Non-genetic Components Research Articles

Morphological and Autofluorescence Dataset for ‘Touch’ Epidermal Cell Populations Collected with Imaging Flow Cytometry

Background New methods for processing ‘touch’ or trace biological samples is an ongoing priority for forensic caseworking laboratories. These samples often contain materials from multiple individuals in varying quantities and/or degrees of degradation. Rapid characterization of cellular material before DNA profiling can allow laboratories to screen samples for the presence of multiple contributors or the amount of biological material present. Methods This dataset contains autofluorescence and morphological profiles of epidermal cell populations analyzed using Imaging Flow Cytometry. The epidermal samples were aged for varying amounts of time prior to analysis. Multiple samples from the same individual were also collected to assess profile variations within and across the contributors. Conclusions This data set may be used to investigate variability in epidermal cell populations from different individuals and potential forensic signatures contained within the non-genetic components that comprise touch biological evidence.

Abstract MP44: Non-Genetic Component of Height as a Surrogate Marker for Childhood Socioeconomic Status and Its Association With Cardiovascular and Brain Health: Results From HCHS/SOL

Introduction: Childhood socioeconomic status (SES) has been recognized to have lifelong effects on health, but often studies rely on recollected information subject to bias. Several studies suggest taller adult height is a surrogate for favorable childhood SES, likely due to better nutrition during skeletal development. We aimed to parse out the non-genetic component of height as a refined surrogate marker of childhood SES. Hypothesis: We hypothesized that the higher non-genetic component of height is associated with higher childhood SES, and further, with better cardiovascular and brain health in adulthood. Methods: Self-reported information such as parental education, nativity (born in US 50 states/DC), age at migration, and economic hardship to pay for basic needs and access to household utilities during childhood were collected from US Hispanics/Latinos aged 18-74 years in HCHS/SOL (2008-2011). A non-genetic component of height (height non-pgs ) was estimated by subtracting genetically predicted height from the measured height, where a height genetic prediction model was fitted on sex-standardized height with Hispanic-specific height polygenic score (height pgs ), genetic ancestry components, and sex. The AHA-LE8 cardiovascular health (CVH) clinical score was derived. Neurocognitive function (6-item screener, Spanish English Verbal Learning Test, Word fluency test, Digit symbol substitution test) was assessed among >45 years. We examined the associations of height non-pgs with childhood environment and health outcomes by survey linear regressions. Results: A higher height non-pgs was associated with better childhood environment - younger generation (mean height non-pgs = 0.37 (SE=0.02) for born in 1980-1999 vs -0.45 (0.03) for born in 1930-1949), higher parental education (0.28 (0.02) for college vs -0.11 (0.02) for elementary), US born or migrated at younger age, and availability of plumbing, electricity, and phone during childhood - but not with childhood economic hardship. Further, height non-pgs was positively associated with better cognitive functions in all domains, adjusting for demographics, participants’ SES, and cardiovascular risk factors. These associations with height non-pgs were more pronounced than those with measured height. Height non-pgs showed positive associations with better clinical CVH score among 45+ aged people. Among <45 years, height non-pgs was negatively associated with clinical CVH score. Conclusions: We observed that height non-pgs reflects childhood SES, potentially through better nutrition and greater access to resources that promote growth. Stronger associations of height non-pgs and health, compared to associations with measured height, suggest that height non-pgs is a good proxy for childhood environment. The paradoxical association of height non-pgs with CVH among <45 years old needs further investigation.

Effects of Various Non-genetic Factors on Fertility Traits in Murrah Bulls in an Organized Herd

Background: In a herd going through improvement by selective breeding, bulls contribute more than half of the genetic gain due to higher selection intensity and wider coverage through AI. On the other side, meritorious sires might have a lower fertility due to various reasons which would render them less fit in their contribution to genetic improvement. Thus dairy buffalo breeders must find a delicate balance between production and reproduction superiority for selection of sires, for which an accurate assessment of genetic and non-genetic component of conception is essential. Methods: Present study was conducted on thirty five years data comprising of 5349 AI records on 1302 Murrah buffaloes using 86 bulls maintained and semen analysis of cryopreserved semen samples of 130 Murrah maintained at ICAR-National Dairy Research Institute Karnal were analyzed to study bull conception rate and sperm quality traits. Data were classified into four periods; four seasons of AI viz., Summer (April-June), rainy (July-September), autumn (October-November), winter (December-March); five parity (first, second, third, fourth, fifth and above) and five female AI number (1,2,3,4 and ³ 5 AI) to study the conception rate. Age at freezing (Years), period of freezing and season of freezing were analysed to observe their effects on semen quality traits. Mixed model analysis (SAS 9.3) and the analysis of variance were used to see the effects of various factors on conception rate as well as on semen quality parameters. Result: The analysis of variance revealed that period of AI and season of AI had significant (P less than 0.05) effect on conception rate while parity and female AI number were not found to affect the conception rate of bulls. For seminal parameters, acrosome integrity and HOST were significantly affected by age at freezing. Season affected all the semen quality traits while the effect of period of freezing was found to be non-significant for all the traits. Over all autumn season was found to be most suitable with respect to better seminal parameters. Considering season of AI, rainy season (July-September) had the lowest conception rate. The overall conception rate (%) in the study was 38.43±0.99; where Sires showed a wide variation for conception rate ranging from 20.00% to 60.46%. Concluding with the fact that there exists variation among bull fertility that can be utilized for not only herd conception rates but also be used as a criteria for bull selection and evaluation.

Cisplatin exposure alters tRNA-derived small RNAs but does not affect epimutations in C. elegans

BackgroundThe individual lifestyle and environment of an organism can influence its phenotype and potentially the phenotype of its offspring. The different genetic and non-genetic components of the inheritance system and their mutual interactions are key mechanisms to generate inherited phenotypic changes. Epigenetic changes can be transmitted between generations independently from changes in DNA sequence. In Caenorhabditis elegans, epigenetic differences, i.e. epimutations, mediated by small non-coding RNAs, particularly 22G-RNAs, as well as chromatin have been identified, and their average persistence is three to five generations. In addition, previous research showed that some epimutations had a longer duration and concerned genes that were enriched for multiple components of xenobiotic response pathways. These results raise the possibility that environmental stresses might change the rate at which epimutations occur, with potential significance for adaptation.ResultsIn this work, we explore this question by propagating C. elegans lines either in control conditions or in moderate or high doses of cisplatin, which introduces genotoxic stress by damaging DNA. Our results show that cisplatin has a limited effect on global small non-coding RNA epimutations and epimutations in gene expression levels. However, cisplatin exposure leads to increased fluctuations in the levels of small non-coding RNAs derived from tRNA cleavage. We show that changes in tRNA-derived small RNAs may be associated with gene expression changes.ConclusionsOur work shows that epimutations are not substantially altered by cisplatin exposure but identifies transient changes in tRNA-derived small RNAs as a potential source of variation induced by genotoxic stress.

Models to estimate genetic gain of soybean seed yield from annual multi-environment field trials

Key messageSimulations demonstrated that estimates of realized genetic gain from linear mixed models using regional trials are biased to some degree. Thus, we recommend multiple selected models to obtain a range of reasonable estimates.Genetic improvements of discrete characteristics are obvious and easy to demonstrate, while quantitative traits require reliable and accurate methods to disentangle the confounding genetic and non-genetic components. Stochastic simulations of soybean [Glycine max (L.) Merr.] breeding programs were performed to evaluate linear mixed models to estimate the realized genetic gain (RGG) from annual multi-environment trials (MET). True breeding values were simulated under an infinitesimal model to represent the genetic contributions to soybean seed yield under various MET conditions. Estimators were evaluated using objective criteria of bias and linearity. Covariance modeling and direct versus indirect estimation-based models resulted in a substantial range of estimated values, all of which were biased to some degree. Although no models produced unbiased estimates, the three best-performing models resulted in an average bias of ±7.41\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm\\, 7.41$$\\end{document} kg/ha-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} (±0.11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm\\, 0.11$$\\end{document} bu/ac-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}). Rather than relying on a single model to estimate RGG, we recommend the application of several models with minimal and directional bias. Further, based on the parameters used in the simulations, we do not think it is appropriate to use any single model to compare breeding programs or quantify the efficiency of proposed new breeding strategies. Lastly, for public soybean programs breeding for maturity groups II and III in North America, the estimated RGG values ranged from 18.16 to 39.68 kg/ha-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} (0.27–0.59 bu/ac-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}/yr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document}) from 1989 to 2019. These results provide strong evidence that public breeders have significantly improved soybean germplasm for seed yield in the primary production areas of North America.

Genomic Editing and Diabetes.

The diabetes types and its complications have varying developmental and metabolic pathways. There is an interplay of nongenetic and genetic components in pathogenesis of diabetes and its complications. There are several established genes such as ABCC8, TCF7L2, SLC2A2, and CAPN10 which are known to influence blood insulin and glucose levels. Current management of diabetes types may include lifetime burdensome use of insulin, insulin sensitizers, insulin secretagogues, etc. There has been increasing interest in improving genetic editing tools such as CRISPR/Cas9 and using genetically edited stem cells to alter diabetes disease course or possibly cure it. Current research on microRNAs and long noncoding RNAs may provide insights into the pathways involved in development of diabetes and its complications. Consequently, developing further understanding of genetics and its messenger pathways in diabetes would enhance our ability to develop precise and accurate genetic editing tools which can translate into clinically useful therapeutics.

Environmental variables and genome-environment interactions predicting IBD diagnosis in large UK cohort

A combination of genetic susceptibility and environmental exposure is thought to cause inflammatory bowel disease (IBD), but the non-genetic component remains poorly characterized. We therefore undertook a search for environmental variables and gene-environment interactions associated with future IBD diagnosis in a large UK cohort. Using self-report and electronic health records, we identified 1946 Crohn’s disease (CD) and 3715 ulcerative colitis (UC) patients after quality control in the UK Biobank. Based on prior literature and biological plausibility, we tested 38 candidate environmental variables for association with CD, UC, and overall IBD using Cox proportional hazard regressions. We also tested whether these variables interacted with polygenic risk in predicting disease, following up significant (FDR < 0.05) results with tests for SNP-environment associations. We performed robustness analyses on all significant results. As in previous reports, appendectomy protected against UC, smoking (both current and previous) elevated risk for CD, current smoking protected against UC, and previous smoking imparted a risk for UC. Childhood antibiotic use associated with IBD, as did sun exposure during the winter. Socioeconomic deprivation was conferred a risk for IBD, CD, and UC. We uncovered negative interactions between polygenic risk and previous oral contraceptive use for IBD and UC. Polygenic risk also interacted negatively with previous smoking in predicting UC. There were no individually significant SNP-environment interactions. Thus, for a limited set of environmental variables, there was strong evidence of association with IBD diagnosis in the UK Biobank, and interaction with polygenic risk was minimal.

Mega-environment analysis of maize breeding data from Brazil

The development and recommendation of single cross maize hybrids (SH) to be used in extensive land areas (mega-environments), and in different crop seasons requires many experiments under numerous environmental conditions. The question we asked is if the data from these multi-environment experiments are sufficient to identify the best hybrid combinations. The aim of this study was to critically analyze the phenotype data of experiments of yield, established by a large seed producing company, under a high level of imbalance. Data from evaluation of 2770 SH were used from experiments conducted over four years, involving the first and second crop seasons, in 50 locations of different years and regions of Brazil. Different types of analysis were carried out and genetic and non-genetic components were estimated, with emphasis on the different interactions of the SH with the environments. Results showed that the coincidence of common hybrids in these experiments is normally small. The estimates of the correlations between of the hybrids coinciding in the environments two by two is of low magnitude. The hybrid × crop season interaction was always expressive; however, the interactions of hybrids and other environmental variables were also important. Under these conditions, alternatives were discussed for making with the information obtained from the experiments, can be more efficient on the process to obtain new hybrids by companies.

Genomic Research in Fishery Sector: Current Status and Future Prospects

Large scale efforts on genomics has resulted in greater understanding of the genomic architecture, their evolution and underlying biological mechanisms in fish. Although, significant achievements in fish genome sequencing initiatives have been undertaken globally, further targeted efforts are needed to realize its full potential. It is expected that the knowledge of structure and function of genomes and their interactions with non-genetic components of production systems will help to improve productivity, as well as in conservation and sustainable management of fisheries. In this direction, integrative multi-omics data intensive projects may be carried out in consortium mode research by pooling the expertise from different domains. These efforts will pave way for efficient resource use, to reduce production cost and increase farmers’ income.

Gene Regulatory Network Dynamical Logical Models for Plant Development.

Mathematical and computational approaches that integrate and model the concerted action of multiple genetic and nongenetic components holding highly nonlinear interactions are fundamental for the study of developmental processes. Among these, gene regulatory network (GRN) dynamical models are very useful to understand how diverse types of regulatory constraints restrict the multigene expression patterns that characterize different cell fates. In this chapter we present a hands-on approach to model GRN dynamics, taking as a working example a well-curated and experimentally grounded GRN developmental module proposed by our group: the flower organ specification gene regulatory network (FOS-GRN). We demonstrate how to build and analyze a GRN model according to the following steps: (1) integration of molecular genetic data and formulation of logical rules specifying the dynamic behavior of each gene; (2) determination of steady states (attractors) corresponding to each cell type; (3) validation of the GRN model; and (4) extension of the deterministic model with the inclusion of stochasticity in order to model cell-state transitions dependent on noise due to fluctuations of the involved gen products. The methodologies explained here in detail can be applied to any other developmental module.

Abstract 11316: Analytical Validation for an Integrated Risk Tool for Cardiovascular Disease Combining Conventional Risk Factors and Polygenic Risk Inferred from Snp Array and Low-Pass Whole Genome Sequencing

Genetic risk factors contribute to cardiovascular disease (CVD) risk and are imperfectly captured by family history. The development of integrated risk tools (IRT) that integrate polygenic risk scores (PRS) with conventional risk factors enables improved risk assessment. However, IRTs rely on accurate estimation of the genetic component across millions of genetic variants, either typed or imputed. To support two real-world implementations of CVD IRTs, we present analytical validation data for three biosample types (whole blood, saliva and buccal swab samples) and two genetic assay technologies (a custom-designed genotyping array (845,608 markers) and low pass whole genome sequencing (lpWGS) with 1.8x average read depth, both combined with computational imputation) across 48 ethnically-diverse donors. IRT measurements (range 4%-23%) were performed on the 10-year CVD risk percentage scale, assuming a non-genetic (pooled cohort equations) risk component of 10%. We find measurement errors are below 1% across the three biosample types, so that an IRT of 4% will at most be measured as 3% or 5%. Array and lpWGS showed different bias-variance trade offs. The array technology was more reproducible, with a standard deviation on the percentage scale of 0.09 (0.072-0.129 95% CI) for blood samples compared with 0.30 (0.201-0.617 95% CI) for lpWGS. In contrast, the array technology showed somewhat larger bias when compared with high depth sequencing data (mean absolute error on the percentage scale of 0.49 (0.217-0.755 95% CI) for blood samples against 0.30 (0.008-0.597 95% CI) for lpWGS, partially reflecting the sequencing basis of the reference data. Pass rate was high for both technologies and all three biosample types. In conclusion, both genotyping array and lpWGS approaches were appropriate and performed well for the purpose of assessing the genetic component of integrated cardiovascular disease risk using either blood, saliva or buccal samples.

Contributions of genetic and nongenetic sources to variation in cooperative behavior in a cooperative mammal.

The evolution of cooperative behavior is a major area of research among evolutionary biologists and behavioral ecologists, yet there are few estimates of its heritability or its evolutionary potential, and long-term studies of identifiable individuals are required to disentangle genetic and nongenetic components of cooperative behavior. Here, we use long-term data on over 1800 individually recognizable wild meerkats (Suricata suricatta) collected over 30 years and a multigenerational genetic pedigree to partition phenotypic variation in three cooperative behaviors (babysitting, pup feeding, and sentinel behavior) into individual, additive genetic, and other sources, and to assess their repeatability and heritability. In addition to strong effects of sex, age, and dominance status, we found significant repeatability in individual contributions to all three types of cooperative behavior both within and across breeding seasons. Like most other studies of the heritability of social behavior, we found that the heritability of cooperative behavior was low. However, our analysis suggests that a substantial component of the repeatable individual differences in cooperative behavior that we observed was a consequence of additive genetic variation. Our results consequently indicate that cooperative behavior can respond to selection, and suggest scope for further exploration of the genetic basis of social behavior.

Interplay between the genetics of personality traits, severe psychiatric disorders and COVID-19 host genetics in the susceptibility to SARS-CoV-2 infection.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus. We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection. Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data. We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10-5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies. We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.

Comparing ecological and evolutionary variability within datasets

Many key questions in evolutionary ecology require the use of variance ratios such as heritability, repeatability, and individual resource specialization. These ratios allow researchers to understand how phenotypic variation is structured into genetic and non-genetic components, to identify how much organisms vary in the resources they use or how functional traits structure species communities. Understanding how evolutionary and ecological processes differ among populations and environments therefore often requires the comparison of these ratios across groups (i.e., populations, sexes, species). Inference based on comparisons of ratios can be limited, however. Variance ratios can remain the same across group despite very different values in the numerator and denominator variances. Moreover, evolutionary ecologists are most often interested in differences in specific variance components among groups rather than in differences in variance ratios per se. Recommendations for how to infer whether groups differ in variance are not clear in the literature. Using simulations, we show how questions regarding the estimation of variance components and their differences among groups can be answered with linear mixed models (LMMs). Frequentist and Bayesian frameworks have similar abilities to identify differences in variance components. However, variance differences at higher levels of organization can be difficult to detect with low sample sizes. We provide tools to conduct power analyses to determine the appropriate sample sizes necessary to detect differences in variance of a given magnitude. We conclude by supplying guidelines for how to report and draw inferences based on the comparisons of variance components and variance ratios Significance statement Many critical questions in ecology and evolution use variance ratios, such as repeatability, heritability, or individual resource specialization, to make inferences about ecological and evolutionary processes. In many cases, these inferences rely on the comparison of variance ratios among datasets (populations, sexes, or environments). In this article, we show that current approaches of drawing inferences about group differences from comparisons of ratios are inappropriate because ratios can differ due to differences in the numerator, denominator, or both. We investigated how questions regarding differences in variance ratios and constituent variance components can be evaluated using linear mixed model (LMM) approaches and provide guidance for appropriate sampling schemes under different scenarios and discuss common pitfalls associated with estimation of differences in variance component among datasets.

Repeatability and heritability of social reaction norms in a wild agamid lizard.

In the evolutionary transition from solitary to group living, it should be adaptive for animals to respond to the environment and choose when to socialize to reduce conflict and maximize access to resources. Due to the associated proximate mechanisms (e.g. neural network, endocrine system), it is likely that this behavior varies between individuals according to genetic and non-genetic factors. We used long-term behavioral and genetic data from a population of eastern water dragons (Intellagama lesueurii) to explore variation in plasticity of social behavior, in response to sex ratio and density. To do so, we modeled individual variation in social reaction norms, which describe individuals' mean behavior and behavioral responses to changes in their environment, and partitioned variance into genetic and non-genetic components. We found that reaction norms were repeatable over multiple years, suggesting that individuals consistently differed in their behavioral responses to changes in the social environment. Despite high repeatability of reaction norm components, trait heritability was below our limit of detection based on power analyses (h2 < 0.12), leading to very little power to detect heritability of plasticity. This was in contrast to a relatively greater amount of variance associated with environmental effects. This could suggest that mechanisms such as social learning and frequency-dependence may shape variance in reaction norms, which will be testable as the dataset grows.

Abstract LB200: A transcriptionally distinct subpopulation of healthy acinar cells exhibit features of pancreatic progenitors and pancreatic ductal adeno carcinoma

Abstract The stochasticity of transcription leads to phenotypic variation within a clonal population. This means that within a healthy tissue, a fraction of cells could assume an “edge” transcriptional state that is primed for a transition toward malignancy. We find such an edge transcriptional state amongst acinar cells in a publicly available PDAC single-cell RNA-seq dataset. These acinar edge cells (AE) cells have transcriptomes that are highly diverged from a typical normal acinar cell, and show transcriptional similarities to pancreatic ductal adenocarcinoma (PDAC). The AE state can be found in several single-cell datasets from healthy human pancreatic tissue. The AE state is not mutationally-driven as AE cells in healthy tissues do not possess any known oncogenic driver mutations, nor are they clonally derived. The transition from a non-edge acinar to an AE state recapitulates key aspects of pancreatic biology; genes associated with this transition are enriched for known markers of acinar de-differentiation and acinar-ductal metaplasia. Further, the AE state represents a partial reversion to a SOX9+PTF1A+ multi-potent embryonic pancreatic progenitor state, likely through the activation of KLF5, HES1 and RBPJ targets. Remarkably, the promoters of genes upregulated in the AE state are significantly hypomethylated in PDAC relative to normal pancreas, while the genes up-regulated in the AE state are up-regulated in PDAC samples in the Cancer Genome Atlas (TCGA) database. It is thus likely that transcriptomic changes in the edge state are fixed epigenetically during tumor initiation and progression. A gene signature derived from the AE cells shows strong association with survival among PDAC patients. Remarkably, we also find that the fraction of AE cells in the healthy pancreas increases with age, which points to a strong non-genetic and transcriptomic component to the known increase in PDAC risk with age. We also observed edge-like transcriptomic heterogeneity among alveolar, ciliated and club cells in the human lung, and amongst hepatocytes in liver tissues. This suggests that, across tissues, gene expression heterogeneity can lead to sub-populations of healthy cells across tissues existing in pre-malignant states. Citation Format: Vishaka Gopalan, Arashdeep Singh, Farid Rashidi, Li Wang, Eytan Ruppin, Efsun Arda, Sridhar Hannenhalli. A transcriptionally distinct subpopulation of healthy acinar cells exhibit features of pancreatic progenitors and pancreatic ductal adeno carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB200.

O21: GENOME-WIDE ASSOCIATION STUDY OF VARICOSE VEINS IN 810,625 INDIVIDUALS IDENTIFIES 45 GENETIC RISK LOCI

Abstract Introduction Varicose veins (VV) impact a third of the UK adult population; 10% of patients develop lipodermatosclerosis and ulceration. VV often requires surgical management, however, there is a high-risk of recurrence. VV is a complex disease, where genetic and non-genetic components contribute to overall phenotypic expression. The genetic architecture of VV is poorly understood; we aimed to uncover its genetic basis. Method We conducted hitherto the largest genome-wide association study of VV. In stage one, using UK Biobank, we compared 22,473 VV patients and 379,183 controls. In stage two, replication and meta-analysis were performed in an independent cohort of 113,041 VV cases and 295,928 controls from 23&amp;Me (California, USA). In-silico analysis was conducted in FUMA, MAGMA, and XGR. Result 109 genome-wide significant (P≤ 5×10-8) loci were identified in UK Biobank, 45 of which successfully replicated in the 23&amp;Me cohort. Twenty-seven loci have not been previously reported. FUMA positionally-mapped 128 genes at the replicated loci, with 84 having a combined annotation-dependent depletion score (CADD) &amp;gt;12.37, suggesting functional, deleterious variants. MAGMA analysis implicated pathways involved in cardiovascular system development (P=1.57×10-08) and tube morphogenesis (P=9.35×10-08). Furthermore, XGR revealed enriched pathways in downstream signalling in naive CD8+ T cells (P=0.0017), and encoding structural and core extracellular glycoproteins (both P=0.007). Conclusion We identified 45 variants conferring risk of VV, which provide insights into disease biology. Implicated genes are enriched in pathways involved in vascular development, immune cell activity and extracellular matrix function, and provide new targets for therapeutic development. Take-home message Unravelling the genetic architecture of varicose veins may facilitate our understanding of the disease and guide therapeutic approaches.

Gene Expression Profiling of Monozygotic Twins Affected by Psoriatic Arthritis.

IntroductionPsoriatic Arthritis (PsA) is a multifactorial disease, where the relative burden of genetic, epigenetic and environmental factors in clinical course and damage accrual is not yet definitively clarified. In clinical practice, there is a real need for useful candidate biomarkers in PsA diagnosis and disease progression, by exploring its underlying transcriptomic and epigenomic mechanisms. This work aims to profile the transcriptome in monozygotic (MZ) twins with psoriatic arthritis (PsA) highly concordant for clinical presentation, but discordant for the radiographic outcomes’ severity.MethodsWe describe i) the clinical case of two MZ twins; ii) their comparative gene expression profiling (HTA 2.0 Affymetrix) and iii) signal pathways and pathophysiological processes in which differentially expressed genes are involved (in silico analysis by the IPA software, QIAGEN).ResultsOne hundred sixty-three transcripts and 36 coding genes (28 up and 8 down) were differentially expressed between twins, and in the brother with the most erosive form, the transcriptomic profiling highlights the overexpression of genes known to be involved in immunomodulatory processes and on a broad spectrum of PsA manifestations.DiscussionTwins’ clinical cases are still a gold mine in medical research: twin brothers are ideal experimental models in estimating the relative importance of genetic versus nongenetic components as determinants of complex phenotypes, non-Mendelian and multifactorial diseases as PsA.

THI Modulation of Genetic and Non-genetic Variance Components for Carcass Traits in Hanwoo Cattle.

The phenotype of carcass traits in beef cattle are affected by random genetic and non-genetic effects, which both can be modulated by an environmental variable such as Temperature-Humidity Index (THI), a key environmental factor in cattle production. In this study, a multivariate reaction norm model (MRNM) was used to assess if the random genetic and non-genetic (i.e., residual) effects of carcass weight (CW), back fat thickness (BFT), eye muscle area (EMA), and marbling score (MS) were modulated by THI, using 9,318 Hanwoo steers (N = 8,964) and cows (N = 354) that were genotyped on the Illumina Bovine SNP50 BeadChip (50K). THI was measured based on the period of 15–45 days before slaughter. Both the correlation and the interaction between THI and random genetic and non-genetic effects were accounted for in the model. In the analyses, it was shown that the genetic effects of EMA and the non-genetic effects of CW and MS were significantly modulated by THI. No significant THI modulation of such effects was found for BFT. These results highlight the relevance of THI changes for the genetic and non-genetic variation of CW, EMA, and MS in Hanwoo beef cattle. Importantly, heritability estimates for CW, EMA, and MS from additive models without considering THI interactions were underestimated. Moreover, the significance of interaction can be biased if not properly accounting for the correlation between THI and genetic and non-genetic effects. Thus, we argue that the estimation of genetic parameters should be based on appropriate models to avoid any potential bias of estimates. Our finding should serve as a basis for future studies aiming at revealing genotype by environment interaction in estimation and genomic prediction of breeding values.

Can paternal effects via seminal fluid contribute to the evolution of polyandry?

Genetic benefits from mating with multiple males are thought to favour the evolution of polyandry. However, recent evidence suggests that non-genetic paternal effects via seminal fluid might contribute to the observed effects of polyandry on offspring performance. Here, we test this hypothesis using the field cricket Teleogryllus oceanicus. Using interference RNA, we first show that at least one seminal fluid protein is essential for embryo survival. We then show that polyandrous females mated to three different males produced embryos with higher pre-hatching viability than did monandrous females mated with the same male three times. Pseudo-polyandrous females that obtained sperm and seminal fluid from a single male and seminal fluid from two additional males had embryos with viabilities intermediate between monandrous and polyandrous females. Our results suggest either that ejaculate mediated paternal effects on embryo viability have both genetic and non-genetic components, or that seminal fluids transferred by castrated males provide only a subset of proteins contained within the normal ejaculate, and are unable to exert their full effect on embryo viability.