One of the biggest challenges in the swine industry is to increase female reproductive efficiency. Recently, vulva score categories (VSC), assessed prior to puberty, has been proposed as an indicator trait of efficient reproductive performance in sows. The objective of this study was to validate the use of VSC as an indicator trait for reproductive performance, and to perform genetic and genomic analyses for VSC.The phenotypic relationship of VSC, using a three-point scale: small (VSC-S), medium (VSC-M), and large (VSC-L), on reproductive performance was evaluated on three farms. VSC was measured at 15 weeks of age, for farms 1 and 2, and at 14 weeks of age for farm 3 on 3981 Yorkshire gilts, in which 1083 had genotypes (~ 50 K SNPs). Genetic parameters for VSC with reproductive traits were estimated using ssGBLUP. A Genome-wide association study (GWAS) for VSC was performed using BayesB.P ≤ 0.05) for the interaction between Farm and VSC for total number dead (TND), and a trend (P < 0.10) for total number born (TNB). There were significant (P ≤ 0.05) pre-defined contrasts of VSC-S versus VSC-M + L on TNB, number born alive (NBA), TND, number of stillborn (NSB), and number of mummies (MUM). Heritability estimates for VSC as a categorical trait (VSCc) and a quantitative trait (VSCq) were 0.40 ± 0.02 and 0.83 ± 0.02, respectively, for across farm, 0.13 ± 0.07 and 0.20 ± 0.10, respectively, for Farm1, 0.07 ± 0.07 and 0.09 ± 0.09, respectively, for Farm2, and 0.20 ± 0.03 and 0.34 ± 0.05, respectively, for Farm3. For across farms, favorable genetic correlations estimates were found for TNB (0.28 ± 0.19) and NBA (0.26 ± 0.17). Within farms, moderate genetic correlations between VSC with reproductive traits were found for TNB (0.61 ± 0.47) and MUM (0.69 ± 0.47) for farm 1, for number of services until first farrow  and unique service with successful first farrow  for farm 3. Multiple genomic regions associated with VSCc were identified. Of these, a QTL located on chromosome 3 at 33–34 Mb accounted for about 7.1% of the genetic variance for VSCc and VSCq. This region harbors the gene PRM1 that has been associated with early embryonic development in pigs.For the phenotypic analysis of VSC across datasets, differences in performance were identified there was a significant effect (The results support potential of VSC for improved reproductive efficiency on first-parity performance, but the results might depend on the interaction between environmental factors and VSC, as well as potentially additive genetics. Recently, the use of vulva size as an indicator trait for reproductive traits has been explored [h2 = 0.57). Corredor et al. [The efficiency of reproductive performance in the swine industry is critical to maximizing productivity. However, genetic selection for reproductive traits in sows  is challenging because of their low heritability . Additioexplored , 3. Pre-explored . Romoserexplored . On the explored  exploredA total of 3981 (Farm1 + 2 + 3) Yorkshire gilts from three farms located in Colorado, USA, were used in this study, with 746, 722, and 2513 from farms 1 (Farm1), 2 (Farm2), and 3 (Farm3). All animals were from the same genetic source and were reared under the same controlled conditions. At 15 weeks of age in Farm1 and Farm2, and at 14 weeks of age in Farm3, all gilts were assigned a VSC, following methodology described in Romoser et al. [y + 1), where y represents the observed phenotype of the trait. In addition, farrowing traits included: age at first service (AFS) in days, number of services until first farrow (NS), and unique service with successful first farrow . In this study, a single service was defined as any service events within 14 days. Therefore, a second service event was any one occurring 14 days after the first event. The summary statistics of these traits is shown on Table First-parity performance traits included number of piglets born alive (NBA), number of stillborn piglets (NSB), and number of mummified piglets (MUM). Total number of born dead piglets (TND) was calculated as NSB + MUM, and total number of born piglets (TNB) was calculated as NBA + TND. Prior to statistical analyses, data on TND, NSB, and MUM were transformed in order to meet the assumptions of statistical inference  using lnGenotype data were available for 193, 202, and 688 animals for Farm1, Farm2, and Farm3, respectively  was evaluated using the single-step BLUP (ssGBLUP) procedure  in the fyijk is the observed phenotype ; μ is the general mean; Farmi is the ith level of the fixed-effect of farm; VSCj is the jth level of the fixed-effect of vulva score category; (Farm × VSC)ij is the interaction term between Farmi and VSCj; ak is the animal random effect of the kth animal, assuming H is the additive genetic relationship matrix including genotyped and non-genotyped animals [eijk is the random error term associated with yijk, assuming I is the identity matrix. For TNB, NBA, TND, NSB, and MUM, the effect of VSC was estimated using a similar model, with the addition of a random effect of contemporary group , assuming P-value< 0.05 and P-value< 0.10, respectively. In addition, we also evaluated the effect of random effect of service sire on these models. However, due to the high number of missing data and lack of effect , this strategy was not further pursued. All analyses were performed in ASReml v4.0 [Where  animals ; and eijr et al. , in whiceml v4.0 .Genetic parameters for VSC were estimated using the following animal model:yij is the observed phenotype; μ is the overall mean; Farmi is the ith level of the fixed-effect of farm; aj is the animal random effect of the jth animal, assuming H is the additive genetic relationship matrix including genotyped and non-genotyped animals [ei is the random error term associated with yij, assuming c) and as a continuous (VSCq) trait. Heritabilities were estimated using a probit mixed model for VSCc and SFS, and using a general mixed linear model for all other traits. Genetic and phenotypic correlations were estimated within farm between VSC and reproductive traits, and genetic correlations between farms for VSC. Due to software limitations, these were estimated for VSCq. The efficiency (E) of correlated response to selection was estimated as:Where  animals ; and ei rG is the estimated genetic correlation between VSC and the reproductive trait of interest; hVSC is the square root of the heritability estimate for VSC; and hTrait of interest is the square root of the heritability estimate for a reproductive trait of interest.Where Genome-wide association studies (GWAS) was performed for VSC for each farm and across farms, using Bayesian genomic prediction methods  using thyi is the observed phenotype; μ is the overall mean; mij is the genotype at the jth SNP for the animal i; aj is the allele substitution effect for the jth SNP, and ei is the error term associated with yi, assuming Where sion 4.4 .https://www.ensembl.org/index.html). The 3-Mb neighboring regions of each side of the identified regions were investigated to account for the resolution of the QTL mapping method used in this study [Putative candidate genes within identified QTL regions and in the neighboring upstream and downstream 3-Mb regions were identified based on the Sscrofa11.1 genome assembly, using the BioMart tool from the Ensembl Genome Browser (is study . QTL regP ≤ 0.05) for the interaction between Farm and VSC for TND, and a trend (P < 0.10) for TNB. Although this interaction was not significant (P ≥ 0.11) for NBA, NSB, and MUM, we observed a significant (P ≤ 0.05) pre-defined contrast of VSC-S versus M + L for these traits, as well as for TNB, and TND.The phenotypic relationship of VSC on reproduction performance is shown in Table P < 0.05) TNB (13.13 ± 0.85) than VSC-M and VSC-L (11.43 ± 0.19) in Farm2, the relationship in Farm3 was opposite; VSC-S gilts had fewer (P < 0.05) TNB (11.69 ± 0.14) compared to the VSC-M and VSC-L (12.06 ± 0.13) gilts. No relationships were found (P > 0.05) between VSC and TNB on Farm1. For NBA, for Farm3, the same relationship found for TNB was observed for this trait, with greater (P < 0.05) performance in VSC-M and VSC-L compared to VSC-S. In contrast, there was no relationships found for Farm2 (P > 0.05), whereas in Farm1, VSC-S had the lower (P < 0.05) performance (9.70 ± 0.63) than VSC-M and VSC-L (10.24 ± 0.18).The phenotypic relationship between VSC and reproductive traits diverged among farms. For TNB, although VSC-S had greater (P < 0.05) number of piglets in VSC-S gilts (0.35 ± 0.05) compared to VSC-M and VSC-L (0.82 ± 0.02). For Farm2, VSC-M had the fewer (P < 0.05) piglets (0.55 ± 0.01) than VSC-S and VSC-L (0.72 ± 0.05), whereas no relationships were found for Farm3 (P < 0.05). For NSB, the same relationships found for TND were found, with greater performance found in VSC-S gilts on Farm1, and lower performance found in VSC-S gilts of Farm2 compared to VSC-M and VSC-L (P < 0.05 for both farms). No relationships were found (P > 0.05) between VSC and NSB on Farm3. Finally, VSC-S gilts showed fewer (P < 0.05) MUM (0.15 ± 0.04) than VSC-M and VSC-L (0.41 ± 0.01) on Farm1, whereas no relationships (P > 0.05) were found for the other farms. These results show that the overall relationship between VSC and reproductive performance depends on the environment  in which these gilts are raised in.The relationships found for TND were more complex. For Farm1, the relationship was the same as for NBA, with lower (P = 0.08), and SFS (P = 0.05), with VSC-S showing overall greater performance than VSC-M and VSC-L gilts. VSC-S required fewer (P < 0.05) NS (1.06 ± 0.04) than VSC-L gilts (1.08 ± 0.01) and had greater (P < 0.05) SFS (0.94 ± 0.03) than VSC-L gilts (0.92 ± 0.01). Although we found a trend effect of Farm×VSC on TNB (P = 0.06) and significant (P < 0.01) pre-defined contrast (S vs. M + L), there was a trend (P = 0.08) for the main effect of VSC, with VSC-M (11.65 ± 0.09) having lower performance (P < 0.05) than VSC-S (11.72 ± 0.36) and VSC-L (11.72 ± 0.13). Finally, there was an effect of Farm for all traits analyzed (P < 0.01), indicating that the environment is a major contributor for the variability of the reproductive data in this study.We also found overall relationships between VSC and reproductive performance. There were relationships between VSC with NS  to 0.08 ± 0.03 (AFS). Farm1 presented moderate heritability estimates for AFS (0.37 ± 0.12), whereas estimates were low for the remaining traits, ranging from < 0.01 ± 0.01 (SFS) to 0.13 ± 0.08 (NBA). For Farm2, heritability estimates for AFS, TND, and NSB were moderate, with 0.27 ± 0.11, 0.28 ± 0.10, and 0.27 ± 0.09, respectively. Heritability estimates for the remaining traits were low, ranging from 0.03 ± 0.07 (NBA) to 0.17 ± 0.11 (SFS). For Farm3, heritability estimates were low, ranging from 0.02 ± 0.03 (TND) to 0.14 ± 0.04 (AFS). For VSCq, estimates of residual variances  when compared to the subset dataset by farms (ranging from 0.02 to 0.13). Estimates of rG for VSCq between farms were high for all comparisons, with estimates of 0.97 ± 0.25 (Farm1 and Farm2), 0.77 ± 0.22 (Farm1 and Farm3), and 0.98 ± 0.16 (Farm2 and Farm3).Estimates of heritability are presented in Table rP) and genetic (rG) correlations between VSCq with reproductive traits for each farm are presented in Table rG were low, ranging from 0.28 ± 0.19 (TNB) to − 0.30 ± 0.13 (SFS). Favorable rG estimates were found for TNB (0.28 ± 0.19) and NBA (0.26 ± 0.17), whereas unfavorable estimates were found for AFS (0.25 ± 0.06), TND (0.12 ± 0.20), MUM (0.27 ± 0.79), NS (0.18 ± 0.13), and SFS (− 0.30 ± 0.13), although standard errors were overall large. For the within farm analyses, Farm1 had moderate to high estimates of rG between VSCq with TNB (0.61 ± 0.47), NBA (0.30 ± 0.37), MUM (0.69 ± 0.47), and SFS (− 0.36 ± 0.95). Estimates of rG between VSCq with the remaining traits were lower, ranging from 0.10 ± 0.55 (NSB) to 0.26 ± 0.51 (TND). For Farm2, estimates of rG were moderate between VSCq with AFS (− 0.51 ± 0.44), NSB (0.38 ± 0.35), MUM (− 0.42 ± 0.58), NS (− 0.31 ± 0.59), and SFS (0.33 ± 0.58). Estimates of rG between VSCq with the remaining traits were lower, ranging from 0.26 ± 0.56 (TNB) to 0.28 ± 0.41 (TND). For Farm3, moderate to high rG estimates were found between VSCq with AFS (− 0.32 ± 0.16), TNB (0.39 ± 0.26), NBA (0.31 ± 0.21), NS (0.69 ± 0.38), and SFS (− 0.71 ± 0.38). For the remaining traits, estimates were low, ranging from − 0.15 ± 0.14 (MUM) and 0.14 ± 0.39 (TND). Estimates of rP were low between VSCq with reproductive traits across all datasets. For Farm1, these ranged from − 0.07 ± 0.04 (SFS) to 0.04 ± 0.05 (AFS), for Farm2 from − 0.03 ± 0.04 (AFS) to 0.04 ± 0.04 (NSB), whereas for Farm3 these ranged from − 0.05 ± 0.02 (SFS) to 0.06 ± 0.02 (NBA). The efficiency of correlated response to selection when selecting for increased VSC, would be 1.80 and 2.37 for TNB and NBA, respectively, for across farms, 1.58 and 0.37 for TNB and NBA, respectively, for Farm1; 0.93 and 0.57 for TNB and NBA, respectively, for Farm3; and 0.25 for TNB for Farm2.Estimates of phenotypic  between prepubertal vulva width and age at first estrus. These authors suggested that VSC measurements in gilts between 95 and 115 days of age could be used as a proxy for ovarian development and onset of puberty. Following this reasoning, Romoser et al. [P = 0.02), and presented greater TNB than VSC-S . In addition to validating these results, we sought to explore the genomic basis of VSC in gilts.In this study, we investigated the relationship between VSC, assigned to 14- and 15-week old gilts, with subsequent reproductive performance. We aimed to corroborate, at both genetic and phenotypic levels, the previous findings from Graves et al.  and Romor et al.  determinIn general, significant phenotypic relationships between VSC and the traits evaluated were observed. However, the direction of the relationship (i.e. positive or negative) between VSC with these traits were not consistent. In Romoser et al. , there wprcomp function in R [The reasons for differences in VSC results between farms are unclear. In addition to the non-genetic factors that are inherited from each farm that do not allow us to separate them in our statistical analyses , two other processes could have resulted in these differences. First, the distributions of VSC-S in Farm1 and Farm2 were very different than in Farm3, with 2.8%, 1.7% and 21.2% in Farm1, Farm2, and Farm3, respectively. With this, the very low frequencies in Farm1 and Farm2 increased the standard error (SE) of the estimates, decreasing the statistical power to identify differences in performance based on VSC. This was not the case for Farm3. When ignoring the large SE, numerically, favorable phenotypic relationships were identified for TNB in Farm1, for MUM, NS, and SFS in Farm2, and for TND, NSB, and MUM in Farm3. Second, a different genetic makeup between farms could explain this. Animals in the three farms were from the same breed (Yorkshire) and were sourced from the same genetic source. In order to investigate the population structure of the three farms, we performed a principal component analysis  model and, therefore, the estimates are in the latent scale.The heritability estimates across farms dataset and for each farm showed that VSC is highly heritable, and therefore, selection for this trait is possible. Heritability estimates for VSCr et al. , with 0.r et al. , with 0.r et al.  and the q between farms were overall high. The high estimate close to unity for Farm1 and Farm2 (0.97 ± 0.25) indicates that the animals with high genetic potential for VSC in one farm would have high genetic potential in the other farm. In fact, the estimates of genetic correlation between VSCq and the reproductive traits for Farm1 and Farm2 had a similar direction and magnitude for TNB, TND, and NSB. This was also the case for Farm2 and Farm3, that had an estimate of genetic correlation close to unity (0.98 ± 0.16) and coincided with the direction of negative genetic correlations between VSCq with AFS, and MUM, and positive genetic correlations between VSCq with TNB, TND, and NSB. Between Farm1 and Farm3, we found a lower genetic correlation estimate (0.77 ± 0.22) for VSCq compared to those for the other farms. This more moderate genetic correlation coincides with some greater differences in the direction of genetic correlations between VSCq and the traits, such as for AFS and MUM. This moderate genetic correlation would indicate re-ranking of animals between Farm1 and Farm3, suggesting the presence of G×E.The estimates of genetic correlation for VSCIn general, the heritability estimates for litter size traits TNB, NBA, TND, NSB, and MUM were low, and in accordance with recent reports in the literature –20, and q with TNB, NBA, TND, and NSB across farms and for all three farms. These estimates indicated that a higher VSC corresponds genetically to larger TNB and NBA, further supporting the idea of using VS as a selection tool to increase NBA [Estimates of genetic correlations were positive between VSCease NBA  but alsoThere were contrasting genetic correlation estimates between VSC with AFS, MUM, NS, and SFS, depending on the dataset analyzed. For across farms and Farm1, all directions were unfavorable, whereas for Farm2, all directions were favorable, and for Farm3, unfavorable for NS and SFS, and favorable for AFS and MUM. The reasons for these differences are unknown. Finally, in general, many of these estimates had moderate to large standard errors, and hence, the value of the estimates should be taken carefully.rG estimates, the correlated response to selection for TNB and NBA would be limited. Although overall results indicate superior response to correlated response to selection for these traits using VSCq, the within farm analysis indicate that a greater efficiency would only be possible based on the results for Farm1. However, although there seems to be a limited impact of VSC on reproductive traits at the genetic level, there was a clear impact of VSC on reproductive traits at the phenotypic level (Farm1 and Farm3), indicating that there is great potential in using VSC for culling criterion. Regardless on this limitation, a combined phenotypic culling and genetic selection strategy could potentially be used to optimize selection for increased NBA in purebred populations. Selecting for VSC instead of a reproductive trait would be beneficial to anticipate selection, since VSC can be measured in gilts prior to insemination. In addition, this would allow for a higher intensity of selection because a larger population of gilts would be available prior to insemination. This advantage of having an early-age indicator of future reproductive performance could also increase response to selection by reduction the generation interval in female pigs. Thus, the use of VSC for both phenotypic and genetic purposes may be beneficial to the swine industry.Given the favorable Finally, as seen for the phenotypic relationship between VSC with reproductive performance, results were somewhat different among the different datasets analyzed, even if for most cases the direction of correlations were similar across them. But, this additional inconsistency in results between farms suggest that 1) relationships within dataset might be real (similar results within datasets), which supports the hypothesis of 2) occurrence of G×E due to the non-genetic differences previously discussed in this study.Results from genomic analyses differed among farms, which is in accordance with all other results presented in this study, further suggesting that non-genetic effects may be playing a role in the expression of VSC phenotypes between the three farms. Although there were no genomic regions identified for Farm2, Farm1 and Farm3 had associations with greater %TGVM for VSC than when the whole dataset was used for analysis . However, analysis using Farm1 + 2 + 3 noted additional regions not identified when analyses were performed for each farm separately. This difference in results could be due to the much larger sample size used for the Farm1 + 2 + 3  which should have improved the statistical power to identify these additional QTL. Nonetheless, it is important to note that none of the Farm1 QTL were identified using Farm1 + 2 + 3. Given that the sample size of both farms was similar, the QTL identified in both Farm1 + 2 + 3 and Farm3 analyses could potentially indicate general QTL for VSC, whereas those identified in Farm1 or Farm3 and not in Farm1 + 2 + 3 could represent the potential occurrence of GxE in this study.While Corredor et al.  investigPRSS35) is located, a gene that has been identified as a novel mouse ovary gene [PRSS35 was highly expressed at the time of ovulation and remained elevated in the developing corpus luteum. Wahlberg et al. [PRSS35 was highly expressed in the theca layers of developing follicles, and it was also expressed in the forming and regressing corpus luteum. Taken together, Miyakoshi et al. [PRSS35 may be involved in ovulation in mice. In a study in humans, Li et al. [PRSS35 and observed that expression in cumulus cells of fertilized oocytes were significantly higher than those in cumulus cells of unfertilized oocytes. Li et al. [PRSS35 may be correlated with oocyte fertility potential.Candidate genes related to reproductive development and performance were proposed for the identified regions that explained more than 3%TGVM, including the regions on SSC 1 (85 Mb and 160 Mb), 3 (33–34 Mb), 5 (91–99 Mb), 7 (103–104 Mb), 15 (52 Mb), and 18 (14 Mb). Within the QTL region on SSC 1 (85 Mb) serine protease 35 . Yang et al. [SERPINB11 in mice uteri during early pregnancy and suggested that SERPINB11 is involved in embryo implantation and decidualization. Similarly, Yang et al. [SERPINB11 in mice testis and suggested that SERPINB11 might be involved in spermatogenesis. Likewise, Lim et al. [SERPINB11 expression in the chicken oviduct, specifically in the luminal and glandular epithelia.The QTL on SSC 1 (160 Mb) harbors serpin family B member 11  is located within the QTL region on SSC 3 (33–34 Mb), which has been associated with sperm quality and embryonic early development in humans and pigs [PRM1 mRNA expression, among other genes, with embryonic development and sperm capacitation in humans. These authors concluded that PRM1 mRNA expression could be used for estimating quality of spermatozoa in humans. However, this relationship has not been demonstrated in pigs [The gene protamine 1 (and pigs –32. Depa in pigs .KITLG). In humans, KITLG has been associated with male infertility [KITLG in the porcine ovary of prepuberal and mature animals by in situ hybridization showed that this gene is expressed in the granulosa cell layer and in the endothelial tissue and throughout the corpus luteum [KITLG have a role in maintaining progesterone secretion by the corpus luteum.Within the QTL region SSC 5 (91–99 Mb) we found KIT ligand  is located within the QTL region on SSC 7 (103–104 Mb). TSHR is a vital element in the pituitary thyroid axis of all vertebrates. TSHR commands to intracellular processes required for the synthesis, storage, and secretion of thyroid hormones, the main regulators of cellular metabolism [TSHR, found that it modulates photoperiodic response in chickens. These authors suggested that TSHR plays a key role in the signal transduction of seasonal reproduction. Rodríguez-Castelán et al. [TSHR in reproductive organs of female rabbits. They found a presence of TSHR in the primordial, primary, secondary, tertiary, and Graafian follicles of virgin rabbits, as well as in the corpora lutea, corpora albicans, and wall of hemorrhagic cysts of pregnant rabbits. These wide presence of TSHR in female reproductive organs could suggests varied effects of TSHR in the reproduction of rabbits.The gene thyroid stimulating hormone receptor  resides within the QTL region on SSC 15 (52 Mb). Studies in mice and chickens have concluded that NRG1 exerts an important regulatory role in oocyte meiotic maturation [NRG1 mRNA increased in the oviducts of chicks treated with a synthetic non-steroidal estrogen. Furthermore, these authors suggested that NRG1 is a novel estrogen-responsive gene closely correlated with the development of the oviduct of chicks.Neuregulin 1  and stimulated by retinoic acid 8 (STRA8) are located within the QTL region on SSC 18 (14 Mb). Multiple gene expression studies in humans and cattle demonstrated that AKR1B1 is strongly associated with prostaglandin production, which is an important regulator of female reproductive function [AKR1B1 functions in prostaglandin metabolism during the estrous cycle and pregnancy [STRA8 expression is required for meiotic initiation in both female and male germ cells [STRA8 in transgenic pigs, from mouse STRA8 promoter, could be useful as an animal model to study male germ cell manipulation and development.The genes aldo-keto reductase family 1 member B (function –48. In pregnancy . Studiesrm cells –53. Thisrm cells . These aIn general, the genomic regions identified in this study for VSC include relevant genes for reproduction-related traits. Most of the genomic regions identified to be associated with VSC were associated with follicular and/or embryonic development. Furthermore, the genetic and phenotypic associations discovered between VSC and reproductive traits in this study additionally corroborate with the biologically relevant findings from our genomic analyses for VSC. Additional research to validate the use of VSC in different environments, using additional parities and genetic lines to continues assessment as an indicator trait of reproductive performance are warranted.In this study, phenotypic analyses support that VSC is associated with improved reproductive performance of sows, with large VSC gilts having greater NBA than small VSC gilts. VSC had moderate heritability among farms, showing that selection for VSC is possible. However, the genetic correlation for VSC between farm indicated the presence of G×E. For each farm, VSC was positively genetically correlated with TNB and NBA indicating that selection for greater VSC could result in increased litter size. Several genomic regions associated with VSC were identified, locating relevant candidate genes with reproductive function. These results support phenotypic relationship between VSC with TNB and NBA but that environmental factors could influence this relationship.


Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density (“Phantom Epistasis”). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation. When reconciling the quantitative variation of complex traits with the Mendelian “scheme of inheritance”, Naturally, the first approaches to predict genetic values with whole-genome marker panels were based on purely additive models , additivOn the other hand, the non-additive models have been proposed with the expectation to a) increase the predictive ability of overall genetic values, b) allow the definition of mate allocation between candidates for selection, and c) exploit non-additive genetic variation through the definition of appropriate cross-breeds or purebred schemes , which parametrize the covariance between genetic values. For additive models, the G matrix of It is well known that, for a suitable choice of parameters, the additive G-BLUP is equivalent to a ridge regression on the markers see . BecauseWe used the equivalent regression models to partition the genotypic values associated to these GRMs into additive and non-additive components. Because variance explained by additive effects can also be partly explained by interactions and vice-versa , the addArabidopsis thaliana, generated in the 1001 genomes project. This project provided 1,135 high-quality re-sequenced natural inbred lines representing the native Eurasian and North African range and the recently colonized North America populations , namely the additive G-BLUP GRM according to i and marker k. .H there is a term for each pair of markers k and l (possibly the same),For These terms build a homogeneous quadratic polynomial.For K there is a term for each d-tuple of markers These terms build an infinite order polynomial, where the terms of lower order have associated higher weights and thus are “cheaper” to use by the regularized regression model . The variance of the residual was considered the contribution of J over the markers indices for G and over marker pairs for H. For K, we sampled separately for tuples of length 1 to 6. After verifying that the contribution to the higher orders was negligible, we averaged the six samples weighted by the number of tuples of each length. For the three GRMs, a sampling with To estimate the contribution of a regressor variable  we simulated ten different traits. Subsequently, we proceeded to conduct the same analysis as with the observed traits described above. The causal loci were excluded for the formation of the GRMs.The GRMs, as well as their variance component decomposition, where built with custom code in the Julia programming language . In parthttps://arapheno.1001genomes.org/. The Rice dataset can be found in https://doi.org/10.7910/DVN/HGRSJG. Derived data supporting the conclusions of this study are available from the corresponding author upon request, and can be replicated with the procedures present within the article, figures, and tables.The article analyses datasets in the public domain. The Arabidopsis dataset can be found in The most obvious pattern is an improvement for all models in their predictive ability with the increase in marker density . This im3 marker panel (h), so we quantified the components of K for two values of h: the usual ”default”, equal to half the median euclidean distance (K(0.5)) and another the quarter of the median euclidean distance (K(0.25)) with only the first one being used in the fitted models.For the Arabidopsis data, we estimated the variance components associated with additive and epistatic effects of varying orders for the 10er panel . The G mμ), and decreased when modeling more population structure variance as fixed effects . All showed a significant epistatic gap with the same qualitative pattern . This has been an obstacle for the applied use of non-additive models in many breeding plans . InsteadArabidopsis thaliana and seed weight in rice, while using low density markers. That these differences in performance vanished when using more markers was suggestive of phantom epistasis. We strengthen this claim by obtaining remarkably similar patterns with simulated traits with a fully additive genetic architecture. The results of this work show that the marker interactions in these genomic models can capture not only the epistatic, but also the additive component of unobserved causal factors.Phantom epistasis under certain conditions is a plausible explanation for the better performance of non-additive models even when non-additive variance on the level of causal loci is expected to be low. In this work we observed such better performance when predicting flowering time in Algorithm 1 Calculate contribution to variance components of varying orders by regression variable Input:M: marker incidence matrix of size J: a tuple with the indices of the markers involved in the regression variableφ: function from Output:d-length vector, with contribution to i, for Auxiliary functions:Poly: a matrix whose columns consists of all monomials of degree d and lower of X’s columnsOls: predictions from regression of matrix X on vector yprocedure1: contributionvarcomp2: 3: 4: 5: whiledo6: 7: 8: 9: 10: 11: 12: return13:


High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-throughput image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat.The reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indicated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively.This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the integration of phenomics and genomics in breeding programs. The efficient and precise phenotyping of a large population is one of the main tasks in breeding programs . For exaSuch barriers in phenotyping have motivated plant breeders to collaborate with engineers and invent modern technologies for high-throughput phenotyping (HTP) in greenhouses and fields . The HTPThe HTP platforms can include instruments such as RGB , multispectral and hyperspectral cameras, spectrometer, normalized difference vegetation index (NDVI) sensors, and light detection and ranging (LiDAR) technology , 11, 12.Drought stress in the Middle East usually occurs at the end of the growing season when spike has already appeared and seed is at the development stage. In the Persian plateau, where most of the environments are arid or semi-arid, farmers are well-trained over the centuries to store rainwater throughout spring and irrigate farms with the stored water at the end of the growing season. The Persian farmers irrigate their farms two to four more times with the stored water after spike appearance to avoid yield loss due to late-season drought stress. This strategy leads to a significant increase in wheat grain yield . The impGenomic prediction (GP)  methods Motivated by this, this study reported the impact of terminal drought stress (TDS) and well-watered (WW) conditions on days to maturity (DTM) in a highly diverse bread wheat germplasm through an ML-based image-phenotyping pipeline.The association panel used in this study included 286 bread wheat accessions from Iran historical germplasm (199 landraces identified during 1931–1968 in the Persian plateau and 87 cultivars released during 1942–2014 in Iran). The plant materials were kindly provided by the University of Tehran (UT) and Seed and Plant Improvement Institute (SPII), Karaj, Iran. The detailed information about the association panel is provided in Additional file PstI and MspI restriction enzymes, barcoded adapters were ligated to each DNA sample using T4 ligase, polymerase chain reactions (PCRs) were done using primers complementary to both adaptors, size-selection for 250–300 bp fragments was conducted using an E-gel system , and the size-selected library was sequenced on an Ion Proton sequencer . Sequence reads were trimmed to 64 bp sequences, and identical reads were grouped. Then, unique sequence tags were assigned to the sequence groups. The unique tags were aligned internally, whereas up to 3 bp interval alignment mismatch was allowed. The Trait Analysis by aSSociation Evolution and Linkage (TASSEL) software [K= 10 was used in LD KNNi. Finally, 9047 SNPs were used for further analysis.We used the genotyping-by-sequencing (GBS)  method fsoftware  was usedsoftware  for SNP software  in the Tsoftware , whereashttps://taylor0.biology.ucla.edu/structureHarvester). Two subpopulations (SBP-I and -II) were identified within the association panel. The SNP calling was performed for each subpopulation and 7714 SNPs for SBP-I and 5873 SNPs for SBP-II were identified. A number of 4785 markers were common between SBP-I and SBP-II, which were systematically separated and named as common markers marker set (CMMS). The molecular markers estimates were assessed for each chromosome using the full matrix option in TASSEL software [The population structure was evaluated by the Bayesian clustering approach with the use of an admixture model in the STRUCTURE software . The numsoftware .2 (GY). For details on measurements of DTH, DTM, DHTM, PH, and GY and time of assessments, please refer to the manual “Physiological breeding II: a field guide to wheat phenotyping” [Phenotypic measurements included days to heading (DTH), days to maturity (DTM), duration of heading-to-maturity (DHTM), plant height (PH), and grain yield/motyping” .A Canon PowerShot SX30 IS camera was installed on a simple handheld phenocart. The phenocart height was 1.7 m. A flat L-shaped metal bar with 0.5 m long was installed on the phenocart. The camera was mounted on the L-shaped metal bar upside down, whereas the camera with the lens opened had 1.6 m distance from the ground. The phenocart was on the right side of the plots during the imagings  was carried out for each phenotype under TDS and WW conditions separately using the sion 9.4 . The datectively . Best liectively  in the sFor five-fold cross-validation (CV), 20% of accessions were randomly assigned to a VS, whereas all of the remaining genotypes were used as a TS. The whole process was repeated 100 times for each GP . The CMMS was used as a marker set for assessing genomic estimated breeding values (GEBVs). The accuracy of the GP was estimated as Pearson’s correlation coefficient among GEBVs and BLUPs over TS and VS. The average of accuracies was reported across folds and repeats . The GPssoftware . A briefsoftware .Four univariate (UV) GP models were defined. Five phenotypes  were evaluated in each of the UV models under TDS and WW conditions, separately. The UV1 model did not contain any covariate. TOR as a covariate was included in the UV2 model. STR as a fixed effect was included in the UV3 model. Both TOR and STR as fixed effects were included in the UV4 model.In total, 280 analyses were conducted including 4 UV models, 5 phenotypes, 2 irrigation conditions, and 7 GP methods.Plantings were conducted at the Kheirabad Agricultural Research Station in Zanjan province in the middle of October and weather conditions were recorded during the cropping season . More accurate results were reported from mixed populations because more diversity in TS and more inbred genotype in VS would be available during the CVs –55. In tThe CT and NDVI as secondary traits in wheat improved the prediction accuracy of GY by 70% . FurtherThe present study activated an ML-enabled image analysis pipeline to identify TOR and STR impact on the GP of the DTM under TDS and WW conditions. The results revealed the reliability of this pipeline for quantifying small phenotypic variations and integrating its advantages in genomic studies. The high prediction accuracy proves the benefit of utilizing TOR and STR as fixed effects in the UV GP models for DTM. The presented high-throughput image analysis pipeline can be generalized for evaluating other crops. In addition, the installation of this pipeline into aerial and ground-based systems promises to accelerate genetic gain in breeding programs.Additional file 1: Tables S1. and S2 are lists of the 199 landraces and 87 cultivars from Iran bread wheat germplasm used in the present study.Additional file 2: Figure S1. demonstrates general conditions of a plot during the imaging.Additional file 3: Scripts S1. and S2 are defined function and written code in MATLAB.Additional file 4: Figure S2. shows climate conditions in the field.Additional file 5: Figure S3. demonstrates ∆K values for population structure.Additional file 6: Table S3. provides information about the members of each subpopulation.


Pasteurella infection) is one of the most common bacterial infections in rabbits on commercial farms and in laboratory facilities. Curative treatments using antibiotics are only partly efficient, with frequent relapses. Breeding rabbits for improved genetic resistance to pasteurellosis is a sustainable alternative approach. In this study, we infected 964 crossbred rabbits from six sire lines experimentally with Pasteurella multocida. After post-mortem examination and bacteriological analyses, abscess, bacteria, and resistance scores were derived for each rabbit based on the extent of lesions and bacterial dissemination in the body. This is the first study to use such an experimental design and response traits to measure resistance to pasteurellosis in a rabbit population. We investigated the genetic variation of these traits in order to identify potential selection criteria. We also estimated genetic correlations of resistance to pasteurellosis in the experimental population with traits that are under selection in the breeding populations .Pasteurellosis  to 0.16 (± 0.06). The resistance score showed very strong negative genetic correlation estimates with abscess (− 0.99 ± 0.05) and bacteria scores (− 0.98 ± 0.07). A very high positive genetic correlation of 0.99 ± 0.16 was estimated between abscess and bacteria scores. Estimates of genetic correlations of the resistance score with average daily gain traits for the first and second week after inoculation were 0.98 (± 0.06) and 0.70 (± 0.14), respectively. Estimates of genetic correlations of the disease-related traits with average daily gain pre-inoculation were favorable but with high standard errors. Estimates of genetic and phenotypic correlations of the disease-related traits with commercial selection traits were not significantly different from zero.Disease response traits are heritable and are highly correlated with each other, but do not show any significant genetic correlations with commercial selection traits. Thus, the prevalence of pasteurellosis could be decreased by selecting more resistant rabbits on any one of the disease response traits with a limited impact on the selection traits, which would allow implementation of a breeding program to improve resistance to pasteurellosis in rabbits. Pasteurella multocida, a gram-negative bacterium, affects various birds and mammals worldwide, including humans  was calculated by dividing the difference in body weight (g) between time points by the number of days in between for three time periods: birth to weaning (ADG-BW), first week post-inoculation (ADG-PI1), and second week post-inoculation (ADG-PI2).Disease-related traits , growth traits  and commercial selection traits  were analyzed using the Reml method with ASReml 3.0 .Each trait was analyzed using specific models, which were all sub-models of the following “global” linear mixed animal model:For the experimental population, the fixed effects tested for each trait were: gender (2 levels), gestation length , batch (5 levels i.e. five birth months within a year), parity of dam (6 levels), and signs of ERE/digestive disorders (3 levels). The scores for the ERE/digestive disorders were defined as follows: “0” for rabbits without signs of disorders, “1” for rabbits that showed signs of digestive disorders but not confirmed as signs of ERE, and “2” for rabbits with signs of ERE. The random environmental effects in the model were BRC and litter, with 196 levels (5 rabbits per level) and 305 levels, respectively, which will be referred to as non-genetic common environment shared by rabbits of the same litter. On average, each litter included 3.21 rabbits (from 1 to 10). Fixed effects were considered significant and included in the final model if the P-value was less than 0.05. To test the significance of the random effects, log likelihood values obtained from ASReml were used to perform a likelihood ratio test in the statistical software R  using a threshold model. Classification of the disease-related traits into different categories may not be fully correct, which could lead to biased estimates of heritability. For abscess (and bacteria) as binary traits, 0 was assigned to rabbits that did not show any abscesses (and bacteria), and 1 to rabbits that showed signs of abscesses (low to severe) and (bacteria). For resistance as a binary trait, 1 was assigned to rabbits survived until the end of the experiment and that did not show any abscess or bacteria, and 0 was assigned to all other rabbits. In the threshold model, the 0/1 phenotype of a rabbit is linked to the explanatory variables in model (1) by a probit link function. Fixed effects were considered significant and included in the model if their P-value was less than 0.05. Random effects that were significant  in the linear mixed animal model for the corresponding trait were fitted as random effects in the final threshold models for that trait. The threshold model gives estimates of the heritability on the underlying scale ed scale  using \ded scale .Of the 953 experimental rabbits used in the analysis, 72 showed no signs of abscess, 79 showed no bacterial growth in tissue samples, and 71 were resistant to pasteurellosis .P. multocida.The mean of each trait with its standard deviation (SD) are in Table ERE/digestive disorders showed a significant effect for all disease-related and growth traits but not for binary disease-related traits, and for ADG-BW Table . In compEstimates of common litter and permanent environment effects, and of heritabilities are in Tables Estimates of the correlations between traits are in Table We investigated the potential of novel response traits as selection criteria for resistance to pasteurellosis, which to our knowledge, is the first study to investigate such diagnostic response traits. The systematic and detailed response trait measures on infected rabbits allowed us to detect genetic variation of resistance to pasteurellosis. The low to moderate estimates of genetic variance for these novel response traits, along with non-significant genetic correlations with the commercial selection traits, suggest that these response traits could be used as selection criteria for resistance to pasteurellosis.We investigated the contribution of random components such as maternal genetics, litter, and BRC to the phenotypic variance for analysis of disease-related traits on a linear scale and for growth traits. BRC and maternal genetic effects showed no significant contributions to the phenotypic variance. A non-significant contribution of the maternal genetic effects for a disease trait in rabbits has already been described by Eady et al. . HoweverFew studies have reported heritability estimates related to pasteurellosis resistance specifically in rabbits but genetic parameters for natural bacterial infections have been estimated in rabbits , 12, 13.P. multocida, whereas in the previous studies field data were used, for which uneven exposure may lead to a low incidence of disease. Such a scenario was also observed in a study on ERE, in which heritability estimates of the disease decreased from 0.21 (± 0.16) for an experimental rabbit population , which could be due to the use of a similar diagnostic approach in both studies. In . Incidences for the binary traits were outside the 10 to 90% range Table , which mEstimates of variance components for NBA and WW were consistent with data in the literature –34, whicHeritability estimates obtained for ADG before inoculation (ADG-BW) were low, which could be explained by the use of data recorded at an early age interval. For rabbits at an early age, the effect of the maternal environment component is larger, which might reduce the effect of the rabbit itself . We alsoThe heritability estimate for ADG during the first week post-inoculation (ADG-PI1) was moderate (0.29 ± 0.07) and similar to that for ADG during the second week post-inoculation (ADG-PI2) (0.20 ± 0.06). Most rabbits in our study showed a strong decrease in daily weight gain during the first week post-inoculation . The reduced body weight gain in the first week post-inoculation appears to result directly from pasteurellosis infection. However, rabbits coped better during the second week post-inoculation, i.e. the mean of ADG-PI2 increased to 19.8 g/days (SD = 17.89) (from − 42.3 to 74.3). Thus, the heritability estimate of 0.29 for ADG-PI1 suggests that it could be advantageous to select rabbits against pasteurellosis resistance at an early phase of infection during which most of the genetic variability in ADG is observed.Heritability estimates of the traits obtained from both univariate analyses and bivariate analyses (results not shown) were similar, which indicates that our models were robust. Because we used crossbred populations, heritabilities might be slightly overestimated. The effect of heterosis was not analyzed because of the lack of phenotypic information for the sires and dams.Disease-related traits showed strong estimates of genetic correlations but comparatively lower phenotypic correlations between each other. All the rabbits with a severe abscess score did not always have a severe bacteria score or vice versa. Since the resistance score is a composite trait based on the abscess and bacteria scores, strong relationships were expected at both the genetic and phenotypic levels. The strong genetic correlation estimates suggest that rabbits that are genetically more susceptible to harbor many bacteria will also show severe abscesses. This indicates that any of the response traits evaluated here could be an effective indicator to improve disease resistance against pasteurellosis.Estimates of genetic correlations between growth pre-inoculation (ADG-BW) and disease-related traits (abscess and bacteria) were negative and favorable but had large standard errors Table . The estPost-inoculation, we observed strong negative genetic correlation estimates between disease-related traits (abscess and bacteria) and growth traits (ADG-PI1 and ADG-PI2) and strong positive genetic correlations between resistance and growth traits. These relationships suggest that rabbits that are genetically resistant to pasteurellosis have a better ADG post-inoculation than less resistant rabbits. Thus, ADG-PI1, measured over the first week post-inoculation could be a good indicator trait for pasteurellosis resistance since recording ADG-PI1 after inoculation is easier, cheaper, and faster than recording disease-related traits .The standard bivariate analyses that we used did not take any underlying simultaneous or recursive causal effects that may exist between traits into account . A betteModerate and favorable genetic correlations between resistance to infectious diseases and WW − 0.34 ± 0.12) were reported in  ± 0.12 w, estimatP. multocida on sibs of the selection candidates in each generation. Growth rate in the first week after inoculation (ADG-PI1) may be the most promising trait to select on because it had the highest heritability (0.29 ± 0.07) among the traits measured post-inoculation, and it had a very high estimated genetic correlation with resistance (0.98 ± 0.06). This trait is also easier to measure than the other response traits because no laboratory analyses or post-mortem examinations are required, and the duration of the challenge can be reduced to 1 week.Our results show that the host genetics contributes to differences in resistance to pasteurellosis in rabbits. The remaining question is how to integrate these useful findings in a breeding scheme. Various options are possible, depending on the financial resources available. The easiest but probably less efficient option would be to keep selecting on weaning weight or growth rate before weaning to get an indirect correlated response on resistance to pasteurellosis. However, the high standard errors on the genetic correlations between resistance and ADG-BW or WW do not provide high confidence on the magnitude of this correlated response. A more efficient (but more costly) option would be to repeat this experimental infection trial with P. multocida strain. Alternatively, future research could focus on the detection of blood markers or on the development of in vitro immune response tests that could predict response to pasteurellosis without experimental challenge.However, performing repeated disease challenges raises ethical issues, and an adjustment of the challenge may be needed to decrease the intensity of the clinical signs, by reducing the inoculation dose or by changing the Another approach that should be explored is the use of genetic markers. DNA samples from the rabbits used in this study and of their parents have been preserved and will be used for genotyping. If a limited number of genes or quantitative trait loci control resistance to pasteurellosis, gene or marker-assisted selection  could beP. multocida were evaluated. Results provide evidence for a genetic basis for these response traits in this experimental crossbred rabbit population and for the potential to decrease the prevalence of pasteurellosis by selecting resistant rabbits on any of the response traits evaluated. Strong favorable estimates of genetic correlations between disease response traits and growth traits post-inoculation suggest that growth after inoculation could be an alternative efficient indicator trait for pasteurellosis resistance. However, implementing the recording of these traits under industrial conditions is not easy. Instead, these traits could be used to facilitate the detection of markers of pasteurellosis resistance in rabbit populations. The moderate heritability of the disease response traits and their non-significant genetic correlations with the commercial selection traits suggest that a breeding program could combine selection for resistance to pasteurellosis and selection for growth rate without infection.Genetic parameters for novel resistance traits to experimental infection


Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post‐transcriptional, translational, post‐translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment‐dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost‐effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment‐dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker‐dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large‐scale functional validation of trait‐specific precision breeding. It is well estimated that by 2050, the world population will reach ~9 billion and ~9–11 billion by 2100. The current practice of food production using conventional breeding approaches is insufficient to satisfy the global demand by 2050, especially with the view to agroecological questions such as productivity, robustness, sustainability and equitability. Moreover, agricultural productivity and environmental changes are well correlated; any abrupt change in environmental conditions will have a consequent harsh impact on plant productivity owing to the direct and indirect impact of biotic and abiotic stresses , leaf features , carbon use efficiency (CUE), nitrogen use efficiency (NUE) and water use efficiency (WUE) determine major traits that contribute for overall crop performance and yield Figure . For exaArabidopsis thaliana. However, such a detailed understanding of crop plants is rather at its infancy.Opportunities for crop improvement include engineering of these complex traits to improve yield. This requires a thorough understanding of the genetic system and information flow from gene, RNA, protein and metabolite to these traits. Furthermore, developing ecophysiological models by integrating the physiological traits with the PANOMICS approach can improve prediction accuracy and marker identification in the environmental context. Considerable progress has been made in deciphering the genetic and molecular basis of the developmental processes that govern these traits, particularly in the model plant species et al., et al., The application of genomics in the field of plant breeding started with the advent of RFLP marker technology  to enhance breeding lines and (b) improve genetic stocks. Genetic stocks represent new types of germplasm collections that include mutants generated using DNA insertion . This information improves the understanding of causal processes and the prediction accuracy for genomic selection  sequencing that can enable a clear understanding of distinct cell identities and transition states and it also provides evidence about the unique mutations in the cell , subcellular localization and compartmentalization, protein complexes, signalling pathways and protein–protein interactions, all this not predictable from the genome sequence  often leads to delayed seed germination, reduced plant growth and decreased crop yield. The role of proteins in plant stress response is crucial, since proteins are directly involved in shaping novel phenotype by adjusting physiological traits to the altered environment  with metabolomics has emerged as a powerful forward genetics strategy to dissect the genetic and biochemical bases of crop plants Luo, . UtilizaArabidopsis thaliana to identify the effects of widespread genetic variants on metabolic diversity across natural populations . However, two main questions still remain open in the mGWAS analysis (i) How to dissect metabolite pathways and characterize gene function in post‐transcription, post‐translation and epigenetic condition? (ii) mGWAS is based on the ratio of each individual metabolite under different conditions, is this approach dynamic enough to dissect mechanisms underlying metabolic responses to environmental stimuli?et al., The phenotypic outcome depends upon single or multiple genes and their interaction with the environment. Phenomics is the systematic study that involves the gathering of phenotypic data from multiple levels of the organization and progress towards the more complete characterization of the holistic phenotype  ; Phenome Networks database (http://phnserver.phenome‐networks.com/); PHOTOSYNQ (https://photosynq.org/); International plant phenotyping network (IPPN) (https://www.plant‐phenotyping.org/IPPN_home); and European Infrastructure for Multi‐scale Plant Phenomics and Simulation (EMPHASIS) (https://emphasis.plant‐phenotyping.eu/). However, in the current scenario, phenotypic data should be cross‐referenced to the other plant resources such as gene banks, specific genomic information, syntenic, genetic and physical maps, SNP marker, metabolomics and proteomics databases. Eventually, such cross‐reference between phenotype and genomic information can be exploited for crop improvement. Moreover, integrating speed breeding platforms  is majorly based on remote sensing; it is a nondestructive and noninvasive approach. It is based on the principle of gathering information provided by visible/near‐infrared and far‐infrared radiation emitted by the crops. It is the most commonly used approaches in crop phenotyping. The devices used for phenotyping include multispectral, hyperspectral, fluorescence, thermal sensors and imagers (RGB colour cameras). Extensive literature is available with detailed information regarding the use of these devices and advancements in field phenotyping  in Spain to track photosynthesis at different phenological and stress stages throughout the season and to support its application in the context of precision breeding  and generate models that can be used to predict complex traits  is gaining momentum in multi‐omics data integration . However, these mapping populations are products from few cycles of recombination events, limiting the resolution of genetic maps that often do not represent germplasm that is actively used in breeding programmes. By contrast, GWAS overcome these limitations and provide greater resolution for identifying genes potentially responsible for variation in a quantitative trait  in plants, it is of utmost importance to understand and gather information about both the specific loci that underlie a phenotype, and the genetic architecture of a trait  with the identification of ~3.6 million SNPs and phenotyped 14 agronomic traits. Interestingly, the obtained result only explained ~36% of phenotypic variances and the complex genetic architecture of these traits , gene knockouts, activation or repression of genes and epigenetic changes are possible . Several public institutes such as ICRISAT , which belongs to the domain of CGIAR , have been accelerating use of genome editing tools  for enhancing crop production and support smallholder farmers (https://www.icrisat.org/icrisat‐and‐corteva‐agriscience‐agriculture‐division‐of‐dowdupont‐collaborate‐for‐sharing‐advanced‐breeding‐technologies‐to‐improve‐crops‐that‐feed‐millions/). This kind of initiative will accelerate the process of bringing healthier legumes (such as chickpea and pigeon pea) to the consumers  will allow one to perform molecular characterization of genotypes, providing the list of candidate genes/gene products that are highly valuable for breeding and engineering stress‐tolerant crops with novel and valuable traits not reachable by classical genome prediction methods. Still, proteomics and metabolomics studies are often regarded as holistic studies due to the fact that not many putative markers are translated into the productive sector. This is because high‐throughput techniques are still emerging and require steady improvement in instrumentation and algorithms. The cost of generating high‐throughput data needs to decrease substantially because it is important to identify relevant genotype‐phenotype associations that are not predictable from the genome sequence. However, metabolomics platforms are nowadays partially cheaper than NGS platforms and have a higher throughput. Metabolite analysis is very important to understand, for example nutritional value of crops, but also stress resistance. Accordingly, the metabolic readout can be a rapid predictor of important traits of large cohorts of samples. Here, we propose the complementation with PANOMICS because these technologies become more and more cost‐effective and will improve genomic prediction. Eventually, the integration of PANOMICS platforms with systems modelling and genome editing techniques will enhance precision breeding and support quick breeding procedures (such as SPEED breeding) with the ultimate outcome of providing the appropriate cultivars for each agroecological scenario.The authors declare no competing financial interest.WW, PC, AG and RKV conceived the conception of the manuscript; PC, AG and WW drafted the manuscript; RKV critically reviewed the manuscript; AG and AB prepared data tables; PC and AG designed the figures. All authors have revised and approved the final version of the manuscript.


HLA-B/C region (p = 6.64 × 10−09). For EBV, associations are identified for VCA . Human leucocyte antigen (HLA) and trans-ancestry fine-mapping substantiate that distinct variants in HLA-DQA1 (p = 5.24 × 10−44) are driving associations for EBNA-1 in Africa. This study highlights complex interactions between KSHV and EBV, in addition to distinct genetic architectures resulting in important differences in pathogenesis and transmission.Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein-Barr Virus (EBV) establish life-long infections and are associated with malignancies. Striking geographic variation in incidence and the fact that virus alone is insufficient to cause disease, suggests other co-factors are involved. Here we present epidemiological analysis and genome-wide association study (GWAS) in 4365 individuals from an African population cohort, to assess the influence of host genetic and non-genetic factors on virus antibody responses. EBV/KSHV co-infection (OR = 5.71(1.58–7.12)), HIV positivity (OR = 2.22(1.32–3.73)) and living in a more rural area (OR = 1.38(1.01–1.89)) are strongly associated with immunogenicity. GWAS reveals associations with KSHV antibody response in the  Disease prognosis after infection with Kaposi’s sarcoma-associated herpesvirus and Epstein-Barr Virus is highly variable. Here the authors carry out epidemiological and genetic analysis of a Ugandan cohort and suggest complex interactions may influence pathogenesis and transmission. While EBV is ubiquitous globally, the prevalence of, and deaths caused by several associated malignancies, display extensive geographic variation. In contrast, KSHV displays striking geographic variation that parallels associated KS incidence, with highest prevalence reported in sub-Saharan Africa3. The inter and intra-continental heterogeneity in the prevalence of KSHV and incidence of associated malignancies, while attributable to a number of factors including differences in pathogen prevalence, may also be influenced by many environmental determinants, host genetics and gene−environment interactions in different populations. Synergistic and antagonistic interactions have been reported for KSHV−EBV co-infection; B-lymphocytes act as a reservoir of latent infection, in dually infected cells, both viruses subvert the host immune response and inhibit lytic reactivation of each other8. While viral genetic factors have been extensively studied10, host genetic factors and their interactions with the environment, leading to potential disease outcomes, are largely unknown and require investigation, particularly in Africa.The gamma-herpesviruses, Epstein−Barr Virus (EBV) and Kaposi sarcoma-associated herpesvirus (KSHV) are oncogenic viruses associated with several malignancies, including >95% of Burkitt’s Lymphoma (BL) in parts of sub-Saharan Africa and 100% of cases of Kaposi’s Sarcoma (KS), respectively14. On the other hand, host genetic studies for KSHV are lacking, with no genome-wide association study (GWAS) performed for traits associated with infection or diseases. The strong correlation of serological status between siblings (not explained by known risk factors), and the familial clustering of KSHV disease19 are all highly suggestive of host genetic influence in addition to shared environmental risk factors. Candidate gene, whole-exome and next-generation sequencing methods have suggested Mendelian causes of rare paediatric KS cases as a result of inborn errors in immunity. However, the role of genetic variants in adult KS and in the viral control in asymptomatic individuals is less clear20. To date, most studies provided marginal evidence using statistically lenient p value thresholds of between 0.01 and 0.05, which failed to correct for multiple testing to declare “association” for variants in immunomodulatory genes with virus biological function, pathogenesis and tumourigenesis24. In addition, they had other limitations including, very small sample sizes (between 1 and 350 cases), failure to adjust for environmental factors such as co-infection with other pathogens, or confounding by strong HLA associations with HIV and AIDS, and did not stratify controls by KSHV serostatus or include KSHV seronegative controls for comparison, and all lack replication in independent samples. Lastly, only two studies25 have been conducted in African populations.We and others have reported associations in the HLA class II region with EBV EBNA-1 antibody responseTo overcome the limitations of previous studies and attempt to identify convincing associations with KSHV and EBV immune response traits, we assess systematic differences in >4000 individuals from an African population cohort, where both viruses are endemic, using socio-demographic and clinical data to assess intrinsic and environmental determinants of infection and then perform a GWAS using antibody responses as markers of infection. We use whole-genome sequence data, dense genotyping array data and imputation to a panel with African sequence data to identify genetic loci associated with both infections and attempt to replicate previously identified genetic loci in the context of the environment.26. Participants were over the age of 13 years and belonged mainly (>70%) to the Baganda ethnolinguistic group. Villages were categorised according to urbanicity quartiles reflecting shared ‘urban’ characteristics based on differences in economic activity, civil infrastructure, and availability of educational and healthcare services as previously described27, with 28% living in quartile 1   collected during medical survey round 22 in 2011). The GPC is a population-based cohort in Kyamulibwa, rural south-west Uganda, comprising inhabitants of 25 neighbouring villages1. The GPr2: 0.1−0.43)  to KSHV and EBV infections, distributions of antibody levels were determined to the latent  and lytic antigens  in 4365 individuals. All antibody responses were highly variable across individuals as shown by the wide range of median fluorescent intensity (MFI) Fig. . For KSH26. We selected age, sex, socio-demographic and infection status factors  and specific IgG antibody response levels [MFI], we investigated 4365 individuals from the GPCrs Table  that cours Table . Living rs Table . We furtrs Table . HIV serrs Table . Age, etWe then assessed the contribution of environmental determinants Table  on EBV sN = 4365) and EBV (N = 4365), we first explored the heritability of anti-KSHV and anti-EBV IgG serological traits in the GPC. As most individuals in the cohort are seropositive (>90%) presumably from childhood30, those who present as seronegative are also likely to have been infected and thus, produced antibodies at very low/undetectable levels; as such, they are also informative and useful to include in analysis. In this study, heritability estimates were high for anti-ORF73 , anti-K8.1  and anti-K10.5 , and for anti-EBNA-1  and anti-VCA IgG . After correction for unmeasured shared environmental correlation, accounted for by GPS coordinates31, our estimates were reduced yet still significant for all traits as previously found in our pilot study11, anti-ORF73 , anti-K8.1  and anti-K10.5 , and for anti-EBNA-1  and anti-VCA IgG .To assess the contribution of human genetic variation on antibody responses to KSHV . Homogeneity in the study population was previously ascertained by principal components analyses with AGVP populations as a reference panel11. This model accounted well for population structure and cryptic relatedness as shown by genomic inflation factor (λ) ~1.0 for all traits  on chromosome 6, mapped as intergenic between HLA-B and HLA-C, reached genome-wide significance threshold (Table p < 5 × 10−8) used in European studies which we highlight as they merit being prioritised for replication in other studies; for K10.5, rs10794590-T , an intergenic variant on chromosome 10  on chromosome 5 mapped in GDNF  on chromosome 6 mapped in HLA-DQA1 (Table 2) was also associated with anti-ORF73 IgG. In the GTEx database, rs10794590 (associated with K10.5 IgG levels) was associated with the differential expression of ACADSB (p = 2.6 × 10−9) and IKZF5 (p = 1.5 × 10−3) in oesophagus mucosa and colon tissues. We also found rs510205 (associated with ORF73 IgG) associated with the differential expression of the following eight genes, HLA-DQA2, HLA-DQB2, HLA-DQB1, HLA-DRB1, HLA-DRB6, HLA-DQA1, CYP21A1P and LY6G5B in whole blood (p < 3.8 × 10−6). Expression data for our other association signals were unavailable in the GTEx database. We were unable to refine signals linked to HLA alleles with no significant associations identified with ORF73 or K10.15 IgG levels , we did not detect significant associations (p > 0.1) of any of the previous suggested variants for any of the traits in this study  and identified a distinct secondary signal, rs3129867 in HLA-DRA   was associated with lower antibody levels . We also identified significant associations for anti-VCA IgG antibody levels also in the HLA-class II region   (Supplementary Data r2 < 0.02) (Supplementary Data HLA-DRB1*0701 (p = 0.86) or HLA-DQB1*0301 (p = 0.84) alleles  > 638 we replicated strong evidence of association in the HLA class II region with lead SNP rs6927022 (log10BF = 44.3)  displayed heterogeneity in effect sizes in the two studies (pQ = 3.56 × 10−8) as identified in our pilot study. To further, refine genetic association signals, using MANTRA results we generated 99% credible sets most likely to drive association signals and contain causal variants  and compared fine-mapping intervals for each associated lead SNP by analysing the variants 500 kb up- and downstream of the lead SNP in the combined dataset as described previously40. This resulted in three distinct SNPs in the credible set, rs6927022, rs9272371 and rs9274247  and consistent with previous findings in Uganda46. For KSHV, we found increased antibody seroprevalence and IgG levels in males compared to females as previously reported30. For EBV, while higher age (>40 years) was associated with modest decrease in antibody response (OR = 0.64), the background antigenic milieu in this population is different to western populations; it is likely that while antibody levels increase in childhood, they could decay with time (in adulthood) as a result of immunomodulation by other factors as observed in other studies47 or as a result of exposure to other pathogens. Environmental factors such as co-infection with other pathogens have been studied and found to influence variation in seroprevalence estimates, and antibody responses associated with infection48. This is consistent with the results presented in this study, where we found HIV infection to be associated with IgG antibody response levels to KSHV and EBV infection. While HIV infection was associated with lower antibody responses to KSHV, the inverse was observed for EBV antibodies. Similarly, lower urbanicity and educational attainment were associated with a decrease in antibody levels to KSHV and increase to EBV  are also lower than the reported estimate of 37% in a Mexican-American ancestry cohort52; this deflation in estimates is similar to what we had previously observed for our pilot study of EBV in Uganda (h2EBNA-1 = 11%) compared to Mexican-Americans or Europeans (h2EBNA-1 = 37–43%)54. These differences in the GPC compared to other populations could be attributed to a number of factors including differences in study/assay design, locus or allelic heterogeneity influencing traits, not fully adjusting for environmental effects and differences in gene−environment interactions.In addition to environmental factors described above, host genetic factors  can also contribute to phenotypic variation in infectious disease response traits. To explore the proportion of variance in antibody responses attributable to the host genetics, we investigated the heritable component of the IgG response to KSHV and EBV antigens in individuals with genotype data, adjusting for unmeasured environmental correlation using spatial distances and similar methodology used in previous studies56, here we used IgG antibody responses as a proxy for disease progression. While anti-KSHV IgG traits are partly heritable after accounting for shared environment, despite having >80% power to detect signals of moderate to large effect sizes  for all traits, only one genome-wide significant locus (p < 1 × 10−8) was identified, the variant rs111664408 in HLA-B/HLA-C (p = 6.64 × 10−09) on chromosome 6 contributing to inter-individual variability in K10.5 IgG responses. Nonetheless, using the standard genome-wide significance threshold (p < 5 × 10−8), two candidate loci associated with anti-ORF73 and anti-K10.5 IgG response levels were identified, reflecting the latent stage/history of infection. For anti-ORF73 IgG, relatively strong associations were in GDNF on chromosome 5  and in HLA-DQA1   has not been previously implicated in KSHV pathogenesis, it has been reported to be critical in the maintenance of latency for Herpes-simplex viruses infections57. The HLA class I and II regions, however, have been reported to play a role in the pathogenesis of KSHV and KS; nevertheless, previous genetic association studies have failed to identify convincing and reproducible associations at these loci25. Activation of CD4+ T cells is particularly important for anti-KSHV immunity and in vitro studies have shown the CD4+ T cells can inhibit viral replication in KSHV-infected tonsillar B cells59. Like EBV, KSHV has evolved strategies to evade immune detection, including negatively regulating the process by which HLA class II molecules present antigens to CD4+ T cells, thereby promoting its survival. Recently, ORF73 and v-IRF3 (K10.5) have been reported to inhibit MHC class II peptide presentation by blocking the transcription of the class II transactivator (CIITA), a master regulator of class II expression62. These findings suggest that variation in genes that play a role in modulating the immune response, particularly, T-cell immunity could contribute to inter-individual differences in anti-ORF73 and anti-K10.5 IgG levels to control KSHV viral infection, and thus, merit prioritisation in further replication studies in other populations.Conducting GWASs for KSHV-associated diseases is challenging as sample sizes are limiting to achieve power for reliable detection of loci. Prior to this study, GWAS had not been performed for any KSHV phenotype. As previous studies have shown correlation of IgG levels with the development of diseaseHLA Class II region driving associations for EBV EBNA-1 antibody response in different populations14; here we replicated these findings in HLA-DQA1   in HLA-DRA  in HLA-DQA1 with anti-VCA IgG levels  for EBNA-1 IgG. Interestingly, however, it did not explain our lead GWAS signal in HLA-DQA1. We also did not replicate HLA alleles DRB1*0701 and DQB1*0301 previously associated with EBNA-1 IgG levels in two GWASs consisting individuals of European descent14. Both studies, however, retained the significance of their lead GWAS SNPs in the HLA region after adjusting for the effect of the lead HLA alleles 14, and thus, could not fully explain the lead GWAS signal, which is consistent with our findings. This suggests the relevance of additional alleles or a non-HLA effect that did not reach significance or is absent in our study. It is also likely that allelic/haplotypic heterogeneity in Africans vs Europeans, an expression modulating effect or HLA imputation errors play a role particularly, given the limited representation of Africans in the multi-ethnic reference panel63. Trans-ancestry meta-analysis of European and African individuals in the HLA class II region confirmed distinct association signals in the two populations. As with our previous findings, rs9272371 and rs6927022 are likely to be distinct variants in the HLA class II region, with a single signal in Europeans (rs6927022) and a signal mostly driven by rs9272371 in Uganda  Fig.  and idenDRA Fig.  contribuels Fig. . We atteIn summary, we characterise systematic differences in >4000 individuals with evidence of infection by KSHV and EBV in Uganda and identify determinants of response to infection. The availability of data on environmental covariates such as co-infection with other pathogens allowed us to capture genetic variation independently of the environment. Co-infection was indeed a strong predictor of inter-individual variability in antibody responses and highlighted significant and complex interactions between EBV and KSHV. Despite similar heritability and power for KSHV and EBV traits, it was interesting to observe less genome-wide statistically significant associations for the former; this suggests differences in underlying genetic architectures between the traits. It is possible that variants with very small effect sizes or low-frequency variants (i.e. EAF <0.5%) influence inter-individual variability in KSHV immune responses; however, this study is under-powered to detect low-frequency variants with small effect sizes and neither the genetic data nor the tools used here are optimal for accurate rare variant detection. As sub-Saharan Africa sustains such high transmission compared to the rest of the world, these insights are useful to better understand fundamental biological processes of gamma-herpesvirus infection and their epidemiological characteristics to inform appropriate therapeutic interventions and to control their transmission. Lastly, given there are no equivalent datasets from African populations, summary statistics derived from this study are a useful resource for others to use.Informed consent was obtained from all participants either in conjunction with parental/guardian consent for under 18-year-olds with signature, or a thumb print if the individual was unable to write. The study was approved by the Uganda Virus Research Institute, Research Ethics committee (UVRI-REC) (Ref. GC/127/10/10/25), the Uganda National Council for Science and Technology (UNCST) (Ref. HS 870), and the UK National Research Ethics Service, Research Ethics Committee (UK NRES REC) (Ref. 11/H0305/5).64. Households are scattered with some concentrated in the trading centres. Villages were categorised by urbanicity quartiles to reflect shared socio-demographic and ‘urban’ characteristics based on seven components: population size, economic activity, built environment, communication services, educational facilities, health services, and diversity as previously described elsewhere27. Blood samples from 7000 GPC study participants, representing 11 self-reported ethnolinguistic groups, were collected during medical survey sampling conducted in the study area between 2010 and 201126. Details of sexual behaviour, medical, socio-demographic and geographic factors were also recorded. Serum was tested for HIV-1, Hepatis B (HBV) and Hepatitis C (HCV) infections and the remainder was stored at −80 °C in freezers in Entebbe prior to further serological testing.The GPC is a population-based cohort in Kyamulibwa, rural south-west Uganda, consisting of 25 neighbouring villages inhabited mainly by farmers who grow subsistence crop, cultivate coffee for trade and also raise livestock28. Pairwise-correlation between phenotypes was tested for using Pearson’s correlation in R. For each virus, seropositivity was defined as having antibody level to any antigen tested above a prespecified cut-off, as previously described29. Of the original ~7000 people genotyped, we were able to link phenotype results from 4365 people  from samples collected from the GPC in 2011.We quantified MFI to measure the levels of serum IgG antibodies to the KSHV encoded antigens ORF73, K8.1 and K10.5; and to the EBV encoded EBNA-1 VCA and EAD covalently bound to fluorescent magnetic beads and read on a multiplexed platform29 and continuous variables for antibody levels (log10 transformed MFI) were generated and analysed with potential risk factors. The following variables were considered to be possible risk factors or confounders: sex, age (years) or age group , ethnic group (Baganda or other), education level attained , village urbanicity quartile (1−4 with 4 being most urban) based on shared socio-demographic or ‘urban’ characteristics as previously described27. HIV infection, HBV infection, HCV infection, and anti-KSHV serostatus and log10 antibody levels , or anti-EBV antibody serostatus and antibody levels . Multivariate logistic or linear regression models were fitted to examine variables predictive of antibody levels. All variables in the model were independent of each other except for urbanicity quartile and education  and age and education ; nonetheless, we do not identify significant collinearity in our models as variance inflation factors were all close to 1. A multiple testing p value of <0.006 was used to determine statistical significance.To investigate factors influencing IgG antibody response to KSHV and EBV infections, categorical outcome variables for antibody serostatus (i.e. positive/negative)65, African genome variation project32 and UG2G (Uganda 2000 Genomes) reference panel in IMPUTE266. Whole-genome sequencing was performed on 2000 samples with 100 base paired end sequencing at 4× coverage on the Illumina HiSeq 2000 platform following the manufacturer’s protocol. Stringent variant and sample quality control (QC) filtering was performed. Low-quality variants that mapped to multiple regions within the human genome or did not map to any region, and duplicate variants genotyped on the chip were removed. We excluded samples with a call rate <97% and heterozygosity >3 SD from the mean, discordant genetic sex and reported sex, and sites deviating from Hardy−Weinberg equilibrium (p < 10−8). Following imputation, we only included high-quality sites (info score > 0.3 and r2 > 0.6) with minor allele frequency (MAF) ≥ 0.5%. We also removed samples without matching phenotype and genotype or sequence data. Of the merged datasets, 343 samples had overlapping genotype and sequence variant calls for which a final concordance of 98% was achieved for all SNPs. The merged datasets post QC filtering resulted in 4365 samples with KSHV and EBV phenotypes and ~17 M SNPs across the autosomes and X-chromosome for analyses. For EBV analyses we first performed primary GWAS of 3289 unique individuals and then in 4365 individuals i.e. in combination with 1076 overlapping samples present in our pilot study11.Of the 7000 GPC samples, 5000 were densely genotyped on the Illumina HumanOmni 2.5M BeadChip array and we then imputed additional variants into the genotype chip dataset using a merged 1000 Genomes phase 3 67. HLA genotypes of 117 alleles (AF > 1%), at 11 loci were imputed with HLA*IMP:0263 using the standard HLA*IMP:02 multi-ethnic reference graphs, representing ~800 reference individuals for HLA-DRB3/4/5 and >6000 reference individuals for all other loci.Human leucocyte antigen alleles were imputed using genotype data available from across the extended MHC region only  and SNP stranded-ness was checked with the HLA*IMP front-end softwareh2) for anti-EBNA-1 IgG, anti-VCA IgG, anti-ORF73 IgG, anti-K8.1 IgG and anti-K10.5 IgG traits were estimated using a linear mixed model (LMM) in FaST-LMM with two random effects, one based on genetic effects and the other on environmental effects using spatial location31 recorded as Global Position System (GPS) coordinates as a proxy for environmental effects.Narrow-sense heritability (http://biostats.usc.edu/software). We conducted analyses for all quantitative antibody traits for KSHV (N = 4365) and EBV (N = 3289 and N = 4365 (including overlapping samples from pilot study) by first applying a multivariable linear regression model adjusting for significant covariates in R. Residuals of MFI values used for analyses were then transformed using inverse, rank-based normalisation in R to ensure a standard normal distribution for the phenotypes and confirmed by visualisation. To control for cryptic relatedness and population structure within the GPC, the GWAS of transformed phenotypes was performed using the standard mixed model approach in GEMMA68. A kinship matrix to define pairwise genetic relatedness among individuals was generated using pooled imputed genotypes and sequence data for all autosomes and X-chromosome using the k = 1 option in GEMMA. The data were LD-pruned (r2 = 0.2) using dosages and an MAF threshold of 1% was applied. Environmental correlation using GPS data as a proxy, genotyping or sequencing method were also adjusted for as additional covariates during analysis in GEMMA. To identify distinct SNPs, conditional analysis was performed in GEMMA. Each SNP within 1 MB of the lead association SNP was conditioned. If any SNP was statistically significant, it was added stepwise onto the mixed model and analysed jointly; this was done until no SNPs with p < 5 × 10−9 33 remained. All SNPs remaining statistically significant were considered distinct association signals. To functionally annotate our most significant associations, we used the Ensembl Variant Effect Predictor (VEP)69 and the gene/tissue expression database  V8 release to access data on expression quantitative trait loci (eQTLs) from tissues. To test associations between imputed HLA allele and amino acid dosages and phenotypes, we performed linear regression of transformed phenotypes in GEMMA as performed in the SNP-based GWAS for that phenotype.The statistical power to identify genetic variants of genome-wide significance and with different effect sizes given the sample size was estimated using QUANTO software  > 6 which is comparable to a p < 5 × 10−8, previously determined by Wang et al.38. Heterogeneity of allelic effect sizes was calculated using Cochran’s Q-test for heterogeneity in METAL70. Using MANTRA results we generated 99% credible sets most likely to drive association signals and contain causal variants  and compared fine-mapping intervals for each associated lead SNP by analysing the variants 500 kb up- and downstream of the lead SNP in the Ugandan and combined Ugandan + European datasets. For this, posterior probabilities were calculated for SNPs and then ranked in decreasing order according to BF, proceeding down the rank until the cumulative posterior probability exceeded 99%40. All SNPs ≥ 0.99 were included in the credible sets.MANTRA was used to perform a genome-wide trans-ethnic meta-analysis for anti-EBNA IgG responses with association summary statistics of 4365 individuals from our Ugandan GWAS combined with publicly available data of 1000 Genomes-imputed European ancestry GWAS from 2162 seropositive individuals, giving a total of 6527 individuals with ~4.6 million shared SNPs for analysis. The MANTRA approach leverages differences in LD structures across populations to account for differences in genetic architecture and accommodates heterogeneity of allelic effects between distantly related populations within a Bayesian partition frameworkFurther information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Reporting Summary