Nasonia vitripennis oviposit multiple eggs in the same host, and siblings thus share a common environment during their development. Here we estimate the genetic parameters of wing morphology in the outbred HVRx population of N. vitripennis, using a sire-dam model adapted to haplodiploids and disentangled additive genetic and host effects. The results show that the wing-size traits have low heritability (h2 ~ 0.1), while most wing-shape traits have roughly twice the heritability compared with wing-size traits. However, the estimates increased to h2 ~ 0.6 for wing-size traits when omitting the host effect from the statistical model, while no meaningful increases were observed for wing-shape traits. Overall, host effects contributed to ~50% of the variation in wing-size traits. This indicates that hosts have a large effect on wing-size traits, about fivefold more than genetics. Moreover, bivariate analyses were conducted to derive the genetic relationships among traits. Overall, we demonstrate the evolutionary potential for morphological traits in the N. vitripennis HVRx-outbred population, and report the host effects on wing morphology. Our findings can contribute to a further dissection of the genetics underlying wing morphology in N. vitripennis, with relevance for gregarious parasitoids and possibly other insects as well.The central aim of evolutionary biology is to understand patterns of genetic variation between species and within populations. To quantify the genetic variation underlying intraspecific differences, estimating quantitative genetic parameters of traits is essential. In Pterygota, wing morphology is an important trait affecting flight ability. Moreover, gregarious parasitoids such as  Winged insects, Pterygota, are often considered to be the most successful terrestrial arthropods. The ability to exploit new habitats and fast dispersal by flight have been recognized as the main causes of their ecological and evolutionary success Mayhew . Many sth2), the relative fraction of the total phenotypical variation due to additive genetic variation in a population. When heritability is high, phenotypic variation is mostly due to additive  effects of genes , which standardizes the additive genetic standard deviation by the trait mean rather than the phenotypic variation. In addition, the short-term response to natural selection depends not only on the heritabilities of the traits, but also on the genetic and phenotypic covariances among traits  natural selection, we constructed a population consisting of half-sib families and estimated the quantitative genetic parameters of intraspecific variation in wing size and shape in an outbred population of Nasonia vitripennis. Our main objective is to partition phenotypic (co)variation in size and shape traits into genetic and non-genetic components. For this purpose, we adapted the linear mixed model known as the “animal model”  are provided for oviposition. To ensure optimal mixing of the wasps, the parasitized hosts are re-distributed over four new mass culture tubes before offspring emerge. Approximately 14 days are needed to complete a cycle at 25 °C and a 16-h light, 8-h dark scheme.We used the We generated a family structure to allow estimation of genetic parameters using a mixed linear model and pedigree relationships among individuals  under coverslips on microscope slides. The right hind tibia was collected in this study, because tibia length can be used as a proxy of body size for parasitoids Godfray . We usedbia Fig. , and 11 bia Fig. . To checCompared with wing size, wing shape is more difficult to define, and we assessed wing-shape variation using different methods. First, wing shape was calculated as the aspect ratio, which was defined as wing length divided by wing width. Second, we investigated wing shape as scaled wing length and width in which both traits were scaled to the tibia length. In addition, we also quantified wing shape using geometric morphometrics, in which the raw coordinates digitized by tpsDig were analyzed in MorphoJ  and e = a vector of residual errors. u was the overall mean of phenotypic records. The sire (us), dam (ud), and host effect (c) were taken as normally distributed and independent random effects. Zs, Zd and Zc were known design matrices assigning observations to the level of us, ud and c, respectively.In total, records of 1569 individuals, representing 55 half-sib and 265 full-sib families, were analyzed for the above eight morphological traits. Data were analyzed with a linear mixed sire and dam model. We used a sire-dam model, rather than a full-animal model, because relationships between paternal sibs differ from those between maternal sibs in haplodiploids . In addition, host identity was included in the model, because individuals developing in the same host share the same environment, and are full siblings that may show a dominance covariance. An analysis with a permanent dam effect for all offspring of the same mother was also performed, but the dam effect was small and not statistically significant. Hence, the permanent dam effect was not included in the statistical model. Therefore, in matrix notation, our final model wasIn haplodiploids, female offspring of the same (haploid) father all share his full paternal haplotype. For this reason, the sire variance in the above model equals half of the additive genetic variance. Female offspring of the same (diploid) mother, in contrast, share only 50% of their maternal haplotype because of Mendelian segregation and recombination. Thus, as in diploids, the dam variance in the above model equals one-quarter of the additive genetic variance. Thus, the above sire-dam model is a type of reduced animal model  distribution with one degree of freedom. When α = 0.05, the critical value was 3.84.The significance of variance components was tested using log-likelihood-ratio tests  for wing size and shape traits from the estimated genetic components as A is the additive genetic trait variance and We also calculated the coefficient of additive genetic variation (CVσs12 is the additive genetic sire covariance between two traits (traits 1 and 2).To evaluate whether different morphological traits share a common genetic basis, we performed a multivariate analysis. As this analysis did not converge, instead, we estimated genetic correlations between traits, using a bivariate version of the linear mixed sire-dam model shown above withIn addition, some wing-shape traits were defined as the ratio between two size traits . To avoid the concern over autocorrelation, we log-transformed all traits and repeated the above analysis with transforming data. We also calculated allometry slopes to examine the patterns of allometry in these traits .N. vitripennis population exhibited variation  were found for size traits, but not for wing-shape traits  -to-moderate (~0.25) heritabilities were found for wing-size and wing-shape traits. However, evolvabilities of all of the traits measured as their CVA were low, ranging from 1.19 to 2.68%. Our evolvability estimates agree with Houle  had a large and highly significant effect, and explained about half of phenotypic variance for wing- and body-size traits, but not for wing-shape traits  for wing length in Nasonia is similar to the value found in Drosophila melanogaster , the effect of the host on a single offspring depends not only on host size , but also on the number of parasitoids developing within the host . In N. vitripennis, sibling competition increases with clutch size, and has stronger negative effects on the body size of females than on males  according to host condition, laying small eggs with a large proportion of males into poor hosts . This result shows that inclusion of a host effect is essential when estimating genetic parameters for size-related traits in  Godfray . In gregDrosophila species . All eigenvalues of our genetic, host and residual covariance matrices were non-negative, meaning the estimates are statistically possible, and the negative correlation does not necessarily imply estimation error. The correlation between wing length and aspect ratio not only depends on the (co)variances of wing length and width, but also on the mean values of those traits (Van Noordwijk and De Jong N. vitripennis population. By applying an adapted version of the “animal model”, we further demonstrated that wing size and shape contained significant additive genetic variation in the N. vitripennis HVRx-outbred population. Remarkably and importantly, we found that hosts rather than genetics explained most of the phenotypical variation in wing-size traits. Our findings also demonstrate the importance of accounting for host effects to avoid very severe bias in the estimates of heritability. Our findings reported here increase the understanding of heritable variation for wing morphology in Nasonia. By combining this knowledge with the wealth of genetic tools available for Nasonia, it facilitates the further genetic dissection of wing morphology in N. vitripennis using tools such as genome-wide association and genomic prediction.To conclude, we found variation for wing size and shape traits among individuals from an outbred SupplementarySupplementary Table S2


Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism.n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European . We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain.Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals  from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (p = 6.1e−13), sibling (p = 4.9e−3) and unrelated (p = 3.0e−3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height—a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children  and parents , but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group . In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants.The ASD  summary statistics used to calculate PGS for ASD and other traits are relatively underpowered.We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens . Autism spectrum disorder (ASD) is a neurodevelopmental condition with significant clinical heterogeneity. Heritability estimates are high (~ 80%), and the genetic architecture is complex, involving de novo, rare and common genetic variants . One appIn autism, the majority of the genetic variation tracked by heritability is accounted for by common genetic variation , 3, as qDe novo and rare inherited variants contribute to ~ 10% of diagnoses of autism , copy nuThe Australian Autism Biobank (AAB)  is an inIn particular, the depth of data in the AAB lends itself to prediction analyses. Polygenic scoring methodology enables prediction of phenotypes based on common genetic markers, based on the assumption that complex traits are underpinned by many additive variants of small effect . Polygenic scores (PGS) leverage summary statistics from independent large-scale GWAS to obtain effect-sizes of trait-associated alleles. Applied to an individual, the effect-sizes for a given allele are multiplied by that individual’s allele dosage, and summed over all alleles to calculate their PGS. Thus, for a given complex trait, an individual’s PGS estimates propensity for that trait, or at least that which is captured by common genetic variation. For disease traits, the PGS are usually called polygenic risk scores, with higher values implying higher risk for the disease.Here, we present analyses using genome-wide SNP data from a total of 2,477 individuals in the AAB. First, we use these genetic data to confirm relationships and ancestry. Second, we leverage publicly-available summary statistics for ASD, IQ and other traits to explore the common genetic basis of autism in the AAB, together with a variety of phenotypes including psychometric testing results and diagnostic features. Third, we call CNVs and identify overlap with publicly-available, curated lists of CNVs and genes that have been associated with ASD, ID and DD.A full description of the AAB biobank is provided by Alvares et al. . ParticiThe AAB includes detailed phenotypic data . For theWe used the following summary scores from the psychometric questionnaires: the ADOS-2 Calibrated Severity Score , the Social Responsiveness Scale total t-score, the CSHQ raw score , and a CSHQ composite score based on variables that were relevant to chronotype , the Communication Checklist-Adult z-score, the WISC-IV composite score, the WASI matrix reasoning t-score, and the MSEL non-verbal developmental quotient.x ≤ 0.8, and GenTrain score ≥ 0.68), prior to strand alignment and standard quality control procedures performed in PLINK1.9 4], IQ 3 and chron = 117), UK Biobank (UKB) controls with European genetic ancestry were selected as an additional control group. These individuals were selected by projecting the n = 1,964 ASD participants of European ancestry onto the PCs derived using 137,102 genotyped SNPs from n = 436,227 UKB participants of European ancestry . For each ASD participant, the n = 5 UKB participants with the least Euclidean distance across 3 UKB principal components were taken as controls  using SBayesR  – a BayeTo generate PGS for each trait, we multiplied the best guess genotypes in the target sample  by the effect sizes .n = 330 individuals diagnosed with ASD and n = 145 undiagnosed siblings with parental genotyping data, we tested for polygenic transmission disequilibrium using the pTDT software [We tested for over-transmission of common genetic variation for ASD from parents to their children with and without autism. For software . Brieflyn = 255,861 common genotyped SNPs with minor allele frequency > 0.05 in the Haplotype Reference Consortium dataset, and only in the European subset of n = 1,973 for whom we generated PGS), which were selected for inclusion based on backwards stepwise regression.We calculated correlations between PGS of multiple traits and various phenotypes of interest recorded within the AAB. We calculated the proportion of variance in standardised phenotype residuals explained by PGS, where phenotype residuals resulted from regression on covariates that included age, sex and 20 PCs . Phenotype-PGS associations that were tested for both variance and correlation were counted as one test, as these statistics are mathematically related. With this threshold, we had ~ 80% power to detect a difference of 0.17 SDs in PGS mean when comparing the ASD (n = 697) and UKB groups . For the PGS prediction analyses of quantitative traits, we had ~ 80% power to detect R2 = 0.014 in a linear regression with 1 predictor, alpha = 1.0e−3 (after multiple testing) and sample size of all 1,222 children (noting that sample size was restricted to the available phenotype data).We used the Bonferroni method to correct for multiple testing across all PGS analyses , as an artificially-split CNV should have concordant type across its segments;Adjacent CNVs with gaps ≤ 100 kb were merged in a greedy algorithm;Adjacent CNVs with gaps ≤ 25% of the length of the neighbouring CNV were merged in a greedy algorithm.We used the Bioconductor GenomicRanges package  to proceWe obtained consensus CNV calls for each individual by intersecting CNV calls from both methods and retaining those with ≥ 50% overlap, ensuring that copy number (gain or loss) was matching in each method. A total of 10,752 consensus CNVs were identified using this approach.n = 137 samples where any of the LRRSD (Log R Ratio), BAFSD (B Allele Frequency Standard Deviation) or GCWF (GC-content Wave Form) statistics  had a value more than three standard deviations from the mean. We also applied a filter for samples where CNVs made up greater than 20% of any chromosome to exclude aneuploidy. This step identified n = 2 individuals – one participant from the ASD group with known Down syndrome (trisomy 21), and one participant with diagnosed Smith-Magenis syndrome whom had been included in the UNR group. CNVs were excluded if:the PennCNV confidence score was < 10;there was any overlap with centromeres or the major histocompatibility complex region , the latter due to the complexity of this region;there was > 50% overlap with a segmental duplication;the CNV was common. This was determined by firstly identifying genomic regions that were overlapped by 25 or more CNVs (a threshold of 25 was chosen as this corresponds to ~ 1% of the AAB population). CNVs with ≥ 50% overlap with these regions were considered “common” and therefore excluded;the CNV was < 10 SNPs in length and/or < 20 kb in length;. [the CNV was common or benign: defined as ≥ 70% overlap with CNVs classified as benign by Zarrei et al.  using th. ; or maxi. , 44; andthe CNV overlapped exclusively with non-coding regions, as determined using RefSeq hg19 gene annotation coordinates; andthe CNV was non-genic.Sample QC was performed using summary statistics from the PennCNV output. We removed After QC, we excluded one additional individual for whom 21 CNVs had been called , leaving 885 CNVs from 723 individuals remaining for subsequent analysis.We annotated CNVs using hg19 cytoband and gene coordinates imported from biomaRt . We defin = 2,477 individuals, n = 546 family groupings, n = 436 families with both parents and at least one affected child) were predominantly of European ancestry , with representation from other populations including South Asian , East Asian  and African . All other individuals of admixed ancestry were classified into an “Other” group  , Fig. A summary of familial relationships and genetic ancestry in the AAB is provided in Fig. p = 6.1e−13) than the UKB group after multiple-testing correction  and UNR (p = 3.0e−3) groups also had higher mean ASD PGS than the UKB at a nominal threshold , nor in the analysis of height PGS , likely due to the small size of this subset.The pTDT analysis provided no evidence for difference in ASD or IQ PGS between the ASD and SIB groups , and the PGS accounted for 27% of the variance after adjusting for covariates , presumably because of age effects . We conASD: We determined whether the ASD PGS based on Grove et al. [r = -0.11, p = 7.4e−3 in the ASD group alone) and marginal association with parental WASI matrix reasoning score      and made a significant contribution to the model     Fig. : Table 5Fig. p = .85e−9   , but we observed a nominally-significant positive correlation  in the ASD group  in CSHQ score, a 34% increase compared to the baseline model, which had adjusted R2 = 4%  across all individuals, in relation to chromosome, cytoband, ASD/ID-associated CNVs and ASD/ID/DD -associated genes is provided in Fig. We report on polygenic variation and rare ASD/ID-associated CNVs within the AAB dataset . We firsp = 6.6e−13); however, there were no significant differences between the AAB groups. This may be explained by the small size of these groups in relation to the UKB control group, or alternatively, it may reflect increased ASD risk within all AAB groups compared to the population, which is possible given the recruitment setting and lack of exclusion criteria. We also found nominal evidence of over-transmission of ASD risk in the pTDT analysis (p = 5.8e−2), consistent with observations of over-transmission of common genetic risk in families with a child diagnosed with ASD [We found that the ASD group had significantly higher ASD PGS scores compared to UKB-selected controls that passed multiple-testing correction  between chronotype PGS and CSHQ raw score in the ASD group, as well as a nominally-significant contribution to the baseline model  . We also performed this analysis for height, again finding no significant evidence for assortative mating . In both analyses, the point estimates are consistent with reported assortative mating correlations [We also looked for evidence of assortative mating by correlating ASD PGS scores in spouse-pairs , whereas this was identified by PennCNV . This reflects the increased sensitivity of PennCNV, as CNVs are called on an individual basis, whereas iPattern calls CNVs within batches.Our study has a number of limitations. Firstly, autism is highly heterogeneous, both genetically and clinically, necessitating large sample sizes for discovery. Hence, we downplay the risk of the identified CNVs that are not already known to overlap high-confidence ASD-associated CNVs. Insufficient power is also an issue for the ASD GWAS summary statistics that were used to calculate PGS. The availability of better-powered GWAS in the near future should improve the power of the prediction analyses performed here. We hope that this dataset will contribute to future meta-analyses. Furthermore, our sample is relatively small, particularly in relation to the SIB and UNR groups, and the results require replication in independent datasets to be generalisable.n = 117), and the standard errors are large, so it is difficult to draw further inference from these results. The majority of UNR participants were recruited from community settings, but some were contacted through association with health care providers and there were minimal exclusion criteria , which could contribute to the observed results.We note that the UNR group had higher mean ASD PGS and lower mean IQ PGS than might be expected in a community sample Fig. a,b. Howep values calculated in this analysis. However, there are other interesting PGS-phenotype relationships that may be interrogated in the AAB that we opted not to test, given the power of the current datasets available to us.In our PGS analyses, we remained cognisant of multiple-testing issues. We selected the statistical tests we performed in a hypothesis-driven manner and applied a Bonferroni correction across all n = 5 UKB individuals per AAB participant), and included 20 PCs from the AAB European cohort in the regression models. To account for the latter, we used a backwards stepwise model to only select the most salient genetic PCs as covariates. Our PGS analyses for height provided support that the AAB and UKB samples were genetically well-matched.We used genetic PCs as covariates in our analysis, and also to select UKB controls. Like many genetic analyses of this nature, we acknowledge that it is difficult to balance the competing issues of residual population stratification with the potential for over-correction. To address the former, we matched UKB controls within 2 standard deviations across 3 UKB European PCs . Black dots denote the projection of the AAB ASD participants of European ancestry (n=698) onto the UK Biobank principal components. Blue dots denote the UK Biobank individuals selected as controls (n=5 per ASD participant). Figure 2 Comparison of mean PGS +/- 95% confidence intervals for a) height and b) chronotype. Note that for chronotype, the input GWAS summary statistics were not fully independent due to overlap with UK Biobank participants. Figure 3 Correlation between height PGS and measured height in a) adults and b) children. Correlation coefficient and p-values are provided in the bottom right corner of each plot. Figure 4 Correlation between maternal and paternal ASD PGS. Figure 5 Correlation between IQ PGS and measured IQ  for a) all children, b) ASD only and stratified by IQ<70, c) SIB/UNR groups, d) parents. Correlation coefficient and p-values are provided in the bottom right corner of each plot. Figure 6 Relationships between mean parent matrix reasoning IQ and child traits. a) Correlation between child’s age of ASD diagnosis and mean parent matrix reasoning IQ score . b) Density plot (n families = 249) for mean parent matrix reasoning IQ score, stratified by the presence of ID in their child. Figure 7 Correlation between chronotype PGS and Children’s Sleep Habits Questionnaire (CSHQ) among a) all children, and b) the ASD group. Correlation coefficient and p-values are provided in the bottom right corner of each plot.Additional file 2.Table 1 Numbers of SNPs from each GWAS, and number remaining after QC . Table 2 SBayesR parRes output. Table 3 Statistics for tests of comparison of PGS between groups . Table 4 pTDT analysis results. Table 5 Summary of statistics for PGS correlations. Table 6 Variance explained by the PGS. Table 7a List of ASD/ID-related CNVs aggregated from ClinGen and DECIPHER. b List of ASD/ID/DD-related genes aggregated from Satterstrom et al. (2020) and DDD Study (2017) and overlapping with ENSEMBL hg19 gene set. Note that some genes are repeated as there are multiple transcripts that were considered for overlap. Table 8a: Identified CNVs in the AAB corresponding with CNVs reported by parents in the AAB medical history survey. Please note that this information is limited to that which was reported by parents, and is intended to be used to help assess replication in our pipeline. b Phenotypic correlates of called AAB CNVs overlapping ClinGen and DECIPHER CNVs. clingen_ddd.* provides start, end and widths of reference CNVs; critgene.* provides start and end of critical gene/s within reference CNVs; overlap_pcent refers to the % overlap of the called CNV, as a proportion of the ClinGen and/or DECIPHER-defined CNV. c Large CNVs (>1 Mb) identified within the AAB. d CNVs overlapping ASD/ID/DD-associated genes from Satterstrom et al. (2020) and DDD Study (2017). Table 9  Overlap of Australian Autism Biobank (AAB) phenotypes with the Simons Simplex Collection (SSC) and Simons Foundation Powering Autism Research for Knowledge (SPARK).


Do genetic factors underlie the association between childhood psychopathology and adult mood disorders and associated traits?This meta-analysis of longitudinal cohorts, which includes data on 42 998 participants, revealed significant associations between childhood psychopathology and adult polygenic scores of major depression, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index but not bipolar disorder.Per this analysis, shared genetic factors exist between childhood psychopathology traits from age 6 years onwards and adult depression and associated traits. This meta-analysis combines data from 7 previous cohorts in the UK, Netherlands, Sweden, Norway, and Finland to investigate whether genetic risk for adult mood disorders and associated traits are associated with childhood disorders. Adult mood disorders are often preceded by behavioral and emotional problems in childhood. It is yet unclear what explains the associations between childhood psychopathology and adult traits.To investigate whether genetic risk for adult mood disorders and associated traits is associated with childhood disorders.This meta-analysis examined data from 7 ongoing longitudinal birth and childhood cohorts from the UK, the Netherlands, Sweden, Norway, and Finland. Starting points of data collection ranged from July 1985 to April 2002. Participants were repeatedly assessed for childhood psychopathology from ages 6 to 17 years. Data analysis occurred from September 2017 to May 2019.Individual polygenic scores (PGS) were constructed in children based on genome-wide association studies of adult major depression, bipolar disorder, subjective well-being, neuroticism, insomnia, educational attainment, and body mass index (BMI).Regression meta-analyses were used to test associations between PGS and attention-deficit/hyperactivity disorder (ADHD) symptoms and internalizing and social problems measured repeatedly across childhood and adolescence and whether these associations depended on childhood phenotype, age, and rater.The sample included 42 998 participants aged 6 to 17 years. Male participants varied from 43.0% (1040 of 2417 participants) to 53.1% (2434 of 4583 participants) by age and across all cohorts. The PGS of adult major depression, neuroticism, BMI, and insomnia were positively associated with childhood psychopathology , while associations with PGS of subjective well-being and educational attainment were negative . There was no moderation of age, type of childhood phenotype, or rater with the associations. The exceptions were stronger associations between educational attainment PGS and ADHD compared with internalizing problems  and social problems , and between BMI PGS and ADHD and social problems , compared with internalizing problems . Furthermore, the association between educational attainment PGS and ADHD increased with age .Results from this study suggest the existence of a set of genetic factors influencing a range of traits across the life span with stable associations present throughout childhood. Knowledge of underlying mechanisms may affect treatment and long-term outcomes of individuals with psychopathology. These include not only internalizing problems, such as depression and anxiety,22 and stability25 of psychopathology over time. Studies of BD in high-risk families also show that children of parents with BD are susceptible to psychiatric disorders and symptoms in childhood,26 adolescence, and early adulthood.28 These results suggest that genetic factors may underlie the persistence of symptoms or the transition from one disorder to another between childhood and adulthood. Polygenic score (PGS) analyses enable the examination of the genetic association between adult traits and childhood symptoms of psychopathology.Both twin/family and molecular genetic studies have reported heritability29 For complex  traits influenced by many genetic variants, PGS summarize genetic risk across loci that are not individually significant in a GWAS. A statistically significant association between measured traits and PGS based on another trait suggests a shared genetic etiology. Results of studies using PGS to investigate the association of childhood psychopathology with mood disorders and associated traits vary. Analyses investigating depression and BD PGS have found no evidence of associations with emotional and behavior problems during childhood and adolescence, although there is evidence of association between depression PGS and emotional problems in adulthood.32 Associations between PGS of EA and ADHD or attention problems have been more consistent, with multiple studies34 showing strong genetic associations between EA and ADHD or attention problems in childhood and adolescence.Polygenic scores are aggregate scores of an individual’s genetic risk for a trait, calculated by summing risk alleles from a discovery genome-wide association study (GWAS), weighted by their effect sizes.36 BD,37 EA,38 and BMI,39 consequently increasing accuracy of PGS.40 Combined with the substantial increase in individuals genotyped in large longitudinal childhood cohorts that assess psychopathology, this provides an opportunity to rigorously investigate whether genetic factors underlie the associations between childhood psychopathology and adult mood disorders and associated nonpsychiatric traits  and determine whether this association depends on age. Using 7 childhood population-based cohorts, we studied 42 998 individuals with repeated measures of ADHD symptoms, internalizing, and social problems. We performed meta-analyses to test whether PGS of adult traits are associated with childhood and adolescent psychopathology and whether this association depends on various factors, including age, type of psychopathology, type of scale used to measure psychopathology, and the informant.The last 2 years have seen ever-larger GWAS for traits, including major depression (MD),We obtained self-rated or maternal-rated measures of ADHD symptoms, internalizing, and social problems from 7 population-based cohorts . Data co35 BD,37 SWB,41 neuroticism,41 insomnia,42 EA,38 and BMI.39 Height39 was included as a control phenotype  to assess the prior at which assessment was optimal. We restricted analyses to common variants, using SNV inclusion criteria of minor allele frequency greater than 5% and imputation quality of 2R greater than 0.90.Polygenic scores were estimated using LDpred, a method that takes into account the level of linkage disequilibrium between measured single-nucleotide variants  to avoid inflation of effect sizes.50In each cohort, associations between childhood psychopathology and adult traits were estimated by regressing each outcome measure  stratified by age and rater, on the calculated PGS of the 8 adult traits at the 5 priors. A wide variety of surveys were used to further characterize the cohort.51 Scales were coded such that higher scores reflected more childhood problems. Both childhood psychopathology scores and PGS were standardized to a mean of 0 and an SD of 1, allowing for comparable βs across cohorts. Sex, age, batch effects, and genetic principal components (which correct for population stratification) were included as covariates in the regression . As a sensitivity check, we repeated all analyses using a prior of 0.50 and compared these results to those using the prior with the highest estimate. We selected the prior of 0.50, because it represents a reasonable estimation of the proportion of associated SNVs across the different types of complex traits we tested.Meta-analyses were performed using the metafor package in R version 3.6.0 .53 To test whether the error covariance matrix alone suitably accounted for differences between cohorts, we applied for each adult trait an analysis of variance (ANOVA) test to compare models with the random effects dropped with those where they were specified along with the error covariance matrix.To correct for dependency in the outcome variables attributable to repeated measures of the same individuals over time, we specified the variance-covariance matrix between their sampling errors. Because errors were assumed to be independent between cohorts, we combined variance-covariance matrices across cohorts by setting correlations between cohorts to 0 in the matrix, further accounting for differences between cohorts.Subsequent meta-analyses to test the association between each adult-trait PGS and overall childhood psychopathology  were performed on the reduced model (no random effects), if dropping them did not result in a significant loss of fit compared with the full model (random effects plus error covariance matrix). We also tested the association between the PGS and each individual childhood psychopathology measure.55 We corrected the meta-analysis results for multiple testing by applying Bonferroni correction (P = .05/number of tests) to the effective number of tests  , and rater  moderated association between childhood psychopathology and adult-trait PGS, we performed multimodel inference analyses using the glmulti package in R version 3.6.0.56 The glmulti package allows the definition of a function that takes into account all potential moderators and generates all possible models for the association of interest, returning the best models based on a specified information criterion; in our study, this was Akaike information criterion.57 Furthermore, it provides parameter estimates based on all possible models, rather than a single-top model, while considering the relative importance of each potential moderator by weighting them. The averaged model avoids relying too strongly on a single best model.To ascertain whether the variables age, type of childhood psychopathology , measurement instrument  to 53.1% (2434 of 4583 participants) by age and across all cohorts.Cohort-specific descriptive statistics and correlation matrices of the 3 psychopathology measures, ADHD symptoms, internalizing problems, and social problems are described in eTables 3, 4, 5, 6, 7, 8, and 9 in the Random-effects meta-analyses corresponding to the 5 priors showed that the prior that provided the strongest association estimates were 0.75 for EA and BMI; 0.50 for MD, insomnia, and height; 0.30 for neuroticism; 0.10 for BD; and 0.03 for SWB , neuroticism , insomnia , and BMI , while associations for SWB  and EA  were negative. There was no evidence for association with BD PGS . No associations were found with the PGS of height.Subsequent meta-analyses of the association between PGS of each adult trait and overall childhood psychopathology  showed that the directions of associations were as expected . SignifiP value threshold of .0125 (α = .05/number of moderators), we found evidence of moderation for EA and BMI PGS  across all possible models. Using a  BMI PGS . The ass BMI PGS . Additio BMI PGS . The ass BMI PGS .P = .047), it did not remain after adjusting for the 4 moderators tested . We revealed strong evidence of associations of PGS for adult MD, SWB, neuroticism, insomnia, EA, and BMI with childhood ADHD symptoms, internalizing problems, and social problems. We found no evidence of associations between BD PGS and childhood psychopathology. In addition, we found no evidence of the moderators age, outcome, measurement instrument, and rater on these associations, except for EA PGS and BMI PGS. While EA PGS was more strongly associated with ADHD symptoms compared with the 2 other outcomes, BMI PGS was more strongly associated with ADHD symptoms and social problems than with internalizing problems. The association between EA PGS and ADHD symptoms increased with age and was stronger for maternal-rated ADHD symptoms compared with self-rated ADHD symptoms.32 Moreover, previous studies focused on separate childhood phenotypes59 as opposed to our approach of simultaneously analyzing multiple childhood problems at different ages. Consistent genetic associations across age suggest a set of genetic variants that influence a range of traits across the life span.Our results indicate a consistent pattern of genetic associations between PGS of adult depression and associated traits and childhood psychopathology across age. This has not been observed previously, which is likely partly attributable to the increased power of our larger discovery and target samples compared with previous studies.58 they were not associated with internalizing problems, or social problems, in the case of EA. The lack of association with internalizing problems was somewhat unexpected, given genetic correlations previously found for BMI and EA with adult MD.36 These results suggest that genetic associations between EA and BMI and MD may become more apparent after adolescence, while they are already present for childhood ADHD and social problems (for BMI).The exceptions to these consistent associations were EA and BMI PGS, which showed moderation on the associations by the different types of childhood outcome. While both were genetically associated with ADHD in accordance with previous research,21 However, previous analyses of BD PGS also found no associations with continuous measures of psychopathology in childhood60 or adolescence.61 These results may be explained by less powerful BD GWAS compared with MD and other traits, which might result in underpowered PGS. Nevertheless, the lack of association with BD PGS may also suggest that genetic risk for BD does not manifest until later in development, but given the higher prevalence rates of childhood psychopathology in offspring of parents with BD, this seems less likely.63 It will be interesting to see if the observation holds as more powerful GWAS become available for BD.We did not identify associations between BD PGS and childhood psychopathology. This is intriguing because moderate genetic correlations with BD have been observed for MD and ADHD, as well as other behavioral-cognitive phenotypes, such as SWB and EA.65 Consequently, associations observed with adult PGS may be the result of both direct and indirect  genetic effects. Third, dropout may have influenced our results. Previous analyses in longitudinal cohorts have reported negative associations between PGS for schizophrenia, ADHD, and depression and participation in childhood and adolescence.67 Nonparticipation in adolescence is also associated with higher psychopathology scores at earlier ages.53 These results suggest that individuals with higher genetic risk for psychiatric disorders and higher childhood psychopathology are more likely to drop out of longitudinal studies. Genetic associations and the magnitude of associations reported may therefore be underestimated. Finally, because we combined data from different cohorts, we introduced heterogeneity in the assessment of childhood psychopathology. However, the meta-regression showed in general, consistent effect sizes across scales and raters. Moreover, combining multiple cohorts resulted in a large sample size, increasing statistical power compared with previous studies, which is a strength of this study.A limitation of our study is that analyses are limited to European ancestry, and therefore results are not generalizable to populations of differing ancestry. Second, associations between PGS and childhood psychopathology measures may be confounded by unaccounted passive gene-environment correlations, an association between a child’s genotype and familial environment resulting from parents providing environments that are influenced by their own  genotypes.68 Polygenic scores by themselves are not sufficient to identify individual children at high risk for persistence (they explain <1% of the variance in childhood psychopathology in this study). Nevertheless, these findings are of major importance because the individuals who are affected across the life span with consequences on other outcomes, such as EA and BMI, should be the focus of attention for targeted treatment. Furthermore, PGS could be combined with other risk factors for risk assessment in clinical samples, as was recently done for psychosis risk using schizophrenia PGS.69 Future studies focusing on samples from high-risk populations are warranted to investigate whether PGS for adult traits, together with other variables, can be used to build risk profiles with reasonable accuracy. These may allow for the stratification of children into high-risk and low-risk groups for persistence, as well as test whether early intervention or more intense treatments for the former group can prevent poor outcomes.70The general lack of an influence of age and type of childhood psychopathology on our identified associations supports evidence of a common genetic psychopathology factor that remains stable across development.In conclusion, we demonstrate the power of combining genetic longitudinal population data to elucidate developmental patterns in psychopathology. Our study provides novel evidence for the presence of shared genetic factors between childhood psychopathology and depression and associated adult traits, as well as their stability across development. Insight into these associations may aid identification of children at risk for a relatively chronic course of illness, ultimately facilitating targeted treatment to this vulnerable group.


Population structure among study subjects may confound genetic association studies, and lack of proper correction can lead to spurious findings. The Genotype-Tissue Expression (GTEx) project largely contains individuals of European ancestry, but the v8 release also includes up to 15% of individuals of non-European ancestry. Assessing ancestry-based adjustments in GTEx improves portability of this research across populations and further characterizes the impact of population structure on GWAS colocalization.cis-eQTL mapping using admixed samples in seven tissues, adjusted by either global or local ancestry. Consistent with previous work, we observe improved power with local ancestry adjustment. At loci where the two adjustments produce different lead variants, we observe 31 loci (0.02%) where a significant colocalization is called only with one eQTL ancestry adjustment method. Notably, both adjustments produce similar numbers of significant colocalizations within each of two different colocalization methods, COLOC and FINEMAP. Finally, we identify a small subset of eQTL-associated variants highly correlated with local ancestry, providing a resource to enhance functional follow-up.Here, we identify a subset of 117 individuals in GTEx (v8) with a high degree of population admixture and estimate genome-wide local ancestry. We perform genome-wide We provide a local ancestry map for admixed individuals in the GTEx v8 release and describe the impact of ancestry and admixture on gene expression, eQTLs, and GWAS colocalization. While the majority of the results are concordant between local and global ancestry-based adjustments, we identify distinct advantages and disadvantages to each approach. Thousands of genome-wide association studies (GWAS) have been published to date. Subsequently, large-scale expression quantitative trait loci (eQTL) datasets are studied to provide insights for genetic variants associated with complex traits. While the majority of such studies focus on single-ancestry populations or relatively homogeneous populations, the latest Genotype-Tissue Expression (GTEx) project (v8) includes up to 17% of individuals with non-European or admixed ancestry , 3. SuchGlobal ancestry (GA), or the proportions of different ancestral populations represented across the entire genome, is routinely used to adjust for population structure in genetic association studies . This apLA adjustment in genetic association studies has been shown to reduce type I error rate  –15 and sM = 1159). We perform cis-eQTL mapping in seven tissues and assess the differences between LA adjustment and GA adjustment in the context of this admixed sub-cohort. For the subset of loci where the two ancestry adjustment methods yield different results, we perform GWAS/eQTL colocalization analyses with 142 previously published GWAS, representing a range of traits, consortia, and cohort ancestry. We characterize 31 loci where a significant colocalization is reported only with one eQTL ancestry adjustment method. Finally, we identify a small subset of GTEx eVariants whose genotypes are highly correlated with LA, providing a resource to enhance functional follow-up of these loci.In this study, we describe the degree of admixture in the GTEx v8 cohort and estimate LA for a subset of 117 individuals with at least 10% admixture from European, African, and East Asian ancestral populations. LA explains at least 7% of the variance in residual expression for 1% of expressed genes . Genome-wide genotype-based principal components (gPCs) reflect GA and have been used to adjust for population structure in both GWAS , 9, 13 acis-eQTL calling based on a minimum admixed sample size of 60 .P values less than 1e−4. COLOC was used to test for colocalization at all of these loci [P value less than 1e−5. If multiple variants within a 1-MB window had a P value less than this threshold, the variant with the smallest P value was selected as the seed variant. For each GWAS seed variant, if there was an eQTL with a P value of less than 1e−4 within 1 MB, the intersection of GWAS and eQTL variants within 1 MB of the GWAS seed variant was tested for colocalization. The same GWAS seed variant was used to perform colocalization with GlobalAA and LocalAA eQTL signals at each locus. Colocalization method-specific parameters are detailed below.We performed colocalization between 142 GWAS and 14 sets of 117AX eQTL summary statistics (one set for each ancestry adjustment in each of seven tissues). Colocalization tests were restricted to the subset of genes where the two eQTL ancestry adjustments yielded different lead variants with nominal ese loci ; an implese loci . Inputs For colocalization analyses between 142 GWAS and 117AX eQTL summary statistics, the same GTEx VCF used for eQTL mapping in 117AX was used to calculate eQTL effect allele frequencies; GWAS effect allele frequencies were extracted from the GWAS summary statistics. The coloc.abf function in the “coloc” R package was used to run COLOC. For binary GWAS traits, case proportion and “cc” trait type parameters were used. For continuous GWAS traits, sample size and “quant” trait type parameters were used. These GWAS characteristics are provided in Table S5 . For binary GWAS traits, case proportion and “cc” trait type parameters were used. For continuous GWAS traits, sample size and “quant” trait type parameters were used. In both cases, imputed or calculated z-scores were used as effect coefficients in Bayes factor calculations. Enloc enrichment estimates [Figure stimates  were usestimates .K variants were then multiplied to calculate a colocalization posterior probability (CLPP):An implementation of FINEMAP was used to test for colocalization at the subset of loci for which COLOC reported a colocalization (PP4 > 0.5) between a GWAS and an 117AX eQTL. After the intersection of GWAS and eQTL variants within 1 MB of the GWAS seed variant was identified for a locus, FINEMAP v1.1 was run independently for the GWAS and eQTL association signals using parameters --n-causal-max 1 --n-iterations 1000000 --n-convergence 1000. The 1000 Genomes Phase 3 VCF was used for LD calculations . As previGWAS,  is the PIP for the ith variant in the vector of K variants tested for the causality of the GWAS signal; PIPieQTL,  is the PIP for the ith variant in the vector of K variants tested for the causality of the eQTL signal. The ith variant in the list of tested GWAS variants is the same as the ith variant in the list of tested eQTL variants for all i.PIPAdditional file 1: Figure S1. Supplementary figures .Additional file 2: Table S1. GTEx IDs for individuals in 117AX cohort.Additional file 3: Table S2. Local ancestry calls for 140 GTEx individuals.Additional file 4: Table S3. Variance in residual 117AX gene expression explained by ancestry.Additional file 5: Table S4. 117AX GlobalAA and LocalAA lead eVariants for all tested genes.Additional file 6: Table S5. Characteristics of GWAS used in colocalization analyses.Additional file 7: Table S6. GTEx v8 eVariants highly correlated with LA (R2 > 0.9).Additional file 8: Table S7. GTEx v8 eVariants correlated with LA (R2 > 0.7).Additional file 9. GTEx Consortium author list.Additional file 10. Review History.


Climate change, including higher temperatures (HT) has a detrimental impact on wheat productivity and modeling studies predict more frequent heat waves in the future. Wheat growth can be impaired by high daytime and nighttime temperature at any developmental stage, especially during the grain filling stage. Leaf chlorophyll content, leaf greenness, cell membrane thermostability, and canopy temperature have been proposed as candidate traits to improve crop adaptation and yield potential of wheat under HT. Nonetheless, a significant gap exists in knowledge of genetic backgrounds associated with these physiological traits. Identifying genetic loci associated with these traits can facilitate physiological breeding for increased yield potential under high temperature stress condition in wheat.p ≤ 9.99 × 10− 4). Ten MTAs with pleiotropic effects detected on chromosomes 1D, 2B, 3A, 3B, 6A, 7B, and 7D are potentially important targets for selection. Five MTAs associated with physiological traits had pleiotropic effects on grain yield and yield-related traits. Seventy-five MTAs were consistently expressed over several environments indicating stability and more than half of these stable MTAs were found in genes encoding different types of proteins associated with heat stress.We conducted genome-wide association study (GWAS) on a 236 elite soft wheat association mapping panel using 27,466 high quality single nucleotide polymorphism markers. The panel was phenotyped for three years in two locations where heat shock was common. GWAS identified 500 significant marker-trait associations (MTAs) (We identified 500 significant MTAs in soft winter wheat under HT stress. We found several stable loci across environments and pleiotropic markers controlling physiological and agronomic traits. After further validation, these MTAs can be used in marker-assisted selection and breeding to develop varieties with high stability for grain yield under high temperature. Worldwide, wheat is grown on more than 218 million hectares of land and provides approximately 20% of dietary calorie needs . High teGenetic improvement of yield under HT via direct selection is hindered by the quantitative nature of grain yield and large genotype by environment interaction. Optimizing carbohydrate partitioning using traits such as spike fertility (SF), internode partitioning, and spike organ partitioning is essential to overcome sink limitations and increase harvest index (HI) , 11. AltAssociation mapping is a powerful approach that utilizes genetic diversity and historical recombination events to provide a high resolution map of trait-linked loci . AlthougP < 0.001) for all measured traits  determinants of phenotypic traits except for MT  among PTs and their relationship with GY, GN, SF, HI, SHI and TGW were calculated using the combined dataset. SPAD was positively correlated with MT (0.31***), GY 0.50***), SF (0.25***), GN (0.30***), HI 0.46***), SHI (0.37***) and TGW (0.26***)  . Briefly2 = 0.2) was estimated at 1182, 1920 and 2916 bp for ranges of 30,000, 40,000, and 50,000 bp, respectively, across the whole genome  during grain filling stages is associated with higher heat tolerance . StudiesThe selective permeability of plasma membrane is highly sensitive to heat stress affecting growth and development of a plant . TherefoAnother parameter that has been frequently used to estimate heat tolerance in wheat is CT . Lower cIn summary, we detected 500 MTAs located in different chromosomes out of which 81 MTAs were linked to the same trait in multiple environments (suggesting stability) and ten MTAs linked to multiple traits (suggesting pleiotropy) Table -4. NotabSome MTAs associated with PTs had pleiotropic effects with GY and other yield related traits . The seeds were collected from different soft wheat breeding programs  from in the south and southeastern USA. The SWAMP was evaluated for PTs in five trials at the two heat stressed locations in Florida: Citra and Quincy. Citra had more frequent episodes of HT (> 30 °C) during the grain filling period than Quincy  that are well adapted to the warm and humid south and southeastern regions of the USA. These lines were developed by public and private soft wheat breeding programs in the south and southeastern USA and the list is available in the NCBI database with accession number PRJNA578088  using a handheld self-calibrated SPAD chlorophyll meter . The SPAD-502 instrument provides a convenient means of assessing relative leaf chlorophyll concentration. The chlorophyll content was measured on intact flag leaves one third of the way from the base of the abaxial surface. The average of eight readings was used for further statistical analysis.Four physiological traits (PTs) including CT, SPAD, NDVI, and MT were measured at different time points. CT was measured three times during grain filling period  using a handheld infrared thermometer  on sunny days when the temperature reached the daily high between 1300 and 1500 h . CT dataSPAD Chlorophyll meter data were taken on the same day or the closest possible day coinciding with CT and MT. NDVI was measured at anthesis  and grain filling stage . A GreenSeeker handheld crop sensor  was used for collecting NDVI readings. The GreenSeeker handheld crop sensor was hold 50 cm above the canopy facing the center of the bed. A 30–40 NDVI readings were recorded/plot and the mean value of those readings represented the NDVI value of the respective plot. To determine MT, flag leaves were collected from ten random main stems at anthesis plus seven days (Zadoks stage 72) from each plot. One cm diameter leaf disks from each leaf were extracted from midway between the base and the tip of the leaf blade using a leaf disc puncher and placed in glass vials containing 20 ml deionized water. The vials were placed in shaker for 24 h at room temperature to ensure diffusion of electrolytes. After 24 h, electrolyte leakage was measured using a conductometer (Thermo Scientific Orion Star A212) followed by autoclaving the vials  to release all the electrolytes from plant tissue. The vials were placed in shaker for 24 h and electrolyte leakage was measured again. MT was expressed in percentage units as the reciprocal of relative leakage [1 is the conductivity reading after heat treatment, and T2 is the conductivity reading after autoclaving. Grain yield (GY) and yield-related traits including grain number (GN), harvest index (HI), thousand grain weight (TGW), spike fertility (SF), and spike harvest index (SHI) were also calculated to determine correlation among traits. The details of the calculation of these traits is described in previous study [where Tus study .Combined analysis of variance (ANOVA) was conducted assuming a mixed linear model. The ‘lme4’ package  and the ijk) is a function of the overall mean (μ), ith genotype (Gi), jth environment, genotype-environment interaction (GEij), kth block (Bk) nested within the jth environment (Ej), and the residual error (εijk).where the phenotypic response , BLUEQ  and BLUEA . Broad sense heritability was calculated assuming random genotypic effect  and was obtained by:H2, broad-sense heritability estimate; σ2G, genetic variance; σ2G × E, genotype-by-environmental variance; σ2e, residual variance; n, number of environments; and r, number of replications.where Pearson’s correlations were calculated from BLUEs in R using the “corrplot” package  and used to determine the direction and magnitude of measured trait associations. Associations between traits were also explored in principle component (PC) biplot analysis using the package “factoextra” in R ..The detail of genotyping process, SNP discovery and filtering criteria has been described in previous study . In brie2 (coefficient of LD) between pairs of SNPs sampled at the range of 30,000, 40,000, and 50,000 bp. The intersection between critical value (r2 = 0.2) and LOESS line was considered as the distance beyond which LD starts to decay. Population structure was observed using Bayesian information criterion (BIC) score provided by discriminant analysis of principal components  [Linkage disequilibrium (LD) and population structure analysis of the SWAMP has been described in detail previously . Brieflyam 2013)  to deterp) = 4.00 (p = 9.99 × 10− 4) was used as the cut-off to define significant MTAs based on Q-Q plots [GWAS was performed in three BLUE datasets  for each trait to identify significant MTAs in SWAMP using Fixed and random model Circulating Probability Unification (FarmCPU) model , 75 exec-Q plots , 78. Can-Q plots . The putAdditional file 1. Weather table showing number of hours in daytime (> 24 °C) and nighttime (> 15 °C) temperature during grain filling stages (Mar 15 - Apr 30). The soft wheat association mapping panel (SWAMP) was planted in three seasons in Citra  and two seasons in Quincy .Additional file 2. Physiological traits for the SWAMP assessed in each year and used in GWAS.Additional file 3. Summary of ANOVA results testing the effects of genotype (G), environment (E), and genotype-by-environment interaction (G × E). The table includes mean square values and significance level of each term. SPAD, soil-plant analyses development; MT, cell membrane thermostability; CT, canopy temperature (°C); NDVIa, normalized difference vegetation index at GS65; NDVIg, normalized difference vegetation index at grain filling.Additional file 4. Pearson’s correlation coefficient (r) between physiological traits in SWAMP. SF, spike fertility (grains g-1 chaff weight); GY, grain yield (kg h-1); GN, grain number m-2; TGW, thousand grain weight (g); SHI, spike harvest index; HI, harvest index; SPAD, soil-plant analyses development; MT, cell membrane thermostability; CT, canopy temperature (°C); NDVIa, normalized difference vegetation index at GS65; NDVIg, normalized difference vegetation index at grain filling.Additional file 5. Principal component bi-plot analysis of measured traits for the SWAMP. SF, spike fertility (grains g− 1 chaff weight); GY, grain yield (kg h− 1); GN, grain number m− 2; TGW, thousand grain weight (g); SHI, spike harvest index; HI, harvest index; SPAD, soil-plant analyses development; MT, cell membrane thermostability; CT, canopy temperature (°C); NDVIa, normalized difference vegetation index at GS65; NDVIg, normalized difference vegetation index at grain filling.Additional file 6. Population structure of the SWAMP based on 27,466 SNPs. (A) bar charts showing posterior probabilities of assignment to three groups based on algorithms of discriminant analysis of principal components (DAPC). (B) Population structure among demes inferred from PC analysis. The populations were colored based on the posterior of probability assigned to three genetic groups inferred from DAPC.Additional file 7. Linkage disequilibrium represented by the r2 against physical distance (in bp) showing LD decay. LOESS regressions of mean r2 between pairs of SNPs vs. physical distance were sampled at 30,000 (red), 40,000 (blue), and 50,000 (green) bp. Grey line represents the critical value beyond which LD is likely caused by physical linkage.Additional file 8. Summary of all significant markers and their functional annotations associated with eight traits in SWAMP.Additional file 9. Summary of significant pleiotropic MTAs associated with PTs, GY and other yield related traits.


From a total of 20 independent genome-wide significant loci, 13 loci replicated of which 8 were novel for depression. These were MUC21 for the broad depression phenotype with self-reported MDD and ZNF804A, MIR3143, PSORS1C2, STK19, SPATA31D1, RTN1 and TCF4 for the broad depression phenotype with schizophrenia. Post-GWAS functional analyses of these loci revealed their potential biological involvement in psychiatric disorders. Our results emphasize the genetic similarities among different psychiatric disorders and indicate that cross-disorder analyses may be the best way forward to accelerate gene finding for depression, or psychiatric disorders in general.Although a genetic basis of depression has been well established in twin studies, identification of genome-wide significant loci has been difficult. We hypothesized that bivariate analyses of findings from a meta-analysis of genome-wide association studies (meta-GWASs) of the broad depression phenotype with those from meta-GWASs of self-reported and recurrent major depressive disorder (MDD), bipolar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for depression. LD score regression analyses were first used to estimate the genetic correlations of broad depression with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia. Then, we performed four bivariate GWAS analyses. The genetic correlations ( SIRT1 gene and in an intron of the LHPP gene [FHIT gene for this broad depression phenotype [A depressive disorder is among the leading causes of morbidity and disability , and it HPP gene . Direk eHPP gene  performeHPP gene  and the HPP gene  involvinhenotype . Finallyhenotype .rg ± SE) was reported between MDD and schizophrenia (0.43 ± 0.06) and between MDD and bipolar disorder (0.47 ± 0.06) [Both MDD and symptoms of depression overlap with bipolar disorder and schizophrenia, and the diagnostic boundaries are sometimes difficult to define , 11. For ± 0.06) . Also, a ± 0.06)  and the  ± 0.06) .CACNA1C, CACNB2 and ITIH3 genes, shared between the major psychiatric disorders [Given these substantial phenotypic and genetic associations , 13, it isorders . Howeverisorders , 16. Recisorders  and bivaisorders , 19 provisorders , recurreisorders  and schiisorders  with theisorders .We performed four consecutive bivariate analyses of the meta-GWASs of the broad depression phenotype , with onFirst, genetic correlations were estimated followed by a series of four bivariate meta-GWAS analyses. We also followed up our findings by annotating the genome-wide significant lead SNPs and conducting post-GWAS analysis as described below.We applied the bivariate LD score regression method , 23 to e++ eLX package. The OB method and the dLC approach [Z-scores) of each SNP and yield a combined significance level based on the bivariate analysis. Power of these methods compares favorably to other popular multivariate methods such as that described by Porter and O'Reilly [A series of four bivariate meta-analyses were performed by combining meta-GWAS data of the broad depression phenotype with self-reported MDD, recurrent MDD, bipolar disorder and schizophrenia, respectively. We chose to do a series of bivariate analyses rather than a single multivariate analysis since the latter would unnecessarily focus the discovery of loci on the overlapping variance among all phenotypes studied. Analyzing combinations of two phenotypes at a time  self-reported MDD (ii) recurrent MDD; (iii) bipolar disorder; and (iv) schizophrenia, respectively) should capture more shared variance thereby facilitating the identification and interpretation of novel loci for the broad depression phenotype. In this study, we applied a powerful bivariate GWAS analytical method using summary statistics from GWAS studies . Our metapproach , 19 combO'Reilly . More deO'Reilly , 28.Z-scores of each SNP for the broad depression phenotype [Z-scores of: (i) self-reported MDD, (ii) recurrent MDD, (iii) bipolar disorder and (iv) schizophrenia. The analyses generate two test statistics and associated p values, one for the OB method and the other for the dLC method. Statistical significance of the bivariate association was determined based on the smaller of the two p values. The results were considered genome-wide significant if: (i) the p value for the bivariate analysis reached genome-wide significance (p < 5 × 10–8); (ii) the bivariate p value was one order of magnitude smaller than the univariate p-values of both analyzed phenotypes; (iii) the univariate meta-GWAS effects were at least nominally significant with a p < 0.001; and (iv) were in the same direction. For each bivariate GWAS analysis, only one independent lead SNP per locus was reported. Nearby SNPs in LD (r2 > 0.10) with the lead SNP were considered dependent and belonging to the same locus.In the bivariate meta-analyses, the full set of GWAS results was used to estimate variance–covariance matrices of test statistics. We combined the henotype  with theSee Supplementary Materials.p < 0.05, (ii) the effect was directionally consistent and (iii) the combined discovery and replication meta-analysis p value was more significant than its corresponding bivariate GWAS. Additionally, we performed a lookup of our genome-wide significant SNPs for probable MDD and ICD-coded MDD [Genome-wide significant SNPs from the four bivariate GWAS analyses were replicated using UK Biobank results for broad depression  and subsoded MDD .p < 1 × 10–5 as input .We used the post-GWAS pipeline reported by Vaez et al.  to annotrg ± SE) between the broad depression phenotype and self-reported MDD in the 23andMe data, calculated using the LD Score regression method, was 0.79 ± 0.07 and highly significant (p = 5.70 × 10–28). In the bivariate meta-analyses of the broad depression phenotype and self-reported MDD, three loci  reached genome-wide significance and all were replicated. MUC21 is novel for depression . In the bivariate meta-GWAS of broad depression phenotype and recurrent MDD, the PLEK2 locus reached genome-wide significance (p = 3.05 × 10–10) but was not replicated . The bivariate meta-GWAS analysis identified three loci  of which ITIH3 replicated . In the bivariate meta-GWAS analysis with schizophrenia, 16 loci reached bivariate genome-wide significance of which 10 replicated  and 6 did not   were novel for depression. Although the TCF4 locus was previously reported by Wray et al. [r2 = 0.004) at this same locus  with 10 nonsynonymous SNPs (nsSNP), 9 of which were in LD with rs2535629 (ITIH3) and located in 7 different genes, 3 of which belonged to the inter-alpha-trypsin inhibitor heavy chain gene family . Two nsSNPs, rs1029871 mapping to NEK4 and rs678 to ITIH1, were considered ”deleterious” by SIFT and “possibly”, respectively, ”probably damaging” by PolyPhen. The nsSNP rs437179 located within the SKIV2L gene was in high LD with the other gSNP rs389883 (STK19)  were in LD (r2 > 0.50) with 4 nsSNPs mapping to 4 genes, i.e., a total of 12 genes containing nsSNPs. Our analyses also revealed 1962 SNPs that are in high LD (r2 > 0.8) with the 56 gSNPs which map to 70 genes . Together with the 2 gSNPs containing nsSNPs in 8 genes described above, these 56 gSNPs included an additional 4 gSNPs in LD  had eQTLs in at least two of the three databases. Of these genes, ITIH4 and GNL3 also contain nsSNPs in LD with rs2535629  and gene-set (SWI/SNF-type complex) provide converging evidence in line with the involvement of epigenetic regulation and chromatin function because the encoded protein is part of the large ATP-dependent SWI/SNF chromatin remodeling complex.Functional network and enrichment analysis that included all four prioritized gene sets  showed enrichment of Gene Ontology (GO) terms involved in the Ras superfamily, the Rab family and GTPase activity, as well as terms involved in the epigenetic machinery and chromatin function , bipolar disorder 0.53) and schizophrenia (0.57), and a more moderate but still highly significant trans-ethnic genetic correlation with recurrent depression (0.24). We identified 20 independent loci in the 4 subsequent bivariate GWASs, of which 13 replicated in the UK Biobank. Eight of these were novel for depression. In the bivariate GWAS with self-reported MDD, we reported one novel genetic locus mapping to 3 and schder GWAS  and fina, bipolarIn order to prioritize particular genes within the 13 identified loci for their likely causal involvement, we performed a series of post-GWAS analyses investigating whether our lead SNPs were in high LD with coding SNPs, were associated with gene expression in blood or brain or had previously been reported in the literature to be associated with similar or other complex traits. These analyses yielded a number of candidates with strong evidence for their functional involvement in the nervous system and brain development. Thus, they may be involved in the pathogenesis of psychiatric disorders including depression .ITIH1, ITIH3, ITIH4), which has been associated with major psychiatric disorders including MDD [ITIH1, ITIH3 and ITIH4, whereas ITIH4 was an eQTL in both blood and brain across all three databases. Furthermore, LD with a nsSNP pointed to SKIV2L as a likely causal candidate for depression, additionally supported by eQTL evidence in blood and brain.One example is the inter-alpha-trypsin inhibitor heavy chain gene family . In the replication phase, a SNP was only considered replicated if a one-sided test was nominally significant (pone sided < 0.05), i.e., the effect was directionally consistent, and the combined meta-analysis of the discovery and replication yielded a p value more significant than the corresponding bivariate p value. Although these are sound precautions similar to those applied in previous recent work [p values. Reassuringly, all 13 replicated SNPs showed an FDR < 0.05 confirming the appropriateness and strictness of our replication criteria.An important asset of our study is that we used most of the currently available data and exploited the biological pleiotropy among psychiatric disorders  to identent work , it can The substantial genetic correlations within depression (ranging from the mild depression continuum to more severe recurrent MDD) and across psychiatric disorders  suggest that psychiatric illnesses may differ, in part, quantitatively rather than qualitatively from each other, and cross-disorder   analysishttp://www.med.unc.edu/pgc/downloads. UK Biobank depression summary data: https://datashare.is.ed.ac.uk/handle/10283/3036. Blood eQTL browser: http://genenetwork.nl/bloodeqtlbrowser. UK Brain Expression Consortium (UKBEC): http://www.braineac.org/. GTEx: http://www.gtexportal.org/home/. Ensembl genome browser: http://www.ensembl.org. DEPICT: www.broadinstitute.org/depict. Online methods: eLX package: https://sites.google.com/site/multivariateyihsianghsu/. LDSC software: https://github.com/bulik/ldsc.Recurrent MDD, bipolar disorder and schizophrenia summary data: Supplementary figuresSupplementary methods and MaterialsSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5Supplementary Table 6Supplementary Table 7Supplementary Table 8Supplementary Table 9


The domestic dog represents an ideal model for identifying susceptibility genes, many of which are shared with humans. In this study, we investigated the genetic contribution to individual differences in 40 clinically important measurements by a genome-wide association study (GWAS) in a multinational cohort of 472 healthy dogs from eight breeds. Meta-analysis using the binary effects model after breed-specific GWAS, identified 13 genome-wide significant associations, three of them showed experimental-wide significant associations. We detected a signal at chromosome 13 for the serum concentration of alanine aminotransferase (ALT) in which we detected four breed-specific signals. A large proportion of the variance of ALT (18.1–47.7%) was explained by this locus. Similarly, a single SNP was also responsible for a large proportion of the variance (6.8–78.4%) for other measurements such as fructosamine, stress during physical exam, glucose, and morphometric measurements. The genetic contribution of single variant was much larger than in humans. These findings illustrate the importance of performing meta-analysis after breed-specific GWAS to reveal the genetic contribution to individual differences in clinically important measurements, which would lead to improvement of veterinary medicine. Through two bottlenecks resulting from domestication2 and the frequent use of specific males, each dog breed shows lower heterogeneity for disease. In humans, higher heterogeneity, meaning multiple variants with different levels of effects influencing the same disease, makes it difficult to identify genetic variants associated with disease3. Therefore, dogs are considered an ideal model animal for identifying genes and genomic loci underlying diseases and phenotypic variation4. Genome-wide association studies (GWAS) using canine single nucleotide polymorphism (SNP) chips have identified susceptibility genes and genomic loci for several complex diseases10 and for many Mendelian disorders16.The domestic dogs share many diseases and phenotypes with human17. A systematic analysis of 6,046 dogs has identified breed-specific differences in hematological measurements18, suggesting also a genetic contribution to the inter-breed variation for these measurements. Identification of the responsible genes would lead to a better understanding of the biological mechanism under the intermediate phenotypes for different diseases, which would improve veterinary medicine.The diagnosis of a disease often involves taking many clinical measurements including blood and urine analysis. These measurements directly reflect different aspects of the health of each individual. Individual differences in these measurements are known to be heritable in humans, and GWAS have identified genetic loci explaining these individual differencesIn this study, 40 clinically relevant measurements were collected from 472 dogs of eight different breeds in five countries. The measurements included 9 morphometric measurements, 4 urinary and 24 clinical blood parameters, and 3 stress responses during the clinical exam . We used the genome-wide efficient mixed model association (GEMMA) algorithm20 to perform association analyses of 301 combinations of phenotypes and breeds. We excluded 19 combinations due to missing phenotypes  the effect may be present in some and absent in others of the studied breeds and (2) if the effect exists in a breed, the effect size is similar between breeds. The inflation factor was 1.140 ± 0.009. After applying a genomic control correction, 13 associations reached a genome-wide association level at p < 5 × 10−7 (Table −8).In order to integrate the results from all eight breeds, meta-analyses were performed using the binary effects model to account for the existence of genetic effect in each breed22. The strongest signal  for ALT was observed at BICF2P496164  and we did not observe significant association in the other chromosomes. Manhattan plot and QQ plot are shown in Fig. GPT encoding ALT, is located in this locus. A causal variant linked with the associated SNP would increase expression level of GPT, which would lead to the production of more ALT protein. Figure 21 (Table −5) which passed threshold at P = 2.89 × 10−4 considering multiple testing with 173 SNPs in this locus.ALT level in blood is used for the diagnosis of liver disease and injury in human and veterinary medicine2 > 0.973). They were considered as one signal. In Finnish Lapphund, BICF2P111900 showed the highest association (P = 5.96 × 10−6) and moderate linkage disequilibrium with BICF2P496164 (r2 = 0.481). We considered BICF2P111900 as another signal. In Newfoundland, TIGRP2P176579 (P = 5.22 × 10−6) and TIGRP2P176993 (P = 1.91 × 10−4) showed independent associations and had low and moderate linkage disequilibrium respectively with BICF2P496164 (r2 = 0.075 and 0.544) as shown in Supplementary Fig. In order to calculate the contribution of this locus for each breed, we searched for the highest association of each breed separately. As shown in Fig. 23. The strongest signal  was observed at BICF2P1369335 . LELP1 is the only protein coding gene located in this region but its association with fructosamine is not reported. Manhattan plot and QQ plot are shown in Fig. −5 was detected in Belgian Shepherd dogs at a different SNP (BICF2P218994), which was 1 Mb from the lead SNP in the meta-analysis. Since linkage disequilibrium between BICF2P218994 and the lead SNP in the meta-analysis (BICF2P1369335) was low (r2 = 0.013), this is independent of the lead signal in the meta-analysis. This was also suggested by the M value of BICF2P1369335 in Belgium Shepherds (M = 0.000). Each lead SNP explained 11.2–45.8% of the variance as shown in Table Fructosamine is a stable condensation product of glucose with serum proteins. It is used as a biomarker for diagnosing and managing diabetes because fructosamine reflects the average blood sugar concentration over the preceding 2 weeks−9, experimental-wise p = 0.032) as shown in Table −6, m-value = 1.000) and Dachshund  at BICF2P1232291 . Supplementary Table We identified experiment wide significant association of stress during physical examination . This SNP explained 68.1% of the variance in morphometric parameter E (body length) in Dachshunds. The length of the back in dogs with the GG genotype was 12.6% (5.9 cm) longer than that in dogs with the AA genotype  and BICF2P67088 at the Supplementary Table In this study, we collected 40 clinically relevant measurements from 472 healthy dogs of eight breeds, and performed breed-specific GWAS and meta-analyses. In total, 13 genome-wide significant associations were identified in meta-analyses. This number was much higher than expected under the null hypothesis. We observed experiment-wise significant association for ALT, fructosamine, and stress during physical exam.25. Taking advantage of the possibility to analyze each dog breed in our study, we investigated a lead SNP in each breed. We have identified four different signals . Note that using the same data, a genome wide significant association on chromosome 3  was reported in Belgium Shepherd30. In the current analysis, we observed a similar tendency, but it was not significant (P = 5.15 × 10−6). This divergence could be explained by different analysis methods and slight difference in the number of dogs .Individual differences in the level of fructosamine were mapped to chromosome 17. As in ALT, association was not found in all breeds but in only three of the studied breeds. One SNP in this locus also explained a large proportion (11.2–45.8%) of the variance. Note that the previous GWAS focusing on fructosamine data of the same dogs did not identify any genome wide significant associations31. However, there are a limited number of studies33, because assessment of behavioral traits is a difficult task. In this study, we used subjective judgment from dog handlers. Although experienced dog handlers might provide better assessment than other behavioral tests, handler assessment should be replaced by a method to assess endophenotype, which has the potential to measure the same phenotype anywhere and to dissect behavior into biological response31.We identified an experimental-wide association with stress during physical examination at chromosome 1. Two breeds, Cavalier King Charles Spaniels and Dachshunds, showed the same tendency. Dogs with the A allele of BICF2P1232291 showed more stress during physical examination. The research field of the genetics of dog behavior trait is of much interest, because dogs are unique animal models that can be used to understand behavioral traitsLCORL and IGF1, respectively. These regions have already been reported to be associated with breed differences in body size34. The lead SNP reported here contributed to individual differences in morphometric parameters in a few breeds only because it was fixed for the other allele in some breeds  and E (body length) were mapped to regions close to 36, close to the gene G6PC2. G6PC2 encodes an enzyme that catalyzes the hydrolysis of glucose-6-phosphate, allowing the release of glucose into the bloodstream. While homozygotes of either allele showed differences of only 2.7% in glucose level (5.24 and 5.10 mmol/l) in humans, they showed differences of 26.6% (1.067 and 0.843 mmol/l) in Finnish Lapphund dogs. Further study is needed to reveal if this SNP influences susceptibility for diabetes.A large contribution of a single variant was also observed for the glucose level. Glucose serum level in this study was linked to a locus of chromosome 36 that corresponds to the locus associated with fasting plasma glucose level in humans37, or other unrecognized phenotyping biases, can cause this kind of “group effect”38. So we had to normalize data within each group instead of merging the original phenotyping data from different groups to avoid this bias of “group effect”. (2) GWAS using dogs of different breeds40 can efficiently map the chromosome region associated with a phenotype. However, the association identified by this kind of analysis is not always applicable to all breeds. Indeed, an association between height and the LCORL locus was found by GWAS using dogs of different breeds24 but in our study the lead SNP contributed to individual difference only in 2 of 8 breeds and the allele was not polymorphic in four breeds. The same is true for ALT, fructosamine and other phenotypes. Therefore, in the context of a genetic marker for the improvement of veterinary medicine, the identification of SNPs for individual differences rather than breed differences is important. It is necessary to analyze the association in each dog breed using a sufficient number of dogs. (3) In this study, we identified three experiment-wide significant associations whose SNPs explained a much larger part of the variance than that in humans. However, the current number of dogs might not have had enough power to identify other associations with smaller effect sizes. Supplementary Table −7 under different scenarios of effect size (0.01–1.5) and total sample size  with GPower 3.141. The total number of dogs in this study (n = 472) was enough for sufficient statistical power to identify the strongest signal for ALT, because its effect size was 1.08–1.37. However, when effect size is 0.5, for example, we need more than 1,000 dogs, and the current number of dogs is not enough for sufficient statistical power. Therefore, we need more dogs, or we need to perform a meta-analysis with other studies examining the same phenotypes in order to identify weaker associations.This study has three main implications for canine GWAS. (1) International collaboration for sampling allows increasing the number of dogs to get a higher statistical power. However, although we have collected phenotypes from the same breed with the same sex in neighboring countries we have observed clear differences in the phenotypes  from five countries  were recruited for this study as part of the EU-funded LUPA project  and carbohydrate metabolism , as well as cortisol level. Plasma was collected to measure cardiovascular hormones . Tubes were centrifuged within 30 minutes of blood sample collection. Plasma and serum were harvested, transferred into plastic cryotubes and samples were frozen and stored at −80 °C. All samples were later transported frozen to different accredited laboratories. All analyses were performed using commercially available assays validated for dogs, according to manufacturers’ instructions. Insulin, cortisol and cardiovascular hormones samples were analyzed in duplicate by personnel blinded to dog identity, and the mean of the two results was used for data analysis. Data were tested for normality using Shapiro Wilk test. Outliers were individually checked and some of them were excluded from the analysis if their results seemed inappropriate.44, breed and sex differences for these phenotypes were expected. But we have also observed differences between countries where Labrador retriever and Belgian Shepherd were sampled. Among the 35 phenotypes collected in female Labrador Retrievers in Denmark and France significant differences were observed in 10 phenotypes (p < 1.43 × 10−3 = 0.05/35) as shown in the Supplementary Fig. −3 = 0.05/36) between Belgium and France for male Belgian Shepherds , low minor allele frequency (MAF) (<0.01), and deviation from Hardy-Weinberg equilibrium (p < 1 × 10−6 for each breed). Finally, 145,741 SNPs were analyzed in this study.Genotypic analyses were conducted using 472 dogs with a 170 K Illumina canine HD SNP array2 < 0.5 according to the indep-pairwise function in PLINK 1.0745 (–indep-pairwise 1000 100 0.5) were selected for principal component analysis (PCA). The analysis was performed using Eigenstrat 5.0.146 to check outliers inconsistent with the recorded dog breeds. A PCA did not reveal any outliers that were inconsistent with the recoded dog breeds  was defined as the ratio of the median of the observed test statistic to the expected median. When λ > 1, population stratification was suggested. We divided the test statistic of each SNP by λ as a corrected test statistic and recalculated a corrected P value as a genomic control correction. We set threshold for genome wide significance at p = 5 × 10−7 because there were 103,740 common SNPs in each breed on average. The contribution of a single variant to explain the variance of each relative rank was calculated47. This method provides a bias-corrected estimate of the proportion of phenotypic variance.Before GWAS, we adjusted confounding effects of breed and sex by normalizing phenotypes separately within each breed and sex. In GWAS, we did not include any covariates. The GEMMA algorithm was applied to perform association analyses for a single phenotype in each breed accounting for population stratification48. A new framework, the binary effects model, was developed to account for the existence of effect to improve the statistical power as compared with fixed effects and random effects models21. This model also provides the posterior probability that the effect could exist in each breed as an m-value. An m-value > 0.9 suggests existence of a genetic effect in that breed.A meta-analysis was also performed to integrate the p-values from the eight breeds for each phenotype. This analysis is similar to a trans-ethnic GWAS of humans that was used to examine several complex diseases such as type 2 diabetes−7 was used for genome-wide significance, and p = 1.25 × 10−8 (= 5 × 10−7/40) was defined as the threshold value for experiment-wide significance. Since the lead SNP in a meta-analysis does not always show the highest association in a breed-specific GWAS, the best association was selected within ±2 Mb of a lead SNP for each dog breed. The regional association threshold was set by Bonferroni correction using the number of SNPs in that locus. We investigated independence between the lead SNP in the meta-analysis and the lead SNP in each breed by the calculation of r2 in each breed separately.We used the Metasoft ver. 2.01 to combine the association results in each breed with the binary model. The binary effects model was applied to the 40 phenotypes with SNPs with MAF ≥ 0.05 in two or more breeds. When the genomic inflation factor was >1, a genomic control correction was applied. The same threshold at p = 5 × 1030. The study was carried out in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and approved by an ethical Committee in Belgium , Sweden , Finland  and Denmark . In France, data was obtained before the creation of the Local Ethical Committee dedicated to clinical research (ComERC-ENVA). As the data were from client owned dogs undergoing normal veterinary exams, there was no animal experiment according to legal definitions in France. However, all local regulations related to clinical procedures were observed. In all countries, informed owners consent was obtained for use of samples and data for scientific research. All undertaken procedures were part of routine veterinary clinical exam in all countries and the local responsible co-investigator, who was also licensed to practice veterinary medicine within the EU, was responsible for collection of the samples.This was already described in the previous paperSupplementary information.