Survival and longevity are very important traits in pig breeding. From an economic standpoint, it is favorable to keep the sows for another parity instead of replacing them and, from the animal’s perspective, better welfare is achieved if they do not experience health problems. It is challenging to record longevity in purebred (PB) nucleus herds because animals are more likely to be replaced based on breeding value and high replacement rates rather than inability to produce. Crossbred (CB) sows are, however, submitted to lower replacement rates and are more likely to be kept in the farm longer if they can produce large and robust litters. Therefore, the objective of this study was to investigate whether the use of CB phenotypes could improve prediction accuracy of longevity for PBs. In addition, a new definition of survival was investigated. The analyzed data included phenotypes from two PB dam lines and their F1 cross. Three traits were evaluated: 1) whether or not the sow got inseminated for a second litter within 85 d of first farrowing (Longevity 1–2), 2) how many litters the sow can produce within 570 d of first farrowing [Longevity 1–5 (LGY15)], and 3) a repeatability trait that indicates whether or not the sow survived until the next parity . Traits were evaluated both as the same across breeds and as different between breeds. Results indicated that longevity is not the same trait in PB and CB animals (low genetic correlation). In addition, there were differences between the two PB lines in terms of which trait definition gave the greatest prediction accuracy. The repeatability trait  gave the greatest prediction accuracy for breed B, but LGY15 gave the greatest prediction accuracy for breed A. Prediction accuracy for CBs was generally poor. The Survival trait is recorded earlier in life than LGY15 and seemed to give a greater prediction accuracy for young animals than LGY15 (until own phenotype was available). Thus, for selection of young animals for breeding, Survival would be the preferred trait definition. In addition, results indicated that lots of data were needed to get accurate estimates of breeding values and that, if CB performance is the breeding goal, CB phenotypes should be used in the genetic evaluation. Survival and longevity of the sow are very important traits in pig breeding because replacing a sow is more expensive than keeping a sow in the herd for an extra parity . In addiA particular challenge with the longevity trait is that the observed longevity/culling is a product of many factors that contribute to a sow’s survival, typically functional traits, such as health and reproduction, but also production traits . FactorsThe goal of this study was to compare the currently used traits for longevity to a new trait, Survival, modeled as a repeatability trait in a random regression model. Furthermore, the value of including CB data in addition to PB data in the genetic evaluation of longevity was investigated.Data recording and sample collection were conducted strictly in line with the legislation requirements in the 14 countries included in the study . The data and samples collected for DNA extraction were obtained as part of routine data recording in the commercial breeding programs of Norsvin SA and Topigs Norsvin.Data was available for two dam lines (breeds A and B) and their F1 cross breed X; . AnimalsTraits analyzed were two longevity traits already included in the breeding program of the PBs, and the new survival trait defined in this study. Longevity was defined as 1) whether or not (0 or 1) a sow was inseminated for a second litter within 85 d of first farrowing [Longevity 1–2 (LGY12)] and 2) how many litters she had within 570 d after the first farrowing . Survival was defined as the “probability of culling.” When animals did not reach the next parity, they were assigned a “1.” Otherwise, they were assigned a “0.” For example, a sow that produced a second litter (parity 2), but failed to produce a third litter, would have observation “0” for parity 1 and “1” for parity 2. However, if the sow is still alive at the end of parity 2, and may yet produce a third litter, she gets a “0” also for parity 2. Hence, if the sow does not have a “1” record, she is still alive and her longevity is censored . This trait was analyzed with a repeatability model.Genotypes were available for 25,619 A, 25,692 B, and 1,994 F1 animals. All animals were genotyped using the Illumina GeneSeek custom 50K SNP chip . Genome positions of the single-nucleotide polymorphism (SNP) were based on the SScrofa11.1 assembly of the reference genome . GenotypA) were used, and the following model was applied:Variance components were estimated using ASReml . For vary was a vector of observations ; X, Z, and V, known incidence matrices; b, a vector of fixed effects (see below); u, a vector of random additive genetic effects, with u ~ N, where σ u2 was the additive genetic variance; v, was a vector of common litter effects, with v ~ N, where σ v2 was the variance of common litter effects; and e, a vector of residuals, with e ~ N, where σ e2 was the residual variance. Matrices Iv and Ie were identity matrices of the appropriate dimensions, A was the additive relationship matrix (pedigree), and H was a matrix of combined pedigree and genomic relationships between individuals , the permanent environmental variance (covariance between records of different parities) is very difficult to estimate :h2 was the heritability of LGY12, LGY15, or Survival when all breeds were in the data set. The reason for correlating with the CP of all models was to compare the model results against each other (because a single gold standard was not available). However, using phenotypes from LGY15 to predict LGY12 is not really of interest because the LGY15 phenotype is recorded later in life than the LGY12 phenotype; thus, these results are not presented. This is to some extent also true for Survival, which will be similar to LGY12 until the third parity (LGY12 is only recorded up until the second parity). That is, Survival is recorded from the first parity onward, whereas LGY12 is recorded at the onset of the second parity. As all the sows in this study had already farrowed once, predicting LGY12 with LGY15 or Survival phenotypes is not really interesting. However, comparing performance of LGY12 and Survival with each other when predicting other traits is interesting because both can be measured early in life.where CP was the corrected phenotype (phenotype corrected for fixed and nongenetic random effects) of LGY12, LGY15, or Survival , EBV was the breeding value of each model , and n survival records, hn2:In addition, the correlation between EBVs when using the full data set for the validation animals (for each breed) were compared to using information (phenotypes) also from other breeds. The correlation between EBVs for the different traits (within breed) were also compared. Because Survival is a repeatability trait, and LGY12 and LGY15 are not, the heritability estimates are not directly comparable. Therefore, an additional equation was used to estimate the heritability of the mean of g2 was the additive genetic variance, σ e2 was the residual variance, σ v2 was the variance of common litter effects, and n was the average number of parities within breed.where σ Descriptive statistics are in hn2) for Survival were 0.048, 0.094, and 0.040 for A, B, and X, respectively, when only one breed was in the data set.Heritability estimates  were simWhen traits were considered different between breeds, larger differences between heritability estimates between the breeds were observed, especially for LGY15 . For LGYPrediction accuracies when traits were considered the same across breeds varied a lot between PB and CB animals –8. PrediLGY15 as gold standard, the prediction accuracies for PB animals were similar between A and B animals, although there was a tendency for breed B to have greater predictive ability for Survival than breed A  across birth years , it is possible to get an idea of when information is available for different animals. Here, the assumption is that greater SD equals greater accuracy. Big differences were seen between the PBs  and 2. RBased on the solutions for the fixed effects for the Survival trait, it seemed that it was somewhat harder for breed B sows inseminated in season 4  to produce another litter . This waThis study investigated the contribution of CB data in addition to PB data on the estimation of breeding values for longevity in pigs. Furthermore, the possibility of the use of an alternative definition of longevity, here called Survival, was investigated. The results were not univocal but indicated that adding CB information in the genetic evaluation is important for improving longevity in CBs.hn2 = 0.049–0.094) was also lower than for LGY15. Heritability estimates vary with the definition of the trait and method of analysis. Other studies have found heritability estimates of longevity using a linear model in the range 0.03–0.10  were low, suggesting that PB and CB longevity are different traits similar to LGY15 as gold standard indicates whether Survival can predict LGY15, which is the trait Topigs Norsvin uses today. For the two traits available early in life , Survival tended to have a greater prediction accuracy than LGY12 both when predicting CPLGY15 and CPSurvival.Using different measures of longevity yield deviations as gold standard can give an indication as to whether phenotypes from one trait are able to predict alternative measures/aspects of longevity. For example, using CPLGY15 and CPSurvival, the same trend was seen in both cases when traits were considered the same across breeds. For breed A, LGY15 tended to give the greatest prediction accuracy (within breed), whereas, for breed B, Survival tended to give the greatest prediction accuracy. However, within breed, when predicting CPLGY15, the prediction accuracies were very similar between the two traits. This suggests that both LGY15 and Survival can predict LGY15 to the same accuracy. As Survival is available earlier in life, it is advantageous to use Survival. In both cases, it also seems that breed A benefited from including CB data, while, for breed B, this was not the case. This may be due to the number of phenotypes for each breed. Breed B had many more phenotypes than breed A, so CB would have contributed more to the data set with breed A when these were included (and traits were considered the same across breeds). As the number of CB phenotypes were close to the number of phenotypes from breed A, when considering the traits as the same between PB and CB, CB phenotypes clearly contributed to breed A breeding values. For breed B, probably there were so many within-breed phenotypes that CB did not contribute to improve prediction of breed B breeding values. In addition, the genetic correlation between CB and the PB differed between breed A and breed B. It was greater between CB and breed B than between CB and breed A, suggesting that CB phenotypes would contribute more for breed B evaluations. As the genetic correlation was not very high (considerably less than 1), this suggests that longevity is a different trait in PB and CB, which would reduce accuracies when the traits are considered the same across breeds. In addition, most of the CB had breed A sires, so the relationship between animals with phenotypes between breed A and CB would have been lower than it was between breed B and CB.For CPFor CB animals, prediction accuracy was very poor, regardless of the trait definition or gold standard. There are several possible reasons for this. One is that the training set was relatively small and that ~61% of animals in the training set had score 5 for LGY15 . Thus, there was little variation in the training and validation sets. In addition, especially for data set X (only CB), no parents had phenotypes, meaning that full and/or half-sibs would have been the closest relatives. Again, because of the low genetic correlation, even when PB animals were included in the training data set , PB information may have contributed very little as longevity does not seem to be the same trait between PB and CB.LGY15, the same was seen for both the PB and the CB as for CPLGY12. For EBVLGY15, CB benefitted from being analyzed with breed B but not with breed A. As the genetic correlation between breed B and CB were greater than for breed A and CB, this makes sense.When predicting LGY12 and the trait was considered different between breeds, analyzing the PB with or without CB gave very similar prediction accuracies. This suggests that CB phenotypes do not really contribute to prediction accuracies of PB for PB performance. The low genetic correlation between PB and CB for LGY12 supports this. In addition, most PB sows would have their own phenotype for LGY12 before any CB offspring get the phenotype. Analyzing CB data together with breed A was favorable for prediction of CB compared to analyzing CB alone, whereas this was unfavorable when analyzing with breed B for LGY12. For gold standard CPPrediction accuracies in the current study were in the range 0.003–0.375, 0.001–0.547, and 0.003–0.183 for breed A, breed B, and CB, respectively, depending on trait and gold standard. Few other studies have reported prediction accuracy of longevity traits, but Based on the SD of EBVs over birth year see  and 2, iWhen looking at the EBVs for the different traits within breed, the greatest correlation between traits was between LGY15 and Survival for PB. This is not surprising as these traits are supposed to measure the same thing, only with different definitions. The advantage of the Survival trait is, however, that the first observation is available much earlier in life than LGY15, which should give more accurate breeding values earlier in life. This was not seen for CB, where the greatest correlation was seen between LGY12 and LGY15. However, the correlation between LGY15 and Survival was a close second. Why LGY15 should be more closely correlated to LGY12 than Survival is unclear but could be because Survival has the potential to have more outcomes than LGY15 and, thus, LGY15 is, therefore, more closely related to LGY12. In addition, only ~15% of the CB had two or fewer parities, and 60% of the CB had LGY15 = 5, so these two traits would be very closely related in the CB and could explain this difference between PB and CB. The lowest correlation was between LGY12 and Survival for all breeds. What is clear from all these correlations, however, is that regardless of the trait used in the breeding program, selecting the best animals based on the EBV would move the traits in the same direction . However, the speed of genetic progress would differ based on the trait chosen and at which age selection takes place.The fixed effects results indicate that there exists a seasonal effect of Survival for breed B. A lower survival rate seems to be achieved when the sows were inseminated in season 4 (October–December). There is some evidence for more culling in certain seasons, mainly due to light or temperature . However, it is unlikely that this has a major effect in modern facilities with light and temperature regulation. Most, but not all of the herds in this study were housed in modern facilities, but about 14% of breed B animals  were in herds without temperature regulations. This could explain why a small effect of season was seen for breed B. This effect was, however, very small: 10% chance of culling compared to 8 or 9%.It seemed that there was a higher replacement rate in breed A than in breed B and CB (based on average number of parities). Some studies have found differences between nucleus and commercial herds, where culling decisions are different . AasmundBetween-breed differences were observed for the effect of parity of dam. In breed B, if the dam’s parity was greater than 1, it was easier for the sow to survive until the next parity. Thus, if the dam had proven that she could have more than one litter, this makes it more likely that her offspring (sows) will too. The same effect was not seen in breed A. It seems the dam effect was less strong in breed A or that culling decisions were based on different criteria. For CB, no effect of parity of dam was seen, perhaps indicating that CBs are kept until the next parity independent of breeding value of parents. This would result in a small heritability as the genetic potential of the animal (the EBV) is not taken into account and, thus, the genetic aspect has little impact on whether the animal stays for another parity. Therefore, CB will mainly be kept based on their own performance in the previous litter rather than the performance of their parents or EBV. This agrees with the low genetic correlation between PB and CB longevity, where dam longevity was not indicative of CB longevity .One of the challenges with longevity traits is that the trait is collected late in life, or after the animal is dead, such as length of productive life . For aniOne disadvantage of the Survival trait in the current study was that the variance components could not be estimated when the traits were considered different between the breeds. This may be due to the binary nature (0–1) of the trait so that genetic (co)variance estimates are rather low. It is also possible that the prediction model needs some adjustment depending on which breeds, or possibly herds, are included. It is possible that the model should not be the same for breed A with CB and breed B with CB. However, as we do not have an estimate of genetic correlations between PB and CB for the Survival trait, it is not possible to conclude whether Survival is the same trait in PB and CB. It is likely that this genetic correlation between PB and CB for the Survival trait would be similar to what was found for LGY15.An advantage of the Survival trait is that its recording pattern fits well with the routine evaluations for maternal traits, which are mostly collected per parity, for example, total number born, litter weight, and body condition score of the sow. Further research is needed to investigate the correlations between Survival and these maternal traits. As litter size seems to have a large effect on whether or not an animal is culled , a multiThe analysis of the Survival trait using a repeatability model assumes a correlation between parities of 1, which is not strictly correct . An alteResults from this study show that longevity traits of PB and CB animals are not genetically the same trait. Thus, it is necessary to collect data in CB populations if the goal is to improve PB breeds for CB longevity. Prediction accuracies for LGY15 and the new Survival trait were similar. As Survival is available earlier in life, this trait would be preferred. This pattern was evident in breed B, but less so in breed A. More research and data will be needed to estimate genetic correlations between PB and CB survival. It is clear from this study that a large amount of data is needed to get accurate predictions of longevity, both in PBs and CBs.txaa073_suppl_Supplementary_TablesClick here for additional data file.


Temperature response of stem elongation in wheat grown under field conditions indicated that temperature response is highly heritable and linked to the flowering pathway. H2=0.81) and positively related to a later start and end of stem elongation as well as final height. Genome-wide association mapping revealed three temperature-responsive and four temperature-irresponsive quantitative trait loci (QTLs). Furthermore, putative candidate genes for temperature-responsive QTLs were frequently related to the flowering pathway in Arabidopsis thaliana, whereas temperature-irresponsive QTLs corresponded to growth and reduced height genes. In combination with Rht and Ppd alleles, these loci, together with the loci for the timing of stem elongation, accounted for 71% of the variability in height. This demonstrates how high-throughput field phenotyping combined with environmental covariates can contribute to a smarter selection of climate-resilient crops.In wheat, temperature affects the timing and intensity of stem elongation. Genetic variation for this process is therefore important for adaptation. This study investigates the genetic response to temperature fluctuations during stem elongation and its relationship to phenology and height. Canopy height of 315 wheat genotypes (GABI wheat panel) was scanned twice weekly in the field phenotyping platform (FIP) of ETH Zurich using a LIDAR. Temperature response was modelled using linear regressions between stem elongation and mean temperature in each measurement interval. This led to a temperature-responsive (slope) and a temperature-irresponsive (intercept) component. The temperature response was highly heritable ( Temperature is a major abiotic factor affecting plant growth and development. As a consequence of global warming, wheat production could decrease by 6% for each degree Celsius of global temperature increase . DespiteSE is a critical phase for yield formation in wheat. It occurs between the phenological stages of terminal spikelet initiation and anthesis . ModifyiRht) genes. The resulting dwarf and semi-dwarf varieties benefit from improved resource allocation from the stem to the spike and reduced lodging, allowing more intensive nitrogen application  were shown to limit cell wall extensibility which decreases growth rates , photoperiod (Ppd), and earliness per se (Eps) genes  temperature-independent elongation; (ii) temperature-dependent elongation; and (iii) the duration of the elongation phase determined by the start and end of the process. To achieve this, we present a method to assess and measure these three processes under field conditions by means of high-frequency, high-throughput phenotyping of canopy height development. The resulting data were combined with genetic markers to identify quantitative trait loci (QTLs) controlling the aforementioned processes.Field experiments were conducted in the FIP at the ETH research station in Lindau-Eschikon, Switzerland . We used a set of ~330 winter wheat genotypes (335–352 depending on the experiment) comprising current European elite cultivars  using a light detection and ranging (LIDAR) scanner  mounted on the FIP (15) and 95% FH (GDD95) were used as proxy traits for the start and end of SE, respectively. SE duration was recorded in thermal time (GDDSE) as well as in calendar days (timeSE), as the difference between GDD95 and GDD15 (GDD) when 50% of the spikes were fully emerged from the flag leaf sheath  were extracted from the height data following t):In order to investigate short-term growth response to temperature, average daily stem elongation rates (SERs) were calculated for each plot as the difference (∆) in canopy height (CH) between consecultive time points , and ε denotes the residual error. bTcrit is the model intercept at the temperature at which the correlation between intercept (intTSER~) and slope is zero (see below). Per definition, the intercept of a linear model would be calculated at T=0 °C—far outside the range of observed temperatures. In the observed data, the intercept at T=0 °C correlated strongly negatively with the slope (Tcrit) at which the correlation between slope and intercept was zero  correction for design factors and spatial trends; (ii) application of linear models to each plot to determine growth response to temperature; and (iii) prediction of adjusted means and calculation of the heritability for all traits across years.The spatial correction of canopy height per measurement time point was done using the R package SpATS  is a smoothed bivariate surface defined over rows and ranges of a virtual grid, Gi is the effect of the ith genotype , Rj is the effect of the jth row in the virtual grid, and Ck is the effect of the kth range. With the number of genotypes, the number of ranges/rows varied across years . The replications were arranged diagonally in this grid with a gap of five rows and ranges between them. Thus, for example in 2017, replication 1 was at rows 1–21 and ranges 1–18; replication 2 was at rows 24–41 and ranges 27–47 of the virtual grid.where f describing the bivariate surface can be decomposed in a nested-type ANOVA structure as described by Rodríguez-Álvarez et al. (2018). The number of spline points was set to two-thirds of the total number of rows and ranges in the virtual grid, respectively.The function JXB online.From this model, the predicted genotypic best linear unbiased estimates (BLUEs) plus residual error were kept as spatially corrected plot values. Thus, these new plot values were corrected for spatial effect as well as for the random row and range effects, and used for the subsequent dynamic model. For a visualization of the applied SpATS correction, see Genotypic BLUEs across the three seasons were calculated for all traits using the R package asreml-4  followinypij is the spatialy corrected plot value of the respective trait , μ is the overall mean, Gi the fixed effect of the genotypes common in all three years , Yj is the fixed effect of the year , GYij is the random genotype-by-year interaction, and ε pij is the residual error.where H2) across years, Gi in In order to estimate best linear unbiased predictors (BLUPs) and heritability . All statistical analyses were performed in the R environment .In order to investigate the relationship between FH, temperature response, and vigour, and to test for confounding yi is FH of the ith genotype, µ is the model intercept, β 1–6 are the main effect estimates of x=slpTSER~, intTSER~, GDDSE, Rht-B1, Rht-D1, or Ppd-D1, respectively. β 1,2–β 5,6 are all two-way interaction effects (n=15) and ε i is the residual error. Genotypic data for Rht-B1, Rht-D1, and Ppd-D1 alleles were available for 301 genotypes obtained from Rht-1 alleles was performed using PCR markers  between phenotypic BLUPs and SNP markers. (i) We used a mixed linear model (MLM) including principal components among marker alleles as fixed effects and kinship as random effect to account for population structure  >5.43 were considered significant.For all methods, a Bonferroni correction was applied to the pointwise significance threshold of α=0.05, to avoid false positives. Hence, only markers above –logr2) calculated with the R package SNPrelate (r2=0.2 (Linkage disequilibrium (LD) among markers was estimated using the squared correlation coefficient  genotypic effects for both slpSER~T and intSER~T across 3 years. Both traits showed high heritabilities across years , FH could be predicted with high accuracy across different years (0.85≤R2≤0.89) by training a linear model on the BLUEs of one year and predicting it on the BLUEs of another independent year. In order to account for possible confounding Rht and Ppd effects, the allelic status of Rht-B1, Rht-D1, and Ppd-D1 was included as contrasts in the model  interaction effect was found between Rht-D1 and Ppd-D1. Furthermore, weak interactions (P<0.1) were found for Rht-B1:Ppd-D1, int~TSER:Rht-B1, and intTSER~:Rht-D1 To evaluate the relationships between the traits measured, Pearson correlation coefficients were calculated for each trait pair. If not indicated otherwise, the reported correlations were highly significant  and vigour (intTSER~) also showed a strong, positive relationship to FH . However, only temperature response correlated with GDD15 and GDD95 , whereas vigour did not  was negatively correlated with GDD15 (r= –0.44) and positively correlated with GDD95 (r=0.4). However, GDDSE did not correlate with FH  or temperature response , although GDDSE negatively correlated with vigour (r= –0.32). In contrast, SE duration in calendar days (timeSE) was negatively correlated with temperature response (r= –0.35) and GDD15 (r= –0.82), indicating a longer SE phase for earlier genotypes. Heading showed strong positive correlations with GDD15 (r=0.61) and GDD95 (r=0.71), and a weak correlation to temperature response (r=0.29). Furthermore, heading correlated negatively with intTSER~ (r= –0.41) and showed no correlation to FH . Other weak correlations (r<0.3), that are not discussed, are shown in As expected, SE duration in thermal time  of the marker genotypes revealed no distinct substructure in the investigated population. The biplot of the first two principal components showed no apparent clusters, with the first component explaining 8% and the second component explaining 3.3% of the variation in the population . This isomosomes .P-value inflation in the haplotype method whereas the P-values were slightly deflated when using the MLM approach , which is in accordance with the high heritability of the trait. In this study, we found 10 significant MTAs for FH  MTA on chromosome 1B (wsnp_Ex_c1597_3045682) and two almost significant (LOD=5.38 LOD=5.01) MTAs on chromosomes 4B (CAP7_c10839_300) and 5D (IAAV7104), respectively  was significantly associated with both GDD15 and GDD95, as well as heading. The lack of overlap of MTAs between temperature response, vigour, and timing of critical stages indicates that they are genetically independent. However, there is a genetic connection between vigour and FH on the one hand and between the start and end of SE and heading on the other.Comparing the GWAS results for temperature response, vigour, FH, GDDEARLY FLOWERING 3, FRIGIDA, and CONSTANS  as well as developmental progress   against the annotated reference genome. Genes putatively underlying the QTLs for heading, GDD15, and GDD95 are listed in 15, and GDD95 on chromosome 5B  were found to be 6.6 Mb upstream of FLOWERING LOCUS T  as well as growth  . Importa LOCUS T . Other fted gene . Moreoveted gene  were fouted gene . The remand BEL1 . Genes n15, GDD95, and FH. In order to examine this interdependency on a genetic level, we used a linear model to predict FH with the SNP alleles of the QTLs for slpTSER~, intTSER~, GDD15, and GDD95 as predictors. The model was able to predict FH with an accuracy R2=0.5; however, clusters in the data showed clear effects of Rht-D1 and Ppd-D1 alleles  as well as between FH and vigour (intTSER~). This is consistent with our hypothesis that FH can be described as a function of temperature-independent growth processes and as a function of temperature response during SE. Importantly, among all components, the temperature response was a significant driver of FH and also had a strong influence on the timing. Temperature response delayed the beginning of SE, leading to a later start and end of the whole phase. This finding might appear counter-intuitive: given the assumption that plants develop faster under higher ambient temperatures, a more responsive genotype should develop faster compared with a less responsive one. The results of the correlation analysis show a clear connection between FH and temperature response , FLOWERING LOCUS C (FLC), and FLOWERING LOCUS T (FT) are major candidate genes for ambient temperature-mediated differences in flowering time . In Arabidopsis, ELF3 was found to be a core part of the circadian clock involved in ambient temperature response -based phenotyping techniques, the growth of canopy cover and canopy height can be measured using image segmentation and structure from motion approaches  and the Together, the results of this study indicate that temperature response may be exploitable as a breeding trait to adjust phenology towards specific environments, through either phenotypic or marker-assisted selection. Furthermore, a better understanding of temperature response may enhance the capability of crop models to predict crop performance under future climate change scenarios.Modern phenotyping platforms hold great promise to map the genetic factors driving the response of developmental processes to environmental stimuli. To the best of our knowledge, this is the first experiment dissecting the SE process into its underlying components: temperature-dependent elongation, temperature-independent vigour, and duration of elongation. The independent loci detected for these traits suggest that it is possible to select them independently. The detected loci may be used to fine-tune height and the beginning and end of SE as they explain a substantial part of the overall genotypic variation. With increases in automation, growth processes may be monitored in the field on a daily basis or even multiple times per day. This will increase the precision in assessing genotype responses to the fluctuation in meteorological conditions and will allow quantification of the relationship of these responses to yield. Remote sensing by means of UAVs in combination with photogrammetric algorithms will allow measurement of these traits in breeding nurseries. We believe that this is paving the way for a more informed selection to climate adaptation within individual growing seasons.JXB online.The following supplementary data are available at Fig. S1. Correction of canopy height for spatial as well as random row and range effects.Fig. S2. Summary of plot-based linear model fits of stem elongation rate versus temperature.Fig. S3. Pearson correlation coefficients among 3 year BLUEs of all investigated traits.Fig. S4. Principal component analysis among marker genotypes.Fig. S5. Manhattan plots and quantile–quantile plots depicting the GWAS results using the MLM approach.Fig. S6. Manhattan plots and quantile–quantile plots depicting the GWAS results using the GLM approach.Table S1. Genes of interest related to floral transition and flowering.r2=0.2Table S2. Chromosome-wise distance thresholds for LD decay <et al. (2016).Table S3. Corresponding marker–trait associations for final canopy height with respect to Zanke GDD from the IWGSC reference genome annotation.Table S4. Selected putative candidate genes for heading95 from the IWGSC reference genome annotation.Table S5. Selected putative candidate genes for GDD15 from the IWGSC reference genome annotation.Table S6. Selected putative candidate genes for GDDGDD, GDD15, GDD95, GDDSE, timeSE, slpTSER~, and intT.SER~Table S7. Three-year BLUEs of the investigated traits FH, headingeraa471_suppl_Supplementary_File001Click here for additional data file.eraa471_suppl_Supplementary_File002Click here for additional data file.


Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava. Manihot esculenta Crantz), a tropical root crop with origins in Latin America, ranks as the 3rd most important crop in the tropics after rice and maize1. In Africa, more than 800 million people rely on cassava as a primary source of calories2. Cassava is widely cultivated due to its high drought tolerance and high carbohydrate-containing storage roots, and although most of the production is for human consumption, its use extends to animal feed and industrially processed products4. In addition to the edible, high-starch storage roots, cassava plants produce thin fibrous roots, which function to absorb water and nutrients from the soil5. The development and differentiation of fibrous roots, as well as the mechanism that triggers root storage formation in cassava, are poorly understood.Cassava . The categorical scores for root size are 3, 5 and 7 for small, medium and large roots, respectively. A single categorical score is given to a harvested plot based on the most frequent size in that plot. The visual rating of shape is 1 , 2 , 3 , 4 (fusiform), 5 (Irregular), and 6 (Combination of shapes). Similar to root size, the shape scoring is based on the most common observation in a plot. These categorical scores suffer from person to person subjectivity and inability to describe the variation in size and shape within a plot. Thus, image analysis of roots offers a more objective means of obtaining unbiased quantitative data on important root traits.Routine assessment of storage root size and shape in breeding plots relies on visual scores  has been used to derive shape-related phenotypes for genome-wide association and genomic prediction14.The development of image software tools and analyses pipelines have gained increased relevance owing to advances in high-throughput phenotyping15 because its implementation can lead to a reduction in cost and time compared to traditional plant breeding programs16. Because cassava is an outcrossing species mostly propagated by stem cuttings, conventional breeding methods can take more than five years to produce superior performing clones (www.nextgencassava.org). Genome-wide association studies (GWAS) are complementary to GS as they have proven effective for the identification of QTL regions associated with several traits that are critical for cassava breeding, including cassava mosaic disease resistance (CMD)17, cassava brown streak disease resistance (CBSD)18, and beta-carotene content and dry matter content19.Genomic selection (GS) is a method first introduced in animal breeding to select candidates for crossing in the breeding program using only genomic information. GS is particularly relevant for the improvement of polygenic traitsTo date, genomic prediction accuracies for root shape and size characteristics have not been reported in cassava. In this study, size and shape related traits describing cassava roots were obtained through automated image analysis. We first estimated their heritability and conducted a genome-wide association study to explore the genetic architecture of cassava roots shape characteristics; then we compared the genomic prediction accuracy of image size and shape traits to those of root yield. Our research contributes to a better understanding of the genetic architecture of cassava root size and shape traits and explores the possibility of in-field high-throughput phenotyping that would allow breeders to use GS to select varieties for quantitative root characteristics.et al., 2016;Wolfe et al., 2017).We processed and analyzed cassava roots images taken from several field trials conducted by the International Institute for tropical agriculture (IITA) as part of their genomic selection breeding program. The cassava germplasm collections that we analyzed are known as Genetic Gain (GG) and the progeny of the first genomic selection event (C1), which are thus progeny of a subset of the GG population. The GG constitutes a large collection of important landraces, breeding lines and released improved varieties of cassava developed by IITA over the last four decades. More detail about the origins and constituency of these populations is available in several published studies . It was important that the roots were not touching each other and also not touching the board edges to get an individual root value . The images were copied in two folders, one for processing and measuring the roots and the second for scaling the measurements. Thus, each image was processed and analysed twice.The first step of the image processing was to convert  the RGB colour images into HSB stacks . We obtained three slices, but we only kept the first slice (the hue image). We then set a threshold from 0 to 255 for the roots and from 125 to 255 for the reference scaling circles before proceeding to run the “threshold” followed by the “make binary” commands. This threshold was determined by doing individual tests on some images. At the end of the processing, each image was binary, with our objects of interest  represented as white pixels and everything else as black. Most steps in the procedure were automated using customized ImageJ macros.http://imagej.net/BioVoxxel_Toolbox#Extended_Particle_Analyzer). This function computes useful parameters of which we chose to keep seven for downstream analysis: Area, Perimeter, Feret, Circularity, Solidity, Roundness, and the Aspect Ratio (AR). The area and the perimeter describe the size of a root. The Feret, is the longest distance between any two points along the selection boundary, also known as maximum caliper. Circularity, Solidity, Roundness and aspect ratio (AR) describe shape.The “analyze particles” command in ImageJ counts each contiguous area of white pixels within a binary image and gives some additional basic measurements. With the aim to get shape related traits, we used the “extended particle analyzer” function in the BioVoxxel Toolbox plugin . Since the exact diameter in centimetres of each circle was known, we used this value to calculate the mean number of pixels per cmWe performed a two-step approach for the genomic analysis. In the first step, we used a linear mixed model to account for the variability in the field design and calculate the broad-sense heritability. The input data was: 1) the mean phenotype value for each plot , 2) the same as (1) but adjusted to account for the potential effects of variation in cassava mosaic disease (CMD) severity among plots and 3) the standard deviation of the root shape and size measurements  per plot, also adjusted to remove the effect of CMD. We fit two different models, with CMD correction and without CMD correction, for each of the two focal populations (GG or C1).For GG, the following models were fitted:crn therefore only reference NOHAV.In both models For C1, model (3) and (4) were fitted:et al. (2017).In model (3) and (4), we replace the range variable with the combination of the location and the range  as the C1 population was planted in several locations unlike the GG. In all models, for all traits, 21. Broad-sense heritability values were calculated using the variance components estimated using the mixed-models described above. EGV and de-regressed EGV are used in downstream analyses as described below.From these models, we extracted the clone-effect BLUP, which estimates the total genetic value (EGV) of each line and de-regressed the EGV by dividing them by their reliability to obtain the de-regressed BLUPIn addition, the correlation across traits was estimated using using the de-regressed BLUP values obtained after fitting the aforementioned linear mixed model.With the combined GG+C1 population, we calculated the phenotypic and genotypic coefficients of variation using the output values of the lmer model (3).Genotypic and phenotypic coefficients of variation were calculated as:22 and the SNP calling was performed with TASSEL 5.0 GBS pipeline v223. Alignment of GBS reads was to the cassava reference genome v6.1 . The condition for the genotype calls was the presence of a minimum of four reads. Extracted SNPs were filtered to remove clones with>80% missing and markers with >60% missing genotype calls. Markers were also removed when they had an extreme deviation from Hardy-Weinberg equilibrium (2 > 20).Both populations (GG and C1) were genotyped using the genotyping-by-sequencing (GBS) method24. For the GG and C1 populations, we had 112,082 and 179,041 markers, respectively, with MAF > 0.01.A combination of custom scripts and common variant call file (VCF); manipulation tools were used to accomplish the above pipeline. The missing data were imputed using Beagle v4.0et al. (2017). Briefly, for each replicate of the process, the population was split into five approximately equal chunks (folds). Five genomic predictions were then made in which each fold (fifth of the population) served as the test set (no phenotypes) and were predicted by the remaining four-fifths . Prediction accuracy for each fold was defined as the correlation of the genome-estimated breeding values , with the de-regressed EGVs from the pre-adjustment stage of the analysis.We estimated genomic prediction accuracy using 5-fold cross-validation repeated 25 times similar to what is described in Wolfe A.mat in the R package rrBLUP26 De-regressed BLUPs were used as the response variable and the GBLUP models were fit with the function emmreml in the R package EMMREML27.For genomic prediction, we used a mixed-model with a genotype (clone) random effect with covariance proportional to the genomic relationship matrix, also called GBLUP. The genomic relationship matrix was constructed using the function 28. Specifically, we followed a leave-one-chromosome-out approach and tested all markers with MAF > 0.05. The leave-one-chromosome-out approach involves excluding all markers on the chromosome of the current candidate SNP from the genomic relationship matrix (GRM) used to control population structure when estimating their marker effects. Manhattan plots were generated using the R package qqman29. SNP markers with a − log10 which exceeded the Bonferroni threshold >6.8 were considered to be statistically significant and were further annotated into coding regions (genes) of the cassava genome.Genome-wide association mapping (GWAS) analyses were performed using a linear mixed-model analysis (MLMA) implemented in GCTA (Version 1.90.0beta)https://phytozome.jgi.doe.gov/biomart/) we searched for genes located 10 kb around the top SNP hits.Candidate gene identification was performed using the significant GWAS results of the standard deviation + CMD correction GWAS results. Using the phytozome 12 portal link to biomart P-values were corrected for multiple testing by computing a Bonferroni threshold similar to the univariate GWAS and Benjamini-Hochberg q-values with a q < 0.1 threshold.Using the plugin BioVoxxel in ImageJ, we extracted quantitative measurements of the Area, Perimeter, Feret, Circularity, Solidity, Roundness, and the aspect ratio (AR) from root images collected in the field. The raw value datasets show similar ranges for root shape and size descriptors in GG and C1 populations . Similarly, root number and root weight were highly correlated in C1 population (r2=0.88). In both datasets, correlations between yield traits were significant and high (r2 > 0.5) and these traits were also positively correlated with Area, Perimeter and Feret. However, a low correlation (r2 < 0.1) was observed between yield traits and root shape descriptors such as Circularity, Roundness, Solidity and AR in both populations.Phenotypic correlations were calculated pairwise using de-regressed BLUPs of the mean values for each population separately Fig. . In the Size-related traits derived from root images  showed the highest positive correlation (r > 0.7) with each other. In both datasets, the highest correlation between size-related traits corresponded to Perimeter and Feret (r=0.97). Additionally, Feret and Perimeter were negatively correlated with shape-related traits  and positively correlated with AR. In the GG dataset, Area showed a negative correlation with Circularity (r= −0.26), Roundness (r= −0.21), Solidity (r= −0.19), and a positive correlation with AR (r= 0.19). While in the C1 population, a low correlation was observed between Area and shape descriptors.Within the shape related traits, the highest correlation was found between Circularity and Roundness  and Solidity . AR showed a negative correlation with Circularity, Solidity and Roundness in both datasets.The analysis of variance per location is presented in Supplementary Table Using the combined GG+C1 populations, the extent of trait variability was assessed in terms of broad-sense heritability values H2) phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) (Table  phenotyp2) for root shape and yield-related traits were calculated for each population (Table 2 of root shape related traits ranged from 0.17 (Perimeter and Circularity) to 0.46 (aspect ratio) and for yield traits, H2 ranged from 0.29 root weight (RTWT) to 0.44 shoot weight (SHTWT). In the GG dataset, Perimeter, Circularity and Solidity exhibited the lowest heritability values at 0.17, 0.17 and 0.12, respectively.In addition, broad-sense heritability values (Hon Table . In the 2 ranged from 0.36 (SHTWT) to 0.61 (RTWT). The heritability of most traits was higher in the C1 population than GG except for Area (0.39 to 0.38) and SHTWT (0.44 to 0.36). The inclusion of the CMD in the calculation of the variance components always reduced the heritability of all the traits in both populations by around 10%.In the C1 population, the heritability of shape-related traits ranged from 0.36 (Perimeter) to 0.54 (Circularity) while for yield traits HP ≥ 6.28). The Manhattan plots of the univariate GWAS results for yield traits are shown in Supplementary Fig. Using a univariate genome-wide association approach for root image traits (root size and shape) and root yield traits we identified significant loci for all traits except for area Fig. . We deteWe detected markers associated with Perimeter and Feret on chromosome 12, and with Solidity on chromosome 1, whereas for AR we identified significant loci on chromosome 1 and chromosome 12. Similarly, for Circularity and Roundness, we detected significant loci on chromosome 1 and chromosome 12.P ≥ 5  were detected for the standard deviation-derived traits of Perimeter (per-sd), Feret (feret-sd) and Aspect Ratio (AR-sd)  Fig. . For persd) Fig. .Figure 4Different markers were significant in the multivariate GWAS model dependent on which phenotypes were included in the multivariate linear mixed model (mvLMM). Although the multivariate model can increase the power for detecting pleiotropic variants when using correlated traits, we identified few significant markers above the Bonferroni threshold  and offspring generation (C1) datasets independently, we calculated the prediction accuracies of size and shape image traits and compared those to root yield traits accuracies using de-regressed BLUPs of 1) the mean phenotype value (average phenotype of 4 plants) Fig. , 2) the Prediction accuracy, calculated as the correlation between the  genomic estimated breeding values (GEBVs) and the de-regressed BLUPs of the mean phenotype value, ranged from 0.32 (SHTWT) to 0.43 (RTNO) in the GG population and from 0.12 (RTNO) to 0.46 (AR) in the C1. For yield traits, accuracies in GG were higher than in C1 but were not different between populations for the shape and size related traits. In the GG population, the shape descriptors Circularity (mean = 0.40), Roundness (mean=0.39), Solidity (mean =0.37) and AR (mean =0.38) showed slightly higher accuracies than the size descriptors Area (mean =0.33), Perimeter (mean = 0.34) and Feret (mean = 0.33). In the C1 population, size and shape image traits exhibited a higher prediction accuracy than root yield traits. Among the size descriptors, Feret showed the highest accuracy (mean=0.34) and Area the lowest (mean=0.29). Among shape descriptors, AR showed the highest predictive value (mean=0.46) and Solidity the lowest (mean=0.33)  breeding program for root shape and size-related traits using image analysis of storage root photographs taken in the field. In both populations, the storage roots exhibited a wide range of shape variation. Roots with a large area were generally heavier and the circularity of storage roots was mostly inversely correlated to its area.  Our results, suggest that rounded-shaped roots in cassava are generally smaller and hence lighter in weight. More importantly, the lowest correlation values found between shape traits ) and yield suggest that there is no negative impact of differences in root storage shape on yield or that it is relatively minor.34 and second, that shape phenotyping can aid the identification of pleiotropic QTL. In our study, using univariate genome-wide association analysis, we detected for most shape and size related traits, significant QTL regions located on chromosomes 1 and 12. The QTL region on chromosome 1 has been previously shown to be segregating for an introgressed segment from M. glaziovii35. Furthermore, the QTL region on chromosome 1 has been associated, in the IITA genetic gain population, with other root traits such as dry matter and total carotenoid content19.In radish, rice and wheat, imaging-based studies of root shape and size traits have demonstrated first, that these have different genetic architectures17. The effect of cassava mosaic disease (CMD) on root yield has been previously investigated in fully and partly infected stands of cassava38. Nonetheless, the identification of new QTL for root number and shoot weight, when these traits were adjusted according to the CMD score, support the notion that CMD can have an effect on root yield traits.For root weight and root number, we identified a significant QTL associated with those traits on chromosome 12. The QTL region on chromosome 12 has been previously associated, using IITA breeding populations, to cassava mosaic disease (CMD) resistance39  suggest that root-related traits are under genetic control by at least a few large effect loci, 2) reveal the impact of disease resistance loci on yield and 3) that introgressed genome regions contain QTL related to root yield, size and shape traits.41, we used a multivariate linear mixed model approach which included groups of correlated root size and root size/shape traits. Considering multiple phenotypes in the mvLMM enabled us to identify new candidate loci on chromosomes 4, 6 and 9 that were not identified in the univariate analyses.To increase the power of our study and to detect pleiotropic loci for size and shape traits20. The study showed promising results particularly for traits with consistent heritability values across programs and stable large-effect quantitative trait loci. Prediction accuracies for RTNO, RTWT and SHTWT were similar with those reported in previous cassava cross-validation analyses20. Root size and shape-related trait accuracies were lower than those reported for dry matter content (DM) and cassava mosaic virus resistance (CMD)20.The potential of GS as a breeding tool to increase the rates of genetic gain was recently tested in three Next Generation Cassava Breeding programsAlthough the heritability of yield traits was higher in the offspring  than the parental generation , the predictive accuracy of traits extracted from root images showed intermediate to high values in both populations. However, the C1 yield traits accuracies being lower than the GG, suggests that it is due to a reduction in variance caused by the strong selection on these yield traits.Nonetheless, predictive accuracies of the mean values of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies in the C1 population. Adjusting the mean and standard deviation phenotypes for the effect of CMD reduced the predictive accuracy. However, that correction is necessary to unlink the effect of CMD from the causal loci that are responsible for the regulation size and shape root traits.The successful identification of QTLs through GWAS analysis demonstrates that image size and shape traits from image analyses can capture genetic variation. The quantitative nature of these traits and the moderate to high predictive accuracy suggest that they can be included in a selection index to breed cassava with uniform storage root shape suitable for mechanical harvest.Although these measurements were laborious in the field and not high-throughput, the analyses of the images are automated and quantitative, they avoid subjectivity in scoring and other human-errors and most importantly, they improve cassava root characterisation.This study has shown that root phenotyping using image capture and analysis is feasible and can be included as part of routine yield data collection in field plots. We used fairly simple equipment including a green board platform, a cheap digital camera (can be substituted with a smartphone) and freely-available open source software for image processing. As expected, additional labour was required to lay and remove roots from the green board, a process that increase cost and time by about 15–20%. However, these costs could be offset through genomic prediction for shape-related traits in subsequent generations using training data from the parents and relatives.https://github.com/PhenoApps/OneKK), an inexpensive and user-friendly tool for automated measurement of seed size, shape, and weight using smart phones. The app is developed under the BREAD PhenoApps project and supported by the National Science Foundation. Still, further work is needed to automate all the steps of cassava root phenotyping to allow the characterization of more lines at different locations in multi-environmental trials that ultimately would allow the creation of larger training population sizes for genomic prediction of root size and shape traits.The methodology described here and the results obtained in this study are promising and open up the opportunity to apply high-throughput methods in cassava. The image capture and analysis can now be performed using the OneKK (one thousand kernels) app (Supplementary information.Supplementary information2.Supplementary information3.Supplementary information4.Supplementary information5.Supplementary information6.Supplementary information7.Supplementary information8.


The availability of (co)variance components and genetics parameter estimatesfor traits included in a selection program is crucial since the estimatedbreeding values of the selected traits are computed based on the available(co)variance components and genetics parameters. The present study aimed toestimate (co)variance components and genetics parameters for linear traitsrelated to foot/leg and udder  in Holstein cattle in Indonesia. Linear traits were measured(instead of scored) on 310 lactating Holstein cows raised in the NationalBreeding Centre for Dairy Cattle and Forage of Indonesia (BBPTUHPTBaturraden). These were nearly all cows in lactation owned by the centre atthe time of study. Lactating cows which were not measured during study werethose which are technically difficult to handle. The Average InformationRestricted Maximum Likelihood (AI-REML) method of the DMU program was used to estimate the(co)variances and genetics parameters of the considered linear traits. Afour-multivariate animal model was employed by including farm (fixed), animal(random), and age (covariate) effects in the model of analysis. Thephenotypic means (standard deviation) for rear legs set, foot angle, udderdepth, and teat length were 139.70 (6.03), 50.65 (5.04), 10.67 (6.19), and5.27 (0.96), respectively. The results showed that the estimated heritability( BBPTUHPT Baturraden, which is themain government institution for genetics improvement of Indonesian dairycattle, serves the country to rear, produce, and improve genetically dairycattle, specifically Holstein cattle. The centre has focused its geneticsimprovement program on milk yield and its component traits. Inclusion oflinear traits or other traits in its genetics improvement program was notconsidered until recently.Milk yield and its components of dairy cows in general have been successfullyincreased through both genetics and management. Milk yield per lactation hasincreased dramatically in the last 40 years and some cows were reported to beable to produce more than 20 000 kg per lactation . The milk yield increase of 5997 kg from 1957 to 2007 is attributedmostly (56 %) to genetics improvement . Many studies haveshown that selecting dairy cows focusing merely on milk yield (and itscomponents) and ignoring other traits, especially linear traits, has resultedin declined performances in animals' health and reproduction performancesdespite the increased production traits.Rauw et al. (1998) reviewed quite extensively the opposite relationshipbetween high-production cows and health performance. The different directionrelationship between high-production cows and health expenses has beenreported from a classical selection study . Ingvartsen etal. (2003) reported the evidence of genetics correlation between milk yieldand the incidence of ketosis, ovarian cyst, mastitis, and lameness. Healthperformances also experienced a similar trend along with the successfulincrease in milk production due to the selection program. The relationshipbetween milk yield and risk of infection of mastitis is clear and if theanimals continue to be selected for high milk yield, the situation willworsen . Milk yield of previous lactation isassociated with retained placenta, mastitis, and milk fever .Lucy (2001) has shown the negative association between the successfulincrease in milk yield and reproductive performances over the last decades.Similar to what has happened in the US, first conception rate has decreasedin Ireland  and the United Kingdom .Despite an increased milk yield from 7800 kg to 10 200 kg within theperiod of 1991–2000, pregnancy rate and cyclicity have declined by up to6 % and 7.6 %, respectively, and incidence of inactive ovariesincreased by up to 8 % . This phenomenon wasaddressed to the presence of undesirable genetic correlation between milkyield and fertility traits  or due to both management andthe negative genetic correlation between those traits .Linear traits are correlated with some economically important traits inlivestock and, to some extent, the correlation is genetic. Selecting animalsbased upon a particular trait will affect the mean of the correlated traits.This is critical especially when the genetics correlation is in the oppositedirection, since the improvement in one trait will decrease the performanceof the other traits. The International Committee on Animal Recording  has recommended recording linear traits on a breeding program since thegenetics correlation is present between linear traits and production traits.Selection of traits that need to be recorded in a genetics improvementprogram needs to be done because it is inefficient and expensive to recordall traits included in the breeding objective. Genetics improvement of atrait can be achieved by including traits that are genetically correlated andeasier/cheaper to record in the selection program. Therefore the informationabout the (co)variance components and genetics parameters of alternativetraits of a population is important. (Co)variance components and parametersgenetics of linear traits of Indonesian Holstein cattle, to the best of ourknowledge, have not been reported. Thus the objective of this present paperwas to estimate the necessary genetics parameters of linear traits forHolstein cows in Indonesia.22.1Linear traits related to foot/leg and udder, including rear legs set, footangle, udder depth, and teat length, were reported in the study. Thecorresponding linear traits of Holstein cattle in Indonesia were measuredduring the months of July and August 2017 at the National Breeding Centre forDairy Cattle and Forage of Baturraden, Indonesia. As many as 310 lactatingdairy cows were measured for their linear traits. The udder depth trait wasmeasured about an hour before milking in the afternoon; animals are milkedtwice daily: early morning and afternoon. All animals were reared in twoseparated farms, namely Tegalsari and Limpakuwus farms. Animal age andpedigree data were obtained from the database available in BBPTUHPTBaturraden. Animals with an unknown date of birth were assigned as datamissing for the age variable and unknown parents were also assigned asmissing. All animals are reared under an intensive system in a free stallbarn where grass and concentrate are provided twice daily and water isprovided ad libitum.®. Foot angle was measured with aconventional angular measure; teat length was measured with a standard tapemeasure; udder depth was measured with a calliper. Rear legs set and footangle were both measured in angle degree unit  definition. Lack of certified classifiers for typing/scoring for theselinear traits inspired the author to measure the traits instead of scoringthem. Rear legs set, foot angles, and teat length were measured for both leftand right parts. Rear legs set was measured with the digital angle level ofNankai2.2The model used in the (co)variance analysis included farm (fixed), animalgenetics additive (random), and animal age (random-fixed covariate) effects.Multivariate genetics analysis including four traits at a time was chosen byemploying the equal design model for all traits being analysed. Themathematical model can be written as in Eq. (1):The standard errors of heritabilities and correlations between random effectsincluding genetic correlations are computed based upon a Taylor seriesapproximation .3It is quite a challenge to have reliable estimates of heritability and(co)variance components in developing countries since the pedigree data ofthe population are not well documented. Pedigree information as the basis ofthe variance structure of additive genetics of the animals is critical. TheDMU software allows the formation of a numerator relationship matrix inversewithout considering the inbreeding coefficient (option 2). Selecting option 2in DMU will command the program to create an approximation of the trueinverse of the additive relationship matrix. Phenotypic means and standarddeviation of the RLS, FA, UD, and TL are presented in Table 2. In the presentstudy, linear traits were measured instead of scored/classified, as opposedto most studies reported in the literature, for instance Toghiani Pozveh etal. (2009), Mazza et al. (2015), Wiggans et al. (2006), and Zink etal. (2012), and therefore comparing directly estimated parameters of thepresent study with those in the literature is not possible except for theratio between estimates such as correlations and heritabilities. Comparingthe estimated parameters with other studies was done cautiously after theestimated parameters obtained were converted into values of the 1–9 scalescoring system. However, phenotypic means of the observed traits werecompared with the 1–9 scale scoring system to get a general overview of thepopulation parameters under study compared to other studies.Table 2 shows that the phenotypic means of the observed linear traits areclosed to the intermediate score in a 1–9 scale scoring system according tothe reference scale of the World Holstein Friesian Federation .The intermediate scores of RLS, FA, UD, and TL according to the WHFF (2005)are 4–6, 4–6, 5, and 4–6, respectively. This indicates that the lineartraits of Indonesian Holstein cows have not undergone selection. The observedphenotypic means of the present study of RLS and FA were slightly higher thanthose reported by Toghiani (2011), whereas UD is about the same. Toghiani(2011) reported that the phenotypic means (standard deviation) of RLS, FA,and UD scored in a 1–9 scale scoring system are 5.4 An additive genetic coefficient of variation (CV) of 13 % has beenreported by Berry et al. (2004) for udder depth in primiparous dairy cows,whereas Fernández et al. (1997) reported a coefficient of variation of34.48 % for udder depth in dairy ewes. The extremely high phenotypiccoefficient of variation obtained from the present study, especially udderdepth (58.01 %), might indicate that the source of variation originatingfrom non-genetic factors, such as the field technician who measured thevariable, is dominant. Measuring udder depth of dairy cows kept in a freestall barn is quite challenging. Obtaining a large coefficient of variationof field data is possible and has been reported in the literature. Forinstance, Royal et al. (2002) reported a phenotypic coefficient of variationof commencement luteal activity (CLA) of 63 %. Samoré et al. (2012)reported CVs of 57 % and 62 % for somatic cell scores of Brown Swissand Holstein Friesian cattle, respectively.3.1The estimated genetic and phenotypic (co)variances of the present study arepresented in Tables 3 and 4. The two tables show that (co)variances, bothgenetically and phenotypically, were observed in the population, and theyvaried in magnitude. Comparing the magnitude of (co)variances obtained in thepresent study with results from studies using the 1–9 scale scoring system,for instance, is irrelevant and thus avoided. The discussion is thereforeemphasized on the variance ratio (i.e. heritabilities and correlations). Theestimated heritabilities along with the standard errors are shown in Table 5.It is shown in Table 5 that rear legs set, foot angle, udder depth, and teatlength of dairy cattle in Indonesia are heritable traits, though themagnitudes of the estimates varied. The estimated heritabilities ranged from0.147 for TL to 0.334 for RLS. These results were different compared to thosereported in the literature. For example, Pérez-Cabal and Alenda (2002)reported heritabilities for RLS, FA, and UD of 0.17, 0.11, and 0.24,respectively. Dal Zotto et al. (2007) reported heritabilities of 0.14, 0.23,and 0.32 for RLS, UD, and TL, respectively, on Brown Swiss cattle. Estimatedheritabilities of 0.19, 0.16, 0.30, and 0.33 for RLS, FA, UD, and TL,respectively, were reported by Pryce et al. (2000). From a study of Holsteincattle in Brazil, Campos et al. (2015) reported heritability estimates of0.21, 0.09, 0.25, and 0.38 for RLS, FA, UD, and TL, respectively. Theheritability estimates could be different from study to study since theestimates are a function of additive genetic and non-genetic variances. Thehigher heritability estimate reflects the higher proportion of additivegenetic variance to the total variance. Results of the present study showthat the linear traits under study are heritable and a significant selectionresponse might be obtained when included in a selection program. The standarderror of all heritability estimates of the present study are slightly larger(0.066–0.181) than those reported in the literature, except for udder depth,which has a standard error for heritability of 0.066. For instance, Pryce etal. (2000), Pérez-Cabal and Alenda (2002), Dal Zotto et al. (2007), andCampos et al. (2015) reported standard errors of heritabilities of 0.01–0.06for the corresponding linear traits using the restricted maximum likelihood(REML) method. The standard error of heritability estimates of the presentstudy could probably be reduced by increasing sampling size of the study bymeasuring new lactating animals. In addition, the smaller standard errorcould possibly be obtained by improving the information accuracy of animals'pedigree such as animals' date of birth so that the true numerator additiverelationship matrix of the animals can be computed by including theinbreeding coefficient. In the present study, the variance structure ofanimals' additive genetic was approximated by excluding the inbreedingcoefficient by allowing the DMU software to run with option 2 .3.2RLS, FA, UD, and TL were correlated with each other both genetically andphenotypically, though the magnitude was variable, ranging from The magnitude and direction of both genetic and phenotypic correlations amongRLS, FA, UD, and TL reported in the literature are not consistent, forinstance Toghiani (2011), Berry et al. (2004), and Němcová etal. (2011), where the same method as in the present study (the restrictedmaximum likelihood method) was used in the genetic analysis. Results of thepresent study were similar regardless of the direction, with those reportedby Berry et al. (2004), Němcová et al. (2011), and Toghiani (2011)for some pairs of correlations, for example the genetic correlation ofRLS–UD (4Some conclusions can be drawn from the present study. Rear legs set, footangle, udder depth, and teat length are heritable traits. Rear legs set, footangle, udder depth, and teat length are correlated and the geneticcorrelations are stronger than those of phenotypic ones. This suggests thatrear legs set, foot angle, udder depth, and teat length could be included inthe selection program. Including the studied traits in the selection programshould be done with proper weights so that an optimum selection response canbe achieved.


MdPG1, a gene controlling firmness, has already been exploited in elite cultivars, the genetic basis of crispness remains poorly understood. To further improve fruit texture, harnessing loci with minor effects via genomic selection is therefore necessary. In this study, we measured acoustic and mechanical features in 537 genotypes to dissect the firmness and crispness components of fruit texture. Predictions of across-year phenotypic values for these components were calculated using a model calibrated with 8,294 SNP markers. The best prediction accuracies following cross-validations within the training set of 259 genotypes were obtained for the acoustic linear distance (0.64). Predictions for biparental families using the entire training set varied from low to high accuracy, depending on the family considered. While adding siblings or half-siblings into the training set did not clearly improve predictions, we performed an optimization of the training set size and composition for each validation set. This allowed us to increase prediction accuracies by 0.17 on average, with a maximal accuracy of 0.81 when predicting firmness in the ‘Gala’ × ‘Pink Lady’ family. Our results therefore identified key genetic parameters to consider when deploying genomic selection for texture in apple. In particular, we advise to rely on a large training population, with high phenotypic variability from which a ‘tailored training population’ can be extracted using a priori information on genetic relatedness, in order to predict a specific target population.Texture is a complex trait and a major component of fruit quality in apple. While the major effect of  Amongst the various physiological and physical ripening processes, texture at maturity is arguably the most important and investigated trait, especially for apple. The apple market is highly structured according to fruit texture, because this feature is important for both consumers’ preference3. As alternative, texture can be dissected and characterized through texture analyzers in a repeatable way5. The most recent instruments can measure two groups of sub-traits, mechanical and acoustic, suitable to distinguish between firm  and crisp (based on acoustic sub-traits) types of apples. These texture parameters have been already described and validated in apple5, and have been used in QTL-mapping studies carried out with biparental populations6, Pedigreed Based Analysis and Genome-Wide Association Studies7. These studies found a complex genetic basis to fruit texture in apple, identifying a large number of QTLs distributed across the apple genome, with the most relevant regions located on chromosome 3, 10 and 16. This genetic complexity is reflected in the regulation of the cell-wall and middle lamella disassembling, a physiological process orchestrated by a myriad of cell-wall modifying enzymes8. Such highly polygenic control can hamper the use of marker assisted selection for improving texture. In the QTL-mapping studies carried out to date, a major QTL located on chromosome 10, close to the polygalacturonase locus MdPG1, has been identified9. This QTL explains a high proportion (about 40%) of the texture phenotypic variance, but still leaves much unexplained variance that could be used to improve this trait. As reported in the above mentioned study7, in modern breeding programs the favorable allele at the locus MdPG1 has been fixed through successive rounds of ad hoc crossing and selection. Given the fixation at MdPG1, the phenotypic variance of modern families, obtained by crossing valuable parents for texture performance, might now be under the control of other loci with minor effects. However, QTL-mapping approaches cannot detect such small effect loci, suggesting that other methods are necessary for further improvement of apple texture11.Although sensory evaluations with trained panelists can be used to assess variation in texture, these measures are highly dependent on the scorer and limited by sample size12). In contrast to marker assisted selection, genomic selection relies on the prediction of a genetic value for a genotype, taking into account all genome-wide markers, making it especially relevant for complex traits13. For establishing genomic selection, genomic predictions are performed considering two sets of genotypes: a training set (TS) of genotyped and phenotyped individuals to train a prediction model, and the validation set (VS), represented by individuals only genotyped, for which genomic estimated breeding values are predicted14. In principle, the most favorable scenario for genomic predictions (and subsequent genomic selection) is to predict highly heritable traits in a VS highly related to the TS. While trait heritability can be increased (to a certain extent) by more accurate phenotyping, relatedness between VS and TS can be optimized with different strategies. Dedicated approaches and tools have been proposed to address this issue using optimization parameters17, and algorithms19. Specifically, two criteria derived from the mixed model equations used in genomic prediction, the mean of the prediction error variance (PEVmean) and the mean of the expected reliabilities, also called coefficients of determination (CDmean), have been proposed and tested to optimize TS composition using the marker data of both the TS and the VS16. In theory, it could thus be feasible to acquire phenotypic and genotypic data for a highly diverse TS and then in silico select subsets of individuals to produce an optimal TS for a given VS.To overcome this limitation, an alternative approach for genome-assisted breeding known as genomic selection has been introduced  and from 0.59 to 0.75 when considering only the genotypes included in the collection  and the families (our 6 VS), which can be visualized with a clustering approach and a heatmap on Fig. collection , while ‘Fuji’ was the most distantly related , and we found here varying levels of relatedness between the ies Fig. .Fig. 2Hecollection. Using the Bayesian information criterion value (BIC), the most probable genetic structure comprised six clusters and was described by five principal genetic components derived from the marker data , whilst cluster 6 was the smallest  using the entire SNP set 8,294 SNPs) identified the pattern of genetic structure in the 94 SNPs i 1 Table .Fig. 3Dicollection, the pairwise Fst-values between clusters indicated low levels of genetic differentiation . The synthetic traits PC1 and PC2 were moderately predictable with accuracies of 0.59 and 0.42 respectively.The prediction of marker effects on each trait was obtained with an additive rrBLUP model, where the BLUPs of across-year phenotypic values and the synthetic traits PC1 and PC2 represented the explained variables. The estimated genetic variance, calculated as the variance of the genomic estimated breeding values using this model in the entire In practice, traits can be predicted in families with any available related genetic material that has been genotyped and phenotyped. For this reason, three different scenarios of training population design were tested: either with or without genotypes from the predicted family (‘TS_coll’ and ‘TS_coll_sibs’), or from a half-sib family , resulting in near-zero to high accuracies, depending on the family, trait, and scenario considered.Texture could be predicted with low to high accuracies in three families, ‘FjPi’, ‘GaPi’ and ‘GaPL’; accuracy values ranging from 0.08 for PC2 in ‘GaPi’ to 0.73 for PC1 in ‘GaPL’. Among these three families, the best predicted trait was PC1 . ‘TS_coll_sibs’ particularly improved the accuracies in ‘FjPi’ (mean accuracies of 0.32 in ‘TS_coll_sibs’ vs. 0.26 with ‘TS_coll’), as it better predicted 12 out of 14 traits. ‘TS_coll_half-sibs’ was the lowest performing scenario overall, although it increased the prediction accuracy of seven traits in ‘GaPi’ . The highest accuracy of 0.81 was obtained for PC1 in the ‘GaPL’ family with only 129 genotypes in the TS, i.e., nearly half of the collection size. When investigating the distribution of accuracies with increasing TS size in each family for the four focal traits, we observed overall similar trends across traits within a family. In families ‘GaPL’ and ‘GaPi’, which had the highest relatedness to the collection , an accuracy of 0.32 was observed with as few as 15 highly related genotypes , which indicates that, despite the experimental and statistical limitations imposed by our design , substantial genotypic effects were assessed with our approach.The twelve texture “sub-traits” showed moderate to high repeatability and the magnitude of genetic variation in traits differed between families, with frequent transgressive segregation patterns. The lower repeatability obtained for traits in the collection could be explained by the larger biological variation caused by the sampling of five fruits across trees , in contrast to genotypes of the families for which the five fruits were sampled within single trees (and where the tree effect is confounded with the genotypic effect). Using BLUPs and marker data only, we could predict texture features with moderate to high accuracy within the collection was further used as main training population to predict texture in families. As we have observed, prediction accuracies were quite heterogeneous between biparental families. Without TS optimization, texture could be accurately predicted for ‘GaPL’ (mean accuracy of 0.57), while ‘GaPi’ and in ‘FjPi’ showed a moderate prediction accuracy (mean accuracy of 0.30). In contrast, near-zero or negative accuracies were obtained for ‘FjDe’, ‘FjPL’ and ‘GDFj’ across all traits (mean accuracy of −0.05). The large negative accuracy values repeatedly obtained in ‘FjDe’ and ‘GDFj’ could reveal an opposite linkage phase at markers closely linked to the relevant QTLs between these families and the collection28. They could also reflect a systematic bias caused in the calculation of the Pearson correlation coefficient itself, in particular when the means of the TS and the means of the VS for the predicted trait are negatively correlated over the sampling procedure29. The low accuracies suggest that genomic selection for fruit texture in families ‘FjDe’, ‘FjPL’ and ‘GDFj’ would be ineffective using the present experimental design.The collection and highest accuracy among traits and family: 0.73 in GaPL), while PC2 showed generally low prediction accuracies. The high phenotypic variability explained by PC1 in the collection  and crispness (as approximated by PC2), the two main components of texture dissected with our PCA  to predict texture within multiple families. Previous works on texture prediction have mainly focused on firmness, and generally showed low accuracies when predicting unobserved genotypes in a set of families or in a collection (between 0.15 and 0.35)27. A much higher accuracy of 0.83 was found for firmness in the work of Kumar et al.25 when performing cross-validations within a 4 × 2 factorial design, with 1080 and 120 genotypes randomly assigned to the TS and VS, respectively. This strategy is expected to yield higher prediction accuracies, but phenotyping such large numbers of genotypes to make accurate predictions for only a few families would be unfeasible in a commercial breeding program. Moreover, relying on a training population derived from a small number of parents makes such an approach less suitable for making predictions across a broad range of breeding material. To this end, the design we proposed is more versatile and enables the user to share the costs of phenotyping the TS on a larger scale, which should be more suitable for the practical use of genomic selection.The prediction design and strategy we adopted involves the phenotyping of one single training population  could be predicted with a reasonable accuracy of 0.42 in the collection, and in most of the families we could achieve accuracy values above 0.2 .Beyond firmness, we also investigated variables highly correlated to sensory crispness, a trait which strongly influences the sensory experience of consumers and thus determines the commercial success of a cultivar. To our knowledge, predictions for crispness have only been reported for sensory crispness with an accuracy around 0.233. Although some degree of correlation was apparent between the genetic clustering of genotypes and their phenotypic distribution , yielded the best predictions.Through the implementation of the DAPC method, six significant, yet marginally differentiated genetic clusters were observed. Families were assigned to one or two specific clusters, reflecting the assignment of their parental genotypes. Our results confirm the weak genetic structure characteristic of the cultivated appleion Fig. , TS opticollection (‘GaPL’ and ‘GaPi’) compared to the remaining ‘Fuji’-related families. This observation confirms that genetic relatedness is a fundamental parameter in genomic prediction34. The addition of closely related genotypes from the same family (‘TS_coll_sibs’) or from a complete half-sib family  to the collection did not improve the prediction accuracy for five of the six families studied. This result suggests that either the collection already contains ‘enough’ diversity to predict families, or that the excess of unrelated genotypes in the collection cannot be corrected by adding more related genotypes. Thus, ‘TS_coll_sibs’ and ‘TS_coll_half-sibs’ scenarios do not seem to effectively improve the TS.The genetic parameter having the largest impact on predictions was genetic relatedness, with texture traits being much better predicted in the two families most related to the a priori information on genetic relatedness derived from marker data. TS optimization based on a priori information on relatedness between genotypes improved the accuracy of predictions relative to other composition rules ‘TS_coll’, ‘TS_sibs’, ‘TS_half-sibs’, with a minimal increase of 0.2 and maximal increase of 0.4 in accuracy  in this study. As mechanical traits are easier to measure than acoustic ones, FNP could in practice replace acoustic traits to assess crispness. Since we also obtained high prediction accuracy for FNP (0.63 in collection and maximum of 0.78 in optimized family prediction), we propose this sub-trait as the most valuable descriptor for fruit texture, minimizing the effort needed to phenotype that trait.The improvement of fruit texture is still limited by the time-consuming and expensive process of phenotyping with texture analyzers. Thus, even though our predictions demonstrate the potential of genomic selection for apple texture, its practical application will be profitable if accurate predictions can compensate for the costly and laborious phenotyping of the TS. Texture analyzers should be preferred over sensory assessments because their measurements are highly repeatable, giving higher heritability estimates for texture traits39, where the prediction accuracy of protein content was improved by using a two-trait model involving yield).Identifying the principal components of texture profiles allowed us to capture the fruit texture phenotypic variability hidden within the twelve measured sub-traits. We exploited the high correlations between sub-traits and PC1 to facilitate and improve the prediction of firmness. This simplified multiple-trait approach could be further exploited by using proper multiple-trait models  to increase marker density to better harness relatedness at causal loci and address potential linkage phase inversions between TS and VS; (ii) to broaden the genotypic diversity of the main TS to better represent the material of interest for breeding ; (iii) to increase the heritability estimates for fruit texture by assessing this trait in the TS using both more replicates and different environments. In the future, the use of reference populations  with high replication, high density genotyping, and high genetic diversity should help address these limitations7. Two types of plant materials were used in this study: a collection of apple genotypes, expected to represent the diversity in the cultivated apple and thus serving as main TS, and six biparental families, which are typical examples of the material used for selection in apple breeding programs, thus serving as six different VS. The apple collection consisted of 259 distinct genotypes, each represented by a single plot of three adjacent trees (clones), at the experimental orchard of the Fondazione Edmund Mach (Trento) in the Northern part of Italy. The six biparental families contained a total of 278 genotypes. Two (‘FjDe’: ‘Fuji’ × ‘Delearly’ and ‘FjPL’: ‘Fuji’ × ‘Pink Lady’) were located at the Fondazione Edmund Mach (in the same orchard as the collection), while the other four  were planted at the experimental orchard of the Laimburg Research Center (Bolzano), located in the same area with near-identical climatic and pedological conditions. In contrast to the collection, each genotype from the six families was represented by a single tree. At the time of the analysis, all plants (from both the collection and families) were in a productive and adult phase. Fruit texture was phenotyped in 2012, 2013, and 2015 for the collection, in 2012 and 2013 for ‘FjDe’ and ‘FjPL’, and in 2012 and 2014 for the four remaining families  equipped with an acoustic envelop device AED , as previously described5. From the harvested apples, a single, homogeneous set of five apples was chosen for each genotype . Four identical discs were taken per fruit, avoiding seeds, seed cavity tissues or skin, for a total of 20 measurements per genotype . Each texture profile was then digitally converted to 12 texture measurements , four related to the acoustic performance and eight to the mechanical force-displacement. In brief, the mechanical sub-traits were coded as: initial, final, maximum, and mean force , area, force linear distance (derived length of the profile), Young’s module  and number of force peaks. The four acoustic sub-traits were the maximum measure of acoustic pressure, the mean of acoustic pressure measures, the acoustic linear distance and the number of acoustic peaks obtained with the texture analyzer. A more detailed description of the texture sub-traits has been previously reported5.The texture of the apple fruit was assessed via mechanical and acoustic measurements with the use of a texture analyzer TA.XT42. The genotyping output was initially analyzed with the software GenomeStudio and further re-edited with ASSiST43. SNPs with minor allele frequencies lower than 0.05 and call rate below 0.2 were filtered out with the package ‘snpStats’44. The final set of markers successfully recovered in the population consisted of 8,294 biallelic SNPs.The DNA used for SNP-genotyping in this survey was extracted from young leaves collected from a tree of each genotype at the beginning of the vegetative phase with the Qiagen DNeasy Plant Kit and further quantified with a Nanodrop ND-8000 . The SNP marker data were obtained with the HiScan  and the apple 20K SNP chip Infinium array ; the chip was assembled within the framework of the European project FruitBreedomicsYi,j,k is explained by the mean μ, the genotype i, the trial j and the error for each combination of genotype, trial and replicate . To note, apples correspond here to pseudo-replications of the genotype levels and the across-year phenotypic values represented by BLUPs encompass the effect of individual trees. This model was fitted separately for all traits with the ‘lme4’ R-package45. As our design did not allow to calculate heritability precisely, the repeatability was calculated instead, as defined by the ratio between the variance of genotypes as assessed by across-year phenotypes and the total phenotypic variance.For each trait, we used a mixed linear model to get the BLUP of across-years phenotypic value for each genotype. These BLUPs were used to explore the phenotypic distributions via a PCA and were subsequently employed in the two-step genomic prediction scheme described below (‘Prediction models’ section). We first calculated the mean of the four technical replicates for each apple to retain only the biological replication level in the model . For genotypes of the collection, the biological variation represented by individual apples encompasses the variation within and between trees of the same genotype. However, it was not possible to account for the effect of individual trees as apples were bulked without recording the specific tree identifiers. Variation in each of the 12 mechanical or acoustic sub-traits, considered as ‘Y’, was modeled using the genotype as a random effect, the trial (location-by-year) as fixed effect and a random effect of error: 46. The values from the collection were used to create the principal components, while the individuals from the families were plotted as supplementary individuals. Coordinates of individuals on the first and the second PCs (‘PC1’ and ‘PC2’) were used for prediction and named ‘synthetic’ traits. The loadings of PC1 and PC2 were used on the raw, replicated data to compute the repeatability estimates of both synthetic traits.A PCA was performed on BLUPs with the ‘FactorMiner’ R-package47 was calculated with the entire SNP dataset with the VanRaden method described in ref. 48 implemented in the ‘A.mat’ function of the ‘rrBLUP’ package49, and depicted in a heatmap plot using the R-function ‘heatmap.2’ (package ‘gplots’50). Genetic clustering was further assessed in the collection to identify potential genetic groups having an impact on the prediction results for texture. To this aim, we performed a DAPC51, carried out with the R-package ‘adegenet’52 using the entire set of 8294 markers. In the first step, six significant clusters were retained with the function ‘find.clusters’ using 300 principal components and selecting the number of clusters with the highest likelihood , μ is the mean of the phenotype, Z is the n × p incidence matrix linking the marker data  to observations of Y, u the p × 1 vector of random marker effects with e is a n × 1 vector of random errors. For the predictions of marker effects, the incidence matrix Z contained the observations from the TS, and observations from the VS were masked. In a second step, the predicted marker effects were employed to obtain the genomic estimated breeding values in the VS.Genomic predictions were computed through a model implemented in the rrBLUP frameworkcollection as main TS, we first performed fivefold cross-validations, repeated 100 times, within the collection. These results served as a baseline to interpret the subsequent predictions of texture in families, using the collection (or part of it) as TS.While the ultimate goal was to predict texture in biparental families with the collection only. In scenario ‘TS_coll_sibs’, 30% of genotypes of the predicted family were added to the collection in the TS, while the remaining 70% formed the VS. In scenario ‘TS_coll_half-sibs’, a single half-sib family  was added to the collection to form the TS, leading to two to four TS possibilities . To give an example focusing on the family ‘GaPi’, we tested the three following scenarios: scenario ‘TS_coll’ corresponded to [TS = collection // VS = ‘GaPi’] (one accuracy estimation only); ‘TS_coll_sibs’ corresponded to [TS = 30% ‘GaPi’ offspring + collection // VS = 70% remaining offspring of ‘GaPi’] ; and ‘TS_coll_half-sibs’corresponded to [TS = ‘GaPL’ or ‘FjPi’ + collection // VS = ‘GaPi’] .When predicting texture within each family (considered as VS), different TS composition rules were tested. In the first approach, the design was made using the information on relatedness known before genotyping between TS and VS . There, we used three composition rules: in scenario ‘TS_coll’, the simplest case, each family was predicted using the N = 10 to N = 259 for (i) and (ii)); or (iii) by starting with a TS composed of the most related cluster and adding less and less related clusters successively . In the principal component-driven approach, TS individuals were selected to constitute several TS with increasing size using the ‘CDmean’ criterion to choose individuals (R-package ‘STPGA’54). The CDmean criterion utilizes the generalized coefficient of determination between contrasts of genotypes16, as a measure of the reliability of prediction, to build the optimal TS via an iterative process. This criterion was selected because it should allow maximizing prediction accuracy without decreasing the genetic variance in the TS17. Specifically, the optimal TS was allowed to vary from 10 genotypes to 259 in increments of 20 genotypes, and was chosen based on the five principal components obtained with DAPC analysis with the algorithm implemented in the function ‘GenAlgForSubsetSelection’ (R-package ‘STPGA’54). Here, genotypes were chosen independently for each TS size, meaning that we did not proceed to a gradual enrichment of the TS.In the second approach, we performed TS optimization with the use of information on genetic relatedness between TS and VS as inferred by marker data. There, we looked for the optimal TS size and composition with a relatedness-driven and a principal component-driven approach. The relatedness-driven approach was tested in three different ways: (i) by starting with the ten most-related genotypes and adding single genotypes with decreasing mean relationship to the family; (ii) with decreasing maximum relationship to the family  and predicted values of the VS genotypes. When standard deviations were not available, we calculated an ~95% confidence interval of the correlation coefficient with a Fisher’s Z-transformation (‘cor.test’ function in base R). Calculations were performed in R statistical softwareSupplementary Figures 1 to 6Table S1. Texture genotypic values and coordinates for PC1 and PC2Table S2. Additive relationship matrixTable S3. Assignments of individuals to genetic clustersTable S4. Pairwise Fst-values between genetic clustersTable S5. Accuracies obtained in cross-validations within the collectionTable S6. Accuracies obtained in family predictions using two models and three TS scenariosTable S7. Accuracies obtained in family predictions with TS optimization with four methods