Biomedical subjects

Matthias Frisch

Publications and source records attributed to Matthias Frisch.

7 recordsLinked to original sources

PubMed2006

Marker-based prediction of the parental genome contribution to inbred lines derived from biparental crosses.

Molecular markers can be employed to predict the parental genome contribution to inbred lines. The proportion alpha of alleles originating from parent P1 at markers polymorphic between the parental lines P1 and P2 is commonly used as a predictor for the genome contribution of parent P1 to an offspring line. Our objectives were to develop a new marker-based predictor xi for the parental genome contribution, which takes into account not only the alleles at marker loci but also their map distance, and to compare the prediction precision of xi with that of alternative methods. We derived formulas for xi for inbreds derived from biparental crosses (F1 and backcrosses) with the single-seed descent or double-haploid method and presented an extension xi* possessing statistical optimum properties. In a simulation study, alpha showed a systematic overestimation of large parental genome contribution that was not observed for xi. The mean squared prediction error of xi was at least 50% smaller than that of alpha for linkage maps with unequal distances between adjacent markers. A data set from a study on plant variety protection in maize was used to illustrate the application of xi. We conclude that xi provides substantially greater prediction precision than the commonly used predictor alpha in a broad range of applications in genetics and breeding.

Computer Simulation↗

PubMed2005

Androgen receptor binding sites identified by a GREF_GATA model.

Changes in transcriptional regulation can be permissive for tumor progression by allowing for selective growth advantage of tumor cells. Tumor suppressors can effectively inhibit this process. The PMEPA1 gene, a potent inhibitor of prostate cancer cell growth is an androgen-regulated gene. We addressed the question of whether or not androgen receptor can directly bind to specific PMEPA1 promoter upstream sequences. To test this hypothesis we extended in silico prediction of androgen receptor binding sites by a modeling approach and verified the actual binding by in vivo chromatin immunoprecipitation assay. Promoter upstream sequences of highly androgen-inducible genes were examined from microarray data of prostate cancer cells for transcription factor binding sites (TFBSs). Results were analyzed to formulate a model for the description of specific androgen receptor binding site context in these sequences. In silico analysis and subsequent experimental verification of the selected sequences suggested that a model that combined a GREF and a GATA TFBS was sufficient for predicting a class of functional androgen receptor binding sites. The GREF matrix family represents androgen receptor, glucocorticoid receptor and progesterone receptor binding sites and the GATA matrix family represents GATA binding protein 1-6 binding sites. We assessed the regulatory sequences of the PMEPA1 gene by comparing our model-based GREF_GATA predictions to weight matrix-based predictions. Androgen receptor binding to predicted promoter upstream sequences of the PMEPA1 gene was confirmed by chromatin immunoprecipitation assay. Our results suggested that androgen receptor binding to cognate elements was consistent with the GREF_GATA model. In contrast, using only single GREF weight matrices resulted in additional matches, apparently false positives. Our findings indicate that complex models based on datasets selected by biological function can be superior predictors as they recognize TFBSs in their functional context.

Binding Sites↗

PubMed2005

Linkage disequilibrium in European elite maize germplasm investigated with SSRs.

Information about the extent and genomic distribution of linkage disequilibrium (LD) is of fundamental importance for association mapping. The main objectives of this study were to (1) investigate genetic diversity within germplasm groups of elite European maize (Zea mays L.) inbred lines, (2) examine the population structure of elite European maize germplasm, and (3) determine the extent and genomic distribution of LD between pairs of simple sequence repeat (SSR) markers. We examined genetic diversity and LD in a cross section of European and US elite breeding material comprising 147 inbred lines genotyped with 100 SSR markers. For gene diversity within each group, significant (P<0.05) differences existed among the groups. The LD was significant (P<0.05) for 49% of the SSR marker pairs in the 80 flint lines and for 56% of the SSR marker pairs in the 57 dent lines. The ratio of linked to unlinked loci in LD was 1.1 for both germplasm groups. The high incidence of LD suggests that the extent of LD between SSR markers should allow the detection of marker-phenotype associations in a genome scan. However, our results also indicate that a high proportion of the observed LD is generated by forces, such as relatedness, population stratification, and genetic drift, which cause a high risk of detecting false positives in association mapping.

Chromosome Mapping↗

PubMed2005

Selection theory for marker-assisted backcrossing.

Marker-assisted backcrossing is routinely applied in breeding programs for gene introgression. While selection theory is the most important tool for the design of breeding programs for improvement of quantitative characters, no general selection theory is available for marker-assisted backcrossing. In this treatise, we develop a theory for marker-assisted selection for the proportion of the genome originating from the recurrent parent in a backcross program, carried out after preselection for the target gene(s). Our objectives were to (i) predict response to selection and (ii) give criteria for selecting the most promising backcross individuals for further backcrossing or selfing. Prediction of response to selection is based on the marker linkage map and the marker genotype of the parent(s) of the backcross population. In comparison to standard normal distribution selection theory, the main advantage of our approach is that it considers the reduction of the variance in the donor genome proportion due to selection. The developed selection criteria take into account the marker genotype of the candidates and consider whether these will be used for selfing or backcrossing. Prediction of response to selection is illustrated for model genomes of maize and sugar beet. Selection of promising individuals is illustrated with experimental data from sugar beet. The presented approach can assist geneticists and breeders in the efficient design of gene introgression programs.

Alleles↗

PubMed2004

Genome-wide in silico mapping of scaffold/matrix attachment regions in Arabidopsis suggests correlation of intragenic scaffold/matrix attachment regions with gene expression.

We carried out a genome-wide prediction of scaffold/matrix attachment regions (S/MARs) in Arabidopsis. Results indicate no uneven distribution on the chromosomal level but a clear underrepresentation of S/MARs inside genes. In cases where S/MARs were predicted within genes, these intragenic S/MARs were preferentially located within the 5'-half, most prominently within introns 1 and 2. Using Arabidopsis whole-genome expression data generated by the massively parallel signature sequencing methodology, we found a negative correlation between S/MAR-containing genes and transcriptional abundance. Expressed sequence tag data correlated the same way with S/MAR-containing genes. Thus, intragenic S/MARs show a negative correlation with transcription level. For various genes it has been shown experimentally that S/MARs can function as transcriptional regulators and that they have an implication in stabilizing expression levels within transgenic plants. On the basis of a genome-wide in silico S/MAR analysis, we found a significant correlation between the presence of intragenic S/MARs and transcriptional down-regulation.

Arabidopsis↗

PubMed2002

S/MARt DB: a database on scaffold/matrix attached regions.

S/MARt DB, the S/MAR transaction database, is a relational database covering scaffold/matrix attached regions (S/MARs) and nuclear matrix proteins that are involved in the chromosomal attachment to the nuclear scaffold. The data are mainly extracted from original publications, but a World Wide Web interface for direct submissions is also available. S/MARt DB is closely linked to the TRANSFAC database on transcription factors and their binding sites. It is freely accessible through the World Wide Web (http://transfac.gbf.de/SMARtDB/) for non-profit research.

Animals↗

PubMed2002

In silico prediction of scaffold/matrix attachment regions in large genomic sequences.

Scaffold/matrix attachment regions (S/MARs) are essential regulatory DNA elements of eukaryotic cells. They are major determinants of locus control of gene expression and can shield gene expression from position effects. Experimental detection of S/MARs requires substantial effort and is not suitable for large-scale screening of genomic sequences. In silico prediction of S/MARs can provide a crucial first selection step to reduce the number of candidates. We used experimentally defined S/MAR sequences as the training set and generated a library of new S/MAR-associated, AT-rich patterns described as weight matrices. A new tool called SMARTest was developed that identifies potential S/MARs by performing a density analysis based on the S/MAR matrix library (http://www.genomatix.de/cgi-bin/smartest_pd/smartest.pl). S/MAR predictions were evaluated by using six genomic sequences from animal and plant for which S/MARs and non-S/MARs were experimentally mapped. SMARTest reached a sensitivity of 38% and a specificity of 68%. In contrast to previous algorithms, the SMARTest approach does not depend on the sequence context and is suitable to analyze long genomic sequences up to the size of whole chromosomes. To demonstrate the feasibility of large-scale S/MAR prediction, we analyzed the recently published chromosome 22 sequence and found 1198 S/MAR candidates.

Algorithms↗