A computational framework for the inheritance pattern of genomic imprinting for complex traits.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang C
PROVIDER: S-EPMC3278998 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: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.
| S-EPMC2390766 | biostudies-literature
Project description:Despite the fact that genetic imprinting, i.e., differential expression of the same allele due to its different parental origins, plays a pivotal role in controlling complex traits or diseases, the origin, action and transmission mode of imprinted genes have still remained largely unexplored. We present a new strategy for studying these properties of genetic imprinting with a two-stage reciprocal F mating design, initiated with two contrasting inbred lines. This strategy maps quantitative trait loci that are imprinted (i.e., iQTLs) based on their segregation and transmission across different generations. By incorporating the allelic configuration of an iQTL genotype into a mixture model framework, this strategy provides a path to trace the parental origin of alleles from previous generations. The imprinting effects of iQTLs and their interactions with other traditionally defined genetic effects, expressed in different generations, are estimated and tested by implementing the EM algorithm. The strategy was used to map iQTLs responsible for survival time with four reciprocal F populations and test whether and how the detected iQTLs inherit their imprinting effects into the next generation. The new strategy will provide a tool for quantifying the role of imprinting effects in the creation and maintenance of phenotypic diversity and elucidating a comprehensive picture of the genetic architecture of complex traits and diseases.
| S-EPMC2904369 | biostudies-literature
Project description:BackgroundGenomic imprinting is an epigenetic source of variation in quantitative traits that results from monoallelic gene expression, where commonly either only the paternally- or the maternally-derived allele is expressed. Imprinting has been shown to affect a diversity of complex traits in a variety of species. For several such quantitative traits sex-dependent genetic effects have been discovered, but whether imprinting effects also show such sex-dependence has yet to be explored. Moreover, theoretical work on the evolution of sex-dependent genomic imprinting effects makes specific predictions about the phenotypic patterns of such effects, which, however, have not been assessed empirically to date.ResultsUsing a genome-scan for loci affecting a set of complex growth and body composition traits from an intercross between two divergent mouse strains, we investigated possible sex-dependent imprinting effects. Our results demonstrate for the first time the existence of genomic imprinting effects that depend on sex and are not related to sex-chromosome effects. We detected a total of 13 loci on 11 chromosomes that showed significant differences between the sexes in imprinting effects. Most loci showed imprinting effects in only one sex, with eight imprinted effects found in males and six in females. One locus showed sex-dependent imprinting effects in both sexes for different traits. The absence of an imprinting effect in one sex was not necessarily indicative of the overall inactivity of the locus in that sex, as for several loci a significant additive or dominance effect was detected. Moreover, three loci exhibited significant additive effects in both sexes but their imprinting effect was restricted to one sex.ConclusionOur results clearly show that imprinting effects can be sex-dependent and also suggest that new candidate imprinted loci can be detected when taking account of sex-specific imprinting effects. However, predictions made about the evolution of sex-dependent imprinting effects and associated phenotypic patterns cannot be unequivocally supported at present and further research into the selection pressures applied to the strains of mice used in our study is required.
| S-EPMC2605465 | biostudies-literature
Project description:BackgroundGenomic imprinting refers to parent-of-origin dependent gene expression caused by differential DNA methylation of the paternally and maternally derived alleles. Imprinting is increasingly recognized as an important source of variation in complex traits, however, its role in explaining variation in muscle and physiological traits, especially those of commercial value, is largely unknown compared with genetic effects.ResultsWe investigated both genetic and genomic imprinting effects on key muscle traits in mice from the Berlin Muscle Mouse population, a key model system to study muscle traits. Using a genome scan, we first identified loci with either imprinting or genetic effects on phenotypic variation. Next, we established the proportion of phenotypic variation explained by additive, dominance and imprinted QTL and characterized the patterns of effects. In total, we identified nine QTL, two of which show large imprinting effects on glycogen content and potential, and body weight. Surprisingly, all imprinting patterns were of the bipolar type, in which the two heterozygotes are different from each other but the homozygotes are not. Most QTL had pleiotropic effects and explained up to 40% of phenotypic variance, with individual imprinted loci accounting for 4-5% of variation alone.ConclusionSurprisingly, variation in glycogen content and potential was only modulated by imprinting effects. Further, in contrast to general assumptions, our results show that genomic imprinting can impact physiological traits measured at adult stages and that the expression does not have to follow the patterns of paternal or maternal expression commonly ascribed to imprinting effects.
| S-EPMC3475036 | biostudies-literature
Project description:Thrombocytopenia-absent radius (TAR) syndrome is characterized by hypomegakaryocytic thrombocytopenia and bilateral radial aplasia in the presence of both thumbs. Other frequent associations are congenital heart disease and a high incidence of cow's milk intolerance. Evidence for autosomal recessive inheritance comes from families with several affected individuals born to unaffected parents, but several other observations argue for a more complex pattern of inheritance. In this study, we describe a common interstitial microdeletion of 200 kb on chromosome 1q21.1 in all 30 investigated patients with TAR syndrome, detected by microarray-based comparative genomic hybridization. Analysis of the parents revealed that this deletion occurred de novo in 25% of affected individuals. Intriguingly, inheritance of the deletion along the maternal line as well as the paternal line was observed. The absence of this deletion in a cohort of control individuals argues for a specific role played by the microdeletion in the pathogenesis of TAR syndrome. We hypothesize that TAR syndrome is associated with a deletion on chromosome 1q21.1 but that the phenotype develops only in the presence of an additional as-yet-unknown modifier (mTAR).
| S-EPMC1785342 | biostudies-literature
Project description:Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function.
| S-EPMC7728203 | biostudies-literature
Project description:BackgroundGenomic prediction has become widespread as a valuable tool to estimate genetic merit in animal and plant breeding. Here we develop a novel genomic prediction algorithm, called deepGBLUP, which integrates deep learning networks and a genomic best linear unbiased prediction (GBLUP) framework. The deep learning networks assign marker effects using locally-connected layers and subsequently use them to estimate an initial genomic value through fully-connected layers. The GBLUP framework estimates three genomic values (additive, dominance, and epistasis) by leveraging respective genetic relationship matrices. Finally, deepGBLUP predicts a final genomic value by summing all the estimated genomic values.ResultsWe compared the proposed deepGBLUP with the conventional GBLUP and Bayesian methods. Extensive experiments demonstrate that the proposed deepGBLUP yields state-of-the-art performance on Korean native cattle data across diverse traits, marker densities, and training sizes. In addition, they show that the proposed deepGBLUP can outperform the previous methods on simulated data across various heritabilities and quantitative trait loci (QTL) effects.ConclusionsWe introduced a novel genomic prediction algorithm, deepGBLUP, which successfully integrates deep learning networks and GBLUP framework. Through comprehensive evaluations on the Korean native cattle data and simulated data, deepGBLUP consistently achieved superior performance across various traits, marker densities, training sizes, heritabilities, and QTL effects. Therefore, deepGBLUP is an efficient method to estimate an accurate genomic value. The source code and manual for deepGBLUP are available at https://github.com/gywns6287/deepGBLUP .
| S-EPMC10392020 | biostudies-literature
Project description:Identification of the genetic basis of common traits may be hindered by underlying complex genetic architectures that are inadequately captured by existing models, including both multiallelic and multilocus modes of inheritance (MOI). One useful approach for localizing genes underlying continuous complex traits is the joint oligogenic linkage and segregation analysis implemented in the package Loki. The method uses reversible jump Markov chain Monte Carlo to eliminate the need to prespecify the number of quantitative trait loci (QTLs) in the trait model, thus providing posterior distributions for the number of QTLs in a Bayesian framework. The current implementation assumes QTLs are diallelic, and therefore can overestimate the number of linked QTLs in the presence of a multiallelic QTL. To address the possibility of multiple alleles, we extended the QTL model to allow for a variable number of additive alleles at each locus. Application to simulated data shows that, under a diallelic MOI, the multiallelic and diallelic analysis models give similar results. Under a multiallelic MOI, the multiallelic analysis model provides better mixing and improved convergence, and leads to a more accurate estimate of the underlying trait MOI and model parameter values, than does the diallelic model. Application to real data shows the multiallelic model results in fewer estimated linked QTLs and that the predominant QTL model is similar to one of two predominant models estimated from the diallelic analysis. Our results indicate that use of a multiallelic analysis model can lead to better understanding of the genetic architecture underlying complex traits.
| S-EPMC2914272 | biostudies-literature
Project description:Imprinted genes represent a curious defiance of normal Mendelian genetics. Mammals inherit two complete sets of chromosomes, one from the mother and one from the father, and most autosomal genes will be expressed from both the maternal and the paternal alleles. Imprinted genes, however, are expressed from only one chromosome, in a parent-of-origin-dependent manner. Because silent and active promoters are present in a single nucleus, the differences in activity cannot be explained by transcription-factor abundance. Thus, transcription of imprinted genes represents a clear situation in which epigenetic mechanisms restrict gene expression and, therefore, offers a model for understanding the role of DNA modifications and chromatin structure in maintaining appropriate patterns of expression. Furthermore, because of their parent-of-origin-restricted expression, phenotypes determined by imprinted genes are susceptible not only to genetic alterations in the genes but also to disruptions in the epigenetic programs controlling regulation. Imprinted genes are often associated with human diseases, including disorders affecting cell growth, development, and behavior.
| S-EPMC1287882 | biostudies-literature
Project description:Genomic imprinting refers to a class of transmissible genetic effects in which the expression of the phenotype in the offspring depends on the parental origin of the transmitted allele. The DNA from one parent may be epigenetically modified so that only a single allele of the imprinted gene is expressed in the offspring. Although imprinting has an important role in the regulation of growth and development through its role in regulating gene expression, its contribution to susceptibility to common complex disorders is not well understood. We summarize current views on the role of imprinting in diabetes and in particular chromosome 6q24-related transient neonatal diabetes mellitus, the best known example of an imprinted genetic disorder that leads to diabetes.
| S-EPMC2945012 | biostudies-literature