R package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads.Log-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the We compared the asymptotic power approximations to the power of analysis attained with Haplin. For external validation, the results were further compared to the power of analysis attained by the EMIM software using data simulations from Haplin. Consistency observed between Haplin and EMIM across various genetic scenarios confirms the computational accuracy of the inference methods used in both programs. The results also demonstrate that power calculations in Haplin are applicable to genetic association studies using either log-linear or multinomial modeling approaches.Haplin provides a robust and reliable framework for power calculations in genetic association analyses for a wide range of genetic effects and etiologic scenarios, based on genotype data from a variety of child-parent configurations.The online version of this article (10.1186/s12859-019-2727-3) contains supplementary material, which is available to authorized users. Statistical power or sample size analysis is an essential first step in the planning of any scientific study. Such analyses ensure that a study is capable of answering its stated research questions and are a prerequisite for optimal study design . FurtherLog-linear and multinomial modeling are closely related approaches that offer a flexible framework for genetic association analysis. Both approaches enable the estimation of genetic effects in addition to hypothesis testing. Beyond the standard case-control design, they are capable of incorporating child, parent-of-origin (PoO) and maternal effects based on genotype data from case-parent triads, as well as a range of other child-parent configurations. They can also handle incomplete triad data as well as independent controls. Moreover, the models are readily extended to haplotype analysis. Due to these appealing features, there has been much interest in the application of log-linear or multinomial models in genetic association studies –12, and General-purpose software tools for statistical power and sample size analysis are not set up to handle the genetic study designs and effect estimates available from case-parent triads with unrelated controls. Although there are tools that offer power calculations for some genetic association studies, e.g., Quanto –16 and GR package Haplin [We propose a complete setup for power calculations tailored to binary disease traits, which we have implemented as a new module in the e Haplin , 13. In Our paper is structured as follows. In the “R package Haplin, which provides an extensive framework for genetic epidemiological analyses of binary traits. The new power calculation module has been integrated into the original setup for genetic association analysis in Haplin and is based on log-linear modeling, as previously described by Gjessing and Lie [Our power calculation tool has been added to the  and Lie . Haplin  and Lie , 19. Hap and Lie –22. In H and Lie . Note th and Lie –27. The  and Lie  is imple and Lie . Further and Lie , 18, 29.Within the Haplin framework, based on the log-linear modeling approach, we have developed a new and complete module for performing power calculations. The basic calculations relate to child, PoO and maternal effects, and our definitions of these genetic effects are provided in Table In this section, we demonstrate how to perform basic power calculations in Haplin, implemented in the function hapPowerAsymp. The power is computed analytically through asymptotic approximations, scaled to the appropriate sample size. We apply the asymptotic normal distribution of the log-scale parameter and use the chi-squared non-centrality parameter of the Wald test. The variance-covariance matrix is computed from a log-linear model which accounts for transmission ambiguities and missing data; its derivation is provided in Additional file In Haplin, the asymptotic power calculations are easy to perform. In general, one only needs to specify the study design and its sample size, the allele frequencies, and the type of genetic effect and its magnitude. Table The genetic effects are determined by the choice of relative risk parameter(s), which also specifies the effect sizes. Corresponding to the parameterization model in Eq. (1)  and RRcf = c. For more details on PoO testing and its relationship to imprinting, see Gjerdevik et al. [As illustrated in Table k et al. .Since children and their mothers have an allele in common, a maternal effect might be statistically confounded with a child or a PoO effect. Corresponding to the parameterization models in Eq. 3) and (4) Table , the pow and (4) ∗ and RRM)∗, we used varying numbers of case-parent triads and a MAF of 0.2. In the right panels (b and d), the power was calculated using varying MAFs and a total of 500 case-parent triads. We used a nominal significance level of 5% throughout.We illustrate the use of our power function hapPowerAsymp by plotting power curves for different scenarios, as shown in Fig. a). Moreover, with 500 case-parent triads and a MAF of 0.1, the power to detect an RR of 1.35 is 57% (panel b). The PoO analysis can be viewed as a statistical interaction. Compared with the child-effect analysis, a higher sample size is therefore required for the PoO analysis to reach the same statistical power for a similar effect size. Approximately 1200 case-parent triads are needed to detect an RRR of 1.35 with 80% power and a MAF of 0.2 (panel c). With 500 case-parent triads and a MAF of 0.2, we have approximately 80% power to detect an RRR of 1.6. Using a MAF of 0.1, the corresponding power is 64% (panel d).In all panels, the green, solid line represents scenarios in which 500 case-parent triads and a MAF of 0.2 were used. For child effects, we have 80% power to detect an RR of 1.35. However, using 250 case-parent triads, the corresponding power decreases to 51% (=1). For the PoO effects, we set RRF=1, so that the value of RRM/RRF is equal to the value of RRM. Panels e and f show the power to detect maternal effects, while adjusting for possible child or PoO effects .The results are shown in Fig. b and e are equivalent because the power to detect a given child or maternal effect is identical when adjusting for possible confounding of the effects with one another. However, this symmetry depends on the study design and will not necessarily hold if case-mothers are unavailable for genotyping (results not shown). PoO effects are essentially estimated in case families, by contrasting the frequencies of alleles transmitted from mother to child with those of alleles transmitted from father to child. Thus, unrelated control families do not add extra power to the case-parent triad design, as can be seen from the overlapping results of the mfc and mfc-mfc designs in panel c. Note that we excluded the mc design from the joint PoO and maternal effect-analyses (panels d and f) because the penetrance model in Eq. (4) (Table Note that panels 4) Table  would beR package Haplin. In Haplin, power analysis can be carried out analytically using the asymptotic variance-covariance structure of the parameter estimator, or, by a straightforward simulation procedure. The two approaches for power calculations complement each other, balancing time efficiency against generality. Haplin enables power calculations to be performed for child, PoO, maternal and GxE effects, based on genotype data from a variety of family-based study designs. An inherent strength of the Haplin framework is its ability to compute power for both single SNPs and haplotypes, either autosomal or X-linked. We plan to continue to expand the present framework for power analysis, adding new features for power calculations as additional methods for genetic association analysis are developed and incorporated into the Haplin software.To our knowledge, a comprehensive software for power analysis based on the full triad design has been lacking. Here, we have developed and showcased extensive, new and easy-to-use functionalities for statistical power analyses based on log-linear modeling, incorporated in the To facilitate power analysis in Haplin, we have provided relevant example commands in Table The standard Haplin implementation assumes haplotype-frequency parameters under HWE instead of a model with all mating-type parameters , 6. ThisWe conducted a thorough comparison of the asymptotic approximation approach with the power attained by Haplin and EMIM in data simulations. Child, PoO and maternal effects were assessed. The concordant results obtained confirm the computational accuracy of the inference methods used in both programs. They also demonstrate that power calculations in Haplin are applicable to genetic association studies analyzed by either log-linear or multinomial modeling approaches. Thus, Haplin provides a robust and reliable framework for power calculations in genetic association analyses for various genetic effects and etiologic scenarios, based on genotype data from a wide range of different child-parent configurations.Project name: HaplinProject home page:https://people.uib.no/gjessing/gene-tics/software/haplinOperating system(s): Platform independentProgramming language: Haplin is implemented as a standard package in the statistical software R. It is available from the official R package archive, CRAN (https://cran.r-project.org).Other requirements: NoneLicense: GPL (>= 2)Any restrictions to use by non-academics: Nonehttps://www.staff.ncl.ac.uk/richard.howey/emim.Information on EMIM and PREMIM is available from Additional file 1Σ. (PDF 184 kb)An asymptotic approximation of Additional file 2Power and sample size calculations in Haplin. (PDF 369 kb)


From an evolutionary perspective, emotions emerged as rapid adaptive reactions that increase survival rates. Current psychobiology includes the consideration that genetic changes affecting neuroendocrine and neurotransmission pathways may also be affecting mood states. Following this hypothesis, abnormal levels of any of the aminergic neurotransmitters would be of considerable importance in the development of a pathophysiological state.HTR1A; HTR2A; HTR2C; HTR3B; TPH1; SLC18A1; SLC18A2; COMT; MAOA; MAOB) and neuroendocrine system .A total of 668 students from the School of Medicine of the University of Malaga  provided self‐report measures of mood states using POMS and GHQ‐28 questionnaires and buccal cells for genotyping 19 polymorphisms from 14 selected neurotransmitter pathways genes  but it is neutral among males in this subscale. On the contrary, it correlates with lower GHQ‐28 depression scores among males . Equivalently, SLC18A1 and HTR2A variants correlated with anger and vigor scores, only among males. From the neuroendocrine system, OPRM1 rs1799971 correlated increasing levels of female's Anxiety, depression and Social Dysfunction scores.Our findings suggest that these polymorphisms contribute to define general population mood levels, although exhibiting a clear sexual dimorphism. Understanding the intimate nature of our emotions, let us give a rational voice to the feelings that condition our behavior in society. Emotions have been explained from the evolutionary perspective as rapid adaptive reactions that increase survival rates among vertebrates  is an instrument originally designed to identify nonpsychotic mental disorders in contexts of general medical practice. It allows to differentiate in a simple way, psychiatric patients from those considered healthy  have been found among families with bipolar disorder   have been studied among major depressive disorder patients and been included in several meta‐analyses that demonstrated a small but significant association to bipolar disorder , which is involved in both the pathogenesis of depression and the mechanism of action of antidepressant treatments  and neuroendocrine . Details are summarized in Supporting Information Table Genotyping was outsourced to Genologica SL. SNP analysis was performed using the TaqMan Open Array Genotyping System from Applied Biosystems. The results obtained were processed using TaqMan Genotyper Software. The selected SNPs were a chosen from the literature among those affecting the neurotransmitter systems  and the Profile of Mood State (POMS) test, basing their responses on their mood status along the past few weeks. GHQ‐28 is a self‐administered questionnaire of 28 items divided into four subscales: A (somatic symptoms), B (anxiety and insomnia), C , and D (Depression)  (RRID: https://scicrunch.org/resolver/SCR_016496). Statistical analysis was performed using IBM SPSS Statistics v22 (RRID: https://scicrunch.org/resolver/SCR_002865). Graphical representations were generated both with the IBM SPSS program and with the Microsoft Excel spreadsheet. Kolmogorov–Smirnov test was used to determine the normality of the quantitative data series. For bivariate correlations studies, both the Pearson's correlation coefficient and Spearman's Rho were calculated. For models that included both the genetic variants and other covariates, the linear regression models were used. The level of significance was 0.05.Hardy–Weinberg equilibrium was assayed using the corresponding online web tool from the Institute of Human Genetics of Munich  although ranged between 18 and 51 years. The series was composed by 41% men and all from Caucasian origin. All of them were sampled for buccal swap for subsequent determination of genetic polymorphisms. Call ratios had an average of 96%, although they ranged from 98% for SNPs such as rs3813929, rs3027452, or rs2254298, and the minimum of 89% obtained with rs6313. Hardy–Weinberg equilibrium (HWE) was determined for those SNPs mapping autosomal chromosomes and only those with p‐value = 0.004) and chronic Anxiety . Age also correlated with different parameters such as vigor, friendship, and new onset Depression, evidencing the need to use them as covariates to determine the potential role of the genetic variants under analyses. Beyond this, we found a relevant intercorrelation between the different variables within the same questionnaire (GHQ‐28 chronic and new onset) as well as a significant correlation between equivalent variables interrogated in POMS and GHQ‐28. As an example, we found that the POMS T‐score measuring fatigue positively correlated with GHQ‐28 chronic anxiety and depression levels   suggesting that those subjects harboring the mutant homozygous genotype reported an increased Vigor than those with the reference genotype. We might mention the associations found for SLC18A1 variants and BDNF rs6265; however, the p‐values obtained do not support multiple correction and therefore should be treated with caution. Of mention, none of the variants analyzed correlated with the Total Mood Disturbance T‐score, pointing that the individual effects of any of the genetic variants under study are not enough per se to generate a significant impact over the general mood state of a subject. We should also highlight that we have assayed a codominant genetic model of inheritance assayed. This can be graphically visualized using Spider diagrams where in a codominant genetic model the effect of the heterozygote genotype is between the two homozygotes  correlated with Anxiety of new onset, but not when defined as a chronic variable. Similar results were obtained for HTR2A rs6313 and MAOA rs3788862 for anxiety and depression. In fact, we found a higher proportion of significant associations within the new onset construct, suggesting that this test might be especially sensitive to mood disturbances. We must however highlight the effect of the OPRM1 variant rs1799971, which was consistently associated, in both constructs, with Anxiety . Moreover, this variant correlated with less Social Dysfunction  and higher somatic scores .Next, we performed a multiple correlation analysis between the three genotypes for each genetic variant and the POMS T‐scores. Results are shown in Table MAOA, whose mutant alleles correlate with decreasing levels of tension (within the POMS questionnaire) among females but it was neutral among males for this subscale. On the contrary, rs3788862 seemed to correlate with less Depression levels among males  when quantified with GHQ‐28 while being neutral among females. Equivalently, SLC18A1 and HTR2A variants correlated with increasing levels of anger and vigor, respectively, but only among males. From the neuroendocrine system‐associated genes, we might highlight the association among females between OPRM1 polymorphism and increasing levels of anxiety and somatization, concomitantly with lower Social Dysfunction scores.Finally, we performed a regression analysis adjusted by age and stratified by sex in order to subtract the effect of these variables and quantify the net effect of the genetic variants. Results recapitulate to a large extend the associations captured in the univariate models; however, we identified a clear sexual dimorphism for several genetic variants Table . Regardi4The present study shows the quantitative evaluation of functionally relevant variants of the neuroendocrine and neurotransmitter systems of the mood state of a cohort representative from the general population. We have characterized the correlation between 16 selected SNPs and the different items included in two widely used and validated psychometric mood questionnaires. The obtained results show a significant correlation between equivalent items of each test. More importantly, we found statistically significant associations between different items with and the subject's genotype. There are three worth noting aspects, first, that our study is based on a young and healthy population where the different mood subscales show a subtherapeutic continuum, in contrast to most of the works reporting differences between cases and controls of a particular pathology. Secondly, we should highlight the clear sexual dysmorphic mood response and genetic correlation, suggesting that the genetic background differentially compromises the mood state depending on the gender. And third, the degree of coherence obtained in the results between the functional attributions of each selected polymorphism and the emotional items to which they have been associated. It is necessary to keep in mind that when a study of genetic association is made, the phenotype must be sufficiently marked to exhibit a statistically significance. In our case, the population phenotypes should be considered as euthymic, what means that the results obtained detect genetic associations with emotional conditions that did not give rise to a psychopathology. This sensitivity on detection mood variability and genotype among euthymic subjects could be supported by the high homogeneity of the age a sociocultural features of the population studied. Perhaps because of this, some statistical association may not support multiple testing correction.DRD2 with the POMS test and find differences between sexes in a similar population. Yarosh, Meda, Wit, Hart, & Pearlson, . This stress pattern is the same for depression, anger, fatigue and TMD and chronic GHQ‐28. On the contrary, friendship and vigor behave inversely, give results of direct correlation with themselves and inverse with all others. It is evident that the nervous alteration is directly related to all the states that are proposed of imbalance and inversely with the states that define more balance, friendship, and vigor. It is noteworthy that the new onset of the GHQ‐28 test items does not correlate with the other items, perhaps due to some general premise about this test that nullifies the possibility of correlation, since this absence of significance is very strange.HTR2A, BDNF to friendship only in women, MAOA to a lesser tension score only among women and SLC18A1 to anger only within men . The same dimorphic and excluding pattern occurs when GHQ‐28 is assayed: HTR2A and OPRM1 for somatization only among women, OPRM1 for new onset and chronic anxiety among women. MAOA genotype behaves as an antidepressant only among males. In order to stablish a functional relationship for these results, we found that our results show a relation of the alleles classified as lower MAO activity with the lesser tension in woman measured by the POMS test and with the Anxiety, in woman, and no depression, with the GHQ‐28 test in man. These results are consistent with those found previously on their association with depression and with the well‐known effect of MAO inhibitors (MAOI) on depressive status  66 to Methionine (Met) change. The most common allele (G) encodes for Val while the mutant allele (A) encodes Met. Statistically significant differences were found between the Val/Met genotypes and the response to emotional expressions measured by functional magnetic resonance imaging signals in the orbitofrontal cortex, amygdala, and hippocampus on the passive view of faces with different expressions , to be replaced by aspartic acid (Asp). According to the literature, mutant allele is related to increased pain, suggesting a compromised protein function (Slavich, Regarding the neuroendocrine system genes, we should highlight that Slavich, . In our Slavich, . We did Slavich, . Overall4.1Whenever a genetic association is reported, the suspect of facing a false positive arises. Given the amount of variables assayed, we might consider that defining an alpha of 0.05 might be too permissive. Here, we analyzed 14 genes and conducted two mood tests, but the different variables under study are not purely independent. MAOA polymorphisms for instance are in linkage disequilibrium. In the same line, several dimensions measured with Goldberg's and GHQ‐28 questionnaires show a statistical correlation. Therefore, some corrections such as Bonferroni adjustment shall be considered too exhaustive. A second limitation of the study relates to the genetic heterogeneity of the Spanish South Eastern population. This would eventually have included a microestratification effect. However, we should highlight the population was composed of volunteers with Caucasian and that the Spanish population is largely homogeneous (Gayan et al., The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. This work was financed using internal sources from the Department of Surgery, Biochemistry and Immunology, Universidad de Málaga. Click here for additional data file.


IL17, IL17F and LIF), and growth-related genes (MC4R and LEP), while the top regions influencing the intercept of the reaction norm included fertility-related genes such as EREG, AREG and SMAD4. In conclusion, our findings validated the G × E interactions for fertility traits across different herds and were helpful in understanding the genetic background of G × E interactions for these traits.Genotype-by-environment (G × E) interactions could play an important role in cattle populations, and it should be considered in breeding programmes to select the best sires for different environments. The objectives of this study were to study G × E interactions for female fertility traits in the Danish Holstein dairy cattle population using a reaction norm model (RNM), and to detect the particular genomic regions contributing to the performance of these traits and the G × E interactions. In total 4534 bulls were genotyped by an Illumina BovineSNP50 BeadChip. An RNM with a pedigree-based relationship matrix and a pedigree-genomic combined relationship matrix was used to explore the existence of G × E interactions. In the RNM, the environmental gradient (EG) was defined as herd effect. Further, the genomic regions affecting interval from calving to first insemination (ICF) and interval from first to last insemination (IFL) were detected using single-step genome-wide association study (ssGWAS). The genetic correlations between extreme EGs indicated that G × E interactions were sizable for ICF and IFL. The genomic RNM (pedigree-genomic combined relationship matrix) had higher prediction accuracy than the conventional RNM (pedigree-based relationship matrix). The top genomic regions affecting the slope of the reaction norm included immunity-related genes ( In the dairy cattle industry, fertility traits are some of the most influential components, as declining fertility prolongs the resume cycles after calving and increases veterinary costs De Vries . Some prIn the Danish milk production system, different herds have different conditions in terms of feed, pharmaceuticals and housing, which may further lead to G × E interactions  , interval from first to last insemination (IFL), non-return rate at 56 days after first insemination (NRR) and interval from calving to first insemination (ICF). Field records during the period of insemination from 2011 to 2016 were collected from 1775 herds. The records in the first three parities were used in this study.https://www.nordicebv.info/).In addition, AIS and IFL included some censored records because of no known date of confirmed successful insemination. In this study, penalty values were used to handle censored data  version 2 ). The SNP data were filtered by removing markers with a minor allele frequency <1%, an average GenCall score lower than 0.60, or an unknown location in the UMD 3.1 assembly. After data editing, 46,342 SNPs were included in the analysis.The EG defined in this study included the production environments of herds, i.e., herd effects. We used a three-step strategy to analyze the G × E interaction for different traits. This three-step approach is an extension of the two-step RNM proposed by Calus et al.  and Kolmy is the vector of observations; b is the vector of fixed effects other than herd effect; f is the vector of fixed herd effects; a is the vector of additive genetic effects, which are assumed to follow the normal distribution A is the numerator (pedigree) relationship matrix and H is the combined pedigree-genomic relationship matrix; c is the vector of herd-year effects, which are assumed to be normally distributed with I is the identity matrix;pe is the vector of permanent environmental effects, which are assumed to be normally distributed with ects; e is the random residual vector following X, F, Z, E and W and are incidence matrices related to the fixed and random effects in the model. The fixed effects included herd, parity, year of first insemination  or year of calving (for ICF), month of first insemination  or month of calving (for ICF) and age at first insemination . The random effects included additive genetic effects, herd-year effects , and permanent effects.Initially, a univariate animal model was used to fit observations for each trait. The model was as follows:A) was built based only on the pedigree, whereas for the ssGBLUP model, a combined pedigree-genomic relationship matrix (H) was used, and the inverse H−1 was calculated as follows G0+ωA22, where ω is the relative weight that could explain the fraction of the genetic variance not captured by markers and was set as 0.2 according to  to y, and Z1 is an incidence matrix containing herd effects estimated in step 1 as covariables to connect y and a1 (slope). Other effects and incidence matrices are the same as those for linear mixed models in step 1. The random residual vector is assumed to follow R is a diagonal matrix with elements for observations in the ith group of herds equal to 1/wi, andni is the number of observations in the ith group of herds and ith group estimated by RJMC. In addition, to evaluate the need to include the G × E interaction in the model, a likelihood ratio test (LRT) between the RNM and the reduced model (RM) of (4) without considering random regression on herd effects was performed based on the statistic of D = −2 * log(likelihood) for the RM, + 2 * log(likelihood) for the RNM. The P-value for the LRT was calculated as Compared with conventional linear mixed model, the RNM included genetic sensitivities represented by random regressions on the herd effects estimated in the step 1 and was analyzed using DMUAI. It was shown as follows:f) were calculated as The heritability To evaluate and compare the prediction ability of different models, the data were divided into training and validation parts according to the birthdates of the bulls. The 20% youngest bulls  with daughter information were regarded as the validation dataset, and the other 80% were regarded as the training dataset. In the RNM, the estimated breeding value of a particular bull obtained from the pedigree-based BLUP (EBV) or from the ssGBLUP (GEBV) was defined asssGWAS as proposed by Wang et al. G matrix with Dii = 1 using (3).1. Calculate the 2. Calculate the GEBV for all the genotyped animals using the reaction norm based ssGBLUP.3. Calculate the SNP effects Dii) for each SNP as pi is the allele frequency for the ith SNP.4. Calculate the weight  or slope (s = 1) of the tth window and can be calculated as Zk is the matrix of gene content of the kth SNP for genotyped individuals, and kth SNP with the tth window for the intercept or slope; Considering that too many iterations may cause some subjective peaks, the ssGWAS was run for three iterations  were very similar  estimated by the RNM based on the lar Fig. . The higlar Fig. .Fig. 1HeP-values < 0.05) different from zero for all traits based on one-tailed t-tests. The genetic correlations between a herd at a particular quantile of herd effects (in the range of mean ± 2.5 × standard deviation of herd effects) and herd level at 5% (lowest), 55% (intermediate) or 95% (highest) quantiles are shown in Fig. A and H are very small for ICF and NRR, while the genetic correlations estimated by A matrix were slightly lower than those estimated by H matrix for AIS and IFL (Fig. The genetic sensitivity to different environments for each trait was indicated by the variance of the slope Table , which wTable IL17, IL17F and LIF.The percentages of genetic variances explained by genomic windows are shown in the Manhattan plot in Fig. In this study, we explored the G × E interaction for female fertility traits using RNMs with or without genomic information and evaluated prediction accuracies for future performance. Further, we tried to locate the particular regions in the genome that probably explain the genetic background of ICF and IFL, which exhibited G × E interactions across extreme EGs. The results showed that the slope variances of the reaction norm for all the traits were statistically significant from zero, while genetic correlations across the extreme EGs could only be identified as significantly different from unity for ICF and IFL. Generally, the inclusion of genotype information improved the prediction accuracy. In addition, some candidate genes influencing intercepts and slopes of reaction norms for ICF and IFL were also identified.The heritabilities estimated from the conventional linear mixed model Table  were simA or H matrices. A previous study on milk yield, dry matter and body weight of Holstein dairy cattle compared genetic variance estimates obtained from ssGBLUP with those obtained from conventional BLUP and showed that variance components estimated with the A matrix were higher than the components obtained from the H matrix, while the latter led to higher accuracy (Veerkamp et al. In this study, we estimated variance components using an RNM with Residual variances across different EGs could be different (Knap and Su In this study, the variance of the slope in the RNM was significantly different from zero for all traits, suggesting that all traits exhibited G × E interaction to some extent Falconer . The corConventional RNMs and genomic RNMs led to similar genetic correlations between different EGs for ICF and NRR Fig. , which wA and H matrices. Compared with pedigree-based BLUP, higher accuracies were obtained with the inclusion of genotype information. In a previous study that evaluated the genomic prediction accuracy using a conventional ssGBLUP model in the same Holstein population, the genomic prediction accuracy of ICF was evaluated to be 0.24 for pedigree-based BLUP and 0.29 for ssGBLUP (Ismael et al. Although the existence of G × E interactions between different EGs was not pervasive for all traits, the LRT results showed that the RNM fit the data better than the RM without considering G × E interactions of all these traits Table . HoweverIL17, IL17F and LIF, which were proved to be related to fertility. For instance, IL17 and IL17F encode interleukin 17 cytokine, and the increased level of this cytokine in plasma has a negative impact on human fertility (Ozkan et al. LIF encodes another interleukin cytokine, leukaemia inhibitory factor, which plays an important role in embryo implantation. It has been reported that aberrant leukaemia inhibitory factor production is linked to implantation failure (Salleh and Giribabu IL17, IL17F, IL17RB, LIF, CHDH, CD59 and TLR4, indicating influences of immunity on fertility in cattle. It has been reported that immunity and fertility could share some common elements (Hurley TFG, EREG and AREG are related to preovulatory follicles in cattle (Li et al. TPR is linked to age at first calving in Nellore cattle (Mota et al. SMAD4 gene, which is necessary for early embryonic development and embryotrophic actions of follistatin in cattle (Lee et al. MC4R and LEP, both of which can regulate obesity and energy expenditure (Dempfle et al. This study performed ssGWAS to locate the particular regions along the genome that were associated with fertility phenotypes and environmental sensitivity. ICF and IFL were chosen to perform ssGWAS as significant G × E interactions were observed across extreme EGs for the two traits Table , and slis Hurley . As diffs Hurley , the ples Hurley . Some geIn conclusion, genetic parameters obtained from the RNM suggested the existence of G × E interactions for all traits, indicating that the breeding value of an individual may be changed in different herds for these traits. However, genetic correlations across extreme EGs indicated significant G × E interactions for ICF and IFL. The RNM resulted in a better goodness of fit than the RM for all the traits and higher prediction accuracies for ICF and IFL than the RM. Genotype information improved the prediction accuracies of both the RM and RNM for all of the traits. Several reproduction-related, immunity-related and growth-related genes were identified in the genomic regions affecting the intercepts and slopes of the reaction norm for ICF and IFL.Data supporting this paper were obtained from the commercial dairy farms in Denmark. The phenotype and genotype data are available only upon agreement with commercial breeding organizations and should be requested directly from the corresponding author.Supplementary Information.


Dominance effects play important roles in determining genetic changes with regard to complex traits. We conducted genomic predictions and genome-wide association studies in order to investigate the effects of dominance on carcass weight, dressing percentage, meat percentage, average daily gain, and chuck roll in 1233 Simmental beef cattle. Using dominance models, we improved the predictive abilities and found several candidate single-nucleotide polymorphisms (SNPs) and genes associated with these traits. Our studies helped us to understand causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle.RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within  Dominance is a result of interactions between genes at the same locus, and it plays an important role in mammalian biology and development ,2. SeverRUNX2, a candidate gene that has been associated with milk production in dairy cattle. In pigs, SNPs located in SSC7 with significant dominance effects for the number of teats were also reported [Dominance effects are important non-additive genetic effects and the inclusion of the dominance effects in prediction models could increase the accuracy of genomic prediction in farm animals. Nishio et al.  conductereported . HoweverSimmental is one of the important introduced beef cattle breeds in China. In this study, we analyzed five economically important traits  in 1233 Simmental beef cattle genotyped with BovineHD Beadchip. Some traits are important in beef cattle production systems and considered as main objectives of the genetic improvement. For example, average daily gain contributes to production efficiency and economic benefits in the beef cattle industry . CarcassAll animals used in the study were treated following the guidelines established by the Council of China Animal Welfare. Protocols of the experiments were approved by the Science Research Department of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS) . The approval ID/permit numbers are SYXK (Beijing) 2008-007 and SYXK (Beijing) 2008-008.A total of 1302 Simmental beef cattle born between 2008 and 2013 were used in this study, and these cattle originated from Ulgai, Xilingol League and Inner Mongolia, China. After weaning, the cattle were moved to JinweifurenCo, Ltd., Beijing, China for fattening. All animals shared the same feeding and management conditions. A more detailed description of the breeding and management has been described previously ,12. The p > 10−6). Moreover, individuals with more than 10% missing genotypes were excluded. After these quality controls, the final data consisted of 1233 individuals and 596,978  autosomal SNPs.We extracted genomic DNA from blood samples using the TIANamp Blood DNA Kit . DNA quality was considered acceptable if the A260/280 ratio (the ratio of absorbance at 260 nm and 280 nm made on a spectrophotometer) was 1.8–2.0. Qualified DNA was genotyped using the Illumina BovineHD Beadchip panel . The SNP chips were analyzed with Infinium GenomeStudio (Illumina). Before statistical analysis, SNPs were pre-processed using PLINK v1.90 . SNPs wey is the vector of phenotypes; b is the vector of fixed effects including year, sex, and body weight at entering the fattening farm, and the number of fattening days. Random additive genetic effects a and random dominance effects d are related to y by incidence matrices e is the vector of random residuals. The linear mixed-model including additive and dominance genetic effects can be written as follows: I is an identity matrix, and G and D are the additive and dominance genetic relationship matrices, respectively. In this study, these matrices were constructed using SNP information as follows:It is assumed thatM is an n × m matrix , which refers to SNP genotype coefficients at each locus. The coefficients of the ith column in the M matrix are |αj||The traditional BayesA only has additive effects in the model. Therefore, The Markov chain for the prediction of the model parameters is generated by Gibbs sampling with a Metropolis–Hastings (MH) step . AccordiWhen given y, the joint posterior distribution of sampling . Given tThe association mapping of dominance effects and additive effects was performed using a multi- locus mixed-model (MLMM) approach in R . This apGoodness of fit for GBLUP-D and GBLUP was assessed by the likelihood value. The superiority of GBLUP-D over GBLUP was also tested using a likelihood ratio test. Moreover, a five-fold cross validation was used to evaluate the accuracy of genomic prediction for additive genetic values in both GBLUP-D and BayesAD. Data were split into five approximately equal-sized groups. For each cross validation, four groups were used as the training sample to estimate the marker effects, and the remaining group was used as the validation sample. The cross-validation procedure was repeated 25 times. Predictive ability was assessed by the correlation between predicted genetic values and corrected phenotypes of the validation group.For the five traits, we decomposed the total genetic variance into the additive and dominance variances. The corresponding components are shown in p < 0.05) superior to GBLUP. The results indicated that the goodness of fit was improved by including dominance effects in the model.Goodness of fit estimations based on likelihood ratio tests are shown in The predictive abilities of the estimated genomic breeding values for five traits using the five-fold cross-validation method with BayesA, BayesAD, GBLUP, and GBLUP-D are shown in p < RIMS2) in BTA14. We also observed that two top SNPs, BovineHD1000015632 and BovineHD1000015492, both showed a strong additive and dominance association with ADG (ALDH1A2). Manhattan plots of the results for DP, MP, and CR with additive and dominance effects were shown in GPR68), guanylate cyclase 1 soluble subunit alpha 2 (GUCY1A2), abl interactor 1 (ABI1), armadillo repeat containing 1 (ARMC1), SKI family transcriptional corepressor 2 (SKOR2), and STAM binding protein like 1 (STAMBPL1). Four of these SNPs contributed to both additive and dominance effects. One SNP was associated with MP near RIMS2. However, we did not detect any significant dominance signal for MP. We observed three SNPs, within or near three genes, that were associated with CR. Interestingly, we exclusively detected the other four SNPs when considering the dominance effects.We performed a whole-genome single-marker scan for additive and dominance effects with five traits in 1233 Simmental using a multiple-locus mixed-model. We found 15 significant additive signals and 10 significant dominance signals  with approximately the same number of significant dominance SNPs and ratio of dominance variance to phenotypic variance. However, for ADG, the accuracy improvement was relatively high with a low dominance variance ratio. These observations may be attributable to a few factors: (1) incomplete decomposition of dominance variance from total genetic variance, and (2) lack of full-sib pairs between reference and prediction populations because full-sibs are the primary source of non-additive relationships .v) increases, GBLUP can be considered as a limiting case of BayesA [In Danish Duroc pigs, the accuracies of predicted breeding values of average daily gain were 28.5% and 29.2% for a model without dominance effects and a model with dominance effects, respectively, and the results showed that the accuracy increased by only 0.7% by using GBLUP-D . In Holsf BayesA . AlthougFGF5) on BTA6 and one intragenic SNP (BovineHD1400017459) located in regulating synaptic membrane exocytosis 2 (RIMS2) on BTA14, which were associated with CW. FGF5 had relationships with embryonic development, cell growth, morphogenesis, tissue repair, tumor growth, and invasion [RIMS2 is associated with carcass weight in Simmental beef cattle [GPR68 has been confirmed as a candidate gene for growth traits of Charolais beef cattle [GUCY1A2 plays a critical role in human blood pressure [ARMC1 was reported as a candidate gene associated with body weight, body length, body height, and chest circumference in Chinese Laiwu pigs [SKOR2 has been reported as a candidate gene for changes in weight and BMI in male and female adolescents [STAMBPL1 was reported as a candidate gene for lung vasculature and immune cell functions in 26 inbred mouse strains [RIMS2 on BTA14 was associated with MP. Three SNPs located in two genes were identified as being associated with ADG; one of these genes was ST6 N-acetylgalactosaminide alpha-2,6-sialyltransferase 5 (ST6GALNAC5) on BTA3, while the other gene was aldehyde dehydrogenase 1 family member A2 (ALDH1A2) on BTA10. It was reported that ST6GALNAC5 is closely related to fat accumulation in human and pigs [ALDH1A2 has been identified to play critical roles in the synthesis of retinoic acid, the active derivative of vitamin A (retinol), which mainly affects body growth and bone development in mice [MPST) on BTA5, member RAS oncogene family (RAB3B) on BTA3, and neuron navigator 3 (NAV3) on BTA5, respectively. MPST was a protein coding gene, and it had been reported as a candidate gene associated with protein production traits in dairy cattle [ABI1, RAB3B, and NAV3 have not been reported associated with our studied traits in beef cattle. However, these genes may play important roles in some metabolic pathways related to DP and CR. We performed GWAS in 1233 Simmental beef cattle to systematically detect additive and dominance variants for CW, DP, MP, ADG, and CR, and we identified 15 additive and 10 dominance signals within or near 13 and 9 genes, respectively. We observed one significant additive signal extragenic SNP near fibroblast growth factor 5  on BTA10, echinoderm microtubule-associated protein like 6 (EML6) on BTA11, and homer scaffold protein 1 (HOMER1) on BTA10, and one SNP was located in upstream of sterile alpha motif domain containing 12 (SAMD12) on BTA14. This may be explained by that these four signals only contribute to dominance variance. The mode of gene action of these SNPs seems purely dominant as the dominance variance in proportion to additive genetic variance of CR was as large as 44%, which indicated that dominance plays an important role in the genetic architecture. However, the effect of these significant signals needs to be further confirmed based on independent studies. Interestingly, we found no significant SNPs in dominance effects association studies for meat percentage. Due to a possible sparseness of dominance effects, MLMM may under-perform for association mapping. Future research is needed to develop more association models for dominance effects.We observed that six significant dominance signals were both characterized as additive and dominant for CW (one SNP), DP (three SNPs), and ADG (two SNPs). The other four significant dominance signals were only associated with CR. Three SNPs were respectively located in ryanodine receptor 3 (We comprehensively evaluated the contribution of dominance effects to five important traits related to growth and carcass in Chinese Simmental beef cattle through analysis of the proportion of dominance variance to the phenotypic variance, genome-wide association studies, and genomic prediction. We found that the proportion of dominance variance to phenotypic variance was 4.2%–16.1% for these five traits, and the inclusion of dominance genetic effects in genomic prediction models can slightly improve the accuracy of genomic breeding values. We also observed that 15 additive and 10 dominance signals were significantly associated with these traits. Our findings could be helpful for further studies of causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle.


Transgressive segregation was observed for all seven traits in both years. The broad-sense heritability estimates for the traits ranged from 0.20 (days to anthesis) to 0.62 (flower length). A genome-wide genetic linkage map consisting of 368 single nucleotide polymorphism bins and extending over 277 cM was searched to identify QTL for these traits. Nineteen QTL were identified and localized to five linkage groups. Eleven of the loci were identified consistently in both years; several loci explained up to 34.0% and 24.1% of the phenotypic variance for flower length and flower diameter, respectively. Multiple loci controlling different traits are co-localized in four intervals in four linkage groups. These intervals contain desirable alleles that can be introgressed into commercial petunia germplasm to expand the genetic base and improve plant performance and flower characteristics in petunia.A major bottleneck in plant breeding has been the much limited genetic base and much reduced genetic diversity in domesticated, cultivated germplasm. Identification and utilization of favorable gene loci or alleles from wild or progenitor species can serve as an effective approach to increasing genetic diversity and breaking this bottleneck in plant breeding. This study was conducted to identify quantitative trait loci (QTL) in wild or progenitor petunia species that can be used to improve important horticultural traits in garden petunia. An F A major bottleneck in plant breeding has been the much limited genetic base and much reduced genetic diversity in domesticated, cultivated agricultural crops . IdentifPetunia ×hybrida) is one of the most economically important ornamental plants. The wholesale value of garden petunia, including plants grown in flats, pots and hanging baskets, in the U.S.A. in 2014, was estimated at over $120 million  . BreakinSo far, several genetic linkage maps have been developed in petunia based on restriction fragment length polymorphism (RFLP)  technologies have brought an efficient, low-cost, and large-scale marker discovery method that was referred as Genotyping-by-Sequencing (GBS)  was produced by single seed descent from an interspecific cross between P. axillaris  and P. exserta  at Michigan State University , whereas P. exserta is a recently discovered species with interesting plant and flower characteristics (An FP. axillaris and P. exserta as well as RIL progeny were sown into 20-row germination trays (27.94 cm × 30.48 cm) filled with Fafard germination mix  and germinated in a growth room with room temperature maintained at 27° and light intensity at 150 μmol m-2 s-1. Two weeks later, germination trays with young seedlings were transferred to a greenhouse where the seedlings were grown on a metal bench, with air temperature maintained between 25° and 30° and a photoperiod of 16 h natural light and 8 h dark. Twelve days later, eight or more seedlings per RIL line were transplanted from the germination trays to 72-cell planter trays (66 cm × 33 cm) filled with Fafard 3B potting mix . Seedlings were fed twice a week with a water-soluble fertilizer containing 15% (w/w) total nitrogen, 5% phosphate (P2O5), and 15% potassium (K2O) . After two weeks, seedlings in planter trays were acclimated in a shade house with 30% light exclusion for one week. Then, four uniform seedlings per RIL and their parents were transplanted to mulched, raised ground beds equipped with an automated drip irrigation system at the GCREC experimental farm . The sandy-textured soil of the raised ground beds was fumigated with Pic-Clor 60  at 45 kg per 1,000 m2 one month prior to transplanting. After transplanting, each plant received 8 g of controlled release fertilizer . The drip irrigation system ran 30 min daily. During the petunia growing season (late Feb. to mid-June), the daily average temperatures ranged from 11° to 28° in 2014, and from 6° to 28° in 2015. Total precipitation was 42.39 cm in 2014 and 46.30 cm in 2015. A randomized complete block design with four replicates was used for the field experiment each year.Two field experiments were conducted in 2014 and 2015, from Jan. to July following the same growing calendar in both years, at the University of Florida’s Gulf Coast Research and Education Center (UF/GCREC) in central Florida. In early Jan., seeds of RIL population progeny and their parents were phenotyped for plant height (PH), plant spread (PS), plant size (PZ), flower diameter (FD), flower length (FL), flower counts (FC), and days to anthesis (DTA). The phenotypic data of PS and PH were collected near the end of the growing season (early to mid-June) to assess maximum plant growth potential. PS (cm) was calculated by averaging the maximal plant spread (cm) and minimal plant spread (cm). Maximal plant spread was measured along the longest axis of the plant from one edge to the opposite edge. Minimal plant spread was taken between plant edges perpendicular to maximal plant spread. PH was measured from the soil surface to the highest point of the plant. PZ was calculated using the formula: PZ = [π ×  ×  × plant height]. Three fully-opened fresh flowers were randomly selected from each plant for FD and FL measurements. FD was measured from one petal edge to the opposite edge of the petal, and FL was measured from the base of the calyx to the plane of the corolla. FC was recorded weekly  for seve2H) estimation was: ijk = µ +y Gi + Ej + Gi × Ej + Bh(j) + εijk, where ijky is the observed value of the studied trait, µ is the population mean, Gi is genotypic effect, jE is environmental effect, i × EjG is the interaction effect between genotype and environment, h(j)B is block effect, and ijkε is random error. All effects were treated as random in the ANOVA. Broad-sense heritability was calculated as the proportion of the genotypic variance over the total phenotypic variance. The level of broad-sense heritability was categorized as low (H2 < 0.30), moderate (H2 between 0.30 and 0.60), or high (H2 > 0.60), according to the criteria proposed by The frequency distribution, Pearson’s correlation coefficients, and broad-sense heritability for each trait were analyzed using JMP Pro 10.0.2 . The stath percentile of LOD score from a permutation test (1000 cycles).A SNP-based genetic map . Once QThttps://datadryad.org/resource/doi:10.5061/dryad.m23ks48). The original GBS data are available under the NCBI GenBank BioProject number PRJNA353949.Phenotyping and genotyping data of all the petunia RILs used in this study, and their linkage mapping data are archived at Dryad (H2) estimates for the seven traits studied are shown in Petunia axillaris showed higher values than P. exserta in PS, PZ, FD, and FL, but had similar values with P. exserta in PH and FC for both years. Petunia axillaris and P. exserta displayed similar DTA values in 2014, but P. exserta had a higher DTA value than P. axillaris in 2015. DTA instability between 2014 and 2015 indicates high sensitivity of DTA to horticultural practices and/or environmental conditions.Mean values, ranges, and broad-sense heritability  and FL (H2 = 0.62); moderate H2 estimates were for PS (H2 = 0.53), PZ (H2 = 0.47), FD (H2 = 0.50), and FC (H2 = 0.36), and low H2 value was for DTA (H2 = 0.20) (bV) for five traits ranged from 0.00% (plant size) to 2.07% (flower diameter), indicating that bV contributed negligibly to the total phenotypic variance (pV). The proportion of environmental components (eV) in the total phenotypic variance was lower for PS (1.72%), FC (0.00%), DTA (0.00%), and FL (0.66%), but higher for FD (6.05%) and PZ (5.47%) (geV) was seen for PZ (19.12%), PS (18.64%), FC (33.95%), and DTA (59.49%) (The RIL population derived from th years . Of the  = 0.20) . As for  (5.47%) . Conside(59.49%) .P < 0.001) correlations (r2 = 0.131), FL and PS (r2 = 0.149), FD and DTA (r2 = 0.114), FD and FC (r2 = 0.127), and FC and PH (r2 = 0.123). Three trait pairs, FD and FL (r2 = 0.435), FC and PS (r2 = 0.314), and PH and PS (r2 = -0.385), had a moderate correlation coefficient level, while two more trait pairs, PH and PZ (r2 = 0.653), and PS and PZ (r2 = 0.840), displayed relatively high correlation coefficients.Pearson’s correlation analysis suggested that 10 of the 21 possible trait pairs had significant  and LG4 (qPH4.1)  2 ((qPH4.1) . Both QTqPS2.1) and LG4 (qPS4.1) were identified for PS (qPS4.1 was consistently detected in 2014 and 2015. The percentage of phenotypic variance explained (PVE) by qPS4.1 was 17.1% in 2014 and 19.6% in 2015. The locus qPS2.1 was detected only in 2014, and its PVE was 8.7%.Two putative QTL in LG2 , LG3 (qFD3.1), LG4 (qFD4.1), and LG7 (qFD7.1)  to 24.1% . The PVE of qFD4.1 varied in two years, 24.1% in 2014 and 11.5% in 2015.Four putative QTL controlling FD were identified in LG1 ((qFD7.1) . Among tqFL1.1), LG2 (qFL2.1), and LG3 (qFL3.1) were identified for this trait , LG2 (qFC2.1), and LG4 (qFC4.1) were detected controlling FC  to 20.1% .Three putative QTL located in LG1 (lling FC . The QTLqDTA2.1) and LG4 (qDTA4.1) were detected controlling DTA  estimates in petunia for PH (H2 = 0.83 to 0.88), DTA (H2 = 0.34 to 0.88), FD (H2 = 0.72 to 0.82), and FL (H2 = 0.72 to 0.96) were previously reported by others , FD (H2 = 0.50), FL (H2 = 0.62), and DTA (H2 = 0.20) , PS (H2 = 0.53), and PZ (H2 = 0.47) were first reported in this study. In other ornamental species, similar or higher H2 values for FC were reported in Dimorphotheca pluvialis (H2 = 0.29) (H2 = 0.67) (H2 = 0.74)   . The lowculation . Should omponent . The H2 r2 = 0.314) was higher than FC and PH (r2 = 0.123), indicating that petunia RILs with larger PS values tended to develop more flowers than RILs with higher PH values. And the negative correlation between PS and PH (r2 = -0.385) suggested that the stem elongation in petunia plants tended to grow either vertically or horizontally. The correlation coefficient between FD and FL (r2 = 0.435) was moderate, suggesting that flowers with larger diameters tend to be longer. Floriculturally speaking, larger and longer flowers would have greater aesthetical impact and contribute positively to the commercial and landscape value of new petunia cultivars. Weak but significant correlations were observed between FD and FC (r2 = 0.127), and between FD and DTA (r2 = 0.114), which were similar to previous results observed in an F2 population of P. axillaris and P. exserta , qFL1.1 , and qFD4.1 . These major QTL could be valuable for further QTL and gene discovery and thus for petunia breeding.The effectiveness of QTL analysis largely depends on how accurately the target traits can be phenotyped in the mapping population . RIL popBrassica napus , and three QTL controlling PH, PS, FC, FL, and DTA in an interval (21.7 cM to 31.9 cM) within LG2 . Previou


Little is known about the heritable basis of gene–environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype–environment interactions.Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.h2) ranging from 24% to 47%. Genotype–environment interactions were detected for age and sex (for the majority of traits), physical activity , smoking (for weight), alcohol intake  and diet pattern . Genotype–age interactions for weight and systolic BP, genotype–sex interactions for BMI and triacylglycerols and genotype–alcohol intake interactions for weight remained significant after multiple test correction.All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities  contains peer-reviewed but unedited supplementary material, which is available to authorised users. Cardiometabolic diseases are the predominant cause of mortality, morbidity and healthcare spending globally , 2, and In contemporary population genetics research, the heritability of a given trait is usually assessed by quantitative genetics approaches to make inferences about the extent to which polygenic variation influences the trait. Assessing heritability is usually done prior to embarking on studies that seek to discover specific loci influencing the trait. While it is equally logical to use quantitative genetics to determine whether traits are influenced by genotype–environment interactions as a prelude to studies focused on specific environmental exposures and genetic loci, this is rarely done in practice –8. The dHere we sought to screen for genotype–environment interactions across a number of environmental exposures and cardiometabolic traits using quantitative genetic analyses in extended pedigrees. Accordingly, we characterised the genealogical structure of a large northern Swedish population, within which detailed measures of environmental exposures, cardiometabolic traits and other personal characteristics exist .The Västerbotten Imputation Databank of Near-Complete Genomes (VIKING) study is nested in a population-based cohort from the county of Västerbotten in northern Sweden. The study capitalises on the extensively mapped genealogies in this low admixture population, in combination with an ongoing health survey in the population that makes available extensive phenotypic data in the cohort . The genThe current study includes 1682 extended pedigrees comprising 193,060 people of whom 16,430 have detailed phenotype data. The most extended genealogy descends from 4255 founders and contains 160,533 people of whom 10,498 are phenotyped. The phenotyped sample includes 8908 first-degree relative pairs, 5794 second-degree relative pairs and 29,706 third-degree relative pairs, in addition to other more distant relatives  were measured once using a mercury sphygmomanometer following a 5 min rest. Capillary blood was drawn after an overnight fast and again 2 h after administration of a standard 75 g oral glucose load , and fibre intakes expressed as per cent of total energy intake [E%]) was conducted, as previously described  24, 2525, whichG2) will be homogenous across the levels of environmental exposure; and (2) the same quantitative trait measured in participants living in different levels of environmental exposure (e.g. active vs inactive or different ages) will have a genetic correlation (ρG) of 1.0 , this mequation ], genetiRejection of the model constraining the genetic variance of the groups to be equal (i.e. presence of a significant class 1 interaction) would imply that the magnitude of the genetic effect on the cardiometabolic trait is significantly different depending on the level of the environmental exposure.Class 2 interaction: The extended model is restricted by constraining the genetic correlation to 1. For a discrete environmental exposure , this mequation ], genetiRejection of the model constraining the genetic correlation between the environmental exposure groups to equal 1 (i.e. presence of a significant class 2 interaction) implies that a different gene or different set of genes are contributing to the variance of the cardiometabolic trait depending on the level of the environmental exposure.To test the null hypothesis, each restricted model is compared with the extended model using the likelihood ratio test (LRT). The LRT statistic to test the null hypothesis of variance homogeneity (G = 0) is distributed as a χ2 random variable with one degree of freedom (χ12); the LRT to test the null hypothesis of genetic correlation equal to 1 (ρG = 1 or λ = 0) is distributed as a 50:50 mixture of a χ2 random variable with a point mass at zero and one degree of freedom (0.5χ02 + 0.5χ12) )Although this is a hypothesis-generating study, and as such one might argue against multiple test adjustments owing to the risk of a type II error , we adopIn conclusion, our results suggest that cardiometabolic traits are heavily influenced by the interactions between the genotype and environmental exposures. Our data indicate that future studies focused on identifying specific genetic variants underlying genotype–environment interactions should focus on the exposures of age, sex and alcohol intake on body composition. Numerous other exposures and outcomes defined here are also plausible candidates for genotype–environment interaction.ESM(PDF 4575 kb)Below is the link to the electronic supplementary material.