Author
Ritsert C. Jansen
Other affiliations: Utrecht University, Hanze University of Applied Sciences, Queen Mary University of London ...read more
Bio: Ritsert C. Jansen is an academic researcher from University of Groningen. The author has contributed to research in topics: Quantitative trait locus & Expression quantitative trait loci. The author has an hindex of 58, co-authored 145 publications receiving 17558 citations. Previous affiliations of Ritsert C. Jansen include Utrecht University & Hanze University of Applied Sciences.
Papers published on a yearly basis
Papers
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University Medical Center Groningen1, University of Tartu2, University of Exeter3, Greifswald University Hospital4, National Institute for Health and Welfare5, University of Washington6, Churchill Hospital7, Technische Universität München8, University of Queensland9, University Medical Center Utrecht10, Erasmus University Rotterdam11, Leiden University Medical Center12, National Institutes of Health13, University College London14, University of Auckland15, QIMR Berghofer Medical Research Institute16, Wellcome Trust Centre for Human Genetics17, University of Düsseldorf18, Ludwig Maximilian University of Munich19, University of Groningen20, Group Health Cooperative21, University of Helsinki22, Wellcome Trust Sanger Institute23
TL;DR: Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
Abstract: Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3' UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
1,627 citations
TL;DR: In this paper, a general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci and markers] in segregating generations obtained from line crosses.
Abstract: A very general method is described for multiple linear regression of a quantitative phenotype on genotype [putative quantitative trait loci (QTLs) and markers] in segregating generations obtained from line crosses. The method exploits two features, (a) the use of additional parental and F1 data, which fixes the joint QTL effects and the environmental error, and (b) the use of markers as cofactors, which reduces the genetic background noise. As a result, a significant increase of QTL detection power is achieved in comparison with conventional QTL mapping. The core of the method is the completion of any missing genotypic (QTL and marker) observations, which is embedded in a general and simple expectation maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. The method is described in detail for the analysis of an F2 generation. Because of the generality of the approach, it is easily applicable to other generations, such as backcross progenies and recombinant inbred lines. An example is presented in which multiple QTLs for plant height in tomato are mapped in an F2 progeny, using additional data from the parents and their F1 progeny.
1,064 citations
Vanderbilt University1, University of Southern California2, University of Texas MD Anderson Cancer Center3, University of Wisconsin-Madison4, University of California, Los Angeles5, National Center for Genome Resources6, Portland VA Medical Center7, University of Colorado Boulder8, University of Pennsylvania9, Hannover Medical School10, Johns Hopkins University11, Oregon Health & Science University12, Cornell University13, University of Michigan14, University of Tennessee Health Science Center15, Washington University in St. Louis16, University of Toronto17, University of Memphis18, Medical Research Council19, University of Massachusetts Medical School20, Hebrew University of Jerusalem21, Université de Montréal22, Purdue University23, University of California, Davis24, Academy of Sciences of the Czech Republic25, University at Buffalo26, Emory University27, University of Cincinnati28, University of Texas Southwestern Medical Center29, New York University30, University of Groningen31, Rutgers University32, Stanford University33, Max Planck Society34, National Institutes of Health35, University of Alabama at Birmingham36, International Livestock Research Institute37, Heidelberg University38, Medical College of Wisconsin39, Icahn School of Medicine at Mount Sinai40, Oak Ridge National Laboratory41, Charité42, University of Antwerp43, RWTH Aachen University44, Paul Sabatier University45, University of California, San Francisco46, McGill University47, Pasteur Institute48, University of Western Australia49, Yale University50, University of Oxford51, Case Western Reserve University52, Roswell Park Cancer Institute53, University of Kentucky54, University of Helsinki55, University of Nebraska–Lincoln56, Harvard University57, Merck & Co.58, King's College London59, Northwestern University60, Shriners Hospitals for Children61, Thomas Jefferson University62, Novartis63, University of North Carolina at Chapel Hill64, Southern Illinois University Carbondale65, University of Rochester66
TL;DR: The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way the authors approach human health and disease.
Abstract: The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.
1,040 citations
01 Jan 2001
TL;DR: Genetical genomics as discussed by the authors combines the power of genomics and genetics in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.
Abstract: The recent successes of genome-wide expression profiling in biology tend to overlook the power of genetics. We here propose a merger of genomics and genetics into ‘genetical genomics’. This involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci. Genetical genomics will combine the power of two different worlds in a way that is likely to become instrumental in the further unravelling of metabolic, regulatory and developmental pathways.
952 citations
TL;DR: This work proposes a merger of genomics and genetics into 'genetical genomics', which involves expression profiling and marker-based fingerprinting of each individual of a segregating population, and exploits all the statistical tools used in the analysis of quantitative trait loci.
924 citations
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28 Jul 2005
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。
18,940 citations
TL;DR: TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.
Abstract: Summary: Association analyses that exploit the natural diversity of a genome to map at very high resolutions are becoming increasingly important. In most studies, however, researchers must contend with the confounding effects of both population and family structure. TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure. For result interpretation, the program allows for linkage disequilibrium statistics to be calculated and visualized graphically. Database browsing and data importation is facilitated by integrated middleware. Other features include analyzing insertions/deletions, calculating diversity statistics, integration of phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable, user manual, example data sets and tutorial document are freely available at http://www. maizegenetics.net/tassel. The source code for TASSEL can be found at http://sourceforge.net/projects/tassel.
5,460 citations
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
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
4,948 citations
01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.
4,833 citations