Hanwoo cattle is considered one of the most economically important species and sources of nutrition for Koreans. Thus, both the reproductive performance and growth traits play an important role in the continuous development and productivity of Hanwoo farming. Therefore, to improve beef production and the profitability of Hanwoo farming, estimations of genetic parameters for birth weight (BW) and weaning weight (WW) and their relationship with age at first calving (AFC) and gestation length (GL) are necessary to understand and improve their responses to selection. Thus, this study estimated the variance components, heritability estimates of birth weight (BW) and weaning weight (WW) and their genetic and phenotypic relationship to age at first calving (AFC), and gestation length (GL). Results revealed a moderate and high heritability estimate for BW and WW, which suggests a sluggish and rapid response of these traits to selection, respectively. The moderate and high genetic correlation between BW and reproductive traits (AFC and GL) revealed that the selection of a heavier BW might result in a longer AFC and GL. Although the genetic correlation for BW and AFC was moderate and positive, the phenotypic correlation of close to zero may indicate that the phenotypic expression for these traits is likely to be influenced by the genotype-environment interaction (GxE). Moreover, the genetic and phenotypic correlation between WW and reproductive traits (AFC and GL) indicates that the selection of a heavier WW may not influence the AFC and GL of Hanwoo cow. These estimated genetic parameters and correlations for the studied traits can be utilized for genetic breeding programs of Hanwoo cattle.Hanwoo is one of the most economically important animal species in Korea due to its significant contribution to nutrition. However, the current selection index only focuses to improve carcass traits of Hanwoo. Thus, this study aimed to estimate the genetic parameters of birth weight (BW) and weaning weight (WW) and their genetic and phenotypic relationship to the age at first calving (AFC) and gestation length (GL) of Hanwoo. The genetic parameters for birth weight (BW) and weaning weight (WW) were estimated using the data obtained from 52,173 and 35,800 Hanwoo calves born from February 1998 to March 2017, respectively. Further, these data were used to determine their genetic and phenotypic correlation to age at first calving (AFC) and gestation length (GL). The heritability estimates of BW and WW and correlation coefficients were obtained using the average information restricted maximum likelihood (AIREML) procedure, fit in single and two-trait linear animal models. The estimated direct heritability for BW and WW was moderate (0.22 ± 0.02) and high (0.51 ± 0.03), respectively, while the maternal heritability for both traits was 0.12 ± 0.01 and 0.17 ± 0.01, respectively. The genetic correlation of BW and reproductive traits (AFC and GL) showed a moderate and high positive correlation coefficient of 0.33 ± 0.06 and 0.53 ± 0.02, respectively, while close to zero and low positive phenotypic correlations of 0.06 ± 0.01 and 0.21 ± 0.06 were also observed between the correlated traits, respectively. For the correlation analysis between WW and AFC, both the genetic and phenotypic correlation showed close to zero values of 0.04 ± 0.06 and −0.01 ± 0.01, respectively. Meanwhile, the genetic and phenotypic correlation between WW and GL showed low and negative correlations of −0.09 ± 0.06 and −0.09 ± 0.01, respectively. These obtained estimated variances for BW and WW and their corresponding genetic and phenotypic correlation to AFC and GL can be used as information for genetic improvement and subsequent economic improvement of Hanwoo farming. Hanwoo (Korean cattle) was previously used for farming, transportation, and religious sacrifice . HoweverThe birth weight (BW) plays a very good economic indicator in the beef cattle industry and is usually the first trait being measured in a calf . This vaThe data on birth weight (BW) and weaning weight (WW) were collected from 52,173 and 35,800 Hanwoo calves born from the period of February 1998 to March 2017, respectively. The BWs of calves were weighed and recorded within 24 h post-partum. Subsequently, these calves were raised from their dam until 90 days of age to determine the WW of each calf. The Hanwoo calves in this study were produced through the purebred mating system done using artificial insemination of semen collected from bulls that were initially selected based on their performance and progeny carcass traits. There were also two reproductive traits included in this study, particularly age at first calving (AFC) and gestation length (GL), with total records of 14,258 and 51,303, respectively. The AFC was determined by determining the birthdate of the heifer and the date of its first calving, while the GL was computed using the last date of insemination and the calving date. Additional details for AFC and GL can be found in our previous studies . Furtherp < 0.05) fixed effects and interactions to be included in the model. Among the non-genetic effects, the following fixed effects were tested for each trait: herd where the animal was raised, birth year, birth month, birth season, parity of the cow, sex of the calf, and age of dam at calving. The analysis was done by way of ordinary least squares using the MIXED procedure of the SAS software . Results of the initial analyses revealed that the herd (98 levels), sex (2 levels), birth year and month (228 levels), and parity of the cow (5 levels) were significant and can be used as fixed effects for BW and WW. In the correlation analysis of BW and WW to reproductive traits, both AFC and GL were analyzed as traits of the calf, with herd and birth year-month of the dam as fixed effects for the earlier trait, while herd, calf birth year-month, and parity were analyzed as fixed effects for the latter trait.Before the genetic parameter estimation, the non-genetic factors were initially analyzed to determine the significant  method. Analyses were conducted using the AIREMLF90 software  considerus study , the addThe direct and maternal heritability estimates and variance components for birth weight (BW) and weaning weight (WW) using a single-trait analysis are presented in The genetic and phenotypic correlations between BW and WW to reproductive traits (AFC and GL) are shown in The results obtained in this study were compared to numerous literature with almost similar model structures except only for studies with non-zero assumptions of the covariance value between the direct additive and maternal variance, and the inclusion of a permanent environmental effect, which was not included in our model since only a few cows have twin calves.Birth weight was considered a very good economic indicator in the beef cattle industry  as it waThe observed direct and maternal heritability estimates for BW of 0.22 ± 0.02  and 0.12In this study, the covariance value between the direct and maternal effects was set to zero. This assumption was different with Choi et al. , Kars etAlongside the covariance assumption, breed differences and large unexplainable residual variances also bring discrepancies to heritability estimates. Specifically, the obtained residual variance in this study contributes to about 66.21% of the total phenotypic variance which was 10.40% to 19.31% higher than those observed by Kars et al. , Mujibi Despite differences, our findings suggest that Hanwoo’s birth weight will sluggishly respond to selection. However, strict caution must be considered when selecting calves with heavy birth weight, since it might increase dystocia cases and subsequent perinatal mortality. Further, our results indicate that maternal effect plays a very important role in cattle BW. Similarly, Chud et al.  stated tIn here, the obtained direct and maternal heritability estimates for weaning weight of 0.51 ± 0.03 and 0.17 ± 0.01 were close to the observation of Gutiérrez et al.  in AsturMeanwhile, numerous researchers who included a permanent maternal environment effect reported lower direct heritability and comparably low maternal heritability estimates. Chud et al.  and BoliFurthermore, the maternal heritability estimates observed in this study fall within the range of 0.10 to 0.17 observed by Schiermiester et al. , Wasike Although differences in estimates were observed, our observation suggests that the differences in the direct heritability estimates can be attributed mainly to breed differences, residual variances, and variations to model structure such as the inclusion of maternal permanent environmental effects. Moreover, our findings indicate that weaning weight cannot be influenced by its genetic potential alone but also with the maternal effects of its dam. Further, the high direct heritability estimates observed for WW suggest a rapid response to selection and might bring an immediate advantage to calves to gain a heavier bodyweight at older ages.g) between BW and AFC of 0.33 ± 0.064 was lower compared with 0.77 observed by Bekele et al. [p) between BW and AFC, the obtained value in this study of 0.06 ± 0.01 was slightly lower than 0.12 observed by Bourdon and Brinks [g between BW and AFC indicates that selecting a calf with heavy growth traits including BW results in a longer AFC [p between these traits can be attributed to the relationship between the additive genetic effects and non-additive genetic effects . According to Wakchaure et al. [The obtained genetic correlation  indicates that to avoid low reproductive performance, the practice of increasing adult size should not be recommended. Meanwhile, Boligon et al. [g (between WW and AFC) of −0.20 and −0.16 in Nellore and −0.12 in female Canchim cattle, respectively. According to these authors, the selection to increase WW and other growth traits result in a favorable response and possible reduction in AFC. Moreover, the obtained rp between WW and AFC in this study did not differ from zero (−0.01 ± 0.01) but was found to be lower when compared with those observed by Boligon et al. [The low and near to zero ra-Correa  in Browna-Correa  of 0.10 n et al. , Boligonn et al. , and Taln et al.  reportedn et al.  and Segun et al.  of −0.18g and rp observed between WW and GL in this study were found to have the same near to zero value of −0.09 ± 0.06 and −0.09 ± 0.01, respectively. The observed rg in this study was comparable to 0.02 ± 0.06 observed by Chud et al. [g ranging from 0.21 to 0.22, and low and positive rp ranging from 0.08 to 01.0 between WW and GL of Hanwoo cattle. The observed similar rg and rp between WW and GL in this study might indicate that both traits were independent of each other. This claim was in agreement with Cervantes et al. [Meanwhile, the rd et al.  in Nellod et al.  reporteds et al. , who repe) coefficients between growth traits and reproductive traits considered in this study were also presented in e coefficients between BW and reproductive traits (AFC and GL) were both low and positive, while negative and close to zero re coefficients were observed between WW and reproductive traits (AFC and GL). According to Kluska et al. [e indicates that the traits of interest were independently influenced by environmental and non-additive effects.To further support earlier claims, the residual correlation  indicate that selecting a calf with a heavier BW might result in a longer AFC and GL. Although there was a moderate and positive genetic correlation between BW and AFC, the low phenotypic correlation may indicate that GxE interaction might influence thephenotypic expression of these traits. Meanwhile, the genetic and phenotypic correlation between WW and reproductive traits  indicates that the selection of a heavier WW may not influence the AFC and GL of Hanwoo cow. These obtained variance components, heritability estimates, and correlation coefficients for growth and reproductive traits in this study can be utilized in future researches which aims to improve the Hanwoo cattle industry.


Quercus petraea and Q. robur), which grow in mixed stands, under even‐aged management regimes. We screened numerous functional traits for univariate selection gradients and for expected and observed genetic changes over two successive generations. In both species, growth, leaf morphology and physiology, and defence‐related traits displayed significant selection gradients and predicted shifts, whereas phenology, water metabolism, structure and resilience‐related traits did not. However, the direction of the selection response and the potential for adaptive evolution differed between the two species. Quercus petraea had a much larger phenotypic and genetic variance of fitness than Q. robur. This difference raises concerns about the adaptive response of Q. robur to contemporary selection pressures. Our investigations suggest that Q. robur will probably decline steadily, particularly in mixed stands with Q. petraea, consistent with the contrasting demographic dynamics of the two species.Most existing forests are subjected to natural and human‐mediated selection pressures, which have increased due to climate change and the increasing needs of human societies for wood, fibre and fuel resources. It remains largely unknown how these pressures trigger evolutionary changes. We address this issue here for temperate European oaks ( We investigated whether contemporary selection pressures may contribute to a marked and heritable shift in the phenotypic traits of oak trees. Large‐scale surveys in European forests emphasized clear phenotypic trends for some traits that have been related to climatic drivers. Continuous increases of growth have been recorded during the last century but also at contemporary time scales .in natura  with different demographic dynamics over two generations  in France, which has been intensively studied over the last 30 years  trees that were about 100 years old at the time they were cut, between 1989 and 2001. Before the final clear cut in 2001, the stand was progressively opened up by removal cuts practiced in 1992, 1993 leaving at the end 116 Q. petraea and 143 Q. robur trees. The final clear cut took place over a period of 3 years , to facilitate the harvesting and manipulation of log samples for later assessments of the wood and tree anatomy. Before they were cut, the trees mated between 1989 and 2001, giving rise to a second generation (G2), as illustrated in Supporting Information The study population was a mixed forest stand composed of 2.2In G1, between 1989 and 2001, when the trees were between 90 and 100 years old, 56 traits correlated with major functional and ecophysiological classes  were measured Table , which uQ. petraea and 390 Q. robur saplings  and circumference (CIRC).Phenology: time at leaf unfolding (LU).13C) and nitrogen isotopic composition (δ15N).Physiology and water metabolism: specific leaf area (SLA), mean leaf area (MLA), carbon content of leaves (C ), nitrogen content (N), carbon/nitrogen ratio (C/N), carbon isotopic composition (δWood structure: wood density (WD) recorded on increment cores with an X‐ray image calibration procedure.Phenotypic traits were monitored in G1 and G2 as follows  and iPLEX® chemistry. SNP arrays were used in a previous study , and all G2 saplings  were genotyped in 2015 with a set of 82 SNPs to estimate reproductive success (see the next paragraph), and for species assignment  derived from targeted sequence capture  and quadratic (γx) selection gradients at generation G1 according to the following linear model:w is the relative fitness , µ is the population mean, xβ is the linear univariate selection gradient and xγ the quadratic selection gradient, and x is the trait value expressed as a standardized value (standardized according to the standard deviation).C, E and P are covariables accounting for the ecological and environmental conditions close to each G1 tree potentially correlated with fitness or to fitness components:We used regression‐based approaches to estimate univariate linear , jD is the diameter at breast height of the subject tree j, iD is the diameter at breast height of tree i standing in the neighbourhood of the subject tree, and Distij is the distance between subject tree j and tree i.x Hegyi, ) measuriE is an environmental index combining ecological variables downscaled to the level of each G1 and G2 tree   suggested by Marrot, Garant, and Charmantier . The AIC (Akaike information criterion) values for the linear regressions were compared, and the eigenvector minimizing AIC was selected as a covariate for subsequent models. Unlike Marrot et al.  operating on generation G1, the number of standard deviations by which selection in G1 shifts the mean values of trait x , by estimating the additive covariance between relative fitness (aw) and the trait of interest (ax):We used the second theorem of selection (STS) Price,  to predieR potentially due to the correlation of environmental effects when the breeder's equation is used to predict eR. This method provides an overall estimate of the expected genetic shift of the trait, whether due to direct or indirect selection via other traits or to other evolutionary forces , a is the vector of random genetic (additive) effect of w and x, ε is the vector of the random effect of the residuals, and Y1, Y2, Y3 and Z are the index matrices related to each effect  and the trait (x), given the bivariate normal distribution assumed for the genetic values in the animal model:G is the additive genetic relationship matrix between the trees of generation G1. We used the realized genomic relatedness to estimate the components of G. In a previous publication  between any pair of G1 trees as follows:n*m matrix  of genotypic arrays scored as −1, 0 or 1 for homozygote, heterozygote and alternative homozygote, respectively, P is an n*m matrix of allele frequencies computed as 2(pi−0.5), and ip is the frequency of the second allele  at locus i, as described by  than in Q. robur (0.0011), and the total number of SNPs used to retrieve realized genomic relatedness was 15,274 in Q. petraea and 16,408 in Q. robur  is the genetic covariance between fitness (BreedR is a linear mixed model (LMM) based on restricted maximum likelihood estimation. GLMMs  are not available in this package. We therefore used LMMs only. For fitness and/or traits not following a Gaussian distribution, we proceeded as follows: (a) the data were log‐transformed, (b) the bivariate LMM was fitted to the transformed data and used to predict evolutionary changes on the transformed scale, and (c) the QGglmm package  implemented in the univariate context and with the addition of generation as a fixed term , we calculated the breeding values of all trees of generations 1 and 2 and their mean values/generation were compared for all traits assessed in both generations. The relatedness matrix G of (8) differs from that of model 5 by including the genetic relatedness between trees of G1, between trees of G1 and G2 and between trees of G2. It therefore combines realized genomic relatedness and the relatedness inferred from the parentage analysis between G1 and G2, as described by Alexandre et al. . In a prIn addition to comparing breeding values, we also used a more empirical method to assess generational shifts at the phenotypic level. We compared the phenotypic means of traits assessed in G2 trees, according to the fitness value of their parents in G1. Indeed, pedigree relationships between the two generations were reconstructed in a previous study  based on the genetic covariance between the traits and fitness of G1 trees with the realized genetic and phenotypic shifts in their G2 offspring. However, comparisons of absolute values would be meaningless, as the two approaches are based on different methods. These comparisons should therefore be seen as a qualitative empirical attempt to compare predicted and realized evolutionary changes. We thus limited our observations to comparisons of the signs of the shifts provided by the two methods.33.1Q. petraea . When the univariate animal model was used with log‐transformed relative fitness data to subdivide the overall phenotypic variance into genetic and residual terms (univariate model 5), additive genetic variance was found to be greater in Q. petraea than in Q. robur (0.468 vs. 0.193)  and three metabolites . In Q. robur, linear gradients were significant for leaf morphological traits  and one secondary metabolite (TWK).In both species, significant linear selection gradients were found for radial growth‐related traits  Table , suggestQuercus robur , leaf morphological traits  and several secondary metabolites, including ellagitannins and volatile compounds.Mixed animal model estimates highlighted significant genetic covariances Table . In Q. pQ. robur, the genetic covariances calculated between fitness and traits , we compared the sign of the selection gradients Table  and the Covwe,Xa and Covwa,Xe being non‐negligible terms in 9. Hence, discrepancies between the signs of the selection gradient and the genetic covariances are probably due to Covwa,Xa and Covwe,Xe having opposite signs. This might be the case for growth components in Q. robur with positive selection gradients but negative genetic covariances. A similar interpretation may explain why more traits in Q. petraea have significant genetic covariances than significant selection gradients.There is no biological support for 3.4Q. petraea  and for which model 8 could be applied, that is traits of interest and covariates (for fixed effects) available for both G1 and G2 trees . This made it possible to make empirical qualitative comparisons between mean phenotypic values in G2 saplings according to the fitness of their parents Tables  and 6.Q. petraea, convergence between predicted genetic and observed phenotypic responses was observed for traits with significant genetic variance and covariance  (Table For C) Table .Q. robur, no consistent pattern emerged for the relationship between predicted genetic and observed phenotypic responses. Conflicting results were even obtained for HGHT, for which STS predicted a significant decrease, whereas a phenotypic increase was observed  were more in agreement with predicted responses for ur Table .4Q. petraea and Q. robur in response to ongoing environmental changes  and its negative correlation with hairiness (HR) and petiole length (PL) indicate that selection is operating in favour of Q. robur‐like phenotypes  and the number of lobes (NL) indicate that Q. petraea‐like trees have a greater fitness, for these traits .For the more integrative leaf morphological traits, such as SLA and MLA, we observed a selective trend towards larger leaves  and thicker and/or denser leaves  in y Evans, . This try Evans, . We obseQ. petraea, whereas no change was predicted for Q. robur. Increases in the levels of ellagitannins and other volatile compounds were predicted, whereas decreases were predicted for other metabolites, consistent with a redistribution within the overall mixture in Q. petraea. The individual roles of these compounds have not been studied, but their overall biological activity is consistent with reactivity in the insect gut, in which they may cause oxidative stress , significant correlations have been found between ellagitannin phenotypes and leaf miner communities . Large annual water deficits were also recorded during these two years , despite the occurrence of drought events during the regeneration period  was detected for traits relating to water use efficiency .The data used in this study  are accessible at the EVOLTREE eLab repository at the TreePop database, available at Supplementary MaterialClick here for additional data file.


Wolbachia) to cause a conditional sterility in crosses between released mass-reared Wolbachia-infected males and wild females, which are either uninfected or infected with a different Wolbachia strain . Herein, we review the current state of knowledge on Wolbachia-tephritid interactions including infection prevalence in wild populations, phenotypic consequences, and their impact on life history traits. Numerous pest tephritid species are reported to harbor Wolbachia infections, with a subset exhibiting high prevalence. The phenotypic effects of Wolbachia have been assessed in very few tephritid species, due in part to the difficulty of manipulating Wolbachia infection . Based on recent methodological advances (high-throughput DNA sequencing) and breakthroughs concerning the mechanistic basis of CI, we suggest research avenues that could accelerate generation of necessary knowledge for the potential use of Wolbachia-based IIT in area-wide integrated pest management (AW-IPM) strategies for the population control of tephritid pests.Members of the true fruit flies (family Tephritidae) are among the most serious agricultural pests worldwide, whose control and management demands large and costly international efforts. The need for cost-effective and environmentally friendly integrated pest management (IPM) has led to the development and implementation of autocidal control strategies. These approaches include the widely used sterile insect technique and the incompatible insect technique (IIT). IIT relies on maternally transmitted bacteria (namely  Anastrepha, Bactrocera, Ceratitis, Dacus, Rhagoletis, and Zeugodacus) contain ∼70 major pest species (Flies in the family Tephritidae (Diptera) include some of the world’s most important agricultural pests. The family is comprised of ∼4,900 described species within 481 genera, of which six ( species . Pest te species . FurtherCochliomyia hominivorax (Coquerel), several species of tsetse fly (Glossina spp.), the codling moth Cydia pomonella (L.) (reviewed in Ceratitis capitata (Wiedemann); Anastrepha ludens (Loew); Anastrepha obliqua (Macquart); Zeugodacus cucurbitae (Coquillett); Bactrocera dorsalis Hendel; and Bactrocera tryoni (Froggatt) (Anastrepha fraterculus (Wiedemann)   . SIT is edemann) ; Dacus ctus Loew  and Bact . The adv , and res .Culex mosquitoes (Wolbachia) to induce a form of reproductive incompatibility known as cytoplasmic incompatibility . Herein we review the current knowledge on taxonomic diversity of Wolbachia-tephritid associations and their phenotypic consequences, and identify gaps in knowledge and approaches in the context of potential application of IIT, alone or in combination with SIT, in AW-IPM programs to control tephritid pests.Another autocidal strategy where mating between mass-reared and wild insects can be used to suppress pest populations is the incompatible insect technique (IIT); coined by squitoes , and intsquitoes . IIT alsWolbachia is the most common and widespread facultative symbiont of insects and arthropods , where unfertilized eggs, which would otherwise develop into males, develop into females. Male killing causes death of infected males to the presumed advantage of surviving infected female siblings, and occurs in Coleoptera, Diptera, Lepidoptera, and Pseudoscorpionida. Cytoplasmic incompatibility (CI)   preventsWolbachia has two functions: mod (for modification), which acts as a toxin or imprint of the male germline; and resc (for rescue), which acts as an antidote. The mod function acts on the nucleus in the male germline, before Wolbachia are shed from maturing sperm  function is active in the egg, the defect caused by mod in the sperm is rescued, and the embryo proceeds through normal development. In the case of unidirectional CI (uni-CI), all or some of the eggs from uninfected females that are fertilized by sperm from Wolbachia-infected males (the “CI cross”) fail to develop , fitness costs of Wolbachia are low, and maternal  transmission is high, the threshold Wolbachia frequency to achieve invasion can be close to zero, and the stable equilibrium frequency can be close to 100%  that contribute to the induction and rescue of CI , nuclease (n), both (nd); “f” is used by certain authors and/or when the nature of the defect is unknown  similarly labeled, but with a “B” replacing the “A”  that seems to function as a toxin. Certain Wolbachia strains have more than one “A–B” pair, and the combination of these is consistent with patterns of incompatibility in Drosophila RNA gene or of a ubiquitous protein-coding gene . Wolbachia detection and identification, revealing that no single PCR protocol is capable of specific detection of all known Wolbachia strains. A related method known as “loop mediated isothermal amplification” . Several higher sensitivity approaches have been devised, particularly for low-titer infections, such as: long PCR (Wolbachia multi-copy genes as PCR targets (The two major shortcomings of utilizing solely PCR (or LAMP) to detect hibitors , by low long PCR ; nested long PCR ; quantitlong PCR ; or the  targets . These mWolbachia is identified as Wolbachia-infected. Several instances have been reported where insect chromosomes carry Wolbachia-derived fragments, presumably from a horizontal gene transfer event that occurred at some point in the host lineage as the result of an active infection that was subsequently lost. The size of the horizontally transmitted fragment can range from ca. 500 bp to the equivalent of an entire Wolbachia chromosome , other insects, as well as nematodes  or of another symbiont of the host. Such false positives are relatively easy to rule out upon sequencing and analysis of the amplified product. Finally, as with any PCR work, false positives can result from contamination of the specimen, the DNA template, or the PCR reagents. Thus, it is important to implement adequate sterile practices and negative controls.False positives occur when a specimen not harboring romosome . In somet genome . The ranematodes . It is ton e.g.,  or antibWolbachia, including the distinction of two different Wolbachia strains  has been developed for identification of specimens infected with  strains . This meWolbachia are assigned to “supergroups”  systems   is defined on the basis of MLST allele combinations, with each allele combination assigned a unique ST number. Further characterization of each MLST-defined strain can be achieved by examination of four hypervariable regions (HVRs) of the wsp gene  . The wspbination . An MLSTwsp gene .Wolbachia. Traditionally, direct Sanger sequencing of PCR products that resulted in sequences with ambiguous base pairs would be subjected to cloning followed by sequencing. The allele intersection analysis method  loci must be evaluated. The multiple locus variable number tandem repeat analysis developed by Wolbachia strains based on PCR and gel electrophoresis.Use of the MLST system alone has two major drawbacks. First, strains of her loci . Secondlergroups . TherefoWolbachia strains, infer their evolutionary relationships, test for recombination, and identify genes of interest  has proven difficult. Recent advances, particularly those based on targeted hybrid enrichment   . The lowWolbachia to a particular host nuclear background can also be achieved through introgression, whereby males of the desired background are repeatedly backcrossed with Wolbachia-infected females  transfer of es e.g., . Under tes e.g., . The draWolbachia in certain host individuals may be used to obtain Wolbachia-free and Wolbachia-infected hosts of equivalent genetic background. Wolbachia removal has also been achieved by “extreme” temperature treatment . Wolbachia does not appear to be efficiently transmitted via ingestion  and Drosophila .Due to less than perfect transmission, passive loss of chondria , which ion e.g., , but seeerations ; this haWolbachia strains are assessed. A significantly lower embryo hatching rate of the CI cross(es) compared to that of the control cross(es) constitutes evidence of CI. CI can be partial or complete (100% embryo failure). As with any fitness assay, care must be taken to prevent potential biases, including crowding and age effects; which have been shown to influence CI  to that of one or more control crosses. For testing bidirectional CI, the reciprocal crosses of hosts infected by the different uence CI . Adequatuence CI , or excl females .Wolbachia infection (excluding pseudogenes) in laboratory and natural populations  of 17 screened to date harbor Wolbachia (Anastrepha species harbor Wolbachia strains assigned to supergroup A. Anastrepha striata Schiner and Anastrepha serpentina (Wiedemann), however, harbor supergroup B in southern Mexico  and three  carry what appear to be Wolbachia-derived pseudogenes, but not active infections  and pseudogenized Wolbachia sequences , in the early ‘1990s’ . Cresson . Both A Zeugodacus (formerly Bactrocera), both Z. cucurbitae and Z. diversa are reported to harbor Wolbachia or Wolbachia pseudogenes (Dacus examined to date are reported to harbor Wolbachia: Dacus axanus Hering (Dacus destillatoria Bezzi (Wolbachia has not been detected in the monotypic genus Dirioxa (Wolbachia (supergroup A) has been reported in Carpomya vesuviana  in natural populations of tephritids. Few of these studies, however, have adequate sample sizes for such inferences . Notwithstanding, inferred Wolbachia prevalence in tephritid populations is highly variable. In Anastrepha, ∼10 species harbor at least one population with prevalence ∼100%, whereas populations of three species reported lower frequencies   . In BactWolbachia prevalence is that of R. cerasi in Europe, which was surveyed over a ∼15-year-period in 59 localities (wCer1–6 have been identified from Europe and the Middle East. In an early (1998) survey, R. cerasi individuals infected by one strain (wCer1), most central and southern European populations harbored an additional strain wCer2 , and at least one Italian population harbored wCer1 + wCer4 (wCer2 (a strain associated with cytoplasmic incompatibility) has been detected. Multiple infections, in various combinations of all five known Wolbachia strains from Europe, have been revealed recently. Samples from Poland, Italy, and Austria, are infected with strains wCer1–5 those from Czech Republic (prior to 2009) and Portugal lacked wCer2 only, while the Swiss samples lacked wCer3  and the possible existence of new Wolbachia strains for the southernmost European R. cerasi population   is fixed in all populations of R. cingulata; a species native to the United States, but introduced into Europe at the end of the 20th century. Invasive populations in Europe harbor wCin1  at frequencies that vary over space and time (up to 61.5%), as a result of horizontal transfer (multiple events) from R. cerasi  . Similar. cerasi . The aboWolbachia in tephritids, the fitness consequences of such associations remain mostly unknown. The studies reporting phenotypic effects of Wolbachia have relied on transinfection and on antibiotic-curing; only two species of tephritids have been successfully transinfected with Wolbachia  originally from R. cerasi to C. capitata also resulted in strong CI . wCer2 in two genetic backgrounds of B. oleae resulted in strong CI as well  exhibit higher embryonic mortality  than their Wolbachia-free counterparts crossed with Wolbachia-free males . Therefore, it appears that wCer2 and wCer4 have additional fertility effects on medfly females, other than CI. It is also possible that these Wolbachia strains can only partially rescue the modification that they induce in sperm  on fitness components of two C. capitata genotypes , as well as the effects of two different Wolbachia strains (wCer2 and wCer4) on a single medfly genotype (Benakeio). The following general patterns emerged (exceptions noted): (a) Wolbachia causes higher egg-to-larva mortality; (b) Wolbachia causes higher egg-to-adult mortality , and reduces life time female net fecundity. However, Wolbachia infection on adult lifespan, and a reduced fecundity in the case of wCer4 infection only. Even though wCer2 and wCer4 in general tended to have consistent effects on medfly, the magnitude of their effects differed. Collectively, the results from these studies indicate that the effect of Wolbachia infection on life history traits depends both on the C. capitata genetic background and on the Wolbachia strain. Furthermore, inconsistencies between the two studies might be indicative of evolution of the host and/or Wolbachia strain during that period. Evidence of Wolbachia evolving reduced fitness costs has been reported in D. simulans . No evidence of bidirectional cytoplasmic incompatibility was detected in reciprocal crosses among singly infected laboratory strains. However, the same work described the presence of slightly detrimental effects on larval survival and a female-biased sex ratio, suggesting the induction of male-killing phenomena. Moreover, Wolbachia reduced the embryo hatching in crosses involving cured females and infected males  within two morphotypes of this cryptic species complex; stronger CI was detected within the Peruvian morphotype than the Brazilian-1 morphotype . No evidence of bidirectional CI was detected in the crosses between the two morphotypes, leading Wolbachia is not directly involved in the speciation process of these morphotypes. Wolbachia in A. obliqua and in “A. fraterculus sp. 1,” which according to wsp sequences, are identical. Nonetheless, confounding effects of the treatment to remove Wolbachia (removed by exposure of pupae to 30°C) or other potential biases cannot be ruled out, as all intraspecific crosses involving at least one cured parent resulted in much lower (<30%) embryo hatching than the intraspecific crosses involving both infected parents (66 and 81% embryo hatching).A recent study by Wolbachia infection can affect male sexual competitiveness of C. capitata. Different Wolbachia strains (wCer2 and wCer4) exerted differential impact on males mating competitiveness, and a single strain (wCer2) had different impact on different medfly genotypes (Benakeio and Vienna 8 GSS laboratory lines) (Recent work demonstrates that y lines) .Wolbachia associated with arthropods in general, at the population level Wolbachia appears to be predominantly maintained by vertical transmission. Above the species level, however, the lack of congruence between the host and symbiont phylogenetic trees implies that Wolbachia horizontal transfers and extinctions are common and underlie its widespread taxonomic and geographic distribution , it would have to invade the egg during oogenesis. Similarly, if Wolbachia were acquired via ingestion , it would have to cross the gut into the hemolymph, before it invaded the egg. Support for the above routes comes from studies reporting: (a) that Wolbachia can retain viability outside cells and infect mosquito cell lines, as well as ovaries and testes that are maintained ex vivo (Wolbachia cells injected into Drosophila hemolymph reach the germline after crossing multiple somatic tissues (Wolbachia can move between parasitic wasp larvae (Trichogramma) sharing the same host egg, and achieve vertical transmission (Bemisia tabaci (Gennadius), can transfer Wolbachia from an infected to a naïve host, as a result of non-lethal probing , whereby the parasitoid ovipositor or mouthparts function as a “dirty needle”  tha tissues ; (c) thasmission ; and (d) needle” .Wolbachia transmission via parasitoids exists in tephritids, but sharing of Wolbachia strains between a parasitoid and several sympatric tephritids , and A. ludens [and the mosquito Aedes aegypti (L.)], only unidirectional CI is feasible. In target populations that harbor one (or more) CI-inducing Wolbachia strain(s)  , it is desirable to ensure that accidentally released females are sterile. In several tephritid systems, female sterility is achieved at a lower irradiation dose than male sterility, such as A. ludens, A. obliqua, Anastrepha suspensa, A. serpentina, B. tryoni, and Z. curcubitae . Nonetheless, the actual outcome is strongly dependent in multiple factors  present in the target population; small or no fitness cost for parameters relevant to the program. These fitness parameters can be classified into those related to a cost-effective mass production  and those related to the success of released males . Some host-Wolbachia combinations result in higher female fecundity, such as D. simulans after many generations  a Wolbachia strain that can rescue the modification (sterility) induced by the strain present in the released males. Generally, such acquisition of a Wolbachia strain during the relatively short lifespan of a release program seems unlikely. Nonetheless, knowledge on the Wolbachia infection status and strain identity of interacting species, such as other fruit flies sharing the same host plant and parasitoids, might aid in the selection of Wolbachia strains that are unlikely to be compatible with strains that can potentially be horizontally acquired by the target population. Permanent screening of wild flies from the target population could provide valuable information in order to foresee potential lack of effectiveness of the method. Laboratory experiments in which the conditions for horizontal transmission are favored (or even forced) might also help to determine the probability of such phenomena to occur in nature.There are two ways in which a target population may become resistant to the effects of released de novo) genetic variation. Evidence consistent with the influence of Wolbachia on premating mechanisms comes from the observation that females and males of Drosophila paulistorum Dobzhansky and Pavan exhibit assortative mating according to the Wolbachia strain they harbor (Wolbachia) decreases mate discrimination in D. melanogaster , refreshing the fly genetic background of mass-reared strain is unlikely to slow down the evolution of resistance to released males in the target population.The second mechanism is host-based, whereby pre- or post-mating selection on wild females to avoid or reduce fertilization by incompatible sperm  as a female strategy to minimize the probability of her eggs being fertilized by sperm from incompatible males  strains present in target population, should be avoided. Secondly, it may be possible in the future to genetically engineer Wolbachia strains with the desired characteristics  for IIT programs, or to replace strains used previously in a control program, as a means of addressing resistance phenomena , this would have multiple benefits in an operational Wolbachia-based population suppression program. Furthermore, a Wolbachia strain that does not induce (strong) CI, but protects against pathogens might be desirable in a program that does not rely on CI  for population suppression. Wolbachia-mediated pathogen protection would enable high production and quality levels, thereby contributing to a cost-effective and sustainable insect pest management program.It has recently been shown that some bacteria . It is vWolbachia appears to be the dominant facultative heritable symbiont of arthropods, numerous other diverse bacteria  form such associations with insects, causing a diversity of reproductive and non-reproductive phenotypes , can yield surprises of bacterial associates   . It is tWolbachia in tephritids and its known fitness consequences in this group of dipterans and in other host taxa, Wolbachia is likely an influential component of tephritid ecology and evolution. Further exploration of Wolbachia-tephritid associations is expected to reveal a diversity of effects, including interactions with other microbial partners, as seen in more extensively studied systems such as Drosophila and mosquitoes. The recent exciting progress in understanding the basis of CI, and many other aspects of Wolbachia biology, should accelerate progress in the development of Wolbachia-based IIT for tephritid species, particularly with the aid of comparative Wolbachia genomics to identify potential CI patterns on the basis of CI gene composition. We consider that one of the major obstacles to effectively implementing IIT will be to avoid population replacement due to accidental release of Wolbachia-infected females. The threshold number of accidentally released females, which is generally much higher in systems that employ bidirectional-CI compared to unidirectional-CI, must be thoroughly investigated prior to any field implementation. Where an unacceptable risk of population replacement exists, we recommend that SIT be explored as a complementary strategy to support IIT.Given the widespread occurrence of https://www.biorxiv.org/content/10.1101/358333v1.An earlier version of manuscript has been released as a Pre-Print at MM led the drafting. HM, PL, JT, BM-A, KG, SL, DS, CC, AA, GT, EA, VD, NP, and GK edited multiple drafts.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


There is a growing interest in worldwide swine breeding programs to genetically select for pork quality and primal cuts in addition to the traditional growth and carcass leanness traits. Accurate population genetic parameters are needed to estimate correlated responses to selection and incorporate novel traits in the selection objective. Therefore, we estimated heritabilities and genetic correlations for 39 pork quality, growth and carcass traits in Duroc pigs. In general, moderate and favorable genetic correlations were observed between pork quality  and carcass traits. Additionally, moderate to low correlations were found among pork quality, growth and carcass traits. Our findings suggest that pig breeders can successfully incorporate pork quality and novel carcass traits in the selection objectives without undesirable impacts upon growth rate and carcass leanness.More recently, swine breeding programs have aimed to include pork quality and novel carcass  and belly traits together with growth, feed efficiency and carcass leanness in the selection indexes of terminal-sire lines, in order to efficiently produce pork with improved quality at a low cost to consumers. In this context, the success of genetic selection for such traits relies on accurate estimates of heritabilities and genetic correlations between traits. The objective of this study was to estimate genetic parameters for 39 traits in Duroc pigs , 10 pork quality and 18 novel carcass traits). Phenotypic measurements were collected on 2583 purebred Duroc gilts, and the variance components were estimated using both univariate and bivariate models and REML procedures. Moderate to high heritability estimates were found for most traits, while genetic correlations tended to be low to moderate overall. Moderate to high genetic correlations were found between growth, primal-cuts and novel carcass traits, while low to moderate correlations were found between pork quality and growth and carcass traits. Some genetic antagonisms were observed, but they are of low to moderate magnitude. This indicates that genetic progress can be achieved for all traits when using an adequate selection index. The North American swine industry produces over 146 million pigs per year, which accounts for over 15 million tons of pork products , which iCurrently, consumers desire high-quality meat products, which are perceived as more tender, juicier and possessing of desirable flavor ,9. Pork Pork quality traits have been previously estimated to have low to moderate heritabilities, while carcass traits have moderate to high heritabilities ,14,15,16The main objective of this study was to estimate heritability and genetic correlations for various novel pork quality, carcass cut weight, belly, carcass leanness and growth traits in a population of purebred, terminal-line Duroc gilts.The animals included in this study were managed in accordance with the “Code of practice for the care and handling of pigs” . All the samples for genotyping were collected in a nucleus breeding farm and the animal owners agreed to be involved in the project. The slaughters, data collection and trait measurements were done by well-trained staff following industry best practices. Phenotypic records for 39 growth, carcass, pork quality and conventional carcass traits were available for 2583 pigs  born between 2010 and 2018. All animals were born and raised on the same nucleus farm and slaughter information was collected at the same slaughter plant on pigs from 159 to 219 days of age. Only female records were collected as the males were used for genetic dissemination from the nucleus farm.The initial pedigree file contained 193,764 animals, in which 3796 were sires and 19,802 were dams. The pedigree was trimmed to 10 generations back from the phenotyped animals. The number of animals in the pedigree file ranged from 2544 to 4917 for the different traits (average ± SD: 4579 ± 689).A total of 39 growth, conventional, carcass and pork quality traits were included in this study. The descriptive statistics are shown in Growth traits. Average daily gain  was defined as total body weight gain divided by days-on-test. Pre-slaughter live weight  was measured five days before slaughter. Hot carcass weight  was defined as the whole carcass weight taken after exsanguination and evisceration, including the head, leaf lard, kidneys and trotters  reported in literature, though it is greater than the value of 0.16 ± 0.02 estimated in another Duroc population . The herPork color scales generally had lower heritability (average of 0.18) compared to the Minolta color measurements (average of 0.33). This difference could be due to human error, as color scores were given by a technician while Minolta L*, a* and b* were measurements taken using a machine. Thus, these measurements would be expected to have less error.The DLP heritability found here (0.28 ± 0.09) is within the range of what was found in literature , though Previous heritability estimates for LNM range from 0.16 ± 0.07 to 0.23 ± 0.05 to 0.31 ± 0.12 ,16,38, wConventional carcass traits were generally moderately to highly heritable, with the exception of GI, which had an estimated heritability of 0.00 ± 0.00. This indicates that GI is not under genetic control, as grades are determined by individual packing plants and fluctuate based on plant and time of the year, making genetic prediction difficult. Therefore, GI (as currently measured) is not a trait that should be included in a genetic or genomic evaluation scheme.A heritability of 0.14 ± 0.05 was found for DP in this study as compared to estimates of 0.32 ± 0.04, 0.40 ± 0.03 and 0.31 ± 0.06 for maternal breeds  . In anotA heritability of 0.38 ± 0.07 was estimated for GBF, which is below the average of the range shown in Clutter : 0.12 – The moderate to high heritabilities estimated for traits such as GBF, LA and RMD indicate that substantial progress can be made by selecting for these traits. Due to their higher heritabilities, genetic progress will be faster while also providing more accurate estimated breeding values.Novel carcass traits had low to high heritabilities . To our To our best knowledge, the current study is the first report of heritability estimates for BLFT, BRW, BLW, BWR, SW and UBW. Thus, the heritability estimates in this study were 0.31 ± 0.11, 0.19 ± 0.05, 0.40 ± 0.06, 0.17 ± 0.12, 0.12 ± 0.05 and 0.15 ± 0.05 for these traits, respectively. Carcass traits had low to high heritability, with the novel and less studied traits tending towards a low to moderate heritability. These results show that these traits are under some degree of genetic control and can be used for the purpose of selecting for specific gains on the primal and subprimal cuts of the carcass. Additionally, the moderate estimate for BLFT indicates that it may be a candidate for consideration to account for belly quality in a selection index.Genetic correlations between conventional carcass traits and pork quality traits can be found in Both color score measurements (LJPC and LNC) had a moderately unfavorable correlation with DP,  which indicates that selection for an increased ratio of internal body contents to lean muscle tissue could lead to paler pork color. A similar correlation was found between LJPC and HCW (−0.36 ± 0.04). However, Miar et al.  found thBackfat depth had moderate correlations of 0.26 ± 0.15, 0.38 ± 0.14 and 0.37 ± 0.15 with L*, a* and b*, respectively. This indicates that selection for backfat is likely to moderately increase the Minolta color score. Inversely, GBF and LNC have a moderate and negative correlation of −0.37 ± 0.21, which indicates that selection for backfat has an inverse relationship with the NPPC color score. The NPPC color scale is based on Minolta L* values, and as NPPC score decreases, the L* value increases. As such, these correlations align.The moderate and favorable correlation between GBF and LNM of 0.30 ± 0.11, was expected, as these traits have been reported to be positively correlated previously ,60. AddiGenetic correlations between pork quality and novel carcass traits are shown in Marbling score also had a moderate inverse correlation with TPW (−0.32 ± 0.15). This indicates that if intra-muscular fat is selected for, there may be higher total fat on the carcass, which will lower the lean yield elsewhere, such as on the picnic shoulder. The moderate correlations between LNM and THW and TW  confirm this observation. In addition, several studies confirm this genetic correlation between leanness and fat, and summaries of correlations can be found in reviews by Ciobanu et al. . The moderate and inverse correlations between DLP and BRW, TBW and UBW  indicate an unfavorable relationship between carcass leanness and drip loss when compared with estimates found in other studies. Suzuki et. al.  reportedFor those traits that had HCW removed as a covariate from the analysis in order to meet convergence criteria, there were both high and moderately positive correlations with HCW (HCW by BLW (0.47 ± 0.11), BRW (0.33 ± 0.03), SW (0.53 ± 0.22) and USW (0.77 ± 0.08)). It is explainable that both primal and subprimal cuts would increase in weight if selection for increased HCW was implemented. In the study performed by Miar et al. , high coA highly favorable correlation was found between GBF and BLFT (0.99 ± 0.07), indicating a high degree of genetic similarity between these two traits. Additionally, a moderately favorable genetic correlation was found between GBF and TBLW (0.51 ± 0.16), a strong and negative correlation was estimated between GBF and THW (−0.73 ± 0.08), and a moderate and negative correlation was observed between BLFT and loin size traits GLD (−0.38 ± 0.28), LA (−0.49 ± 0.25) and LC (−0.68 ± 0.62). GBF also had moderately unfavorable correlations (average of −0.49) with most primal and subprimal cuts except TBLW, as shown above. This correlation is unfavorable because if GBF is included in a selection index, producers do not want to decrease the amount of lean product produced in the rest of the carcass. In this context, these correlations can be explained by considering the fat and lean contents the traits possess, as the literature indicates that fat content and lean content will have an inverse genetic correlation ,60. Genetic correlations among growth and conventional carcass traits can be found in A moderate, but favorable correlation of 0.44 ± 0.19 was found between DP and ADG, and HCW by GI had a highly favorable correlation of 0.79 ± 0.61, indicating first that selection for increased feed gain will increase the ratio in a carcass of lean and fat to body contents, and second, that selection for a higher carcass weight will likely create a higher value product on a scale like the grade index used in this study.Genetic correlations amongst the pork quality traits can be found in The favorable correlation between LNC and LJPC of 0.96 ± 0.00 suggests that these traits are nearly genetically identical. L* also shows a high degree of correlation with both LNC (−0.84 ± 0.13) and LJPC (−0.79 ± 0.02). Suzuki et al.  also fouAmong the Minolta values, moderate to high correlations were observed. Correlations between L* and a* 0.47 ± 0.13), L* and b* (0.82 ± 0.06) and a* and b* (0.86 ± 0.05) are all positively related. Lee et al.  found a , L* and Moderate correlations were found between LNM and DLP (−0.30 ± 0.19), L* 0.34 ± 0.13), a* (0.54 ± 0.12) and b* (0.45 ± 0.12). Marbling and drip loss had a low and negative correlation of −0.06 ± 0.19 as estimated previously by Miar et al. [, a*  and USW . TBLW also shares moderately inverse correlations with TBW (−0.60 ± 0.16), THW (−0.33 ± 0.17), TW (−0.42 ± 0.17), BRW (−0.31 ± 0.24) and UBW (−0.55 ± 0.25). UBLW has similar correlations with UBW (−0.51 ± 0.26), TBW (−0.43 ± 0.18) and TW (−0.41 ± 0.18). As the belly would be expected to have more fat than lean mass, these inverse correlations are explainable. These estimates do not agree with what was found by Miar et al.  where coMeanwhile, cuts that contain more lean mass tend to have a moderate and positive relationship amongst each other. THW had positive correlations with TPW (0.37 ± 0.16), BRW (0.44 ± 0.16), and SW (0.47 ± 0.26). UHW has similar correlations with these traits as well. Among the loin traits  the average correlation was 0.47. TPW and TBW had a correlation of 0.40. These correlations indicate that the genetic relationship between leanness traits will be positive and favorable. Generally, these estimates are in agreement with previous estimates between leaner carcass traits . The belly flop test was developed to determine the degree of firmness of the fat as a measurement of overall belly quality and is phenotypically related to fatty acid profile . The disOnly THW was highly correlated with BLFT (−0.87 ± 0.06), though it is a strong and negative correlation. This indicates that selection for increased lean in the ham may decrease the distance measured between the ends of the bacon in the belly flop test, which is ultimately an undesirable impact upon the belly. Likewise, BLFT is moderately and inversely correlated with BRW (−0.36 ± 0.07), SRW (−0.58 ± 0.15), TBW (−0.47 ± 0.01), TPW (−0.58 ± 0.31), UHW (−0.49 ± 0.14) and UPW (−0.45 ± 0.31). With this unfavorable relationship, if producers consider including BLFT as a trait in a selection index, it may be important to include it alongside other carcass traits such as trimmed or untrimmed primal cuts to prevent any negative impacts upon bacon quality. One expected correlation found was between BL and BLFT with a correlation of −0.66 ± 0.64, meaning that a longer belly may produce a smaller distance between the ends of the belly during the belly flop test will be closer together. BW and BWR are also closely related (0.87 ± 0.83), indicating they are similar traits and may not need to be measured separately. Using the BLFT to determine quality and BW to determine the preferable width of the belly could be beneficial to selection programs that wish to meet a packer’s preferences, and these results show promise in regard to the degree of genetic correlation between belly traits while also providing a measure of caution when selecting for primal or sub-primal cuts.Pork quality, conventionally-measured and novel carcass and growth traits in purebred Duroc pigs are heritable and can be improved through genetic selection. Genetic correlations between growth and conventional carcass traits and pork quality traits were predominantly low to moderate and unfavorable, while growth and conventional and novel carcass traits were mostly moderate and favorable in relation, with few high correlations. Meanwhile, among growth and conventional carcass traits, pork quality traits and novel carcass traits, there were higher and moderate correlations which tended to be favorable. An important finding in this study was that some trait measurements may be redundant due to their high and positive correlations, such as the correlations estimated between HCW, LW and ADG; between L*, LNC and LJPC; and between trimmed and untrimmed cuts. It may not be necessary to measure all of these traits or to include every color score in a selection index for example. Carcass and pork quality traits are not good predictors for one another due to their moderate correlation. Similarly, growth and conventional carcass traits may not have a large impact on pork quality traits, as many correlations were low . However, growth and conventional and novel carcass traits had many favorable correlations and show potential for use within a selection index. The estimation of genetic parameters for belly traits is a valuable contribution of this paper as it fills a gap in knowledge and provides insight into using a measurement such as the belly flop test as a predictor for belly quality. These estimated parameters show potential for developing a selection index combining growth, pork quality and carcass traits.


The profiling of the aroma was carried out on fresh and roasted kernels enabling the detection of 150 mass peaks. Sixty eight, for the most related with sulfur compounds, furan containing compounds, and aldehydes formed by Strecker degradation, significantly increased during roasting, while the concentration of fifty-four mass peaks, for the most belonging to alcohols and terpenes, significantly decreased. Four hundred and seventy-one robust SNPs were selected and employed for population genetic studies. Structure analysis detected three subpopulations with the Sicilian accessions characterized by a different genetic stratification compared to those collected in Apulia  and the International cultivars. The linkage-disequilibrium (LD) decay across the genome was equal to r2 = 0.083. Furthermore, a high level of collinearity (r2 = 0.96) between almond and peach was registered confirming the high synteny between the two genomes. A preliminary application of a genome-wide association analysis allowed the detection of significant marker-trait associations for 31 fresh and 33 roasted almond mass peaks respectively. An accurate genetic and phenotypic characterization of novel germplasm can represent a valuable tool for the set-up of marker-assisted selection of novel cultivars with an enhanced aromatic profile.Almond is appreciated for its nutraceutical value and for the aromatic profile of the kernels. In this work, an almond collection composed of 96 Sicilian accessions complemented with 10 widely cultivated cultivars was phenotyped for the production of volatile organic compounds using a proton-transfer time-of-flight mass spectrometer and genotyped using the Illumina Infinium Prunus dulcis Mill. D.A. Webb; syn. Prunus amygdalus Batsch.; Amygdalus communis L.; Amygdalus dulcis Mill.), belongs to the genus Prunus, family Rosaceae a taxonomic group that includes numerous species of agronomical interest such as: apple, pear, peach, apricot, cherry, prune, and several berry fruits. Among tree nuts, almond ranks third in worldwide production behind cashew and walnut, with the US being the largest producer1. In ancient times, its cultivation rapidly spread throughout the Mediterranean regions from central Asia reaching Sicily during the Greek domination2. Nowadays, almond is widely cultivated all over the Mediterranean basin.Almond  and it is perceived in the mouth, aroma is the result of the interplay of a large array of volatile organic compounds (VOCs) and it is perceived largely by the olfactory receptors. In light of this, aroma profiles of raw and roasted almond have been dissected through several approaches at harvest and during storage. VOC profile of raw almonds is composed for the most by aldehydes such as hexanal, nonanal and benzaldehyde9, although several ketones, alcohols, alkanes and heterocyclic compounds have been reported10. Pyrazines, pyrroles, furans and aldehydes comprised the main volatile compound classes in roasted almonds10. The chemical reactions behind the formation of the majority of VOCs in roasted almonds are the Maillard reactions11 which produce branched-chain aldehydes, alcohols, sulfur-containing and heterocyclic compounds, while straight chain volatiles reflect heat-induced oxidation during roasting8. Several sulfur-containing aroma compounds are de novo produced during roasting by the degradation of sulfur-containing amino-acids, such as dimethyltrisulfide and 2-furfurylthiol, respectively formed by methionine and cysteine12.One of the leading aspects guiding the choice of the almond cultivar is the kernel quality. Such multi-factorial trait encompasses the physical appearance , its nutritional properties and the flavour (aroma and taste)8, most plant breeding programs have historically neglected this trait, given its intrinsic complexity and costs to phenotype10. To correct this inconsistency and incorporate flavour into breeding program routines, it is necessary to identify the sources of flavour variability, understand their genetic architecture, and define cost-effective methods of selection.Since aroma involves the perception of a plethora of VOCs, their assessment is crucial to guarantee the selection and marketability of high-quality fruit. Thus, high priority should be given to replace poor flavour cultivars with favourable ones, exploiting the variability already available in nature. However, the analysis of the aroma trait in many samples, necessary to overcome the significant biological and genetic variability among samples, may be laborious and time consuming. VOC phenotyping is currently a limiting step in breeding programs, due to high costs and complex analytical techniques. Another limitation also raised by the elevated, and difficult to be controlled, the interaction between fruit genetics, environmental effects, and product transformation. Even though different cultivars are often characterized by substantial variations in flavour12. The rapidity and the moderate cost of DI-MS analysis may allow to perform a detailed aroma characterization with a peculiar attention to the VOC fold changes caused by ad hoc storage and transformation experiments. Indeed, this technique has been already applied for the VOC characterization of transformed products, such as fermented cocoa13 and coffee beans14, and for genetic association studies of different fruit species16.According to recent publications, direct injection mass spectrometry (DI-MS) techniques, like Proton Transfer Reaction -Time of Flight- Mass Spectrometry (PTR-ToF-MS), are powerful high-throughput phenotyping tool for both genetic and quality-related studies9.The production of these VOCs is controlled by two classes of genes: those encoding enzymes responsible for the synthesis of the end products and those encoding factors regulating the biochemical pathways19 has happened in the last decades. To this extent several segregating populations were developed to build the first genetic maps of almond22. Such studies laid the foundations for QTL analysis approaches to detect genomic regions linked to phytosterol content23 and other traits related to the physical traits of almond nut and kernel22. However, none of these genetic association studies was focused on understanding the genetic aroma regulation of almond kernels.A significant increase in the use of both high-throughput DNA-derived data and advanced phenotyping approaches to dissect the causative genes underlying traits of agronomical interest through marker-trait association approaches19 allowed the development of interspecific almond x peach segregating population and their use for QTL analyses for traits of agronomical interest such as chilling and heat requirement24, brown rot resistance25 and ‘stone-adhesion/flesh-texture’26. The availability of high-throughput genotyping platforms enabled the use of genome-wide association study (GWAS) approaches on germplasm collections composed by unrelated individuals27. GWAS approaches proved its efficiency in almond28 as well as in many other outcrossing tree crops, since they are capable of assessing higher allelic variability and smaller linkage blocks compared to other methods.The high genetic similarity between peach and almond30. Overall, such almond germplasm collection encompasses accessions showing both high resistance/tolerance to biotic and abiotic stresses and/or quality traits of interest. Such a genetic reservoir could play a fundamental role in future breeding plans in which specific traits characterizing local selections could be introgressed into elite cultivars through marker-assisted breeding selection approaches.In light of this, the set-up of an ex situ germplasm collection is a strategic step both for conservation and breeding purposes. The present work is based on the analysis of an ex situ germplasm collection that was already characterized both phenotypically and genetically highlighting a variability both within Sicilian accessions and between those and the Italian and international elite cultivars®18 K Peach SNP array developed by RosBREED consortium31. Genetic data were employed for synteny analysis and to decipher both the genetic stratification and the linkage disequilibrium (LD) extent within the collection in the analysis. The same germplasm was phenotyped for the production of VOCs both on raw and roasted kernels using a PTR-ToF-MS.In this survey, our almond collection was both phenotyped using a proton-transfer time-of-flight mass spectrometer and genotyped using the Illumina InfiniumThe aims of this work were (i) to estimate the volatilome variability among almond different genotypes; (ii) to evaluate the effect of roasting on the VOC composition of the almond kernel; (iii) to identify the best performing accessions to be used as superior parental lines for future breeding programs; (iv) to detect molecular markers linked to VOCs of interest. In addition, the results of this study might be useful in defining an objective VOC phenotyping protocol to apply in all production stages, from breeding to the food industry transformation. This study is a first, preliminary, step toward the definition of molecular markers that can be readily employed for marker-assisted selection (MAS) approaches and provide novel insights on the genetic mechanisms regulating the VOCs profile in almond.11. VOC mass peaks from the raw PTR-ToF-MS spectra were reduced from 422 to 150, applying noise and correlation coefficient thresholds , m/z 49.011 (t.i. methanethiol), m/z 63.029 , and m/z 105.039 , and with furan containing compounds produced by thermal degradation of sugars such as m/z 69.033 (t.i. furan), m/z 83.051 (t.i. methylfuran), m/z 97.048 , m/z 99.046 (t.i. 2-furan methanol), and m/z 139.114 (t.i. 2-pentyl furan). Moreover, roasting enhanced the concentration of aldehydes formed by Strecker degradation of valine, isoleucine, leucine and phenylalanine40, such as 2-methylpropanal (m/z 73.064), 2- and 3-methylbutanal . Other relevant VOC mass peaks that significantly increased during roasting were methanol , cyanamide (m/z 43.03), acetic acid (m/z 61.028), 1-hydroxy-2-propanone (m/z 75.044), and γ-Butyrolactone  .Among the 150 mass peaks, 68 significantly increased their content during roasting (Table m/z 47.049), butanol (m/z 57.07), 2-pentanol (m/z 71.086), hexanol (m/z 85.10), phenyl ethyl alcohol , and nonanol (m/z 127.148). Other relevant VOCs that significantly decreased during roasting were aminoacetonitrile (m/z 57.043) and limonene (m/z 137.13)  were significantly reduced during roasting , ethyl benzene (m/z 107.088), pyrazine (m/z 81.041) and hexanal  were not significantly modified by roasting. Among them, several compounds have an important role in the characterization of almond aromaThe VOC variability, assessed on raw and roasted almonds, is graphically represented by the PCA plot Fig.  defined m/z 55.054 , m/z 83.086 (t.i. hexenol), m/z 85.102 (t.i. hexanol), m/z 101.097  and m/z 119.105; and of m/z 79.06 (t.i. benzene), m/z 91.057 (t.i. diethyl sulfide), m/z 105.039 , m/z 107.044 , m/z 107.088 (t.i. ethyl benzene) and m/z 125.059 .According to solely to the VOC profile assessed on fresh kernels, an accurate prediction of the profile after roasting is quite complex, since several compounds, like sulfur compounds, furans, and few aldehydes, are produced by the degradation of primary metabolites only during roasting Fig. . However9. Noticeably, it was possible to define several clusters of cultivars, among the Sicilian accessions, characterized by a considerable high content of compounds with a specific, and easy to be perceived, aroma note, like benzaldehyde , “Pizzuta grande” (#80), “Comunista” (#28), “Pilusedda” (#76), “Mennula du nigliu” (#57) and “Vaiana” (#101) were some of the cultivars of our germplasm collection characterized by highest phenyl ethyl alcohol concentration. This feature might be interesting not only for the agro-food sector but also for the cosmetic industry47.Moreover, these volatilome results evidenced that all almond elite cultivars assessed in this study, except “Ferraduel” (#44), were characterized by a less intense VOC profile than many of the Sicilian accessions. As for many other horticulture products, this lower VOC content might be the indirect consequence of a cultivar selection for the most oriented to the fruit productivity rather than to the qualityyde Fig.  or phenyyde Fig. . While bPrunus dulcis genotypes. However, without a detailed sensory analysis, quantifying the relevance of each VOC might be too speculative, bearing also in mind the non-linear interaction of these molecules in determining consumer preference. For this reason, in order to reduce any possible statistical bias in the result interpretation, all data were analysed with unsupervised multivariate statistical methodologies . Nonetheless, considering each quality trait independently  was grouped based on the distribution quantile , calculated for both raw and roasted assessment  robust polymorphic markers spanning the eight almond chromosomes. Among the discarded markers, 11,743 (73.2%) were monomorphic, 2321 (14.5%) failed in the amplification and the remaining 1503 (9.3%) were characterized by the presence of null alleles. The relatively low number of failed SNPs confirmed the high synteny between peach and almond genomes, nevertheless, the high fraction of monomorphic markers well reflected the fact that the probes were designed to target SNPs characterizing a different, although similar, species.The original set of 16,038 SNPs was filtered using the ASSIsT software54 to 246 Mb (‘cultivar ‘Laurenne’)55. Pd1 was the longest linkage group (43.1 Mb) while the remaining ranged from 27.5 Mb (Pd6) to 17.5 Mb (Pd8) e group 4.1 Mb whie group 4.1 Mb whi54 and peach genome56 was highly consistent (r2 = 0.96) highlighting a high synteny and collinearity between the two species. 13 SNPs (2.7%) mapped on different chromosomes in the two species. Pd1 and Pd6 did not show any inconsistencies and the SNP positions along the almond and peach linkage groups showed an r2 = 0.997 (Pd1) and 0.998 (Pd6),  followed by K = 7 and K = 2 showing similar likelihoods  of one of the three subpopulations, in particular: 19 accessions were predominantly characterized by Subpop1 while both Subpop2 and Subpop3 were represented by 13 accessions each. The remaining 61 genotypes showed a higher level of admixture  influencing a trait of agronomical interest. In this study, molecular and phenotypic data were employed for a preliminary application of a GWAS analysis to identify molecular markers linked to the VOC production of the fresh and roasted almond kernel.Among the 150 mass peaks related to the VOC profile of fresh almond, 31 were characterized by significant marker-trait associations for at least one of the SNP tested. Although with different relative frequencies, significant SNPs were detected in all linkage groups except Pd7. Pd8 resulted significantly associated with 21 VOC mass peaks while for the other linkage groups, the number of VOC mass peaks exceeding the significance threshold ranged from 1  to 10 (Pd2)  and 17.4 Mb (19 and 14 SNPs respectively) suggesting the existence of either a cluster of genes underlying the synthesis of different aromatic compounds or the presence of common genetic regulation systems  to 0.181 (Pd5). The LD decay detected in this study was slightly higher than in previous reports (mean r2 = 0.0423). The highly significant r2 threshold, calculated as the 95th percentile of the r2 distribution, was 0.41, corresponding to LD blocks of ~60 Kb  is probably the most important factor influencing the different LD decays in Prunus. While almond and cherry, with some rare exceptions, are out-crossing species, peach and apricot are self-compatible and the subsequent self-pollination results in lower heterozygosity and slower LD decay66.The analysis of the non-random association between loci through a whole-genome LD decay scan provides insights on the population genetic forces structuring the germplasm collection in the analysis. The mean 36 were detected in this broad germplasm collection.Future breeding programs, focused on the optimization of consumer perceived quality, need to consider almond VOC modification related to genetic variability, environmental effect, and transformation. This can be achieved only with a more objective and precise identification of the best performing cultivars to be used as superior parental lines in combination with a reliable phenotyping methodology and genotyping assay. This investigation supports the use of PTR-ToF-MS as an accurate and objective phenotyping tool to evaluate the VOC profile of almonds. Indeed, most of the molecules that were previously identified on a limited number of almond accessions by gas chromatographic analysisA preliminary GWAS analysis enabled the identification of 63 VOC mass peaks (related to fresh and/or roasted treatment) showing a significant phenotype–genotype association. The detection of molecular markers in close linkage to several aroma components could be of great interest for the set-up of marker-assisted selection (MAS) approaches in novel breeding schemes to enhance the almond aroma. However, a better understanding of genes and enzymes involved in the VOC production, during kernel ripening or during roasting, is still needed. Further studies aimed at a real-time VOC assessment during almond roasting will provide a more complete overview of the volatilome of almond kernel, while the availability of a dedicated almond SNP array will allow a better genetic resolution for the detection of candidate genes regulating the aromatic characteristics of almond.67.The germplasm was composed by 106 almond accessions maintained in the ex situ germplasm collection held at the ‘Museo vivente del mandorlo Francesco Monastra’, located in Sicily . The germplasm collection was mainly composed of Sicilian almond accessions selected through centuries for their agronomic traits of interest  complemented with widely known national and international cultivars as outlined in Supplementary Table 12). The drift tube conditions were as follows: 110 °C drift tube temperature, 2.8 mbar drift pressure, 428 V drift voltage, ion funnel (18 V). This leads to an E/N ratio of about 130 Townsend (Td), with E corresponding to the electric field strength and N to the gas number density (1 Td = 10−17 Vcm−2). The sampling time per channel of ToF acquisition was 0.1 ns, amounting to 350,000 channels for a mass spectrum ranging up to m/z = 400. The sample headspace was withdrawn through PTR-MS inlet with 40 sccm flow for 60 cycles resulting in an analysis time of 60 s/sample. Pure nitrogen was flushed continuously through the vial to prevent pressure drop. Each measurement was conducted automatically after 25 min of sample incubation at 40 °C and 5 min between each measurement was applied in order to prevent memory effect. All steps of measurements were automated by an adapted GC autosampler  coupled to PTR-ToF-MS. After the PTR-ToF-MS measurement of fresh almonds, each vial, without cup, was transferred into an oven  at 150 °C for 15 min to achieve a medium roast. These roasting conditions were decided based on literature information36 and on preliminary tests performed on almond kernel genotypes, characterized by different shapes and sizes profile of roasted almonds was assessed in the same way of fresh samples.Almond kernels were collected at full ripening stage according to the maturity period of the different accessions (from mid-August to mid-September 2019) and conserved at 4 °C prior to the analysis. Three biological replicates of 3 g of sliced sample, each obtained by five fresh unpeeled almond kernels, were inserted into 20 mL glass vials equipped with PTFE/silicone septa . Measurements of almond VOCs were performed on three biological replicates with a commercial PTR-ToF–MS 8000  and for the “Lollipop graph” employed in this work72.R.4.0.273 following the protocol described by Distefano and colleagues29. The almond germplasm collection was genotyped employing the Illumina Infinium®18 K Peach SNP array31. The use of an SNP array developed for peach is due both to the lack of SNP arrays specifically designed for almond and the high marker transferability between the two species57. Robust SNPs were filtered using the ASSIsT software53 with default parameters . Markers were ordered along the eight linkage groups using the Prunus dulcis Texas Genome v2.054, while the Prunus persica Whole-Genome Assembly v2.056 was employed for collinearity analysis.Total DNA was extracted from fresh leaf tissue using the CTAB extraction method proposed by Doyle and DoyleK) characterizing the 106 accessions was assessed using the STRUCTURE software v2.3.458. The K tested ranged from 1 to 10. For each K, five independent runs were carried out with a burn-in period of 10,000 and 100,000 Markov chain Monte Carlo replications after burn-in. The K value that best fits the data was assessed by calculating the DeltaK value74 as implemented in the STRUCTURE HARVESTER program75 (http://taylor0.biology.ucla.edu/structureHarvester/). The five independent runs were integrated using the CLUMPP software76 and resulting Q matrices were displayed in R68. Accessions showing a membership coefficient (Qi) equal or higher than 0.8 were assigned to a subpopulation, while the others were classified as ‘admixed’77.The most probable number of subpopulations  implemented in the ‘GWAS’ function of the rrBLUP R packageβ is a vector of fixed effects modelling both environmental factors and population structure, the variables g and τ models the genetic background of each line as a random effect and the additive SNP effect as a fixed effect respectively; ε summarizes the residual variance. The GWAS model employed takes genetic structure and kinship matrix as covariates to correct for genetic stratification and parental relationship. To minimize type-one errors, significant associations were detected after correcting the p value for multiple testing using the false discovery rate (FDR) method79. FDR is computed using the q value package in R80; SNPs exceeding the FDR threshold rate of 0.05 (represented by a dashed line in the Manhattan plot) were considered significantly associated with the phenotype.where 81. The genome-wide LD decay was visualized using the R software68 plotting the LD parameter r2 against the corresponding physical distance. The 95th percentile of the r2 distribution was taken as the threshold value to consider two markers in close LD82. The r2 threshold baseline was matched to the locally weighted polynomial regression-based fitting curve (LOESS) to estimate the average LD decay distance using the ‘stats’ R package68.The LD decay was calculated using the R package sommer v2.9Supplementary table 1Supplementary table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary figure 1Supplementary figure 2Supplementary figure 3Supplementary figure 4Supplementary figure 5Supplementary figure 6


Optimized breeding goals and management practices for the improvement of target traits requires knowledge regarding any potential functional relationships between them. Fitting a structural equation model (SEM) allows for inferences about the magnitude of causal effects between traits to be made. In recent years, an adaptation of SEM was proposed in the context of quantitative genetics and mixed models. Several studies have since applied the SEM in the context of animal breeding. However, fitting the SEM requires choosing a causal structure with prior biological or temporal knowledge. The inductive causation (IC) algorithm can be used to recover an underlying causal structure from observed associations between traits. The results of the papers, which are introduced in this review, showed that using the IC algorithm to infer a causal structure is a helpful tool for detecting a causal structure without proper prior knowledge or with uncertain relationships between traits. The reports also presented that fitting the SEM could infer the effects of interventions, which are not given by correlations. Hence, information from the SEM provides more insights into and suggestions on breeding strategy than that from a multiple‐trait model, which is the conventional model used for multitrait analysis. One is the fundamental or sustainable breeding goal, which is not dependent on market trends and focuses on properties such as reproduction, feed efficiency, and longevity. The other is the flexible breeding goal that reflects temporal market or economical demands and focuses on properties such as the qualities of milk and meat products. When we are confronted with the need to improve a new trait because of changing market demands, such as fatty acid composition, which is a meat quality trait and is related to the sweet aroma  allows for inferences of the magnitude of the causal effects between traits  algorithm Pearl, , which iValente, Rosa, de los Campos, Gianola, and Silva  adapted This review will first describe an overview of the IC algorithm and SEM, and then introduce some papers that apply these techniques to the context of animal breeding.2The IC algorithm, which was proposed by Pearl  and Verm2.1y1y2|y3, ρy1y2|y4, and ρy1y2|y3y4) and correlation between y1 and y2 (ρy1y2) are different from 0 (significant), the two traits are connected by an undirected edge (y1 − y2). The graph with undirected edges is finally generated by iterating the same process for the remaining possible set of traits . For example, when there are four target traits for inferring the causal structure , as shown on the left graph of Step 1 in Figure  − y2. Fo2.2y1y3, ρy1y3|y2, ρy1y3|y4, and ρy1y3|y2y4) are not significantly different from 0, arrowheads pointing to the common adjacent trait (y2) are added  with a common adjacent trait  are dependent conditional on any possible set including the adjacent trait (y2), that is, all partial correlations  intervals. If the interval contained the value 0, the correlation is declared as null. Outputs may differ according to the probability content used for the decisions.In the context of the mixed‐model analysis, after fitting the MTM to the traits of interest, the IC algorithm can be applied to samples from the posterior distribution of the residual (co)variance matrix obtained from the multiple‐trait analysis because the residual (co)variances are considered to be information from the joint distributions of the traits conditional on genetic effects, which corrects the search for confounding because of such effects when they are correlated variance matrix and Ψ0 is the diagonal residual variance matrix.In the case of existing causal relationships between traits, as mentioned in the previous section, fitting a SEM variance matrix and R0∗ is the residual (co)variance matrix). Like this, the SEM can be transformed into a MTM, which ignores causal relationships between traits. Therefore, the likelihood between MTM and SEM is equivalent , but u1 is also the indirect genetic effect on y2; e1 and e2 are the residual effects; and λ21 is the causal effect from y1 to y2. In addition, the equation described above can be transformed as follows:u1∗ and u2∗, e1∗ and e2∗ are the additive genetic and residual effects in the MTM, respectively. This equation shows that the genetic effects in the SEM affect not only the corresponding phenotypes directly but also the other phenotypes indirectly via the causal structure. In addition, it suggests that the genetic effects in the MTM represent the overall effects, which also include the direct and indirect effects via the causal effects (u2∗=λ21u1+u2).By reducing the above SEM for 4y1 in Figure y1 is fixed. The equation, which represents the intervention shown in Figure c is a constant and the indirect genetic effect of u1toy2 via the causal structure no longer exists. Examples of the external intervention in management practices in livestock production systems are cross‐fostering for litter size in sows and cesarean sections for CD.As shown in the previous section, the SEM can infer direct and indirect genetic effects separately but the MTM cannot. Therefore, a breeding strategy based only on estimated genetic parameters obtained from the MTM would cause a delay in achieving the breeding goal if causal relationships and external ‘interventions,’ which would block indirect genetic effects, exist among the traits variance matrices that are considered phenotypic correlations conditional on additive genetic correlations between traits; this approach was proposed by Valente et al. . They usBouwman, Valente, Janss, Bovenhuis, and Rosa  exploredIn Japanese Black cattle, Inoue et al.  inferredInoue et al.  also infMore recently, techniques for inferring causal relationships by using the IC algorithm and SEM have applied to genome‐wide association studies (GWAS). Momen et al.  applied 6The results of the papers that are introduced in this review showed that the application of the IC algorithm to infer a causal structure is one of the most helpful tools for detecting an underlying causal structure without proper prior knowledge or with uncertain relationships between traits. The reports also indicated that visualization of the causal structure among traits on a graph and fitting the SEM to the causal structure could infer the effects of interventions, which are not given by correlations. Hence, information from the SEM provides more insights into and suggestions on breeding strategies than does that from a MTM, which is a conventional model usually used for multiple‐trait analysis. These new procedures could be utilized in the field of animal breeding.