All five of these genetic correlation estimates were positive. Two-trait genome-wide association studies (GWAS) for each of these five pairs of traits revealed common regions of association on BTA1 and BTA8 for pairs that included DD or SU as one of the traits, respectively. Other regions of association were unique to the pair of traits and not observed in GWAS for other pairs of traits. The positive genetic correlation estimates between foot disorders and other health disorders imply that selection against foot disorders may also decrease susceptibility to other health disorders. Linkage disequilibrium blocks defined around significant and suggestive SNPs from the two-trait GWAS included genes and QTL that were functionally relevant, supporting that these regions included pleiotropic loci.Lameness is an animal welfare issue that incurs substantial financial and environmental costs. This condition is commonly caused by digital dermatitis (DD), sole ulcers (SU), and white line disease (WLD). Susceptibility to these three foot disorders is due in part to genetics, indicating that genomic selection against these foot lesions can be used to reduce lameness prevalence. It is unclear whether selection against foot lesions will lead to increased susceptibility to other common diseases such as mastitis and metritis. Thus, the aim of this study was to determine the genetic correlation between causes of lameness and other common health disorders to identify loci contributing to the correlation. Genetic correlation estimates between SU and DD and between SU and WLD were significantly different from zero ( Despite these prevention efforts, lameness remains highly prevalent in the United States, affecting 16.8% of cows and 3.2% of bred heifers . These nAdditionally, certain foot lesions are genetically correlated with mastitis or indicator traits of mastitis. For example, the genetic correlations between clinical mastitis and sole hemorrhage or SU were estimated at 0.35 and 0.32, respectively, in Swedish Red cows . For HolAll procedures were conducted in accordance with ethical standards set by the University of California, Davis and approved by the Institutional Animal Care and Use Committee (protocol #22099).Five large commercial dairies  in Northern and Central California participated in this study. Phenotypes were derived from hoof trimming and other health records provided by the dairies beginning from the cows’ first lactation. Three hoof trimmers recorded the foot lesions used for phenotyping foot lesions: one who serviced Dairies A, B, and C; one who serviced Dairy D; and another who serviced Dairy E. Hoof trimmer experience and hoof trimming regimens were described previously . Foot dip < 1E-6) to exclude systematic genotyping errors, and SNPs with minor allele frequency <5% to exclude rare variants. Missing genotypes for each cow were imputed using BEAGLE 5.1 . DNA samples were quantified using the NanoDrop (ND-2000 v3.2.1) spectrophotometer  and sent to GeneSeek  for SNP genotyping on the high-density BovineHD BeadChip . Genotype calls were made using Illumina’s GenCall algorithm. SNP genotypes from a subset of the cows used in this study were used in our past studies  and are AGLE 5.1  using thAGLE 5.1 , and defkth cow from the ith farm at the jth SNP was modeled asith farm to the risk of disease, jth SNP genotype to risk of the disorder, and A was the GRM. kth cow from the ith farm at the jth SNP was modeled using the same components for trait 2 asGenetic correlation was estimated between each foot lesion and other health traits, including other foot lesions  using cows that had phenotypes for both traits and at least 40 case cows for each disease. PLINK 2.0 was used to filter cows by requiring phenotypes for both traits . GCTA wap < 0.05 from the likelihood ratio test, and suggestive genetic correlation estimates were those with p < 0.1.The covariance of additive genetic effects was computed as a function of the numerator relationship matrix, and genetic correlation was calculated as the covariance of additive genetic effects divided by the product of the standard deviations of the genetic effect of traits 1 and 2. All genetic correlation estimates were transformed from the observed scale (0/1) to the underlying liability scale to account for case ascertainment using the prevalence of each disorder obtained from the literature . Geneticp < 0.05/Me and suggestive SNPs, as those with LRT p < 1/Me  with significant or suggestive SNPs were considered belonging to the same LD block. LD blocks were queried in the region search of FAANGMine , and estimates between DD and mastitis, DD and milk fever, and SU and metritis were suggestive  scale] for genome-wide suggestive significance and 3.1 × 10−7 [6.5 on the −log10(p) scale] for genome-wide significance. Manhattan plots from the two-trait GWAS are shown in The effective number of markers after accounting for LD was 162,435 SNPs, corresponding to a suggestive threshold of 6.2 × 10vice versa  and other health traits . For pairs of traits having significant or suggestive genetic correlation, the loci that were contributing to the correlation were examined using two-trait GWAS. To our knowledge, this is the first study to estimate genetic correlation between foot disorders and diseases other than mastitis from individual-level genotype data rather than pedigree data and identify loci potentially contributing to the correlation. Genetic correlation estimates that were significant or suggestive included SU or DD as one of the traits and estimates were positive, indicating a favorable genetic correlation between pairs of disease traits such that genetic selection against one disease will lead to selection against the other disease. Significant and suggestive SNPs were detected in the same regions on BTA1 and BTA8 for two-trait GWAS datasets that had DD and SU as one of the traits, respectively, suggesting that DD and SU were driving the association in these genomic regions. Other significant and suggestive SNPs were specific to the dataset from which they were detected and not detected in GWAS for other pairs of traits.vice versa; however, SU was correlated with metritis and DD whereas WLD was not correlated with either disorder. This divergence would suggest that although SU and WLD share a genetic component, differences exist in the location or direction of the effect for susceptibility loci between SU and WLD, as indicated by the opposite signs of suggestive SNP effects between the traits and the lack of association of WLD to metritis or DD in the two-trait GWAS.Compared to previous estimates of genetic correlation between foot disorders and other health traits, estimates from this study were higher and had larger standard errors. Previous estimates of genetic correlation between foot disorders and mastitis or somatic cell count were significantly different from zero (0.15–0.35)  or closeCompared to our previous one-trait GWAS for DD and SU , 2021, tIn addition to detecting the same regions as the one-trait GWAS, the two-trait GWAS detected other regions that were not detected in the one-trait GWAS for DD, SU, and/or WLD. The DD and mastitis GWAS detected a region on BTA28 that the DD GWAS did not find . The SU SLC9A9 (solute carrier family 9 member A9)  , which hmber A9) . Notablymber A9) , corrobomber A9) . Previoumber A9) , suggestmber A9) . This LDmber A9) , and coumber A9) , though mber A9) . The LD mber A9) . Cows wimber A9) , though The main limitations of this study were the small sample size of genotyped cows and the variation in the number of case cows across the various disorders. At the expense of a larger sample size, we minimized the environmental variation by constraining the sample population to cows to a small geographical region under similar management and nutrition practices and minimized the number of hoof trimmers to reduce variation in phenotyping foot lesions. Minimizing environmental and consistent phenotyping improves the power to detect significant genetic correlation; however, the resulting small sample size limited the accuracy of genetic correlation estimates. For instance, one workaround for the inflation of genetic correlation estimates due to shared controls is to randomly partition the controls between the two traits before estimating genetic correlation; however, the small sample size prevented using this approach. The small sample size also limited the benefit of using genomic data instead of pedigree data to estimate genetic correlation. Although using genomic data to estimate relationships may be more accurate than using pedigree data  due to rA genomic relatedness matrix calculated from SNP genotypes was used to estimate genetic correlation between individual foot disorders  and other health disorders . The positive estimates of genetic correlation between individual foot disorders and other health disorders indicate that direct selection against foot disorders will not increase the incidence of other health disorders and may in fact reduce their prevalence. Genomic assessment for pairs of traits that were genetically correlated revealed multiple associated regions. Whereas some of these chromosomal regions were shared across multiple pairs of traits that included SU or DD as one of the traits, others were unique to the pair of traits, indicating the complexity of genetic contributions within and between traits. The LD blocks defined from associated SNPs included protein-coding genes and QTL that were functionally relevant to both traits, suggesting that selection for markers in these LD blocks would affect susceptibility to both traits.


Acne vulgaris is a highly heritable skin disorder that primarily impacts facial skin. Severely inflamed lesions may leave permanent scars that have been associated with long-term psychosocial consequences. Here, we perform a GWAS meta-analysis comprising 20,165 individuals with acne from nine independent European ancestry cohorts. We identify 29 novel genome-wide significant loci and replicate 14 of the 17 previously identified risk loci, bringing the total number of reported acne risk loci to 46. Using fine-mapping and eQTL colocalisation approaches, we identify putative causal genes at several acne susceptibility loci that have previously been implicated in Mendelian hair and skin disorders, including pustular psoriasis. We identify shared genetic aetiology between acne, hormone levels, hormone-sensitive cancers and psychiatric traits. Finally, we show that a polygenic risk score calculated from our results explains up to 5.6% of the variance in acne liability in an independent cohort. Better understanding of the genetic basis of acne can pave the way to more effective treatments. Here, the authors perform a genome-wide association study meta-analysis of >20,000 cases and identify 29 new acne susceptibility loci, uncovering genetic links to Mendelian hair and skin disorders and other complex traits. According to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD)3 acne was estimated to be responsible for nearly 5 million disability-adjusted life years (DALYs) globally in 2019, of which the majority occurred in those aged 15–49 years. This is greater than other chronic inflammatory conditions such as psoriasis or rheumatoid arthritis. Severe acne often persists into adulthood, and scar formation is more prevalent in this severe adult population. Acne is associated with impaired quality of life, including lower rates of employment in patients with acne, and reduced school and work performance5, as well as a substantial impact on mental health that correlates with clinical severity6. Individuals with acne have elevated rates of depression and higher rates of mental health illness than those without acne7, with rates of suicidal ideation and other mental health disorders being up to three times higher in adolescents with severe acne than their peers with little or no acne4. Several studies report increased clinical depression, anxiety and hospitalisation rates due to mental health disorders in adults and adolescents with acne, with the magnitude of the effect larger in adults9.Acne vulgaris is a common skin disorder that results from inflammation of the pilosebaceous unit leading to the characteristic comedones, papules, pustules, nodules and cysts. Lesions are generally restricted to the face, neck, chest and back, and onset typically occurs during puberty. Prevalence estimates of acne vary substantially; it is estimated that more than 85% of teenagers are affected to some degree, and up to 8% have been reported with severe disease10, restricts use to only those with severe disease. Isotretinoin is also a powerful teratogen whose use during pregnancy is restricted due to its association with severe and life-threatening birth defects. Better-tolerated acne treatments are required and there is a particular need for effective options without the risk of teratogenicity.Despite considerable recent advances in new treatments for other inflammatory skin diseases, including psoriasis and atopic dermatitis, there is a substantial unmet medical need in the treatment of acne. Early and effective intervention strategies are often necessary to avoid irreversible scarring in severe acne. Treatment typically consists of topical and systemic agents that suppress the microbiome repertoire or the activity of sebaceous glands, while other treatments include hormonal treatment and phototherapy. The most effective agent to treat acne is isotretinoin, which may induce remission through its effect on epidermal differentiation, but its side effect profile, which includes dry skin, lips (cheilitis) and mucous membranes, muscle aches, itching of the skin and headaches15. Recent molecular genetic studies identified 17 genomic loci harbouring alleles associated with the disease—15 loci with a reported effect in European populations17 and two in a Han Chinese population18. Functional characterisation of these genetic association signals has implicated a series of causal genes whose genetic perturbation impacts the development and maintenance of the hair follicle and wound healing.The genetic contribution to acne susceptibility has been demonstrated in several twin studies, with heritability consistently estimated around 80%To further characterise the genetic architecture of acne vulgaris and identify additional genomic loci contributing to the disease susceptibility, we have performed a meta-analysis of genome-wide association studies (GWAS) of acne undertaken in nine independent cohorts that in total comprise 615,396 study participants . Next, we combine fine-mapping and genome-wide analytical approaches to gain insights into the underlying genes and pathways through which the associated loci contribute to disease susceptibility, and the relationship between the genetic architecture of acne and other traits.P < 5 × 10−8) associations at 43 loci  genome-wide significant associations at three of the previously established loci .We conducted case-control GWAS of acne in fourteen datasets from nine independent European ancestry cohorts (Supplementary Note). Ascertainment of acne case status varied across the individual cohorts from clinical diagnoses of acne vulgaris by a dermatologist to self-reported disease  and minimal evidence of effect in our separate cohorts , and acne risk allele of rs260643 itself has been reported to be associated with hair curl in a GWAS in Japanese women20. The range of phenotypes that result from functional variation at this locus closely resembles the known acne susceptibility locus harbouring WNT10A, at which genetic variation influences acne risk, hair curl, male pattern baldness and Mendelian forms of ectodermal dysplasia.Statistical fine-mapping of each association signal revealed two loci at which a single variant was identified as the putative causal variant with a posterior probability >0.95 Table . This inLAMC2, TGFB2, WNT10A, LGR6, FGF2 and GLI2 but also highlights the potential consistency of biological processes involved in acne pathogenesis with the identification of other members of these gene sets located near new acne association signals.To identify additional putative causal genes at acne susceptibility loci, we used a combination of approaches: (a) colocalisation of the acne association signals with skin eQTL signals , where patterning defects of the hair follicle lead to alopecia.CSTA as the putative causal gene at this locus. CSTA encodes Cystatin A, a protease inhibitor with an established role in cell-cell adhesion. The acne susceptibility signal at 3q21.1 shares a common causal variant with skin eQTLs for CSTA with high probability , with the acne risk allele lowering expression of CSTA. Homozygous loss-of-function of CSTA causes a peeling skin syndrome that results from extensive hyperkeratosis (OMIM:607936). There is also evidence of a potential shared biological mechanism with pustular psoriasis at 2q13. The acne susceptibility association signal colocalises with IL36RN eQTLs in both sun-exposed and non-sun-exposed skin . The acne risk allele is associated with decreased IL36RN expression, which is directionally consistent with the rare putative loss of function missense variants in IL36RN underlying pustular forms of psoriasis 22.At 3q21.1, there is further evidence supporting the importance of cell-cell adhesion processes in the skin in acne susceptibility through the identification of Assuming a population prevalence of 30% for acne, the genome-wide significant acne risk loci explain an estimated 6.01% of the variance in acne liability. However, estimation of heritability explained by all common SNPs, i.e., the SNP-based heritability, indicates that 22.95% (s.e. = 0.02) of the variance in acne liability is explained by common genetic variation across the genome. We utilised this extensive polygenicity to examine the genetic correlation and potential causal relationship between acne and a series of 935 human diseases and traits, finding 45 traits with statistically significant genetic correlations (Supplementary Data P < 5 × 10−8) and a PRS leveraging genome-wide effects across all SNPs with the SBayesR algorithm23. Whilst both scores were strongly associated with acne (defined as moderate or severe acne) in this independent cohort , the cumulative risk derived from genome-wide significant association signals explained 2.8% (s.e. = 1.32%) of variance in acne liability compared to 5.6% (s.e. = 1.79) using SBayesR  that estimates an individual’s genetic liability of acne to predict the phenotypic expression in an independent cohort of 2,058 people for whom the history of acne had been evaluated by questionnaire. The polygenicity of acne susceptibility was further assessed by comparing a PRS constructed with the lead variants at genome-wide significant loci , and variants with MAF < 1% and an imputation accuracy score <0.7 were excluded prior to the meta-analysis. Only variants present in at least 9 of the 14 datasets were included in the final meta-analysis, resulting in 7,072,770 variants.We conducted an inverse-variance-weighted meta-analysis of 615,396 individuals  from each of the cohorts described above (14 datasets) using METAL32 with the following parameters: a P-value cut-off of 5 × 10−8, 1 Mb distance between variants and r2 < 0.001 for variants within the genomic distance cut-off, using the linkage disequilibrium structure of the European ancestry subset of the 1000 Genomes Project as a reference panel. Due to the complexity of the major histocompatibility complex region (chr6:26-34 Mb), only the most significant variant in this locus has been reported.Linkage disequilibrium (LD) clumping was used to identify the positions of loci containing acne-associated variants. Clumping of the results from the meta-analysis was conducted using Plink v1.933, assuming a prior variance on the log odds ratios of 0.04 ; W is prior variance on the log odds ratios.An approximate Bayes factor was calculated from the effect size and standard error of each variant in each associated locus (lead SNP ± 1 Mb), using the approach defined by WakefieldP  < 1 × 10−4). A Bayesian test for colocalisation between the acne association signal and the skin eQTL signal was performed using a set of variants that overlapped between the two studies using the R package coloc34, with a prior probability of colocalisation defined as P: 10−5. A posterior probability exceeding 50% was used as evidence of colocalisation.We examined the colocalisation between acne association signals and skin cis-eQTLs from the GTEx (The Genotype-Tissue Expression project) consortium. Candidate skin eQTLs were defined as any variant located within an acne risk locus (±1 Mb from lead SNP) that was also associated with variation in the expression of a nearby gene , all variants present in the 95% credible sets were annotated using Ensembl Variant Effect Predictorhttps://github.com/perslab/depict) to prioritise causal genes and identify gene-sets and tissue types in which these genes are enriched36. DEPICT uses 14,461 reconstituted gene-sets, in each of which a z-score of association is calculated for each gene across the genome. DEPICT prioritises genes at associated loci by calculating how highly correlated they are with other genes in that gene-set. We used a P-value threshold of 1 × 10−5 to define loci associated with acne. We corrected for multiple testing using Benjamini-Hochberg’s False Discovery Rate (FDR < 5%). Further annotation of links, including membership of gene families, co-existence within curated pathways, co-expression across tissues and experimentally determined interactions was performed between genes located in acne susceptibility loci was performed using Ensembl37 and STRING databases38 databases. The low-confidence threshold (0.150) was used to define links between genes in STRING.To gain insights into the underlying genes and pathways that explain our associations, we used DEPICT  of the acne meta-analysis. We also estimated the variance explained by the genome-wide significant SNPs alone using R package Mangrove (https://CRAN.R-project.org/package=Mangrove). For both analyses, we assumed a population prevalence of 30%. We used the Complex Traits Genetics Virtual Lab  to calculate genetic correlations between our acne meta-analysis and 935 complex behavioural and disease traits and screen these traits for a potential causal association with acne using the Latent Causal Variable (LCV) method40. LCV does not directly test for causality but instead estimates a genetic causal proportion (GCP) parameter that mediates an association between two traits; a GCP of 0 indicates no genetic causal association, and a GCP of 1 indicates full genetic causality. GCP values lower than 1 but greater than 0 indicate partial causality. In this study, we considered a GCP > 0.7 as an indication of a causal relationship. We applied a multiple testing correction (FDR < 5%) to determine statistical significance.We used LD-Score (LDSC) regression41. Participants were asked about the presence and severity of acne as a teenager to which they could answer None, Mild, Moderate or Severe. We conducted two regression analyses: first, using all participants with cases coded on an ordinal scale and second, using only individuals that answered Moderate or Severe included as cases in our analysis (N = 527), and individuals that reported not having acne considered controls (N = 645). Individuals were genotyped as described in the Supplementary Note. To avoid bias due to potential sample overlap between PISA and the other QIMR cohorts included in the meta-analysis, we used genotype information to exclude all individuals with an IBD > 0.125—which equates to third-degree relatives.We leveraged our meta-analysis results to build acne PRS and assess their predictive ability in an independent sample. We used acne data collected in the Prospective Imaging of Aging Study (PISA) at QIMR Berghofer as our target sample. PISA currently consists of ~3000 genotyped individuals who have completed extensive behavioural, psychological and medical questionnaires and cognitive testing and brain imaging23. This method re-scales the GWAS SNP effects with many SNPs assumed to have an effect size of zero. SBayesR has been shown to outperform other PRS methods in the prediction of complex traits. For the LD reference, we used the same sparse LD matrix as in Lloyd-Jones et al.23, where the LD matrix was built based on the HapMap3 SNPs of randomly selected and unrelated 50,000 UK Biobank individuals. The posterior SNP effects were used to generate PRS for each individual using the --score function in PLINK. PRS were calculated using the C+T method across eight different SNP P-value significance thresholds: P < 5 × 10−8, P < 1 × 10−5, P < 0.001, P < 0.01, P < 0.05, P < 0.1, P < 0.5, P < 1. For each individual, at each threshold, an acne PRS was calculated by multiplying the dosage score and effect size for each SNP, and then these values were summed across all loci. For more detail on C+T PRS calculation see Mitchell et al.42.We calculated PRS using both SBayesR v2.03 and the traditional clumping and thresholding (C+T) methods. SBayesR is a Bayesian method that assumes that SNP effects are drawn from a mixture of four zero-mean normal distributions with different variances43. A partial R2 was used to estimate the variance explained by the PRS. The predictive ability of the PRS was further evaluated using receiver operating characteristic (ROC) curves using ROC curves in R v3.6.1 using the pRoc package44. Significance values were calculated using a two-tailed Student’s t-test.For each PRS, a mixed model regression was conducted with the acne PRS as a predictor variable while accounting for sex, age and the first ten genetic principal components  as fixed effects; relatedness among individuals was accounted for as a random effect with a genetic relatedness matrix, as implemented in GCTA 1.91.7Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1–7Reporting Summary


Stiff-stalk lines with a temperate background tended to have higher MVNs than plants in other genetic backgrounds. MVN was significantly positively correlated with plant height, tassel maximum axis length, ear length, and kernel number per row, which indicates that MVN may affect plant morphological development and yield. In addition, MVN was extremely significantly negatively correlated with brace root radius, but significantly positively correlated with brace root angle (BRA), diameter, and number, thus suggesting that the morphological function of some brace root traits may be essentially determined by MVN. Association analysis of MVN in brace roots combined 1,253,814 single nucleotide polymorphisms (SNPs) using FarmCPU revealed a total of nine SNPs significantly associated with MVN at P < 7.96 × 10–7. Five candidate genes for MVN that may participate in secondary wall formation  and regulate flowering time (GRMZM2G381691 and GRMZM2G449165). These results provide useful information for understanding the genetic basis of MVN in brace root development. Further functional studies of identified candidate genes should help elucidate the molecular pathways that regulate MVN in maize brace roots.Metaxylem vessels in maize brace roots are key tissue, and their number (MVN) affects plant water and inorganic salt transportation and lodging resistance. Dissecting the genetic basis of MVN in maize brace roots can help guide the genetic improvement of maize drought resistance and lodging resistance during late developmental stages. In this study, we used 508 inbred lines with tropical, subtropical, and temperate backgrounds to analyze the genetic architecture of MVN in maize brace roots. The phenotypic variation in MVN in brace roots was evaluated in three environments, which revealed broad natural variation and relative low levels of heritability ( Water is an important environmental factor with a profound effect on plant growth and development. As a vascular plant species, maize has evolved a xylem vessel system that transports materials such as water and inorganic salts to support plant growth and development. Interestingly, maize brace roots, which are specialized structures, have also developed metaxylem vessels. As an important part of postembryonic roots, brace roots provide water and inorganic substances for maize plants during later stages of growth and development, when seminal roots and primary roots have degraded . In addinecrotic upper tips1 (nut1) plants have weak stems and fewer vascular bundles in inflorescence nodes, which results in drought sensitivity and abortion at the flowering stage. Through gene cloning, NUT1 has been found to encode the NAC transcription factor NAC91, a member of the subfamily of secondary cell wall NAC gene. Mutation of NAC91 leads to a reduction in xylem vessels and defects in plant water transport  is an effective method to analyze natural variation in quantitative traits on the basis of linkage disequilibrium, thereby providing a theoretical foundation for dissecting the genetic architecture and facilitating the molecular improvement of complex traits in maize . The gen1 The significant SNPs uncovered in this study can be used to dissect the genetic basis of MVN in maize brace roots. In addition, the identified candidate genes can serve as a useful resource for further functional study to improve the transport capacity and lodging resistance of maize during later stages of growth and development.In this study, we used an association panel of 508 maize inbred lines with a wide range of sources and abundant genetic diversity to conduct GWAS of MVN in maize brace roots in three environments. Our aim was to reveal the underlying phenotypic diversity and genetic basis of MVN in maize brace roots. We also analyzed the correlation between MVN and other agronomic traits, including seven morphological traits, seven yield related traits, three maturity traits, and five brace root traits. Using the B73 V2 reference genome, we identified a series of candidate genes associated with brace root MVN. Finally, we analyzed the TPM-based expression levels of these candidate genes in different B73 tissues using information downloaded from the MaizeGDB qTeller database.The association panel used for the GWAS contained 508 diverse maize inbred lines. Among them, 60 inbred lines were from the Germplasm Enhancement of Maize in the United States, 223 were from the International Maize and Wheat Improvement Center in Mexico, and 225 were germplasm resources from China. Most of the inbred lines from the International Maize and Wheat Improvement Center were tropical and subtropical, whereas inbred lines from the United States and China were mostly temperate. In a previous study, kinship modeling of the 508 maize inbred lines divided the association panel into four subgroups: 27 stiff stalk (SS), 70 non-stiff stalk (NSS), 196 tropical-subtropical (TST), and 215 admixed (MIXED) lines . The SS All 508 inbred lines of the association panel were planted in three environments in China: Siping City, Jilin Province in 2017 , Shenyang City, Liaoning Province in 2018 , and Xinxiang City, Henan Province in 2020 . All lines were planted using a randomized complete block design with two replicates. In each plot, each line was planted in a single 2-m long and 0.6-m wide row, with 0.4-m spacing between rows. During the reproductive growth stage, six maize plants of each inbred line were randomly selected. Three brace roots, which were closest to ground level and growing into the soil, were cut with shears. The collected materials were washed with distilled water and placed in a prepared FAA fixative solution for 3–7 days for short-term storage, which softened the roots and made them easily sliceable. Three transverse sections were obtained from each brace root by free-hand sectioning. To clearly visualize metaxylem vessels, the sections were stained with safranin dye, and the MVN of each section was counted under a light microscope .Yijk = μ + Repi + Genk + εijk for individual environment analysis, Yijk = μ + Envi + Repj(Envi) + Genl + Envi×Genl + εijkl for across environments analysis, where Yijk is the MVN of interest, μ is the mean effect, Envi is the effect of the ith environment, j (Envi)Rep is the effect of the jth replicate within the ith environment, Genl is the effect of the lth genotype, Envi × Genl is the environment × genotype interaction, and εijk is the error. Broad-sense heritability was, respectively, calculated at individual environment and multiple environments as follows: n represents the number of environments and replications  analysis of MVN in maize brace roots were performed under the following linear mixed model in the lme4 package of META-R : Yijk = ications . For calwww.maizego.org/Resources , which resulted in a P-value of 7.96 × 10–7 for FarmCPU.For the GWAS, a genotyping dataset comprising 1,253,814 SNP markers  obtained from 50K SNP array, 600K SNP array, RNA-Seq, and genotyping-by-sequencing data combined into a whole genetic map was downloaded from esources . The assesources . We usedesources . The sugR2 of each significant SNP was calculated using two linear models: Y = Xiαi + Pβ + ε, which was used to estimate the total variance of all significant SNPs, and Y = Xα + Pβ + ε, which was used to estimate the variance of individual significant SNPs. In these equations, Y and X represent phenotype and SNP genotype vectors, respectively, P is the matrix of the three subgroups , and α, β, and ε are SNP, subgroup and random effects, respectively.The contribution of SNPs to the phenotypic variance was estimated using ANOVA function in the R software package. After adjustment for population structure effects, the r2 < 0.2). The physical locations of SNPs were determined in reference to the B73 RefGen_v2 genome. Genes within a 50-kb range upstream and downstream of the significantly associated SNP locus were searched , where n is the TPM value.The expression levels of candidate genes from different maize tissues of B73 were downloaded from the MaizeGDB qTeller database (see text footnote 1). The values used for heat map construction were calculated as logh2) of MVN ranged from 0.43 in the across environments analysis to 0.74 in the JL environment (P < 0.01) in single environments and across all environments, while the genotype × environment (G × E) interaction was highly significant (P < 0.01) for across environments but environment variance was not significant.According to our analysis of phenotypic variation, the MVN of brace roots in the association panel exhibited extensive phenotypic variation that followed a normal distribution . The broironment . VariancThe phenotypic variance of MVN in different environments  was analyzed independently. Significant differences were detected in MVN among the three environments . The medBecause the MVN of maize brace roots can affect plant growth and development by influencing the transport of water and inorganic substances, we investigated the correlations between MVN and other agronomic traits. Pearson’s correlation coefficients were calculated after comparison of MVN with 17 agronomic traits previously measured in the same association panel, including seven morphological traits: plant height (PH), ear height (EH), ear leaf width (ELW), ear leaf length (ELL), tassel maximum axis length (TMAL), tassel branch number (TBN), and leaf number above ear (LNAE), seven yield traits: ear length (EL), ear diameter (ED), cob diameter (CD), kernel number per row (KNPR), cob grain weight (CGW), cob weight (CW), kernel width (KW), and three maturity traits: days to anthesis (DTA), days to silking (DTS), and days to heading (DTH) . MVN wasIn addition, MVN can also affect plant lodging resistance by influencing the mechanical strength of brace roots. Pearson’s correlation coefficients were also calculated after comparison of MVN with five brace root morphological traits: brace root tier number (BRT), brace root radius (RBR), brace root number (BRN), brace root angle (BRA), and brace root diameter (BRD) (Unpublished results), which can affect plant yield and lodging resistance . MVN wasP < 7.96 × 10–7. We detected four independent significant SNPs above this threshold on chromosomes 2, 4, 7, and 8 in LN, which accounted for 4.88–6.72% of phenotypic variation and 30.00% of total phenotypic variation  protein  were used for the association study. The GWAS was performed using the FarmCPU method with a threshold of ariation . Three iariation . Only onariation . An iden protein .P-value of 1.83E-09, which was consistent with FarmCPU results , which was highly homologous with VOZ1 in Arabidopsis. AtVOZ proteins mediate the first step of the phyB signal transduction pathway to regulate flowering, voz1voz2 mutant suppressed the expression of FT, and repressed the early flowering phenotype of the phyB mutant  protein. SWEET proteins participate in the transport of hexose and sucrose in plants . Interfascicular fiber wall thickness is increased in the basal internodes of the ofp4 mutant and ofp1ofp4 double mutant in Arabidopsis, similar to knat7 mutants (GRMZM2G028982 may regulate MVN in brace roots by affecting secondary cell wall formation.A transcriptional regulatory pathway in which NAC transcription factors act as the main regulatory switch to control other downstream transcription factors or functional proteins to regulate secondary cell wall formation and PCD has also been identified in Arabidopsis . The can mutants . KNAT7 i mutants . Subsequ mutants . ConsequIn this study, we have revealed the genetic architecture of natural variation in MVN in maize brace roots by the GWAS approach. MVN showed a relatively low heritability and showed broad variation in the association panel used in this investigation. According to the GWAS, several genetic loci with small effects regulate the natural variation in MVN in maize brace roots, and flowering time might have an indirectly role for shaping MVN in brace roots during maize domestication. Five candidate genes were detected that possibly impact MVN by affecting flowering time and secondary wall formation. These candidate genes are a valuable resource for further studies to dissect the molecular network regulating MVN in maize brace roots. In addition, the significantly associated SNPs found in this study should be helpful in facilitating marker assisted selection of appropriate brace-root MVNs in maize breeding programs.The original contributions presented in the study are included in the article/CL and YR initiated and designed the overall study. XL and MZ performed and coordinated the field experiments and phenotypic data collection. ML and MZ carried out the data analysis. ML interpreted the results and wrote the manuscript. All authors contributed to manuscript editing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


In the past decades, genomic prediction has had a large impact on plant breeding. Given the current advances of high-throughput phenotyping and sequencing technologies, it is increasingly common to observe a large number of traits, in addition to the target trait of interest. This raises the important question whether these additional or “secondary” traits can be used to improve genomic prediction for the target trait. With only a small number of secondary traits, this is known to be the case, given sufficiently high heritabilities and genetic correlations. Here we focus on the more challenging situation with a large number of secondary traits, which is increasingly common since the arrival of high-throughput phenotyping. In this case, secondary traits are usually incorporated through additional relatedness matrices. This approach is however infeasible when secondary traits are not measured on the test set, and cannot distinguish between genetic and non-genetic correlations. An alternative direction is to extend the classical selection indices using penalized regression. So far, penalized selection indices have not been applied in a genomic prediction setting, and require plot-level data in order to reliably estimate genetic correlations. Here we aim to overcome these limitations, using two novel approaches. Our first approach relies on a dimension reduction of the secondary traits, using either penalized regression or random forests (LS-BLUP/RF-BLUP). We then compute the bivariate GBLUP with the dimension reduction as secondary trait. For simulated data (with available plot-level data), we also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP). In our second approach (GM-BLUP), we follow existing multi-kernel methods but replace secondary traits by their genomic predictions, with the advantage that genomic prediction is also possible when secondary traits are only measured on the training set. For most of our simulated data, SI-BLUP was most accurate, often closely followed by RF-BLUP or LS-BLUP. In real datasets, involving metabolites in Arabidopsis and transcriptomics in maize, no method could substantially improve over univariate prediction when secondary traits were only available on the training set. LS-BLUP and RF-BLUP were most accurate when secondary traits were available also for the test set. Genomic prediction is increasingly applied as standard tool in many animal and plant breeding programs. Since it was first introduced by Meuwissen et al. , the maiAt the same time, advances in high-throughput phenotyping and cell biology technologies provide increasing amounts of phenotypic data, in addition to the “primary” or “target” traits of interest, such as yield or disease resistance. Such additional traits are typically high-dimensional, and collected using various types of technology, e.g., remote-sensing . Lopez-Cruz et al. , relying on penalized regression (or random forest regression) of the target on the secondary traits. We then compute the bivariate GBLUP with the dimension reduction as secondary trait. Second, we extend existing multi-kernel methods by replacing the secondary traits by their genomic predictions, the main advantage being that genomic prediction for the test set is always possible, also when secondary traits are only measured on the training set. For simulated data (with available plot-level data), we will also use bivariate GBLUP with the penalized selection index as secondary trait (SI-BLUP).Y1, …, Yp+1, where each Yj is a column vector. The first one (Y1 = Yf) is the focal or target trait, for which genomic predictions are required; Y2, …, Yp+1 are the secondary traits. n = nt + no genotypes, including no genotypes for which the target trait is observed (the training set), and nt for which it is to be predicted (the t referring to test set). We will use subscripts t and o to indicate that we take the subset of values on the test, respectively training set, for example Yo and Yf, o.To a large extent we follow the notation of Runcie and Cheng , assuminn × n genetic relatedness matrix K is partitioned as:The secondary phenotypes are either observed only on the training set  and observed (training) genotypes. We will also write Kt· = [KttKto] and Ko· = [KotKoo]. Similarly, we can decompose the genetic and residual covariance matrices Σu and Σe aswhere the where the scalars Y =  is assumed to beThe joint distribution of wheree can assumed to be diagonal. Σu and Σe are usually unknown, and need to be estimated from the data. For p larger than 5 − 10, this usually requires approximations. Below we describe several dimension reduction approaches, which reduce the dimensionality of the secondary phenotypes to 1, and exact REML-estimates of Σu and Σe can be obtained with standard software.The genetic covariances . In our simulations we assess prediction accuracy in terms of the Pearson correlation (r) between the simulated and predicted genetic effects, on the test set. For real data, we consider the correlation between the predicted genetic effects and the trait values observed on the test sets. Although it is well-known that this is a biased estimator of the true accuracy , the bias is likely to be constant among methods, as long as the target and secondary traits are observed on different plants  linear unbiased predictor, at least given the true values of the variance components.where The multivariate GBLUP in scenario 1 isu and Σe are obtained from the multivariate mixed model for Yf and Ys. As pointed out by Runcie and Cheng  is larger than 5 − 10, estimation of the required genetic covariances quickly becomes challenging and often infeasible  and (5) are valid regardless whether there is just a single secondary phenotype, or multiple ones. However, when the dimension of the secondary phenotype (S = ĥ(Ys), where ĥ(Ys) is a prediction of Yf based on Ys, obtained either with LASSO or random forests. Genomic prediction in scenarios 1 and 2 is then performed using (4) and (5), with S = ĥ(Ys) as secondary trait. We will refer to the resulting genomic predictions using LS-BLUP and RF-BLUP, depending on whether the dimension reduction was achieved by respectively LASSO or random forests. In a GWAS context, such dimension reductions have been used by van Heerwaarden et al. , which we explore for the real data.Ys for the column vector containing all secondary traits, we will now also use Ys(j) for the column-vector containing the jth secondary trait, the dimension being either no × 1 (scenario 1) or n × 1 (scenario 2). We will use i. Recall that the individual secondary traits are still labeled Y2, …, Yp+1, Y1 being the target trait.In addition to the notation p × 1 vector γ of coefficients is obtained by minimizing, for each individual i, the expectation of Ys times the vector of genetic covariances between Ys and Yf, i.e., SI one could plug in estimates p) is large, A well-known alternative dimension reduction approach is to use a selection index J(γ) is either To address these issues, Lopez-Cruz et al.  proposedWe will follow the implementation by Lopez-Cruz et al.  in theirh(S)ρG. After selecting a value for λ, genomic prediction in scenarios 1 and 2 is performed using (4) and (5), with a single secondary trait, i.e., the selection index Following again  is partly a phenotypic prediction: instead of the genetic component of the secondary traits, it directly relies on these traits themselves, which are assumed to be available on the test set. As a consequence, the use of (8) is limited to scenario 2.To overcome these limitations we propose the GM-BLUP:where as considered by Töpner et al.  and GrotFinally, following many other authors  and residuals (ri) for each accession i, and the final trait values were obtained using a structural equation model, describing functional relations between traits. More specifically, for each individual i, the (p + 1) × 1 vector of trait values is defined by yi = yiΛ + gi + ri, Λ being the (p + 1) × (p + 1) matrix of structural coefficients. The th entry of Λ contains the effect of trait k on trait l, and the vectors gi and ri have zero mean Gaussian distributions with covariance matrices Σg and Σr, respectively. The joint distribution of all n(p + 1) trait values is then as in (1), with Σu = ΓtΣgΓ and Σe = ΓtΣrΓ, where Γ = (I − Λ)−1  + G1 + R1, and we do not assume any functional relations among the secondary traits. Hence, if λ ≠ 0, there is a causal effect from Y2, Y3, and Y4 on Y1, but the algorithms under consideration do not know which of the 300 secondary traits are the actual causal ones. We consider λ values on the grid {−1, −0.5, 0, 0.5, 1}. Σg has diagonal elements , i.e., the variances of the direct genetic effects are 0.2 for Yf and 0.7 for each of the secondary traits. The off-diagonal elements corresponding to Y1 vs.  are G ∈ {−0.5, 0, 0.5}. Similarly, Σr has diagonal elements 0.8 for Yf and 0.3 for the secondary traits, and the off-diagonal elements between Y1 and  are E ∈ {−0.5, 0, 0.5}. The other off-diagonal elements in Σg and Σr are zero.The target trait is defined as I, Σu = Σg and Σe = Σr, and Yf will have a heritability of 0.2. The secondary traits will have heritability 0.7, and there is no causal effect of  on Y1. Genomic prediction for Y1 can however still benefit from the genetic correlation between these traits (which is present when ρG ≠ 0). When λ ≠ 0, the causal effect of (Y2 + Y3 + Y4) on Y1 will introduce additional genetic and residual covariance in Σu and Σe.For the special case λ = 0 we have Γ = G and ρE we simulate 50 data-sets; for each of them we predicted the simulated genetic effects for the test set, with the different methods.For each of the 125 combinations of λ, ρY1 − Y4, using the true (simulated) variance components.In addition to the methods described above, we evaluate a benchmark prediction, by computing (4) and (5) for the four-dimensional mixed model with e in Equations 4 and 5).To test the methods on real data, we consider four data-sets with various target and secondary phenotypes. To assess accuracy, each data set was randomly split into training (70%) and a test genotypes (30%). This was repeated 160 times, and we report accuracy averaged over the 160 test sets. Because of the required computing time, only 50 test sets were analyzed for RF-BLUP with hyper-parameter-optimization (for the Arabidopsis data-sets), and 30 test-sets for the maize data . With one exception (mentioned below), the target and secondary phenotypes were measured on different plants; therefore, all bivariate mixed models were fitted with diagonal residual covariance , using 5, 760 transcripts  and flowering time (FT) of 388 inbred maize lines (The data that support the findings of this study are available at:https://doi.org/10.1105/tpc.19.00332 (Maize data)https://doi.org/10.1105/tpc.17.00232 https://doi.org/10.1111/nph.14220 https://doi.org/10.1111/tpj.14659 https://figshare.com/s/5d01062711ce33bb327e.All data-sets (except the maize transcriptomics) are included in an Rdata file available at: n and p we refer to Zhou and Stephens , and randomForest . We focus on three cases, with different values for the correlations between the direct genetic effects on Y1, …, Y4, as well as the corresponding residuals (see section 2): (A) ρG = 0.5 and ρE = −0.5, (B) ρG = ρE = 0, and (C) ρG = 0.5 and ρE = 0.5. In scenario 1 ].enario 1  as well enario 1 , accuracYf and secondary traits Y2, Y3, and Y4 always outperforms univariate GBLUP, except when ρG = λ = 0, in which case accuracies are equal. When ρG ≠ 0, the benchmark always benefits from the genetic correlations between the target trait and the secondary traits, even if the latter do not have a causal effect on Yf.The multi-trait benchmark with perfect information on the genetic and residual covariance between the target trait r = 0.44 and r = 0.70, while the benchmark had accuracy between 0.50 − 0.70 (scenario 1) and 0.50 − 0.92 (scenario 2). The difference between scenario 2 (secondary traits observed on the test set) and scenario 1 (secondary traits only observed on the training set) was bigger for large values of |λ|. This is because for large |λ|, the total genetic correlation  between Yf and the causal secondary traits  is larger.The accuracy of univariate GBLUP varied between Ys → Yf (λ = 0) and residual genetic and residual correlations having opposite sign (case A), our simulation setup appeared to be too challenging, and none of the methods performed better than univariate GBLUP. Something similar occurred in case C, for λ = −0.5. On the positive side, for large values of |λ|, both SI-BLUP and LS-BLUP have near-benchmark accuracy, where the latter did not rely on plot-level observations. In scenario 2, RF-BLUP appeared to be an interesting alternative, with somewhat lower accuracy on the extreme sides, but relatively good performance at unfavorable values of λ.In absence of a causal effect Prediction based on the secondary traits only  is generally one of the least successful. The multi-kernel methods (Multi-BLUP and GM-BLUP) are somewhere in between, GM-BLUP often having an accuracy similar to that of RF-BLUP. GM-BLUP appears to be slightly better than Multi-BLUP, but in most cases the difference is smaller than the standard errors of the accuracy estimates.In scenario 1 , none ofr = 0.03 to r = 0.23 (LS-BLUP) for the Salt5 trait in data-set 1, or from r = 0.55 to r = 0.65 (RF-BLUP) for Maize yield in data-set 3, with transcriptomics as secondary traits.The remainder of this section we focus on scenario 2 , in whicr = 0.65 vs. r = 0.51).LS-BLUP had the highest accuracy in all Arabidopsis datasets, with a small but consistent improvement over RF-BLUP (0.02–0.03 higher), also when optimized with the caret/scikit-learn packages. This hyperparameter optimization appeared to be rather important for the Maize data; using the default settings from the randomForest package (as in the simulations), accuracy was considerably lower  had around zero accuracy in all Arabidopsis data-sets, but Given the importance of genomic selection in plant breeding and the rapid development of phenotyping technology, it becomes increasingly important to know if and how the availability of additional phenotypic traits can improve prediction accuracy for a target trait. Here we proposed new methods to incorporate large numbers of such additional traits in genomic prediction, and compared these to existing methods, in simulated and real data. In many of the simulated data-sets, some of our methods indeed greatly improved univariate genomic prediction. In these cases, the accuracy was often close to that of penalized selection indices, without requiring plot-level data. In other cases, none of the methods did very much better than univariate prediction, while the multi-trait benchmark indicated that there is in fact scope for improvement. This happens especially when genetic and residual correlation have opposite sign. Moreover, our study indicates that current methods do not perform well when the secondary traits are available only on the training set : while there was often some improvement in many of the simulations, accuracy in scenario 1 was hardly improved for any of the real data-sets.While scenario 1 is probably most common, scenario 2  may arise in a number of applications. In particular, it has become increasingly common to screen large collections for metabolites or other types of -omics data, and scenario 2 may also arise in a biomedical context when biomarkers could be used to predict disease. Our results for various stress traits in Arabidopsis showed that metabolites can indeed improve accuracy, even if they were measured in a different study. While Multi-BLUP and the LS- and RF-BLUP require balanced data, the GM-BLUP is more flexible, and can also handle an intermediate scenario where only some of the secondary traits are measured for all (or some of) the test genotypes.s on the training set, but for the prediction of breeding values on the test set that leads to overfitting. The methodology implemented in the hdi-package  might be useful. Second, specifically for LS-BLUP, the cross-validation in the first (dimension reduction) step appears to select too many variables. Often, this may still result in an accurate prediction ŶWe attempted to improve existing multi-kernel methods with our GM-BLUP approach, replacing secondary traits by their genomic predictions. Unfortunately, this led to only minor improvements. In case secondary traits have high heritability, there is little shrinkage and genomic predictions and trait values are highly correlated, leading to similar accuracies. In case secondary traits have lower heritabilities, the methods may potentially differ more, but at the same time, in such a scenario there is much less scope for improvement with multi-trait methods in the first place. Both Multi-BLUP and GM-BLUP were often less accurate than competing methods. To some extent this may be explained by the absence of variable selection, or, compared to RF-BLUP, the assumed linearity. Nonetheless, GM-BLUP extended the use of Multi-BLUP to scenario 1, without ever being less accurate.For the case of a single secondary trait, Runcie and Cheng  studied G = 0.5), each of the three relevant secondary traits  has heritability 0.7, the heritability of the target trait being 0.2. Consequently, we have Y1 − Y4 is more accurate than with a mixed model for Y1 alone.Statistical methods for high-dimensional data often benefit from initial screening, for example by removing variables with very low marginal correlation (see e.g., Fan and Lv, More generally, the methods presented here could be extended in several ways. First, for all of them, prediction relies on the GBLUP: either bivariate GBLUP, or univariate GBLUP extended with additional relatedness matrices. This corresponds to a Gaussian prior on the marker effects, which could be generalized to a mixture of Gaussians and a point mass at 0, as for example in Bayes-R (Moser et al., BA performed the research. WK, BA, and FE designed the research. BA and WK wrote the paper, with input from TT and FE. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Phenotypic correlation estimates of plasma metabolites with performance and carcass traits were generally very low. Significant genetic correlation estimates with performance and carcass traits were found for several measures of growth and feed intake. Interestingly the plasma concentration of oxoglutarate was genetically negatively correlated with treatments received across the challenge nursery and finisher  and creatinine was positively correlated with mortality in the challenge nursery . These results suggest that some plasma metabolite phenotypes collected from healthy nursery pigs are moderately heritable and genetic correlations with measures of performance and resilience after disease challenge suggest they may be potential genetic indicators of disease resilience.Metabolites in plasma of healthy nursery pigs were quantified using nuclear magnetic resonance. Heritabilities of metabolite concentration were estimated along with their phenotypic and genetic correlations with performance, resilience, and carcass traits in growing pigs exposed to a natural polymicrobial disease challenge. Variance components were estimated by GBLUP. Heritability estimates were low to moderate (0.11 ± 0.08 to 0.19 ± 0.08) for 14 metabolites, moderate to high (0.22 ± 0.09 to 0.39 ± 0.08) for 17 metabolites, and highest for  Metabolomics has been used in pharmacology studies, drug development, food technology, toxicology, plant biotechnology, and human health. In the latter case, metabolomics has been used successfully for disease diagnosis and determination of disease state, biomarker discovery, and risk determination3. Metabolite testing is increasingly gaining attention in animal science, including in animal genetics, animal health, and milk and meat quality4. Applications in livestock include discovery of disease biomarkers in dairy cows6, animal health7, growth performance8, feed efficiency in beef cattle and pigs10, and swine diseases11.Metabolites are small molecules with molecular size < 1.5 kDa and metabolomics is the study of these small molecules to provide a more detailed and comprehensive understanding of how cells function. Metabolites are involved in cellular metabolism, such as energy conversion, growth, signaling, and stimulatory and inhibitory effects on enzymes13. It has been proposed that resilience can be an effective approach to select for both disease resistance and tolerance in animals13. Results from studies with dairy cows, beef cattle, pigs including human medicine10, suggest that metabolomics can be useful in helping increase understanding of the processes involved in disease resilience. In addition, metabolomics offers the potential to identify new phenotypes or traits that can be used for selection of resilient animals. There is evidence in the literature that blood metabolites in humans and cattle are heritable17. Given the similarities that exist between porcine and human, blood metabolites in porcine might be expected to be heritable.The large impact of infectious disease in swine on animal welfare and productivity has increased interest in selection for pigs that are less susceptible to pathogens and that are more disease resilient, which has been defined as the ability to maintain relatively undiminished performance levels when exposed to disease18. It is therefore important to understand the genetic architecture of these potential new phenotypes.Heritability is a prerequisite for the use of a trait in breeding strategies and, ideally, indicators of resilience should be expressed in healthy animals, such that they can be measured in high-health nucleus farms to provide prediction of resilience in commercial environments18 with collection of resilience related traits, including average daily gain (ADG), feed intake and feed intake duration (ADFI and ADFD), number of individual health treatments (nTRT) and mortality, residual feed intake (RFI), and feed conversion ratio (FCR). In addition, new resilience traits based on day-to-day variation in feeding patterns were proposed by Putz et al18. The overall purpose of the current study was to use data and plasma samples collected on healthy pigs prior to pathogen/disease challenge to estimate heritability of 44 metabolites and 2 amino acid indices, the phenotypic and genetic parameters of plasma metabolite concentration in relation to their subsequent performance, disease resilience, and carcass traits under the natural disease challenge.This study is part of a larger project entitled “Phenomics for genetic and genome-enabled improvement of resilience in pigs”, which had identification of predictors of resilience on young healthy pigs as one of its main objectives. The project uses a natural polymicrobial disease challenge modell-arginine, 3-hydroxybutyric acid, ethanol, formate, acetoacetate, succinate, acetone, methanol and l-acetylcarnitine. Among the 33 heritable metabolites, 14 showed low estimates of heritability, ranging from 0.11 ± 0.08 to 0.19 ± 0.08, while 17 metabolites had moderately high estimates of heritability, ranging from 0.22 ± 0.09 to 0.42 ± 0.08. Finally, two metabolites, i.e. l-glutamic acid and hypoxanthine, had the highest estimates of heritability, 0.41 ± 0.09 and 0.42 ± 0.08, respectively. Low and moderate estimates of heritability were obtained for two amino acid indexes, i.e. ketogenic amino acids (ketoAA) (0.23 ± 0.08) and branched-chain amino acids (BCAA) (0.19 ± 0.08).Table l-Glutamine (0.12 ± 0.04), l-ornithine (0.12 ± 0.04), betaine (0.18 ± 0.04), citric acid (0.21 ± 0.04), l-lysine (0.13 ± 0.04) and l-methionine (0.15 ± 0.04) showed positive phenotypic correlation estimates with quarantine nursery ADG (qNurADG) (P < 0.05), while creatinine (− 0.21 ± 0.04), l-histidine (− 0.20 ± 0.04) and isobutyric acid (− 0.22 ± 0.04) showed negative phenotypic correlation estimates with qNurADG (P < 0.05). In addition, l-glutamic acid showed positive phenotypic correlation estimates with ADFI  and l-aspartate showed positive phenotypic correlation estimates with ADFD . None of the phenotypic correlations of metabolites with challenging nursery ADG (cNurADG), finisher ADG (FinADG), treatment, and mortality rates, and carcass traits were significant (Supplementary Table P > 0.05).In general, phenotypic correlations of the metabolites with performance, resilience, and carcass traits were small .Moreover, we estimated the phenotypic correlations between the metabolites that are involved in the same pathway  and the results showed that betaine was positively correlated with dimethylglycine and Genotypic correlations of the metabolites with performance, resilience, and carcass traits were larger than phenotypic correlation  genetic correlations with FinADG.No significant genetic correlations were estimated between metabolites and cNurADG. Only two metabolites namely: l-Glutamic acid was positively correlated with ADFI (P < 0.001), while several other metabolites, including dimethylglycine and l-aspartate, tended to be positively genetically correlated with ADFI (0.05 < P < 0.1). Four metabolites that were positively correlated with ADFD (Table P < 0.05): dimethylglycine, l-glycine, betaine and citric acid were visualized in a compound network  and isobutyric acid was negatively correlated with RFI (P < 0.05).In addition, the P < 0.05).Estimates of genetic correlations of metabolites with carcass traits are presented in Table P < 0.05), while creatinine had a significant positive genetic correlation with mortality in the challenge nursery (Table P < 0.05).Finally, only oxoglutarate had a significant negative genetic correlation estimate with the number of treatments across the challenge nursery and finisher (Table The objectives of this study were to estimate (1) heritabilities of metabolites in plasma of young healthy pigs and (2) their genetic correlations with production, disease resilience, and carcass traits in growing pigs that were exposed to a natural polymicrobial disease challenge. The metabolomics samples were collected from healthy pigs at an average of 26 days of age in the quarantine nursery and the traits analyzed included those in the quarantine nursery, as well as in the challenge nursery and finisher, and carcass traits at slaughter. This study contributes to one of the main overarching objectives of the polymicrobial natural disease challenge model, which is to identify genetic predictors of resilience using samples from healthy pigs. Such predictors would be very useful for breeding for disease resilience, as they can be measured in high health genetic nucleus herds as indicator traits for disease resilience in commercial farms.16. Metabolites can be grouped into primary and secondary metabolites. Primary metabolites are directly involved in primary metabolic processes, such as normal growth, development, reproduction, and immune response, e.g. amino acids and products derived from glycolysis and the TCA cycle. Primary metabolites are highly conserved across species and serve as precursors for the synthesis of secondary metabolites19. For example, amino acids, in addition to being the building blocks of proteins, are also regulators of innate and adaptive immune responses in living cells. Many studies have demonstrated that glutamic acid, glutamine, histidine, methionine, leucine, isoleucine, and valine are functional regulators of macrophages, dendritic cells, and T-cells.23. Our results showed that 31 metabolites had low to moderate heritability and two metabolites had relatively high heritability (> 0.4). In a study of beef cattle, only 11 of 33 metabolites measured (29 in common with this study) were reported to be heritable17. Similar heritability estimates to Li et al.17 were reported here for betaine, creatinine, pyruvic acid and citric acid. In our study, other metabolites, such as ketone bodies , creatine, succinate, formate, and methylhistidine, showed negligible heritability estimates, suggesting that they are primarily influenced and manipulated by environmental effects such as diet, and/or age, health status etc.We measured the concentration of a panel of 44 metabolites in plasma, including amino acids, short chain fatty acids, sugars, alcohols, organic acids, amines, and TCA cycle intermediates, and urea cycle intermediates. Variation in metabolite concentration can be due to environmental effects, diet, gender, age, physiological conditions, and genetic effects. Literature indicates that in humans, approximately 50% of phenotypic differences in metabolite levels is due to genetics, but heritability estimates differ across metabolite classesl-leucine, l-isoleucine and l-valine and BCAA did not have higher heritability than the amino acids that contributed to the index.Branched-chain amino acids, was calculated as the sum of 15. However, genetic correlation analyses found that dimethylglycine (0.28), betaine (0.39), l-methionine (0.45), and l-serine (0.54) were positively correlated with ADG in the quarantine nursery, while creatinine (− 0.60) was negatively correlated with this trait. These metabolites are involved in glycine, serine, alanine and threonine pathway, which suggests that this pathway might be a target to improve ADG in young healthy piglets. Indeed, supplementation with dimethylglycine has been shown to improve growth performance, significantly increasing total body weight gain and feed intake, and improving feed efficiency in low-birth-weight piglets24. We found that l-methionine (0.15), l-glutamine (0.12) and betaine (0.18), had positive phenotypic correlation estimates with qNurADG. Methionine supplementation has been reported to improve growth rate in nursery pigs25. Positive effects of methionine supplementation on intestine structure have also been observed, with greater average daily gain from days 7–14 of age and improved feed efficiency25.Phenotypic correlations of metabolites with production, disease resilience, and carcass traits were generally very low. These results are in line with those reported previously in dairy cows by Buitenhuis et al.27. Conversion of muscle creatine into creatinine can reflect protein degradation28 and serum creatinine concentration has been proposed as an indicator of protein deposition29. Animals with low ADG are expected to have lower protein or muscle deposition and our results suggest that increased plasma creatinine concentration might be a genetic indicator of low ADG in healthy nursery piglets. In addition, creatinine was phenotypically negatively correlated with ADG of young healthy pigs (− 0.21). Moreover, creatinine was genetically positively correlated with mortality in the challenge nursery, suggesting that young healthy pigs that have higher plasma creatinine, genetically have lower ADG as healthy nursery pigs but are more likely to die when challenged by disease. Further research is necessary to validate creatinine as a potential genetic marker for ADG in healthy nursery pigs and as an early genetic indicator trait for mortality under disease.Creatinine was genetically highly negatively correlated (− 0.60) with ADG in the quarantine nursery. Variation in creatinine concentration can arise as result of environmental and genetic factors. For example, phenotypic variation in creatinine concentration can be expected due to batch, transportation and fighting or re-grouping of animals. Creatinine is considered a waste product produced by muscles from the breakdown of creatine and is removed from the blood and released into urine by the kidneys. Typically, 95% of creatinine is found in muscle and an increase of creatinine concentration in blood is often considered as an indicator of kidney malfunctionl-glutamic acid showed positive genetic correlation estimates with ADG (0.72) and with ADFI (0.62) in the finisher. Oxoglutarate, also known as alpha-ketoglutarate, is a key organic acid of the TCA cycle and a source of glutamic acid and glutamine, and stimulates protein synthesis and inhibits protein degradation in muscles31. Positive effects of oxoglutarate on the protein synthesis and skeletal system have been reported in various farmed species, including turkeys32, pigs34, and sheep36. Our results suggest that young healthy pigs that had greater oxoglutarate and l-glutamic acid concentration, genetically have greater ADG in the finisher and received fewer health treatments across the challenge nursery and finisher. Interestingly, none of the metabolites were genetically correlated with ADG in the challenge nursery. For the data used here, Cheng et al.37 reported that the estimate of heritability of ADG was lower in the challenge nursery (0.19) than in the quarantine nursery (0.31) and in the finisher (0.30), which might have impacted the ability to accurately estimate the genetic correlations of metabolites with ADG in the challenge nursery.Plasma oxoglutarate concentration in the quarantine nursery was genetically positively correlated with ADG in the finisher and negatively correlated with nTRT across the nursery and finisher. Moreover, l-glycine, betaine and citric acid) had moderate to high positive genetic correlation estimates with ADFD (0.30–0.52). These metabolites are involved in the TCA cycle and in glycine, serine, alanine, and threonine metabolism. Interestingly, dimethylglycine and betaine were also positively correlated with ADG in the quarantine nursery, which suggests that pigs with greater dimethylglycine and betaine concentration genetically have greater ADG as young healthy pigs and might spend more time eating in the finisher stage.Four metabolites  between citric acid and carcass backfat was estimated, which is in line with the fact that citric acid is involved in fatty acid metabolism. Citrate, the conjugated base of citric acid, is formed from oxaloacetate and acetyl-coenzyme A (acetyl-CoA) by citrate synthase. Once transported to the cytosol, citrate is converted to acetyl CoA and oxaloacetate. Acetyl-CoA is then converted to malonyl CoA and can be used as a substrate for fatty acid synthesis39. It has been reported that SCFAs such as acetic, propionic, and butyric acids, derive mostly from carbohydrates, while other SCFAs such as isobutyric acid are mostly derived from proteins40, specifically from degradation of branched-chain amino acids, and greater concentration of isobutyric acid could be indicative of better utilization of dietary protein by the microbiota41. Literature indicates that low RFI animals (efficient) have greater ileal isobutyric acid concentrations42. Thus, our results suggest that low RFI pigs genetically might have a better utilization of dietary protein by the microbiota. Further research is necessary to investigate the role of isobutyric acid as a potential genetic biomarker of RFI in pigs with or without disease.Interestingly, isobutyric acid was estimated to be negatively correlated, genetically, with RFI (− 0.38), which suggests that young healthy pigs that have higher plasma isobutyric acid content genetically have better feed efficiency under disease conditions. Isobutyric acid is a carboxylic or short chain fatty acid (SCFA) that is generated via microbial (gut) metabolism. Isobutyric acid has been described and quantified in the faeces of human, rats, horses, and pigsIn conclusion, the results suggest that some metabolic phenotypes measured in plasma of young healthy pigs are moderately heritable. The present work contributes to our understanding of the genetic parameters of plasma metabolite concentrations in young healthy pigs and their relationships with production, resilience, and carcass traits under disease. To the best of our knowledge, this is the first study to report estimates of heritabilities of plasma metabolite concentrations and of their genetic correlations with production, resilience, and carcass traits in pigs following polymicrobial disease challenge. Metabolites have the potential to be used in high health genetic nucleus herds as indicator traits for disease resilience in commercial farms. Further studies are warranted to validate the identified possible genetic indicators of resilience.43. All procedures were carried in accordance with the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines44. The Animal Care protocol was approved by the Animal Protection Committee of the Centre de Recherche en Sciences Animales de Deschambault (15PO283) and Animal Care and Use Committee at the University of Alberta (AUP00002227).This study was carried out in accordance with the Canadian Council on Animal Care guidelines (CCAC)18, Cheng et al.37, and Bai et al.45. The challenge was established in late 2015 at the Centre de développement du porc du Québec inc (CDPQ) test station in Québec, Canada, with the aim to mimic a commercial farm with high disease pressure to maximize expression of genetic differences in resilience. For purpose of clarity, here we briefly describe the three phases of the experiment, which included a pre-challenge quarantine nursery (19 days on average beginning at 3 weeks of age), the challenge nursery (27 days on average), and lastly, the finishing phase (100 days on average). The natural disease challenge protocol was established by introducing naturally infected animals with known diseases into the challenge nursery and finisher barn at CDPQ. Some of the introduced pathogens included: viruses (PRRSV and swine influenza virus A), bacterial pathogens such as Brachyspira hampsonii, Haemophilus parasuis, Mycoplasma hyopneumoniae, Salmonella enterica serovar Typhimurium, and Streptococcus suis), and two parasites (Ascaris suum and Cystoisospora suis)45. Moreover, environmental enrichment (inedible point source objects) was applied in 50% of quarantine nursery pens in some of the batches, with the purpose to evaluate the impact of environmental enrichment on disease resilience. Pigs that received enrichment in the quarantine nursery, continued to receive enrichment in the challenge nursery and finisher37. Throughout the study feed was available ad libitum . In the quarantine nursery all pigs were fed the same commercial diet appropriate for pigs’ age and weight ).This study is part of a larger research project which investigates the underlying genetic mechanisms of disease resilience in grow-finisher pigs exposed to a natural polymicrobial disease challenge. The details of the polymicrobial challenge and phenotypes/traits collected were previously described in Putz et al.37. Finally, carcass traits included: carcass weight, backfat, and loin depth, dressing percentage, and lean yield. Details of the recording and derivation of the resilience, production, and carcass traits can be found in Putz et al.18 and Cheng et al.37. Cheng et al.37 used two data sets for analysis of traits recorded in the finisher but in the current study we only use the data set that included phenotypic finisher data on pigs that survived to slaughter (survivor data), except for mortality. For the challenge nursery, phenotypes on all pigs were used. Details of the number of animals and traits considered here are described in Cheng et al.37. For purpose of brevity: data from 958 young healthy pigs were included in the present study for metabolomics analysis, nearly 3200 animals for nursery traits, around 2500 animals for finisher traits, and 2000 animals for carcass traits.Production, resilience, and carcass traits were collected from a total of 3205 F1 crossbred (Landrace × Yorkshire) barrows. The phenotypes used in this study included: average daily gain recorded in the quarantine nursery, challenge nursery, and finisher stages. Finisher traits considered in the present study included feed intake and duration, feed conversion ratio, and residual feed intake. The number of parenteral treatments provided to individual pigs were also tabulated separately for the challenge nursery, finisher and combined challenge nursery and finisher as the number of treatments per 180 days. Mortality  in the challenge nursery, finisher, and across the nursery and finisher were also included45. After collection, the blood samples were centrifuged at 3000 rpm at 4 °C for 10 min, plasma collected and immediately frozen and stored at − 80 °C and only thawed for the metabolomics analysis. Two weeks after the first sampling, pigs were transferred to the test station and naturally exposed to multiple pathogens, as described in Putz et al.18 and Bai et al.45.For metabolomics analysis we used blood samples collected from 958 young healthy animals which were introduced in 15 batches of 60 or 75 pigs. Blood was collected from the jugular vein into K2 ethylenediaminetetraacetic acid (EDTA) tubes , on all pigs in the quarantine nursery at an average age of 26 days, 5 days post-arrival from their farm of origin37 and Bai et al.45. After quality control, a total of 417,443 SNPs in 3205 pigs remained and were used for analysis.All animals (n = 3205) were genotyped using a 650k Affymetrix Axiom Porcine Genotyping Array by Delta Genomics . Raw Affymetrix SNP data were processed by Delta Genomics, separately for each cycle, with the Axiom Analysis Suite, using all defaults. Details of genotyping and quality control are described in Cheng et al.https://www.metabolomicscentre.ca/).Forty-eight plasma metabolites were quantified using NMR, following established protocols at The Metabolomics Innovation Center at University of Alberta (TMIC), AB, Canada , were rinsed five times each with 0.5 mL of H2O and centrifuged  to remove residual glycerol bound to the filter membranes. Aliquots of each plasma sample were then transferred into the centrifuge filter devices and spun  to remove macromolecules (primarily protein and lipoproteins) from the sample. The filtrates were collected and the volumes for each sample were recorded. If the total volume of the sample was under 250 µL an appropriate amount of 150 mM KH2PO4 buffer (pH 7) was added and the dilution factor was annotated and taken into account in the analysis. Subsequently, 46.5 µL of a standard buffer solution , 5.84 mM 2-chloropyrimidine-5 carboxylate, and 0.1% NaN3 in H2O) was added to the sample.Plasma samples were thawed on ice and a deproteinization step, involving ultra-filtration was performed as previously described1H-NMR spectra were collected on a 700 MHz Avance III (Bruker) spectrometer equipped with a 5 mm HCN Z-gradient pulsed-field gradient (PFG) cryoprobe. 1H-NMR spectra were acquired at 25 °C using the first transient of the NOESY pre-saturation pulse sequence (noesy1dpr), chosen for its high degree of quantitative accuracy47. All FID’s (free induction decays) were zero-filled to 250 K data points. The singlet produced by the DSS methyl groups was used as an internal standard for chemical shift referencing (set to 0 ppm) and for quantification all 1H-NMR spectra were processed and analyzed using an in-house version of the MAGMET automated analysis software package using a custom metabolite library. MAGMET allows for qualitative and quantitative analysis of an NMR spectrum by automatically fitting spectral signatures from an internal database to the spectrum48. Each spectrum was inspected by an NMR spectroscopist in order to minimize compound misidentification and misquantification.The plasma samples (250 µL) were transferred in 3 mm SampleJet NMR tubes for spectral analysis. All , enrichment), covariable (age), and random effects (pen and litter) using linear regression models implemented in R statistical software49. The residuals of the model were plotted and visually inspected for the presence of outliers, which were excluded from the dataset. Data normalization (log10) of metabolite concentrations that were not normally distributed  was done prior to statistical analysis. Two indexes were also computed for statistical analysis: (1) branched-chain amino acids (BCAA), which was calculated as the sum of l-leucine, l-isoleucine and l-valine and (2) ketogenic amino acids (ketoAA), calculated as the sum of l-lysine and l-leucine. A summary of descriptive statistics of all metabolites after quality control is in Supplementary Table Prior to statistical analysis, a quality control step was applied to the metabolite data. Four metabolites that were frequently (> 20%) below the limit of detection or with at least 20% missing values were removed from consideration. A total of 44 metabolites and two amino acid indexes remained in the dataset. Other missing values (15 data points) were replaced by the median value of each metabolite in the original data. First, we assessed the significance of each fixed ; Batchi is the fixed batch effect ; Ageijk is the covariate of age when the pig entered the quarantine nursery; Penj is the random effect of pen by batch corresponding the different phases , with Penj ~ N  where σ2P is pen variance; Litterijk is the litter environmental effect, with Litterijk ~ N  where σ2L is the litter environmental variance; uijk is the random additive genetic effect, with the vector u ~ N , where G is the genomic relationship matrix and σ2A is the additive genetic variance; and eijk is the residual effect, with eijk ~ N  where σ2e is the residual variance. The genomic relationship matrix, G, was created separately for each of the seven companies supplying pigs using the software preGSf9050 and the method described by VanRaden51, and then combined into one G matrix, with genetic relationships between companies set to zero in order to focus on within-company variance components, as described by Cheng et al.37. For six metabolites namely: l-ornithine, l-leucine, l-valine, l-asparagine, 3-methyl 2-oxovaleric acid and formate, environment enrichment was included as a fixed effect because the effect of enrichment was significant (P ≤ 0.05).Variance components were estimated by GBLUP using the BLUPF90 programs37.For metabolites with heritability estimates greater than 0.20, genetic correlations with production, disease resilience, and carcass traits were estimated using bivariate models. For these traits, phenotypes from batches that were not measured for metabolites were also included. Models used for these traits were as described by Cheng et al.2a/ (σ2a + σ2L + σ2e) and the standard error (SE) for the heritability estimate was calculated according to the Monte-Carlo method suggested by Meyer and Houle52. The proportion of variance explained by sow or maternal effects, referring to effects that are common to individuals with the same mother, was estimated as σ2L/ (σ2L + σ2e). Genetic correlations between two traits were estimated as the estimate of the genetic covariance from the bivariate analysis divided by the product of the genetic standard deviations for the two traits. Significance of estimates of heritabilities and genetic correlations were determined using likelihood ratio tests with 1 degree of freedom. For heritability estimates, the resulting P-values were divided by 2 because the estimates are restricted to be positive53.Heritability was estimated as σ54 in Cytoscape 3.8.255 was used. The metabolites that had significant (P ≤ 0.05) genetic correlations with qNurADG and ADFD were used for pathway visualization. The file with the list of KEGG elements was loaded into Metscape to generate a compound network. Only metabolites with KEGG IDs were considered for compound network and pathway analysis. In a compound network, metabolites are represented as nodes and reactions are represented as edges. A compound node with an outgoing edge is a substrate, while a compound with an incoming edge is the product of a specific biochemical reaction. Finally, we estimated phenotypic and genetic correlations among metabolites that belong to the same pathway.For exploration and visualization of the biochemical pathway that metabolites are involved in, the Metscape pluginSupplementary Figure S1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.