Biomedical subjects

Ina Hoeschele

Publications and source records attributed to Ina Hoeschele.

10 recordsLinked to original sources

PubMed2006

Nucleoplasmin facilitates reprogramming and in vivo development of bovine nuclear transfer embryos.

Successful cloning by somatic cell nuclear transfer (NT) involves an oocyte-driven transition in gene expression from an inherited somatic pattern, to an embryonic form, during early development. This reprogramming of gene expression is thought to require the remodeling of somatic chromatin and as such, faulty and/or incomplete chromatin remodeling may contribute to the aberrant gene expression and abnormal development observed in NT embryos. We used a novel approach to supplement the oocyte with chromatin remodeling factors and determined the impact of these molecules on gene expression and development of bovine NT embryos. Nucleoplasmin (NPL) or polyglutamic acid (PGA) was injected into bovine oocytes at different concentrations, either before (pre-NT) or after (post-NT) NT. Pre-implantation embryos were then transferred to bovine recipients to assess in vivo development. Microinjection of remodeling factors resulted in apparent differences in the rate of blastocyst development and in pregnancy initiation rates in both NPL- and PGA-injected embryos, and these differences were dependent on factor concentration and/or the time of injection. Post-NT NPL-injected embryos that produced the highest rate of pregnancy also demonstrated differentially expressed genes relative to pre-NT NPL embryos and control NT embryos, both of which had lower pregnancy rates. Over 200 genes were upregulated following post-NT NPL injection. Several of these genes were previously shown to be downregulated in NT embryos when compared to bovine IVF embryos. These data suggest that addition of chromatin remodeling factors to the oocyte may improve development of NT embryos by facilitating reprogramming of the somatic nucleus.

Animals↗

PubMed2005

Approximating identity-by-descent matrices using multiple haplotype configurations on pedigrees.

Identity-by-descent (IBD) matrix calculation is an important step in quantitative trait loci (QTL) analysis using variance component models. To calculate IBD matrices efficiently for large pedigrees with large numbers of loci, an approximation method based on the reconstruction of haplotype configurations for the pedigrees is proposed. The method uses a subset of haplotype configurations with high likelihoods identified by a haplotyping method. The new method is compared with a Markov chain Monte Carlo (MCMC) method (Loki) in terms of QTL mapping performance on simulated pedigrees. Both methods yield almost identical results for the estimation of QTL positions and variance parameters, while the new method is much more computationally efficient than the MCMC approach for large pedigrees and large numbers of loci. The proposed method is also compared with an exact method (Merlin) in small simulated pedigrees, where both methods produce nearly identical estimates of position-specific kinship coefficients. The new method can be used for fine mapping with joint linkage disequilibrium and linkage analysis, which improves the power and accuracy of QTL mapping.

Algorithms↗

PubMed2005

Finite mixture model analysis of microarray expression data on samples of uncertain biological type with application to reproductive efficiency.

Common goals of microarray experiments are the detection of genes that are differentially expressed between several biological types and the construction of classifiers that predict biological type of samples. Here we consider a situation where there is no training data. There is considerable interest in comparing expression profiles associated with successful pregnancies (SP) and unsuccessful pregnancies (UP) in model and farm animals. Successful pregnancy rate is known to be much higher in embryos generated by in vitro fertilization (IVF) than in nuclear transfer (NT) embryos, and higher under induced ovulation for large follicles (LF) than for small follicles (SF). The tasks of identifying genes differentially expressed between SP and UP, and predicting SP for future samples are not well accomplished by comparing IVF and NT, or LF and SF. A suitable method is finite mixture model analysis (FMMA), which models each observed class (IVF and NT, or LF and SF) as a mixture of two distributions, one for SP and one for UP, with different known or unknown proportions (here known to be 0.50 SP for IVF and 0.02 SP for NT). The means of the two distributions differ for the differentially expressed genes, which we identify via a likelihood ratio test. We confirm by simulation that FMMA strongly outperforms hierarchical clustering and linear discriminant analysis using the known class labels (NT, IVF). We apply FMMA to a real data set on IVF and NT embryos, and compute their posterior probabilities of SP, which confirm our prior knowledge of the SP proportions for IVF and NT.

Animals↗

PubMed2005

Genetical genomics analysis of a yeast segregant population for transcription network inference.

Genetic analysis of gene expression in a segregating population, which is expression profiled and genotyped at DNA markers throughout the genome, can reveal regulatory networks of polymorphic genes. We propose an analysis strategy with several steps: (1) genome-wide QTL analysis of all expression profiles to identify eQTL confidence regions, followed by fine mapping of identified eQTL; (2) identification of regulatory candidate genes in each eQTL region; (3) correlation analysis of the expression profiles of the candidates in any eQTL region with the gene affected by the eQTL to reduce the number of candidates; (4) drawing directional links from retained regulatory candidate genes to genes affected by the eQTL and joining links to form networks; and (5) statistical validation and refinement of the inferred network structure. Here, we apply an initial implementation of this strategy to a segregating yeast population. In 65, 7, and 28% of the identified eQTL regions, a single candidate regulatory gene, no gene, or more than one gene was retained in step 3, respectively. Overall, 768 putative regulatory links were retained, 331 of which are the strongest candidate links, as they were retained in the expression correlation analysis and were located within or near an eQTL subregion identified by a multimarker analysis separating multiple linked QTL. One or several biological processes were statistically significantly overrepresented in independent network structures or in highly interconnected subnetworks. Most of the transcription factors found in the inferred network had a putative regulatory link to only one other gene or exhibited cis-regulation.

DNA↗

PubMed2005

A note on joint versus gene-specific mixed model analysis of microarray gene expression data.

Currently, linear mixed model analyses of expression microarray experiments are performed either in a gene-specific or global mode. The joint analysis provides more flexibility in terms of how parameters are fitted and estimated and tends to be more powerful than the gene-specific analysis. Here we show how to implement the gene-specific linear mixed model analysis as an exact algorithm for the joint linear mixed model analysis. The gene-specific algorithm is exact, when the mixed model equations can be partitioned into unrelated components: One for all global fixed and random effects and the others for the gene-specific fixed and random effects for each gene separately. This unrelatedness holds under three conditions: (1) any gene must have the same number of replicates or probes on all arrays, but these numbers can differ among genes; (2) the residual variance of the (transformed) expression data must be homogeneous or constant across genes (other variance components need not be homogeneous) and (3) the number of genes in the experiment is large. When these conditions are violated, the gene-specific algorithm is expected to be nearly exact.

Algorithms↗

PubMed2004

Identification of differentially expressed genes in individual bovine preimplantation embryos produced by nuclear transfer: improper reprogramming of genes required for development.

Using an interwoven-loop experimental design in conjunction with highly conservative linear mixed model methodology using estimated variance components, 18 genes differentially expressed between nuclear transfer (NT)- and in vitro fertilization (IVF)-produced embryos were identified. The set is comprised of three intermediate-filament protein genes (cytokeratin 8, cytokeratin 19, and vimentin), three metabolic genes (phosphoribosyl pyrophosphate synthetase 1, mitochondrial acetoacetyl-coenzyme A thiolase, and alpha-glucosidase), two lysosomal-related genes (prosaposin and lysosomal-associated membrane protein 2), and a gene associated with stress responses (heat shock protein 27) along with major histocompatibility complex class I, nidogen 2, a putative transport protein, heterogeneous nuclear ribonuclear protein K, mitochondrial 16S rRNA, and ES1 (a zebrafish orthologue of unknown function). The three remaining genes are novel. To our knowledge, this is the first report comparing individual embryos produced by NT and IVF using cDNA microarray technology for any species, and it uses a rigorous experimental design that emphasizes statistical significance to identify differentially expressed genes between NT and IVF embryos in cattle.

Animals↗

PubMed2004

Discovery of meaningful associations in genomic data using partial correlation coefficients.

MOTIVATION: A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS: The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY: Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION: Supplementary information can be found at: URL to be decided.

Algorithms↗

PubMed2004

Conditional probability methods for haplotyping in pedigrees.

Efficient haplotyping in pedigrees is important for the fine mapping of quantitative trait locus (QTL) or complex disease genes. To reconstruct haplotypes efficiently for a large pedigree with a large number of linked loci, two algorithms based on conditional probabilities and likelihood computations are presented. The first algorithm (the conditional probability method) produces a single, approximately optimal haplotype configuration, with computing time increasing linearly in the number of linked loci and the pedigree size. The other algorithm (the conditional enumeration method) identifies a set of haplotype configurations with high probabilities conditional on the observed genotype data for a pedigree. Its computing time increases less than exponentially with the size of a subset of the set of person-loci with unordered genotypes and linearly with its complement. The size of the subset is controlled by a threshold parameter. The set of identified haplotype configurations can be used to estimate the identity-by-descent (IBD) matrix at a map position for a pedigree. The algorithms have been tested on published and simulated data sets. The new haplotyping methods are much faster and provide more information than several existing stochastic and rule-based methods. The accuracies of the new methods are equivalent to or better than those of these existing methods.

Animals↗

PubMed2003

ATP-binding cassette transporter A1 locus is not a major determinant of HDL-C levels in a population at high risk for coronary heart disease.

ATP-binding cassette transporter A1 (ABCA1) transports cellular cholesterol to lipid-poor apolipoproteins. Mutations in the ABCA1 gene are linked to rare phenotypes, familial hypoalphalipoproteinemia (FHA) and Tangier disease (TD), characterized by markedly decreased plasma high-density lipoprotein cholesterol (HDL-C) levels. The aim was to test if the ABCA1 locus is a major locus regulating HDL-C levels in the homogenous Finnish population with a high prevalence of coronary heart disease (CHD). Firstly, the ABCA1 locus was tested for linkage to HDL-C levels in 35 families with premature CHD and low HDL-C levels. Secondly, 62 men with low HDL-C levels and CHD were screened for the five mutations known to cause FHA. Thirdly, polymorphisms of the ABCA1 gene were tested for an association with HDL-C levels in a population sample of 515 subjects. The ABCA1 locus was not linked to HDL-C levels in the CHD families, and no carriers of the FHA mutations were found. The AA596 genotype was associated with higher HDL-C levels compared with the GG and GA genotypes in the women, but not in the men. The G596A genotypes explained 4% and the A2589G genotypes 3% of the variation in plasma HDL-C levels in women. The data suggest that the ABCA1 locus is of minor importance in the regulation of HDL-C in Finns.

ATP Binding Cassette Transporter 1↗

PubMedDate not supplied

Bayesian QTL mapping using skewed Student-t distributions.

In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.

Algorithms↗