A quantitative trait locus was identified on chromosome 7 (∼25 Mb), at the major histocompatibility complex (MHC) region, explaining ∼30% of the genetic variance for S/P ratio, mainly by SNPs ASGA0032113, H3GA0020505, and M1GA0009777. This same region was identified in the bivariate GWAS of S/P ratio and reproductive traits, with SNP H3GA0020505 explaining up to 10% (for NBA P1) of the genetic variance of reproductive performance. The heterozygote genotype at H3GA0020505 was associated with greater S/P ratio and NBA P1 (P = 0.06), and lower MUM P3 and NSB P3 (P = 0.07). Genomic prediction accuracy for S/P ratio was high when using all SNPs (0.67) and when using only those in the MHC region (0.59) and moderate to low when using all SNPs excluding those in the MHC region (0.39). These results suggest that there is great potential to use antibody response to PRRSV vaccination as an indicator trait to improve reproductive performance in commercial pigs.We proposed to investigate the genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) vaccination and its relationship to reproductive performance in non-PRRSV-infected commercial sows. Nine hundred and six F1 replacement gilts (139 ± 17 days old) from two commercial farms were vaccinated with a commercial modified live PRRSV vaccine. Blood samples were collected about 52 days after vaccination to measure antibody response to PRRSV as sample-to-positive (S/P) ratio and for single-nucleotide polymorphism (SNP) genotyping. Reproductive performance was recorded for up to 807 sows for number born alive (NBA), number of piglets weaned, number born mummified (MUM), number of stillborn (NSB), and number of pre-weaning mortality (PWM) at parities (P) 1–3 and per sow per year (PSY). Fertility traits such as farrowing rate and age at first service were also analyzed. BayesC0 was used to estimate heritability and genetic correlations of S/P ratio with reproductive performance. Genome-wide association study (GWAS) and genomic prediction were performed using BayesB. The heritability estimate of S/P ratio was 0.34 ± 0.05. High genetic correlations ( In addition, these authors showed that S/P ratio was highly genetically favorably correlated with litter size traits during PRRSV infection, such as number born alive (NBA), with an estimate of 0.73 and number of stillborn (NSB) of -0.72. rg estimate between S/P ratio and NSB (-0.73). Nonetheless, these authors obtained a weak rg estimate between S/P and NBA (0.05) and a low h2 estimate for S/P ratio (0.17). However, waiting for PRRS outbreaks to occur to collect data might limit the use of S/P ratio as a selection tool. Ideally, this strong relationship between S/P ratio and performance in PRRSV-infected sows would also be favorable in non-infected pigs.Antibody response, measured as sample-to-positive (S/P) ratio, to porcine reproductive and respiratory syndrome (PRRS) in PRRS virus (PRRSV)-infected sows has been proposed as an indicator trait for selection for improved reproductive performance in sows during a PRRS outbreak . These ah2 estimates ranging from 0.28 to 0.47, as the proportion of seroconverted animals increased in the dataset. Although there was no confirmation on whether the replacement gilts in h2 estimate of 0.69. Also, they developed a phenotyping criterion to discriminate susceptible and resilient sows based on viral load at 7 and 21 days post vaccination  vaccine stimulates the same mechanisms of evasion as are developed during natural infection . Initialcination . At the Thus, in our study, we proposed to investigate the genomic basis of S/P ratio to PRRSV vaccination and its relationship with reproductive performance in non-infected commercial sows in an independent dataset (from a different breeding company) than the animals from All methods described in this study were approved by the Institutional Animal Care and Use Committee at Iowa State University (IACUC# 6-17-8551-S).Nine hundred and six naïve F1 (Landrace × Large White) replacement gilts from two commercial farms in North Carolina, United States, were vaccinated (139 ± 17 days old) intramuscularly with a commercial PRRS MLV vaccine , following the manufacturer’s guidelines. Throughout the study, the farm performed periodic diagnostic tests for PRRSV and all animals (included or not in the study) did not show PCR-positive tests or signs of natural PRRSV infection. Blood samples were collected using Lavender Top Vacutainer tubes  at approximately 50 days after vaccination in three contemporary groups (CGs), in which each CG represented 1 day of blood collection . After collection, a drop of blood from each sample was used on blood cards  for subsequent genotyping. The remaining blood samples were shipped to the Veterinary Diagnostic Laboratory at Iowa State University , where samples were processed using the laboratory’s standard procedures, for measurement of Immunoglobulin G against PRRSV, as S/P ratio, using a commercial ELISA test .A subset of 807 animals from the animals with information of S/P ratio, located on PRRS-negative commercial farm, had farrowing performance recorded for up to three parities from January 2018 (∼150 days after blood collection) to December 2018 for the following litter size traits: NBA, number of stillborn (NSB), number born mummified (MUM), number of piglets weaned (NW), and number of pre-weaning mortality from the total number being weaned (PWM). Number born dead (NBD) was calculated as the sum of MUM and NSB, and total number born (TNB) was calculated as the sum of NBA and NBD. For animals with three parities, the number of piglets per sow per year (PSY) was calculated for each trait as the sum of the phenotype across the three parities divided by the difference in days between the third and first farrowing, multiplied by 365 days. We also analyzed fertility traits such as farrowing rate (FR), which was defined as the probability of an inseminated sow to farrow, age at first service (AFS), and farrowing interval (FI), which was the difference in days between the farrow date of parities 1 and 2, and between parities 2 and 3. The summary statistics of the data are presented in Sus scrofa 11.1 assembly.Blood cards were shipped to Neogen Genomics  for DNA extraction according to their standard procedures. Then, DNA from each animal was used for genotyping using the GGP Porcine HD (Neogen GeneSeek) for a total of 50,697 single-nucleotide polymorphisms (SNPs). Genotypes were set to missing if GC score < 0.50, SNPs with call rate < 0.90 were removed, and animals with genotype call rate < 0.90 were removed. After quality control, 45,536 SNPs and 906 animals were used for subsequent analyses. Positions of SNPs on the genome were based on the Bayesian analysis BayesC0;  was usedyij is a vector of phenotypic response (S/P ratio or reproductive performance); μ is the intercept; X is the incidence matrix relating the fixed effects to the response; b is a vector of fixed effects: CG for S/P ratio, farm for farrowing performance, and number of piglets cross-fostered and NBA as covariate (for NW and PWM); W is the incidence matrix relating the random effects to the response; u is the vector of random effects: combination of month/year of farrowing for NBA, NSB, MUM, NBD, and TNB, month/year of weaning for NW and PWM, and month/year of birth for AFS, PSY, FR, and FI traits; zi is the vector of genotypes for SNP i ; αi is the allele substitution effect of SNP i; δi is an indicator whether SNP i was included (δi = 1) or excluded (δi = 0) in the model for a given iteration of the Monte Carlo Markov chain (MCMC) ; and e is the vector of residuals. FR was a binary trait and was analyzed with a threshold model.where h2). Then, the estimate of h2 was calculated as the posterior mean of the sampled h2. The posterior standard deviation of the h2 samples was used as the standard error of the estimate.Bayesian analyses consisted of 50,000 MCMCs, with the first 5,000 discarded as burn-in. At every 100th iteration of the chain, the breeding value of each individual used in the analysis was calculated as the sum of its genotypes multiplied by the sampled marker effects. The variance of the sampled breeding values was used as the sampled additive genetic variance in that iteration. The sampled additive genetic variance was divided by the sampled phenotypic variance  at each iteration to obtain the sampled heritability  traits for individual j; μ is a vector of overall means for t traits; X is X1 and X2are the incidence matrices relating the fixed effects to the response for S/P ratio (k = 1) and reproductive performance (k = 2), respectively; b = b1 and b2are the vectors of fixed effects for S/P ratio and reproductive performance, respectively; W is the incidence matrix relating the random effects to the response (reproductive traits); u is the vector of random effects (month/year of farrow); zij is the genotype covariate at locus i for individual j ; m is the number of genotyped loci; Di is a diagonal matrix with elements diag(Di) = δi = , where δik is an indicator variable indicating if the marker effect of locus i for trait k is zero or not and, in this case, there are 2t = 4 combinations for δi: , , , and , with  representing the proportion of markers not being fitted in both traits and so on ; βi is the vector of marker effect for loci βi, where ∼ MVN , where ej is the vector of residuals of t traits for individual j, where ej∼ MVN , where where rg) was estimated as the posterior mean of the correlation between the sampled genomic breeding values for each animal for each trait at each iteration and its standard deviation across iterations as the standard error. The proportion of the covariance between S/P ratio and reproductive performance that was explained by a 1-Mb window was calculated as the covariance between sampled window breeding for each animal obtained based on the SNPs in the 1-Mb window divided by the covariance between sampled breeding values for each animal obtained based on all SNPs across the genome. All analyses were performed in the JWAS package  for each trait were simultaneously fitted as categorical fixed effects in the model used for estimation of genetic parameters. Orthogonal contrasts were used to test the additive and dominance effects for each marker. Significant associations and tendency were considered at the significance levels of eml v4.0 .Genomic prediction was performed for S/P ratio using the same statistical model as used for GWAS, using BayesB (π = 0.995) and BayesC0. We used BayesB based on our results and on the literature, which showed that S/P ratio has a major QTL accounting for ∼25% of the genetic variance. The BayesB method assumes different genetic variance per locus, allowing for the QTL to be well captured while reducing the effect of the small-effect QTL. Due to this major QTL for S/P ratio, we also analyzed the data removing SNPs located in this major QTL. With many more markers to be estimated and assumption equal variance, we chose to use BayesC0, as this method is more suitable under these circumstances. Nonetheless, this is the first study showing the genomic prediction for S/P ratio to vaccination, and thus, we investigated both methods to identify the one that would be more accurate.A three-fold cross-validation was used for genomic prediction analyses. For this, data from two CGs were used for training, and the data from the remaining CG were used as validation. This was repeated until all CGs were used as validation. The number of individuals in each validation dataset was 378, 257, and 252 for CGs 1, 2, and 3, respectively. Marker effects were estimated  based on the training population in three scenarios: (1) using the whole genome (ALL), using markers for the MHC QTL on SSC 7 for each training population (MHC), and using the rest of markers not included in the MHC scenario (REST). For the MHC scenario, a GWAS was performed for each training population to preselect the markers. The regions defined for each training population were SSC 7 25–26 Mb (29 SNPs) for two training populations  and SSC 7 23–26 Mb (81 SNPs) for the other training population (CGs 1 with 2). GPA was calculated as follows:ni is the number of individuals in the ith validation dataset , ri is the correlation between the genomic estimated breeding values (GEBVs) and phenotypes adjusted for estimates of fixed-effects (y∗) for the ith validation dataset, and h2 is the heritability estimate using the whole dataset. All analyses were performed using the JWAS package.where h2 are presented in h2 estimates for S/P ratio and AFS were moderate, with 0.34 and 0.29, respectively, and higher than those for reproductive performance. This indicates that a faster genetic progress could be obtained for S/P ratio compared with reproductive performance. All other fertility and litter size traits showed low h2 (<0.20) and were similar across parities and sows per year. The highest and lowest h2 estimates for these traits were 0.16 (FR P3) and <0.001 , respectively. In general, the average h2 for FR (0.16) across parities was higher than for litter size traits (0.05). As expected, fertility and litter size traits had low h2 estimates, indicating slow genetic progress for these traits.Estimates of rg estimates between S/P ratio with reproductive traits are presented in rg estimates with favorable direction, such as between S/P ratio with NBA P1 (0.61), PWM P2 (-0.70), NSB P3 (-0.84), MUM P3 (-0.83), and NSB PSY (-0.90). These estimates indicate that selection for increased S/P ratio would result in improved farrowing performance for these traits. A moderate non-favorable rg estimate was observed between S/P ratio and NW P1 (-0.35). For other traits, rg estimates were overall low and favorable.The Sus scrofa chromosome (SSC) 7 (23–26 Mb), the MHC region, explaining 30% of the TGVM (PPI = 1). This variance was mainly explained by SNPs ASGA0032113, H3GA0020505, and M1GA0009777, which explained 21% (PPI = 1), 10.5% (PPI = 0.93), and 3.5% (PPI = 0.78) of the TGVM, respectively.The GWAS results are presented in rg > 0.50 with S/P ratio  for S/P ratio across all bivariate GWASs. Additionally, this region explained 34, 25, 26, 35, and 90% of the genetic covariance explained for by the markers (TGCoVM) between S/P ratio with NBA P1, PWM P2, NSB P3, MUM P3, and NSB PSY, respectively. These results indicate that this major region on the MHC for S/P ratio is also associated with farrowing traits.For the bivariate analysis between S/P ratio and reproductive traits showing /P ratio , results/P ratio . This reP < 0.001) associated with S/P ratio to PRRSV vaccination. The additive effect was significant (P ≤ 0.06) for all of them, and the dominance effect was significant for H3GA0020505 (P = 0.002). For H3GA0020505 and M1GA0009777, genotypes AA  and AB  had greater S/P ratio than BB , while for ASGA0032113, S/P ratio increased from AA to BB, with 1.5 ± 0.06, 1.7 ± 0.06, and 1.8 ± 0.08, for AA, AB, and BB, respectively  and bivariate analysis  is shown in ectively .P ≤ 0.07) effect on NBA P1, MUM P3, and NSB P3. For these traits, there was a dominance effect (P ≤ 0.06), whereas there was an additive effect (P = 0.01) also for MUM P3  associated with S/P ratio to PRRSV vaccination. There was a tendency (P ≤ 0.06) for the additive effect for all of them, and the dominance effect was significant for H3GA0020505 (P = 0.002). For H3GA0020505 and M1GA0009777, genotypes AA  and AB  had greater S/P ratio than BB , while for ASGA0032113, S/P ratio increased from AA to BB, with 1.5 ± 0.06, 1.7 ± 0.06, and 1.8 ± 0.08, for AA, AB, and BB, respectively  and bivariate analyses  is shown in ectively .P ≤ 0.07) for NBA P1, MUM P3, and NSB P3. For NBA P1, there was a tendency for the dominance effect (P = 0.06), whereas for MUM P3 and NSB P3, the additive and dominance effects were significant  to 0.59 for ALL (BayesB). GPAs were higher for BayesB than for BayesC0 for scenarios ALL and MHC, but not for REST. This may be explained by the major QTL on the MHC region identified for all training population and was excluded in the scenario REST. We observed a large standard deviation for the MHC scenario when using BayesC0, much larger than when using BayesB. The GPAs for training population without CGs 1, 2, and 3 were 0.71, 0.68, and 0.17, respectively, for BayesC0, and were 0.58, 0.68, and 0.51, respectively, for BayesB. Thus, selection and training of markers using CGs 1 and 2 and validating CG 3 did not work well using BayesC0. Among scenarios, ALL showed better accuracy than did MHC and REST for BayesB, whereas ALL and MHC were comparable for BayesC0. Although there was substantial decrease in GPA from MHC to REST for BayesB, this was not the case for BayesC0. In fact, overall, BayesC0 had similar GPAs across all scenarios.h2 estimates reported for litter size traits in this study were slightly lower than estimates from literature using commercial F1 gilts (h2 estimate for AFS was moderate but higher than previously shown in F1 (Large White × Yorkshire) gilts . h2 of 0.11 ± 0.01 in a study including Landrace, Large White, and F1 sows. To the best of our knowledge, there are no reports in the literature regarding the mortality traits PSY. In our study, the h2 for FR was low but slightly higher to what has been reported by The F1 gilts . Nonethets 0.16; . Our estts 0.16; . The h2 In this study, F1 replacement gilts followed the standard acclimation procedures for the farms enrolled in our study. Moreover, these animals were sourced from high health multiplier herds and represent standard high-producing sows in the US swine industry. Therefore, our results are well representative of current genetics used for commercial production.h2 estimate for S/P ratio, with 0.34 ± 0.05. The first h2 estimate for S/P ratio reported in the literature was by h2 estimate, of 0.17 ± 0.05 during the PRRS outbreak. In their study, antibody response to PRRSV was measured at about 60 days after the PRRS outbreak. There were three main differences between h2 observed. First, the time of sample collection in h2 estimates of S/P in Duroc (0.33 ± 0.06) and Landrace (0.28 ± 0.07) sow populations during a PRRS outbreak, measured about 54 days after the outbreak using the same ELISA test used in our study. As in h2 estimate found in The number of studies investigating the genetic variation for antibody response to PRRSV has increased in the literature in the past few years. Studies vary on the age of animals (young and adult), type of exposure to PRRSV , and the assay used to measure antibodies. We measured S/P ratio ∼52 days after vaccination using a commercial ELISA test, when animals were ∼34 weeks old, and we obtained a moderate h2 estimates ranging from 0.28 ± 0.04 to 0.47 ± 0.06, as the proportion of seroconverted animals increased in the dataset; however, there was no confirmation on whether the replacement gilts in were PRRSV-vaccinated or PRRSV-infected or even both. More similar to our study, h2 for S/P ratio at 42 days after PRRSV vaccination and obtained a higher h2 estimate than we observed in our study (0.69 ± 0.10). Although they used the same ELISA test as us, the animals in their study were growing pigs at 6–7 weeks of age, and it is possible that the redirect of energy being used to growth for antibody production may have affected future performance. Another important point to be highlighted is that in the study by h2 for S/P ratio in PRRS-vaccinated F1 replacement gilts.Differently than in the studies described above, in our study, we investigated the relationship between S/P ratio and vaccination with reproductive performance in non-infected sows. h2 estimates observed across the different studies, such as the time of sample collection, the assay used for antibody measurement, the strains affecting the population, breeds, and genetic background, among others. The difference observed between our studies and PRRS outbreak studies may be the higher viral load during an outbreak compared with vaccination, although the use of MLV stimulates a similar immune response when compared with natural infection  and NSB P1 (-0.05) may explain the lower rg between S/P ratio with TNB P1 compared with the rg with NBA P1, which indicates that selection for increased S/P ratio would increase NBA and decrease mortality traits at parity 1. Also, S/P ratio was highly negatively correlated with MUM P3 (-0.83) and NSB P3 (-0.84), indicating that the use of S/P ratio as a selection tool would have positive long-term effects. For the rg estimates between S/P ratio with NSB P3 (-0.84) and MUM P3 (-0.83), although the standard errors for these estimates were low, these estimates were inconsistent with the one for NBD (-0.19), since NBD is calculated as a function of NSB and MUM. The h2 estimates for NSB P3 (0.001) and MUM P3 (0.003) were very low in our study and, thus, have very low genetic variances for these traits in comparison with their respective phenotypic variances. Therefore, although highly genetically correlated with S/P ratio, the very low h2 estimates for these traits indicate that there is limited genetic improvement of these traits. On the other hand, the posterior probabilities of these rg estimates being smaller than zero were of 1 (data not shown), indicating that there is a negative genetic correlation between S/P ratio with NSB P3 and MUM P3.In our study, however, S/P ratio had high and moderate positive genetic correlations with NBA P1 (0.60) and TNB P1 (0.30), respectively. The low negative rg estimates of S/P ratio with NBA and NSB are similar to those reported by rg between S/P with NSB, of -0.73 during a PRRS outbreak but not for NBA, which was positive but very low (0.05). Although not directly comparable, our rg between S/P ratio and AFS was moderate and negative (-0.25) while null with FR, indicating that selection for increased S/P ratio would result in younger gilts being serviced. Analysis of PSY traits has the advantage of taking in consideration the interval between farrow events, which also reflects the overall farrowing efficiency of the sows. In general, the rg estimates between S/P ratio with PSY trait were low to moderate, with exception of NSB, which was high and negative (-0.90), in accordance with the rg estimate between S/P ratio and NSB P3 (-0.84).In addition to these results, we also investigated the relationship of S/P ratio with AFS, FR, and PSY, which had not yet been reported in the literature. The rg > 0.50 with S/P ratio -DR, SLA-DQ, SLA-DM, and SLA-DO proteins; transporter-associated with antigen processing genes, such as ATP binding cassette subfamily B member transporter 1 (TAP1) and 2 (TAP2); and proteasomes, such as Proteasome 20S subunit beta 8 (PSMB8) and 9 (PSMB9)  . Interesin swine . In addiTNF), lymphotoxin alpha (LTA), and lymphotoxin beta (LTB), components of the complement cascade, such as complement C2 (C2), complement C4A (C4A), and complement factor beta (CFB); heat shock proteins, such as HSP1A, HSP1B, and HSP1L; and genes with complex functions, such as tenascin XB (TNXB) and notch receptor 4 (NOTCH4). Some of them are potential candidate causal genes given their previous association with PRRSV. Among them, C2 has been associated with PRRSV susceptibility and was downregulated in pregnant sows with higher antibody response at 35 days post PRRSV vaccination  and the MHC region. However, in our study, we used SNP data to perform associations between them and variation in S/P ratio. Although we obtain strong and somewhat narrow associations, including the identification of specific SNPs explaining the majority of the variation, our analyses do not allow us to make any sort of cause and effect. Therefore, additional studies are needed to further investigate the causal variants that could be explained by the SNPs identified in our studies.TAP1 gene, located in this region, were more PRRS resilient than others (HSD17B8) gene, located at 25.2 Mb, has been associated with reproductive traits in pigs , which is involved in the innate immune response and induces NF-kappa-B activation. We also constructed an LD map of this region . Similarly, Interestingly, the other major QTL found by s region , but conSummarizing, the major QTL identified in this study for vaccination was the same as the one previously identified in PRRSV-infected animals, supporting that the genetic control to vaccination may be similar to infection. This is reasonable, as we used an MLV vaccine, which has an attenuated form of the PRRSV. Also, the high genetic correlation between S/P ratio to PRRSV vaccination and reproductive performance, which was partially explained by this QTL, could be due to LD between genes controlling immune response and reproductive performance, or pleiotropy in the MHC region.P ≥ 0.14) associated with S/P ratio in our dataset (data not shown).The main SNPs associated with S/P ratio were responsible for most of the variance accounted by the window, and after fitting them simultaneously as fixed effects in the model, the percentage of the variance explained by that window dropped to about null, supporting that these SNPs are playing a major role explaining the variability in S/P ratio. These three SNPs were not the same as the ones identified in The SNP H3GA0020505 was associated with S/P ratio and farrowing performance in the bivariate analysis. Although this was the SNP explained most of variance for all traits analyzed, after fitting it as a fixed effect in the statistical model, this SNP tended to be associated with NBA, MUM, and NSB. The AA genotype was favorable for S/P ratio, NBA, and NSB but not for MUM, being the heterozygous genotype  the only simultaneously favorable for all these three traits. Assuming that greater S/P ratio is favorable, this SNP seems to have a complete dominance mode-of-action for the A allele, and the highest values of S/P ratio could be obtained with AA and AB genotypes. However, the AA genotype was unfavorable for MUM P3, with the other two genotypes (AB and BB) having the highest performance. For this trait, the dominance seems to be the opposite than for S/P ratio, with the B allele dominating the A allele. The biological explanation for this is beyond what can be concluded using SNP data, but in case of pleiotropy, the transcripts for of AB genotypes could be the only ones participating on complex pathways that favor both traits simultaneously. This same rationale could be used for PMW P2 and NSB P3, where AB and BB genotypes had similar and favorable performances, although no significant association between H3GA0020505 and these two traits were identified. The trend for these two traits was clear  and similar to the that for MUM P3, but the lack of significant associations for PWM P2 and NSB P3 could be due to the high standard error of the AA genotype, which can be explained by its very low frequency in this population. The frequency of the AA, AB, and BB genotypes were 0.074, 0.464, and 0.462, respectively. The consistent superiority of the heterozygote across all of these traits facilitates selection for improved performance in this situation. Since these F1 gilts were generated from two maternal lines (Landrace and Large White), the selection for opposite alleles could be performed for each breed in order to result in 100% heterozygote F1 animals for this locus. Investigation of genotypic frequencies in the purebred populations used to create these F1 gilts indicates that this locus is in Hardy–Weinberg equilibrium (data not shown), suggesting that this novel locus has not yet been selected for.We further explored the impact of H3GA0020505 on the relationship between S/P ratio and these traits discussed in this section. After fitting this SNP as fixed effect in the model, the covariance explained by the window containing this SNP dropped from 34 to 26% and 90 to 74% for NBA P1 and NSB PSY, respectively, but not for the other traits. The genetic correlation between these two traits and S/P ratio also decreased for NBA, from 0.61 to 0.50 for NBA P1, supporting the results that this SNP plays a major role in the relationship between these two traits. In addition, we observed that the genetic variances for S/P ratio and reproductive performance, and the genetic covariances between these traits decreased as well. Although this happened for all the traits, the genetic correlations between S/P ratio with PWM P2, NSB P3, and MUM P3 did not decrease , showing that the remaining SNPs were capable of maintaining the relationship between these traits. Altogether, our results provided potential genetic markers  that could be explored in marker-assisted selection schemes to increase S/P ratio and farrowing performance in commercial sows.The GPAs were moderate to high, showing that genomic information is able to accurately predict S/P ratio in commercial gilts vaccinated for PRRS. GPAs for ALL and MHC were similar and greater than for REST. Interestingly, results for REST using BayesC0 were greater than using BayesB. These results are in accordance with the GWAS results, in which the MHC region plays an important role in the prediction of S/P ratio and using only SNPs in the region promotes a substantial GPA. In contrast, no regions outside the MHC were identified in the GWAS, supporting the result using BayesC0 for REST, since this method assumes that no major QTLs control the trait being analyzed.SLA-DRA gene expression  in these regions explained the vast majority of the genetic variance for S/P ratio within the MHC. In addition, SNP H3GA0020505 was associated with S/P ratio and reproductive performance, with the heterozygote genotype yielding the most favorable performance across these traits. Finally, the accuracy of genomic prediction was fairly high when using all SNPs available and only those located in the MHC region. Altogether, these results indicate that genetic selection for increased S/P ratio after PRRS MLV vaccination would result in indirect response for genetic improvement of farrowing performance in commercial sows. However, the improvement of reproductive performance may not be observed in all parities. Nonetheless, the majority of the genetic correlations that were not high were close to zero and/or in the right direction. Thus, genetic selection for S/P ratio might not generate unfavorable response in the reproductive performance in other parities. Future work is needed to validate our results for S/P ratio to PRRSV vaccination and its relationship with farrowing performance. In addition, it is necessary to evaluate if this genetic relationship exists in different populations , while evaluating additional time points for collection of S/P ratio and using data on sows with greater number of parities .The data that support the findings of this study are not publicly available. Data may be available from authors upon request and authorization from the company that generated the data.The animal study was reviewed and approved by Institutional Animal Care and Use Committee at Iowa State University (IACUC# 6-17-8551-S).LS performed the data analyses, interpreted the results, and drafted the manuscript. KG, JD, MN, and NS developed the research project. LS, RF, JD, and NS conceived the statistical analyses. LS and NS prepared the first draft of the manuscript. KG and DL coordinated the collection of materials. MN and DL coordinated the antibody measurement analysis. All the authors contributed to the final manuscript, read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Maize is rapidly replacing traditionally cultivated dual purpose crops of South Asia, primarily due to the better economic remuneration. This has created an impetus for improving maize for both grain productivity and stover traits. Molecular techniques can largely assist breeders in determining approaches for effectively integrating stover trait improvement in their existing breeding pipeline. In the current study we identified a suite of potential genomic regions associated to the two major stover quality traits—in-vitro organic matter digestibility (IVOMD) and metabolizable energy (ME) through genome wide association study. However, considering the fact that the loci identified for these complex traits all had smaller effects and accounted only a small portion of phenotypic variation, the effectiveness of following a genomic selection approach for these traits was evaluated. The testing set consists of breeding lines recently developed within the program and the training set consists of a panel of lines from the working germplasm comprising the founder lines of the newly developed breeding lines and also an unrelated diversity set. The prediction accuracy as determined by the Pearson’s correlation coefficient between observed and predicted values of these breeding lines were high even at lower marker density (200 random SNPs), when the training and testing set were related. However, the accuracies were dismal, when there was no relationship between the training and the testing set. Crop residues provide major feed resources for livestock in low and middle income countries (LMCs). In India, maize is now the third most important crop after rice and wheat, and it is cultivated on over 8.7 million ha with 24.0 million ton of grain produced at an average yield of 2.6 tha−15. Most of the increase has been recorded in non-traditional maize growing areas where it is grown as an irrigated crop for commercial purpose during the dry season. In these areas, maize is largely replacing sorghum, an important dual-purpose crop. The stover of sorghum is highly valued by livestock keepers and fodder traders7. Given the prevalent fodder shortage in India, maize stover would need to substitute for the loss in sorghum stover8. Increases in maize area have also been reported in East Africa, where there is a similar need to look into the fodder value of maize stover10. However, breeding programs focused on maize improvement primarily in regions of South Asia are directed largely towards grain improvement with diminutive focus on stover traits.Recent studies have highlighted the potential of maize stover as an important source of fodder in livestock farming systems globally11. However, simultaneous improvement of stover and grain in maize would require repeat phenotyping for both grain yield and stover quality traits substantially increasing the conventional breeding costs and time per cycle. With the advent of molecular markers, maize breeding programs across several regions had made a paradigm shift and incorporated marker based technologies for various trait improvements in the breeding pipeline particularly due to its advantages with high throughput efficient selections and cost reduction of the breeding processes. With the advancements in molecular marker systems, the identification of candidate genomic regions that have significant effects on fodder quality, and deployment of these regions through breeding has become more feasible. Earliest reports for Quantitative trait loci (QTL) mapping for stover quality traits dates to 199713. Several studies have since identified various QTLs for different silage traits in maize23. In an attempt to resynthesize the information on stover quality Truntzler et al.23 conducted a meta analysis on the combined results of 14 such studies in maize, and identified regions (QTLs) with significant importance and smaller confidence interval on chromosomes 1, 2, 3, 4, 5 and 9 for digestibility traits and cell wall component traits. In addition, studies with candidate genes had also identified specific regions associated with digestibility and/or cell wall synthesis on chromosomes 9, 4, 3 and 1 using a limited set of maize24 lines. However, QTL mapping approaches are often restricted by the limited recombination in the mapping population, that are widely different from the once observed in natural population25 and hence often have narrow applicability. Genome-wide association study (GWAS) is another powerful molecular tool for crop improvement, which has been successful in identifying genomic regions for plant height, root traits, flowering time and a range of disease resistance traits28. GWAS studies on stalk strength and stalk components have also been reported in maize31. GWAS on maize stover digestibility traits  have also been studied previously32 on a panel of 296 maize test crosses, that had revealed several significant genomic regions particularly on chromosome 3 (bin 3.05) and chromosome 9 (bin 9.08). However, validation of these genomic regions in an independent population or panel, and also with in the working germplasm of the breeding program is an important pre-requisite for their integration in marker assisted breeding. In addition, the loci identified for these complex traits often explained only a small portion of the observed phenotypic variation, which often restricts their applicability in breeding program33.Improving the quality of maize stover has been addressed through conventional breeding strategies34 and is being utilized successfully in plant breeding as well37. However, successful integration of genomic selection with the breeding pipeline circumventing the need for conventional selection is determined largely based on the cost and time involved in phenotyping the trait as well as the genotypic prediction accuracy between the testing set and training set38. Prediction accuracy as determined by the correlation between the breeding value and the genomic estimated values of individuals, varies with different parameters39. Trait complexity and the degree of similarity between the training and testing sets are two such factors that determine the accuracy of prediction. Zhang et al.40 reported moderate to high accuracies when training and test populations were related. Morgante et al.33 further suggested that the accuracies can often go low in cases where the actual loci affecting the traits are far fewer than the molecular markers used and if large epistatic interactions are involved in a trait. Prediction accuracies are affected by the genetic architecture of the trait, marker densities, minor allelic frequencies and population relationships41. In this context the current study focused on (i) identifying genomic regions for two major stover quality traits through GWAS in an independent association panel and (ii) determining if a genomic prediction approach could be followed to identify non-phenotyped lines with good stover quality traits.Reduction in the cost of genotyping and use of high-throughput genotyping facilities, has paved the way for deploying genomic selection strategies in routine breeding programs. Genomic selection has been widely practiced in animal breedingP = 0.006 for IVOMD and 0.007 for ME). IVOMD ranged from 46.50 to 59.30% with a mean of 52.32% and ME ranged from 6.65 to 8.75 Mj kg−1 with a mean of 7.60 Mj kg−1. Broad sense heritability for the entries ranged between 0.22 and 0.23 for the two traits.Phenotypic distribution for the two traits (IVOMD and ME) in the panel was close to normal Fig. a,b. Mean2 of 0.2 and 11.6 Kb at r2 of 0.1  were detected amongst 9 SNPs for IVOMD and ME across the genome. Phenotypic variance explained by these individual SNPs identified for IVOMD ranged between 3.2 and 4.0% and the allelic effects ranged from -0.45 to 0.55. The phenotypic variance explained by these highly significant SNPs associated with ME ranged between 3.2 and 4.9% and the effects ranged from − 0.08 to 0.09  was used for comparison of the SNPs from this study with the results with several previous studies on digestibility traits in maize , a large number of SNPs were found on chromosome 2, 3 and 6 for IVOMD and on chromosome 2 and 6 for ME , 5 (bin 5.06), 6 (bin 6.01) and 7 (bin 7.02–7.04) for IVOMD with P value < 9.3 × 10−5 and on chromosome 1 (bin1.01), 2 (bins 2.05 and 2.09), 3 (bin 3.09), 5 (bins 5.00 and 5.06) and 7 (bins 7.03 and 7.04) for ME with P value < 9.5 × 10−5. While significant SNPs were identified for IVOMD across the chromosomes (1–10), the study could not detect any significant SNPs for ME on chromosome 10  to determine the prediction accuracies at different marker densities. The sub-sets of the genotype for the different marker densities were made at least twenty times and the prediction accuracies averaged over these iterations. For comparison, genomic predictions were also estimated for the selected DH lines by using the association mapping panel set as a training set.−1) Table −1 with a mean of 7.15 Mj kg−1  and ME  for the stover traits, IVOMD and ME. The entries selected with in the top 5% matched across both the traits evaluated with an exception of only 4 entries with 2 entries each that had high IVOMD predictions but moderate ME predictions and vice versa. However, for all further estimations, these four entries were considered within the high phenotype groups for both IVOMD and ME. Based on these predictions, the set of 100 lines selected were test crossed and phenotyped for the two stover quality traits. The observed phenotypic value of IVOMD and ME ranged from 50.02 to 54.88% and 7.19 to 7.91 Mj kg4. Crop residues of cereals—the by-products after harvest of the primary product (grains), are emerging as the major alternative to feed resources in developing countries where feed demand is steadily increasing while the availability of natural resources, particularly arable land and water, is declining42. Crop residues do not cost additional input such as water, land, etc., as crops are invariably grown for the harvest of grain4. Among the cereal crops, maize, which is grown throughout the year in tropical climate, has a very high demand and potential to be a successful dual-purpose crop. Most of the maize breeding programs of south Asia largely focus on improving grain yield—under optimal conditions with reduced losses under various biotic and abiotic stress conditions. However, global increase in demand for fodder has created an impetus for breeders to concentrate on ways of whole plant multiple trait improvement, concomitantly targeting food, feed and fodder improvement.Sustainability for marginal and poor livestock holders can be achieved by ensuring the availability of feed throughout the year43, Mexico44, Ethiopia and Tanzania9, South Africa45 and South Asia46 However, targeted breeding for these dual purpose (grain and stover) maize would entail additional phenotyping and increase in cost of the breeding pipeline, a major bottleneck for whole plant improvement for multiple traits. Use of molecular markers circumventing the need for additional phenotyping could support breeders in early selections of genotypes with superior yields and stover quality. Genomic prediction/selection approach could also help breeders design population improvement schemes for simultaneous improvement of multiple traits and complete multiple selection cycles within the time period conventionally used for improving a single trait49. However, applicability of GS model for trait of interest and/or reliability of the results of an association mapping for integration in breeding pipeline needs a deeper assessment. Two stover quality traits IVOMD and ME were selected for this study, as they are considered two of the most desired traits of stover quality and a small improvement in these traits can positively impact animal productivity50. We reviewed the previous association mapping studies and identified a suite of associations that co-localize for both the traits and address their wider applicability and integration into the maize breeding pipeline. In addition, we attempted to assess the applicability of the genomic prediction model for these traits in the current breeding pipeline.Considerable progress has been made in detecting and exploiting genetic variations in stover fodder quality in maize. Screening of many of the released and pipeline hybrids of maize for stover quality has resulted in the identification and preferential promotion of dual-purpose hybrids across the globe, including USA2 of 0.2 and 11.6 Kb at r2 of 0.1) and was similar to that observed in previous studies on tropical germplasm51. The low LD decay as seen in the current study further suggests that the panel is diverse, and hence the use of higher number of SNPs should provide a good resolution. The current association study used 181,768 SNPs for the association mapping. The association tests in our study were corrected for both structure and kinship. First 10 principal components used to correct for structure in the current analysis explained close to 81% of the variation. Considering the bonferroni corrected P value was too stringent to identify significant SNPs, a lower threshold of P value < 1 × 10−4 was used as a cut off for identifying potential SNPs. A set of 64 SNPs for ME and 163SNPs for IVOMD were identified for both the traits  through single locus MLM approach.The set of entries used as the association mapping panel consisted largely of tropical germplasm from across South Asia. Linkage disequilibrium (LD) decay of this set was high  and chromosome 6 (11 SNPs at bin 6.01). The bin 2.04 has been identified previously to contain QTLs for several fodder quality traits in maize 56, while Meta QTLs and cell wall digestibility traits have also been reported on bin 6.0123. These regions have also been found to be associated with gene models contributing to proteins involved in various cell wall components. The gene model GRMZM2G479018 (bin 2.04) associated with a SNP (S2_39751613) was found to encode for ABC like transporters. These proteins are associated with transport of lignin precursors from cytoplasm through plasmalemma and sequestration in vacuoles in Arabidopsis57. Lignin an important component of plant stem cell wall reduces the enzymatic degradability of biomass and cell wall polysaccharides in the rumen59. The study also identified SNPs (S6_2043652 and S6_35895666) on bin 6.01 associated with gene model (GRMZM2G035741) encoding G protein beta subunit, WD 40 repeat and BRI1-KD interacting protein 130 (GRMZM2G035741) that phosphorylates G protein for sugar signaling. Role of G protein and WD 40 repeats in cell wall biogenesis has been reported in earlier studies in Arabidopsis61.The two stover quality traits are reported to be highly correlated62. A meta QTL for cell wall digestibility traits has also been reported23 on bin 4.1. This region was also identified to harbor QTLS for ADF and NDF53.The highest variation among the putative SNPs along with the lowest P value was accounted by SNPs on chromosome 2 (bin 2.05 and 2.09), chromosome 5 (bin 5.06) and chromosome 7 (bin 7.03 and 7.04) across both the traits IVOMD and ME. QTLs have been found for various cell wall component digestibility traits such as ADF, NDF and IVDMD on bin 2.0556 and meta QTL for cell wall traits was reported in this region23. QTLs for p-coumaric esters (PCA) involved in lignin biosynthesis have also been reported in this region. Meta QTL for cell wall component and digestibility traits have also been reported23 on bin 2.08–2.09, bin 5.06, bin 7.03 and 7.04. Bin 7.04 has also been found to harbor QTLs for cell wall digestibility traits ADF and NDF53. QTLs for cell wall component arabinose (7.03) and Klason lignin (bin 7.04) has also been reported previously62 on chromosome 7. Arabinoxylon is responsible for cross linking of lignin with ferulate bridges and is known to contribute to the reduced rumen degradability of maize cell walls64. In addition to these, three of the identified putative SNPs on chromosome 9 (bin 9.07)  and one on chromosome 3 (bin 3.05) (S3_149247409) co-localized (within 1 Mb) to the identified SNP for IVOMD from the previous study32. These SNPs are co-localized to the regions coding for the enzymes protein phosphatase and urate hydroxylase, involved in signal transduction. This region on chromosome 9 (bin 9.07) has also been previously identified as a major hotspot region for cell wall components and digestibility in a meta-analysis study23. While several significant associations were detected in the current study, most of these associations for the traits observed had minor effects and were associated with low phenotypic variance. Previous studies have also identified a large number of minor effect QTLs for forage quality23 in maize restricting the use of SNPs in breeding programs. Further, the need of repeat phenotyping of such traits with low heritability can often unsettle the breeding pipeline and substantially increase the cost of the breeding program. Hence, genomic selection strategy might be the best possible approach for their rapid improvement.Another hot spot with clusters of SNP for IVOMD and ME was observed on chromosomes 3 (bin 3.09) and 4 (bin 4.1). QTL for cell wall composition and neutral sugar component of cell wall, glucose and ferrulic acid have been identified previously on bin 3.0941. In the current study, the training set exhibited substantial variation (P < 0.0001) and good repeatability (h2 > 0.50) for the two traits, IVOMD and ME. In addition, as GWAS did not identify any major regions for deployment, the use of genomic prediction strategy for these traits might be an effective way of improving the trait. To understand the applicability of whole genome predictions for these traits, a set of 1026 DH lines derived from 10 bi-parental breeding crosses from the program were used as the prediction set. The genomic selection model was trained based on two different training sets (i) The first training set consisted of 276 test crosses of active breeding lines from the Asia program, that included the founders of the DH lines and (ii) the second training set consists of the association mapping panel used in the study comprising diverse inbred lines.Trait heritability, relationship between the training and testing set, variability for the trait of interest in the training population are few of the major factors determining success of genomic selection69. Moreover, the success of prediction of traits through genome-wide genotyping depends on various features like marker density relative to the population’s effective size, persistence of LD in the breeding material, etc.72 and similarity of the training and test set. In the current study, the parental lines of the crosses used for developing the DH populations were also part of the breeding lines involved in the training set indicative of the close similarity of the training and test sets.Based on the predicted trait values using the first training set consisting of the test crosses of the active breeding lines, a set of 100 inbred lines were selected (top and bottom ranking) and evaluated for the traits IVOMD and ME. The correlation between the observed phenotype of the selected lines of the testing population and the corresponding predicted high phenotype of those lines was significantly positive. The level of prediction accuracy in the present study was similar to or higher than the level for several agronomic and disease traits in other studies reported earlier73. The highest prediction accuracies were found when training data represented the whole population and had a strong relationship to the test data74, while low prediction accuracies for genomic selection have been reported with less related individuals in the training and test population76. Besides relationship between training and test set, marker densities also affect in the prediction accuracies39. Considering these the prediction accuracies were also repeated with a suite of marker densities  chosen at random from the total high density SNP markers. The average prediction accuracies as determined by Pearsons correlation coefficient across the different marker densities were comparable indicating that markers at lower densities could predict for these traits in a test set with accuracies similar to those obtained from higher densities across the three training sets. Zhang et al.41 found for less complex traits the increase in marker density could reduce the prediction accuracy, while for complex traits increased marker densities increased the prediction accuracy which stabilized after certain marker densities. However, in our study we did not find any significant differences in prediction accuracies at marker densities.To determine if similar prediction accuracy could be achieved when the training set was unrelated to the test set, the association mapping panel, which is more like a diversity panel, was used as a training set to predict the performance of the selected DH lines. The results clearly showed poor prediction accuracies in this scenario, when there was no relation between the training and test set. However, the prediction accuracies improved when the training set constituted a combination of both the breeding lines and association panel. Further, the prediction accuracies were comparable to the results obtained when the training set comprised solely of the breeding lines. Relationship between test set and training set in a genomic prediction model affects genomic selectionThese preliminary leads in predicting fodder quality phenotype in bi-parental populations from prediction equations trained on an independent panel suggest that (i) the genomic selection approach could be effectively used to improve the two major stover quality traits in maize breeding populations; and (ii) that the approach could also be used as an effective tool of selecting parents for breeding starts for fodder quality trait improvement from the existing maize repositories without the need for extensive phenotyping. Further, good prediction accuracies for these traits even at low marker densities suggests that tailoring the current breeding program to integrate stover quality as a trait of interest might not be difficult.The study presented several significant SNPs for in vitro organic matter digestibility and metabolizable energy. The suite of SNPs identified co-localized to several QTLs previously reported, indicating their suitability for use in breeding programs. Hot spots with cluster of SNPs had been identified on chromosome 2 and 6 for both the traits, that would be interesting to further investigate. However, considering the traits are associated with several minor effect QTLs each accounting for low phenotypic variance, incorporating genomic selection in breeding programs in Asia to improve this trait seems to be the most optimum strategy. Good prediction accuracies between the training and testing sets for the two traits of interest even at a low density of markers as detailed in the study, further supports its use for improving stover quality.Three different sets of germplasm were used in generating experimental datasets in the current study, including an association mapping panel comprising 424 maize lines, a training set for genomic prediction that was constituted using a set of 276 Asia-adapted breeding lines from the working germplasm of the CIMMYT —Asia breeding program, and a testing set for genomic prediction that comprised a set of untested 1026 double haploid (DH) lines derived from ten bi-parental breeding crosses that were genotyped and their breeding values estimated. A brief description of three set of germplasm is presented below.An association mapping panel was constituted that comprised of 424 diverse maize inbred lines sourced from CIMMYT maize program , Purdue University, USA (15 temperate lines) and the Maize and Millets Research Institute (MMRI), Pakistan . The panel was test-crossed with a CIMMYT tester line with high general combining ability (CML451) and phenotyped for two key stover quality traits—in vitro organic matter digestibility (IVOMD) and metabolizable energy (ME) during post rainy season of 2013. This panel was also used as a secondary training set for predicting the performance of 100 DH lines selected for the two stover traits (IVOMD and ME).Phenotypic and genotypic datasets from 276 advanced stage maize inbred lines from the working germplasm of CIMMYT Asia maize breeding program were used as a primary training set in the genomic selection model for the two stover quality traits (IVOMD and ME). Lines involved in the study were Asia-adapted advanced elite breeding lines that originated from the sub-tropical and tropical maize breeding program of CIMMYT and being currently actively used as parental lines of several breeding crosses.A set of 1026 double haploid (DH) lines derived from ten elite × elite bi-parental crosses were used as the testing set. The founder lines of these 10 DH populations were part of the primary training set used for prediction. These untested DH lines were genotyped using genotype-by-sequencing (GBS) platform and the breeding values for the two major stover quality traits were estimated using the model trained on the training set with > 150,000 random SNPs across the genome. Based on the genomic estimated breeding values (GEBVs), a sub-set of 100 DH lines was selected (50 high ranking and 50 low ranking) from the 1026 DH lines. Considering the two traits were highly correlated, there was little discrepancy in the selection, with only 4 entries not consistently falling under high IVOMD and high ME criterion. Two of these entries each had high IVOMD value but moderate ME values and vice versa. However, for the all estimation purposes these entries were considered in high groups. The selected 100 DH lines were planted as replicated trials and phenotyped for the two stover quality traits at Hyderabad during 2014 post Rainy season. Prediction accuracies were estimated at different marker densities based on the correlation coefficient between the predicted values and the observed phenotypic values on those 100 DH lines for the two stover quality traits.−1 in the form of urea, 60 kg phosphorus ha−1 as single super phosphate, 40 kg potassium ha−1 as muriate of potash and 10 kg zinc as zinc sulfate were applied as a basal dose. The second and third doses of N (each 30 kg ha−1) were side-dressed when plants were at knee-high and at tasseling stage, respectively. Pre-emergence application of pendimethalin and atrazine  were used to keep the crop weed-free at early growth stages. Standard agronomic and plant protection measures were followed throughout the cropping period to prevent from biotic or abiotic stress affecting the stover quality. At the maturity stage after harvesting of the cobs, five representative plants were selected from each plot for stover quality analysis.Test-cross progenies of association mapping panel and the primary training set were planted using alpha-lattice design with two replications during the spring seasons of 2013 respectively at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) campus, Hyderabad, India. Each entry was planted in a 4-m long plot with a plant-to-plant spacing of 20 cm and a row-to-row spacing of 70 cm. Prior to planting, 60 kg nitrogen (N) haBased on the predicted values (GEBVs) for the two stover quality traits, a subset of 100 DH lines was selected out of 1026 DH lines. Test-crosses of these 100 DH lines were then planted in two replications at ICRISAT campus, Hyderabad, India following a similar design as detailed earlier during post Rainy season of 2014. The recommended agronomic practices were followed for a good crop stand. At maturity stage, five representative plants per plot were harvested for stover quality analysis.32.Five whole plants were sampled from each of the plot in the experimental trials and processed for the various stover quality traits. The analysis for IVOMD and ME was done at the Livestock Nutritional Laboratory, International Livestock Research Institute (ILRI) at ICRISAT campus using Near Infrared Spectroscopy (NIRS), calibrated against conventional laboratory analyses. The NIRS instruments used were FOSS Forage Analyzer 5000, 6500 and XDS with the software package WinISI II. The procedure for analysis of maize stover samples for these quality traits using NIRS has been described in detail in Vinayan et al.77 at the Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA. A total of 955,690 SNPs were generated through GBS v2.7 and the SNPs were filtered for the analysis, based on the method described by Suwarno et al.78 with slight modifications. A call rate of > 0.85 and minor allelic frequency (MAF) > 0.05 criteria resulted in a set of 181,768 SNPs for association mapping analysis. Marker density for association mapping panel was one SNP per 11.604 kb. Structure of the association panel was determined using principal components, wherein the SNPs were further filtered based on a call rate of 0.9 and MAF < 0.1. In addition, Linkage Disequilibrium (LD) pruning based on adjacent markers was also carried out. This criterion resulted in a set of 55,807 SNP markers for principal component analysis.All the three set of germplasm  used in the current study were genotyped using the GBS platformTo train the genomic model for the two traits, the genotypic dataset from both training set (276 breeding lines) and a prediction set (1026 DH lines) were combined and later filtered using a call rate of 0.9 and MAF of 0.1. This filtering criterion resulted in a set of 156,884 SNPs, which were then used to predict phenotypes of 1026 DH lines derived from 10 bi-parental breeding crosses, based on the marker effects obtained from the training sets. The study also used the association mapping panel as a secondary training set and the testing set comprised of 100 DH lines for estimating the prediction accuracies. For this purpose, the filtered genotyping dataset estimated from the primary training set, association mapping panel and DH lines were merged. Based on the commonality a final set of 127,623 markers were then used for the prediction. In addition, to the whole set of random markers  used for prediction, SNP marker densities of 200, 500, 1000, 3000, 5000, 10,000, 50,000 and 100,000 SNPs were also used to estimate the breeding value of the 100 DH lines.79,A linear mixed model was used for the analysis of the stover quality traits using the web handle META-R developed by CIMMYT80. The additive model was used for the analysis and the missing genotype values were set to 0. The kinship matrix used as covariates were treated as random effects. Manhattan plots were plotted using the − log 10 P values of all SNPs in the model and Quantile–quantile plots were plotted using the observed − log 10 P values and the expected − log 10 P values. A suite of significant SNP associations was observed from the analysis. These significant associations were then used to look for congruence with results from previous studies. The associated SNPs were considered co-localized if found to be in close proximity (within 1 Mbp) to the previously reported SNP for the trait81. The analysis were carried out using SNP and Variation Suites v8.6.082. In addition, the gene models with the putative SNP associations were identified from maize GDB genome browser at http://www.maizegdb.org (B73 RefGen_V2) and their respective protein were obtained from Uniprot genome browser at http://uniprot.org.Association tests in between markers and association mapping panel phenotypes were corrected for population structure and kinship following a single locus mixed linear model (MLM) (G + Q + K) following the efficient mixed-model association expedited (EMMAX) variance component approach83. The primary training set comprised of phenotypic and genotypic datasets of 276 CIMMYT Asia-based breeding lines, and the prediction set comprised of 1026 DH lines derived from 10 bi-parental crosses involving Asia-adapted elite breeding lines. The GBLUP model used here was82. Based on the GEBVs of the DH lines estimated using the full set of 156,884 SNPs, a sub-set of 100 DH lines were subsampled. The test crosses form these lines were evaluated for the two traits to obtain the observed phenotypic value. The prediction accuracies were estimated as simple Pearson’s correlation coefficient between estimated breeding values and observed breeding values. Further, the prediction accuracies were re-estimated for these selected 100 DH lines at different marker densities. In addition, for comparison of prediction accuracies when a diversity panel is used in the breeding scheme, predicted values for these 100 DH lines were also estimated using the association mapping panel as the training set. The predicted values for DH lines were estimated by sampling genotype dataset at designated marker density 20 times. The correlation coefficients across these 20 samples for each marker density were then averaged following Fisher Z transformation as described by Alexander et al.84.Genomic predictions were done following genomic best linear unbiased prediction (G-BLUP) methodSupplementary Information.


Most fish breeding programs aim at improving growth rate and include feed conversion ratio (FCR) neither in the breeding goal nor in the selection index, although decreasing FCR is known to increase farm profit and decrease environmental impacts. This is because FCR is difficult to measure in fish that live in groups and FCR is assumed to have a favourable (negative) genetic correlation with growth, although the magnitude of this correlation is unknown. We investigated the effect of the genetic correlation between growth and FCR on the economic and environmental responses of a two-trait breeding goal (growth and FCR), compared to a single-trait breeding goal (growth only). Next, we evaluated the weights to assign to growth and FCR in a two-trait breeding goal to maximize sustainability of fish production.We used pseudo-best linear unbiased prediction (BLUP) index calculations to simulate a breeding program for sea bass. For the single-trait breeding goal, the trait in the breeding goal and in the index was thermal growth coefficient (TGC) and for the two-trait breeding goal, the traits in the breeding goal were TGC and FCR and the traits in the index were TGC and percentage of fat in the dorsal muscle (an indirect measure of FCR). We simulated responses to selection for genetic and phenotypic correlations between TGC and FCR ranging from 0 to − 0.8. Then, in the two-trait breeding goal, we calculated the economic return and the change in eutrophication when using economic values (EV) or environmental values (ENV).When the genetic correlation between TGC and FCR was lower than − 0.45, we found major differences in economic returns and in eutrophication between single and two-trait breeding programs. At a correlation of − 0.25, the two-trait breeding goal based on EV increased economic return by 25% compared to the single-trait breeding goal, while using ENV decreased eutrophication by 1.34% per ton of fish produced after one generation of selection.The genetic correlation between TGC and FCR affects the magnitude of economic losses due to omitting FCR in the breeding program. In addition, the genetic correlation affects the importance of choosing EV or ENV to reduce eutrophication and increase profit. Most fish breeding companies consider growth rate as the major trait to be improved in their breeding program . When a eview in ), and bement}FCR , 12, whement}FCR –15).The genetic response of a breeding program depends on the phenotypic and genetic correlations between the traits in the breeding goal and in the corresponding selection index. In the case of a single-trait breeding goal where the trait of interest is e.g. growth rate, the response depends only on the heritability and phenotypic variance of growth rate, and on the intensity of selection. A correlated response in FCR will depend on the genetic standard deviation of However, EV might not be the best weights to enhance the environmental sustainability of fish production. In a previous study , we calcIn this paper, we explored different strategies to enhance the economic and environmental sustainability of fish production. First, we explored the potential gain in economic return of upgrading a simple breeding program for growth rate only by including FCR in the breeding goals and percentage of fat in the dorsal muscle as an indirect criterion of FCR in the index. We explored this potential gain as a function of the genetic and phenotypic correlation between growth and In a previous study , we calcDicentrarchus labrax) in a hypothetical sea cage farm producing 1000 tons of sea bass per year, where the instant biomass present on site was constrained to 435 tons (“standing stock” or “biomass” quota). The farm was composed of 34 circular cages of 600 m3 for pre-growing and 34 circular cages of 1800 m3 for on-growing. Fish were stocked at 10 g and sold at a fixed harvest weight of 400 g. Stocking occurred all year round. The bio-economic model was divided into four model parts.The fish model estimates individual fish growth using perature . \documet et al.  that modt et al.  that modt et al. , 22.The batch model estimates the average stocking density of a batch depending on individual fish performances (from the fish model) and mortality. A batch is defined as the group of fish stocked at the same time in the same pre-growing cage.The farm model estimates the number of batches produced to calculate annual fish production, emission of pollutants, and annual feed consumption, while complying with the quota on biomass.Finally, in the economic model, annual profit is calculated by combining results of the farm model with economic parameters.The bio-economic model estimated the production of sea bass . The impact_farm values were subsequently used to calculate ENV.Each flow of resources and pollutants observed in the system was assigned to eutrophication potential. We chose to investigate only eutrophication because quotas are essentially designed to limit the eutrophication caused by fish farming. The characterization factors in the CML2 Baseline 2000 version 2.04 method were used to compute eutrophication. The categories of impact were calculated using the SimaproThe EV and ENV of a trait were calculated for a one genetic standard deviation change in the mean of the trait while the means of the other traits remained constant. When calculating EV and ENV, increasing Unlike previous studies , 24, we We used the eutrophication per year per farm  per year considering an average generation interval of 2.5 years . We expressed genetic gain in σg to compare the genetic gain achieved for the three traits on a standardized basis.We simulated a simple breeding program for sea bass using SelAction , in whice et al. . GeneticWe used the single trait breeding goal Oncorhynchus mykiss), whereas correlations between Genetic parameters of the three traits are in Tables In the single-trait breeding goal, the response to selection for mentclass2pt{minimmentclass2pt{minimmentclass2pt{minimIn a two-trait breeding goal, the response to selection achieved is the result of a complex interaction between weights assigned to each trait and the additive genetic variances for those traits, and their correlations. The response to selection for mentclass2pt{minim2 = 0.43) and because the ENV of Interestingly, using EV or ENV caused different responses for mentclass2pt{minimAs expected, response for %fat was positive when using EV due to the positive correlation of mentclass2pt{minimWith the single-trait breeding goal, economic returns increased linearly . The reduction in eutrophication per year did not differ between EV and ENV when the correlation between Using ENV in the breeding goal generated a reduction of eutrophication of at least 1.55 kg POrovement . With coTo our knowledge, this is the first study that explores the influence of the correlation between growth rate . This suggests that, in such cases, an easy and affordable single-trait breeding program for Although %fat is acknowledged to be an important driver of the results obtained, we did not investigate the effect of a potential change of the correlation between %fat and TGC and FCR on selection response when the correlation between TGC and FCR changed. Mainly because we do not know precisely how the genetic correlations between three traits would behave when the genetic correlation between two of these traits would change. Nevertheless, if we consider that the genetic correlation between TGC and %fat is close to the 0.75 value tested here, the correlation between FCR and %fat would have a strong effect on the response to selection when the genetic correlation between TGC and FCR is weak. In that case, the response to selection for FCR would probably be higher if the genetic correlation between FCR and %fat is stronger.So far, in fish, there are strong indications that the correlation between .4, e.g. ). Hence,proposed , 34. Theurements . In the urements  reportedrrelated , 37. In rrelated  showed trrelated . Surpris}FCR − 0. resultedment}FCR .We also explored what would be the best type of weighting factor for a two-trait breeding goal  of a sea bass farm running under a quota on biomass.