Quantitative Genetics, Pleiotropy, and Morphological Integration in the Dentition of Papio hamadryas

Variation in the mammalian dentition is highly informative of adaptations and evolutionary relationships, and consequently has been the focus of considerable research. Much of the current research exploring the genetic underpinnings of dental variation can trace its roots to Olson and Miller's 1958 book Morphological Integration. These authors explored patterns of correlation in the post-canine dentitions of the owl monkey and Hyopsodus, an extinct condylarth from the Eocene. Their results were difficult to interpret, as was even noted by the authors, due to a lack of genetic information through which to view the patterns of correlation. Following in the spirit of Olson and Miller's research, we present a quantitative genetic analysis of dental variation in a pedigreed population of baboons. We identify patterns of genetic correlations that provide insight to the genetic architecture of the baboon dentition. This genetic architecture indicates the presence of at least three modules: an incisor module that is genetically independent of the post-canine dentition, and a premolar module that demonstrates incomplete pleiotropy with the molar module. We then compare this matrix of genetic correlations to matrices of phenotypic correlations between the same measurements made on museum specimens of another baboon subspecies and the Southeast Asian colobine Presbytis. We observe moderate significant correlations between the matrices from these three primate taxa. From these observations we infer similarity in modularity and hypothesize a common pattern of genetic integration across the dental arcade in the Cercopithecoidea

Genetic variation in the pleiotropic association between physical activity and body weight in mice

<p>Abstract</p> <p>Background</p> <p>A sedentary lifestyle is often assumed to lead to increases in body weight and potentially obesity and related diseases but in fact little is known about the genetic association between physical activity and body weight. We tested for such an association between body weight and the distance, duration, and speed voluntarily run by 310 mice from the F₂generation produced from an intercross of two inbred lines that differed dramatically in their physical activity levels.</p> <p>Methods</p> <p>We used a conventional interval mapping approach with SNP markers to search for QTLs that affected both body weight and activity traits. We also conducted a genome scan to search for relationship QTLs (<it>rel</it>QTLs), or chromosomal regions that affected an activity trait variably depending on the phenotypic value of body weight.</p> <p>Results</p> <p>We uncovered seven quantitative trait loci (QTLs) affecting body weight, but only one co-localized with another QTL previously found for activity traits. We discovered 19 <it>rel</it>QTLs that provided evidence for a genetic (pleiotropic) association of physical activity and body weight. The three genotypes at each of these loci typically exhibited a combination of negative, zero, and positive regressions of the activity traits on body weight, the net effect of which was to produce overall independence of body weight from physical activity. We also demonstrated that the <it>rel</it>QTLs produced these varying associations through differential epistatic interactions with a number of other epistatic QTLs throughout the genome.</p> <p>Conclusion</p> <p>It was concluded that individuals with specific combinations of genotypes at the <it>rel</it>QTLs and <it>epi</it>QTLs might account for some of the variation typically seen in plots of the association of physical activity with body weight.</p

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

<p>Abstract</p> <p>Background</p> <p>Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).</p> <p>Results</p> <p>In this paper, we further explore the differences and links between the F_∞and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞model. A new way of linking the Cockerham and F_∞model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞models are applied.</p> <p>Conclusion</p> <p>The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞model can then be calculated by translating formulas derived for the Cockerham models.</p

Genome-Wide Analysis Reveals a Complex Pattern of Genomic Imprinting in Mice

Parent-of-origin–dependent gene expression resulting from genomic imprinting plays an important role in modulating complex traits ranging from developmental processes to cognitive abilities and associated disorders. However, while gene-targeting techniques have allowed for the identification of imprinted loci, very little is known about the contribution of imprinting to quantitative variation in complex traits. Most studies, furthermore, assume a simple pattern of imprinting, resulting in either paternal or maternal gene expression; yet, more complex patterns of effects also exist. As a result, the distribution and number of different imprinting patterns across the genome remain largely unexplored. We address these unresolved issues using a genome-wide scan for imprinted quantitative trait loci (iQTL) affecting body weight and growth in mice using a novel three-generation design. We identified ten iQTL that display much more complex and diverse effect patterns than previously assumed, including four loci with effects similar to the callipyge mutation found in sheep. Three loci display a new phenotypic pattern that we refer to as bipolar dominance, where the two heterozygotes are different from each other while the two homozygotes are identical to each other. Our study furthermore detected a paternally expressed iQTL on Chromosome 7 in a region containing a known imprinting cluster with many paternally expressed genes. Surprisingly, the effects of the iQTL were mostly restricted to traits expressed after weaning. Our results imply that the quantitative effects of an imprinted allele at a locus depend both on its parent of origin and the allele it is paired with. Our findings also show that the imprinting pattern of a locus can be variable over ontogenetic time and, in contrast to current views, may often be stronger at later stages in life

Phylogeny, Diet, and Cranial Integration in Australodelphian Marsupials

Studies of morphological integration provide valuable information on the correlated evolution of traits and its relationship to long-term patterns of morphological evolution. Thus far, studies of morphological integration in mammals have focused on placentals and have demonstrated that similarity in integration is broadly correlated with phylogenetic distance and dietary similarity. Detailed studies have also demonstrated a significant correlation between developmental relationships among structures and adult morphological integration. However, these studies have not yet been applied to marsupial taxa, which differ greatly from placentals in reproductive strategy and cranial development and could provide the diversity necessary to assess the relationships among phylogeny, ecology, development, and cranial integration. This study presents analyses of morphological integration in 20 species of australodelphian marsupials, and shows that phylogeny is significantly correlated with similarity of morphological integration in most clades. Size-related correlations have a significant affect on results, particularly in Peramelia, which shows a striking decrease in similarity of integration among species when size is removed. Diet is not significantly correlated with similarity of integration in any marsupial clade. These results show that marsupials differ markedly from placental mammals in the relationships of cranial integration, phylogeny, and diet, which may be related to the accelerated development of the masticatory apparatus in marsupials

A wild derived quantitative trait locus on mouse chromosome 2 prevents obesity

<p>Abstract</p> <p>Background</p> <p>The genetic architecture of multifactorial traits such as obesity has been poorly understood. Quantitative trait locus (QTL) analysis is widely used to localize loci affecting multifactorial traits on chromosomal regions. However, large confidence intervals and small phenotypic effects of identified QTLs and closely linked loci are impeding the identification of causative genes that underlie the QTLs. Here we developed five subcongenic mouse strains with overlapping and non-overlapping wild-derived genomic regions from an F2 intercross of a previously developed congenic strain, B6.Cg-<it>Pbwg1</it>, and its genetic background strain, C57BL/6J (B6). The subcongenic strains developed were phenotyped on low-fat standard chow and a high-fat diet to fine-map a previously identified obesity QTL. Microarray analysis was performed with Affymetrix GeneChips to search for candidate genes of the QTL.</p> <p>Results</p> <p>The obesity QTL was physically mapped to an 8.8-Mb region of mouse chromosome 2. The wild-derived allele significantly decreased white fat pad weight, body weight and serum levels of glucose and triglyceride. It was also resistant to the high-fat diet. Among 29 genes residing within the 8.8-Mb region, <it>Gpd2, Upp2, Acvr1c, March7 </it>and <it>Rbms1 </it>showed great differential expression in livers and/or gonadal fat pads between B6.Cg-<it>Pbwg1 </it>and B6 mice.</p> <p>Conclusions</p> <p>The wild-derived QTL allele prevented obesity in both mice fed a low-fat standard diet and mice fed a high-fat diet. This finding will pave the way for identification of causative genes for obesity. A further understanding of this unique QTL effect at genetic and molecular levels may lead to the discovery of new biological and pathologic pathways associated with obesity.</p

A search for quantitative trait loci controlling within-individual variation of physical activity traits in mice

<p>Abstract</p> <p>Background</p> <p>In recent years it has become increasingly apparent that physical inactivity can predispose individuals to a host of health problems. While many studies have analyzed the effect of various environmental factors on activity, we know much less about the genetic control of physical activity. Some studies in mice have discovered quantitative trait loci (QTL) influencing various physical activity traits, but mostly have analyzed inter-individual variation rather than variation in activity within individuals over time. We conducted a genome scan to identify QTLs controlling the distance, duration, and time run by mice over seven consecutive three-day intervals in an F₂population created by crossing two inbred strains (C57L/J and C3H/HeJ) that differed widely (average of nearly 300%) in their activity levels. Our objectives were (a) to see if we would find QTLs not originally discovered in a previous investigation that assessed these traits over the entire 21-day period and (b) to see if some of these QTLs discovered might affect the activity traits only in the early or in the late time intervals.</p> <p>Results</p> <p>This analysis uncovered 39 different QTLs, over half of which were new. Some QTLs affected the activity traits only in the early time intervals and typically exhibited significant dominance effects whereas others affected activity only in the later age intervals and exhibited less dominance. We also analyzed the regression slopes of the activity traits over the intervals, and found several QTLs affecting these traits that generally mapped to unique genomic locations.</p> <p>Conclusions</p> <p>It was concluded that the genetic architecture of physical activity in mice is much more complicated than has previously been recognized, and may change considerably depending on the age at which various activity measures are assessed.</p