Information on phenotypic and genetic (co)variance for production traits inturbot is required to improve breeding programs. So far, information onmorphometric growth traits is sparse and completely lacking on qualitycarcass traits like fillet weight or fillet yield for turbot. As part of along-term study we explored the phenotypic and genetic (co)variance of 16biometrical and carcass traits of three different European turbot strains.Fish were reared under commercial grow-out conditions, including sizegrading. We used molecular relatedness (MR) methods based on genotyping with96 microsatellite markers and animal models. We included an adapted conditionfactor for Pleuronectiformes (FCI In Europe, turbotis mainly valued as a high-priced food fish on the upscale gourmet market,with the vast majority of production taking place in Spain and Portugal.Smaller aquaculture facilities for turbot are located in France, Norway,Denmark, and the Netherlands. Turbot is primarily sold whole, with a smallerportion of aquaculture production being sold either gutted or filleted. Regardless of thesedifferent marketing strategies, the improvement and harmonization of growthtraits remains one of the biggest and economically most relevant challengesof turbot breeding programs. Despite a recent rise in the development ofgenetic and genomic resources for turbot, reliable estimates of geneticparameters are still needed to improve existing breeding programs. Studies onheritability and genetic correlations in turbot have mostly been focusedexclusively on body weight as the trait of the highest economic value  and have revealed moderate levels ofgenetic variation and therefore potential for selection. However, even whenfish are marketed whole, traits like fillet weight, fillet yield, and dressingpercentage are of great importance with regard to customer preferences andfinancial profits  and have therefore been incorporatedas additional selection criteria in many of the established breeding programsfor other important aquaculture species . Additionally,carcass traits like the hepatosomatic index (HSI) and morphometric traitslike length, width, or Fulton's condition index (FCI) can be used as readilyobtainable indicators for health and fitness conditions. Possibleinteractions on a genetic level between traits of interest have to bequantified to avoid unfavorable correlated selection response. As far as weknow, this is the first time that a quantitative genetic analysis of carcasstraits in turbot has been done. As part of a long-term study we explored thegenetic and phenotypic (co)variance of 16 biometrical and carcass traits ofdifferent turbot strains under commercial grow-out conditions using molecularrelatedness (MR) and restricted maximum likelihood (REML) methods. In addition to standard traits like weightand length, we included FCI and average daily weight gain (ADG) between theages of 300 and 500 d post-hatch (dph) to explore their potential correlationwith body weight at harvest. Fulton proposed his condition index to measurefish health in 1904, assuming that the standard weight of the fish isproportional to the cube of its length. Since this assumption is based on themorphometrical traits of roundfish, we additionally included an adaptedcondition factor for Pleuronectiformes . Fish weregenotyped using a panel of 94 polymorphic microsatellite markers (Table S1 inSupplement) that were tested in a small subset of fish and chosen based ontheir high polymorphism information content. Markers were selected to beevenly distributed across the genome , covering 22 of24 linkage groups with an average inter-marker distance of 10.5 cm. Primerpairs were designed with the Primer 3 software .Primer sequences and marker information are presented in Table S1. MultiplexPCR amplifications were performed using fluorescence labeled primer pairs,the Qiagen Multiplex PCR kit , and an MJ ResearchPTC-200 thermal cycler under the following cycling protocol: initial denaturation at 95 2.4All data handling and statistical computing were done in R  unless otherwise specified.As neither pedigree information nor DNA samples of possible parents wereavailable for strains C and D, we estimated molecular relatedness for thetotal dataset based on co-ancestry using the similarity index described byEding et al. (2001):2.5For the estimation of heritability we used a univariate additive animalmodel:The model includes the fixed effects of strain, contemporary housing group,and gonadal sex. The filleting person was included in the model only for traitsthat were affected. The age curve was modeled by applying Legendre polynomialsof degree 5. Polynomials of this order were chosen since we experiencedconvergence issues in some traits with lower-order polynomials. Polynomialsof degree 5 yielded consistent results across all traits. We utilized therestricted maximum likelihood method as implemented in the WOMBAT softwarepackage  to calculate variance components. The random effect of“animal” is distributed proportionally to the “arbitrary” relationshipmatrix, which has been supplied to WOMBAT as the inverse. Molecularrelationship information was supplied to WOMBAT as a positive-definite,general inverse matrix file. Fixed effects were tested for significance foreach trait by stepwise forwards selection comparing Akaikes information criterionand Bayes information criterion (AIC and BIC) values and bychi-square statistics  for all traits at the time of slaughter. In the complete dataset,mean BW was 765.5 g with a high standard deviation of 281.9 g. Mean FW was227.5 g, which translates to a mean fillet yield (FY%) of 29.7 %. Theadapted Fulton's condition index . Heritability estimates for theeconomically most important traits BW, FW, and FY% were low with 0.19,0.18,and 0.15, respectively. Estimates of heritability for individual carcasselements GU, CA, IW, and HE were also low between 0.14 and 0.19, with theexception of LI, which had a moderate heritability of 0.24. For morphologicalproduction traits heritability estimates were within the same range (L: 0.18and A: 0.12), apart from W with a heritability of 0.08. A comparison of FCI(0.25) and FCISeparate analysis of strains using the same models generally resulted inhigher heritability estimates, particularly for BW (0.27–0.31), FW(0.25–0.29), and FY% (0.14–0.23). Standard errors of all estimates wereslightly higher than in the complete dataset. Estimates for biometricaltraits were also higher in both separate strains, although trends andpatterns between those traits remained the same with W (0.12–0.16) showingthe lowest estimates and L (0.24–0.35), ADG (0.16–0.36), and DP%(0.12–0.43) showing the highest heritabilities. Whileestimates for FCI were similar between strains, heritability forFCI3.3Genetic and phenotypic correlations between BW (the target trait forselection) and all other traits are given in Table 4, while a complete tableof all correlations for all traits can be found in the Supplement (Table S2).Phenotypic correlations between BW and other weight-based, as well asmorphometric, traits were generally high (0.70–0.94). FW, HE, and CA hadphenotypic correlation estimates higher than 0.9. Genetic correlations forthese combinations were also above 0.9. Of the morphometric traits A and Whad the highest positive correlations (> 0.9) with BW. Whilephenotypic correlations of both condition indices with BW were weak, we foundmoderate positive genetic correlation for BW–FCI3.4The heritability estimates we obtained in this study are low to moderate,indicating potential for improvement via selective breeding programs. Anumber of studies on genetic parameters for body weight at harvest exist inturbot, which generally report slightly higher estimates.Sánchez-Molano et al. (2011) reported heritability estimates of 0.45 forbody weight in their study. Xu et al. (2015) estimated heritability estimatesranging from 0.2 to 0.4 for body weight under different temperature regimes,whereas Gjerde (1997) reported higher estimates ranging from 0.4 to 0.7. Wesuspect that our results might be slightly underestimated due to both the useof molecular relatedness and the joint analysis of multiple strains. So far,there are only two publications using MR to estimate genetic parameters inflatfish: Blonk et al. (2010) calculated heritability for body weight incommon sole (0.11–0.13), while Guan et al. (2015) compared the MR method tothe classical pedigree-based approach and found heritability estimates forbody weight at 100 dph of 0.19 (MR) and 0.66 (pedigree). Lower values forestimated variance components based on molecular relatedness have also beenreported in a number of other species  and have been potentially linked to the denseness of the relationshipmatrix. Powell et al. (2008), among others, discussed a lack of comparabilityof different populations within the same species, which might partly be due togenetic differences in founder animals. Although the calculation of the genomicrelationship matrix includes a standardization step to minimize foundereffects, we received higher estimates in all traits when we analyzed strainsseparately, an effect that can be expected for within-family varianceestimation. Since most turbot breeding programs in the different Europeancountries treat their populations as separate from each other, these resultscould be considered more realistic.To our knowledge, this is the first time that genetic parameters for carcassand fillet traits are being reported for this species. Despite the obviousdifficulties in direct comparability with other aquaculture species,particularly roundfish, it can be stated that our results seem to be within areasonable range. Saillant et al. (2009) reported heritability estimates of0.25 for FY% in sea bass, while Navarro et al. (2009) presentedheritability of 0.15 for FW and 0.12 for FY% in gilt-head sea bream. Neiraet al. (2004) found very similar estimates  in cohosalmon, whereas heritability estimates in other salmonids are usually muchhigher . Many of theabove studies reported problems in the analysis of mathematically derivedratio traits, which we experienced as well, especially regarding correlationsinvolving those traits. Powell et al. (2008) and Rutten et al. (2005), inparticular, argued the usefulness of yield traits as selection criteria sinceratios are calculated from closely proportional traits. Consequently, wetried an alternative, indirect method to calculate heritability for filletyield by including body weight as an extra covariable in the model for filletweight, but we received close to identical results from both methods (resultsnot shown here). Alternative methods to measure and quantify ratio traits areneeded to make them more useful as selection target traits. Efforts have beenmade in other livestock species to develop direct, in vivo methods for filletor carcass yield determination based on X-rays, computer tomography, or ultrasound .Genetic correlations between weight-based traits that were directly measuredwere all positive and high (> 0.7) . We also found high positive correlations for the target traits BW andFW with morphometric traits. The highest correlations were found for A,which makes it a very suitable alternative selection trait. The use ofmorphometric traits such as length, width, or area as alternative selectioncriteria is particularly interesting since selection candidates do not haveto be sacrificed to determine these measurements. With the advance ofnoninvasive, camera-based measurement techniques handling stress could befurther minimized as well.Another interesting selection trait could be ADG, since it is comparativelyhighly heritable (0.3) and strongly genetically correlated with body weight atharvest (0.88), but it can be measured nonlethally at significantly earlierlife stages (300–500 dph in this case).While a prior study  suggested the adapted conditionindex for Pleuronectiformes FCI4This study presents heritability estimates and genetic correlations forweight and carcass traits in turbot. Estimates of heritability forweight-related traits were moderate, thus indicating that there is inter-individualvariation and selection on these traits is beneficial. We were able todemonstrate the usefulness of biometrical body traits as alternatives forselection and found no potential for inadvertent negative selection responsebetween body weight and carcass quality traits.10.5194/aab-62-265-2019-supplementThe supplement related to this article is available online at: https://doi.org/10.5194/aab-62-265-2019-supplement.


PPP1R36-PLEKHG3 and rs1028727 near SERPINE3 at genome-wide significance that replicate in independent Asian cohorts imputed to 1000 Genomes. At this point, validation of these variants in POAG cohorts is hampered by the high degree of heterogeneity. Our results show that multi-trait analysis is a valid approach to identify novel pleiotropic variants for ONH.A new avenue of mining published genome-wide association studies includes the joint analysis of related traits. The power of this approach depends on the genetic correlation of traits, which reflects the number of pleiotropic loci, i.e. genetic loci influencing multiple traits. Here, we applied new meta-analyses of optic nerve head (ONH) related traits implicated in primary open-angle glaucoma (POAG); intraocular pressure and central corneal thickness using Haplotype reference consortium imputations. We performed a multi-trait analysis of ONH parameters cup area, disc area and vertical cup-disc ratio. We uncover new variants; rs11158547 in  PPP1R36-PLEKHG3 and SERPINE3 by multi-trait analysis in European-ancestry cohorts that were replicated in independent Asian cohorts.The International Glaucoma Genetics Consortium reports a genome-wide association study meta-analysis of optic disc parameters relevant to primary open-angle glaucoma. They identify two novel variants in  Primary open angle glaucoma (POAG) is the most prevalent type of glaucoma accounting for 74% of all glaucoma cases3. Intraocular pressure (IOP) and the morphology of the optic nerve head (cup area (CA), disc area (DA), and vertical cup–disc ratio (VCDR)) are important features of the glaucomatous process. For each of these traits, twin studies showed a high heritability 4. Central corneal thickness (CCT) is also a highly heritable trait (h2CCT = 0.68–0.95)5, which is most likely non-physiologically associated with POAG, but rather biases IOP measurement, the major risk factor of POAG7. CA, DA, and VCDR, are significantly correlated both at the genetic level8. The high genetic correlation found between the optic nerve head (ONH) traits (Rg = 0.31–0.83) raises the question whether multi-trait analyses will improve the statistical power of the individual GWAS and will find variants with pleiotropic effects9.Glaucoma is the most common cause of irreversible blindness in the worldnMAX = 31,269) to haplotype reference consortium (HRC) release 1 imputation panel, which includes over 39 million variants10. A meta-analysis of these 12 European ancestry studies served as a discovery cohort in the analyses. Replication was performed in five Asian ancestry cohorts that were part of the IGGC. The cohorts of Asian descent were imputed to 1000 Genomes as there is little gain in HRC imputation in this ancestry group because there are no additional Asian samples included in HRC (http://www.haplotype-reference-consortium.org/participating-cohorts)11. We evaluated the added value of multi-trait analyses using two programs: CPASSOC and multi-trait analysis of GWAS (MTAG). Both use aggregated GWAS results. Whereas CPASSOS performs a meta-analysis assuming homogeneous and heterogeneous effects across traits by applying a inter-trait correlation matrix, MTAG basically increases the power of a single trait analyses by incorporating the GWAS findings of correlated traits.For this study, we generated new data on these 5 quantitative traits by imputing 12 European ancestry cohorts from the International Glaucoma Genetic Consortium (IGGC)  and Iglesias et al.12  based on 1000 Genomes imputation. Supplementary Fig. 8 and Iglesias12 based on 1000 Genomes. Out of 113 (95%), 107 available variants in HRC replicated at a Bonferroni significance level.As a validation we first confirmed previously identified loci for CA, DA, VCDR, IOP, and CCT by Springelkamp et al.nCA = 24,493, LDSC interceptca = 1.024 (SE = 0.0083); nDA = 24,509, LDSC interceptDA = 1.041 (SE = 0.0071); nVCDR = 25,180, LDSC interceptVCDR = 1.029 (SE = 0.0081) Supplementary Data 13, we identified three new loci at p < 5 × 10−8 by CPASSOC’s SHom   , rs11158547 near PPP1R36-PLEKHG3 in the CA analysis (p = 2.13 × 10−08) and rs1028727 near SERPINE3 in the DA analysis (p = 4.50 × 10−09) (Supplementary Data PPP1R36-PLEKHG3) and rs1028727 (SERPINE3) displayed nominally significant association in the multi-trait analysis (CPASSOC and MTAG) in individuals of Asian ancestry (Supplementary Datas r2 < 0.1) with neighboring known variants near SIX6, DDHD1, and DLCK1.In the single trait meta-analyses of ONH traits  in those of European descent  near TCF4 on 18q21.2. rs9853115 failed replication in the Asians (p = 0.9315) and rs150202082 could not be examined since this variant was monoallelic in the Asian individuals. A GWAS by Choquet et al.15 also identified rs9853115 as new variant associated with IOP in multiethnic cohort of predominately (83%) European ancestry. The same study also identified a novel variant near TCF4, rs11659764, approximately 300 kb upstream of rs150202082 which was in relatively weak LD (r2 = 0.4). In a recent study from the UKbiobank by Khawaja et al.16 the same variant near DGKG showed genome-wide significant association with similar effectsize, however, rs150202082 near TCF4 could not be validated in this study.Next, we conducted a single trait meta-analysis for IOP and for CCT, the two traits that are not likely physiologically related. For IOP, we meta-analyzed a total of 31,269 participants . We identified 31 independent genome-wide significant signals of which three were novel Table , includir2 ≥ 0.8) with the lead SNPs at the two new and replicated ONH variants, rs11158547 and rs1028727, using a combination of bioinformatics tools (see Method section). A total of 70 variants in LD with the 2 novel variants were queried. None of the examined variants were predicted to damage protein structure by SIFT, Polyphen, or alternative splicing using Ensembl’s Variant Effect Predictor. As all queried variants are noncoding, we reviewed the possible regulatory annotation of these SNPs in experimental epigenetic evidence, including DNAse hypersensitive sites, histone modifications, and transcription factor-binding sites in human cell lines and tissues from the ENCODE17 and ROADMAP EPIGENOMICS18 projects, integrated in Haploreg19. Annotations of chromatin states indicated that the two novel variants were located in, or in LD with, an active chromatin state region from at least one of the tissues investigated (Supplementary Data cis-expression quantitative trait loci (eQTL) in several databases (see Methods). In both novel loci PPP1R36-PLEKHG3 and SERPINE3, variants were found to be eQTL’s and based on Regulome DB-scores both ONH loci contained variants that were likely to alter binding . For, rs11158547 (PPP1R36-PLEKHG3) 9 genes were assigned to this locus and for rs1028727 (SERPINE3) 21 genes were mapped to this locus (Supplementary Data 21 and Wikipathways22) found 5 and 11 Bonferroni significant gene-sets comprising genes mapped to SERPINE3 locus and PPP1R36-PLEKHG3 locus respectively. These pathways were in particular involved in immune response and cancer development  and showed Bonferroni significance (p = 0.025). Given the high degree of heterogeneity of effects at this locus a random effect meta-analysis was carried out which could not confirm this finding in POAG , from European origin. For rs1028727 a negative effect on the VCDR in the present study predicts a decreased risk of POAG which was seen in NEIGHBOR study and the UKBiobank but not in the Southampton study  as a molecular mechanism of increased plasmin activity leading to neurodegeneration in high ocular pressure conditions26. In the trabecular meshwork, serpins (plasminogen activator inhibitor) may mediate the inhibition of matrix metaloprotienase (MMPs) activity induced by transforming growth factor-beta enhancement28. Inactivity of MMPs were found to increase aqueous humor outflow resistance leading to rising IOP29. rs11158547 downstream the PLEKHG3 gene, a pleckstrin homology domain containing protein, is also a relatively unknown gene. It contains a guanide nucleotide exchange factor (GEF) domain which is important for Rho-dependent signal transduction30. In mice PLEKHG3 knockout is associated with an abnormal anterior chamber depth of the eye (IMPC release 3.2 http://www.mousephenotype.org/data/experiments?geneAccession=MGI:2388284). The other gene close to this locus, PPP1R36, is less likely a candidate gene for ONH. PPP1R36, has been described in connection with autophagy during spermatogenesis. PPP1R36 encodes a regulatory subunit of protein phosphatase 1 which is involved in multiple cellular functions such as metabolism, immune response, apoptosis, meiosis, mitosis, cytoskeletal reorganization, and synthesis. However, its function to the eye has not been characterized.Our results implicate two novel loci, one downstream r = 0.71, p < 2.2 × 10−16; r = 0.68, p < 2.2 × 10−16, respectively). The correlation in MTAG for CA, DA, and VCDR was considerably low yet very significant .A general insight from this study was that the strength of genetic correlation is an important condition for investigating the pleiotropic effect of genetic variants on traits. The high genetic and phenotypic correlation observed between the ONH traits enabled multi-trait analyses and yielded plausible and replicable results. We validated these novel variants identified by CPASSOC with another multi-trait analysis method MTAG, which also uses summary statistics as input. The main difference between both methods is that CPASSOC test whether a SNP is not associated with any of the traits under the null hypothesis. MTAG, on the contrary produces trait-specific effect estimates for each SNP. The variants discovered in the European CPASSOC analysis that replicated in the Asian CPASSOC analysis also replicated in the MTAG analysis. Sensitivity analysis excluding the Rotterdam studies showed a high correlation of the Shom and Shet statistic with the SHom/SHet statistic from the full analysis including the Rotterdam studies  for rs11158547 in PPP1R36-PLEKHG3, random effect meta-analysis that takes into account the heterogeneity could not confirm this finding in POAG. The source of the high variability in estimates is unknown and may involve clinical variability and ethnic differences. It is important to realize that the identification of POAG genes is far from complete and work in progress.We have no doubt that association of the variants to ONH are of interest to the biology community. To evaluate the implications of our findings in the context of glaucoma we studied three independent POAG studies. Up until now we are not able to link our finding to POAG. Although fixed effect meta-analysis showed Bonferroni significance . Subsequently, we performed multi-trait analysis for CA, DA, VCDR, IOP, and CCT. Replication was carried out for the single trait as well as the multi-trait analysis in a meta-analysis of 5 Asian cohorts imputed to 1000 genomes. We also tested significance of lead SNPs in three independent POAG cohorts.All studies included in this meta-analysis are part of the International Glaucoma Genetics Consortium (IGGC). A description of the details of all cohorts participating in this study can be found in Supplementary Note and Supplementary Tables https://imputation.sanger.ac.uk) or the Michigan imputation server (https://imputationserver.sph.umich.edu) with reference to the HRC panel, version 1 or 1.134.Genotyping was performed using commercially available Affymetrix or Illumina genotyping arrays (Supplementary Table r = 0.59 in Rotterdam Study I), the CA GWAS was adjusted for DA in all discovery cohorts prior to meta-analysis. Linear regression was employed for studies with unrelated individuals, and linear mixed effects models were used to account for family structure in the family-based studies.Within each discovery cohort, each genotyped or imputed variant was tested for association with each of the traits, assuming an additive genetic model. The measurements were adjusted for sex, age, and five principal components in all cohorts and if necessary also for cohort-specific covariates  and expected minor allele count >6. Additional checks for quality control were applied on the already filtered datasets including review of P–Z-plots, allele frequency plots and calculation of genomic inflation factor λ.Before meta-analysis, a centralized quality control procedure implemented in EasyQC was applied to individual study association summary statistics to identify outlying studies36, since there was no sample overlap or cryptic relatedness as checked by LD score regression (see methods genetic overlap). This tool also applies genomic control by correcting the test statistics to account for small amounts of population stratification or unaccounted relatedness. We also assessed heterogeneity by calculating I2 values and Cochrans Q-test for heterogeneity as implemented in METAL. After meta-analyses of all available variants, we excluded the variants that were not present in at least three studies. This resulted in 11,830,838 variants for CA, 11,764,957 for DA, 11,901,698 variants for VCDR, 12,426,120 for IOP, and 9,249,813 variants for CCT. The remaining variants per trait were used to create Manhattan plots and QQ-plots, see Supplementary Figs. p value less than 5 × 10−8 for CA, 2029 for DA, 2473 for VCDR, 156 for IOP, and 1288 for CCT. Re-running the meta-analysis excluding TEST-BATS study to show that the significantly younger mean age in this study did not distorted our findings showed nearly perfect correlation between effect estimates from the full analysis and the effect estimates from in the analysis excluding TEST-BATS . Furthermore, the mean differences between effect estimates found in the full analysis and the effect estimates found in the analysis excluding TEST-BATS were zero , this also suggests that the younger age in the TEST-BATS study has not biased the results.The association results of all studies were combined in a fixed effect inverse variance meta-analysis in METAL37. This tool performs a stepwise selection procedure to select multiple associated SNPs by a conditional and joint (–CoJo) analysis approach using summary-level statistics from a meta-analysis and LD corrections between SNPs. The three Rotterdam Study cohorts (N = 5815), imputed with the HRC reference panel version 1, were used as the reference to calculate the LD, because it represents the largest discovery studies. LD was calculated between pairwise SNPs, but any SNP further than 10 Mb apart were assumed to not be in LD. All autosomal chromosomes were analyzed, with MAF restricted to ≥0.01 estimated from the three Rotterdam Study cohorts. The independent variants were annotated by Haploreg19, see Supplementary Table We examined whether multiple independent variants at a given locus influenced a trait and if they were independent of previous findings, we used the genome-wide complex trait analysis software (GCTA)39, Iglesias et al.12, Hysi et al.40, and Lu et al.41 identified various loci associated with CA, CCT, DA, IOP, and VCDR by GWAS with the HapMap and 1000 Genomes as a reference panel for imputations. To identify new variants, we investigated if any of the independent variants were within 1 Mb of a known loci identified for the same trait by Springelkamp et al.39, Iglesias et al.12, Hysi et al.40, and Lu et al.41. We created locuszoom plots and forest plots of all potential novel variants, see Supplementary Figs. Previously, Springelkamp et al.13. We used CPASSOC for two analyses to combine the association results from CA, DA, VCDR, and from IOP and CCT. CPASSOC generates two statistics, SHom and SHet. SHom is similar to the fixed effect meta-analysis method but accounts for the correlation of summary statistics because of the correlated traits. SHom uses the sample size of a trait as a weight instead of variance, so that it is possible to combine traits with different measurement scales. SHet is an extension of SHom, but power can be improved when the genetic effect sizes are different for different traits. To compute statistics SHom and SHet, a correlation matrix is required to account for the correlation among traits or induced by overlapped or related samples from different cohorts. We followed the approach previously described by Park et al.42, to calculate this correlation matrix. Briefly, we used all independent SNPs (r2 < 0.2) present in datasets that were not associated with any of the traits (−1.96 > Z score < 1.96), and took the Pearson’s correlation of their Z-scores13. For both tests QQ-plots were created  that were not implicated in the single-trait analysis were validated using a second multi-trait method, MTAG. Similarly, MTAG also utilizes summary statistics as input, but performs LD score regression to estimate the genotypic and phenotypic variance-covariance matrices. In contrast to CPASSOC, MTAG performs association tests for each individual trait by boosting the power of a signal and providing an estimation of the underlying association via the multi-trait variance-covariance structure. We applied MTAG to SNPs MAF > 0.01 for combing the analysis of CA, DA, and VCDR, and the analysis of IOP and CCT. For the European sample we used the 1000Genomes European pre-calculated LD scores and for the Asians the 1000Genomes East-Asian pre-calculated LD scores . We then validated each of the genome-wide significant signals identified by CPASSOC in the MTAG results.For multi-trait genome-wide association analysis we applied the CPASSOC package developed by Zhu et al.p < 5 × 10−8 in the discovery stage  were carried forward for replication in Asians. For single-trait analyses, we validated these signals in fixed effect meta-analyses previously reported by Springelkamp et al.  and Iglesias et al. (CCT). Similar as in the discovery stage, we also performed a multi-trait CPASSOC and MTAG analysis of CA, DA, VCDR, and IOP, CCT in the Asians using the 1000Genomes summary statistics. Association replication was sought at nominal (p < 0.05) levels. A brief description of the cohorts participating in this study can be found in the Supplementary Note. Descriptive statistics, phenotyping methods, genotype, and 1000 Genomes phase I version 3 (March 2012) imputation quality and control has been described previously in Springelkamp et al.8 and Iglesias et al.10.All independent SNPs identified with p < 5 × 10−8) that replicated at nominal significance (p < 0.05) in Asians 1000Genomes have a shared component with primary open-angle glaucoma we validated these SNPs in three POAG case–control studies from NEIGBOHR/MEEI, Southampton and UK Biobank Eye and Vision Consortium. Phenotyping and genotyping methods are provided in Supplementary Note 43. Statistical significant level was corrected for the number of queried SNPs by the Bonferroni method.To evaluate whether SNPs identified in the European HRC discovery stage (44 to examine the pattern of genetic correlations. The LD score for each SNP measures the amount of pairwise LD(r2) with other SNPs within 1-cM (centimorgan) windows based linkage disequilibrium. Bivariate LD score regression can estimate the extent to which two phenotypes share genetic variance.To further investigate the genetic overlap among CA, DA, VCDR, CCT, and IOP we used the LD Score regression implemented in LDSChttps://github.com/bulik/ldsc). We used pre-calculated LD scores provided by the developers for each SNP using individuals of European ancestry from the 1000 Genomes project that are suitable for LD score analysis in European populations. SNP heritability estimates for all traits and genetic correlations were then calculated between the traits, see Supplementary Data Summary statistics of the five meta-analysis were formatted to LDSC input files, we followed quality control as implemented by the LDSC software package  based on epigenetic signatures. We examined whether these variants (GWAS SNPs and variants in LD with the GWAS SNPs) overlapped with regulatory elements including DNAse hypersensitive sites, histone modifications, and transcription factor-binding sites in human cell lines and tissues from the ENCODE Project and the Epigenetic Roadmap Project. We then used the RegulomeDB score to assess their potential functional consequence, as described previously46.Using the software HaploReg (version 4.1)23, Blood eQTL browser47, BIOS eQTL browser48, and BRAINEAC49 were selected for the mapping. Chromatin interaction was based on GSE87112 (Hi-C) database as implemented in FUMA. We explored possible biological functions by pathway analysis for all variants that reached genome wide significance in the discovery stage and were nominal significant in the Asian replication set. These 55 associated variants  were assigned to genes by FUMA mapping strategies. Prioritized genes for ONH traits were highly overlapping and were combined to form a set of 295 unique genes for further functional annotation in FUMA. For IOP and CCT 11 and 116 genes were prioritized respectively. We further investigated the FUMA-mapped genes for enrichment using hypergeometric enrichment tests on pre-defined gene-sets derived from MsigDB and WikiPathways. p Values were corrected based on Bonferroni method for the number of tested gene-sets.We applied FUMA, which uses a three way gene-mapping strategy, to assign genome-wide significant SNPs to genes of interest. For positional mapping, SNPs in LD with the independent SNPs were mapped to genes using a window of 10 kb. eQTL mapping was performed by mapping SNPs to genes up to 1 Mb (cis-eQTL). eQTLs from all tissues available in GTEx v6https://genome.sph.umich.edu/wiki/METAL), EasyQC (www.genepi-regensburg.de/easyqc), GCTA (http://cnsgenomics.com/software/gcta/), FUMA (https://fuma.ctglab.nl/), LDSC (https://github.com/bulik/ldsc), CPASSOC , and MTAG (https://github.com/omeed-maghzian/mtag). In the single trait analyses and the multi-trait analyses variants surpassing a p value less than 5 × 10−8 were considered genome-wide significant and followed up for replication in the Asian replication sample. Replication was defined as variants surpassing nominal significance level p value < 0.05. Variants were only considered novel when located >1 Mb away from a previously reported variant.Software used for the data analysis of this study: METAL (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1–14Reporting Summary


Local ancestry, defined as the genetic ancestry at a genomic location of an admixed individual, is widely used as a genetic marker in genetic association and evolutionary genetics studies. Many methods have been developed to infer the local ancestries in a set of unrelated individuals, a few of them have been extended to small nuclear families, but none can be applied to large (e.g. three-generation) pedigrees. In this study, we developed a method, FamANC, that can improve the accuracy of local ancestry inference in large pedigrees by: (1) using an existing algorithm to infer local ancestries for all individuals in a family, assuming (contrary to fact) they are unrelated, and (2) improving its accuracy by correcting inference errors using pedigree structure. Applied on African-American pedigrees from the Cleveland Family Study, FamANC was able to correct all identified Mendelian errors and most of double crossovers. Investigating the different ancestral proportions across an individual’s genome, i.e. the local ancestries, is useful for estimating population-specific genetic effects via admixture mapping studies6, capturing natural selection signals9, understanding population migration history10, and correcting for local population structure in genome-wide association studies12.There has been an increasing interest in studying the ancestral spectrum of admixed individuals, such as African Americans and Latino Americans13, HAPMIX14, LAMP-LD15, and PCAdmix3, are widely used because of their high accuracy and resolution. In brief, these methods model the observed genotypes of admixed individuals conditioning on the hidden states of their ancestral reference alleles or haplotypes, which are assumed to follow a Markov process. All the above methods can be applied to phased haplotypes or diploid genotypes using a joint HMM applied to the two haplotypes in an admixed individual.Many methods have been developed to infer the local ancestries in unrelated admixed individuals from a given study sample using dense genotype data. In particular, Hidden Markov Model (HMM)-based methods, including SABER+16 [CFS] and the San Antonio Family Heart Study17 [SAFHS]) are available for family-based admixture mapping and association analyses. However, existing local ancestry inference methods for family data are limited. LAMP-HAP15 and PCAdmix3 were extended to small nuclear families by fitting joint HMMs on shared haplotypes among family members. For example, in a parent-offspring pair, the parent and child share one common haplotype. The family-wise local ancestries can be estimated from a joint HMM of the three independent haplotypes. In a parent-offspring trio, the child inherited one haplotype from each of the parents. The family-wise local ancestries can be estimated from the joint HMM of the four independent haplotypes. However, this design requires a complex computationally intensive process to phase the family members. The model complexity increases quadratically with the number of founders. Therefore, it can be hard to apply to large pedigrees. The common approach for inferring local ancestries in large pedigrees is currently to incorrectly assume individuals are unrelated. This approach may result in multiple Mendelian errors that violate the assumption of family-based genetic analyses.Family data have multiple advantages in genetic studies compared to population-based data, both by increasing the statistical power to identify risk variants through better control of environmental confounding effects and by more precise modeling of heritability. Genomics data in large admixed pedigrees  using existing software (e.g. SABER+ and HAPMIX) to infer local ancestries for all individuals in a family, temporarily assuming they are unrelated, and then (2) using FamANC to improve the local ancestry inference accuracy by using the known pedigree structure to correct inference errorsFamANC was developed for admixed pedigrees with two genetic ancestors, such as African-Americans, who were admixed from West-African and European ancestral populations. The general flow of the FamANC algorithm is described in Fig. τ be the number of generations since admixture occurred. Without loss of generality, we use 1 for an African (true/inferred) allele and 0 for a European (true/inferred) allele at locus t of individual i. Let λ and 1 − λ be the admixture proportions of African and European ancestry in the population. In a pedigree of n individuals and L dense markers on a chromosome, let tth locus; Ped be the set of all possible Xt at any t in a pedigree satisfying Mendelian inheritance. For example, in a nuclear family with two parents and one offspring (n = 3), the set of all possible Ped = {, , , , , , , , , , , , , , }.Let In this study, the accuracy of the local ancestry inference is evaluated by the dosage error rate, defined as the average difference between the inferred and true local ancestries across all individuals and all loci in a given sample:ε be the average allelic local ancestry inference error rate,Ti,t given the true local ancestry Xi,t as shown in Supplementary Table ε and λ as follows:Let R in an interval of d Morgans follows a Poisson distribution, τ is the number of generations since original admixture. Letting τ = 8, the average number of generations since admixture in African-Americans, in a region of 2 cM the probability of observing two or more recombination events is low: After applying existing software, FamANC firstly scans local ancestry errors that arise from: (1) Mendelian inconsistencies identified from the pedigree structure; (2) double crossovers occurring within 2 cM, described as follows. We assume that the number of crossover events Yt at position FamANC corrects identified local ancestry errors using the known pedigree structure. Suppose that local ancestry is inferred at loci t = 1, thenIf an error is observed at the first locus of a chromosome, i.e. when t = L, then,If an error occurs at the last locus of a chromosome, i.e. when Notably, the accuracy of correcting the inference errors for loci at the boundaries of a chromosome will be worse compared with the interior, because we can collect less information at the boundaries.Xt and i, the status of The joint probability in Eq.  can be d18. For haploid data,τ is the number of generations since admixture occurred; and dt is the genetic distance (in Morgans) between the tth and t + 1th loci, which is usually small enough that for current GWAS array data at most one recombination event can occur. The diploid transition probability matrix is thusThe transition probabilities are estimated from the recombination events, which are the same as those used in HMMsWe next estimate the inference probability ε and λ.mentclass2pt{minimε from the local inferred ancestries using existing software that incorrectly assumes all individuals are unrelated. Here we modified the method of estimating genotyping errors from Mendelian inconsistency in nuclear families proposed by Saunders et al.19. Some of the local ancestry inference errors will lead to Mendelian errors and others will not. From the observed number of Mendelian errors in the data, we can estimate ε. We first divide a large pedigree into smaller non-overlapping nuclear families. For simplicity, we assume only one member in a nuclear family can have an error at a particular position. We list all possible patterns of true and inferred local ancestries in the family members and count the number of Mendelian errors for each corresponding pattern ..NME obsed is the genetic distance (in Morgans) and τ is the number of generations since admixture. We set τ = 8 for all founders. If a recombination event occurs, a new haplotype would be sampled. In the second step, we simulated the offspring given their parents’ haplotypes. The offspring inherits one chromosome from each of the parents. A crossover event in the parent was generated with probability τ = 1.We constructed a large pedigree  and Toscani in Italy (TSI) as the reference panel. We used SABER+ε from all markers on chromosome 22 using Eq. . The CFS is a family-based longitudinal study consisting of laboratory-diagnosed sleep apnea patients, their family members, and neighborhood control families, as described previouslyε from the observed Mendelian errors using Eq. . We divided the three families into 10 small nuclear families and estimated the allelic error rate sing Eq.  in thosePed with at most two values different from the observed Yt.The ancestry inference error rates of SABER+ and HAPMIX for our simulated data are low. We modeled the probabilities of observing different numbers of individuals with ancestry inference errors at one locus in a family using a binomial distribution with inference error probability 0.01 .FamANC source code is available on GitHub  assume study individuals are unrelated. Specifically, pedigree structure is used to identify and correct Mendelian errors and double crossovers. When applied on family data, this method reduces the systematic errors in local ancestry inference, thus having the potential to improve the performance of disease mapping studies and population genetics inference in recently admixed populations.We have also provided a method to estimate the local ancestry inference error rate for existing software using the observed number of Mendelian errors in nuclear families while falsely assuming the individuals are unrelated. In this method, we assumed that in any small nuclear family only one member can have a local ancestry inference error at a locus. This assumption may be violated for an extremely large nuclear family with two parents and multiple children and lead to underestimation of the error rate.We estimated a higher local ancestry inference error rate in real data than in simulated data. This could be due to genotyping errors in the real data. However, it is also possible that our simulation strategy may only reflect an ideal mixture of ancestral haplotype segments, which may not represent the complex admixture process of ancestral populations in evolutionary history. This simulation method has been commonly used in many genetic studies, so this possibility raises a concern about the performance of many local ancestry inference methods. Developing mathematical models that mimic a complex and historically accurate admixture history is a topic for future research.22. The performance of FamANC when used following other recently released ancestry inference software such as RFMix23 should be evaluated in the future.Our method has some limitations. FamANC performs well when the local ancestries inferred in the sample at the first step, assuming independence, are estimated with sufficient accuracy (err < 0.1). It may not be suitable for poorly inferred data. At a given locus, our method works by detecting either a Mendelian or a cross-over error, and correcting it using local ancestry inference from neighboring loci, namely In summary, we developed a novel method, FamANC, which can improve the accuracy of local ancestry inference in large pedigrees and will benefit future family-based genetic studies.Supplementary Information.


Bos grunniens) is an important domestic animal living in high-altitude plateaus. Due to inadequate disease prevention, each year, the yak industry suffers significant economic losses. The identification of causal genes that affect blood- and immunity-related cells could provide preliminary reference guidelines for the prevention of diseases in the population of yaks. The genome-wide association studies (GWASs) utilizing a single-marker or haplotype method were employed to analyze 15 hematological traits in the genome of 315 unrelated yaks. Single-marker GWASs identified a total of 43 significant SNPs, including 35 suggestive and eight genome-wide significant SNPs, associated with nine traits. Haplotype analysis detected nine significant haplotype blocks, including two genome-wide and seven suggestive blocks, associated with seven traits. The study provides data on the genetic variability of hematological traits in the yak. Five essential genes  were identified, which affect the HCT, HGB, RBC, PDW, PLT, and RDWSD traits and can serve as candidate genes for regulating hematological traits. The results provide a valuable reference to be used in the analysis of blood properties and immune diseases in the yak.Yak ( Bos grunniens) is an iconic symbol of the Qinghai–Tibet Plateau and adjacent high-altitude areas [Yak  2, 7, 8, 12, 13, 15, and X [Leptin genes [Genome-wide association studies (GWASs) represent a powerful tool for mapping genetic loci associated with common disease and quantitative traits. Hematologic parameters are important indicators reflecting the immune status and health of the human or animal organism and are used as biomarkers in GWASs . In rece5, and X . Zou and5, and X . Wang an5, and X . To the in genes .The current study employs the bovine HD SNP chip to locate the QTLs of blood traits in Polled yaks by GWAS analysis. The objective of this work is to provide crucial preliminary information necessary for the identification of genes affecting blood traits and causal mutations in yak.All yaks involved in this study were in compliance with the guidelines for the care and use of laboratory animals issued by the State Council of the People’s Republic of China. Additionally, the current investigation was approved by the Animal Management and Ethics Committee of the Lanzhou Institute of Animal Science and Veterinary Medicine, Chinese Academy of Agricultural Sciences (Permit No. SYXK-2016-0039)The cohort of yaks used in this study was from the Ashdan Mountain area of Qinghai Province, China. Blood samples, 5 mL each, were collected from the jugular vein of 315 yaks. The animals had no genetic relationship to avoid inclusion of yaks with the same pedigree. DNA was extracted by the phenol–chloroform method [http://personality-project.org/r/psych.manual.pdf).Standard sets of hematological data were recorded using the Sysmex pocH-100iV Diff whole blood analyzer  within 24 h of specimen collection at the Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences. The remaining portion of the blood sample was stored at −20 °C until DNA extraction (see above). To perform single-marker GWASs, 15 blood traits were used. They included 8 erythroid traits: hematocrit (HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC), red blood cell volume distribution width-SD (RDW-SD), and red blood cell volume distribution width (RDW). Three leukocyte traits were also analyzed: lymphocyte count (LYM), white blood cell count (WBC), and medium white blood cell count (OTHR). The remaining four traits were related to the platelets and included platelet distribution width (PDW), platelet count (PLT), platelet-large cell ratio (P-LCR), and mean platelet volume (MPV). The presence of correlations between the 15 hematological parameters analyzed was established using the R psych package , were excluded. Moreover, yaks with more than 10% of missing genotypes or with Mendelian errors greater than >5% were excluded. Only SNP markers that were consistent with family separation rules have been included in subsequent statistical analyses.Genomic DNA was processed according to Illumina’s chip protocol and the chip was scanned using the iScan instrument . The results of the chip scan were analyzed with GenomeStudio (version 2011.1) software. Quality control was performed using PLINK (version 1.07)  and resu−6  and 4.7 × 10−7 .The Bonferroni calibrated multiple test  was usedFor each SNP, the linear trend of alleles and phenotypic traits was assessed by the general linear mixed model ,35. The y is the phenotype, bi is the regression coefficient of phenotype on SNP genotypes, X represents the vector of the SNP genotype indicators and assumes values of 0, 1, or 2 corresponding, respectively, to the three genotypes 11, 12 and 22, v is a variable dependent on population structure, Q is the corresponding principal components matrix, and e is the vector of residual errors with e, N . The single-marker GWASs were conducted using the Tassel 5.2.43 software package [The model was described as follows: package .Individual haplotype blocks and haplotype frequency were detected using the standard expectation maximization (EM)  algorithThe mean, standard deviation and coefficient of variation (CV) for 15 phenotypic observations of blood traits in the experimental population are presented in p-value less than 10−5). These SNPs were excluded from further analysis so that a total of 104,864 SNPs remained. Additional 185 SNPs, including unmapped SNPs, were also removed, yielding a total of 104,679 SNPs used for subsequent GWASs.After quality control, none of the animals had a genotyping call rate lower than 95%; all 315 yaks were included in the association analyses. A total of 42,276 SNPs failed the missingness test (GENO > 0.05), 653,185 SNPs had a minor allele frequency (MAF) of less than 0.05, and 8710 SNPs were severely departing from the Hardy Weinberg Equilibrium (HWE) (Bos taurus chromosome) 5, 16, 14, and 23. The most significant association of SNP was with the HCT trait (p = 6.21 × 10−8)  .GPLD1 gene and was associated with the HGB trait.In haplotype analysis, a total of six significant haplotype blocks, including two genome-wide and four suggestive blocks, were identified for four erythrocyte traits. One block each was associated with HCT, HGB, and RDWSD, while three blocks were associated with RBCs . These sp = 7.71 × 10−7) marker, bovine HD0100040184 was associated with OTHR and was located in the ATP2C1 gene on chromosome 1  PLT trait marker was bovine HD2100007261, located in the SH3GL3 gene on chromosome 4 . A significant degree of correlation implies that the QTL on BTA23 might be pleiotropic. However, this association was not found in leukocyte and platelet traits.The results of this GWAS showed a significant association pattern of erythrocyte phenotypes. The Manhattan plot pattern was similar for HGB, HCT, and RBC and the three erythrocyte traits which shared common regions at BTA23, from 32,982 Mb to 32,983 Mb, containing two SNPs (BovineHD2300009627 and BovineHD2300009628). Both of these parameters are directly related to Erythrocytes, respectively correlation analysis of hematological traits by comparison . MoreoveBy applying a single-marker analysis, the current study identified 43 significant SNPs on 12 different chromosomes associated with 15 hematological traits . TogetheEDNRA gene is also involved in the vasoconstriction mechanism, which is closely related to pulmonary hypertension [p =1.68 × 10−6). Moreover, all these markers were located within the APOB gene. The expression of the APOB gene is positively correlated with the degree of atherosclerosis, and the composition and function of platelets in patients with hyperlipidemia is altered. These changes may be related to the effect of circulating lipoproteins on platelet phenotype [GPLD1 gene, which is also associated with erythrocyte traits. The product of the GPLD1 gene, also known as GPI-PLD and PLD, belongs to the glycosyl phosphatidyl inositol (GPI) family of proteolytic enzymes. The concentration of GPLD1 in human serum can be as high as 5–10 μg/mL [GPLD1 gene in the pathways affecting the properties of red blood cells, making GPLD1 a candidate gene controlling the characteristics of red blood cells.The SNP BovineHD1700003099 located in the gene explained 13.35% of phenotypic variants of PDW . EDNRA, rtension . In addihenotype . Since yhenotype . There ihenotype . In the henotype ,49. Furt10 μg/mL . GPI has10 μg/mL . TogetheGPLD1 and TINAg, located on BTA23. Also, RBC, HCT, and HGB were shown to have common significant blocks located on the GPLD1 gene. Another erythrocyte trait, RDW-SD, was associated with the block at BTA23, from 3150 to 3156 Mb containing seven SNPs , a region that also includes the HIST1H1E and HIST1H2BI genes. These genes are associated with the function of blood and immune cells. Kordbacheh and coworkers have demonstrated that histones promote erythrocyte aggregation and sedimentation in a concentration-dependent manner, increasing their osmotic fragility and lysis [In the present work, nine significant haplotype blocks located mostly in BTA8, BTA23, and BTA24 were identified by haplotype analysis. Among them, three significant blocks for RBC were found within two annotated genes, nd lysis . In addiGPLD1, EDNRA, APOB, HIST1H1E, and HIST1H2BI, affecting the HCT, HGB, RBC, PDW, PLT, and RDWSD traits, and serving as candidate genes for the future dissection of molecular mechanisms regulating hematological traits. The present investigation provides a valuable reference for the analysis of blood properties in the yak and immune diseases impacting this species.In summary, our research provides important information about the genetic determinants of hematological traits in yaks and points out five genes, namely,


Triticum turgidum L. subsp. durum (Desf.) Husnot) represent an important reservoir of economically useful genes for development of new wheat cultivars. Two field experiments were conducted at Foggia , in the 2004 to 2006 and 2012 to 2015 growing seasons. In the first, 77 Khorasan wheat [T. turgidum subsp. turanicum (Jakubz.) Á. Löve & D. Löve] accessions from 23 countries of four geographic regions  were evaluated to explore breeding opportunities. Seven agronomic traits were used to describe the diversity among the accessions: days to heading (HT), plant height (PH), grain yield (GY), specific weight (SW) as an indication of the density of the grain, 1000-grain weight (TGW), protein content, and gluten content. The total Shannon–Weaver diversity index was used to estimate phenotypic diversity, which ranged from monomorphic for PC (0.39) to highly polymorphic for TGW (0.67). A high level of total variation (87%) was attributed to the within-region diversity. The accessions grouped into six clusters, and seven elite accessions were selected as parents for crosses with durum wheat. In the second experiment, ten parents (seven Khorasan accessions and three durum wheat cultivars) and 790 F6 recombinant inbred lines (RILs) from the different T. durum × T. turgidum subsp. turanicum crosses were included to study genotypic and phenotypic variability of the same agronomic traits, plus the susceptibility index (SI) for disease. The genotypic coefficients of variation were lower than the phenotypic ones for all of the traits, which showed an environment effect on expression of these traits. High broad-sense heritability (h2b > 86%) was recorded for all traits, and high h2b coupled with high genetic gain as percentage of the mean (ΔG) was observed for HT, PH, GY, and SW, and for SI. This suggests that selection for these traits will provide good responses. Four principal components explained 70% of the total variation, and the genotypes were clustered into 20 groups. According to the results, some lines could be tested in varietal registration trials, and/or could be used as a significant breeding pool for durum wheat cultivar development in Mediterranean area.Tetraploid relatives of durum wheat ( Triticum turgidum subsp. durum (Desf.) Husnot, 2n = 4x = 28, AABB) is a staple crop that is adapted to the hot, dry conditions of the Mediterranean Basin and to similar climates in other parts of the world, with Mediterranean countries accounting for ∼75% of the world durum wheat production . For these reasons, new wheat varieties that can adapt to future challenges must be developed. More efficient use of biodiversity in breeding programs is essential to this progress  Á. Löve & D. Löve , which is commonly known as Oriental or Khorasan wheat. It is a neglected and underutilized species, which appears to have survived over the centuries in subsistence farming systems in the Near East, Central Asia, and Mediterranean areas  index  index  based on′) index . These s′) index . AccordiIn this context, a breeding plan was developed to initially analyze the degree of similarity/dissimilarity in a Khorasan wheat collection from different eco-geographic regions of the world. The aim of the experiments was to (i) select superior genotypes of Khorasan wheat to be used as parents to be crossed with three durum wheat lines and (ii) introgress these genes to enlarge the genetic diversity and to select superior genotypes.Seventy-seven accessions of Khorasan wheat that were collected from different eco-geographic regions of the world and maintained by the Research Centre for Cereal and Industrial Crops . Seed samples were kindly provided by the Institute of Biosciences and Bioresources gene bank .The genotypes used in this study were developed through pure-line selection from the available germplasm and sown for three consecutive years, 2005 to 2007. Based on their 23 countries of provenance, the 77 accessions were assigned to 10 groups according to their geography: East Africa (1), North Africa (8), North America (1), Central Asia (9), western Asia (40), Australia (1), eastern Europe (6), southern Europe (6), western Europe (1), and unknown origin (4) Table 1.Seeds from F1 hybrids derived from 21 crosses of three durum wheat cultivars and seven Khorasan genotypes were used as the starting materials to develop RILs that segregated for high grain yield, specific weight, 1000-grain weight, and superior quality traits. The three durum wheat cultivars were “Iride” , “Saragolla” , and “PR22D89” . These were released in 1996, 2004, and 2005, respectively. The seven Khorasan genotypes were PI67343, PI525355, PI330552, PI10391, PI68287, CItr14599, and PI115815. Durum wheat and Khorasan genotypes were used as female and male parents, respectively. The F1-crosses were single backcrossed to durum wheat and Korasan genotypes used as the recurrent male parents  were used for the study of the genetic parameters for the qualitative and quantitative traits.A population of 790 lines was maintained at the CREA-CI  by selfing, starting from the FTwo field experiments were conducted at the CREA-CI , the collection experiment and the breeding population experiment. In the collection experiment, the 77 Khorasan wheat were evaluated over three growing seasons (2004 to 2006), and in the breeding population, 790 RILs and 10 parents were evaluated over three successive growing seasons (2013 to 2015).-1, mean minimum and maximum temperatures of 7.6 ± 4.0°C and 18.3 ± 6.3°C, respectively, and annual mean rainfall and temperature of 506 mm and 15.8°C, respectively. These data were obtained from an on-site weather station.Based on 15-year average meteorological data (2000–2014), the climate through the plant growth season (November–June) was characterized by mean precipitation of 357 ± 12 mm yr2O), available phosphorous (Olsen method), 15 mg kg-1; exchangeable potassium K(NH4Ac), 800 mg kg-1; total nitrogen, 1.4 g kg-1; and total organic matter , 21 g kg-1. The experiments were arranged in a randomized complete block design, with three replications. The plot area was 5.06 m2, which contained 8 rows that were 3.7 m long and 0.17 m apart. The experimental blocks were separated by 1.5 m walkways.The trials were performed in a clay–loam soil (Typic Chromoxerert) with clay, 36.9%; silt, 50.5%; sand, 12.5%; pH 8 , and at plant tillering 200 kg ha-1 NH4NO3 . Weed control was carried out at the end of tillering, using Buctril Universal , mixed with Axial .The fertilizer used at sowing was 200 kg haEach year, the central six rows of each plot were combine harvested after physiological maturity. For the collection, this occurred on June 22, 2004, June 15, 2005, and June 21, 2006. For the breeding population, this occurred on June 23, 2013, July 1, 2014, and June 15, 2015. A plot combine harvester was used for threshing and to obtain the grain yield for each plot.-1) was determined and expressed at 13% moisture level. The grain was stored in a cool chamber at 5°C until analysis. Thousand-grain weight  was calculated as the mean weight of five sets of 200 grains per plot. Specific weight , an indication of the density of the grain, was measured on 250-g samples per plot, using a Schopper Chondrometer equipped with a 1-L container, without reference to the moisture content. Protein content  and gluten content  were determined on samples of 500 g kernels, using a near-infrared spectrometer . For the breeding population, during the vegetative period, the susceptibilities against four fungal diseases were evaluated, according to a 1 to 9 scale , where 1 represented low performance for a trait; i.e., no disease susceptibility. These fungal diseases were: powdery mildew ; septoria leaf spot ; leaf brown rust ; and yellow rust .Heading time  was recorded as the number of days from April 1 until the ears of 50% of the tillers had emerged from the flag-leaf sheaths by approximately half of their length  and suggested by For the collection experiment, descriptive statistics include mean, standard error (SE), range (min-max), and coefficient of variation , which were calculated over the three growing seasons to describe the variability among the Khorasan accessions. The mean and standard deviation (SD) calculated for seven quantitative traits were used to group the accessions into five classes according to pi is the proportion of the entries in the ith class of an n-class trait, n is the number of the phenotypic classes for a trait, pi is the relative frequency, and loge is the natural logarithm. Each value of H′ was divided by its maximum value, logen, and normalised, to keep the values between 0.0  and 1.0 . H′ was estimated on the whole dataset where P ≤0.05.Analysis of variance (ANOVA) was performed for each trait according to GLM procedure, using JMP software . Homogeneity of the error variance was verified using Bartlett’s tests. Mean comparison was performed by applying the Tukey-Kramer tests, with statistically significant differences determined at the probability level of In the breeding population, the total phenotypic variation of each trait was partitioned into the variance components due to genetic and nongenetic factors, according to Broad-sense heritability (where where Response to selection (R) was calculated as:where, i is the standardized selection differential at 5% selection intensity (i = 2.063).Then, the R as a percentage of the mean (ΔG) was calculated to compare the extent of predicted R of the different traits under selection, using the following formula:p) and genotypic (rg) correlation coefficients for the different traits for all possible combinations were estimated using variances and covariances, according to the method described by The phenotypic .The statistical analyses were performed using two software programs: the JMP software ; and the R package Agricolae (version 1.2-1) , TGW (70.9 g), PC (15.9%), and GC (12.2%). HT was 25.0 days to 39.0 days, and PH was 90.0 cm to 125.0 cm. The widest range was recorded for GY (CV = 21.3%), followed by TGW (CV = 13.1%), with the narrowest range for SW (CV = 4.8%). Broad-sense heritability (h2b) for the whole collection was high (>85%) for all traits except for GY (29.2%).The descriptive statistics and the ANOVA results for the recorded phenotypic traits of the 77 Khorasan accessions of the collection population are summarised in st = 27, 15, and 14%, respectively); the remaining traits showed much lower Gst (8–12%).The Y (0.55) Table 3.A heat map was constructed to visualize the traits that had weak (r ≤0.21), moderate (r = 0.22–0.27) and strong (r = 0.28–1.00) correlations , Year (Y), and G × Y interaction effects for all of the traits examined. Compared to durum wheat, the Khorasan parent accessions had later HT (+10 days), higher PH (+38 cm), lower GY (-2 t ha-1), higher SW (+10 kg hl-1) and higher protein content (+1%) and gluten content (+0.8%) (-1), SW (67.9 kg hl-1), TGW (85 g), protein content (17.9%), and gluten content (14.3%) were seen for the breeding population genotypes. Furthermore, some of the RILs produced significantly early heading and shorter plants. The extent of the variability was assessed by estimation of the genetic parameters (2p), i.e. more than 68%, was accounted for by the genetic component (σ2g). The phenotypic coefficient of variation (PCV) was higher than the genotypic coefficient of variation (GCV) for all of the traits. The PCV and GCV were high (>20%) for GY and SI for diseases, intermediate (10–20%) for HT, PH, and SW, and low (< 10%) for the other traits. Broad-sense heritability for the whole collection was high, and ranged from 68.2% (STB score) to 98.2% (PH). Heritability was >90% also for HT, GY, and TGW. Selection gains of more than 10% would be expected in 10 traits if 5% of the lines were selected. ΔG was particularly high for SI for diseases (>69.4%) and GY (63.7%), while it was minimal for TGW (4.6%). High heritability and genetic gain were recorded for SI for diseases, GY, PH, and HT.Significant differences (P < 0.0001) were seen among the  (+0.8%) Table 4.p = 0.31, P ≤ 0.05), and significant negative association with HT , PH , YR score , and LR score   and SW , and the association of protein content with gluten content was positive and significant . The genetic correlation coefficients were higher than their corresponding phenotypic ones.Grain yield had significant positive phenotypic correlation with TGW (r ≤ 0.01) Table 5.Due to the high phenotypic variation, the PCA showed wide dispersion along both of the PC axes. For the RILs population, four principal components had eigenvalues >1 and accounted for 70% of the total variance (-1) genotypes. Clusters VIII and XIV were composed of genotypes with good qualitative traits, i.e. protein content  and gluten content . Cluster IX comprised genotypes that were characterized by high PH (+116.0 cm). Genotypes that constituted clusters X and XII showed higher PH , higher SW (+82.0 g) and lower SI for diseases . Cluster XIII comprised genotypes that were characterized by late HT (+34.6 days). Cluster XVIII was composed of genotypes with high GY (+4.1 t ha-1) and low SI for diseases . Finally, early HT (-28.4 days), short PH (-78.9 cm) and high GY (+4.7 t ha-1) were the special features of cluster XX.Cluster analysis was performed considering all measured traits, and this divided the 800 genotypes into 20 major clusters, I to XX Table 6.-2 for the autumn crop grown in Central Europe. These values are comparable to the best accessions evaluated in the present study, in which we adopted a similar sowing density (300–350 seeds m-2). Also, the means observed in the present study for SW (77.7 kg hl-1) and TGW (54.0 g) were similar to those reported by Phenotypic variation in Khorasan wheat has not been studied in great detail. However, knowledge of the extent of variability for plant traits and the association of specific traits within the collected material would help to revisit the breeding programs .The dataProtein content represents an important factor for price determination in wheat trading, and semolina PC >12% is considered one of the parameters required to obtain good pasta quality . AccordiH′t estimates. According to H′ ≥ 0.60), intermediate (0.40 ≤ H′ ≤ 0.60), and low (0.10 ≤ H′ ≤ 0.40). The overall genetic diversity (H’t) was medium to high (0.55) in the present study, which indicates that the Khorasan wheat accessions had a substantial level of genetic diversity, with relatively high frequency for the desirable classes of many of the agronomically important traits. While most traits showed relatively high levels of polymorphism, only protein content had low H’t estimates which might reflect unequal frequencies of different classes rather than the absence of the desirable class for a particular trait  , or it iTo the best of our knowledge, the present study is the first to evaluate variability of agronomic and qualitative traits in a wide collection of Khorasan wheat under field conditions. These data show a great genetic potential, and the selection of the best genotypes from the clusters with the best combinations of yield-related traits for direct use as cultivars is feasible for Khorasan wheat. Based on univariate and multivariate assessments, seven promising accessions that showed desirable expression for many traits simultaneously were identified as donors for durum wheat improvement. In particular, we identified: PI68287 from Azerbaijan , PI67343 from Australia and PI115815 from Turkey , PI525355 from Morocco , PI330552 from United Kingdom (with high GY and PC), PI10391 from Egypt (with high GY and low PH), and CItr14599 from Ethiopia .T. durum × T. turanicum crossing is a rich source of genetic diversity for breeding purposes. High variation was observed here for the SI for diseases, GY, PH, SW, and HT traits. Appreciable variability for several durum wheat traits was reported by The highly significant genotypic differences observed among all of the traits investigated indicate that the germplasm derived from 2p) and PCV were a little higher than the corresponding genotypic variance (σ2g) and GCV for all of the traits, which suggested the presence of environmental influence to some extent in the expression of these traits , which suggests that these traits are controlled by the genotype more than by the environment, in comparison with SW. Similar data were reported by Triticum aestivum RILs and improved lines.Nevertheless, the proportion of genetic variability that is transmitted from parents to all offspring is reflected by the heritability, which gives information on the relative values of the phenotypic selection. According to 2b and ΔG for GY, PH, HT, SW, and SI for diseases indicates that selection for these traits would be more effective. High h2b coupled with moderate ΔG were reported for protein content and gluten content, which indicated the presence of intra-allelic and inter-allelic interactions in the expression of these traits . The lower R for TGW, protein content, and gluten content despite high heritability estimates are due to low variability. This shows the importance of genetic variability for improvements through selection.e traits . High h2g values were close, and a little higher than their corresponding phenotypic values, which might be due to masking or modifying effects of the environment on the genetic association among the traits  was accumulated by the first four principal components, which was explained by the bio-agronomic traits studied. On the basis of the cumulative effects of the individual traits examined, the genotypes were clustered into 20 groups, each of which included 16 to 89 genotypes. The genotypes in each cluster have defined traits, and thus represent resources for specific breeding programs. The results indicated that based on each trait, many RILs from the complex crossing program exceeded the parents . The best breeding lines showed low increments for TGW. Accordingly, Grouping of genotypes by multivariate methods allow us to choose elite lines from different clusters. The superior wheat genotypes identified were: 382 , 422 , 630 , 696 , 721  and 754 . All these genotypes were highly resistant to plant diseases examined.T. turanicum collected from different eco-geographic regions around the world. In contrast to others tetraploid species, i.e., T. dicoccoides and T. dicoccum, the involvement of the T. turanicum in practical durum wheat breeding programs, by hybridization, still remains limited. Our results indicate that Khorasan wheat offer abundant genetic diversity for multiple agronomic adaptive traits and gives us the opportunity to exploit favorable alleles that were excluded from the domesticated genepool. Seven accessions were identified with a combination of desirable traits for high GY and other useful characteristics that can be further used in durum wheat breeding programs. Moreover, no studies were found in the literature that have considered the heritability, or phenotypic or genotypic correlations, for GY and its related traits, and the quality traits of lines derived from T. durum × T. turanicum crossing. The current research shows that the backcross strategy used is appropriate for transferring of novel alleles from wild relative species into durum wheat elite cultivars without a significant reduction in their valuable agronomic qualities. The promising introgressed lines have been selected for use in durum wheat breeding programs, based on their diverse and complementary agronomic traits that are adapted to the Mediterranean environment.The present study represents the first comprehensive study of the variability of agronomic and qualitative traits in a large sample of AI conceived, designed the experiment, analyzed the data, and wrote the paper. PC performed the experiments and recorded the data. All authors read and approved the final manuscript.This study was self-financed by the Council for Agricultural Research and Economics within the scope of a larger project supported by the Italian Ministry of Agricultural, Food and Forestry Policies.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Non-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the “animal model” of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance. Genetic and nongenetic sources of phenotypic variation and similarity between individuals are often studied in isolation, with different research aims. Laboratory experiments have demonstrated genetic and nongenetic sources of similarity , but theQuantitative genetics assesses the extent to which related individuals show similar phenotypic traits, making the assumption that this is because of shared genetic effects . This asThe animal model is mostly used to estimate additive genetic variance (and heritability), but phenotypic variance can sometimes be affected by other factors that reduce the precision of these estimates. Genetic variance can include dominance and epistatic variance , in addiNongenetic factors can also cause misestimation of additive genetic variance in some systems. The main assumptions underlying the animal model are that relatives share genes, and therefore the genetic effects that shape their genotype, but that any effects shaping unexplained variance are independent between individuals . Shared Shared environments can increase the phenotypic resemblance between relatives and alter heritability estimates. Shared rearing environments in particular can increase sibling similarity. To address this problem, the common environmental factor causing individuals to resemble one another can be included as a random effect in the animal model. For example, models may include the year of measurement  or year In many cases, some portion of the environment that an individual experiences can be attributed to other individuals. For example, shared rearing environments can include environmental parameters generated by one or both parents ,25. PareMore generally, the variance in a focal individual’s phenotype can be affected by indirect genetic effects –33. ThesEucalyptus to show that these processes may act simultaneously and interact.Environments are often heterogeneous over both space and time ,48, and Parus major). Subsequently, three studies have included spatial autocorrelation in animal models for wild populations , a, aVa esttability .Va, Vr, and h2, will depend on whether the causes of similarity increase the resemblance between relatives or non-relatives. Nongenetic resemblance amongst relatives artificially inflates a trait’s estimated additive genetic variance when not accounted for. Conversely, increased resemblance between unrelated individuals will affect estimates of the residual variance, but heritability should be unbiased. In the merpeople simulation, relatives were similar in their continuous environmental measures, so that genetic variance was inflated by environmental effects when they were not accounted for.The relative effect of including of nongenetic similarity on For body size and swim speed, maternal effects were also included in the models, showing that it is important to account for maternal traits when they affect the trait of interest. For traits where maternal effects don’t exist, or have no effect on the estimation of additive genetic variance, the inclusion of maternal effect variance is unlikely to change estimates of additive genetic variance. Maternal environmental and genetic variance proved challenging to partition completely, but neglecting maternal environmental variance might cause over-estimation of maternal genetic, as well as additive genetic, variance.Inclusion of such matrices with real-world data should be hypothesis driven, such that researchers need to have reason to expect that nongenetic similarity may account for some portion of phenotypic variance. These matrices are also non-sparse, and the models run more slowly and require more computer memory than an animal model containing only the additive genetic relatedness matrix. In addition, caution must be taken with considering whether other variance components may be confounded with the nongenetic variance. Dominance variance, for example, can contribute to phenotypic variance . HoweverMultiple nongenetic parameters themselves can have genetic influences . For example, if there is a genetic basis to social behaviour , the socThe additive genetic variance and heritability of traits, can change over ontogeny  or senesCarrying out these analyses may also be made more challenging by the presence of missing data for the nongenetic components.  includedEnvironmentally induced resemblance between relatives will depend upon the heterogeneity of the environment, and the rate and distance of dispersal within and between environment types. For example, in cases where dispersal away from the mother is low relative to the rate of environmental change, full- and maternal half-siblings are likely to share both genes and their environment, increasing their resemblance. In the extreme case, this may lead to the two variance components being completely confounded. Cross-fostering ,81,95, tEnvironmental measures can have non-linear and interacting effects on phenotypes. Fitting separate environmental measures as fixed effects makes assumptions about the linearity or interactions of such effects. These assumptions are not made with the environmental similarity matrix, though it does assume that its component environmental measures have equal weight. Thus the choice of how to include environmental measures in an animal model may depend on the questions researchers want to address. For example, tail-fin colour in merpeople could be strongly affected by salinity and less strongly affected by other environmental measures, which could potentially water down the strength of the covariance between the environment and the phenotype. If we were interested specifically in how salinity affects tail-fin colour we could choose to fit salinity as a fixed effect, rather than incorporating it into the matrix with the other nine environmental measures. For factorial environmental measures, these could be fitted as an additional random effect. Conversely, if we wished to estimate environmental variance dependent on all the measures, then we would retain it within the matrix.The simulation that we show here is an oversimplified view of how epigenetics could be measured, but is used here to illustrate how such effects could be accounted for. We use a simulation of methylation at CpG islands ,98, but vs. maternal epigenetic effects).Additionally, the genomic architecture of epigenetic marks may be complex. For example, epigenetic marks could be in linkage disequilibrium with genetic variance , particuNongenetic effects may directly affect evolutionary potential , and mayVepi is inherited. This animal model containing epigenetics does not distinguish between the portion of variance that is due to inherited (directly or reconstructed) or non-inherited epialleles, but it does imply that nongenetic inheritance may be having a significant effect on trait variation. If heritable epialleles are known a priori, these could be included as a separate matrix, allowing distinction between heritable and non-heritable epigenetic variation. In addition, comparison of trait covariances between different types of relatives may reveal the rate of epigenetic inheritance and reset [Multiple matrix models can provide novel insights for these problems: first, they allow us to demonstrate consistent nongenetic effects on the phenotype, and quantify the proportion of variance explained by these nongenetic effects. Second, where there is separate evidence for the stable transmission of the nongenetic effect (e.g. epigenetic inheritance), the model will show the relative effect of these nongenetic mechanisms compared to genetic mechanisms. For the traits simulated in this paper, a portion of the epigenome is maternally inherited, which in turn implies that a portion of nd reset .Vc is likely to represent the inheritance of the trait, rather than just that attributable to similarity.Furthermore, in a population where individuals show close affinity to parental environments, such as inheriting a territory , niche tA major advantage of the multiple matrix approach is its flexibility in where and how it can be applied. Nongenetic similarity matrices are not restricted to those demonstrated in this tutorial, but could also include a range of other measures of similarity, for example gene expression , histoneThe multiple matrix extension to the animal model has potential applications to both wild and laboratory data. Wider adoption of multiple-matrix models has substantial promise for improving our understanding of ecological and evolutionary processes. The complex interaction of genetic and nongenetic sources of variance with dispersion, the rate of loss or reset of nongenetic information, and population structure may help to explain some of the differences between populations in estimates of genetic variance . TherefoS1 FileDetailed tutorial for carrying out the analyses shown in this paper.(PDF)Click here for additional data file.S2 FileThorough detailing on the simulation used to create the data in this paper.(HTML)Click here for additional data file.S3 FileSupplementary analyses using reduced data sizes to show the effect on estimation of variance components.(DOCX)Click here for additional data file.