An association between individual levels of indigenous Chilean Mapuche ancestry and the risk of gallbladder cancer has been reported in observational studies, and the current program for gallbladder prevention in Chile takes the number of Mapuche surnames into account for prophylactic cholecystectomy. However, the association between Mapuche ancestry and gallbladder cancer could be due to known and unknown confounders  and non-random measurement errors . To investigate whether Mapuche ancestry and gallbladder cancer risk are statistically correlated or causally related, we used ancestry-informative markers for instrumental variable analysis. We aim to provide robust evidence on the potential of accounting for ethnic differences  for disease interception . The methodology used and the results obtained may also guide future admixture mapping studies.p = 6.7 × 10−5) and also on gallstone disease , pointing to a mediating effect of gallstones on the association between Mapuche ancestry and GBC. In contrast, the proportion of Mapuche ancestry showed a negative effect on BMI . The results presented here may have significant implications for GBC prevention and are important for future admixture mapping studies. Given that the association between the individual proportion of Mapuche ancestry and GBC risk previously noted in observational studies appears to be free of confounding, primary and secondary prevention strategies that consider genetic ancestry could be particularly efficient.A strong association between the proportion of indigenous South American Mapuche ancestry and the risk of gallbladder cancer (GBC) has been reported in observational studies. Chileans show the highest incidence of GBC worldwide, and the Mapuche are the largest indigenous people in Chile. We set out to assess the confounding-free effect of the individual proportion of Mapuche ancestry on GBC risk and to investigate the mediating effects of gallstone disease and body mass index (BMI) on this association. Genetic markers of Mapuche ancestry were selected based on the informativeness for assignment measure, and then used as instrumental variables in two-sample Mendelian randomization analyses and complementary sensitivity analyses. Results suggested a putatively causal effect of Mapuche ancestry on GBC risk (inverse variance-weighted (IVW) risk increase of 0.8% per 1% increase in Mapuche ancestry proportion, 95% CI 0.4% to 1.2%,  Every year, around 116,000 people are diagnosed with gallbladder cancer (GBC), and 85,000 die due to this aggressive disease worldwide ,3.The development of GBC is probably driven by a combination of environmental exposures and genetic predisposition ,5. SymptThe incidence of GBC shows wide geographical and ethnic variation . High-inIn addition to any still unknown risk factors for GBC in high-incidence regions of South America, other potential confounders of the relationship between the individual proportion of Mapuche ancestry and GBC risk include socio-economic status and access to prophylactic cholecystectomy, multiparity in women, chronic inflammation, and lifestyle choices such as smoking and alcohol consumption ,6,15,16.In response to the high GBC mortality rates and lack of treatment options, but based on weak scientific evidence, the Chilean government is implementing a GBC prevention program that provides financial support to gallstone patients for prophylactic cholecystectomy . PrioritThe upper part of To assess the confounding-free impact of Mapuche ancestry on GBC risk, we relied on the informativeness for assignment measure  to presen = 28), Aymara (n = 63), European , and African  individuals relying on the informativeness for assignment measure yielded 21,854 candidate markers. Of these, 985 genetic variants were robustly associated (p < 5 × 10−8) with the individual proportion of Mapuche ancestry, which was previously estimated in a dataset with genome-wide genotype data from 1861 genetically admixed Chileans. Exclusion of markers associated with GBC or established GBC risk factors (PheWAS p < 5 × 10−8) complemented with LD pruning (r2 < 0.01) resulted in 429 instrumental variables being preliminarily retained for the subsequent MR analyses. Radial MR, based on summary statistics of the association between the instrumental variables and the proportion of Mapuche ancestry (sample I: 1861 admixed Chileans) and that between the instrumental variables and GBC status (sample II: 412 Chilean GBC patient-control pairs), detected 33 outlying instruments. The subsequent MR analysis of the association Mapuche ancestry → GBC was therefore based on 396 instrumental variables, which explained 13.2% of the variance in the proportion of Mapuche ancestry (F-statistic = 284.01).The flowchart in the lower part of p = 0.99) and no horizontal pleiotropy  and weighted median estimates  were consistent with IVW estimates . Steiger filtering and more stringent thresholds for LD pruning (r2 < 10−3) and PheWAS (p < 5 × 10−6) resulted in overlapping 95% CIs of the estimated effect sizes  Cochran’s Q statistic stimates . As descct sizes .p = 0.95) nor horizontal pleiotropy  to investigate the potential mediating effects of gallstone disease and BMI on the link between Mapuche ancestry and GBC. For a two-step MR mediation analysis, we first assessed the unconfounded effect of Mapuche ancestry on gallstone disease. Neither heterogeneity among instrumental variables (Q statistic  × 10−21 . These rp = 0.57). In agreement with the results of two-step MR, MVMR revealed a causal effect of gallstones on GBC risk  but no concurrent causal effect of Mapuche ancestry on GBC risk , suggesting complete mediation of the association between Mapuche ancestry and GBC risk by gallstones.Since the proportion of Mapuche ancestry showed a confounding-free effect on gallstone disease in the present study and a causal effect of gallstones on GBC risk has already been demonstrated for Chileans, the two-step MR analysis suggests that gallstones mediate the association between Mapuche ancestry and GBC risk. To validate this hypothesis, we also performed MVMR. The instrumental variables used for MVMR were weak , but horizontal pleiotropy was not found  nor horizontal pleiotropy  and weighted median estimates , and the variance in the proportion of Mapuche ancestry explained by the instrumental variables was relatively high. It should be noted that, in order to be able to rule out potential confounding by instrumental variable analysis, we did not directly investigate the association between individual proportions of Mapuche ancestry  and GBC risk but rather between genetic variants strongly associated with the individual proportion of Mapuche ancestry (13.2% explained variance) and GBC risk. In addition to low statistical power, another common limitation of MR studies is pleiotropy. First-order inverse variance weights keep the type I error rate under the causal null, and we calculated Cochran’s Q statistic using first-order weights to detect heterogeneity, which often reflects pleiotropy. We also visually inspected scatter and funnel plots, performed MR-Egger regression to detect potential bias attributable to horizontal pleiotropy, used radial MR to detect outlying instruments, and excluded instrumental variables associated with GBC and GBC risk factors not investigated in our study.Previous studies have demonstrated the importance of subdividing indigenous American ancestry into its main subcomponents; in the case of admixed Chileans, the two major indigenous American subcomponents are Mapuche and Aymara ancestry . While cBeyond their practical relevance for GBC prevention, the results presented here may also be useful for designing future studies. On the one hand, the evidence of a putatively causal effect of Mapuche ancestry on GBC risk underpins the potential of admixture mapping to identify novel GBC susceptibility variants, possibly in combination with subsequent association testing (note that the genome-wide significance level is much higher for admixture mapping than for association mapping) . For exaPatient and public involvement: A representative of the Chilean Foundation of Gastrointestinal Cancer Patients  reviewed the informed consent forms and is a permanent member of the External Advisory Board of the European-Latin American Consortium towards Eradication of Preventable Gallbladder Cancer—EULAT Eradicate GBC , which meets annually to discuss the project objectives, the progress of the project, and the relevance of the project results to patients. The EULAT Eradicate GBC dissemination videos are also available in Mapudungun, the language of the Mapuche people . Our study did not include Mapuche individuals directly, but genetically admixed Chileans who consented to the use of their genetic data for the present investigation. The manuscript was reviewed by an expert in Mapuche culture from the Universidad de la Frontera in Temuco, Chile. This study spans genetics, epidemiology, public health, and anthropology. Interdisciplinary collaborations are essential to advance our understanding of complex diseases and improve current prevention, early detection, and treatment [https://www.klinikum.uni-heidelberg.de/medizinische-biometrie/forschung/arbeitsgruppen/statistical-genetics/research-training, accessed on 3 August 2023) to discuss how to involve communities and individuals in the design and implementation of genetic research projects and improve the dissemination of study results.reatment ,29. We rStudy participants: The Chilean genome is a mixture of chromosomal segments from two major indigenous American peoples, the Aymara in the north and the Mapuche in the south of the country; Europeans; and, to a lesser extent, Africans [Africans ,30,31. TAfricans  and 28 MAfricans ,33, as wAfricans .Sample I for two-sample MR analyses of the associations Mapuche ancestry → GBC, Mapuche ancestry → gallstone disease and Mapuche ancestry → BMI consisted of 1861 Chileans from the Consortium for the Analysis of the Diversity and Evolution of Latin America (CANDELA) . Sample Preselection of markers of Mapuche ancestry: To preselect genetic variants robustly associated with our exposure of interest, the individual proportion of Mapuche ancestry, we first chose markers of Mapuche ancestry based on the informativeness for assignment measure nI [j = 1, …, N possible alleles in i = 1, …, K subpopulations, we calculated:jp denotes the average frequency of allele j in all K subpopulations, ijp the average frequency of allele j in subpopulation i, Q the (random) assignment of an individual to a subpopulation, and J the (random) genotype of one of the two alleles of an individual. nI conditioning on Q and J is a general approach to quantifying the amount of information that multi-allelic markers provide about individual ancestry composition. We used the reference panels described above to estimate nI for each of the 43,625 genetic variants available for all individuals in this study, considering two subpopulations at a time . We selected the 10,000 genetic variants with the highest nI for each comparison and retained the genetic variants that were present in at least one comparison for the subsequent analyses.asure In . For eacSelection of instrumental variables: MR relies on the random assortment of genetic variants during meiosis, which mimics a randomized trial and thus circumvents potential confounding. Individual proportions of genetic ancestry are fixed at birth, but the alleles of the ancestry-informative markers used as instrumental variables in this study were randomly assigned at meiosis. To ensure that the preselected ancestry-informative markers were valid instrumental variables fulfilling the first MR assumption (relevance), we used the software ADMIXTURE (version 1.3) for supervised estimation of the individual proportions of Mapuche ancestry in sample I, relying on the reference panel of Aymara, Mapuche, European, and African individuals [p < 5 × 10−8) with the estimated proportion of Mapuche ancestry, adjusted for age and sex. The second MR assumption (independence) was assessed by a phenome-wide association study (PheWAS), excluding genetic variants associated (p < 5 × 10−8) with GBC risk or potential confounders  in MR-Base [β) and the minor allele frequency (MAF) to calculate the variance in Mapuche ancestry proportion explained by each variant [ividuals , and ret MR-Base . We then variant  using th2 > 0.01) with other variants that explained a larger variance. The explained variance, Ins, and subpopulation allele frequencies for the 50 genetic variants that explained the greatest variance in Mapuche ancestry proportion are shown in We determined the pairwise linkage disequilibrium between genetic variants using the R package ‘genetics’  in samplIn an attempt to fulfill the third MR assumption (exclusion restriction), radial MR was applied separately for each investigated outcome to detect and subsequently exclude outlying and influential genetic variants in combination with further techniques described in the next section.Two-sample MR, sensitivity, and mediation analyses (two-step and multivariable MR): For the previously selected instrumental variables, we calculated summary statistics of genetic association with the individual proportion of Mapuche ancestry in sample I and summary statistics of genetic association with GBC, gallstones, and BMI in the respective sample II. We fitted linear (proportion of Mapuche ancestry and BMI) or logistic  regression models, assuming an additive genotype model and considering age and sex as covariates. We then used the summary statistics to examine the association between Mapuche ancestry and GBC risk using two-sample MR as the primary analysis. To guarantee instrumental validity, we also (1) visually inspected the funnel and scatter plots of summary statistics to detect weak instrument bias; (2) calculated Cochran’s Q statistic using first-order inverse variance weights to detect heterogeneity, which indicates a possible violation of the instrumental variable or modeling assumptions, of which pleiotropy is a likely major cause; and (3) used the p-value for a non-zero MR-Egger intercept to assess horizontal pleiotropy. Our primary objective was to identify statistical causal effects, which require weaker MR assumptions than estimation of their magnitude, and we tested causality based on random-effect inverse variance-weighted (IVW) p-values. As a secondary objective, we estimated the causal effect sizes and assessed their robustness by comparing IVW, weighted median, and MR-Egger regression parameter estimates .2 < 10−3) and PheWAS (p < 5 × 10−6).Population stratification is particularly relevant in MR studies of genetically admixed individuals. To deal with potential stratification, association statistics are typically adjusted for the main principal components of genetic variability. However, the individual proportion of Mapuche ancestry was the exposure of interest in this study of Chileans, who show continuous gradients of ancestry. To check the sensitivity of MR results to population stratification, we estimated the genetic principal components in samples I and II using the eigenstrat function , examineSince a causal effect of gallstones and BMI on GBC risk has recently been reported , we inveMR analyses were conducted using the R version of MR-Base, which provides convenient tools for the harmonization of the association statistics, including standardization of the effect alleles and removal of problematic palindromic genetic variants . The R pIn summary, we assessed the confounding-free relationship between the individual proportion of indigenous Chilean Mapuche ancestry and GBC risk using ancestry-informative markers as instrumental variables. To our knowledge, this is the first MR study that considers the proportion of genetic ancestry as the exposure of interest. It is important to keep in mind that the study was based on genetically admixed Chileans who showed continuous gradients of Mapuche ancestry. Since evidence of an unconfounded effect of ancestry proportion on disease development is generally more relevant than an observational correlation, the present findings provide more refined information on the potential of accounting for ethnic differences (in this case Mapuche ancestry) in disease prevention (in this case GBC). Individual ancestry proportions are not a modifiable exposure, but individuals with a high proportion of Mapuche ancestry could be prioritized for gallstone screening and prophylactic cholecystectomy—after extensive complementary research into potential side effects and cost-effectiveness. Future admixture mapping studies could also benefit from the methodology applied in the present investigation to test the confounding-free effect of genetic ancestry on disease outcomes. From a more applied point of view, instrumental variable results suggested a putatively causal effect of Mapuche ancestry proportion on GBC risk, most likely mediated by gallstones, with direct implications for the development of more efficient GBC prevention strategies.


To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.The online version contains supplementary material available at 10.1186/s40104-023-00876-7. DGAT1 determined the variation of milk production traits [Cattle are commonly raised as livestock animals for meat and milk. In the recent decade, thousands of candidate variants and genes for agronomic traits have been detected by genome-wide associated studies (GWAS) in cattle . For exan traits . Howevern traits , and hown traits –6. So fan traits –9, muscln traits , and Neln traits , and livn traits . Howevern traits . Using sn traits –16, TWASn traits . MR is an traits , which hn traits , 20.longissimus dorsi muscle is more tender, contains more intramuscular fat, and has better flavor, which is the most valuable part of beef. Backfat thickness is an important economic trait in beef cattle. Backfat thickness can affect both yield [longissimus dorsi muscle, backfat and liver, and their roles in the mechanisms underlying GWAS of agronomic traits in beef cattle, remain unknown.Although the cattle Genotype-Tissue Expression (cGTEx) project released highly valuable multi-tissue eQTLs by analyzing publically available RNA-sequencing data , the biath yield  and flavth yield . The livth yield . Howeverlongissimus dorsi muscle, backfat, and liver) using 356 samples from 120 cattle .One Hundred and Twenty healthy Simmental beef cattle born between 2017 and 2018 in Wulagai (Inner Mongolia Autonomous Region in China) were selected for sampling. The yearling steers were delivered to a contract feeder where they were fed a typical feedlot diet consisting of corn, protein, vitamins, and minerals until they reached an average age of 24 months. The 120 blood samples were collected before they were taken to the slaughter room. To ensure consistency of sampling location for each tissue, we collected backfat (subcutaneous adipose) and longissimus dorsi, 120 liver, and 116 backfat (4 failures). The transcriptome libraries were sequenced using Illumina 150 bp paired-end strategy libraries. Quality trimming and adaptor removal of the Illumina reads were carried out using Cutadapt v2.8 [Total RNA was extracted from muscle, liver, and adipose tissue using the Trizol method according to the manufacturer’s instructions. We successfully generated 356 transcriptome libraries from 120 apt v2.8  and Trimapt v2.8 . After qapt v2.8 . We usedapt v2.8 . The genapt v2.8 . All exphttps://www.ebi.ac.uk/ena/browser/view/ERZ1738264 [R-squared (DR2) < 0.8. After quality control (same as above), 8,221,244 autosomal variants were obtained for GWAS and eQTL mapping. The average DR2 of the imputed variants was 0.92. Before performing GWAS, the phenotype was adjusted by year, sex, age, and the first two principal components (PCs) of the genotype generated by PLINK and normalized by rank-transformation using the transform function in GenABEL [y is adjusted phenotype; X is a vector of genotypes of a variant at the locus tested; β is the effect size of the variant; u is a vector of random polygenic effects ~ N  analysis was implemented in GCTA software [P-value < 5 × 10−8 as significance thresholds of GWAS for all traits. We compared GWAS results with Cattle QTLdb  [The phenotype and genotype data of 21 traits were collected from 2,622 individuals over the past decade, including our newly collected data from the above 120 cattle. The summary of phenotype records is shown in Table SINK 1.90 . After qINK 1.90 . The refZ1738264 . We remo GenABEL . We perfsoftware . We usedcis-eQTLs mapping using fastQTL [cis-eQTLs. We applied the nominal P-value threshold that corresponds to false discovery rate (FDR) ≤ 0.05 for each gene. To identify independent secondary signals, we treated the most significant corresponding eVariant as a covariate. We repeated cis-eQTL mapping until no additional independent signals were detected. The genotype, expression and covariates were used to map trans-eQTLs using the MatrixQTL’s linear model [P-values were adjusted by multiple testing using Benjamini–Hochberg (BH) method to obtain FDR. The eQTLs were annotated in genomic regions using the Variant Effect Predictor [cis-eQTLs in external data sets, we compared the eQTL results with the cattle GTEx project's eQTL summary data, which included 27 tissues and cell types [1 statistic using the qvalue R package [The genes with expression ≥ 0.1 TPM in ≥ 20% of samples were obtained and their values were normalized across samples by the inverse normal transformation. To control for population effects, the first two PCs of genotype were included in eQTL analyses. We estimated latent covariates for gene expression levels for each tissue using the probabilistic estimation of expression residuals (PEER) . We used fastQTL . The SNPar model . The P-vredictor . Chromatredictor . Enrichmredictor . KEGG anredictor . To evalll types . The rep package . The gen package . The eGer2 > 0.25) among SNPs. The priors were estimated by TORUS based on the distances of variants to the TSS of target genes [We conducted fine-mapping of eQTLs for each gene using priors by deterministic approximation of posteriors (DAP) , which cet genes . Any varet genes , which icis-SNP heritability . Circos software was used to plot the multi-omics results [Colocalization between GWAS signals and eQTLs was performed using the Coloc R package . All var results .P < 5 CARD11 (caspase recruitment domain family member 11) was associated with six carcass traits. We observed that 1,372 associations were newly identified in our analysis, while 208 associations were previously reported in the cattle QTLdb [We analyzed the GWAS between imputed whole-genome variants and 21 agronomic traits using a mixed model approach  was 1,952, 3,091, and 2,950 for adipose, liver, and muscle, respectively , CPLTM1L (CLPTM1 Like) and COQ4 (coenzyme Q4) in muscle  were examined to determine whether they exhibited consistent directionality in tissues in which they had not yet been identified as eQTLs. With decreasing P-value criteria, we observed a progressive decrease in consistency for the second tissue from 100% to near 50%  that had near-threshold P-values in one tissue (P < 0.001) and were significant in another tissue demonstrated 95% consistency, suggesting that even looser cis-eQTL discovery thresholds would still yield additional significant eQTLs. We observed an improvement in cis-eQTL discovery using multi-tissue eQTL mapping. The increase rates of eGene discovery were 61.5%, 28.6%, and 36.9% for adipose, liver, and muscle, respectively  and bta-mir-147, as evidenced by the colocalization probability > 0.95. FERMT2 (FERM domain containing kindlin 2) had colocalization probabilities < 0.12 with C10H15orf48 and bta-mir-147, leading us to believe that they were regulated by their distinct genetic signals  in muscle was associated with daily weight gain  in liver was associated with backfat thickness  were colocalized with GWAS signals of stature. The eQTLs of NDUFS3 (NADH: ubiquinone oxidoreductase core subunit S3) were associated with PH and rib eye area. The eQTLs of LTF (lactoferrin) were associated with somatic cell score. The eQTLs of KIFC2 (kinesin family member C2) were detected colocalized with milk fat  is a cholesterol transfer gene. We found the eQTLs of GRAMD1C were colocalized with the signals of rear thigh circumference  and ZNF613 (zinc finger protein 613) were associated with body conformation traits. In muscle tissue, the eQTLs of TIGAR (TP53-induced glycolysis and apoptosis regulator) were colocalized with rump width, stature, strength, body depth, sire calving ease, and productive life  were associated with daily weight gain and rib eye area, respectively. In general, the use of GWAS, eQTL mapping, TWAS, colocalization, and SMR analysis improved our ability to identify potentially causal genes and comprehend the genetic basis of complex traits in cattle , eQTLs showed > 95% consistency, suggesting that even looser thresholds can identify additional significant eQTLs. Most eQTLs are tissue-specific, implying that the genetic mechanisms of gene expression are different and complex in different tissues. The cis-eQTLs are enriched for regulatory elements of promoter and enhancer, probably reflecting that only a tiny percentage of variants in these extended regions have true regulatory effects [trans-eQTL in functional domains is limited, reflecting the low power of trans-eQTL discovery due to the small sample size. Similar to human GTEx V8, the cis-eQTLs mediate about one-third of trans-eQTLs [We observed a high density of signals near the respective gene's TSS, which is confirmed by another study . Consist tissues . Even ne effects . In contns-eQTLs . The eQTns-eQTLs , 61. We longissimus dorsi muscle and backfat. The second reason is the breed difference: cGTEx used multiple breeds to perform the eQTL mapping, and most cattle were Holsteins (44.5%), while Simmental represented only 1% [Compared with the cGTEx results, we detected more than one-third of the eGenes were newly identified, especially for muscle and adipose. One reason for this is the tissue difference: the cGTEx used a variety of muscle and adipose types and the results were not specific for the  only 1% . The thicis-eQTL offer a collection of hundreds of functional variants that are most likely causative for eGenes. This TWAS strategy reveals hundreds of genes for which changes in genetically predicted expression are associated with 11 agronomic traits. IGFBP5 is a crucial focal regulator of the local action of IGF-I [IGFBP5 was detected to be related to daily weight gain. LPIN2 played a significant part in regulating fatty acid metabolism at various levels that were associated with type 2 diabetes and fat distribution [LPIN2 was related to backfat thickness. The tubulin alpha-1D chain (TUBA1D) is involved in cytoskeletal organization and cell motility [UNC45B) is involved in myocyte maturation and striation formation [TUBA1D and UNC45B in muscle was related to rib-eye weight.Our results provide important insights into the pleiotropy of variants. The eVariants regulate multiple genes, and this phenomenon occurs clustered in distinct regions of the cattle genome. The largest of these clusters contains at least 47 eGenes of the MHC region, which is essential for immunological response . This clof IGF-I  by sequeof IGF-I , 66. IGFribution , 68. We motility . Unc-45 ormation . The expcis-eQTLs in three tissues helps identify causal genes for hitherto unsolved association signals. Multiple GWAS signals were detected to colocalize with cis-eQTLs in three tissues. The eQTLs of NADSYN1, NDUFS3, LTF and KIFC2 in liver were related to height, PH and Rib eye area, somatic cell score and milk fat, respectively. NADSYN1 is related to calcium metabolism [NDUFS3 was correlated with the juiciness and flavor of beef in a previous study [LTF is a milk glycoprotein favorably associated with the immune system of cows [KIFC2 is involved in vesicle-mediated transport [GRAMD1C in adipose were related to rear thigh circumference. GRAMD1C is a cholesterol transfer gene that contributes to cholesterol transfer activity [TMTC2 and ZNF613 were GWAS fine-mapped candidate genes for body conformation traits in a previous study [TMTC2 and ZNF613, which affects body conformation traits.Colocalization of GWAS signals and tabolism , 72. NDUus study . LTF is  of cows . KIFC2 iransport . The eQTactivity . Both TMus study . In thisTIGAR can improve mitochondrial functions and reduce muscle cell autophagy [TIGAR were related to stature, strength, and reproductive traits. NDUFS3 contributes to energy metabolism of transformed cells [NDUFS3 gene in muscle would induce myopathy phenotype in mice [L3HYPDH was a candidate gene associated with the rib eye area [longissimus dorsi muscle, backfat and liver, provides a resource for resolving additional genetic association signals emerging from ongoing extensive efforts to improve production and quality traits of cattle, for which these three tissue types are significant.utophagy , 78, whiutophagy , 80. We ed cells . Deletio in mice . L3HYPDHeye area , which wKIFC2, TIGAR and GRAMD1C. Moreover, these new candidate genes or causal DNA variants will help improve genomic prediction accuracy and the benefit of genetic improvement programs in beef cattle [Interpretation of the genetic mechanism of complex traits based on molecular phenotype of primary tissues is relatively late in beef cattle. We demonstrate an efficient multi-omics data strategy for agronomic traits using three primary tissues of beef cattle, which can serve as a valuable approach that moves from fundamental discovery to decipher genetic mechanisms of complex traits of cattle. By integrating eQTL and GWAS data, we constructed a molecular QTL map in cattle that helps to resolve genetic association signals by detecting candidate genes such as f cattle , 85.Additional file 1: Fig. S1. The sample size of primary tissue in beef cattle. The number of individuals with data for each tissue. The 770K SNP arrays of 120 individuals were imputed to whole-genome SNPs using the 1,847 multiple-breed cattle reference panel.Additional file 2: Fig. S2. RNA sequencing information. a The proportion of the mapped reads of RNA sequencing in the genome region. Most Reads were mapped to the coding areas. b The clean reads and mapping rate of 356 samples. The average clean-read number and mapping rate were 45.6 million and 97%, respectively.Additional file 3: Fig. S3. Hierarchical clustering of 356 samples. The clustering of 356 samples shows that the tissue is the most differentiated among all samples. The batch factor could affect several samples. The sample difference caused by age was limited.Additional file 4: Fig. S4. Characterization of PEER factors and PCA of genotypes. a–c Factor weight variances are computed up to 30 factors in three tissues. Factor weight variances were stable for all three tissues when the number of PEER factors reaches five. d PCA of genotype data of 120 cattle.Additional file 5: Fig. S5. Biological covariates analysis. a–c Percentage of variance explained by covariates in each of the three tissues. Data are presented as a percentage (%) of the total variance explained. d–f Pairwise correlation between the covariates. Red and white indicate high and low correlation, respectively.Additional file 6: Fig. S6. Correlation of gene co-expression modules with agronomic traits in liver and adipose. The modules were denoted by different colors. Correlation of module eigengene with each agronomic trait displayed in the corresponding box . The color of each box represents a positive correlation (red) or a negative correlation (blue).Additional file 7: Fig. S7. The summary of cis-eQTL and trans-eQTL results.Additional file 8: Fig. S8. Manhattan plot of cis-eQTLs in liver and backfat. a Manhattan plot showing the nominal P-value (y-axis) for all cis-eQTLs in the liver. b Manhattan plot of backfat cis-eQTLs.Additional file 9: Fig. S9. Dot plot showing the locations, P-value and effect sizes for all significant trans-eQTL in liver and adipose. a Locations of trans-eQTL in liver. Variants and gene positions are shown on the x-axis and y-axis, respectively. Each dot was a significant trans-eQTLs (FDR < 0.05). The size of each dot represents the -log10 scaled P-values. The color of each dot represents the direction of slope effect. b Locations of trans-eQTL in adipose (backfat).Additional file 10: Fig. S10.P-value distributions of most significant cis-eQTLs per gene relative to TSS in three tissues. The scatter plots show the -log10  of the most significant SNP per gene for eGenes and their distance to the TSS (in kilobases).Additional file 11: Fig. S11. The independent eQTLs. a The number of independent signals in three tissues. b The primary signals (golden) and the secondary signals (blue) relative to TSS are shown using point plot (left). The right plot is a boxplot of the absolute distance of primary and secondary signals to the TSS. The Wilcoxon test is used to compute significance. c The distance to TSS for adipose tissue.Additional file 12: Fig. S12. The characters of eQTLs. a The SNP-heritability compares between eGene and non-eGene. b–d The most significant P-value of cis-eQTLs per gene with function of their heritability for adipose, liver and muscle. e The genomic location enrichment of the top significant cis-eQTLs (red dot) and all significant cis-eQTLs (blue dot) in three tissues. f The gene expression comparison between eGenes and non-eGenes.Additional file 13: Fig. S13.Cis-eQTLs across tissues. a Venn diagram of cis-eQTLs pairs across three tissues. b Venn diagram of eVariants across three tissues. c The effect compares specific and shared cis-eQTLs. d The pathway analysis of common tissue is shared by three tissues. e The P-value distribution of the shared eQTL of tissue 1 in tissue 2. f The eQTL effect size of the tissue-2 (y-axis) increases with the significance of P-value (x-axis) for both positive and negative effect tissue-1 eQTLs. g The directionality consistency of the shared eQTL increases with the significance of the P-value (x-axis) for both positive and negative effect tissue-1 eQTLs.Additional file 14: Fig. S14. The correlation of eQTL slope effect sizes between tissues. a Correlation between muscle and adipose. b Correlation between muscle and liver. c Correlation between liver and adipose. d Correlation of common eQTL effect sizes across all three tissues using three dimensions. e Correlation matrix plot of common eQTL effect sizes across three tissues.Additional file 15: Fig. S15. The pleiotropic character of eQTLs. a The circos plot of the trans-eQTL pleiotropic example. b The number of cis-eGenes regulated by eVariant. c The number of trans-eGenes regulated by eVariant. d The distribution of eQTL clusters in the cattle genome. The green, yellow and red highlights represent muscle, adipose and liver, respectively. e Data showing the colocalization posterior probabilities (H4) for all 47 eGenes located in cluster 23:27612341-31472505 in adipose. H4 was colored in red with increasing intensity.Additional file 16: Fig. S16. The fine-mapping results of cis-eQTLs by DAP-g. a–c The posterior probability distribution of cis-eQTL fine mapping using DAP-g in adipose, liver, and muscle. d–f Gene enrichment for these genes with fine-mapping variants in adipose, liver, and muscle.Additional file 17: Fig. S17. Manhattan plot of TWAS between liver gene expression and backfat thickness. Manhattan plot showing the genomic position (x-axis) and the P-value for association (y-axis).Additional file 18: Fig. S18. Manhattan plot showing the colocalization results (H4 > 0.8) between eQTL and GWAS signals. The x-axis is the P-value of lead eQTLs (points) across traits (colors) in adipose.Additional file 19: Fig. S19. Manhattan plot showing the colocalization results (H4 > 0.8) between eQTL and GWAS signals. The x-axis is the P-value of lead eQTLs (points) across traits (colors) in liver.Additional file 20: Fig. S20. An example of GWAS–eQTL colocalization for KIFC2 in the liver. The colors of variants are based on their LD with the most significant variant.Additional file 21: Table S1. The information of GWAS results for 21 agronomic traits.Additional file 22: Table S2. The information of 356 RNA-seq samples and their mapping results.Additional file 23: Table S3. The significant modules-trait pairs in three tissues, and KEEG annotation of significant module genes.Additional file 24: Table S4. Overview of cis-eQTL clusters and posterior probability for colocalization of genetic signals underlying eGenes of the same cluster in three tissues.Additional file 25: Table S5. Overview of fine-mapping results in three tissues.Additional file 26: Table S6. TWAS results for 11 agronomic traits in three tissues.Additional file 27: Table S7. The results of GWAS-eQTL colocalization by Coloc or SMR in three tissues.


CT1 and CT2) measured in purebred broiler chickens. The MT models also included information on indicator traits measured in vivo [Growth and feed efficiency trait (FE)]. We proposed an approach termed (quasi) multitask SVR (QMTSVR), with hyperparameter optimization performed via genetic algorithm. ST and MT Bayesian shrinkage and variable selection models  were employed as benchmarks. MT models were trained using 2 validation designs (CV1 and CV2), which differ if the information on secondary traits is available in the testing set. Models’ predictive ability was assessed with prediction accuracy , standardized root-mean-squared error (RMSE*), and inflation factor (b). To account for potential bias in CV2-style predictions, we also computed a parametric estimate of accuracy . Nonetheless, the higher predictive accuracy of QMTSVR over MTGBLUP and MTBC was replicated across accuracy metrics, besides the similar performance between the proposed method and the MTRKHS model. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike–slab multivariate priors.This study investigates nonlinear kernels for multitrait (MT) genomic prediction using support vector regression (SVR) models. We assessed the predictive ability delivered by single-trait (ST) and MT models for 2 carcass traits ( Genomic selection (GS) is a suitable alternative to the traditional pedigree-based genetic evaluation, generally providing higher prediction accuracy for young animals and contributing to faster genetic progress in different livestock species  methods have been viewed as capable of dealing with complex high-dimensional data from modern genotyping technologies  is smaller than p (number of markers).A popular class of ML procedures applied to whole-genome prediction is the so-called kernel-based regression methods, e.g. reproducing kernel Hilbert space (RKHS) regression and support vector regression (SVR). These methods rely on kernel functions for building a covariance structure among target observations based on the similarity (or dissimilarity) of their predictor variables  is then optimized via a stochastic evolutionary algorithm. A case study with carcass traits in commercial broilers is presented.The objective of this study was to propose and investigate the performance of nonlinear kernels for MT-GP using SVR models. The high computational burden for hyperparameter fine-tuning is alleviated by considering trait-common values for the model bias, regularization parameter, and epsilon constant, leading to an optimization problem that can be handled with standard quadratic programming. The ith individual and p-dimensional row vector of single nucleotide polymorphisms (SNPs), coded as 0, 1, and 2, for genotypes AA, AB, and BB, respectively. This data set can be represented in matrix form as Let e is the vector of random errors.in which A common cost function for SVR optimization is the equation  are founequation :C is a positive regularization hyperparameter that controls the penalty imposed on residuals larger than the epsilon margin (ε), and N × 1 vector of ones. The where equation  yields tN × 1 and N × 1 vector of ones).where It can be shown that minimizing the primal objective function in equation  is equivN × 1 vectors of positive Lagrange multipliers and in which t inequality constraints at once  algorithm which solves  at once . Althougt refers to the number of traits, k, t-dimensional vector of ones, t traits standardized to the same scale, and Q is a block symmetric matrix, partitioned into t2 blocks of dimension N × N, each. We propose a MT scaled RBF kernel, as follows:where D is an N × N Euclidean distance matrix (EDM) for the markers’ vectors, considering all genotyped individuals for at least 1 of the traits analyzed, p is some scalar representing the number of predictor variables (e.g. number of SNPs), k and where Using the dual formulation in equation , the funi and every column j in the matrix Q. In equation (5), typically only a subset of data points will have nonnull Lagrange multipliers, the so-called support vectors (SVs). The predicted value C), and the epsilon threshold (t − 1 correlated traits. This model has C and k and t[t + 1]/2.in which quations  and (5);D with a weighted Euclidean distance matrix (WD), with elements computed as:The block matrix equation  can be fi and j in the N × N block, computed for traits k and k = 1, 2, … t), i and j at locus l, p is the total number of loci, and locus l considering the traits k and t[t + 1]/2 different WD must be computed for plugging into Q and the constant p−1 is replaced with where l as l, lth locus on traits k and G; We computed the weighting factor for locus atrix G;  were alsWe compared the predictive ability of the proposed approach with conventional single-trait (ST) and MT genomic prediction models; these methods are briefly described below.The ST GBLUP can be described as follows:N × 1 vector of observations, N × 1 vector of ones, μ is the model intercept, Z is a design matrix connecting observations to the genomic breeding values (DGVs), N × 1 vector of residual terms.where p.Under a Bayesian paradigm, the following priors are assumed for the model unknowns: p matrix , σu2 is The MT counterpart for the GBLUP has the following form:Y is a matrix of response variables of dimension N × t, N × t matrix of predicted DGVs, and where The prior distributions for the model unknowns are assumed independent and can be represented as follows :pvariances, respectively; i = u and e).G matrix with the same nonlinear RBF kernel (K) used to fit the SVR method. Accordingly, prior distributions of animal random effects are given by p(u) ∼ N is the replacement of the The general form of a Bayesian regression model is represented as follows:N × p matrix of marker genotypes and p × 1 vector of allele substitution effects. The BayesC (BC) assumes a spike–slab prior for all marker effects, which refers to a mixture distribution comprising a point of mass at zero, with probability π, and a univariate normal distribution with probability 1 − π  and the remaining terms are defined as in equation variance matrix for the markers’ effects, with where equation . One posribution .G) as the kernel instead of the RBF kernel (K).Lastly, we also assessed the prediction performance of the ST SVR with a linear kernel [linear SVR (SVR_Lin)]. The optimization of this method is performed as represented in equation . The onlCT1 and CT2) and 2 in vivo traits [Growth and feed efficiency trait (FE)]. Before genomic analyses, pedigree-based breeding values (EBV) were estimated for all traits using all available observations . The EBV for CT1, CT2, and FE were estimated with a 2-trait animal model, including Growth as a regressor trait for attenuating selection bias. The pedigree-based analysis considered the effects of contemporary group (GC) and sex (only for Growth and FE) as fixed. The CG combined the effects of the hatch, source, and MG. The (co)variance components of the pedigree-based analyses were estimated using the AIREML algorithm, available in the BLUPF90 suite programs  and comprised phenotypic observations from broiler purebred lines, bred in 23 overlapping mating generations . The phenotypic data included 2 carcass traits  were used for fine-tuning and training the models, whereas the data from MG20 to MG23  were held out as the testing set. In the ST prediction, only observations for the target trait available in the training set were used for fitting the models. For the MT analyses, each target trait (CT1 and CT2) was analyzed with an indicator trait using a 2-trait model (i.e. CT1 × Growth and CT2 × FE).The predictive ability of the ST and MT models was assessed by splitting the data into training and testing data sets according to the chronological information of MG . The animals from MG1 to MG19 (As discussed in CT1 or CT2) in the testing set, b), i.e. the slope of the linear regression of realized observations on predicted values in the testing set. Values of b above or below 1 represent upward- or downward-biased predictions, respectively.The first criterion used to assess the models’ predictive ability was the corrected accuracy (ACC), defined as Because both the focal (election . To accoequation ) to the C, ɛ, We used a genetic algorithm (GA) to perform a heuristic search over the QMTSVR 6D parameter space , and between 0.1 and 0.3 for The GA aimed to maximize the Pearson correlation between observed and predicted responses measured from the 17th to 19th MG in the training data. Therefore, during the hyperparameter fine-tuning, these observations (17th to 19th MG) were omitted within the GA optimization. After defining the best model hyperparameter configuration for each trait and training strategy, the SVR models were retrained with the full training data (MG1–MG19) for predicting the observations of the testing set (from MG20 to MG23). The hyperparameter search in the GA algorithm was restricted between the following intervals: 0.1 and 4 for qmtsvr package  were fitted using the BGLR package , 2022. F package , which uCT1, CT2, Growth, and FE, respectively. Heritability estimates (h2) obtained with the pedigree-based models were high for carcass traits, with values of 0.56 ± 0.01 for CT1 and 0.63 ± 0.03 for CT2. For the indicator traits, heritability estimates were moderate, 0.34 ± 0.01 for Growth and 0.35 ± 0.01 for FE. The genetic and environmental correlations were, respectively, 0.23 and 0.47 between CT1 and Growth, and 0.38 and 0.25 between CT2 and FE.The mean ± standard deviation of the traits analyzed were 26.14 ± 2.46 (%), 1.64 ± 0.41 (%), 4660 ± 495.55 (g), and −16.84 ± 231.53 (g) for Growth (P < 0.05), evidencing that this trait is under selection, as suggested by the density plots (CT1 × Growth) and 0.205 (CT2 × FE). Pearson's correlation among the remaining trait combinations ranged from null to weak (between −0.163 and 0.02ns). Additionally, there was no change in direction and magnitude for the correlation coefficients according to the training and testing samples.ty plots . The assty plots , with PeCT1. Note that the number of necessary generations to get rid of local minima increases as the evolutionary process proceeds. The highest optimization progress for CT1 generally occurred at the early stages of the evolutionary process (up to the 10th generation of the GA), followed by a modest linear increase in the fs afterward , ranging from 0.71 to 0.84 for ACC , 0.80 tob , conside for ACC , from 0.r ACC  compared to models assuming linear effects only  (K) relative to using the linear kernel (G) in the kernel-based regression methods (SVR vs SVR_Lin and GBLUP vs RKHS). On the other hand, predictive ability was rather similar between GBLUP and SVR_Lin, which indicates that using different loss functions (quadratic form in GBLUP vs hinge function in SVR) played a negligible impact on the predictive ability for both traits.When considering only the ST models, kernel methods incorporating nonlinear feature mapping  had the highest ACC and the lowest RMSE, but worse SVR_Lin)  and 7. TCT1 and CT2, respectively. No differences in ACC were found between QMTSVR-CV2 and MTRKHS-CV2  and 0.82 ± 0.03 (QMTSVR-CV1). Similarly, the CT2. Spearman correlations between ACC and the posterior average for CT1 and CT2, respectively. This indicates that despite these 2 metrics being highly associated, model selection may be sensitive to the chosen accuracy metric, especially for CT1. Credible intervals at 95% of probability (CI95%) for the Since CV2-style predictions for the focal trait are prone to the environmental influence of indicator traits , we alson design . TherefoCT1, there was relatively weak evidence that the difference between QMTSVR-CV1 and GBLUP is negative  . Results= 0.319) . MoreoveThe computational time for running the 20,000 Gibbs samples (averaged over different traits and training strategies) in the multivariate kernel regression models (MTGBLUP and MTRKHS) was 43.11 ± 0.19 (min). Conversely, the computational time for the MTBC model was much larger, taking on average 262.75 ± 0.16 min to run the same number of iterations. The computational time required for the GA-based optimization process for the QMTSVR method was somewhat in the middle, needing on average 78.34 ± 8.99 min. This was the time necessary for building the EDMs based on the marker matrix and running the GA over 25 hyperparameter combinations  during 30 generations, totalizing 750 specific models assessed for each trait and training strategy. For the weighted SVR, there is an additional running time for computing the EDM weighting factors, which will vary depending on the method used.N × N kernels) while being capable of exploring nonlinear relationships within and between traits, without any strong assumption for the markers’ effects. Results showed that the proposed method was competitive with conventional MT Bayesian regression models using either Gaussian (i.e. MTGBLUP and MTRKHS) or Gaussian–spike–slab (MTBC) multivariate priors.Since early developments by CT1 and CT2), compared to linear models that assumed only additive effects for the marker effects. Breeding values are represented as linear combinations of allelic substitution effects. Accounting solely for additive inheritance attributed to marker effects generally performs better when this assumption holds (We used deregressed EBVs (DRP) as target variables, which can be considered as smoothed data points with the influence of parent average effects removed  for the MT methods compared to their respective ST versions in both traits, especially for QMTSVR and MTRKHS. This is in line with As observed in other studies (par) mostly for CT1, which presented a higher residual correlation with Growth than that estimated between CT2 and FE. Still, both metrics seem to provide pieces of evidence for better predictive accuracy for the proposed method (QMTSVR) compared to conventional prediction methods such as GBLUP, BC, MTGBLUP, and MTBC, besides a similar performance between QMTSVR and MTRKHS (CT1. We believe the main reasons for this limited superiority observed were as follows: (1) the target traits have high heritability and therefore prediction accuracy is already satisfactory for ST models and (2) genetic correlations between target and secondary traits are weak. We expect that the advantage of the proposed method over the univariate case will become clearer in more favorable scenarios .Nonetheless, because both target and secondary traits are measured on the same individual, predictions for the target trait using a CV2 design can be contaminated by nongenetic factors shared between traits, leading to a possible bias in the estimate of accuracy based on Pearson's correlation (ACC). The extent of this bias will depend on the magnitude and direction of residual covariances between the focal and secondary traits . For thid MTRKHS , and 9. While the findings of this study suggest that QMTSVR outperformed MTGBLUP and MTBC, it is important to note that this superiority may not hold for other traits and species. As animal and plant breeding lacks a universally best genome-enabled prediction method , it is cDespite the plethora of existing methods, kernel-based methods as genome-assisted prediction machines for complex traits have become popular in animal and plant breeding over recent years, mostly due to the attractive feature of higher flexibility for accommodating hidden nonlinear relationships such as epistasis and genome–environment interactions  receive nonzero regression coefficients. Both QMTSVR and MTRKHS are potentially able to map nonlinear relationships by using nonlinear kernels. A major difference between these 2 methods is that the implemented MTRKHS uses the regular scaled RBF kernel, which needs a single bandwidth hyperparameter to control the kernel variance. On the other hand, the QMTSVR uses a MT scaled RBF kernel that assigns A key difference between the QMTSVR and the kernel ridge regression methods  lies in the loss function used to solve the regularized optimization problem. As for SVR and SVR_Lin, the QMTSVR uses a Moreover, in the QMTSVR, the genetic and residual covariance components do not need to be estimated for incorporating MT information in genome-enabled prediction. Our approach might be useful for predicting novel traits with small training sizes based on the additional information provided by correlated traits. However, there is a lack of interpretability in terms of effects captured by the QTMSVR. It is important to note that QMTSVR-predicted values are not construed as estimated breeding values since conditioning predictions on indicator traits alter the variance component accordingly. Hence, predicted values must be interpreted on the scale of the training variables rather than as DGVs. Additionally, ML methods generally need several hyperparameters to be tuned during training, which can be computationally intensive. Here, the burden of fine-tuning was alleviated by adapting some global hyperparameters for all traits, hence reducing the space of possible combinations.A GA was then employed for avoiding an extensive grid search in the hyperparameter space. GA is a metaheuristic approach used to solve nonconvex optimization problems in discrete and continuous systems. The GA uses a population of chromosomes  for exploring the search space in different directions simultaneously. The main features of this algorithm are inspired by natural selection, i.e. the fittest individuals  have higher chances to generate new chromosomes for the next generation. Although GA does not guarantee global optima for the solutions, this nonlinear optimization procedure is very appealing for ML since it is flexible enough to address different complex problems  and can be easily parallelized. Results showed that finding an appropriate hyperparameter configuration with GA for QMTSVR would considerably take less computing time than fitting a Bayesian regression model with multivariate spike–slab priors. The multivariate Bayesian kernel regression models (MTGBLUP and MTRKHS) were the most efficient in terms of computational time. Nevertheless, one must consider that for MTRKHS, the time needed to fine-tune the bandwidth hyperparameter  was ignored, as we set the same value as used for SVR (optimized via GA). Notice that comparisons performed here are only valid for the number of iterations used for the GA and Gibbs sampling algorithms.Similar to what has been proposed for GBLUP (In summary, we introduced a fully nonparametric and nonlinear procedure for integrating MT information into genomic prediction tasks. Preliminary results suggested that QMTSVR has the potential to achieve a competitive or even better performance than the compared multivariate Bayesian shrinkage and variable selection models. The main advantages of QMTSVR are its flexibility for capturing extra variance due to nonlinear effects and the unnecessity of estimating covariance parameters, which generally require a large amount of data and may hamper the joint analysis of many traits with small training samples. This scenario is very common for novel traits that are hard or expensive to measure. QMTSVR uses the standard SMO algorithm for solving constrained optimization equations, which does not require the repeated inversion of dense matrices. Furthermore, the fine-tuning process is performed internally using a GA with a multithread parallelization process.We assessed strategies for extending the SVR method to a MT-GP context. Results showed that the proposed approach is competitive with conventional MT Bayesian regression models using either Gaussian or spike–slab multivariate priors. QMTSVR represents a nonparametric alternative to MT-GP, and it is capable of handling nonadditive effects without adding much computation complexity. Future research performing a comprehensive comparison of the proposed approach with other methods and different traits is encouraged. Genomic prediction of postmortem carcass traits in broilers can be improved when correlated secondary traits measured in vivo are available for target individuals. Nonetheless, differences in accuracy observed between ST and MT models were small, possibly because target traits investigated in this study were highly heritable and presented weak genetic correlations with the indicator traits.jkad109_Supplementary_DataClick here for additional data file.


Novel sources of genetic resistance to tan spot in Australia have been discovered using one-step GWAS and genomic prediction models that accounts for additive and non-additive genetic variation.Pyrenophora tritici-repentis (Ptr) and has been reported to generate up to 50% yield losses under favourable disease conditions. Although farming management practices are available to reduce disease, the most economically sustainable approach is establishing genetic resistance through plant breeding. To further understand the genetic basis for disease resistance, we conducted a phenotypic and genetic analysis study using an international diversity panel of 192 wheat lines from the Maize and Wheat Improvement Centre (CIMMYT), the International Centre for Agriculture in the Dry Areas (ICARDA) and Australian (AUS) wheat research programmes. The panel was evaluated using Australian Ptr isolates in 12 experiments conducted in three Australian locations over two years, with assessment for tan spot symptoms at various plant development stages. Phenotypic modelling indicated high heritability for nearly all tan spot traits with ICARDA lines displaying the greatest average resistance. We then conducted a one-step whole-genome analysis of each trait using a high-density SNP array, revealing a large number of highly significant QTL exhibiting a distinct lack of repeatability across the traits. To better summarise the genetic resistance of the lines, a one-step genomic prediction of each tan spot trait was conducted by combining the additive and non-additive predicted genetic effects of the lines. This revealed multiple CIMMYT lines with broad genetic resistance across the developmental stages of the plant which can be utilised in Australian wheat breeding programmes to improve tan spot disease resistance.Tan spot is a foliar disease in wheat caused by the fungal pathogen The online version contains supplementary material available at 10.1007/s00122-023-04332-y. Triticum aestivum L.) caused by the necrotrophic fungal pathogen Pyrenophora tritici-repentis (Ptr) of the order Pleosporales. It may cause substantial yield losses by decreasing both kernel weight and numbers of grain per head , Ptr ToxA, Ptr ToxB and Ptr ToxC, that interact with host specific sensitivity genes to cause necrosis or chlorosis . Triggering of plant susceptibility through the interaction of the host with any combination of these effectors is known as the inverse gene-for-gene model  panel with varying levels of tan spot resistance consisting of bread wheat lines from the International Maize and Wheat Improvement Centre (CIMMYT), the International Centre for Agriculture in the Dry Areas (ICARDA) and Australia (AUS). These lines were then screened for tan spot severity using Australian Ptr isolates in various controlled environment and field locations around Australia, with disease assessment conducted at various stages of plant development. Experiments were repeated over two consecutive years. In addition, the IWD panel was screened for sensitivity to Ptr ToxA and Ptr ToxB using purified effector proteins in plant bioassays and assessed for necrosis and chlorosis. Unfortunately, screening for Ptr ToxC sensitivity using plant bioassay was not possible in this study as the structure of Ptr ToxC has yet to be elucidated. The IWD panel was then genotyped using the 90 K Illumina iSelect SNP array designed for wheat  lines (AUS-1 to AUS-47), including 23 Australian commercial wheat varieties with varying levels of tan spot resistance, 121 lines from the CIMMYT bread wheat breeding programme (CI-1 to CI-121) and 24 lines from the ICARDA bread wheat breeding programme (IC-1 to IC-24). Full names of each line from each sub-population region are given in Supplementary Table S1.The IWD panel was phenotyped for resistance against a mixture of local isolates at various growth stages and environments in 2015 and 2016 at South Perth, Western Australia ; at Horsham, Victoria ; and at Toowoomba, Queensland . Additionally, the IWD panel was screened for sensitivity to Ptr ToxA and Ptr ToxB at South Perth, Western Australia .For all controlled environment, glasshouse and field trials conducted in this research, experimental designs were generated as spatially optimal row-column designs with layout configurations defined in Table All experimental designs were computationally generated using the model-based optimal design R package odw Butler . To achiA complete description of the protocols relating to the phenotyping experiments conducted in 2015 and 2016 at each location is given in the proceeding sections, and the collated summary of these protocols, as well as information about numerical and analytical aspects of the resulting tan spot severity traits, is given in Table The IWD panel was assessed at the seedling and adult plant stages under controlled environment conditions and at the adult plant stage in an irrigated field nursery in both years. For all trials, an equal mix of the following ten contemporary isolates obtained from the Western Australian Plant Pathology Reference Culture Collection (WAC) was used for inoculation: WAC13611, WAC13614, WAC13769, WAC13651, WAC13767, WAC13768, WAC13770, WAC13772, WAC13774 and WAC13793. Inoculum was prepared as described by Shankar et al. . The con−2 s−1. Plants were fertilised with soluble all-purpose Thrive N/P/K 25:5:8.8  at a concentration of 0.8 g/L and a rate of 60 mL/pot on a weekly basis and with a trace element solution of Liberal BMX (BASF) at a concentration of 0.5 g/L and a rate of 30 ml/pot on a fortnightly basis. At heading (Zadoks 55), flag leaves of individual plants in each pot were inoculated as described above. Fourteen days after this inoculation, percentage leaf area diseased (PLAD) was visually scored on the flag leaves and PLAD values were averaged over the sampled leaves for each pot to ensure a single numerical tan spot symptom severity value was attributed to each experimental unit.For the controlled environment experiments in both years, experimental design blocks were arranged on separate benches. Lines were grown in a controlled environment with 24/22 °C day/night temperatures and 12 h of natural day light. Four seeds per line were sown within each 120 mm diameter pot containing a sand-loam mix with 1 g of Osmocote (slow-release fertiliser). At Zadoks growth stage 12.5 , by spraying flag leaves with the conidial suspension to run-off. High humidity was ensured by watering the site just before inoculation and by using plastic bags secured over PVC rings  to cover individual plots for 48 h after inoculation. Before being used to cover the plots, the plastic bags were misted internally with water. To shade the inoculated plants from direct sunlight, the plastic bags themselves were covered with shade-cloth bags (84–90% cover factor). At 390 °C thermal days (sum of mean daily temperatures) after inoculation, PLAD was scored on the flag leaves of five individual plants selected randomly in each plot and averaged to ensure a single numerical tan spot symptom severity value was attributed to each experimental unit.Field experiments were conducted using methods that mitigated possible confounding effects of plant maturity and height on disease expression at the adult plant stage as described by Shankar et al. . Plots c2 plugs of each cultured isolate were then sub-cultured onto clarified V8 juice agar plates and incubated in darkness at 22 ºC for 5 days, after which the hyphal growth was flattened using a sterile metal rod. Plates were then incubated under white fluorescent and gro-lux lights at 24 °C for 24 h, and then in darkness at 16 °C for 24 h to produce conidia. Inoculum was prepared by scraping conidia from the surface of the plates using a spatula and then suspended in microfiltered sterile water. Two seeds of each line were sown into 5 cm deep pots containing potting mixture, fertiliser and trace elements. Experimental design blocks were arranged across four trays in a 2 by 2 arrangement. The experiments were conducted under natural light at 20 ± 2 ºC, and seedlings were inoculated at the two–three leaf stage (Zadoks 12–13) with the conidial suspension with concentration of ~ 3,500 spores/ml. Inoculated plants were incubated at 95–100% relative humidity in total darkness at 20 ± 1 ºC for 24 h while maintaining the same block structure and row by column layout. Inoculated seedlings were then returned to a glasshouse for 7 days to allow for symptom development. At this point, leaves that had been fully emerged at inoculation were assessed for symptom severity using a 1–9 scale in increments of 1, where 1 was low disease severity and 9 is high disease severity  and septoria tritici blotch (Zymoseptoria tritici) development. Infection was established by spreading approximately 0.5 t/ha of wheat stubble naturally infected with locally occurring Ptr from the previous year during June in both years. The infected stubble was sourced from a block of wheat planted to a variety highly susceptible to yellow spot but resistant to other important diseases. This block was also managed with flutriafol as described above. Also, during the 2016 experiment, the site received a foliar spray of a conidial suspension  of an equal mix of eight virulent isolates . No supplementary irrigation was applied during either year as there was sufficient in-crop rainfall. Disease severity was rated using the 1–9 scale described above. During 2015, assessments were conducted when most lines were at late stem elongation (Zadoks 36) and booting (Zadoks 45), while during 2016, assessments were made when most plants were at early stem elongation (Zadoks 32), late stem elongation to early booting (Zadoks 39–41), late booting (Zadoks 49) and anthesis (Zadoks 61 to 65). Middle leaf layers were randomly assessed in most cases. For assessments made at late anthesis, the top 4 leaf layers were randomly assessed. At each assessment stage, the symptom severity across sampled leaves was averaged to ensure a single tan spot symptom severity value was attributed to each of the experimental units.Field design blocks aligned with pre-watered irrigation field bays and plants were sown in May. Each experimental unit consisted of a 50-cm row in which approximately 20 seeds were sown. Rows were 30 cm apart. At planting plots were fertilised with mono-ammonium phosphate fertiliser (70 kg/ha) treated with flutriafol fungicide (75 g /ha) to suppress stripe rust , containing Searles Native Mix potting soil (Searles Pty Ltd Kilcoy QLD). Seeds were pre-germinated at 5 °C and four seeds per line were planted in each pot. Experimental design blocks were aligned with separate benches. For all the experiments conducted in Toowoomba, a mixture of ten contemporary Ptr isolates  was used. Inoculum was prepared as described by Shankar et al.  and anthesis (Zadoks 61–65). Whole plants were assessed at the early tillering stage while the top two layers were assessed at anthesis and averaged to ensure ratings were attributed to experimental units.E. coli SHuffle and purified using affinity chromatography (IMAC) as described by See et al.  algorithm  of the IWD panel sub-populations as well as empirical best linear unbiased predictions (eBLUPs) of the individual IWD panel lines were extracted for numerical and graphical summary. A generalised broad sense heritability was calculated using the formula developed by Cullis et al. , namely,.Ptr ToxA-induced necrosis and Ptr ToxB-induced chlorosis were mostly consistent across the replicates of each line in the IWD panel of the plant bioassays. The lack of replicate variation indicated a single numerical value for each line may be better suited in the analysis approaches that follow. These collapsed traits could then be considered a proxy for total genetic effects of the Ptr ToxA or Ptr ToxB effectors across the population. To ensure these traits could be used in the whole-genome analyses that follow a simple effector placeholder LMM required developing. Let , and To determine the association strength of the Ptr ToxA and Ptr ToxB effectors with the complete set of tan spot severity traits defined in Table .Let Substituting (5) into (3) the initial whole-genome analysis LMM becomesM in the model and placed as an additive random covariate in the baseline LMM (3) as well as the whole-genome analysis LMM (6). Both models are re-estimated and the process of finding a significant marker is then repeated. An informative flow diagram of how the WGAIM algorithm proceeds can be found in Verbyla et al.  which is similarly defined in Norman et al. . Key elein Mrode  where, fu et al.  and provhttps://www.vsni.co.uk/). Diagnostic assessment of models was conducted using ASReml-R functions as well as functions from the linear mixed modelling post-processing package ASExtras available for download from https://mmade.org/. All whole-genome analyses were conducted using minor modifications of the WGAIM V2 R package  observed. A whole-genome analysis was performed for the Ptr ToxA and Ptr ToxB sensitivity traits scored across the IWD panel, and a Manhattan plot of the QTL results is given Supplementary Figure S3. As expected, for Ptr ToxA the results indicated near 100% of the genetic variation was attributed to a single locus on the long arm of 5B linked to “BS00010590_51”, the 764p-value < 0.0001; LOD = 71.62) occurred for Prt ToxA with seedling tan spot severity assessed at the seedling stage in the glasshouse at Horsham 2015 (A), with only smaller associations for other traits from the same year and location (B and C). In Horsham 2016, a very significant association  was found for Ptr ToxA and tan spot severity measured at early stem elongation (E) and, interestingly, the strength of this association was dramatically reduced with the remaining tan spot traits measured at later stages of the plants’ growth in the same experiment (F-I). In South Perth, the strongest associations with Ptr ToxA sensitivity occurred with the tan spot severity trait measured at the seedling stage of the 2015 experiment (J) and the tan spot traits measured at the seedling and adult stages of the field and controlled environment experiments in 2016 . Other traits obtained from South Perth controlled environments exhibited much weaker association. Tan spot severity assessed at seedling stage of the 2015 field experiment of Toowoomba (R) had the strongest association with Ptr ToxA , with other tan spot traits from Toowoomba in 2015 and 2016 having much weaker or no association. In contrast to Ptr ToxA association results, the bottom panel of Fig. The strength of the association between wheat Ptr ToxA and Ptr ToxB sensitivity and the complete list of tan spot severity traits defined in Table Whole-genome analysis of the complete set of tan spot severity traits defined in Table Here, we have focussed on repeatability of QTL where markers in LD have an association with at least three different tan spot severity traits Table . To idenFor each tan spot trait, the eBLUEs of the sub-populations were added to the additive eBLUPs and non-additive eBLUPs to form total genetic predictions of the each of the lines in the IWD panel. For all traits except tan spot severity in Horsham 2016 assessed at the seedling stage (D), genomic prediction accuracies indicate a strong influence from the genetics  and this aligns with the high heritabilities obtained from the phenotypic modelling. As the scoring system differed between traits and some traits were transformed for analysis purposes, the genomic prediction variety rankings were used to provide a standardisation of the relative resistance of each variety within a trait. To visualise this resistance, rankings for each variety were averaged across the tan spot traits and varieties were ordered from most resistant to most susceptible by the average ranking  panel with broad levels of tan spot resistance and then conducted extensive phenotypic and genotypic analyses of tan spot severity traits with Australian Ptr isolates, with assessment over two years and multiple locations. Phenotypic analyses revealed strong heritability of tan spot severity traits, with ICARDA lines on average showing reduced tan spot symptoms compared to other sub-populations. One-step whole-genome analyses detected a large QTL on 1AS that is most likely co-locating with the ToxC sensitivity gene Supplementary file2 (DOCX 1359 KB)Supplementary file3 (CSV 6 KB)Supplementary file1 (CSV 14 KB)Below is the link to the electronic supplementary material.


Thuja plicata) is a conifer of the Pacific Northwest of North America prized for its durable and rot‐resistant wood. WRC has naturally low outcrossing rates and readily self‐fertilizes in nature. Challenges faced in WRC breeding and propagation involve selecting trees for accelerated growth while also ensuring enhanced heartwood rot resistance and resistance to ungulate browsing, as well as mitigating potential effects of inbreeding depression. Terpenes, a large and diverse class of specialized metabolites, confer both rot and browse resistance in the wood and foliage of WRC, respectively. Using a Bayesian modelling approach, we isolated single nucleotide polymorphism (SNP) markers estimated to be associated with three different foliar terpene traits and four different heartwood terpene traits, as well as two growth traits. We found that all traits were complex, being associated with between 1700 and 3600 SNPs linked with putatively causal loci, with significant polygenic components. Growth traits tended to have a larger polygenic component while terpene traits had larger major gene components; SNPs with small or polygenic effect were spread across the genome, while larger‐effect SNPs tended to be localized to specific linkage groups. To determine whether there was inbreeding depression for terpene chemistry or growth traits, we used mixed linear models for a genomic selection training population to estimate the effect of the inbreeding coefficient F on foliar terpenes, heartwood terpenes and several growth and dendrochronological traits. We did not find significant inbreeding depression for any assessed trait. We further assessed inbreeding depression across four generations of complete selfing and found that not only was inbreeding depression not significant but that selection for height growth was the only significant predictor for growth during selfing, suggesting that inbreeding depression due to selfing during operational breeding can be mitigated by increased selection intensity.Western redcedar (WRC;  WRC is endemic to North America's Pacific Northwest and is held in high regard by coastal First Nations and Native Americans, for whom WRC has served for millennia as a keystone societal resource. WRC wood has been used in the construction of buildings, watercrafts, totem poles and ceremonial masks, among other important practical and spiritual uses  and dimethylallyl diphosphate (DMAPP). Terpenes constitute a large group of metabolites in plants, with over 40,000 structures involved in both general and specialized metabolism  and molecular mechanisms controlling these growth and chemistry traits in WRC will be essential for understanding how WRC grows and adapts to pest and pathogen attacks, and for improving the growth and survival prospects of trees in the wild and during operational forestry. It is also unknown which genomic loci may have an impact on inbreeding depression, or lack thereof, in growth or chemistry traits in WRC. Recently, large‐scale genotyping of WRC has been carried out for the implementation of genomic selection for growth and chemistry traits in WRC, with the goal of expediting breeding cycles for these traits  for genomic selection was comprised of 1520 progeny trees from 26 half‐sib families in a polycross mating design growing at three different British Columbia Ministry of Forests (BCMoF) progeny test sites in BC, Canada. A total of 520, 520 and 480 trees were sampled from sites containing large progeny trials at Jordan River, Powell River and Port McNeill respectively , and then moved outside for chilling. In the 2nd year, trees were moved back into the greenhouse and were completely encased in pollination bags to allow for selfing, and cones were then collected to begin the process again, allowing for one generation every 2 years. Seedlings from each FS family were planted in three replicates in a greenhouse.Breeding and propagation of selfing lines (SLs) were carried out between 1995 and 2007 at the Cowichan Lake Research Station (CLRS) at Lake Mesachie, British Columbia (BC), Canada. Fifteen pairs  of unrelated grafted parent trees taken originally from various locations across Coastal BC and Vancouver Island  and brought through the entire selfing process to mitigate the likelihood of line extinction. Ten of the 60 lines went extinct during the process. Foliar tissue was collected for DNA extraction and foliar chemistry analysis from FS – S4 trees between 4 and 15 years of age growing in outdoor plots at CLRS; and 1‐year‐old S5 seedlings growing in a greenhouse at CLRS, for a total of –S5 and b2.3g, the amount of Qiagen MagAttract solution was increased to 7 μl, 1‐min wait time for binding on the magnetic block was added and 115 μl TE buffer was used for DNA dilution. The concentration and quality of DNA were verified with a Nanodrop 2000c (Fisher Scientific), Quantiflor (Promega Corporate) and 0.8% agarose gel to cross‐check for the quantity and quality of the DNA.DNA was isolated as described by Shalev et al. . Briefly2.4PRJNA704616)  was generated for both the TP and the selfing lines using the ‘A.mat’ function from rrBLUP v4.6.1 , wood thujaplicins  and height and diameter at breast height at 15 years of age Dataset . For the2.5.1Height and diameter at breast height  were measured for trees in the TP at approximately 15 years of age in 2014. Height was measured for 4‐month‐old seedlings of all SLs up to S4 between 1997 and 2005.2.5.2Foliar chemistry was analysed from foliar tissue sampled from TP trees at 15 years of age and SL trees between 1 and 15 years of age at Glynn Road Analytical Chemistry Lab, as described in Gamal El‐Dien et al. . Briefly2.5.3For wood chemistry, increment cores were sampled from TP trees at 18 years of age in the fall of 2017. Cores were taken at breast height (1.3 m) and were oriented to intercept the pith of the stem to ensure all heartwood material was captured in the sample while capturing the entire diameter of the tree. One‐half (one radius) of each core was retained for dendrochronological analysis. The other half was ground for 5–10 s in an electric grinder. The grinder was swept after each sample and cleaned with methanol after every 10th sample. Ground samples were conditioned at 40°C for 48 h before extraction to ensure consistency between samples, as the small sample weights did not allow for individual moisture contents.Extraction and analysis of wood metabolites were carried out at FPInnovations using the approach of Daniels and Russell  with som2.5.4For dendrochronology, each core was scanned into the program Coordinate Recorder (Cybis Elektronik & Data AB). Each image was assessed to (1) identify ring boundaries and detect false rings ; and (2) assign calendar years to each ring, assuming the last ring with complete latewood was 2016 for all samples. A constant threshold was applied to automatically detect the earlywood–latewood boundary within each ring. Three time series of data representing total ring, earlywood and latewood widths were recorded for each tree.The calendar year of the pith or inner ring was recorded. The number of years from this year to sampling date was designated as ‘pith age’. The majority of cores intercepted the pith and included the bark. Ring counts represented age of the trees (at coring height) and ring widths were summed to represent growth over their lifespan. For cores that included arcing rings adjacent to the pith, we calculated correction factors for the missed portion of the radius and number of rings Duncan, :(1)MisUsing these corrections, the radius from the pith to the latewood of the 2016 ring was calculated by summing the measured ring widths and adding the estimated missed portion of the radius. Total number of rings was calculated as the number of measured rings plus the estimated number of missing rings. For cores missing the bark, there is no standard method to estimate the number of potentially missing sapwood rings, so no correction was applied to address this potential source of error. Their ring counts and summed ring widths provide minimum estimates of age and growth. The number of sapwood rings per sample was counted and ring widths were summed. The number of heartwood rings was calculated as total number of rings minus number of sapwood rings. Heartwood width was calculated as total radius minus sapwood width.2.5.5h2) for our traits in the TP from SNP data using univariate mixed linear models  and from our corrected pedigree data (ABLUP) in ASReml‐R v4.1.0.149  to test and compare included random effects. The models used for each trait can be found in Table Restricted effects maximum‐likelihood (REML) ratio test was implemented using the asremlPlus v4.2‐18 package in R , as described above Dataset . In this2.5.7N (no additive genetic effect), N (polygenic effect), N  and N (large effect), respectively, where σg2 is the additive genetic variance.BayesR  draft genome , generated previously for the WRC genome . This metric, designated Fhat3 in PLINK, is defined by Yang et al.  using PLINK (flags: ‐‐indep‐pairwise 2168 kb 1 0.1) based on previous estimates of LD in WRC  and the interaction between the ith selfed generation and kth line type, respectively; fl, gifl, fllm and gifllm are the random effects of the lth family, the interaction between ith selfed generation and lth family, the mth line within the lth family and the interaction between ith selfed generation and mth line within lth family, respectively; and eijklmn is the random error associated with trees within replication.Due to limitations in data availability, we only had access to breeding values (BVs) for height in our SLs. BVs for height were predicted for each generation in each line as described in Russell and Ferguson . Brieflyu is the population mean; gi and lj are the fixed effects of the ith selfed generation and the jth growing location at CLRS, respectively; ak is the additive genetic effect of the kth tree as estimated from the inverse GRRM from SNP data and eijk is the random error associated with trees within each generation and location.BVs for foliar monoterpenes were calculated using a mixed linear model (GBLUP) in ASReml‐R:2.6.3F for height BVs and total foliar monoterpene BVs in the SLs using ordinary least squares (OLS) in the following linear model:u is the population mean, Fi is the fixed effect of the ith tree's inbreeding coefficient and ei is the random error associated with inbreeding coefficients. Assumptions of independent and identical distribution of error terms were tested in R using residual plots and normality histograms.We assessed significance of 2.6.4u is the population mean; Fi, tj, Fitj and fk are the fixed effects of the ith tree's inbreeding coefficient, the jth line type (random or selected), the interaction between the ith inbreeding coefficient and the jth line type, and the kth family, respectively; and eijk is the random error associated with these fixed effects.We used OLS to estimate the effect of selection on height BVs in the following linear model:33.1n = 1504. We corrected erroneous SNP calls for the SLs using the approach of Shalev et al. (n = 28) or S5 (n = 11) with a total of n = 151 trees with genotyping data.Using a targeted genotyping approach, we captured 51,638 polymorphic loci from 20,858 probes. Filtering across all trees yielded 36,262 SNPs Dataset . Among Sv et al. , resulti3.23.2.1n = 1499 trees. The most abundant foliar monoterpene by far in our TP was α‐thujone, with a mean (SD) concentration of 23,249 (7538) μg/g DW, followed by sabinene with a mean concentration of 5718 (2314) and finally, β‐thujone with a mean of 4732 (1679). There was substantial variation in foliar terpene concentration between trees, with α‐thujone concentrations ranging from 6256 to 57,408, sabinene from 559 to 22,903 and β‐thujone from 1468 to 13,445 μg/g DW .Foliar chemistry data were obtained for W Figure . The sam3.2.2n = 1492 trees. Of the four thujaplicins analysed in this study, α‐ and γ‐thujaplicin were the most abundant, with mean (SD) concentrations of 396.5 (275.6) and 361.1 (340.8) μg/g conditioned wood (CW), respectively, followed by β‐thujaplicin with 210.2 (225.6) and β‐thujaplicinol with 67.5 (66.8) μg/g CW. The distribution of these phenotypes was right‐skewed, with more trees exhibiting lower‐than‐average concentrations . Pith age had a significant effect on α‐ and γ‐thujaplicin and β‐thujaplicinol . The sapwood‐to‐heartwood ratio had a significant effect on α‐thujaplicin and β‐thujaplicinol .Wood chemistry data were obtained for s Figure . For all3.2.3p = 0, one‐tailed t‐test)] and Port McNeill [683.1 (119.2) cm for height and 98.6 (27.7) mm for DBH ] .Both height and DBH were significantly greater overall at the Powell River site, with a mean height (SD) of 1084.5 (156.8) cm and DBH of 143.0 (32.7) mm compared to the means of Jordan River [761.6 (102.0) cm for height and 110.0 (24.5) mm for DBH , with a lower h2 for β‐thujone (0.16) , while the estimate for β‐thujone was lower (0.14). For wood thujaplicins, β‐thujaplicin had the highest genomic h2 (0.30), although the pedigree‐based estimate was lower (0.21). α‐ and γ‐thujaplicin and β‐thujaplicinol all had moderate‐to‐low genomic h2 , with similar pedigree‐based results. Height and DBH both had low genomic h2 . In all cases, genomic h2 had lower standard error than pedigree estimates.We recorded moderate genomic 6) Table . Pedigre3.4As the foliar terpenes in this study are in the same biosynthetic pathway, we hypothesized that genetic and phenotypic correlations between them would be strong. Genetic and phenotypic correlations were similar and high between α‐ and β‐thujone  Table . GeneticDue to the structural similarity of the wood thujaplicins, we expected to observe genetic and phenotypic correlations between them. We recorded moderate‐to‐high genetic and phenotypic correlations between the different wood thujaplicins Table . OverallBetween groups of traits, the largest significant genetic correlation was observed between β‐thujone and DBH (0.39). There were very small or no significant genetic correlations between wood thujaplicins and other traits, with the exception of sabinene and α‐thujaplicin (0.31). There was a moderate but significant negative genetic correlation between β‐thujaplicinol and α‐thujone (−0.20); there were also negative phenotypic correlations between almost all wood thujaplicins and growth traits Table . Standar3.5−2 × σg2), suggesting that traits with a greater degree of control by major genes have less loci overall associated with them. There was a strong correlation (0.99) between number of trait‐associated SNPs and number of genes associated with each trait; indeed, only 28% of annotated trait‐associated SNPs were in intergenic regions, indicating that an increase in number of trait‐associated SNPs also entails an increase in the number of genes controlling a trait.Using BayesR, we identified trait‐associated SNPs for each of the traits in this study Table . There wHeight and DBH were expected to be mainly polygenic, given their highly quantitative nature  – likely in large part due to their strong genetic correlation (0.75) and the larger number of SNPs overall associated with these traits Figure . Of thes3.5.2Using a putative linkage map for WRC established by Shalev et al. , we mappp = 0.0614, Fisher's exact test) . For β‐thujone and sabinene, the SNPs with the largest ES were in intergenic regions on LG 8 and 10 respectively.Large‐effect SNPs for α‐thujone were distributed across all putative LGs, with more than 20% on LG 2, although this was not significantly more than would have been expected given the distribution of all SNPs  Figure . This inp = 0.0202), while large‐effect SNPs for γ‐thujaplicin and β‐thujaplicinol were significantly over‐represented on LG 9 (p < 8 × 10−4). Interestingly, the SNPs with the largest ES for α‐ and β‐thujaplicin were both on LG 9, while for γ‐thujaplicin and β‐thujaplicinol, they were on LG 10 and LG 6, respectively. The largest‐effect SNPs for α‐thujaplicin and β‐thujaplicin were on LG 9, downstream of a gene encoding for a plant protein of unknown function, and in a gene putatively encoding for an ABC family transporter respectively. The largest‐effect SNPs for γ‐thujaplicin and β‐thujaplicinol were in intergenic regions on LG 10 and 6 respectively.Large‐effect SNPs for α‐thujaplicin were significantly over‐represented on LG 2 , inbreeding depression was not significant for any of the growth or chemistry traits we tested. Russell and Ferguson (F with each selfing generation and that even in random lines, inbreeding depression is not a significant predictor of height. Selfing rates tend to be higher in seed orchards (up to 60%; El‐Kassaby et al., F estimates can be effective targets for selection and breeding, underscoring the importance of understanding the interactions of causal loci influencing the expression of key traits and increasing selection intensity for these loci, rather than attempting to drastically reduce inbreeding in WRC breeding populations.Previous studies have found inbreeding depression for seed traits to be practically nonexistent, while the effect on growth and productivity appears to be low (Russell et al., Ferguson  found pr5The aim of this study was to identify the genomic regions which may be associated with ecologically and economically important terpene chemistry and growth traits in WRC, and to explore whether WRC's tendency towards inbreeding might suggest a lack of inbreeding depression for such traits. We found evidence of complex polygenic genetic control for all terpene and growth traits surveyed, with most terpene traits potentially retaining some level of major gene control on specific LGs in the genome. Moreover, we found no evidence of inbreeding depression for either terpene or growth traits in WRC. In addition to providing an important resource for WRC management and improvement, our results showcase the importance of assessing the polygenicity and interconnected nature of traits and the loci associated with them in models of genetic architecture.The authors declare no conflict of interest.Appendix S1Click here for additional data file.Dataset S1Click here for additional data file.Dataset S2Click here for additional data file.Dataset S3Click here for additional data file.Dataset S4Click here for additional data file.