Unreplicated field trials and genomic prediction are both used to enhance the efficiency in early selection stages of a hybrid maize breeding program. No results are available on the optimal experimental design when combining both approaches. Our objectives were to investigate the effect of the training set design on the accuracy of genomic prediction in unreplicated maize test crosses. We carried out a cross validation study on basis of an experimental data set consisting of 1436 hybrids evaluated for yield and moisture for which genotyping information of 461 SNP markers were available. Training set designs of different size, implementing within environment prediction, within year prediction, across year prediction, and combinations of data sources across years and environments were compared with respect to their prediction accuracy. Across year prediction did not reach prediction accuracies that are useful for genomic selection. Within year prediction across environments provided useful correlations between observed and predicted breeding values. The prediction accuracies did not improve when adding to the training set data from previous years. We conclude that using all data available from unreplicated tests of the current breeding cycle provides a good accuracy of predicting test crosses, whereas adding data from previous breeding cycles, in which the genotypes are less related to the tested material, has only limited value for increasing the prediction accuracy. With simulations Integrating genomic prediction in breeding programs and at the same time reducing the expenses for field trials was the subject of recent studies. Unreplicated trials can be used in early stages of a breeding program, when either the amount of seeds available is limited or when a given number of selection candidates have to be tested with limited resources. In unreplicated trials, the effect of the genotype is confounded with the genotype by environment interaction effect. This confounding reduces the efficiency of selection. However, a prerequisite of a successful variety is, that its performance is high across environments. Therefore a superior genotype should be among the best selection candidates in any of the evaluation environments. In addition to those genotypes that show a high performance due to their high genotypic value, genotypes that perform well due to a favorable genotype by environment interaction or a large experimental error are selected. The latter genotypes will be selected out in later stages of the breeding program due to their weaker performance in the additional environments.Application of unreplicated trials was suggested in cereal breeding programs by Our goal was to use a data set of 1436 test cross hybrids evaluated in unreplicated field trials for yield and moisture in a cross validation study to investigate the design of the training set for genomic prediction. In particular, our objectives were to compare training set designs implementing (a) within environment prediction, (b) within year prediction, (c) across year prediction, and (d) combinations of data sources across years and environments with respect to their prediction accuracy.22.1The genetic material used for this study consisted of 1436 Dent x Flint test cross hybrids from a commercial breeding program. The parental components were doubled haploid lines. The hybrids were evaluated in an unreplicated trial in years 2013, 2014, and 2015 at seven locations for yield and moisture. We use the term unreplicated trial in this study for a trial, in which a selection candidate was evaluated in one single plot at one environment. No control or standard genotypes were used across trials. The number of hybrids tested in an environment ranged from 89 to 110 Table 1.f1 and f2 are frequencies of the two SNP variants in the set of parental lines) of 10%, and were available for at least 90% of the parental lines. The frequency of missing SNPs in a parental line did not exceed 10%. The preprocessing of the data reduced the number of SNPs to 461. The distribution of the markers on the chromosomes as well as their average and maximum distance per chromosome are presented in the Markers have been obtained by a custom Illumina chip. The number of 481 SNPs markers were assessed for the 726 parental lines of the 1436 hybrids. We preprocessed the data so that all SNPs had exactly two variants, a minimum expected heterozygosity  from the fixed effects modelIn a first analysis step, we adjusted the phenotypic data for environmental effects . We estimated with analysis of variance methodology the year effects y are the unreplicated plot values, μ is the general mean and e is the experimental error. Then we calculated adjusted plot valueswhere The estimated effects for years and environments are presented in 2.3Genomic prediction was carried out with the modely* is the vector of adjusted plot means, β0 is the intercept, Zf and Zd are the design matrices linking the marker effects to genotypes and uf,ud are the marker effects vectors for testcross performance for the Flint and Dent parental components, respectively, of which the elements are e is the vector of residuals. The marker effects were estimated with the ridge regression best linear unbiased prediction (RR-BLUP) method (where ) method  using th) method .We further investigated the methods Bayes A, Bayes B , and Bay2.4We used a re-sampling procedure to investigate different training set designs. In a first step, a random sample of 80 hybrids from each environment was drawn. We call these 80 hybrids the complete set for a certain environment. Each complete set was then divided randomly in two parts: a set called training set of size 60 and a set called prediction set of size 20. The complete set, training set, and prediction set were used according to the subsequently described designs for genomic prediction. The correlation between the predicted and observed performance of the hybrids was used as a measure for the prediction accuracy. To avoid stratification effects, correlations were computed independently at each environment using the Pearson’s correlation. For each investigated design the prediction was replicated for 100 cross validation runs. In each replication, the assignment of hybrids to the training set and the prediction set was the same for all designs.We investigated three groups of designs: Across year designs, within environment designs, and within year designs Figure 1The within environment designs comprised designs CV3, CV4, and CV5. In CV3, the design of a prediction model consisted of the training set of a certain environment. The model was used to predict the prediction set of the respective environment. CV4consisted of the design of CV3completed by the complete set of the same location in one or two previous years. The model was used to predict the prediction set of the respective location. CV5consisted of the design of CV3completed by the complete sets from one or two previous years. This model was used to predict the prediction set of the current year.The within year designs comprised designs CV6, CV7, and CV8. In CV6, the design of a model consisted of all training sets from one year. The model was used to predict the prediction sets of the respective year. CV7consisted of the design of CV6completed by the complete set of a certain location in one or two previous years. The model was used to predict the prediction set of the same location in the current year. CV8consisted of the design of CV6completed by the complete sets from one or two previous years. These models were used to predict the prediction sets of the current year.3The prediction accuracy, assessed by the median of the correlation between predicted and observed phenotypes obtained in 100 cross validation runs, reached for the training set design CV3(within environment with no previous data) values between 0.03 and 0.52 for yield and between 0.27 and 0.59 for moisture using the RR-BLUP prediction method Figure 2When one previous year of data was used in the designs, prediction accuracies of the across year design CV1(across year with one previous location) ranged between 0 and 0.04 for both traits Figure 3When two previous years of data were available in year 2015, we compared the effect of using data from one or two previous years in the training set Figure 3The results presented in 44.1r2 values around 0.3. We conclude that for the presented investigation the linkage map is sufficiently dense for precise genomic predictions.The structure of our data set is characterized by the rather low number of 481 SNPs employed. These SNPs provided in previous cycles of the investigated breeding program prediction accuracies that were considered as sufficient by practical breeders (results not shown). This observation is supported by theoretical considerations. The lines used as parents of the investigated hybrids are related, as they are belonging to a heterotic pool of a hybrid breeding program. They consist of full-sibs (both crossing partners are the same) and half-sibs (one common crossing partner) derived from related crossing partners. Per definition, on a chromosome segment of 100 cM there occurs on average one crossing-over per meiosis. The maize chromosomes have lengths between 150 and 250 cM on most linkage maps. Consequently, common chromosome stretches that are shared by the hybrids are expected to be long and cover considerable parts of the chromosomes. For example, if a chromosome of length 200 cM consists of four long stretches in LD, then a marker distance of about 5 cM is expected to cover the recombination events on that chromosome with sufficient precision. Adding further markers is expected to increase the costs of genotyping without resulting in a substantial increase in the prediction accuracy. The average marker distances in our data set S4 suppoThe Roger’s distance is a measurement of the genetic distance between two populations based on the SNP composition of the individuals. It indicates how genetically close two populations are from each other and is related to heterosis and therefore often used in studies on hybrid breeding . On averThe prediction accuracies observed for the CV6designs ranged mostly between 0.5 and 0.8 3 and caWe conclude that unreplicated tests of small training sets and genotyping with a rather low number of markers allowed the screening of large numbers of selection candidates in our data set from early stages of a maize breeding program. Compared to phenotyping with replicated tests, the combination of unreplicated tests with genomic prediction seems to be a promising strategy for increasing the selection intensity.4.2In genomic prediction with replicated trials the phenotypic values are usually adjusted with a linear model. The resulting BLUPs of the genotypes (when regarding genotype as a random factor) or the least square means  are used for the subsequent genomic prediction. Such a procedure is not possible for unreplicated trials, because if each genotype is evaluated only in one plot, the genotypic effect cannot be separated from the error term, and neither BLUPs nor LSMeans can be estimated. We therefore estimated the effects of year and environment within year with Equation 1 using standard analysis of variance methodology, and then adjusted the unreplicated phenotypes for each hybrid with the effects of years and environments. This is a common procedure in evaluating unreplicated trials.4.3The statistical model in Equation 3 is a marker-based model that estimates testcross effects. Testcross effects cover additive gene effects and in addition a part of the dominance gene effects. The degree to which dominance effects are covered depends on the genetic separation of the two breeding pools. If the allele frequencies in the pools are differing to a large extent, then large portions of the dominance effects are included in the testcross effects. The fact that testcross effects include dominance is the backbone of hybrid breeding, and the only reason why hybrid breeding programs that employ testcrosses in early stages can successful realize selection gain. Our data set is from an ongoing commercial hybrid breeding program that employs testcrosses in early stages and the two pools are genetically separated  effects and the interaction effects between fathers and mothers are called specific combining ability (SCA) effects. The GCA and SCA effects of such a factorial can be predicted with so called ‘animal models’ where the marker information is used to establish genomic relationship matrices , an examInteraction effects between SNPs and environment were not included in our genomic prediction model. Omitting the correction for environmental effects (Equation 1) and including environmental effects on the individual level and genotype by marker interaction effects in the genomic prediction model could accomplish this. Such an analysis would require that all different chromosome stretches in linkage disequilibrium that are occurring in the plant material are tested in a comparable replication number in all environments. Only then the unconfounded estimation of marker effects, environmental effects and marker × environment interaction would be possible. Such an analysis would need a planned balanced experiment, specifically designed to detect marker × environment interactions. In our data set such a balancedness is not guaranteed, neither for the tested lines nor for the testers. Employing a model as described above to our data would result in a strong confounding of genetic, environmental and interaction effects. Due to the rather high environmental effects this could result in severe under- or overestimation of the performance of the hybrids. We therefore decided to employ a correction for environmental effects (Equation 1) and to not include environmental effects and their interaction with genomic effects in the model.4.4The effect of the prediction model on the accuracy of genome wide prediction was investigated in numerous studies e.g.,  but with4.5Test cross trials for the two heterotic pools of a hybrid breeding program are typically carried out in parallel. For example, in 2015 we tested at each of locations 1 and 2 the number of 106 Flint lines with a Dent tester. At locations 6 and 7 a total of 206 Dent lines were tested with four Flint testers Table 1.4.6In a stand-alone one year test, the training set designs CV3and CV6are the only options that can be employed. From a quantitative genetics point of view, these designs differ in the genetic meaning of the estimated marker effects. In the design CV3, the marker effects are estimating the genotypic effect plus the genotype by environment interaction effect. In the design CV6, the effects estimate the genotypic effect only (neglecting for both cases the factor year and its interactions). While in replicated trials, estimating the genetic effect only is expected to provide the more precise estimation of genotypes, the situation in unreplicated trials is different. Each hybrid is tested only in one location, and therefore including the appropriate genotype by environment effect in the marker estimate might result in more precise prediction of the phenotype than the genetic effect alone. From a statistical point of view, the greater size of the training set for the CV6design is expected to result in more precise estimates of the genetic effects, which is expected to result in a more precise prediction of the phenotypes ([13]). These considerations were the motivation to compare the prediction accuracy of the CV6design with that of the CV3design. The CV6training set design resulted for all environments investigated in greater prediction accuracy than the CV3training set design Figure 2We conclude that the greater precision of the effects estimated with the CV6training set design, consisting of several hundreds of hybrids, outweighs a putative positive effect from inclusion of the genotype by environment interaction in the CV3design. The CV6training set design delivers a sufficient prediction accuracy to be considered as an effective means to increase the selection gain in an unreplicated test cross trial.4.7Across year prediction using training data from previous years is, under the condition of a sufficient prediction accuracy, an efficient method for pre-selection of promising candidates before testing them in the field . In the We conclude that with a data structure similar to our investigated experiment, the prediction accuracy reached with across year prediction is not sufficient for effective pre-selection of promising hybrids.4.8The training set designs CV3and CV6can be extended by including into the training set data from one previous year, either from the same location  decreases the prediction accuracy compared to the initial training set design, while adding data from all locations (Designs CV5and CV8) increased it. For moisture in 2015, the situation is the opposite. For CV6based designs, adding same location increases the prediction accuracy while adding all locations decreases it. This increase can be explained by a more precise estimation of the phenotypes due to a more precise estimation of the marker by location effects. These results indicate that prediction accuracy in unreplicated trials is highly influenced by the year/environmental effects when considering across year predictions. Depending on the importance of the genotype by location effects in the considered years, adding one or all locations could decrease instead of increase prediction accuracy. Therefore there is a need to define in which conditions each approach should be privileged. From an application point of view, adding data from previous years to the training sets of designs CV3or CV6seems not to be advantageous due to the variability of its effect, which might increase or decrease the prediction accuracy depending on the situation.4.9Extending the training set designs to incorporate two years of previous data instead of one Figure 3When the environmental effects of the predicted year are not available in the training population, then using multiple years of previous data could have a similar effect than using multiple locations. A similar result was observed by The lack of improvement in precision for within environment and within year designs could be explained by the major contribution of the data from the current year to a precise estimation of genome by environment effect. In comparison to this major contribution, earlier years can only contribute marginally to the prediction accuracy.4.10The population size is often seen as one of the main components contributing to a high prediction accuracy . HoweverDesigns CV4and CV5compared to design CV3, and designs CV7and CV8compared to design CV6, have a much bigger population sizes (80 genotypes more for CV4and CV7and between 320 and 480 more for CV5and CV8) but show similar prediction accuracies. This result is confirmed when we consider prediction with data from two previous years. Here the number of extra genotypes is doubled while the prediction accuracy remains constant. Adding more genotypes from previous years to the training set of Design CV3or CV6do not increase the prediction accuracy, in most cases it even slightly decreases it.These results suggest that for a precise prediction of unreplicated test crosses in maize a high grade of relatedness among the selection candidates and similar environmental conditions are more important than the pure number of genotypes in the training set. Including data from loosely related material evaluated in environments with different genotype by environment to the training set designs might even result in a negative effect on the prediction accuracy.The data analyzed in this study is subject to the following licenses/restrictions: The data set is available on request from the authors. Requests to access these datasets should be directed to biometry.popgen@uni-giessen.de.MF conceived the study in collaboration with FR and CF. Analyses were performed by JT. The manuscript was written by JT and MF. FR and CF contributed to writing the manuscript. All authors contributed to the article and approved the submitted version.


We estimated individual differences in synaesthesia to be heritable and influenced by environmental factors not shared between twins. The association between individual differences in synaesthesia and autistic traits was estimated to be predominantly under genetic influence and seemed to be mainly driven by non-social autistic traits . Our study suggests that the link between synaesthesia and autism might reside in shared genetic causes, related to non-social autistic traits such as alterations in perception. Future studies building on these findings may attempt to identify specific groups of genes that influence both autism, synaesthesia and perception.Synaesthesia is a sensory phenomenon where external stimuli, such as sounds or letters, trigger additional sensations (e.g. colours). Synaesthesia aggregates in families but its heritability is unknown. The phenomenon is more common in people on the autism spectrum compared with the general population and associated with higher autistic traits. Using classical twin design, we assessed the heritability of individual differences in self-reported synaesthesia and the genetic and environmental contributions to their association with autistic traits within a population twin cohort ( For insSynaesthesia aggregates in families –9, and mAnother objective of our study was to gain insights into the link between synaesthesia and autism spectrum conditions, henceforth called ‘autism’. Autism is a neurodevelopmental condition, characterized by alterations in social communication and interaction, alongside restricted/repetitive patterns of behaviour, interests or activities . SynaestWe applied classical twin modelling in a general population twin sample in order to estimate the genetic and environmental contributions to synaesthesia and its covariance with autism. Classical twin models partition the factors explaining the variance in one measure and the correlation between measures into genetic and environmental components, and estimate the genetic and environmental correlations between them. We used dimensional definitions of both synaesthesia and autism. This approach is justified: (i) because it allows modelling more variance, (ii) because autism is defined as the extreme end of continuously distributed autistic traits  and https://www.people.vcu.edu/~bverhulst/power/power.html), we estimated that our study was well powered to detect an additive genetic component ‘A’ of around 0.50, but underpowered to find a smaller shared environmental effect ‘C’, which is a common issue in classical twin design [Data were collected from twins born between 1999 and 2003, participating in the Child and Adolescent Twin Study in Sweden (CATSS), a population-based twin study aiming to include all twins born in Sweden . The twin design . The stu (b) (i)After a short general description of synaesthesia, participants were screened for eight relatively common synaesthesia types, including examples of each type . More specifically, the questions included four types of sequence-colour synaesthesia; letter-colour, digit-colour, week-day-colour and month-colour synaesthesia. Further, the screening covered auditory-visual synaesthesia (sounds like music or noises triggering colour or shape experiences), sequence-space synaesthesia (where sequences such as the days of the week or the months of the year together build a certain shape in space), ordinal linguistic personification  and people-colour synaesthesia (associating people with specific colours). Response possibilities were ‘yes’ (scored as 1), ‘yes, to some extent’ (scored as 0.5), ‘no’ (scored as 0) and ‘I don't know/don't want to answer’ (coded as missing), leading to a maximum possible score of 8, and 17 possible scores. (ii)Autistic traits were assessed using an abbreviated version of the autism module of the Autism-Tics, attention-deficit/hyperactivity disorder (AD/HD) and other comorbidities inventory (A-TAC) , used as (c) (i)α). Participants answering less than 80% of a scale/sub-scale  did not contribute to analyses involving the scale in question. Variables were log-transformed (log (1 + x)) to reduce positive skew, regressed on birth year and sex, and finally the residuals were standardized.Variables were explored in terms of skewness, mean values and internal consistency (Cronbach's  (ii)ACE models, i.e. models that estimated the relative influences of an additive genetic component termed ‘A’, a shared environmental component, ‘C’, and a non-shared environmental component, ‘E,’ on the observed variance. A bivariate ACE model assesses the degree to which genetic influences are correlated between two phenotypes (referred to as ‘genetic correlation’), as well as the correlations between these phenotypes' environmental influences, and the degree to which the phenotypic correlation between the two traits can be explained by genetics and environment .For both the univariate (heritability of synaesthesia) and the bivariate analysis (association between synaesthesia and autistic traits), we fit These ACE models are based on the correlations between twins with regard to each trait (twin correlations), and the bivariate models are additionally based on the phenotypic correlation between traits within individuals, and the correlations between one trait on twin 1 to the other trait on twin 2 . Further details on the twin design are given elsewhere .Univariate quantitative genetic analysis. First, we tested the model assumptions that means and variances did not differ across twin order or zygosity . Second, we fitted a univariate ACE twin model to estimate the contributions of A, C and E on the individual differences in synaesthesia. Third, we tested the statistical significance of each component by comparing nested models to the ACE model.(1) Bivariate quantitative genetic analyses. In addition to the twin correlations for each phenotype, the CTCT correlations between synaesthesia score in one twin to autistic traits in the other twin were estimated from the constrained saturated models. Three bivariate correlated factors solutions ACE and nested models were then fitted to the data , and the statistical significance of each component was assessed by comparing nested models to the ACE model.(2) https://openmx.ssri.psu.edu/). Sex and birth year were regressed prior to running the models (see data preparation).All analyses were performed using R v.4.2.1, including the OpenMx package We performed a sensitivity analysis in order to check whether the results would be sensitive to the scoring method applied to the synaesthesia screening. More specifically, based on recommendations from a previous study that showed that some types of synaesthesia cluster together and might represent different expressions of one synaesthesia type , we repe. 3 (a)All variables were skewed and internal consistency was acceptable to good for the synaesthesia score and acceptable for autistic traits total and sub-scores . See als (b)The main results comprise two parts: first, univariate quantitative genetic results and second bivariate results of the associations between synaesthesia screening score and autistic traits . (i)a), hence DZ twins of the same and OS were collapsed in a single group in the ACE models.Twin model assumptions were met, since means and variances were not statistically significantly influenced by twin order or zygosity as revealed by assumptions testing . The correlations between the synaesthesia screening score between twins (cross-twin correlations) were stronger  in MZ twins  compared with both opposite  and same sex DZ twins , indicating genetic influences on the individual differences in synaesthesia. The twin correlations were similar for DZ twins of OS and DZ twins of the same sex a, hence b), additive genetics (A) were estimated to explain 46% (95% CI: 41–51%) of the variance in the synaesthesia screening score while the remaining 54% (95% CI: 49–59%) was attributed to non-shared environment (E).The ACE model fit was not significantly worse than the model fit of the saturated model of the observed data . Further, dropping the shared environment component (C) did not statistically significantly worsen the fit of the model, while the other nested models had a statistically significantly worse model fit . The AE model (where A and E are modelled but C is fixed to be zero) was, therefore, chosen as a more parsimonious alternative to the ACE model. In the AE model b, additi (ii)The phenotypic correlations (estimated from the bivariate models across zygosity groups) between synaesthesia screening score and autistic traits were similar for total autistic traits  and the RRBI-D sub-domain , but smaller for SIC as indicated by non-overlapping 95% CIs . The CTCT correlations between the synaesthesia screening score in one twin and RRBI-D in the other twin tended to be larger in MZ compared with DZ twins, but the 95% CIs overlapped slightly .Table 2For all three bivariate models, the AE model did not fit the data worse compared with the ACE model  and was hence selected. The genetic correlation between synaesthesia screening score and total autistic traits was estimated to be rA = 0.26 (0.17–0.36), with 71% (95% CI: 49–93%) bivariate heritability (i.e. the degree to which the phenotypic correlation between two traits can be explained by genetics) and the remaining 29% (95% CI: 7–51%) of the covariance being attributed to non-shared environment . The gen (iii)The results of the sensitivity analysis, were highly similar to the main results .. 4To the authors’ best knowledge, this study was the first to investigate the genetic and environmental architecture underlying individual differences in synaesthesia and their association with autistic traits, using classical twin modelling in a population-based twin cohort. Our results indicated genetic influence on individual differences in synaesthesia and additionally a significant role of non-shared environment in its aetiology. Moreover, the association between synaesthesia and autistic traits was estimated to be primarily explained by shared genetics, with some non-shared environmental contribution.Our findings of a genetic contribution to individual differences in synaesthesia are in line with findings that synaesthesia aggregates in families –9. FurthNon-shared environmental factors, which accounted for the remaining variance 54%), comprise all factors  that make twins more different from each other. This includes for instance infections or trauma affecting only one twin, differences between twins in social or school environments or engagement in certain activities. For example, people with synaesthesia were reported to engage more in creative activities [%, compriWe further found that the association between synaesthesia and autistic traits was primarily (greater than 70%) genetic, in line with the findings of familial aggregation of synaesthesia and autism ,19. Our In the current study, the phenotypic association between synaesthesia screening score and the RRBI-D domain of autistic traits was similar to its association with overall autistic traits (or even slightly larger). By contrast, the association between synaesthesia screening score and the SIC domain of autistic traits was estimated to be very small to negligible. These findings correspond to previous studies that found increased autistic traits in synaesthetes primarily on the attention to detail domain of the autism spectrum quotient (AQ) ,22. SimiOur findings that non-shared environment influences both synaesthesia and its link to autism also correspond to the outcomes of a previous study in a different, smaller twin cohort, showing that twins with more consistent grapheme-colour associations assessed with an objective synaesthesia test scored higher on the attention to detail domain of autistic traits, compared with their co-twins. This latter study underlined the importance of non-shared environmental factors in the association between the degree of synaesthesia and attention to detail, complementing the results of the current study methodologically.Some synaesthesia types cluster together more than others , and som. 5Since we only screened for a sub-set of common rather than all known synaesthesia types . Another limitation is that we have not included a negative control variable as has been done in other studies on the link between synaesthesia and autism, for instance assessing the link between synaesthesia and body mass index  or synae. 6In sum, we found individual differences in self-reported synaesthesia to be heritable and to be associated with autistic traits, especially the RRBI-D domain, while this association seemed to be primarily under genetic influence. We also found evidence for environmental factors not shared by twins to influence individual differences in synaesthesia and to some extent also its association with autistic traits. We hope that our findings will inspire future research on environmental factors influencing synaesthesia, the shared genetic mechanisms of autism and synaesthesia, and the specific behavioural features shared between the two phenomena, informing their shared and non-shared aetiology.


Harmful social behaviours, such as injurious feather pecking in poultry and tail biting in swine, reduce animal welfare and production efficiency. While these behaviours are heritable, selective breeding is still limited due to a lack of individual phenotyping methods for large groups and proper genetic models. In the near future, large-scale longitudinal data on social behaviours will become available, e.g. through computer vision techniques, and appropriate genetic models will be needed to analyse such data. In this paper, we investigated prospects for genetic improvement of social traits recorded in large groups by (1) developing models to simulate and analyse large-scale longitudinal data on social behaviours, and (2) investigating required sample sizes to obtain reasonable accuracies of estimated genetic parameters and breeding values (EBV).Latent traits were defined as representing tendencies of individuals to be engaged in social interactions by distinguishing between performer and recipient effects. Animal movement was assumed random and without genetic variation, and performer and recipient interaction effects were assumed constant over time. Based on the literature, observed-scale heritabilities (We developed models to simulate and genetically analyse social behaviours for animals that are kept in large groups, anticipating the availability of large-scale longitudinal data in the near future. We obtained promising accuracies of EBV with ~ 100 interactions per individual, which would correspond to a few weeks of recording. Therefore, we conclude that animal breeding can be a promising strategy to improve social behaviours in livestock.The online version contains supplementary material available at 10.1186/s12711-023-00840-z. Modern animal production systems, which are characterized by group-housing with more opportunity for animal movement and ample contacts between individuals, are believed to improve animal welfare. However, domestic animals regularly engage in social interactions, and the high demand for animal productivity has led to intensification of the pig/poultry industry. This intensification has brought new challenges for animal welfare and health, including an increase in harmful social interactions, such as injurious feather pecking in poultry and tail biting in swine.To reduce the occurrence of harmful social behaviours, management strategies can be applied. Physical alteration of animals (e.g. beak trimming in poultry or tail docking in pigs) has been widely applied, but this has undesired implications for animal health and welfare  and has Over the past century, animal breeding has made a substantial contribution to improving the efficiency of animal production . HoweverHowever, a limitation of these models is that they assume that each individual affects the phenotype of each of its pen mates equally. Therefore, they are particularly useful in the case of small groups, where animals have ample interactions with all their pen mates , 24. HowRadersma  presenteAssuming a single social tendency, as in Radersma , is not Genetic analysis of phenotypes that are a result of pairwise social interactions requires extensive records of the social interactions, including not only the event itself but also the identity of the participants. Recording all interactions between individuals in a large population used to be time- and labor-demanding, but it is gradually becoming feasible due to recent developments in animal detection and tracking techniques. By combining a variety of sensors and artificial intelligence (AI) algorithms, researchers have successfully detected animal behaviour in several species , 28. TheThe objectives of this paper were to (1) extend the social genetic model of Radersma  for geneFor each individual, two latent traits were defined to represent its tendency to be engaged in a behavioural interaction as a performer  i.e. when distinguishing As mentioned above, the variance of the BV and permanent environment effects were assumed equal for both traits Table . Three l2. Thus, pen size was 100 m2 in the default scenario (10 m × 10 m). For each pen, 20 feeders were uniformly located on two opposite sides of the pen . In each time step, an animal moved 1 m if it was motivated for walking, either in a random direction or towards a feeder when motivated for eating.Pen size followed from a fixed density of one animal per mTo obtain the behavioural pattern described in Fig. To evaluate the impact of the total number of individuals, we varied the number of pens included in the statistical analysis from 1 to 20 (where 20 was the default). Thus, the number of animals ranged from 100 to 2000. To accomplish this, we did not change the simulations but assumed data were available from a range for numbers of pens. Because families were distributed randomly across pens, variation in the number of pens included in the analysis resulted in variation in the amount of sib information included in breeding value estimation. In the default situation with 2000 individuals, an animal has own performance and records of four full sibs and 15 half-sibs. When the number of individuals was reduced to 100, an average individual had own performance and records on 0.2 full-sibs and 0.75 half-sibs.To evaluate the impact of the number of observed interactions per pen, we extended the simulated time until at least 100k interactions were obtained for each pen. The GLMM was then fitted by including the first 5k, 10k (default), 20k, 50k, or 100k interactions.2, irrespective of the number of groups.To evaluate the impact of group size, we randomly divided the 2000 individuals into 10, 20 (default), 50, or 100 groups. The corresponding group sizes were 200, 100 (default), 40 and 20 individuals. The average number of interaction events per individual was set at 100 for each group size. Hence, simulations were continued until the number of interactions recorded in each pen was at least 100 × group size. With Compared to the default scenario, three parameters were varied, one at a time: (a) the total number of individuals, (b) the number of observed interactions per pen, and (c) group size.Table ual see “” sectionmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimThe results also show that estimated genetic parameters did not differ from their true values . The average number of social interactions per individual was identical for the four group sizes. However, individuals interact with more pen mates in scenarios with a larger group size. Therefore, with a fixed total number of records, the average number of records per pair of individuals is smaller in larger groups. This can become problematic when the total number of records is small and a lot of zeros show up in the data, which may lead to poor estimation of probabilities and BV. In these cases, one could record more interaction events or use statistical techniques to overcome this issue.Group size had only a minor impact on the accuracy of EBV Fig. . In the We investigated prospects for genetic improvement of social traits recorded in large livestock populations with hundreds of animals. For this purpose, we developed an individual-based simulation method to generate data on social interactions in a population with individual genetic and permanent environmental effects, and with spatial movement of individuals over time. We also presented a GLMM to estimate genetic parameters and BV for performer and recipient effects from the simulated data. We chose input values to ensure that estimated observed-scale heritability agrees with typical values of the heritability for social behaviour traits found in the literature. Thus, we believe that our simulations reflect the magnitude of genetic effects in actual livestock populations. These low observed-scale heritabilities corresponded to relatively large genetic differences between individuals in the tendency to express a behaviour.The GLMM successfully estimated genetic parameters that were not significantly different from the true values. Results also showed a considerable accuracy of total EBV. With the half-sib family structure used in this study and the presence of permanent environmental effects, the maximum possible accuracy is 0.77 . when the amount of data is smaller, random variation in the number of encounters among pairs of individuals will be larger and the number of interactions per pair will deviate more from a normal distribution. Thus, when the amount of data is limited, we expect GLMM to be more suitable for dealing with the resulting nonlinear relationship between the frequency of social interactions and underlying BV. When the amount of data is large enough such that the interaction frequencies are close to a normal distribution and all pairs of individuals have ample interaction, we expect little difference between the accuracy of EBV for performer and recipient effects obtained from LMM and the GLMM we used here. Some additional results shown in Table We investigated the effects of the number of phenotyped individuals on the accuracy of EBV. For this comparison, we assumed a situation in which a large population is housed in several pens  and where the video monitoring system is installed by pen. We found that a larger number of phenotyped individuals resulted in a higher accuracy Fig. . Thus, eIn terms of the number of records, we found that ~ 100 interactions per animal yielded a fairly high accuracy of EBV (0.71) and increasing the number of interactions tenfold increased accuracy by only 7% (0.78). Given that automated phenotyping is still relatively complicated and costly, we suggest to increase the number of phenotyped individuals rather than increase the number of interactions recorded per individual . One way to achieve this is to equip a few pens with a system for automated behaviour detection, and to replace the individuals in those pens, for example, every few weeks. Nevertheless, a key challenge remains to collect a sufficiently large amount of annotated data to train AI algorithms for automated behaviour detection.We found a minor effect of group size on the accuracy of EBV. Although there was a statistically significant difference for one of the comparisons (n = 40 vs. n = 200), differences in accuracy were still minor. In larger groups, animals can interact with more group mates but the number of interactions for specific pairs will be smaller . Apparently, these two opposing effects largely compensate each other. Given the minor impact of group size on accuracy, we suggest that group size should mainly be determined by practical considerations.A variety of factors play a role in social interactions. However, due to the lack of quantitative data on these factors, it is difficult to incorporate them into the simulation at present. Therefore, we made a number of assumptions in this study. First, we assumed that animal movement is completely random and we did not simulate genetic differences in movement between animals. However, genetic differences in movement between individuals probably exist and more active animals will have more opportunity to interact with pen mates. Factors such as walking speed and resting time may also show large variation between individuals , 37, whiSecond, animals typically develop a hierarchy, which affects their role in social interactions, but we did not include this in this study. Animals may carefully assess their opponent and take into account what happened in previous interactions before deciding how to interact . MoreoveThird, we assumed that the (expected) frequency of social interactions is constant for a pair of animals, while it has been shown that social behaviours can spread across the group like an epidemic , 42. ThiAll above-mentioned factors have been proven to exist but there is still a lack of quantitative studies, so they were not included in the present study. However, rapid developments in AI and computer vision techniques promise to deliver quantitative information on these factors in the near future , 28. In Given the accuracy of EBV that we found and given the fact that relevant data will become available in the near future, genetic improvement appears to be a promising strategy to improve social behaviours in livestock. However, there is still lack of knowledge on the genetic basis of directional social behaviours and on the relationship between behavioural traits and other traits. Animals that show less harmful social behaviour may be less fearful or less stressed animals but could, for example, also be inactive or dim-sighted. Although the first results on selection for indirect genetic effects in pigs and laying hens suggest the opposite , it is nWe investigated prospects for genetic improvement of social traits. For this purpose, we developed methods to simulate social behaviours and to analyze the resulting data. Results showed unbiased genetic parameter estimates and promising accuracies of EBV of social tendency, even with only ~ 100 interactions per individual, which corresponds to a few weeks of recording. The analysis model is applicable to large-scale longitudinal data on behavioural interactions between animals kept in large groups. Given the facts that (1) phenotype data will become available in the near future and (2) genetic analysis of these phenotype data is feasible, we conclude that animal breeding can be a promising strategy to improve social behaviours in the future.Additional file 1: Text S1. Determination of rate of motivation for eat, walk and rest behaviour. The file explains how matrix Figure S1. Simulated behavioural pattern of the individuals.Additional file 2: Table S1. Estimation of genetic parameters, accuracy and bias of EBV from GLMM for all combinations of Additional file 3: Text S2. Estimation of the upper bound of accuracy in the GLMM. The file shows the calculation of the upper bound of the accuracy one could get with


Alternative splicing exists in most multi-exonic genes, and exploring these complex alternative splicing events and their resultant isoform expressions is essential. However, it has become conventional that RNA sequencing results have often been summarized into gene-level expression counts mainly due to the multiple ambiguous mapping of reads at highly similar regions. Transcript-level quantification and interpretation are often overlooked, and biological interpretations are often deduced based on combined transcript information at the gene level. Here, for the most variable tissue of alternative splicing, the brain, we estimate isoform expressions in 1,191 samples collected by the Genotype-Tissue Expression (GTEx) Consortium using a powerful method that we previously developed. We perform genome-wide association scans on the isoform ratios per gene and identify isoform-ratio quantitative trait loci (irQTL), which could not be detected by studying gene-level expressions alone. By analyzing the genetic architecture of the irQTL, we show that isoform ratios regulate educational attainment via multiple tissues including the frontal cortex (BA9), cortex, cervical spinal cord, and hippocampus. These tissues are also associated with different neuro-related traits, including Alzheimer’s or dementia, mood swings, sleep duration, alcohol intake, intelligence, anxiety or depression, etc. Mendelian randomization (MR) analysis revealed 1,139 pairs of isoforms and neuro-related traits with plausible causal relationships, showing much stronger causal effects than on general diseases measured in the UK Biobank (UKB). Our results highlight essential transcript-level biomarkers in the human brain for neuro-related complex traits and diseases, which could be missed by merely investigating overall gene expressions.The online version contains supplementary material available at 10.1007/s43657-023-00100-6. Alternative splicing is an essential mechanism in diversifying the genetic and proteomic landscapes in eukaryotes , have been successively discovered in large-scale genome-wide association studies (GWAS)  across brain tissues, where the isoform expressions were quantified using our previously developed isoform expression estimation method, X-matrix alternating expectation–maximization (XAEM) . We considered 13 brain tissues, consisting of 1,236 RNA sequencing samples from 172 individuals from the GTEx project  project  data for these individuals were obtained from the GTEx portal under version phs000424.v7.p1. There are 6,496,708 markers called from WGS, including 5,987,177 SNPs and 509,531 InDels. The final sample size used for QTL analysis is 1,191 samples, for which both RNA-sequencing and whole-genome sequencing data are available.Cis-eQTL are genomic loci near the corresponding coding genes that explain a fraction of the genetic variance of the gene expression phenotypes . Individuals with half or more than half of their genes having zero counts were removed for subsequent analyses, which reduced the original 1,671 viable samples in GTEx to the 1,236 retained samples we started with. For the principal component analysis (PCA) and QTL mapping, only variants with minor allele frequency (MAF) In this study, we identify irQTL for 13 brain tissues. For genes with multiple isoforms, isoform ratios were defined as TPM values of isoforms divided by their respective gene-level TPM. PCA was carried out on the genomic kinship matrix constructed via the whole-genome sequencing genotype data using PLINK  diseases with international classification of diseases (ICD) codings from Neale’s lab GWAS results. From the original 152 neuro-related traits, we removed the duplicated traits from different sources and highly correlated traits e.g., defined by alternative phenotype codings. This resulted in 114 neuro-related phenotypes for subsequent analysis. We conducted an MR analysis between the isoform ratios and 114 neuro-related traits and the 200 UKB diseases using the standard inverse-variance weighted (IVW) method for all the cis-irQTL and the MR Egger regression  Fig.  by the Gmentclass2pt{minimThe majority of the detected irQTL lead variants are centered at the transcription start sites (TSS) Fig. c, which p-values significantly deviated from the null . With FDR of less than 0.05, three brain tissues were found to be significantly associated with 13 neuro-related traits via the genetic regulation of isoform proportions per gene instead of gene expression levels . This phenomenon can be generalized to other composite phenotypes too. In general, we would need to go deeper into the specific genetically regulated phenotype  instead of only studying the composite phenotype .We decided only to use age, sex, and three PCs as covariates mainly due to two perspectives: (1) we aimed to control the number of covariates in such small-sample association analysis to save degrees of freedom, as long as the inflation factor can be well controlled, and we found that the current setting was sufficient Supplementary Fig. 3 Number of potential causal isoform biomarkers from MR for the UKB disease phenotypes. GWAS summary statistics for 200 disease phenotypes with ICD codes were used in the MR analysis. The results with FDR less than 0.05 were counted (PDF 6 KB)Supplementary Fig. 4 Uniformp-value distribution of the reverse causal inference analysis. To check reverse causality, generalized summary-statistics-based MR (GSMR) was applied to the complex traits as exposures and the isoforms as outcomes, where the cis-irQTL-based MR estimates were statistically significant. Fifteen randomly selected isoforms (for about 10% of the MR discoveries) were analyzed (PDF 150 KB)Supplementary Fig. 5 Quantile-quantile plot for the heritability enrichment signals across all the investigated tissue-trait associations. The significant enrichment tests with a false discovery rate less than 0.05 were highlighted in blue (PDF 969 KB)Supplementary Table 1 List of significant irQTL for the 13 brain regions. chr: chromosome; pos: base pair position; ref allele: reference allele; effect allele: coding allele; maf: minor allele frequency; n: sample size; beta: effect size; se: standard error; p: p-value (XLSX 269 KB)Supplementary Table 2 The number of mapped irQTL in each tissue compared to that of established sQTL. The irQTL lead variants were cross-referenced in the GTEx v8 sQTL analysis, and the irQTL that were also genome-wide significant sQTL were counted (XLSX 9 KB)Supplementary Table 3 Types of the genes involved in the discovery of irQTL. This gives an overview of the distribution of gene biotype groups across the genes used in irQTL analysis. A complete list of the genes and their corresponding isoforms is also provided (XLSX 743 KB) Supplementary Table 4 Stratified LD score regression analysis for the enrichment of neuro-related complex traits’ heritabilities at the reported irQTL. Enrichment_p and Enrichment_p_1s are the two-sided and one-sided p-values for the heritability enrichment fold parameter, and FDR the corresponding false discovery rate calculated based on all the one-sided p-values in the same tissue, testing whether the enrichment fold is greater than 1 (XLSX 273 KB)Supplementary Table 5 Summary of the significant MR results for neuro-related phenotypes. Significance was determined as a false discovery rate of less than 5%. Effect: irQTL effect; SE: standard error for the irQTL effect; beta: genetic effect on the complex trait; se: standard error for beta; MR effect: causal effect estimate of the isoform on the trait based on MR; MR se: standard error for the MR effect (XLSX 351 KB)Supplementary Table 6 Summary of the significant MR results for UKB disease phenotypes. Significance was determined as a false discovery rate of less than 5%. Effect: irQTL effect; SE: standard error for the irQTL effect; beta: genetic effect on the complex trait; se: standard error for beta; MR effect: causal effect estimate of the isoform on the trait based on MR; MR se: standard error for the MR effect (XLSX 447 KB) Supplementary Table 7 Comparison of heritability enrichment at irQTL genes and other coding genes for the traits with significant S-LDSC discoveries. Results for irQTL genes annotations are labeled with “irQTL”. Results for the other coding genes annotations are labeled with “other”. The last three columns give the enrichment fold estimates at irQTL genes relative to the other coding genes, where the standard errors were computed using the Delta method.2 (XLSX 14 KB)Below is the link to the electronic supplementary material.


We identified markers associated with GRD resistance after screening an Africa-wide core collection across three seasons in UgandaGroundnut is cultivated in several African countries where it is a major source of food, feed and income. One of the major constraints to groundnut production in Africa is groundnut rosette disease (GRD), which is caused by a complex of three agents: groundnut rosette assistor luteovirus, groundnut rosette umbravirus and its satellite RNA. Despite several years of breeding for GRD resistance, the genetics of the disease is not fully understood. The objective of the current study was to use the African core collection to establish the level of genetic variation in their response to GRD, and to map genomic regions responsible for the observed resistance. The African groundnut core genotypes were screened across two GRD hotspot locations in Uganda (Nakabango and Serere) for 3 seasons. The Area Under Disease Progress Curve combined with 7523 high quality SNPs were analyzed to establish marker-trait associations (MTAs). Genome-Wide Association Studies based on Enriched Compressed Mixed Linear Model detected 32 MTAs at Nakabango: 21 on chromosome A04, 10 on B04 and 1 on B08. Two of the significant markers were localised on the exons of a putative TIR-NBS-LRR disease resistance gene on chromosome A04. Our results suggest the likely involvement of major genes in the resistance to GRD but will need to be further validated with more comprehensive phenotypic and genotypic datasets. The markers identified in the current study will be developed into routine assays and validated for future genomics-assisted selection for GRD resistance in groundnut.The online version contains supplementary material available at 10.1007/s00122-023-04259-4. Arachis hypogaea L.) (2n = 4x = 40), also known as peanut, is an important cash and food crop worldwide  in many sub-Saharan African (SSA) countries, groundnut productivity is extremely low, owing to various biotic and abiotic challenges. One of the most important foliar diseases in SSA is Groundnut Rosette Disease (GRD), which is endemic to SSA and was first reported in Tanzania in 1907  are the principal transmission vectors for the GRD agents  breeding lines from nine African countries that were part of the African core collection were used in this study . Genotypes Ug-43_Oug-RED_BEAUTY_UG and Gh2-54_GhaII-NUMEX_03 were used as susceptible checks, while Ug-41_Oug-DOK_1_RED_UG and Ug-194_Oug-ICGV_90099 were used as resistant checks (Supplementary Table 1).Field evaluation was done in Eastern Uganda at two GRD hotspot locations, Serere and Nakabango  at the ith observation and n is the total number of observations  using the formula:Yijk is the kth observation for the ith genotype; µ is the overall mean; Gi is the Genotype effect, Rj is the replication effect while R/Bjk is the effect of blocks nested in replicates, respectively; εijk is the error term associated with Yijk.Best linear unbiased predictions (BLUPs) and thereafter variance components within environments were estimated in the lme4 package  for GRD using the formula:σ2g is the genetic variance component and σ2e is the residual (error) component and nr is the number of replications.BLUP variance components estimated within environments were appropriated to calculate Broad-sense heritability  in Senegal West Africa. Thinning was done to retain one plant per genotype. Twenty mg of oven-dried young leaves from a single plant were collected 21 days after planting. DNA was isolated using the MATAB protocol . The raw SNPs were filtered at a call rate > 0.95 and minor allele frequency > 0.05. The distribution of the final filtered high-quality SNPs was plotted across the chromosomes using CMplot ; β represents unknown fixed effects, as well as population structure and marker effects; u is a vector of size s (number of groups) for unknown random polygenic effects following a distribution with mean of zero and covariance matrix of K is the group kinship matrix with element i and j, and X and Z are matrices for β and u while e is a vector of random residual effects that are normally distributed with zero mean and covariance I is the identity matrix and Marker trait associations (MTAs) were calculated by combining the filtered SNP dataset of the genotypes with the corresponding BLUPs in R software using the Genome Association and Prediction Integrated Tool (GAPIT) version 2 package  (Benjamini and Hochberg P < 0.05). Candidate genes were identified within 250 kbp distance of the significant marker using Arachis duranensis and Arachis ipaensis reference genomes. Information on the location of the genes and their annotations were obtained from the A. ipaensis, A. duranensis and annotation files (https//peanutbase.org/).The resulting associations were displayed as Manhattan plots alongside quantile–quantile (Q–Q) plots to demonstrate the model fitness using qqman package in R Turner . The P vR using package DescTools  were observed among the genotypes for AUDPC across all the Nakabango trials (Table P < 0.05) among the genotypes in seasons 2020A and 2020B. There were no significant differences observed for Serere 2021B. The broad sense heritability was low (0–30%) for environments Serere 2020B and Serere 2021B; moderate (31–60%) for environments Serere 2020A, Nakabango 2020A, 2020B and high (> 60%) for Nakabango 2021B. The frequency distribution graphs for AUDPC showed near normal distribution for environments Serere 2020A and Nakabango 2020A while for Serere 2020B and 2021B, AUDPC values were skewed to the right  high quality SNP markers were retained that had a genotype call rate > 0.95 and Minor Allele Frequency (MAF) > 0.05. The SNPs were well distributed across the 20 chromosomes . GWAS for Serere 2020A and 2020B yielded no significant markers  and for that reason, were not used for any further analysis or data interpretation. All the results presented below are for Nakabango.Due to low disease pressure in Serere that resulted in the lack of significant genetic variation in the response of genotypes to GRD for season 2021B, this dataset was not included in the GWAS analysis. Both the genotypic and phenotypic datasets that were used for GWAS have been made available at this link (P < 0.05) were detected in at least one season and/or from combined seasons in Nakabango (Table R2 ranged from 0.25 to 0.29 (2020A), 0.34–0.42 (2020B), 0.32–0.38 (2021B) and 0.40–0.49 (combined dataset) (Supplementary Table 2). The combined dataset picked up an association with one additional SNP that was not detected with any of the single season’s data (AX-147219934).Thirty-two significant marker-trait associations (MTAs)  while the remaining 130 genes were from the B sub-genome (Araip) (Supplementary Table 4). Of the 383 candidate genes identified, 62 (43 from sub-genome A and 19 from sub-genome B) were unknown proteins while an additional 37 (31 from sub-genome A and 6 from sub-genome B) were uncharacterized (Supplementary Table 4). A total of 17 markers were localized within genes, 10 on the A sub-genome and 7 on the B sub-genome (Supplementary Table 4). Two markers from sub-genome A, AX-147219924 and AX-147219925, were localized within a disease resistance protein  as shown in Fig. We identified a non-redundant set of 383 genes within 250 kbp of all significant SNPs, of which 253 were from the A sub-genome  resistance using a diverse set of cultivated groundnut germplasm that had been carefully selected using genotypic data and breeders’ preferences. We identified significant Marker Trait Associations (MTAs), favourable haplotypes and candidate genes tightly linked to significant markers. Our key findings reveal important aspects on choice of germplasm, environments, molecular markers, the identification of candidate genes and their implications on future genetic and genomic studies for GRD resistance in groundnut.We have demonstrated that the African core collection was suitable for the identification of GRD resistance loci. We also captured the predominant groundnut market classes used across Africa. Collections with useful diversity such as core and mini core sets have been recommended as more appropriate for association studies as a result of numerous rounds of historical recombination  and increased disease pressure  and A. ipaensis (sub-genome B)  is the non-random association between alleles at different loci in a breeding population and is the result of interplay of several factors including linkage, population structure, relatedness, selection and genetic drift  and high (68%) broad sense heritability (BSH) estimates reported for Nakabango location in our study was lower than in other studies that had fewer germplasm, but also with enhanced disease pressure either using artificial inoculation  genes, which encode mostly nucleotide-binding site and leucine-rich repeat (NBS-LRR) proteins (Dangl and Jones Avr proteins such as in the tobacco mosaic virus (TMV)  , a gene silencing mechanism deployed by plants to fight viral infections by hindering expression of genes during and post transcription , Rapid Generation Advance (RGA), digital data capture, precision phenotyping, as well as gene editing should be deployed appropriately to speed up the varietal development process and enhance our understanding of this disease.Supplementary file1 (DOCX 998 KB)Supplementary file2 (XLSX 16 KB)Supplementary file 3 Table 2: A summary of significant marker trait association for 2020A, 2020B, 2021B and combined analysis.Supplementary file4 (XLS 33 KB)Supplementary file5 (XLSX 31 KB)Below is the link to the electronic supplementary material.


Hnf1a (MODY3 gene in humans), as a strong candidate. The pleiotropic effects of meQTL.5a could contribute to variations in body size and metabolic traits, and influence CpG methylation and epigenetic ageing that could have an impact on lifespan.DNA methylation is influenced by genetic and non-genetic factors. Here, we chart quantitative trait loci (QTLs) that modulate levels of methylation at highly conserved CpGs using liver methylome data from mouse strains belonging to the BXD family. A regulatory hotspot on chromosome 5 had the highest density of trans-acting methylation QTLs (trans-meQTLs) associated with multiple distant CpGs. We refer to this locus as meQTL.5a. Trans-modulated CpGs showed age-dependent changes and were enriched in developmental genes, including several members of the MODY pathway (maturity onset diabetes of the young). The joint modulation by genotype and ageing resulted in a more ‘aged methylome’ for BXD strains that inherited the DBA/2J parental allele at meQTL.5a. Further, several gene expression traits, body weight, and lipid levels mapped to meQTL.5a, and there was a modest linkage with lifespan. DNA binding motif and protein–protein interaction enrichment analyses identified the hepatic nuclear factor,  Genome-wide patterns in DNA methylation (DNAm) are established during development and are critical for cell differentiation and cell identity . The canAlong with ageing and modifications by extrinsic factors, CpG methylation can also be influenced by underlying genetic sequence variants. Several studies in humans have identified genetic loci that are associated with DNAm . AnaloguModel organisms provide a powerful tool for interrogating the interactions between meQTLs, eQTLs, and experimental conditions such as diets and drugs. However, although genome-scale meQTL studies in humans date back to the 2010s , there iBXDs are a family of recombinant inbred (RI) and advanced intercross (AI) mouse strains. We have previously described the BXDs in greater detail . In brieHnf1a, as one of the important candidate genes in meQTL.5a. In humans, mutations in HNF1A results in MODY3 (maturity onset diabetes of the young 3) . Th. ThTet3,ethylome . For then by HFD . These ty weight . OverallDD genotype in meQTL.5a had higher average methylation than the BB genotype for both diet groups  or DD (n = 972), and tested two predictions: (1) that the BB genotype in meQTL.5a will be associated with higher body weight at age 6 months, and (2) although higher body weight is associated with shorter lifespan, we predicted that at this locus, based on a more ‘aged methylome’, the DD genotype will have slightly shorter lifespan. As the longevity cohort has no DNAm data, we could not directly verify whether the DD in this group indeed has a more aged methylome, and this is purely an assumption based on the meQTL data. As predicted, the BB genotype had significantly higher mean body weight . Also, consistent with prediction, the DD genotype was associated with a slightly higher risk of death between the ages of 650 and 1100 days compared to the BB genotype, but only in the CD group . This small difference in the CD group resulted in a median lifespan of 702 days and maximum LS of 1250 days in the BB genotype (n = 399), compared to a median of 685 days and maximum of 1197 days in the DD genotype (n = 510). Using a multivariable regression model with genotype, diet, and body weight at 6 months as predictors, we find that the strongest predictors of lifespan were weight at 6 months, followed by diet, and then by the meQTL.5a genotype (BB genotype becomes more apparent when we adjust the longevity data for the weight at 6 months (BB mice were predicted to have a median lifespan that was 46 days longer than the DD mice (log-rank p < 0.0001). In HFD, the median lifespan of BB was only 7 days longer than that of DD mice (log-rank p < 0.03). Our results suggest a pleiotropic effect of a genetic locus on multiple traits that are also modified by diet and ageing. Here, we use the terminology by Tyler et al. [BB) having a slight lifespan advantage.We obtained bodyweight and lifespan data from a different BXD cohort that were allowed to survive until natural mortality . We segrgenotype . Given t6 months . For ther et al. ,80, and Hnf1a’ as the term in the PheWAS search [Hnf1a locus, and not to a Hnf1a allele. At -Log10p ≥ 3, there were 10 BXD traits that included one immune related phenotype, four traits related to the nervous system and five metabolic traits related to fat content and amino acid ratios (Data S6). The brain-related traits were also little distant from meQTL.5a  [BB genotype had higher liver fat content.The BXDs have accrued a vast collection of traits over decades, and we next performed a phenome-wide association analysis (PheWAS) to identify other higher-order traits that may be modulated by the meQTL.5a interval . Note thS search , the resd ratios . The stro et al.  and at 1o et al. ). The me Fig S8) . AlthougHNF1A using two GWAS databases: GWAS Atlas, and the NHGRI-EBI GWAS Catalog [p < 1e-05, the GWAS Atlas identified 85 traits, and the GWAS Catalog identified over 400 traits associated with variants in HNF1A and HNF1A-AS1, the antisense RNA transcribed from HNF1A . The trait with the strongest association was C-reactive protein levels, a measure of inflammation and cardiovascular health, followed by levels of Gamma glutamyl transpeptidase (GGT), a measure of liver damage [HNF1A included age at puberty, birth weight, and diabetes [HNF1A (rs6489785) is reported to be one of the 37 ‘longevity SNPs’ that have a small effect on human lifespan [Complementing the murine family-based PheWAS, we also searched for GWAS hits associated with variants in  Catalog ,90. At mr damage . These wr damage . Other tdiabetes . While nlifespan . This isWe have provided an overview of the regulatory loci that influence methylation of conserved CpGs in the murine liver. Overall, the results show complex interrelationships and genetic pleiotropy that influence DNA methylation and physiological traits. As expected, cis-meQTLs are associated with higher LOD scores compared to the trans-meQTLs . Given tThe cohort we used for the meQTL mapping consisted of 41 BXD progeny strains, F1 hybrids, and the parent strains, and had a variable number of biological replicates per genotype. Due to this, we performed the meQTL mapping using a stringent regression model that adjusted for age, diet, weight, and genetic relatedness, and corrected for unmeasured variance by including the top principal component as a cofactor. The use of linear mixed models improves both precision and power . NonetheOasl2, Srsf9, Pxn, Rab35, Cit, Prkab1). Among these, Prkab1 codes for the regulatory subunit of AMP-activated protein kinase (AMPK), and the AMPK signalling pathway, along with the Sirtuins, play important roles in metabolic regulation and ageing, and possibly in epigenomic maintenance [Sirt4 and Coq5, both of which map as strong cis-eQTLs in the liver [Oasl2, involved in the interferon-induced signalling pathway [Hnf1a as a functionally highly relevant prime suspect in meQTL.5a, and one of the potential links between development, ageing, and the epigenome.The meQTL hotspot Eaa19 is also noteworthy. Eaa19 is linked to epigenetic clock acceleration, and potentially influences susceptibility to entropy accumulation in the liver methylome . In our ntenance ,105. Thehe liver ,75. Anot pathway , forms aKdm6b, Prdm16, Sox2, Foxp2, Esrrb, Tet3, Foxa2, etc.). The prime candidate in meQTL.5a, Hnf1a, is a member of the hepatocyte nuclear factor family of TFs, and primarily expressed in the liver, kidney, and pancreas [Igf1 (insulin-like growth factor 1) [HNF1A, is the most common form of maturity onset diabetes of the young [HNF1A mutations also lead to dysregulation in fatty acid synthesis and transport, which can cause fatty acid accumulation in the liver [HNF1A (rs6489785) is one of the 169 variants that jointly contribute to human longevity [HNF1A do not cause MODY but increase the susceptibility to type 2 diabetes and lower BMI [Hnf1a causes Laron dwarfism and hyperglycaemia [The list of genes with CpGs trans-modulated by meQTL.5a included several key players in early development and cell differentiation  . MODY3, he young ,110. HNFongevity . Some muower BMI ,112. In lycaemia .Hnf1a in mice causes a change in the local chromatin structure and affects the spatial location of its target regions in the nucleus [Hnf1a has a modest cis-eQTL that is associated with a positive additive effect for the B allele at meQTL.5a.Although HNF1A is not a direct regulator of DNAm, there is some intriguing evidence that it contributes to the epigenetic state. For instance, deletion of  nucleus . Further nucleus . This su nucleus ,25. In t nucleus . The expJarid2 CpG and the mean methylation of the trans-CpGs in D variant of HNF1A has a lower DNA binding affinity, and the BXD strains with DD at meQTL.5a could begin life with heightened methylation at the target sites. If we consider this in terms of epigenetic entropy, then a hypomethylated state presents a low-entropy landscape [DD genotype, however, the methylation beta-values at these CpGs will be closer to 0.5 compared to BB, and will approach a more random epigenetic state at an earlier age compared to strains that have a BB genotype at meQTL.5a. In other words, rather than differences in the age-dependent rate of change, we posit that these trans-CpGs have baseline variation in methylation that is dependent on the genotype at meQTL.5a. Although speculative at this point, the baseline variation in DNAm may be due to differential binding affinities of HNF1A variants at the downstream targets that overlap the trans-CpGs, many of which are in or near other developmental genes.Many of these CpGs that are trans-modulated by meQTL.5a are characterized by a low methylation profile (‘hypomethylated’ with methylation beta-scores closer to 0) and an increase in methylation with ageing  ,119. Accin vivo. ,123Hnf1a gene is that the promoter and first intron overlap the long non-coding RNAs (lncRNA), Hnf1aos1 and Hnf1aos2 [Hnf1a  is also seen in Hnf1a-null mice, and HNF1A variants in humans. Generally, when downstream targets of Hnf1a are deleted, it results in smaller stature and longer lifespan in both humans and mice. For instance, deficiency in growth hormone or IGF1 confers longer lifespan and healthspan [Hnf1a in mice or deleterious mutations in HNF1A in humans do not appear to confer any lifespan advantage despite the mice having a form of Laron dwarfism and humans have lower BMI [In the BXDs, low body weight at a young age predicts longer lifespan and slower epigenetic ageing ,37,130. althspan ,132. In althspan ,134. Moualthspan ,136. Howower BMI .D allele increasing methylation at sites that typically have low methylation when young, and the B allele increasing body weight and lipid levels. The methylation of the target CpGs, which are also under convergent influence of ageing and diet, then contributes to variation in survival trait, with the D allele associated with slightly shorter lifespan. In this model, lifespan is a distal complex trait that shows only a modest linkage to meQTL.5a, while the intermediate traits (the CpGs) have a stronger linkage.We suggest that HNF1A, in addition to its role as a TF for developmental and metabolic genes, also influences the epigenome early in life, and contributes to epigenomic maintenance in adulthood and ageing. We present a model in which the meQTL.5a locus exerts horizontal pleiotropic effects on physiological traits and CpG methylation. The pleiotropic influence results in the Hnf1a is a key candidate in this locus, and the potential influence of the HNFs on the epigenomic state during development could contribute to ageing and longevity.In conclusion, we have identified meQTL.5a as a trans-meQTL hotspot that modulates several CpGs in trans. The pleiotropic effect of meQTL.5a could contribute to variations in body size, metabolic traits, CpG methylation and lifespan. Data S1. Detailed annotations of the CpG probes including probe sequence, genomic coordinates and locations, gene annotations, and local CpG density (computed using the ‘Repitools’ R package) [Data S2.The data we use in this study have been previously reported, and the full data are available from NCBI Gene Expression Omnibus (GEO accession ID GSE199979) . In briepackage)  are provData S12. We performed QTL mapping using a univariate linear mixed model that accounts for genetic relatedness. We first computed genotype probabilities and employed that to obtain genetic relatedness matrices (GRM), or the kinship, using a Leave One Chromosome Out (LOCO) scheme. Genome scans included age, diet and the top PC as covariates (variables provided in Data S1) and were implemented using a ‘scan1’ function with genotype probabilities as input while adjusting for relatedness outside the chromosome of interest. We next estimated the genetic effects, and genetic directions between the two genotypes were computed as (DD – BB). The R codes are provided as Supplemental information (Data S13).Each CpG was mapped against 7127 informative autosomal genotype markers distributed across the autosomal chromosomes using the R/qtl2 software . The fulData S14).We used the WGCNA R package to cluster the CpGs into inter-correlated modules . The fulData S6. The QTL mapping for the higher-order traits identified by the PheWAS was also done using GEMMA, and for these, the data are at the strain levels , and instructions on trait retrieval are provided in Data S6.Aside from the main meQTL mapping that was done using R/qtl2, additional QTL analyses were done on the web platform, GeneNetwork, which provides interface to few different mapping algorithms ,57. We uTable S1). In addition to genomic locations, the CpGs have been classified as differentially methylated by age, diet  and body weight based on a multivariable epigenome-wide association analysis [Table S2). All other statistical tests  were done using JMP (version 16).As previously described, we have annotated each CpG by genomic context  and chromatin state ,49,50. Fanalysis . The freFor the meQTL.5a trans-modulated CpGs, and CpGs in the Blue module, biological functions and transcription factor motif enrichment analysis were done using the R package for Genomic Regions Enrichment of Annotations Tool  ,59. The Hnf1a locus in the BXDs [HNF1A as the search term and retrieved GWAS hits from two databases: the GWAS Atlas, and the GWAS Catalog [For candidate gene selection, we searched for cis-eQTL in the BXD liver RNA-seq data using the GeneNetwork search tool . To identhe BXDs ,82. For  Catalog ,90.Click here for additional data file.Click here for additional data file.


Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative. The aim of transcriptome-wide association studies (TWAS) is to find genetic relationships between the expressions of genes and specific human traits of interest, and they do so using large-scale genetic association results from existing studies of those traits. However, the statistical methods traditionally used to perform these TWAS studies do not directly test such genetic relationships, and in this study we examine the implications of this limitation. Using both simulations as well as analyses of real data, we demonstrate that when traditional TWAS methods are interpreted as testing a genetic relationship between gene expression and a trait, this results in a considerable inflation in the false positive rates for these analysis. This suggests that these TWAS methods cannot reliably be used to study such genetic relationships, with their statistically valid interpretion being much more limited. We also show that methods such as local genetic correlation analysis can serve as potential alternative. Transcriptome-wide association studies (TWAS) –8 constiFrom a significant TWAS association, it would be tempting to conclude that there is a genetic relationship between the expression of the tested gene and the phenotype of interest, and this interpretation is indeed commonly found in applied TWAS studies –13. Suchpredicted genetic component of the gene expression, without accounting for the uncertainty in that prediction ), excGenotype data for simulations came from the UK Biobank , a natiohttps://ctg.cncr.nl/software/magma) was used as genotype reference data to estimate LD for the real data analysis for both LAVA and FUSION, removing SNP with a minor allele frequency (MAF) below 0.5%. For eQTL data we used the GTEx [The European panel of the 1,000 Genomes  data    = 0. The simulations were based on the UK Biobank data, generating gene expression and outcome phenotypes while varying their sample sizes and the local heritabilities, as well as the number of SNPs per gene. The number of SNPs K in the genes was varied across values of 100, 500, 1,000 and 2,500 SNPs; sample sizes of either 250 or 1,000 were used for the gene expression (denoted NE), and 10,000, 50,000 or 100,000 for the outcome phenotype (denoted NY); and local heritabilities, defined as the percentage of variance explained by the SNPs in the gene, were set at either 1%, 5% or 10% for the gene expression  , and 0.0N used in the simulations, the first N individuals from this subset were used for that sample, to ensure that samples of different sizes are all nested. The MAF for all SNPs was computed based on the N = 100 subsample, discarding all SNPs with MAF below 5%. Since the subsamples are nested, this ensures that all SNPs have a sufficient minor allele count at every sample size used. Ten blocks of 2,500 consecutive SNPs were then selected to represent simulated genes, one block each from around the middle of the first ten chromosomes (avoiding the centromere region). The number of SNPs K was varied across conditions by using only a subset of these 2,500 SNPs, always selecting the first K SNPs from each block. For each simulation condition, the total number of iterations for that condition was evenly distributed over these ten blocks, and results were aggregated over the blocks. Type 1 error rates per condition were computed as the proportion of iterations for that condition for which the p-value was smaller than the significance threshold, which was set to 0.05 unless otherwise specified.To create the samples to use, we first drew a random subset of 100,000 individuals from the UK Biobank data. For each sample size E and outcome phenotype Y for the standardized genotypes X of the SNPs in a gene, X was projected onto standardized principal components W, pruning away redundant components based on the cumulative genotypic variance explained by the components . For each iteration, true genetic effect sizes δE and δY for the principal components under the null hypothesis of cov = 0 were generated by drawing values from a normal distribution, then regressing one vector on the other and retaining only the residuals for the outcome vector to ensure that δE and δY were exactly independent. The two genetic components were then computed as GE = WδE and GY = WδY, and both were subsequently standardized to a variance of one.To simulate gene expression E = WδE+ξE and Y = WδY +ξY, drawing the residuals ξE and ξY from normal distributions with variance parameters equal to E and Y were then regressed on each individual SNP in X using simple linear regression, and the resulting test statistics as well as the simulated E and Y themselves were stored for subsequent analysis.Simulated gene expression and phenotype values were then generated as K = 1,000, NE = 1,000 and the remaining parameters across their full range, was generated and analysed with all methods: LAVA-TWAS, LAVA-rG, FUSION-BLUP, FUSION-BSLMM, FUSION-ElastNet, FUSION-LASSO and CoMM. LAVA analyses were performed using the summary statistics as input, FUSION analyses with the raw gene expression values for the first stage and outcome phenotype summary statistics for input, and CoMM using the raw values for both gene expression and outcome phenotype. For the first stage of FUSION, cross-validation was turned off and the true K being equal to NE, and the simulations were rerun setting K to 100 instead to resolve this. Moreover, an additional set of simulations was performed with To compare the different methods, a baseline set of simulation conditions with 1,000 iterations, with K = 1,000, NE = 1,000, For LAVA-TWAS, further of simulations with 10,000 iterations were performed across the full range of parameter settings for analysis, to evaluate the impact of these parameters on type 1 error rates. To determine the effect of filtering genes on the presence of eQTL signal, as is common practice in TWAS analyses, for these simulations we also computed the LAVA univariate p-value for the gene expression. In addition to the regular type 1 error rates, filtered type 1 error rates were also computed based on only the iterations for which the univariate p-value was below either 0.05 or 0.0001. Finally, to allow for reliable computation of type 1 error rates at lower significance thresholds, for the subset of these conditions with δE and the corresponding GE, from which E was then simulated. This was used to estimate SNP summary statistics for the associations between X and E, from which the estimate vector δY was then generated to be exactly independent of this Additional simulations were also performed under the TWAS null hypothesis of TWAS and local genetic correlation analyses were performed on the GWAS data as follows, separately for each of the five phenotypes. In all the analyses, SNP filtering was applied to remove all SNPs with a minor allele frequency below 0.5%, and only SNPs available in the 1,000 Genomes data, the GTEx data, and the GWAS sample for that phenotype were used.For every gene, univariate LAVA analysis for the eQTL signal, LAVA-rG and LAVA-TWAS analyses, and FUSION analyses with the Elastic Net and LASSO models was performed. For FUSION, the BLUP and BSLMM models were not used as these were not available in the precomputed model files. FUSION univariate p-values for the eQTL signal were also obtained from these model files. Note that separate SNP filtering was applied prior to these models being computed, and as such the LAVA and FUSION analyses are based on different sets of SNPs per gene.−6  of the LAVA-TWAS model for the null hypothesis of no genetic covariance  = 0), at different levels of local heritability (h2) for outcome phenotype . Results are shown for sample sizes of 1,000 and 10,000 for the expression and outcome respectively, at 5% local heritability for the expression. As shown, the number of SNPs interacts with the local heritability of the outcome, with lower numbers of SNPs resulting in a lower type 1 error rate at lower heritability, but increasing more rapidly as a function of heritability than conditions with higher numbers of SNPs. Fig B. Type 1 error rate simulation results as a function of univariate input filtering. Shown is the type 1 error rate (at significance threshold of 0.05) of the LAVA-TWAS model for the null hypothesis of no genetic covariance  = 0), at different levels of local heritability (h2) for outcome phenotype  and different gene filtering settings. The univariate filtering is based on the test of genetic signal for the gene expression implemented in LAVA, applying either no filtering or subsetting to iterations of the simulation with univariate p-value smaller than 0.05, 0.001 or 0.0001  before computing the type 1 error rate on the remaining iterations. Results are shown for a sample size (N) of 1,000 and a local heritability of 5% for the expression, and 1,000 SNPs. As shown, filtering at a univariate p-value threshold of 0.05 has little effect, but at the stricter thresholds the type 1 error rate becomes further increased. Fig C. Type 1 error rate simulation results for LAVA-TWAS under the TWAS null hypothesis. Shown is the type 1 error rate (at significance threshold of 0.05) of LAVA-TWAS under the TWAS null hypothesis GE, GY) = 0, Fig D. Type 1 error rate simulation results for FUSION models. Shown is the type 1 error rate (at significance threshold of 0.05) of the four penalized regression models in FUSION for the null hypothesis of no genetic covariance  = 0), at different levels of local heritability (h2) for outcome phenotype  and gene expression (separate lines). Results are shown for sample sizes of 1,000 and 100,000 for the expression and outcome respectively, with 1,000 SNPs, thus corresponding to the results for LAVA-TWAS shown in the bottom-right panel of Fig E. Type 1 error rate simulation results for CoMM. Shown on the top row is the type 1 error rate (at significance threshold of 0.05) of CoMM for the null hypothesis of no genetic covariance  = 0), at different levels of local heritability (h2) for outcome phenotype  and gene expression (separate lines). Results are shown for sample sizes of 1,000 for the expression, and sample sizes of 10,000 (left) and 100,000 (right) for the outcome. Results in the top-right panel thus correspond to the LAVA-TWAS results in the bottom-right panel of Fig F. P-value bias in the real data analysis for BMI. Shown is the relation between the -log10 p-values of LAVA-TWAS model against those of the LAVA-rG model, demonstrating the bias in the LAVA-TWAS p-values as a result of its failure to account for the uncertainty in −10 to better visualize the region around the Bonferroni-corrected significance threshold . In green the genes significant for both LAVA-TWAS and LAVA-rG, in red the invalid associations significant in LAVA-TWAS only. Fig G. Comparison of -log10 p-values for different implementations of LAVA-rG. P-values in the published implementation of LAVA-rG are computed using a partially empirical, sampling-based approach. Shown here is a comparison with p-values computed using a normal approximation, results for which are used throughout this paper rather than those based on the empirical p-values. Here a comparison is given of all the LAVA-rG p-values from the simulations (left) and real-data analysis (right). As shown, these p-values are virtually identical, and as such results based on either approach can be considered interchangeable. Fig H. Type 1 error rate simulation results for FUSION models with post hoc permutation test. Shown is the type 1 error rate (at significance threshold of 0.05) of the four penalized regression models in FUSION for the null hypothesis of no genetic covariance  = 0), at different levels of local heritability (h2) for the outcome phenotype . Results are shown for 5% heritability for the gene expression, and sample sizes of 1,000 and 100,000 for the expression and outcome respectively, with 1,000 SNPs. Significance is declared either when the permutation p-value is below 0.05 (black lines), or when the permutation p-value and the main analysis p-value are both below 0.05 (yellow lines). As shown, type 1 error rates are generally well controlled for the Elastic Net and LASSO models, and well controlled for the Bayesian LMM model when using both p-values, though for the BLUP model inflation remains even when using both p-values. Table A. Summary of results of TWAS and local genetic correlation analyses of published summary statistics for five phenotypes, with FUSION univariate filtering. Table B. Summary of results of TWAS and local genetic correlation analyses for genes with no detectable eQTL signal. Table C. Summary of results of FUSION permutation tests for published summary statistics for five phenotypes.Contains additional derivations and explanation of the core properties of TWAS and related tests and models, as well as figures and tables with additional results. (DOCX)Click here for additional data file.