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R C Jansen

Publications and source records attributed to R C Jansen.

9 recordsLinked to original sources
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↗