Assessing dominance and additive effects of casein complex single-nucleotide polymorphisms (SNPs) , and their epistatic relationships may maximize our knowledge on the genetic regulation of profitable traits. Contextually, new genomic selection perspectives may translate this higher efficiency into higher accuracies for milk yield and components’ genetic parameters and breeding values. A total of 2594 lactation records were collected from 159 Murciano-Granadina goats (2005–2018), genotyped for 48 casein loci-located SNPs. Bonferroni-corrected nonparametric tests, categorical principal component analysis (CATPCA), and nonlinear canonical correlations were performed to quantify additive, dominance, and interSNP epistatic effects and evaluate the outcomes of their inclusion in quantitative and qualitative milk production traits’ genetic models . Milk yield, lactose, and somatic cell count heritabilities increased considerably when the model including genetic effects was considered . Components standard prediction errors decreased, and accuracies and reliabilities increased when genetic effects were considered. Conclusively, including genetic effects and relationships among these heritable biomarkers may improve model efficiency, genetic parameters, and breeding values for milk yield and composition, optimizing selection practices profitability for components whose technological application may be especially relevant for the cheese-making dairy sector. The Murciano-Granadina goat is one of the main dairy goat breeds in Spain. This special relevance has been achieved through its census, geographical distribution, and its milk quality and production, which on the whole, is well-suited for the dairy industry .The demands for goat milk have increased in recent decades, hence selection programs have faced the need to adapt through new technological and methodological advances to more suitably and appropriately respond to them. Contextually, the optimization of genetic evaluations has become an important point to consider these days, as the success or failure of any potential selection strategy for a certain trait fundamentally depends on the accuracy with which we are able to evaluate it. The production of milk and its components is regulated by complex inheritance mechanisms and is influenced by many pairs of genes whose expression depends on the interaction with the environment in which the animals are located. One of the most relevant sets of genes involved in the regulation of the expression of milk quantity and quality is the casein complex. The genetic variants for caseins  influence the traits related to the components of milk and its profitability . KnowledThe obtaining of accurate estimates of heritability, variance components, and correlations is necessary to predict the expected selection response and predicted breeding values. Traits related to production, conformation, reproduction, production of milk, and its components are already considered in dairy cattle breeding programs in many countries . Dairy gIncluding environmental effects and genetic factors facilitates the design of more efficient and accurate models, whose prediction abilities improve the determination ability for future features and productive potential of resulting products. This not only helps to cover market demands earlier in the production chain but also at a lower cost, thus maximizing profitability. The use of multivariate techniques, such as categorical principal components and nonlinear canonical correlations , could bOnce the groups of animals with desirable traits have been identified, these individuals constitute the basis for breeding programs aimed at improving traits such as productivity or fertility, among others ,9,10. OnFor these reasons, the objective of this study was to evaluate and quantify the genetic repercussions of the inclusion of the additive and dominance components of SNP biomarkers clustered in principal component dimensions for the genes in the casein complex and of the epistatic relationships among such heritable units in predictive models on the estimation of genetic parameters (heritabilities and variance components), breeding values, and their accuracies for milk yield and components in Murciano-Granadina goats.j) was computed through the equation described in Pizarro Inostroza et al. [th April 2005, regulating official milk yield controls for the genetic evaluation in the bovine, ovine, and caprine species of the Spanish Ministry of Agriculture (2005). Milking policies varied across farms depending on whether milking was performed every 4 or 6 weeks, during the morning or afternoon . First control (d1) and the last (d2) were assessed individually for every goat computing the days between the day the animal was born (BD) and the date of the first control (FC), and the days between the penultimate control (PC) and the last control (LC), respectively using the formulas in Pizarro Inostroza et al. [The productive management of Murciano-Granadina, as a polyestric breed is characterized by two annual kidding periods, with lactations lasting longer than 210-240 days . Each goa et al. . Officiaa et al. . To savea et al. . Milk sa1 is the weight for milk yield, W2 for fat, W3 for protein, and W4 for lactose in kg and standardized to 210 days; and Given the costs involved in genotyping, a selection process of goats that had been considered for milk yield standardization and composition analysis was implemented. This sample selection process aimed at genotyping a representative sample of animals for 48 SNPs in the casein complex from which complete records for several lactations existed. Hence, animals present in the herdbook of the National Association of Breeders of Goats of Murciano-Granadina breed (CAPRIGRAN) were ranked considering the most recent and updated official breeding values for milk yield and composition reported for all the animals published in 2015. Provided multiple traits are considered, we developed combined selection index (ICO) procedures following the premises in Van Vleck  to summaDNA isolation was performed through a modification of the procedure of Miller et al. . We seleAdditive and dominance effects for each SNP were determined and encoded, as described in Pizarro Inostroza et al. . Normal 2 (linkage disequilibrium coefficient of determination). The total length of casein loci and distances between adjacent loci were determined following the premises presented by Dagnachew et al. [Linkage disequilibrium (LD) and Categorical Principal Component Analysis (CATPCA) results were evaluated to select the minimum number of SNPs capturing the highest genetic variability of a given trait. The number of SNPs depends on the distribution of SNP allele frequencies and the existence of inter SNP LD. Minor allele frequency (MAF) enables differentiating between populations’ common and rare variants (MAF < 0.05). MAF was calculated using default settings for all SNPs by PLINK v1.90 . Casein w et al. . Using aw et al. ,21. Thisw et al. ,22. Hornw et al.  proposedw et al. . Furtherw et al. ,25. KaisAfter identifying clustered dimensions from CATPCA analysis, the evaluation of the relationships established among such dimensions, using nonlinear canonical correlations (NLCC), can quantify the epistatic relationships that exist between SNPs that act as a single unit. NLCC can help identify similarities between SNP clusters. Despite several methods being available to infer the study of epistasis, such as standard linear canonical correlation (CCA) and Artificial Neural Network analysis (ANN), these normally fail in two aspects, namely the issues of local minima and overfitting . The bacAdditionally, NLCC can be used to validate whether a presumed ordinal or nominal condition for the factors analyzed was in fact correctly assigned. Hence, if we consider the different possibilities (levels) within the same factor as nominal, either using normal parametrization, that is, encoding the dominant homozygous possibility as -1, the heterozygous possibility as 0, and dominant recessive possibility as 1 or alternatively encoding the dominant homozygous possibility as 1, the heterozygous possibility as 2 and the dominant recessive possibility as 3, the interrelationship across possibilities remains the same. Moreover, at the same time we prevented the problems arising from the use of negative numbers as a nominal category (level) occurring with many statistical programs. In this context, NLCC validation stems from the fact that this technique permits to classify factors into two or more sets and scale them as multiple nominal, single nominal, ordinal, or numerical. Then, the interpretation of their direction is obtained from the position of projected centroids which can be used as a validation for the encoding pattern used.Explicitly, considering a variable as ordinal implies that the order of the levels (categories) within each factor must be preserved. Then, if actual and projected centroids are not separated, a presumable ordinal relationship does not exist, and ordinal variables should have been considered as nominal as in our study. The bidimensional solution for noncanonical linear correlation analysis explained 79.65% of the total variance in the phenotypic traits evaluated across SNP groups (57.56% of the variance for the first dimension and 42.43% for the second dimension). Out of the 48 SNPs evaluated, SNP18 was the most frequent  and one of the most relevant when explaining intergroup epistatic variability for milk yield and components. Milk yield and components have commonly been assumed to be normally distributed according to historical research. However, data obtained from the field often challenge researchers, as the methodology implemented or study conditions promote data to violate regular parametric assumptions. In this context, new alternatives for the assessment of such data offer new opportunities to fit the tools used to the characteristics of the data to be assessed . Walsh’sParametric assumptions can be violated as a result of distribution irregularities or bias occurring during the selection process of research samples. However, despite parametric assumptions could have been violated by Murciano-Granadina historical records of milk yield and composition, data for milk performance and composition from the goats at the same lactational stage has been reported to presumably follow a normal distribution ,12,32, wAs stated by Bidanel , increas2) was computed as it expresses inter category differences and their associated error variance as a percentage. Afterwards, univariate tests must be carried to isolate the pairs of categories of each factor between which a significant difference in the mean value for the independent variable exist. Past research suggests ηp2 may be more appropriate than eta square (η2) after performing a multifactorial design, as ηp2 provides a score for the association strength between independent factors and dependent variables, excluding the variance produced by the rest of factors considered in the model as suggested by Brown [Following these premises and the preliminary analyses performed, parametric assumptions were also tested on our field data. As preliminary tests and our study data had violated parametric assumptions, a nonparametric approach was suggested. The Kruskal–Wallis H test was performed to identify differences in the distribution of the data for milk yield (Kg), fat, protein, solids, lactose content (%), and somatic cell count (cs/mL) across the levels of the same factor (genetic factors whether they are additive or not (dominance and epistasis) and nongenetic factors). Out of all possible level comparison pairs, only statistically significant pairwise comparisons were considered by Dunn Test. Then, Bonferroni correction was applied to reduce the likelihood of an increased Type I error potentially deriving from redundancies resulting from the inclusion of an excessive number of factors considering the relatively limited sample of our study. Then, partial eta square . Kruskal–Wallis H statistic is based upon a single independent factor accounting for the variance explained of a dependent variable with no additional factor contributing to the explanation of such variance at the same time, a particular situation which makes ηpo et al. . A complo et al. .The analysis of the relationship between factors such as Days in Milk (DIM), Days to first control (DFPC), and Days from last control to drying (DLD), number of kids born alive and number of kids born dead and milk yield (kg) and component variables was performed using Pearson product-moment correlation coefficient. Kruskal–Wallis H test, Dunn test, and Bonferroni’s correction and Pearson Product-Moment correlation analysis were carried out with SPSS Statistics software for Windows, version 25.0 was used to perform statistical tests.yfpdmlsc is the separate score of milk yield (y) and components (fat (f), protein (p), solids (dm), lactose (l) in kg and somatic cells (sc) count cs/mL) for a given animal; μ is the overall mean; Fara is the fixed effect of the ath farm/owner (a = 28 farms); Pyeb is the fixed effect of the bth year of parturition (b = 2005–2018); Pmonc is the fixed effect of the cth month of parturition (c = January to December); Psed is the fixed effect of the dth season of evaluation ; Bnume is the fixed effect of the eth birth number (e = 1-9 Birth); Cyef is the fixed effect of the fth control year (f = 2005–2018); Cmong is the fixed effect of the gth control month (g = January to December); Cseh is the fixed effect of the hth control season ; Nci is the fixed effect of the ith number of control (i=1-31 controls); Mroutj is the fixed effect of the jth milking routine ; Btyk is the fixed effect of the kth birth type ; Dyel is the fixed effect of the lth drying year (l = 2005–2018); Dmonm is the fixed effect of the mth drying month (m = January to December); Dsen is the fixed effect of the nth drying season ; DIMo is the fixed effect of the oth number of days in milk (o = 1-183 days); DFPC is the fixed effect of the pth number of days to first control (p = 1-226 days); DLDq is the fixed effect of the qth number of days from last control to drying (q = 1-61 days); Alnr is the fixed effect of the rth number of kids born alive (r = 0-5 kids); Dens is the fixed effect of the sth number of kids born death ( s= 0-3 kids); PC1t is the fixed effect of the tth additive and dominance effect of SNPs in PC1 (t = 1-17); PC2u is the fixed effect of the uth additive and dominance effect of SNPs in PC2 (u = 1-6); PC3v is the fixed effect of the vth additive and dominance effect of SNPs in PC3 (v = 1-17); PC4w is the fixed effect of the wth additive and dominance effect of SNPs in PC4 (w = 1-20); PC5x is the fixed effect of the xth additive and dominance effect of SNPs in PC5(x = 1-15); PC6y is the fixed effect of the yth additive and dominance effect of SNPs in PC6 (y = 1-14); PC7z is the fixed effect of the zth additive and dominance effect in PC7 (z = 1-12); NLCCaa is the fixed effect of the aath epistatic nonlinear canonical correlation between SNPs (NLCC) (aa = 1-10); age in months was considered a linear and quadratic covariate, hence ab), Animalac is the random additive genetic effect of the acth goat, PEad is its permanent environmental effect of each goat, and εyfpdmlsc is the random residual effect. Genetic analyses were performed using the data provided by the Murciano-Granadina Breeders Association, derived from the actions implemented within the scope of its breeding program. Overall, the pedigree included records of 244,046 animals with indirect or direct records related through at least one known ancestor , while the phenotype field database comprised 2594 direct records of 159 goats evaluated from 2005 to 2018 that have been genotyped. Therefore, a multitrait animal mixed model with repeated measures was used to estimate (co) variance components, and heritability, repeatability, phenotypic and genetic correlations and standard errors of such correlations. In matrix notation, the following multitrait animal model with repeated measures was used:Afterward, genetic factors  were then excluded from the model to isolate their potential effects on the traits measured. The rest of the terms included in the model were the same as those defined above. In matrix notation, the multitrait animal model with repeated measures excluding genetic factors was as follows: −12. Link functions and their mathematical development is shown in Boldman et al. [MTDFREML software package  was usedn et al. After reaching convergence, best linear unbiased estimators for non-genetic fixed effects and covariates (BLUE) and best linear unbiased predictors for random effects, i.e., predicted breeding values , were directly estimated with MTDFREML software . AccuracThe genetic evaluation performed in the present study was undertaken using BLUP, restricted maximum likelihood methodology and a deep pedigree of 244,046 animals that ranged from zero to six complete generations or zero to fifteen maximum generations in length . Pearson product-moment correlation analysis between the predicted breeding values (PBV) for milk yield, protein, fat, and solids (expressed in kg) obtained using both the model including the αS1-casein genotype and the one excluding it for all the animals included in the pedigree  of Murciano-Granadina breed goats was carried out to check for the replicability of the results across models.The present study works with records provided by the National Association of Breeders of Murciano-Granadina breed Goats (CAPRIGRAN) rather than live animals directly, hence no special permission was compulsory.Once the additive and dominance effects have been encoded for each of the 48 SNPs, LD and Categorical Principal Components (CATPC) were studied to select which SNPs may capture the highest genetic variability in milk yield and its components. As suggested by Pizarro Inostroza et al. , we can Seven dimensions were identified at a Cronbach’s alpha level of over 0.803 , which Out of the 48 SNPs studied, a total of 40 SNPs contributed to the seven-dimensional model in a meaningful way (factor loadings>|0.5| for CATPCA), then the different components  were best described by the SNPs highlighted included in the red rectangle in The analysis of nonlinear canonical correlations identified two highly loaded dimensions . Average loss (2-1.593 = 0.407). Hence, a bidimensional solution was chosen, so 1.593/2 = 79.65% of the variation was explained by the SNPs highly loading (component loading>|0.5|) for both dimensions. 0.917/1.593 = 57.56% of the actual fit was calculated by the first dimension and 0.676/1.593 = 42.43% by the second dimension was not high. 2), and Pearson Product-Moment Correlation Coefficient (ρ) providing information regarding the existence of differences in the distribution across levels within the same factor and the variance explanatory power of these independent variables factors.CSN1S1, CSN2S2, CSN2, CSN3) considered as a fixed effect were 0.46; 0.22; 0.25; 0.21; 0.30, and 0.43, respectively. Somehow, comparatively, when these factors were excluded, heritabilities were, 0.21; 0.24; 0.24; 0.24; 0.22, and 0.20, respectively (G) and phenotypic (rP) correlations [The estimates for heritability, genetic, phenotypic, and permanent environmental variance obtained through restricted maximum likelihood methods for models including and excluding the additive and dominance effects for SNPs in Principal Components from 1 to 7 and the epistatic nonlinear canonical correlation between SNPs as fixed effects are shown in ectively . Geneticelations  are showThe comparison of predicted breeding values did not report significant differences in regard to whether genetic effects  were included or not. Contrastingly, 2), thus, variance explanatory power, when compared to those models, comprises higher numbers of SNPs used in genomic selection. This is supported by Pizarro et al. [Genomic selection could be basically considered a form of marker-assisted selection in which a very large number of genetic markers (10.000 up to ≈800.000 SNPs) are used. Quantitative trait loci (QTL) are sections of DNA that correlate with the variation of a quantitative trait in the phenotype of a population. All QTL are closely linked at the chromosomes with at least one marker. Under these premises, conclusions drawn from high scale genomic selection may be applicable to marker-assisted selection using lower numbers of SNPs. The high number of genetic markers used in genomic selection can be used as input in a genomic formula that predicts the breeding value of an animal, so does the lower set of SNPs used in marker-assisted selection. Statistically, this reduction in the number of SNPs translates into a reduction in the values of adjusted determination coefficients  reduces the number of originally correlated variables into a smaller set of uncorrelated variables, maintaining most of the original variability and reducing dimensionality to a new set of variables, under the assumption of losing as little information as possible, which improves the descriptive performance of models . The PCABenradi et al.  reportedIn regards to somatic cells count (cs/mL), lower results were reported by recent studies, suggesting heritabilities may range from 0.20 to 0.24 in Saanen and Alpine goats ,49,50,51Moderate heritability values for lactose content were reported. These values are in agreement with the heritability values of 0.27 for lactose content reported in the literature for Polish White Improved (PWI) and Polish Fawn Improved (PFI) does . ContextCSN1S1, CSN2S2, CSN2, CSN3) as fixed effects , suggested the outcomes from the models tested report reliable enough values. When such genetic effects were not considered in the model the values for standard error of prediction were 0.01; 0.01; 0.02; 0.01, 0.01; 0.05 for milk yield, fat, protein, solids, lactose, and somatic cell counts, respectively , milk and lactose performance (−0.07), lactose and protein content (−0.20), and lactose content and somatic cells (−0.22). When SNPs genetic effects were excluded from the model genetic correlations between milk yield, fat, protein, solids contents and somatic cell count , fat and lactose content (−0.18), protein and lactose (−0.16), and lactose and somatic cells (−0.32). Very similar values were found in the Saanen and Alpine breeds [These negative values have been reported for genomic correlations between loci and have been attributed to the alteration of the relationship between traits under directed selection , as it wNegative genetic and phenotypic correlations between milk yield and fat content, protein solids, lactose, and somatic cells revealed unfavorable associations, which could negatively affect the quality of dairy products, particularly those of special relevance for the cheese industry. Therefore, dairy goats should be selected using indexes that consider the phenotypic relationships between these traits but also include SNPs epistatic interactions . Including the existing association of such traits with yield and a firmer cheese curd  can maxiThe highest genetic correlations were found for fat and solids contents (0.85), protein and solids contents (0.60), and fat and protein (0.43) when genetic effects  were considered in the model. Contrastingly, when such genetic effects were excluded, the genetic correlation between the same traits reduced to 0.73; 0.51 0.39, respectively. In this context, genetic correlations may be considered to improve the profitability of the outputs of selection given the implicit potential increase in the content of components that are intrinsically related to cheese production, as has been reported for other high-quality cheese production dairy goat breeds ,63. PhenIt should be noted that there is a moderate similarity between the values of genetic correlations in both models since we evaluated the same traits in the same population set. The positive correlations found for fat and other components such as protein or solids had been previously described by Verdier-Metz et al.  who asseThe relationship between fat and protein is not only important from a nutritive point of view, but also from a cheese performance-enhancing perspective. Milk component enrichment must be balanced as increases in protein that are not simultaneous to fat increases, will result in very aqueous and less fatty cheeses, which may not fulfill the needs of the markets .The use of functional models provides an alternative vision that reflects the physiological or molecular interactions that more precisely describe the gene mapping of the economically important phenotypes involved in productive traits such as milk or cheese production . A substDespite many empirical studies that demonstrate genetic interactions, key empirical evidence is scarce on how the epistasis influences heritability, affects functional traits, or can be controlled and modeled. As a common factor, almost all the studies found in literature lack a clear population context which is crucial given heritability is a specific parameter, not only of the population being measured but, the tools used to measure it and the model implemented.The lack of significant differences in regard to whether genetic effects  were included or not may suggest the prediction of breeding values for each individual may be similar across models. Significant correlations were higher for standard error of prediction of milk yield, fat, and protein content predicted breeding values. However, despite being significant, these values drastically reduced when the results for reliability and accuracy of predicted breeding values were compared between the two models, including and excluding genetic effects. This may suggest the inclusion of genetic effects as fixed effects may increase the estimative power and accuracy of the model used to perform genetic evaluations for economically important traits linked to milk yield and its components.These findings are in agreement with those in literature according to which when a relatively low number of genotyped animals is assessed through a shallow pedigree, that is, individuals which are likely to be distantly related or unrelated are contrasted within a considerably large pedigree and all breeding values estimated using BLUP are zero. Conclusively, BLUP may still predict a breeding value with a significant accuracy when animals in the test population  and reference population (whole pedigree) may share a distant relationship and accuracy reduced to close to zero when animals in the pedigree and study sets are poorly related, distantly related or unrelated .The heritabilities and correlations between fat, protein, and solids content must be considered given their implication in the nutritional and commercial quality of several dairy products. Additionally, lactose and somatic cells count genetic parameters may also be critical traits to consider given their implications with milk’s health quality. These intertrait relationships may address which selective paths may be more profitable, hence, they should be considered when targeting the maximization of the commercialization potential of goat milk as a raw material for cheesemaking industry. Indexes comprising the evaluation of these traits considering additive, dominance, and epistatic relationships among casein complex genes or SNPs could lead to remarkable increases in the outcomes derived from selective practices and indirectly in the economic value of dairy breeds. As suggested by our results, the inclusion of casein complex SNPs additive, dominance, and epistatic effects in the model used in genetic evaluations for milk yield and components may increase models’ estimative power and the accuracy of the breeding values obtained after dairy goat genetic evaluations when compared to models lacking such effects. This may translate into the enhancement of the genetic progress of the breed and strengthen the international competitiveness of Murciano-Granadina breed in the dairy goat industry.


Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding.Quinoa . Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance.We infected 132 South American genotypes, three Danish cultivars and the weedy relative The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding. Chenopodium quinoa Willd.) is a grain crop that was domesticated in South America and cultivated from Chile to Southern Colombia for thousands of years [Peronospora variabilis Gaüm, previously known as Peronospora farinosa f.sp. chenopodii [P. variabilis also infects the closely related and widespread weed C. album  [C. album occurs throughout Europe and is frequently infected by downy mildew-causing pathogens that seem to be conspecific with P. variabilis infecting C. quinoa. Therefore, C. album may act as alternative host for P. variabilis and constitute a reservoir for this pathogen [C. murale (nettle leaf goosefoot), C. ambrosioides (Indian goosefoot) are also susceptible [C. quinoa has not been reported so far. Since the disease is seedborne, tolerance to this pathogen is a critical trait in the development of new quinoa varieties [Quinoa  , 15 whicpathogen , 16. Othceptible –19, but arieties .P. variabilis - quinoa interaction, or about the genetic basis of downy mildew tolerance and the role of other phenotypic traits in disease susceptibility. Previous studies for quinoa tolerance using greenhouse experiments, seedlings, detached leaves and field scorings primarily focused on quantitative measures by scoring disease symptoms [Currently, very little is known about the physiological mechanisms involved in the symptoms , 21, 22.symptoms . Anothersymptoms . ReliablP. variabilis enters leaf tissues through the stomata [P. variabilis infection and conducted a genome-wide association study (GWAS) with whole genome sequences of a subset of accessions to identify genomic regions with putative tolerance genes.The objectives of the present study were to investigate the variation of quinoa genotypes from its native range in South America  in their response to inoculation with the downy mildew pathogen. We investigated the robustness of phenotypic scoring under controlled conditions and characterized the relationship of the disease traits severity of infection, sporulation and incidence with other phenotypic traits. These traits included size and density of leaf stomata because  stomata , 24, and stomata , 26. We Chenopodium album) to 83.0% (Accession G9) with a mean of 46.2%, whereas sporulation ranged from 0.2% (Variety Puno) to 83.6% (Cultivar CV21) with a mean of 42.6%. Incidence of infection showed a smaller range among genotypes from 36.8% (Accession G41) to 92.0% (Accession G92) with a mean of 71.6%.In total, 132 genotypes  were successfully grown, inoculated with mildew, phenotyped and scored in three independent greenhouse experiments. Severity of infection ranged from 5.0% (p < 0.05) except for a single model . The genotype by experiment For the trait incidence of infection, we used a GLMM because it allows to fit non-normally distributed data like discrete proportions and to include random effects. We used a R = 0.91, p < 0.001; Fig. R = 0.9, p < 0.001). The correlation of mildew incidence with both severity and sporulation was markedly lower .The similar variance component structures of the three disease traits Fig.  suggestsWe also estimated heritability, H2¯ (Eq. ). Both pC. album and Puno had the lowest estimates for severity  to 83.6% (CV21). Transformation of the sporulation data had a small effect on the distribution of estimated means, independent of whether control varieties were included or excluded , and only 6 accessions in the present study.r = 0.18, p = 0.041). We also tested whether saponin content in seeds is correlated with disease susceptibility and carried out foam tests with seed harvested in two locations because saponin content varies between phenological stages and environments. 105 genotypes had seed available from both locations, Bolivia and Denmark, while 26 genotypes were harvested only in Denmark. Foam height measurements were not correlated between sources of seed . In a comparison of estimated average severity and sporulation of genotypes with and without saponins, we did not observe any systematic pattern based on the seed source or if accessions or cultivars were compared separately (t-test with p > 0.05).The only significant correlation of disease and stomatal traits was between severity and width of stomata . Spores harvested from these plants and inoculated onto Chenopodium spp. showed low disease severity (4%) and lowest sporulation (0.4%) to C. album compared to the C. quinoa genotypes .The isolate of pes Fig. . This isGiven the highly significant genotypic effect on response to mildew infections, we sequenced a subset of 88 accessions and cultivars representing the range of phenotypic variation to conduct a GWAS. The sequence genotypes included two control varieties (Puno and Titicaca), 18 cultivars, and 68 genebank accessions of which 39 originated from Bolivia, 19 from Peru, two from Ecuador and 1 from Chile. The average severity of the sequenced genotypes ranged between 11% and 83%, with an average of 47%.11 bp in 2.6 × 109 read pairs with a length of 150 bp each. After mapping processed sequence reads to the quinoa reference genome version 1, sequence coverage per genotype ranged from 0.38 X (control variety Titicaca) to 9.17 X (Accession G37) with an average of 3.24 X. The proportion of mapped reads per sample ranged from 99.3% to 99.9%, with an average of 99.8%. In contrast to the nuclear genome, the chloroplast and mitochondrial genomes were overrepresented in our sample, with a coverage of 109 X and 32.6 X, respectively. Such a high coverage is expected because there are multiple copies of mitochondrial and plastid genomes per haploid nuclear genome.Sequencing of 88 samples produced 7.9 × 10r2, drops to 0.1 within 22-25Kb in the sequenced genotypes.Variant calling with GATK identified 18,017,831 biallelic SNPs across the genome. After filtering for minor allele frequency and sample missingness, and testing for departure from Hardy-Weinberg Equilibrium 4,131,562 variants remained. We imputed genotypes with coverage <8 X, and sample and position missingness below <0.7, which produced 606,791 SNPs from 61 samples with an estimated accuracy of 96.9% based on 5,000 hidden markers. Fig. A principal component analysis (PCA) shows that all genotypes classified as cultivars cluster together  in the model without correction for population structure using principal components   and is not biased to genomic regions included in a reference sequence. This analysis was based on an average of 570,741,731 k-mers of length 31 per sample. The control variety Titicaca  and genebank accessions G37  were the genotypes with the lowest and highest numbers of k-mers counts, respectively. In total, 992,946.265 k-mers passed the filters and were included in the kinship matrix estimation and the subsequent GWAS. For the first stage of the GWAS, 880,137,481 k-mers were tested and 10,001 passed the first filter to be fit with GEMMA. The smallest p-value for any k-mer was 9.19 × 10−10 for a single k-mer. Therefore, this analysis also did not detect any significant associations with the trait severity of infection given a permutation-based 5% p-value threshold of 1.505 × 10−10 for the k-mer analysis.We also used a Peronospora variabilis revealed substantial variation in the three infection related traits severity of infection, sporulation and incidence of mildew . To evaluate the robustness of parameter estimates, we used linear mixed models and various combinations of data transformation and inclusion or exclusion of control varieties. Although the use of data transformation is under debate –30, it dly small .A limitation in fitting LMM and GLMM is that reliable estimation of the variance of a random effect requires at least five levels, i.e. locations, experiments, years, etc. . The lowPrevious work on quinoa yield traits found high estimates of genotype-by-environment variance components . The gennd years , or unband years .R = 0.9) is larger than the coefficient reported in an evaluation of scoring methods for downy mildew in cucumber [C. album, control variety Puno and several gene bank accessions.To identify different reactions of host plants, we measured three different disease-related traits. A comparison of severity of infection and sporulation is of interest because pathogen populations typically harbor high levels of genetic variation for both virulence and fecundity , 39. Somcucumber . HoweverVitis vinifera) was around 0.40 [Consistent with estimates of genetic variance, heritability estimates were moderately high and very similar between models with a range from 0.72 to 0.78 for severity and from 0.78 to 0.81 for sporulation. In comparison, estimated heritability of sporulation of downy mildew in grapevine   and indiAlthough our panel includes the main quinoa ecotypes , there wC. album and the Puno control variety, which was one of the genotypes with the lowest severity, sporulation, and incidence of downy mildew  and Mañiqueña Real (CV21) were inoculated with a Bolivian isolate of P. variabilis and classified as tolerant and susceptible, respectively, as evaluated by the disease progression of downy mildew P. variabilis [2:6 population [An important limitation of our study is the use of a single isolate only of riabilis . Our resriabilis , a high pulation .C. quinoa cv. Atlas in 2001 [Comparison of passport data with our results is limited by missing information on scoring methods, phenotypical stage of the plant, genetic constitution of accessions  and information about field trials in Bolivia. In addition, our data on severity of infection is based on one pathogenic isolate whereas the passport data is based on natural infections of local races whose virulence might differ from the Danish isolate used in this study . Previou in 2001 .P. variabilis enters host tissues through stomata, and its haustoria emerge from the stomatal pore to release spores [e spores , 24, whie spores . We therDowny mildew remains dormant in the pericarp of the seed  and the Therefore, our results do not support the hypothesis that mildew tolerance is substantially influenced by other traits such as stomata characteristics or saponin content.k-mers with severity of infection. The power of GWAS depends on the sample size of the association panel and on the genetic architecture of a trait of interest [k-mer analysis) accessions, and possibly by the genetic architecture of severity of infection because distribution of phenotypic values suggests it is a polygenic quantitative trait  genotypes suggested that mildew tolerance shows a dominant inheritance [Our results provide a perspective for a more efficient resistance breeding in quinoa. The large variation found in mildew tolerance and high heritabilities of disease traits allows the development of QTL mapping populations by crossing genotypes from both ends of the distribution  effects in the field are difficult to control. Furthermore, symptoms of pathogen infection are influenced by the position and age of leaf tissue  which rePeronospora variabilis infections. We have shown that cross-infection from C. album to C. quinoa and vice-versa is feasible and this widely distributed weed is likely a reservoir for the pathogen and an alternate host for the P. variabilis, which has implications for quinoa cultivation in the presence of C. album. The substantial variation in mildew tolerance between genotypes has a strong genetic component is therefore is amenable to selection in breeding programs. However, inferences based on a single experiment - or a single location field trial - should be taken with care because a large genotype by experiment interaction was found, so future work on the resistance of C. quinoa to P. variabilis must take this into consideration during the design and planning phases.Our study revealed a high level of variation of quinoa varieties and accessions to http://germoplasma.iniaf.gob.bo).The quinoa genotypes analyzed in this study consist of 106 accessions stored in the National Germplasm Bank of Bolivia. They include landraces collected in Bolivia (55 accessions), Peru (33), Ecuador (7) and Chile (4), in altitudes ranging from 2 m.a.s.l. to 4082 m.a.s.l.  based on spontaneous infections by Chenopodium album L.) were used as control varieties in greenhouse experiments. The four quinoa varieties include the cultivar ‘Blanca’, which is adapted to the Northern highlands and Inter-Andean valleys of Bolivia and partially resistant to downy mildew [Chenopodium album is closely related to quinoa and a widely distributed weed. C. album seeds used in this study were collected in 2017 and 2018 at a former quinoa breeding field at the experimental station of the Faculty of Science, University of Copenhagen .Four quinoa varieties and the wild relative common goosefoot  using a protocol by Danielsen and Ames [Previous research recognized the role of alternate hosts on the evolution and spread of pathotypes  P. variaC. album , 18, 20.and Ames . We usedand Ames . The latP. variabilis. A spore suspension (1 × 1011 spores/ml) produced from the maintained inoculum was filtered with a nylon filter with 20 μ m pore size (Merck Millipore Ltd.) to capture P. variabilis spores, which were transferred to 1.5 ml microcentrifuge tubes containing glass beads (425–600 μ m) and kept on ice. 200 μ l lysis buffer  were added and mycelia were pulverized with a sterile pestle. Further 200 μ l of lysis-buffer with 4 μ l of RNase were added. DNA was extracted with a DNeasy Plant Mini Kit (Qiagen) following manufacturer’s instructions. Primers designed to amplify a 1150 bp fragment covering ITS-1 and ITS-2 region in members of the oomycete family Peronosporaceae, including species of Peronospora, Pythium, and Phytophthora, were amplified from genomic DNA by polymerase chain reaction (PCR) using Oomyc Fw-1: 5′ cggaaggatcattaccacac and Oomyc-Rv1: 5′ cgcttattgatatgcttaagttca as forward and reverse primers, respectively. PCR amplification was carried out with one cycle of 95C for 3 min; 35 cycles of 94C for 30 s, 55C for 30 s and 72C for 40 s, and one cycle of 72C for 3 min. Amplification products were purified using QIAquick PCR purification columns (Qiagen) and the DNA concentrations were determined on a NanoDrop Lite Spectrophotometer. DNA sequencing of the PCR amplified ITS was performed at Eurofins Genomics. DNA sequences were submitted to NCBI (accession MT895880) and compared against the NCBI nr database using BLASTN (https://blast.ncbi.nlm.nih.gov).We used DNA sequencing of the Internal Transcribed Spacer (ITS) region to confirm that the isolate was Peronospora solution mounted on a glass slide. Histopathological slides were prepared from Blanca leaf pieces of ±5 cm 2 collected 7 days after infection. Leaf samples were coated with nail polish on the abaxial side and dried for 24 h. The imprints were then removed and stained with Lactophenol Aniline Blue [in planta on Vikinga and Blanca. The detailed protocol for the isolation and propagation of the pathogen is available in The identity of the pathogen was also confirmed by visual analysis with microscopic and histopathology slides. Microscopic analysis was carried out with a sample of the ine Blue . Both miPhenotypic data were collected in greenhouses of the University of Copenhagen between February and May 2019. The response of quinoa genotypes to downy mildew inoculation was evaluated in three sequential identical experiments that each included the complete set of 132 genotypes and the four control varieties with a randomized complete block design with four blocks each. Each experiment occupied a greenhouse allocated exclusively for the experiment to avoid infestations with insects or risk of cross contamination as well as the provision of biological control agents. Experiments started 2 weeks after the end of the prior one. Within blocks, accessions and cultivars were represented by a single plant while control varieties were represented by 2 to 5 plants.3 pots and grown for 3 weeks in greenhouses under the same conditions. The greenhouse management included biological control agents against common greenhouse-borne pests.Prior to the experiment, the Bolivian gene bank accessions were self-pollinated once to increase homozygosity because the heterozygosity of genebank accessions was unknown. Seeds were produced in a greenhouse between February and August 2017 with a 12 h photoperiod, an average temperature of 24C during the day, 18C at night, and irrigated with fertilized water (NPK 14–3-23 + mg EC 1.9). After the day length surpassed the plants requirements, greenhouse curtains were used to maintain the photoperiod. Seeds were harvested, cleaned and stored at natural conditions until January 2018. Harvested seeds were sown in jiffy pots containing peat to assure the provision of plantlets for transplantation and grown for seven to ten days. To avoid infestation with flies, the compost was watered with a solution of gnatrol (10% v/v). Plantlets were then transplanted to 550 cm 5 spores/ml) of P. variabilis spores and Tween 20 (1%). The solution was sprayed comprehensively onto each plant with a pressure paint gun. 50 ml of solution were used for each block. Blocks with inoculated plants were covered with a plastic sheet 5 days after inoculation  of the k-th genotype in the j-th block of the i-th experiment, μ is the general mean, ρi is the effect of the i-th experiment, βij is the effect of the j-th block nested within the i-th experiment, αk is the genotype effect, ραij is the genotype-experiment interaction, and eijk is the residual error term. The effects for experiments, blocks within experiments and the genotype-experiment interaction were treated as random effects because experiments were considered as a random factor and hence all effects involving a random factor need to be modelled as random [The following mixed model was used to estimate the mean severity and sporulation of the disease for each genotype in the panel, using ASREML-R package version 3.0 :1\docums random , whereass random , as descs random .n is the total number of observations. In addition, we fitted a model such that the variance-covariance matrix of the vector of errors (sorted by experiments) was n is the total number of observations. In addition, we fitted a model with independent variance components for each experiment [The residual error for the experiments was initially modeled as normally distributed and independent with a common variance component, where periment :\documeR1, R2 and R3 are variance-covariance structures for each experiment, each taking the form nj is the number of observations in the j− th experiment.where pijk), ρi is the effect of the i-th experiment, βij is the effect of the j-th block nested within the i-th experiment, αk is the genotype effect, and ραik is the genotype-experiment interaction. The model included a scale parameter account for overdispersion of the data through the residual keyword in the RANDOM statement of PROC GLIMMIX in SAS version 9.0 [We used a Generalized Linear Mixed Model (GLMM) to analyze the incidence of downy mildew in quinoa and fit the following model with the PROC GLIMMIX procedure of SAS software:sion 9.0 .The ad-hoc broad-sense heritability was estimated as:ed means . To estiY and W are vectors with 0 for reference varieties and 1 for cultivars and accessions, αk is the genetic random effect. The remaining effects are the same as in Eqs. . Pearson correlation coefficients were estimated for each pair of variables with a significance threshold ge Hmisc .t-test using the adjusted means obtained from a LMM with heterogeneous variances between experiments using an untransformed data without the control varieties. This set of means was used because there was no indication from the previous analysis that fitting models with transformed data improved accuracy of the estimates.Saponin content of seeds was assessed using the foam test , which cFor DNA extraction, two plants per genotype were grown in a greenhouse of the Taastrup campus at the University of Copenhagen, and two healthy leaves from a single two-months old plant were collected and stored with silica gel for drying. DNA was extracted using the AX Gravity DNA extraction kit  following manufacturer’s instructions. Purity and quality of DNA were controlled by agarose gel electrophoresis and concentration determined with a Qubit instrument using SYBR green staining. DNA sequencing libraries were constructed using the protocol of Baym et al. . Whole-gProcessing of the raw reads, mapping, and variant calling were done with a custom Snakemake pipeline . Raw reap> 0.01 using a likelihood ratio test [r2, using the final VCF file as input for PopLDdecay [Variants (SNPs and indels) were called using GATK 3.8  by usingtio test . LinkagepLDdecay  with defk-mer-based method. FarmCPU (Fixed and random model Circulating Probability Unification) uses SNP in a two-step iterative process with fixed and random effects models to improve computation times, reduce the confounding effects of structure and improve power to identify significant marker-trait associations in comparison to other methods [p-value threshold of 0.01 (Bonferroni corrected) for both the inclusion of a marker during the first iteration of the model as well as the genome-wide significance threshold.We used two methods for association mapping, FarmCPU, for use with sequence variants (SNPs) and a  methods , 96. Thek-mer based method by [k-mer length of =31 nucleotides, minor allele count =3 minor allele frequency =0.05. This method requires a kinship matrix, which was estimated with a method used by EMMA (Efficient Mixed-Model Association) and consists of an identical-by-state (IBS) allele-sharing matrix under the assumption that every variant has a small random effect on the phenotype [The ethod by  identifihenotype .Additional file 1: Figures S1 to S5 and Tables S1 to S3. Supplementary Figures and Tables.Additional file 2: Supplementary File 1. List of genebank accessions and passport data; Mildew infection raw data; saponin and stomatal measurements.Additional file 3: Supplementary File 2. Detailed protocol for the isolation and maintenance of the downy mildew pathogen Peronospora variabilis.


Both age and sex are influential factors, and six novel SNPs are identified for potential association with abnormal ECG. With the selected SNPs, a polygenic risk score (PRS) differentiates the case–control subgroups, and correlates well with increased risk of abnormal ECG. The findings are reproduced in an independent validation cohort. The derived PRS may function as a potential biomarker for prospectively screening the high-risk subgroup of heart issues in the Chinese population.Electrocardiography is a common and widely-performed medical examination based on the measurement and evaluation of electrocardiogram (ECG) to assess the up-to-date cardiac rhythms and thus suggest the health conditions of cardiovascular system and on a larger level the individual’s wellness. Abnormal ECG assessment from the detection of abnormal heart rhythms may have clinical implications including blood clots in formation, ongoing heart attack, coronary artery blockage, etc. Past genetic-phenotypic research focused primarily on the physical parameters of ECG but not the medical evaluation. To unbiasedly uncover the underlying links of genetic variants with normal vs Additionally, the PR reflects the time the electrical impulse takes to travel from the sinus node through the atrioventricular node. Abnormal rhythms exist as abnormally slow or fast heart rate or an irregular cardiac rhythm. An abnormal P-wave may serve as predictors for atrial arrhythmias and atrial enlargement4. A premature ventricular contraction or a ventricular rhythm can be revealed as a prolonged QRS duration which is associated with mortality in both the general population and in patients with cardiac diseases9. T waves can be helpful in a variety of pathologies: as an example, tall T waves in anterior chest lead III, aVR, and V1 with a negative QRS complex may suggest acute myocardial ischemia10. QT interval measures myocardial depolarization and repolarization time, while shortening or prolongation of QT interval is associated with an increased risk for arrhythmias and sudden cardiac death (SCD)12. Abnormal PR interval is a risk factor for atrial fibrillation and heart block13. Through ECG examination and the following evaluation by certified medical staffs , abnormality on cardiac rhythms could be detected early. Therefore, normal or abnormal ECG assessment is a key medical and clinical evaluation that provides the doctors with instrumental evidence-based revelation on individuals’ health conditions.Electrocardiogram (abbreviated as ECG or EKG) is a non-invasive test of the electrical activity of cardiac rhythms that may indicate normal or abnormal cardiovascular conditions including cardiac hypertrophy, arrhythmias, conduction defects etc. ECG tracings consist of three major components during a single heartbeat: the P wave 14, validating the underlying genetic basis19. The QT interval was one of the first traits investigated at the outset of the GWAS era20. Heritability estimates of QT interval are between 30 and 40%21. Previous GWAS identified 45 loci, of which 35 have been proved in QT-IGC, containing variants in genes that modestly influence QT interval, such as GBF1, AZIN1, CREBBP, and KCNQ123. QRS interval duration is a quantitative trait influenced by multiple genetic factors and as well by both age and gender25. In addition, the heritability of QRS duration is estimated to be 35–55% from twin and family studies29. An exome-chip meta-analysis has identified 34 single nucleotide polymorphisms (SNPs) associated with QRS duration30, confirming 20 out of the 29 previously identified loci, including KLHL3832, DLEC133, NACA34, and ADAMTS635. However, there were so far few GWASs with the qualitative assessment of ECG results as the outcome so it remains unclear if any genetic variants may be linked to abnormal ECG status.Both genetic and environmental factors contribute to abnormal cardiac rhythms, and regarding the mode of inheritance for such a complex trait, it is highly likely that a collection of rather than a single genetic variant may be involved. Genome-wide association study, or GWAS, aims to genotype the genome of a population in high-throughput manner and unbiasedly screen for loci significantly associated with the phenotype-of-interest. A few pioneering GWASs for various ECG phenotypes have found over 500 risk variants37. Most of the published GWASs for ECG traits have been carried out in populations of European descent40, but not in populations residing in populous regions of high incidence in cardiovascular diseases such as China. In-parallel GWASs in different ethnic groups are informative in both validating common biomarkers and discovering ethnicity-specific genetic links. To our knowledge, no large-sample GWAS on the assessment of ECG has been conducted for a cohort of Chinese population, and this study aims to fill this gap to explore the association of any known or novel SNPs with normal vs. abnormal ECG assessment in a Chinese cohort.Moreover, like many biometric parameters, ECG features are known to vary by ethnicityA group of adult residents living in Sichuan Province of P.R. China were recruited as a regional cohort and among them, 1006 individuals participated in the project studying the association between genomic polymorphisms and the evaluation of electrocardiogram status. For ECG measurement, individual participant was informed of the standard procedure in advance and was given a grace period of 20 min to calm down before measurement by certified medical staffs using a portable electrocardiogram machine that measures and records heart activity (setting at 1 cm/mV). A conventional 12-lead electrocardiogram  measures the overall magnitude and direction of the individuals’ electrical depolarization throughout the cardiac cycle. The ECG results were assessed by a group of certified ECG medical practitioners. For each individual’s ECG, the certified staffs were responsible for evaluating the examination to label any identifiable issues . The ECG issues include: sinus bradycardia (heart rate < 60 beats/min), sinus tachycardia (heart rate > 100 beats/min), sinus arrhythmia (irregular timing between successive P waves), T wave change , high voltage of the left ventricle  etc. After ECG evaluation was completed for all cohort members, each individual was categorized into a binary phenotype of normal vs. abnormal ECG based on the presence or absence of labelled ECG issues. The “normal” refers to the group with none of the labelled issues (issue count = 0), and the “abnormal” covers individuals who display at least one out of the various subtypes of ECG issues (issue count ≥ 1). It should be noted that as the phenotype-of-interest, abnormal ECG in this study refers to the feature assessment regarding general ECG issues but does not directly infer clinical abnormity .Related health and personal information of the participants were collected through physical examination and face-to-face survey by trained staffs. For biological samples, participants spitted 1–2 ml of saliva into sterilized sample tubes, which were transported back to a genetic laboratory within 24 h and subjected to DNA purification. Upon quality validation, DNA samples were genotyped on a high-throughput Affymetrix Axiom Precision Medicine Array chip on a GeneTitan Multi-Channel Instrument platform (Thermo Fisher Scientific). The methods were carried out in accordance with the relevant guidelines and regulations.–7  and a less stringent candidate threshold of 2 × 10–5 were used exclusively for screening hit and candidate SNPs. Statistical analysis and data visualization were conducted in version 4.0.2 of the R statistical environment ; parallel computing in GWAS was run on a Windows computer with an Intel CPU .A dataset of cohort individuals with their categorized ECG status, genotypes, and related personal information  was assembled by the completion of on-site ECG measurements and lab experiments. Only SNPs on autosomes were selected. Genetic dataset is pre-processed for quality checks at both individual and SNP levels. Identity-by-descent (IBD) analysis iteratively disqualifies individuals exceeding a kinship threshold to remove cryptic relatedness. Hardy–Weinberg equilibrium (HWE) test excludes potential population substructure or genotyping errors. Details of the data pre-processing steps were annotated Table . PrincipAll participants in this study have signed the Informed Consent Form before participation to allow their data for scientific research purposes only. The health and genetic data from measurements were subjected to strict anonymization for the respect and protection of personal privacy. In observance of the Regulatory Articles of Human Genetic Resources of P.R. China, all work of genotyping and data analysis were conducted in China, and individuals’ genetic data could not be made accessible without official authorization and approval from the supervisory agencies. The study design and procedure were approved by the Research Ethics Committee at West China School of Public Health, Sichuan University.–5). The median age of the cohort is 46 years-old, and there is no apparent difference in age distribution regarding sex : notably, 70.8% of the most senior group (70–79 years-old) displays at least one subtype of issues in ECG assessment  of the participants are males while the other 567 (56.4%) are females so the cohort is slightly unbalanced towards the females  with the six candidate SNPs weighted by their respective coefficients Figure . The PRSnts Fig. A. The PRnts Fig. B.Figure The GWAS and PRS evaluations above are carried out in the same cohort, so there would be potential issues of overfitting. To address such concerns, we recruit an independent cohort of 100 individuals as the validation set to objectively assess the robustness and effectiveness of the candidate SNPs and the PRS. The limited sample-size of the validation set leads to the rarity of homogenous minor allele individuals, and consequently, associations of the candidate SNPs to abnormal ECG could not be effectively confirmed in the perspective of hypothesis test  Table . HoweverA clear uptrend of abnormal ECG with regard to aging is observed in the cohort, matching the progressive deterioration of cardiovascular functions throughout individuals’ lifetime. On the other hand, sex also plays a role as the males and the females display similar but not identical pattern regarding the percentage of abnormal ECG. Therefore, incorporating age and sex into a statistical learning model is instrumental to facilitate the robust forecast of individuals’ risk in being assessed for abnormal ECG.vs. abnormal binary phenotype based on the labelling of potential issues by certified medical staffs. This partially explains why previously identified genetic variants do not score significantly in this study, similar to the phenomenon observed in the treatment of blood pressure as a quantitative trait (e.g. systolic pressure) vs. as a qualitative trait (e.g. hypertension)44. In addition, different backgrounds of ethnicity in these studies might also contribute to the disparate hits of genetic variants. Undoubtedly, there are various subtypes of ECG issues so generalization of all issues into a single abnormal group may be possibly confounding. However, participants may display multiple subtypes  so the subtypes are themselves complex and non-independent outcomes to evaluate for individuals. Moreover, both the examination and the evaluation of ECG and as well the genotyping of a cohort are time and resource consuming, so subtype-based analysis suffers from significantly reduced sample size which is the major obstacle for discovering positive hits in heavily-adjusted genomic analysis. By evaluating ECG assessments into a binary outcome, this study benefits from the statistical power conferred by the full cohort. Subtype association analysis would be better suited if sample size would expand for our cohort in the future or when established large-scale cohorts such as the UK Biobank are analyzed45.While previous GWASs on electrocardiography were mainly focused on the specific quantitative metrics of ECG, one unique feature of this study is the categorization of ECG assessments into a general normal PDGFD (platelet derived growth factor D), TMC5 (transmembrane channel like 5), FBXO10 (F-box protein 10), and OR7E24 (olfactory receptor family 7 subfamily E member 24). The mechanisms underlying these potential associations remain elusive but factors such as transcriptional regulation or alternative splicing might be involved. Higher statistical power from a larger-size cohort would better reveal the genetic-phenotypic link. Nevertheless, assembling information from all six candidate SNPs, a polygenic risk score displays robust and reproducible difference between the normal and abnormal groups, and sequentially correlates with the increased risk of abnormity in ECG. Validation of the PRS features in the independent set not only supports the credibility of our GWAS but also suggests the potential application of the PRS as a novel biomarker to early detecting Han Chinese individuals with high-risk of heart issues in a precision medicine perspective.Abnormal ECG proves to be a complex trait for the mapping to hit genetic variants. None of the six candidate SNPs identified in this study lead to alterations in the primary sequence of proteins, but four of them locate to the noncoding regions of certain genes: To our knowledge, this is the first GWAS using the professional assessments of ECG as the phenotype-of-interest, and it remains to be investigated to what extent our discoveries in a Han Chinese cohort could be reproduced in other ethnicities. It should also be noted that neither the SNPs nor the PRS lead to optimal classification performance, suggesting the genetic association only contributes partially to the phenotype of abnormal ECG. This should not be surprising because ECG status is itself a complex trait that should be influenced and determined by many factors . Future research should aim to collect information on such variables within large cohorts in a longitudinal timeframe to enhance both the association analysis and the predictive modeling of ECG features and status.Supplementary Information.


Feather pecking (FP) is damaging behavior in laying hens leading to global economic losses in the layer industry and massive impairments of animal welfare. The objective of the study was to discover genetic variants and affected genes that lead to FP behavior. To achieve that we imputed low-density genotypes from two different populations of layers divergently selected for FP to sequence level by performing whole genome sequencing on founder and half-sib individuals. In order to decipher the genetic structure of FP, genome wide association studies and meta-analyses of two resource populations were carried out by focusing on the traits ‘feather pecks delivered’ (FPD) and the ‘posterior probability of a hen to belong to the extreme feather pecking subgroup’ (pEFP).SPATS2L, ZEB2, KCHN8, and MRPL13 which have been previously connected to psychiatric disorders with the latter two being responsive to nicotine treatment. Gene set enrichment analysis revealed that phosphatidylinositol signaling is affected by genes identified in the GWAS and that the Golgi apparatus as well as brain structure may be involved in the development of a FP phenotype. Further, we were able to validate a previously discovered QTL for the trait pEFP on GGA1, which contains variants affecting NIPA1, KIAA1211L, AFF3, and TSGA10.In this meta-analysis, we discovered numerous genes that are affected by polymorphisms significantly associated with the trait FPD. Among them We provide evidence for the involvement of numerous genes in the propensity to exhibit FP behavior that could aid in the selection against this unwanted trait. Furthermore, we identified variants that are involved in phosphatidylinositol signaling, Golgi metabolism and cell structure and therefore propose changes in brain structure to be an influential factor in FP, as already described in human neuropsychiatric disorders. Feather pecking (FP) is a worldwide problem in layers substantially impairing animal welfare and causing economic losses. The propensity to show this behavior is a complex trait affected by various environmental factors and numerous genes. Heritability estimates of around 0.15 were reported  indicatip-value (rs734668878) is an intron-variant of the SPATS2L gene and is located on GGA7. SPATS2L has been associated with schizophrenia in numerous studies [It is, however, unpredictable in commercial layer populations, whether and when FP will occur. Thus, most studies have been conducted in layer lines divergently selected for FP behavior . Genomic studies –17.2 = 0.740 (SD = 0.351), GGA7, R2 = 0.767 (SD = 0.342). R2 values for all tested markers are summarized in Additional File An average of 250,771,841  sequencing reads per sample were aligned to the reference genome with an average mapping efficiency of 98.39%. The mean coverage of sequenced F0 individuals was 14.42 and of HS individuals 23.49. In total 12,864,421 SNPs were detected, 1,219,711 of which were novel as of dbSNP version Galgal_variation_release-92. With respect to the number of INDELs, 1,561,896 out of 2,142,539 variants were novel. The Ti/Tv of SNPs was 2.403, whereas known SNPs had a Ti/Tv of 2.449 and novel SNPs had a Ti/Tv of 1.965. The coefficients of determination for the imputation accuracy with the HS population were as follows: GGA1, Rp-value < 7.421 × 10− 8) were detected in the F2 design and 185 significant variants  were detected in the HS design. Variants with -log10(p) > 5 are summarized in Additional File Based on the SNP-chip data, the phenotypic variance explained by all markers (chip-heritability) was estimated as 0.18 (S.E. 0.05) in the F2 and 0.20 (S.E. 0.08) in the HS design, respectively. Using the imputed sequence level data, a GWAS was performed for the trait ‘feather pecks delivered’ (FPD) in each of the designs. After Bonferroni correction, 16 significant variants , of which 10 variants were also statistically significant in the F2 GWAS and 5 variants in the HS design. A Manhattan plot, which displays the results of the meta-analysis for the phenotype FPD is shown in Fig. p-values are listed in Table UNC50, has been previously associated with FP [After performing a meta-analysis of the two FPD data sets, we identified 15 significant variants  was also detected in the meta-analysis for FPD . VEP produced two different results for variant rs740633021. It was predicted to be an intron variant of the NRBP1 gene and an upstream gene variant of ncRNA LOC107053158. An intron variant affecting SPATS2L  was detected in the meta-analyses of both phenotypes as well.However, among variants with –log10(p) > 5 the traits FPD and pEFP datasets have 16 affected genes in common Table . It is nhttps://www.animalgenome.org/cgi-bin/QTLdb/GG/index) related to QTL for production traits and only identified a single gene. LPP, noteworthy one of the genes found in common between FPD and pEFP  but not in the F2 cross  > 5 associated with FPD in the meta-analysis with STRING revealed significantly more interactions than expected  and ‘posterior probability of belonging to the subgroup of extreme feather peckers’ (pEFP) were performed with both populations. We detected 16 variants that are significantly associated with the trait FPD. The variant with the lowest p-value  is an intron-variant in the SPATS2L gene. Polymorphisms of SPATS2L have been linked to hippocampal volume, intelligence, and schizophrenia [ZEB2 , a gene which has been identified to be a risk locus for schizophrenia [KCHN8 we discovered five significant variants . KCNH8 was identified in the context of the neuropsychiatric 15q13.3 microdeletion which is associated with several neuropsychiatric disorders including autism, schizophrenia, and attention deficit hyperactivity disorder [LRP1B  to be significantly associated with the trait FPD. LRP1B was identified in a GWAS where it was found to be associated with increased ventricular volumes in psychosis [FGF18. We found a significantly associated intron-variant  in FGF18, a gene that has been connected to dyslexia [MRPL13 , which was found to be upregulated after nicotine treatment [ZEB2 gene. We found genes encoding subunits of nicotinic acetylcholine receptors (nAchR), the major receptors for nicotine [In the study presented here, we imputed medium-density SNP-chip genotypes of two layer lines divergently selected for FP to sequence level. GWAS and subsequent meta-analyses for the traits ‘ophrenia –17, whicophrenia  and to bophrenia . In proxdisorder . Furthersychosis . Anotherdyslexia . Anotherreatment  like thenicotine . Both finicotine ). Gene cnicotine .In summary, the polymorphisms affecting the genes discussed above substantially contribute to the explanation of the genetic architecture behind FP behavior. While we estimated a chip heritability that corresponds well with previously published genetic parameters –3, the aIGF2 gene is especially interesting since it is currently under discussion as a pharmacological target in various human psychiatric conditions . Interaiewed in .GABRA5, GABRB3, and GABRG3) as potential candidate genes. That study was carried out using SNP-chip data providing limited mapping resolution. Inspecting the genes in our meta-analysis, using VEP revealed that NIPA1, KIAA1211L, AFF3, and TSGA10 are genes affected by variants in this region. Copy number variations in NIPA1 were found in psychiatric disorders [KIAA1211L was identified in a schizophrenia twin-study [AFF3 is involved in intellectual disability and cellular migration in the cerebral cortex of mice [GABRB2  > 5) between the F2 and HS populations  according to the manufacturer’s protocol. For genotyping with Illumina SNP chips the concentration was adjusted to 50 +/− 10 ng/μl.Illumina PCR-free TruSeq fragment libraries with 350 bp insert size were prepared. We collected on average 128 million 2 × 150 bp paired-end reads on a NovSeq6000 instrument.https://github.com/wurmlab/flo) with both genomes as input files. The success rate of the dbSNP liftover was 99.35%. Base quality score recalibration was performed with the aforementioned dbSNP version as the knownSites dataset. Truth datasets used for Variant Quality Score Recalibration (VQSR) were as follows. SNPs: Illumina chicken 60 k BeadChip and 600 k Affymetrix Axiom HD chicken genotyping array. INDELs: High confidence fraction  of a variant calling performed on a dataset of 50 laying hens (25 brown layers and 25 white layers) that were already described [Mapping and variant calling was performed according to the GATK best practice pipeline using GATK v. 4.0  and chicescribed . Trainin2) for each variant on GGA1 and GGA7 was calculated with an in house R script. In total, 24 of the HS animals were sequenced for the reference population. None of the F1 and F2 animals were sequenced.Haplotype phasing and imputation of the various datasets was performed with Beagle 5.0. Beagle 4.0 was used when pedigree data was involved, since it is the latest version to support pedigree data. The phasing/imputation workflow is summarized in Fig. y = Wα + Xβ + u + ϵis fitted, where y is an n × 1 vector of phenotypes n hens  and hatch were included in the analysis of the HS design, while only hatch was included for the F2 design. X is an n × 1 vector of marker genotypes at the locus tested and β is the corresponding effect size; and u is a vector of random genetic effects, with A− is the genomic relationship matrix based on all SNP-chip markers except those on the chromosome currently analyzed. Finally, ε is a random residual term, with I represents an identity matrix.GWAS were performed with the Genome-wide Complex Trait Analysis (GCTA) software version 1.92.3 beta3 using the imputed sequence level genotype data filtered for MAF (> 0.01). In order to correct for relatedness between animals and stratification , a randoThe variance explained by all markers on the SNP-chip was estimated using the same model without the SNP effect. In the F2 design, the variance explained by all significantly associated markers was estimated by including a second GRM based on the significant SNPs. As in the HS design only a single significant QTL was found with several adjacent markers, we partitioned the variance into a component explained by the SNP chip markers within a 5 Mb QTL window and the rest of the genome.Meta-analysis of the GCTA output for each trait was performed with METAL version 1.1 using the sample size based approach with default settings .The Ensembl Variant Effect Predictor release 98, which is part of the Ensembl advanced programming interface (API), was used for variant effect prediction with the following settings: --cache --species chicken --refseq --force_overwrite --variant_class --symbol --nearest symbol --sift b --tab.p-value correction. Protein interaction networks were computed with STRING v. 11 [Gene cluster analyses were conducted using the R package clusterProfiler  along wiNG v. 11  with defAdditional file 1. Coefficients of determination of imputation accuracy on GGA1 and GGA7 for the HS population.Additional file 2. Variants (−log10(p) > 5) from all GWAS and meta-analysis performed with the traits FPD and pEFP.Additional file 3. Manhattan plots of the GWAS performed with the F2 cross and the HS structure with the traits FPD and pEFP.Additional file 4. Venn diagrams of associated genes from the GWAS and meta-analyses with the traits FPD and pEFP.Additional file 5. List of genes concordant and discordant between the GWAS and meta-analyses with the traits FPD and pEFP.Additional file 6. Results of the VEP of genes affected by variants (−log10(p) > 5) from the GWAS and meta-analyses with the traits FPD and pEFP.Additional file 7. Results of the gene set enrichment analysis of genes affected by variants (−log10(p) > 5) from the GWAS meta-analyses with the traits FPD and pEFP with clusterProfiler.Additional file 8. Protein interactions maps of genes affected by variants (−log10(p) > 5) from the GWAS with the traits FPD and pEFP.