Biology subjects

Brockmann, G. A.

Publications and source records attributed to Brockmann, G. A..

3 recordsLinked to original sources

bioRxiv2022

LMM-MQM time series mapping - An application in a murine advanced intercross line identifies novel growth QTLs

The Berlin Fat Mouse Inbred line 860 (BFMI860) is a mouse model for juvenile obesity. Previously, a recessive major effect locus (jObes1) was identified on chromosome 3 explaining around 26% of the body weight variance in an BFMI860xC57BL/6NCrl advanced intercross line. The aim of this study was to discover additional QTL. Time series body weight data were modeled using linear mixed models (LMM), while a multiple QTL mapping (MQM) approach compensated for the jObes1 locus effect. LMM-MQM identified five additional loci significantly associated with body weight. Variance explained by the jObes1 locus increased to 38.1% when using LMM-MQM mapping, while the additional loci explained between 2.0% and 3.9% of the body weight variance. Several positional candidate genes within the novel QTL regions were found in KEGG pathways for insulin signaling and insulin resistance. Strong distortion with preference for the BFMI allele was observed within a newly identified QTL containing the well-known Foxo1 regulator of adipocyte differentiation. Here, we present a novel method for QTL detection in time series data: LMM-MQM time series mapping. We show that our method is more powerful in detecting QTLs compared to single timepoint mapping approaches. Thus, the time series structure should be considered for optimal detection of small effect QTLs. LMM-MQM time series mapping can be used to find genetic determinants of all kind of "phenotypes over time" be it lactation curves in cattle, plant biomass, drug clearance in human clinical trials, or cognitive decline during disease.

genetics↗

bioRxiv2021

Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

Background/ObjectivesWhile direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 (BFMI). Subjects/Methods341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays (MegaMUGA). TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data was used to test for overrepresentation of non-synonymous SNPs in TRD regions. Genetic incompatibilities were tested using the Bateson-Dobzhansky-Muller two-locus model. Results62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more non-synonymous SNPs than randomly selected regions (182 vs. 125.9 {+/-} 17.0, P < 1x10-4). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions (P(BF) < 0.05). Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA (siRNA) biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein-protein interactions influencing obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6 and Nme8. ConclusionsUsing an AIL significantly improves the resolution at which we can investigate TRD. Our analysis implicates distortion of protein-protein interactions as well as meiotic drive as the underlying mechanisms leading to the observed TRD in our AIL. Furthermore, genes with large amounts of non-synonymous SNPs located in TRD regions are more likely to be involved in pathways that are related to the phenotypic differences between the parental strains. Genes in these TRD regions provide new targets for investigating genetic adaptation, protein-protein interactions, and determinants of complex traits such as obesity.

genetics↗

bioRxiv2020

Content and performance of the MiniMUGA genotyping array, a new tool to improve rigor and reproducibility in mouse research

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well annotated genome, wealth of genetic resources and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost effective, informative and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole genome sequencing. Here we describe the content and performance of a new Mouse Universal Genotyping Array (MUGA). MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: 1) chromosomal sex determination, 2) discrimination between substrains from multiple commercial vendors, 3) diagnostic SNPs for popular laboratory strains, 4) detection of constructs used in genetically engineered mice, and 5) an easy to interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA we genotyped 6,899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived and recombinant inbred lines. Here we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, the extension of the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. There is preliminary but striking evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the main stock for a significant action of the genotyped inbred samples. We conclude that MiniMUGA is a valuable platform for genetic QC and important new tool to the increase rigor and reproducibility of mouse research.

genetics↗