Biomedical subjects

Gudrun A Brockmann

Publications and source records attributed to Gudrun A Brockmann.

7 recordsLinked to original sources

PubMed2006

Genetic, sex, and diet effects on body weight and obesity in the Berlin Fat Mouse Inbred lines.

Mouse lines long-term selected for high fatness offer the possibility to identify individual genes involved in the development of obesity. The Berlin Fat Mouse (BFM) line has been selected for low protein content and afterward for high fatness. Three Berlin Fat Mouse Inbred (BFMI) lines, which are derivates of the selection line BFM and an unselected control line (C57BL/6; B6) were systematically phenotyped between 3 and 20 wk. The body weights and body compositions were measured on a weekly basis. We demonstrated that the BFMI lines dispose of more body weight, body fat mass, and body lean mass than the control line B6 because of a better feed efficiency in these lines. In contrast to other growth-selected mouse lines, the BFMI lines exhibited a general increase in body fat mass but only a marginal increase in body lean mass. The three BFMI lines also showed line- and sex-specific patterns and varied in their response to high-fat diet. The phenotypic differences between the BFMI lines can be traced back to different sets of fixed alleles contributing to fat accumulation and diet-induced obesity. Our results demonstrate that the genetically related BFMI lines are novel models to study the genetic as well as the nutritional aspects of obesity.

Adipose Tissue↗

PubMed2005

Chromosome-wise dissection of the genome of the extremely big mouse line DU6i.

The extreme high-body-weight-selected mouse line DU6i is a polygenic model for growth research, harboring many small-effect QTL. We dissected the genome of this line into 19 autosomes and the Y chromosome by the construction of a new panel of chromosome substitution strains (CSS). The DU6i chromosomes were transferred to a DBA/2 mice genetic background by marker-assisted recurrent backcrossing. Mitochondria and the X chromosome were of DBA/2 origin in the backcross. During the construction of these novel strains, >4000 animals were generated, phenotyped, and genotyped. Using these data, we studied the genetic control of variation in body weight and weight gain at 21, 42, and 63 days. The unique data set facilitated the analysis of chromosomal interaction with sex and parent-of-origin effects. All analyzed chromosomes affected body weight and weight gain either directly or in interaction with sex or parent of origin. The effects were age specific, with some chromosomes showing opposite effects at different stages of development.

Age Distribution↗

PubMed2005

Simultaneous mapping of epistatic QTL in DU6i x DBA/2 mice.

We have mapped epistatic quantitative trait loci (QTL) in an F2 cross between DU6i x DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%-128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype-phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.

Animals↗

PubMed2004

QTLs for pre- and postweaning body weight and body composition in selected mice.

In an intercross between the high-body-weight-selected mouse line NMRI8 and the inbred line DBA/2, we analyzed genetic effects on growth during the suckling period and after weaning during the juvenile phase of development. QTL mapping results indicated that a switch of gene activation might occur at the age of three weeks when animals are weaned. We found QTLs for body weight with major effects at the age of two and three weeks when animals are fed by their mothers, and QTLs with highest effects after weaning when animals have to live on their own under ad libitum access to food. Specific epistatic effects on body weight at two and three weeks and epistatic interaction influencing growth after weaning support this finding. QTL effects explained the greatest variance during puberty when animals grow fastest and become fertile. In the present study, all except one QTL effect for early body weight had dominance variance components. These might result from direct single-locus-dominant allelic expression, but also from the identified epistatic interaction between different QTLs that we have found for body weight at all ages. Beside body weight, body composition traits (muscle weight, reproductive fat weight, weight of inner organs) were analyzed. Sex-dimorphic QTLs were found for body weight and fat deposition. The identified early-growth QTLs could be the target of epigenetic modifications which might influence body weight at later ages.

Age Factors↗

PubMed2003

Expressionview: visualization of quantitative trait loci and gene-expression data in Ensembl.

We present here a software tool for combined visualization of gene-expression data and quantitative trait loci (QTL). The application is implemented as an extension to the Ensembl project and caters for a direct transition from microarray experiments of gene or protein expression levels to the genomic context of individual genes and QTL. It supports the visualization of gene clusters and the selection of functional candidate genes in the context of research on complex traits.

Animals↗

PubMed2002

Using mouse models to dissect the genetics of obesity.

Mice have proved to be powerful models for understanding obesity in humans and farm animals. Single-gene mutants and genetically modified mice have been used successfully to discover genes and pathways that can regulate body weight. For polygenic obesity, the most common pattern of inheritance, many quantitative trait loci (QTLs) have been mapped in crosses between selected and inbred mouse lines. Most QTL effects are additive, and diet, age and gender modify the genetic effects. Congenic, recombinant inbred, advanced intercross, and chromosome substitution strains are needed to map QTLs finely, to identify the genes underlying the traits, and to examine interactions between them.

Age Factors↗

PubMedDate not supplied

Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle.

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.

Animals↗