FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance. The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age. In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis. Finally, we identify a pathway worthy of further study: haem metabolism.Ageing phenotypes, such as years lived in good health , total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically. Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near  Ageing phenotypes are of great interest but are difficult to study genetically, partly due to the sample sizes required. Here, the authors present a multivariate framework to combine GWAS summary statistics and increase statistical power, identifying additional loci enriched for aging. A long and healthy life is determined by many different factors, including lifestyle, environment, genetics, and pure chance. Recent estimates suggest the genetic components of both human lifespan (i.e. the number of years lived) and healthspan  are only around 10%3, which makes genetic studies of these traits challenging, as noise tends to obscure effects unless sample sizes are large.Human ageing is characterised by a progressive decline in the ability to maintain homeostasis, leading to the onset of age-related diseases and eventually death. However, there is much variation between individuals, with some experiencing chronic disease early on and dying before age 60, while others being able to reach an exceptional old age, often free of disease until the last few years of life4,  lifespan7 and survival to exceptional old age 9, with some overlap between findings. Multivariate analyses of correlated traits offers the prospect of increased power10, especially where samples do not overlap, and may be able to identify variants influencing a common underlying ageing process.However, with sufficiently large samples, genome-wide association studies (GWAS) of ageing traits have the potential to identify genes and pathways involved in the human ageing process. GWAS have attempted to identify loci and pathways related to healthspanHere, we assess the degree of genetic overlap between published GWAS of three different kinds of ageing phenotypes—healthspan, parental lifespan, and longevity —and perform a multivariate meta-analysis to identify genetic variants related to healthy ageing. We subsequently characterise the sex- and age-specific effects of loci which affect all three ageing traits and look up reported associations with age-related phenotypes and diseases. Finally, we link the observed signal in these loci to the expression of specific genes, including some that are currently studied in model organisms, and identify pathways involved in healthy ageing.N = 300,477 individuals, 28.3% no longer healthy), parental lifespan , and longevity . The traits show substantial genetic correlations (P < 5 × 10–8) despite differences in age demographics, trait definition, and study design. Parental lifespan correlates strongly with both healthspan  and longevity , while healthspan and longevity show a weaker correlation with each other   Fig. . We perf09) Fig. .Fig. 1HeP < 0.05/81; Phet > 0.05) with coronary artery disease , stroke , chronic obstructive pulmonary disease , and years of schooling . However, we also find evidence for differences in correlations across the traits (Phet < 0.05): healthspan correlated more strongly with metabolic traits (such as type 2 diabetes) than the other studies, and showed negative genetic correlations with depression and cancers, especially melanoma , which were not observed in the other datasets. Conversely, parental lifespan correlated uniquely with alcohol intake  and longevity showed a unique correlation with Alzheimer’s disease .  , associated with an average increase in parental lifespan of 12.7 months per allele (95% CI: 11.4–14.0) and an increased odds ratio of reaching longevity of 1.66 (1.56–1.77). However, noting that <2% of the healthspan study sample experienced Alzheimer’s disease, the same allele was associated with an average healthspan increase of only around 50 days (2–98).Given the correlations amongst the traits, a combined MANOVA offers the prospect of increased power. We therefore performed a meta-analysis of GWAS of healthspan, parental lifespan, and longevity, which identified 24 loci at genome-wide significance (P < 0.05) in two or more datasets  were mostly associated with skin cancers and metabolic traits, while parental lifespan-specific loci were associated with smoking and risk taking  in all three GWAS datasets (Table SLC4A7), rs17499404 (LINC02513), rs1159806 (FOXO3), rs61905747 (ZW10), and rs12830425 (FGD6)  (r2 > 0.4) with rs2802292, a well-known candidate SNP from longevity studies13. Given that some of the loci show P values near the genome-wide significance threshold (i.e. SLC4A7 and LINC02513), replication of these loci in large independent cohorts, which were not yet available to us, is warranted.Ten loci reached nominal significance  and evidence of no sexual dimorphism for lead SNPs near LINC02513, SLC4A7, LPA, TOX3, and FOXO3 (<20% difference or Pdiff > 0.50). For the remaining loci, near CDKN2B-AS1, ZW10, FGD6 and LDLR, effect size point estimates may differ by more than 20%, but we would need a larger sample size to be able to detect this difference with confidence , with the effect size of the ε4 allele increasing by 32% (25–39%) for every 10-year increase in parental survival. While we are underpowered to confirm the trends for the remaining loci, we find that, collectively, the average effect of the protective alleles of these nine loci decreases by 24%  for every 10-year increase in parental survival  of cis-acting eQTL colocalisation for eight out of 10 loci. In total, we highlight 27 unique genes acting across 32 tissues, especially whole blood (12 genes) and the tibial nerve (7 genes) (Supplementary Data BCL3 and CKM (near APOE); CTC-510F12.2, ILF3, KANK2 and PDE4A (near LDLR); USP28 and ANKK1 (near ZW10); and CDKN2B are linked to an increase in multivariate ageing traits , while the opposite is true for EXOC3L2 (near APOE), TTC12 (near ZW10), and FOXO3. For the multivariate signal near SLC4A7 we find colocalisation with expression of NEK10 (liver); for the signal near LPA we find colocalisation with expression of SLC22A1/A3 (multiple tissues) and MAP3K4 (pituitary); and for the signal near FGD6 we find colocalisation with expression of FGD6 itself . Including trans-acting eQTL from blood, while keeping the same thresholds for colocalisation, we additionally discover higher expression levels of FOXO3B colocalises with the life-extending signal near FOXO3. When we include genes which could not be tested for heterogeneity (NeQTL < 3), we identify one additional cis-acting and 49 additional trans-acting genes  , we find strong evidence  and two trans-acting genes (E2F2 and PDZK1IP1) in the LDLR locus show a similar effect . The most interesting, however, seems to be the LINC02513 locus, which showed multiple trans-acting genes to be strongly downregulated with age, while the lead life-extending variant increases expression. LEF1, CCR7, and ABLIM1 even belong to the most significantly affected genes in the whole transcriptomic dataset. This indicates that this long intergenic non-protein coding RNA may serve as a master regulator of age-related transcription in whole blood.To determine the age-related expression of the identified cis- and trans-acting genes for gene set enrichment in 50 hallmark and 7350 biological process pathways, we find significant enrichment (Padjusted < 0.05) in seven hallmark gene sets and 32 biological processes. The hallmark gene sets with the strongest enrichment include haem metabolism, hypoxia, and early oestrogen response  of serum iron, transferrin levels, and transferrin saturation. These effects are not driven by only one of the loci, including the hereditary haemochromatosis locus, as confirmed by leave-one-out analyses , they are difficult to interpret. We therefore repeated the procedure for the individual component traits: healthspan, parental lifespan, and longevity, recognising the reduction in effective sample size was likely to yield underpowered effect size estimates, although these give a sense of direction and magnitude of the effect in measurable units , against our GWAS results. In a univariate MR framework, we find evidence of a causal effect for serum iron (FDR < 5%), which appears to be consistent with the MR assumptions and is robust to outliers  and never at genome-wide significance. The effects of loci of interest on male and female lifespan are largely the same, although their effect on survival may be slightly stronger in middle age (40–60 years) compared to old age (>80 years). The ApoE ε4 allele is exceptional in this regard, as its effect is stronger in females and increases with age, likely due to its well-known link to Alzheimer’s disease16. We find these loci of interest colocalise with the expression of 28 cis-genes and 50 trans-genes, some of which are known to become differentially expressed with increasing age. Lastly, we find these genes are enriched for seven hallmark gene sets (particularly haem metabolism) and 32 largely overlapping biological pathways (including apoptosis and homeostasis), and in line with the highlighted pathways, we find a causal role for iron levels in healthy life in a MR framework.Genetic correlations between publicly available healthspan, parental lifespan, and longevity GWAS reveal these traits share 50% or more of their underlying genetics. Performing a multivariate meta-analysis on the GWAS summary statistics, we highlight 24 genomic regions influencing one or more of the traits. Ten regions are of particular interest as they associate with all three ageing traits and are as such likely candidates to capture intrinsic ageing processes, rather than extrinsic sources of ageing. Five of the loci of interest are not associated at a genome-wide significant level in any individual dataset, including the region near 17. In summary, we used an inverse-variance weighted approach in a two-sample MR setting using independent GWAS summary statistics to provide corroboration for the haem metabolism finding (see Methods). The sample overlap between iron-related GWAS and our study was <2.5%. The instrumental variables were independent genome-wide significant SNPs (P < 5 × 10–8), supported by knowledge of biological plausibility as they included several iron transporters and the hereditary haemochromatosis locus, the latter of which had the greatest effect on iron, in line with expectations18. The causal effect of iron is further supported by two sensitivity analyses: one showing no evidence of pleiotropy (MR-Egger) and the other showing the observed effect is robust to exclusion of outliers (leave-one-out).Interpretation of MR results should be treated with some caution and transparency of the applied method as well as a sensitivity analysis are necessary20. While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities. It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region of LD. Should the former be true, this would add to existing evidence for antagonistic pleiotropy in humans, which includes two recent studies that showed antagonistic pleiotropic effects for genes involved in coronary artery disease21 and ageing22. However, almost all loci of interest associate strongly with cardiovascular and blood cell phenotypes, without apparent antagonistic effects, in line with established knowledge that cardiovascular disease is a leading cause of mortality and morbidity worldwide23.The antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive windowAPOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan27, while knockout of IGF1R has the opposite effect28. Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster29 and the SNCA orthologue in Caenorhabditis elegans30 have shown to extend their respective lifespans. Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing31. For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction32, but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3. In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing. This pathway includes genes involved in processing haem and differentiation of erythroblasts33. Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest  are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis.Several of the genes we identify have previously been shown to influence lifespan in experiments on model organisms. For example, knockouts of the orthologs of 34. However, the strong signal for haem metabolism, in combination with the MR results, suggests the evidence for the involvement of this pathway in human ageing is reasonably robust. Haem synthesis declines with age and its deficiency leads to iron accumulation, oxidative stress, and mitochondrial dysfunction35. In turn, iron accumulation helps pathogens to sustain an infection36, which is in line with the known increase in infection susceptibility with age37. In the brain, abnormal iron homeostasis is commonly seen in neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease and multiple sclerosis38. Plasma ferritin concentration, a proxy for iron accumulation when unadjusted for plasma iron levels, has been associated with premature mortality in observational studies39, and has been linked to liver disease, osteoarthritis, and systemic inflammation in MR studies41.The pathway analysis has potential limitations due to the correlative nature of the genes used to test for enrichment, which can inflate type 1 errors42, to further improve power.A particular strength of this study is the ability to identify loci shared by multiple traits, without the need for additional sample collection. Comparing the strength of the multivariate association at our 10 loci of interest with the strength of association within each individual GWAS, we estimate the combined statistics are equivalent to a median sample size increase of 127%  for the healthspan study, 76%  for the parental lifespan study, and 415%  for the longevity study. This gain in power is particularly important for the latter since the sample size of GWAS for longevity will likely not improve in the near future due to limited availability of data on long-lived people. Having demonstrated the advantages of jointly studying three ageing traits, we encourage future studies to incorporate additional large-scale age-related trait GWAS, such as a recent study on frailty in UK BiobankAPOE and FOXO3 that we are capturing the human ageing process to some extent. However, some judgement is involved in definitions. For one, there are currently no widely accepted standards for measuring healthspan43. Zenin et al.3 define healthspan based on the incidence of the eight most common diseases increasing exponentially in incidence with age in their sample. As such, their trait is highly dependent on the characteristics of the UK Biobank cohort, who were aged 40–69 years when they were recruited in 2006–2010 and of which two-thirds have yet to experience an age-related disease. Therefore, loci with effects on diseases of middle age (cancer and heart disease) are likely overrepresented in our analysis. The rarity of Alzheimer’s disease in the UK Biobank sample also explains the limited association of APOE in the healthspan GWAS, compared to the other ageing traits. Similarly, the parental lifespan GWAS is dependent on the most common causes of death in the parental generation. As such, the observed cardiometabolic associations may, to some extent, reflect the large effect of these diseases on death in Europe a few decades ago. Future studies on additional cohorts with wider age ranges, disease frequencies, and causes of death  would be able to show if loci shared between ageing traits are indeed independent from cohort-specific characteristics and reflect common biological ageing mechanisms.It is clear from the association of age-related diseases and the well-known ageing loci Z scores of the underlying traits to assign a direction to Z scores derived from MANOVA P values. This works well for SNPs with concordant effects on ageing traits but is less accurate when SNPs have heterogeneous or antagonistic effects. For example, a SNP associated with an increase in healthspan and an equal decrease in parental lifespan—while likely rare—will have a large Z score in the MANOVA, but no clear direction of effect. This limitation will introduce some heterogeneity in the colocalisation analysis, and as a result inflate the HEIDI statistic. Furthermore, gene expression colocalisation is limited by the number of tissue eQTL (with some tissues being underpowered) and does not capture the effect of coding variation. There may be additional genes with highly tissue-specific effects or effects dependent on structure or splicing isoforms, which we are unable to detect.Multivariate analysis of traits does not provide a natural combined effect size or direction of effect. Colocalisation of eQTL with loci of interest requires effect directions to test for heterogeneity of instruments. As such, we used the direction of the sum of the 44 and thus the relative importance of haem metabolism in ageing.The pathways we have highlighted mostly relate to biological processes for chemical and cellular homeostasis and are therefore likely to be generalisable across populations. However, it is important to note that all GWAS summary statistics used in our study were derived from individuals of European ancestry and more follow-up work is necessary to validate our findings in individuals from other ethnic backgrounds. For example, certain population characteristics, such as levels of obesity and meat intake, can affect the bioavailability of iron45, and this prediction is supported by a recent study on the relation between recessive mutations involved in haemochromatosis and morbidity41. Heritability studies of lifespan show that a small but significant amount of phenotypic variation may be explained by dominance effects46, and, as such, future studies should also try to study the effect of recessive variants on ageing traits.Another limitation is that our meta-analysis, like many others, is focused on the identification of additive genetic variants. The evolutionary theory of ageing predicts that recessive variants may have larger effects on fitnessFOXO3 in blood, which itself becomes increasingly expressed with increasing age, but experiments suggest the gene has many protective functions including detoxification of reactive oxygen species and DNA damage repair15. The observed inverse relationship between healthy life and FOXO3 expression may reflect healthy individuals have less oxidative damage and require less FOXO3 to mitigate this damage.Importantly, the genes we have highlighted show natural variation in the human population and some of them show altered levels of expression with increasing age, which makes them good candidates for therapeutic intervention. However, colocalisation of gene expression could be due to pleiotropy rather than causality, and there is a need to validate the effects of genetic variants in experimental models to confirm their role in disease aetiology. For example, we have found life-extending variants colocalise with decreased expression of In conclusion, the challenge of studying ageing genetics in humans—low heritability and limited samples—can be overcome to some extent by combining large studies of closely related phenotypes that capture elements of the ageing process. Focusing on the overlap between different populations and age-related traits has revealed that several ageing pathways discovered in model organisms also apply to humans, and has highlighted genes and pathways in humans which can now be further tested in model organisms. This study, and follow-up work on the genes we have highlighted, will eventually lead to therapeutic targets that can reduce the burden of age-related diseases, extend the healthy years of life, and increase the chances of becoming long-lived without long periods of morbidity.47 (10.5281/zenodo.1302861), parental lifespan48 (10.7488/ds/2463), and longevity9 (https://www.longevitygenomics.org/downloads), whose derivation is briefly described here.We downloaded three publicly available sets of summary statistics on healthspan3 for details. After removing single nucleotide polymorphisms (SNPs) with duplicate rsIDs  summary statistics were available for 5,429,268 common (MAF ≥ 0.05) and 5,860,562 rare (MAF < 0.05) SNPs.The Healthspan GWAS consists of 300,477 unrelated, British-ancestry individuals from UK Biobank. The statistics were calculated by fitting Cox-Gompertz survival models with events defined as the first incidence of one of seven diseases  or death itself. Martingale residuals from this model were then regressed against HRC-imputed dosages. Of the 84,949 individuals who had experienced an event , 51.3% experienced a cancer event, 18.0% a diagnosis of diabetes and 17.1% a myocardial event. Less than 5% of the individuals experienced their first event due to one of the remaining diseases. See Zenin et al.7 for details. Summary statistics were available for 5,526,246 common (MAF ≥ 0.05) and 3,559,402 rare (MAF < 0.05) SNPs.The Parental Lifespan GWAS consists of unrelated, European-ancestry individuals reporting a total of 512,047 mother and 500,193 father lifespans, of which 60% were complete. The statistics for each participating cohort were calculated by fitting Cox survival models to father and mother survival separately, adjusted for subject sex, at least 10 principal components, and study-specific covariates such as genotyping batch and array. Martingale residuals of the survival models were regressed against subject dosages (HRC-imputed). Father and mother results were combined into two separate ways: father and mother residuals from UK Biobank were combined before regression, while father and mother summary statistics from other cohorts were meta-analysed, adjusting for the phenotypic correlation between parents. See Timmers et al.Ncases = 11,262) or whose age at the last follow-up visit (or age at death) was at or before the 60th percentile age . The statistics for each of the participating cohorts were calculated using logistic regression and 1000 G Phase 1 version 3-imputed dosages, adjusted for clinical site, known family relationships, and/or the first four principal components (if applicable) and subsequently combined using a fixed-effect meta-analysis. See Deelen et al.9 for details. After removing SNPs with duplicate IDs , summary statistics were available for 6,657,238 common (MAF ≥ 0.05) and 2,181,962 rare (MAF < 0.05) SNPs.The Longevity GWAS consist of unrelated, European-ancestry individuals who lived to an age above the 90th survival percentile . In addition, we excluded individuals who had withdrawn their consent to participate as of 16 October 2018 and all but one of each related set of individuals . Related individuals were excluded as mixed modelling is not well understood in the context of the kin-cohort method7. The remaining 325,614 individuals reported 312,088 and 322,672 father and mother lifespans, respectively, of which 67.7% were complete. Parent lifespans were then split into three age bands, 40–60, 60–80, and 80–120, excluding parents who died before the start of the age band and treating any parent who survived at least until the end of the age band as alive (i.e. right-censored). Sample descriptives of each age band are detailed in Supplementary Table h(x) is the hazard of the parent at age x, h0(x) the baseline hazard, and β1,β2, …,βn the effect sizes  associated with the covariates X1,X2, …,Xn. Martingale residuals of these models were taken50, divided by the proportion dead to scale effects to hazard ratios and doubled to account for parental genotype imputation5, and then regressed against subject allelic dosage in an additive model using RegScan51. Individual parental lifespan statistics were combined using inverse-variance meta-analysis, inflating standard errors by rp).We carried out a series of additional, age-stratified GWAS using a sample of 325,614 unrelated, British-ancestry individuals from UK Biobank (as determined by genomic PCA and 3rd degree kinship or closer)52 was used to calculate genetic correlations between ageing trait GWAS, age-stratified parental lifespan (described above) and 27 European-ancestry GWAS of developmental, behavioural, and disease traits  and low frequency (MAF < 0.05) SNPs, as well as those located in the Major Histocompatibility Complex, were discarded before merging the summary statistics with a HapMap3 reference panel to reduce statistical noise. An average of 1,086,952 SNPs  were used to calculate genetic correlations per set of summary statistics, based on LD-score regression weights derived from European individuals.LD-score regression10. Correlations were calculated from summary statistics by taking the correlation in effect estimates from independent SNPs between studies . These correlation estimates ranged from 0.013 between healthspan and longevity to 0.094 between healthspan and parental lifespan, reflecting a small degree of sample overlap and/or phenotypic correlation. Summary association statistics were calculated for the 7,320,282 SNPs shared between studies, of which 5,278,109 were common (MAF ≥ 0.05) and 2,042,173 were rare (MAF < 0.05). These statistics represent the significance of each SNP affecting one or more of the traits, giving a P value against the null hypothesis that effect sizes are zero in all studies. The method does not provide a combined effect size.Healthspan, parental lifespan, and longevity summary statistics were meta-analysed using MANOVA, while accounting for correlations between studies due to (limited) sample overlap and correlation amongst the traits, as implemented in MultiABEL v1.1-6r2 < 0.1) with other lead SNPs. LD-score regression was used to assess inflation of the GWAS statistics, using 1,138,687 SNPs from the MANOVA and LD weights from European samples from the 1000 Genomes project. Loci with lead SNPs showing a nominally significant effect (P < 0.05) in all three datasets were considered more likely to capture intrinsic ageing pathways. We refer to them as loci of interest throughout this study.Loci were defined as 500 kb regions flanking the lead genome-wide significant SNP in linkage equilibrium /sqrt(SEfathers2 + SEmothers2), where SE is the standard error of the effect estimate. This statistic follows a Z distribution, assuming errors in measured effects are independent.Lead SNPs of loci of interest were looked up in individual father and mother survival statistics from Timmers et al.α is the fold change in effect, βband is the effect estimate of the age-specific band, βall is the unstratified effect estimate, and SE is the standard error of the respective effects.Age-specific survival statistics were retrieved for the same loci from our age-stratified parental lifespan GWAS in UK Biobank. In order to standardise effects for each locus, we expressed the age-specific effect as a fold change from the unstratified effect in UK Biobank, inflating standard errors using the Taylor series expansion to account for the uncertainty in the denominator:P > 0.05/10) were meta-analysed, again weighted by the inverse of their variance, to provide a collective estimate. A sensitivity analysis examining the collective trend estimate using all loci of interest (instead of only underpowered loci) was performed using the meta R package and found substantial heterogeneity (I2 > 89%) driven by APOE, which represented almost 70% of the regression weights. As such, the P values for age-specific effects reported in the main text were Bonferroni-adjusted for a total of 12 statistical tests.This provided a relative change in effect size per parental age band. We then calculated the median survival from Kaplan-Meier survival curves of each age band, allowing us to place the effects on a years-of-life scale. For each locus individually, effect sizes of age bands were regressed against median survival of the age band, inversely weighted by the variance of the effect estimate . Coefficients of the loci underpowered to detect a trend individually (r2EUR > 0.6) were looked up in the GWAS catalog54 and PhenoScanner55 (accessed 14 October 2019). All genome-wide associations were included except triallelic SNPs, associations without effect sizes, and associations with healthspan, parental lifespan, longevity, or medications. Similar traits were then grouped together using approximate string matching—verified manually—keeping only the strongest association and the shortest trait name. For example, Body mass index, Body mass index in smokers, and Body mass index in females greater than 50 years of age were grouped and renamed to Body mass index. Associations were then categorised into seven disease phenotypes based on keywords and manual curation: Cardiovascular, metabolic, neuropsychiatric, immune-related, smoking-related, cancer, and age-related. Cardiovascular phenotypes included lipid levels, vascular traits, and diseases concerning the heart; Metabolic phenotypes included body (fat) mass and glycaemic traits; Neuropsychiatric phenotypes included neurodegenerative diseases and disorders of brain signalling such as restless leg syndrome and epilepsy; Immune-related phenotypes included measures of immune cells, and inflammatory and autoimmune disorders; Smoking-related phenotypes included smoking and lung function-related traits; Cancer included all neoplasms and carcinomas; Age-related phenotypes included traits typically associated with advancing age, such as age at menopause, male pattern baldness, age-related macular degeneration, hearing loss, and frailty. See Supplementary Data Lead SNPs from the multivariate GWAS and close proxies (57. The gene expression studies included Westra (cis-eQTL), CAGE (cis-eQTL), Vosa (cis- and trans-eQTL), and GTEx v761, the latter with eQTL P < 10–5 only. Estimates of SNP effects are needed for SMR but are not directly provided by the multivariate analysis. Instead, we derived Z scores from multivariate P values and signed these based on the sign of the sum of underlying healthspan, parental lifespan, and longevity Z scores. The HEIDI statistic is dependent on the heterogeneity between effect estimates. We therefore recalculated standard errors and effect sizes based on allele frequency and sample size, using formula 6 from Zhu et al.56. For sample size, we used the sum of individual studies’ effective samples  and performed a sensitivity test using the sum of all samples . Differences in PHEIDI between analyses were <0.0006, i.e., had no practical effect on results. A Benjamini–Hochberg multiple testing correction was applied separately to each eQTL dataset to account for the number of genes tested. Determining an optimal threshold for heterogeneity pruning is less straightforward: Wu et al.57 consider 5% to be too conservative, especially when using summary-level data and SNPs with different sample sizes, and set a 1% threshold to correct for three colocalisation tests. We apply the same threshold, which may still be conservative in our study as we test many  tissues and we expect additional heterogeneity due to inferred Z scores (see Discussion).For each locus of interest, gene expression was tested for colocalisation with SNP effects within 500 kb of the lead SNP using SMR-HEIDIcis or trans at FDR < 5% were tested for enrichment in 50 GO hallmark and 7350 biological process gene sets from the Molecular Signatures Database33, using a procedure analogous to Gene2Func in FUMA62. First, we translated all unique gene symbols from the eQTL datasets to Entrez IDs , and subsetted hallmark and GO biological process gene sets to only include genes for which eQTL were available. We then used a hypergeometric test to assess whether our genes were overrepresented in each pathway compared to all genes with eQTL. A minimum of three genes had to be present in a gene set for it to be tested for enrichment. Seven hallmark gene sets and 383 biological process gene sets met this requirement. Bonferroni correction was applied to account for multiple testing, separately for hallmark and biological process sets. Gene sets with Pbonferroni < 5% are reported.Genes colocalising with loci of interest in 18 to be used as exposures in a Mendelian randomisation (MR) analysis. Univariate MR was performed using the R package TwoSampleMR63, with instrumental variables defined as the lead genome-wide significant SNPs  shared between each iron-related trait and our meta-analysis . We calculated the effect estimates of our multivariate meta-analysis using the same method as described for SMR-HEIDI. For each iron-related trait, two inverse variance-weighted regressions were run with the iron-related trait as exposure and the inferred multivariate effect sizes as outcome: one without a fixed intercept to test for pleiotropy violation (MR Egger) and one with the intercept set to zero. We adjusted P values for multiple testing using the Benjamini–Hochberg method with an FDR threshold of 5% for significance. We also performed a leave-one-out analysis to assess whether the observed effects were robust to outliers.Summary statistics for serum iron, ferritin, transferrin, and transferrin saturation were obtained from Benyamin et al.64, which could fit a random-effects model and provides an estimate of the multivariate regression intercept but is otherwise identical to TwoSampleMR. As instrumental variables, we used the same iron trait-related lead SNPs as before, keeping only the SNP with the strongest association if multiple lead SNPs were located in the same 1 Mb locus (N = 15). We fitted a random-effects, inverse variance-weighted model, with and without fixed intercept, with the multivariate ageing trait as outcome, and as before, performed a sensitivity analysis using the leave-one-out method. The main analysis was then repeated using the same SNPs and effects derived from the original healthspan, parental lifespan, and longevity GWAS as outcomes. P values were adjusted for eight tests  using the Benjamini–Hochberg method.Multivariate MR was performed using the R package MendelianRandomizationFurther information on research design is available in the Supplementary InformationPeer Review FileDescriptions of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Reporting Summary


Plant breeders regularly evaluate multiple traits across multiple environments, which opens an avenue for using multiple traits in genomic prediction models. We assessed the potential of multi-trait (MT) genomic prediction model through evaluating several strategies of incorporating multiple traits  into the prediction models with two cross-validation schemes  in barley. The predictive ability was similar for single (ST-CV1) and multi-trait (MT-CV1) models to predict new lines. However, the predictive ability for agronomic traits was considerably increased when partially phenotyped lines (MT-CV2) were used. The predictive ability for grain yield using the MT-CV2 model with other agronomic traits resulted in 57% and 61% higher predictive ability than ST-CV1 and MT-CV1 models, respectively. Therefore, complex traits such as grain yield are better predicted when correlated traits are used. Similarly, a considerable increase in the predictive ability of malting quality traits was observed when correlated traits were used. The predictive ability for grain protein content using the MT-CV2 model with both agronomic and malting traits resulted in a 76% higher predictive ability than ST-CV1 and MT-CV1 models. Additionally, the higher predictive ability for new environments was obtained for all traits using the MT-CV2 model compared to the MT-CV1 model. This study showed the potential of improving the genomic prediction of complex traits by incorporating the information from multiple traits (cost-friendly and easy to measure traits) collected throughout breeding programs which could assist in speeding up breeding cycles. Then, the model is validated through cross-validation. Finally, genotypic or genomic estimated breeding values from a breeding or selection population are predicted based on their genotypic information  genomic prediction models use the information from individual lines and multiple traits simultaneously. Recent studies have evaluated the impact of MT prediction in genomic prediction models. For instance, a study of 495 wheat advanced breeding lines has included multiple baking quality traits in the genomic prediction model and identified that the predictive ability of unobserved individuals was increased compared to the single-trait (ST) model . The useInstituto Nacional de Investigacion Agropecuria (INIA), Uruguay . A total of 145 doubled haploid lines from those crosses were used for the model comparisons . The 145 lines were evaluated for agronomic traits for two years (2015 and 2016) in three locations  and three years  in another location [EEMAC ]. This resulted in a total of nine environments  for evaluating agronomic traits. Malting quality traits were evaluated from samples of three of the experiments EEMAC15, EELE15, and MUSA15. All the experiments were conducted in a partially replicated experiment (-1), number of grains per square meter, thousand grain weight (g), and grain plumpness . The thousand grain weight and number of grains per square meter were not measured in 2016 at MUSA. The malting quality traits evaluated were beta-glucan content (ppm), malt extract (% dry basis), soluble nitrogen (mg per 100 g), and grain protein content (%). Micro-malting was performed at the Laboratorio Tecnológico del Uruguay (LATU) and Malteria Oriental S.A. following a standard malting procedure.Four crosses using five parents were performed to create a large inter-connected population of 980 doubled haploid lines  at the ijkl is the trait of interest; i is the effect of the i-th environment ; Bj(i) is the effect of the j-th block nested within the i-th environment; jk is the effect of the genotype by environment interaction; and eijkl is the residual error. Genotype was assumed as a fixed effect, whereas environment, block nested within the environment, and genotype by environment interactions (GEI) were assumed as random effects. For individual experiments, BLUEs were estimated using the above-mentioned model excluding environment (E) and GEI effects. These analyses were performed using PROC MIXED in SAS 9.4 (The estimation of the genotypic best linear unbiased estimates (BLUEs) was obtained from the following model: SAS 9.4  using re2gσ is genotypic variance estimated using the ‘sommer’ package in R  was estimated using the ‘A.mat’ function in the ‘rrBLUP’ package in R  of phenotype on n genotypes for a single trait; 1 is a vector (n x 1) of ones of length n; μ is the overall mean; Z is the incidence matrix (n x p) with fixed known values of p markers for these genotypes, u(nx1) is a genotypic predictor with u∼N as shown in Genomic prediction models were evaluated with the 145 lines that had genotypic and phenotypic information. Single trait (ST) and multi-trait (MT) models for genomic prediction of agronomic and malting quality traits were obtained following Σ) and residual matrix (R) with 1,500 burn-in and 3,000 iterations for the Gibbs sample algorithm implemented in the ‘MTM’ R package (y is a vector of n x t length (n genotypes and t traits); 1 is a vector of ones of length nxt; μ is the overall mean; Znxt)xp][x1] is a genotypic predictor with u ∼ MVN and e is the residual errors vector with e ∼ MVN, where Σ is the variance-covariance matrix across traits, K is the realized additive relationship matrix among individuals estimated from the markers, R is the variance-covariance matrix for the residual effects for each individual in all traits, and I is the nxn identify matrix. Σ was estimated as an unstructured matrix and R as a diagonal matrix following The MT model was built using a Bayesian multivariate Gaussian model estimating an unstructured variance-covariance matrix between traits  to train the model. The remaining lines (40%) were predicted for the trait of interest using genotypic and phenotypic information from other correlated traits. This process was repeated 100 times where each iteration included different lines in the training and testing sets. The first cross-validation scheme was used for the ST (designated as ST-CV1) and MT (MT-CV1) models, whereas the second cross-validation scheme was used only for the MT (MT-CV2) model. In short, the CV2 scheme uses full genotypic information on every individual (training and prediction sets), full phenotypic information for correlated traits on every individual (training and prediction sets), and phenotypic information of trait of interest only on the training set. Therefore, CV2 only applies to multiple traits problems. Pearson’s correlation (PAr) between the genotypic values and genotypic BLUEs was estimated to determine the predictive ability of the models.Two genomic prediction models (ST and MT) were used to compare two main cross-validation schemes described previously . BrieflyGenomic prediction models for agronomic traits were tested using the BLUEs from an individual experiment, experiments across locations , years (2015 and 2016), and all combined (designated as ‘ALL’). Similarly, genomic prediction models for malting quality traits were tested using BLUEs from individual experiments conducted in 2015 , experiments combined across locations in 2015 (‘ALL15’) and using agronomic trait information from all nine experiments combined (ALL). Agronomic traits used in the genomic prediction models were grain yield, thousand grain weight, number of grains per square meter, and grain plumpness. For the MT model, two strategies were used for predicting agronomic traits of interest. The first strategy included predicting the agronomic trait of interest using all other three agronomic traits (AGRO). The second strategy included predicting the agronomic trait of interest using the remaining three agronomic traits and all four malting quality traits (A+M). Similarly, five strategies were used for predicting malting quality trait of interest as follows: (i) all four agronomic traits (AGRO), (ii) both agronomic and malting traits (A+M), (iii) positively correlated traits with grain protein content , and (iv) traits explaining the largest variance in the first principal component ; and (v) traits explaining the largest variance on the second principal component .https://doi.org/10.25387/g3.9172619.File S1 contains supplementary Tables S1-S6 and Figures S1-S6. Table S1 contains basic summary statistics with BLUEs, standard error, minimum, maximum, coefficient of variation, broad sense heritability, and proportion of variance component for genotype by the environment and genotypic effect for agronomic and malting quality traits evaluated across multiple locations and years. Table S2 contains an analysis of variance with mean squares for agronomic and malting quality traits. Table S3 contains genomic predictive ability for un-phenotyped environments using MT-CV1 and MT-CV2 models. Table S4-S6 contains predictive ability for agronomic and malting quality traits using MT-CV1 and MT-CV2 models. Figure S1 shows a principal component analysis of all individuals. Figure S2 contains a distribution of the 6,482 single nucleotide polymorphisms (SNPs) across the seven chromosomes of barley in 145 doubled haploid lines. Figures S3-S6 contain Pearson’s correlation among environments (locations-years) for grain yield, number of grains per square meter, grain plumpness, and thousand grain weight, respectively. File S2 contains the BLUEs for the agronomic traits  from individual experiments , experiments across locations , years , and all experiments combined (ALL). File S3 contains the BLUEs for the malting quality traits  from the experiments conducted in 2015 in three locations  and for the combined experiments (ALL15). Supplemental material available at figshare: -1), followed by EELE , EEMAC , and MUSA . Similarly, a wide variation across environments was observed for other traits.Basic summary statistics and analysis of variance of the agronomic and malting quality traits are in Variance component analysis showed that the proportion of the variance explained by the GEI effects to genotypic effects was greater than one for most of the agronomic traits whereas lower than one for malting quality traits (Table S1). Broad-sense heritability estimates for agronomic traits were low to high ranging from 0.20 to 0.80 (Table S1). Low to moderate heritability was observed for grain yield (0.20 to 0.66) and plumpness (0.25 to 0.53) whereas moderate to high heritability was observed for number of grains per square meter (0.46 to 0.71) and thousand grain weight (0.42 to 0.80). Relatively high heritability was observed for malting quality traits ranging from 0.66 (beta-glucan content) to 0.74 (soluble nitrogen) (Table S1).Grain yield was positively correlated with plumpness and the number of grains per square meter, negatively correlated with grain protein content, and not correlated with grain weight . Grain pIn the principal component analysis (PCA), the first two PCs explained ∼52% of the total variation . The PC1The model (MT-CV2) that included other agronomic traits from individuals to be predicted performed better for agronomic traits than either single (ST-CV1) or multi-trait (MT-CV1) models without this information  prediction model including both agronomic and malting (A+M) traits and compared it with the MT model including agronomic (AGRO) traits only  in the prediction model for both individual and combined experiments  had relatively lower predictive ability compared to the prediction for a location using information from other locations  (Table S3). In general, genomic prediction using the MT-CV2 model had a higher predictive ability for all traits compared to the MT-CV1 model for unobserved environments. For instance, grain yield predictive ability across locations using the MT-CV1 was up to 0.26 and the MT-CV2 model was up to 0.41. For prediction across years, the MT-CV1 model was up to 0.27 and the MT-CV2 was up to 0.32. The highest predictive abilities using MT-CV2 for grain plumpness, thousand grain weight, and number of grains per square meter were 0.52, 0.53, 0.52, and 0.46, respectively. For locations, on average, the highest predictive ability was observed at EEMAC for grain yield, MOSA for plumpness, EELE for thousand grain weight, and MUSA for number of grains per sq. m using the MT-CV2 model.Genomic prediction for a location using the information from other years . However, only a few studies have been conducted using multi-trait multi-environment genomic selection methods due to complexity of model and requirements of high computing power  and four malting quality traits  for use in a multi-trait genomic selection model. The proportion of variance explained by GEI was larger for agronomic traits than malting traits (Table S1), suggesting a large influence of GEI effects on agronomic traits compared to quality traits. The larger influence of GEI on agronomic traits resulted in a lower heritability for agronomic traits compared to quality traits. Lower heritability estimates for agronomic traits such as grain yield was expected as they are governed by many genes and are genetically complex in nature  were strongly positively correlated to each other. The positive correlation of grain yield with number of grains per square meter and no correlation with grain weight in the current study indicates that the grain yield was largely defined by the number of grains per square meter component. The negative relationship between grain yield and protein content identified in this study is well understood in cereal crops and is mainly associated with the dilution effect  across all combined experiments. For instance, the MT-CV2 model for grain yield across all combined experiments increased the predictive ability by 57% and 61% compared to the ST-CV1 and MT-CV1 models, respectively. This may be associated with the higher heritability estimates of combined environments compared to individual environments. The higher predictive ability in genomic prediction models for a trait is expected when the heritability is higher . Genomic selection using multiple traits collected throughout the breeding program could be useful for improving the genomic prediction of complex traits of interest. We evaluated strategies for adding multiple traits and compared their impact on the predictive ability of the single (ST-CV1) and multi-trait genomic prediction models (MT-CV1 and MT-CV2) on the predicted genotypic values for a trait of interest in barley breeding program. We found that including agronomic traits that are relatively easy to score and inexpensive improves the prediction of more complex traits such as grain yield and expensive/difficult to measure traits such as malting quality traits. These results can have a large impact on breeding programs where improving grain yield is a major goal and malting quality evaluations are not performed routinely in all individuals, but agronomic evaluations are. Our results suggest that including genotypic information and a model that includes all the collected data, breeders can improve the identification of superior lines with a small increase in the costs. Using these strategies could significantly change the way that phenotyping trials are set up in the future, and also the way that individuals are chosen to be phenotyped for malting quality traits.


Erasmoneura vulnerata, a Nearctic leafhopper occurring on grapevine which is rarely damaging in North America, has become a new pest in European vineyards. Winegrowers are worried because of severe leaf symptoms potentially associated with yield losses and the nuisance posed when large numbers of adults occur at harvest time. Outbreaks were detected in conventional vineyards despite the use of broad-spectrum insecticides as well as in organic vineyards treated with pyrethrins. Therefore, the identification of effective control tools is required. Studies on E. vulnerata phenology have found that the second generation produces the largest population densities. We planned field trials to establish the most effective insecticides to be applied in conventional and organic vineyards. The most effective synthetic insecticides were acetamiprid, flupyradifurone and lambda-cyhalothrin, while the most effective natural product was kaolin.Erasmoneura vulnerata, detected in Europe in the early 2000s, has recently become a pest in North-Italian vineyards. Infestations were recorded in organic and conventional vineyards despite the application of insecticides targeting other pests. Erasmoneura vulnerata completes three generations per year, and the second generation is frequently associated with large populations. The selection of appropriate active ingredients and the timing of their application is crucial for effective pest control. Field trials were carried out in Northeastern Italy, using a randomized design, to evaluate the impact of insecticides applied against other grapevine leafhoppers on E. vulnerata populations. The beginning of the second generation was selected as the best time for insecticide application. For natural products, two applications were planned. Among the selected insecticides, the most effective were acetamiprid, flupyradifurone and lambda-cyhalothrin. Regarding natural products, the most effective was kaolin which could represent an alternative to pyrethrins in organic vineyards. The identification of pest threshold levels and the evaluation of side effects of the most effective insecticides on key natural enemies occurring in vineyards are required.The American leafhopper  Empoasca vitis (Göthe) and Scaphoideus titanus Ball. Empoasca vitis has been considered a pest in France, Italy, Switzerland, and other countries [E. vitis declined, probably because of the selection of strains resistant to pesticides [E. vitis populations are limited by a number of natural enemies, namely the Hymenoptera Mymaridae  involved in the Flavescence dorée, a destructive disease of European vineyards [E. vitis, the efficacy of organophosphates towards S. titanus has remained satisfactory [ineyards ,18,19. Iineyards ,21,22. Isfactory  and thussfactory ,24. Recesfactory ,26,27. Hsfactory ,22,28.Erasmoneura vulnerata (Fitch) (Hemiptera: Cicadellidae), first detected in Europe in 2004 [E. vulnerata was localized in unsprayed vineyards in Northeastern Italy, then it spread to Northwestern Italy, Slovenia and Switzerland [E. vulnerata have been detected, although insecticides were applied against S. titanus. Investigations on E. vulnerata biology, ecology and behavior have been planned to implement effective control measures. This pest can complete three generations per year. Overwintered adults can damage shoots at sprouting, but the first nymphal generation is usually not harmful. The second generation is associated with the highest population densities, while the third is sometimes a problem [E. vulnerata second generations. The results obtained in these trials are reported here.In this context, the American leafhopper  in 2004 , has bec in 2004 . Althoug in 2004 ,32. Inittzerland ,34,35. Ctzerland . Winegro problem . We condE. vulnerata populations were evaluated in three conventional vineyards located in Vicenza and Verona provinces  during the 2017, 2018 and 2019 growing seasons. In 2017, trials were carried out in a vineyard located in the Vicenza plain . In 2018, a hilly vineyard  located in the Verona province was selected for trials. The vineyard selected in 2019  was also located in the Verona province. In these vineyards, the occurrence of E. vulnerata had been reported in the season preceding the study. Insecticides commonly applied in vineyards  and other products  potentially useful for leafhoppers control were selected for trials  levels under a dissecting microscope.Trials were carried out according to a completely randomized design where each treatment comprised four replicates of 8–10 vines. Insecticides were applied (1 or 2 applications depending on label instructions) against the second generation of ulnerata . Samplin® . Data obtained in each field trial were analyzed separately. In all models, the number of nymphs per leaf was considered as a response variable with repeated measures made at different times. Insecticide, time of sampling, and their interaction were considered as sources of variation in the model and tested using an F test (α = 0.05). Multiple comparisons of the abundance of E. vulnerata on different treatments were performed using t-test (α = 0.05) on the least-square means. The degrees of freedom were estimated with Kenward–Roger method, which can calculate non-integer values for error terms. Prior to the analysis, data were checked for model assumptions. The model was run on data transformed to log (n + 1), while untransformed data are shown in the figures. The SLICE option of the LSMEANS statement was used to test treatment effect variation during observation periods.Data were analyzed using a repeated measures linear mixed model with the MIXED procedure of SASE. vulnerata nymphs. Adults of this species were rarely detected. Therefore, statistical analyses were carried out on total nymphs, early (I-II instar) nymphs and older (III-V instar) nymphs. In 2017, the effects of treatment and time were significant . Interaction “treatment*time” was not significant . No differences among treatments were found prior to the first insecticide application . Among insecticides, acetamiprid was more effective than potassium salts  (p = 0.058). The effect of time emerged for early  as well as for older nymphs . Interaction “treatment*time” was not significant for early  nor for older nymphs .When early (I-II instar) and older (III-V instar) nymphs were considered separately, differences among treatments emerged only for the former  . In this= 0.008) . ConsideE. vulnerata nymphs . Nymph densities fluctuated over sampling dates  and interaction “treatment*time” was significant  because the effect of treatment emerged after the first insecticide application onwards (p = 0.496). Higher E. vulnerata nymph densities were found in the control compared with the acetamiprid, lambda-cyhalothrin, mineral oil and kaolin treatments  or older nymphs  separately (p = 0.384) and older nymphs  separately, while differences among treatments emerged later. Considering early nymphs, acetamiprid and lambda-cyhalothrin were confirmed to be the most effective insecticides . Nymph densities changed over time  and a significant interaction “treatment*time” was found . There were no differences among treatments before insecticide applications , while differences emerged later  , while the effect of treatment emerged later  . No diffed later . Flupyraed later .E. vulnerata populations in two out of three trials. In 2019, its impact was slightly lower than that of flupyradifurone (IRAC Group 4D), a novel insecticide belonging to the Butenolides and recommended against sucking pests. Its effectiveness against E. vulnerata is consistent with those reported in the control of Erythroneura elegantula Osborn and E. ziczac Walsh in North America [E. vulnerata population densities at low levels for some weeks. Another neonicotinoid (IRAC Group 4A), i.e., thiamethoxam, proved to be very effective against E. vulnerata in 2017. It was largely employed against sucking insects, e.g., E. vitis and S. titanus [E. vulnerata in 2017 but not in 2018. Different vineyards were selected for these trials, and thus a variation in the susceptibility of leafhopper populations could explain the different results we obtained. It should be mentioned that the first outbreaks of E. vulnerata in Northern Italy were detected in vineyards frequently treated with chlorpyrifos-methyl. This observation suggests that resistance to insecticides could be a key factor explaining the unexpected outbreaks of this species [S. titanus, berry moths, and scales [E. vitis was strongly suspected [Among the products tested in this study, those based on acetamiprid (IRAC Group 4A) were the most effective in controlling  America . A singl titanus ,26,27,38 titanus ,40 and t titanus . It was  species . It shouuspected . ChlorpyS. titanus and other leafhoppers in organic vineyards in France, Italy and Switzerland [E. vulnerata. In 2017, pyrethrins significantly reduced nymph densities compared with the control, in 2018 they showed some effectiveness on older nymphs only, while in 2019 they showed unsatisfactory results in controlling leafhoppers. Mineral oils were effective in 2018 and it was expected they could increase the impact of pyrethrins when mixed; this assumption was verified in 2017 but not in 2019. Potassium salts were slightly effective in 2017 but were associated with poor results in 2018. Finally, kaolin (an inert white clay not classified as an insecticide) significantly reduced E. vulnerata densities in 2017 and 2018. It was more effective than chlorpyrifos-methyl, pyrethrins and potassium salts against early nymphs in 2018 trial. Kaolin was active on some grapevine pests [E. vitis and Z. rhamni [E. vulnerata and mechanisms underlining its mode of action are worthy of study.With regard to natural products, pyrethrin-based insecticides (IRAC Group 3A) are widely used against tzerland . In the ne pests , particu. rhamni . InhibitE. vulnerata in Europe have caused concern for winegrowers and suggest the value of testing the impact of a number of conventional or natural insecticides on this species in vineyards. Among the insecticides tested, the most effective were acetamiprid, flupyradifurone and lambda-cyhalothrin. A single application of these compounds reduced leafhopper population densities to low levels for some weeks. Regarding natural products, the most effective was kaolin, which could represent an alternative to pyrethrins in organic vineyards and a complementary tool in conventional vineyards. The use of insecticides should be selected at the correct time and once threshold levels are exceeded. Our knowledge of the biology of E. vulnerata allowed us to identify the best timing for insecticide application  but threshold levels have not been defined yet [Recent outbreaks of ined yet . Finallyined yet ,46. Infoined yet ,48 but k


Current selection in the Italian Heavy Draught Horse (IHDH) is based on linear type traits scored on foals, but the studbook admission of candidate mares and stallion requires an additional evaluation of linear type traits at adult age, about 30 months. The study intended estimating the genetic parameters of these traits to evaluate the possible shift from type scored on foals to adult. Results showed a moderate heritability of traits, and a positive genetic trend was observed in the traits of selection interest. This suggests the feasibility of using linear type traits scored at adult age for genetic improvement of the IHDH.The Italian Heavy Draught Horse (IHDH) breed is selected based on linear type traits (LTT) evaluated at young age on six-month-old foals. However, animals retained for reproduction are scored also at adults age (about 30 months), and the evaluation is mandatory for the final official admission to the stud book of candidate mares and stallions. This study aimed to estimate genetic parameters of LTT scored at 30 months to consider if they are feasible for selection instead of using foal data and to reduce costs of selection plan. Data included 19 years of evaluation for 14 LTT and an overall score. Analyses were performed on 5835 females and 856 males via animal model. The heritability ranged from 0.03 (upper line length) to 0.40 (frame size). Traits of selection interest  reported heritability between 0.21 and 0.31. High genetic correlations were obtained among traits related to muscular development, 0.73 on average. Positive genetic trends were found in traits of selection interest, already selected from foal type trait data. Accounting for genetic parameters estimated in adult animals instead in foals is feasible in IHDH selection. The goal of breeding organizations and breeders is to improve the genetic value of animals over generations. Breeding for conformation accounts aspects as the morphology and movement, and it is an important aspect of breeding decisions in many livestock species including pigs, sheep, cattle and horse . Traits The IHDH  is a natGenetic improvement is based just on the linear type traits obtained on six-month-old foals, five of them weighted in a total merit index (TMI) indicating the selective value of the horse ,47. The Traits scored on 30-month animals are not directly used for genetic improvement, but they are mandatory for the final admission of candidate stallions and mares to the stud book . They arFollowing these considerations, this study aimed to estimate genetic parameters of liner type traits in the IHDH evaluated at the age of about 30 months, when animals are young mares and stallions. Moreover, the study also aimed to assess the genetic correlations among these traits and to estimate the genetic trends realized for traits. In terms of genetic improvement of the breed, this is a challenge of using linear traits scored at 30 months rather than obtained at six months.An amount of 7133 records was obtained from data of the stud book of IHDH breed and from the linear type traits information routinely recorded by the National breeder association . Records prior to 1992, as well as horses without father and mother, horses lacking stud-farm, birth date or evaluation date were eliminated.The dataset suitable for analyses included single records of 6691 horses , aged about 30 months  and 11,012 individuals in pedigree. Linear type traits scored by 33 classifiers in 19 subsequent years of evaluation were considered. As in foals, linear type evaluation used a 9-point scale system .The traits were classified in three classes as follows: (i) Traits of general aspect: head size and expression (HS), temperament/movement (Te/M), frame size (FS), fleshiness (Fl), bone incidence (BI); (ii) Traits of the trunk: thorax depth (TD), fore diameter (FD), rear diameter (RD), upper line length (UL), upper line direction (UD); (iii) Traits of the limbs: legs side view (LS), fore feet (FF), rear feet (RF), hind legs back view (HL). In IHDH breeding management, animals with a light head (HS) are preferred for selection, as well as with a great reactivity to environmental stimuli and a regular trout (Te/m). High scores of FS must be preferred for selection. A great development of muscles masses of croup, thigh, buttock, loins and withers , a fine-boned frame (BI), a depth thorax (TD) and large chest (FD) and croup (RD) are also desirable. Intermediate optima are related to traits scoring the correctness of conformation that are UL, UD, LS, FF, RF and HL. The last three traits are scored only at 30 months. A detailed description of the traits is reported in The study also considered the overall score (OS) of conformation, subjectively scored only on 30-month animals and assigning a final morphologic judgement from “fair” to “excellent”.The traits measured at six months were not included in the present study, just focused on traits of 30-month animals. Anyway, an evaluation of the genetic correlation among six and 30 month-traits is currently under study and could be useful for future decisions about linear type traits evaluations.The non-genetic effects considered for the analysis included the sex of the animals and the age at scoring (in month). The combined effect of the classifier and of the year of evaluation was also considered, as for Italian the Haflinger .A variable called “stud group” was formed to consider the effect of the small studs, i.e., with less than two animals scored within a year of evaluation. This was carried out on the basis of geographical position and management , the farm’s production goal , the general prophylaxis on foals (vaccination or not) and the mean value mares ‘body condition registered at foals’ evaluation. In this way, groups were created for neighboring studs with similar nutrition and management. A detail description of stud group constitution and evaluation has been reported in a previous study on linear type traits in IHDH foals . TherefoA preliminary ANOVA  was run on the non-genetic effects to be included in the genetic model: the combination of stud-group classifier—year of birth ; the sex of animals (2 levels); the age at scoring . The classes of age at scoring were built on the basis of the frequency of the individual ages.y was the vector of observations for one of the 14 traits recorded on a single animal; β was the vector of the same fixed effects of the ANOVA; u was the vector of the random additive genetic effect ; e was the vector of the random residual terms; X and Z were the incidence matrices assigning observations to the related effects.Variances of traits and (co)variance components for all the 14 linear type traits and the OS and for each pairwise combination between traits, were estimates via Average Information REML method (AIREML ) using sThe assumptions about the structure of (co)variances for bivariate analysis run on each trait pair were written as:r) were computed as follows [gr or phenotypic, pr) and the other terms are the (co)variances of the traits (1 and 2) and their predicted error (co)variances. Significance of phenotypic and genetic correlations was tested following Kohn and Atchley [The standard errors of the heritability SEh2= follows :(4)SEr=r Atchley .The genetic trends for all traits of the study were traced from the average breeding values (EBVs) of individuals born in the same year found running a BLUP analysis after AIREML estimations  ). EBVs wMeans and standard deviations of traits, as well as minimum and maximum values of their scores are reported in p < 0.001), as well the sex, except for temperament, thorax depth, rear diameter, legs side view, hind legs back view and overall score. The age at evaluation was significant only for temperament, frame size, fleshiness, thorax depth, fore and rear diameter, legs side view and overall score. Residual variance, expressed as root mean square error, ranged from 0.08 (hind legs back view) to 0.43 .The results of preliminary ANOVA are reported in Estimated variances, heritability and standard error are presented in a2 = 13.98), overall score (σa2 = 13.47) and head size (σa2 = 10.09), while the traits with the lowest values were upper line direction (σa2 = 0.28), rear feet (σa2 = 0.40) and hind legs back view (σa2 = 0.54). The standard errors (SE) of genetic variances ranged from 0.19 (legs back view) to 1.47 . Residual variances were higher than genetic ones; higher values were found for overall score (σe2 = 30.22), head size (σe2 = 22.20) and frame size (σe2 = 20.92), whereas lower values were found for legs back view (σe2 = 7.98) and upper line direction (σe2 = 8.41). Their SE values were in the same range of the ones of the genetic variances.The traits showing the greatest genetic variance  were fra2 = 0.03 to h2 = 0.40. Looking at the results, the most heritable traits were frame size (h2 = 0.40), head size (h2 = 0.31), fore diameter (h2 = 0.31) and overall score (h2 = 0.31) while the lowest values were obtained for the correctness traits of upper line direction (h2 = 0.03), rear feet (h2 = 0.03), legs back view (h2 = 0.06) and fore feet (h2 = 0.08). Temperament/movement, fleshiness, thorax depth and rear diameter had moderate heritabilities (ranging from h2 = 0.21 to h2 = 0.27). Standard errors of heritability were low and ranged from SEh2 = 0.017 (rear feet) to SEh2 = 0.033 (frame size).Heritabilities obtained for linear type traits were low or moderate depending on traits and ranged from hgr) were found between upper line direction and rear feet (gr = −0.99) and between bone incidence with both temperament (gr = −0.74) and head size (gr = −0.64). Legs side view was negative correlated with most traits . The greatest gr were found between fleshiness and both fore diameter (gr = 0.74) and rear diameter (gr = 0.91) and between rear diameter with fore diameter (gr = 0.85). Thorax depth showed as well a great gr with fleshiness (gr = 0.55), fore and rear diameter . The trait was also highly correlated with frame size (gr = 0.71), the latter highly correlated also with rear diameter (gr = 0.73) and moderately with fleshiness (gr = 0.45), fore diameter (gr = 0.52) and upper line length (gr = 0.41) and direction (gr = 0.38). Moderate and significant positive gr of bone incidence regarded frame size (gr = 0.22) and fore feet (gr = 0.36). This latter trait was also positively related with fleshiness (gr = 0.33) and fore diameter (gr = 0.35). Head size has a great positive gr with temperament/movement (gr = 0.67) and a moderate, but significant rg with fore diameter (gr = 0.18). Temperament/movement was moderately correlated also with frame size (gr = 0.24) and legs back view (gr = 0.25). The overall score was significantly positively correlated with most of traits: head size (gr = 0.45), temperament/movement (gr = 0.47), frame size (gr = 0.85), fleshiness (gr = 0.61), thorax depth (gr = 0.72), fore and rear diameter . Negative, but not different from zero gr were found only with fleshiness, rear feet and legs back view. The approximate genetic standard errors ranged from SEgr = 0.007 (udder depth vs. rear size) to SEgr = 0.327 (udder depth vs. legs side view). Genetic correlations with great SEgr were almost not different from zero.Estimates of genetic and phenotypic correlations between traits pairs are reported in pr) had the same sign, but lower values than the respective genetic correlations. Different signs were found when the correlations were not different from zero. The trait that showed the greatest correlations with the others was the overall score with frame size (pr = 0.58), rear (pr = 0.54) and fore diameter (pr = 0.52). Rear diameter exhibited as well a positive and medium-high correlation with fleshiness (pr = 0.51), as well as fore diameter (pr = 0.50). Fleshiness showed positive pr with fore diameter (pr = 0.47) and rear diameter (pr = 0.51). Another positive pr was fore between diameter with frame size (pr = 0.47). The legs side view was negatively correlated with most traits (pr from −0.10 to 0.11). The lowest pr were found between head size and bone incidence (pr = −0.29) and temperament/movement with bone incidence (pr = −0.18). The standard errors of pr were lower than genetic ones and ranged from SEpr = 0.011  to SEpr = 0.016 (head size vs. fleshiness).Generally phenotypic correlations  in Spanish Asturian beef cattle [2 = 0.25 to h2 = 0.34 for shoulder, back and rump muscling scores were found in Czech Beef Cattle [2 = 0.12) to the one of this study.Linear type evaluation in IHDH involves 12 traits that are individually scored plus two traits that are a combination of others, frame size and fleshiness. Differently, the final overall score of conformation, considered only at 30 months evaluations, is subjectively scored. Genetic parameters estimated in the present study concern several traits that have been also valued in a number of horse populations, like the traits related to correctness of body and legs. But looking at the traits related to muscular development (summarized in fleshiness in IHDH), the estimates obtained in this study are difficult to compare within other horse breeds, due to the lack of similar studies. The IHDH a bulky horse mainly selected for meat, so comparison can be made with cattle hypertrophic breeds. As a matter of fact, some associations of traits under selection and myostatin gene (MSTN), which functional mutations contribute to hyper-muscularity in various mammal species, have been recently found in IHDH . Lookingese cows  reportedlue cows , while af cattle . Then, vf Cattle . Anotherf Cattle  and was 2 = 0.16 for fore quarter in Noriker horse, whereas Molina et al. [2 = 0.18, whereas the work of Vicente et al. [2 = 0.12 on chest and thorax trait. Finally, Bakhtiari et al. [2 = 0.22 for the morphometric measurement of chest width in the Iranian Thoroughbred. Some works [2 = 0.40 in Andalusian and h2 = 0.12 in the Lusitano horse).Other traits of interest for meat purpose, fore diameter and rear diameter, were valued also in other horse breeds than IHDH because they are important for the heavy draught. Heritability values not in agreement with those obtained in this study were reported by other Authors that linearly scored the same body part. Specifically, Druml et al.  found a a et al.  and Misea et al.  estimatea et al. , reportee et al.  on Lusiti et al.  found a me works ,28 inclu2 = 0.11) and in the Lusitano horse for croup ([2 = 0.15). Values of heritability similar to the present study were found in the Dutch Warmblood ([2 = 0.28), in the Noriker ([2 = 0.20) and in the Bardigiano ([2 = 0.25), whereas a greater value of h2 = 0.59 was found in the Pantaneiro horse [The rear diameter heritability found in IHDH in the present study is higher than those found in the Italian Haflinger  for crour croup .The combined trait of frame size reported heritability greater than the values of the single traits constituting this phenotype that are diameter, thorax and of the height. An overall evaluation of the frame was also found in some other horse breeds, such as the Hanoverian Warmblood  and in Italian Haflinger ([2 = 0.24 as average of head volume and expression). Similar heritability values were found for head and neck in Andalusian horses ([2 = 0.23), in Dutch Warmbloods ([2 = 0.21) and in Lusitanos ([2 = 0.18). Differently, in the Bardigiano horse three h2 values of head  were reported [2 = 0.55) and head width (h2 = 0.27) were found [2 = 0.39 for the morphometric measure of head length was found in the Iranian Thoroughbred [2 estimates may be explained by breed variability, the selection goal and the evaluation method . About IHDH, a light head is preferred (higher score) because it relies with a greater elegance of the individual and also correlates with a lower bone incidence, preferred for the meat purpose.Regarding the other traits under selection in IHDH, head size is widely evaluated in horse breeds regardless the breeding purpose. Lower heritabilities than in this study were found in Noriker  for the same trait and in the Haflinger for gait ([2 = 0.19). In this breed a low heritability (h2 = 0.06) was found for temperament, but the definition of the trait is a bit different than in IHDH. Furthermore, in Andalusian [2 (0.08) was found for temperament. Notwithstanding, movement, which definition partly overlaps the one of temperament in IHDH, had an heritability of 0.15 in this Andalusian and of 0.20 in the Noriker [The last trait included in the selection index, the temperament/movement, is not a type traits, but it is often scored in horse breeds because it concurs to the general framework of individual body appearance. In IHDH the trait evaluated both the docility and the regularity of the movement, often separately considered in other horse breeds. The heritability for this trait is similar to the value found in the Bardigiano  than in IHDH.The heritabilities of the traits scoring the correctness of body and legs are low; this is probably because they are related to a proper conformation of the animal and have intermediate optima. The heritabilities for the linear scores of legs ranged from hand pony , from h2aflinger , from h2Lusitano . An estier horse  and was 2 = 0.37 close to the IHDH overall score. Lower estimates were found in the Lusitano horse ([2 = 0.14) and in the Sardinian Anglo Arab Horse ([2 = 0.23). Different aspects are likely to be valued for providing an overall morphologic judgement in breeds with different purposes and the heritabilities estimated, as well as the genetic correlations with the other traits scored, reflect the selection purposes of the breeds [A final morphologic overall score was also considered in other horse breeds to summarize the conformation evaluation. The type in the Noriker draught horse showed a ho horse  were found. However, the genetic correlation between head–neck and croup was moderate (gr = 0.24). In the Andalusian [gr = 0.22 for head–neck with both chest-thorax and croup-tail were found. In the Noriker [gr = 0.52 between the morphometric measures of head length and chest width was found in Iranian Thoroughbred horses [Genetic correlations between head and traits related to muscularity excluding fore diameter did not differ from zero. Similarly, in the Lusitano  low genedalusian , genetic Noriker , a genetd horses . Differed horses .The high and positive genetic correlations of frame size with thorax depth and rear and fore diameter arise because the evaluation of frame size also include the two traits. Conversely, the high and positive genetic correlations of fleshiness with fore and rear diameter and thorax depth reflect the fact that wide diameter offer more space for muscle masses and the thorax develops consistently with diameter. Fleshiness had a low and not different from zero genetic correlation with bone incidence, as expected since bone incidence is measured in relation to the muscle development.gr (0.52) between chest-thorax and was found in Lusitano [gr = 0.37 was reported in the Banei Draught Racehorse [Briefly looking at horse literature, an high value of Lusitano  and a vaLusitano . An averacehorse .gr = 0.61) between correctness and hind feet was reported in the Noriker horse [The genetic correlations among traits related to conformation correctness suggest strong relationships between the defects occurred in different body parts, between the upper line direction and in the rear feet and between fore and rear feet. An example of high genetic correlation rg = 0.1 betweengr = 0.88), lower for meat traits like fleshiness and, indirectly, thorax depth (average gr = 0.61) and intermediate for temperament. The evaluations at the two ages are able to take into account different aspects of animal career. The six-month evaluation intends to appreciate the meat attitude of the breed. Fleshiness is more heritable indeed in young animals, as well as traits related to the skeletal development, including fore diameter and rear diameter. On the other hand, also the heavy draught is important and evaluating the traits more related to this attitude (head size and temperament/movement) at 6 months or 30 months does not provide many differences, due to the similar heritabilities of traits [The genetic trends of traits are consistent with traits heritability, the genetic correlations among traits, the selection purposes of the breed and the biologic meaning of the linear scoring. Linear type traits are typically defined with the extreme scoring corresponding to biologic extremes, therefore a minimum score of one measured today is different from the score of one measured years ago, in terms, e.g., of body measurement of the trait. This is due to the occurrence of genetic improvement, that is able to change the average value of traits over time . The tref traits  and to tTo conclude, it is possible to observe that genetic parameters of traits recorded at 30 months of age are consistent with estimates obtained in traits recorded in young foals  and curr2 = 0.21 to h2 = 0.31). The traits involved in the muscular development that are fleshiness and fore and rear diameter, had moderate high heritability estimates, indicating that selection can be used for these traits and a suitable response will be found. These traits are all positively correlated, and the genetics correlations were very high, ranging from gr = 0.74 to gr = 0.91, while phenotypic correlations were lower, ranging from gr = 0.47 to gr = 0.51. Most of the genetic correlations between traits included in the selection index are moderate-high, meaning that selection for one of these traits should result in an increase in the other traits of interest. Positive genetic trends were observed for traits of selection interest despite that breeding values are estimated on traits scored at young age. Evaluations at the age of 30 months are however mandatory for the final admission at stud book of candidate stallions and mares. This study has shown that the use of traits scored in young mares and stallions instead that in foals is feasible in IHDH selection.The results about morphologic evaluation indicate that the goal of selection is to obtain an animal with a good muscular mass, but elegant and brilliant in the movements. The traits included in the TMI are head size, temperament/movement, fleshiness, fore diameter, rear diameter that have a medium high heritability (ranging from h