Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank  with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15  to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice. However, these understandings and predictions are bounded by the heritability of the traits, and for many complex traits, large phenotypic variation remains unexplained, suggesting substantial environmental contributions to phenotypic variance.Both genetic and environmental factors underlie phenotypic variance of complex traits. Understanding the influences of these factors not only helps explain why individuals differ from one another in phenotypes but also helps predict future phenotypes, such as disease diagnoses. The proliferation of genotypic data in the past decades, along with developments in relevant analytic tools, have already contributed a great deal to understanding phenotypic variations of complex traits15. Similar to genotypes, exposomic variables are standardised across cohorts16. Since the inception of the concept, considerable efforts have been made to assess and characterise the exposome17. For example, the Human Early-Life Exposome project is a European collaborative effort established to characterize the early-life exposome which includes all environmental hazards that mothers and children are exposed to18. Despite the progress in the construction of the exposome, few analytic tools exist to date that can integrate genomic and exposomic data for complex trait analyses. We hypothesize that exposomic variables do not only affect phenotypes on their own but also interact among each other20 and with genotypes21. In addition, the estimation of exposomic effects and genomic effects on phenotypes could be biased, if these effects are correlated but the estimation model assumes otherwise22. Hence, tools that integrate genomic and exposomic data are required to capture variance as well as covariance components of phenotypes.Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures, encompassing external as well as internal environments over the lifetime of a given individualHere we propose a versatile linear mixed model that fulfils these requirements. The proposed approach jointly models the random effects of the genome and exposome and can be extended to capture genome-exposome and exposome-exposome interactions and genome-exposome correlations in the phenotypic analysis of a complex trait. It also allows us to model exposomic effects modulated by one or a few specific environmental variables. We demonstrate the proposed approach using traits from the UK biobank with 11 complex traits and 28 lifestyle exposures that were measured using a standard protocol.22. Second, the model can capture both additive and non-additive effects of the exposome and genome, i.e. pairwise interactions between exposomic variables  and interactions between exposomic variables and genotypes . Third, the model can handle correlated exposomic variables (see Simulations in “20).We used a novel linear mixed model (LMM) to jointly model the effects of the genome and exposome on the phenotypes of a complex trait. The exposome here is restricted to 28 lifestyle exposures that were measured using a standard protocol see “”. Our mohttps://nealelab.github.io/UKBB_ldsc/), along with 28 lifestyle variables, including alcohol use, smoking, physical activity and dietary composition , we found significant SNP-based heritability for all selected traits, with estimates ranging between 0.08 (years of education) and 0.52  was highly significant for 7 out 11 traits Table . By contBy extending the proposed model to a reaction norm model , the prediction accuracies of the model y = g + ε for the selected traits are only between 1/3 and 1/2 of the theoretical maximums  as a function of the sample size of the discovery dataset, variances explained by the genome and exposome, and effective numbers of (independent) SNPs and exposomic variables see “”, using ion [Eq. ], we comion [Eq.  in “Meth2 from 0 to 0.3 improves AUC from 0.5 to ~ 0.6 when the sample size of the discovery set is 50 k. This is in contrast to a twofold improvement, from 0.5 to ~ 0.7, when the sample size is 500 k. Thus, genomic prediction accuracy heavily relies on sample size, such that for a disease trait with a moderate heritability, a clinically meaningful level of accuracy (AUC ≥ 0.7) may not be attainable unless the sample size of the discovery dataset is substantially large (≥ 500 k). On the other hand, the benefit of using exposomic information to disease prediction can be realised with a relatively small discovery sample. This is evidenced by that when setting h2 to 0 , increasing the value of When setting The key model parameters of the proposed integrative analysis of genomic and exposomic data (IGE) compared to existing linear mixed models that incorporate genetic and environmental effects on phenotypes are outlined in Table 36 are whole-genome approaches to the estimation of heritability and gxe interactions, although IGE is considered more comprehensive and versatile, which models variances explained by additive effects of exposomic variables, by exposome × exposome interactions, and by exposome × covariate (such as demographics) interactions; and covariance between genetic effects and exposomic effects (Table https://sites.google.com/site/honglee0707/mtg2).Both IGE and GxEMM20 that examines one SNP at a time with a focus on association tests  for G × E interactions between the SNP genotypes and multiple exposomic variables. Using the well-established SNP BLUP method38, IGE can also provide GWEIS summary statistics, including estimated allele substitution effects of all SNPs across environments, their standard errors and p-values. Note that SNP BLUP implemented in IGE can model all SNP jointly (a whole-genome approach). Nonetheless, one of the main scopes of this study is to provide unbiased estimates of exposomic variances, e.g., In contrast, StructLMM has been developed primarily for a genome-wide by environment interaction study (GWEIS)mentclass2pt{minim39.Using our approach, we demonstrate the importance of the exposome for understanding individual differences in phenotypes. Although the ‘exposome’ constructed in this study comprises only 28 lifestyle factors, when integrated with genomic data, it explained between 2 to 10% additional phenotypic variance and significantly improved phenotypic prediction accuracy to a level equivalent to a 1.2 to 14-fold increase in sample size. The additional phenotypic variance is not only from additive effects of the exposome but also from its non-additive effects (exe) and genome–exposome interactions (gxe). We expect that as the construction of the exposome continues to progress, more phenotypic variance will be explained and greater improvements in phenotypic prediction accuracy will be gained. This would be particularly promising for phenotypic analysis and prediction of traits with small to little heritability component, such as ovarian and colorectal cancerE in Table 20 proposed the structured linear mixed model (StructLMM) that incorporates random effects of multiple environments in order to study the interactions between these environments and genotypes of a single SNP . However, the environmental variables in StructLMM are not transformed, even though they are very likely correlated, which would have biased the variance estimate of environmental effects. Consequently, it remains uncertain the extent to which the estimation bias affects the StructLMM-based test statistics for detecting gxe interactions.We noted that when exposomic variables are correlated, the variance estimate of additive effects of exposomic variables is biased unless these variables are transformed using a principal component analysis . We considered two possibilities. First, the exposome may mediate part of additive genetic effects on phenotypes. For example, some SNPs affect smoking status, which in turn affect BMI. A model that simultaneously includes genetic and exposomic data would account for smoking status and their genetic effects, and hence gives arise to reduced heritability estimates. Second, there is a genuine correlation between exposomic and genomic effects in some latent mechanism. It is noted that there are marginally significant correlation estimates, which were not significant after Bonferroni correction. Such correlation may be because people who have similar genotypes may somehow share similar exposures i.e. genotype-environment correlationIn conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, for which our proposed method can provide reliable estimates. We show that the integrative analysis of genomic and exposomic data has a great potential for understanding genetic and environmental contributions to complex traits and for improving phenotypic prediction accuracy, and thus holds a great promise for future clinical practice.http://www.ukbiobank.ac.uk/) for our analyses. The UK Biobank’s scientific protocol has been reviewed and approved by the North West Multi-centre Research Ethics Committee (MREC), National Information Governance Board for Health & Social Care (NIGB), and Community Health Index Advisory Group (CHIAG). UK Biobank has obtained informed consent from all participants. Our access to the UK Biobank data was under the reference number 14575. The research ethics approval of the current study was obtained from the University of South Australia Human Research Ethics Committee. All methods were performed in accordance with the relevant guidelines and regulations.We used data from the UK Biobank  < 0.6, a MAF < 0.01, a Hardy–Weinberg equilibrium p-value < 1e−4, or a call rate < 0.95. We then selected HapMap phase III SNPs, which are known to yield reliable estimates of SNP-based heritability44, for downstream analyses. We filtered individuals who had a genotype-missing rate > 0.05, were non-white British ancestry, or had the first or second ancestry principal components outside six standard deviations of the population mean. We also applied quality control on the degree of relatedness between individuals by excluding one of any pair of individuals with a genomic relationship > 0.025. From the remaining individuals, we selected those who were included in both the first and second release of UK Biobank genotype data. Eventually, 288,837 individuals and 1,133,273 SNPs passed the quality control of genotype data. Among these, 38,921 individuals had no missing data for any of the exposomic variables used in the present study, which were used in the main analysis. Depending on the missingness of the main phenotypic data, sample size varies across traits  greater than 0.05. These traits are standing height, sitting height, body mass index, heel bone mineral density, fluid intelligence, weight, waist circumference, hip circumference, waist-to-hip ratio, diastolic blood pressure and years of education.We chose eleven UK Biobank traits available to us that have a heritability estimate  in three sequential steps: (1) adjustment for confounders such as age, sex, birth year, social economic status (by Townsend Deprivation Index), population structure , assessment centre, and genotype batch using linear regression; (2) standardization; and (3) removal of data points outside  ± 3 standard deviations from the mean.We deliberately selected lifestyle-related variables that are known to affect some of the selected traits to construct the exposome in this study. These variables include smoking, alcohol intake, physical activity, and dietary composition. Details of these variables are listed in Supplementary Table 22. In these models, a genomic relationship matrix (G) was constructed using an n x m1 genotype coefficient matrix (A) as G = 1 is the number of SNPs. Similarly, an exposomic relationship matrix (E) was estimated using an n x m2 exposomic variable matrix (B) as E=2 is the number of exposomic variables  are inflated due to multicollinearity problem.When exposomic variables in W can be expressed asU is a matrix whose columns contain the left singular vectors of W, D is a diagonal matrix having a vector containing the singular values of W and V is a unitary matrix (i.e. VVt = I45) whose columns contain the right singular vectors of W.From the singular value decomposition, V can be also obtained from the eigen decomposition of the covariance matrix of the variables, i.e. 45 proposes to transform W to a column-orthogonal matrix, Ω, multiplied by V, which can be written asThe principal component regression approachW with Ω in the model asNow, we can replace V is a unitary matrix such that VVt = I (identity matrix) can be cancelled out45.The estimated regression coefficients from the model (2) are 2 values obtained from models (1) and (2) are equivalent as.Therefore, RΩ, i.e. ΩΩt/m where Ω is column-standardised.However, Eq.  can avoiy is a vector of phenotypes for n individuals; W is a n x m2 matrix that contains m exposomic variables; β is a vector of random exposomic effects, each assumed normal with mean zero and variance ε is a vector of residuals, each assumed normal with mean zero and variance Suppose a LMM of the formI is the n × n identify matrix.Under this model, phenotypic variance is partitioned asW, denoted as Ω, should be used, to avoid biased When exposomic variables are highly correlated, a transformed In a similar manner to the linear models (1) and (2), LMM (3) can be rewritten asSince ThenW for Eq.  by introducing exc interaction terms, where e is the additive effects of exposomic variables and c is a covariate. Given the significant additive effects found in the above, the interest of fitting RNMs is determine whether these effects vary depending on covariates, which would be evidenced by the presence of significant exc interactions.We extend the proposed model to a reaction norm model , age and ethnicity measured using the first two ancestry principal components. For each covariate, we fitted the RNM that allows the covariate to interact with exposomic effects and compared the fit of this model with a null model that assumes no exc interactions  validate significant variance components identified above  with SNP-based heritability h2 is analogous to M1 and can be thought of as the effective number of (independent) exposomic variables. Similar to M1, M2 can be estimated using the inverse of the variance of the off-diagonal elements of exposomic relationships ,,5), and Assuming that the genomic and exposomic effects on phenotypes are uncorrelated, the prediction accuracy of the model y = g + e + ε can be written asEquation  is verif47, which can then be converted to an AUC value25.Considering a disease trait with a population prevalence k, we derived the expected prediction accuracy of the model y = g + e + ε for the disease in terms of the correlation coefficient between the true underlying disease liability and predicted values from the modelg and re, the expected prediction accuracies for the disease on the liability scale, denoted as 47 as the followings.2 is the SNP-based heritability on the liability scale, N is the discovery sample size, k is the population prevalence, p is the ratio of cases in the discovery sample, and z is the density at the threshold on the standard normal distribution curve.Similar to rAs in Eq. , we comb28 that has been verified by a comprehensive analysis of real data26, we converted 2 (= − ik/(1 − k)) is the mean liability for non-diseased individuals, t is the threshold on the normal distribution that truncates the proportion of disease prevalence k and Ф is the cumulative density function of the normal distribution.Following a well-established theory1 to 50,000 and M2 to 28. M1  was estimated from the inverse of the variance of genomic relationships (G) between the discovery and target samples30. Similarly, M2 (28) was estimated from the inverse of the variance of exposomic relationships (E) between the discovery and target samples, which agrees with the number of transformed exposomic variables by a principal component analysis in this study (see the correlated exposomic variables section in “2 up to 100 would not yield expected prediction accuracies that notably differ from those from setting M2 = 28.To derive the AUC values shown in Fig. Supplementary Information.


Genomic regions with a high frequency of runs of homozygosity (ROH) are related to important traits in farm animals. We carried out a comprehensive analysis of ROH and evaluated their association with production traits using the BovineHD (770 K) SNP array in Chinese Simmental beef cattle.ECT2, GABRA4, and GABRB1 have been previously reported for those traits in beef cattle.We detected a total of 116,953 homozygous segments with 2.47Gb across the genome in the studied population. The average number of ROH per individual was 99.03 and the average length was 117.29 Mb. Notably, we detected 42 regions with a frequency of more than 0.2. We obtained 17 candidate genes related to body size, meat quality, and reproductive traits. Furthermore, using Fisher’s exact test, we found 101 regions were associated with production traits by comparing high groups with low groups in terms of production traits. Of those, we identified several significant regions for production traits (P < 0.05) by association analysis, within which candidate genes including Our study explored ROH patterns and their potential associations with production traits in beef cattle. These results may help to better understand the association between production traits and genome homozygosity and offer valuable insights into managing inbreeding by designing reasonable breeding programs in farm animals.The online version contains supplementary material available at 10.1186/s12864-021-07992-6. Runs of homozygosity (ROH) is defined as contiguous regions of the genome where an individual is homozygous across sites. ROH arises when the haplotypes transmitted from the parents are identical and inherited from a common ancestor . IncreasMany studies revealed the negative impact of high homozygosity on fertility traits including bull semen quality , calvingPrevious studies have been carried out to explore ROH and its potential association with diseases in humans –12. InveAllocating ROH into different classes based on length can be used to separate recent and ancient inbreeding. Long ROH may reflect recent inbreeding with a very low probability of recombination, while short ROH may indicate ancient inbreeding . Based oThe objectives of this study were to evaluate the homozygous segment pattern at the whole genome level in Chinese Simmental beef cattle and assess the association between ancient homozygous segments and production traits.In this study, we identified a total of 116,953 ROH with 2.47Gb across the genome in 1181 Chinese Simmental beef cattle. We found an average number of 99.03 ROH segments per individual with an average length of 117.29 Mb. Most ROH (~ 69.60 %) belong to the short class, ranging from 0.5 Mb to 1 Mb, while the large class only occupies a small portion (2.1 %) (Supplement Table SWe divided ROH into three classes: (A) Small (500 kb to 1 Mb), (B) Medium (1 Mb to 5 Mb), and (C) Large (> 5 Mb) . The ROHDNAJC18, ECSCR, MATR3, PAIP2, SNHG4, SPATA24, TMEM173, UBE2D2, KED, DCAF16, LCORL, NCAPG, etc. Moreover, we found several quantitative trait locus (QTLs) for weight gain and calving index overlapped with these regions based on the cattle QTLdb .To investigate the distribution of ROH enrichment within the population, we carried out an analysis of consensus ROH using PLINK with –homozyg-group option. We observed the highest frequency of consensus ROH (> 42 %) was located in the middle part of BTA6. Two ROH regions with frequencies of 34 % and 25 % were located on BTA7 and BTA11, respectively. We also detected 14, 6, and 1 consensus ROH (showing a rate of more than 25 %) on BTA6, BTA7, and BTA11 respectively. The genome-wide plot of the distribution of ROH enrichment was shown in Fig. To assess the association between homozygosity level and production traits, we performed the correlation analyses between the total length of ROH for each animal and adjusted phenotype including net meat weight (NMW), carcass weight (CW), average daily gain (ADG), and live weight (LW). Notably, we found the Pearson’s correlation coefficient between the sum of ROH and four traits  were 0.02, 0.04, 0.04, and 0.05 and LW (P = 2.26 × 10− 3) between individuals with ROH and those without ROH , we obtained four unique candidate regions. Totally, we observed four genes related to production traits, which may play an important role in promoting growth and metabolic functions. These candidate genes were found in the ROH regions located on BTA1, BTA5, BTA6, and BTA9. Of these, we found one region located at  for average daily gain and live weight, which was largely overlapped with ECT2  associated with CW (P = 0.0110) and NMW (P = 0.0052). However, no significant differences were observed for adjusted CW and NMW between individuals with ROH and those without ROH. In addition, no gene was detected in two candidate regions at BTA5 and BTA9.Consensus ROH that occur prevalently throughout the genome due to LD . Our finCT2 Fig.  C. We alThe increasing use of key sires has been coupled with a drastic reduction in the generation interval for most cattle populations, representing both an opportunity and a challenge . ArtificIn this study, we characterized homozygosity regions for the first time in Chinese Simmental cattle using a high-density SNP array. We found a large proportion of small ROH (~ 69.6 %), which was similar to our previous report . The higThe selection of superior animals can yield large phenotypic changes and reshap the ROH patterns in specific regions across the genome. Previous studies have identified the most homozygous region  on BTA6 within QTLs affecting milk fat and protein concentrations in local dairy cattle breeds . Our stuLAP3, NCAPG, and LCORL). Of those, we identified the largest ROH (425.68 kb) embedded with both NCAPG and LCORL. These two genes have been indicated that were related to growth and carcass merit traits in many previous studies [NCAPG and LCORL using multiple strategies in various cattle breeds [Notably, the small ROH are mainly selected and derived from ancient haplotypes . Therefo studies . Moreovee breeds –35. These breeds –41. RemaTo assess the effect of homozygosity on the production traits, we estimated the relationship between the total length of ROH for each individual and the production traits . Our study revealed the total length of ROH in autosomes across individuals was weakly correlated with production traits, but not statistically significant.Negative effects of inbreeding depression was observed on production traits in various dairy cattle –45. DespOur study showed that the inbreeding coefficient of the Chinese Simmental beef cattle estimated from ROH is around 0.047, suggesting the population has not experienced inbreeding. In our study, we observed an abundance of short ROH across the genome in the studied population. The short ROH derived from ancient haplotype which may have favorable inbreeding effects as compared with recent age of inbreeding. This finding may support the previous findings that ancient inbreeding occurred from a distant common ancestor is expected to display less unfavorable effects due to genetic purging , 26.FAM229B, LAMA4, and TUBE1). LAMA4 was localized and upregulated in damaged muscle fibers, and this gene appears to contribute to fiber survival in zebrafish [LAMA4-/- mice were reported that exhibited reduced weight gain in response to both age and high-fat diet [LAMA4 was a candidate gene for meat quality [We compared the distribution of ROHs for high and low production trait groups . Notably, a clear distribution difference was observed between the two groups in terms of the total length of ROH, which implied the difference in the levels of homozygosity between the two groups. Notably, we identified 101 nonredundant regions associated with production traits using Fisher’s exact test by comparing the top 300 high groups against the bottom 300 low groups. These regions account for a total of 120 candidate genes. Of those genes, several genes with candidate regions were observed that showing potential associations with reproduction traits as reported in previous publications. For instance, the region on BTA9 was identified for LW, NMW, ADG, and CW harboring three candidate genes ergic Synapse, have been previously reported related to reproduction trait [GABRB1 and GABRA4 were reported as candidate genes affecting meat color traits in Nellore cattle [Moreover, on trait . These gon trait . GABA ison trait . In addie cattle .We characterized the ROH and evaluated the association between ROH and production traits in Chinese Simmental beef cattle. Our study suggested that consensus ROHs derived from ancient haplotype may have a positive impact on production traits in beef cattle. Our findings provided a better understanding of the molecular basis underlying production traits from the aspect of homozygosity across cattle genomes.No ethics statement was required for the collection of genetic material. The data from animals included in this study were derived from previous analyses that obtained specific permissions.The dataset includes the phenotypes of 1,181 Chinese Simmental beef cattle born between 2008 and 2015 from Ulgai, Xilingol League, and Inner Mongolia, China. Average Daily Gain (ADG) was obtained with body weight gain divided by the number of fattening days. Carcass’s merit traits were measured as described in our previous analysis , 55.Samples were genotyped with Illumina BovineHD BeadChip and were processed with Genome Studio software. The individual call rate > 95 % was kept, and SNP quality controls were carried out using PLINK v1.9  based onIn this study, we used the PLINK v1.9 to detect ROH across cattle genomes . The speAssociation analysis was performed between ROH and four production traits . Prior to inclusion in the analysis, all phenotypes were adjusted using the general linear model . Then we considered the residuals as the adjusted phenotype for later analysis. In this study, we first estimated the relationship between the total length of ROH for each individual and the production traits  using the Pearson correction test.https://genome.ucsc.edu/) was used to retrieve genes within genomic regions of interest based on bovine genome assembly (UMD 3.1).Then, we divided the population into two subgroups with extreme phenotypes: the high 300 group and the low 300 groups. Analysis of consensus ROH was performed using PLINK with –homozyg-group option. We defined consensus ROH as segments of overlapping ROH that had a minimum of five SNPs. Statistical significance for the proportion of individuals with ROH differed between the high and low groups were assessed using Fisher’s exact test based on a 2 × 2 table for each ROH. The UCSC Genome Browser . X and Z are the design matrices relating phenotypic traits to fixed and random effects, respectively; u is the vector of additive genetic effects and e is the vector of residual effects. The effects u and e were distributed as u ~ N (0, GN (0, RG is the additive genomic relationship matrix, which was constructed by synbreed packages based on SNPs [R is an identity matrix. The associations between each ROH region and phenotypic traits were evaluated using linear mixed model analysis in ASReml v3.0 [where  on SNPs , 58. R ieml v3.0  with a sAdditional file 1:Additional file 2:Additional file 3:Additional file 4:Additional file 5:Additional file 6:Additional file 7:Additional file 8:


Salvelinus alpinus), and investigated the consequences of potential patterns of trait covariance on the phenotype of their hybrids. These traits were measured along ontogeny and involved growth, yolk sac resorption, developmental timing (hatching and the onset of exogeneous feeding), head morphology and feeding behaviour.Studying the development of fitness related traits in hybrids from populations diverging in sympatry is a fundamental approach to understand the processes of speciation. However, such traits are often affected by covariance structures that complicate the comprehension of these processes, especially because the interactive relationships between traits of different nature  remain largely unknown in this context. In a common garden setup, we conducted an extensive examination of a large suit of traits putatively involved in the divergence of two morphs of Arctic charr  but this was the only strong signal of covariance observed between all the measured traits. Furthermore, we did not report evidence for differences in overall phenotypic variance between morphs, nor for enhanced phenotypic variability in their hybrids.Our study shed light on the multivariate aspect of development in a context of adaptive divergence. The lack of evidence for the integration of most traits into a single covariance structure suggested that phenotypic constraints may not always favour nor impede divergence toward ecological niches differing in numerous physical and ecological variables, as observed in the respective habitats of the two charr. Likewise, the role of hybridization as a disruptive agent of trait covariance may not necessarily be significant in the evolution of populations undergoing resource polymorphism.The online version contains supplementary material available at 10.1186/s12862-021-01904-8. Understanding how phenotypic traits subjected to divergent selection evolve is essential to comprehend the processes of adaptive divergence and speciation –4. In th1 hybrids) in a multivariate framework would be a first significant step to understand the effects of trait covariance in speciation. Additive mechanisms generating intermediate mean trait values in F1 hybrids are expected to be fairly common , SB × SB: 2.98 , hybrids: 2.98 ). Furthermore, lower slopes and small second order polynomial terms were observed in the SB × SB offspring and the hybrids compared to the PL × PL offspring . These results indicate a slower and a more decelerating growth in the SB × SB offspring and the hybrids than in the PL × PL offspring.Longitudinal size measurements (standard length) indicated that the SB × SB and the hybrids differed from the PL × PL offspring in their growth trajectories Fig. . We obseUsing Geometric morphometric data from photographs of the embryos, we did not observe strong differences between the types of crosses in mean yolk sac area at hatching nor in the rate of yolk sac resorption Table . The hybp-value > 0.1 in all the pairwise comparisons).Head shape variation between cross types was estimated with Analyses of the Procrustes residuals  of Geometric Morphometric data from the same set of photographs used for the yolk sac analyses. These analyses indicated that size was related to most of the variation among specimens while no effect of the cross type in itself was observed Table . The ontFinally, we estimated the date of the onset of exogenous feeding of each individual through daily observations and studied variations in feeding behaviour among cross types (3–4 weeks after the onset of exogeneous feeding) by conducing three sessions of behavioural observations per individual . We didP matrix) for each cross type. We first compared the cross types on the basis of each component of P (trait variances and trait correlations), then on the general properties of P , and finally by assessing through Krzanowski’s common subspaces method [P (i.e. particular suits of covarying traits) differed in variance.We studied the patterns of trait covariance (excluding head shape) by generating a phenotypic matrix of variance–covariance  [P . Together with the shape change grids, the loadings of the first singular vectors indicate that shape changes are mostly associated with the standard length at the onset of exogeneous feeding  to covary with the variables in the (2B-PLS) . The 2-Bing Fig. . Note th1 hybrids of two sympatric morphs of Artcic charr showed subtle phenotypic differences with the offspring of the two pure morph crosses. First, while SB and PL charr differed in their growth trajectories , the hyn et al. .The lack of evidence for size-independent head shape variations among cross types contrasts with previous observations of differences between PL and SB embryos in the morphology of craniofacial cartilage elements . These dOverall, we did not observe evidence of multivariate trait divergence between the two Artic charr morphs. The PL × PL and the SB × SB offspring differed in average value for some traits , but did not show clearly distinct trait variance–covariance structures. Besides, most of the studied traits appeared uncorrelated. Under multivariate divergent selection, the evolutionary trajectories of populations are expected to be biased in the direction of the phenotypic space with the largest variance (i.e. “lines of least resistance” ). These 1 hybrids. The hybrids from our study were not strictly intermediate nor transgressive but rather show parental bias . Because the two hybrid cross types were pooled for the analyses, we were not able to test for differences between reciprocal hybrids nor to assess whether one type of hybrids accounted for most of the parental bias. Still, this observation is contrasting with other common-garden experiments reporting intrinsic developmental deficiencies or transgressive characters with obvious ecological implications in F1 hybrids between recently diverged populations [Coregonus cluteaformis [1 hybrids because of the common effects of dominance [We did not find differences in average trait values that would imply substantial fitness consequences in wild Fulations , 69. Forulations , and higeaformis . Consideeaformis  and may ominance . Such fuominance , suggest2) and backcrossed hybrids, as observed in many systems [The lower variance for growth traits observed in the hybrids goes contrary to our predictions of increased phenotypic variability through hybridization. Together with growth in hybrids being as low as in the smallest morph, these observations might be the only hints of developmental deficiencies in the hybrids. Growth-related traits are known for often being highly related to fitness , 73, so  systems , 74, 75. systems . FurtherOur results provided little support to the hypothesis of intrinsic postzygotic isolation between the PL and the SB charr (i.e. reproductive barrier produced by environment-independent hybrid deficiencies). Moreover, the singularity of hybrids in terms of average trait values and trait covariance suggests that selection against hybrids might be effective, although these observed differences were subtle, and their fitness consequences are unknown. Thus, the question of reproductive isolation in the two charr remains unresolved. In a recent study on the genetic structure of the two charr, about ten percent of the fishes were identified as potential hybrids , so subsIncreased trait dimensionality is expected to facilitate local adaptation, sometimes to such an extent that phenotypic divergence can easily occur in the face of high gene flow . Althoug2, mean depth: 34 m) that formed within a graben of the Mid-Atlantic ridge during the last glacial retreat  [Nitella opaca, and a profundal zone (25 m and deeper) covered by a diatomic gyttja substrate [Thingvallavatn is a deep postglacial lake (surface 84 kmears BP) , 80. Theubstrate , 81. Theubstrate . The twoubstrate –53. The ubstrate . The difubstrate , but canubstrate . The SB ubstrate . The youubstrate .2 mesh size), and placed into a EWOS tray (60 × 250 cm) with flow-through water. All families and cross types hatched at a similar developmental time point . We useWe measured the craniofacial development, pre- and post-feeding growth, and yolk-sac resorption using morphometric data from photographs taken at four points throughout ontogeny: at hatching (ca. 445 °C d), 20 days post-hatching (ca. 530 °C d), 3 to 4 weeks after the onset of exogeneous feeding ca. 840 °C d) and 9 to 11 weeks after the onset of exogeneous feeding (ca. 1100 °C d). The fish were anaesthetized with 2-phenoxyethanol , positio0 °C d anThe timing of the onset of exogeneous feeding was determined through “One-zero” sampling (i.e. records of the occurrence or non-occurrence of an event within defined observation periods) . Direct Several key aspects of feeding behaviour were estimated by conducting three focal sampling sessions  over 3 cData on size (standard lengths) and morphology were extracted from photographs using Geometric morphometrics methods . We placWe modelled the growth trajectories of every specimen in each cross type using polynomial random regressions . We thenV = 1, nu = 0.002 or the number of traits for the multi-response models) and determined the optimal number of iterations for model convergence through the examination of trace plots, posterior density plots and effective sample sizes .All the GLMMs were run with the R package MCMCglmm . MCMCglmWe studied between-individual variations in feeding behaviour by comparing repeatability estimates among the three cross types. The repeatability of each behavioural variable measured across the three repeated observational trials  was calculated according to the formula of adjusted repeatability in . The repP matrix) for each cross type. P matrices are reliable surrogates of genetically based patterns of trait covariances (i.e. of the G matrices) when no pedigree is available [P matrices are especially likely to be good proxies in our particular study because the effects of the environment were mitigated by the use of common-garden conditions, and because the parental effects were accounted for by including in the subsequent models the family of origin (i.e. the egg clutch) of all individuals. We estimated the components of the three matrices by running three separate Multi-Response Generalized Mixed models [We studied the patterns of trait covariance by generating a phenotypic matrix of variance–covariance  were used to compare the types of crosses in the overall phenotypic variance and were calculated as the sum of their eigenvalues (Eq. 2 in [Ω) was used as a measure of the shape of the matrices and was calculated as the ratio of the first two eigenvalues [θ) between the first eigenvector of each P matrix. Briefly, if the patterns of trait covariances were not conserved but have rapidly evolved among the two morphs, we expected the two types of pure-morph offspring to show differences in the overall size of P (Vtot), which should suggest a response to two selective regimes eroding genetic variations to different extents. Similarly, differences in eccentricity (Ω) between the two purebred offspring were expected  were also expected [P matrices with larger sizes and reduced eccentricity [P between the hybrid and the purebred offspring should indicate whether the remaining constraints on the hybrid phenotypes are intermediate, under dominance and conserved relative to one morph, or transgressive (i.e. biased toward a unique direction of the phenotypic space).The entation . The matEq. 2 in ) [94, 95y forces , 97. Thuexpected . Regardintricity . MeanwhiP (i.e. which suits of covarying traits) differed the most among cross types in their variance by using Krzanowski’s common subspaces method [H) that can be used to determine the groups’ similarities in parts of the trait space. Eigenvalues of H indicates the degree of resemblance between principal components of the trait subspaces of each group while the eigenvectors are informative of the variables associated with this resemblance. We used the approach of [P matrices .Next, we assessed which part of s method . This meroach of , which iP matrices were projected into a subspace composed by the first three eigenvectors P matrix of the PL × PL offspring by modifying the plotsubspace function from [P with the angles between the first eigenvector of one cross type (depending on the comparison) and the first eigenvector of a “random” P matrix. The simulated matrix was generated by sampling 150 individuals from the two cross types being compared.For visualisation purposes, ion from . BecauseP matrices. Instead, we relied on Two-Blocks Partial Least Squares (2B-PLS) analyses [P matrices. We relied on the method of Adams and Collyer [Because of the complex multivariate nature of shape data, univariate proxies of shape changes were not used to generate the analyses  to asses Collyer  for pairAdditional file 1. Additional Tables and Figures.Additional file 2. Datsets.


Statistical geneticists employ simulation to estimate the power of proposed studies, test new analysis tools, and evaluate properties of causal models. Although there are existing trait simulators, there is ample room for modernization. For example, most phenotype simulators are limited to Gaussian traits or traits transformable to normality, while ignoring qualitative traits and realistic, non-normal trait distributions. Also, modern computer languages, such as Julia, that accommodate parallelization and cloud-based computing are now mainstream but rarely used in older applications. To meet the challenges of contemporary big studies, it is important for geneticists to adopt new computational tools.We present TraitSimulation, an open-source Julia package that makes it trivial to quickly simulate phenotypes under a variety of genetic architectures. This package is integrated into our OpenMendel suite for easy downstream analyses. Julia was purpose-built for scientific programming and provides tremendous speed and memory efficiency, easy access to multi-CPU and GPU hardware, and to distributed and cloud-based parallelization. TraitSimulation is designed to encourage flexible trait simulation, including via the standard devices of applied statistics, generalized linear models (GLMs) and generalized linear mixed models (GLMMs). TraitSimulation also accommodates many study designs: unrelateds, sibships, pedigrees, or a mixture of all three.  We consider an assortment of trait models and study designs to illustrate integrated simulation and analysis pipelines. Step-by-step instructions for these analyses are available in our electronic Jupyter notebooks on Github. These interactive notebooks are ideal for reproducible research.The TraitSimulation package has three main advantages. (1) It leverages the computational efficiency and ease of use of Julia to provide extremely fast, straightforward simulation of even the most complex genetic models, including GLMs and GLMMs. (2) It can be operated entirely within, but is not limited to, the integrated analysis pipeline of OpenMendel. And finally (3), by allowing a wider range of more realistic phenotype models, TraitSimulation brings power calculations and diagnostic tools closer to what investigators might see in real-world analyses. Modern genome-wide association studies (GWAS) confront researchers with new computational and analytic challenges. Genetic data sets are becoming bigger and more varied.  TheThere are a variety of existing phenotype simulators, each with its unique virtues and implementation. Three prominent examples are –5. Some Fortunately, more recent computer languages are better suited to accommodate modern software engineering practices that protect users from common programming mistakes. Languages devised before the advent of ubiquitous parallelization and big data sets are struggling to stay relevant. On the other hand, most modern languages handle these opportunities with aplomb. Julia is a new, dynamic language that is purpose-built for high-performance computing (HPC) and widely adopted by leading computational statisticians , 7.In this article we introduce TraitSimulation, an open-source Julia-based package integrated into the OpenMendel software suite . OpenMenAlthough phenotype simulation can play a vital role in many downstream applications, including power analyses, phenotype generation itself is rarely the most time-consuming step. Usually, most time is taken by the post-simulation analyses, which may or may not be performed within our OpenMendel suite. For this reason we do not focus here on speed benchmarking of the phenotype simulation itself. Rather, we are interested in providing researchers with a tool for more flexible and perhaps more realistic phenotype simulation, while leveraging the modern language features of Julia for efficiency and ease of programming.To demonstrate how TraitSimulation may be used as an element in the OpenMendel toolkit, we present case studies that include application of our simulation software under settings that require the wide range of possible phenotype models unique to TraitSimulation. The unified nature of the OpenMendel environment makes it easy to craft code for simulating traits conditional on real or simulated genetic and covariate data for both unrelated individuals and multi-generational pedigrees. In common with other OpenMendel packages, TraitSimulation conveniently embraces modern computing architectures and encourages reliable and efficient programming practices.In the sections below, we first explain the advantages of using Julia to develop the OpenMendel project and demonstrate some of its key language features. We then present OpenMendel’s efficient SNP data management tool SnpArrays. Finally, we outline the trait simulation procedure with example data and results in various case studies that critically employ the simulated phenotypes in downstream analyses. The realistic trait simulation models used in these case studies are not all available in any other simulation software that we are aware of. Users are not limited to the analysis options provided in OpenMendel or Julia. After simulating the desired trait, they can call other analysis program, including popular R, C++ or Python packages, while staying within the Julia or Jupyter notebook environments. Alternatively, they can output the simulated traits to files for more customizable downstream analyses.The Julia programming language provides an excellent computing environment for genetic association studies. Among Julia’s many features is its just-in-time compiler that allows the language to combine the speed and efficiencies of low-level languages such as C or C++ with the ease-of-use and understandable syntax of high-level languages such as R or Python. Julia’s speed is also enhanced by its automatic use of the tremendous parallelization built into modern CPUs. For example, Julia includes automatic instruction-level parallelism, vectorization , and multi-threading ; Julia even includes tools for distributed computing across massive computing clusters –11. To mOur Julia-based package SnpArrays  is a verOur new TraitSimulation program provides the broad range of underlying models listed in Table Load the required packages: SnpArrays and TraitSimulation.Read in PLINK data files via SnpArrays and estimate the GRM, if applicable.Construct the simulation model, including relevant parameters such as the genetic and non-genetic predictors, the variance components, etc.Call the simulation routine to sample from the constructed model.Output the simulated phenotypes to a file or pass them to other analyses.The following Julia code snippet is an example of commands used to perform the above steps for model (4) in Table To run our TraitSimulation package the typical five steps are: X is the matrix of predictors and B is the corresponding matrix of regression coefficients. Themacro provides a convenient way to specify the variance components of the model. Here Table More details on running TraitSimulation under various settings, and additional step-by-step instructions for model specification, can be found in our interactive Jupyter notebooks at . TraitSiB are provided as a 2-element vector of formulas, one for each trait. The matrix of predictors is specified as a DataFrame with column names coordinated with those appearing in the formulas.In this alternative specification, the regression coefficients in G, X) and the corresponding regression coefficients Another model specification mechanism provides greater flexibility for users who wish to include many SNPs without having to convert the model genotypes from the compressed SnpArray. Here, the genetic and non-genetic predictors  framework appear in Fig. ained in .Table 3Fk specified SNP predictors for a sample size of n people, for Case Studies 1 and 2, respectively, as reported by the Julia BenchmarkTools package. All computer runs were performed on a standard 3.5 GHz Intel i9 CPU with 12 cores; they were run under Linux but we find the operating system has no appreciable effect on runtimes. As mentioned above, these power calculation runtimes are dominated by the post-simulation analyses. Thus, for variance component analyses, the runtimes scale linearly in k, but not in n, as is usual for a variance components statistical analysis. Of course, overall runtimes are linear in the number of replications chosen to perform. However, since each replication is an independent process and our programs can easily be distributed across multiple machines, using even extremely large numbers of replications, for example, for precise type 1 error estimation, is certainly feasible on a computational cluster or in the cloud.Tables Genetic epidemiology and computational statistics are inexorably linked. The increasing size and complexity of genetic data drive improvements in algorithm design, and statistical advances push new genetic analyses. To continue this progress, we have introduced TraitSimulation, a software package that employs the Julia language to achieve impressive computational efficiencies and easy coding for a broad range of trait simulation models, including many unavailable in other simulation packages.Simulation is a vital step in estimating the power of a proposed study to map genetic influences. To obtain the best power estimates, one must exploit all available study subjects , impute realistic genotypes , and possibly recombination events), incorporate pertinent non-genetic predictors, and critically, simulate realistic trait values.For example, if one is planning a family-based study and wanted to do a power analysis before collecting any data, then one would start with a collection of pedigree structures, including possibly singletons, that mimicked to the best of one’s knowledge the potential sample collection. At the founders of each pedigree one would want to simulate the genetic data using ethnic-specific allelic frequencies based on the admixture of the target population. The correct LD structure should also be maintained within these founder genomes. One way to accomplish this is to find a real genotyping or sequencing study, for example, the International Genome Sample Resource (IGSR) , that inThe model generality, ease of use, and speed of TraitSimulation, and indeed OpenMendel as a whole, promote the agenda of modern epidemiology. TraitSimulation’s wide range of generating models improves model realism and therefore power estimation. This generality allows statistical analysis to escape the straitjacket of the Gaussian assumption by allowing case/control and ordinal disease models, and more profoundly, any GLM or GLMM structure. Our choice of the Julia computer language makes it straightforward to code software and for users to adapt existing code to fit their modeling needs. Julia enhances the speed, flexibility, and overall ease-of-use of TraitSimulation. Julia’s speed stems from its just-in-time compiler, thorough use of parallelization, and its promotion of bit-wise linear algebra operations.TraitSimulation is part of the OpenMendel family of Julia packages . OpenMenProject name TraitSimulationProject home pagehttps://github.com/OpenMendel/TraitSimulation.jlOperating systems Linux, MacOS, WindowsProgramming language Julia 1.0 or higherOther requirements NoneLicense MITAny restrictions to use by non-academics None


Fragaria × ananassa) caused by the ubiquitous necrotroph Botrytis cinerea, renders fruit unmarketable and causes economic losses in the postharvest supply chain. To explore the feasibility of selecting for increased resistance to gray mold, we undertook genetic and genomic prediction studies in strawberry populations segregating for fruit quality and shelf life traits hypothesized to pleiotropically affect susceptibility. As predicted, resistance to gray mold was heritable but quantitative and genetically complex. While every individual was susceptible, the speed of symptom progression and severity differed. Narrow-sense heritability ranged from 0.38 to 0.71 for lesion diameter (LD) and 0.39 to 0.44 for speed of emergence of external mycelium (EM). Even though significant additive genetic variation was observed for LD and EM, the phenotypic ranges were comparatively narrow and genome-wide analyses did not identify any large-effect loci. Genomic selection (GS) accuracy ranged from 0.28 to 0.59 for LD and 0.37 to 0.47 for EM. Additive genetic correlations between fruit quality and gray mold resistance traits were consistent with prevailing hypotheses: LD decreased as titratable acidity increased, whereas EM increased as soluble solid content decreased and firmness increased. We concluded that phenotypic and GS could be effective for reducing LD and increasing EM, especially in long shelf life populations, but that a significant fraction of the genetic variation for resistance to gray mold was caused by the pleiotropic effects of fruit quality traits that differ among market and shelf life classes.Gray mold, a disease of strawberry ( Fragaria × ananassa), tomato (Solanum lycopersicum), and many other horticulturally important plants are susceptible to postharvest decay by gray mold, a devastating disease caused by the necrotrophic fungal pathogen Botrytis cinerea . Genetic studies in tomato and Arabidopsis leaves have identified multiple small-effect quantitative trait loci (QTL) that only account for a small fraction of the genetic variation for resistance, seldom translate across genetic backgrounds, and have not solved the problem of breeding for resistance to gray mold . Although genetic studies of similar depth and breadth have not been undertaken in strawberry, previous studies have not uncovered strong sources of resistance to gray mold .B. cinerea increases during ripening  to gray mold in strawberry. While hypotheses can be formulated from insights gained from genetic studies in tomato and other hosts  for increased resistance to gray mold in strawberry, we developed and studied training populations segregating for fruit quality traits predicted to affect shelf life. Genomic prediction approaches are particularly attractive for postharvest traits that are difficult and costly to phenotype in strawberry but still require sufficient accuracy to complement phenotypic selection and achieve genetic gains . The traShelf life studies were conducted with fruit harvested from five day-neutral cultivars  and three “summer-plant”  cultivars (the “UCD” prefixes are hereafter dropped from the cultivar names) grown on organic and conventional farms using the standard production practices of commercial growers in coastal California. The day-neutral cultivars were grown in 20-plant plots on three commercial farms, one organic and two conventional, in Santa Maria and Prunedale, California in 2017–2018 with harvests for postharvest studies on June 22 and 27, July 30, and August 1, 2018, July 30, 2018. The summer-plant cultivars were grown in 20-plant plots on three commercial farms, one organic and two conventional, in Oxnard and Santa Maria, CA in 2018–2019 with harvests for postharvest studies on September 27 and 30, November 1 and 18, and December 4, 2019.lme4 . Harvest dates were June 22 and 27, July 30, and August 1, 2018, for day-neutral cultivars and September 27 and 30, November 1, 18, and 21, and December 4, 2019, for summer-plant cultivars. Two 0.45 kg samples of fruit were collected at each harvest and placed in 18.4 cm × 12.1 cm × 6.2 cm vented plastic clamshells, one of which was stored undisturbed for visual phenotyping and another of which was used for destructive fruit quality trait measurements at three time points for summer-plant experiment  and four time points for the day-neutral experiment . Soluble solid content , fruit firmness (g-force), fruit weight (g/fruit), fungal decay incidence (% of clamshell), and marketability were recorded at each time point. The latter was visually scored on a 1–5 scale, where 1 = very good, 2 = good, 3 = fair, 4 = poor, and 5 = very poor . Four fr, Royal Royce × Madame Moutot (PI551632), Royal Royce × Tangi (PI551481), Royal Royce × Earlimiss (PI551862), and 05C197P002 × 16C108P065. These families constituted the multifamily training population developed for genomic prediction and other analyses. Seeds were scarified and germinated the week of June 18–22, 2018. Seedlings were established and grown in a greenhouse until they were transplanted to the field on October 5, 2018, at the UC Davis Wolfskill Experiment Orchard (WEO), Winters, CA, USA. The field site was prepared with a disk-harrow and ring-roller and smoothed with a spring-tooth harrow. The soil was fumigated on May 14, 2018, with Pic-Clor 60®  at a rate of 474.7 kg/ha. Subsequent to fumigation, planting beds were mechanically shaped to a height of 30.5 cm, width of 50.8 cm, base of 121.9 cm with center-to-center spacing of 152.4 cm between beds, and a furrow width of 30.5 cm. Seedlings were transplanted to planting beds on October 15, 2018, in a single-centered row with 55.9 cm between plants within the row. The number of individuals that produced sufficient fruit for the postharvest study of resistance to gray mold were n = 86 for Royal Royce × Primella, n = 82 for Royal Royce × Madame Moutot, n = 78 for Royal Royce × Tangi, n = 92 for Royal Royce × Earlimiss, and n = 42 for 05C197P002 × 16C108P065. The parents were planted from bare-root plants produced in a commercial high-elevation nursery in Dorris, CA, USA. The population was grown through June 30, 2019, irrigated as needed to prevent water stress, and hand weeded throughout the growing season. Fruit were harvested on from May 15 to June 7, 2019. Additional Royal Royce × Tangi full-sib individuals (n = 155) were grown, handled, and phenotyped in 2019–2020 exactly as described above for the 2018–2019 experiment . Fruit were harvested May 8–29, 2020.Seeds of five et al. 2020), and SNP calls were generated using the Affymetrix Axiom Suite (v1.1.1.66). The raw genotypic data were filtered to identify polymorphic SNP markers with clear and well separated homozygous and heterozygous genotypic classes and eliminate individual SNP markers with minor allele frequencies < 0.05 and any missing data. This process yielded 11,946 SNPs for the training population (n = 380) studied in 2019 and 9962 SNPs for the Royal Royce × Tangi full-sib population (n = 233) studied in 2019 and 2020.DNA was extracted from 0.2 g of dried young leaf tissue with the E-Z 96 Plant DNA Kit  per the manufacturer’s instructions, though the protocol was modified by adding Proteinase K to the lysis buffer to a final concentration of 0.2 mg/ml and extending lysis incubation to 45 min. at 65°C to increase the quality and yield of the DNA. Single-nucleotide polymorphisms (SNPs) were genotyped using the 50K Axiom SNP array (Hardigan B. cinerea disease progression and symptom development on ripe fruit. Spore suspensions of the B. cinerea strain B05.10 (B. cinerea growth and development with a minimum of contamination from other postharvest decay pathogens. Fruit were placed on 30-cell plastic egg hatching trays with dimensions of 29 cm × 29 cm and 4.5 cm × 4.5 cm cells. The fruit were punctured once near the center with a 3 mm sterile pipette tip to an approximate depth of 1–2 mm. Ten μl of the B. cinerea conidia suspension (500 conidia/μl) was placed on the surface of the puncture. The inoculated fruit were incubated in a growth chamber at 10°C and 95% humidity for 14 days. Disease symptoms were assessed daily after inoculation by manually measuring lesion diameter (LD) and determining the number of days until external mycelium  was evident on the surface of the fruit near the wound site. Fruit were phenotyped until mycelia covered the entire surface of the fruit. Spoiled fruit with infections outside of the inoculation site or caused by decay organisms other than B. cinerea were removed from the experiment. Genome-wide association study (GWAS), QTL mapping, and GS analyses were applied to LD at 8 days postinoculation (dpi) and EM.We developed a high-throughput protocol for postharvest phenotyping of α 6000 camera equipped with an E PZ 16–50 mm F3.5–5.6 OSS lens . Photographs were processed with a custom macro in Fiji  for quantifying titrable acidity , SSC (°BRIX), and total anthocyanin concentration . TA percentages were quantified with a Metrohm Robotic Titrosampler System from 1 to 5 ml of the defrosted homogenized fruit juice . SSC was measured from approximately 200 µl of juice on an RX-5000α-Bev Refractometer . Total AC was measured from a 25 µl sample of juice in 200 µl 1% HCl in methanol by reading absorption at a wavelength of 520 nm on a Synergy HTX platereader equipped with Gen5 software . A standard curve  coded −1, 0, and 1, respectively (λ) was 0.60 for LD and 0.71 for EM for analyses of the multifamily population and 1.09 for LD and 1.00 for EM for analyses of the Royal Royce × Tangi population. The Bonferroni-corrected threshold for statistical significance was −log10(0.05/k) where k is the number of SNPs used in the analysis. The Bonferroni-corrected threshold was −log10(4.2 × 106) for the multifamily population and −log10(5.0 × 106) for the Royal Royce × Tangi population.GWAS approaches were applied to search for marker-trait associations in the multifamily training population and the Royal Royce × Tangi population. EMMs for LD and EM were estimated from subsamples and biological replications using the  emmeans . GWAS anectively . The genectively . The genBatchMap and onemap (Parent-specific genetic maps were developed for each full-sib family using a custom PERL script pipeline utilizing the R packages qtl (Aa is a heterozygote and aa is a homozygote. The null hypothesis of no QTL effect was rejected when the likelihood odds (LOD) ratio for the SNP marker effect exceeded the genome-wide LOD significance threshold empirically estimated by permutation with 1000 randomly drawn samples  was estimated for each trait from multiple subsamples/individual  by lme4 (h2) was estimated for each trait from the mean of 1000 REML estimates of the additive genetic and phenotypic variance components from 1000 Markov Chain Monte Carlo (MCMC) samples drawn for cross-validation using G-BLUP  were estimated for each trait by applying three whole-genome regression methods in both training populations—genomic best linear unbiased prediction (G-BLUP), reproducing kernel Hilbert spaces (RKHS), and support vector machine (SVM). G-BLUP, RKHS, and SVM mixed model analyses were performed using the kinBLUP function of rrBLUP , the BGLUP  or summer-plant cultivars (P = 0.98) or between organically and conventionally produced fruit . Cultivar × location interactions were nonexistent—REML estimates of the cultivar × environment interaction variance component were zero in both studies (P = 1).Our studies were partly motivated by the observation that gray mold infections were uncommon between 0 and 14 dpi in a series of postharvest shelf life studies of modern LSL strawberry cultivars  for GS studies by crossing “Royal Royce,” one of the LSL cultivars assessed for natural infections .From common knowledge and earlier surveys of phenotypic diversity for resistance to gray mold . Althougfections , with foB. cinerea strain (B05.10), introducing a known concentration of spores into the wound site, and monitoring disease development on individual fruit stored undisturbed under high humidity , every individual was susceptible and the phenotypic ranges were comparatively narrow .Genome-wide searches failed to identify large-effect loci for LD or EM . These srawberry . The othitinases  and protitinases . This waRoyal Royce, the firm-fruited LSL parent, was more resistant to gray mold than the soft-fruited SSL parents . Lesions were smaller and mycelium appeared later in Royal Royce than the other parents (resistance increased as LD decreased and EM increased). Royal Royce was the more resistant parent for both traits in the four full-sib families with that parent . Hence, we concluded that breeding values cannot be accurately predicted without subsampling fruit.One of the challenges of breeding for resistance to gray mold and other postharvest traits is phenotyping throughput. Collectively, 2563 fruit were harvested and individually stored, tracked, and phenotyped in our study . Our exppulation . NaturalOne of our working hypotheses was that selection for increased fruit firmness and other shelf life-associated fruit quality traits pleiotropically decreased susceptibility to gray mold in strawberry. The additive genetic correlations support this hypothesis and highlight between family differences driven by breeding history, the phenotypic diversity of the parents, and transgressive segregation .e.g., SSL vs LSL. LD was negatively genetic correlated with titratable acidity , nearly 20,000 phenotypic observations were collected to quantitatively assess resistance to gray mold, which included image analyses at each postinoculation time point . Supplementary File S2 stores 50K Axiom SNP array genotypic data for individuals in the training populations (five full-sib families). Supplementary File S3 stores the phenotypic data for the training populations. Custom R and PERL scripts developed for genetic mapping analyses are stored in Supplementary File S4. Supplementary File S5 stores chromosome numbers, genetic and physical coordinates for SNP markers, and other information for the 10 parent-specific genetic maps constructed for each of the five full-sib families, in addition to summary statistics for each genetic map, e.g., number of mapped SNP marker loci and map length (cM). Supplementary File S6 stores information on candidate genes linked to gray mold resistance QTL with physical addresses and genome annotations from the F. × ananassa “Camarosa” v1.0 reference genome (https://www.rosaceae.org/species/fragaria_x_ananassa/genome_v1.0.a1). Supplementary File S7 stores the EMMs and GEBVs for gray mold resistance and fruit quality phenotypes among 380 individuals in a multifamily training population and 233 individuals in the Royal Royce × Tangi population. Supplementary Figure S1 displays time-series photographs of fruit of the LSL summer-plant cultivars “UCD Finn” and “UCD Mojo” at 0, 7, and 14 days of postharvest storage. Supplementary Figure S2 displays the distributions and parent EMMs for gray mold resistance phenotypes within full-sib families. Supplementary Figure S3 displays kernel density plots for genomic prediction accuracies estimated by cross-validation for G-BLUP, RKHS, and SVM.Genotypic, phenotypic, and other supplemental data files, figures, and tables are centrally available at https://doi.org/10.25387/g3.14732073Supplementary material is available at figshare:


The presence of transposable elements (TEs) in close physical proximity of effector genes can have important consequences for gene regulation and sequence evolution. Species-wide investigations of effector gene loci remain rare hindering our ability to predict pathogen evolvability.Plant pathogens cause substantial crop losses in agriculture production and threaten food security. Plants evolved the ability to recognize virulence factors and pathogens have repeatedly escaped recognition due rapid evolutionary change at pathogen virulence loci . The locus showed also strong signatures of genomic defenses against TEs (i.e. RIP) contributing to the rapid diversification of the locus.Here, we performed genome-wide association studies (GWAS) on a highly polymorphic mapping population of 120 isolates of In conjunction, our work highlights the power of combining GWAS and population-scale genome analyses to investigate major effect loci in pathogens.The online version contains supplementary material available at 10.1186/s12864-021-07691-2. Phytophthora infestans has 1249 predicted effector candidates, whereas the white rust pathogen of Arabidopsis thaliana, Albugo laibachii, has only 143 predicted effector candidates [Plant pathogens are a major threat to food security and cause annual losses of 20–30% of global harvest due to the lack of durable control strategies –3. The endidates . Identifndidates .Zymoseptoria tritici [Effector gene polymorphism can be a major factor driving host-pathogen interactions , 15. The tritici –21. The  tritici –24. Henc tritici . Such ef tritici –28. Brea tritici –32. Effe tritici , 33, 34.Magnaporthe oryzae [AVR-Pita effector gene has been shown to undergo multiple translocations in the genome contributing to the evolution of virulence on specific hosts [AvrPi9 led to a loss-of-function mutation enabling M. oryzae to escape host resistance [Brassica napus (canola) carrying the Rlm1 resistance gene suffered a breakdown of resistance against the fungal pathogen L. maculans [AvrLm1 locus [The rapid evolution of effector gene sequences is often driven by features of the chromosomal sequence in which the effector genes are embedded. Effector genes can be located on lineage-specific accessory chromosomes –37. Suche oryzae , 41. Theic hosts . The inssistance . The trasistance , 44, 45.sistance –48. Brasmaculans . The brem1 locus . SequencZymoseptoria tritici is one of the most destructive pathogens of wheat leading to yield losses of ~ 5–30% depending on climatic conditions [i.e.Stb), the corresponding effector genes remain unknown [The haploid ascomycete nditions , 51. Patnditions , 52–54. nditions , 17. In nditions , 20. GWAnditions . TE dynanditions , 19, 44.nditions . Phenotynditions , 57. Ide unknown .In this study, we aimed to identify the genetic basis of virulence on the wheat cultivar Claro using GWAS performed on a genetically highly diverse mapping population established from a single wheat field. We analyzed the expression patterns of genes in proximity to the top associated SNP, the presence of TEs and genetic variation at the locus in populations across the world to build a comprehensive picture of sequence dynamics at the newly identified virulence locus.Z. tritici. The collection originates from a multi-year experimental wheat field in Switzerland planted with 335 wheat cultivars [Z. tritici [To build a mapping population for GWAS, we used a subset of a previously established collection of 177 isolates of ultivars , 59  and the percentage of leaf area covered by lesion (PLACL) ; ρlesion ranged from 0 to 13.8  and tolerance ranged from 0.15–0.3   Fig. d-e. We c lesions . We founh2snp) for each trait using a genomic-relatedness-based restricted maximum-likelihood approach to partition the observed phenotypic variation  exceeding h2snp for ρlesion . Pathogenicity-related traits have overlapping genetic architectures leading to phenotypic and genetic correlations [rp = − 0.08; Fig. p-value inflation due to non-random degrees of relatedness among isolates, we used a mixed linear model that included a kinship matrix. We assessed the allelic effects across all SNPs for all traits to estimate the degree of genetic correlation among trait pairs. We found the genetic correlations (rp) to vary from − 0.1 to 0.98  while the endopeptidases gene Zt09_1_01591 is mainly expressed towards the end of the infection cycle  for all isolates of the GWAS panel. Both genes were conserved in all the isolates and appear transcriptionally active with variable expression levels among the isolates. The candidate effector gene was transcribed between 12 and 14′750 reads per kilobase of transcript per million mapped reads (RPKM) , Switzerland (n = 37 + 185), Israel (n = 30) and Australia , Israel (n = 17) and Switzerland (n = 6 and 50)  Fig. . The disZt09_1_01590: Student’s t-test, p > 0.15; serine-type endopeptidase Zt09_1_01591: Student’s t-test, p > 0.05). We also found no significant association with the TE presence-absence polymorphism and reproduction on the host .We annotated the scaffolds matching the top GWAS locus using consensus sequences of known TE families. Overall, the TEs between the two adjacent genes grouped into 11 superfamilies and 25 families Fig. d. The twZ. tritici on the wheat cultivar Claro. The identified locus is rich in TEs and is flanked by genes encoding an effector candidate and a serine-type endopeptidase. We analyzed a worldwide set of populations to analyze sequence variation at the pathogenicity locus. We found significant length variation caused by the insertion of a diverse set of TEs.We used whole-genome sequencing data and association mapping to unravel the genetic architecture of pathogenicity of i.e. preventing lesion damage despite high reproduction of the pathogen). Using GWAS, we identified several loci significantly associated with different pathogenicity traits. The most significant associations were found for pycnidia counts and ρleaf both related to reproductive success of the pathogen. Pathogen reproduction showed a strong single locus association while host damage (i.e. lesions) revealed no single gene effects. The difference between traits may be due to the fact that the genetics underlying host damage is more complex. Lesions are caused by host cell death triggered as a response to pathogen attack [Variation in pathogenicity on the wheat cultivar Claro was largely quantitative. We found that heritability was higher for pathogen virulence (damage to host) than pathogen reproduction (production of pycnidia). This is in contrast to analyses of heritability across 12 different wheat cultivars where heritability for pathogen reproduction was typically higher compared to lesion damage . Howevern attack , 69. Henn attack , 70. Furn attack . Interesn attack , 72. ThiZ. tritici and underlie variation in melanin production, virulence and fungicide resistance [We identified a highly polymorphic chromosomal locus associated with pathogenicity on the cultivar Claro. The most significant SNPs mapped in an intergenic region flanked by a large cluster of diverse TEs. We found no clear evidence for a coding sequence in immediate proximity of the most significantly associated SNPs. The closest genes encode functions, which may be relevant for host infection though. Serine-type endopeptidases play pivotal roles in nutrient degradation and subsequent assimilation, as well as protection from the host immune system . Serine sistance , 77, 78.Mycosphaerella fijiensis [Cochliobolus heterostrophus [, Fusarium species [, L. maculans [Verticillium species [The effects of gene-TE proximity have been studied mainly in animal , 80 or pijiensis , Cochliostrophus , Fusariu species , L. macumaculans  and Vert species . HoweverZ. tritici isolates were collected from the Field Phenotyping Platform (FIP) site of the ETH Zürich, Switzerland . We analyzed a total of 120 isolates collected during the 2015/2016 growing season from 10 winter wheat cultivars, which are commonly grown in Switzerland [n = 62) were collected when wheat plants were in Growth stage (GS) 41 while the second collection (n = 58) was performed when the plants were in GS 85 stage. After sampling, spores of each isolate were stored in either 50% glycerol or anhydrous silica gel at − 80 °C. Additional information regarding sampling schemes and genetic diversity is available [tzerland . We analvailable .°C and 140–180 rpm on a shaker-incubator. After 8 days of incubation, the cultures were passed through four layers of meshed cheesecloth and washed twice with sterile water to remove media traces. The filtering step also largely eliminated hyphal biomass but retained spores. The Swiss winter wheat cultivar Claro was used for virulence assays . Four seeds were sown in pots with commercial compost soil in triplicates. The pots were frequently in the growth chamber. The plants were grown under controlled conditions as follows: 16/8 h day/night periods at 18 °C throughout the experiment. The growth chamber was maintained at 70% humidity. Plants were grown for 3 weeks before infection with Z. tritici. To initiate infections, washed spores were diluted to 2 × 105 spores/ml in 15 ml of sterile water containing 0.1% TWEEN20. For each isolate, plants from three pots were infected using spray bottles. After spray inoculation, the plants were allowed to dry before sealing them in clear plastic bags to maintain 100% humidity for 48 h. Plastic bags were removed after 48 h and conditions were kept as described above.Isolates were revived from glycerol stock by adding 50 μl fungal stock solution to a 50 ml conical flask containing 35 ml liquid YSB (yeast-sucrose broth) medium. The inoculated flasks were incubated in the dark at 18http://imagej.nih.gov/ij/, 1997–2012). Briefly, the macro recorded the total leaf area, total lesion area, the number of pycnidia, mean size of pycnidia and pycnidia grey value. The percent leaf area covered by lesions (PLACL) was calculated as the ratio of the total lesion area and total leaf area [Twenty-one days post inoculation (dpi), the second leaf of each plant was cut and fixed on a barcoded white paper. Leaves were scanned immediately using a flatbed scanner at 1200 dpi. The scanned images were batch-processed using a macro , 87 baseeaf area .Approximately 100 mg of lyophilized spores were used to extract high-quality genomic DNA using the Qiagen DNeasy Plant Mini Kit according to the manufacturer’s protocol. We sequenced paired-end reads of 100 bp each with an insert size of ~ 550 bp on the Illumina HiSeq 4000 platform. Raw reads are available on the NCBI Sequence Read Archive under the BioProject PRJNA596434 . For RNAhttps://fungi.ensembl.org/Zymoseptoria_tritici/Info/Index) and the mitochondrial sequence (European Nucleotide Archive EU090238.1) using Bowtie2 v. 2.4.1 [We performed sequencing quality checks using FastQC v. 0.11.9.  and extracted read counts. Sequencing reads were then trimmed for adapter sequences and sequencing quality using Trimmomatic v. 0.39  using thv. 2.4.1 . Multi-sv. 2.4.1 . We retav. 2.4.1 . We furtv. 2.4.1  with thep-value inflation due to non-random phenotype-genotype associations [h2snp; equivalent to narrow-sense heritability) for each trait was estimated using the genome-wide complex trait analysis (GCTA) tool v.1.93.0 [h2snp was estimated using a genome-based restricted maximum likelihood (GREML) approach using the phenotypic values of each trait and considering the additive effect of all the SNPs represented by the GRM.Population structure and relatedness among individuals in the mapping population may be a source of ciations , 98. To ciations , adegeneciations , ade4 v.ciations  and ggplciations . We alsociations . File fociations . To idenciations . Isolateciations  for morev.1.93.0 . The h2sggcorr function from the GGally R package v2.1.1 [qqman v 0.1.4 [p-values were smaller than the Bonferroni threshold at the nominal α = 0.05 (here p < 1.1e-7). The Bonferroni threshold was calculated by dividing the nominal threshold of 0.05 by the total number of SNPs used for GWAS. False discovery rate (FDR) thresholds of 5% were determined using the p.adjust function in the stat package in R. We explored the genomic regions containing significantly associated loci using the “closest” command in bedtools v. 2.29.0 [r2 between marker pairs using the “option–hap-r2” in VCFtools v. 0.1.15 [r2 values with the R package LDheatmap v 0.99–7 [We performed GWAS based on mixed linear models accounting for genetic relatedness using either only a kinship matrix (MLM K) or a kinship matrix along with principal components of a PCA (MLM K + Q). We estimated relatedness among isolates by computing a kinship matrix using the scaled identity-by-state (IBS) algorithm implemented in TASSEL v. 20,201,110 . We incle v2.1.1 . Similar v 0.1.4 . We cons. 0.1.15  with “--v 0.99–7 .Zt09_1_01590 and Zt09_1_01591 in multiple completely assembled genomes of isolates collected in Switzerland, the United States, Australia and Israel covering the global distribution range of the pathogen . For synteny plots, the available repeat-masked chromosome-scale assemblies were analyzed using pairwise BLASTN. Information on BLAST hits among homologous chromosomes was visualized in R using the genoplotR package [https://theripper.hawk.rocks/ [We analyzed the locus surrounding the genes  package . We analk.rocks/ .https://github.com/crolllab/datasets) generated for the species [To analyze sequence polymorphism at the locus, we used draft genome assemblies of 432 isolates from previously analyzed field populations in the United States, Switzerland, Israel and Australia , 88. Ill species . Consens species , 116. Th species , ignorinAdditional file 1: Supplementary Fig. 1: The first two principal components (PC) of 788′313 genome-wide SNPs in 114/120 isolates. Isolates are color-coded by the cultivar of the origin. The six excluded isolates grouped into two clone groups of three isolates each and were collected from cultivar CH Combin. Supplementary Fig. 2: Phenotypic trait values. Red and blue lines represent the mean and median, respectively. A) The percentage of leaf area covered by lesion (PLACL). B) pycnidia count. C) Tolerance expressed as the pycnidia count divided by PLACL. D) ρleaf is the pycnidia count per cm2 of leaf area and E) ρlesion is defined as the total number of pycnidia divided by per cm2 lesion area. Supplementary Fig. 3: Manhattan and QQ-plot representing of the genome-wide association mapping analyses using mixed linear models based on a kinship matrix. The blue line corresponds to the Bonferroni threshold  and the red line corresponds to the 5% FDR. A-B) Percentage of leaf area covered by lesions (PLACL), C-D) tolerance E-F) ρlesion. Supplementary Fig. 4: Manhattan and QQ-plot representing of the genome-wide association mapping analyses. The GWAS was performed using mixed linear models including a kinship matrix and the first two principal components as random factors. The blue line corresponds to the Bonferroni threshold  and the red line corresponds to the 5% FDR. A-B) Percentage of leaf area covered by lesions (PLACL), C-D) Pycnidia count, E-F) tolerance G-H) ρleaf and I-J) ρlesion. Supplementary Fig. 5: Gene expression in reads per kilobase of transcript, per million mapped reads (RPKM) for genes closest to the top-associated SNP. A) Putative effector gene (Zt09_1_01590) and B) serine-type endopeptidase gene (Zt09_1_01591). Supplementary Fig. 6: The Large RIP Affected Regions (LRARs) composite index was calculated using The RIPer tool  and shown in black. The region flanked by the genes Zt09_1_01590 and Zt09_1_01591 is shown in complete genome assemblies of seven isolates from global population.Additional file 2 Supplementary Table S1: Phenotypic trait values used for GWAS. The percentage of leaf area covered by lesion (PLACL); ρleaf is the pycnidia count per cm2 of leaf area; ρlesion is defined as the total number of pycnidia divided by per cm2 lesion area. Tolerance is expressed as the pycnidia count divided by PLACL. Supplementary Table S2: Groups of clonal genotypes identified in the GWAS population with information about the collection time point and cultivar of origin. The clonal genotype columns provides a unique identifier. See Singh et al. (2020) for more detailed analyses. Supplementary Table S3: List of significantly associated SNPs above 5% FDR for pycnidia counts. Supplementary Table S4: Number of isolates from populations on different continents analyzed for transposable element content. Total assembled genomes and total of isolates per population where a scaffold was retrieved containing both genes Zt09_1_01590 and Zt09_1_01591.