Thalassiosira. We demonstrate that within Thalassiosira, phenotypic diversity cannot be predicted from genotypic diversity, and moreover, plasticity can create highly divergent phenotypes that are incongruent with taxonomic grouping. Significantly, multivariate phenotypes can be represented in reduced dimensional space using principal component analysis with 77.7% of the variance captured by two orthogonal axes, here termed a ‘trait-scape’. Furthermore, this trait-scape can be recovered with a reduced set of traits. Plastic responses to the new environments expanded phenotypic trait values and the trait-scape, however, the overall pattern of response to the new environments was similar between strains and many trait correlations remained constant. These findings demonstrate that trait-scapes can be used to reveal common constraints on multi-trait plasticity in phytoplankton with divergent underlying phenotypes. Understanding how to integrate trait correlational constraints and trade-offs into theoretical frameworks like biogeochemical models will be critical to predict how microbial responses to environmental change will impact elemental cycling now and into the future.Trait-based approaches to phytoplankton ecology have gained traction in recent decades as phenotypic traits are incorporated into ecological and biogeochemical models. Here, we use high-throughput phenotyping to explore both intra- and interspecific constraints on trait combinations that are expressed in the cosmopolitan marine diatom genus  Trait-based approaches to ecology focus on how an organism’s physical, chemical, and physiological characteristics (traits) contribute to its population size and dynamics . Traits The application of trait-based approaches to phytoplankton has gained traction in the last two decades –13. PhytThese models utilise a large body of literature which seeks to identify potential trade-offs between traits, where organisms increase functionality or efficiency in one aspect of their physiology at the cost of another , 19–21. Furthermore, trait-based models rely on fixed trait relationships derived from interspecific variation –29. As sChlamydomonas [While the incorporation of phenotypic plasticity into biogeochemical models is needed, this is a daunting task due to the complexity of the problem. An improved understanding is needed as to the breadth of intraspecific trait variation, correlations between traits and their plastic responses, the degree of phenotypic plasticity within phytoplankton functional groups, and constraints on this plasticity. It is experimentally intractable to rigorously test all possible trait combinations across many phytoplankton lineages and under a range of environmental conditions. Thus we need to develop an understanding of how traits are interrelated through multi-dimensional trait-based approaches. Due to trade-offs and correlations between traits, it has been hypothesised that variability over many traits can be captured in a reduced number of underlying axes . Such a ydomonas  but how ydomonas , 35.Thalassiosira. Diatoms contribute significantly to C and Si cycling in the oceans, due to their contribution to global primary productivity  are measured, traits co-vary and thus constrain the variability of observed phenotypes. We used multivariate analyses that emphasize the relationships between traits to examine patterns in inter- and intraspecific phenotypic variability and plasticity in the cosmopolitan diatom genus y  and they  to create a two-dimensional ‘trait-scape’  that allThalassiosira strains could not predict phenotypic diversity within the trait-scape, and phenotypes resulting from plastic responses to two new growth environments were more divergent than between-strain differences. However, consistent over-arching “strategies” were identified when strains were exposed to new environments, indicating that changes in phenotype due to plastic responses are constrained and follow a pattern.Genetic/taxonomic differences between These findings highlight the utility of multi-trait approaches for observing patterns and constraints on both standing trait variation and plastic responses to environmental change in phytoplankton that in turn could be used to anticipate how phytoplankton traits may shift in future climate scenarios.Thalassiosira spp. were obtained from the Provasoli-Guillard National Centre of Marine Phytoplankton , and one strain from the Australian National Culture Collection, representing 7 species in total  and CCMr study; ). Strain−1. All 13 strains were grown in a “standard” environment  with 9 phenotypic traits measured to describe the initial trait-scape. Five strains  were grown in two additional environments in triplicate: a high temperature and light treatment , and a low nutrient treatment . Cultures for the two additional treatments were inoculated with 10,000 cells ml−1 (LN) and 5,000 cells ml−1 (HT) in anticipation of limited growth.Experimental cultures (200 mL) were grown in 250 mL polystyrene tissue culture flasks in triplicate, at a starting concentration of 2500 cells mlGrowth was tracked daily using in vivo fluorescence as a proxy for cell density . One mL Phenotypic traits were selected to capture different commonly measured base physiological functions, and to include traits that are used in biogeochemical models. We also selected traits that demonstrated independence and orthogonality , based on pilot studies, in order to successfully define the multivariate trait-scape .Growth rates for each time step were calculated from the daily in vivo fluorescence measurements according to the calculation:Maximum growth rates were determined by the average growth over 2–4 consecutive steps depending on the duration of exponential growth.For flow cytometry trait measures , 1 mL aliquots of experimental culture were fixed with EM grade paraformaldehyde  in 1.6 mL cryopreservation tubes , frozen in liquid nitrogen, then stored at −80 °C prior to analysis. All measures were performed using a Cytoflex LX .Cell counts were done by gating the diatom population using chlorophyll a  and forward scatter channel thresholds. Cell size was estimated using forward scatter values calibrated against spherical beads . This resulted in a conversion equation of equivalent spherical diameter (ESD) = (FSC + 194636)/75775, which was used to assess relative changes in cell size .Chlorophyll a (Chl-a) fluorescence of the gated diatom population was quantified using 488 nm excitation, 690/50 nm detection. A standard bead  was used to calibrate the performance of the instrument and ensure comparable measures across samples. Chlorophyll values were divided by ESD to account for cell size differences.Side scatter is an indicator of the internal complexity of a cell or “granularity”. This trait is measured in tandem with other flow cytometry measures and was included as a phenotypic trait. The interpretation of this trait is not straight forward, but is independent of other flow cytometry traits measured and has been used in other flow cytometry studies of microalgae . This tr−1 in DMSO) was added to each sample, resulting in a final BODIPY concentration of 2 μg mL−1. Samples were incubated for 10 min in the dark before being read again on the flow cytometer. Neutral lipid content was defined as the difference in median fluorescence per cell between the pre- and post-stained sample. This value was then divided by the ESD size to account for size-related effects.Relative neutral lipid content was determined using the fluorescent stain BODIPY™ 505/515  which is commonly used to assess neutral lipid content in phytoplankton –47. BackPhotophysiological measures were taken by conducting a rapid light curve  with a w2DCFDA; Thermo Fisher, MA, USA) which has been used in a number of phytoplankton studies [−1 H2DCFDA was made in DMSO) was added to one aliquot, with the other acting as a blank. The plates were sealed  and incubated in the dark at growth temperature (20 or 30 °C) for 2 h. Incubation was done in the dark because of the effects of light on the dye itself, therefore the effects of the excess light treatment were not captured in this trait. Fluorescence of H2DCFDA was read using a plate reader with 488 nm excitation 525 nm emission . ROS concentration was estimated as the difference in fluorescence units per cell between the stained and unstained aliquots of each culture. This metric was also divided by ESD size to account for size effects.The development of reactive oxygen species (ROS) was measured using the fluorescent probe 2’,7’-dichlorodihydrofluorescein diacetate (H studies –51. Two DNA from stock cultures (10 mL) was extracted using a DNeasy PowerSoil kit  and checked for quality with a NanopDrop™ 2000 , before amplification and sequencing at the Australian Genome Research Facility . PCR conditions and primers used were those developed by Chappell et al.  for the Chaetoceros atlanticus isolate TPV2 1146 obtained from GenBank. Percentage similarity between strains according to the alignment was used as a metric of genetic relatedness.Bioinformatics processing was conducted using Geneious Prime . Strain sequences were compared to GenBank using the BLAST function to confirm species identity. Nucleotide sequences were aligned using the MUSCLE alignment , followeThalassiosira strains using principal component analysis (PCA). The input variables were the 9 independent trait measurements made on each replicate culture . Trait data was standardized  for each trait prior to PCA analysis to account for differences in the units and scale of measurements. The resulting PCA plot was defined as the ‘trait-scape’.We assessed the multivariate phenotypes for the n = 3 per strain).Hierarchical clustering analysis was performed on the 9-trait dataset used to assess similarity in multivariate phenotypes between each replicate for each strain  within the trait-scape. Distances between multivariate means (centroids) were calculated using the equation:To assess whether a trait-scape generated using fewer input traits (4 rather than 9) was representative of the full, 9-trait plot, we conducted PCA using 4 input traits, and then assessed whether the inter-strain distances (distances between centroids) within the plot were correlated using linear regression. This provided a quantitative assessment of whether the strains were in the same relative positions to each other within the trait-scape.To compare the pairwise relationships between traits across the strains, correlation matrices were made using data collected in the standard environment, and for the HT and LN environments.The change in phenotypes in the new environments were assessed firstly by conducting PCA on the full dataset, including trait data from the 13 strains grown in the standard environment, plus the 5 strains grown in the two additional environments. This generated an “expanded trait-scape”. In addition, correlation matrices were generated for the new environments' trait dataset to assess differences in trait-trait relationships between the ‘standard’ and “expanded” datasets.Relative changes in trait values for each trait in the new environments were calculated as follows:We used PCA to assess whether the relative changes in trait values were consistent between strains in the two different environments. i.e., was the relative change in whole phenotype consistent. If the changes were consistent across strains, we expected to see clustering in the PCA based on treatment.Statistical analyses were performed in R , Matlab,Thalassiosira, we measured nine traits in 13 taxa  showing higher growth rates and similar overall phenotypes  can be created with a curated set of traits thus facilitating experimental and modelling studies.To test the robustness of this results, we repeated this analysis with four input traits representing 3 of the 4 trait groups (i.e. no cell size-related traits). This resulted in a trait-scape that less accurately resembled the original trait-scape Fig. , with inInterspecific relationships are often used to predict phenotypic changes under different environmental conditions. Here we test the robustness of this assumption. A subset of five strains encompassing diverse phenotypes were selected and grown in two new environments . For all strains, the phenotypes resulting from plastic responses to the new environments were more separated from the ‘standard’ environment along PC2 Fig. . DiffereR2 ≥ 0.85, P < 0.05; Fig. R2 ≤ 0.66, P < 0.05; Fig. Cell size and related traits in the new environments were all positively correlated, as in the standard environment , as well as choosing a sufficient, but not exhaustive, number of genotypes. By reducing the number of traits and genotypes, experimental resources can be focussed on testing multi-trait responses to different environments, which is essential for understanding the degree of plasticity within and between species.The implication of constraints in how trait-scapes are filled is significant: constrained variation among phenotypes potentially makes representing responses in biogeochemical and ecosystem models more tractable. In principle, there are an infinite number of trait combinations and trait-scapes that could be measured on a large number of diverse phytoplankton taxa . HoweverThalassiosira, phenotypic diversity could not be predicted from genotypic diversity or taxonomic classification, and plasticity created divergent phenotypes that did not cluster according to genetic similarity. This suggests that estimates of trait diversity based on sequence variation are unlikely to be accurate for closely related organisms , as evidenced in freshwater chlorophytes [For rophytes . Trait-brophytes , but ourrophytes , 61–63. rophytes .Although the uncoupling of genotype and phenotype indicates a cautionary tale for making assumptions about any strain or species’ phenotype based on genotype, the constraints demonstrated in the trait-scape show that there are underlying rules of phenotypic expression and plasticity that hold across genotypes. This has the potential to simplify ecological modelling by constraining the trait value combinations available within species or broadly similar taxa, and can inform models of longer-term (evolutionary) responses .To understand and model phytoplankton responses to environmental change, we need to consider how complex phenotypes vary in response to environmental perturbations. This requires a multi-dimensional representation of traits, incorporating not only pairwise, but multi-dimensional trade-offs within and between species. However, there is a paucity of evidence of multi-directional trade-offs in the phytoplankton, and an even greater lack of empirical data, partially due to the daunting logistics of carrying out the necessary experiments, as well as the challenges of representing the results in a meaningful way.Thalassiosira. One direction for future work would be to determine the limits of genetic distance over which these apparent constraints hold.Here we demonstrate that the constraints on the phenotype in response to environmental change can be described in terms of multivariate trade-offs Fig. , that maThalassiosira pseudonana cells can modify their lipid composition and increase lipid stores within as little as 24 h after a reduction in nitrogen availability [Thalassiosira in the presence of high CO2 [Trait correlations observed in multiple strains or species, such as cell size and growth rate when considered across diatoms, dinoflagellates, cyanobacteria etc. , are genlability . Convershigh CO2 .Fig. 7TrThe distinction between across-species and short-term physiological trade-offs is a challenge for modelling applications as the “rules” for how phytoplankton respond to short term changes may not be reflected by trait correlations resulting from longer term evolution. Our results suggest that further studies to explore short-term physiological trade-offs through experimentation are required to determine the limits and patterns of phenotypic plasticity, which can influence both the speed and direction of evolutionary processes .Thalassiosira taxa in extended culture show that vessel type and time in cultivation can affect trait values [While we kept our culturing processes consistent across all strains to minimise any phenotypic variance due to cultivation factors, assessing the contribution of evolutionary or culturing history to phenotypic responses was beyond the scope of this study. Our observation of t values  but confPlastic and adaptive responses will play a key role in how diatoms fare in response to climate change, as the frequency of extreme events and overall variability in the oceanic environment increases . DiatomsSupplementary Information


Field pea is the most commonly grown temperate pulse crop, with close to 15 million tons produced globally in 2020. Varieties improved through breeding are important to ensure ongoing improvements in yield and disease resistance. Genomic selection (GS) is a modern breeding approach that could substantially improve the rate of genetic gain for grain yield, and its deployment depends on the prediction accuracy (PA) that can be achieved. In our study, four yield trials representing breeding lines' advancement stages of the breeding program  were assessed with grain yield, aerial high-throughput phenotyping , and bacterial blight disease scores (BBSC). Low-to-moderate broad-sense heritability (0.31–0.71) and narrow-sense heritability (0.13–0.71) were observed, as the estimated additive and non-additive genetic components for the three traits varied with the different models fitted. The genetic correlations among the three traits were high, particularly in the S0–S2 stages. NDVI and BBSC were combined to investigate the PA for grain yield by univariate and multivariate GS models, and multivariate models showed higher PA than univariate models in both cross-validation and forward prediction methods. A 6–50% improvement in PA was achieved when multivariate models were deployed. The highest PA was indicated in the forward prediction scenario when the training population consisted of early generation breeding stages with the multivariate models. Both NDVI and BBSC are commonly used traits that could be measured in the early growth stage; however, our study suggested that NDVI is a more useful trait to predict grain yield with high accuracy in the field pea breeding program, especially in diseased trials, through its incorporation into multivariate models. Pisum sativum L.) is an annual pulse crop widely grown in all major cropping regions, including North America, Russia, India, Australia, etc.  has become one of the most important diseases in Australia's field pea production  have the capacity to become high-throughput platforms (HTP) that can assess many thousands of lines in a short period of time  is a modern breeding method that combines genotypes and phenotypes of a training population to predict breeding values in genotyped but not phenotyped individuals by using appropriate statistical models. The breeding values can then be used for selection in the breeding process , 1 (S1), 2 (S2), and 3 (S3), which had decreasing numbers of entries due to the artificial selection conducted in the breeding cycle. Breeding lines in each cycle were selected from the respective previous staged breeding lines from 2017, i.e., S3 lines were selected from the 2017 S2 lines. The S0 stage consisted of 960 newly generated lines, with S1, S2, and S3 trials having 420, 240, and 144 breeding lines, respectively. Pedigree of all breeding lines are included in the All trials were sown in Horsham, Victoria  in a completely randomized block design with two replicates, apart from the S0 trial which was partially replicated. The plots in all trials were arranged in 12 columns, with each plot being 5 m long and 1.25 m wide with five rows spaced at 0.25 m each. Visual scores of BBSC were recorded on a 1–10 scale from each plot when disease infection was at its peak (pod filling stage). Briefly, score 1 represented a plot that had 0–10% plants affected by the disease, 2 represented a plot with 10–20% plants affected by the disease, and so on. Plots were machine-harvested, and grain weights were recorded. The plot weights were converted to yield (tons/ha) using a plot width of 1.75 m. Weather data during the growing season were obtained from a weather station installed at the site . Chemicahttps://www.arcgis.com/) to estimate plot level NDVI values. The rectangular polygons with a unique number (plot number) were created and overlaid on the NDVI orthomosaics image to extract plot level average NDVI values for each trial.The aerial images were acquired when disease infection was at maximum, at the early pod filling stage, using a multispectral Parrot Sequoia camera  attached to a UAS. The camera had four discrete channels, namely, red (660 nm), green (550 nm), red-edge (735 nm), and near-infra-red (790 nm), and an RGB sensor. The application “Tower” was used to automate the flight mission with a front and side overlap of 75% each, at a speed of 5 m/s, and conducted at 40 m above ground level to estimate NDVI. These flight parameters delivered high-resolution orthomosaics with a ground sampling distance of 2.69 cm. The acquired images were processed using the software Pix4D mapper Pro  to generate high-resolution orthomosaics and NDVI maps . RNA-Seq libraries were prepared using SureSelect stranded RNA library preparation kit  following the manufacturer's instructions  was calculated for all lines according to VanRaden . Each brAll statistical analyses were conducted in ASReml  model, g is a vector of random genetic effects of lines ~ N  were calculated using a univariate model similar to the BLUP model, but the breeding lines were fitted as fixed effects. BLUEs were used as adjusted phenotypic data to calculate the genomic PA in different models .The multivariate model with three traits, also referred to as the BLUP multivariate model, was illustrated as follows:where the model terms for traits 1, 2, and 3 were similar to the previously described univariate model. In this model, I is the incidence matrix. When I was replaced with G, the model was referred to GBLUP multivariate model.H2) for each trait was calculated as the proportion of phenotypic variance explained by genetic variance (Broad-sense heritability (h2) was represented as Narrow-sense heritability (A), and G). When non-additive genetic effects were decomposed, narrow-sense heritability could be estimated the same way but the variance of line effects was added to the total phenotypic variance.where rG) between traits was calculated using the variance components estimated from BLUP multivariate models in four breeding populations, where Genetic correlation (rg) between traits was calculated in the same way but with additive genetic variance, and covariance of traits was based on G.In GBLUP multivariate model, the additive genetic correlation  and BLUEs. In the cross-validation method, all lines within each breeding cycle were randomly divided into five equal subsets. Each subset was, in turn, chosen as the validation set and was subsequently predicted by using the other four subsets as the training set. The cross-validation process was repeated five times, and mean PA and standard deviation (SD) were calculated across all 25 validation sets. In the forward prediction method, the training set consisted of one or multiple breeding stages previous to the validation breeding stage. A total of six scenarios were tested using GBLUP models, including using traits in the S0 to independently predict S1, S2, and S3; combining S0 and S1 to predict S2 and S3; and combining S0, S1, and S2 to predict S3.Summary statistics of grain yield, NDVI, and BBSC for the four breeding stages are shown in In total, 1,453 breeding lines with 47,352 SNPs were used to calculate the GRM. With all stages combined, three groups could be observed according to the dendrogram with different levels of relatedness among lines within and between the groups . When thh2 was reduced in most cases for all traits when including non-additive genetic components while the estimated error variances were not reduced. The proportion of total genetic variation explained by additive genetic effects, represented as the estimated Estimating variance components from different breeding stages will help us understand the breeding population, especially when selection is conducted at each breeding cycle. The genetic parameters estimated for each trait would help us to observe the traits' genetic architecture. Overall, the non-additive genetic variance was observed for all three traits, but its proportion varied with models fitted and varied across breeding stages. The estimated H2) was 0.58, 0.47, and 0.38 for grain yield, NDVI, and BBSC, respectively  can be estimated using the pedigree information, the proportion of variance explained by numerator A matrix , minimum temperatures early in the season, and maximum temperatures during seed filling. In Australia, ≈70% of the field pea crop is grown in low rainfall (<230 mm) environments (Leonforte et al., In conclusion, this study estimated the heritability for grain yield, NDVI, and BBSC in a field pea breeding program, and it also evaluated the genomic PA for grain yield with univariate and multivariate models. The results showed that heritability for all traits was low to moderate. However, strong genetic correlations were observed among traits apart from S3. In both cross-validation and forward prediction methods, we found that the multivariate model outperformed the univariate models by substantially improving PA. We also confirmed that with a larger training set, a higher PA could be achieved when different breeding stages were included in the training population. Field phenotyping is costly and labor-intensive for large breeding trials. Both NDVI and BBSC are commonly used traits in breeding programs that could be measured in the early growth stage. Our study has shown the potential of adopting NDVI with multivariate models to improve grain yield PA. GS is becoming popular among crop breeders to predict phenotypes, especially yield. We showed that GS holds great potential for improving grain yield in field pea breeding.https://doi.org/10.6084/m9.figshare.19817509.The details of the data and results supporting the discussions and conclusions are included in the Supplementary Materials. The genotype data used for this study are deposited in the figshare repository, can be accessed from here: HZ, BP, SKant, and GR conceived and designed the experiment. BP, SS, SJ, SKant, and Skaur performed and supervised the phenotyping and genotyping. HZ performed the statistical analysis and wrote the draft. BP and MK assisted in the data analysis and interpretation. SKaur and GR supported the study. All authors revised the manuscript.This study was funded by Agriculture Victoria Research and Grains Research Development Corporation through a research project DAV00153.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This cross-sectional study investigates if genome-by-trauma exposure interactions are associated with major depressive disorder. Are genome-by-trauma exposure interactions associated with major depressive disorder?In this cross-sectional study, a mixed linear model design was implemented using 148 129 UK Biobank participants with available genomic and trauma exposure data to explore depression and neuroticism variance attributable to genomic, trauma exposure, and genome-by-trauma exposure interaction associations. Findings suggest genome-by-trauma exposure interactions can explain up to 20% of variation in major depressive disorder and more often in male vs female participants.Exploring mechanisms underlying genome-by-trauma exposure interactions may be useful in identifying at-risk individuals and intervention targets; insight into these mechanisms may provide explanations for depression prevalence differences across the different sexes. Self-reported trauma exposure has consistently been found to be a risk factor for major depressive disorder (MDD), and several studies have reported interactions with genetic liability. To date, most studies have examined gene-environment interactions with trauma exposure using genome-wide variants (single-nucleotide variations [SNVs]) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance.To reexamine genome-by-trauma interaction associations using genetic measures using all available genotyped data and thus, maximizing accounted variance.The UK Biobank study was conducted from April 2007 to May 1, 2016 . The current study used available cross-sectional genomic and trauma exposure data from UK Biobank. Participants who completed the mental health questionnaire and had available genetic, trauma experience, depressive symptoms, and/or neuroticism information were included. Data were analyzed from April 1 to August 30, 2021.Trauma and genome-by-trauma exposure interactions.Measures of self-reported depression, neuroticism, and trauma exposure with whole-genome SNV data are available from the UK Biobank study. Here, a mixed-model statistical approach using genetic, trauma exposure, and genome-by-trauma exposure interaction similarity matrices was used to explore sources of variation in depression and neuroticism.Analyses were conducted on 148 129 participants  of which 76 995 were female (52.0%). The study approach estimated the heritability (SE) of MDD to be approximately 0.160 (0.016). Subtypes of self-reported trauma exposure  accounted for a significant proportion of the variance of MDD, with heritability (SE) ranging from 0.056 (0.013) to 0.176 (0.025). The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction revealed estimates (SD) ranging from 0.074 (0.006) to 0.201 (0.009). Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in male participants (0.441 [0.018]) than in female participants (0.086 [0.009]).This cross-sectional study used an approach combining all genome-wide SNV data when exploring genome-by-trauma interactions in individuals with MDD; findings suggest that such interactions were associated with depression manifestation. Genome-by-trauma interaction accounts for greater trait variance in male individuals, which points to potential differences in depression etiology between the sexes. The methodology used in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions. Furthermore, meta-analyses of genome-wide association studies (GWAS) have uncovered many single-nucleotide variations (SNVs) associated with depression; however, SNV-based heritability estimates of 5% to 10%6 are much lower than estimates obtained from twin studies, the difference being termed missing heritability. Explanations for missing heritability include heterogeneity in sample processing, phenotyping methodology, and phenotype heterogeneity across medical systems, countries, and cultures within GWAS used in meta-analyses, as well as inflation of twin-based heritability estimates attributable to shared environmental effects and gene-environment interplay.10Depression is a highly prevalent psychiatric disorder with a lifetime risk of approximately 16%13 In turn, trauma exposure in childhood has been consistently associated with adverse outcomes including increased risk of MDD in adolescence and adulthood.15 These findings suggest the role of trauma exposure in depression’s etiology needs further exploration. Moreover, research has shown self-reported trauma exposure and MDD are genetically correlated, suggesting shared genetic risk factors for both16 or potentially more complex interplay effects on depression manifestation.Self-reported trauma exposure has been found to play a role in depression, with case-control studies suggesting individuals diagnosed with major depressive disorder (MDD) report higher levels of trauma exposure.18 Minimal evidence of interactions has been yielded from studies exploring SNV-by-trauma associations.22One key form of interplay that has been explored is the interaction effect of genetics and trauma exposure on depression manifestation. Gene-by-environment interactions refer to the differential associations between an environmental exposure on traits in individuals with differing genotypes. This can be conceptualized as an individual’s genetic sensitivity to certain environments, which may result in an exacerbated risk of a disorder.24—have yielded inconsistent findings. Some studies have highlighted sex differences25 and found significant interaction associations with MDD outcomes,27 whereas some replication attempts reported null findings.30 An explanation for inconsistent findings may lie in the predictive accuracy and validity of PGSs.10 PGSs build on the information provided by GWAS, which still have limited statistical power for detecting trait associated genetic variants and their effect sizes.32 These power limitations of GWAS are greater for traits that have a substantial environmental component, such as depression, as opposed to traits with higher genetic etiology.33 This is reflected in the fact that current PGSs capture less than 3%6 of the phenotypic variance proposed for depression.Moreover, research using polygenic scores (PGSs)—genetic measures that can be calculated for each individual by identifying, weighting, and summing genotyped risk variants found to be associated with depression34 These matrices capture genetic similarity between individuals within a sample based on the number of genotyped SNVs they have in common, including matrices representing genetic, environmental, and interaction association similarity within a sample. In mixed linear models, these matrices can provide estimates of the genetic, environmental, and genome-by-environment interaction components of trait variance.35One way to circumvent the issues associated with PGSs is to make use of genetic measures that capture greater variance. Genomic similarity matrices have been able to do this by using all genotyped SNVs.37 and has a strong phenotypic link with MDD, suggesting the exploration of neuroticism to be useful in understanding the genetic etiology of both of these traits.39 As the existing literature has highlighted the role of trauma exposure in sex differences observed in MDD, we also explored these associations in male and female participants separately.40 Here we show that using the entirety of genotyped genetic variants can improve statistical power in the exploration of genome-by-trauma interactions. More importantly, our findings suggest that genome-by-trauma interactions may play a much larger role in depression manifestation than previously thought.Here, we estimated the contribution of trauma exposure and its interaction with genetic variation to depression and neuroticism. We chose to explore neuroticism as this trait has been shown to have a greater genetic componentSTROBE) reporting guidelines.Data for the current study were obtained from the UK Biobank (UKB), a national study that explored environmental and genetic determinants of health. Individuals in UKB were recruited from 22 centers across the UK. The UKB study received ethical approval from the NHS National Research Ethics Service and has approval from the North West Multi-Centre Research Ethics Committee. This study has been approved by the UKB Access Committee. Participants in the UKB study provided written informed consent. In the current study, we explored variance components of Composite International Diagnostic Inventory (CIDI) depression, broad depression, and neuroticism. Information on participants, phenotypes, and genotyped data is available in the eMethods and eTables 1-14 in the Covariance matrices were used to explore genetic, environmental, and genome-by-environment components of variation. As the number of participants with available depression/neuroticism and trauma information is large, analyzing the pairwise covariance matrices jointly for the whole cohort was computationally intractable. To work around these computational issues, participants were split into 5 different clusters based on the geographic location of their recruitment centers  and all subsequent analyses were replicated across the 5 clusters. Information on how clusters were formed, and their demographic characteristics is available in eFigure 2 and eTables 23 to 31 in the 34 Supplementary Gs were computed using only unrelated individuals .Genomic similarity matrices (G) represent the expected genetic similarity between individuals. We computed 1 G for all individuals in each of the geographical clusters using genetic variants that passed quality control  represent similarity between individuals based on their trauma eigenvector principal components (PCs) (eMethods and eTables 15-22 in the 41 To control for genetic covariance between trauma exposure and our phenotypes of interest (depression/neuroticism), the impact of G was removed from our trauma variables. Trauma eigenvectors were precorrected for the full G, by regressing each trauma PC on G. The residuals obtained from these analyses were used to compute an additional supplementary E. If the presence of gene-environment correlations bias our results, we expect estimates of variance attributable to G × E to be different to estimates observed from models using E matrices without controlling for covariance between G and E.Evidence suggests that gene-environment correlations and interactions often co-occur and disregarding 1 effect can result in biased findings.42 to compute these matrices. More information on available algorithms is available in eAppendix 1 and eTable 32 in the We used OSCA, version 0.45 default algorithm 144Gene-environment interaction similarity matrices (G × E) represent shared genome-by-trauma interactions. These were computed by multiplying G and Es using a cell-by-cell (Hadamard) product.Using the first eigenvector (PC1) for the subtypes of trauma, the SNV heritability of each trauma variable was explored. Heritability estimates of the trauma variables were obtained by fitting the trauma variables as the dependent variable and Gs as random effects within a mixed linear model framework (estimates of trauma PC1 variance attributable to G) noted in model 1. The genetic correlations between trauma PC1 and depression variables were explored using the moment-based method, Haseman-Elston regression analyses, where SEs were calculated using a leave-one-individual-out jackknife technique. Age, sex, genotyping array, and the first 15 principal components of the G were included as covariates.Variance components of depression/neuroticism were explored within mixed linear model frameworks. Four models were explored with varying levels of complexity:y = Xβ + g + ε1. y = Xβ + e + ε2. y = Xβ + g + e + ε3. y = Xβ + g + e + g × e + ε4. y is a n × 1 vector of observed depression/neuroticism phenotypes; β is a vector of fixed effects , and X is its design matrix; g is an n × 1 vector of SNV effects (representing additive genetic effects) with g approximately Norm; e is an n × 1 vector representing common environmental effects of childhood, adult, catastrophic, or all trauma with e approximately Norm; g × e is an n × 1 vector representing interactions between genetic and trauma effects with g × e approximately Norm; and ε is an n × 1 vector of residual effects.Where 34Estimates of variance attributable to the G, E, and G × E components are obtained from analyses using CIDI and broad depression as dependent variables and are converted to the liability scale within GCTA.45 Prevalence rates used were 0.28 and 0.35 for joint sex analyses, 0.35 and 0.43 for female participant analyses, and 0.19 and 0.27 for male participant analyses for CIDI and broad depression, respectively. We conducted the analyses using different prevalence estimates  as sensitivity analyses. Although estimates are slightly different, the overall pattern of significance remain unchanged  were meta-analyzed using R package metafor .Analyses were repeated using only unrelated individuals. All analyses were replicated across the 5 geographic cluster samples using GCTA, version 1.91.4beta,Note that interactions between main effects (G and E) with covariates were not included in these mixed linear models. This would have required additional matrices capturing covariate similarity, and thus, multiple additional matrices to be included in the models, which would have made analyses computationally intractable. Analyses were conducted for male and female participants separately, which is aimed to work around these computational issues . Initial analyses explored SNV heritability of the first PC of full trauma and subcategories of trauma. Trauma variables meta-analyzed SNV heritability (SE) estimates including the following: full trauma, 0.17 (0.008); childhood trauma, 0.15 (0.008); adult trauma, 0.063 (0.008); and catastrophic trauma, 0.11 (0.008) (eTable 33 in the All genetic correlations between PC1 of trauma variables and depression/neuroticism phenotypes are shown in Heritability (SE) estimates  of CIDI depression were stable across the different UKB subsamples, approximately 0.16 (0.016). The meta-analyzed estimates (SE) for proportion of variance attributable to trauma exposure (Es) were 0.18 (0.025) for full trauma, 0.101 (0.027) for childhood trauma, 0.113 (0.03) for adult trauma, 0.05 (0.013) for catastrophic trauma. The meta-analyzed estimates (SE) for proportion of variance attributable to the interaction effect (G × E) were highest when exploring full trauma 0.201 (0.009) and were 0.084 (0.006), 0.081 (0.005), and 0.074 (0.006) when exploring childhood, adult, and catastrophic trauma separately, respectively.Similar results were obtained when using only unrelated individuals as well as when mixed linear models used Es computed from trauma eigenvectors precorrected for the full sample G , female, and male samples. Compared with the analyses of the full sample , the meta-analyzed interaction variance was smaller when explored within the female sample and larger when explored within the male sample. Similar results were observed when using only unrelated individuals , approximately the same magnitude as the variance captured by self-reported trauma exposure itself (5%-18%). Exploring variance components of depression/neuroticism within male and female individuals separately indicates that the proportion of CIDI depression variation captured by genome-by-trauma interactions differs substantially between the sexes, with estimates being approximately 5-fold greater in male individuals.27 We computed trauma exposure and genome-by-trauma interaction similarity to explore trait variance attributable to these effects by incorporating the genetic, trauma exposure, and interaction terms as random effects as opposed to fixed effects within linear models.27 Moreover, we used all related individuals, with appropriate sensitivity analyses . By using all available genotyped data, the mixed linear models implemented have greater statistical power to identify phenotypic variation attributable to genetic and genome-by-trauma interactions. Hence, this method is able to uncover a greater proportion of trait variance than observed with individual SNVs or PGSs. We explored general (full trauma) and specific  measures of trauma.To more comprehensively capture genome-by-trauma interactions within depression and neuroticism, the approach uses all genotyped variants to compute genetic similarity instead of individual SNVs or PGSs as in some previous studies.46 Our results suggest statistically significant and modest genetic correlations between the trauma and depression/neuroticism variables. Genetic correlations are 2-fold greater with CIDI depression as opposed to broad depression and neuroticism. A perception component of these traits, ie, the more extreme one is on the depression scale the more likely one is to either remember or perceive an event as traumatic, may explain these genetic correlations. This caveat should also be considered and curb interpretation of the causal role of trauma exposure in depression manifestation. Future research that can combine longitudinal designs with prospective measures will be able to greatly enhance this line of research.Our heritability estimates of the trauma measures support findings from the literature.49 Findings also suggest the subcategories, childhood, adult, and catastrophic, trauma explain a substantial proportion of CIDI depression variance.Our results provide depression/neuroticism heritability estimates and estimates for proportion of variance attributable to self-reported trauma exposure in line with previous literature.As mentioned previously, we observed significant contributions of genome-by-trauma interactions to depression/neuroticism phenotypic variation. This finding was consistent  across the subcategories  of trauma exposure . Lower, Our findings suggest that model fit was significantly improved when mixed linear models used environmental relationship (Es) and genome-by-trauma interaction (G × E) matrices. This was also observed when Es were computed using only the first eigenvector of trauma items. However, the variance components estimates were substantially attenuated when compared with results from models including Es computed using all PCs of trauma items. This suggests that important trauma exposure and genome-by-trauma interactions may be distributed across the different dimensions of self-reported trauma exposure. The inclusion of more self-reported trauma exposure PCs may additionally capture the differential impact of subtypes of trauma exposure.The substantial difference between the sexes in the proportion of depression variation captured by genome-by-trauma interactions was also observed for broad depression and neuroticism phenotypes. These results, alongside the evident prevalence differences, highlight the importance and need to explore these associations within the sexes separately. Our findings suggest that trauma exposure and sensitivity to trauma exposure accounted for greater variance in depression/neuroticism outcomes for male individuals.Although using PCs enables the use of all trauma exposure variables, it is difficult to interpret directions of associations as higher PC values do not necessarily mean higher levels of trauma exposure. Further, research can be conducted to explore the direction of these associations. Exploring individual trauma  measures may provide a better understanding of the effect of specific trauma and genome-by-trauma experiences.30Our findings suggest evidence of differential associations between trauma exposure dependent on differences in individual genetic liability with depression. Our research design and analyses are repeated across 5 geographic cluster samples. These within-sample replications, although not independent samples, yielded relatively consistent estimates and SEs, thereby increasing confidence in our results. It is evident that the method employed here has major advantages when exploring genome-by-trauma exposure interactions as opposed to much of the literature making use of PGSs.50 Moreover, results show that our environmental variables, full trauma and the subcategories of trauma, have moderate heritability estimates, and these are genetically correlated with our outcome phenotypes (depression/neuroticism). This highlights that our environmental measures captured both genetic and environmental variances. As genomic relationship matrices are not capturing the entirety of the genetic variance within depression/neuroticism outcomes, the variance captured by the trauma (and subtrauma) measures may capture residual genetic variance.There are limitations to this study that need to be considered when interpreting results. Hence, results of this study may not be generalisable to the whole population.Although the Gs computed used all genotyped SNVs and subsequently accounted for greater variance than PGSs, discrepancies between twin study heritability and SNV heritability estimates of depression were still apparent. Twin study estimates may be biased upward owing to the presence of gene-environment interplay effects, and thus, real heritability estimates are likely to fall between SNV heritability and twin study heritability estimates.50 which would be useful in addressing the limitations outlined previously. Moreover, future work could entail the simulation of correlated genetic and environmental data with absent G × E associations, to explore how this would affect the mixed linear model results, particularly the variance attributable to G × E.To control for genetic covariance between our environmental and outcome variables, we also explored measures of trauma precorrected for the available genetic measure (Gs). The differences in estimates of variance components were negligible  may be able to provide a measure of trauma exposure that is less susceptible to reporting bias and thus, measurement error. For instance, the availability of methylation data has increased substantially and can be used as good proxy measures of environments as seen with smoking.52 Evidence suggests there may be a methylation profile associated with trauma exposure.54 Genome-by-environment interaction effects using methylation data can then be dissected to explore biologic pathways with nonadditive effects on outcomes that can be directly targeted. Findings could also further clarify the relationship between genetic liability and trauma exposure.The UK Biobank study is a homogeneous cohort with a healthy volunteer bias, which means that participants tend to have relatively better health and higher socioeconomic status.In conclusion, findings of this cross-sectional study suggest empirical evidence of depression/neuroticism variation associated with genome-by-trauma interactions. The magnitude of these associations were much larger for male individuals than for female individuals. These findings can be further explored to identify both risk groups and modifiable environments/biological pathways that yield greater risk of depression manifestation, which would be useful in personalized/preventive interventions.


Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement. Genotyping platforms are prerequisite for genomic research, genetic analysis, and marker-assisted breeding in animals and plants. Compared with other marker types, single nucleotide polymorphisms (SNPs), as the most extensive and stable genomic variations of multiple species, are ideal markers for genotyping because of their advantages in ultra-high-throughput detection and easy integration of genotypic data .Array-based and sequencing-based technologies (next-generation sequencing) are the major genotyping platforms which are available for the screening of SNP markers. In the former technology, the fixed nature of SNPs on an array is helpful for cross-project comparisons because the same markers are used . HoweverGenotyping by target sequencing (GBTS), a newly developed sequencing-based genotyping platform, involves the capture of target genomic loci by probes . GBTS inHybrid breeding plays a great role in improving maize and many other crops. It mainly involves the development of inbred lines with high general combining ability (GCA) and specific combining ability (SCA) and the identification of hybrids with high yield potentials . The estGenomic selection, first proposed by Although several SNP genotyping platforms were developed through GBTS in maize, no GBTS system was evaluated in genomic selection for GCA and hybrid performance. In addition, the current GBTS platform still needed to be specifically customized and optimized according to different applications in genetic and molecular breeding. In the present study, we designed a low-density GBTS panel from diverse resources and evaluated its applications in genotyping, population structure classification, and genomic prediction for GCA and hybrid performance.1 hybrids  were sequenced through RNA-seq technology at seven seed developmental stages (Dataset II). A total of 1,973 heterosis-related SNPs were selected from the above data using two following criteria: (1) SNPs were retained when the corresponding genes were significantly differentially expressed between F1 hybrids and one of their corresponding parents in at least one F1 hybrid and one developmental stage using edgeR , and these differentially expressed genes were significantly correlated with the mid-parent heterosis of hundred-kernel weight (HKW) and fresh HKW in at least one developmental stage using weighted gene co-expression network analysis , which generated 540 F1 hybrids. Two hybrid populations were evaluated in field experiments at Xinxiang and Zhoukou, Henan, but Pop1 and Pop2 were grown in 2020 and 2021, respectively. Entries were evaluated in one-row plot using randomized complete block design with two replicates. The plot size was 4 m and 3.3 m in length in Xinxiang and Zhoukou, respectively, all with 0.60 m between rows and 0.22 m between plants. Traits determined were grain yield per plant (GYP), ear weight (EW), HKW, and kernel number per row (KNR) in both populations. The analysis of variance was calculated following a linear mixed model.To verify the effectiveness of the low-density GBTS genotyping platform, two populations (Pop1 and Pop2) were used in the present study. Pop1, a genetic population, contained 254 inbred lines from China (150) and USA (104), of which 250 inbred lines and four testers  produced 942 Fy=μ+E+R+GCAL+GCAT+SCA+GCAL×E+GCAT×E+SCA×E+ϵ , where y indicates the phenotypic value of hybrids, μ denotes the overall mean, E represents environment effect, R represents replicates. GCAL and GCAT are effects of inbred lines and testers, respectively; SCA is the effect of the combinations of inbred lines and testers; GCAL×E , GCAT×E , and SCA×E indicate GCAL , GCAT , and SCA interaction effects with environment, respectively. The variance components and GCA effects were calculated using R package lme4. The heritability of GCA effects was calculated using the following formula which was modified from t , r , and e are the numbers of tester lines, replicates, and environments, respectively. Heritability at per mean level and best linear unbiased estimate (BLUE) values of hybrid traits were calculated using QTL IciMapping v4.2 software (software .http://www.gramene.org/). GATK v4.1.2.0  was estimated as:K) was set from 1 to 8 in Pop1, whereas that was 1 to 10 in Pop2.The length of burnin period and the number of MCMC replicates after burnin were 5,000 and 50,000, respectively. The Structure output was visualized by Structure Harvester  program in Structure v2.3.4 . The numarvester , and delP v1.1.2  was usedP v1.1.2 . The cirh was set at 0.1, 0.5, and 2.5. For the GCA prediction, the genotypes were coded by -1 for one homozygote, 0 for the heterozygote, and 1 for the other homozygote. Randomized imputation was used for missing markers, according to the known genotype frequency , genomic best linear unbiased prediction (GBLUP), and reproducing kernel Hilbert space (RKHS) were adopted for genomic prediction using all filtered genotyping data. For RKHS, three kernels were used and their bandwidth parameter requency . The aboAll GS models and prediction strategies were performed using the R package, BGLR . For allThe fixed nature of the GBTS technology allowed the comparisons between different projects. The genotyping of the two populations was conducted in two batches, therefore we found common SNPs between the filtered genotyping data of both populations. Based on these common SNPs, the prediction accuracy of GCA and hybrid performance was calculated using the RKHS method. As all filtered genotyping data, the same cross-validation and parameters were used.Among the 5,521 target SNPs, 2,644 SNPs were identified from the weighted gene co-expression network analysis, allele-specific expression analysis, known genes, and published references Table 1,r value ranging from 0.98 to 0.99  were also observed between the GCA effects of GYP and EW in the two populations. For GCA effects of other traits, similar correlation values were found as those of the hybrid traits.Hybrid phenotypes and parental GCA were analyzed in this study. Descriptive statistics were shown in H2 = 0.16-0.17), whereas those were high (H2 = 0.82-0.84) in Pop2. For other traits, the heritabilities of GCA effects ranged from 0.50 to 0.84. Big variations observed in the heritabilities of GCA for GYP and EW across populations could be attributed to genetic backgrounds, environmental effects, or the interactions between them.Analysis of variance showed that significant GCA, SCA, GCA-by-environment interaction, and SCA-by-environment interaction variances were revealed for all traits except for one source in KNR of Pop1  to 3,446 (chromosome 1) in Pop1, whereas that ranged from 376 (chromosome 6) to 2,391 (chromosome 1) in Pop2  to 0.43 (HKW) in Pop1, whereas those varied from 0.52 (HKW) to 0.65 (EW) in Pop2 Figure 3Except for GCA effects, the predict power of hybrid phenotypes was also evaluated for the genotyping panel. The above five GS methods incorporating additive effect only and additive plus dominance effect were adopted. In Pop1, the prediction accuracies for the performance of hybrids ranged from 0.22 (GYP) to 0.65 (HKW) in the five methods including additive effects, whereas those were improved and ranged from 0.51 (EW) to 0.66 (HKW) in the AD model Figure 4Based on all filtered SNPs, the RKHS method showed superior performance over the other four methods particularly when the additive effect was considered in Pop1 and showed similar or slightly lower advantages in other circumstances, therefore the method was used for the cross-project comparisons and the prediction for potentially functional markers. Due to the fixed nature of target genomic loci, 7,743 SNPs were simultaneously detected between 20,210 and 11,734 SNPs, accounting for 38-66% of those filtered genotyping data  and 1-40K mSNP, were developed, which made the technology an effective and efficient tool for genotyping and population structure classification . In the For Pop1, 254 maize accessions were divided into five subpopulations by the Structure analysis, 76% of which was in agreement with the phylogenetic tree . This diGS has obvious advantages for improving genetic gains in animal and plant breeding, but the price of genotyping can be prohibitive for many species . TherefoDecomposing of variances of hybrid performance into GCA and SCA variances could reflect the role of additive and non-additive effects. In Pop1, the inclusion of the dominance effects could effectively improve the prediction accuracy of hybrid phenotypes in GBLUP and the three Bayesian methods. In particular, the AD model boosted the prediction accuracy for GYP and EW by more than two-fold compared with the A model, which agreed with a previous study where GBLUP with AD effects doubled the predictive capacity for maize grain yield under water-stressed trial in comparison to the A model . In geneper se can capture non-additive effects in hybrid populations  of GCA effects for GYP in Pop2 based on 11,734 SNPs was higher than that (0.49-0.55) achieved using 39,659 SNPs from the DArT-seq platform . For hybThe data presented in the study are deposited in the Sequence Read Archive repository, accession number PRJNA728476, PRJNA649667, PRJNA681326, PRJNA843481, and PRJNA843784, and in the China National GeneBank DataBase Sequence Archive repository, accession number CNP0003317.JM designed the study, collected the phenotypic data, analyzed the phenotypic and genomic data including population structure and genomic prediction analyses, wrote and revised the manuscript. JM, YC, YW, and YD provided the DNA resequencing and RNA sequencing data for GBTS panel development. All authors contributed to the article and approved the submitted version.This work was supported by the Science and Technology Project of Henan Province (222102110043) and by the Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences (2020YQ04).We would like to thank Dr. Jiankang Wang, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS) for providing valuable suggestions for this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program’s relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton ( Gossypium spp.) breeding there is a strong negative association between total fiber yield and major fiber quality traits, especially fiber length and strength [Plant breeders play a crucial role in the development of new cultivars by selecting characteristics of contrasting traits in order to ultimately obtain rare individuals that are recombinant for otherwise opposing phenotypes. For example, in cotton  and G. barbadense  [G. barbadense has a higher quality  fiber, whereas G. hirsutum has a shorter growing season, average fiber quality, and substantially elevated yield [Two allotetraploid 2n = 4x = 52) species comprise the vast majority of the cotton fiber and cottonseed production worldwide. The two predominant species are x = 52 sped yield .Currently, public-sector cotton breeders in the US screen phenotypic characteristics on tens of thousands of early generation progeny (F3 and F4) for fiber quality, yield, and yield components, as well as smaller numbers (10’s or ~100’s of entries) of advanced progeny (F5 and forward). This annual screening process is expensive, laborious, and requires the collection of a large volume of hand-picked and/or machine-harvested seed cotton samples. Following harvest, this large volume of samples must be individually ginned on a laboratory gin to separate seeds and fibers to obtain information on the gin turnout (ratio of fiber to combined weight of seed and fiber) and physical fiber properties , measured using standardized equipment such as the High Volume Instrument (HVI) and/or the Advanced Fiber Information System (AFIS). Little is known about the trait architecture of many complex traits in cotton due to the low co-occurrence of identified significant loci shared between studies . Since mThe Pee Dee (PD) Germplasm Enhancement Program has been a long-term cotton improvement project with USDA-ARS in Florence, SC, USA, since 1935. Over time, breeders have maintained genetic diversity while combining fiber quality traits, especially fiber strength and length, with insect/disease resistance and acceptable levels of fiber yield and yield components . It has Selection for fiber quality and yield is complicated by the contribution of genotype × environment (G × E) interactions on these traits ,10. GeneResearch assessing genomic selection across multiple environments in cotton is limited. A group in Australia studied the utility of genomic selection for cotton fiber length and strength on historical data and found a similarly favorable performance using a range of statistical models with a wide variance in prediction accuracy in different environments . Little The goal of this study was to evaluate the genetic underpinnings of critical quantitative traits, including seed composition, yield, and fiber quality measured with both HVI and AFIS, across multiple environments in a set of historical cotton breeding materials (the Pee Dee breeding program), with the longer-term goal of conducting a preliminary analysis for implementing a genomics-based selection in public cotton breeding programs. We aimed to explore loci with estimable effects using association mapping and the polygenic background based on realized genomic relationships.p-values.A total of 15,177 SNPs remained after filtering to retain only polymorphic SNPs from the CottonSNP63K array. Based on historical pedigree records, this set of 80 lines is related mostly within four or so cycles of breeding, with dozens of rounds of successive recombination resulting in admixture between groups [2 = 0.22, compared with H2 ≥ 0.75 for all others). Gin turnout (the ratio of lint to seed and lint) and seed index (seed weight) were the more heritable yield components, whereas bolls per square meter and boll weight were the least heritable yield components. Lint yield, the multiplication product of seed cotton yield and gin turnout, was highly heritable (H2 = 0.96).In line with prior estimates , the fibThe average single year–location environment broad-sense heritabilities were consistently lower than the overall heritabilities, often having high standard errors . This suWe performed an association analysis between each of the 14 replicated field trials (by environment) and the filtered genome-wide SNP data on the panel of 80 historical cotton lines from the Pee Dee breeding program  yielded little to no marker–trait associations in 5+ environments, indicated as “-” in 2 < 0.85) were slightly less likely to have at least one significant GWAS hit in at least three environments  are more promising, with hundreds of marker–trait associations discovered in multiple environments. The markers discovered here , with the exception of AFIS immature fiber content, HVI micronaire, seed protein/oil, and boll weight from seed cotton , immature fiber content, seed oil, seed protein, and boll weight prediction abilities also had the highest standard deviations, indicating large variability in the prediction ability, depending on how the data were randomly partitioned during cross-validation  or differences in the phenotype distribution .Some traits varied substantially depending on the year–location of the trait means analyzed in the environment dataset. For example, lint yield prediction ability ranged from close to zero for both the Rocky Mount 2005 and 2006 means all the way up to 0.64 in Florence 2006, and topping out at 0.66 for the overall means. Especially for yield, these results indicate the importance of replicated, multi-location, multi-year trials to build models for employing genomic selection in highly complex traits, such as yield in cotton. For other traits, model implementation based on one year  of data seems more plausible. The prediction ability for fiber strength and length was more uniform across different subsets of the data, with prediction ability >0.29 in all of the single environment models .2 = 0.27), had a uniformly poor prediction ability . The seed protein % had a strong environmental effect and a small genetic component [2 = 0.88), but a long tail in the phenotype distribution, with some lines having very high and very low seed oil content . For fiber length and strength, in contrast to yield, it may be better to perform trials at multiple locations regardless of the year, since this is the situation that seems to be most challenging to the model examined here. These results further support the immediate application of genomic selection to these high-value fiber quality traits.The GBLUP model is designed to capture the additive genetic component for a trait by connecting overall genetic relatedness to the lines’ breeding value. The model operates under the assumption of the infinitesimal model, which states that an individual’s phenotype is due to the additive effects of many loci across the genome, each with a very small effect. GBLUP can have a poor prediction ability in the presence of violations to this assumption—for example, if dominance, epistasis, or a small number of high-effect loci exert substantial effects on the phenotypes. Since GBLUP is only capturing the additive component of genetic variance for a trait, it is important to investigate how alternative genetic models (kernels) can be used to improve the prediction ability .We investigated the possibility of integrating individual GWAS hits into the GBLUP model by comparing models that include and do not include one significant SNP. This procedure was repeated for each of the SNPs that were stable (detected in ≥3 environments). For each GWAS hit, we calculated the change in prediction ability observed from including that GWAS hit as a fixed effect in the model (only in the environments the SNP was detected in). At the threshold established and used in the preceding portion of this study , most (%) GWAS hits improved the model prediction ability . With stGenomic prediction has demonstrated success in many economically important crop species. The vast majority of cultivated varieties of cotton in the United States are bred by private companies, carrying patented transgenes for insect resistance and herbicide tolerance. Although the role of public-sector cotton breeders is constantly evolving, public breeders have always played key roles in pre-breeding for germplasm improvement, basic and applied genetic studies, and the introgression of alleles from uncultivated lines or specific genotypes into elite backgrounds. Genomic selection presents an opportunity for cotton breeders to accelerate genetic gain in the cotton gene pool by increasing the efficiency and accuracy of selection.As a first step, we used GWAS to identify SNPs associated with the 20 traits in this study. Many loci significant in one environment were not significant in other environments, a frequent problem in GWAS that can be due to a number of practical or statistical matters, potentially including a lack of power to detect small-effect loci in this small panel, normal difficulties in obtaining accurate trait measurements in field trials, and genotype × environment interactions. The GWAS results, especially those stable associations discovered in 3+ environments, can be utilized by breeders to develop genetic screens for marker assisted selection.Brassica spp. and soybean [In this study, we evaluated the prediction ability of a standard genomic prediction model in 20 traits that cotton breeders frequently evaluate in their breeding materials. The analysis of AFIS and HVI fiber quality traits, seed oil/protein, and yield and its substituent components revealed heterogeneity in the prediction ability between traits and scenarios. Generally, heritability was a decent predictor for prediction ability, as revealed by cross-validation, although seed oil % stood alone in having a high heritability but low prediction ability. A poor genomic prediction performance can occur when the genetic model differs from that of the underlying genetic architecture. Seed oil is an oligogenic trait with a small number of major genes in some crop species such as  soybean , suggest5 stage and forward due to challenges in management and logistics for larger sizes of field trials, as well as increased residual error due to field heterogeneity. However, with a well-designed training set, perhaps using historical data and lines from within the breeding program, genomic prediction could be implemented for fiber length and strength immediately. However, the current genotyping platforms available to cotton breeders are cost-prohibitive to use at any early stage in the breeding cycle because of the amount of plants that need to be screened (thousands to tens of thousands), so a low-density, high-throughput system for assessing genomic relationships is crucial to make genomic selection a reality for cotton breeders.More field trials with a larger training population and high replication are needed before the genomic prediction for yield or yield components can be feasible in practice. Public-sector cotton breeders usually limit replicated field trials to <100 entries at the FFor fiber length, one group used direct measures of expression and single nucleotide variants in 400 known fiber length genes to predict fiber length  in a biparental population . With a Others have modeled fiber quality using a marker × location interaction term to predict cotton fiber quality, finding that adding this information to the model significantly improves the prediction accuracy when trying to predict performance across environments . They weEighty historical cotton lines from the PD breeding program were genotyped with the CottonSNP63K array , as repoStatistical analysis was performed using the method of Campbell et al. (2009)  with PRO2) and the associated standard errors were calculated for the overall and per environment datasets using a random-effects model fitted with PROC MIXED in SAS v9.4 [2 < 0.01), indicative of a very low genotypic effect relative to the error component in the model, were excluded from the corresponding single environment analysis.Family-mean broad-sense heritability (HSAS v9.4 . BrieflyGenome-wide association study (GWAS) was performed in PLINK v1.9 ,35 for tThe cross-validation accuracy of the genomic best linear unbiased predictor (GBLUP) was estimated using the Reproducing Kernel Hilbert Spaces (RKHS) model for the overall and environment datasets in an 80% train, 20% test arrangement over 100 iterations in BGLR statistical package . The reaIn total, our results support the development of further genomic resources to enable breeders to tailor genetic marker development and utilization within their own breeding program’s gene pool and outline the need for more field trial data with larger numbers. Multiple years or locations of data should be used to ensure the chosen model can account for genotype × environment interactions, either directly or indirectly. Genomic selection on fiber quality should be possible to implement in the near term, as long as a suitable genotyping platform for estimating genomic relationships can be identified. Although the utilization of these resources will come down to the scope and scale of each individual breeding program, uniform phenotyping and genotyping will allow breeders to share some information to better estimate marker effects and empower more accurate predictions, hopefully reducing the number of early generation progeny needed to screen for these complex traits. More data for fiber and seed cotton yield, as well as seed protein and oil content, will help determine whether or not a genomic prediction for these traits is possible in cotton. Other important traits in cotton, such as abiotic and biotic stress, theoretically have the potential for genomic prediction, although the selection efficiency will be limited by trait heritability and other practical limitations of large-scale field experiments with controlled stress conditions. To enable genomic prediction in cotton breeding at scale, the community needs a low-cost genotyping system that can be used to accurately estimate relatedness between selection candidates. Shared phenotyping methods and data tracking systems will also enable data exchange and integration, bringing genomic prediction to fruition in public cotton breeding programs sooner.


Genetic progress for fertility and reproduction traits in dairy cattle has been limited due to the low heritability of most indicator traits. Moreover, most of the quantitative trait loci (QTL) and candidate genes associated with these traits remain unknown. In this study, we used 5.6 million imputed DNA sequence variants  for genome-wide association studies (GWAS) of 18 fertility and reproduction traits in Holstein cattle. Aiming to identify pleiotropic variants and increase detection power, multiple-trait analyses were performed using a method to efficiently combine the estimated SNP effects of single-trait GWAS based on a chi-square statistic.SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow , and sire  traits.There were 87, 72, and 84 significant SNPs identified for heifer, cow, and sire traits, respectively, which showed a wide and distinct distribution across the genome, suggesting that they have relatively distinct polygenic nature. The biological functions of immune response and fatty acid metabolism were significantly enriched for the 184 and 124 positional candidate genes identified for heifer and cow traits, respectively. No known biological function was significantly enriched for the 147 positional candidate genes found for sire traits. The most important chromosomes that had three or more significant QTL identified are BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several novel and biologically important positional candidate genes were strongly suggested for heifer (These findings contribute to better understand the underlying biological mechanisms of fertility and reproduction traits measured in heifers, cows, and sires, which may contribute to improve genomic evaluation for these traits in dairy cattle.The online version contains supplementary material available at 10.1186/s12864-022-08555-z. Fertility, reproduction, calving, and fertility disorders represent a group of traits that directly impact the economic efficiency and animal welfare in the dairy industry , 2. HoweBos taurus autosomes (BTA) and in the X chromosome are associated with fertility and reproduction traits. However, most of these studies used the low- or medium-density single-nucleotide polymorphism (SNP) panels, which might hinder the discovery of candidate genes and causal variants. Due to the reduced cost of high-throughput sequencing approaches, several large-scale genome resequencing initiatives have been undertaken, such as the 1,000 Bull Genomes Project [Genome-wide association studies (GWAS) have been popularly performed as a standard method for QTL mapping and candidate gene discovery in both humans and other species . For ins Project  and the  Project . Based o Project , 16. The Project . Previou Project –20).There is a plethora of traditional and novel trait definitions for measuring fertility and reproductive performance in females and males, which are further complicated by alternative definitions across breeding programs , 21, 22.In this study, we first performed single-trait GWAS analyses for 18 fertility and reproduction traits in North American Holstein cattle, using imputed WGS genotypes. Following the method proposed by Bolormaa et al. , the mulThe analyzed traits and their phenotypic summaries  are shown in Table The single-trait GWAS revealed a total of 1,484 SNPs that were significantly associated with the 18 fertility and reproduction traits with no overlapping SNPs between traits. The highest and lowest numbers of significant SNPs were observed for DO (days open) and NRRh (non-return rate heifer), respectively Fig. . The higFor the multiple-trait analyses, the defined trait categories of heifer, cow, and sire traits, and their combinations are shown in Table The multiple-trait analysis of six heifer traits revealed 87 significant SNPs broadly distributed across 22 chromosomes Fig. . Among tBOLA-DRA), MHC class I antigen (BOLA-NC1), MHC class I heavy chain (BOLA), and MHC Class I JSP.1 (JSP.1). Another significantly enriched GO term, the “prolactin receptor binding”, was found related to three genes, named growth hormone E4 (GHE4), prolactin-related protein 14 (PRP14), and prolactin-related protein IX (PRP9), within the region of BTA23:34.80–35.03 Mb.The functional analyses revealed that the identified 184 candidate genes were significantly enriched into 21 Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways Fig. S. Among tThere were 72 SNPs significantly associated with cow traits Fig. . These sMOGAT2; 2-acylglycerol O-acyltransferase 2, LOC785379; ENSBTAG00000017443; ENSBTAG00000015091) within the region of BTA15:54.88–55.08 Mb and to one gene  within the region of BTA18:63.08–63.28 Mb. Three GO terms of “integrin complex”, “protein complex involved in cell adhesion”, and “plasma membrane signaling receptor complex” were linked to six genes on BTA25 , BTA18 , and BTA10 . The KEGG pathway of “Olfactory transduction” was also associated with 12 genes on BTA5 and BTA10.Eight GO terms and one KEGG pathway were significantly enriched for the 124 identified candidate genes Fig. S. AcylglyHEXB (Hexosaminidase subunit beta) and LOC786974 (Beta-hexosaminidase subunit beta) were also involved in this biological function.Eighty-four significant SNPs were associated with sire traits, which were broadly distributed across 24 chromosomes Fig. . Two genWhen the 14 female fertility and reproduction traits were combined (heifers and cows), 96 significant SNPs were identified across 28 chromosomes Fig. . The thrThe multiple-trait chi-square statistics was also applied to all 18 traits of heifers, cows, and sires combined. Eighty-six significant SNPs and 178 candidate genes were identified across 26 chromosomes Fig. . All sigDue to the great economic importance of fertility and reproduction traits in cattle industry, GWAS have been frequently carried out especially since genotyping become affordable for large sample sizes. Besides the fact that SNP panels have been used in former GWAS of cattle fertility and reproduction traits –39, wholMost fertility and reproduction traits are lowly heritable in cattle. Berry et al.  reportedIt is well known that the genetic basis and physiological processes underlying reproduction differ between heifers and lactating cows. In general, heifer traits have higher heritability, but lower genetic correlations with each other compared to cow traits , 44. TheIn previous GWAS for fertility and reproduction traits, different variants, candidate genes, and QTL were found significantly associated with heifers and cows , 27, 37.The mean CR of Holstein heifers was estimated to be 56.3% in the United States, which is influenced by the age at breeding, month of breeding, age of service sire, and other factors . AccordiBoLA-NC1, found in this study and involved in the KEGG pathway of allograft rejection, was suggested to increase maternal immunity against the fetus [JSP.1) was found to play a crucial role during early pregnancy in heifers in a previous study [PIK3C3) was identified in this study. These findings suggest the importance of immunological tolerance of dam to fetus and other immune responses in establishing successful pregnancy.For the CR at first service and the number of times bred in Holstein heifers, Galliou et al.  found thhe fetus . Anotherus study . Similarus study  found thus study , anotherSOD2) for meiotic defects in mouse oocytes [WTAP) for spermatogenesis in mice [ACAT2) for DPR in Holstein [TCP1) for mediating sperm-oocyte interaction in mice [TGFBR2) for female fertility in pigs and mice [DLEC1) and solute carrier family 22 member 14 (SLC22A14) regulating male fertility in mice [ACVR2B) associated with premature ovarian failure in human [PFKFB4) involved in spermatogenesis in mice [TRIM27) and PRP14 on BTA23, were suggested to be associated with male and female fertility, respectively [TMEFF2) on BTA2 involved in early oocyte development in human [HECW1) on BTA4 required for estrogen-induced degradation of Scribble [CST8) on BTA13 involved in sperm maturation [DNAH17) on BTA19 associated with human sperm fertility [ADAM3A) on BTA27 regulating sperm migration in mice [ZP4) on BTA28 regulating embryo development in rabbits [Notably, a QTL, that was located on BTA9:95.88–96.08 Mb and supported by five previously reported QTL, contained four candidate genes having biological implications in reproduction, including the superoxide dismutase 2  located on BTA25 were associated with CTFS in Iranian Holstein cattle [KIF13B) involved in oocyte meiosis [ANXA1) with regulatory functions during early pregnancy in mice [PCSK5) that contributed to ovarian follicle development in rats [ZNF483), which is associated with age of puberty in Brahman heifers [FEM1B) and sperm equatorial segment protein 1 (SPESP1) on BTA10, were reported to be involved in polycystic ovary syndrome in humans [CADM2) on BTA1 with the number of piglets born dead in pigs [CACNB4) on BTA2 with CTFS in Iranian Holstein Cattle [ST6GALNAC5) on BTA3 with fertility index in Holstein cattle [AGBL1) on BTA21 with out of season lambing in sheep [PTPRZ1) on BTA4 differentially expressed between high- and low-fertility Holstein cows [LRBA) on BTA17 differentially methylated between high- and low-fertility bulls [PPARG) on BTA22 involved in the release of oocytes each estrous cycle [JMJD1C) on BTA28 required for long-term maintenance of male germ cells in mice [Interestingly, numerous candidate genes identified in this study also have the known biological functions associated with reproduction in literature. Two genes  on BTA20 have no functional evidence in literature with respective to reproduction. However, we found many novel candidate genes that have direct functional implications in reproduction. For example, the engulfment and cell motility 1 (ELMO1) gene on BTA4 was found to play crucial roles in clearance of apoptotic germ cells and spermatogenesis in mice [JMJD1C and cilia and flagella associated protein 70 (CFAP70) on BTA28, are required for long-term maintenance of germ cells in mice [ELMO1 and JMJD1C genes were suggested by the significant SNPs located in intronic regions. One well-known candidate gene on BTA15 , associated with semen quality and fertility in bulls [Only one GO term  was significantly enriched among all candidate genes of sire traits. The two involved genes  associated with fertility traits in pigs [ADAM19) differentially expressed between different quality of bovine blastocysts [SOX30) required for male fertility in mice [VCAN) involved in embryo implantation in rabbits [RAB33B, member RAS oncogene family (RAB33B), associated with freezability in boar spermatozoa [TANGO2) influencing bull fertility [DGCR8) involved in human spermatogenesis [MAPK3), septin 14 (SEPTIN14), and poly(A) polymerase beta (PAPOLB) were found to regulate ovulation in mice [SYPL1) on BTA4 contributing to sperm maturation in sheep [MEGF11) on BTA10 associated with lifetime productivity in pigs [FUT8) on BTA10 differentially expressed between matured oocytes from older and younger women [MYT1) on BTA13 involved in reduced fertility of anovular dairy cows [ARHGEF3) on BTA22 associated with number of piglets weaned [MGLL) on BTA22 influencing metabolism of endocannabinoids in bovine endometrium [NEDD4L) on BTA24 associated with AFC in buffaloes [BCL2) on BTA24 influencing oocyte and early embryo survival in humans [There were four suggestive candidate genes on BTA7, including the PRELI domain containing 2  on BTA9, cadherin 1 (CDH1) on BTA18, fanconi anemia complementation group M (FANCM) on BTA23, DPY30 domain containing 1 (DYDC1) on BTA28, and neural EGFL like 2 (NELL2) on BTA5 [NELL2 was recently found to be involved in lumicrine system essential for testis-epididymis-spermatozoa signaling and male fertility [We combined multiple trait categories for multiple-trait analysis for exploiting potential pleiotropic effects for fertility and reproduction traits. When the 14 female traits were analyzed together, 10 out of the 33 suggestive candidate genes in heifer and cow traits were identified. In addition, 15 out of the 54 suggestive candidate genes in heifers, cows, and sires were identified when also combining the four male traits together. There were five additional identified candidate genes in the combined analysis that have biological implications in reproduction in the literature, including Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2  can further facilitate the mapping of QTL and variants associated with fertility and reproduction. Future studies could further investigate the most promising candidate genes using gene editing or gene knock-out experiments and real WGS data (instead of imputed WGS), which would enable the investigation of rare alleles  and structural variation in the genome . For the significant intergenic SNPs found in this study, their possible functional roles will be explored when a comprehensive annotation of genomic regulatory elements are available.SOD2, WTAP, DLEC1, PFKFB4, TRIM27, HECW1, DNAH17, and ADAM3A), cow , and sire  traits. More than one-third of the QTL identified in this study overlapped with previously reported reproduction-related QTL in cattle, and more than 50 candidate genes were supported by functional implications in reproduction, as found in the literature. In addition, many important candidate genes were identified via significant SNPs located in their intronic regions. These observations indicate high detection power and mapping resolution of this study. Therefore, a comprehensive investigation on underlying genetic basis of fertility and reproduction in heifers, cows, and sires are provided in this study, whose findings can be used for improving genomic evaluation for fertility and reproduction traits in Holstein cattle.The multiple-trait GWAS of 18 fertility and reproduction traits in North American Holstein cattle revealed several QTL and candidate genes associated with heifer, cow, and sire traits. These QTL were broadly distributed across the entire genome, which are consistent with the polygenic nature of fertility and reproduction traits. The biological functions of immune response and fatty acid metabolism were significantly enriched in heifer and cow traits, respectively, whereas no known functional enrichment was found for sire traits. The most important chromosomes, which had three or more significant QTL, were BTA22 and BTA23 for heifer traits, BTA8 and BTA17 for cow traits, and BTA4, BTA7, BTA17, BTA22, BTA25, and BTA28 for sire traits. Several candidate genes that have not been previously reported in cattle and other livestock were strongly suggested for heifer (The phenotypic and genotypic datasets used in this study were provided by the Canadian Dairy Network (CDN), a member of Lactanet Canada . A total of 18 fertility and reproduction traits were analyzed in this study (Table P > 1.0 × 10–8). The SNPs that were poorly imputed were also removed from the analyses. This was done in a previous step where accuracy of imputation was calculated based on genotype concordance rate and allelic R2 on a per-SNP basis, where the SNPs retained had a concordance rate and allelic R2 greater than 0.95 and 0.85, respectively. All QC were conducted using the PLINK software [Imputed WGS datasets containing 29,548,077 SNPs were available for 9,131 animals. The detailed genotype imputation process using the FImpute software , includisoftware , after wAs all known fixed effects were fitted when predicting the EBVs used to compute the dEBVs, only SNP and polygenic effects were included in the mixed linear model used for the association analyses, i.e.:X is a vector containing the genotype score for the tested SNP; where puted as :\documenith SNP; where software .r2 > 0.9) with another SNP that have a lower P in the GWAS, using the PLINK software [P values from GWAS [The strongly-linked SNPs were clumped out if they had high LD  was used as the reference genome. Functional enrichment analyses of the candidate genes were conducted using the g:GOSt function of the g:Profiler web server [The QTL were defined as chromosomal regions of ± 100 Kb around the significant SNPs, therefore, considering a flanking distance where, on average, high LD is expected in North American Holsteins . The ideb server , includib server  and KEGGb server . The defb server .Additional file 1: TableS1. Position distribution and minor allele frequencies of SNPs that passed thegenotype quality control; Table S2. Distribution of significant SNPs of single-traitGWAS; Table S3. Numbers of quantitative trait loci revealed by single-traitGWAS and their overlapping among traits; Table S4. The previously reported andreproduction-associated QTL for the significant SNPs revealed by multiple-traitanalysis of six heifer traits; Table S5. The previously reported andreproduction-associated QTL for the significant SNPs revealed by multiple-traitanalysis of nine cow traits; Table S6. The previously reported andreproduction-associated QTL for the significant SNPs revealed by multiple-traitanalysis of four sire traits; Table S7. Significant SNPs and candidate genesfrom multiple-trait analysis of 14 heifers and cows’ traits; Table S8. Thepreviously reported and reproduction-associated QTL for the significant SNPsrevealed by multiple-trait analysis of 14 heifer and cow traits; Table S9. Thesignificantly enriched GO terms and KEGG from multiple-trait analysis of 18heifers, cows, sire traits; Table S10. Significant SNPs and candidate genesfrom multiple-trait analysis of 18 heifers, cows, sire traits; Table S11. Thepreviously reported and reproduction-associated QTL for the significant SNPsrevealed by multiple-trait analysis of 18 heifer, cow, and sire traits; Figure S1. Manhattan plots (left) and Quantile-quantile plots (right) of thesingle-trait GWAS results based on imputed WGS data; Figure S2. Numbers of significant SNPs foreach trait category and their overlaps found by multiple-trait analysis. Figure S3. Significantly enrichedbiological functions of candidate genes revealed by multiple-trait analysis. Figure S4. Potential protein-protein interaction among biologically relevantgenes that were identified for heifer, cow, and sire traits in this study.