Genetic analysis of complex traits in the emerging Collaborative Cross
TL;DR: It is demonstrated that the genetic diversity of the CC, which derives from random mixing of eight founder strains, results in high phenotypic diversity and enhances the ability to map causative loci underlying complex disease-related traits.
Abstract: The Collaborative Cross (CC) is a mouse recombinant inbred strain panel that is being developed as a resource for mammalian systems genetics. Here we describe an experiment that uses partially inbred CC lines to evaluate the genetic properties and utility of this emerging resource. Genome-wide analysis of the incipient strains reveals high genetic diversity, balanced allele frequencies, and dense, evenly distributed recombination sites-all ideal qualities for a systems genetics resource. We map discrete, complex, and biomolecular traits and contrast two quantitative trait locus (QTL) mapping approaches. Analysis based on inferred haplotypes improves power, reduces false discovery, and provides information to identify and prioritize candidate genes that is unique to multifounder crosses like the CC. The number of expression QTLs discovered here exceeds all previous efforts at eQTL mapping in mice, and we map local eQTL at 1-Mb resolution. We demonstrate that the genetic diversity of the CC, which derives from random mixing of eight founder strains, results in high phenotypic diversity and enhances our ability to map causative loci underlying complex disease-related traits.
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TL;DR: How high-throughput genomic methods are revealing the importance of the kinetics of cytokine gene expression and the remarkable degree of redundancy and overlap in cytokine signaling is highlighted.
Abstract: The cytokine storm has captured the attention of the public and the scientific community alike, and while the general notion of an excessive or uncontrolled release of proinflammatory cytokines is well known, the concept of a cytokine storm and the biological consequences of cytokine overproduction are not clearly defined. Cytokine storms are associated with a wide variety of infectious and noninfectious diseases. The term was popularized largely in the context of avian H5N1 influenza virus infection, bringing the term into popular media. In this review, we focus on the cytokine storm in the context of virus infection, and we highlight how high-throughput genomic methods are revealing the importance of the kinetics of cytokine gene expression and the remarkable degree of redundancy and overlap in cytokine signaling. We also address evidence for and against the role of the cytokine storm in the pathology of clinical and infectious disease and discuss why it has been so difficult to use knowledge of the cytokine storm and immunomodulatory therapies to improve the clinical outcomes for patients with severe acute infections.
1,501 citations
Cites background from "Genetic analysis of complex traits ..."
...The Collaborative Cross is a recombinant inbred mouse resource (5) designed to capture the genetic heterogeneity of the human population, supporting systems genetics and predictive biology....
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TL;DR: 20 arguments for and against each of these models of the genetic basis of complex traits are reviewed and it is concluded that both classes of effect can be readily reconciled.
Abstract: Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.
1,225 citations
TL;DR: Recent insights into the molecular nature of regulatory variants are reviewed and examples of complete chains of causality that link individual polymorphisms to changes in gene expression, which in turn result in physiological changes and, ultimately, disease risk are presented.
Abstract: We are in a phase of unprecedented progress in identifying genetic loci that cause variation in traits ranging from growth and fitness in simple organisms to disease in humans. However, a mechanistic understanding of how these loci influence traits is lacking for the majority of loci. Studies of the genetics of gene expression have emerged as a key tool for linking DNA sequence variation to phenotypes. Here, we review recent insights into the molecular nature of regulatory variants and describe their influence on the transcriptome and the proteome. We discuss conceptual advances from studies in model organisms and present examples of complete chains of causality that link individual polymorphisms to changes in gene expression, which in turn result in physiological changes and, ultimately, disease risk.
882 citations
TL;DR: A large cross between two yeast strains is used to accurately estimate different sources of heritable variation for 46 quantitative traits, and to detect underlying loci with high statistical power, and it is found that the detected loci explain nearly the entire additive contribution to heritability for the traits studied.
Abstract: For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic-mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this 'missing heritability' have been proposed. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits, and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to approximately 50 per cent. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits.
472 citations
Tel Aviv University1, University of Western Australia2, University of Oxford3, University of North Carolina at Chapel Hill4, North Carolina State University5, National Institutes of Health6, Oregon Health & Science University7, University of Arizona8, University of Colorado Denver9, University of Washington10, Hebrew University of Jerusalem11, University of Tennessee Health Science Center12, University of Veterinary Medicine Vienna13, Duke University14, University of Nebraska–Lincoln15, University of Wisconsin-Madison16, University of Calgary17, University of California, San Francisco18, Washington University in St. Louis19
TL;DR: The Collaborative Cross Consortium reports here on the development of a unique genetic resource population, a multiparental recombinant inbred panel derived from eight laboratory mouse inbred strains, which shows that founder haplotypes are inherited at the expected frequency.
Abstract: The Collaborative Cross Consortium reports here on the development of a unique genetic resource population. The Collaborative Cross (CC) is a multiparental recombinant inbred panel derived from eight laboratory mouse inbred strains. Breeding of the CC lines was initiated at multiple international sites using mice from The Jackson Laboratory. Currently, this innovative project is breeding independent CC lines at the University of North Carolina (UNC), at Tel Aviv University (TAU), and at Geniad in Western Australia (GND). These institutions aim to make publicly available the completed CC lines and their genotypes and sequence information. We genotyped, and report here, results from 458 extant lines from UNC, TAU, and GND using a custom genotyping array with 7500 SNPs designed to be maximally informative in the CC and used a novel algorithm to infer inherited haplotypes directly from hybridization intensity patterns. We identified lines with breeding errors and cousin lines generated by splitting incipient lines into two or more cousin lines at early generations of inbreeding. We then characterized the genome architecture of 350 genetically independent CC lines. Results showed that founder haplotypes are inherited at the expected frequency, although we also consistently observed highly significant transmission ratio distortion at specific loci across all three populations. On chromosome 2, there is significant overrepresentation of WSB/EiJ alleles, and on chromosome X, there is a large deficit of CC lines with CAST/EiJ alleles. Linkage disequilibrium decays as expected and we saw no evidence of gametic disequilibrium in the CC population as a whole or in random subsets of the population. Gametic equilibrium in the CC population is in marked contrast to the gametic disequilibrium present in a large panel of classical inbred strains. Finally, we discuss access to the CC population and to the associated raw data describing the genetic structure of individual lines. Integration of rich phenotypic and genomic data over time and across a wide variety of fields will be vital to delivering on one of the key attributes of the CC, a common genetic reference platform for identifying causative variants and genetic networks determining traits in mammals.
451 citations
Cites background from "Genetic analysis of complex traits ..."
...In mice, these include panels of chromosome substitutions strains (i.e., consomics), recombinant inbred lines (RIL), and subcongenics (Bailey 1971; Taylor et al. 1971; Hudgins et al. 1985; Demant and Hart 1986; Nadeau et al. 2000)....
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References
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Harvard University1, Brigham and Women's Hospital2, University of Wisconsin-Madison3, University of California, Berkeley4, Technical University of Denmark5, Icahn School of Medicine at Mount Sinai6, Vienna University of Technology7, University of Erlangen-Nuremberg8, German Cancer Research Center9, University of Milan10, Johns Hopkins University11, University of Washington12, Scripps Research Institute13, Walter and Eliza Hall Institute of Medical Research14, University of Iowa15
TL;DR: Details of the aims and methods of Bioconductor, the collaborative creation of extensible software for computational biology and bioinformatics, and current challenges are described.
Abstract: The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.
12,142 citations
"Genetic analysis of complex traits ..." refers methods in this paper
...Normalization was applied with the rma function in the affy R package from Bioconductor (Gentleman et al. 2004)....
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TL;DR: This work proposes an approach to measuring statistical significance in genomewide studies based on the concept of the false discovery rate, which offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted.
Abstract: With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
9,239 citations
"Genetic analysis of complex traits ..." refers background or methods in this paper
...The rate of inbreeding is rapid at first and slows with each subsequent generation....
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...These thresholds correspond to false discovery rates of 2% and 4%, respectively (Storey and Tibshirani 2003)....
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...We used this null distribution to calculate adjusted P-values for each genome scan, and used these to determine the FDR for the eQTL analysis (Storey and Tibshirani 2003)....
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TL;DR: An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand, and is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations.
Abstract: The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.
4,964 citations
Book•
01 Jan 1975TL;DR: Rank Tests for Comparing Two Treatments and Blocked Comparisons for two Treatments in a Population Model and the One-Sample Problem as discussed by the authors were used to compare more than two treatments.
Abstract: Rank Tests for Comparing Two Treatments.- Comparing Two Treatments or Attributes in a Population Model.- Blocked Comparisons for Two Treatments.- Paired Comparisons in a Population Model and the One-Sample Problem.- The Comparison of More Than Two Treatments.- Randomized Complete Blocks.- Tests of Randomness and Independence.
3,355 citations
TL;DR: A new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping and takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows for substantially increase the computational speed and reliability of the results.
Abstract: Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.
1,765 citations
"Genetic analysis of complex traits ..." refers background in this paper
...Others have suggested trans-bands are caused by intersample correlations introduced experimentally that can be statistically corrected (Kang et al. 2008)....
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