Paternal inheritance of egg traits in mice: a case of genomic imprinting

Eggs from reciprocal hybrids between the C57BL/6By and BALB/cBy strains were tested for their susceptibility to attack by hyaluronidase and pronase. There were significant reciprocal differences between the F1 females in the responses of their unfertilized eggs to both enzymes. The F1 hybrids from BALB mothers showed the increased susceptibility characteristic of C57BL whilst the F1 hybrids with C57BL mothers were more resistant to both enzymes, like BALB mice. Eggs from the four kinds of reciprocal F2 hybrid females also showed patroclinous patterns of susceptibility. A patroclinous difference was found between reciprocal crosses of the CXBD and CXBE recombinant inbred strains but not in crosses between recombinant inbred strains with similar phenotypes. Cross fostering did not alter the phenotypes of the C57BL and BALB females or those of their reciprocal F1 hybrids. The findings are interpreted in terms of differential genomic imprinting of paternally inherited information. The possible general usefulness of patroclinous differences between reciprocal F1 females in revealing differences in imprinting is noted.     

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.     

Effects of genomic imprinting on quantitative traits

Standard Mendelian genetic processes incorporate several symmetries, one of which is that the level of expression of a gene inherited from an organism’s mother is identical to the level should that gene have been inherited paternally. For a small number of loci in a variety of taxa, this symmetry does not hold; such genes are said to be “genomically imprinted” (or simply “imprinted”). The best known examples of imprinted loci come from mammals and angiosperms, although there are also cases from several insects and some data suggesting that imprinting exists in zebra fish. Imprinting means that reciprocal heterozygotes need not be, on average, phenotypically identical. When this difference is incorporated into the standard quantitative-genetic model for two alleles at a single locus, a number of standard expressions are altered in fundamental ways. Most importantly, in contrast to the case with euMendelian expression, the additive and dominance deviations are correlated. It would clearly be of interest to be able to separate imprinting effects from maternal genetic effects, but when the latter are added to the model, the well-known generalized least-squares approach to deriving breeding values cannot be applied. Distinguishing these two types of parent-of-origin effects is not a simple problem and requires further research.     

Parametric proportional hazards model for mapping genomic imprinting of survival traits

A number of imprinted genes have been observed in plants, animals and humans. They not only control growth and developmental traits, but may also be responsible for survival traits. Based on the Cox proportional hazards (PH) model, we constructed a general parametric model for dissecting genomic imprinting, in which a baseline hazard function is selectable for fitting the effects of imprinted quantitative trait loci (iQTL) genotypes on the survival curve. The expectation–maximisation (EM) algorithm is derived for solving the maximum likelihood estimates of iQTL parameters. The imprinting patterns of the detected iQTL are statistically tested under a series of null hypotheses. The Bayesian information criterion (BIC) model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameterisation. We applied the proposed approach to analyse the published data in an F₂ population of mice and concluded that, among five commonly used survival distributions, the log-logistic distribution is the optimal baseline hazard function for the survival time of hyperoxic acute lung injury (HALI). Under this optimal model, five QTL were detected, among which four are imprinted in different imprinting patterns.     

Genomic imprinting effects on complex traits: A phenotype-based perspective

Parent-of-origin dependent gene expression associated with genomic imprinting is expected to result in the phenotypic signature of a difference between the average phenotypes of the reciprocal heterozygotes.     

Sex dependent imprinting effects on complex traits in mice

Background  

Genomic 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.     

A computational framework for cortical learning

Recent physiological findings have revealed that long-term adaptation of the synaptic strengths between cortical pyramidal neurons depends on the temporal order of presynaptic and postsynaptic spikes, which is called spike-timing-dependent plasticity (STDP) or temporally asymmetric Hebbian (TAH) learning. Here I prove by analytical means that a physiologically plausible variant of STDP adapts synaptic strengths such that the presynaptic spikes predict the postsynaptic spikes with minimal error. This prediction error model of STDP implies a mechanism for cortical memory: cortical tissue learns temporal spike patterns if these spike patterns are repeatedly elicited in a set of pyramidal neurons. The trained network finishes these patterns if their beginnings are presented, thereby recalling the memory. Implementations of the proposed algorithms may be useful for applications in voice recognition and computer vision.     

Mammalian viviparity: a complex niche in the evolution of genomic imprinting

Evolution of mammalian reproductive success has witnessed a strong dependence on maternal resources through placental in utero development. Genomic imprinting, which has an active role in mammalian viviparity, also reveals a biased role for matrilineal DNA in its regulation. The co-existence of three matrilineal generations as one (mother, foetus and post-meiotic oocytes) has provided a maternal niche for transgenerational co-adaptive selection pressures to operate. In utero foetal growth has required increased maternal feeding in advance of foetal energetic demands; the mammary glands are primed for milk production in advance of birth, while the maternal hypothalamus is hormonally primed by the foetal placenta for nest building and post-natal care. Such biological forward planning resulted from maternal–foetal co-adaptation facilitated by co-expression of the same imprinted allele in the developing hypothalamus and placenta. This co-expression is concurrent with the placenta interacting with the adult maternal hypothalamus thereby providing a transgenerational template on which selection pressures may operate ensuring optimal maternalism in this and the next generation. Invasive placentation has further required the maternal immune system to adapt and positively respond to the foetal allotype. Pivotal to these mammalian evolutionary developments, genomic imprinting emerged as a monoallelic gene dosage regulatory mechanism of tightly interconnected gene networks providing developmental genetic stability for in utero development.     

Non-conflict theories for the evolution of genomic imprinting

Theories focused on kinship and the genetic conflict it induces are widely considered to be the primary explanations for the evolution of genomic imprinting. However, there have appeared many competing ideas that do not involve kinship/conflict. These ideas are often overlooked because kinship/conflict is entrenched in the literature, especially outside evolutionary biology. Here we provide a critical overview of these non-conflict theories, providing an accessible perspective into this literature. We suggest that some of these alternative hypotheses may, in fact, provide tenable explanations of the evolution of imprinting for at least some loci.     

A comparative genomic framework for the fish-tetrapod transition

Science China Life Sciences -     

A computational framework for influenza antigenic cartography

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.     

A computational framework for topographies of cortical areas

Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored the conditions under which these multi-feature SOFMs contained two features that were mapped monotonically and aligned orthogonally with one another (i.e., “globally orthogonal”), as well as the conditions under which the map of one feature aligned with the longest anatomical dimension of the modeled cortical area (i.e., “dominant”). In a single SOFM with more than two features, we never observed more than one dominant feature, nor did we observe two globally orthogonal features in the same map in which a dominant feature occurred. Whether dominance or global orthogonality occurred depended upon how heavily weighted the features were relative to one another. The most heavily weighted features are likely to correspond to those physical stimulus properties transduced directly by the sensory epithelium of a particular sensory modality. Our results imply, therefore, that in the primary cortical area of sensory modalities with a two-dimensional sensory epithelium, these two features are likely to be organized globally orthogonally to one another, and neither feature is likely to be dominant. In the primary cortical area of sensory modalities with a one-dimensional sensory epithelium, however, this feature is likely to be dominant, and no two features are likely to be organized globally orthogonally to one another. Because the auditory system transduces a single stimulus feature (i.e., frequency) along the entire length of the cochlea, these findings may have particular relevance for topographic maps of primary auditory cortex. This research was supported by The McDonnell Center for Higher Brain Function, The Wallace H. Coulter Foundation and NIH grant DC008880.     

A statistical framework for genomic data fusion

MOTIVATION: During the past decade, the new focus on genomics has highlighted a particular challenge: to integrate the different views of the genome that are provided by various types of experimental data. RESULTS: This paper describes a computational framework for integrating and drawing inferences from a collection of genome-wide measurements. Each dataset is represented via a kernel function, which defines generalized similarity relationships between pairs of entities, such as genes or proteins. The kernel representation is both flexible and efficient, and can be applied to many different types of data. Furthermore, kernel functions derived from different types of data can be combined in a straightforward fashion. Recent advances in the theory of kernel methods have provided efficient algorithms to perform such combinations in a way that minimizes a statistical loss function. These methods exploit semidefinite programming techniques to reduce the problem of finding optimizing kernel combinations to a convex optimization problem. Computational experiments performed using yeast genome-wide datasets, including amino acid sequences, hydropathy profiles, gene expression data and known protein-protein interactions, demonstrate the utility of this approach. A statistical learning algorithm trained from all of these data to recognize particular classes of proteins--membrane proteins and ribosomal proteins--performs significantly better than the same algorithm trained on any single type of data. AVAILABILITY: Supplementary data at http://noble.gs.washington.edu/proj/sdp-svm     

A survey of tissue-specific genomic imprinting in mammals

In mammals, most somatic cells contain two copies of each autosomal gene, one inherited from each parent. When a gene is expressed, both parental alleles are usually transcribed. However, a subset of genes is subject to the epigenetic silencing of one of the parental copies by genomic imprinting. In this review, we explore the evidence for variability in genomic imprinting between different tissue and cell types. We also consider why the imprinting of particular genes may be restricted to, or lost in, specific tissues and discuss the potential for high-throughput sequencing technologies in facilitating the characterisation of tissue-specific imprinting and assaying the potentially functional variations in epigenetic marks.     

A statistical model for dissecting genomic imprinting through genetic mapping

As a result of nonequivalent genetic contribution of maternal and paternal genomes to offsprings, genomic imprinting or called parent-of-origin effect, has been broadly identified in plants, animals and humans. Its role in shaping organism’s development has been unanimously recognized. However, statistical methods for identifying imprinted quantitative trait loci (iQTL) and estimating the imprinted effect have not been well developed. In this article, we propose an efficient statistical procedure for genomewide estimating and testing the effects of significant iQTL underlying the quantitative variation of interested traits. The developed model can be applied to two different genetic cross designs, backcross and F₂ families derived from inbred lines. The proposed procedure is built within the maximum likelihood framework and implemented with the EM algorithm. Extensive simulation studies show that the proposed model is well performed in a variety of situations. To demonstrate the usefulness of the proposed approach, we apply the model to a published data in an F₂ family derived from LG/S and SM/S mouse stains. Two partially maternal imprinting iQTL are identified which regulate the growth of body weight. Our approach provides a testable framework for identifying and estimating iQTL involved in the genetic control of complex traits.