Biology subjects

Valdar, W.

Publications and source records attributed to Valdar, W..

At least 19 recordsLinked to original sources

bioRxiv2026

DetectGxT: detecting gene-by-treatment interactions on molecular count phenotypes accounting for allelic additivity

MotivationIdentifying the mechanisms by which genetic variants affect the molecular response to an applied treatment is important across multiple biological fields, and an effective approach to this end is interaction molecular QTL mapping. However, the statistical models commonly used to detect such gene-by-treatment interactions (GxT) are non-trivially misspecified, and this can lead to decreased power. ResultsWe developed an R software package, DetectGxT, that uses nonlinear regression to more accurately model the relationship between the genotype and the transformed molecular count phenotypes. It also optionally models donor or polygenic random effects. Simulations show that nonlinear regression can increase the power to detect interactions. In existing interaction expression QTL mapping data from primary human neural progenitor cells, nonlinear and linear regression approaches identified overlapping but distinct sets of gene-SNP pairs with significant GxT interactions. Overall, our results suggest an advantage of nonlinear regression over linear regression in detecting GxT interactions on molecular phenotypes. AvailabilityThe DetectGxT software is available at https://github.com/yharigaya/detectgxt. Contactmilove@email.unc.edu, william.valdar@unc.edu

genetics↗

bioRxiv2026

Cross-coronavirus host susceptibility loci influence disease severity through immune mediators

Severe disease following infection with SARS-CoV or SARS-CoV-2 is driven in part by genetically regulated immune responses that promote lung injury. Previously, we showed that genetic risk for severe disease is conserved across viruses and between mouse and human, identifying the HrS43 locus as a shared determinant of severity. Here, to resolve immune pathways linking host loci to disease outcomes, we apply an integrative statistical framework combining Bayesian identification of predictive immune traits with QTL mapping and mediation analysis across infection conditions. This approach identifies immune predictors of disease severity across both viruses, reveals extensive genetic control of the immune system at homeostasis and during infection, and supports locus-specific causal mechanisms of immunopathology. Notably, HrS43 appears to influence disease severity through distinct immune mediators in SARS-CoV versus SARS-CoV-2, demonstrating that conserved genetic susceptibility can drive virus-specific immunopathology with translational relevance across species.

genetics↗

bioRxiv2025

Genetic mapping in Collaborative Cross mouse strains identifies loci that affect initial sensitivity to cocaine

We identified two Collaborative Cross (CC) strains, CC004/TauUncJ (CC004) and CC041/TauUncJ (CC041), that differ significantly for locomotor response and self-administration of cocaine. In the current study, we crossed each of these strains to C57BL/6NJ (B6N) mice to produce two F2 populations and identify genetic loci that influence locomotor response to cocaine. We identified three significant loci on chromosomes 7, 11 and 14 in the CC041 F2 mapping cross that collectively explain 14% of the phenotypic variance for locomotor response to cocaine. We used a bioinformatic approach to identify high quality candidate genes that are genetically plausible, have functional relevance and are suitable for further exploration. Our study is the first to use CC strains to perform QTL mapping for addiction-related phenotypes and proposes several candidate genes for follow-up analyses.

genetics↗

bioRxiv2023

Sarbecovirus disease susceptibility is conserved across viral and host models

Coronaviruses have caused three severe epidemics since the start of the 21st century: SARS, MERS and COVID-19. The severity of the ongoing COVID-19 pandemic and increasing likelihood of future coronavirus outbreaks motivates greater understanding of factors leading to severe coronavirus disease. We screened ten strains from the Collaborative Cross mouse genetic reference panel and identified strains CC006/TauUnc (CC006) and CC044/Unc (CC044) as coronavirus-susceptible and resistant, respectively, as indicated by variable weight loss and lung congestion scores four days post-infection. We generated a genetic mapping population of 755 CC006xCC044 F2 mice and exposed the mice to one of three genetically distinct mouse-adapted coronaviruses: clade 1a SARS-CoV MA15 (n=391), clade 1b SARS-CoV-2 MA10 (n=274), and clade 2 HKU3-CoV MA (n=90). Quantitative trait loci (QTL) mapping in SARS-CoV- and SARS-CoV-2-infected F2 mice identified genetic loci associated with disease severity. Specifically, we identified seven loci associated with variation in outcome following infection with either virus, including one, HrS45, that is present in both groups. Three of these QTL, including HrS45, were also associated with HKU3-CoV MA outcome. HrS45 overlaps with a QTL previously reported by our lab that is associated with SARS-CoV outcome in CC011xCC074 F2 mice and is also syntenic with a human chromosomal region associated with severe COVID-19 outcomes in humans GWAS. The results reported here provide: (a) additional support for the involvement of this locus in SARS-CoV MA15 infection, (b) the first conclusive evidence that this locus is associated with susceptibility across the Sarbecovirus subgenus, and (c) demonstration of the relevance of mouse models in the study of coronavirus disease susceptibility in humans.

genetics↗

bioRxiv2023

A mutation in Themis contributes to peanut-induced oral anaphylaxis in CC027 mice

BackgroundThe development of peanut allergy is due to a combination of genetic and environmental factors, although specific genes have proven difficult to identify. Previously, we reported that peanut- sensitized CC027/GeniUnc (CC027) mice develop anaphylaxis upon oral challenge to peanut, unlike C3H/HeJ (C3H) mice. ObjectiveTo determine the genetic basis of orally-induced anaphylaxis to peanut in CC027 mice. MethodsA genetic mapping population between CC027 and C3H mice was designed to identify the genetic factors that drive oral anaphylaxis. A total of 356 CC027xC3H backcrossed mice were generated, sensitized to peanut, then challenged to peanut by oral gavage. Anaphylaxis and peanut-specific IgE were quantified for all mice. T-cell phenotyping was conducted on CC027 and five additional CC strains. ResultsAnaphylaxis to peanut was absent in 77% of backcrossed mice, with 19% showing moderate anaphylaxis, and 4% having severe anaphylaxis. A total of eight genetic loci were associated with variation in response to peanut challenge, six associated with anaphylaxis (temperature decrease) and two associated with peanut-specific IgE levels. There were two major loci that impacted multiple aspects of the severity of acute anaphylaxis, at which the CC027 allele was associated with worse outcome. At one of these loci, CC027 has a private genetic variant in the Themis (thymocyte-expressed molecule involved in selection) gene. Consistent with Themis described functions, we found that CC027 have more immature T cells with fewer CD8+, CD4+, and CD4+CD25+CD127- regulatory T cells. ConclusionOur results demonstrate a key role for Themis in the orally-reactive CC027 mouse model of peanut allergy.

genetics↗

bioRxiv2022

Sex and genetic specific effects on behavioral, but not metabolic, responses to a high fat diet in heterogeneous stock rats

Obesity is a growing epidemic associated with a range of comorbidities, including anxiety and depression. Genetics and environmental factors such as diet contribute to both adiposity and anxiety/depression. Heterogeneous stock (HS) rats are an outbred colony and useful for genetic mapping of complex traits. We have previously shown that HS male rats exhibit worsened metabolic and behavioral health in response to high fat diet (HFD). This study aims to determine if females have similar response to diet and if response to diet interacts with genetic background. We measured multiple metabolic (body weight, fat pad weight, glucose tolerance, fasting glucose and insulin) and behavioral (elevated plus maze, open field test, and forced swim test) outcomes in a large cohort of male and female rats on either HFD or low fat diet (LFD). We estimated overall heritability as well as heritability of response to diet for each outcome. Both sexes showed worsened metabolic measures when fed HFD compared to LFD. In contrast, only males exhibited altered behavioral responses to HFD relative to LFD, with no effect in females. Most metabolic and behavioral measures showed overall heritability in both sexes. In contrast, although there was some evidence for gene by diet (GxD) interactions for behavioral measures in males, GxD interactions were generally not found for the metabolic measures. These data demonstrate an important role of diet, sex and genetics in metabolic and behavioral phenotypes in HS rats, with a potential role of gene by diet interactions for behavioral outcomes only in males.

genetics↗

bioRxiv2022

Transcriptome-wide Analyses of Adipose Tissue in Outbred Rats Reveal Genetic Regulatory Mechanisms Relevant for Human Obesity

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene co-expression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the African American Genetics of Metabolism and Expression and Metabolic Syndrome in Men. We used weighted gene co-expression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight co-expressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b) or no previously known role in obesity (e.g., St14, Msa4a6). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.

genomics↗

bioRxiv2022

Genetic mapping of multiple metabolic traits identifies novel genes for adiposity, lipids and insulin secretory capacity in outbred rats

Despite the successes of human genome-wide association studies, the causal genes underlying most metabolic traits remain unclear. We used outbred heterogeneous stock (HS) rats, coupled with expression data and mediation analysis, to identify quantitative trait loci (QTLs) and candidate gene mediators for adiposity, glucose tolerance, serum lipids and other metabolic traits. Physiological traits were measured in 1519 male HS rats, with liver and adipose transcriptomes measured in over 410 rats. Genotypes were imputed from low coverage whole genome sequence. Linear mixed models were used to detect physiological and expression QTLs (pQTLs and eQTLs, respectively), employing both SNP- and haplotype-based models for pQTL mapping. Genes with cis-eQTLs that overlapped pQTLs were assessed as causal candidates through mediation analysis. We identified 15 SNP-based pQTLs and 19 haplotype-based pQTLs, of which 11 were in common. Using mediation, we identified the following genes as candidate mediators of pQTLs: Grk5 for a fat pad weight pQTL on Chr1, Krtcap3 for fat pad weight and serum lipids pQTLs on Chr6, Ilrun for a fat pad weight pQTL on Chr20 and Rfx6 for a whole pancreatic insulin content pQTL on Chr20. Furthermore, we verified Grk5 and Ktrcap3 using gene knock-down/out models, thereby shedding light on novel regulators of obesity.

genetics↗

bioRxiv2021

A Bayesian model selection approach for mediation analysis

Mediation analysis is a powerful tool for discovery of causal relationships. We describe a Bayesian model selection approach to mediation analysis that is implemented in our bmediatR software. Using simulations, we show that bmediatR performs as well or better than established methods including the Sobel test, while allowing greater flexibility in both model specification and in the types of inference that are possible. We applied bmediatR to genetic data from mice and human cell lines to demonstrate its ability to derive biologically meaningful findings. The Bayesian model selection framework is extensible to support a wide variety of mediation models.

genetics↗

bioRxiv2020

Skewed X inactivation in genetically diverse mice is associated with recurrent copy number changes at the Xce locus

Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176 kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function-structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross strains. Using published sequence data and applying a Bayesian "Polya urn" model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20-30 cells contributing to brain. Second, NOD/ShiLtJ has a novel and unique functional allele, Xcef, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xceb), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.

genetics↗

bioRxiv2020

Inferring the Allelic Series in a Multiparental Population

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully-Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

genetics↗

bioRxiv2019

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues--liver, lung, and kidney--in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.\n\nAuthor summaryGenetic variation can drive alterations in gene expression levels and chromatin accessibility, the latter of which defines gene regulatory elements genome-wide. The same genetic variants may associate with both molecular events, and these may be connected within the same causal path: a variant that reduces promoter region chromatin accessibility, potentially by affecting transcription factor binding, may lead to reduced expression of that gene. Moreover, these causal regulatory paths can differ between tissues depending on functions and cellular activity specific to each tissue. We identify cross-tissue and tissue-selective genetic regulators of gene expression and chromatin accessibility in liver, lung, and kidney tissues using a panel of genetically diverse inbred mouse strains. Further, we identify a number of candidate causal mediators of the genetic regulation of gene expression, including a zinc finger protein that helps silence the Akr1e1 gene. Our analyses are consistent with chromatin accessibility playing a role in the regulation of transcription. Our study demonstrates the power of genetically diverse, multi-parental mouse populations, such as the Collaborative Cross, for large-scale studies of genetic drivers of gene regulation that underlie complex phenotypes, as well as identifying causal intermediates that drive variable activity of specific genes and pathways.

genomics↗

bioRxiv2018

Bayesian decision theoretic design of two-founder experimental crosses given diallel data

In designing experimental crosses of inbred strains of model organisms, researchers must make a number of decisions. These include the selection of the appropriate strains, the cross design (eg. F2 intercross), and the number of progeny to collect (sample size). These decisions strongly influence the potential for a successful quantitative trait locus (QTL) mapping experiment; good design decisions will lead to efficient and effective science. Thus experimental design deserves careful consideration and planning. Experimental outcomes can be quantified through utility functions using a Bayesian decision theoretic approaches. For QTL mapping experiments, the power to map a QTL is an appealing utility function to maximize. Using any utility function to aid in experimental design will be dependent on assumptions, such as the QTL effect size in the case of power. Rather than arbitrarily selecting QTL effect size values, they can be estimated from pilot data using a Bayesian hierarchical model. The information in the pilot data can be propagated to the utility function, using Markov Chain Monte Carlo (MCMC) to sample from the posterior distribution. Key features of this approach include: 1) distributional summaries of utility, which are preferable to point estimates, and 2) a comprehensive search of the experimental space of crosses of inbred lines for well-designed experiments. We evaluate this Bayesian theoretic approach using diallel crosses as the pilot data. We present results from simulations as well as present examples from both Mendelian and complex traits in the founder strains of the mouse Collaborative Cross. All analyses were performed using our R package, DIDACT (Diallel-Informed Decision theoretic Approach for Crosses Tool), developed to perform Bayesian cross selection based on diallel pilot data.

genetics↗

bioRxiv2018

Determinants of QTL mapping power in the realized Collaborative Cross

The Collaborative Cross (CC) is a mouse genetic reference population whose range of applications includes quantitative trait loci (QTL) mapping. The design of a CC QTL mapping study involves multiple decisions, including which and how many strains to use, and how many replicates per strain to phenotype, all viewed within the context of hypothesized QTL architecture. Until now, these decisions have been informed largely by early power analyses that were based on simulated, hypothetical CC genomes. Now that more than 50 CC strains are available and more than 70 CC genomes have been observed, it is possible to characterize power based on realized CC genomes. We report power analyses based on extensive simulations and examine several key considerations: 1) the number of strains and biological replicates, 2) the QTL effect size, 3) the presence of population structure, and 4) the distribution of functionally distinct alleles among the founder strains at the QTL. We also provide general power estimates to aide in the design of future experiments. All analyses were conducted with our R package, SPARCC (Simulated Power Analysis in the Realized Collaborative Cross), developed for performing either large scale power analyses or those tailored to particular CC experiments.

genetics↗

bioRxiv2018

A diallel of the mouse Collaborative Cross founders reveals strong strain-specific maternal effects on litter size

Reproductive success in the eight founder strains of the Collaborative Cross (CC) was measured using a diallel-mating scheme. Over a 48-month period we generated 4,448 litters, and 24,782 weaned pups were used across 16 different published experiments. We identified factors that affect the average litter size in a cross by estimating the overall contribution of parent-of-origin, heterosis, inbred, and epistatic effects using a Bayesian zero-truncated overdispersed Poisson mixed model. The phenotypic variance of litter size has a substantial contribution (79%) from unexplained and environmental sources, but no detectable effect of seasonality. Most of the explained variance was due to additive effects (9.2%) and parental sex (maternal vs paternal strain; 5.8%), with epistasis accounting for 3.4%. Within the parental effects, the effect of the dams strain explained more than the sires strain (13.2% vs. 1.8%), and the dams strain effects account for 74.2% of total variation explained. Dams from strains C57BL/6J and NOD/ShiLtJ increased the expected litter size by a mean of 1.66 and 1.79 pups, whereas dams from strains WSB/EiJ, PWK/PhJ, and CAST/EiJ reduced expected litter size by a mean of 1.51, 0.81, and 0.90 pups. Finally, there was no strong evidence for strain-specific effects on sex ratio distortion. Overall, these results demonstrate that strains vary substantially in their reproductive ability depending on their genetic background and that litter size is largely determined by dam.strain rather than sire.strain effects, as expected. This analysis adds to our understanding of factors that influence litter size in mammals, and also helps to explain breeding successes and failures in the extinct lines and surviving CC strains.

genetics↗

bioRxiv2018

Mean-Variance QTL Mapping Identifies Novel QTL for Circadian Activity and Exploratory Behavior in Mice

We illustrate, through two case studies, that \"mean-variance QTL mapping\" can discover QTL that traditional interval mapping cannot. Mean-variance QTL mapping is based on the double generalized linear model, which elaborates on the standard linear model by incorporating not only a linear model for the data itself, but also a linear model for the residual variance. Its potential for use in QTL mapping has been described previously, but it remains underutilized, with certain key advantages undemonstrated until now. In the first case study, we use mean-variance QTL mapping to reanalyze a reduced complexity intercross of C57BL/6J and C57BL/6N mice examining circadian behavior and find a mean-controlling QTL for circadian wheel running activity that was not detected by traditional interval mapping. Mean-variance QTL mapping was more powerful than traditional interval mapping at the QTL because it accounted for the fact that mice homozygous for the C57BL/6N allele had less residual variance than the other mice. In the second case study, we reanalyze an intercross between C57BL/6J and C58/J mice examining anxiety-like behaviors, and identify a variance-controlling QTL for rearing behavior. This QTL was not identified in the original analysis because traditional interval mapping does not target variance QTL.

genetics↗

bioRxiv2018

Mean-Variance QTL Mapping on a Background of Variance Heterogeneity

Most QTL mapping approaches seek to identify \"mean QTL\", genetic loci that influence the phenotype mean, after assuming that all individuals in the mapping population have equal residual variance. Recent work has broadened the scope of QTL mapping to identify genetic loci that influence phenotype variance, termed \"variance QTL\", or some combination of mean and variance, which we term \"mean-variance QTL\". Even these approaches, however, fail to address situations where some other factor, be it an environmental factor or a distant genetic locus, influences phenotype variance. We term this situation \"background variance heterogeneity\" (BVH) and used simulation to explore its effects on the power and false positive rate of tests for mean QTL, variance QTL, and mean-variance QTL. Specifically, we compared traditional tests, linear regression for mean QTL and Levenes test for variance QTL, with tests more recently developed, namely Caos tests for all three types of QTL, and tests based on the double generalized linear model (DGLM), which, unlike the other approaches, explicitly models BVH. Simulations showed that, when used in conjunction with a permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also discovered that the rank-based inverse normal transform, often used to corral unruly phenotypes, can be used to mitigate the adverse effects of BVH in some circumstances. We applied the DGLM approach, which we term \"mean-variance QTL mapping\", to publicly available data on a mouse backcross of CAST/Ei into M16i and, after accommodating BVH driven by father, identified a new mean QTL for bodyweight at three weeks of age.

genetics↗

bioRxiv2018

Reciprocal F1 hybrids of two inbred mouse strains reveal parent-of-origin and perinatal diet effects on behavior and expression

Parent-of-origin effects (POEs) in mammals typically arise from maternal effects or from imprinting. Mutations in imprinted genes have been associated with psychiatric disorders, as well as with changes in a handful of animal behaviors. Nonetheless, POEs on complex traits such as behavior remain largely uncharacterized. Furthermore, although perinatal environmental exposures, such as nutrient deficiency, are known to modify both behavior and epigenetic effects generally, the architecture of environment-by-POE is almost completely unexplored. To study POE and environment-by-POE, we employ a relatively neglected but maximally powerful POE-detection system: a reciprocal F1 hybrid population. We exposed female NOD/ShiLtJxC57Bl/6J and C57Bl/6JxNOD/ShiLtJ mice, in utero, to one of four different diets, then after weaning recorded their whole-brain gene expression, as well as a set of behaviors that model psychiatric disease. Microarray expression data revealed an imprinting-enriched set of over a dozen genes subject to POE; the POE on the most significantly affected gene, Carmil1 (a.k.a. Lrrc16a), was validated using qPCR in the same and in a new set of mice. Several behaviors, especially locomotor behaviors, also showed POE. Interestingly, Bayesian mediation analysis suggests Carmil1 expression suppresses behavioral POE, and Airn suppresses POE on Carmil1 expression. A significant diet-by-POE was observed on one behavior, one imprinted gene, and over a dozen non-imprinted genes. Beyond our particular results, our study demonstrates a reciprocal F1 hybrid framework for studying POE and environment-by-POE on behavior.

genetics↗