proposed a simple regression-based method to map Quantitative Trait loci underlying function-valued phenotypes. In order to better handle the case of noisy phenotype measurements and accommodate the correlation structure among time points, we propose an alternative approach that maintains much of the simplicity and speed of the regression-based method. We overcome noisy measurements by replacing the observed data with a smooth approximation. We then apply functional principal component analysis, replacing the smoothed phenotype data with a small number of principal components. Quantitative Trait locus mapping is applied to these dimension-reduced data, either with a multi-Trait method or by considering the Traits individually and then taking the average or maximum LOD score across Traits. We apply these approaches to root gravitropism data on Arabidopsis recombinant inbred lines and further investigate their performance in computer simulations. Our methods have been implemented in the R package, funqtl.
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the dissection of expression Quantitative Trait locus hotspots
arXiv: Applications, 2015Co-Authors: Jianan Tian, Mark P Keller, Aimee Teo Broman, Christina Kendziorski, Brian S Yandell, Alan D Attie, Karl W. BromanAbstract:Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effect on gene expression: expression Quantitative Trait locus (eQTL) hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression Traits that map to the locus: the direction of the effects, as well as the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (such as with linear discriminant analysis) and compare the phenotype distribution in the non-recombinants to that in the recombinant individuals: If the recombinant individuals display a different expression pattern than the non-recombinants, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression Trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.
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r qtlcharts interactive graphics for Quantitative Trait locus mapping
bioRxiv, 2014Co-Authors: Karl W. BromanAbstract:Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map Quantitative Trait loci (QTL; genetic loci that influence Quantitative Traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl's static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.
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poor performance of bootstrap confidence intervals for the location of a Quantitative Trait locus
Genetics, 2006Co-Authors: Ani Manichaikul, Josee Dupuis, Saunak Sen, Karl W. BromanAbstract:The aim of many genetic studies is to locate the genomic regions (called Quantitative Trait loci, QTL) that contribute to variation in a Quantitative Trait (such as body weight). Confidence intervals for the locations of QTL are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the nonparametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals behave poorly and so should not be used in this context. The profile likelihood (or LOD curve) for QTL location has a tendency to peak at genetic markers, and so the distribution of the maximum-likelihood estimate (MLE) of QTL location has the unusual feature of point masses at genetic markers; this contributes to the poor behavior of the bootstrap. Likelihood support intervals and approximate Bayes credible intervals, on the other hand, are shown to behave appropriately.
Jonathan K Pritchard - One of the best experts on this subject based on the ideXlab platform.
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wasp allele specific software for robust molecular Quantitative Trait locus discovery
Nature Methods, 2015Co-Authors: Bryce Van De Geijn, Graham Mcvicker, Yoav Gilad, Jonathan K PritchardAbstract:Allele-specific sequencing reads provide a powerful signal for identifying molecular Quantitative Trait loci (QTLs), but they are challenging to analyze and are prone to technical artifacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and chromatin immunoprecipitation sequencing (ChIP-seq) reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL-mapping approaches.
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wasp allele specific software for robust discovery of molecular Quantitative Trait loci
bioRxiv, 2014Co-Authors: Bryce Van De Geijn, Graham Mcvicker, Yoav Gilad, Jonathan K PritchardAbstract:Allele-specific sequencing reads provide a powerful signal for identifying molecular Quantitative Trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.
Richard Mott - One of the best experts on this subject based on the ideXlab platform.
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using progenitor strain information to identify Quantitative Trait nucleotides in outbred mice
Genetics, 2005Co-Authors: Binnaz Yalcin, Jonathan Flint, Richard MottAbstract:We have developed a fast and economical strategy for dissecting the genetic architecture of Quantitative Trait loci at a molecular level. The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a Quantitative Trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a Quantitative Trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis. We apply the method to a 4.8-Mb region on mouse chromosome 1 that contains a QTL influencing anxiety segregating in a heterogeneous stock and show that, under the assumption that a single QTN is present and lies in a region conserved between the human and mouse genomes, it is possible to reduce the number of variants likely to be the Quantitative Trait nucleotide from many thousands to <20.
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strategies for mapping and cloning Quantitative Trait genes in rodents
Nature Reviews Genetics, 2005Co-Authors: Jonathan Flint, William Valdar, Sagiv Shifman, Richard MottAbstract:Over the past 15 years, more than 2,000 Quantitative Trait loci (QTLs) have been identified in crosses between inbred strains of mice and rats, but less than 1% have been characterized at a molecular level. However, new resources, such as chromosome substitution strains and the proposed Collaborative Cross, together with new analytical tools, including probabilistic ancestral haplotype reconstruction in outbred mice, Yin-Yang crosses and in silico analysis of sequence variants in many inbred strains, could make QTL cloning tractable. We review the potential of these strategies to identify genes that underlie QTLs in rodents.
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genetic dissection of a behavioral Quantitative Trait locus shows that rgs2 modulates anxiety in mice
Nature Genetics, 2004Co-Authors: Binnaz Yalcin, Saffron A G Willisowen, Janice M Fullerton, Anjela Meesaq, Robert M J Deacon, Nicholas J P Rawlins, Richard R Copley, Andrew P Morris, Jonathan Flint, Richard MottAbstract:Here we present a strategy to determine the genetic basis of variance in complex phenotypes that arise from natural, as opposed to induced, genetic variation in mice. We show that a commercially available strain of outbred mice, MF1, can be treated as an ultrafine mosaic of standard inbred strains and accordingly used to dissect a known Quantitative Trait locus influencing anxiety. We also show that this locus can be subdivided into three regions, one of which contains Rgs2, which encodes a regulator of G protein signaling. We then use Quantitative complementation to show that Rgs2 is a Quantitative Trait gene. This combined genetic and functional approach should be applicable to the analysis of any Quantitative Trait.
Aldons J Lusis - One of the best experts on this subject based on the ideXlab platform.
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expression Quantitative Trait loci replication tissue and sex specificity in mice
Genetics, 2010Co-Authors: Atila Van Nas, Leslie Ingramdrake, Janet S Sinsheimer, Susanna S Wang, Eric E Schadt, Thomas A Drake, Aldons J LusisAbstract:By treating the transcript abundance as a Quantitative Trait, gene expression can be mapped to local or distant genomic regions relative to the gene encoding the transcript. Local expression Quantitative Trait loci (eQTL) generally act in cis (that is, control the expression of only the contiguous structural gene), whereas distal eQTL act in trans. Distal eQTL are more difficult to identify with certainty due to the fact that significant thresholds are very high since all regions of the genome must be tested, and confounding factors such as batch effects can produce false positives. Here, we compare findings from two large genetic crosses between mouse strains C3H/HeJ and C57BL/6J to evaluate the reliability of distal eQTL detection, including “hotspots” influencing the expression of multiple genes in trans. We found that >63% of local eQTL and >18% of distal eQTL were replicable at a threshold of LOD > 4.3 between crosses and 76% of local and >24% of distal eQTL at a threshold of LOD > 6. Additionally, at LOD > 4.3 four tissues studied (adipose, brain, liver, and muscle) exhibited >50% preservation of local eQTL and >17% preservation of distal eQTL. We observed replicated distal eQTL hotspots between the crosses on chromosomes 9 and 17. Finally, >69% of local eQTL and >10% of distal eQTL were preserved in most tissues between sexes. We conclude that most local eQTL are highly replicable between mouse crosses, tissues, and sex as compared to distal eQTL, which exhibited modest replicability.
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identification of pathways for atherosclerosis in mice integration of Quantitative Trait locus analysis and global gene expression data
Circulation Research, 2007Co-Authors: Susanna S Wang, Leslie Ingramdrake, Eric E Schadt, Thomas A Drake, Hui Wang, Xuping Wang, Weibin Shi, Aldons J LusisAbstract:We report a combined genetic and genomic analysis of atherosclerosis in a cross between the strains C3H/HeJ and C57BL/6J on a hyperlipidemic apolipoprotein E–null background. We incorporated sex and sex-by-genotype interactions into our model selection procedure to identify 10 Quantitative Trait loci for lesion size, revealing a level of complexity greater than previously thought. Of the known risk factors for atherosclerosis, plasma triglyceride levels and plasma glucose to insulin ratios were particularly strongly, but negatively, associated with lesion size. We performed expression array analysis for 23 574 transcripts of the livers and adipose tissues of all 334 F2 mice and identified more than 10 000 expression Quantitative Trait loci that either mapped to the gene encoding the transcript, implying cis regulation, or to a separate locus, implying trans -regulation. The gene expression data allowed us to identify candidate genes that mapped to the atherosclerosis Quantitative Trait loci and for which the expression was regulated in cis . Genes highly correlated with lesions were enriched in certain known pathways involved in lesion development, including cholesterol metabolism, mitochondrial oxidative phosphorylation, and inflammation. Thus, global gene expression in peripheral tissues can reflect the systemic perturbations that contribute to atherosclerosis.
Bryce Van De Geijn - One of the best experts on this subject based on the ideXlab platform.
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wasp allele specific software for robust molecular Quantitative Trait locus discovery
Nature Methods, 2015Co-Authors: Bryce Van De Geijn, Graham Mcvicker, Yoav Gilad, Jonathan K PritchardAbstract:Allele-specific sequencing reads provide a powerful signal for identifying molecular Quantitative Trait loci (QTLs), but they are challenging to analyze and are prone to technical artifacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and chromatin immunoprecipitation sequencing (ChIP-seq) reads, we demonstrate that WASP has a low error rate and is far more powerful than existing QTL-mapping approaches.
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wasp allele specific software for robust discovery of molecular Quantitative Trait loci
bioRxiv, 2014Co-Authors: Bryce Van De Geijn, Graham Mcvicker, Yoav Gilad, Jonathan K PritchardAbstract:Allele-specific sequencing reads provide a powerful signal for identifying molecular Quantitative Trait loci (QTLs), however they are challenging to analyze and prone to technical artefacts. Here we describe WASP, a suite of tools for unbiased allele-specific read mapping and discovery of molecular QTLs. Using simulated reads, RNA-seq reads and ChIP-seq reads, we demonstrate that our approach has a low error rate and is far more powerful than existing QTL mapping approaches.
