The observable phenotype is the manifestation of information that is passed along different organization levels  of a biological system. The widespread use of various omic technologies  has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait. The observable phenotype is the manifestation of numerous biological process that occur across organizational levels  in the plant. In the last 20 years significant progress has been made to query phenotypes at these levels and elucidate the molecular mechanisms  that shape variation in conventional traits like plant architecture, disease resistance, productivity and fitness. Omics technologies have provided a means to query the phenotypic space at a molecular level and quantify these phenotypes across organizational levels and query these mechanisms in large populations that are typically required in genetic studies. The term “endophenotype” has been coined to describe these molecular phenotypes  that affect these endophenotypes can instead be directly introduced into the prediction frameworks. Predictions for the focal traits should be improved by allowing variance components to be estimated separately for putative functional  and non-functional markers. This approach would also remove the requirement to have endophenotypes measured on the population used for prediction. Of course, this assumes that effects will be somewhat consistent across populations and locations, and does not account for genotype-by-environment effects. Several studies have used domain/prior knowledge to partition genomic markers into potentially functional  and non-functional sets . Unlike the genomic relationship matrices defined by VanRaden , which aZhang et al.  used a tThese statistical frameworks that use TGRM offer opportunities to improve selection for conventional traits by including genetic effects for related endophenotypes. In this study, we evaluated the potential of TGRM to improve genomic prediction of seed composition traits in oat. We measured endophenotypes in a large diverse population, allowing inferences on these endophenotypes to be leveraged to improve predictions for related phenotypes in new populations. The abundances of nine fatty acid methyl esters were quantified in the mature seed of 336 oat lines using gas chromatography-mass spectrometry (GC-MS). These data were used to estimate marker effects for TGRMs using five Bayesian regression approaches: Bayesian ridge regression, Bayes A, Bayes B, Bayes Cπ, and Bayesian LASSO. Two datasets were used for validation. The first dataset consists of fatty acid abundances measured on an independent population of 213 elite oat lines. The second study quantified seed protein and lipid content using near-infrared spectroscopy (NIRS) in 210 elite oat lines. These datasets allow us to answer two questions: (1) Are estimated marker effects consistent across populations? (2) Can predictions for a trait be improved by using TGRM for component traits ? The utility of these TGRM prediction frameworks is demonstrated through comparisons with single-trait genomic best linear unbiased prediction (gBLUP) and multi-trait gBLUP approaches (MT-gBLUP). This work broadly tests if endophenotype relationships are transferable between populations. Further, it assesses the efficiency of endophenotyping for plant breeding: the cost of such phenotyping will make it efficient only if knowledge obtained from core populations can be transferred to multiple breeding populations.This study used three datasets. The first dataset was used to infer marker effects for nine fatty acids. These data consist of fatty acid phenotypes measured on an oat diversity panel of 375 lines derived from breeding programs in North America and Europe. We refer to this panel as the “Diversity Panel.” The Diversity Panel was grown in an augmented field design in Ithaca, NY, in 2018. A total of 368 unreplicated entries were randomly allocated to 18 blocks with 21–23 plots per block. One primary check, “Corral,” and one of six secondary checks were included in each of the blocks. These secondary checks were replicated four times in total, while the primary check was replicated 19 times . A total of 336 lines with genotypic data were used for downstream analyses.The second dataset consists of fatty acid measurements on 227 lines from a second oat panel, and was used to validate marker effects estimated in the Diversity Panel. This panel is constructed from breeding materials and varieties that were used to develop oat varieties for the northern Midwestern United States, which will be referred to as the “Elite Panel” throughout the remainder of this manuscript. The panel was grown in three locations  using an augmented block design. Each experiment included 220–224 unreplicated entries and three check lines.The third experiment measured total lipid content using Near Infrared Spectroscopy (NIRS) in six trials for 210 lines in the Elite Panel. The experiments followed an augmented block design. Entry means were downloaded from the Triticeae Toolbox  data were collected from 11 genotyping experiments for 539 lines . QC sample were injected after every 6 samples. Standard curves for C14:0, C16:1, C16:0, C18:0, C18:1, C18:2, C18:3, C20:0, and C20:1 were established.One micro-liter of the upper hexane layer containing FAME was injected into a TG-WAXMS column  in a Trace1310 GC (Thermo Scientific) coupled to a Thermo Scientific ISQ-LT mass spectrometer. The injector temperature was 260°C, and the split ratio was 15:1. A constant flow rate of the carrier gas (He) was controlled at 1.2 mL · minBest linear unbiased predictors (BLUPs) were calculated to remove systematic effects for each fatty acid phenotype. Given that both experiments that quantified fatty acids followed the same type of experimental design (augmented block), the linear mixed model is nearly identical and is given bycheck is a fixed effect for each of the check varieties; new is an indicator variable where 0 indicates a check variety and 1 indicates an unreplicated entry, and is nested within entry; DTH is a fixed covariate that provides days to heading for each observation; block and batch are random effects to account for field blocks and injection batch for GC-MS, respectively. Heading dates were only available for the experiments performed in Ithaca, so the linear model used to compute BLUPs for fatty acid phenotypes in the Elite Panel did not include this term. The terms μ and e represent the overall mean and the vector of residuals, respectively. We assume entries are unrelated in this step. The above model was fitted using the sommer package in R , and e is a vector of residuals. In all cases, we assume where t density, allowing differential shrinkage of marker effects. Scale-mixture densities are used as priors for Bayes B and Bayes Cπ. Some effects are sampled from a point mass at zero and others are sampled from a scaled-t density, as is the case in Bayes B, or a Normal distribution in Bayes Cπ. The mixing parameter specifies the probability of a marker being sampled from either density and is treated as an unknown in implementations of Bayes B and Bayes Cπ used in this study  were constructed using the estimated marker effects for each of the nine fatty acid phenotypes in the Diversity Panel. For each fatty acid phenotype, the TGRM are defined asM is an n × P scaled and centered matrix of allele dosages with n being the number of individuals and P the number of markers. D is an P × P diagonal matrix that contains the marker weights. The weight for marker p is given by ap is the additive effect.where To predict each fatty acid trait the following model was fittedy is a vector of deregressed BLUPs for each line in the six trials; uZ is an n × q incidence matrix that assigns the q genomic values to n observations; u is a vector of genomic values; and eZ is an n × e incidence matrix that assigns observations to trials and s are the corresponding effects. Both TGRM-BLUP and gBLUP follow the same model, what separates the two methods are the assumptions on u. For TGRM-BLUP, we assume *G is the TGRM defined above, and for gBLUP we assume G is a genomic relationship matrix calculated using VanRaden's second definition  in cross-validation each sample is drawn from the same dataset and are not independent, which violates one of the assumptions of the t-test; and (2) the magnitude of the t-statistic is dependant on the sample size, which is the number of resampling runs. Our approach is not dependent on the sample size and should be a more robust alternative to the t-test.where Prediction of total lipid content was performed using multi-kernel TGRM-BLUP (MK-TGRM-BLUP), multi-trait gBLUP, and gBLUP approaches. The model for MK-TGRM-BLUP is given bytu is a vector of genomic breeding values computed using the TGRM for fatty acid trait t. Prediction accuracy was assessed using Pearson's correlation between the predicted genomic estimated breeding values and the BLUPs for each trial. Prediction accuracies from the model above were compared to gBLUP to determine if TGRM affected genomic predictions.with all matrices and vectors defined as above; however, The multi-trait BLUP model isY is a n × T matrix of phenotypes and U is a n × T matrix of genomic breeding values. BLUPs were averaged across the six trials and were used to construct Y. These data were also used to fit MK-TGRM-BLUP models that were compared to multi-trait gBLUP and are given by BGLR package as described earlier  using targeted GC-MS . GeneralGiven that a significant portion of phenotypic variation in these lipid phenotypes could be explained by additive genetic effects, we sought to leverage these effects to better predict lipid-related traits in an independent population. We constructed trait-specific genomic relationship matrices (TGRMs), which differentially weight markers based on their additive genetic effects on the phenotype. Since the genetic architectures of the fatty acid phenotypes differ, we used five Bayesian whole-genome regression approaches to estimate marker effects: Bayesian ridge regression (BRR), Bayes A, Bayes B, Bayes Cπ, and Bayesian LASSO BL; . These aWith the exception of C18:1 and C18:3, prediction accuracies were significantly improved by using a TGRM, indicating that the signal captured by TGRMs is relevant in this second independent population . CompariThe previous analyses showed that TGRMs can be used to improve genomic prediction for fatty acid traits in an independent population. While these outcomes provide support for the use of TGRMs in breeding programs, the quantification of these compounds may not be feasible in breeding programs due to the high cost of GC-MS. Seed compositional traits measured via indirect methods, e.g., near-infrared spectroscopy (NIRS), is a more feasible approach to quantify total seed lipids in a large breeding program  compared to other approaches, while Bayes A showed the lowest prediction accuracy among the MK-TGRM-BLUP approaches (r = 0.473).All MK-TGRM-BLUP approaches significantly increased prediction accuracies compared to gBLUP . Improver = 0.578), which showed a 14.41% increase in prediction accuracy over MT-gBLUP. Collectively, these results suggest that the use of a TGRM approach can significantly improve prediction accuracies over conventional genomic prediction approaches, even when information on related phenotypes is included in the prediction model.Given that the MK-TGRM-BLUP leverages information on related traits to improve prediction accuracies, we also compared the MK-TGRM-BLUP approach to a multitrait gBLUP (MT-gBLUP) model that jointly modeled all nine fatty acid traits in the Diversity Panel and total lipid content in the Elite Panel. Thus, MT-gBLUP contains all the data that was used to compute the TGRM for fatty acids used in the MK-TGRM-BLUP model. A total of 12 lines in the Elite Panel had phenotypes for individual fatty acids and their sum. Five-fold cross validation was used for the remaining 198 lines in the Elite Panel with phenotypes for total lipid content. All TGRM-BLUP approaches showed significant improvements in prediction accuracies over the MT-gBLUP approach . PredictFinally, we asked whether it was necessary to quantify and construct TGRM for all fatty acids, or whether similar improvements in prediction accuracy could be achieved by using kernels for the most abundant fatty acids. In both panels, C16:0, C18:1, and C18:2 were the most abundant fatty acids, while C14:0 C16:1 and C20:0 were present at much lower levels . Two MK-Omics technologies provide an easy and effective way to measure thousands of endophenotypes in large mapping populations. Many research groups are using these approaches to improve prediction for complex traits , and it seemed to be affected by at least one large-effect QTL in both panels  and exhibited a much more complex genetic architecture compared to other fatty acids  can be found in the article/Metabolomic data were generated by HH, TY, KS, LG, and MC-T. Analyses were performed by MC under the guidance of MG and J-LJ. MC wrote the manuscript with guidance from J-LJ and MG. Comments were provided by HH, LG, LB, MS, MG, and J-LJ. This study was supported by grants secured by KS, LG, MC-T, MS, MG, and J-LJ. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with the authors MC and MG.


Background: Previous studies suggested an association of sleep disorders with inflammatory bowel disease (IBD) and indicated that using pharmacological treatments for the modulation of circadian rhythms might prevent IBD pathogenesis or aggravation, but whether the effect of sleep traits on IBD was causal is inconclusive and, therefore, prevents drug repurposing based on the previous studies. We aimed to examine the causal effect of different sleep traits on the pathogenesis of IBD.Methods: Genetic instruments for sleep traits were selected from the largest GWAS studies available in the UK Biobank  and the 23andMe Research . A two-sample Mendelian randomization (MR) study was conducted to examine the association of the genetic instruments with IBD , ulcerative colitis , and Crohn’s disease . We applied the inverse-variance weighted (IVW) method to estimate causal effects, and we used the weighted median and MR-Egger method for sensitivity analyses.Results: We found that sleep duration , short sleep duration , morningness , daytime napping , frequent insomnia , any insomnia , and snoring  had no causal effect on IBD, and these sleep traits had no causal effect on ulcerative colitis and Crohn’s disease either. Most of the sensitivity analyses showed consistent results with those of the IVW method.Conclusion: Our MR study did not support the causal effect of sleep traits on IBD. Pharmacological modulation of circadian rhythms for the prevention of IBD pathogenesis was unwarranted. Inflammatory bowel disease (IBD) is a chronic, autoimmune disease of the gastrointestinal tract, including two subtypes—Crohn’s disease (CD) and ulcerative colitis (UC). The pathogenesis of IBD includes genetic predisposition, environmental risk factors, and impaired immune activation . IBD hasSleep disorders affect both innate and adaptive immune function, which is gradually recognized as one of the potential environmental triggers of IBD . The thrMendelian randomization (MR) analyses used genetic instruments to infer a cause-and-effect relationship between exposures and outcomes. Genetic variants and allelic randomization minimize the problem of confounding issues and reverse causation, which provides stronger evidence than traditional observational studies in inferring cause–effect relationships . TherefoWe performed a two-sample MR study, using summary-level data from publicly available genome-wide association studies (GWAS). The data of the GWAS studies were obtained from UK Biobank and 23andMe Research. The study design and reporting conformed to STROBE-MR .Our analysis used published studies or publicly available GWAS summary data. No original data were collected for this manuscript, and thus, no ethical committee approval was required. Each study included was approved by their institutional ethics review committees, and all participants provided written informed consent.n = 106,192) with the reference group , and the group of long sleep duration  was also compared with the reference group. The SNPs for long sleep duration were not included in our analysis, since the number of SNPs for long sleep duration was small, and they were under the risk of weak instrument bias  and 23andMe Research   . The parn = 453,379), in which the participants were asked “Do you have trouble falling asleep at night or do you wake up in the middle of the night?” The participants who responded “usually” were classified as frequent insomniacs , and those who responded “usually” or “sometimes” were classified as non-insomniacs   .n = 408,317), in which the participants were asked “Does your partner or a close relative or friend complain about your snoring?” with response options of “Yes”, “No”, “Don’t know”, or “Prefer not to answer”. The participants who answered “Don’t know” or “Prefer not to answer” were excluded from the dataset.Genetic variants that correlated with snoring were acquired from the UK Biobank population , we excluded the single nucleotide polymorphisms (SNPs) with r2—a measurement of the pairwise-linkage disequilibrium—larger than 0.001.Genetic instruments should be associated with the sleep traits (the relevance assumption of MR). To ensure the relevance assumption, the correlation The Wald ratio, obtained by dividing the SNP-outcome association estimate by the SNP-exposure association estimate, was used to evaluate the size of the causal effect of sleep traits on IBD. The ratio estimates  for the SNPs were then pooled by using inverse-variance weighted (IVW) meta-analysis; and the pooled β values and corresponding 95% confidence intervals (95% CIs) were calculated through the meta-analysis. The β values were transformed into odds ratios (ORs) using the formula—β = ln (OR). Scatter plots of SNP-outcome associations versus SNP-exposure associations were provided for the MR analysis.I2 statistics, and an I2 value smaller than 40% was considered an indication of unimportant heterogeneity—as stated by the Cochrane Handbook  have greater robustness than the inverse-variance weighted and MR-Egger methods when individual genetic variants with strongly outlying causal estimates are included. The pooled ratio estimates from the simple median method were obtained by computing the ratio estimates from each genetic variant and finding the median estimate; while the estimates from the weighted median method were obtained by computing normalized inverse-variance weights for each genetic variant and pooling the estimates with the incorporation of the acquired weights. The MR-Egger method allows one or more genetic variants to have pleiotropic effects. The ratio estimates from the MR-Egger method were obtained by regression of the ratios of the SNP-outcome associations versus the SNP-exposure associations, with the weights being the inverse-variances of the associations with the outcome.To evaluate the potential pleiotropy in the IVW model, we performed the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis. To validate the independent causal effects of sleep traits on IBD, we performed a multivariable MR analysis. To evaluate whether the study results were affected by confounding bias, we performed linkage disequilibrium score (LDSC) regression analysis; an LDSC intercept larger than 1.3 indicated that the results might be affected by confounding bias . In addiI2 = 37.9%; mean F-statistic = 1.636), UC , or CD . A further analysis studying the causal effect of short sleep duration showed similar results , UC , or CD  . The LDSC analysis showed a regression intercept of 1.656 .The IVW analysis showed that morningness had no causal effect on IBD  . The sen= 2.797) , and the= 2.797) . The ana= 2.797) 4. HoweveI2 = 27.8%; mean F-statistic = 1.384), UC , or CD . The LDSC analysis showed a regression intercept of 1.62 .The IVW analysis showed that daytime napping had no causal effect on IBD , UC, or CD , UC , or CD  on IBD—an explanation for the relationship between sleep disorders and IBD pathogenesis shown in previous studies—since the bidirectional relationship between sleep disorders and anxiety or depression is acknowledged . The infOur study adopted genetic data from the two study populations that were of European ancestry, which ensured the genetic homogeneity and robustness of the study results. However, it limits the generalizability of the study findings to other populations. Sleep disorders might have causal effects on the pathogenesis of IBD in other populations, and our study did not test the results in other populations because of lack of relevant data. It should also be noted that SNPs associated with the IBD outcome were determined based on the diagnosis and presence of IBD, but not on disease progression or disease severity. Accordingly, whether sleep disorders have impacts on the progression of IBD  is still unknown, and our study results could not be generalized to guide whether an aggressive treatment for sleep disorders is needed for patients with IBD to prevent disease progression.Owing to the two-sample MR design, we used summary-level statistics instead of individual-level statistics. However, regarding the fact that the included data were adjusted for similar characteristics  and that the two-sample MR design is less susceptible to weak instrument bias , we specOur study did not support a causal effect of differential sleep traits on the pathogenesis of IBD, and the result could be robust because of the large sample size of the study, multiple sensitivity analyses, and verification in two GWAS study populations of sleep traits. Future studies focusing on the causal effect of psychological disorders on IBD pathogenesis are warranted, as well as future studies conducted in populations other than European ancestry.


In cage-free systems, laying hens must lay their eggs in the nests. Selecting layers based on nesting behavior would be a good strategy for improving egg production in these breeding systems. However, little is known about the genetic determinism of nest-related traits. Laying rate in the nests (LRN), clutch number (CN), oviposition traits (OT), and nest acceptance for laying (NAL) of 1,430 Rhode Island Red (RIR) hens and 1,008 White Leghorn (WL) hens were recorded in floor pens provided with individual electronic nests. Heritability and genetic and phenotypic correlations of all traits were estimated over two recording periods–the peak (24–43 weeks of age) and the middle (44–64 weeks of age) of production–by applying the restricted maximum likelihood method to an animal model. The mean oviposition time (MOT) ranged from 2 h 5 min to 3 h and from 3 h 35 min to 3 h 44 min after turning on the lights for RIR and WL hens, respectively. The mean oviposition interval ranged from 24 h 3 min to 24 h 16 min. All heritability and correlation estimates were similar for RIR and WL. Low to moderate heritability coefficients were estimated for LRN (0.04–0.25) and moderate to high heritability coefficients for CN and OT (0.27–0.68). CN and OT were negatively genetically correlated with LRN (-0.92 to -0.39) except during peak production for RIR (-0.30 to +0.43). NAL was weakly to moderately heritable (0.13–0.26). Genetic correlations between NAL and other traits were low to moderate (-0.41 to +0.44). In conclusion, CN and OT are promising selection criteria to improve egg production in cage-free systems. NAL can be also used to reduce the number of eggs laid off-nest in these breeding systems. However, variability in MOT must be maintained to limit competition for the nests. To provide animals with living conditions that consider their sensitivities better , cage-frLaying rate, which equals the number of eggs laid divided by the number of days of the recording period, is the trait that has the highest economic weight in breeding programs for laying hens. Laying rate is associated with a clutch, which is defined as consecutive days of oviposition. Successive clutches are separated by one or more days of pause. The length and number of clutches are controlled by the ovulatory cycle, directly observed by the oviposition cycle, and governed by the circadian rhythm and the internal cycle of follicular growth and maturation , 8. Egg-Long-term selection has decreased the genetic variability in the laying rate . Since tThe total number of saleable eggs from cage-free systems also depends on the acceptance of the nests for laying. Eggs laid off-nest must be collected by hand and decrease the flock’s production because they are usually downgraded or lost. Nest acceptance for laying is influenced by many factors, such as nest design , lightinContinuous recording of the laying activity of hens by electronic nests allows these “new” egg-production traits to be observed. The present study aimed to identify the most relevant traits for improving egg production in cage-free systems through the estimation of genetic parameters for traits recorded by electronic nests in a cage-free environment. To represent a wide phenotypic and genetic variability, populations from two pure lines–Rhode Island Red (RIR) and White Leghorn (WL)–were studied, as were two breeding periods: peak production (24–43 weeks of age) and the middle of production, when the laying rate starts to decrease (44–64 weeks of age).All data were registered as part of the commercial and selection activities of Novogen . These animals and the scientific investigations described herein are therefore not to be considered as experimental animals per se, as defined in European Union directive 2010/63 and subsequent national application texts. Consequently, we did not seek ethical review and approval of this study as regarding the use of experimental animals. All animals were reared in compliance with national regulations pertaining to livestock production and according to procedures approved by the French Veterinary Services.ad libitum with a commercial feed .RIR (brown-egg layers) and WL (white-egg layers) are the two main breeds of laying hens used for egg production in the world. This study involved two pure lines–a RIR line and a WL line–created and selected by the Novogen breeding company . These two lines are specifically selected for egg number, egg quality, body weight, and behavior-related traits such as feather pecking. Data were obtained from 2017–2019 for three batches of RIR, which contained 1,430 laying hens (146 sires and 546 dams), and two batches of WL, which contained 1,008 laying hens  with individual electronic nests (20 Width × 40 Length × 27 Height cm). The density was about 5 hens for 1 nest . These nIn the present study, only the time of entry for nest visits with oviposition was used to define and calculate traits, for two reasons. First, oviposition time may be incorrect if the egg laid remains stuck in the nest until or after the hen leaves the nest. When this occurs, the oviposition time recorded by the egg collection system is later than the true oviposition time. These data had to be excluded for analyses, unlike the corresponding time of entry in the nest. Second, when the oviposition time was known to be correct, the time of entry in the nest correlated strongly with oviposition time for both lines (phenotypic correlation = +0.99). To facilitate comparison of mean oviposition time (MOT) with results in the literature, we calculated phenotypes of MOT in the nests when oviposition time was known to be correct  and the middle of egg production . For all traits, only data for hens that survived for at least half of a given period were kept for analysis; consequently, 0.5–1.1% of the data for each period and each line were excluded. Moreover, an infection in batch B from 59–64 weeks of age strongly influenced laying rate and nesting behavior; thus, data from these six weeks were excluded. The total amount of data recorded  varied by batch .The first trait studied was the laying rate in the nests (LRN). For each hen, LRN was calculated by dividing the number of eggs laid in the nests by the number of days alive during a given period and multiplying the result by 100. LRN was analyzed only for hens with LRN ≥ 50% to exclude those that laid in the nests less frequently. Several phenomena can explain why hens do not lay in the nests, such as a proclivity to lay off-nest, molting, or repeated pauses. It was difficult to differentiate these phenomena for hens with low LRN, and they could increase uncertainty in interpretation of LRN. Thus, LRN was used to evaluate the “good” layers in the nests. The second trait studied was the clutch number (CN), which required a preliminary quality control to define it . CN was To characterize oviposition, four traits were calculated from the time of entry in the nest, for visits with oviposition: MOT, logarithm of the variance of oviposition time (LVOT), mean oviposition interval (MOI), and logarithm of the variance of oviposition interval (LVOI). The oviposition interval is the duration between two consecutive times of entry in the nests for visits with oviposition (i.e. for two consecutive days). Hens with less than 10 data available to calculate oviposition traits were excluded from analysis; thus, 0.05–0.10% of data for each period and each line were excluded.Finally, nest acceptance for laying (NAL) was defined to estimate the ability of hens to lay eggs in the nests. Hens with LRN < 50% or ≥ 50% were assigned the value 0 or 1, respectively.y is the vector of observations for the traits analyzed; X and b are the incidence matrix and vector, respectively, related to fixed effects of the hen’s hatch date; Z and a are the incidence matrix and vector, respectively, related to random additive genetic effects; a ~ N×A), where v(a) is the variance of additive genetic effects and A is the kinship matrix; e is the vector of random residual effects and e ~ N×I), where v(e) is the residual variance and I is the identity matrix.Distributions of the data were transformed towards a normal distribution using the “bestNormalize” R package . Geneticrp, ra, and re are phenotypic, additive genetic, and residual correlations, respectively, between traits i and j; h is the square root of the heritability for traits i and j; e is the square root of the proportion of residual variance for traits i and j.Phenotypic correlations between traits were calculated using the formula :rp=hihjGenetic parameters were estimated by applying the restricted maximum likelihood method (REML) to an animal model. For this, the tools REMLF90  and AIREMLF90  of the BLUPF90 family of programs  have beePhenotypic distributions of LRN, CN, MOT, LVOT, MOI, LVOI, NAL were calculated for both lines and breeding periods . Mean LRHens’ laying patterns in the nests varied greatly during the breeding season. We classified them into three types based on the CN: hens with more than four short clutches and many pauses, with two to four long clutches, or with one long clutch without pauses .At peak production, most RIR hens showed the pattern without pauses (54%) or with a few long clutches 39%; . In the Most nest-related traits displayed moderate to high heritability estimates in both lines. Heritability estimates for LRN were low in RIR , LVOT (-0.82 to -0.67), MOI (-0.87 to -0.61), and LVOI (-0.88 to -0.79), except for peak production in RIR (-0.30 to +0.27 between these traits and LRN). CN, LVOT, MOI, and LVOI were strongly positively correlated (+0.42 to +0.97). In both lines, genetic correlations between LRN and CN or oviposition traits were stronger in the middle than at the peak of production, except for LVOI in WL. NAL was weakly to moderately correlated with LRN, CN, and oviposition traits (-0.41 to +0.44). Phenotypic correlations were lower but consistent with genetic correlations. Phenotypic correlations for NAL with LRN and CN could not be estimated because all hens that were kept for analysis of LRN and CN performances had a value of 1 for NAL.Most of the nest-related traits displayed a strong genetic correlation between the two periods despite modest phenotypic correlation . In bothThis study estimated moderate to high heritability for CN and oviposition traits in a cage-free system, and strong genetic correlations between these traits and LRN. Therefore, nest-related traits hold potential as selection criteria to improve egg production in cage-free system. Moreover, low to moderate heritability estimates of NAL and low to moderate genetic correlations between it and other nest-related traits suggest that selection can improve this trait.In the present study, phenotypic data recorded by individual electronic nests in a cage-free system are consistent with those in the literature. Here, MOT ranged from 2 h 5 min to 3 h 44 min after turning on the lights, which is consistent with times reported in individual cages or other cage-free systems. In individual cages, 51% of RIR eggs are laid 30 min to 3 h 30 min after turning on the lights . LikewisThe three laying patterns in the nests identified in the present study are also consistent with those in the literature . In a prHeritability estimates were moderate to high for nest-related traits and similar for RIR and WL lines, although they are distant genetic types. Moderate heritability estimates for CN have also been reported in the literature for hens in individual cages. Heritability of 0.46 ± 0.02 for CN was estimated in a population of 5,826 dwarf brown-egg layers selected for 16 generations for increased average clutch length . These rModerate to high heritability estimates for MOT 0.49–0.68) are relatively consistent with the heritability previously estimated for this trait. Heritability values of 0.81 ± 0.20 and 0.72 ± 0.23, respectively in a line selected for egg mass and a line selected for egg mass and food consumption, were estimated when heritability was lower in a line selected for egg number , betweeGenetic correlations for nest-related traits between the peak and middle production are strong. This suggests the potential to group peak and middle production into a single period or to use records from only one period to assess breeding values of selection candidates for nest-related traits. The latter option would decrease costs by decreasing the operating time of the recording system and generating fewer data to store. In this study, the middle of production (i.e. 44–64 weeks of age) seems to be the most favorable breeding period for phenotypes (i.e. most of the variability) and genetic parameters of CN and oviposition traits (i.e. higher heritability and stronger genetic correlations with LRN).Selecting hens as breeders with a low CN, low MOT, short MOI, and low variance in oviposition traits will help improve LRN for hens in cage-free systems. These traits are also strongly and favorably correlated with each other, indicating that there are no negative interactions for selection. We suggest that CN and MOT are the most favorable traits for increasing egg production in cage-free systems, since they tend to have higher heritability and stronger genetic correlation with LRN than other traits.However, hens may compete for the nests in cage-free systems, which risks increasing off-nest laying and suffocation of hens in the nests. Selecting for CN and oviposition traits, which are strongly genetically correlated with MOT can increase this competition. In this study, hens in the RIR and WL lines that laid in the nests without pauses had long time slot of MOT . It would be important to maintain this long time slot for highly productive hens, which may help maintain the variability in oviposition time and control nest occupancy rates during the day. A study of the variability in oviposition time suggests that the time slot of laying can be increased by divergent selection of MOT and combining different genotypes in the same flock . SecondlThe low to moderate heritability of NAL suggests that it has a genetic background. The objective of the NAL trait is to determine whether hens prefer to lay in the nests or not. However, NAL also depends on hens’ physiological ability to lay eggs or not, and the data recording system cannot differentiate the two phenomena. We assume that NAL is a combination of two phenomena that influence egg production negatively in cage-free systems , and selection against these phenomena could help improve egg production in cage-free systems.In conclusion, the cage-housing system will be forbidden in the European Union in a few years. Electronic nests provide the advantage of recording individual nesting behavior daily in cage-free systems and seem to be a good way to decrease the use of individual cages for selection. Phenotypic and genetic variabilities observed in this study show that laying performances could be improved in these group-housing systems by using information about oviposition time in the nests. In this study, CN, MOT and NAL could be the most interesting traits to include in breeding programs in the aim to increase LRN and reduce off-nest laying. The middle of production seems also to be the best period to improve these nest-related traits. Future studies that include genomics and more animals would increase the accuracy of results. Genetic relationships between these traits and egg-quality traits also need to be studied to be able to include these traits in breeding programs. Finally, the genetic gain expressed by commercial crossbreed hens should be studied, as should possible genotype-by-environment interactions between the nucleus farm, using individual electronic nests, and commercial farms, using collective nests.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Appendix(DOCX)Click here for additional data file.


To determine the extent to which these differences are heritable, we quantified acute responses to a single dose of THC  in males and females from 20 members of the BXD family of inbred strains derived by crossing and inbreeding B6 and D2 mice. Acute THC responses  were quantified as changes from baseline for: 1. spontaneous activity in the open field (mobility), 2. body temperature (hypothermia), and 3. tail withdrawal latency to a thermal stimulus (antinociception). Initial sensitivity to the immobilizing, hypothermic, and antinociceptive effects of THC varied substantially across the BXD family. Heritability was highest for mobility and hypothermia traits, indicating that segregating genetic variants modulate initial sensitivity to THC. We identified genomic loci and candidate genes, including Ndufs2, Scp2, Rps6kb1 or P70S6K, Pde4d, and Pten, that may control variation in THC initial sensitivity. We also detected strong correlations between initial responses to THC and legacy phenotypes related to intake or response to other drugs of abuse . Our study demonstrates the feasibility of mapping genes and variants modulating THC responses in the BXDs to systematically define biological processes and liabilities associated with drug use and abuse.Cannabinoid receptor 1 activation by the major psychoactive component in cannabis, Δ9-tetrahydrocannabinol (THC), produces motor impairments, hypothermia, and analgesia upon acute exposure. In previous work, we demonstrated significant sex and strain differences in acute responses to THC following administration of a single dose (10 mg/kg,  Recombinant inbred (RI) rodent populations are a valuable resource for forward genetic mapping and systems genetics analysis. Individual RI lines are stable and each genotype can be resampled to boost mapping power and improve quantitative trait loci (QTL) detection . In addiWith well over 100 BXD lines currently available , the BXDi.p.), the major psychoactive component in cultivars of cannabis (1) receptor in discrete neuronal populations  differ greatly in their initial responses to a single dose of THC  is not the cause of variation in THC responses between B6 and D2. In other previous work, we quantified striatal protein levels of the CB1 receptor and found them to be significantly higher in B6 relative to D2 mice . A summary of actual ages, sex, strains, and litter numbers obtained for the study can be found in Three strategies were incorporated into the study to control for batch effects due to the somewhat arbitrary availability of BXD subjects. First, strain and sex were counterbalanced across batches when possible. Based on availability from the BXD breeding colony, mice were assigned to testing cohorts consisting of a maximum of 32 mice tested each week. Within each cohort, mice were assigned to one of up to four batches. Each batch consisted of up to eight mice that were phenotyped together within the same test session. A summary of cohort and batch assignments for each sex and strain can be found in i.p.) injection at a dose of 10 mg/kg such that a 30 g mouse received a 100 μl injection volume. Each formulation was used for the entire testing schedule of each cohort (see details below). Previously we demonstrated that each THC formulation contains 95.2% of the initial amount of THC when measured 8 days after preparation  vehicle followed by filter sterilization. The resulting formulation was stored in the dark at 4°C in a septum sealed vial. Vehicle (VEH) was prepared in the same manner. THC and VEH were administered by intraperitoneal (paration .Treatment and trait measurements are described in anova.lm function). Effect sizes  for each parameter in the ANOVA were calculated in R using the omega_squared function in the effectsize package. Response during habituation on day −1 was not analyzed and individual BXD data points were only removed for technical reasons .BXD response data from day 0 (VEH treatment) to day 1 (THC treatment) were used in a three-way between-subjects (treatment × strain × sex) analysis of variance (ANOVA). The purpose of this analysis was twofold: we wanted to determine if THC treatment had a large and significant effect on responses and quantify any interactions between treatment and sex that would justify additional sex-specific analyses. ANOVA was performed using base functions in R (2 web service (RRID:SCR_002388) for the Mouse species, the BXD Family group, the Traits and Cofactors type, and the BXD Published Phenotypes dataset.Because THC treatment effects were significant and of moderate to large effect for all traits, initial responses to THC were calculated as the difference between day 1 and day 0 (baseline) for each trait . Differey), one-way between-subjects ANOVA in the form of y ∼ strain was performed using base functions in R (anova.lm function). Effect sizes (omega-squared or ω2) for each ANOVA were calculated in R using the omega_squared function in the effectsize package. Heritability (h2), an estimate of the proportion of the trait variation explained by genetic factors, can be calculated based on the variance components of a one-way ANOVA (y ∼ Strain). We used ω2 as an estimate of h2.To quantify and visualize the effect of strain on initial response to THC for each trait  are not included. For both HK and GEMMA, a dense panel of 7,321 markers was used for mapping. QTL were considered noteworthy if: (1) a genome-wide suggestive level  was reached following permutation (1000 tests) using the HK method, and (2) the peak marker association [−log(p) > 3] detected using GEMMA overlapped the QTL mapped by HK.Genetic mapping is performed using strain means and not individual level data. Power in genetic mapping studies is primarily determined by the number of unique individual genomes. Recombinant inbred panels allow for replication of genomes within strain which can further increase power to detect QTLs. Here we applied two different methods to compute QTL probability given strain genotypes and initial responses to THC using GN . The firGenome-wide efficient mixed model association permutation was performed for each trait as described previously . For traQuantitative trait loci scans on small subsets of BXDs, as in our study, are expected only to identify QTLs of large effect. For example, a mapping population comprised of 20 BXD strains with four replicates per strain is only powered to detect a QTL explaining 50% of the trait variance, whereas a population size of ∼80 strains with four replicates per strain is well powered to detect a QTL explaining 20% of the trait variation at 80% power .RRID:SCR_005780) Table Browser Tool .A 1.5-LOD drop from the peak marker (HK mapping) was used to define an approximate ∼95% confidence interval (CI) for a QTL of interest . For eacRRID:SCR_011784). Specifically, SNPs, InDels, and structural variants (SVs) distinguishing B6 and D2 and their predicted consequences were retrieved and compared (by gene symbol) to reference genes located within the boundaries of each QTL CI. Next, legacy mRNA datasets available in GN for BXD brain tissue were queried to identify genes located in each QTL CI whose expression was modulated by variants located within or near the location of the gene itself – a cis expression QTL (eQTL). Brain region data sets  were selected based on known functional regulation by cannabinoid receptor signaling, moderate to high expression of CB1 receptor in each region, and functional involvement of each region in initial response traits . Datasets included: Hippocampus Consortium M430v2 (Jun06) (GN Accession: GN110 and GEO Series: GSE84767); INIA Hypothalamus Affy MoGene 1.0 ST (Nov10) (GN Accession: GN281 and GEO Series: GSE36674); HQF BXD Neocortex ILM6v1.1 (Feb08) RankInv (GN Accession: GN284); VCU BXD NA Et vs. Sal M430 2.0 (Oct07) (GN Accession: GN156); VCU BXD PFC Sal M430 2.0 (Dec06) RMA ; HQF Striatum Affy Mouse Exon 1.0 ST Gene Level (Dec09) RMA (GN Accession: GN399). Cis eQTL data are summarized in cis eQTLs with higher expression associated with the B6 (B) allele (ILM6v1.1 and M430 data sets only) are flagged in this table as expression measurements that could be biased in the direction of B alleles  due to technical probe hybridization artifacts. Finally, a search for relevant biological function  was conducted using the GeneCup 3 web service (RRID:SCR_021281) to search through PubMed (RRID:SCR_004846) literature abstracts for associations between cannabinoid-related terms and gene symbols for each QTL CI. A secondary search was performed for high priority candidate genes using GeneCup to place these genes in a broader context of addiction and psychiatric diseases.Genes in each QTL CI were then prioritized based on several criteria. First, all variants distinguishing B6 and D2 within each QTL CI were identified using Sanger’s Mouse Genome Project Mouse_SNPViewer/rel-15050 . This resulted in 790 traits. The BXD legacy traits mined from GN were retained for correlation analysis if there were at least 12 matched BXD strains shared with our THC initial response traits. This resulted in retention of 757/790 traits. Traits were further filtered if descriptions included the terms “Morphine response,” “morphine withdrawal,” “Cocaine response,” “Intravenous cocaine,” OR “Ethanol response” and DID NOT include the terms “Saline control,” “inactive,” OR “unconditioned.” A final total of 302 traits  were included for correlation analysis. Traits were assigned to general categories based on phenotype and whether the trait was measured in females, males, or both. Category descriptions along with GN record IDs, trait descriptions, and values for drug response and cocaine self-administration can be found in p-values were calculated for each of the four THC initial response traits and all 302 BXD legacy drug response traits. Significant correlations were associated with a p < 0.05. Note that all correlations were performed on BXD strain averaged trait data and do not include parental B6 and D2 strains.Two recent studies quantified initial responses to multiple drugs of abuse  or intravenous cocaine self-administration among BXDs . Both stp-values < 0.001) and moderate to large main effects on mobility, hypothermia, and antinociception in our BXD cohort. Relative to the VEH treatment on day 0, THC treatment on day 1 greatly and significantly reduced mobility . THC treatment caused a large and significant decrease in body temperature . Acute THC treatment resulted in a moderate and significant increase in tail withdrawal latency .We found that treatment  had significant , hypothermia , and antinociception . Significant main effects of sex were observed for hypothermia  and antinociception .Relative to treatment effects, main effects of strain or sex were generally smaller. Strain effects contributed significantly to response variation for mobility  and a smaller significant treatment-by-strain interaction effect for hypothermia . Smaller effect trends for sex-by-treatment , strain-by-sex , and treatment-by-strain-by-sex  interactions were observed for the hypothermia trait.We observed a moderate and significant treatment-by-strain interaction effect for mobility . Average mobility across BXD strains was reduced by 238.40 ± 28.08 s .Acute THC resulted in an average 1.20 ± 0.11 s increase in tail withdrawal latency across all strains measured . InitialF16,35 = 2.42, p < 0.05, ω2 = 0.30, 90% CI ). In males the effect was also significant, but larger . Average body temperature across BXDs was reduced by 3.36 ± 0.33°C in females and by 2.42 ± 0.27°C in males. BXD62 (−6.07 ± 1.35°C) and BXD101 (−5.73 ± 0.60°C) females and BXD16 (−4.86 ± 0.35°C) males demonstrated greater relative sensitivity to the hypothermic effects of THC  in the parents of the BXD family – B6 and D2 . Here weIn general, THC response traits demonstrated high heritability . Tail wip < 0.01) and positive correlation between time mobile  and hypothermia in males, but not in females, following the first exposure to THC  that leverages linear mixed-models and accounts for population family structure . Suggestcis eQTLs in naive BXD brain tissue (Ndufs2 (Chr 1 antinociception QTL); Scp2 (Chr 4 antinociception QTL); Rps6kb1/p70s6K ; Pde4d ; and Pten   . These gNdufs2 encodes a core subunit of the mitochondrial Complex 1. The gene also contains both missense and putative splice region variants and demonstrates evidence of genetic regulation of expression in the form of cis eQTLs detected in naive BXD hippocampus, hypothalamus, neocortex, nucleus accumbens, and prefrontal cortex. Moreover, Ndufs2 has been implicated in mediating response to cannabinoids. Activation of mitochondrial CB1 receptors resulted in a decrease in PKA-dependent phosphorylation of oxidative phosphorylation proteins, including NDUFS2, and a concomitant decrease in brain mitochondrial function that was associated with cannabinoid-induced synaptic depression and amnesia ( amnesia .Scp2 gene contains a putative splice region variant and demonstrates genetic modulation of expression (cis eQTL) in naive BXD neocortex and prefrontal cortex.The SCP2 protein has been proposed to regulate brain endocannabinoid levels and, thus, endocannabinoid system function . The ScpRps6kb1 encodes the 70-kDa ribosome protein S6 kinase (p70S6K) and contains a predicted splice region variant. Expression of Rps6kb1 is modulated by a cis eQTL in neocortex. Activation of the G-protein coupled CB1 receptor by THC triggers cascades of intracellular signaling, including activation of phosphoinositide-3 kinase (PI3K)/Akt/glycogen synthase kinase 3 (GSK-3) and subsequent activation of the serine/threonine kinase mammalian target of rapamycin (mTOR). P70S6K is a downstream target of mTOR whose activation is associated with protein synthesis and regulation of different cellular states . The CB1/mTOR/p70S6K signaling pathway in hippocampal GABAergic interneurons was found to mediate THC-induced long-term memory deficits  we explored broader associations with over 300 addiction and psychiatric disease key words using a newly developed resource: GeneCup . This search finds sentences in PubMed that contain both the gene of interest and keywords relevant to drugs of abuse, addiction, and psychiatric disease. Our overall goal was to place provisional endocannabinoid signaling QTGs identified in our study into a broader biological context. Given the broad modulatory effects of the endocannabinoid system on brain function, we hypothesized that THC initial response QTGs would also be associated with diverse signaling in response to other drugs of abuse and psychiatric disorders.Pten , Rps6kb1/p70S6K , and Pde4d  were the most well connected QTGs in terms of associations with drugs of abuse and psychiatric disease . disease . All thrNdufs2 (Chr 1 antinociception QTL) and Scp2 (Chr 4 antinociception QTL) were also associated with neuroplasticity, signaling, transcription, stress, and hippocampus terms  correlations (i.p.) from 90 to 150 min post-injection. Other patterns emerged as well: (1) significant and positive correlations between THC-induced hypothermia  and naloxone-induced (30 mg/kg i.p.) withdrawal from morphine (50 mg/kg i.p.), (2) significant and positive correlations between cocaine self-administration and THC-induced immobility, (3) sex-specific and significant correlations between THC-induced hypothermia and preference for cocaine, and (4) significant correlations between both THC-induced immobility and hypothermia  and initial motor response and tolerance to alcohol/ethanol.From our background list of 302 BXD legacy traits, we found 43 significant . Instead, these correlation patterns indicate shared biological mechanisms and more complex genetic modulation driving response to THC and other drugs of abuse.1 receptor signaling. Following this line of reasoning, we identified multiple QTLs and positional candidate endocannabinoid signaling QTGs  that may control variation in initial response to THC  have been associated with cannabis use or dependence in human genome-wide association studies (Nrg1 (Neuregulin) mutant mice, in which one copy of the gene contains a deletion of the transmembrane region, are more sensitive to the initial locomotor suppressant and behavioral effects of THC . Second, endocannabinoid pathways represent common points of convergence that are highly relevant for understanding the molecular response to many drugs of abuse and molecular mechanisms underlying addiction processes. To this end, we have identified strong correlations between initial responses to THC and initial response or self-administration of cocaine, morphine, and/or alcohol among BXD strains that could be indicative of shared genetic and biological co-regulation.1 receptors in some rodent models has been shown to enhance the rewarding effects of alcohol and opioids through both dopamine dependent and independent pathways . CB1 receptor-mediated plasticity and signaling is also important for mediating response to opioids  plays an important role in modulating synaptic plasticity, behavior, and reward circuitry. Cannabis, cannabinoids and non-cannabinoid drugs of abuse have been shown to disrupt endocannabinoid system function and signaling, and these changes may contribute to addiction processes . For exapathways . Specifipathways  and lossehaviors . THC and in rats . Both co in rats  and alco in rats  exposure opioids . Thus, tWe hypothesized that correlations between initial response to THC and responses to alcohol, cocaine, or opioids in the BXD population resulted from the convergence of genes and pathways that mediate both effects. Indeed, we observed striking correlations between THC and opioid responses , THC and cocaine responses (enhanced sensitivity to the motor suppressant effects of THC and higher active lever presses for cocaine), THC and ethanol responses (enhanced sensitivity to the motor suppressant and hypothermic effects of THC and enhanced sensitivity to the motor stimulant effects of ethanol), and THC and withdrawal from alcohol and opioids . The results of these exploratory correlational analyses hint at underlying shared genetic and biological regulation. Of interest, no single shared locus could account for the covariance in drug response traits, indicating complex regulation by multiple loci, genes, and variants. Importantly, our exploratory analysis identified individuals of the BXD population with extreme responses to multiple drugs of abuse. These individuals can be leveraged to directly test predictions about drug response and to identify mediators of behavioral responses to both cannabinoid and non-cannabinoid drugs using both genetic (QTL mapping) and pharmacological (through targeting of specific pathways) methods.Our study marks the first and largest attempt to quantify genetic factors mediating initial responses to THC in males and females of a genetic population. While we have made an important contribution to the field, there are some limitations of our study that will need to be addressed by future experiments. First, THC traits were profiled in a subset of 20 BXD strains which limits our ability to detect QTLs of small effect size. All QTLs identified in our study are suggestive after accounting for the effects of genome-wide testing. It is likely that these loci will be replicated in an independent or larger cohort of BXD strains. We emphasize that this replication will be required to confirm QTLs and QTGs prioritized by our study. Other aspects of our study design limit translatability somewhat and should be addressed by future studies. These include the use of a single major component of cannabis (THC), use of a single dose (10 mg/kg), and quantification of acute physiological responses to THC . Finally, we were agnostic to genetic variation in cannabinoid metabolic pathways in this study.∗3 allele associated with slower clearance of THC . It will be important in the future to establish whether sex or strain differences in THC metabolism contribute to variation in response and whether there are functional variants that impact THC metabolism in rodents. Several functional human variants  have been identified for cannabinoid metabolism genes [e.g., CYP3A4 and CYP2C9, reviewed in e of THC .Ultimately, our study demonstrates the feasibility of genetic mapping in the BXDs to identify underlying biological and genetic factors contributing to response to THC and other drugs of abuse. We have taken important first steps toward the identification of loci and genes that modulate initial responses to THC. We have also provided evidence of covariation among drug response traits, although the underlying causal factors remain elusive. Finally, we have identified BXD individuals with extreme responses to acute THC exposure. Identification of BXD individuals with greater initial sensitivity to THC will facilitate more detailed genetic analysis of intake, reward, withdrawal, tolerance, and the behavioral impact of cannabinoids on motivation, cognition, and health. The genetic architecture of behavioral and pharmacological responses to cannabis and cannabinoids is difficult to reconstruct using currently available human data sets. A better understanding of trait correlation structure and regulation by underlying genetic variation in diverse rodent models is expected to help bridge these gaps and direct the focus of future human studies.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The animal study was reviewed and approved by The University of Tennessee Health Science Center Institutional Animal Care and Use Committee.BJ, BM, and MM designed the study. BM, HC, and RW provided databases, tools, reagents, and animals. CP and MM performed the experiments and wrote the manuscript. CP, CR, PP, and MM analyzed the data. BJ, BM, RW, HC, and MM performed the major editing. The study was carried out in the laboratory of MM with intellectual guidance and support from BJ, BM, and RW. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MD declared a past co-authorship with the authors HC, RW, and MM to the handling editor.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Research on the genetics of complex traits overwhelmingly focuses on the additive effects of genes. Yet, animal studies have shown that non-additive effects, in particular homozygosity effects, can shape complex traits. Recent investigations in human studies found some significant homozygosity effects. However, most human populations display restricted ranges of homozygosity by descent (HBD), making the identification of homozygosity effects challenging. Founder populations give rise to higher HBD levels. When deep genealogical data are available in a founder population, it is possible to gain information on the time to the most recent common ancestor (MRCA) from whom a chromosomal segment has been transmitted to both parents of an individual and in turn to that individual. This information on the time to MRCA can be combined with the time to MRCA inferred from coalescent models of gene genealogies. HBD can also be estimated from genomic data. The extent to which the genomic HBD measures correspond to the genealogical/coalescent measures has not been documented in founder populations with extensive genealogical data. In this study, we used simulations to relate genomic and genealogical/coalescent HBD measures. We based our simulations on genealogical data from two ongoing studies from the French-Canadian founder population displaying different levels of inbreeding. We simulated single-nucleotide polymorphisms (SNPs) in a 1-Mb genomic segment from a coalescent model in conjunction with the observed genealogical data. We compared genealogical/coalescent HBD to two genomic methods of HBD estimation based on hidden Markov models (HMMs). We found that genomic estimates of HBD correlated well with genealogical/coalescent HBD measures in both study genealogies. We described generation time to coalescence in terms of genomic HBD estimates and found a large variability in generation time captured by genomic HBD when considering each SNP. However, SNPs in longer segments were more likely to capture recent time to coalescence, as expected. Our study suggests that estimating the coalescent gene genealogy from the genomic data to use in conjunction with observed genealogical data could provide valuable information on HBD. Inbreeding leads to increased homozygosity and has a negative effect on phenotypes . This phFounder populations give rise to higher HBD levels. The French-Canadian founder population originated at the beginning of the 17th century with the immigration of French settlers , which eThe probability of an individual's allele being HBD within a fixed genealogy depends on the number of meiosis on the transmission paths of the individual's two copies of the alleles through a specific most recent common ancestor (MRCA) and also on all the possible paths linking those two alleles through this common ancestor. When the MRCA occurs further back in time than the founders of a fixed pedigree, time to MRCA for two genomic segments is available from the gene genealogy , which dSharing of chromosomal segments within individuals can be observed with genomic data. HBD can then be estimated by searching for runs of homozygosity (ROHs) exceeding a given length  along a genomic region . Alternahttps://balsac.uqac.ca/). BALSAC contains over 4.3 million records providing information on over 6 million individuals and allowing the reconstruction of ascending genealogies from present-day individuals going back over four centuries  were recorded at each of the single-nucleotide polymorphism (SNP) locations in the segment.r2 threshold of 0.5. For FEstim, default values were used for all parameters.HBD was also estimated from the probands' simulated genomic segments using the HMM-based approaches implemented in the IBDLD software version 3.13  and the All analyses were performed using the R environment version 4.1.1 .We calculated descriptive statistics of the two study genealogies using GENLIB. These characteristics include the completeness of the genealogical structures , the number of lowest common ancestors shared by the parents of the probands, the kinship coefficients among pairs of probands, and the inbreeding coefficients of each proband . The lenk  yielded two Correlations between IBDLD and FEstim HBD probabilities were calculated. Specifically, in each study, each simulation replicate HBD status inferred from the time in generations to MRCA within 30 generations (as described above) was averaged across SNPs in the simulated genomic region to obtain an estimate of the HBD probability in the region. This estimate was then compared graphically to IBDLD and FEstim HBD probabilities using scatter plots with fitted regression lines. Lastly, we used scatter plots to examine the relationship between genomic HBD probabilities from FEstim and IBDLD and time to coalescence in generations for each The completeness of the genealogies at each generation is shown in In The relationship between average (over SNPs and simulation replicates) FEstim and IBDLD HBD probabilities is illustrated in It is clear that IBDLD and FEstim differ in their estimated HBD probabilities. To better understand which probabilities might be closer to the true HBD probabilities, we define a gene genealogy-based measure that captures similar information as the HBD probability. For each SNP, we determine if the MRCA of each individual two alleles occur within 30 generations . Given the small number of generations since the ancestor, the two alleles are very likely to be identical. We then average across the region to obtain an estimate of the HBD probability for each individual in the region. We compare this genealogical/coalescent-based HBD probability to the average HBD probability from both FEstim and IBDLD in Finally, in The distribution of coalescence generation time captured when different genomic HBD probability cutoffs are used is displayed by boxplots in In this study, we simulated chromosomal segments using a coalescent model combined with genealogical data from two study samples from the French-Canadian founder population. We used these simulations to compare two genomic measures of HBD (FEstim and IBDLD) with HBD and relatedness measures based on the study and coalescent/gene genealogies. We found that the genealogies from the two studies had different levels of inbreeding, with the SLSJ study genealogy displaying overall higher levels of inbreeding in concordance with the settlement history of the SLSJ region of Quebec . Interesr2 > 0.01 within a 50-marker window were removed) or averaged over several subsets in low LD (of one SNP selected every 0.5 cM) performed as well as or better than HMM modeling of LD when estimating the inbreeding coefficient using ROHs. In contrast, In the two studies, the FEstim and IBDLD genomic-based measures of HBD were well correlated when averaging across the simulated chromosomal region but less so on a SNP-by-SNP basis. The correlation between the two methods was also slightly higher in the SLSJ study with higher inbreeding. Hence, the two methods could lead to different conclusions on the HBD status of specific SNPs, especially in study samples with lower inbreeding. Both genomic HBD measures correlated well with the genealogical/coalescent-based measure in both study samples, although IBDLD estimates of HBD probabilities were closer in values to the genealogical/coalescent-based estimates. This could impact HBD status inference from FEstim compared to IBDLD. The difference between the two methods could be due to the fact that IBDLD uses all SNPs in estimating the HBD probabilities since it models LD and thus does not need to restrict the analysis to a subset of SNPs in low LD, as required by FEstim. In defining the genealogy/coalescent HBD measure used in the comparison with the genomic HBD probability estimates, we considered whether the MRCA occurred within 30 generations  for each SNP and averaged over the region. Results comparing this genealogy/coalescent HBD to the genomic HBD probabilities (averaged over simulation replicates) shown in When using genomic estimates of HBD probabilities, a cutoff can be used to determine if a SNP is HBD or not. A probability cutoff of at least 0.5 is the default in the IBDLD software. Using a cutoff is useful in order to infer HBD segments or to assess associations between HBD at specific genomic regions and health-related phenotypes . It is tOur study has some limitations. First, the genealogical data from the two French-Canadian studies are not complete. The incomplete genealogical links could affect our simulation results in terms of the generation time to coalescence. However, since our simulations are based on these incomplete genealogies, the missing genealogical links are taken into account in the simulation results. Second, it would be interesting to vary the parameters used for the coalescent simulations. Third, using genome-wide SNPs instead of a 1-Mb segment would allow comparison with a wider range of HBD detection methods  and would more closely reflect studies of HBD, which are typically genome-wide. In this study, we prioritized computational feasibility to be able to trace back the generation of coalescence in a large number of simulation replicates. Finally, studying more parameters and conditions  for LD pruning or the HMM LD modeling would provide more guidance on the application of these analysis tools.In addition, the two study genealogies used in our simulations come from participants selected based on disease status. Thus, our results may not generalize to samples obtained without ascertaining on a disease status. However, the two studies considered in these simulations investigate complex diseases influenced by many genetic and environmental factors. Hence, although participants were ascertained based on their disease status, we do not expect to see a large difference in overall (genome-wide) inbreeding levels in the study samples compared to the general French-Canadian founder population. Indeed, the kinship and inbreeding levels observed in the two studies correspond to those observed in In summary, our simulation results provide insight for the interpretation of genomic estimates of HBD in a large founder population. We studied two genealogical datasets from different study designs yielding different levels of inbreeding. These differences in inbreeding led to different genomic estimates of HBD, which correlated well with genealogical/coalescent HBD. Generation time to coalescence captured by genomic HBD estimates was similar in the two studies. Time to coalescence captured was very variable when considering each SNP separately, but SNPs in longer segments were more likely to capture recent time to coalescence, as expected. Our study suggests that estimating the coalescent gene genealogy from the genomic data  and comb


Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds.–6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model.Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds  genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN.We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis.The online version contains supplementary material available at 10.1186/s12711-021-00671-w. Pork accounts for 35% of meat consumption worldwide, representing an important component of many human diets . To aligIGF2 [MC4R [LEPR [https://www.animalgenome.org/QTLdb). Results from these studies showed that backfat thickness is a polygenic trait that is regulated by a large number of small-effect variants. With the advent of single-nucleotide polymorphism (SNP) genotyping arrays, gene expression analyses, and other high-throughput genotyping technologies, many more candidate genes for backfat thickness have been reported that are involved in very diverse biological functions and metabolic pathways, such as: adipogenesis [Over the last two decades, there has been great interest in identifying candidate genes that regulate backfat thickness. Prominent genes that were discovered by linkage analysis include IGF2 –9, MC4R F2 [MC4R , 11, and4R [LEPR . Since togenesis , 14; lipogenesis , 15, 16;ogenesis –20 or evogenesis ; the adiogenesis , 19; theogenesis ; and nerogenesis .The accumulation of evidence for the association of genomic regions with backfat thickness across diverse genetic backgrounds could disentangle which of the reported QTL represent the most prevalent genes and pathways that underlie backfat deposition. In turn, it could also be hypothesized that less prevalent and population-specific associations may derive from variants with larger effects that have been (nearly) fixed in intensely selected populations. In this study, we performed a large genome-wide association study (GWAS) for backfat thickness in eight pig breeding lines of diverse genetic backgrounds, with ~ 15,000 to ~ 55,000 pigs each, for a total of 275,590 pigs. Our main objectives were to determine the genetic architecture of backfat thickness and to identify the main genes and pathways that underlie its genetic variance.Data comprised 278,112 purebred pigs from eight lines (A to H) of diverse genetic backgrounds . In order to detect potential functional candidate genes, gene annotation was retrieved from databases of the Gene Ontology project and the KEGG Pathway Database integrated in the Enrichr gene analysis .Data belonging to previously reported QTL that mapped to the reference genome version Sscrofa11.1 were downloaded from the Animal QTLdb  (Februar–6) and their location in the genome are in Table We found significant genome-wide associations with backfat thickness for 264 SNPs in 27 genomic regions, of which six were detected in three or more lines. Genome-wide associations by line are shown in Fig. Sus scrofa chromosome (SSC) 1 at ~ 160 Mb (158.31–162.35 Mb) was detected in five lines . Three other regions were detected in four lines: on SSC5 at ~ 66 Mb , on SSC11 at ~ 8 Mb , and on SSC18 at ~ 10 Mb . Two regions were detected in three lines: on SSC1 at ~ 52 Mb  and on SSC7 at ~ 30 Mb .A region on Table MC4R on SSC1 at ~ 160.8 Mb (significant association in five lines). Other candidate genes with fat-related functions were identified, such as MEDAG on SSC11 at ~ 7.5 Mb, which is involved in adipocyte differentiation and showed a significant association in four lines. While this region did not show significant associations in the other lines, it explained 1.6% of the additive genetic variance in one other of the lines for which the genomic region was not statistically significant. Another example is the genomic region that contains the IGF2 gene on SSC2 at ~ 1.5 Mb, which is involved in regulation of fat deposition. This region was significantly associated with backfat thickness in only one line but explained 1.1% of the additive genetic variance in at least one other line. The results also suggest that genes involved in phosphate, calcium, and vitamin D homeostasis pathways, such as FGF23 on SSC5 at ~ 66.0 Mb, contribute to backfat thickness development.Within the 27 genomic regions, we identified 1219 positional candidate genes, of which 64 are annotated to functions that can be related to fat metabolism (Table –4). For seven of the regions we found no previously reported QTL, and for 12 of the regions we found previously reported QTL for fat metabolism and deposition or for feed efficiency traits but not for backfat thickness traits. These 12 regions showed a significant association in only one of the lines and, in general, explained a low proportion of the genetic variance.Table To our knowledge, this is the largest-to-date GWAS for backfat thickness in pigs. We report results from eight large populations, which ranged from ~ 15,000 to ~ 55,000 genotyped pigs and differed in breed of origin and selection history. Large sample sizes are required for high power of GWAS and, thus, this dataset provides valuable insight into the genetic architecture of backfat thickness and the main genes and pathways that underlie its genetic variance.MC4R, MEDAG, FGF23, DGKI, and PTN. Together with the candidate genes that were found in the other genomic regions, the results support the involvement of energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways, and suggest the contribution of other metabolic pathways, which are less well understood, to genetic variation for backfat thickness in pigs, such as the phosphate, calcium, and vitamin D homeostasis pathways.We found significant associations for 27 genomic regions, of which one region was detected in five out of the eight lines, three regions in four lines and two regions in three lines. Moreover, some of these genomic regions explained a relatively large proportion of the additive genetic variance of backfat thickness in lines for which the GWAS detected no significant association. The genomic regions that were detected in five or four of the lines contained candidate genes In the light of these findings, we will focus our discussion on: (1) the genetic architecture of backfat thickness, (2) the role of energy homeostasis genes on backfat thickness, (3) the role of adipogenesis, fatty acid metabolism, and insulin signalling genes on backfat thickness, and (4) the role of phosphate, calcium, and vitamin D homeostasis genes on backfat thickness.The results of the detected SNP associations and genetic variance partitioning confirm the polygenic architecture of backfat thickness, with many loci with small individual effects and only a small fraction of SNPs with larger effects. The effect of the significant SNPs was largely maintained across the eight studied lines. Our moderate-to-high estimates for SNP-based heritability are in the range of previous SNP-based estimates in purebred Duroc lines  and 5.3% (MC4R region) of the additive genetic variance in the lines studied here.We detected several genomic regions that significantly affected backfat thickness and individually explained up to 8.0% of the genetic variance for the trait. Genomic regions that were detected in more than one line or that explained a large proportion of genetic variance generally overlapped with entries for backfat-related QTL with evidence of enrichment and with candidate genes with plausible annotated functions. The fact that a genomic region was detected in a single line or in multiple lines was not always related to the proportion of genetic variance explained by the region in those lines. Moreover, some genomic regions explained a relevant proportion of genetic variance in lines for which that genomic region was not significant. Previous reports in a Duroc population estimated that a single genomic region, in particular the region on SSC6 where the hickness . We obsevariance . These tAfter accounting for significant genomic regions from the GWAS, the majority of the additive genetic variance remained distributed across non-significantly associated genomic regions; in the genetic variance partitioning analysis, the residual polygenic term due to these non-significant SNPs explained from 80.5 to 96.8% of the additive genetic variance in each line. Thus, most of the genetic variance is explained by minor loci that were not detected in the GWAS. Taken together, these results confirm that backfat thickness has a polygenic architecture, although some major genes that agree with previous studies , 17, 22 LEPR and IGF2 have been reported to have dominance [Previous studies have estimated that dominance effects account for 4 to 15% of the phenotypic variance, and imprinting effects for 1 to 3% , 31. Indominance , 34 and ominance  effects,ominance . StatistMC4R is located in this region. Energy homeostasis in mammals is a feedback system that balances energy intake and expenditure. The melanocortin-4 receptor that is encoded by MC4R has been described as a critical coordinator of mammalian energy homeostasis and body weight [MC4R are well known in pigs and are involved in regulating appetite [MC4R in human monogenic obesity was described in the late 1990s [The GWAS results support the role of energy homeostasis genes for genetic variation in backfat thickness in pigs. Significant SNPs in the genomic region on SSC1 at ~ 160 Mb were found in five lines. The functional candidate gene y weight . Mutatioappetite , 37. Thete 1990s , 39 and te 1990s –42.LEPR gene. A mutation in this gene was described in an Iberian LEPR gene at 146.8–146.9 Mb. Non-significance of this region in the other lines could be the result of intense selection for feed efficiency in the studied lines [JAK1 and LEPROT also map to this region. In fact, the significant SNP at 147.5 Mb is located in an intron of the JAK1 gene. The annotated functions of JAK1 and its effect on backfat thickness are difficult to disentangle from the effects of other genes, such as LEPR or LEPROT.Another key regulator of feed intake and energy homeostasis is the ce cross  that redce cross . Similarce cross  and in ate cross . In factte cross . We onlyed lines . A studyed lines . The canMEDAG, which promotes adipocyte differentiation and lipid accumulation in mature adipocytes [ALOX5AP, which is involved in subcutaneous fat deposition in pigs [The results of the GWAS also support that other pathways are involved in the development of backfat in pigs, such as adipogenesis, fatty acid metabolism, and insulin signalling. Adipogenesis involves differentiation from pre-adipocytes to adipocytes. The region on SSC11 at ~ 8 Mb, which was identified in four lines, contains the adipogenic gene ipocytes  and was ipocytes . The sam in pigs .PTN, which is in the region on SSC18 at ~ 10 Mb that was identified in four lines, has a role in a signalling pathway that negatively regulates adipogenesis [PTN plays an essential role in the dynamics of adipose lipid turnover and plasticity, as it preserves insulin sensitivity and regulates energy metabolism and thermogenesis [DGKI, which maps to this same region, has been reported to be under positive selection in polar bears and could be related to the development of corporal fat to provide thermal isolation [The gene ogenesis . Recent ogenesis . The gensolation .LRP5 (body fat distribution in humans) [BRSK2  [DOLK (subcutaneous fat in lambs) [LRP5 and BRSK2 are in the 0.00–4.82 Mb genomic region on SSC2. Although we detected a significant association for this region in only one line, several GWAS on backfat thickness in pigs have revealed a significant association of this genomic region with backfat thickness, average daily gain, and meat-to-fat ratio in diverse genetic backgrounds, from F2 populations derived from breeds such as Pietrain, Large White and Landrace [INS gene, which encodes insulin that regulates blood glucose levels, promotes cell fat storage, and regulates the activity of enzymes that intervene in lipid metabolism [IGF2 gene, which encodes the insulin-like growth factor 2, and is widely considered as a major candidate gene for muscle mass and fat deposition in pigs [IGF2, such as CTSD, which encodes a protease [CPT1A, which is involved in the oxidation of long-chain fatty acids, which are the main storage lipids that form backfat. The FADS1, FADS2, and FADS3 genes, which encode fatty acid desaturases, are located near this region (at ~ 9.6–9.7 Mb). Genes related to fatty acid oxidation, such as PLAAT3 (at ~ 8.4 Mb), and fibroblast growth factor genes, such as FGF19 [, have also been considered as potential candidate genes for growth and fat deposition traits in this region.Other genes with annotated functions related to adipogenesis that were implicated in this GWAS include genes that have been previously associated with body fat content in other species, such as  humans) , BRSK2 (chicken) , and DOLn lambs) . Of thesLandrace  to crossLandrace , and mantabolism , and the in pigs , 54, 55.as FGF19 , 32, havPLIN5 gene, which is located in the significant SSC2 region at ~ 74.3 Mb, is involved in the control of intracellular lipid deposition and some results indicate that it may be involved in regulation of the expression of hormone-sensitive lipase [The e lipase . Other ge lipase .SOAT2 gene [SLC51B [ATP8B1 [AKR1D1 [BAAT gene that was proposed in that previous study was not in any of the significant genomic regions identified in our study.Across the genomic regions we identified candidate genes with functions in bile acid metabolism. Bile, which is predominantly formed by steroid bile acids synthesized from cholesterol, breaks down fat into monoglyceride and fatty acids that can be absorbed by the digestive tract. The AT2 gene  is invol [SLC51B , ATP8B1  [ATP8B1 , and AKR [AKR1D1  genes ar [AKR1D1 , althougFGF23 is located in this region. This gene is responsible for phosphate homeostasis through a pathway that involves feedback regulation by phosphate, calcium, and vitamin D [FGF23 genomic region with backfat thickness in pigs, although this gene was previously linked to mineral utilization and homeostasis in Landrace pigs (not significant after correction for multiple testing) in relation with bone development [FGF23 and adiposity. Lean adipose tissue secretes adiponectin, which causes a significant reduction in the expression of FGF23 in osteocytes [FGF23 expression in osteocytes [FGF23 signalling in the metabolic status of individuals, including insulin resistance, dyslipidemia, and obesity [Due to the biological complexity of polygenic traits, some metabolic pathways that have been less explored could nonetheless also have an impact on backfat thickness. For instance, calcium , 63 and itamin D –68. To oelopment . Howeverteocytes , while eteocytes . In huma obesity , 73. For obesity  and that obesity .PTN gene was detected in the same four lines as the SSC5 genomic region that contains FGF23. As well as having a role in regulation of adipogenesis (as discussed above) [PTN gene is also involved in vitamin D-dependent regulation of calcium and phosphate homeostasis [PTN and FGF23 [The genomic region on SSC18 at ~ 10 Mb that includes the d above) , the PTNeostasis . To our nd FGF23 .DHCR7 gene, which is located in the genomic region on SSC2 at ~ 2.4 Mb close to the IGF2 and other genes, has also been associated with backfat thickness in different pig populations [DHCR7 catalyses the conversion of 7-dehydrocholesterol to cholesterol, the final step in the production of cholesterol. 7-dehydrocholesterol is also a precursor for vitamin D and, therefore, DHCR7 plays an important role in vitamin D metabolism in humans [DHCR7 and other genes such as FGF23 and PTN with functions related to phosphate, calcium and vitamin D homeostasis could shed new light on genetic variation for backfat thickness.The ulations , 32. Then humans , 79. Hown humans , 81. AltMC4R, IGF2, and LEPR has been studied during the last decades, the genomic regions detected here also contained more novel candidate genes, such as DHCR7, FGF23, MEDAG, DGKI, and PTN. We quantified that these and other genomic regions could individually contribute up to 8.0% of the genetic variance in the studied lines. The characterization of genes with annotated functions that are not well understood is challenging but can shed new light on the genetic and physiological mechanisms that control adiposity. Further research on these candidate genes is encouraged in order to identify putative causal genomic variants that contribute to the genetic variance in backfat thickness in pigs and to assess their potential application in swine breeding programmes.Our GWAS results obtained on 275,590 pigs from lines with diverse genetic backgrounds confirmed the polygenic architecture of backfat thickness and the importance of genes associated with energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggested that genes involved in phosphate, calcium, and vitamin D homeostasis contribute to backfat development. While the association with backfat thickness of genes such as Additional file 1: Figure S1. Distribution of the SNP effects on backfat thickness (mm) in the eight pig lines. Figure S2. Q-Q plots for the genome-wide association study of backfat thickness for the eight pig lines.