Introduction: In genetic epidemiology, log-linear models of population risk may be used to study the effect of genotypes and exposures on the relative risk of a disease. Such models may also include gene-environment interaction terms that allow the genotypes to modify the effect of the exposure, or equivalently, the exposure to modify the effect of genotypes on the relative risk. When a measured test locus is in linkage disequilibrium with an unmeasured causal locus, exposure-related genetic structure in the population can lead to spurious gene-environment interaction; that is, to apparent gene-environment interaction at the test locus in the absence of true gene-environment interaction at the causal locus. Exposure-related genetic structure occurs when the distributions of exposures and of haplotypes at the test and causal locus both differ across population strata. A case-parent trio design can protect inference of genetic main effects from confounding bias due to genetic structure in the population. Unfortunately, when the genetic structure is exposure-related, the protection against confounding bias for the genetic main effect does not extend to the gene-environment interaction term.Methods: We show that current methods to reduce the bias in estimated gene-environment interactions from case-parent trio data can only account for simple population structure involving two strata. To fill this gap, we propose to directly accommodate multiple population strata by adjusting for genetic principal components (PCs).Results and Discussion: Through simulations, we show that our PC adjustment maintains the nominal type-1 error rate and has nearly identical power to detect gene-environment interaction as an oracle approach based directly on population strata. We also apply the PC-adjustment approach to data from a study of genetic modifiers of cleft palate comprised primarily of case-parent trios of European and East Asian ancestry. Consistent with earlier analyses, our results suggest that the gene-environment interaction signal in these data is due to the self-reported European trios. G, environmental exposures E, and a gene-environment interaction term G × E. The G × E term allows genotypes to modify the effect of the exposure or, equivalently, the exposure to modify the effect of genotypes on the relative risk of developing the disease. Including a G × E term can improve model accuracy and provide a more detailed picture of disease etiology compared to models with just G and E main effects . Stratum-specific differences in the GG′ haplotype frequencies can lead to differences in G′ risk across the population strata where none exist for G  computed from genetic markers that are unlinked to the test locus. This data-based approach may be applied to arbitrary exposures, including continuous exposures, and does not require siblings. However, its properties have not been evaluated in the case of more than two population strata.Methods to mitigate this bias may be classified as design- or data-based. For a binary environmental exposure, the roach of  augmentsroach of . Accordiroach of  takes a osure of  with theG′ × E in case-parent trios. The analysis of  at a causal locus G. Under this model, G × E is equivalent to GRRs that depend on the exposure E. We then derive the GRRs at a non-causal test locus G′ in linkage disequilibrium with G and show that, in the absence of G × E, the G′-GRRs can depend on E when there is dependence between E and GG′ haplotypes in the population. Such dependence can lead to spurious inference of G′ × E in the absence of G × E. However, valid inference is obtained if we adjust the risk model for any variable X for which E and GG′ haplotypes are conditionally independent given X . We reviG = 0, 1 or 2 denote the number of copies of the variant allele at the causal locus and E denote the exposure variable. The disease-risk model of (G = 0Let model of  can be oβg and fg(⋅) are, respectively, genotype-specific main effects and functions that allow for G × E interaction. We can also write disease risk in terms of the baseline risk η(e) and the GRRs as follows. First define GRR0(e) ≡ 1. Next, note thatThe parameters βg and fg(⋅), g = 1, 2, from case-parent trio data can be derived under the assumption that G and E are conditionally independent given parental genotypes Gp. As shown in η(⋅) that parametrizes the environmental main effect drops out of the likelihood and cannot be estimated from case-parent trio data.A likelihood for estimation of the GRR parameters G′ denote genotypes at a non-causal test locus in linkage disequilibrium with the causal locus G. We assume D and G′ are conditionally independent given G and E, so thatLet G′ and E can be written asTherefore, the risk of disease given G as the latent class having probabilities P. Eqs G′ to be written in terms of the latent-class probabilities and the GRRs at G as follows:Eq. G × E, GRRs at G do not depend on E. Importantly, though, the log-GRRs at G′ can depend on E through the latent-class probabilities P. In fact, as shown in E whenever GG′ haplotypes and E are associated, as happens when the population has exposure-related genetic structure. Since G′ × E is equivalent to GRRg′ varying with E, Eq. G′ × E.Without E and a causal locus G. We now consider an augmented disease-risk model that depends on E, G and a third variable X:βg and fg are, respectively, genotype-specific main effects and functions that allow for G × E × X interaction. DefiningX-adjusted log-GRRs at G′:The development so far has considered a disease-risk model that depends only on X that eliminate E from the latent-class probabilities for G, and hence eliminate spurious G′ × E arising from exposure-related genetic structure.In the next section we discuss choices for GG′ haplotypes and E from exposure-related genetic structure in the population. In the figure, S is a categorical variable that indicates population strata. The categorical variable XE is a “coarsening” of S such that different levels of XE correspond to different E distributions, and, similarly, XGG′ is a coarsening of S such that different levels of XGG′ correspond to different GG′ haplotype distributions.The diagram in E and GG′ in blocked by each of the variables XE, S and XGG′ [  = P. Consequently, from Eq. The path connecting XGG′ [ , Definitr XGG′ . As showG′ will thus depend on E if and only if GRRs at G do.GRRs at g′ and x, log  GRRg′ varies with e if and only if the GRRg do. We can therefore test for G × E by fitting a model for log  GRRg′ that allows separate curves in e for each combination of g′ and x . The slopes, βg′E+βg′EXx, are the G′ × E interaction terms in stratum x. We use a likelihood-ratio test of the null hypothesis that βg′E = βg′EX = 0 for g′ = 1, 2, versus the alternative hypothesis that at least one of these slope parameters is non-zero to detect G × E. We emphasize that the simplified log-GRR curves in e characterize G × E rather than environmental main effects, which are not estimable from case-parent trio data. Genetic main effects are estimable however and flexibly parametrized by the intercept terms of the log-GRR curves. The flexibility in the intercept terms avoids mis-specification of the genetic main effects which can lead to biased inference of interaction effects  as a surrogate S. Though S is not known, it is reflected in principal components (PCs), G′. The expectation of E given E on E is continuous, or by logistic regression when E is binary. For EEGM adjustment, the expected exposure within genetic strata is estimated by X = S. In particular, if the population has K+1 genetic strata, indexed 0, … , K, we let S denote a vector of K dummy variables that distinguish these strata such that the kth element Sk = 1 for trios in stratum k > 0 and 0 otherwise, for k = 1, … , K.Following , Shin e set X tobserved, , consideE|S^)^. . showed E|S^)^.  indicateS reflects genetic ancestry and is not generally known. Since adjustment for self-reported ancestry can lead to bias . Thus, using more PCs than are necessary reduces the power of the test for G′ × E. We seek methods to select a parsimonious set of PCs that provides enough adjustment to control type 1 error rate, without sacrificing power. We consider three PC-selection methods. The first . Trios with an affected child and at least one heterozygous parent at the test locus are retained. The data recorded on each trio are G′, and E, where For a given stratum, informative trios were simulated following the methods proposed by . BrieflyG′ × E is induced by specifying different distributions of E and GG′ haplotypes in the four strata of GG′ distributions for strata S = 0 and S = 1 are as in (G are denoted R (risk) and N (non-risk), while alleles at G′ are denoted 1 and 0. We summarize the haplotype distributions by the implied allelic correlations between the index alleles R and 1. Under the GG′ haplotype frequencies given in r0 = −1 in stratum S = 0, r1 = 1 in stratum S = 1, r2 = 0.5 in stratum S = 2 and r3 = −0.5 in stratum S = 3.Spurious re as in . AllelesE are chosen to be normal with common variance σ2 = 0.36, and means μ0 = −0.8, μ1 = 0.8, μ2 = 2.4 and μ3 = 4.0 in strata 0, 1, 2 and 3, respectively. The E distributions for strata S = 0 and S = 1 are as in (The stratum-specific distributions of re as in .βg = log(3)/2 for g = 1, 2, corresponding to a G × E. To evaluate the type 1 error rate of the G × E test we set fg(e) = 0 in our simulations. To investigate power we choose a linear interaction model for the G × E term, setting fg(e) = βgEe with βgE = −0.10, −0.15, −0.20 or −0.25.The disease-risk model is specified as follows. The genetic main effect is r2 ≤ 0.1  to the haplotypes in that stratum, chromosome by chromosome, using fastPHASE , and useG × E, used the PC selection method of , marker simulations were based on the CHB and IBS population groups. For three strata , simulations were based on the CHB, IBS and CDX population groups.In K+1 = 4 populations we require K = 3 PCs. In 5,000 simulation replicates, the method of , but not when the sizes were unequal. We illustrate with simulation results involving datasets of 3,000 trios sampled from four unequal-sized strata. For ethod of  always sethod of  always sethod of  selectedethod of  to selecG′ × E using (i) adjustment with the true stratum membership S, (ii) the EEGM adjustment of , which includes the first 8–9 weeks of gestation when palatal development is completed. Exposure status is summarized in r2 threshold of 0.1 yielded 63,694 markers. In a principal component analysis of these markers, the first PC explains 6.3% of the total variance and all others explain less than 0.4%. Not surprisingly, the method of  were similar (results not shown). The first model, as in  instead. In particular, we calculate PCs from a genomic region unlinked to the test locus and select a parsimonious subset using the method of (G × E as an oracle approach based directly on S. We illustrate our approach by applying it to an analysis of real data from case-parent trios in the GENEVA Oral Cleft Study. In our analysis of the GENEVA data, we focussed on SNPs and exposures identified by (G × E analysis that adjusts for population structure. However, our results (G × E, but rather suggest that any G × E signal is due to the self-reported European trios. Further data collection aimed at self-reported European trios may provide stronger conclusions regarding the presence of G × E.We consider a log-linear model of GRRs at a causal locus ethod of . We thenified by . In a di results  and thos results  do not cTo reduce bias from exposure-related genetic structure, direct PC adjustment has advantages over the EEGM approach and design-based strategies such as the tetrad approach of  and the E given the genetic markers and then convert the predictions to a Normal density score that takes low values for predictions far from their observed value. These density scores could be used either as predictors (G′ × E from case-parent trios with continuous exposures, particularly when there are more than two population strata.Development of alternative approaches based on propensity scores is an area for future work. The EEGM approach is attractive in that it reduces the genetic principal components to a single score, ty score . For conty score . With coedictors  or weighedictors  in subse


Ghir_A08G009110 and Ghir_D13G023010) were associated with BW and that Ghir_D13G023010 was involved in artificial selection during cotton breeding by population genetic analysis. The transcription level analyses showed that these two genes were significantly differentially expressed between high-BW accession and low-BW accession during the ovule development stage. Thus, these results reveal valuable information for clarifying the genetic basics of the control of BW, which are useful for increasing yield by molecular marker-assisted selection (MAS) breeding in cotton.Boll weight (BW) is a key determinant of yield component traits in cotton, and understanding the genetic mechanism of BW could contribute to the progress of cotton fiber yield. Although many yield-related quantitative trait loci (QTLs) responsible for BW have been determined, knowledge of the genes controlling cotton yield remains limited. Here, association mapping based on 25,169 single-nucleotide polymorphisms (SNPs) and 2,315 insertions/deletions (InDels) was conducted to identify high-quality QTLs responsible for BW in a global collection of 290 diverse accessions, and BW was measured in nine different environments. A total of 19 significant markers were detected, and 225 candidate genes within a 400 kb region (± 200 kb surrounding each locus) were predicted. Of them, two major QTLs with highly phenotypic variation explanation on chromosomes A08 and D13 were identified among multiple environments. Furthermore, we found that two novel candidate genes ( PV for BW in nine environments was evaluated across four representative agroecological regions. In addition, several QTLs and candidate genes were further identified by a GWAS. This study provides information regarding a valuable cotton germplasm potentially useful for MAS in cotton breeding practice for raising yield in upland cotton.http://www.cricaas.com.cn/). Among these accessions, 263 (90.7%) representative cultivars were collected from four major cotton production regions of China: Northern-Specific Early-Maturity region (NSER), Yellow River region (YRR), Yangtze River region (YZRR), and Northwest Inland region (NIR). The remaining 27 (9.3%) cultivars were introduced from six different countries . Complete GWAS population material of each accession is shown in A total of 290 elite upland cotton accessions were obtained from CRICAAS (H2) of BW progressed with the “sommer” , and the correlation analysis results were exhibited with the “corrplot” . All accessions were genotyped with the Illumina HiSeq2500 platform. The detailed protocols used for library preparation and sequencing using the SLAF strategy have been described previously  value was 4.43, which was used as 1/n  according to the Bonferroni-corrected method. The phenotypic variation explained (PVE) of each marker was calculated by the formula as follows: PVE = [2β2 × MAF × (1 – MAF)] / [2β2 × MAF × (1 – MAF) + ((se(β))2 ×2 × N × MAF × (1–MAF))], where β and MAF were obtained by the GEMMA software, and N represented the sample size according to previous reports  less than 0.05 and a missing rate greater than 80%. Second, population structure was calculated as the covariate to reduce false positives . Finallyhttps://www.cottongen.org/), which was in the upstream and downstream of 200 kb regions by significant markers according to previous reports , 20 DPA, 30 DPA, and 40 DPA) were analyzed using the published RNA-seq dataset PRJNA248163  were planted at Zhejiang A&F University in Hangzhou. Flowers were tagged on the day of anthesis. We collected bolls at 0, 5, 15, 20, and 25 DPA, and then, the young seeds with fibers were stripped of hulls, frozen in liquid nitrogen, and stored at −80circC. Total RNA was extracted from the frozen 0, 5, 15, 20, and 25 DPA fibers and ovule using the MolPure® Plant Plus RNA Kit , and cDNA was synthesized using the MonScript™ RTIII Super Mix with dsDNase . Then, real-time PCR was performed to identify transcript levels using LightCycler 480 II PCR System  and MonAmp™ ChemoHS qPCR Mix . The 2ΔCT−Δ method was applied to analyze the gene transcript abundance with three biological replicates , with Pearson's correlation coefficients ranging from 0.26 to 0.75 (P < 0.001). This finding confirmed that a large number of genetic variations existed in the natural population. The H2 for BW was calculated as 69.65%, indicating that BW was mainly affected by the genotype, which was suitable for making further efforts association analysis  values ranged from 4.53 to 6.13 (10(P) = 5.04) on chromosome A08 explained 9.38% of the PV. Notably, another major QTL region on chromosome D13  was stably detected in six environments, and the BW-BLUP values were based on two SNPs and an InDel. The PV explained and -log10(P) values ranged from 10.32 to 10.95% and 6.06 to 6.13, respectively.A GWAS of boll weight was performed with a linear mixed model (LMM) . In tota to 6.13 . A total to 6.13 . Among tArabidopsis thaliana reference genome, which included 215 annotated genes and 10 novel genes (Ghir_D13G021550 (PLA2-BETA) has been reported to be involved in pollen development, germination, and stomatal opening in response to light  have been reported to be involved in the regulation of indoleacetic acid (IAA) signaling  were shown to be involved in response to stimulus, which is consistent with previous reports  encodes a protein predicted to have a three-stranded helical structure, which has been previously shown to modulate early seedling development in Arabidopsis   carrying the AA allele than in “CRI12” (BW = 5.28 ±0.59 g) and “CRI16” (BW = 5.08 ±0.97 g) with GG allele during ovule development stage  . In addi < 0.05)  and show < 0.05) , while ts region . The quant stage . ThroughGhir_D13G023000, Ghir_D13G023010, and Ghir_D13G023020) were observed and tightly linked within the candidate region , which was not reported to control the boll weight of cotton in previous studies. Thus, these stable QTLs that are responsible for BW may have a significant effect on further yield improvement in cotton with appropriate BW.BW is an important determinant of yield and profitability in cotton and is controlled by multiple genes. Indeed, cotton breeding has constantly focused on the improvement of BW. Thus far, most QTLs for BW have been identified based on linkage analysis in the CottonQTLdb by using traditional molecular markers . Thus, it is possible that the locus rsA08_30171616-A associated with excellent BW was screened out during the breeding process, so it is necessary to use rsA08_30171616-A as a tagging SNP in MAS of cotton lines to further improve yield.It is known that BW is a complex quantitative trait controlled by many genes. Here, based on the association analysis, candidate gene expression analysis, and genetic diversity analysis of BW in 290 diverse cultivated upland cotton accessions,  variety . In addiGhir_D03G011310 was considered a candidate gene underlying the natural variation in cotton that controls early maturity in a natural population during long-term artificial selection, as stated in our previous report  can be found in the article/SY and ZF supervised the study and were involved in writing—reviewing and editing. LL was involved in funding acquisition, investigation, visualization, and writing—original draft. MT conceptualized the study and was involved in data curation and formal analysis. QL and CH designed software and data curation. DS validated the study. ZJ, GL, SZ, and GZ investigated the study. YZ designed software and visualized the study. All authors contributed to the article and approved the submitted version.This research was sponsored by the Program for Research and Development of Zhejiang A&F University (2021LFR005).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


For efficient mechanical harvesting, low grain moisture content at harvest time is essential. Dry-down rate (DR), which refers to the reduction in grain moisture content after the plants enter physiological maturity, is one of the main factors affecting the amount of moisture in the kernels. Dry-down rate is estimated using kernel moisture content at physiological maturity and at harvest time; however, measuring kernel water content at physiological maturity, which is sometimes referred as kernel water content at black layer formation (BWC), is time-consuming and resource-demanding. Therefore, inferring BWC from other correlated and easier to measure traits could improve the efficiency of breeding efforts for dry-down-related traits. In this study, multi-trait genomic prediction models were used to estimate genetic correlations between BWC and water content at harvest time (HWC) and flowering time (FT). The results show there is moderate-to-high genetic correlation between the traits (0.24–0.66), which supports the use of multi-trait genomic prediction models. To investigate genomic prediction strategies, several cross-validation scenarios representing possible implementations of genomic prediction were evaluated. The results indicate that, in most scenarios, the use of multi-trait genomic prediction models substantially increases prediction accuracy. Furthermore, the inclusion of historical records for correlated traits can improve prediction accuracy, even when the target trait is not measured on all the plots in the training set. Genomic selection (GS) is especially beneficial when dealing with complex traits that are affected by many quantitative loci each with very small effects . GenomicIn this study, multi-trait genomic best linear unbiased prediction (MT-GBLUP) models were used to estimate genetic correlations between BWC and HWC and FT. MT-GBLUP was performed using different model training approaches to investigate optimal prediction strategies and investigate prediction accuracy for BWC when using HWC and FT as secondary traits.The population used in this study contained 397 diverse maize inbred lines with a wide genetic background. These lines were sourced from China (281 lines), United States (105 lines), and CIMMYT (11 lines). Most of the inbred lines from the United States and China are from a temperate environment background, whereas the inbred lines from CIMMYT are from tropical backgrounds.All 397 inbred lines were planted in three locations in China: Shenyang City in 2019 (19SN) located in northeastern China , Shenfu City in 2017 (17SF) located in northeastern China , and Hainan Province in 2017 (17HN) located in southern China . Since grain moisture at physiological maturity is a component of dry-down rate calculations , it was 1 A total of 56,563 SNP markers were used.Genotyping-by-sequencing data with 600 K single-nucleotide polymorphism (SNP) markers were available for the 397 inbred lines. After quality control for missing rate (< 10%), minor allele frequency (MAF > 0.05), and LD pruning (at 0.9), missing genotypes were imputed using TASSEL 5.0 .1 A totaDry-down rate was calculated for the temperate and tropical regions as follows in Equations 1 and 2.DRTRO and DRTEM are dry-down rate (DR) for the tropical and temperate climate zone, respectively. BWC is grain moisture content when black layer appeared, and HWC is grain moisture content at harvest time.where Single-trait genomic best linear unbiased predictions (ST-GBLUP) were used to estimate genetic and residual variances in each location using the following model for each trait :y is the vector of raw phenotypes, μ is the overall mean, b is the fixed effect of replication, u is the vector of random additive genetic effects for inbred lines, X is a design matrix for the fixed effect of replicate, Z is the design matrix for additive genetic effects, and e is the vector of residuals. The distribution of the random effect u was assumed to be G is the additive genomic relationship matrix between the inbred lines  explains the proportion of phenotypic variation due to additive genetic variance. Heritability was calculated as follows:Narrow sense heritability , harvest time water content (HWC), and flowering time (FT). The general MT-GBLUP model within each ecological zone was as follows:1y, 2y, and 3y are the vectors of phenotypes for BWC, HWC, and FT, respectively, 1μ, 2μ, and 3μ are the overall mean for each trait, 1b, 2b, and 3b are the fixed effects of location and replication nested within location, 1u,2u, and 3u are vectors of the random additive genetic effects for each trait, 1X, 2X, and 3X are the design matrices for the fixed effect of replication, 1Z, 2Z, and 3Z are the design matrices for the random genetic effect, and 1e, 2e, and3e are the vectors of residuals. It was assumed that  ∼ N, where Go is the variance–covariance matrix of the genetic effect of the traits as follows:where 0G represents a symmetrical 3 × 3 variance–covariance matrix of the genomic effect of genotypes in the environments. The diagonal of the 0G matrix is the additive genetic variance for three traits, while the off-diagonal elements represent the genetic covariance between the traits.where G is the same as Eq. 3, and residual errors were assumed to be distributed as  ∼ N, where I is the identity matrix and R is a symmetrical unstructured matrix of the residual (co) variances:ST-GBLUP and MT-GBLUP models were fit separately for each ecological region. For the temperate zone where there were two locations, both Eqs 3 and 6 were modified to account for the random location effect and the effect of replicate was nested within location. However, since there is only one location in the tropical ecological zone, the location effect in both Eqs 3 and 6 was ignored.Genomic predictions were performed using MT-GBLUP and ST-GBLUP using Eqs 6 and 3 for different cross-validation scenarios. This was done to assess prediction accuracy for BWC using information on correlated traits, HWC and FT, in each ecological zone. For that purpose, three cross-validation scenarios were considered.In the third scenario (CV3), the validation and training sets were constructed in such a way that one replication or one location was selected and used as a validation set, with the remaining locations and replications used as a training set. In the validation set, the phenotypic value for BWC was set to missing while keeping phenotypic information for HWC and FT. The purpose of this scenario was to simulate a breeding program where BWC is measured only in one replicate or in one location while HWC and FT are recorded in all replicates and locations.2.The prediction models were run 10 times, and the Pearson correlation between phenotypic values for BWC (corrected for fixed effects) and predicted values was calculated in each run. The result presented here is the average of the 10 runs. All single- and multi-trait analyses were done using ASReml 4 2.Heritability estimates for BWC, HWC, and DR were obtained using the ST-GBLUP model. As shown in Development of maize varieties with low HWC is an ideal situation that ensures efficient mechanical harvesting can be applied. To achieve this goal, a good breeding strategy which can reduce the consumption of resources while achieving desired rates of genetic gain for the target traits is essential. In this study, we consider the genetic architecture of dry-down-related traits and effective prediction strategies for genomic-enabled breeding, leveraging correlated traits (HWC and FT) that are relatively easy to phenotype . CompareThe advantage of MT-GBLUP model, however, depends on the genetic correlation between the target and the secondary traits . EstimatThe first cross-validation scenario (CV1) focused on breeding strategies that rely on generating predictions for lines that have yet to be tested in the field. As such, no phenotypic information on correlated traits is available on lines in the validation or prediction set. As seen in In the first cross-validation scenario (CV1), we further investigated the impact of including records in the training set that have no BWC phenotypic information but do have phenotypic records for correlated traits like FT and HWC. Many maize breeding programs record HWC and FT as agronomic traits for many generations , while BIn CV2, the phenotype for BWC was set to missing for a subset of the population in order to mimic a breeding program that collects data on a trait that is expensive and difficult to measure on a subset of the population and predicts the phenotype for the rest of the population using routinely collected data on correlated traits. This is not an uncommon scenario as in most breeding programs, resource and time efficiency are important factors to consider . The resAs shown in Multi-location and multi-replication trails play an important role in agronomic research and plant breeding programs . In suchIn this study, multi-trait genomic prediction was tested using different cross-validation scenarios to investigate prediction strategies for genomic-enabled breeding for dry-down-related traits in maize. The results clearly show that the use of correlated traits, like HWC and FT, and sparse phenotyping can yield high prediction accuracies while reducing the cost of extensively phenotyping for difficult to measure traits like BWC. While the sparse phenotyping approaches consistently yielded very high prediction accuracies, the need to phenotype selection candidates on correlated traits places limitations on gains that can be made by increasing selection intensity and reducing generation intervals. Examining strategies for predicting untested lines, the accuracy of model prediction drops substantially when compared to sparse phenotyping; however, this strategy does enable gains in response to selection through increased selection intensity and reductions in the generation interval. Regardless of the breeding strategy, the results of this study show clear advantages to using correlated traits when information on the target trait is sparse in historical datasets.GVM000350 at http://bigd.big.ac.cn/gvm/getProjectDetail?project=GVM000350; Further inquiries can be directed to the corresponding author.The phenotype data presented in the study are included in the article/PN was involved in manuscript preparation, phenotypic data collection, statistical analysis, development of analysis code, visualization, revision, and check. KR and MA were involved in manuscript preparation, statistical analysis, visualization, revision, and check. SW and YR were involved in conceptualization, research design, revision, and check. DD and LL were involved in phenotypic data collection, revision, and check. NM and ML were involved in revision and check. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The effectiveness of genomic selection in pig breeding depends largely on genomic prediction accuracy. The relatively short generation interval and the high selection intensity make the issue of the selection criterion accuracy extremely important. Genotyping animals by SNP markers makes it possible to increase the accuracy of breeding criteria for young replacement animals. However, the applied computational algorithm plays an equally important role, that is, the method of calculating the genomic estimates. The study compared three methods  for the assessment of pigs’ breeding value according to five main breeding traits.Changes in the accuracy of the genomic estimates obtained by the ssGBLUP and wssGBLUP methods were evaluated using different reference groups. The weighting procedure’s reasonableness of application Pwas considered to improve the accuracy of genomic predictions for meat, fattening and reproduction traits in pigs. Six reference groups were formed to assess the genomic data quantity impact on the accuracy of predicted values . The datasets included 62,927 records of meat and fattening productivity ), muscle depth  and precocity up to 100 kg  and 16,070 observations of reproductive qualities  and the number of live-born piglets (NBA), according to the results of the first farrowing). The wssGBLUP method has an advantage over ssGBLUP in terms of estimation reliability. When using a small reference group, the difference in the accuracy of ssGBLUP over BLUP AM is from −1.9 to +7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the wssGBLUP is also maintained for the largest group of genotyped animals: from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. However, for all analyzed traits, the number of markers explaining 5% of genetic variability varied from 71 to 108, and the number of such SNPs varied depending on the size of the reference group . The results of the genetic variation distribution have the greatest similarity between groups of about 1000 and about 1500 individuals. Thus, the size of the reference group of more than 1000 individuals gives more stable results for the estimation based on the wssGBLUP method, while using the reference group of 500 individuals can lead to distorted results of GEBV. The improvement of methods for assessing the farm animals breeding values has two main directions: attracting as much data about the animals and their relatives as possible and developing methods that allow the more efficient use of available information. Thus, genomic evaluation methods have significantly improved the prediction accuracy of the individual genetic value by using marker genotype data ,2,3. TheThe primary assumption of genomic assessment methods based on linear mixed models is that all markers have the same contribution to the overall genetic variability of a given trait. However, it is difficult to agree that such a situation occurs ,10. The Therefore, our study aimed to assess the impact of the SNP genotypes’ data amount on individual genomic estimate accuracy based on the ssGBLUP and wssGBLUP methods. The validity of applying the weighting procedure to improve the accuracy of genomic predictions for pigs’ meat, fattening, and reproduction traits were considered.The phenotypic features of meat, fattening and reproductive traits of Large White pigs were used. Animals born between 2015 and 2020 were included in the dataset. The following characteristics of meat and fattening productivity were evaluated: fat thickness over 6–7 ribs , muscle depth  and precocity up to 100 kg . In addition, according to the results of the first farrowing, the number of all-born piglets (TNB) and the number of live-born piglets (NBA) were taken into account. Descriptive statistics of the complete data set are given in To assess the genomic data amount effect on the accuracy of predictive values for several traits, we formed six reference groups  and calculated them according to the scenarios presented in The groups were selected in such a way as to simulate a situation where genotyping was carried out on the part of the livestock in the estimated population at three stages, with a cumulative effect. An analysis of meat and fattening qualities was carried out on a dataset that included 62,927 records of in vivo measurements of backfat thickness, muscle depth and precocity in gilts and boars when they reached 100 kg live weight. Studies of reproductive traits were carried out on a dataset, including results of the first farrowing . Accordingly, the reference groups of genotyped individuals according to the meat and reproduction traits were different but formed according to a single algorithm. Thus, the first group for meat and fattening qualities included 530 pigs  from the 2010–2015 year of birth, and the second group included 1178 individuals, including the first group  born in 2010–2017. The third group consisted of animals from the second group, to which 315 more heads were added, for a total of 1493 individuals born from 2010–2021. According to the reproduction traits, only genomic data of sows were used in the analysis. Therefore, the following values characterized the groups: the fourth included 396 individuals (sows born from 2010–2015), the fifth included 870 animals (sows born from 2010–2017), and the sixth included 1228 sows born from 2010–2020 . Based oThe most complete (numerous) reference groups of individuals 3 and 6 were characterized by the following average indicators and standard deviations of phenotypic values (mean (SD)): group 3, BF1—14.61 (3.13), MD—57.95 (5.96), age—156.1 (9.78); group 6, TNB—13.48 (3.87), NBA—12.76 (3.75). The phenotype’s limiting values (ranges) of the reference groups did not differ from those of the general population. A set of partial data was formed for validation by deleting records of validated animal phenotypes. The relationships between the validated animals and their descendants were reset in the pedigree. The group of validated animals for meat and fattening traits included 200 individuals: 79 boars (mean rel = 0.954) and 121 sows (mean rel = 0.849). The validated animals for reproduction traits group included 165 sows (mean rel = 0.572).We utilized the GGP Porcine HD hybridization chips  to genotype boars and sows used in this study to form reference groups. The GGP Porcine HD hybridization chip contains 70,000 SNPs with a mean intermarker interval of approximately 42 kb and bears 20 major causative mutations. These SNPs are identified within and between all major pig breeds. Genome-wide genotyping data were presented as 1483 individual genotypes exported from the GenomeStudio 2 program .−6; and animal records with Mendelian errors of allele inheritance . Afterward, the QC genomic data included 50,740 SNPs of 1483 genotyped animals.We performed data quality control (QC) by using Plink 1.9  to ensurDescriptive statistics calculations were performed in the program STATISTICA 10, and animal pedigrees were analyzed by the CFC software package . PrimaryReference  was usedy represents the vector of phenotype observations on the trait ; b represents vector of fixed effects ; a represents vector of random animal effect; e represents the vector of the random residual effect; and X and Z represent the incidence matrices.The mixed linear model on the BLUP AM method is:G was obtained using the linear method proposed by Van Raden [m is the number of SNPs, ip is the frequency of the minor allele of SNP I, and M is the centered genotype matrix.The significance of the fixed effects was assessed based on the exact Fisher criterion. To optimize the set of random effects, the Akaike criterion was used . The genan Raden :G=M′M2∑iA−1) is replaced by (H−1).A is the relationship matrix, A22−1 is the inverse relationship matrix for genotyped animals, G is the genomic relationship matrix, and the coefficients α = 0.95, β = 0.05, τ = ω = 1 (by default) [Fixed and random effects in the ssGBLUP and wssGBLUP models are the same as in the BLUP AM model. The relationship matrix (default) .PEV is the estimated error variance, and varA is the additive genetic variance.The EBV reliability (GEBV) was calculated as follows:G) is replaced by a weighted G* matrix, which is constructed as follows:In the wssGBLUP approach (weighted ssGBLUP) genomic matrix :dii=1.1G* , (2) estimate the effects of SNP values of genomic scores in the previous step, (3) evaluate the effect of each SNP, (4) normalize the SNP effect vector to obtain SNP weights , (5) use the SNP weights to construct a new D-matrix, and (6) move to step (1).The wssGBLUP approach is based on an iterative algorithm with the following steps: (1) perform ssGBLUP with matrix The pig breeding value estimates and their reliability were calculated according to three meat and fattening traits and two reproduction traits. Genomic scores (GEBV) were obtained using all three compared reference groups (Scenarios 1–3) for genotyped animals of the smallest groups: the first and fourth. A comparison of the average reliability values of the GEBV estimates indicates that accuracy grows for each analyzed method with increasing the reference group size from +4.0 to +9.9% points and from +0.0 to +2.4% points for the ssGBLUP and wssGBLUP methods, respectively. Thus, the accumulation of genomic data for the reference group gives the most significant increase in accuracy when using the ssGBLUP method. In contrast, the reference group size enlargement leads to a relatively smaller increase in the prediction accuracy for the wssGBLUP method.In the previous article, we discussed the increase in the accuracy of breeding value estimates by including genomic data on the characteristics of meat and fattening productivity and reproduction traits in pigs . HoweverWe note that the wssGBLUP method has an advantage over ssGBLUP in estimated reliability. For example, when using a small reference group (Scenario 1), the difference in the accuracy of ssGBLUP over BLUP AM is from −1.9 to + 7.3 percent points, while for wssGBLUP, the change in accuracy varies from +18.2 to +87.3 percent points. Furthermore, the superiority of the second method is also maintained for the largest group of genotyped animals (Scenario 3): from +4.7 to +15.9 percent points for ssGBLUP and from +21.1 to +90.5 percent points for wssGBLUP. The application of the wssGBLUP method using the genomic data for a reference group of 500 animals (Scenario 1) at first glance already makes it possible to obtain highly reliable predictive values of the pig breeding values in terms of meat fattening productivity and reproduction indicators.The correlation coefficients between animal estimates of the first group  GEBVwss and GEBVss based on meat and fattening productivity, obtained using three reference groups, were close to 1 (from 0.950 to 0.994 for GEBVss and from 0.970 to 0.995 for GEBVwss). In our opinion, this indicates the high reliability of both methods concerning highly inherited traits. On the other hand, for reproduction traits, the correlation coefficients between estimates were characterized by lower Pearson correlation values: from +0.823 to +0.980 for GEBVss and from +0.878 to +0.981 GEBVwss (results not shown in the table).The validation of genomic estimates using ssGBLUP and wssGBLUP methods was carried out by comparing genomic predictions based on a partial data set (PD) (Scenario 5) with the most accurate genomic estimates based on complete information about animals, whole data set (WD) (Scenario 3) .PD, GEBVssPD, GEBVwssPD) were compared with their most accurate breeding value estimates (GEBVwssWD) on the whole data set.A preliminary evaluation of young animals was performed based on the pedigree and was a prediction based on the parent average (PA). In our study, the predictive indicators of a validated group of individuals on a partial data set . Estimates based on weighted values are either close to or slightly inferior to the GEBVssPD estimates. The final estimates  have the highest correlation with predictions calculated using the same method. However, highly reliable estimates based on BLUP are more closely interrelated with genomic predictions. The final weighted estimates correlate almost at the same level with the predictions of GEBVssPD and GEBVwssPD. For traits with low heritability , GEBVwssWD scores are also inferior to GEBVssWD. In the case of traditional BLUP, the greatest relationship is characterized by PAPD. It follows from the results that genomic methods are most justified in predicting the true breeding value of individuals for traits with relatively high heritability. For low-inherited traits, the ssGBLUP method has an advantage over wssGBLUP.A genomic estimation method validation was carried out on genomic predictions correlated with highly reliable estimates for each method (Scenario 5). Of note is the fact that for traits with relatively higher heritability , the relationship of the predicted values with the final estimates was the lowest for PAWD, GEBVssWD, GEBVwssWD on PAPD, GEBVssPD, GEBVwssPD). The level of the regression coefficient (b), when predictions and final estimates were based on the same method, varied. For the BLUP method, it ranged from 0.64 to 0.72 (R2 from 0.11 to 0.15) and 1.06 (R2 from 0.59 to 0.62); for the ssGBLUP method, it ranged from 0.78 to 0.87 (R2 from 0.26 to 0.34) and from 0.99 to 1.01 (R2 from 0.77 to 0.77); and for the wssGBLUP method, it ranged from 0.57 to 0.78 (R2 from 0.21 to 0.32) and from 0.76 to 0.77 (R2 = 0.51) for meat and fattening traits and reproduction traits, respectively. In the case when different methods obtained the prediction and final estimates, the best values of inflation (close to one) and the coefficient of determination were identified for GEBVssPD (from 0.70 to 0.88) (R2 from 0.20 to 0.32) for meat and growth traits, from 0.69 to 1.27 (R2 from 0.47 to 0.48) for reproduction traits), the worst values of b for meat and fattening traits had PAPD, and for reproduction traits—GEBVwssPD . However, the validation of genomic methods (ssGBLUP and wssGBLUP), a test of the accuracy of genomic estimates , indicatWe analyzed the levels of the EBV relationship for BLUP AM (Scenario 4) in individuals of the first and fourth groups, obtained for three methods using genomic data from different reference groups (Scenarios 1–3) .wss estimates, apparently due to the adjustment of marker effects.As can be seen from the results, an increase in the size of the reference group for both methods reduces the relationship level between EBV and GEBV scores, which is most pronounced for the GEBV varies from +0.887 to +0.999 depending on the group; for reproduction traits it varies from +0.737 to +0.868), and from the point of view of pigs practical selection, these differences in results are not so significant. However, the change in the correlation coefficients indicates a stable decrease in the relationship between genomic scores and EBV for meat and fattening traits with the accumulation of genomic information, while changes in reproductive qualities are less predictable.It should be noted that the level of correlation between the genomic estimates themselves, obtained by the two compared methods, is very high  = +0.929, r = +0.901, and r = +0.959 for meat and fattening traits, and r = +0.936, r = +0.904, and r = +0.959 for reproduction traits.The correlation coefficient analysis of the variance proportions explained by SNP markers indicates a redistribution of these values for groups that differ significantly in size , which was also confirmed by the relatively low consistency of the SNP markers’ weight values. Each marker’s contribution varies depending on the reference group examined (Scenarios 1–3). A similar trend was observed for the The power of the method to take into account the individual SNP marker weight as a predictor of breeding value becomes significantly lower in situations when the true QTLs of the greatest influence on the trait variability are unknown and the traits themselves are polygenic. The traits analyzed in the study are characterized by very low proportions of variance described by individual SNP markers . Thus, aIt is noteworthy that the results obtained for the genetic variance distribution over the entire set of SNP markers for the analyzed traits have the most remarkable similarity between the groups Scenario 2 and 3. A reference group size of about 1000 (Scenario 2) and about 1500 heads (Scenario 3) gives more consistent results for evaluation based on the wssGBLUP method. Thus, applying this procedure using a reference group of 500 heads can lead to somewhat distorted GEBV results.Paralichthys olivaceus) based on disease resistance showed that a significant increase in the prediction reliability was not observed with the preliminary selection of SNPs based on both SNP-GWAS  and ssGWAS (by a fraction of the marker explained variance) [Causal QTN information can be included in the ssGBLUP procedure using a weighted genomic relatedness matrix. For example, a study by  performeariance) . In thisA comparison of the genomic estimate accuracy obtained using wssGBLUP with traditional BLUP estimates was carried out by  for Holswss relative to EBV ranges from +18.2 to +90.5 percent points. Mehrban et al. (2021) showed a non-uniform improvement in the accuracy of beef trait weighted scores in beef cattle compared to ssGEBV [The most accurate marker weights in the study by Atashi et al.  were obto ssGEBV . AlthougEmbedding genomic assessment into the overall livestock breeding value assessment system implies the presence of a reference population (group) of genotyped animals of sufficient size with optimal parameters to obtain the most reliable genomic prediction results. However, the issue of determining these parameters and volume still cannot be resolved unambiguously. Alvarenga et al. (2020)  comparedArdestani et al. (2021)  proved twssWD (Scenario 3): for BF1, values ranged from 0.51 to 0.56; for MD, values ranged from 0.45 to 0.50; for age, values ranged from 0.43 to 0.48; for TNB, values ranged from 0.69 to 0.71; and for NBA, values ranged from 0.69 to 0.72, which exceeds the accuracy of predictions based on the pedigree using the BLUP AM method .The accuracy of genomic estimates in the study by Grossi et al. (2018)  on pigs It should be noted that the genomic assessment procedure (based on the ssGBLUP and wssGBLUP methods) can significantly improve the preliminary prediction accuracy of the individual genetic values within a particular population. An increase in the reference group size provides more stable individual marker weight values, and the proportion of genetic variance explained by each of them, especially when the reference group is closely related to the assessed population . Since tUsing the ssGBLUP and wssGBLUP methods makes it possible to assess pigs’ genomic breeding values based on the main breeding traits  with high precision. Using the weighting procedure to evaluate SNP markers effects significantly improves the accuracy of the genomic predictions. The wssGBLUP implementation is reasoned for genotyped reference groups of as few as 1000 individuals  or 1500 animals (reproduction indicators). Further replenishment of the reference group involves the mandatory recalculation of the SNP markers weights for genomic assessment of polygenic traits influenced by a large number of QTLs, such as meat, fattening and reproduction qualities in pigs.


Wolbachia is one of the most abundant intracellular symbionts of arthropods and has profound effects on host biology. Wolbachia transmission and host phenotypes often depend on its density within the host, which can be affected by multiple biotic and abiotic factors. However, very few studies measured Wolbachia density in natural host populations. Here, we describe Wolbachia in the pear psyllid Cacopsylla pyri from three populations in the Czech Republic. Using phylogenetic analyses based on wsp and multilocus sequence typing genes, we demonstrate that C. pyri harbours three new Wolbachia strains from supergroup B. A fourth Wolbachia strain from supergroup A was also detected in parasitised immatures of C. pyri, but likely came from a hymenopteran parasitoid. To obtain insights into natural Wolbachia infection dynamics, we quantified Wolbachia in psyllid individuals from the locality with the highest prevalence across an entire year, spanning several seasonal generations of the host. All tested females were infected and Wolbachia density remained stable across the entire period, suggesting a highly efficient vertical transmission and little influence from the environment and different host generations. In contrast, we observed a tendency towards reduced Wolbachia density in males which may suggest sex-related differences in Wolbachia-psyllid interactions. Wolbachia is one of the most abundant intracellular symbionts of nematodes, insects and other arthropods2. It is particularly well-represented among terrestrial arthropod species, with 40–60% of all species estimated to be infected with Wolbachia3. Wolbachia has strong impacts on host biology, ecology and evolution, including manipulation of host reproduction via male-killing, feminization, parthenogenesis or cytoplasmatic incompatibility (CI) to promote its transmission8. On the other hand, there is strong evidence that Wolbachia acts as a nutritional mutualist in certain species, providing its host with vitamins and nutrients that are important for host survival13. Sixteen genetically distinct evolutionary lineages of Wolbachia (supergroups) have been found so far, which differ from each other in host range and biology17. The majority of the known Wolbachia strains fall into two major supergroups A and B, mostly consisting of reproductive parasites.Wolbachia is mainly vertically transmitted from mother to offspring but can also be transmitted horizontally to new host lineages and infect populations of both related and unrelated host taxa22. Moreover, Wolbachia is used to limit the transmission of numerous human pathogens and agricultural pests28, which makes Wolbachia a promising tool for controlling vector-borne diseases31. Thus, several Wolbachia strains showed their efficiency in inhibiting mosquito-borne diseases when introduced into natural mosquito populations34. Likewise, several studies on agricultural pests suggested that Wolbachia affects the transmission of plant pathogens38.Wolbachia’s influence on their hosts was intensively investigated in the last two decades, e.g.40, factors that might affect its density dynamics were less often considered. Maintaining an optimal density is crucial for Wolbachia to ensure long-term and stable relationships with its host41. Hence, a reduced infection density may impede the vertical transmission of Wolbachia, while an excessive bacterial density could be detrimental for the host42. Moreover, many Wolbachia-induced phenotypes  depend on sufficient Wolbachia density43. For instance, the most frequently observed Wolbachia-induced reproductive phenotype (CI) results in embryonic death when Wolbachia-infected males reproduce with uninfected females8. Hence, sufficient Wolbachia density in males is required to induce CI, while females are under selective pressure to maintain a sufficient Wolbachia concentration to protect themselves against CI. Nonetheless, it has also been observed that Wolbachia can be suppressed in males, potentially to reduce CI-related mortality when mating with uninfected females8. Environmental conditions , host factors  and interactions with other microorganisms can also have considerable effects on Wolbachia titer48. The importance of host genotype and Wolbachia strain for the regulation of Wolbachia density was demonstrated in fruit flies, parasitoid wasps, beetles, mosquitoes and isopods54. Competition for resources and space as well as interactions with other microorganisms, including viruses, bacteria and different Wolbachia strains, can also lead to changes in Wolbachia titer57. Wolbachia density can also fluctuate during the lifespan of a host, with varying outcomes depending on the host species. While the density declined with age in flies and Aedes mosquitoes60, it increased in aged Culex pipiens mosquitoes61. Similarly, a recent study on the flies Drosophila simulans and D. melanogaster demonstrated that their respective Wolbachia strains varied differently with host age: while Wolbachia density decreased in ageing males of D. simulans, its density in D. melanogaster increased62. These differences might be linked to differences in host immune response to Wolbachia infection62. In addition, the host mating pattern affected Wolbachia density dynamics in mites63.While Wolbachia density65. Depending on the Wolbachia-host association, high temperatures eliminated Wolbachia from their hosts68, increased the Wolbachia titer69or reduced the efficiency of Wolbachia transmission in in vitro experiments70. Thermal sensitivity of Wolbachia abundance in the fly Drosophila melanogaster on a global scale was recently demonstrated71. In contrast to experiments under controlled laboratory conditions, environmental factors such as temperature are much more variable in the wild, making it difficult to translate patterns from laboratory experiments to natural populations. Very few studies investigated the seasonal variations of Wolbachia infection in natural host populations75. Of these, only one study73 measured Wolbachia abundance using quantitative PCR (qPCR), and demonstrated a decrease of Wolbachia density across several generations of the pale grass blue butterfly, Pseudozizeeria maha, from early summer to autumn in Japan. The other studies on Wolbachia dynamics in natural host populations75 made their conclusions based on the relative abundance of Wolbachia in metabarcoding data. As a result, we are currently lacking insights into the dynamics of Wolbachia infections in natural host populations, particularly for non-model organisms.The effect of temperature is especially important for Wolbachia was detected in many psyllid species (Insecta: Hemiptera: Psylloidea)80 but it was thoroughly studied only in pest species, such as the citrus psyllid Diaphorina citri and the potato psyllid Bactericera cockerelli. Particularly, the studies on D. citri suggest that Wolbachia could mitigate and even impede the transmission of the pathogen ‘Candidatus Liberibacter asiaticus’82. In addition, several Wolbachia strains from B. cockerelli are able to induce CI86. The recent metabarcoding study by87 revealed high relative abundance of Wolbachia in several populations of the pear psyllid Cacopsylla pyri (Psyllidae) from the Czech Republic. In one of these populations, Wolbachia was detected across different developmental stages and seasonal generations throughout an entire year.Wolbachia infection and density dynamics throughout an entire year in natural populations of C. pyri. This psyllid is an economically important pest that transmits ‘Candidatus Phytoplasma pyri’, the causative agent of the Pear Decline disease of pear trees in Europe88. In contrast to many other psyllid species from northern temperate latitudes, C. pyri is a non-migrating species that usually spends its entire life cycle on pear trees and therefore it is present in pear orchards all year long, producing several summer generations and one overwintering generation90. In Central Europe, C. pyri starts reproducing in the middle or at the end of spring, giving rise to the first summer generation after overwintering on its host-plant89. This makes C. pyri a suitable model system to characterise Wolbachia seasonal dynamics in a natural environment. In the current study, we (i) characterise Wolbachia in C. pyri from three localities from the Czech Republic, (ii) analyse the phylogenetic relationships between the different Wolbachia strains and (iii) investigate the density dynamics of the most prevalent Wolbachia strain in one population throughout an entire year using qPCR. To our knowledge, this is the first study demonstrating Wolbachia density dynamics in a natural host population across an entire year using a targeted qPCR approach. Our results provide new insights into the impact of seasonal dynamics on Wolbachia density in a natural population of psyllids and will broaden our general understanding of Wolbachia-host interactions in natural environments.In the present study, we aim to investigate C. pyri , while in CZ1 (N = 1/10) and CZ3 (N = 1/5) the Wolbachia prevalence reached only 10% and 20%, respectively , whereas the strain wCpyr3 was more divergent from both wCpyr1 and wCpyr2 for three of the five analysed genes   and two strains from the mosquitoes Aedes albopictus (wAlbB) and Culex quinquefasciatus (wPip)  Fig. a.Figure wsp, coxA, fbpA and gatB) of wCpyr3 were identical to those of the strain wEjen from the butterfly Erebia jeniseiensis   Fig. a.wsp sequences of previously identified Wolbachia strains from other psyllid species. The results demonstrated that the Wolbachia strains from C. pyri belonged to different clades from the strains of other psyllid species, including those from other Cacopsylla species   of wSaph were different from previously sequenced strains present in this database , whereas fbpA of wSaph was identical to wBif.Two of the five immature individuals of se Table . AdditioC. pyri population CZ2 had the highest Wolbachia prevalence among the studied populations , only two males  compared to individuals sampled in all other months (mean Wolbachia titer/host cell: 1.62 in February—16.91 in June) (Tukey’s post-hoc test: p ≤ 0.0002). When considering also the sex of the tested individuals, our data showed that the Wolbachia titer in females was relatively stable throughout the year, while it was more variable in males    Fig. b. This o29) Fig. . In addiWolbachia density plays an important role in the evolution of symbiotic associations and ecological interactions, mainly through its impact on symbiont transmission and host extended phenotype71. Although different environmental factors have a strong influence on Wolbachia titre91, the majority of studies focusing on Wolbachia density dynamics were performed under controlled laboratory conditions. Given that host-microbe associations in the wild are likely influenced by multiple environmental factors , host factors  and interactions with other organisms, controlled laboratory environments cannot encompass the full spectrum of these factors and their potential impacts92. In the current study, we present one of the few examples of the dynamics of Wolbachia in a natural host population across different generations and seasons.Wolbachia density dynamics in a natural population of the pear psyllid C. pyri, across several generations throughout an entire year. Cacopsylla pyri completes its life cycle on pear trees and therefore represents an interesting model to study Wolbachia density dynamics through different seasons. This psyllid species produces several summer generations and one overwintering generation on pear trees90, which allows studying the impact of varying environmental conditions on the Wolbachia density throughout the year. Only a few studies attempted to describe Wolbachia seasonal dynamics in natural host populations so far74. In the butterfly Pseudozizeeria maha, the Wolbachia density decreased across several host generations from spring/early summer to autumn73. Likewise, the relative abundance of Wolbachia within the microbiome declined from spring to summer in the flea Synosternus cleopatrae74. A decrease in Wolbachia relative abundance, through its vertical transmission to the subsequent host generation, was correlated with higher temperatures in natural populations of the mosquito complexes Aedes vexans and Culex pipiens/restuans25. However, as none of these studies included individuals sampled in winter, the effect of harsh winter conditions on Wolbachia dynamics in overwintering hosts has not been analysed yet.We assessed the Wolbachia density dynamics in a natural population of C. pyri across all seasons, we measured the titer of the most frequently occurring strain (wCpyr2) at a single locality throughout an entire year. Although the bacterial density in natural populations can be influenced by multiple factors, Wolbachia density was stable in the analysed females of C. pyri, which were abundantly infected with Wolbachia throughout the sampling period, suggesting a highly efficient vertical transmission of wCpyr2 from mothers to daughters across different host generations. Regardless of their fluctuations , the environmental conditions had no impact on Wolbachia density in females. This might indicate a strong selective pressure on females to maintain a sufficient Wolbachia density in order to protect themselves against cytoplasmatic incompatibility (CI)4. As such, several Wolbachia strains from supergroup B that are closely related to wCpyr2 are known to induce CI, i.e. two strains from the fly Drosophila simulans  and two strains from the mosquitoes Aedes albopictus (wAlbB) and Culex quinquefasciatus (wPip)95. Taken together, this suggests that wCpyr2 might also be able to induce CI but this needs to be validated experimentally.To investigate Wolbachia and its titer in males was more variable compared to the females. Considering the efficient Wolbachia transmission from mothers to daughters, it is unlikely that male embryos received less Wolbachia from their mothers. Instead, Wolbachia could multiply less in males or could be suppressed in males in order to reduce CI-related mortality when mating with an uninfected female. This would result in reduced Wolbachia titers in aged males. Such a pattern was predicted by population genetic models and observed in the mosquito Aedes albopictus and the fly Drosophila innubila96. In accordance with these predictions, male individuals in our study showed a general tendency towards reduced Wolbachia density compared to females, culminating in a significant drop in Wolbachia titer in May. However, since no females were sampled in May, we cannot exclude that the titer might have been low also in females at this time point. In Central Europe, a new annual cycle of C. pyri begins in April–May, giving rise to the first summer generation after overwintering and newly-emerged adults from this generation can be already found in the second half of May89. At this time, aged adults from the previous overwintering generation can still be alive and present in the orchard as well. In contrast to D. innubila and A. albopictus mosquitoes, the Wolbachia titer of mosquitoes from the Culex pipiens group increased with ageing and therefore young males of C. pipiens had a lower Wolbachia titer compared to older individuals61. Consequently, the males of C. pyri collected in May with a low Wolbachia titer could have been either old individuals from the overwintering generation (if Wolbachia titer decreases with age) or young summer-form adults (if Wolbachia titer increases with age). Both scenarios would agree with those from the previous studies that demonstrated the impact of age on Wolbachia titer in laboratory studies of different systems62. Moreover, the number of males collected throughout the study period was half the number of females. While this could be a coincidence, it could also be the result of another Wolbachia-induced reproductive manipulation, e.g. male-killing or feminization8. However, the presence of Wolbachia in males, albeit at more variable titer, makes this scenario less likely.In contrast to females, not all tested males harboured wCpyr2 but also two additional Wolbachia strains (wCpyr1 and wCpyr3) which all belong to Wolbachia supergroup B. Each strain was detected as a single infection in individuals from three different localities in the Czech Republic. Hence, multiple Wolbachia strains occur in this host species, but they appear not to co-occur in a same individual of C. pyri. Currently, we cannot draw any conclusions whether the observed patterns of Wolbachia infection reflect stable host-symbiont associations with at least two divergent strains (e.g.97), an ongoing spread of one or more newly-acquired Wolbachia strains (e.g.98), or replacement of a pre-existing strain by a new, more competitive strain (e.g.99). To predict Wolbachia infection spread and potential introductions into new host populations, further studies should investigate the infection dynamics of the newly discovered Wolbachia strains on a larger geographical scale. In future studies, it is also necessary to sequence the whole genome of the new Wolbachia strains to understand how they diverged. This is particularly relevant for the strains wCpyr1 and wCpyr2 that differed from each other only slightly based on the MLST genes. Wolfe et al.100 demonstrated that small differences in the MLST genes can in fact reflect bigger structural differences in the whole genome of the symbiont. Additionally, it was striking to find that neither of the strains wCpyr1, wCpyr2 and wCpyr3 showed close relatedness to the Wolbachia strains from other psyllid species, including other Cacopsylla species (e.g.79).In the current study, we characterised not only Wolbachia strain was detected in immatures of C. pyri parasitised by a hymenopteran wasp that was tentatively identified as Syrphophagus aphidivorus based on insect DNA barcoding. Syrphophagus spp. are relatively frequent parasitoids or hyperparasitoids of various psyllids, including Cacopsylla spp. on pear trees101. This Wolbachia strain clustered within supergroup A and was closely related to Wolbachia strains from other hymenopteran parasitoid wasps. Despite a high importance of parasitoids for a biological control of psyllids, only limited information is available on the microbial communities of their parasitoids103. Particularly, Wolbachia was recently detected in the parasitoids of the citrus psyllid Diaphorina citri103, whereas to our knowledge, our study represents the first detection of Wolbachia in parasitised individuals of Cacopsylla. Considering the high incidence of parasitoids in pear psyllid populations, particularly in immatures105, horizontal transmission of Wolbachia between psyllids via shared parasitoids might occur, as it was described in other host-parasitoid associations108. However, a horizontal transmission from parasitoids to their hosts is quite unlikely, as parasitised hosts die and therefore are not able to maintain the bacteria throughout later life stages and pass them onto next generations102. Thus, it is more likely that bacterial symbionts hitchhike from hosts to parasitoids. In the case of wSaph, we can only speculate whether this Wolbachia strain originally belonged to the parasitoid or to C. pyri. To investigate the possibilities of horizontal transmission of wSaph in the wild, further experimental studies on host-parasitoid interactions in pear orchards are required.An additional Wolbachia strain wCpyr2 of the pear psyllid C. pyri across an entire year, spanning several host generations. We demonstrated that Wolbachia density remained stable in females, while we observed a tendency towards reduced Wolbachia density in males, with a significant drop in Wolbachia titer in May. However, in light of a relatively low sample size of C. pyri, these results should be interpreted with caution. Moreover, in similar studies we highly recommend to do the non-destructive DNA extractions and retain voucher specimens in order to be able to identify the generations of the analysed individuals. Nevertheless, our study is the first demonstration of the seasonal dynamics of Wolbachia in psyllids and the first study to monitor Wolbachia abundance in a natural host population across an entire year. The identification of three different Wolbachia strains in C. pyri and another strain in parasitised individuals of C. pyri highlight that additional studies are needed to study phenotypic effects and spatial dynamics of Wolbachia in natural host populations of this important pest species. Taken together, this study provides insights into the ecological and evolutionary dynamics of Wolbachia-host interactions in the natural environment and opens new research questions on this study system.In the current work, we studied the density dynamics of the newly-described Cacopsylla pyri  based on the closest BLAST hit. The immatures of C. pyri from CZ1 were included in the current study to explore Wolbachia infection in parasitised psyllid individuals.Several immatures of C. pyri from the three aforementioned populations, CZ1 (N = 15), CZ2 (N = 36) and CZ3 (N = 5), were tested for Wolbachia infection .Fifty-six individuals  of Wolbachia strains and supergroups was based on the amplification of the Wolbachia surface protein wsp111 and the five multilocus sequence typing (MLST) genes coxA, fbpA, ftsZ, gatB and hcpA112. Due to its fast-evolving rate, wsp provides many informative characters that help resolving the relationships between different Wolbachia strains114. The MLST genes are more conserved and were broadly applied for Wolbachia strain detection in numerous studies (e.g117). Each 25 μl PCR reaction was composed of 1X DreamTaq PCR Master Mix (Thermo Fisher), 7 μl of sterile water, 0.7 μM of each primer and 2 μl of DNA template. PCR conditions were as follows: 95 °C for 5 min; 40 cycles of 95 °C for 15 s, the optimal annealing temperature (see below) for 30 s and 72 °C for 1 min; 72 °C for 10 min. The optimal PCR annealing temperature was 53 °C for hcpA, 54 °C for both gatB and ftsZ, 55 °C for coxA, and 59 °C for fbpA112. PCR conditions for wsp were as follows: 95 °C for 5 min; 40 cycles of 95 °C for 15 s, 58 °C for 30 s and 72 °C for 1 min; 72 °C for 10 min, using the primer pair 81F and 691R111. PCR products were purified and Sanger sequenced by Eurofins Genomics . Each PCR product was sequenced in both directions and the consensus sequences were obtained using ‘MEGA X’118.Identification of wsp sequences were aligned with the 21 most closely related Wolbachia strains  analyses using IQ-TREE v1.6.12122 and Bayesian Inference (BI) analysis using MrBayes v3.2.7a123. As an outgroup, we used the Wolbachia strain of Folsomia candida from supergroup E (Supplementary Table 124 based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) scores . Nodes were considered strongly supported with PP > 90%. Both ML and BI trees were visualized with iTOL v6126.The Consensus sequences obtained in this study were submitted to GenBank with the following accession numbers: ON146571–ON146574 and ON157498–ON157516  of Wsp and the host gene wingless (wg) were amplified using primers specific for the analysed Wolbachia strain wCpyr2 and the wg gene of C. pyri. For wsp, the primer set from Le Clec'h et al.127 was modified, producing a 202 bp-amplicon: wCpyr_Fq (5'-TGGTGCAGCATTTACTCCAAC-3') and wCpyr_Rq (5'-TTGCTTGATAAGCAAAACC-3'). PCR conditions were as follows: 95 °C for 3 min; 40 cycles of 95 °C for 15 s, 55 °C for 30 s and 72 °C for 30 s. For wg, we designed new primers amplifying a 186 bp amplicon: Wg-202Fq (5'-CTCGTCTACCTGGAGACCTC-3') and Wg-362Rq (5'-ACGCAGGAAATCACTGTT-3'). PCR was performed with the following conditions: 95 °C for 3 min; 40 cycles of 95 °C for 15 s, 58 °C for 30 s and 72 °C for 30 s. Each 20 μl reaction contained 1X IQ SYBR Green Supermix (Bio-Rad), 6 μl of sterile water, 0.5 μM of each primer and 2 μl of DNA template. The amplification efficiency of the wsp and wg primer pairs was tested using a standard curve at different annealing temperatures to determine the optimal annealing temperature for the highest amplification efficiency, which was 92% for wsp and 95% for wg. To verify the amplification of the target PCR product across all reactions, a melting curve analysis was performed at the end of each run. Gene copy numbers were determined based on standard curves consisting of tenfold serial dilutions of longer PCR products of the same genes. The longer PCR products for the standard curves were amplified by standard PCR using the DreamTaq PCR Master Mix (Thermo Fisher) and the following primer sets: 81F and 691R for wsp111 and 19F 5'-ACATGYTGGATGAGAYTACCA-3' and 388R 5'-TCTTGTGTTCTATAACCACGCCCAC-3' for wg (this study). PCR conditions for wg were as follows: 95 °C for 5 min; 40 cycles of 95 °C for 15 s, 58 °C for 30 s and 72 °C for 1 min; 72 °C for 10 min. The resulting amplicons were purified using AMPure XP beads (Beckman-Coulter) and quantified using the Qubit dsDNA High Sensitivity Assay Kit (Invitrogen).All samples were run in duplicates on a CFX96 real-time PCR system . Wolbachia titer/host cell ratio, the mean number of wsp copies was divided by the mean number of wg copies for each specimen. All qPCR data were log-transformed and analysed in R v3.6.3 using the packages agricolae and car130. Psyllid specimens that were not infected with Wolbachia as well as immatures , were eliminated from the statistical analyses. The dataset was tested for normality and homogeneity of variance using the Shapiro–Wilk and Levene tests, respectively. Welsh’s t-test was used to test for potential differences in Wolbachia titer between males and females. Analysis of variance (ANOVA) followed by Tukey’s post-hoc test for multiple comparisons was used to test for differences in Wolbachia titer between different months of the year. The data was visualized using the ggplot package in R131. The map showing the representation of different Wolbachia strains in each studied population of C. pyri (Fig. To calculate the Supplementary Information.