Daniel Pomp

Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors

In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.

The nature and identification of quantitative trait loci: a community’s view

This white paper by eighty members of the Complex Trait Consortium presents a communitys view on the approaches and statistical analyses that are needed for the identification of genetic loci that determine quantitative traits. Quantitative trait loci (QTLs) can be identified in several ways, but is there a definitive test of whether a candidate locus actually corresponds to a specific QTL?

A medium-density genetic linkage map of the bovine genome

A cattle genetic linkage map was constructed which covers more than 95 percent of the bovine genome at medium density. Seven hundred and forty six DNA polymorphisms were genotyped in cattle families which comprise 347 individuals in full sibling pedigrees. Seven hundred and three of the loci are linked to at least one other locus. All linkage groups are assigned to chromosomes, and all are orientated with regards to the centromere. There is little overall difference in the lengths of the bull and cow linkage maps although there are individual differences between maps of chromosomes. One hundred and sixty polymorphisms are in or near genes, and the resultant genome-wide comparative analyses indicate that while there is greater conservation of synteny between cattle and humans compared with mice, the conservation of gene order between cattle and humans is much less than would be expected from the conservation of synteny. This map provides a basis for high-resolution mapping of the bovine genome with physical resources such as Yeast and Bacterial Artificial Chromosomes as well as providing the underpinning for the interpolation of information from the Human Genome Project.USDA-MARC family and data for validating this family. P. Creighton, C. Skidmore, T. Holm, and A. Georgoudis provided some validation data for the BOVMAP families. R. Fries, S. Johnson, S. Solinas Toldo, and A. Mezzelani kindly made some of their FISH assignments available before publication. We wish to thank all those researchers who kindly sent us probes and DNA primers.

Genetic analysis of complex traits in the emerging Collaborative Cross

The Collaborative Cross (CC) is a mouse recombinant inbred strain panel that is being developed as a resource for mammalian systems genetics. Here we describe an experiment that uses partially inbred CC lines to evaluate the genetic properties and utility of this emerging resource. Genome-wide analysis of the incipient strains reveals high genetic diversity, balanced allele frequencies, and dense, evenly distributed recombination sites-all ideal qualities for a systems genetics resource. We map discrete, complex, and biomolecular traits and contrast two quantitative trait locus (QTL) mapping approaches. Analysis based on inferred haplotypes improves power, reduces false discovery, and provides information to identify and prioritize candidate genes that is unique to multifounder crosses like the CC. The number of expression QTLs discovered here exceeds all previous efforts at eQTL mapping in mice, and we map local eQTL at 1-Mb resolution. We demonstrate that the genetic diversity of the CC, which derives from random mixing of eight founder strains, results in high phenotypic diversity and enhances our ability to map causative loci underlying complex disease-related traits.

Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease.

BackgroundObesity is a particularly complex disease that at least partially involves genetic and environmental perturbations to gene-networks connecting the hypothalamus and several metabolic tissues, resulting in an energy imbalance at the systems level.ResultsTo provide an inter-tissue view of obesity with respect to molecular states that are associated with physiological states, we developed a framework for constructing tissue-to-tissue coexpression networks between genes in the hypothalamus, liver or adipose tissue. These networks have a scale-free architecture and are strikingly independent of gene-gene coexpression networks that are constructed from more standard analyses of single tissues. This is the first systematic effort to study inter-tissue relationships and highlights genes in the hypothalamus that act as information relays in the control of peripheral tissues in obese mice. The subnetworks identified as specific to tissue-to-tissue interactions are enriched in genes that have obesity-relevant biological functions such as circadian rhythm, energy balance, stress response, or immune response.ConclusionsTissue-to-tissue networks enable the identification of disease-specific genes that respond to changes induced by different tissues and they also provide unique details regarding candidate genes for obesity that are identified in genome-wide association studies. Identifying such genes from single tissue analyses would be difficult or impossible.

Quantitative trait loci for physical activity traits in mice

The genomic locations and identities of the genes that regulate voluntary physical activity are presently unknown. The purpose of this study was to search for quantitative trait loci (QTL) that are linked with daily mouse running wheel distance, duration, and speed of exercise. F(2) animals (n = 310) derived from high active C57L/J and low active C3H/HeJ inbred strains were phenotyped for 21 days. After phenotyping, genotyping with a fully informative single-nucleotide polymorphism panel with an average intermarker interval of 13.7 cM was used. On all three activity indexes, sex and strain were significant factors, with the F(2) animals similar to the high active C57L/J mice in both daily exercise distance and duration of exercise. In the F(2) cohort, female mice ran significantly farther, longer, and faster than male mice. QTL analysis revealed no sex-specific QTL but at the 5% experimentwise significance level did identify one QTL for duration, one QTL for distance, and two QTL for speed. The QTL for duration (DUR13.1) and distance (DIST13.1) colocalized with the QTL for speed (SPD13.1). Each of these QTL accounted for approximately 6% of the phenotypic variance, whereas SPD9.1 (chromosome 9, 7 cM) accounted for 11.3% of the phenotypic variation. DUR13.1, DIST13.1, SPD13.1, and SPD9.1 were subsequently replicated by haplotype association mapping. The results of this study suggest a genetic basis of voluntary activity in mice and provide a foundation for future candidate gene studies.

Mouse chromosome 2

mKimmel Cancer Center, Jefferson Medical College, Department of Microbiology and Immunology, 233 South 10th Street, Philadelphia, Pennsylvania 19107-5541, USA 2Human Genetics Unit, Molecular Medicine Center, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK 3The Jackson Laboratory, 600 Main Street, Bar Harbor, Maine 04609, USA 4Departrnent of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska 68583-0908, USA SMRC Mammalian Genetics Unit, Harwell, Didcot Oxon OX 11 ORD, UK

Mapping of the melatonin receptor 1a (MTNR1A) gene in pigs, sheep, and cattle.

and Implications Human and sheep Melatonin receptor 1a (MTNR1A) gene information was used to clone a portion of the coding region of this gene in pigs, and to identify polymorphisms of the gene for its assignment to both the genetic linkage and physical maps. MTNR1A maps to pig chromosome 17, establishing a new region of conserved synteny between this chromosome and human chromosome 4. Furthermore, we have assigned MTNR1A to bovine chromosome 27 and sheep chromosome 26. The addition of genes like MTNR1A to livestock genome maps allows questions about evolutionary events and the genetic basis for quantitative traits in livestock to be addressed.

Genetic Dissection of Obesity in Polygenic Animal Models

In contrast to diseases caused by single-gene defects, many of the most common human maladies such as obesity, atherosclerosis, diabetes, and hypertension exhibit continuous phenotypic variation and a predominantly multifactorial and polygenic basis. Genes with roles in energy balance, nutrient partitioning, lipid and insulin metabolism, and a variety of behavioral traits are likely interacting with environmental stimuli to regulate obesity phenotypes. With the current proliferation of highly polymorphic genetic markers and the refinement of experimental approaches, it is now possible to screen thoroughly the genomes of model organisms for the individual genes or quantitative trait loci (QTL) that control measurable polygenic traits such as obesity. With the growing wealth of comparative mapping, it will be possible to predict the location of a homologous locus in the human after first mapping it in the mouse. Many experiments have been conducted in mice, rats, and pigs to estimate the number, location, and effect of QTL controlling obesity and related traits. This review describes the design and strategies of such studies and summarizes the results and their implications toward understanding the complex nature of obesity in humans.

Dopaminergic Dysregulation in Mice Selectively Bred for Excessive Exercise or Obesity

Dysregulation of the dopamine system is linked to various aberrant behaviors, including addiction, compulsive exercise, and hyperphagia leading to obesity. The goal of the present experiments was to determine how dopamine contributes to the expression of opposing phenotypes, excessive exercise and obesity. We hypothesized that similar alterations in dopamine and dopamine-related gene expression may underly obesity and excessive exercise, as competing traits for central reward pathways. Moreover, we hypothesized that selective breeding for high levels of exercise or obesity may have influenced genetic variation controlling these pathways, manifesting as opposing complex traits. Dopamine, dopamine-related peptide concentrations, and gene expression were evaluated in dorsal striatum (DS) and nucleus accumbens (NA) of mice from lines selectively bred for high rates of wheel running (HR) or obesity (M16), and the non-selected ICR strain from which these lines were derived. HPLC analysis showed significantly greater neurotransmitter concentrations in DS and NA of HR mice compared to M16 and ICR. Microarray analysis showed significant gene expression differences between HR and M16 compared to ICR in both brain areas, with changes revealed throughout the dopamine pathway including D1 and D2 receptors, associated G-proteins (e.g., Golf), and adenylate cyclase (e.g., Adcy5). The results suggest that similar modifications within the dopamine system may contribute to the expression of opposite phenotypes in mice, demonstrating that alterations within central reward pathways can contribute to both obesity and excessive exercise.