Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free  approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen  Zymoseptoria tritici, as a case study. We analyzed a panel of 145 strains collected across the global distribution range and gathered trait variation data on 49 distinct traits. We integrated complex sequence variation among strains systematically into the association mapping and found that multiple approaches are needed to cover satisfactorily causal loci for trait variation. Our approach is generalizable to many microbial species and will uncover novel mechanisms driving rapid host adaptation in microbial populations.Mapping trait variation to polymorphism within species has become a cornerstone of modern biology. Applications in microbial pathogens (both bacteria and fungi) have produced major insights into their ecology, emergence of resistance, gains in virulence and climatic adaptation. However, microbial populations collected from the environment often express major adaptive traits governed by complex genetic variation. Standard genome-wide association studies (GWAS) based on single nucleotide polymorphisms have typically failed to reveal the full extent of loci contributing to such traits. We provide the first quantitative assessment of GWAS in a fungal pathogen comprehensively accounting for complex sequence variation. We used an environmental collection of the global fungal pathogen of wheat,  Rapid genetic change in plant pathogens has led to significant damage to agricultural production over recent decades –3. The ri.e. k-mers  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,David E. Cook, PhDGuest EditorPLOS PathogensBart ThommaSection EditorPLOS PathogensKasturi HaldarEditor-in-ChiefPLOS Pathogensorcid.org/0000-0001-5065-158XMichael MalimEditor-in-ChiefPLOS Pathogens <span style="font-family: "Noto Sans", sans-serif; font-size: 12px;"> orcid.org/0000-0002-7699-2064 </span> ***********************Dear Dr. Croll,The submitted manuscript has undergone peer review by three independent experts. The reviewer’s all commented on the value of the analysis, the demonstration of bias in single-reference GWAS studies, and that while the approach is not entirely new, it is a good demonstration and guide-post for use in fungal systems.There are questions raised by the authors that require addressing and warrant the decision of major revision. Some reviewers suggested the revisions could be considered minor, which can be interpreted that the authors did not think major new analyses are necessary, but that more explanation, justification, and discussion are warranted. Please pay attention to the comment by reviewer 2 asking for specific examples demonstrating PAV as causative for trait association differences, and comments by all authors regarding statistical approaches and justification.Reviewer's Responses to QuestionsPart I - SummaryPlease use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.Reviewer #1: The manuscript entitled “Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen” collected 49 phenotypic traits of 145 strains of the fungal wheat pathogen Zymoseptoria triticim, and genotyped these strains through whole genome sequencing. Genome-wide association studies (GWAS) were conducted using a traditional SNP-based approach along with multiple reference genomes and a k-mer method to analyze various collected traits. The results revealed significantly associated SNPs, k-mers, and candidate genes. While GWAS has been extensively utilized in genetic research involving humans, animals, and plants, its application in microorganisms is relatively limited. Therefore, the utilization of GWAS, particularly k-mer GWAS given the prevalence of structural variation among Z. triticim fungal genomes, holds significant value. This study devoted significant efforts to data collection, and the methods employed for data analysis are well-described, providing ample detail. A few comments are provided for the authors' consideration.Reviewer #2: Dutta et al. report the application of innovative association mapping techniques to discover hidden variation in Zymoseptoria tritici associated with virulence/ecological traits. As fungal pathogens often have highly variable genomes within a species including structural variation, classic GWAS methods that rely on SNPs called from a single reference genome inherently miss much of the standing variation, which can fail to detect true genetic associations. This manuscript takes a comprehensive approach using a vast phenotypic dataset to assess the use of SNPs called from multiple reference genomes, as well as a reference-free k-mer approach. Overall, the manuscript is very well written. The methods used are clearly described and the conclusions are generally well-founded. These results advance our understanding of functional genomic variation in fungi and provide a framework that can be applied in other systems. I have identified a couple of areas that could be expanded and some relatively minor points that I feel could bolster some conclusions and increase the clarity of the results.Reviewer #3: The authors use a variety of their previously published population-level data from Z. tritici  in order to address an important issue - for species with abundant structural variation and differences in gene and marker presence-absence, how much is missed in association mapping studies when defining markers solely based on a single reference genome? They compare the performance of GWAS using SNPs called with respect to the single canonical reference genome to GWAS with SNPs called with respect to an additional 18 reference genomes as well as to GWAS using a reference-genome free k-mer approach. Not surprisingly, they find that the reliance on just a single canonical reference genome generally leads to missing the identification of other significant trait-associated markers that are absent or cannot be called with the canonical reference. The k-mer GWAS approach has already been demonstrated in plant and bacterial species, this is a novel and significant demonstration of the utility of this approach in fungi, in particular in an important plant pathogenic fungus.The manuscript is generally well-written and executed, though some areas require some clarification or revision (see below). A strength of the study is the availability of so much data from their previous studies, such that this study can focus on a comparison of analysis methods and remain fairly concise. The authors do a great job of providing important data in supplemental tables so the reader can follow along with their conclusions well. The most significant weaknesses - described immediately below - are probably something the authors can address simply - either by providing a little more information and interpretation or a bit of justification of some choices they have made in their analyses.The manuscript refers in the abstract and other places to a set of 49 traits for GWAS, but then results of GWAS are only shown for 20 or so different traits. It is not clear why this is so, and should be explained - the biggest concern without any additional explanation is that the authors chosen subset of traits are not representative of the full results in some important aspects that could affect the study's conclusions. (more related comments on this issue in PartII of review).I agree with the authors on the relative merits of the k-mer approach in contrast to performing GWAS separately against multiple SNP sets each called against a different reference. The latter approach clearly entails a lot of redundancy (most of the SNPs are presumably redundant across most of the SNP sets). The other major weakness in the study has to do with the tough statistical question related to the approach of using multiple SNP sets. How to account for multiple testing when performing GWAS with the same trait data and not just 1 set of SNPs called against 1 reference, but 18 additional SNP sets called against different references? If the additional SNPs were independent (and they clearly are not), then defining 5% significance with Bonferroni correction would mean dividing the original p-values not only by the number of SNPs in each set (>800k) but also by the total number of SNP sets (19). In reality, there should be a very high correlation in SNP sets , but still the additional tests that are actually being performed with each new SNP set are not properly being taken into account by the Bonferroni correction. Simply, with each SNP set we expect some small amount of false positives, and by doing 19 sets of tests, we of course expect some increase in false positives, even if small.**********Part II – Major Issues: Key Experiments Required for Acceptanceabsolutely required to validate study conclusions.Please use this section to detail the key new experiments or modifications of existing experiments that should be Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".Reviewer #1: (No Response)Reviewer #2: Although I agree with the authors that the differential detection of loci depending on the reference genome used is likely due to presence/absence variation or issues with reliably calling SNPs, I feel that the results could be bolstered by investigating this further. Are the genes in close proximity to these reference-genome specific loci truly absent in the isolates where the locus was not detected? Or if the gene is present in reference datasets where the locus was not detected, were those regions devoid of SNPs and/or aligned reads? Choosing a few of these scenarios and exploring them further may help illustrate the impact of these results a little more clearly.L221-234: Why limit the k-mer mapping to the IPO323 genome? One of the major novelties of this manuscript is capturing and associating unexplored genetic variation to phenotypic traits and by only mapping significant k-mers to IPO323, I feel that some important associations to novel genes may be missed. Would mapping the identified k-mers to the alternate references help place some of the unmapped k-mers? Or could k-mers be assembled into contigs and used to search the pangenome gene space? I think this would help further demonstrate the utility of this approach if all available data can be used and would also more accurately reflect the proportions of mapped/unmapped k-mers.Reviewer #3: As mentioned above, the authors state using 49 traits, but although 2 supplemental tables are used to provide the raw measurements taken for the traits, the full list of 49 traits are never properly listed anywhere . In the methods they describe how some of the trait values are calculated based on the raw measurements taken, but without the full list of 49 traits I do not know if the description of calculating any other traits is missing. Please include a full listing of the 49 traits.Related - what is EAM  - is this the same as MCA? And overall virulence or overall reproduction, and reproduction specificity - these are derived phenotypes mentioned later in the manuscript but not otherwise defined within the Methods section of the paper.And related to the number of traits, the authors focus their conclusions  on a subset of only 20 traits, but it is never explained why - some additional explanation here is required!I found it odd that in table S5 there are no cases where 0 significant SNPs were recovered across all of the different reference genomes. Does this mean the authors filtered out their results to drop any cases where 0 significant SNPs were recovered in 1 or more of the reference genome analyses? Or traits were dropped because no significant SNPs were found in ANY of the reference genome analyses? Were other traits dropped, and if so, for what reason? And could any of the study's conclusions change if additional traits were included in the paper?Please address this issue.The other major issue requires some discussion of multiple testing and the Bonferroni corrections used, in the context of their study design. For the most part, the value of the study is in the comparisons of the results when performing 1 individual GWAS analysis  versus the results when performing a different GWAS analysis. In this case, the Bonferroni corrections are probably fine. But by suggesting that other researchers might take their approach of defining multiple SNP sets and performing GWAS across all of them in order to maximize the number of significant markers identified is statistically more problematic - here is where the Bonferroni correction as carried out is probably not quite adequate, or at least misleading, and this needs to be stated somewhere. Perhaps there is some other way (independent of the k-mer approach) to combine SNP markers across the different SNP sets  that would avoid so much redundancy in tests performed, where the total number of tests could be reduced and a better/more conservative Bonferroni correction could be applied to help reduce the chance of false positives. I am not asking the authors to alter their analyses methods in this paper to address the issue, but at a minimum they must point out the issue, so that other researchers would know not only is using multiple reference assemblies as they have done costly in effort (making the assemblies and defining each SNP set), but it would also be more statistically costly too, because it does involve performing (many) more tests.**********Part III – Minor Issues: Editorial and Data Presentation ModificationsPlease use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.Reviewer #1: 1. First, I noticed that two different statistical approaches were used to account for multiple tests in GWAS. In SNP GWAS, a very stringent approach, the Bonferroni method, was used, while a permutation approach was employed for the k-mer GWAS. The Bonferroni correction method assumes that each marker is independent, which is not true and may limit the power of SNP GWAS. I am curious why the permutation approach cannot be used as well for SNP GWAS.2. Three reasons were provided to justify the selection of 25-mer (L197-200). I think the selection of the k-mer size is related to the size of the complexity of genomes. 25-mer seems to be a reasonable selection for the small microbial genome. However, even for small genomes, the longer k-mer can facilitate accurate genomic mapping to find k-mers’ genomic location. In the justification, Reason 2 is a little hard to understand. Sequence sharing should not be a big issue among closely related genomes since the density of genomic polymorphisms among them should be low. In addition, given the high accuracy of Illumina read, sequencing errors mentioned in Reason 3 are not a big issue either as long as the k-mer length is not too long. Maybe the justification can be revised.3. Trait-associated k-mers are frequently found in TE. Because presence/absence k-mers were used as markers, here are possibilities: 1) An associated TE is present in some strains but in others; 2) A TE have many copies in all genomes (or strains) and a particular copy carrying variants was associated with traits. For the latter case, the TE copy carrying variants might insert to certain genes for phenotypic impacts. This is likely to be hard to investigate but it would be nice if more details can be provided.Minor comments:4. Although genomic inflation factors were calculated and controlled, if I understand this correctly, the p-value inflation based on Q-Q plots in Figure 4 seems to be high. Please help explain.5. Of p-values in Figure 5F and Figure 6F, it is probably not correct to make numbers after “e” be superscript, 1.6e-08 is very different from 1.6e(-08 as superscript).Reviewer #2: L132-133: It is not clear if GIFs were calculated for each individual reference-based GWAS (each trait/SNP dataset combination).L128-149: The variation observed among the various alternative reference genome datasets is very interesting. However, instead of reporting the number of SNPs, it would be informative to report the number of distinct loci. Presumably, multiple SNPs in LD are being detected at a single locus. I suggest delimiting these results further into the number of individual loci  which may more accurately reflect the number of genes detected for each trait. Although the ortholog accumulation analysis somewhat gets to this point, I still think this information should be relayed when discussing the genetic analyses.L483-484: Filtering for adapter sequences and quality were previously described, but how were the raw sequences filtered for contamination?Figure 2B: The y-axes have different maximum limits. I suggest making the y-axis ranges uniform to make the visualization more comparable. Also, is the left Manhattan plot showing associations with virulence on landrace 1204? The specific line should be mentioned in the figure title and/or caption.Figure S3 was blurry and difficult to read. Also, “Pathogen” is misspelled in the upper left quadrant.-Some references in the Materials and Methods are cited in a different format Reviewer #3: Please indicate in which analyses PCs were used as a factor or not! The Methods vaguely describes on what basis this decision was made, but it may be important in interpreting the results to know exactly when PCs were used or not.Based on your plots, it appears the GIFs are averages across the different reference genomes? Please clarify this. Also, why not show some q-q plot comparisons, at least for a few examples of analyses using PCs and not using them - wouldn't this be more informative than only using GIF?In table S5 and in the results - the variance of number of significant SNPs identified is reported (and emphasized). But of course we expect a higher variance when the mean is also higher. I suggest also computing the coefficient of variation (cv) in these cases, and reporting that instead. You will see that different traits end up being extreme when looking at cv instead of variance.SNP effect size was only estimated for the canonical reference - why? I suppose it appears in the manuscript just for illustrative purposes, and these effect sizes are not expected to be very different against other references, but it seems maybe not difficult to estimate all effect sizes. If you keep effect sizes only for the canonical reference, please provide a justification.Fig 3 please provide a statement that you are summing the distinct genes (or orthologous groups) across these sets of traits for the plot (not taking an average).Fig 2B - list cultivar 1204.Fig 4 - am I correct in assuming that the blue dots (significant k-mers) always come in pairs because they represent the alternative SNP alleles (now coded as presence-absence for discrete k-mers)? If so, it would help to explicitly explain this.Lines 159-161: State that you are only counting a gene once even when it is significant for multiple (related) traits.Fig S3 is cited before S2 - is that a problem for the journal?In figure S2A and S3, the figure label reads pathoge rather than pathogen.In Fig5B and line 241 - given the gene orientation, shouldn't the text read downstream rather than upstream? (with respect to gene's transcription?)Line 251 - maybe cite Fig 6A not 6B? Because the q-q plot does not indicate from which region significant SNPs come.If initial SNP filtering requires genotype call rate >80%, then why is the 50% maximum missing values filter needed later?**********what does this mean?). 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Read more information on sharing protocols at  24 Oct 2023AttachmentResponse_reviewers.docxSubmitted filename: Click here for additional data file. 6 Nov 2023Dear Prof Croll,We are pleased to inform you that your manuscript 'Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen' has been provisionally accepted for publication in PLOS Pathogens.Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. 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Cook, PhDGuest EditorPLOS PathogensBart ThommaSection EditorPLOS PathogensKasturi HaldarEditor-in-ChiefPLOS Pathogensorcid.org/0000-0001-5065-158XMichael MalimEditor-in-ChiefPLOS Pathogens <span style="font-family: "Noto Sans", sans-serif; font-size: 12px;"> orcid.org/0000-0002-7699-2064 </span> ***********************************************************Dear Dr. Croll,The revised manuscript by you and your co-authors has adequately addressed reviewer comments. Congratulations on the improved manuscript, and I am happy to inform you that the manuscript has been recommended for acceptance. 10 Nov 2023Dear Prof Croll,We are delighted to inform you that your manuscript, "Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen," has been formally accepted for publication in PLOS Pathogens.We have now passed your article onto the PLOS Production Department who will complete the rest of the pre-publication process. All authors will receive a confirmation email upon publication.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. 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Eucalyptus.Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts.Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals  compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees.Genomic RPAs ≥0.80 were obtained as the genetic relatedness between GWe demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding. Eucalyptus have been one of the main workhorses of GS experimental work toward breeding applications in forest trees  developed in the 1980s in Brazil  programs between  studies . In the The RRS strategy is a two-step breeding cycle that takes considerable time because the advancement of the pure species reciprocal populations depends on the hybrid progeny trial data for backwards selection, although a forward selection variant has been proposed  and evalWhile GS across breeding generations is already standard commercial practice in domestic animals  and annuIn this work, we evaluated the effectively realized predictive ability (RPA) for volume growth at harvest age by forward GS across generations in a eucalypt hybrid progeny trial. To ensure merging of data from different experiments deployed at different times, we applied age adjustment models on prior generation data used for model training. Additive only, additive plus dominance, and single-step genomic models were used for predictions. For the production component of the RRS program, the RPA was evaluated for selecting individuals and families in a terminal “urograndis” hybrid progeny trial. The ranks of the estimated breeding (EBVs) or genotypic values (EGVs) for volume growth at harvest age were matched to their genomically predicted counterparts (GEBVs and GEGVs) estimated when they were seedlings. For the breeding component of the RRS program, we estimated the predictive abilities (PA) by cross-validation for the reciprocal pure species advancement, assessing the different contributions of pure species or hybrid data to train prediction models.22.1E. grandis and E. urophylla, here called G0 generation. These trees were selected between 2004 and 2007 based on their performance as parents of “urograndis” hybrids in diverse hybrid progeny trials evaluated between 1985 and 2005. For each one of the two species separately, the 30 elite trees were recombined using a polymix design with a mixture of pollen collected from all 30 parents, generating 30 intraspecific half-sib families for each species. Between 2008 and 2009, these half-sib families were tested in 16 pure species progeny trials (PSPT), 8 for each species. The trials were established in a randomized complete block design with 36–50 blocks and 30 families planted as single tree plots plus four or five commercial clones as checks. In total, these PSPT ultimately provided data for 9,931 trees for E. grandis and 10,518 for E. urophylla as part of the G1 breeding population for this work. Between 2010 and 2012, half-sib hybrid families were generated again by using a polymix design of the 30 G0E. grandis parents pollinating the 30 G0E. urophylla parents and vice versa. These 60 hybrid families derived from the top G0 parents plus checks were established in the G1 hybrid progeny trial HPT249 in a randomized complete block design with 50 replications in single tree plots in April 2013. For this HPT249, 1,712 G1 trees were measured and used in this work to train prediction models.The study was carried out with operational breeding populations of the RRS breeding program of Cenibra S.A. in Brazil Figure 11 PSPTs were subject to between- and within-family selection, and 30 breeding parents were selected for each species. Using 9 of the 30 parents for each species that flowered synchronously, the G2 hybrid progeny trial HPT267 was created and deployed in 2016 to serve as a terminal trial for individual tree selection. Selected hybrid individuals would not be used as parents for the next generation, but subsequently clonally propagated and tested as clones towards final commercial recommendation. The HPT267 was established in randomized complete block design with 35 full-sib families, 28 unique and 7 reciprocal, with 40 replications in single tree plots, totaling 1,400 trees tested plus three commercial checks. Prior to the establishment of the field trial of HPT267, a sample of 200 seedlings out of the 1,400 were randomly sampled in 34 of the 35 families, covering 27 unique families, for SNP genotyping and genomic prediction  and tree height (HT) were measured at around ages 3 and 6 years in all the trials described above. Wood volume (VOL) in cubic meters was estimated using a taper factor of 0.45. Mean annual increment (MAI) in m2.21 trees were genotyped and used either individually or combined as training sets for genomic prediction of individuals of the G2 target population HPT267: (1) 1,842 F1 individuals of HPT249; (2) the 18 parents of progenies in HPT267, 9 of E. grandis and 9 of E. urophylla; and (3) 99 E. grandis and 115 E. urophylla trees randomly sampled in the same G1 PSPTs where the 18 parents of HPT267 were selected  ≥0.01 and common to the two SNP genotyping arrays were used. All SNPs were of the type A/G, A/C, T/G, or T/C. SNP data were exported either from GenomeStudio 2.0 or from Axiom Analysis Suite 4.0.1 formatted as AA, AB, and BB where the A allele is the SNP nucleotide base A or T and the B allele is nucleotide base C or G at the SNP. Missing data were imputed based on the expected value 1 the SNP data were consistent with their half-sibship status, but no maternal G0 genotypes were available for full pedigree verification.SNP genotype data were obtained with the EuCHIP60K chip   or with selected Figure 1 samples , quantif2.31 trials had been measured at slightly different ages, around age 2.76 (2.64 to 2.98 years) and 5.40 (4.94 to 6.14 years). To allow data consolidation to equal ages, linear and non-linear models were fitted to the raw volume growth data to adjust the age differences of the trees across the experimental trials to the same two ages points, 2.76 and 5.40 years, hereafter called ages 3 and 6 years for brevity, corresponding to the arithmetic mean of the measurement ages of all trials. Different random regression adjustments were evaluated: a linear model (Eq. 1) and three non-linear models: Logistic 1 (Eq. 2), Logistic 2 (sigmoid) (Eq. 3), and the Gompertz curve (The different Gtz curve  (Eq. 4) ith tree, where nearized  in the Rnearized . Illustr2.41 and G2 individuals were obtained for the two measurement ages using two alternative univariate individual tree mixed models, one additive  and one additive-dominance :ABLUP estimated EBVs or EGVs were used as pseudo phenotypes in genomic predictions. EBVs and EGVs for the GX, W, Z, and Q are the incidence matrices relating fixed and random effects in the model to the measurements in vector ith trial, respectively, and where ge nadiv  in R  were also estimated for our training datasets by dividual . Parentadividual . To asse2.6Genomic evaluations were carried out using genomic best linear unbiased prediction (GBLUP) in the Sommer R package  with twoX, Z, and Q are the incidence matrices relating fixed and random effects in the model to the measurements in vector M is the total number of SNPs, with MAF ≥ 0.01. The ith SNP; Equations 7 and 8 were used to predict the individual tree GEBVs and GEGVs, respectively, where  WW'/M , where  matrix  was esti matrix  in R, gi2.7Single-step genomic BLUP (HBLUP) was fitted in the Sommer R package with the following additive model (Eq. 9):X and Z are the incidence matrices relating fixed and random effects in the model to the measurements in vector where  matrix  was obtavice versa; and where  package  in R. Thge , trained with different combinations of the prior generation data as follows: (1) the preceding G1 hybrid generation represented by the HPT249, henceforth called HYBRIDS with 2,840 individuals, 1,842 of which were genotyped; (2) the nine E. grandis and nine E. urophylla G1 genotyped parents mated to create the HPT267, henceforth called PARENTS; (3) the preceding G1 generation pure species progeny trials, henceforth called UNCLES, which included phenotype data for 5,143 individuals of E. grandis and 5,190 individuals of E. urophylla, out of which genotype data were obtained for 108 and 124 trees for the two species, respectively; (4) the combined HYBRIDS+PARENTS dataset; (5) the combined HYBRIDS+UNCLES dataset; (6) the combined PARENTS+UNCLES dataset; and (7) the combined HYBRIDS+PARENTS+UNCLES dataset. The analysis therefore encompassed five phenotypic datasets (one unadjusted and four age-adjusted datasets); two individual tree genetic models (A and A+D); three genomic models ; and seven training sets for each of two measurement age, totaling 420 analytical strategies.Genomic predictions in the G2. The RPAs were calculated by the Pearson correlation between the individuals’ GEBVs and their EBVs for the GBLUP_G and HBLUP models, or their GEGVs and EGVs for the GBLUP_G+D model. For the GBLUP models, the RPAs were estimated for the 197 genotyped individuals. HBLUP predictions were carried out for the 197 genotyped individuals to see whether the inclusion of phenotypic data of ungenotyped trees in training would improve predictions over GBLUP. GEBVs for the 1,203 ungenotyped individuals were also estimated by HBLUP with the inclusion of different types of G1 genotyped individuals in training and compared with the EBVs. Besides the estimates at the individual tree level, estimates of RPA at the family level were also calculated based on the family average EBVs or GEBVs for the 27 sampled families. Bias of the RPAs was estimated as 1−b where b is the slope of the regression line between EBVs and GEBVs or EGVs and GEGVs. A slope of b>1 indicates underestimation of the RPA, and b<1 denotes overestimation.All analytical strategies were evaluated for their genomic RPA for MAI in the target HPT267 hybrid generation G2.92 selection candidate trees that would be genomically selected at the seedling stage and the number of trees that would be selected at harvest age based on their EBVs or EGVs for different proportions selected (2.5% to 50% selected).The individual MAI tree ranks based on their GEBVs or GEGVs were compared to their ranks based on their EBVs or EGVs from ABLUP, respectively. The relationship between these two ordinal variables was evaluated by a Spearman rank correlation. The two ranks were subsequently used to calculate the coincidence rate (%) between the number of G2.10E. urophylla and E. grandis, breeding cycles. Prediction models were trained for each species separately using either (1) only the pure species data from the respective species PSPTs, (2) only the preceding hybrid generation HPT249 data, or (3) the combined HPT249 and PSPT data  value of the heterozygote cluster >0.2; (iii) mean normalized theta of the heterozygote cluster between 0.20 and 0.80; and (iv) >99% reproducibility across the replicated samples and >99% correct inheritance between generations. Additionally, only SNPs with call frequency ≥90% and MAF ≥ 0.01 for samples with call rate ≥85% were retained for further analyses. Out of the 60,904 unique SNPs present on the chip, data for 28,521 SNPs were ultimately retained after quality control. The 232 trees of the PSPTs were genotyped with the Axiom Euc72K Eucalyptus array   , a second-generation Eucalyptus SNP platform improved over the EuCHIP60K chip that includes 67,683 autosomic Eucalyptus specific SNPs, 28,177 of which were selected from the previous EucHIP60k for quality and polymorphism across several Eucalyptus species. SNP data were exported using the Axiom Analysis Suite 3.1, using a dish quality control (DQC) value >0.82 and call rate CR >0.97 following the recommended “Best Practices Workflow”   were exporkflow” . A total3.21 and G2 trials combined were ha2= 0.240 and 0.210, and broad-sense heritabilities were slightly higher at hg2 = 0.250 and 0.220 at ages 3 and 6, respectively. The slight improvement of age adjustment was also reflected in the estimates of narrow-sense (ha2) and broad-sense (hg2) heritabilities for all G1 and G2 trials combined , heritabilities decreased to 0.45 at age 3, and more pronouncedly to 0.23 at age 6. Narrow- and broad-sense genomic heritabilities were estimated for the HPT267 under the three genomic models, using the seven G1 training sets. Genomic heritabilities, both narrow and broad sense, estimated using training data were considerably higher than the standard heritabilities estimated using the pedigree or hybrid matrix for the target trial, reaching values close to unity when PARENTS and UNCLES were included in training and when the HBLUP model was used. Under the GBLUP_G+D model, broad-sense genomic heritabilities were higher than narrow sense by 20% to 35% , despite the 100× larger number of individuals involved in training . The distantly related set of G1 individuals composed by the pure species UNCLES resulted in negative additive RPAs or non-convergence of the estimates. When combined with other training sets, UNCLES did not improve additive RPAs. However, when dominance was also predicted (GBLUP_G+D), PARENTS alone resulted in a low RPAs of 0.107, while the addition of HYBRIDS and UNCLES had a major positive impact on RPAs, increasing them 0.705 and 0.790 when HYBRIDS and UNCLES, respectively, were included in training  when HYBRIDS alone were used, and overestimated (1−b >1) when UNCLES alone were used in training. RPA estimates using PARENTS were largely unbiased, with a value of 1−b close to zero (average of -0.111). The estimates of RPAs were underestimated for GBLUP_G+D for all training sets except when PARENTS alone were used. The lowest overall bias of RPA was seen when using HBLUP with PARENTS included in training  between the number of Gportions Table 2.The 197 genotyped individuals ranked by their GEBV were plotted according to the 27 unique families they belonged, and the 27 families, in turn, were ranked according to their average EBV  than HBLUP  were similar to previous estimates in “urograndis” eucalypts , more in line with previous estimates in similar “urograndis” trials  or standard EBVs as pseudo phenotypes to train prediction models would result in any appreciable difference in genomic predictions. De-regression of EBVs  was propst trees  as a wayst trees , or no est trees .2 varies among individuals in the training population”In practice, the benefit of deregression and the subsequent weighting of alternative information sources will depend on the extent to which the number of repeat records, number of progeny and/or r  using exclusively the 46 G0 and 62 G1 parents to predict G2 individuals  than in E. urophylla (0.21) (data not shown). These results highlight the fact that the expected advantage of HBLUP relies heavily on precise pedigree records and the level of relationship between the genotyped and ungenotyped individuals  information, although the distinction between these two components is somewhat subjective . In praceN = 50, and genomic predictions will be carried out across one or at most two generations before model updating is done. Furthermore, both components of an individual’s breeding value are very relevant: the parent average breeding value and the within-family Mendelian term due to the sampling of gametes from its parents, the latter accounting for 50% of interindividual genetic differences in breeding values. Additionally, the non-additive variance is also typically captured by clonal propagation. Thus, marker-based prediction of differences among full-sibs due to Mendelian sampling is very important in achieving genetic gain. While pedigree-based BLUP predictions can yield accurate estimates of parental average when ancestors data are abundant, Mendelian segregation terms require records from progeny. As we have shown in this study, genomic data can accurately predict additive Mendelian sampling without progeny records, enabling ultra-early and accurate selective ranking at the seedling stage, although less efficiently when the total genotypic value was predicted, at least in this experiment. Moreover, the granular estimates of relatedness within families provided by genomic data will also allow much better management of inbreeding and maintenance of Mendelian segregation variance for continued gains when compared to pedigree alone (At least in industrial eucalypt improvement, GS will be practiced within specific breeding populations with effective sizes rarely exceeding ee alone .Finally, besides providing experimental data on cross-generational GS in the hybrid terminal trials, our study also provided indications that training models with pure species data should yield high predictive abilities that can be boosted by an HBLUP approach to accelerate the progress of the reciprocal populations in RRS breeding. Realized predictive abilities across generations for the pure species populations will await ongoing experimental work, although we speculate that similarly positive results are likely to emerge. As GS in forest tree breeding is now “climbing the slope of enlightenment” as a workable breeding “technology” , we expe10.6084/m9.figshare.23582370. Full results of data analyses presented in the study are included in the The datasets presented in this study can be found in the online repository figshare.com with the following D.O.I.: DG, ET, and RR contributed to the conception and design of the study. ET and JS performed the field experimental work. GS carried out data analysis with feedback from RR. DG wrote the manuscript and all co-authors subsequently contributed to it by editing the final version. All authors contributed to the article and approved the submitted version.


Murciano-Granadina has drifted towards better dairy-linked conformation traits without losing the grounds of the zoometric basis which confers it with an enhanced adaptability to the environment. Consequently, international Linear Appraisal Systems (LAS) may not fit the zoometric variability reality of autochthonous breeds, such as the Murciano-Granadina goat. LAS panels comprise large numbers of traits which makes selection for dairy conformation a complex and time costly task, hence selection practices focusing on major areas is often suggested. The evaluation of genetic, phenotypic and environmental parameters for each zoometric/LAS trait individually and of the pairwise relationships among traits may permit the design of a solid selection strategy towards the maximization of dairy potential while making selection tasks time- and resource-efficient. Results suggest that zoometrics and LAS-derived genetic and phenotypic parameters are translatable as long as the process of collection is performed objectively by trained operators. Selection of major areas is feasible but may be conditioned to the restructuration and modification of the scales that are currently used for dairy goats. The strategies that were designed help to evaluate the momentum of selection for dairy-linked zoometric traits of the Murciano-Granadina population and its future evolution to enhance the profitability and efficiency of breeding plans.Selection for zoometrics defines individuals’ productive longevity, endurance, enhanced productive abilities and consequently, their long-term profitability. When zoometric analysis is aimed at large highly selected populations or in those at different levels of selection, linear appraisal systems (LAS) provide a timely response. This study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimating genetic and phenotypic correlations among all traits, and determining whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43, and the accuracy of estimation has improved after decades, rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggests that negative selection against thicker bones and higher rear insertion heights indirectly results in the optimization of selection practices in the rest of the traits, especially those in the structure, capacity and mammary system major areas. The integration and implementation of the strategies proposed within the Murciano-Granadina breeding program maximizes selection opportunities and the sustainable international competitiveness of the Murciano-Granadina goat in the dairy goat breed panorama. Linear Appraisal Systems (LAS) were developed as time cost-effective alternatives to provide a timely relatively accurate response to the need to perform large scale zoometric evaluations. The biggest problem associated with in vivo zoometry  is the difficulty of restraining animals in a natural position long enough to make an accurate measurement, especially when the differences being measured are small .In this context, the complementarity between LAS and zoometry must be performed at an acceptable repeatability level across appraisers. This means that it must be possible to define the trait and all its components and the associated evaluation criteria precisely enough for the trait to be evaluated by appraisers with acceptable repeatability.Despite few preliminary applications , the NatIn these regards, Cabrandalucía Federation has implemented the concept of smart farming, relying on a precision livestock farming (PLF) platform (Web-App RUMIA). Web-App RUMIA incorporates PLF-like principles based on the integration of individual animal data points to optimize decision-making through a smartphone-based terminal and substitutes the previous “Escardillo” software used by CAPRIGRAN . The impThe productive levels that the Murciano-Granadina breed has reached currently provides it with a prominent position within the international dairy goat breed panorama . In thisCAPRIGRAN LAS routinely comprises the numerical description of 17 zoometric linear traits  on a one-to-nine-point scale to represent the zoometric linear biological range for each particular trait . The terOne of the main drawbacks derived from the extrapolation of ADGA LAS to build CAPRIGRAN LAS, relies on the scarce amount of information which exists on the heritability of structural traits in dairy goats. Indeed, although genetic parameters may be similar, according to relative indications and experience, the absolute heritability of traits is not known or expected to be the same for dairy cattle and dairy goats. For example, the heritabilities used in the selection of traits for ADGA LAS are based on 4 years of dairy cattle linear data. Hence, they were inferred in other species, and specifically barely any information is present in regards the genetic correlations across zoometric or LAS traits.Selection for zoometrics defines an individual’s productive longevity, endurance, enhanced productive abilities and consequently, its long-term profitability ,9. When Recent studies have suggested Murciano-Granadina goats’ inherent highly rustic nature may make CAPRIGRAN LAS not to optimally meet the ADGA and USDA’s LAS standards (upon which CAPRIGRAN LAS was formerly based) . ContextContextually, CAPRIGRAN international disagreement compelled the evaluation, optimization, validation and suggestion of restructuration measurements for CAPRIGRAN LAS ,5. RecenThe value of CAPRIGRAN LAS relies in the possibility of dairy goat breeders using the information provided by animal evaluation programs as guidance in making their management decisions, such as mating plans that involve the selection of sires or dams used in their breeding programme . In turnTo this aim, this paper determines whether phenotypic, genotypic and environmental parameters for traditional zoometric analysis and CAPRIGRAN LAS are comparable. Afterwards, the appropriateness of the clustering of zoometric or LAS items comprising each mayor category will be tested to propose enhancement measures to ensure the potential of Murciano-Granadina breed selection strategies is maximized. This may help to evaluate the viability of selection strategies based on the relationship across zoometric and LAS traits as a base for future studies evaluating potential benefits linked to an increased productive longevity.ad libitum water, forage and supplemental concentrate were provided. A detailed description of the analytical composition of the diet supplied to the animals is reported in The Murciano-Granadina whole pedigree datafile comprised 279,264 animals , and was used as the pedigree matrix for genetic analyses. Animals had been born from June 1966 to November 2019. The linear appraisal had been historically performed in 41,418 animals. Animal records were collected from 76 farms  in the South of Spain from 9 June 2010 to 18 December 2019. All the farms considered in the study had received official National and International Sanitary Certificates. All farms were controlled and officially declared tuberculosis-free (C3), brucellosis-free (M4) (Order of 22 June 2018 ). Goats Records from 95 individuals were discarded due to their zoometric and linear appraisal observations being missing or incomplete. A total of 41,323 records, belonging to 22,727 herdbook-registered primipara does, 17,111 multipara does and 1485 bucks were considered in the analysis. Average ages for primipara, multipara does and bucks in the sample were 1.61 ± 0.35 years, 3.96 ± 1.74 years and 2.43 ± 1.49 years (μ ± SD), respectively.Each observation comprises each animal’s rater score in the following four major categories for primipara and multipara does ; structure and capacity, dairy structure, mammary system  and legs and aplomb. In primipara and multipara does, each record comprised information on 17 linear traits rated on a 9-point scale. Bucks were not scored for the mammary system major category, hence only 10 9-point scale traits were scored with the aforementioned animals. Body depth from the structure and capacity major category and the dairy structure and legs and feet major categories followed the same criteria independent of sex and sexual status. The same trained rater scored all animals. Once all major categories were scored, the final score represents how close the overall animal comes to the optimal dairy standard. Murciano-Granadina LAS establishes that each major category contributes to the final score based on 25% for structure and capacity, 15% for dairy structure, 20% for legs and feet and 40% for mammary system for primipara/multipara does (any doe producing milk). In bucks and young males, such percentages change to 50% for structure and capacity, 20% for dairy structure and 30% for legs and feet.Rater’s scores are assigned one of the six category qualifications considered as follows: insufficient (IN) for animals displaying less than 69% of the optimal standard for Murciano-Granadina dairy goats , mediocre (R), 70 to 74% of optimal standard (70 and 74 points), good (B) from 75 to 79% of optimal standard (75 to 79 points), quite good (BB) from 80 to 84% of optimal standard (80 to 84 points), very good (MB) from 85 to 89% of optimal standard (85 to 89 points), or excellent (E) when at least 90% of optimal standard is displayed . The scales used and the translation process from zoometric traits to LAS traits is described in detail in Sánchez Rodríguez et al. , Table 1p < 0.01), suggesting redundancies if both age components were simultaneously considered. Thus, lactation stage was considered and results for primipara and multipara goats were reported separately.Age elements, such as doe age or lactation order, affect the dairy linear or type appraisal-related traits . These eHmisc package’s hoeffd function  interval exceeds 5% (p > 0.05), the analysis could lead to rejecting the hypothesis of normality of residuals, which would render ANCOVA outputs invalid for conclusions to be issued.Given the assumptions of the linear regression model evaluated in ANCOVA, standardized residuals should normally distribute, which implies 95% of the residuals should be in the interval , with all the observations falling outside this interval potentially being outliers, or indicative of the normality assumption not being met. The ijklmn = μ + Fari · Ai + LacStatj · Bj + KMonk · Ck + IntFarm/KYearl · Dl + b1DIMm · Em + b2An · Fn + n · Fn + eijklmn,Yijklmn is the vector of observations for each separate measure of each zoometric or LAS trait ;FarLacStatj is the vector for the fixed effect of the jth lactation stage (j = primipara/multipara does);k is the vector for the fixed effect of the kth kidding month (k = January to December);KMonl is the vector for the fixed effect of the lth level of interaction between farm/herd and kidding year (l = 400 interaction levels possibilities combining the 76 farms and kidding years from 2005 to 2019);IntFarm/KYear1 is the linear regression coefficient on days in milk (DIMm);days in milk was considered a linear covariate, hence b2 and n);age in years was considered a linear and quadratic covariate, hence bijklmn is vector of random residual effects;ei, Bj, Ck and Dl are incidence matrices relating records to their respective fixed effects, while Em and Fn are incidence matrices relating records to their respective random effects.and AThe complete kinship matrix used for genetic analyses comprised all the 279,264 animals  in the Murciano-Granadina goat breed pedigree. As the literature suggests, when bucks start rutting, that is, when male goats display the behaviours associated with the urge to breed, they go through physical changes which even alter specific variables, such as a rump angle decrease of 3 degrees [Only the direct genetic effect  was fitted in each model due to because zoometrics/LAS scores were recorded only once on each individual animal.−12. Link functions can be found in Boldman et al. [The MTDFREML software package  was usedn et al. . The stan et al.  as well.Bivariate routine of the Correlate procedure of the Analyze package in SPSS Statistics for Windows statistical software, Version 25.0. was used. Starting values for genetic, phenotypic and environmental variates and covariates are shown in As suggested by Navas González et al. , we usedAfter convergence was reached, we directly estimated non-genetic factors estimators through the best linear unbiased estimators for fixed effects (BLUES) using the MTDFREML software .p > 0.001) which was supported by the values for skewness statistics ranging from—½ to ½, which evidenced the symmetry of the profile of the curve described by the distribution of the data for all the variables evaluated. According to the evaluation of kurtosis, most of the variables presented a distribution with kurtosis < 3 (excess kurtosis < 0) or platykurtic with low and broad central peaks and short thin tails. Exceptionally, a distribution with kurtosis >3 (excess kurtosis > 0) or leptokurtic was reported for motility of movements in bucks.After the study of the distribution and symmetry properties of zoometric traits and the scale readjustment proposal suggested in Fernández Álvarez et al. . ParametVariance inflation factor evaluation suggested the model was free from redundancies after two rounds of multicollinearity analyses were performed . MulticoAs suggested in Significance levels over 0.05 were reached and 0 was not contained in the confidence interval for almost all the levels within all fixed effects and covariates considered in the model . This deThe comparison between predicted values and observed values suggested 5% of standardized residuals /692,096) could be identified as potential outliers. Hence residual normality assumption was met and ANCOVA outputs can be used to draw valid conclusions.G) and phenotypic correlations (rP) estimated are shown in Estimates of non-genetic fixed effects (BLUES) obtained from the REML quantitative genetic analysis, including days in milk as a linear covariate and age as a linear and quadratic covariate, the fixed effects of farm/herd, lactation stage, kidding month, and the interaction between farm/herd and kidding year are shown in Genetic correlations ranged from −0.010 to 0.870, with the highest positive genetic correlation occurring between Mobility and Rear Legs Rear View (0.870) and the lowest positive genetic correlation between Udder Depth and Rump Width (0.000). The most negative genetic correlation (−0.570) occurred between udder depth and anterior insertion, while the least negative genetic correlation was −0.010 and occurred between nipple placement and rump width and nipple diameter and mobility. The standard errors associated with the genetic correlations were low and negligible (µ = 0.0001), with the highest error associated with rear legs rear view.Phenotypic correlations values were low to high and ranged from −0.230 to 0.450, with standard errors being 0.0001 on average. The highest positive phenotypic correlation (0.450) occurred between rump width and chest width while the lowest positive genetic correlation occurred between rump angle and bone quality (0.010). The only variable pair which did not genetically correlate was that comprising nipple diameter and body depth.The highest negative phenotypic correlation was −0.210 and occurred between rear insertion height and chest width, while the lowest negative phenotypic correlation was −0.010 and occurred between rear legs rear view and udder depth. Mobility and stature, mobility and chest width, between rear legs side view and nipple placement, rear legs side view and udder depth, rear legs side view and rump angle, nipple diameter and rump angle, and rump angle and udder depth were neither positively nor negatively phenotypically correlated.As reported by Fernández Álvarez et al. , the MurAlthough the average heritability of zoometric measures has reportedly been described as higher than the heritability of LAS traits and usually present larger standard errors , the proIndeed, such dissimilarities may occur when LAS scale units are not able to represent the same range of units found in the population for a particular zoometric trait, thus LAS is unable to capture all the population’s variability for such traits. This normally occurs due to the lack of implementation of a process of scale validation and optimization, and trained score operators not being used to collect the information as it has occurred in the Murciano-Granadina goat breed .The progressive gain in heritability values and reduction in heritability standard errors may be ascribed to the technification and improvement of the efficiency of the methods used to collect either phenotypic or genealogic data in terms of quantity and quality. Relative increases in heritability may evidence faster trait evolution; this means fewer generations may be required for traits to evolve either positively or negatively. According to Haworth et al. , heritabWhen compared to the values in Fernández Álvarez et al. , mobilitChanges in heritability must be regarded with caution given heritability does not measure the proportion of a trait caused by genes, but the proportion of variation in a trait that can be attributed to genes. As a result, when the environment relevant to a given trait uniformly changes affecting all members of the population, that is, the variation or differences among individuals in the population remains the same, the mean value of the trait will change without any change in its heritability. This not only becomes evident for traits for which convergence had not been reached at previous evaluations , but for those, such as stature, which accounts with a high heritability of 0.4300, even if average stature continues to increase through the years to reach the international optimal standard for the dairy goat type . This meTotal phenotypic variance is the denominator of heritability and it is estimated as the variance of the trait being evaluated after correcting for known fixed effects such as sex, and covariates such as age, as it occurred in this study. As extended among animal breeders, the best prediction of future performance is obtained by considering the amount of variation that is not accounted for by known environmental effects. The lack of knowledge in regards these factors increases the estimates of phenotypic variance thus reduces the estimate of heritabilities. However, zoometry needs to follow a rather evolutionary perspective and focus on the total variation between individuals.Visscher et al. , suggestThe highest estimates were generally associated with the udder- and teat-related traits, whereas those estimated for the legs and feet were lower. The highest estimates were generally associated with the stature, the udder (nipple-related traits) and those traits involved in teat suspensory system, whereas those estimated for mobility, legs, feet and other body areas which are involved in movement development were the lowest ones. The individual traits with the overall highest and lowest heritability estimates were stature (0.4300) and rear legs side view (0.0906), respectively. Other authors ,22 have The evaluation of legs aplomb-related traits in Murciano-Granadina goats (rear legs side and rear views and mobility) reported heritability values ranging from 0.0906 to 0.2213. This range covers the values reported for standardized breeds  and was also reasonably similar to those of 0.13 reported by McLaren et al. [This situation may be ascribed to zoometric/LAS criteria differing in terms of the methods that were implemented for their collection . Furthermore, the rather advanced level of standardization of the breeds that were evaluated may be a source for reduction in the variability for specific zoometric/LAS traits.When the same or similar scoring methods are used, values closely resemble those in our study 0.2213), as shown by the 0.21 heritability values presented by Luo et al.  for a mu13, as shFernández Álvarez et al. ,21 suggeConsidering the mammary system major area, the estimates observed in the present study were in close agreement with those observed by Manfredi et al.  and RuppAlthough the results in this study are in the range of the aforementioned studies ,32,33,41McLaren et al. , suggestHowever, differences may not only derive from accuracy issues. For instance, selection of specific traits may involve a reduction in variability and in turn this may translate into the progressive reduction of heritability estimates. The implementation of on-farm selection policies tends to remove effectives displaying undesirable conformations from an early age before animals are able to disseminate their genetics and become established in the herd. The optimization of zoometric and LAS systems and of the ability of operators to collect information may be responsible for the increases in heritability experienced by almost all traits, as reported in Fernández Álvarez et al. , as thesSimilar values to those genetic correlations found in this study were reported by McLaren et al. , who alsThere was a general lack of parallelism in the magnitude of genetic correlations and the respective phenotypic correlations for the same pair of variables. This means that although the moderate to high values of genetic relationship among variable pairs permits the determination of a well-defined relationship between trait pairs in either direction (positive or negative), phenotypic correlations are low to mild. This situation challenges selection if we only consider what we can visually see of zoometric traits, and is typical of breeds which are immersed in a process of standardization such as the Murciano-Granadina . This beIn line with these findings, Fernández Álvarez et al.  reportedThe evaluation of genetic and phenotypic parameters across major areas revealed the set of traits comprised in the structure and capacity major area genetically correlate following a positive pattern, which at the very least duplicates the magnitude of phenotypic correlations. There are only two exceptions to note, which are the low negative phenotypic correlation between stature (height at withers) and body depth against the moderate positive genetic correlation for the same pair of traits, with the latter presenting the lowest heritability of the traits in the structure and capacity major area. This demonstrates the more limited range of possibilities for selection to be efficient and the existence of redundancies based on the rump angle trait as evidenced by the studies of Fernández Álvarez et al. . These aThe previously described genetic/phenotypic pattern is also shared by the angularity trait of the dairy structure major area. Contrastingly, angularity and bone quality, both from the dairy structure major area describe an opposite relationship. This means that our focus seeks the improvement in one of them, and at the same time we hinder the selection in favour of the other.As a consequence, the first suggestion is to discard of the rump angle variable from the structure and capacity major area and to include the angularity trait, which may ensure all traits in the same major area behave similarly, which in turn may enhance the potential of selection strategies. This becomes even more important when these results are compared to those in the literature given the same pattern sustained across research experiences in the topic across goat breeds ,22,33,37As suggested by our results, the high negative genetic correlations of bone quality and rear insertion height with almost all of the rest of traits makes of these traits potential candidates to be used as references in negative selection practices. In this sense, quantitatively selecting against thicker bones and higher rear insertion heights, which indirectly means selecting for animals with finer and flatter bones, with shorter rear insertion heights (which is the optimum that breeders seek) may indeed result in the optimization of selection practices in the rest of traits, specially of those in the structure and capacity and mammary system major areas.Within the mammary system major area, rear insertion height described the exact opposite genetic and phenotypic correlation patterns to the rest of traits in the same major area. This stems from the fact that, while performing the extrapolation of zoometrics to LAS, the optimum in LAS scale  corresponds to fewer centimetres (3 cm) than the minimum level in the scale (1 point and 11 cm). This may need to be considered when implementing selection strategies as although distribution properties and descriptive statistics are equivalent, the direction of the correlations is inversed when compared to the rest of traits in the same major area. The proposal would be to invert the current LAS scale so that upper levels in the LAS scale correspond to longer rear insertion heights, hence making the LAS system and zoometrics follow the same direction, without the need to remove the trait from the major area in which it has traditionally been evaluated.This has also been described in other breeds, as CAPRIGRAN LAS is based upon the system which was traditionally applied in international goat breeds such as Alpine and Saanen breeds, as a direct extrapolation from dairy cows, in respect to the direction of the relationships among trait pairs that were considered a priori. In these regards, Manfredi et al.  indicateIn this sense, if sturdy, tall, thick-boned animals presenting sloping rumps are selected against, we may indirectly seek for rather average-sized fine-boned animals with raised rumps, which is the exact opposite to that recommended in the literature for Holstein Friesian dairy cows ,43, but In regards the legs aplomb major area, all traits considered (rear legs side and rear views and mobility) describe the similar negative genetic correlation trend with the rest of the traits in the rest of the major areas, which permits the consideration of these traits as a solid cluster in terms of the planification of selection strategies, even if the direction of selection must be the opposite to that to be performed for the structure and capacity and mammary system major areas.Changes occurring along lactations in Murciano-Granadina does and during rutting in Murciano-Granadina bucks need to be accounted for while measuring individuals due to the zoometric alterations that they promote. Certain traits may not be able to account for the variability described in international 9-point scoring scales, but a 5-point scale with the consequent reduction of heritability may be an improvement. A priori on-farm selection criteria may have selected animals with better aplomb and mobility patterns, which is the source for their relative fixation in the population, variability and heritability reduction. Discarding the rump angle trait from the structure and capacity major area and including the angularity trait ensures all traits in the same major area behave similarly, which enhances the potential of selection strategies. Scale inversion on specific zoometric traits may help to address disagreement in the patterns of the rest of the traits in the same major area without impairing the aim of selection for the trait whose scale has been modified. The legs aplomb major area conforms to a solid cluster in terms of the planification of selection strategies, even if the direction of selection must be the opposite to that for the structure and capacity and mammary system major areas. Future breeding programs would benefit from modifying the collection system and the manner in which the zoometric traits are managed at a genetic level to ensure that selection for zoometrics/LAS does not translate into any unwanted change in functional fitness, maximizing the outcome of selection strategies to fit the particular reality of the goat species and the diverse range of breeds that it comprises.