Zm00001d051328 for ear perimeter and width, zma-MIR159f for ear shape, Zm00001d053080 for kernel width and row number per ear, and Zm00001d048373 for the blue color channel of maize kernels in the red-green-blue color model. This study emphasizes the importance of researching novel phenotypic traits in maize by utilizing high-throughput phenotypic measurements. The identified genetic loci enrich the existing genetic studies related to maize ears.The morphology of maize ears plays a critical role in the breeding of new varieties and increasing yield. However, the study of traditional ear-related traits alone can no longer meet the requirements of breeding. In this study, 20 ear-related traits, including size, shape, number, and color, were obtained in 407 maize inbred lines at two sites using a high-throughput phenotypic measurement method and system. Significant correlations were found among these traits, particularly the novel trait ear shape (ES), which was correlated with traditional traits: kernel number per row and kernel number per ear. Pairwise comparison tests revealed that the inbred lines of tropical-subtropical were significantly different from other subpopulations in row numbers per ear, kernel numbers per ear, and ear color. A genome-wide association study identified 275, 434, and 362 Single nucleotide polymorphisms (SNPs) for Beijing, Sanya, and best linear unbiased prediction scenarios, respectively, explaining 3.78% to 24.17% of the phenotypic variance. Furthermore, 58 candidate genes with detailed functional descriptions common to more than two scenarios were discovered, with 40 genes being associated with color traits on chromosome 1. After analysis of haplotypes, gene expression, and annotated information, several candidate genes with high reliability were identified, including  Maize not only is consumed directly but also is extensively used as animal feed and industrial raw material, leading to an increased demand for maize in contemporary society , carotenoid cleavage dioxygenase (GRMZM2G446858), MYB-CC–type transcription factor, and phosphate starvation response protein 3. For the number of kernel rows, KRN4 was found to control quantitative variation in maize kernel row number , has been widely used to study the relationship between phenotype and genotype at the population level, not only in human diseases but also as a routine tool for gene mining of quantitative traits in crops, such as maize vascular bundle , plant hreported  to incluw number . Zhang eIn this study, 20 ear-related traits were obtained from 407 inbred lines from the maize association analysis population  using a 22.1The materials used in this study were 407 inbred lines from the maize association mapping panel published by 2.2Maize ear-related phenotype acquisition was carried out by a high-throughput phenotypic measurement method and system . As show−2 cm/pixel. OpenCV was used for image processing, and Visual C++ was used to develop modules for corncob state detection, image acquisition, image stitching, phenotype calculation, and statistical analysis modules  industrial camera  with resolution of 1.25 million pixels. The working frame rate for camera image acquisition was approximately 15 fps, and the image was saved in a JPEG format with a resolution of 1,292 × 964 pixels and a pixel size of 3.126 × 10 modules .By analyzing the color differences between the background and the roller chain in ear images, the state of the ear was detected, triggering image acquisition upon placing the ear in the imaging area. Ear segmentation employed color features, and the ear’s shape was enhanced through morphological calculations. It is important to note that the ear image represents a two-dimensional projection of the ear on a plane. On the surface of the ear, the kernels exhibited complex distortions within the image, and the shape and size of the kernels varied significantly across different positions. Comparatively smaller distortions were observed in the small region near the central axis of the ear. Extracting regions with the least distortions from the sequence of ear images allowed for stitching them together to obtain a panoramic image of the ear’s surface. Effective kernels were identified and extracted using ear boundary detection, kernel segmentation, and validity identification techniques. 2.3Descriptive statistical analysis of phenotypic data distributions was conducted using R (version 3.6.3) software, including the generation of histograms. An analysis of variance (ANOVA) was carried out to assess the differences in traits across subpopulations, utilizing the Holm method  to accouThe BLUP for the 20 phenotypic traits was estimated with the following linear mixed model in ASReml-R v.4.0 by using the “asreml” function of R package asreml . The forY is the vector of phenotypic values, X is the design matrix for fixed effects, β is the vector of the coefficients for fixed effects, Z is the design matrix for the random effects, u is the vector of the random effects, and ε is the vector of residuals. The BLUP values were considered as new phenotypic values in the subsequent phenotypic analysis and GWAS analysis.where 2H) was calculated for each trait as follows:Broad-sense heritability . Quality control of genotypic data of 407 inbred lines was performed, leaving 798,048 SNPs with a minor allele frequency (MAF) more than 0.05 and a call rate greater than 0.9. The GWAS for 20 ear-related traits from BJ, SY, and BLUP were conducted using GEMMA  was adopted to define statistical significance. Finally, Manhattan plots and quantile-quantile (QQ) plots were utilized to visualize the results. All candidate genes were annotated by annotate variation (ANNOVAR) (http://plants.ensembl.org/Zea_mays/Info/Index) and national center for biotechnology information (NCBI) Gene database (https://www.ncbi.nlm.nih.gov/gene).Genotypic data of maize association mapping panel were obtained from Maizego (ng GEMMA . GEMMA blculator  was usedANNOVAR)  accordinThe degree of explanation of phenotypic variation (PVE) for each SNP was calculated as follows:N is the sample size, se is the standard error of effect size for the genetic variant of interest, beta is the effect size for the genetic variant of interest, and af is the MAF for the genetic variant of interest.where 2.5Haplotype analysis was performed on the SNPs that were significantly associated with each trait. The Confidence Intervals method in Haploview 4.2  was utilhttps://qteller.maizegdb.org/) and visualized using data from the Kaeppler lab , and kernel size but also the multi-scale traits such as ear shape (ES) and color. A total of 2,333 valid image samples from 407 inbred lines were obtained, resulting in the acquisition of 20 ear-related traits. These traits were categorized into two groups on the basis of the acquisition method: seven traditional traits and 13 novel traits. The high-throughput phenotypic measurement method and system employed in this study facilitated a more efficient evaluation of phenotypic traits for future research. Detailed descriptions and definitions of the 20 ear-related traits can be found in 3.2The statistical analysis of 20 ear-related traits from two sites Figure 22H > 0.82), warranting further exploration of their underlying genetic mechanisms. In contrast, both bald tip traits [barren tip length (BTL) and barren tip area (BTA)] exhibited modest heritability , suggesting that environmental factors may have a greater impact on their formation. Overall, 70% of ear traits displayed heritability greater than 0.5, indicating that genetic factors still greatly influence maize ear traits. These findings provide valuable insights into the genetic and environmental factors that contribute to phenotypic variation in maize ear traits.A comprehensive analysis of heritability was carried out for 20 ear-related traits using high-throughput phenotypic measures and systems. The results revealed a range of heritability values from 0.3002 to 0.8320  and kernel number per ear (KNE) with r = 0.85 and adjusted P < 0.05. Furthermore, these two traits were significantly and positively correlated with EL, EV, and ear surface area (ESA) with r > 0.51 and adjust P < 0.05 but were significantly negatively correlated with ES and kernel thickness (KT) with r < −0.49 and adjust P < 0.05. In addition, the two bald tip traits (BTL and BTA) exhibited a strong positive correlation with each other with r = 0.97 and adjust P < 0.05. They were also found to have a significant negative correlation with average kernel area (AKA)  but a significant positive correlation with ES . Notably, ES, a novel trait investigated in this study, was found to be correlated with traditional traits of interest, KNR and KNE, as well as bald tip traits (BTL and BTA), highlighting its importance as a reference for the screening and determination of typical traits of maize ear. Finally, all six color traits showed a significant positive correlation with each other  as shown in The correlation between traits was determined using Spearman’s rank correlation coefficient method, and the conclusive correlation results were obtained by applying multiple testing corrections and reporting the corrected 3.4P < 0.05). Among them, RNE, KNE, and ER showed significant differences between TST and the other three subpopulations (adjust P < 0.05) , and kernel G (KG) did not show significant differences among subpopulations, indicating that these traits alone cannot be used as selection characteristics to differentiate between tropical or subtropical materials from temperate ones in maize varieties.3.5In this study, a comprehensive GWAS analysis was conducted on 20 ear-related traits, utilizing GEMMA under three scenarios: BJ, SY, and BLUP. The analysis yielded 275, 434, and 362 significant SNPs for BJ, SY, and BLUP, respectively. Of these, the number of significant SNPs detected varied from 0 to 25 for traditional traits and from 2 to 247 for novel traits in a single scenario Table 2.The phenotypic interpretation of the significantly associated SNPs varied across different traits, with the PVE by all SNPs ranging from 3.78% to 24.17%. Most of the loci showed a small PVE, averaging 7.06% Table 2.3.6Using the maize B73 reference genome (B73 RefGen_v4) available in EnsemblPlants, gene annotation was performed on all SNPs that displayed significant associations with 20 ear-related traits across three distinct scenarios. As a result, 174, 203, and 174 candidate genes were identified for BJ, SY, and BLUP, respectively. The number of candidate genes varied widely depending on the scenario and trait evaluated. For traditional traits, the number of candidate genes was generally modest, with a range of 0 to 15, whereas, for novel traits, there was a broader range, spanning from 2 to 95. Interestingly, our comparison of SNP results with gene annotations revealed that some genes were associated with multiple SNPs. This was particularly notable for color traits, suggesting that these genes could play a key role in regulating ear-related traits.To further investigate the candidate genes obtained from the three scenarios, we searched for their detailed functional descriptions in NCBI Gene databases. A total of 58 candidate genes associated with ear-related traits were found to have detailed functional descriptions common to more than two scenarios Table 3.3.7Zm00001d011060 on chromosome 8 (chr8.s_137865777 and chr8.s_137865788) were identified in two scenarios (BLUP and BJ) and significantly associated with EL. These SNPs formed two haplotypes, GG and AA, with respective population frequencies of 0.762 and 0.237. Inbred lines with the AA haplotype had longer EL compared with those with the GG haplotype (Zm00001d007100 on chromosome 2 formed two haplotypes (CT and TC), which exhibited notable differences in ear volume (EV) (Zm00001d048372 on chromosome 9 were significantly associated with KB and identified in two scenarios (BLUP and BJ). These SNPs formed two haplotypes (AT and GC) with respective population frequencies of 0.886 and 0.101, and inbred lines with the AT haplotype had a higher KB value compared with those with the GC haplotype  Figure 53.8Zm00001d028725, encodes beta3-glucuronyltransferase and had the highest expression level of 26.7 observed 18 days after pollination (DAP), compared with other tissues. Another candidate gene for ER (BJ and BLUP), Zm00001d028810, which encodes transaminase/transferase motifs, had sustained high expression in the endosperm. Moreover, we found that candidate genes for two novel ear-related traits (ESA and EV) were highly expressed in specific tissues. For example, Zm00001d033905, a candidate gene for ESA(SY) and EV , encodes growth-regulating factor-interacting factor 1 and had the highest expression level observed in immature cob of V18 stage, with a value of 75.9 as compared to other tissues. Furthermore, we observed that Zm00001d033905 had elevated levels of expression in the tip of ear primordium, indicating its potential candidacy as a candidate gene for ESA (SY) and EV (BLUP and SY).In this study, we utilized maizeGDB qteller to analyze the expression of 58 candidate genes in different maize ear-related tissues or organs. It was found that nearly all of these genes were expressed in one or more tissues (expression levels exceeded 1), highlighting their potential importance in ear development Figure 744.1Maize is the primary grain crop cultivated in China due to economic and social development and the continuous reduction in arable land. An urgent need exists to further enhance maize production, with breeding and planting new varieties being among the most direct and crucial methods to achieve this. Morphological traits of maize ears are essential parameters for selecting and breeding new varieties, including EL, diameter, number of rows, number of kernels in row, and number of kernels in ear. In this study, a high-throughput panoramic image–based method and system for maize ear testing was used to obtain the ear-related traits of the 407 inbred lines. The method and system employed achieved a better balanced the efficiency and accuracy of maize ear testing, with an average efficiency of image acquisition and calculation efficiency of 15 ears/min and four ears/min, respectively. Furthermore, a calculation accuracy of 99% for EL and 98.89% for ear row number was achieved . Because4.2The current study achieved a significant breakthrough by utilizing a high-throughput phenotypic measurement method and system to acquire a comprehensive and detailed profile of 20 traits related to maize ears, encompassing morphology, size, color, and kernel number. For clarity, the 20 traits were classified into two groups: traditional and novel. Seven of the traits  were traditional traits typically assessed manually in maize variety testing. The other 13 traits consisted of six color traits  and seven ear and kernel morphology traits . These traits, which could not be quantitatively evaluated using traditional methods, were identified as novel ear-related traits.Zm00001d026129, Zm00001d017241, and Zm00001d023468. In addition, ESA and EV showed a strong correlation coefficient of 0.96, and their correlation coefficients with two traditional traits, namely, KNE and KNR, exceeded 0.68. These four traits shared two genes, namely, Zm00001d007100 and Zm00001d033905. Importantly, even negatively correlated traits shared genes, which suggested the possibility of an antagonistic relationship in phenotypic expression, although they may be regulated by the same gene. For instance, the correlation coefficient between ES and KNE was −0.76, and they shared the gene Zm00001d045513, whereas KW and RNE had a negative correlation coefficient of −0.53 and shared the gene Zm00001d053080. The identification of shared genes across different phenotypes provided insights into the genetic foundation of diverse traits. These insights were crucial for understanding the genetic mechanisms underlying complex traits in maize. Although the shared genes served as a foundation for understanding the relationships among different traits, each trait possessed its own locus and gene and, therefore, maintained its individual significance. Specifically, this study revealed numerous novel traits sharing loci with traditional traits, emphasizing their potential importance in the selection and breeding of high-yield maize varieties. Moreover, these newly identified traits could be used as references for screening and determining typical ear-related traits of maize.The results of the correlation analysis revealed significant correlations among different ear-related traits. In addition, the GWAS analysis indicated the existence of shared genes among these traits. For instance, the correlation coefficient between the traditional trait BTL and the novel trait BTA was exceptionally high at 0.97, and they shared three genes, namely, This study offers significant insights into the complex genetic mechanisms underlying traits in maize and establishes a foundation for future research in this field. Through the identification of novel ear-related traits, researchers can enhance their understanding of the genetic and molecular pathways involved in maize growth and development. Furthermore, the findings of this study have practical consequences for efforts in maize breeding, providing new avenues for selecting and cultivating desirable traits. Overall, this study makes a significant contribution to the field of maize genetics and is likely to inform future research in this area.4.3The findings of this study highlight the significance of traits obtained through the high-throughput phenotypic measurement method and system, particularly novel traits that cannot be assessed by traditional methods. The high heritability observed for several traits (P = 0) between EP and EW in this study. This correlation is further supported by the identification of common genes  through GWAS. One of the identified genes, Zm00001d051328, also known as GRMZM2G377217, was reported to play a fundamental role in maize growth and development (Zm00001d051328 in the plant ontology (PO) term “central spike of ear (PO:0006505)” and “ear inflorescence (PO:0020136)” ” . These fzma-MIR159f has been identified as a candidate gene for ES. It has demonstrated that zma-MIR159f is involved in encoding the transcription factors MYB33 and MYB101  has been identified as a probable receptor protein kinase gene located on chromosome 4. Remarkably, this gene has shown significant associations with KW and RNE, with a correlation coefficient of up to −0.53. In addition, gene expression analysis has indicated that Zm00001d053080 exhibits relatively higher expression levels in the whole seed of B73 at 6 and 8 DAP compared with other tissues  and KB (BJ and BLUP) in our study. The selection for yellow- and white-kernel types has been central to postdomestication improvement of maize. It has previously been reported  gene that encodes a carotenoid-degrading enzyme, playing a role in evolution of kernel color in maize. These findings suggested that Zm00001d048373 could be an important candidate gene for the color trait in maize, specifically EB and KB. In contrast to previous research that primarily focused on classifying yellow and white maize kernels, our study took a more detailed approach by specifically examining the single color channel of the kernel in the RGB color model. By isolating and analyzing this specific color channel, we were able to gain deeper insights into the genetic factors that influence the precise coloration of maize kernels. This finer-grained investigation is of significant importance for understanding the underlying mechanisms that govern maize kernel color development, thus providing valuable insights for future breeding and improvement strategies.Notably, reported  that theer study  demonstrZm00001d028915 , which positively regulates maize EL. In this study, this gene was identified as a candidate gene for EG (SY) and ER (SY), and the phenotype analysis displayed a significant positive correlation between EL and EG, as well as ER after multiple verifications (It has been reported that a major effect quantitative trait locus (QTL) (qEL1) with significant association to maize EL was identified on chromosome 1 through GWAS a. Four cications Figure 3This study emphasizes the significance of investigating novel phenotypic traits in maize through the utilization of high-throughput phenotypic measurements. The genetic loci identified in this research make a substantial contribution to our understanding of the traits associated with maize ears and enhance current genetic studies on maize ears.The original contributions presented in the study are included in the article/CZ and XG contributed to the study conception and design; JW and SZ analyzed the data, interpreted the results, and drafted the manuscript; XG and YZ revised the manuscript; XL, JD, CW, and WW performed field experiments and obtained phenotypic data. All authors contributed to the article and approved the submitted version.


Multi-trait linear mixed models build on the linear mixed model to jointly model multiple traits. Existing estimation methods, however, are limited to the joint analysis of a small number of genotypes; in fact, most approaches consider one SNP at a time. Estimating multi-dimensional genetic and environment effects also results in considerable computational burden. Efficient approaches that incorporate regularization into multi-trait linear models (no random effects) have been recently proposed to identify genomic loci associated with multiple traits (Yu et al. in Multitask learning using task clustering with applications to predictive modeling and GWAS of plant varieties. mixed models along with scalable approaches for estimation in the presence of high-dimensional genotypes and a large number of traits. We evaluate the effectiveness of the proposed methods using datasets in maize and sorghum diversity panels, and demonstrate benefits in both achieving high prediction accuracy in GS and in identifying relevant marker-trait associations.This work addresses this gap by proposing a novel class of regularized multi-trait linear The proposed regularized multivariate linear mixed models are relevant for both GWAS and GS. We hope that they will facilitate agronomy-related research in plant biology and crop breeding endeavors.The online version contains supplementary material available at 10.1186/s12859-023-05519-2. The effective use of DNA markers from high-throughput genomic data in breeding will be critical for increasing agronomic productivity to levels that will be able to sustain the population by 2050 . In partThe type of data sets most widely used for plant GWAS is the diversity panel .None of these approaches, however, are able to perform estimation for the complete mtLMM where the effects of all SNPs are considered simultaneously. In such a case cal [see , 16, forp can be significantly larger than the number of observations n. Regularization has been a very active topic in univariate and multivariate linear models. It has also been employed in univariate mixed effect models for variable selection [see [To address the aforementioned issues, we propose a class of regularized mtLMMs that enables estimation in the high-dimensional setting where the number of fixed effects ion [see , 25, andion [see , regularion [see  considerion [see  proposedThe framework proposed in this paper minimizes a class of penalized negative log-likelihood functions:Examples of regularization functions include the following:Example 1: Variable Selection.Variable selection can be performed using as SCAD .Example 2: Variable Selection and Trait-wise Clustering.In several applications it may make sense to assume that there is an underlying grouping of traits, where traits in the same group are impacted by a similar set of SNPs. To identify and leverage such a grouping, one can employ the convex clustering penalty as proposed in , adaptedons. See  for moreAn optimization procedure to efficiently estimate regularized mtLMMs is presented in Additional file Zea mays L.) diverse lines [We evaluated the performance of regularized mtLMMs against alternate approaches by first analyzing real traits from two crop diversity panels. The first of these two datasets is the Goodman diversity panel, which consists of 281 maize  lines that have been previously described in [The second dataset analyzed is a diversity panel of 320 sorghum , a total of six different models were fitted to the maize and sorghum data. Such an evaluation is a critical first step for determining the potential application of these models to GS in plant breeding. The specific models we evaluated include the multitrait linear mixed model with L1 regularization (mtLMM-L1), the multitrait linear model with L1 regularization (mtLM-L1), the multitrait mixed linear model with convex clustering regularization (mtLMM-clust), and the multitrait linear model with convex clustering regularization (mtLM-clust). Each of these models used all available traits within a given data set as the vector of response variables and all available genome-wide markers as explanatory variables. As an additional comparison approach, we also included two applications of random-regression best linear unbiased prediction (RR-BLUP) model , 37, as P-value [For the GWAS task, we compared the four aforementioned multi-trait approaches to the single-trait LMM with single marker (st-LMM-sm) and multitrait LMM with single marker (mt-LMM-sm) approaches. These two models test for the association of one marker at a time, on single traits and multi-traits respectively. We ran 50 experiments, where for each experiment a subsample of 80% of the data is drawn. For each method we counted the number of times each marker was detected over the 50 experiments. For st-LMM-sm and mt-LMM-sm, a marker was considered to be detected for a subsample if its Benjamini–Hochberg(BH)-adjusted P-value  was statRandom Structure: The QTNs randomly impacted the various traits. Hence the pattern of QTNs were trait-specific. In this case, j and trait Clean Clustering Structure: The traits were divided into three groups, and the traits within a group share a common set of QTNs. In this case, Mix of shared and specific: This was a superposition of random and clean clustering structures. The matrix No fixed effects.In scenarios 1 and 2, a total of 100 QTNs were simulated per trait. Similarly, each trait simulated under scenario 3 considered 20 QTNs from scenario 1 and 80 from scenario 2. For scenario 1, nonzero values in N. For scenario 2, we considered three groups of traits: two groups of three traits each, and one group of q is the total number of traits. The non zero entries within each group were generated as We also conducted a simulation study to quantify the advantage of regularized mt-LMM approaches in terms of the detection accuracy of quantitative trait nucleotides (QTNs) underlying simulated traits, where the ground truth is known. To make data simulation as plausible as possible, we use the actual genotype matrices and kinship matrices from the two real datasets above, and covariance matrices mentclass2pt{minimFor each scenario we generated 50 replicates of traits. We then applied methods stLMM-sm, mtLMM-sm, mtLMM-L1, mtLM-L1 mtLMM-clust, mtLM-clust to every replicate.P-value was statistically significant at an FDR of For each replicate, a non-regularized method (st-LMM-sm and mt-LMM-sm) was said to have detected a true positive QTN if its BH-adjusted For each replicate, a regularized method  was said to have detected a true positive QTN whenever a QTN was included in the estimated model, whereas it was said to have detected a false positive whenever it included a marker that was not a QTN in the “true” model.For scenarios 1–3, we then reported the average F1 score over the 50 replicates, which is the harmonic mean between precision and recall, while for scenario 4 we reported the false positive rate.We present the results of our evaluation of the potential of using the multi-trait regularized models for genomic prediction, using the maize and sorghum data sets. The results of the analysis of the sorghum data are presented in Fig. ZmVTE4 to those that were published in [The circular Manhattan plots presented in Fig. ished in , while tThe F1 scores under scenarios 1–3, and false positive detection rate under scenario 4, for the various comparison approaches are provided in Tables One of the primary goals of this study was to evaluate the extent to which accounting for subpopulation structure and familial relatedness via mtLMMs improved overall performance relative to mtLMs. We also sought to determine if the mtLMM could offer additional benefits to GWAS practices through its inclusion of multiple loci and multiple traits in a single model. The results of our experiments suggest empirical confirmation of these potential benefits. In the simulated GWAS study, we clearly saw that mtLMMs provides a substantial improvement in the accuracy of marker-trait association detection, indicating in particular that it is adequately controlling for false positives. Our expectation is that the same would be true in the actual GWAS studies, wherein the added random effects in mtLMM’s would help reduce the false positive rates - this will need to be validated by plant geneticists via downstream experiments. Although this additional complexity essentially results in fitting each marker as a fixed and random effect in the model, our results suggest that the additional complexity introduced in the formulation of mtLMMs is well-motivated because of the improved model accuracy we observed. Despite this inherent additional complexity, we were able to achieve sufficient computational efficiency to merit the use of the mtLMM in data sets of similar size to the maize and sorghum diversity panels we evaluated. Collectively, these promising results suggest that the mtLMM makes it feasible to include a large number of traits in a single, unified model. Such a model would not only streamline the overall process of conducting GWAS for multiple traits, but also improve the model accuracy by leveraging structural information common across the traits.P-values from the model [as in e.g., P-value derivation for regularized methods. One future research direction that can investigate this limitation would be to develop easy-to-use approaches to enable unbiased estimation and hypothesis testing of the peak associations identified using regularized approaches such as those evaluated in this study.While the ideal and ultimate test of effectiveness is an actual GWAS study where all ground truth associations are known, there are proxies that can provide partial evidence and meaningful insights. Such proxies motivated our simulation study, where we simulated traits controlled by population structure and familial relatedness in addition to the simulated QTNs. Another proxy for examining a GWAS method’s ability to control for false positives due to population structure and familial relatedness is via the “QQ-plot” of the While GWAS was the main application of this work, we also evaluated the potential for the application of the mtLMM to GS. In the ensuing analyses, the mtLMM yielded prediction accuracies that were competitive to the popular RR-BLUP model. This suggests that the use of the mtLMM for GS is reasonable, and in particular that it has comparable potential as RR-BLUP to accelerate genetic gain. Although we are not suggesting that the mtLMM be used instead of the vast amount of available GS models that are proven to be effective, the observed prediction accuracies from the mtLMM are encouraging. If the mtLMM were to be used in GS, it would then be possible to make accurate inferences on the genetic architecture of multiple traits within the training population. Excitingly, it should be relatively straightforward to incorporate the regularized approaches of the mtLMM into multi-kernel GS models  performing downstream biological analysis of the SNPs selected by our approaches on the real GWAS datasets, and (ii) extending the theory of statistical significance testing in the high-dimensional setting to develop a principled test for the variables selected by regularized mtLMMs.Additional file 1. An algorithmic description of the cross-validation procedure considered when evaluating prediction accuracy in the genomic selection study.Additional file 2. Optimization procedure for efficiently estimating multi-trait linear mixed models (mtLMMs).


Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data. Although the factor structure of psychopathology was generally genetically and phenotypically consistent, conditions related to externalizing  and compulsivity  exhibited cross-level disparities in their relationships with other conditions, plausibly due to environmental influences. Domain-level factors, especially thought disorder and internalizing factors, were more informative than a general psychopathology factor in genome-wide association and polygenic index analyses. Collectively, our findings enhance the understanding of comorbidity and shared etiology, highlight the intricate interplay between genes and environment, and offer guidance for psychiatric research using polygenic indices. Indeed, complex patterns of comorbidity are perhaps the norm in psychopathology, as more than 50% of people who meet diagnostic criteria for one disorder will also meet criteria for a second1.Psychiatric disorders are among the leading contributors to the global disease burden. They are the main cause of years lived with disability and will affect more than 25% of the world’s population at some point in their lifetime. Critically, these disorders often co-occur with one another5. By cutting across fuzzy diagnostic boundaries, transdiagnostic research aims to identify and understand core mechanisms in psychopathology8. Such insights are uniquely poised to reduce the burden of mental illness, as they may advance knowledge of multiple disorders simultaneously, contribute to the development of therapeutics with broad utility, and even inform future nosological systems in the field11.In response to these observations of pervasive comorbidity, researchers in clinical psychology and psychiatry have increasingly turned toward transdiagnostic approaches in the study of mental conditions12 and highly polygenic – influenced by thousands of causal variants with probabilistic effects14. Notably, many disorder-linked variants appear to have pleiotropic effects, conferring risk in a transdiagnostic manner11. With overlap at the genetic level typically mirroring that at the phenotypic level15, these patterns of widespread pleiotropy support the hypothesis that comorbidity among disorders may, in part, arise from shared etiology.Advances in statistical and psychiatric genetics have substantiated this fundamental change in perspective, especially for biological investigations. Results from large-scale genome-wide association studies (GWAS) have confirmed that all forms of psychopathology are partly heritable21. Generally, these studies report that individual disorders comprise higher-order factors of internalizing, externalizing, and thought disorder problems that help explain comorbidity22. They even suggest that a general transdiagnostic dimension – the ‘p’ factor – explains features that are common to all mental disorders24. However, recent data-driven genomic studies report genetic factor structures that differ from the phenotypic literature, raising questions about the utility and interpretation of the p factor9. In fact, one recent genomic study found that the p factor produced few biological insights, as it obfuscated patterns of associations with individual variants, genetic correlations with biobehavioral outcomes, and enrichment within specific biological annotations9.Concurrently, factor analytic research has revealed a transdiagnostic structure of psychopathology that is observed at both phenotypic and genetic levelsp factor in biological investigations of comorbidity, using GWAS and polygenic indices (PGIs) as exemplars. In doing so, our results provide substantive insights into the factor structure of psychopathology and practical guidance on how to leverage these insights.Here, we aim to advance the understanding of psychiatric comorbidity with two extensive sets of analyses. First, we report a comprehensive factor analysis of 10 psychiatric conditions in the UK Biobank, and we characterize model (dis)similarity across phenotypic and genetic levels of analysis. Second, we evaluate the utility of the p and g, respectively .In the present study, we investigated the factor structure of 10 diverse psychiatric conditions in the UK Biobank: generalized anxiety (ANX), alcohol use disorder (AUD), bipolar disorder (BIP), depression (DEP), eating disorder (EAT), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), schizophrenia (SCZ), suicidality (SUI), and substance use disorder (SUD). As described in the and genetic factor structure of psychiatric conditions, we first conducted univariate genome-wide association analyses (GWASs) for the 10 psychiatric conditions in up to 402,411 UK Biobank participants. However, as some phenotypes had relatively few cases in UK Biobank (rg| = 0.073); To investigate the phenotypic  Biobank , we perf Biobank . UK Biob Biobank  and cros Biobank . The difh2 = 0.057 – 0.476; GC = 1.023 – 1.699, mean χ2 = 1.03 – 1.955; All psychiatric conditions were significantly heritable (rp and rg) for all study phenotypes . We then used exploratory and confirmatory factor analyses  to characterize the multivariate system of relationships between psychiatric conditions at phenotypic and genetic levels.To better understand patterns of comorbidity and shared etiology among psychiatric conditions, we calculated the pairwise phenotypic and genetic correlations (enotypes . In bothR2 and change in root mean square error of approximation (ΔRMSEA) between the three- and four-factor models were less than 0.05 and 0.015, respectively. We observed a similar pattern in the genetic data, where the inclusion of a fourth factor did not substantially increase the variance explained over the three-factor model , another factor by internalizing conditions , and a final factor by externalizing/substance use conditions (AUD and SUD). In the genetic EFA, we observed a similar thought disorder factor (BIP and SCZ), but the internalizing factor now included substance use conditions  and the third factor was now composed of conditions characterized by compulsivity (EAT and OCD). The full results are reported in p factor of psychopathology explains variance in first-order psychopathology factors; and (iv) a bifactor model, where four orthogonal factors  explain variance in psychiatric conditions  a first-order factor model, (ii) a correlated factors model with three latent factors; (iii) a second-order factor model, where a second-order 37 in the phenotypic and genetic data.While all models showed relatively good fit within their respective level of analysis , the correlated factors and second-order factor models optimized the balance between parsimony and goodness-of-fitAs our EFAs suggested differences in the phenotypic and genetic factor structure of psychopathology, we sought to more formally characterize these dissimilarities. Specifically, we aimed to identify which psychiatric conditions contribute to this discordance and probe the extent to which measurement models were specific to phenotypic and genetic levels of analysis.r = 0.873, p = 9.99e-4, several pairwise relationships differed across levels of analysis. To quantify these dissimilarities, we calculated the ‘disparity’39 between bivariate phenotypic and genetic correlations (D = rp – rg) (D| = 0.104), though there were considerable deviations (range D = −0.290 – 0.317) (DSUI = 0.091), and exhibited large disparities with compulsive conditions . Conversely, DEP had smaller phenotypic than genetic correlations (mean DDEP = −0.051), and showed some marked differences with frequently comorbid conditions . Notably, observed disparities were not systematically related to imprecision (standard errors) in genetic correlation estimates , or the GWAS Neff  (Although the phenotypic and genetic correlation matrices were highly correlated (rp – rg) . Dispari– 0.317) . For exa > 0.05) . This suDINT,COMP = 0.449; r = 0.998 and 0.999 for the loadings across models for the phenotypic and genetic data, respectively). Sensitivity analyses suggest these results were robust to several potential confounds, such as sampling differences across datasets , the correlated factors from the genetic model , and two versions of a general psychopathology factor from the phenotypic models: one from a first-order factor model and one from a second-order factor model  with their respective factors in the genetic models, as were p factors across models , which was primarily attributable to polygenic architecture , which indicates that many of the associated loci plausibly operate through a common pathway . Coupled with the low number of associated loci with either configuration of the p factor  with a well-powered externalizing factor model41. Similarly, our INTp and TDp factors were strongly correlated with genomic factors of internalizing and thought disorder psychopathology42, respectively . Although models from the literature included UK Biobank data, they also used data from other cohorts and included conditions that were not well represented in UK Biobank , supporting the generalizability of our inferences.Comparison to models in the literature provided strong evidence of concurrent validity for our latent factors of psychopathology. For instance, our EXT factor was genetically correlated at 1  were significant in the QTrait analyses  and neuroanatomy  in a UK Biobank holdout sample of approximately 24,100 individuals (Method). We calculated PGIs for the eight factors described above: TDp, INTp, EXTp, TDg, INTg, COMPg, first-order p, and second-order p.Finally, we examined whether the p and TDg were associated with a phenotypic thought disorder factor) and most showed cross-trait patterns of association  across all scenarios. This was also true for the first-order p factor PGI, except for compulsive disorders, where it explained more variance in compulsive disorders than the three PGIs from the correlated factors models . Generally, the PGIs of the genetic and phenotypic correlated factors model of well-powered GWAS  tended to explained more or similar variance to the p factor PGIs . Once again, we found that the domain-level PGIs varied in their associations with health, genetic, and lifestyle outcomes  analyses for each PGI in the Mass General Brigham Biobank similarity between these levels of analysis, which yielded novel insights into the form, validity, and utility of transdiagnostic factors in biological psychopathology. Three key insights emerged from these efforts.Psychiatric disorders are a common cause of human suffering44 , which states that phenotypic and genetic correlations between complex traits can be assumed to be highly similar38. While the structure of thought disorder and internalizing psychopathology was largely consistent, our results suggest that externalizing and compulsive disorders may be differentially related to other conditions at phenotypic and genetic levels. As disparities were not a consequence of heterogeneous data or low statistical power, differences might reflect environmental influences that cause phenotypic correlations to deviate from genetic ones. For instance, compulsive conditions were more strongly phenotypically (r= 0.889) than genetically (r= 0.449) correlated with internalizing conditions. Such deviations also occurred at the indicator level: OCD and AUD were phenotypically but not genetically correlated. One possible explanation is that overlapping symptoms may arise from a combination of different genetic predispositions and environmental factors. In contrast, the genetic correlation of depression and anxiety was greater (r= 0.908) than its phenotypic correlation (r= 0.618), which could suggest that while these disorders share genetic predispositions, environmental factors may drive different phenotypic expressions. These results provide new hypotheses to be tested by future gene-environment studies.First, the factor structure of mental conditions was only partially concordant across phenotypic and genetic levels of analysis, somewhat supporting Cheverud’s conjecture5. The internalizing factor, serving as an exemplar with the greatest number of indicators, was shown to be particularly robust to configural changes to the model. Moreover, despite some structural differences across levels of analysis, all four of these factors improved statistical power compared to the p factor GWAS and exhibited low rates of heterogeneity in genetic analyses, as demonstrated in the QSNP and QTrait results.Second, we provide novel evidence for the validity and utility of multiple transdiagnostic factors in biological psychopathology, especially those characterized by internalizing, externalizing, compulsive, and thought disorders. With few exceptions, correlations between these factors were quite moderate, underscoring that they are largely modeling (co)variances that are unique from one anotherp factor had relatively low validity and utility at the genetic level. While first-and second-order p factor models can be fit to the data, they offer limited insights into biology due to heterogeneous genetic effects across mental conditions. Specifically, QSNP and QTrait results indicated that many SNP-level effects and nearly all genetic correlations were inconsistent with a general factor of psychopathology. Moreover, PGI results from the cerebral and PheWAS analyses revealed that information is lost when opting to use PGIs for p over correlated-but-distinct domain-level factors . Collectively, our findings build upon recent studies that suggest the p factor will generate fewer robust and comprehensive findings than the domain-level factors in the study of psychiatric comorbidity46 – at least with the currently available data.Third, the 9 and limited the available data for the thought disorder, externalizing, and compulsive factors, which were each modeled with two indicators. While these findings should be replicated in models with more data, our thought disorder and externalizing factors did have genetic correlations that were indistinguishable from unity (rg = 1) with more expansive models from the literature42. Finally, our analyses were limited to individuals of European ancestries in the UK Biobank due to data availability and methodological constraints47. Extending these analyses to additional cohorts with more diverse data48 and less healthy volunteer bias49 will be critical to evaluating the robustness and generalizability of our findings.Although the present study has taken important steps to address potential confounds and shortcomings, several limitations should be considered when interpreting results. For instance, the present study does not comprehensively sample the full spectrum of psychopathology and related conditions, as the UK Biobank did not have adequate data for all phenotypes of interest . This resulted in the omission of a neurodevelopmental factorand genetic levels, refining our understanding of comorbidity and shared etiology10. We add to the relatively scarce literature characterizing Cheverud’s conjecture and its exceptions in this domain, identifying instances where environmental influences may have a particularly pronounced effect on comorbidity. We also extend recent investigations into the validity of transdiagnostic factors in psychiatric genetic research51, providing practical insights that can guide the development and use of transdiagnostic PGIs in the field. Ultimately, our results highlight the intricate interplay between genetic and environmental factors in mental disorders52, paving the way for targeted studies of the shared etiological landscape for these conditions.In conclusion, the present study generates novel insights into the structure of psychopathology by reporting results at phenotypic 49. Participants provided informed consent  and the UK Biobank received ethical approval from the Research Ethics Committee (reference 11/NW/0382). This study was conducted based on application 46007.The UK Biobank has phenotypic, genotypic, and imaging data from more than 500,000 participants. Participants were recruited between 2006 and 2010 across 22 assessment centers in England, Wales, and Scotland, between the ages of 40 to 69 years old53. Genotypes were imputed to the Haplotype Reference Consortium (HRC) reference panel26 (Version 1.1) and UK10K and the 1000 Genomes Project Phase 3 reference panel by the UK Biobank. We kept SNPs from the 1000 Genomes Project Phase 154 that were filtered on minor allele frequency ≥ 0.01, genotyping call rate ≥ 0.05, and a Hardy-Weinberg equilibrium threshold ≥ 5×10–6. Samples were filtered on missingness rate ≥ 0.1. In total, 5,319,661 variants and 487,409 people pass filters and QC, including 431,006 individuals from British ancestry with genetic principal components.Participants  were genotyped with the UK BiLEVE or the UK Biobank Axiom array. The array design, genotyping, and quality control procedures have been previously described by the UK Biobank investigators56). Analyses were conducted in R57. The MRI data were collected with a standard Siemens Skyra 3 T running VD13A SP4 with a standard Siemens 32-channel RF receive head coil. The UK Biobank Imaging team analyzed the 3D MPRAGE T1-weighted volumes with pipeline scripts that primarily call for FSL and Freesurfer tools. The UK Biobank Imaging Protocols provide details of the acquisition protocols, image processing pipeline, image data files, and derived measures of brain structure and function. There were three scanner sites located in Cheadle ; Reading ; and Newcastle .We analyzed the cortical mean thicknesses and surface areas of the Desikan-Killiany-Tourville (DKT) atlas from the first Magnetic Imaging Resonance (MRI) visit generated by an image-processing pipeline developed and run by the UK Biobank Imaging team , generalized anxiety (ANX), suicidality (SUI), eating disorders (EAT), posttraumatic stress disorder (PTSD), obsessive-compulsive disorders (OCD), Hazardous Alcohol Use or Dependence (AUD), substance use disorder (SUD), bipolar disorder(BIP), and schizophrenia (SCZ)) generated in a previous studyiagnoses . We did iagnoses . These aWe created a validation holdout sample of UK Biobank individuals with neuroimaging data that were not related to individuals in the UK Biobank discovery sample. This yielded 24,162 individuals  for which we calculated polygenic indices.We calculated the phenotypic and genetic correlations across psychiatric disorders and conducted exploratory factor analyses to identify models of psychopathology to validate in the confirmatory factor analyses.rp and rg) for all study phenotypes to better understand patterns of comorbidity and shared etiology among psychiatric disorders. For the phenotypic data, we randomly selected 50% of the sample as the training sample and calculated the tetrachoric correlation matrix of our psychiatric conditions using the mixedCor function from the psych package59 in R57. The phenotypic correlation here assesses the degree to which two psychiatric conditions tend to vary together in a population. For the genetic data, we used LD Score Regression25 to calculate the genetic correlation from all SNPs. A high genetic correlation suggests that there is a strong genetic overlap between the two traits, indicating that the same genes are likely involved in the expression of both traits.We calculated the bivariate phenotypic and genetic correlations (39 between bivariate phenotypic and genetic correlations (D = rp − rg) in R57. To evaluate whether the calculated disparities were systematically related to technical factors, we computed Pearson correlations between D estimates and (1) the standard errors of genetic correlations, (2) the proportion of external cases in contributing GWAS, and (3) the effective sample size of contributing GWAS. For the latter two tests, we used a permutation-based correlation approach. Specifically, for each disorder, we shuffled the vector values during each of the 1,000 permutations and then computed the correlation between the matrix and this permuted vector. This yielded a distribution of correlations under the null hypothesis of no association specific to each disorder. The observed correlation for each disorder was then compared to its respective null distribution to compute empirical p-values. When these p-values are non-significant, it suggests that observed discrepancies might reflect environmental influences leading to differences in phenotypic expression.To quantify differences between the phenotypic and genetic correlations of the 10 psychiatric conditions, we calculated the ‘disparity’fa function from the psych package59 in R57. Since indicators were categorical and some loadings were under 0.5, we used the difference in the Root Mean Square Error Approximation (ΔRMSEA) with a 0.015 cut-value to determine the number of factors to retain60. We first examined the ΔRMSEA across EFAs with 1–4 factors. We applied an oblimin rotation and the principal axis factoring (PAF) method, which is more appropriate for non-normal data than the maximum likelihood method61. We report results on the sample with missingness for some psychiatric conditions because the model fit and variance explained across psychiatric disorders or conditions were similar when excluding individuals with missing data.We ran exploratory factor analyses (EFA) on the tetrachoric correlation matrix of our psychiatric conditions from the training sample using the lavaan package 62 in R57 to conduct confirmatory factor analyses (CFA) in the test sample  with and without missing data using diagonally weighted least squares (DWLS) estimation and pairwise likelihood estimation for missing cases  > 0.95, a root mean square error approximation (RMSEA) < 0.06, and a standardized root mean square residual (SRMR) < 0.08 (Hu and Bentler 1999). We report the model fit and parameters from the models with participants that had missing data or one or more disorders since the model parameters were similar when including and excluding individuals with missing data.36 to evaluate the fit of these data-driven models in the test data in R57, a genetic covariance matrix constructed from even chromosome SNPs, before testing model fit on all SNPs. Finally, we also used Genomic SEM to estimate SNP effects on the latent genetic factors. Estimated sample size 42.To characterize the genetic structure of psychopathology, we first performed EFA with promax rotation and modeled up to 4 factors in the training data, a genetic covariance matrix constructed from odd chromosome SNPs. We then used Genomic SEM64) from the Genome-wide Complex Trait Analysis (GCTA) package65. We controlled for relatedness, sex , center, genotyping chip, birth year, and the first 40 PCs of the genotyped data. We included information provided by the UK Biobank on the genetically inferred kinship of respondents (estimated in the KING software66).We ran GWASs of the 10 psychiatric conditions in the UK Biobank  in 395,367. Meta-analyzed GWAS summary statistics (META GWASs) were generated for all disorders except for SUD, which lacked publicly available summary statistics due to the broad definition of case status. Meta-analyses were conducted using a sample size weighted analysis. The weight was set to the effective N for univariate summary statistics )*(N controls + N cases)) and to the sum of the N Effective  for meta-analyzed summary statistics68. When the minor allele frequency (MAF) was unavailable, we calculated the MAF from the reported frequency of the effect allele or used the MAF from the 1000 Genome Project Phase 3 reference panel54.Since several psychiatric conditions had few cases in the UK Biobank, we sought to improve statistical power of our GWASs via meta-analysis. For this procedure, we obtained GWAS summary statistics for similar phenotypes  that (i)36. Model fit was assessed using the following cut-offs: comparative fit index (CFI) > 0.95 and Standardized Root Mean Square Residual (SRMR) < 0.08 indicative of good fit, and CFI > 0.90 indicative of acceptable fit. Diagonally-weighted least squares estimation was used in all Genomic SEM analyses.Prior to analysis, all summary statistics were munged using Genomic SEM, where we applied the conventional filters of imputation quality ≥ 0.90 and minor allele frequency (MAF) ≥ 0.01. The genetic covariance and sampling covariance matrices were estimated using multivariable LDSC in Genomic SEM36 to evaluate the fit of these data-driven models in the test data, a genetic covariance matrix constructed from even chromosome SNPs, before testing model fit on all SNPs. Finally, we also used Genomic SEM to estimate SNP effects on the latent genetic factors. Estimated sample size 42.To characterize the genetic structure of psychopathology, we first performed EFA with promax rotation and modeled up to 4 factors in the training data, a genetic covariance matrix constructed from odd chromosome SNPs. We then used Genomic SEM69) to extract (i) the number of lead SNPs and genomic loci associated with a factor and (ii) the number of lead SNPs and genomic loci identified in the QSNP analyses (see the section below) using the multivariate GWAS results from Genomic SEM as input. We then compared the number of lead SNPs from the first-order factor analyses to those from the QSNP analyses (we set the maximum cutoff P threshold to 1). The remaining parameters were set to their default value . FUMA was applied on about 5,319,661 SNPs for each multivariate GWAS output.We used the “functional mapping and annotation of genetic associations” method  conducting 2-, 3-, and 4-factor EFAs on the UK Biobank and the META GWAS results, (iii) estimating the correlation of the indicator loadings of the UK Biobank GWAS and META GWAS across genetic and phenotypic models, and (iv) examining whether the correlation between the meta-analyzed genetic correlation matrix was more similar to the phenotypic correlation matrix than the UK Biobank genetic correlation matrix. Second, we tested the phenotypic model in the genetic data and vice-versa to test whether the models fit similarly across data types. Third, when applied to the genetic and phenotypic data, we calculated the correlation of factor loadings across the genetic and phenotypic models.We conducted three sets of analyses to compare phenotypic and genetic factor structure of our phenotypes. First, we examined the consistency of the UK Biobank and META GWAS results to ensure that differences in factor model structure were not due to idiosyncrasies between the genetic and phenotypic data that were introduced by including public GWAS results. This was accomplished by (i) examining differences in the genetic correlation and the heritability of the indicators from the UK Biobank and META GWASs using LDSC. We also estimated the genetic correlation between our internalizing factor and a factor indexing mood disturbance , as well as the genetic correlation between our thought disorder factor and a factor indexing serious mental illness . We recreated the latent factors from the literature using the model structure and indicator GWAS from the literature and calculated the genetic correlation between the latent factors from the literature and ours with Genomic SEM36. Our latent factors were estimated with our indicator GWAS.We examined the concurrent validity of our psychopathology factors by estimating genetic correlations between our factors and factors from models in the literature with additional indicators and larger samples. Specifically, we examined the correlation between our externalizing factor and the externalizing factor from a well-powered multivariate GWAS excluding 23andMe dataQTrait analyses to examine whether the pattern of genetic correlation between an outcome ad the indicators of the model are well accounted for by the latent factor. The calculation of this statistics is described elsewhere71. In brief, it is an index of the decrement in model fit between a model where the factor of interest is regressed onto a given trait and a model where the indicators for the factor of interest are regressed onto a given trait. We compare these models to test whether the observed pattern of association plausibly operates via the latent factor or if it is better described by indicator-specific relationships.We performed QTrait analyses, we used GWAS summary statistics from a previously published pipeline68, while removing phenotypes that were redundant with the investigated latent factor . Summary statistics were chosen due to their broad relevance to human health and well-being, as previously described42. Briefly, they correspond to 4 main domains: health and disease outcomes, personality and risky behavior, psychopathology and cognition, and demography and socioeconomic status. The number of external traits for which QTrait analyses were conducted depended on whether the QTrait model converged. In some cases, the QTrait model did not converge because of negative variance estimates.For the 72, we created polygenic indexes (PGIs) for individuals with neuroimaging data and their siblings with the summary statistics of the latent variables of the most parsimonious and best-fitting genetic and phenotypic models.Using sBayesRp factor PGI because including the MHC region led to counterintuitive results . Removing the MHC region did not affect the PGI prediction of the other psychopathology factors.We created residualized PGIs by adjusting the PGIs for the year of birth, genetic sex, and the first 40 principal components of the genotyped data. We excluded individuals with related individuals in the discovery and individuals that were related in the target sample. The major histocompatibility complex (MHC) region was excluded from the PGI analyses for the COMP and first-order R2 of each PGI for all eight psychopathology factors. The following cutoffs were set to establish a good model fit: CFI > 0.95, RMSEA < 0.06, and SRMR < 0.0873.To examine the predictive power of the PGIs on their respective latent variables, we quantified the degree to which each PGI predicted the latent psychopathology factors in the present study .. The lop factors and domain-specific factors and (2) brain-PGI associations between domain-specific factors.To evaluate associations between the PGIs of psychopathology and cortical brain measures, we examined the association between the PGIs and 64 regional mean thicknesses and 64 surface areas from the cortical Desikan-Killiany-Trouville atlas. We conducted linear regressions with PGIs, age at the MRI, sex, quadratic age at imaging, sex and age interactions, and sex and quadratic age interactions predicting each cortical measure. Age corresponds to age at MRI and the PGI were adjusted for year of birth and the 40 first genetic principal components provided by the UK biobank. Continuous variables were unit standardized to have a mean of 0 and a standard deviation of 1. We report the correlations between the brain-PGI associations to compare (1) brain-PGI associations between the 74. It has enrolled 138,042 participants, 65,265 of whom have also been genotyped. The recruitment strategy, genotyping procedures, and quality control procedures for this dataset have been described in previously published studies74. All participants provided written consent upon enrollment, and analyses were conducted under MGB Institutional Review Board protocols #2009P002312 and #2021P003641.To evaluate associations between the PGIs of psychopathology and a wide variety of medical outcomes, we performed PheWAS analyses in the Mass General Brigham Biobank (MGBB). The MGBB is a biorepository from the MGB healthcare system with patient data on electronic health record, genetic, and lifestyle variables76 was used to assign case status for 1,817 medical outcomes, requiring the presence of at least two International Classification of Disease 10 Clinical Modification (ICD-10-CM) codes. To reduce the risk of population stratification, PheWAS analyses were restricted to 43,323 patients of European ancestry. PGIs were computed using the sBayesR method64 in these individuals, as described above, and standardized prior to analysis. PheWAS analyses were then conducted using the PheWAS package in R77 (https://github.com/PheWAS/PheWAS), where logistic regression models were fit for each of the 1,817 medical outcomes under study. Sex assigned at birth, current age, genotyping chip, and the first 10 genetic principal components were included as covariates. Statistical significance was evaluated at a Bonferroni-corrected threshold (p < 2.75e-5).A standard “phecode” approach43 and our correlated factors of psychopathology. The genetic correlation estimates were non-zero in all cases , which implies the cross-trait intercept can be used to detect sample overlap. We found the cross-trait intercept was not significantly different from zero in all cases, indicating that there is no detectable overlap between the GWAS and PheWAS data in the present study.To evaluate whether there was any sample overlap between our GWAS and PheWAS data, we used LD Score regression to estimate the cross-trait intercept between a published GWAS of tobacco use disorder in the MGBBSupplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 6Supplement 7Supplement 8Supplement 9Supplement 10Supplement 11Supplement 12Supplement 13Supplement 14Supplement 15Supplement 16Supplement 17


TPK1, POU6F2, PBX3, UNC5C, and BMPR1B, were defined as central in affecting internal organ weight in pigs. Moreover, APK1, ANO6, and UNC5C were identified to be pleiotropic in multi-trait GWASs. These results can be applied to various types of genomic studies of pigs.In this study, bioinformatics approaches were used to better understand the genetic architecture of internal organ weights in three-way crossbred commercial pigs and to map genetic markers and genes. For this purpose, we used single-trait and multi-trait genome-wide association studies (GWASs) followed by a haplotype block analysis. We explored the key genetic markers and genes from the internal organ weight genome-wide association study results of three-way crossbred commercial pigs. In this manner, five genes, TPK1, POU6F2, PBX3, UNC5C, and BMPR1B, as being associated with the six internal organ weight traits analyzed. Multi-trait GWAS identified four SNPs with polymorphisms localized on the APK1, ANO6, and UNC5C genes and improved the statistical efficacy of single-trait GWASs. Furthermore, our study was the first to use GWASs to identify SNPs associated with stomach weight in pigs. In conclusion, our exploration of the genetic architecture of internal organ weights helps us better understand growth traits, and the key SNPs identified could play a potential role in animal breeding programs.Internal organ weight is an essential indicator of growth status as it reflects the level of growth and development in pigs. However, the associated genetic architecture has not been well explored because phenotypes are difficult to obtain. Herein, we performed single-trait and multi-trait genome-wide association studies (GWASs) to map the genetic markers and genes associated with six internal organ weight traits  in 1518 three-way crossbred commercial pigs. In summation, single-trait GWASs identified a total of 24 significant single- nucleotide polymorphisms (SNPs) and 5 promising candidate genes, namely,  Body weight, which can reflect growth performance and thus affect economic efficiency, has attracted a lot of attention in animal breeding programs. The body weight of cattle is the sum of various elements, including fat weight, internal organ weight, muscle weight, and bone weight, among others. Of these components, internal organ weight constitutes 14% of the total body weight of cattle . MoreoveGenome-wide association studies (GWASs) are widely used to identify quantitative trait loci (QTL) and candidate genes associated with complex traits in animals and plants. To date, the number of QTL associated with Heart WT, Liver WT, Spleen WT, Lung WT, and Kidney WT are 29, 31, 19, 5 and 8, respectively, and no QTL have been reported to be associated with Stomach WT in the pig QTL database  (accesseThe difficulty (and high cost) of obtaining phenotypes for internal organ weight studies has led to fewer studies on its genetic architecture. Moreover, previous studies conducted single-trait GWASs for internal organ weight to map the genetic markers and genes; however, internal organ development is mutually coordinated by each different organ, and the single-nucleotide polymorphisms (SNPs) in the genome may act on multiple organs at the same time. Therefore, it is difficult to identify SNPs and candidate genes that affect multiple internal organs simultaneously using single-trait GWASs. Therefore, herein, we performed multi-trait GWASs to identify polymorphic SNPs and improve statistical efficiency, which mainly depends on the genetic correlation between traits ,10. In tPrevious studies demonstrated the superiority of conducting multi-trait GWASs in terms of uncovering the genetic architecture of complex traits in animals. For instance, Zhou et al.  performeThe aim of this study was to map the genetic markers and candidate genes associated with internal organ weight in pigs. To this end, we conducted single-trait and multi-trait GWASs for six internal organ weight traits in 1518 crossbred commercial Duroc × (Landrace × Yorkshire) DLY pigs. The results from the current study advanced our understanding of the genetic basis for internal organ weight and further revealed the complexity of the genetic architecture of internal organ weight in pigs. Integrating SNP results from GWASs as a source of prior biological information in the improvement program enhances the selection process by assigning higher weight to key SNPs that are critical for improving internal organ weight traits.All animals used in this study were treated in accordance with the guidelines for the use of laboratory animals of the Ministry of Agriculture of China and with the approval of South China Agricultural University , No. 2018F089.Experimental animals were selected from a DLY three-way crossbred commercial line with no overlapping blood relations, through random selection based on genealogy, in which 89 Duroc boars were mated with 397 Landrace × Yorkshire sows to produce a large number of offspring. All pigs were raised in four farms of the Guangdong Wens Food Group Co., Ltd. . In brief, a total of 1518 individuals (757 boars and 764 sows) were reared with free access to water and feed and were fattened to 115 kg. They were euthanized in 13 batches with a 24 h interval between each batch and had an average slaughter age of about 7 months. After the pigs were euthanized, their phenotypes were recorded, and their internal organs were excised, emptied, flushed, blotted dry, and weighed immediately using an electronic scale with a range of 0.0 kg to 300 kg and accuracy of ±100 g. The scale was calibrated using the linear calibration method with 20% MAX or 60% MAX weight. The organ distribution is shown in p > 10−6 for the Hardy–Weinberg equilibrium test. Moreover, SNPs in sex chromosomes and unmapped regions were excluded. After QC, a final set of 31,941 eligible SNPs remained for subsequent single-trait and multi-trait GWASs.Ear samples were collected from all 1518 individuals, and genomic DNA was extracted from the ear tissue of each pig using a standard phenol–chloroform method and subsequently diluted to 50 ng/μL for the genotyping procedure, controlling the quality OD260/280 between 1.8 and 2.0. The 1518 DLY pigs were genotyped using the GeneSeek Porcine 50K SNP BeadChip , which contained 50,703 SNPs. After genotyping, to ensure the accuracy and validity of the GWAS results, we performed a quality control (QC) procedure using the PLINK v1.07 software [r2) with a window size of 1000.PCA was conducted using the GCTA software  to assesy is a vector of phenotypic values for each internal organ weight; W is the correlation matrix of covariates (fixed effects), including the top five eigenvectors of PCA, farm, sex, and slaughter lot; α is a vector of corresponding coefficients including the intercept; X is the genotypic vector of the SNP markers; β denotes the effect size of the SNP markers; u is a random effects vector, −1K)u~MVNn ε~MVNn  was employed as a correction to the threshold [p-value was calculated as N is the number of SNPs with p-value less than 0.01, and M refers to the total number of SNPs after quality control. Moreover, quantile–quantile (Q–Q) plots were constructed for the six internal organ weight traits to further assess the population structure.Furthermore, the Bonferroni correction can lead to an overcorrection and can be too conservative, this can result in a limited number of labeled association hreshold . Thus, tp-value < 10−3 for the HWE test) were used to define the linkage disequilibrium (LD) blocks of SNPs.In addition, the PLINK v1.07 and Haploview v4.2 software  were impy is the phenotypic value of each internal organ weight trait; β is the vector of fixed effects, including the top five eigenvectors of PCA, farm, sex, and slaughter lot; X is an association matrix; g is the vector of total genetic effect of all the qualified SNPs for the 1518 DLY pigs; In the present study, the restricted maximum likelihood (REML) method was used to assess the SNP-based heritability of each internal organ weight trait, and the percentage of phenotypic variation that could be explained by significant SNPs was calculated using GCTA software. SNP-based heritability and the percentage of phenotypic variation explained by significant SNPs were calculated as follows :y=Xβ+g+εr2 of 0.2 is about 200 kb apart [r2 = 0.2) of the populations [http://ensemble.org/Sus_scrofa/Info/Index, accessed 20 September 2022). Genes nearest the significant SNPs are list in Tables. We conducted a search of both PubMed and the relevant literature to examine the correlation between the nearest peak SNPs of all the candidate genes and the internal organ weight traits being analyzed.Our previous studies on this population showed that the average kb apart ; the ranulations . We usedulations  in R, bah2) for analysis of the internal organ weights are listed in The descriptive phenotypic statistics and estimated heritabilities . The top SNP, ALGA0098928, explained 2.22% of the phenotypic variation and is located within POU6F2. POU6F2 is a suppressor associated with nephroblastoma (WT) that regulates cell proliferation and specific differentiation [POU6F2 is associated with renal morphogenesis [POU6F2 may be closely associated with spleen weight traits.The GWAS results of Spleen WT identified three significant SNPs, located on SSC3, SSC9, and SSC18, with a λ of 0.972 . All thrntiation . Accordiogenesis , suggest−4. An SNP named ALGA0110225 explained 1.81% of the phenotypic variance and is located 97 kb downstream of PBX3, indicating that PBX3 and ALGA0110225 may both play a role in Lung WT. A literature review revealed that PBX3 is directly regulated by targeting NBPF10, miR-144, and miR-224, which are directly associated with lung cancer cell proliferation [PBX3 promotes the proliferation of A549 cells (lung cancer histiocytes) [PBX3 to be a promising candidate gene for influencing Lung WT, and the regulatory mechanism needs further investigation.For lung weight, four significant SNPs were detected on SSC1, SSC7, and SSC11 with a λ of 1.008 . These fferation . In addiiocytes) . Therefop < 1.03 × 10−4)  . Figure p < 1.17 × 10−4 . The insufficient density of 50K microarray markers resulted in a low number of SNPs with linkage disequilibrium, which limited the resolution of the genetic architecture of key SNPs for the trait to some extent.In order to improve the statistical effect, multi-trait GWASs were individually performed for each SNP by combining the joint analysis of six internal organ weight traits. This revealed the genetic factors with significant interactions among different traits in the same individual under the same environment. Manhattan plots of the multi-trait GWASs are shown in ANO6 gene. The overexpression of CCR7 was observed to enhance the migration of BxPC-3 cells under the induction of the ANO6 gene, which is a potential mediator of ANO6 expression through the ERK signaling pathway. This promotion of migration was also seen in pancreatic ductal adenocarcinoma cells [TPK1 and UNC5C genes on the weight of the liver and stomach. These SNPs were not only significant in the single-trait GWAS but were also found to be simultaneously associated with the weight of all six internal organs, suggesting that these four SNPs have pleiotropic effects. Furthermore, no additional SNPs, independent of the single-trait GWAS results, were found. Similar results were previously reported by Guo et al. [The multi-trait GWASs combining six internal organ weight traits identified four significant SNPs with polymorphisms affecting the phenotypes, ALGA0032998, H3GA0028070, MARC0052872, and ALGA0106192 . SNP ALGma cells . The sino et al. , in whicTPK1, POU6F2, PBX3, UNC5C, and BMPR1B were highlighted as potential genes responsible for differences in Liver WT, Spleen WT, Lung WT, and Stomach WT among individuals according to their gene functions. In summary, the results of this study contribute to our understanding of the genetics of internal organ weight traits in DLY pigs by assigning higher weights to relevant SNPs and key genes in the genome.In this study, we conducted single-trait and multi-trait GWASs on the internal organ weights of 1518 DLY pigs. A total of 24 significant SNPs were detected in the single-trait GWAS results for six internal organ weight traits. The four significant pleiotropic SNPs identified via multi-trait GWASs were associated with six internal organ weight traits, confirming the results of the single-trait GWASs and improving our ability to reveal the genetic architecture of organ weight traits.


Malt for craft “all-malt” brewing can have high quality, PHS resistance, and malted in normal timeframes. Canadian style adjunct malt is associated with PHS susceptibility.HvMKK3 on chromosome 5H in the Seed Dormancy 2 (SD2) region responsible for PHS susceptibility. Soluble protein (SP) and soluble over total protein (S/T) both shared a common association with a marker in the SD2 region. Significant genetic correlations between PHS resistance and the malting quality traits AA, FAN, SP, S/T were detected across and within HvMKK3 allele groups. High adjunct malt quality was related to PHS susceptibility. Selection for PHS resistance led to a correlated response in malting quality traits. Results strongly suggest pleiotropy of HvMKK3 on malting quality traits and that the classic “Canadian-style” malt is caused by a PHS susceptible allele of HvMKK3. PHS susceptibility appears to benefit the production of malt intended for adjunct brewing, while PHS resistance is compatible with all-malt brewing specifications. Here we present our analysis on the effect of combining complexly inherited and correlated traits with contrasting goals to inform breeding practice in malting barley, the general principles of which can be extended to other breeding programs.Expansion of malting barley production into non-traditional growing regions and erratic weather has increased the demand for preharvest sprouting (PHS) resistant, high quality malting barley cultivars. This is hindered by the relatively unknown relationships between PHS resistance and malting quality. Here we present a three-year study of malting quality and germination at different after-ripening durations post physiological maturity. Malting quality traits alpha amylase (AA) and free amino nitrogen (FAN) and germination rate at six days post PM shared a common association with a SNP in The online version contains supplementary material available at 10.1007/s00122-023-04257-6. Hordeum vulgare L.) is grown worldwide for the production of malt, an ingredient in many alcoholic beverages and food products. Malting is a process of controlled germination where barley is steeped, germinated, and then dried (kilned) to develop flavor compounds and promote shelf stability  locus in regard to the dormancy associations. Nakamura et al. (HvMKK3) in the SD2 region are responsible for changes in dormancy. Vetch et al. , “non-dormant” (MKK3N), and “dormant” (MKK3D) according to their phenotypes.Many studies have focused on the genetic mapping of malting quality traits  is important when determining the relationship between PHS and malting quality. Germination/dormancy traits are not static  selected lines. 88 lines derived from the top performing 25% of F2 individuals in C1G based on genomic selection using the initial index were used as the C1G. A random subset (n = 108) of the C1P were used as the C1P representatives. In total the population used was comprised of the eight parents, n = 105 C0 , 87 C1G, 108 C1P, and 114 C2G lines. See Supplemental Fig. 1.The population development for the lines used in this study were described in detail in Sweeney et al.  and Sween = 66 C0, 37 C1G, 64 C1P, 58 C2G, and the eight parental lines) was grown in one location in 2021  in Madison, Wisconsin. Malting data wer collected on grain from one trial each year in 2019, 2020, and 2021 according to Schmitt and Budde  on one rHvAlaAT1 identified by Sato et al. , the “non-dormant” (MKK3N), and the “dormant” (MKK3D) based on their phenotypes.The genetic data in this study was generated and described by Rooney et al. . All lino et al.  and the o et al.  in HvMKK Sweeney  used theAll data analyses were performed within the R statistical language version 4.0.3 , and eie is normally distributed error. The ratio of the plot variance to the total variance (σpl2 + σerror2) corresponds to a technical heritability or repeatability of the malting quality assays.Raw malting quality data collected in duplicate  or triplicate  technical replicates per plot per timepoint, were used to fit models. Malting timepoint and year effects were included as fixed effects in all models. Variance components across years, within year, and within year-timepoint combination were estimated. For all traits per timepoint per year, a model of the formYijk is a vector of the malting quality trait phenotypes, T is a fixed effect for timepoint i, g is a random effect for line j ~ N, g:Tij is a random effect for timepoint i’s interaction with line j ~ N, and eijk is normally distributed error. Most lines were only malted once per timepoint, therefore residual errors were approximately equal to those from the first model. The σg:TP2 contained the variance explainable both by the malting timepoint specific changes that occurred to each line, but also a plot level error variance (σpltE2), i.e., the variance of each trait, separate from the malting assay’s extraction error variance (variance of repeated measurement of the same sample) as estimated within the error term in model 2, when the same plot is sampled repeatedly and malted. If the assumption is made that the plot level variance is identical at each timepoint for each line and these variables are independent, thenSecond, on a per year (for 2020 and 2021) per trait basis, the relative importance of the per line by timepoint deviation from a main line effect was assessed. Models of the formσg:TPΔ2 is the variance associated with phenotypic change across timepoints. σpltE2 was estimated with an internal check, c.v. “Tradition,”  that was included and replicated every 15 samples with grain sourced from the same lot. The variance of Tradition on a replicate plot basis was estimated as the variance of Tradition trait values minus the error variance associated with the assay (from model 2 or 3). σg:TPΔ2 was estimated and gives an indication of how much of the σg:TP2 was due to plot level error versus changes in the malting quality that occurred during after-ripening. This is an approximation, and the output should be treated with caution.where Yyijk is a vector of the malting quality trait phenotypes, E is a fixed effect for year y, T is a fixed effect for timepoint i, E:T is a fixed effect interaction term for year-timepoint combination yi, g is a random effect for line j ~ N, g:T is a random effect for each line j’s interaction with timepoint i ~ N, and e is normally distributed error ~ N. 2019 data were assigned a value of TP6 for the malting timepoint based on the results from the previous models. Heritability of traits across years was assessed asThird, per trait  models of the formy = 3). All the previous mixed model analyses were performed using the lme4 R package  and used as the input to GWA per year.The raw malting quality data as before were used as the input. For 2020 and 2021 on a per year basis, model 3 was reparametrized as a fixed effect model. Line best linear unbiased estimators (BLUEs) at each timepoint (labeled as “TP4” or “TP6”) were then extracted from the model and used as the input to GWA per timepoint. A second model was fit per year per trait of the formYyijk is a vector of the malting quality trait phenotypes, E was a fixed effect for year y, T was a fixed effect for timepoint i, E:T was a fixed effect for year y’s interaction with timepoint i, G was a fixed effect for the line effect j, and e was a normally distributed heterogeneous error variance per year. BLUEs per line were then extracted (labeled as “TP4/TP6”) and used as the input to GWA. Lastly, model 8 was fit to all the data  to estimate line effects overall years, labeled as ‘Combined’. See Supplemental Table 2 for a table summary of GWA analyses performed. Any models with heterogeneous variance components were fit with ASReml-R  and for 2019, 2020, and 2021  model 5 was reparametrized as a fixed effect model with a heterogeneous error variance per year and line BLUEs extracted for each timepoint (labeled as “TP4” or “TP6”). A second model was fit per trait of the formGWA analysis was performed within the GAPIT framework  where σg2 was a diagonal matrix with a unique genetic variance per cycle of selection. Error was distributed as ~ N where σerror2 was a diagonal matrix with a unique error variance per year. Model 10 was compared to an identical model except for using a homogeneous variance structure for the random line effect, with a likelihood ratio test to assess increased model fit and changes in genetic variance between the cycles of selection. The cycle fixed effects were tested for significant differences from zero using a Wald test in ASReml-R. Kenward − Roger degrees of freedom were estimated from the wald object and used within a t-distribution to determine a p-value. The test statistics  were estimated as the cycle fixed effect estimate divided by its standard error.Since all-malted lines were part of a selection experiment  with the resulting values ranging from 1.15 for 2021 BG to 0.082 for 2021 SP. Most of the ratios were less than 0.5. Heritability across years (model 5) was 0.52 for AA, 0.37 for BG, 0.31 for CCRU_Adj, 0.17 for CCRU_allMalt, 0.59 for DP, 0.59 for FAN, 0.49 for ME, 0.68 for MP, 0.73 for SP, and 0.39 for S/T. Supplemental Table 5 contains variance components from model 5.Malting quality trait distributions per year and timepoint are shown in Supplemental Fig. 2 and Supplemental Table 3. Technical heritability for all traits was high, rarely decreasing below 0.80 within any timepoint, year combination . Analysis of model 3, variance of the Tradition malt check, and the heritability on a per year basis are shown in Supplemental Table 4. Heritability on a per year basis was generally moderate to low with BG being the lowest in both years at about 0.32, and SP being the highest in both years at 0.6 to 0.7. Estimation of SD2 region approximately 2.4 Mbp proximal to the HvMKK3. This SNP was in moderate LD (r = −0.76) with MKK3_E165Q and was less correlated with the other alleles of HvMKK3 . Traits DP, FAN, MP, and SP were associated with a region on chromosome 6H. All significant markers in the region were in high LD (r > 0.94) centering around the 50 k marker JHI-Hv50k-2016–385,463. Alpha Amylase was associated with a different region on chromosome 6H. Free amino nitrogen was associated with one marker near the distal end of chromosome 7H, and SP and MP were associated with a region near the distal end of chromosome 1H.Figure r = 0.01) with the MTA found for AA on chromosome 2H in 2019. For FAN, MTA were found on the proximal end of chromosome 4H and near the middle of chromosome 3H in 2020/2021 and 2020, respectively. A ME MTA was found on the middle of chromosome 2H for 2020 and 2020/2021. For MP, a MTA centering at 365 Mbp on chromosome 2H was found for TP6 in the combined 19/20/21 dataset. For SP, a marker that colocalized with the FAN MTA (r = 0.97) in the combined analysis on chromosome 7H was significant in 2021. Another FAN MTA was found near the middle of chromosome 5H for 2020/2021. This marker was unrelated (r = −0.17) to the BG MTA found in the region. A S/T MTA at 527 Mbp on chromosome 3H for TP6 in the combined 19/20/21 dataset.The other combinations of year/malting quality timepoint generally repeated these results, except for a few markers reaching significance in only one year or year group . In 2019 and 2021, AA MTA were found on the chromosome 7H and end of chromosome 2H, respectively. In 2020, 2021 and 2020/2021 only for TP4, a region on chromosome 5H at 517 Mbp was significantly associated with BG. A DP MTA was found with a marker in almost perfect LD (correlation = 0.998) to the chromosome 6H AA MTA in the combined dataset in 2020 and the 2020/2021. Diastatic power also had MTA with markers on chromosomes 4H and 2H in 2021. The marker on chromosome 2H was not in LD  correlated with GI TP1 and TP6, but not GI TP4. Free amino nitrogen, S/T, SP, and AA were correlated with GI TP1  and retained high correlations to GI TP4 and TP6. Beta glucan was moderately negatively correlated with GI TP1 (r = −0.517) and remained negatively correlated over time. Malt extract was weakly positively correlated with GI TP1 (r = 0.30). The CCRU_Adj score was strongly correlated with GI TP1 (r = 0.90) and retained a high correlation to the GI over time. The CCRU_allMalt was weakly correlated with GI TP6 (r = 0.20 to 0.40). Malt protein was weakly correlated with GI TP1 (r = 0.26). Supplemental Table 7 shows results for variations of model 9 including different groups of years or malting timepoints. These results were similar to the results in Fig. Genetic correlations of malting traits with GE or GI per timepoint were similar until later timepoints when the genetic variance of GE declined as most lines reached 100% germination . Only TP1, TP4, and TP6 had germination data for three full years and will be discussed here. Figure ly r < 0. correlatHvMKK3 allelic groups multiple significant correlations were detected, particularly with FAN, SP, S/T, and AA. For MKK3N*, FAN was correlated with GI TP1 (r = 0.85) and BG was negatively (r = −0.58) correlated with GI TP1. For MKK3N, SP, S/T, and FAN were correlated (0.3 < r < 0.5) with GI TP1 and TP4. Germination rate at TP1 was not significantly correlated with AA (r = 0.26). CCRU_Adj was moderately (r ~ 0.7) correlated with GI at all timepoints. BG was not significantly correlated with GI TP4 and TP6, while DP was weakly positively related to GI TP4 and TP6. For MKK3D the highest number and magnitude of significant correlations were observed. GI TP1 was weakly to moderately correlated with AA, BG, FAN, and SP. These correlations generally increased in magnitude with after-ripening and remained significant. The CCRU_Adj score was strongly (r > 0.7) correlated with GI at TP4 and TP6. The correlation of GI TP1 with DP was not significant, but increased in strength and became weakly correlated with GI TP6.Within each HvMKK3 alleles and within allele are shown in Supplemental Table 8. Overall, there were weak to moderate correlations between most malting traits, with especially strong correlations between FAN and SP. Biplots  the genetic basis of malting quality traits using GWA analysis to find common associations with dormancy loci, 2) the genetic correlations between PHS resistance, germination traits over time, and malting quality, and 3) the effect of genomic or phenotypic selection against PHS susceptibility on malting quality traits across two cycles of selection. There were common MTAs for PHS susceptibility  variance estimated indicated a relatively low importance for most traits of the σg:TPΔ2, as the σg2 generally was at least double σg:TPΔ2 , and that the TP:line deviations were mostly caused by plot level assay error , rather than large changes in malting quality between 48 and 110 days post PM. This was likely due to the small quantity of grain sampled. Rooney et al.  on an individual line basis. The magnitude of the plot level error  is strong evidence that HvMKK3, especially the MKK3N* allele, has pleiotropic effects on malting quality.In the combined GWA analysis . For other combinations of the year-timepoint data SP was significantly associated with the MKK3_E165Q marker  which could suggest error within particular years or timepoints. Another plausible explanation would be the presence of two genes in the larger SD2 region affecting malting quality, HvMKK3 which has effects on AA and FAN, while another unknown gene affects SP and S/T. Additional experiments to knock out the HvMKK3 alleles would add stronger evidence for pleiotropic effects on malting quality traits.Surprisingly, for the combined GWA, SP and S/T were not most strongly associated with the MKK3_E165Q mutation or another SNP in tighter linkage with it. They most strongly associated with a SNP 2.4 Mbp away from the mutation only moderately correlated with the MKK3u et al.  show theN* allele leads to an extremely high germination rate. Germination rate affects timing and amount of enzymes production, starch breakdown, and modification. MKK3N* could lead to higher modification  because of the allele per se or because it mediates higher germination rate. If germination rate leads to the changes instead of the allele per se, modifying the malting schedule or the germination rate with exogenously produced plant hormones could allow maltsters to fine tune the malt profile without the risk of a PHS susceptible allele of HvMKK3.The nature of these pleiotropic effects remains unclear. The MKK3The common association of BG, DP, FAN, MP, and SP all with a single region on chromosome 6H is similar to results in in Montana State University germplasm  > 0.4) with AA, BG, FAN, SP, or S/T at the time of malting. Previous work has shown that these germination traits are highly heritable and predictable with genomic prediction  and at time of malting (47 to 200 days post PM) which could play an important role in selection decisions. The importance of correlations may be lessened because malting quality trait goals are related to absolute ranges rather than purely increased or decreased values. It is helpful to look at these values in relation to the American Malting Barley Association breeding targets  would be useful. Correlations may be different in different sets of germplasm, environments and even within the germplasm from which these cultivars originated. Representatives of European, Canadian, and western US lines from North Dakota State University and Montana State University showed significant correlations of the malting quality traits to germination. Exact appraisal of the usefulness and estimated cost of using GI to select for both good malting quality and PHS resistance will need to be further examined in detail given the correlations and estimated cost of phenotyping each trait. Repeating this work under different steeping regimes may also be valuable. The S/T values obtained under the steeping regime from Schmitt and Budde,  point too et al.  who usedBreeding programs exist to improve crop genetics for increased production efficiency, whether that be by increasing the yield, quality, or any other number of relevant traits. Malting barley may serve as just one cautionary example that directly breeding for a primary trait may have undesirable consequences for other traits. We found that selection for high adjunct malt quality likely led to higher PHS susceptibility. This limits the adaptability of the bred varieties to new environments and increases the risk to malting barley producers. Any breeding undertaking should be carefully examined for unintended effects that selection for a target trait may have, weigh the cost and benefits of each effect, and modify the breeding program according to those conclusions. Most prominently, this may include incorporating balancing selection for the target and correlated traits so as to advance the target trait while minimizing the negative effect on the correlated traits.HvMKK3 to malting quality traits, especially AA, FAN, SP, and S/T. Given these correlations, germination traits could be used in selection for potentially good malting candidates and PHS resistance, thus saving time and money on costly phenotyping. The Canadian style adjunct malting profile is associated with PHS susceptibility because the MKKN* allele is also associated with malting quality traits. There is strong evidence this association is causative. PHS resistance comes with a cost, namely lower average FAN, AA, SP, and S/T trait values and a decreased probability of achieving an adjunct style malting quality trait profile. Here we provide evidence that selection of a primary trait can have either a desired or adverse effect on market value depending on the target market. All-malt target malting profiles are compatible with relatively high levels of PHS resistance and PHS resistant lines can be malted successfully even with relatively short periods of after-ripening. However, given that PHS resistance is unfavorably correlated with malting quality traits desired by the adjunct style malt brewers, constant care must be taken within active breeding programs during selection for adjunct malting quality to prevent PHS susceptibility.Germination traits, specifically GI at TP1, a proxy for PHS resistance, are significantly correlated across and within alleles of Supplementary file1 (DOCX 183 KB)Below is the link to the electronic supplementary material.