MultiABEL.The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package  The implementation of these methods only requires summary association statistics. We suggest a four-level replication strategy where the above four replication methods are applied sequentially. To demonstrate the application of the four-level replication strategy, we studied the SNPs discovered by MANOVA, using the GWAS summary statistics from the GIANT consortium as an example, and try to replicate them in the UK Biobank (UKB).To verify a locus detected by multi-trait methods, the first task is to replicate the association between the locus and the set of traits tested in the discovery phase. The next task is to evaluate the consistency of multi-trait genetic associations between discovery and replication samples across the traits in question, which calls for stronger replication strategies. To accomplish these two tasks in the replication of multi-trait signals, in this perspective article, we investigated and compared the performance of four replication methods. We began by reviewing the MANOVA and phenotype score, which we suggest as methods for locus replication. Then, we evaluated the consistency of effects that the locus exhibits onto multiple traits in question, i.e., replication of multivariate effects. A discovery study may have enough power to establish the overall association between a locus and a set of traits but insufficient power to robustly estimate all the effects the locus exhibits on multiple traits. In this case, one would expect that a locus would pass a locus-level replication but would fail to pass the multivariate genetic effects replication. To evaluate the similarity between estimates of multivariate genetic effects in discovery and replication, we introduced a Monte-Carlo (MC)-based correlation method. Through simulations of different scenarios, we have illustrated the strength and complementarity of four methods that cover different aspects of multivariate replication: (i) MANOVA, as a representative of multivariate methods providing unsigned omnibus k traits Y1, ..., Yk are dependent variables. If we denote the true marginal effects of a SNP on the k traits by β, then the null hypothesis in MANOVA is H0:β = 0. Let g of a single SNP and the k phenotypes, and R* ≡ Cor(t ∣ β) = Var(t ∣ β). If R* is available, the test statisticIt has been shown that MANOVA can be performed using summary statistics , b, can be obtained bywhere f is the coding allele frequency of the SNP ≈R* . Becauseand (ii) Kendall's rank correlation coefficientβ and τβ. Based on this distribution, the parametric bootstrap confidence intervals (CI) are attained, which can be used to evaluate the similarity between estimates of multivariate genetic effects.which measures the ordinal association between We performed a series of simulations to compare the replication results of four methods: MANOVA, phenotype score, Pearson correlation, and Kendall correlation. Four different scenarios of cross-phenotype effects were simulated: (i) null, where the SNP did not affect any trait; (ii) unmatched multi-trait effects, where the SNP exhibited effects on all traits, but the effects were independently simulated for discovery and replication samples; (iii) matched single-trait effect, where the SNP affected only one trait, and the true effect was the same in the discovery and replication samples; and (iv) matched multi-trait effects, where the SNP had effects on all the traits, and the true effects were the same in the discovery and replication samples. Two different numbers of traits, 6 or 32, were simulated. In practice, the phenotypic correlation matrices between the discovery and replication samples can be consistent or inconsistent, e.g., when estimated from two samples with different sample overlaps among the phenotypes. Its potential impact on replication was also evaluated. In the simulations, the phenotypic correlation matrices were based on the true observed correlation matrices in the GIANT and UKB data. More details can be found in P-value thresholds. The curves of MANOVA were above the diagonal in scenarios (ii), (iii), and (iv). This means with MANOVA, an SNP is considered replicated as long as it affects at least one trait in the replication sample no matter how the cross-phenotype effect compares to that in the discovery sample. The curves of phenotype score replication did not deviate much from the diagonal in scenarios (ii). It shows when there is a large discrepancy in the cross-phenotype effect between the samples; the phenotype score does not consider it as replicated. However, the curves of phenotype score replication were above the diagonal in scenario (iii). So even when the SNP affects only one trait, the association between the SNP and the phenotype score can still be replicated. When there were 32 traits, the curves of the Kendall correlation obviously deviated from the diagonal in scenario (iv) but only slightly deviated from the diagonal in scenario (iii). This indicates that Kendall correlation replication is more specific for confirming the cross-phenotype effect where an SNP affects multiple traits. The performance of the Pearson correlation is similar to that of the Kendall correlation; the exception is in scenario (iii), where the curves of the Pearson correlation were between those of the Kendall correlation and those of the other two methods. Although the correlation methods were more specific to evaluate the pattern of the multi-trait effects, their curves were under those of MANOVA and phenotype score, which indicates lower sensitivity.The results of the simulations are summarized in According to the above simulation results, the four methods are with different replication strength. Therefore, we suggest the following four-level replication strategy, summarized in p-value test (including MANOVA and many other multivariate methods) verifies whether there is indeed an association between the discovered locus and the set of traits in a replication sample. As long as the association is present in the replication sample, the locus is considered as replicated regardless of whether the multivariate genetic effects are similar between discovery and replication or not. This is the weakest type of multivariate replication.Omnibus The phenotype score method  in the simulation, the Pearson correlation method would consider the multivariate genetic effects similar.Kendall correlation method is the strongest one among the four methods. Since Kendall's correlation is based on the ranks of estimated marginal genetic effects, any factor disturbing the ranks would weaken the correlation. For example, if an SNP does not affect all the tested traits, then its estimated marginal effects around zero on those irrelevant traits will be randomly ranked, which reduces Kendall's correlation . An extrp-value <5 × 10−8. Considering SNPs <1 Mb distant from most significant SNP as one locus, the 21,240 SNPs were clumped into 449 loci. For each locus, we defined its top SNP as the SNP with the smallest MANOVA p-value in GIANT. After, we used the top SNPs to represent loci.In order to compare the performance of different replication methods using real data, we investigated the loci discovered in the GIANT consortium and replicated them in UKB by MANOVA. We used six anthropometric traits for multi-trait analysis: BMI, height, weight, hip circumference (denoted here as HIP), waist circumference (WC), and waist-to-hip ratio (WHR). Among the 2,348,642 SNPs in the GIANT data, 21,240 SNPs had MANOVA p-value threshold of 0.05/449. To illustrate the performance of different replication methods, among the 317 loci, we looked into 24 “multivariate-only” loci as an example. These 24 loci could be discovered and replicated by MANOVA, but none of them could reach p-value <5 × 10−8 in univariate GWAS for any of the six anthropometric traits. The replication results were summarized in A total of 317 out of the 449 top SNPs could be replicated in UKB with a MANOVA p-value < 0.0021 (experiment-wise P < 0.05 after Bonferroni correction for 24 tests) in UKB. Additionally, all of their According to the results from the phenotype score method, all except two SNPs had a β was  for rs2138275, and  for rs4968164. This means the multi-trait marginal genetic effects across samples were similar for rs2138275 but not for rs4968164. The contrast indicated that the underlying six-trait cross-phenotype association was more plausible at the locus led by rs2138275. For the locus led by rs4968164, although it could be detected and replicated by MANOVA, the SNP might be associated with only a small subset of the six traits.Results from correlation methods indicated different levels of similarity of multivariate genetic effects between discovery and replication across the 22 loci. Both the two correlation methods identified the same 19 SNPs for which the pattern of multi-trait association was significantly similar between the discovery and replication samples. For some loci, the lower bounds of their 95% confidence intervals (CI) were higher than the others, suggesting a higher level of similarity of multivariate genetic effects between discovery and replication. Because the association of a locus with multiple traits is necessary but not sufficient for pleiotropy  to claim replication of an association. This criterion is, however, weaker than that conventionally used in single-trait GWAS in which we ask not only for significance but also for consistency. The requirement of overall consistency is reached with the phenotype score replication step. At this stage, we can consider the locus to be established, that is, we can claim consistent and significant association of the locus to (at least some of the) investigated traits. The question of whether the discovery phase was informative enough to establish the genotype-multi-phenotype map of the associations of the locus is addressed in the second phase. In principle, given score replication was passed, one does expect that the Pearson correlation between effects estimated in discovery and replication will be positive. However, it may be weak, meaning that a researcher can not trust individual estimates of effects. In such a situation, any attempt to utilize observed associations of individual traits, e.g., the benefit of functional interpretation, is likely doomed. However, the Pearson correlation may be high because the locus exhibits a strong effect on just a few—or even only one—traits in the set or because it exhibits effects of many traits in the set. The fourth method, the Kendall correlation, allows one to distinguish between the two scenarios .To detect which traits are more likely to be irrelevant for an SNP, we can compare the ordinal discordance generated by each trait in Kendall's correlation. For an SNP, if the effects of the locus on a trait introduce much discordance, then the association between the SNP and the trait would be more suspicious. For example, the marginal effect of height generated much discordance for rs11231693, which indicated that height was more likely not contributing to the multivariate association between rs11231693 and the group of traits. This could be verified by the non-significant association in height GWAS (R”) could perform better than the “consistent R” scenario in the six traits settings. The phenomenon can be attributed to the choice of R for the replication cohort. In the six traits setting, R was from GIANT for the discovery cohort. Therefore, for “consistent R,” R for the replication cohort was also from GIANT; whereas for “different R,” R was from UKB instead. The T2 statistic for MANOVA based on R from UKB could be on average larger than those based on R from GIANT, as for MANOVA, the replication results are fully determined by the replication cohort without comparing with the multi-trait genetic effects in the discovery cohort.In the simulation, as we aim to replicate multi-trait genetic effects, for a good replication method, the performance should not be driven by whether the phenotypic correlation matrices are the same between the discovery and replication cohort. However, we found that for MANOVA, replication samples with a different phenotypic correlation structure .The developed methods are implemented and freely available in the R package R represent the phenotypic correlation matrix of the k phenotypes. According to Zhu et al.  SNPs to compute correlation matrix . In total, 128,670 independent SNPs were used to estimate the correlation matrix for GIANT and 166,000 SNPs for UKB. All estimated correlation matrices can be found in For correlation matrix estimation, we used previously proposed approach based on correlation of Z-statistics between independent unassociated SNPs , waist circumference (WC), and waist-to-hip ratio (WHR). BMI data are from Locke et al.  and filtered for autosomes only and position missings. In total, we ended with 2,348,642 SNPs.UKB participants were recruited from the general UK population across 22 centers between 2006 and 2010. Subjects were aged 40–69 at baseline, underwent extensive phenotyping by questionnaire and clinic measurements, and provided a blood sample. Genotyping has been done with the first wave released in July 2015 and the second wave released in July 2018. Because the objective of this study is to demonstrate the replication methods, we use the first wave data of UKB as an example dataset. Data access to UKB was granted under Project No. 14302 and No. 19655. Phenotypes and genotypes were downloaded directly from UKB. In total 502,664 subjects had phenotypic information available, of whom 152,732 had been genotyped, of these 120,286 were identified as genetically British by UKB, of which 118,182  had completed phenotyping. These subjects were taken forward for analysis.Participants provided full informed consent to participate in UKB. This study was covered by the generic ethical approval for UKB studies from the NHS National Research Ethics Service . The authors in this study were completed blinded to individual-level data collection and preparation. The phenotypes involved in this study were adjusted for age, sex, and batch before being standardized to have mean zero and variance one.The original contributions presented in the study are included in the article/XS and YA initiated the study. XS, YA, YP, and CH coordinated the study. ZN, XS, YP, and YA developed statistical methods. ZN and YT performed the data analysis. ZW contributed to the method development. ZW, SS, AG, XF, MS, PJ, and JW contributed to the data analysis. ZN and XS drafted the manuscript. YT, PJ, JW, YP, CH, and YA contributed to the manuscript writing. All authors contributed to the article and approved the submitted version.YA was a co-founder and co-owner of private research organizations PolyOmica and PolyKnomics. The remaining 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.


Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Translating these associations into mechanisms is complicated by two factors: they fall into gene regulatory regions; and they are rarely mapped to one causal variant. One way around these limitations is to find groups of traits that share associations, using this genetic link to infer a biological connection. Here, we assess how many trait associations in the same locus are due to the same genetic variant, and thus shared; and if these shared associations are due to causal relationships between traits. We find that only a subset of traits share associations, with many due to causal relationships rather than pleiotropy. We therefore suggest that simply observing overlapping associations at a genetic locus is insufficient to infer causality; direct evidence of shared associations is required to support mechanistic hypotheses in genetic studies of complex traits. Christiane Gasperi et al. used a shared association mapping approach to identify pairs of immune cell traits and gene expression traits showing shared genetic associations. Using polygenic risk score analysis and Mendelian randomization approaches, they find that while many such shared associations indicate a causal relationship between traits, observation of overlapping genomic associations alone is not sufficient to infer causality. Genetic mapping studies have identified thousands of associations between common variants and hundreds of human traits. Uncovering the mechanisms that underlie these traits requires understanding the molecular, cellular and physiological events altered by causal genetic variants. Incomplete fine mapping due to linkage disequilibrium and the possible action of causal variants across diverse cell types, contexts and genes currently limits our ability to infer the mode of action of causal variants, and hence the biology underlying traits. Experimentally testing multiple such mechanistic hypotheses across thousands of associations rapidly becomes a problem of scale; we thus need principled approaches to generating and testing such mechanistic hypotheses.4. Such sharing is often defined as two traits associated with variation in the same general genome region, often within an arbitrary window of physical distance. A more robust alternative is to identify pairs of traits that share an underlying causal effect, rather than a shared genomic segment, and several methods have been developed to this end12.We and others have suggested such an approach, building on the concept of pleiotropy. The molecular and cellular events altered by causal genetic variants are, by definition, also genetic traits, and they must be associated with the same variant. To link traits together and thus form mechanistic hypotheses, one can thus look for shared genetic associations between traits12. This result appears paradoxical given the strong enrichment of risk heritability in gene regulatory regions15, which suggests that the majority of risk effects should alter gene regulation, and therefore expression. This paucity may be because we are not interrogating the right cell types, or the right physiological or stimulation conditions for those cells; or it may be that we lack power to detect such overlaps, though our simulations suggest the latter is not a major factor12.We have reported a relative paucity of overlaps between expression quantitative trait loci (eQTLs) and disease risk associationsIt is tempting, therefore, to assume that requiring a demonstration of shared association between traits is overly stringent, and simply identifying associations to the same region is a more productive approach. A further temptation is then to assume causality, especially when the two traits are drawn from different levels of physiology: it is natural to assume that a gene expression trait is causal for disease risk rather than the other way around, for example. This assumption of causality is made implicitly when pairs of associations are used to propose mechanistic hypotheses of pathophysiology. However, it is also possible that the two traits are either associated to different variants in the same locus, or to a single pleiotropic variant and otherwise share no biological underpinning . To our knowledge, there is no a priori way to set a prior expectation for causality or pleiotropy. How useful, therefore, is it to identify shared effects between two traits, rather than simply identify associations to the same broad locus?17 to gene expression traits in monocytes, neutrophils and T cells from the BLUEPRINT consortium18. We first select pairs of immune traits and gene expression traits with associations at the same genetic locus, and then identify which of these pairs share an association and which are associated with different variants in close proximity. We find that trait pairs with shared genetic associations are more likely to share a broader genetic correlation and are more likely to share a causal relationship, as assessed by two Mendelian randomization approaches. Our results show that a substantial proportion of shared associations between traits is likely to be due to causal relationships. We therefore suggest that simply observing associations of different traits to the same genetic locus is insufficient to infer causality, and direct evidence of shared association must be the minimum evidence required to link traits.Here, we answer this question by first assessing how many associations to different traits in the same locus are due to the same underlying effect, and thus shared; and if these associations shared between traits are likely due to a causal relationship between these traits, or if horizontal pleiotropy is widespread. We compare 164 distinct immune cell phenotypes from the Milieu Intérieur projectp < 1 × 10−5; 32 loci at p < 5 × 10−8). In 83/1,379 loci, we found more than one independent effect for the same immune phenotype .To identify effects shared between immune and gene expression phenotypes, we first identified associations for each of the 164 immune phenotypes included in the Milieu Intérieur project data (Supplementary Table p < 1 × 10−3). Most of these 7365 genes were influenced by more than one conditionally independent eQTL within a 1.2 megabase window around the TSS: 3762/5748 (65.4%), 3476/5004 (69.5%), and 4146/5906 (70.2%) in T cells, neutrophils and monocytes, respectively  of the loci. We found that 7365/14,634 (50.3%) genes in 1201/1318 (91.1%) of loci have an eQTL in at least one immune cell type profiled by the BLUEPRINT Consortium  of genes, at 1199/1379 (86.9%) of the discovered loci. We found evidence for a shared underlying causal variant for 207/22,379 (0.9%) pairs, involving 92/164 (56.1%) immune phenotypes and 127/7060 (1.8%) distinct genes, at an FDR < 0.05. These 207 shared effects include 15 instances where a gene expression and immune phenotype pair shared two (13 pairs) or three (two pairs) genetic effects. For 190 distinct combinations of a gene expression trait and an immune phenotype at least one shared association could be identified. Thus, though we test the vast majority of cases where immune phenotypes and eQTLs overlap, we find limited statistical evidence for shared effects between them. This is consistent with our previous observations of limited sharing between autoimmune disease associations and SELL and the level of its protein product L-selectin .Some of our results highlight clear relationships between traits: for example, we see a shared effect between expression level of ils Fig. . Howevercis-eQTLs are not in the same immune cell subpopulation as their cognate immune phenotype. In some cases, we did not have expression data for the subpopulation in which an immunophenotype was measured. We found, for instance, a shared effect between the expression level of CR2 in T cells and the level of its protein product, complement receptor type 2 or CD21 in multiple B cell populations  to 16,462 trait pairs that do not share the same causal variant per our JLIM analysis. For each trait pair, we calculated a genome-wide PRS for the gene expression trait, and determined the variance of the paired immune phenotype explained by that PRS  where we found evidence for at least 2 independent variants associated with the gene expression trait . Using these variants as TSMR instruments, we found that the estimated causal effects of gene expression traits on immune phenotypes were higher in the 190 pairs with a shared association compared to the 16,441 non-sharing pairs , and if so, identify all variants independently associated with those transcripts too. We thus gather a larger set of instruments for our MR analysis. We remove transcripts whose overall expression is highly correlated to the initial gene expression trait, to avoid over-estimating the effects of variants, as previously suggested33. We then perform the same inverse variance-weighted MR analysis as above, using the expanded instrument sets.To address the limited number of instruments available for gene expression traits, we broadened our analysis to include other transcripts in the locus influenced by the same variantsp-value and the estimated causal effects of the gene expression traits on immune phenotypes  as compared to trait pairs without a shared association (12.5%) in this Mendelian randomization analysis  expression and surface protein levels illustrates this principle: an allele that increases SELL transcript levels in neutrophils and monocytes also increases the mean fluorescence intensity of the L-selectin protein product CD62L expressed on neutrophil and eosinophil surface membranes17. L-selectin is a cell adhesion molecule used by diverse immune cells to enter target organs by interacting with resident endothelial cells34. It is particularly important for the entry of naive T cells into secondary lymphoid tissues as part of the maturation process36. CD62L levels are thought to be predictive of treatment response in leukemia37 and risk of adverse events in multiple sclerosis therapy38; the effect of the L-selectin eQTL on protein levels could thus be misconstrued in a clinical setting.These shared associations can uncover previously unknown facets of immune cell biology. The shared effect between RPS20P15 in monocytes and the number of naïve CD4 + T cells  with many cellular traits  to identify pleiotropic effects. Two broad approaches to this cross-trait pleiotropy have been articulated: the first is to look for shared heritability between two traits genome-wideAfter identifying pairs of traits that share genetic effects (either locus-specific or genome-wide), it is tempting to immediately conclude that one trait directly causes the other. This conclusion is particularly appealing when considering traits across different levels of physiology, as we do here with gene expression and cellular measurements. Our results, however, suggest that simply observing association of different traits at the same genetic locus is not sufficient. Rather, we must look for direct evidence for causality between traits, and shared association mapping is a useful approach to do so. Ultimately, only with explicit proof of causality can we construct mechanistic hypotheses about trait physiology.46.A schematic overview of our analysis is presented in Fig. 17. We removed two phenotypes due to the low number of non-zero values  > 0.05. We performed additional QC, removing all individuals with excess heterozygosity of more than five standard deviations from the sample mean (n = 6), one sample from each pair showing cryptic relatedness  > 0.1875, n = 3) and population outliers with a distance in the first four principal components of more than 4 standard deviations (n = 10). These QC steps were all based on a set of variants with MAF > 0.05, genotyping rate >98% and a Hardy–Weinberg equilibrium (HWE) test p-value > 1 × 10−3 and pairwise linkage disequilibrium (LD) < 0.2. From the complete dataset, we then removed all variants out of Hardy–Weinberg equilibrium (p < 1 × 10−5) and MAF < 0.05, and insertions, deletions and multiallelic variants. Our final dataset was thus 5,231,477 variants across 797 individuals.All 816 individuals had been genotyped using the HumanOmniExpress-24 BeadChip and most of them  have also been genotyped using the HumanExome-12 Beach Chip and quality control (QC) and genotype imputation has been performed as described in the original publication+ T cells , CD14+ monocytes  and CD16+ neutrophils  from the BLUEPRINT consortium, ascertained to be free of disease and representative of the United Kingdom (UK) population18. We downloaded FASTQ files and used the GTEx pipeline for RNA-seq alignment, quantification and quality control . Briefly, we performed alignment to the human reference Genome GRCh38/hg38 using STAR v2.5.3a47, based on the GENCODE 26 annotation and gene-level quantification with RNA-SeQC v1.1.948. We produced read counts and “transcript per million” (TPM) values as described in the GTEx pipeline. We then selected genes with expression values of >0.1 TPM and ≥6 reads in at least 20% of the samples and normalized between samples using “trimmed mean of M-values” (TMM) as implemented in edgeR49. We then normalized expression values across samples using an inverse normal transformation. All samples had at least 10 million unique reads. From the BLUEPRINT data release, we obtained genotype data for all individuals for 7,008,524 variants acquired by whole genome sequencing. Sequencing, alignment, variant calling and quality control had been performed as described in the original publication18. We additionally filtered out insertion/deletion and multiallelic variants, and all variants with a MAF < 0.05 and a Hardy-Weinberg equilibrium chi-square p-value of <1 × 10−5. We performed sample QC as described above for the MIP dataset, which did not lead to the removal of any individuals, yielding a final genotype data set of 197 individuals and 4,853,096 single nucleotide polymorphisms (SNPs) (GRCh37 build). In total, we found 4,355,418 SNPs present in both the BLUEPRINT and the Milieu Intérieur project data sets.We obtained RNA-seq data for naive CD451. Allele inconsistencies between the two data sets were resolved by transforming the regression coefficients accordingly and ambiguous SNPs were removed for the PRS.Both the BLUEPRINT and the Milieu intéreur project genotype data sets were available in the GRCh37 build, but version 8 of the GTEx pipeline for RNA-seq alignment and quantification uses GRCh38. We reconciled the different genome builds by back-lifting the RNA-seq data to GRCh37, determining the transcription start site for each gene with R/BiomaRt v2.34.352, assuming an additive model of inheritance for all variants. We adjusted all regression analyses on the immune phenotypes from the MIP data set for age, sex, as well as two environmental factors—smoking  and latent CMV infection —as these have been identified as the main non-genetic factors affecting immune phenotype variation in the original study17. Additionally, we corrected the regression models for the top five principal components to adjust for population stratification. For the association analyses on gene expression data  we included age, sex, the first five principal components as well as 30 PEER factors53  as covariates. We used the same covariates to generate permutation data for JLIM.We performed all association regression analyses with plink v1.9JLIM compares association data for a primary trait to association data for a secondary trait. In all analyses, we use the Milieu Intérieur immune phenotypes as primary traits and BLUEPRINT gene expression traits as secondary. We thus first identify potential associations in the immune phenotypes and then look for overlapping BLUEPRINT eQTLs.p < 1 × 10−5); (ii) are not within 100 kilobases from another lead SNP; and (iii) are not within 500 kilobases from another lead SNP and in LD (r2 > 0.2) with another lead SNP. To identify conditionally independent associations, we performed stepwise conditional association analyses for all markers within 200 kilobases of each lead SNP. At each step, we identified the most associated SNP not in LD with any other lead SNP (r2 < 0.2); if this SNP had p < 1 × 10−3, we added it to the model and repeated the analysis until no independent SNP satisfied the p-value threshold. For each conditionally independent signal, we then calculated residual association statistics, where we condition on all other independent effects in a locus. All association signals with a lead SNP with an association p < 1 × 10−5 were carried forward to subsequent analyses. These represent strong independent associations, with any residual weak effects removed (identified by the more lenient p < 1 × 10−3 threshold). We deliberately chose lenient thresholds for inclusion to maximize our chances of identifying associations which may be shared across traits.We first identified all independent immune phenotype associations by selecting lead SNPs that (i) had suggestive levels of association  within 1 megabase of each lead SNP . We then looked for cis-eQTL associations for each such gene in T cells, monocytes and neutrophils, independently. For each identified eQTL (p < 1 × 10−3), we then performed stepwise conditional association analyses as described above, for all SNPs within 1.2 megabases of the TSS  as an external LD reference panel. We permuted the secondary trait for each pairwise comparison 100,000 times to obtain empirical significance levels, and used a false discovery rate (FDR) < 0.05 as a significance threshold.We compared 16,652 unique combinations of immune phenotype and gene expression trait in one of three cell types across 1,199 genetic loci. These pairs encompassed all 164 immune traits and 7,060 genes with eQTLs in at least one of the three BLUEPRINT cell types. As we considered up to three conditionally independent associations per gene and locus, we made a total of 22,379 comparisons.54 with default parameters for clumping and an additive genetic model. A PRS ith individual over m independent SNPs is defined as:Xj is the number of minor alleles carried at the jth SNP, and jth SNP26. To account for shared effects between some trait pairs but not others, we condition eQTL traits on the main variant from the association signal with the strongest JLIM p-value (even if not significant), and use the now conditionally independent eQTL data for the PRS calculation. We used ten different significance thresholds to select these SNPs: 1 × 10−2, 1 × 10−3, 1 × 10−4, 5 × 10−5, 1 × 10−5, 5 × 10−6, 1 × 10−6, 5 × 10−7, 1 × 10−7, and 5 × 10−8. We then calculated the proportion of immune phenotype variance (R2) explained by these PRS and their empirical significance, also using PRSice.We used polygenic risk scores (PRS) to assess the global genetic overlap between immune/gene expression trait pairs, beyond the shared associations identified by JLIM. For each pair, we selected independent variants associated with the expression trait at some threshold and then calculated PRS for all individuals in the immune trait cohort, using PRSice v2.2.11.bR2 values) with the Mann–Whitney–Wilcoxon test, and the correlation between JLIM p-values and PRS R2 with univariate linear regression.We then compared PRS results between trait pairs with a shared effect and trait pairs with no sharing, using two approaches. We compared the proportion of immune phenotype variance explained  approaches to assess evidence that gene expression traits are causal for the immune phenotype traits for which they share an association. First, we used two-sample Mendelian randomization (TSMR)33. As described by Porcu et al, we first identified all variants associated with the gene expression trait in each trait pair in each cell type . We then identified all genes within 1 megabase of the gene’s TSS, and selected all SNPs associated with the expression levels of any of these genes. We then removed genes with highly correlated expression values to the original gene (r2 > 0.2), and selected pairwise-independent SNPs from the remaining list (pairwise LD r2 < 0.1). We used the resulting set of variants as instruments in a multivariate MR model to estimate the causal effect on the immune phenotype. Ambiguous SNPs were removed for both MR analyses.We also used transcriptome-wide summary statistics-based Mendelian Randomization (TWMR), an extension of TSMRp-values (strength of evidence of shared effect) with the estimated causal effect sizes and corresponding p-values using univariate linear regression.We compared causality estimates from both methods between trait pairs with a shared effect and trait pairs with no sharing. We compared estimated causal effect sizes with the Mann–Whitney–Wilcoxon test; and, as a continuous measure, the correlation between JLIM Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary


PRDM13, MAP3K7, CNR1, HTR1E, IL4, IL5, IL13, KIF3A, EFHD2, SLC38A7, mTOR, CNOT1, PLCB2, GABRR1, and GABRR2, which have biological roles in neuropsychiatric processes. The results of biological pathway and gene network analyses also support the association of the neuropsychiatric processes with SGE on ADG in pigs. Additionally, a total of 11 QTL windows for DGE on ADG in SSC2, 3, 6, 9, 10, 12, 14, 16, and 17 were detected with positional candidate genes such as ARL15. We found a putative pleotropic QTL for both SGE and DGE on ADG on SSC6. Our results in this study provide important insights that can help facilitate a better understanding of the molecular basis of SGE for socially affected traits.In livestock social interactions, social genetic effects (SGE) represent associations between phenotype of one individual and genotype of another. Such associations occur when the trait of interest is affected by transmissible phenotypes of social partners. The aim of this study was to estimate SGE and direct genetic effects  on average daily gain (ADG) in Landrace pigs, and to conduct single-step genome-wide association study using SGE and DGE as dependent variables to identify quantitative trait loci (QTLs) and their positional candidate genes. A total of 1,041 Landrace pigs were genotyped using the Porcine SNP 60K BeadChip. Estimates of the two effects were obtained using an extended animal model. The SGE contributed 16% of the total heritable variation of ADG. The total heritability estimated by the extended animal model including both SGE and DGE was 0.52. The single-step genome-wide association study identified a total of 23 QTL windows for the SGE on ADG distributed across three chromosomes . Positional candidate genes within these QTL regions included  Although explicit observation and evaluation of the impact of social interactions on a phenotype is challenging, a quantitative genetic model was proposed that can decompose phenotypic values into a direct effects that originated from an individual’s own characteristics and indirect effects  that are derived from others2. As the direct effect can be partitioned into an inheritable component  and an environmental component, the indirect effect also can be partitioned into a genetic component and an environmental component. The genetic components of an indirect effect are called indirect genetic effects, or social genetic effects (SGE). Hence, SGE represent associations between phenotype of one individual and genotype of another.Livestock are usually raised in groups. Accordingly, social interactions and/or hierarchy of animals in the group can affect phenotypic variations such as growth rate. Competition is a ubiquitous example of social interaction and can cause considerable negative impacts on productivity  and animal welfare3. In this extended best linear unbiased prediction (BLUP) model, the random SGE can be considered as competitive effects. In 2002, Muir and Schinckel showed that the incorporation of the competitive effects into their extended BLUP model used for Japanese quail breeding resulted in substantially increased selection response compared with the conventional BLUP approach that incorporated only DGE4. Subsequently, more comprehensive linear mixed models were developed to better estimate both DGE and SGE7. SGE have been estimated and shown to be responsible for many phenotypes across several animal species, including pigs8, chickens9, quail5, deer10, mink11, and laboratory mice12. In particular, the magnitude of SGE in pigs has been estimated for average daily gain (ADG)14, feed intake and muscle depth8, and carcass traits15; these studies reported that the genetic variation in pen mates  substantially influenced the phenotypic variation of diverse traits in these animals.The SGE can be estimated using linear mixed model frameworks by incorporating SGE as a random effect together with the conventional random DGE17. Until recently, however, a limited number of GWASs have been performed to identify QTLs responsible for SGE, and little is known about their genetic architecture or individual genes implicated19. The aims of this study were to estimate variance components of SGE and DGE for ADG in purebred Landrace pigs and to perform a single-step GWAS (ssGWAS) to identify QTLs and positional candidate genes associated with the traits of interest. In addition, post-GWAS functional annotation analyses  were conducted to elucidate the biological background of the underlying SGE using the list of positional candidate genes identified based on the ssGWAS results.Thanks to the noteworthy advancements in molecular and statistical genetic methodologies, genome-wide association studies (GWASs) to detect quantitative trait loci (QTLs) and their positional candidate genes have become feasible8. An explanation of the observed differences is due to the fact that the breeder of these maternal lines for pig selection have focused on reproductive traits, such as litter size, there has been little chance of genetic improvement for growth. Bergsma et al. suggested that the absence of competition between mate growth and an individual’s own growth might be a result of neutral or marginally cooperative social interactions8. According to Bijma et al., the positive covariance between direct variance and indirect  variance is likely to increase the 6. Canario et al. reported that individuals may improve the growth of their social mates without personal cost7. In this study, the positive correlation in Landrace pigs could suggest that their social partners might have stimulated a greater ADG. Moreover, in addition to being influenced by environmental factors, this results indicate that SGE contribute to the additive genetic component together with DGE. Therefore, the GWAS was conducted to delineate the genetic architecture of SGE and DGE in Landrace pigs.The (co)variances and parameters obtained from the studied model for Landrace pigs are presented in Table Non-overlapping 1-Mb windows that explained more than 0.5% of the additive genetic variance were determined to be QTLs for SGE on ADG in this study. The Manhattan plot of the single-step GWAS (ssGWAS) for SGE is shown in Fig. PRDM13. It has been known that GABA (gamma-aminobutyric acid) plays a critical role in the control of neurotransmitters as an inhibitor of neuronal activities related with neuropsychiatric processes20. PRDM13 is required to generate the precise number of GABA (gamma-aminobutyric acid) associated neurons 21. There is a report that a microdeletion of PRDM13 locus has been implicated in autism and intellectual disability in human22. The QTL window on SSC1 at 58–59 Mb includes MAP3K7, which is known to be involved in autophagy. Autophagy is known to be related to psychiatric diseases such as schizophrenia23. CNR1, which encodes the type 1 cannabinoid receptor protein, is localised to the QTL window on SSC1 at 55.6–56.6 Mb. This window accounts for 3.52% of additive genetic variance for SGE on ADG. The endocannabinoid system plays a crucial role in the regulation of neurological activity throughout the central nervous system24. This gene is known to be associated with neurological phenotypes in mice25 and humans26. Also on SSC1, another QTL window at 56.9–57.9 Mb, accounting for 1.21% of additive genetic variance, harboured GABRR1 and GABRR2, which encode receptors for GABA. The GABA makes use of its effects via their receptors. Hence, GABA receptors influence neurological and behavioural phenotypes in many animal species29. It was reported that GABRR2 is a plausible positional candidate gene on SGE of feed conversion rate in Yorkshire pigs19. The QTL window on SSC1 at 54.4–55.4 Mb, accounting for 1.09% of additive genetic variance, harboured HTR1E, which encodes 5-hydroxytryptamine 1E receptor protein. The 5-hydroxytryptamine, which is also known as serotonin, plays an important role in mood control30, HTR1E has been implicated in autism spectrum disorders31. On SSC1, the QTL window at 51.5–52.5 Mb, explaining 0.58% of additive genetic variance, contained RIMS1. The protein encoded by RIMS1 is a RAS gene superfamily member that controls synaptic vesicle exocytosis32 and influences behavioural traits linked to schizophrenia33.The top QTL, which explained 5.36% of the additive genetic variance, was mapped on SSC1 at the 61–62 Mb region. However, this window did not harbour any known gene. Among the 22 other QTLs, sixteen were also mapped on SSC1. The QTL window on SSC1 at 66–67 Mb explained the 2nd highest percentage of additive genetic variance (4.96%) for SGE on ADG and included a positional candidate gene, 34. It is well documented that T cells are linked to the homeostasis of the central nervous system and therefore may be related to neurological or psychological traits36. KIF3A was also located in this region; this gene encodes the kinesin-like protein 3A, which has been implicated in a disorder involving motor neuron degeneration37. Furthermore, this gene was identified as a component of gene networks associated with conditional fear phenotypes in mice38.On SSC2, only one QTL at the 134–135 Mb region was identified. This QTL region accounted for 0.55% of additive genetic variance for SGE on ADG. Three genes are located in this region that encode IL4, IL5, and IL13. These interleukins are involved in T cell receptor signaling pathwayEFHD2, which encodes EF hand domain containing 2 protein, is located in this QTL. EFHD2 is expressed in human and mice brains. EFHD2 protein is a Ca2+ sensor protein which is likely involved in synaptic plasticity and affects various behavioural traits in mice39. Another QTL window at 19.9–20.9 Mb, which accounted for 0.87% of additive genetic variance, contained SLC38A7, which encodes solute carrier family 38 member 7 . Hägglund et al. reported that SLC38A7 is a sodium-coupled amino acid transporter in glutamatergic neurons in the brain40, and it has been implicated as a risk-conferring glutamatergic gene in schizophrenia41. In the QTL region of SSC6:70.8–71.8 Mb, the mammalian target of rapamycin (mTOR) gene was found to be associated with SGE on ADG. mTOR is a broadly expressed serine-threonine kinase that coordinates major cellular processes, including synaptic and autophagy activation. Altered mTOR signal transduction is known to be associated with neuropsychiatric disorders such as schizophrenia42.On SSC6, five windows were identified as QTLs. The most significant QTL on SSC6 was located in the 73.7–74.7 Mb region, and explained 0.95% of additive genetic variance for SGE on ADG. ARL15 encoding a small GTP-binding protein. In humans, the ARL15 locus was found to be associated with plasma insulin, HDL cholesterol, adiponectin levels, and obesity. Moreover, a functional characterisation revealed that ARL15 influences adiponectin secretion and adipocyte differentiation43.Eleven QTL windows that affected DGE on ADG were identified by ssGWAS. These QTLs were located on SSC2, 3, 6, 9, 10, 12, 14, 16, and 17 Fig. . Among tSLC38A7 located in this QTL window is expressed in GABAergic and other neurons in brain, and in liver and skeletal muscle. In addition to neuropsychiatric traits, this gene has been implicated in energy metabolism and cell growth44. Therefore, SLC38A7 is considered as a potential candidate gene for both SGE and DGE on ADG.To evaluate the presence of putative pleiotropic QTL regions associated with SGE and DGE on ADG, we compared the two GWAS results; only one QTL  was co-localised for SGE and DGE in this study. The previously mentioned 45. In the previous study, the mean phenotypic value of ADG for unrelated pen partners and the phenotypic value of individual ADG were used as the naïve SGE and DGE for the GWAS, respectively. Therefore, several other fixed and random effects were not previously corrected compared with the current study. However, an extended animal model was used to compute BLUP estimates of SGE and DGE on ADG in this study. Subsequently, the two BLUP estimates were used as dependent variables for the ssGWAS. We found that there is a lack of concordance between our previous and current GWAS results. No overlapping QTL was identified in SSC1 and SSC2. The previous study identified significant SNP markers in SSC6. However, these markers were not co-localised with the QTL identified in the current study. This lack of concordance may not be surprising, because we used two different types of dependent variables for the two different GWAS approaches. Nevertheless, we argue that the current ssGWAS results are more plausible, because the extend BLUP model in this study is likely to be better fit for the social interactions between pigs. Moreover, we identified a list of positional candidate genes involved in neuropsychiatric processes that are more relevant to SGE in this study.We previously reported the identification of several QTLs for SGE and DGE using a standard GWAS approach46. Functional annotation of the positional candidate genes to biological processes is presented in Table PLA2G4F, PLA2G4D, PLA2G4E, PLA2G4B, and PLCB2) were overrepresented in both long-term depression and serotonergic synapse categories. Another neuropsychiatric function for SGE on ADG were retrograde endocannabinoid signaling. Five genes  were overrepresented in this pathway. As a complementary method to biological pathway analysis, a network-based analysis was conducted to establish gene network associated with the SGE on ADG using Ingenuity Pathway Analysis (IPA)47. The IPA identified a gene–gene interaction network with a score of 31. This network contained 18 of the positional candidate genes detected by ssGWAS  of them were listed in the SZDB, which is a database for schizophrenia genetic research48. Moreover, the IPA revealed that this network is associated with cell morphology, developmental disorder, and nervous system development and function.To evaluate the association of a curated set of genes  with the SGE on ADG, a total of 199 positional candidate genes, which are located within the QTL windows, were uploaded to the Enrichr databaseLDHB, SDS, SDSL, and GOT2) involved in this amino acid metabolism were overrepresented in this pathway. Castellano et al. reported that cysteine and methionine levels can affect preadipocytes proliferation and differentiation which can be relevant to fatness traits in pigs49. The Enricher database identified a signaling pathway that regulates pluripotency of stem cells for the DGE on ADG. Three genes  were overrepresented in this signaling pathway. It is well documented that WNT protein-mediated signaling plays an important role in the pathogenesis of obesity51. Additionally, the mTOR signaling pathway was also detected by this analysis; this pathway included three genes . The mTOR pathway orchestrates multiple major cellular processes, such as cell growth53. The IPA detected a gene–gene interaction network with score of 49. This network contained 23 of the positional candidate genes detected by ssGWAS for DGE on ADG  have biological roles that are strongly associated with neuropsychiatric processes. Furthermore, the post-GWAS pathway and network analyses also supported the association of neuropsychiatric processes with SGE on ADG. This study contributes to our understanding of the molecular basis of SGE.Using an extended BLUP model, we found that genetic variation in pen mates  influences the variation in ADG in Landrace pigs. We also provide a list of QTLs and positional candidates associated with SGE and DGE on ADG using ssGWAS. The identified positional candidate genes for SGE on ADG  farm in the Republic of Korea. Both parents were known for a total of 22,983 individuals. The average inbreeding coefficient was 0.064. The range of inbreeding coefficients was 0.00009–0.304. The observed average family size was 4.12 with ranges of 2–23. The population structures of these breeds were determined using the CFC v1.0 software55. On average, fewer than 160 days were required to attain this target weight. The mean and standard deviation of ADG were 804 ± 91 g/day.At the same closed nucleus farm, the phenotypic dataset for ADG was obtained by performance tests of Landrace  pigs between years 2001 and 2015. A total of 4 − 13 pigs of the same sex were placed in each pen to form the groups of pigs. The average group size was 6.8 ± 1.9. As Hong et al. described, the performance evaluations of ADG of pigs started soon after each animal reached a live body weight of 30 kg, and continued until a target weight of 90 kg was attained; pigs were fed ad libitum, and water was constantly accessible through nipple drinkers56. This population was previously used in GWAS for the naïve SGE45. The quality control process of the genotype data included the removal of individuals with pedigree errors, omission of monomorphic SNP genotypes, SNPs on sex chromosomes or SNPs with minor allele frequencies (< 0.95), genotype call rate of < 0.90, animal missing rate of > 0.90, Hardy–Weinberg equilibrium of 0.15, and the SNPs with displaced segregation distortion58. After quality control, the final dataset included genotypes from a total of 1,029 pigs. The total number of autosomal SNPs was reduced to 39,136 after 36.4% of SNPs from the original Illumina marker panel were removed.The genomic DNA of pigs was extracted from blood samples collected from jugular veins using a standard protocol. A total of 1,041 Landrace pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which included 61,565 SNP markers. accounted for early-life environmental effects in the SGE model to avoid bias in the estimated genetic parameters for social effects7. The following animal model, which includes social genetic and early-life environmental effects was described as Model 7 of Canario et al.7:y is the vector of observations (ADG), b is the vector of the fixed effects, aD is the vector of the random additive DGE, aS is the vector of the random additive SGE, c is the vector for the random pen with g is the vector of the random group with pe is the vector for the random nongenetic permanent effects of the mother with l is the vector for the random birth litter, k is the vector for the random early-life environment (birth litter of its group mates), and e is the vector of the residuals with X, ZD, ZS, W, V, U, T, and Q are the corresponding incidence matrices. I is an identity matrix of appropriate dimensions. To take into account differences in group size, as suggested by Canario et al.7, an additional covariate term known as dilution The ADG trait was analysed by the extended animal model below. Bayesian inference using Gibbs sampling procedure was used to estimate (co)variance components of the studied trait. The effects of birth year-month (168 levels), sex , and group size (10 levels) were fitted as fixed effects. In the model, age at target weight was fitted as a covariate. The models also included the random effects of the physical pen (112 levels), group identity , litter of birth , and permanent environment of the mother . Animals were fitted as a random effect in the model. Canario et alDGE and SGE had the following multivariate normal (MVN) distribution:C ⊗ A), in which C is defined by the matrix C ⊗ H denotes the Kronecker product of two matrices. The relationship matrix H, in single-step evaluation, defines the relationship among genotyped and non-genotyped animals. The inverse of the H matrix is rather simple in structure60 and can be given as:A and G is the relationship matrix among individuals based on genomic information. In addition, birth litter and early-life environmental effects had the following multivariate normal (MVN) distribution:MVN61, and the Gibbs samplers were run as single chains 550,000 rounds. The first 50,000 rounds were discarded as burn-in with thinning every 50 samples. This resulted in a total of 10,000 samples used for post-Gibbs analyses, which were completed using POSTGIBBSF9061.To fit a SGE model, GIBBS2F90 with Bayesian inference using Gibbs sampling was used62, for traits affected by heritable social effects, the variance of total breeding value (TBV) represents the total heritable variation that is exploitable for selection. The TBV of the ith animal was defined as follows:n − 1 group mates. Bijma (2011) also stated that the total heritable variance determines the population’s potential in response to selection and can be expressed as62:According to Bijma7:According to Canario et al., the phenotypic variance for such a model can be calculated as followsThe total heritable variance can be expressed relative to phenotypic variance  as follows:aS). In this ssGWAS approach, greater power and more precise estimates of variance components can be achieved by including non-genotyped animals if the number of genotyped animals is limited63. The G matrix was constructed with method 1 in VanRaden (2008)64:Z is a matrix of gene content that contains genotype adjusted for allele frequencies; D is a diagonal matrix of weights for variances of SNPs ; and q is a weighting factor. This factor can be derived by ensuring that the average diagonal in G is close to that of A2265. The SNP effects and weights were derived as follows:Let D = I in the first step.Calculate breeding valuesConvert breeding values to SNP effects Calculate the weight for each SNP: Normalise SNP weight to retain the total genetic variance constant.A ssGWAS, which jointly uses genotype, phenotype, and pedigree information in one step, was conducted on the vector of random additive SGE  that accounted for equal to or greater than 0.5% of the additive genetic variance from GWAS were determined to be the QTL regions46. The gene interaction network analysis was conducted using IPA tools as a complementary analysis to the Enricher biological pathway analysis47. The same list of candidate genes used for the pathway analysis were uploaded into the IPA for network analysis. These analyses were conducted with their default settings.The selected candidate genes were uploaded into the Enricher database to investigate relevant biological pathwaysAll experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) at the National Institute of Animal Science (NIAS), Republic of Korea. All methods in this study were carried out in accordance with relevant guidelines and regulations. Necessary approval was obtained from the IACUC of the NIAS, Republic of Korea .Supplementary Information.


Eucalyptus grandis is one of the most important species for hardwood plantation forestry around the world. At present, its commercial deployment is in decline because of pests and pathogens such as Leptocybe invasa gall wasp (Lepto), and often co-occurring fungal stem diseases such as Botryosphaeria dothidea and Teratosphaeria zuluensis (BotryoTera). This study analyzed Lepto, BotryoTera, and stem diameter growth in an E. grandis multi-environmental, genetic trial. The study was established in three subtropical environments. Diameter growth and BotryoTera incidence scores were assessed on 3,334 trees, and Lepto incidence was assessed on 4,463 trees from 95 half-sib families. Using the Eucalyptus EUChip60K SNP chip, a subset of 964 trees from 93 half-sib families were genotyped with 14,347 informative SNP markers. We employed single-step genomic BLUP (ssGBLUP) to estimate genetic parameters in the genetic trial. Diameter and Lepto tolerance showed a positive genetic correlation (0.78), while BotryoTera tolerance had a negative genetic correlation with diameter growth (−0.38). The expected genetic gains for diameter growth and Lepto and BotryoTera tolerance were 12.4, 10, and −3.4%, respectively. We propose a genomic selection breeding strategy for E. grandis that addresses some of the present population structure problems. Eucalyptus trees. Eucalyptus species are adaptable, fast-growing, generally resilient to pests and pathogens, and have the desired wood qualities for diverse wood products . It has spread across the globe, infesting a wide range of commercially grown Eucalyptus species and their hybrids, resulting in severe losses in young plantations and nursery seedlings  markers have been identified that jointly explained 3–37% of the variation of resistance in sistance . Due to olerance . A recenolerance .Teratosphaeria zuluensis, a fungal pathogen that causes stem canker, previously known as Coniothyrium canker, is a devastating stem disease of Eucalyptus species and is one of the most severe pathogens of plantation-grown Eucalyptus spp. , and tolerance to leaf gall caused by L. invasa (Lepto). The study aimed to obtain genetic parameters and genetic gains for growth, pest, and pathogen tolerance in this multi-generation breeding trial comparing ABLUP  and ssGBLUP models. We further investigated the additive genetic correlations and genotype-by-environmental (G × E) interactions of diameter growth and tolerance to Lepto and BotryoTera. Based on the results, we discuss the utility of genomic selection in E. grandis for simultaneous improvement of growth and tolerance to the gall wasp and fungal stem disease.In this study, we measured breeding trials of Eucalyptus grandis W. Hill ex Maiden was introduced to South Africa in the early 1900s and included various government breeding populations as a timber resource for the mining industry. Private breeding programs only started in the early 1970s, initiated from government landrace breeding populations. Breeding objectives for these landrace breeding populations gradually shifted to target traits for pulp and paper products rather than timber production in successive generations and trial series  SNP chip as described by Silva-Junior . The a-Junior  availabla-Junior . Of the a-Junior , there a package  and the Linear mixed models were fit to estimate variance components and solve mixed model equations to obtain solutions for fixed and random effects. The matrix notation for the linear mixed models used is as follows:y is a vector of phenotypes, X is the design matrix for the fixed effects (site), β is the vector of the fixed effect coefficients (intercept site), Z is an incidence matrix for the random effects of individual trees, u is the vector of random effect coefficients , and ε is the vector of residual effect coefficients. The expectations of y, u, and e are E(y) = Xβ, E(ε) = 0 and the variances are A is the relationship matrix of the random effects, R was relaxed to have a heterogeneous error variance across the environments. Similarly, the assumption of the G matrix was relaxed to model full G × E and heterogeneous genetic variances at each site (s + 1 variance parameters), where s is the number of environments  × p (markers). The base pair calls were transformed into gene content values of the minor alleles at each SNP loci in each individual in matrix Z, with elements −1 , 0 (heterozygote), and 1 . The frequencies of the genotypes were 0.584, 0.338, and 0.078, respectively. The allele frequencies in matrix P are presented as 2(pi−0.5), where pi is the observed allele frequency at the marker i for all individuals. The 2∑pi(1−pi) is the variance of alleles summed across all the loci. A ssGBLUP model was fitted using a blended relationship (H) matrix, incorporating the (G) matrix of genotyped trees that are linked to the non-genotyped trees by the half-sib pedigree (A) matrix (where ) matrix .H matrix used in the ssGBLUP was formulated as follows: where u is a vector of genetic effects with variances u), there are non-genotyped and (u1) and genotyped (u2) individuals partitioned in the A matrix as:The A11 is the relationship matrix of non-genotyped individuals, A22 is the relationship matrix for the genotyped individuals, and A12 and its transpose A21 are the covariances between the genotyped non-genotyped individuals. We then replaced the u2 genetic effects with the pedigree relationship of A22 with their G matrix as constructed in Eq. 3. The relationship between the non-genotyped and (u1) and genotyped (u2) individuals in A12 and A21 is then adjusted by the G matrix via the pedigree relationship of all other individuals in the H matrix . The design matrix X has the dimensions n = (p1), where p is the number of fixed estimators, which are replication nested in location for the traits, and the additional column is added for the intercept. β is the matrix of coefficients of fixed predictor effects to be estimated with dimensions (p1) = d. The rows of β correspond to predictor variables, and the columns are response variables. The design matrix of Z has dimensions n = r, where r is the number of random effects  per trait, and u is a r = d matrix of the random effects.where G and R variance–covariance matrices of the multivariate model were designed with the variances for the three traits on the diagonal and the covariances between the traits on the off-diagonals:The G matrix is the direct product of the A matrix (pedigree relationship) for the ABLUP model and substituted with the H matrix for the ssGBLUP model with an unstructured, heterogeneous variance and covariance structure, where each environment has a unique genetic variance, and each pair of the environments has a unique covariance, with an s(s1)2 variance parameter (R matrix is the direct product of the identity matrix (Im) with m dimensions, m is the number of genotypes with variance BotryoTera, and Lepto and their covariances nested within.where the arameter . The R mA matrix) and the realized genomic (G) was calculated using the package synbreed 0.10-2 . Overall, the Kwambonambi site produced the highest heritability values ranging from 0.29 to 0.63 (ABLUP) and from 0.45 to 0.70 (ssGBLUP) across the traits  was high, ranging from 0.77 to 0.81 for the three traits associated with small standard errors  with diameter growth at 48 months  clone that was highly susceptible to L. invasa. The G × C hybrid genotype has been shown in the literature to be susceptible to L. invasa  clone  clone . There aU) clone . These aUP model . It is nLepto tolerance showed similar heritability estimates between ABLUP (0.69) and ssGBLUP (0.70) and for diameter growth ABLUP (0.63) and ssGBLUP (0.58)  . The simtionship .BotryoTera tolerance score 3 in Kwambonambi  were selected in the absence of the BotryoTera disease explaining the higher susceptibility of the second generation families. The environmental conditions at the Kwambonambi site are optimal for diameter growth, and, due to the negative correlation with BotryoTera tolerance, there was high susceptibility to BotryoTera in Kwambonambi , and stem (later in the growth cycles); therefore, the correct timing of scoring is crucial. Third, although present, pests and pathogens may differ in their infestation and infection severity due to many factors, leading to highly varying levels of challenge and incomplete expression of tolerance or susceptibility. Fourth, the emergence of pests and pathogens sometimes may reveal inadequacies of already established selection strategies, thereby requiring revision, as is the case for BotryoTera.a psidii , L. inva. invasa , T. zuluuluensis , and B. dothidea . This stLepto tolerance and then only consider selecting BotryoTera tolerant individuals from high ranked families .Circumventing field trials and the inconsistency of pest infestations or pathogen infections, tree breeders may consider a proposed serial selection strategy with genomic selection and controlled pollination in potted trials . This aprategies . The proMeanwhile, the second set of ramets from the same clones (Set B) is planted in field trials to validate the expected correlated diameter growth response, while the third set of ramets (Set C) are subjected to flower induction to produce control-pollinated next-generation families. The clonal phenotypic data can be used together with genome-wide genotyping to train a genomic selection model for implementation  can be found in the article/MM carried out the experimental design, data collection, data analysis, and drafting of this manuscript as part of his Ph.D. thesis. FI assisted with the modeling and scripts of the data analysis tools. FI, GH, and AM helped with the data interpretation and supervision. All authors have read and approved the final manuscript.MM was employed by the company Mondi South Africa (Pty) Ltd. The authors declare that this study received funding from Mondi South Africa (Pty) Ltd. The funder was not involved in the study design, collection, analysis, and interpretation of data, the writing of this article, or the decision to submit it for publication.


The objective of this study was to characterize the number of loci affecting growth traits and the distribution of single nucleotide polymorphism (SNP) effects on growth traits, and to understand the genetic architecture for growth traits in Hanwoo (Korean cattle) using genome-wide association study (GWAS), genomic partitioning, and hierarchical Bayesian mixture models.GWAS: A single-marker regression-based mixed model was used to test the association between SNPs and causal variants. A genotype relationship matrix was fitted as a random effect in this linear mixed model to correct the genetic structure of a sire family. Genomic restricted maximum likelihood and BayesR: A priori information included setting the fixed additive genetic variance to a pre-specified value; the first mixture component was set to zero, the second to The GWAS revealed common genomic regions of 2 Mb on bovine chromosome 14 (BTA14) and 3 had a moderate effect that may contain causal variants for body weight at 6, 12, 18, and 24 months. This genomic region explained approximately 10% of the variance against total additive genetic variance and body weight heritability at 12, 18, and 24 months. BayesR identified the exact genomic region containing causal SNPs on BTA14, 3, and 22. However, the genetic variance explained by each chromosome or SNP was estimated to be very small compared to the total additive genetic variance. Causal SNPs for growth trait on BTA14 explained only 0.04% to 0.5% of the genetic varianceSegregating mutations have a moderate effect on BTA14, 3, and 19; many other loci with small effects on growth traits at different ages were also identified. High-density single nucleotide polymorphism (SNP) panels are available as a result of genome sequencing, and large-scale genome-wide association studies (GWASs) have been conducted to identify many novel loci associated with complex polygenic traits in humans and livestock. A good example is a GWAS for human height, which is a polygenic trait. The heritability of human height is approximately 80% ,2. HowevNowadays, as the sequencing information of livestock was released and SNP array was developed, GWASs can consider that addresses three objectives of the livestock industry. First, GWASs aim to identify genomic regions, such as quantitative trait loci (QTL), harboring causal variants underlying genetic variation in polygenic traits. Second, GWASs are used to investigate the genetic architecture of polygenic traits by estimating the genetic variance explained by a genomic region containing causal variants. Finally, GWASs can be used to predict genomic estimated breeding value and future phenotypes to rank animals for selective breeding . Growth The previous genome-wide QTL mapping studies have identified a major QTL for bovine body stature and growth traits on chromosome 14 ,9. KarimRecent developments in bovine high-density SNP arrays, such as the BovineSNP50 BeadChip , allow for an understanding of the genetic architecture of complex traits and to more accurate prediction of genomic breeding value in livestock. Moreover, genomic prediction using genome-wide SNP markers is a promising breeding technology to accurately predict breeding value at an early stage of identifying candidate animals for selection . The genThe aim of the present study was to characterize the number of loci affecting growth traits and the distribution of SNP effects on growth traits using a whole-genome association study in Hanwoo cattle.The animals (n = 904) used in the study were born from 2000 to 2010 and belonged to the National Progeny test program . Phenotype data were live body weight (BW) at different ages . The number of genotyped individuals and phenotypic growth traits and the trait summary statistics and heritabilities are shown in A single-marker regression-based mixed model was used to test the association between the SNPs and the causal variants. A genotype relationship matrix (GRM) was fitted as a random effect in this linear mixed model to correct the genetic structure of the sire family. The basic concept of a GWAS is that the SNP is assumed to be in linkage disequilibrium (LD) with causal variants in close proximity. The effect evaluated was an additive effect. SNP genotypes were recoded as allele counts , which represent copies of the variant allele. The following mixed linear regression model was fitted to map QTL for growth traits using a genome-wide complex trait analysis (GCTA) . The staYijkl is the phenotype of the growth traits, μ is the overall mean, CGi is a contemporary group for birth year and season and test-round effects, b1 is a regression coefficient, Sagej is slaughter age as a covariate, b2 is genotypic effect, SNPk is SNP genotype , Al is polygenic as a random effect e is a residual effect as a random where Two genomic regions were identified on BTA14 and 3, that may harbor causal variants of large genetic effect. We estimated the GRM of all individuals from autosomal SNPs using GCTA software to partition the genetic variance explained by restricted maximum likelihood analysis in each chromosome . The stay is a vector of the growth traits, β is a vector of fixed effects of the contemporary group , X is an incidence matrix, gG is a vector of genetic effects of all autosomal SNPs with Ag is the GRM for all autosomal SNPs. The proportion of variance explained by all autosomal SNPs was defined as where gc is a vector of genetic effects to each chromosome, and where A priori conditions included setting the fixed additive genetic variance to a pre-specified value, such as setting the first mixed component to zero, the second to π =  which are constrained to be positive and to sum to unity: be et al , we assuσ2 is the additive genetic variance explained by SNPs that fixed the marker variance at a pre-specified value.Here, The 50K SNP information obtained for GWAS and genomic partitioning analysis using growth traits and genomic information was imputed as follows. A total of 731,261 SNPs was imputed using 48,845 samples of 777K SNPs as a reference. After QC assessment, 644,726 SNPs were included for further analyses, and 782, 82,450, and 4,085 SNPs were removed by QC criteria of low call rate, MAF, and HWE, respectively. The majority of SNPs were mapped to the intergenic and intron regions, i.e., 59% and 31% of SNPs were located in intergenic and intron regions of the bovine reference genome, respectively . The exoThe GWAS revealed that common 2-Mb genomic regions may contain a causal variant on BTA14 for body weight at 6, 12, 18, and 24 months . This geWe estimated the proportion of genetic variance explained by the common SNPs on each chromosome to determine whether the growth trait was a polygenic characteristic in the Hanwoo population studied. The proportion of total additive genetic variance attributed to each chromosome averaged across three growth traits against chromosome length  was estia priori assumption of a mixture of a normally distributed mixture of SNP effects.Given the high density of SNPs, it was impossible to fit all of the SNP markers in the model at the same time. The GWAS used a large number of SNP markers so that the number of parameter estimates was larger than the number of samples. Therefore, we reduced the number of SNP markers using variable selection methods, either by discarding unimportant predictors or by shrinking the marker effect to zero. Among the variable selection methods, Bayesian methods such as the BayesA, B , and C  models hσ2) explained genetic variance, i.e., 25% of BW6, 25% of BW12, 23% of BW18, and 7% of BW24. The large genetic contribution to the effect size of the SNP produced a moderate effect (0.001×σ2): 65% of genetic variation in BW6, 49%, 30%, and 55% of genetic variation in BW12, 18, and 24 were explained by SNPs with a moderate effect (0.001×σ2). These results confirm that the average 70% of genetic variance for small and moderate effects explains the growth traits of Hanwoo cattle. Therefore, growth traits are controlled by many SNPs with small and moderate effect sizes in Hanwoo.Comparisons between a normal GWAS and BayesR were assessed based on their ability to identify a genomic region of causal variant for growth trait on BTA14 in Hanwoo cattle. Our results revealed that differences exist in the genetic architecture of different complex traits in Hanwoo cattle. Segregating mutations with a moderate effect on BTA14, 3, and 19 and many other loci with small effects were found for growth traits at different ages. The distribution of the effects was assumed to be normal. The genetic architecture of growth traits will provide important information for predicting the genomic breeding value of animals for selection in the Hanwoo breeding industry.