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R C Jansen

Publications and source records attributed to R C Jansen.

17 recordsLinked to original sources
PubMed2006

Analysis of human serum by liquid chromatography-mass spectrometry: improved sample preparation and data analysis.

Discovery of biomarkers is a fast developing field in proteomics research. Liquid chromatography coupled on line to mass spectrometry (LC-MS) has become a powerful method for the sensitive detection, quantification and identification of proteins and peptides in biological fluids like serum. However, the presence of highly abundant proteins often masks those of lower abundance and thus generally prevents their detection and identification in proteomics studies. To perform future comparative analyses of samples from a serum bank of cervical cancer patients in a longitudinal and cross-sectional manner, methodology based on the depletion of high-abundance proteins followed by tryptic digestion and LC-MS has been developed. Two sample preparation methods were tested in terms of their efficiency to deplete high-abundance serum proteins and how they affect the repeatability of the LC-MS data sets. The first method comprised depletion of human serum albumin (HSA) on a dye ligand chromatographic and immunoglobulin G (IgG) on an immobilized Protein A support followed by tryptic digestion, fractionation by cation-exchange chromatography, trapping on a C18 column and reversed-phase LC-MS. The second method included depletion of the six most abundant serum proteins based on multiple immunoaffinity chromatography followed by tryptic digestion, trapping on a C18 column and reversed-phase LC-MS. Repeatability of the overall procedures was evaluated in terms of retention time and peak area for a selected number of endogenous peptides showing that the second method, besides being less time consuming, gave more repeatable results (retention time: <0.1% RSD; peak area: <30% RSD). Application of an LC-MS component detection algorithm followed by principal component analysis (PCA) enabled discrimination of serum samples that were spiked with horse heart cytochrome C from non-spiked serum and the detection of a concentration trend, which correlated to the amount of spiked horse heart cytochrome C to a level of 5 pmol cytochrome C in 2 microl original serum.

Animals↗
PubMed2005

Genetic association studies in complex disease: disentangling additional predisposing loci from associated neutral loci using a constrained - permutation approach.

In the process of genetically mapping a complex disease, the question may arise whether a certain polymorphism is the only causal variant in a region. A number of methods can answer this question, but unfortunately these methods are optimal for bi-allelic loci only. We wanted to develop a method that is more suited for multi-allelic loci, such as microsatellite markers. We propose the Additional Disease Loci Test (ADLT): the alleles at an additional locus are permuted within the subsample of haplotypes that have identical alleles at the predisposing locus. The hypothesis being tested is, whether the predisposing locus is the sole factor predisposing to the trait that is in LD with the additional locus under study. We applied ADLT to simulated datasets and a published dataset on Type 1 Diabetes, genotyped for microsatellite markers in the HLA-region. The method showed the expected number of false-positive results in the absence of additional loci, but proved to be more powerful than existing methods in the presence of additional disease loci. ADLT was especially superior in datasets with less LD or with multiple predisposing alleles. We conclude that the ADLT can be useful in identifying additional disease loci.

Diabetes Mellitus, Type 1↗
PubMed2001

Genetical genomics: the added value from segregation.

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.

Animals↗
PubMed2000

Identification of the SAAT gene involved in strawberry flavor biogenesis by use of DNA microarrays.

Fruit flavor is a result of a complex mixture of numerous compounds. The formation of these compounds is closely correlated with the metabolic changes occurring during fruit maturation. Here, we describe the use of DNA microarrays and appropriate statistical analyses to dissect a complex developmental process. In doing so, we have identified a novel strawberry alcohol acyltransferase (SAAT) gene that plays a crucial role in flavor biogenesis in ripening fruit. Volatile esters are quantitatively and qualitatively the most important compounds providing fruity odors. Biochemical evidence for involvement of the SAAT gene in formation of fruity esters is provided by characterizing the recombinant protein expressed in Escherichia coli. The SAAT enzyme showed maximum activity with aliphatic medium-chain alcohols, whose corresponding esters are major components of strawberry volatiles. The enzyme was capable of utilizing short- and medium-chain, branched, and aromatic acyl-CoA molecules as cosubstrates. The results suggest that the formation of volatile esters in fruit is subject to the availability of acyl-CoA molecules and alcohol substrates and is dictated by the temporal expression pattern of the SAAT gene(s) and substrate specificity of the SAAT enzyme(s).

Acyltransferases↗
PubMed1998

High frequency of interactions between lung cancer susceptibility genes in the mouse: mapping of Sluc5 to Sluc14.

Although several genes that cause monogenic familial cancer syndromes have been identified, susceptibility to sporadic cancer remains unresolved. Animal experiments have demonstrated multigenic control of tumor susceptibility. Recently, we described four mouse lung cancer susceptibility (Sluc) loci, the main effects of which are masked by their mutual interactions. Because such interactions can considerably affect the strategies for identification of cancer susceptibility genes in humans, it is necessary to establish whether they are common or rare. Here, we report the mapping of 10 additional Sluc loci and show that 13 of the 14 Sluc loci are involved in one or more interactions, demonstrating that interactions of tumor susceptibility genes are frequent and that they probably form complex networks.

Animals↗
PubMed1998

Genetics of refractoriness to Plasmodium falciparum in the mosquito Anopheles stephensi.

We previously selected a line of the malaria vector mosquito Anopheles stephensi refractory (resistant) to the human malaria parasite Plasmodium falciparum, using in vitro infections with P. falciparum gametocytes. This report presents data on the genetic background of refractoriness. The results of F1-crosses and backcrosses show that refractoriness to P. falciparum in our A. stephensi line is autosomal and semi-dominant to susceptibility. The expression of refractoriness is apparently affected by a cytoplasmic factor. Interpretation of data from the crosses by quantitative trait locus analysis shows that one gene or two unlinked interacting autosomal genes, or groups of closely linked genes, are involved.

Animals↗
PubMed1998

A mixture model approach to the mapping of quantitative trait loci in complex populations with an application to multiple cattle families.

A mixture model approach is presented for the mapping of one or more quantitative trait loci (QTLs) in complex populations. In order to exploit the full power of complete linkage maps the simultaneous likelihood of phenotype and a multilocus (all markers and putative QTLs) genotype is computed. Maximum likelihood estimation in our mixture models is implemented via an Expectation-Maximization algorithm: exact, stochastic or Monte Carlo EM by using a simple and flexible Gibbs sampler. Parameters include allele frequencies of markers and QTLs, discrete or normal effects of biallelic or multiallelic QTLs, and homogeneous or heterogeneous residual variances. As an illustration a dairy cattle data set consisting of twenty half-sib families has been reanalyzed. We discuss the potential which our and other approaches have for realistic multiple-QTL analyses in complex populations.

Animals↗
PubMed1997

QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping.

The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecotypes Ler and Col, both showing a low level of seed dormancy, were investigated. The analysis was performed with 98 recombinant inbred lines (RILs) derived from the cross between the two ecotypes, and these lines had previously been analysed for molecular marker composition by Lister and Dean (Norwich, UK). The analysis of germination was performed on seeds grown in three different maternal environments and each seed batch was tested in three different germination environments: in light, in darkness and in the presence of the gibberellin inhibitor paclobutrazol. Fourteen loci were identified using the multiple-QTL-model (MQM) procedure for mapping quantitative trait loci. At nine loci no significant interaction between the detection of the locus and environmental factors could be detected. However, three other distinct loci controlling the germination behaviour in the presence of the gibberellin inhibitor paclobutrazol had a much lower or no effect when germination was tested in water either in light or darkness. Two other loci affecting germination in darkness and/or light had practically no effect on germination in the presence of paclobutrazol.

Arabidopsis↗
PubMed1997

Dissection of a synthesized quantitative trait to characterize transgene interactions.

Six transgenic tobacco lines, each homozygous for the beta-glucuronidase (GUS) gene at a different locus, and wild type were selfed and intercrossed to evaluate GUS activity in all possible hemizygous, homozygous and dihybrid combinations of GUS alleles. The transgenic lines are characterized by their GUS activity (two low, three intermediate, one high), T-DNA complexity (four single-copy, two more complex single-locus) and the presence of the chicken lysozyme matrix-associated region (MAR) around the full T-DNA (two lines). Gene action and interaction was analyzed by weighted linear regression with parameters for additivity, dominance and epistasis. The analysis showed that each of the four single-copy lines acted fully additively. In contrast, the two complex single-locus lines showed classical single-locus overdominance and were epistatic dominant over all other GUS alleles. The latter is manifested in severe suppression of GUS activity in dihybrid lines, irrespective of the presence of MAR elements around the GUS gene. Such elements apparently do not protect against epistatic dominance. The quantitative data suggested that the epistatic dominance and overdominance are based on the same molecular mechanism. Our approach of a genetic analysis of quantitative variation in well-characterized transgenic lines provides a powerful tool to gain insight into complex plant traits.

Alleles↗
PubMed1996

Complex interactions of new quantitative trait loci, Sluc1, Sluc2, Sluc3, and Sluc4, that influence the susceptibility to lung cancer in the mouse.

Many complex traits, including susceptibility to lung cancer, are controlled by multiple genes--quantitative trait loci (QTLs). We facilitated the mapping of QTLs by making use of recombinant congenic strains (RCS), a system of mouse inbred strains in which the genetic complexity is reduced, and by applying MQM-mapping (multiple-QTL models or marker-QTL-marker), a multilocus method with an increased power of detecting of individual QTLs and interacting QTLs (epistasis). The mouse strain O20 develops significantly larger N-ethyl-N-nitrosourea induced lung tumours than mice of the RC strain OcB-9 (ref. 5); the latter share approximately 87.5% of their genes with strain O20 and 12.5% with strain B10.O20 (refs 6,7). QTL analysis of 222 (OcB-9 x O20) F2 mice revealed four new loci that influence susceptibility to lung cancer (Sluc genes). They are involved in two significant, partly counteracting interactions which mask their individual main effects: Sluc1 (on chromosome 19) interacts with Sluc2 (chromosome 2), and Sluc3 (chromosome 6) interacts with Sluc4 (chromosome 11). Together with the data of van Wezel et al. in the accompanying report, our results indicate that interactions between tumour susceptibility genes are a common phenomenon which complicates their mapping.

Animals↗
PubMed1996

A general Monte Carlo method for mapping multiple quantitative trait loci.

In this paper we address the mapping of multiple quantitative trait loci (QTLs) in line crosses for which the genetic data are highly incomplete. Such complicated situations occur, for instance, when dominant markers are used or when unequally informative markers are used in experiments with outbred populations. We describe a general and flexible Monte Carlo expectation-maximization (Monte Carlo EM) algorithm for fitting multiple-QTL models to such data. Implementation of this algorithm is straightforward in standard statistical software, but computation may take much time. The method may be generalized to cope with more complex models for animal and human pedigrees. A practical example is presented, where a three-QTL model is adopted in an outbreeding situation with dominant markers. The example is concerned with the linkage between randomly amplified polymorphic DNA (RAPD) markers and QTLs for partial resistance to Fusarium oxysporum in lily.

Algorithms↗
PubMed1994

High resolution of quantitative traits into multiple loci via interval mapping.

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.

Chromosome Mapping↗
PubMed1994

Controlling the type I and type II errors in mapping quantitative trait loci.

Although the interval mapping method is widely used for mapping quantitative trait loci (QTLs), it is not very well suited for mapping multiple QTLs. Here, we present the results of a computer simulation to study the application of exact and approximate models for multiple QTLs. In particular, we focus on an automatic two-stage procedure in which in the first stage "important" markers are selected in multiple regression on markers. In the second stage a QTL is moved along the chromosomes by using the preselected markers as cofactors, except for the markers flanking the interval under study. A refined procedure for cases with large numbers of marker cofactors is described. Our approach will be called MQM mapping, where MQM is an acronym for "multiple-QTL models" as well as for "marker-QTL-marker." Our simulation work demonstrates the great advantage of MQM mapping compared to interval mapping in reducing the chance of a type I error (i.e., a QTL is indicated at a location where actually no QTL is present) and in reducing the chance of a type II error (i.e., a QTL is not detected).

Chromosome Mapping↗
PubMed1993

Interval mapping of multiple quantitative trait loci.

The interval mapping method is widely used for the mapping of quantitative trait loci (QTLs) in segregating generations derived from crosses between inbred lines. The efficiency of detecting and the accuracy of mapping multiple QTLs by using genetic markers are much increased by employing multiple QTL models instead of the single QTL models (and no QTL models) used in interval mapping. However, the computational work involved with multiple QTL models is considerable when the number of QTLs is large. In this paper it is proposed to combine multiple linear regression methods with conventional interval mapping. This is achieved by fitting one QTL at a time in a given interval and simultaneously using (part of) the markers as cofactors to eliminate the effects of additional QTLs. It is shown that the proposed method combines the easy computation of the single QTL interval mapping method with much of the efficiency and accuracy of multiple QTL models.

Chromosome Mapping↗