Genomic imprinting effects on adult body composition in mice

Parent of origin genetic effects on milk production traits in a population of Iranian Holstein cows

The aim was to estimate the relative contribution of imprinting effects from both paternal and maternal sides to phenotypic variation in milk production traits including 305 days milk yield (MY), average daily milk production (ADM), fat percentage (F%), protein percentage (P%), 305 days fat yield (FY), 305 days protein yield (PY), ratio of fat percentage to protein percentage (F:P) and somatic cell score (SCS) in Iranian Holstein cows. To do this, each trait was analysed with a series of four animal models, which were identical for fixed and additive genetic effects but differed for combinations of paternal and maternal imprinting effects. The log-likelihood ratio test (LRT) and Akaike's information criteria (AIC) were used to select the best model for each trait. Correlations between traits due to additive and imprinting effects were estimated by bivariate analyses. For all traits studied, fitting the imprinting effect led to a better data fit. Also, it resulted in a noticeable decrease in additive genetic variance from 8% (SCS) to 28% (F:P). A significant maternal imprinting effect was detected on all traits studied. Estimates of maternal imprinting heritability (h mi 2 ) were 0.07 ± 0.02, 0.04 ± 0.01, 0.06 ± 0.01, 0.05 ± 0.01, 0.5 ± 0.01, 0.09 ± 0.02, 0.07 ± 0.02 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. For F:P, in addition to the maternal imprinting effect, a significant paternal imprinting component was also detected with a 7% contribution to phenotypic variance of F:P. Estimates of direct heritability (h a 2 ) were 0.29 ± 0.02, 0.17 ± 0.01, 0.22 ± 0.02, 0.11 ± 0.01, 0.18 ± 0.02, 0.22 ± 0.02, 0.15 ± 0.04 and 0.06 ± 0.01 for MY, ADM, F%, P%, FY, PY, F:P and SCS, respectively. Maternal imprinting correlations (rmi) were in a wide range between -0.75 ± 0.15 (P%-SCS) and 0.95 ± 0.11 (MY-ADM). Additive genetic correlations (ra) ranged between -0.54 ± 0.05 (MY-P%) and 0.99 ± 0.01 (MY-ADM) and phenotypic correlations (rp) ranged from -0.30 ± 0.01 (MY-F%) to 0.93 ± 0.01 (MY-ADM). The Spearman's correlation between additive breeding values including and excluding imprinting effects deviated from unity especially for top-ranked animals implying re-ranking of top animals following the inclusion of imprinting effects in the model. Since including imprinting effects in the model resulted in better data fit and re-ranking of top animals, including these effects in the genetic evaluation models for milk production traits was recommended.

Relative contribution of Imprinting, X chromosome and Litter effects to phenotypic variation in economic traits of sheep

Data on Zandi sheep were analysed to quantify maternal and paternal imprinting, X chromosome and litter effects' contribution to phenotypic variation in birth weight (BW), weaning weight (WW), growth rate (GR), Kleiber ratio (KR), efficiency of growth (EF) and relative growth rate (RGR). To this end, a two-step approach was adopted. In the first step, each trait was analysed with a series of 16 animal models, which were identical for fixed and autosomal additive genetic effects but differed for combinations of maternal permanent environmental, maternal genetic, X chromosome and litter effects. For each trait, the best model was selected by the Akaike information criterion (AIC) and likelihood ratio tests (LRTs). In the second step, three additional models were fitted by adding maternal imprinting, paternal imprinting or both (models 17, 18 and 19) to the best model selected in the first step. Estimators of bias, dispersion and accuracy of breeding values estimated within 19 models with whole, and partial data were used to evaluate how well were the 19 models in estimating breeding values for the animals when their records were masked. For all traits studied, fitting the litter effect led to a better data fit. Also, it resulted in noticeable decreases in residual variance and other maternal variances. For growth traits, models containing the X-linked effects fitted the data substantially better than corresponding models without the X-linked effects. For BW, WW and GR, estimates of X-linked heritability ( h s 2 $$ {h}_s^2 $$ ) ranged between 0.09 (GR) and 0.14 (BW). Ignoring X-linked effects from the genetic evaluation model resulted in significant inflated autosomal additive genetic variance. For BW, WW, EF and RGR, models containing the imprinting effects provided a better fit of the data than otherwise identical models. Imprinting effects contributed significantly to the phenotypic variation of these traits in a range between 5% (RGR) and 8% (BW, WW). A sharp decline was observed in autosomal additive genetic variance following including imprinting effects in the model (27% to 40% depending on the trait). The least bias and dispersion, as well as greater accuracies for breeding values of focal animals, were for a model which included imprinting, X-linked and litter effects. It was concluded that imprinting, X-linked and litter effects need to be included in the genetic evaluation models for growth and efficiency-related traits of Zandi lambs.

A complex genetic architecture underlies mandibular evolution in big mice from Gough Island

Some of the most compelling examples of morphological evolution come from island populations. Alterations in the size and shape of the mandible have been repeatedly observed in murid rodents following island colonization. Despite this pattern and the significance of the mandible for dietary adaptation, the genetic basis of island-mainland divergence in mandibular form remains uninvestigated. To fill this gap, we examined mandibular morphology in 609 F2s from a cross between Gough Island mice, the largest wild house mice on record, and mice from a mainland reference strain (WSB). Univariate genetic mapping identifies 3 quantitative trait loci (QTL) for relative length of the temporalis lever arm and 2 distinct QTL for relative condyle length, 2 traits expected to affect mandibular function that differ between Gough Island mice and WSB mice. Multivariate genetic mapping of coordinates from geometric morphometric analyses identifies 27 QTL contributing to overall mandibular shape. Quantitative trait loci show a complex mixture of modest, additive effects dispersed throughout the mandible, with landmarks including the coronoid process and the base of the ascending ramus frequently modulated by QTL. Additive effects of most shape quantitative trait loci do not align with island-mainland divergence, suggesting that directional selection played a limited role in the evolution of mandibular shape. In contrast, Gough Island mouse alleles at QTL for centroid size and QTL for jaw length increase these measures, suggesting selection led to larger mandibles, perhaps as a correlated response to the evolution of larger bodies.

Fruit quality and DNA methylation are affected by parental order in reciprocal crosses of tomato

Reciprocal effects were found for tomato fruit quality and DNA methylation. The epigenetic identity of reciprocal hybrids indicates that DNA methylation might be one of the mechanisms involved in POEs. Crosses between different genotypes and even between different species are commonly used in plant breeding programs. Reciprocal hybrids are obtained by changing the cross direction (or the sexual role) of parental genotypes in a cross. Phenotypic differences between these hybrids constitute reciprocal effects (REs). The aim of this study was to evaluate phenotypic differences in tomato fruit traits and DNA methylation profiles in three inter- and intraspecific reciprocal crosses. REs were detected for 13 of the 16 fruit traits analyzed. The number of traits with REs was the lowest in the interspecific cross, whereas the highest was found in the cross between recombinant inbred lines (RILs) derived from the same interspecific cross. An extension of gene action analysis was proposed to incorporate parent-of-origin effects (POEs). Maternal and paternal dominance were found in four fruit traits. REs and paternal inheritance were found for epiloci located at coding and non-coding regions. The epigenetic identity displayed by the reciprocal hybrids accounts for the phenotypic differences among them, indicating that DNA methylation might be one of the mechanisms involved in POEs.

Modification of Experimental Design and Statistical Method for Mapping Imprinted QTLs Based on Immortalized F2 Population

Genomic imprinting is an epigenetic phenomenon, which plays important roles in the growth and development of animals and plants. Immortalized F₂ (imF₂) populations generated by random cross between recombinant inbred (RI) or doubled haploid (DH) lines have been proved to have significant advantages for mapping imprinted quantitative trait loci (iQTLs), and statistical methods for this purpose have been proposed. In this paper, we propose a special type of imF₂ population (R-imF₂) for iQTL mapping, which is developed by random reciprocal cross between RI/DH lines. We also propose two modified iQTL mapping methods: two-step point mapping (PM-2) and two-step composite point mapping (CPM-2). Simulation studies indicated that: (i) R-imF₂ cannot improve the results of iQTL mapping, but the experimental design can probably reduce the workload of population construction; (ii) PM-2 can increase the precision of estimating the position and effects of a single iQTL; and (iii) CPM-2 can precisely map not only iQTLs, but also non-imprinted QTLs. The modified experimental design and statistical methods will facilitate and promote the study of iQTL mapping.

Facial shape and allometry quantitative trait locus intervals in the Diversity Outbred mouse are enriched for known skeletal and facial development genes

The biology of how faces are built and come to differ from one another is complex. Discovering normal variants that contribute to differences in facial morphology is one key to untangling this complexity, with important implications for medicine and evolutionary biology. This study maps quantitative trait loci (QTL) for skeletal facial shape using Diversity Outbred (DO) mice. The DO is a randomly outcrossed population with high heterozygosity that captures the allelic diversity of eight inbred mouse lines from three subspecies. The study uses a sample of 1147 DO animals (the largest sample yet employed for a shape QTL study in mouse), each characterized by 22 three-dimensional landmarks, 56,885 autosomal and X-chromosome markers, and sex and age classifiers. We identified 37 facial shape QTL across 20 shape principal components (PCs) using a mixed effects regression that accounts for kinship among observations. The QTL include some previously identified intervals as well as new regions that expand the list of potential targets for future experimental study. Three QTL characterized shape associations with size (allometry). Median support interval size was 3.5 Mb. Narrowing additional analysis to QTL for the five largest magnitude shape PCs, we found significant overrepresentation of genes with known roles in growth, skeletal and facial development, and sensory organ development. For most intervals, one or more of these genes lies within 0.25 Mb of the QTL's peak. QTL effect sizes were small, with none explaining more than 0.5% of facial shape variation. Thus, our results are consistent with a model of facial diversity that is influenced by key genes in skeletal and facial development and, simultaneously, is highly polygenic.

Sexes on the brain: Sex as multiple biological variables in the neuronal control of feeding

Neuronal interactions at the level of vagal, homeostatic, and hedonic circuitry work to regulate the neuronal control of feeding. This integrative system appears to vary across sex and gender in the animal and human worlds. Most feeding research investigating these variations across sex and gender focus on how the organizational and activational mechanisms of hormones contribute to these differences. However, in limited studies spanning both the central and peripheral nervous systems, sex differences in feeding have been shown to manifest not just at the level of the hormonal, but also at the chromosomal, epigenetic, cellular, and even circuitry levels to alter food intake. In this review, we provide a brief orientation to the current understanding of how these neuronal systems interact before dissecting selected studies from the recent literature to exemplify how feeding physiology at all levels can be affected by the various components of sex.

The genetics of morphological and behavioural island traits in deer mice

Animals on islands often exhibit dramatic differences in morphology and behaviour compared with mainland individuals, a phenomenon known as the 'island syndrome'. These differences are thought to be adaptations to island environments, but the extent to which they have a genetic basis or instead represent plastic responses to environmental extremes is often unknown. Here, we revisit a classic case of island syndrome in deer mice (Peromyscus maniculatus) from British Columbia. We first show that Saturna Island mice and those from neighbouring islands are approximately 35% (approx. 5 g) heavier than mainland mice and diverged approximately 10 000 years ago. We then establish laboratory colonies and find that Saturna Island mice are heavier both because they are longer and have disproportionately more lean mass. These trait differences are maintained in second-generation captive-born mice raised in a common environment. In addition, island-mainland hybrids reveal a maternal genetic effect on body weight. Using behavioural testing in the laboratory, we also find that wild-caught island mice are less aggressive than mainland mice; however, laboratory-raised mice born to these founders do not differ in aggression. Together, our results reveal that these mice have different responses to the environmental conditions on islands-a heritable change in a morphological trait and a plastic response in a behavioural trait.

Offspring genes indirectly influence sibling and maternal behavioural strategies over resource share

Family members show behavioural strategies predicted to maximize individual fitness. These behaviours depend directly on genes expressed in focal individuals but also indirectly on genes expressed in other family members. However, how sibling and parental behavioural strategies are modified by genes expressed in family members, and to what degree, remains unclear. To answer this question, we have used a split litter design in an experimental population of genetically variable mouse families, and identified loci that indirectly affected sibling and maternal behaviour simultaneously. These loci map to genomic regions that also show a direct effect on offspring behaviour. Directly and indirectly affected traits were significantly correlated at the phenotypic level, illustrating how indirect effects are caused. Genetic variants in offspring that influence solicitation also impacted their siblings' and maternal behaviour. However, in contrast to predictions from sibling competition, unrelated litter mates benefited from increased solicitation. Overall, such indirect genetic effects explained a large proportion of variation seen in behaviours, with candidate genes involved in metabolism to neuronal development. These results reveal that we need to view behavioural strategies as the result of conjoint selection on genetic variation in all interacting family members.

A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis

Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.

Genome-wide random regression analysis for parent-of-origin effects of body composition allometries in mouse

Genomic imprinting underlying growth and development traits has been recognized, with a focus on the form of absolute or pure growth. However, little is known about the effect of genomic imprinting on relative growth. In this study, we proposed a random regression model to estimate genome-wide imprinting effects on the relative growth of multiple tissues and organs to body weight in mice. Joint static allometry scaling equation as sub-model is nested within the genetic effects of markers and polygenic effects caused by a pedigree. Both chromosome-wide and genome-wide statistical tests were conducted to identify imprinted quantitative trait nucleotides (QTNs) associated with relative growth of individual tissues and organs to body weight. Real data analysis showed that three of six analysed tissues and organs are significantly associated with body weight in terms of phenotypic relative growth. At the chromosome-wide level, a total 122 QTNs were associated with allometries of kidney, spleen and liver weights to body weight, 36 of which were imprinted with different imprinting fashions. Further, only two imprinted QTNs responsible for relative growth of spleen and liver were verified by genome-wide test. Our approach provides a general framework for statistical inference of genomic imprinting underlying allometry scaling in animals.

Identification of Genomic Somatic Variants in Cancer: From Discovery to Actionability

The perfect method to discover and validate actionable somatic variants in cancer has not yet been developed, yet significant progress has been made toward this goal. There have been huge increases in the throughput and cost of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) sequencing technologies that have led to the burgeoning possibility of using sequencing data in clinical settings. Discovery of somatic mutations is relatively simple and has been improved recently due to laboratory methods optimization, bioinformatics algorithms development, and the expansion of various databases of population genomic information. Tiered systems of evidence evaluation are currently being used to classify genomic variants for clinicians to more rapidly and accurately determine actionability of these aberrations. These efforts are complicated by the intricacies of communicating sequencing results to physicians and supporting its biological relevance, emphasizing the need for increasing education of clinicians and administrators, and the ongoing development of ethical standards for dealing with incidental results. This chapter will focus on general aspects of DNA and RNA tumor sequencing technologies, data analysis and interpretation, assessment of biological and clinical relevance of genomic aberrations, ethical aspects of germline sequencing, and how these factors impact cancer personalized care.

Incorporating parent-of-origin effects in whole-genome prediction of complex traits

Background

Parent-of-origin effects are due to differential contributions of paternal and maternal lineages to offspring phenotypes. Such effects include, for example, maternal effects in several species. However, epigenetically induced parent-of-origin effects have recently attracted attention due to their potential impact on variation of complex traits. Given that prediction of genetic merit or phenotypic performance is of interest in the study of complex traits, it is relevant to consider parent-of-origin effects in such predictions. We built a whole-genome prediction model that incorporates parent-of-origin effects by considering parental allele substitution effects of single nucleotide polymorphisms and gametic relationships derived from a pedigree (the POE model). We used this model to predict body mass index in a mouse population, a trait that is presumably affected by parent-of-origin effects, and also compared the prediction performance to that of a standard additive model that ignores parent-of-origin effects (the ADD model). We also used simulated data to assess the predictive performance of the POE model under various circumstances, in which parent-of-origin effects were generated by mimicking an imprinting mechanism.

Results

The POE model did not predict better than the ADD model in the real data analysis, probably due to overfitting, since the POE model had far more parameters than the ADD model. However, when applied to simulated data, the POE model outperformed the ADD model when the contribution of parent-of-origin effects to phenotypic variation increased. The superiority of the POE model over the ADD model was up to 8 % on predictive correlation and 5 % on predictive mean squared error.

Conclusions

The simulation and the negative result obtained in the real data analysis indicated that, in order to gain benefit from the POE model in terms of prediction, a sizable contribution of parent-of-origin effects to variation is needed and such variation must be captured by the genetic markers fitted. Recent studies, however, suggest that most parent-of-origin effects stem from epigenetic regulation but not from a change in DNA sequence. Therefore, integrating epigenetic information with genetic markers may help to account for parent-of-origin effects in whole-genome prediction.

Miniature Swine for Preclinical Modeling of Complexities of Human Disease for Translational Scientific Discovery and Accelerated Development of Therapies and Medical Devices

Noncommunicable diseases, including cardiovascular disease, diabetes, chronic respiratory disease, and cancer, are the leading cause of death in the world. The cost, both monetary and time, of developing therapies to prevent, treat, or manage these diseases has become unsustainable. A contributing factor is inefficient and ineffective preclinical research, in which the animal models utilized do not replicate the complex physiology that influences disease. An ideal preclinical animal model is one that responds similarly to intrinsic and extrinsic influences, providing high translatability and concordance of preclinical findings to humans. The overwhelming genetic, anatomical, physiological, and pathophysiological similarities to humans make miniature swine an ideal model for preclinical studies of human disease. Additionally, recent development of precision gene-editing tools for creation of novel genetic swine models allows the modeling of highly complex pathophysiology and comorbidities. As such, the utilization of swine models in early research allows for the evaluation of novel drug and technology efficacy while encouraging redesign and refinement before committing to clinical testing. This review highlights the appropriateness of the miniature swine for modeling complex physiologic systems, presenting it as a highly translational preclinical platform to validate efficacy and safety of therapies and devices.

Gene expression profiling of white adipose tissue reveals paternal transmission of proneness to obesity

Previously, we found that C57BL/6J (B6) mice are more prone to develop obesity than PWK mice. In addition, we analyzed reciprocal crosses between these mice and found that (PWK × B6) F1 mice, which have B6 fathers, are more likely to develop dietary obesity than (B6 × PWK) F1 mice, which have B6 mothers. These results suggested that diet-induced obesity is paternally transmitted. In this study, we performed transcriptome analysis of adipose tissues of B6, PWK, (PWK × B6) F1, and (B6 × PWK) F1 mice using next-generation sequencing. We found that paternal transmission of diet-induced obesity was correlated with genes involved in adipose tissue inflammation, metal ion transport, and cilia. Furthermore, we analyzed the imprinted genes expressed in white adipose tissue (WAT) and obesity. Expression of paternally expressed imprinted genes (PEGs) was negatively correlated with body weight, whereas expression of maternally expressed imprinted genes (MEGs) was positively correlated. In the obesity-prone B6 mice, expression of PEGs was down-regulated by a high-fat diet, suggesting that abnormally low expression of PEGs contributes to high-fat diet-induced obesity in B6 mice. In addition, using single-nucleotide polymorphisms that differ between B6 and PWK, we identified candidate imprinted genes in WAT.

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Background

Genomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model. Analytical deductions from the proposed imprinting model indicated a non-negligible contribution of imprinting to genetic variation of complex traits. Also, we performed a whole-genome scan analysis on mouse body mass index (BMI) aiming at revealing potential consequences when existing imprinting effects are ignored in genetic analysis.

Results

10,021 SNP markers were used to perform a whole-genome single marker regression on mouse BMI using an additive and an imprinting model. Markers significant for imprinting indicated that BMI is subject to imprinting. Marked variance changed from 1.218 ×10⁻⁴ to 1.842 ×10⁻⁴ when imprinting was considered in the analysis, implying that one third of marked variance would be lost if existing imprinting effects were not accounted for. When both marker and pedigree information were used, estimated heritability increased from 0.176 to 0.195 when imprinting was considered.

Conclusions

When a complex trait is subject to imprinting, using an additive model that ignores this phenomenon may result in an underestimate of additive variability, potentially leading to wrong inferences about the underlying genetic architecture of that trait. This could be a possible factor explaining part of the missing heritability commonly observed in genome-wide association studies (GWAS).

Mapping complex traits as a dynamic system

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a "system" in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.

An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins

DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits.

Type 2 diabetes (T2D) is a highly heterogeneous disease with a strong genetic component. Here the authors examine genome-wide methylation patterns in T2D-discordant, T2D-concordant and healthy concordant monozygotic twin pairs, and identify DNA methylation signals that may represent new biomarkers or drug targets for T2D.

Fine mapping and candidate gene search of quantitative trait loci for growth and obesity using mouse intersubspecific subcongenic intercrosses and exome sequencing

Although growth and body composition traits are quantitative traits of medical and agricultural importance, the genetic and molecular basis of those traits remains elusive. Our previous genome-wide quantitative trait locus (QTL) analyses in an intersubspecific backcross population between C57BL/6JJcl (B6) and wild Mus musculus castaneus mice revealed a major growth QTL (named Pbwg1) on a proximal region of mouse chromosome 2. Using the B6.Cg-Pbwg1 intersubspecific congenic strain created, we revealed 12 closely linked QTLs for body weight and body composition traits on an approximately 44.1-Mb wild-derived congenic region. In this study, we narrowed down genomic regions harboring three (Pbwg1.12, Pbwg1.3 and Pbwg1.5) of the 12 linked QTLs and searched for possible candidate genes for the QTLs. By phenotypic analyses of F2 intercross populations between B6 and each of four B6.Cg-Pbwg1 subcongenic strains with overlapping and non-overlapping introgressed regions, we physically defined Pbwg1.12 affecting body weight to a 3.8-Mb interval (61.5-65.3 Mb) on chromosome 2. We fine-mapped Pbwg1.3 for body length to an 8.0-Mb interval (57.3-65.3) and Pbwg1.5 for abdominal white fat weight to a 2.1-Mb interval (59.4-61.5). The wild-derived allele at Pbwg1.12 and Pbwg1.3 uniquely increased body weight and length despite the fact that the wild mouse has a smaller body size than that of B6, whereas it decreased fat weight at Pbwg1.5. Exome sequencing and candidate gene prioritization suggested that Gcg and Grb14 are putative candidate genes for Pbwg1.12 and that Ly75 and Itgb6 are putative candidate genes for Pbwg1.5. These genes had nonsynonymous SNPs, but the SNPs were predicted to be not harmful to protein functions. These results provide information helpful to identify wild-derived quantitative trait genes causing enhanced growth and resistance to obesity.

Mapping of imprinted quantitative trait loci using immortalized F2 populations

Mapping of imprinted quantitative trait loci (iQTLs) is helpful for understanding the effects of genomic imprinting on complex traits in animals and plants. At present, the experimental designs and corresponding statistical methods having been proposed for iQTL mapping are all based on temporary populations including F2 and BC1, which can be used only once and suffer some other shortcomings respectively. In this paper, we propose a framework for iQTL mapping, including methods of interval mapping (IM) and composite interval mapping (CIM) based on conventional low-density genetic maps and point mapping (PM) and composite point mapping (CPM) based on ultrahigh-density genetic maps, using an immortalized F2 (imF2) population generated by random crosses between recombinant inbred lines or doubled haploid lines. We demonstrate by simulations that imF2 populations are very desirable and the proposed statistical methods (especially CIM and CPM) are very powerful for iQTL mapping, with which the imprinting effects as well as the additive and dominance effects of iQTLs can be unbiasedly estimated.

Developmental programming mediated by complementary roles of imprinted Grb10 in mother and pup

Developmental programming links growth in early life with health status in adulthood. Although environmental factors such as maternal diet can influence the growth and adult health status of offspring, the genetic influences on this process are poorly understood. Using the mouse as a model, we identify the imprinted gene Grb10 as a mediator of nutrient supply and demand in the postnatal period. The combined actions of Grb10 expressed in the mother, controlling supply, and Grb10 expressed in the offspring, controlling demand, jointly regulate offspring growth. Furthermore, Grb10 determines the proportions of lean and fat tissue during development, thereby influencing energy homeostasis in the adult. Most strikingly, we show that the development of normal lean/fat proportions depends on the combined effects of Grb10 expressed in the mother, which has the greater effect on offspring adiposity, and Grb10 expressed in the offspring, which influences lean mass. These distinct functions of Grb10 in mother and pup act complementarily, which is consistent with a coadaptation model of imprinting evolution, a model predicted but for which there is limited experimental evidence. In addition, our findings identify Grb10 as a key genetic component of developmental programming, and highlight the need for a better understanding of mother-offspring interactions at the genetic level in predicting adult disease risk.

Body composition and gene expression QTL mapping in mice reveals imprinting and interaction effects

BACKGROUND

Shifts in body composition, such as accumulation of body fat, can be a symptom of many chronic human diseases; hence, efforts have been made to investigate the genetic mechanisms that underlie body composition. For example, a few quantitative trait loci (QTL) have been discovered using genome-wide association studies, which will eventually lead to the discovery of causal mutations that are associated with tissue traits. Although some body composition QTL have been identified in mice, limited research has been focused on the imprinting and interaction effects that are involved in these traits. Previously, we found that Myostatin genotype, reciprocal cross, and sex interacted with numerous chromosomal regions to affect growth traits.

RESULTS

Here, we report on the identification of muscle, adipose, and morphometric phenotypic QTL (pQTL), translation and transcription QTL (tQTL) and expression QTL (eQTL) by applying a QTL model with additive, dominance, imprinting, and interaction effects. Using an F2 population of 1000 mice derived from the Myostatin-null C57BL/6 and M16i mouse lines, six imprinted pQTL were discovered on chromosomes 6, 9, 10, 11, and 18. We also identified two IGF1 and two Atp2a2 eQTL, which could be important trans-regulatory elements. pQTL, tQTL and eQTL that interacted with Myostatin, reciprocal cross, and sex were detected as well. Combining with the additive and dominance effect, these variants accounted for a large amount of phenotypic variation in this study.

CONCLUSIONS

Our study indicates that both imprinting and interaction effects are important components of the genetic model of body composition traits. Furthermore, the integration of eQTL and traditional QTL mapping may help to explain more phenotypic variation than either alone, thereby uncovering more molecular details of how tissue traits are regulated.

Genomic imprinting and parent-of-origin effects on complex traits

Parent-of-origin effects occur when the phenotypic effect of an allele depends on whether it is inherited from the mother or the father. Several phenomena can cause parent-of-origin effects, but the best characterized is parent-of-origin-dependent gene expression associated with genomic imprinting. The development of new mapping approaches applied to the growing abundance of genomic data has demonstrated that imprinted genes can be important contributors to complex trait variation. Therefore, to understand the genetic architecture and evolution of complex traits, including complex diseases and traits of agricultural importance, it is crucial to account for these parent-of-origin effects. Here, we discuss patterns of phenotypic variation associated with imprinting, evidence supporting its role in complex trait variation and approaches for identifying its molecular signatures.

A model for family-based case-control studies of genetic imprinting and epistasis

Genetic imprinting, or called the parent-of-origin effect, has been recognized to play an important role in the formation and pathogenesis of human diseases. Although the epigenetic mechanisms that establish genetic imprinting have been a focus of many genetic studies, our knowledge about the number of imprinting genes and their chromosomal locations and interactions with other genes is still scarce, limiting precise inference of the genetic architecture of complex diseases. In this article, we present a statistical model for testing and estimating the effects of genetic imprinting on complex diseases using a commonly used case-control design with family structure. For each subject sampled from a case and control population, we not only genotype its own single nucleotide polymorphisms (SNPs) but also collect its parents' genotypes. By tracing the transmission pattern of SNP alleles from parental to offspring generation, the model allows the characterization of genetic imprinting effects based on Pearson tests of a 2 × 2 contingency table. The model is expanded to test the interactions between imprinting effects and additive, dominant and epistatic effects in a complex web of genetic interactions. Statistical properties of the model are investigated, and its practical usefulness is validated by a real data analysis. The model will provide a useful tool for genome-wide association studies aimed to elucidate the picture of genetic control over complex human diseases.

Empirical testing of hypotheses about the evolution of genomic imprinting in mammals

The close interaction between mother and offspring in mammals is thought to contribute to the evolution of genomic imprinting or parent-of-origin dependent gene expression. Empirical tests of theories about the evolution of imprinting have been scant for several reasons. Models make different assumptions about the traits affected by imprinted genes and the scenarios in which imprinting is predicted to have been selected for. Thus, competing hypotheses cannot readily be tested against each other. Further, it is far from clear how predictions about expression patterns of genes with specific phenotypic effects can be tested given current methodology of assaying gene expression levels, be it in the brain or in other tissues. We first set out a scenario for testing competing hypotheses and delineate the different assumptions and predictions of models. We then outline how predictions may be tested using mouse models such as intercrosses or recombinant inbred (RI) systems that can be phenotyped for traits relevant to imprinting theories. Further, we briefly discuss different molecular approaches that may be used in conjunction with experiments to ascertain expression patterns of imprinted genes and thus the testing of predictions.

Effect of the myostatin locus on muscle mass and intramuscular fat content in a cross between mouse lines selected for hypermuscularity

Background

This study is aimed at the analysis of genetic and physiological effects of myostatin on economically relevant meat quality traits in a genetic background of high muscularity. For this purpose, we generated G₃ populations of reciprocal crosses between the two hypermuscular mouse lines BMMI866, which carries a myostatin mutation and is lean, and BMMI806, which has high intramuscular and body fat content. To assess the relationship between muscle mass, body composition and muscle quality traits, we also analysed intramuscular fat content (IMF), water holding capacity (WHC), and additional physiological parameters in M. quadriceps and M. longissimus in 308 G₃-animals.

Results

We found that individuals with larger muscles have significantly lower total body fat (r = −0.28) and IMF (r = −0.64), and in females, a lower WHC (r = −0.35). In males, higher muscle mass was also significantly correlated with higher glycogen contents (r = 0.2) and lower carcass pH-values 24 hours after dissection (r = −0.19). Linkage analyses confirmed the influence of the myostatin mutation on higher lean mass (1.35 g), reduced body fat content (−1.15%), and lower IMF in M. longissimus (−0.13%) and M. quadriceps (−0.07%). No effect was found for WHC. A large proportion of variation of intramuscular fat content of the M. longissimus at the myostatin locus could be explained by sex (23%) and direction-of-cross effects (26%). The effects were higher in males (+0.41%). An additional locus with negative over-dominance effects on total fat mass (−0.55 g) was identified on chromosome 16 at 94 Mb (86–94 Mb) which concurs with fat related QTL in syntenic regions on SSC13 in pigs and BTA1 in cattle.

Conclusion

The data shows QTL effects on mouse muscle that are similar to those previously observed in livestock, supporting the mouse model. New information from the mouse model helps to describe variation in meat quantity and quality, and thus contribute to research in livestock.

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 F(2) 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 and genetic effects on muscle traits in mice

BACKGROUND

Genomic 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.

RESULTS

We 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.

CONCLUSION

Surprisingly, 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.

Detecting maternal-effect loci by statistical cross-fostering

Great progress has been made in understanding the genetic architecture of phenotypic variation, but it is almost entirely focused on how the genotype of an individual affects the phenotype of that same individual. However, in many species the genotype of the mother is a major determinant of the phenotype of her offspring. Therefore, a complete picture of genetic architecture must include these maternal genetic effects, but they can be difficult to identify because maternal and offspring genotypes are correlated and therefore, partially confounded. We present a conceptual framework that overcomes this challenge to separate direct and maternal effects in intact families through an analysis that we call "statistical cross-fostering." Our approach combines genotype data from mothers and their offspring to remove the confounding effects of the offspring's own genotype on measures of maternal genetic effects. We formalize our approach in an orthogonal model and apply this model to an experimental population of mice. We identify a set of six maternal genetic effect loci that explain a substantial portion of variation in body size at all ages. This variation would be missed in an approach focused solely on direct genetic effects, but is clearly a major component of genetic architecture. Our approach can easily be adapted to examine maternal effects in different systems, and because it does not require experimental manipulation, it provides a framework that can be used to understand the contribution of maternal genetic effects in both natural and experimental populations.

Bayesian mapping of genome-wide epistatic imprinted loci for quantitative traits

Genomic imprinting, an epigenetic phenomenon of parent-of-origin-specific gene expression, has been widely observed in plants, animals, and humans. To detect imprinting genes influencing quantitative traits, the least squares and maximum likelihood approaches for fitting a single quantitative trait locus (QTL) and Bayesian methods for simultaneously modeling multiple QTL have been adopted, respectively, in various studies. However, most of these studies have only estimated imprinting main effects and thus ignored imprinting epistatic effects. In the presence of extremely complex genomic imprinting architectures, we introduce a Bayesian model selection method to analyze the multiple interacting imprinted QTL (iQTL) model. This approach will greatly enhance the computational efficiency through setting the upper bound of the number of QTLs and performing selective sampling for QTL parameters. The imprinting types of detected main-effect QTLs can be estimated from the Bayes factor statistic formulated by the posterior probabilities for the genetic effects being compared. The performance of the proposed method is demonstrated by several simulation experiments. Moreover, this method is applied to dissect the imprinting genetic architecture for body weight in mouse and fruit weight in tomato. Matlab code for implementing this approach will be available from the authors upon request.

A computational framework for the inheritance pattern of genomic imprinting for complex traits

Genetic imprinting, by which the expression of a gene depends on the parental origin of its alleles, may be subjected to reprogramming through each generation. Currently, such reprogramming is limited to qualitative description only, lacking more precise quantitative estimation for its extent, pattern and mechanism. Here, we present a computational framework for analyzing the magnitude of genetic imprinting and its transgenerational inheritance mode. This quantitative model is based on the breeding scheme of reciprocal backcrosses between reciprocal F(1) hybrids and original inbred parents, in which the transmission of genetic imprinting across generations can be tracked. We define a series of quantitative genetic parameters that describe the extent and transmission mode of genetic imprinting and further estimate and test these parameters within a genetic mapping framework using a new powerful computational algorithm. The model and algorithm described will enable geneticists to identify and map imprinted quantitative trait loci and dictate a comprehensive atlas of developmental and epigenetic mechanisms related to genetic imprinting. We illustrate the new discovery of the role of genetic imprinting in regulating hyperoxic acute lung injury survival time using a mouse reciprocal backcross design.

Characterization of genomic imprinting effects and patterns with parametric accelerated failure time model

Genomic imprinting, a genetic phenomenon of non-equivalent allele expression that depends on parental origins, has been ubiquitously observed in nature. It does not only control the traits of growth and development but also may be responsible for survival traits. Based on the accelerated failure time model, we construct a general parametric model for mapping the imprinted QTL (iQTL). Within the framework of interval mapping, maximum likelihood estimation of iQTL parameters is implemented via EM algorithm. The imprinting patterns of the detected iQTL are statistically tested according to a series of null hypotheses. BIC model selection criterion is employed to choose an optimal baseline hazard function with maximum likelihood and parsimonious parameters. Simulations are used to validate the proposed mapping procedure. A published dataset from a mouse model system was used to illustrate the proposed framework. Results show that among the five commonly used survival distributions, Log-logistic distribution is the optimal baseline hazard function for mapping QTL of hyperoxic acute lung injury (HALI) survival; under the log-logistic distribution, four QTLs were identified, in which only one QTL was inherited in Mendelian fashion, whereas others were imprinted in different imprinting patterns.

Disentangling prenatal and postnatal maternal genetic effects reveals persistent prenatal effects on offspring growth in mice

Mothers are often the most important determinant of traits expressed by their offspring. These "maternal effects" (MEs) are especially crucial in early development, but can also persist into adulthood. They have been shown to play a role in a diversity of evolutionary and ecological processes, especially when genetically based. Although the importance of MEs is becoming widely appreciated, we know little about their underlying genetic basis. We address the dearth of genetic data by providing a simple approach, using combined genotype information from parents and offspring, to identify "maternal genetic effects" (MGEs) contributing to natural variation in complex traits. Combined with experimental cross-fostering, our approach also allows for the separation of pre- and postnatal MGEs, providing rare insights into prenatal effects. Applying this approach to an experimental mouse population, we identified 13 ME loci affecting body weight, most of which (12/13) exhibited prenatal effects, and nearly half (6/13) exhibiting postnatal effects. MGEs contributed more to variation in body weight than the direct effects of the offsprings' own genotypes until mice reached adulthood, but continued to represent a major component of variation through adulthood. Prenatal effects always contributed more variation than postnatal effects, especially for those effects that persisted into adulthood. These results suggest that MGEs may be an important component of genetic architecture that is generally overlooked in studies focused on direct mapping from genotype to phenotype. Our approach can be used in both experimental and natural populations, providing a widely practicable means of expanding our understanding of MGEs.

Genome Scan for Parent-of-Origin QTL Effects on Bovine Growth and Carcass Traits

Parent-of-origin effects (POE) such as genomic imprinting influence growth and body composition in livestock, rodents, and humans. Here, we report the results of a genome scan to detect quantitative trait loci (QTL) with POE on growth and carcass traits in Angus × Brahman cattle crossbreds. We identified 24 POE–QTL on 15 Bos taurus autosomes (BTAs) of which six were significant at 5% genome-wide (GW) level and 18 at the 5% chromosome-wide (CW) significance level. Six QTL were paternally expressed while 15 were maternally expressed. Three QTL influencing post-weaning growth map to the proximal end of BTA2 (linkage region of 0–9 cM; genomic region of 5.0–10.8 Mb), for which only one imprinted ortholog is known so far in the human and mouse genomes, and therefore may potentially represent a novel imprinted region. The detected QTL individually explained 1.4 ∼ 5.1% of each trait’s phenotypic variance. Comparative in silico analysis of bovine genomic locations show that 32 out of 1,442 known mammalian imprinted genes from human and mouse homologs map to the identified QTL regions. Although several of the 32 genes have been associated with quantitative traits in cattle, only two (GNAS and PEG3) have experimental proof of being imprinted in cattle. These results lend additional support to recent reports that POE on quantitative traits in mammals may be more common than previously thought, and strengthen the need to identify and experimentally validate cattle orthologs of imprinted genes so as to investigate their effects on quantitative traits.

Imprinting in the schizophrenia candidate gene GABRB2 encoding GABA(A) receptor β(2) subunit

Schizophrenia is a complex genetic disorder, the inheritance pattern of which is likely complicated by epigenetic factors yet to be elucidated. In this study, transmission disequilibrium tests with family trios yielded significant differences between paternal and maternal transmissions of the disease-associated single-nucleotide polymorphism (SNP) rs6556547 and its haplotypes. The minor allele (T) of rs6556547 was paternally undertransmitted to male schizophrenic offsprings, and this parent-of-origin effect strongly suggested that GABRB2 is imprinted. 'Flipping' of allelic expression in heterozygotes of SNP rs2229944 (C/T) in GABRB2 or rs2290732 (G/A) in the neighboring GABRA1 was compatible with imprinting effects on gene expression. Clustering analysis of GABRB2 mRNA expressions suggested that imprinting brought about the observed two-tiered distribution of expression levels in controls with heterozygous genotype at the disease-associated SNP rs1816071 (A/G). The deficit of upper-tiered expressions accounted for the lowered expression levels in the schizophrenic heterozygotes. The occurrence of a two-tiered distribution furnished support for imprinting, and also pointed to the necessity of differentiating between two kinds of heterozygotes of different parental origins in disease association studies on GABRB2. Bisulfite sequencing revealed hypermethylation in the neighborhood of SNP rs1816071, and methylation differences between controls and schizophrenia patients. Notably, the two schizophrenia-associated SNPs rs6556547 and rs1816071 overlapped with a CpG dinucleotide, thereby opening the possibility that CpG methylation status of these sites could have an impact on the risk of schizophrenia. Thus multiple lines of evidence pointed to the occurrence of imprinting in the GABRB2 gene and its possible role in the development of schizophrenia.

A statistical design for testing transgenerational genomic imprinting in natural human populations

Genomic imprinting is a phenomenon in which the same allele is expressed differently, depending on its parental origin. Such a phenomenon, also called the parent-of-origin effect, has been recognized to play a pivotal role in embryological development and pathogenesis in many species. Here we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. The design is integrated with population and cytogenetic principles of gene segregation and transmission from a previous generation to next. The implementation of the EM algorithm within the design framework leads to the estimation of genetic parameters that define imprinted effects. A simulation study is used to investigate the statistical properties of the model and validate its utilization. This new design, coupled with increasingly used genome-wide association studies, should have an immediate implication for studying the genetic architecture of complex traits in humans.

The hunt for the epiallele

Understanding the origin of phenotypic variation remains one of the principle challenges of contemporary biology. Recent genome-wide association studies have identified association between common genetic variants and complex phenotype; however, the minimal effect sizes observed in such studies highlight the potential for other causal factors to be involved in phenotypic variation. The epigenetic state of an organism (or 'epigenome') incorporates a landscape of complex and plastic molecular events that may underlie the 'missing link' that integrates genotype with phenotype. The nature of these processes has been the subject of intense scientific study over the recent years, and characterisation of epigenetic variation, in the form of 'epialleles', is providing fascinating insight into how the genome functions within a range of developmental processes, environments, and in states of health and disease. This review will discuss how and when mammalian epialleles may be generated and their interaction with genetic and environmental factors. We will outline how an epiallele has a variable relationship with phenotype, and how new technologies may be used for their detection and to facilitate an understanding of their contribution to phenotype. Finally, we will consider epialleles in population variation and their teleological role in evolution. variation and their teleological role in evolution.

Diet-dependent genetic and genomic imprinting effects on obesity in mice

Although the current obesity epidemic is of environmental origin, there is substantial genetic variation in individual response to an obesogenic environment. In this study, we perform a genome-wide scan for quantitative trait loci (QTLs) affecting obesity per se, or an obese response to a high-fat diet in mice from the LG/J by SM/J Advanced Intercross (AI) Line (Wustl:LG,SM-G16). A total of 1,002 animals from 78 F₁₆ full sibships were weaned at 3 weeks of age and half of each litter placed on high- and low-fat diets. Animals remained on the diet until 20 weeks of age when they were necropsied and the weights of the reproductive, kidney, mesenteric, and inguinal fat depots were recorded. Effects on these phenotypes, along with total fat depot weight and carcass weight at necropsy, were mapped across the genome using 1,402 autosomal single-nucleotide polymorphism (SNP) markers. Haplotypes were reconstructed and additive, dominance, and imprinting genotype scores were derived every 1 cM along the F₁₆ map. Analysis was performed using a mixed model with additive, dominance, and imprinting genotype scores, their interactions with sex, diet, and with sex-by-diet as fixed effects and with family and its interaction with sex, diet, and sex-by-diet as random effects. We discovered 95 trait-specific QTLs mapping to 40 locations. Most QTLs had additive effects with dominance and imprinting effects occurring at two-thirds of the loci. Nearly every locus interacted with sex and/or diet in important ways demonstrating that gene effects are primarily context dependent, changing depending on sex and/or diet.

Genetic, epigenetic, and gene-by-diet interaction effects underlie variation in serum lipids in a LG/JxSM/J murine model

Variation in serum cholesterol, free-fatty acids, and triglycerides is associated with cardiovascular disease (CVD) risk factors. There is great interest in characterizing the underlying genetic architecture of these risk factors, because they vary greatly within and among human populations and between the sexes. We present results of a genome-wide scan for quantitative trait loci (QTL) affecting serum cholesterol, free-fatty acids, and triglycerides in an F(16) advanced intercross line of LG/J and SM/J (Wustl:LG,SM-G16). Half of the population was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. Here we examine genetic, environmental, and genetic-by-environmental interactions of QTL overlapping previously identified loci associated with CVD risk factors, and we add to the serum lipid QTL landscape by identifying new loci.

Methylation profiling in individuals with uniparental disomy identifies novel differentially methylated regions on chromosome 15

The maternal and paternal genomes possess distinct epigenetic marks that distinguish them at imprinted loci. In order to identify imprinted loci, we used a novel method, taking advantage of the fact that uniparental disomy (UPD) provides a system that allows the two parental chromosomes to be studied independently. We profiled the paternal and maternal methylation on chromosome 15 using immunoprecipitation of methylated DNA and hybridization to tiling oligonucleotide arrays. Comparison of six individuals with maternal versus paternal UPD15 revealed 12 differentially methylated regions (DMRs). Putative DMRs were validated by bisulfite sequencing, confirming the presence of parent-of-origin-specific methylation marks. We detected DMRs associated with known imprinted genes within the Prader-Willi/Angelman syndrome region, such as SNRPN and MAGEL2, validating this as a method of detecting imprinted loci. Of the 12 DMRs identified, eight were novel, some of which are associated with genes not previously thought to be imprinted. These include a site within intron 2 of IGF1R at 15q26.3, a gene that plays a fundamental role in growth, and an intergenic site upstream of GABRG3 that lies within a previously defined candidate region conferring an increased maternal risk of psychosis. These data provide a map of parent-of-origin-specific epigenetic modifications on chromosome 15, identifying DNA elements that may play a functional role in the imprinting process. Application of this methodology to other chromosomes for which UPD has been reported will allow the systematic identification of imprinted sites throughout the genome.

A model for transgenerational imprinting variation in complex traits

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.

High-throughput analysis of candidate imprinted genes and allele-specific gene expression in the human term placenta

Background

Imprinted genes show expression from one parental allele only and are important for development and behaviour. This extreme mode of allelic imbalance has been described for approximately 56 human genes. Imprinting status is often disrupted in cancer and dysmorphic syndromes. More subtle variation of gene expression, that is not parent-of-origin specific, termed 'allele-specific gene expression' (ASE) is more common and may give rise to milder phenotypic differences. Using two allele-specific high-throughput technologies alongside bioinformatics predictions, normal term human placenta was screened to find new imprinted genes and to ascertain the extent of ASE in this tissue.

Results

Twenty-three family trios of placental cDNA, placental genomic DNA (gDNA) and gDNA from both parents were tested for 130 candidate genes with the Sequenom MassArray system. Six genes were found differentially expressed but none imprinted. The Illumina ASE BeadArray platform was then used to test 1536 SNPs in 932 genes. The array was enriched for the human orthologues of 124 mouse candidate genes from bioinformatics predictions and 10 human candidate imprinted genes from EST database mining. After quality control pruning, a total of 261 informative SNPs (214 genes) remained for analysis. Imprinting with maternal expression was demonstrated for the lymphocyte imprinted gene ZNF331 in human placenta. Two potential differentially methylated regions (DMRs) were found in the vicinity of ZNF331. None of the bioinformatically predicted candidates tested showed imprinting except for a skewed allelic expression in a parent-specific manner observed for PHACTR2, a neighbour of the imprinted PLAGL1 gene. ASE was detected for two or more individuals in 39 candidate genes (18%).

Conclusions

Both Sequenom and Illumina assays were sensitive enough to study imprinting and strong allelic bias. Previous bioinformatics approaches were not predictive of new imprinted genes in the human term placenta. ZNF331 is imprinted in human term placenta and might be a new ubiquitously imprinted gene, part of a primate-specific locus. Demonstration of partial imprinting of PHACTR2 calls for re-evaluation of the allelic pattern of expression for the PHACTR2-PLAGL1 locus. ASE was common in human term placenta.

Epigenetics and transgenerational transfer: a physiological perspective

Epigenetics, the transgenerational transfer of phenotypic characters without modification of gene sequence, is a burgeoning area of study in many disciplines of biology. However, the potential impact of this phenomenon on the physiology of animals is not yet broadly appreciated, in part because the phenomenon of epigenetics is not typically part of the design of physiological investigations. Still enigmatic and somewhat ill defined is the relationship between the overarching concept of epigenetics and interesting transgenerational phenomena (e.g. 'maternal/parental effects') that alter the physiological phenotype of subsequent generations. The lingering effect on subsequent generations of an initial environmental disturbance in parent animals can be profound, with genes continuing to be variously silenced or expressed without an associated change in gene sequence for many generations. Known epigenetic mechanisms involved in this phenomenon include chromatin remodeling (DNA methylation and histone modification), RNA-mediated modifications (non-coding RNA and microRNA), as well as other less well studied mechanisms such as self-sustaining loops and structural inheritance. In this review we: (1) discuss how the concepts of epigenetics and maternal effects both overlap with, and are distinct from, each other; (2) analyze examples of existing animal physiological studies based on these concepts; and (3) offer a construct by which to integrate these concepts into the design of future investigations in animal physiology.

Parent-of-origin effects on voluntary exercise levels and body composition in mice

Despite the health-related benefits of exercise, many people do not engage in enough activity to realize the rewards, and little is known regarding the genetic or environmental components that account for this individual variation. We created and phenotyped a large G(4) advanced intercross line originating from reciprocal crosses between mice with genetic propensity for increased voluntary exercise (HR line) and the inbred strain C57BL/6J. G(4) females (compared to males) ran significantly more when provided access to a running wheel and were smaller with a greater percentage of body fat pre- and postwheel access. Change in body composition resulting from a 6-day exposure to wheels varied between the sexes with females generally regulating energy balance more precisely in the presence of exercise. We observed parent-of-origin effects on most voluntary wheel running and body composition traits, which accounted for 3-13% of the total phenotypic variance pooled across sexes. G(4) individuals descended from progenitor (F(0)) crosses of HRfemale symbol and C57BL/6Jmale symbol ran greater distances, spent more time running, ran at higher maximum speeds/day, and had lower percent body fat and higher percent lean mass than mice descended from reciprocal progenitor crosses (C57BL/6Jfemale symbol x HRmale symbol). For some traits, significant interactions between parent of origin and sex were observed. We discuss these results in the context of sex dependent activity and weight loss patterns, the contribution of parent-of-origin effects to predisposition for voluntary exercise, and the genetic (i.e., X-linked or mtDNA variations), epigenetic (i.e., genomic imprinting), and environmental (i.e., in utero environment or maternal care) phenomena potentially modulating these effects.

Bayesian model selection for characterizing genomic imprinting effects and patterns

MOTIVATION

Although imprinted genes have been ubiquitously observed in nature, statistical methodology still has not been systematically developed for jointly characterizing genomic imprinting effects and patterns. To detect imprinting genes influencing quantitative traits, the least square and maximum likelihood approaches for fitting a single quantitative trait loci (QTL) and Bayesian method for simultaneously modeling multiple QTLs have been adopted in various studies.

RESULTS

In a widely used F(2) reciprocal mating population for mapping imprinting genes, we herein propose a genomic imprinting model which describes additive, dominance and imprinting effects of multiple imprinted quantitative trait loci (iQTL) for traits of interest. Depending upon the estimates of the above genetic effects, we categorized imprinting patterns into seven types, which provides a complete classification scheme for describing imprinting patterns. Bayesian model selection was employed to identify iQTL along with many genetic parameters in a computationally efficient manner. To make statistical inference on the imprinting types of iQTL detected, a set of Bayes factors were formulated using the posterior probabilities for the genetic effects being compared. We demonstrated the performance of the proposed method by computer simulation experiments and then applied this method to two real datasets. Our approach can be generally used to identify inheritance modes and determine the contribution of major genes for quantitative variations.