Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets. Decline in diastolic function is also a hallmark of cardiac ageing which occurs through multiple pro-fibrotic and energetic pathways3. While several candidate genes have been implicated in various systolic function phenotypes through genome wide association studies (GWAS)5, the genetic architecture of diastolic function and causal associations with disease are largely unknown. Efforts to better define the molecular mechanisms of diastolic dysfunction could enable the development of innovative therapies for many cardiovascular disease states.Diastole is not a passive phase of the cardiac cycle, but is a complex sequence of inter-related physiological processes dependent on myocardial relaxation, stiffness and recoil, that are modulated by loading conditions, heart rate, and contractile function. Diastolic function therefore plays a central role in determining left ventricular filling and stroke volume with dysfunction shown to be a predictor of major adverse cardiovascular events and all cause mortality6. Such tissue level changes can be assessed at macroscopic scale in human populations through analysis of diastolic mechanics. Here we use data from participants in UK Biobank with cardiac magnetic resonance imaging (CMR)7, and apply deep learning computer vision techniques for precision motion analysis to derive image-based phenotypes of diastolic function9. In a GWAS of diastolic traits we identify associated loci that map to genes involved in actin assembly, cardiac myocyte survival, and heart failure phenotypes. We also describe the relationship between diastolic function and cardiovascular risk factors, and identify potential causal relationships with disease through Mendelian randomisation.Pre-clinical models of diastolic dysfunction are associated with alterations in left ventricular stiffness on atomic force microscopy that occur at the level of the cardiomyocyte sarcomere as well as due to extracellular matrix protein expansionrr and PDSRll) (imax)10. A flow chart of the analysis steps is depicted in We analysed CMR data from 39,559 participants in UK Biobank using machine learning segmentation and motion tracking to measure three validated parameters of diastolic function - radial and longitudinal peak early diastolic strain rate (PDSR PDSRll) , and maxP < 10−16 for both associations) . Significant associations were also observed for body surface area (BSA) and systolic blood pressure (SBP). Diabetes also added significantly to the associations with the diastolic function traits in the model .Strain rates declined with age and were lower in men (iations) , but no s) (maxi . Multipls) , a circulating biomarker of inflammation, showed a positive relationship with serum triglycerides, but we found no circulating biomarkers independently associated with diastolic function. We found that reduced peak diastolic strain rates were associated with reduced LAVll, 13% for PDSRrr, and 21% for LAVimax. The observed genetic correlation between the diastolic function traits was 0.22 (SE 0.07) between PDSRll and LAVimax, 0.12 (SE 0.08) between PDSRrr and LAVimax, and 0.85 (SE 0.04) between PDSRll and PDSRrr.The SNP-based heritability  was 12% for PDSRrr, 4 for PDSRll, and 2 for LAVimax (2 loci are shared between PDSRrr and PDSRll). Within the discovery set, we identified 5 independent loci  reaching genome-wide significance . Considering the full dataset, the number of significant independent loci increased to 9 with 2 additional loci associating with PDSRrr, 1 additional with LAVimax, and 1 additional with PDSRll , and in the functional maintenance and stress-response of the cytoskeleton 12. Moreover, we were also able to identify a novel link between the NPR3 locus and left atrial volume. The signal co-localizes with a previously discovered association with blood pressure traits . The C-allele of the lead SNP (rs1173727) at this locus increases NPR3 expression, and is associated with increased blood pressure and LAVimax, and an increase in risk of heart failure , making it a potential therapeutic target.Summary information for the 9 loci identified using the full GWAS dataset and two predicted loss of function variants are presented in  failure . The NPRNPR3 and genes encoding other proteins in the natriuretic peptide pathway with traits linked to the lead SNP (rs1173727) are shown in The relationship between common variants in rr, 15 SNPs for PDSRll, and 8 for LAVimax. The PIVS explained 1.5 % of the variability of PDSRrr, 1.1 % of PDSRll and 0.2 % of LAVimax. There was good agreement between the distribution of the PIVS in the UK Biobank participants with and without CMR indicating no systematic bias in genetic architecture . PheWAS was undertaken and we considered traits that have been previously associated with cardiac phenotypes in the literature, but in addition included an unbiased selection of phenotypes for exploration. In total, we considered 71 quantitative phenotypes and 63 (binary) disease endpoints (adj < 0.05) with at least one of the diastolic function PIVSs after leave-one-out cross validation (13 and IGF-114). Furthermore, we identified an association of PDSRrr to heart failure, cardiomyopathy and dilated cardiomyopathy, implicating diastolic function in cardiovascular endpoints.PIVSs for each diastolic function trait consisted of 20 SNPs for PDSRitecture . The Peandpoints . Out of lidation . Some of15. Here we used Mendelian Randomization (MR) to identify potential causal relationships between diastolic function as an exposure and two key clinical outcomes . We also assessed causal effects of biochemical, metabolic and haemodynamic exposures on diastolic function. These were chosen on the basis of clinical plausibility and the findings of the phenotype correlation analysis.Diastolic dysfunction is a substrate for the subsequent development of heart failure and, in observational studies, diabetes and hypertension are associated risk factorsrr, PDSRll and LAVimax  and increased risk of heart failure 18 from the HERMES consortium , suggesting sarcomere homeostasis during mechanical stress may affect diastolic function in both health and disease33. Phospholamban (PLN) is a key regulator of cardiac diastolic function, which modulates sarcoplasmic reticulum calcium-ATPase activity34. Common variants in this gene are also associated with trabeculation which has been implicated in promoting ventricular filling9. Speckle-tracking echocardiography of Pln knockout mice reveals alterations in longitudinal strain but not radial strain35, which is concordant with our observed associations with diastolic function and may relate to associated changes in ventricular geometry36. Although there is a genetic correlation between strain rate vectors the majority of SNPs used as polygenic instruments were independent of each other for these traits. We also identified a potential therapeutic target through the association of variants at the locus of NPR3 influencing diastolic function and risk of heart failure. Previous studies have highlighted its role in blood pressure control37, and in mediating the cardioprotective effects of cardiomyocyte and fibroblast-released CNP23.Our study provides insights into the biological basis of diastolic function with potential implications for therapy development. We identified common variants within genes implicated in cardiomyopathies . Our study was conducted under terms of access approval number 28807 and 40616. A range of available data were included in this study comprising genotyping arrays and whole exome sequencing, cardiac imaging, health-related diagnoses, and biological samples.There are 488,252 genotyped participants of which 200,640 have whole exome sequencing. We partitioned 39,559 participants with both CMR imaging and genotyping array data into two tranches by date of release from UK Biobank providing a discovery dataset of 26,893 participants and a validation dataset of 12,666 participants.8. Participants also underwent a resting 12 lead electrocardiogram which was automatically analysed using proprietary software .A standardised CMR protocol was followed to assess cardiac structure and function using two-dimensional retrospectively-gated cine imaging on a 1.5T magnet . A contiguous stack of images in the left ventricular short-axis plane from base to apex was acquired, with long axis cine imaging in the two and four chamber views. Each cine sequence had 50 cardiac phases with an acquired temporal resolution of 31 ms7. Transverse cine imaging was also performed in the ascending and descending thoracic aorta. All imaging phenotypes used for the analysis underwent quality control assessment9. The accuracy of image segmentation on the UK Biobank dataset is equivalent to expert human readers45. End-diastolic volume, end-systolic volume, stroke volume, and ejection fraction were determined for both ventricles. Left ventricular myocardial mass was calculated from the myocardial volume assuming a density of 1.05 g.ml –1. Left atrial volume was calculated from the segmented images using the biplane area–length formula ACh2 and ACh4 indicate the atrial area on the two and four-chamber cines respectively, and L indicates the longitudinal diameter averaged across two views. Measurements were indexed to body surface area (BSA) according to the Du Bois formula: 0.20247 * (Weight0.425) * (Height0.725), with weight in kg and height in m. The heart was divided into 16 standardised anatomical segments, excluding the true apex, according to American Heart Association nomenclature46.Segmentation of the short-axis and long-axis cine images in UK Biobank was made using fully convolutional networks, a type of deep learning neural network, which predict a pixel-wise image segmentation by applying a number of convolutional filters onto each input image for feature extraction and classification47. The maximum and minimum cross-sectional areas were derived from the segmentation and distensibility calculated using estimates of central blood pressure obtained using peripheral pulse-wave analysis 8.The aorta was segmented on the cine images using a spatio-termporal neural network49. To reduce the accumulation of registration errors motion tracking was performed in both forward and backward directions from the end-diastolic frame and an average displacement field calculated8. This motion field was then used to warp the segmentation contours from end-diastole onto successive adjacent frames. Circumferential (Ecc) and radial (Err) strains were calculated on the short axis cines by the change in length of respective line segments  strain. Peak strain for each segment and global peak strain were then calculated (rr) and longitudinal (PDSRll) directions was detected using an algorithm to identify local maxima (in GitHub repository peak_detection) (Motion tracking was performed on the cine images using non-rigid image registration between successive frames (in GitHub repository ukbb_cardiac)segments  as Edir=lculated . Strain tection) .http://biobank.ctsu.ox.ac.uk/crystal/). It should be noted that the biochemical markers used here were acquired at the initial assessment visit that preceded imaging assessment. Also, note that not all phenotypes were used in both the phenotype and the genetic analysis . We refer to the In total we consider 110 non-imaging cardiovascular-related phenotypes in UK Biobank participants for the phenotype regression analysis and the genetic analysis. These phenotypes contain information acquired by touch screen questionnaire, interview, biophysical measurement, hospital episode statistics, primary care data and biochemical analysis of venous blood. Details of how each phenotype was acquired are available on the UK Biobank Showcase  are described in the respective sections and/or in the We consider in all phenotype analysis a ± standard deviation (SD). Differences in continuous variables between groups were performed using Student’s t-test. Univariable and multiple linear regression analysis was used to explore the phenotype relationship between each diastolic parameter and cardiovascular risk factors. To identify relationships between diastolic function and a broader range of imaging and non-imaging phenotypes, including circulating biomarkers, we used the least absolute shrinkage and selection operator (LASSO) with stability selection, to optimise the model coefficients. We then ran regression diagnostics on the model with the selected variables, to exclude a possible collinearity inappropriately influencing our model  were used. Whole exome sequencing (WES) was performed on data released in 2020 collected from 200,640 UK Biobank participants51. The sequencing methods and variant calling procedures have been described in detail52. In the present study, genotypes in their released PLINK-format files are utilized, and samples were restricted to the European population. Quality control of the genetic data was performed as recommended by UK Biobank  were conducted with PLINK2 and adjusted for the first ten principal components, sex, age at baseline and the genotyping array. Post-GWAS filtering removed any SNPs with a Hardy-Weinberg equilibrium p-value < 0.05 and MAF < 0.005.For the genetic analysis, there were 34,242 participants of European ancestry  v1.0.1, PMID 25642630) was used to obtain a genetic correlation score between each pair of traits.55. Where eQTL data was available for the locus, the full summary statistics were downloaded to assess colocalisation .Lead variants for each locus were assigned causal genes, where possible, using a combination of variant annotations and additional functional genomic data sources . Each lead variant was systematically tested for any evidence of functional consequence using VEP. In addition, QTL evidence was extensively searched using Open Targets Genetics56 and Loss-of-Function Transcript Effect Estimator (LOFTEE)57 plugin were applied on all genomic variants of WES data. In the present study, we considered the genomic variants predicted by LOFTEE with high-confidence label "HC", non-dubious , and minor allele frequency < 0.05, as a Loss-of-function (LoF) mutation.Variant Effect Predictor (VEP)α = 0.05 . We identified 18,660 participants with both whole exome sequencing data and CMR imaging data.A Loss-of-Function (LoF) carrier indicator was created for each WES sample and each of the human protein-coding genes based on the collapsed information of LoF annotations. A subject was considered as an LoF carrier of the gene if there was at least one LoF mutation (based on methods in the variant annotation section), and a non-carrier if there was none. We then conducted the association test between LoF carrier indicator and the three diastolic function imaging phenotypes. Linear regression was performed with the adjustment of sex, age at time of MRI, and the top ten genetic principal components. The association results were further filtered as those with at least two carriers and the endpoint available. The association was considered significant after multiple testing correction at imax, PDSRll, PDSRrr) were obtained based on the respective GWAS (full imaging cohort) results by performing clumping (PLINK 1.9) using an LD threshold of R2 = 0.1 (in a window of 1000kb) and considering all SNPs with P < 10−6. Unlike more traditional polygenic risk scores we do not use thousands of variants as instruments but aim to identify a set of instrumental variables that are minimally correlated. This comes with the price of a relatively small set of instruments that explains less variability of a trait, but can be used as proper instruments for the Mendelian randomization analysis. Candidate variants were included in multivariate linear modelling evaluated on the European subset of the full imaging cohort with the first ten genetic principal components, age at MRI, sex, genotyping array and the MRI center as additional covariates and the respective diastolic function trait as dependent variables. The diastolic function traits were scaled to standard deviation (sd) 1 prior to the model estimation - therefore, a unit change in the PIVS score represents a change of 1 sd unit in the respective diastolic function trait. PIVS estimates per individual were then calculated by multiplying the observed genotype with the estimated beta from the multivariate linear model for each SNP and summing these values up. Missing genotypes were imputed using a mean imputation. The variance explained for the PIVS is measured by R2, estimated in a linear regression with the PIVS as only variable and the respective diastolic function trait as endpoint.Candidate variants for PIVS for the three diastolic function traits  and, as a sensitivity analysis, for which all leave-one-SNP out cross validations analysis lead to a significant result at α = 0.05 after multiple testing correction (FDR) for the number of considered phenotypes. The latter condition is supposed to exclude spurious results which are only driven by one single variant. Leave-one-SNP out cross-validation is performed by excluding one SNP from the list of candidate variants, then re-estimating the PIVS and performing the PheWAS as described above. For the leave-one-SNP out cross-validation, FDR adjustment is performed per combination of diastolic trait and phenotype, considering the number of included SNPs.Next, we conducted a PheWAS using the obtained PIVS  approach, i.e. two MR analysis are performed: first, an MR analysis using the first chosen trait as exposure is conduced and secondly a MR analysis using the selected second trait is run. By considering both results, evidence can be gathered for a one-directional causal relationship, a bi-directional causal relationship or no causal relationship at all. We performed this analysis taking into account one diastolic and one non-diastolic function trait and for that, we selected non-diastolic function traits of interest by taking into account the results from the observational correlation analysis and clinical expertise. This approach lead to the consideration of six dichotomous risk factors associated with diastolic dysfunction, Arteriosclerosis, Atrial Fibrillation, Heart failure, Hypertension and Diabetes - considering Type I and Type II separately. Further, we considered four physiological variables as potential causes or consequences of changes in diastolic function, as well as five quantitative lipid traits as surrogate for arteriosclerotic risks as potential confounder source for changes in diastolic function. In total we analysed 15 non-diastolic phenotypes and the 3 diastolic phenotypes in our MR.We established a workflow for the MR analysis which briefly described in this section. Full details are provided in the We aimed to remove potential confounding instruments by two filtering steps. First, we ran phenotype association analysis to identify and remove instruments that associate significantly with any of the traits Arteriosclerosis, Triglycerides, Apoliprotein B and LDL-Cholesterol. Second, we ran Steiger Filtering to remove instruments with potentially wrongly inferred causal directions.58 by using inverse-variance weighted method (IVW) as the main analysis and applying several other MR methods for ensuring robustness of the obtained results as sensitivity analyses. We used weighted median-based methods, MR-PRESSO and MR-Egger. Consistent effect estimates across the different methods improves our confidence in a truly causal effect. We consider an association as "potential causal" if the main analysis indicates a causal relationship (P < 0.01), at least two of the sensitivity analyses indicate at least a suggestive causal relationship (P < 0.05) and none of the sensitivity analyses indicate associations with inconsistent effect directionality, i.e. none of the methods showed a suggestive association with conflicting directionality (P < 0.05). No explicit multiplicity adjustment is performed for MR experiments. For "potential causal" associations, we next conducted a supplementary sensitivity analysis using published GWAS results as described in the Supplementary Material - if published GWAS data was available.All MR analysis are based on the point estimates and standard deviations obtained from the respective GWAS. We follow a similar approach to van Oort et al.All analysis, which involved diastolic and non-diastolic function traits, were conducted in a two-sample approach, i.e., the diastolic function trait GWAS was calculated in the full imaging cohort and the non-diastolic function trait GWAS was calculated in the non-imaging cohort.For comparison of the effect estimates from the MR-analysis to the observed correlation of diastolic function measurement and disease status, we restricted the analysis population to subjects which were disease-free at the CMR visit. We then fitted a logistic regression model by coding subjects who experienced a first event of the selected disease during follow-up time as 1 and event-free subjects during follow-up as 0. As covariates, we included age at CMR visit, gender, diabetes status, diastolic blood pressure and BMI. Note that this analysis was only performed for relationships judged as potential causal and that involves a disease endpoint (and not a quantitative measurement like pulse rate).imax and to further characterize the role of natriuretic peptides, we looked for additional genetic associations within genes of the natriuretic peptide pathway . We conducted GWAS using BOLT-LMM for all imaging traits listed in NPR3) across the 4 loci (NPPA and NPPB share the same locus). The GWAS summary statistics were filtered to a 1MB window around each gene . Across these summary statistics, we performed clumping with a p-value threshold of 10−5 and R2 < 0.1.In order to increase our understanding of the association of NPR3 with LAV59 and pQTL data from Sun et al.60) and SNPs with only a clear association with the gene of interest and traits of interest were kept .For the identified tag SNPs and associated variants in LD from the clumping analysis, we then tested which of these variants we could confidently link to the natriuretic gene in the locus. If any variant was classified as missense, we selected that variant directly. For eQTL variants, we used colocalisation analysis to link these SNPS to the natriuretic genes in each locus. Relevant eQTL and pQTL data was used  × β values with the βs aligned to have a negative sign on the diastolic blood pressure. P < 10−5) are shown in the Supplementary Material (Hierarchical clustering was then performed on the −log(Material .Supplementary DataSupplementary informationSupplementary Table


Reproductive performance of beef cows affects the profitability of beef production systems. Heritability of fertility traits is generally low, which means that there is a great influence of the nongenetic environmental factors. Selection based on breeding value is an effective way to improve fertility traits, and the knowledge of genetic parameters is necessary for this approach. In this study, the two most common fertility traits—the age at first calving and first calving interval are evaluated. It was found that genetic parameters and genetic correlation between these two traits differ according to population structure . A breeding strategy should be developed within a breed.The objective of this study was to estimate genetic parameters for age at first calving (AFC) and first calving interval (FCI) for the entire beef cattle population and separately for the Charolais (CH) and Aberdeen Angus (AA) breeds in the Czech Republic. The database of performance testing between the years 1991 and 2019 was used. The total number of cows was 83,788 from 11 breeds. After editing, the data set contained 33,533 cows, including 9321 and 4419 CH and AA cows, respectively. The relationship matrix included 85,842 animals for the entire beef population and 24,248 and 11,406 animals for the CH and AA breeds, respectively. A multibreed multitrait animal model was applied. The estimated heritability was low to moderate. Genetic correlations between AFC and FCI varied depending on the breeds from positive to negative. Differences between variance components suggest that differences between breeds should be considered before selection and breeding strategy should be developed within a breed. Recently, an increasing effort to include fertility traits in the genetic evaluation of beef cattle has been observed worldwide. Reproductive performance of beef cows affects the profitability of beef production systems but for a long time, the economic importance of fertility was neglected ,2. This However, the economic value of fertility traits is considerable because poor fertility results in a low profitability in beef cattle production . One of Age at first calving (AFC) and first calving interval (FCI) are the most mentioned traits because birth date is collected in all production systems. Moreover, AFC is a complex trait encompassing puberty and the ability to conceive, gestate, and deliver a calf . Low AFCThis study aimed to estimate and compare genetic parameters for the early maturing breed Aberdeen Angus, later maturing breed Charolais, and the whole population of beef cattle in the Czech Republic and to assess their impact on selection for fertility traits.Animal Care and Use Committee approval was not required for this study because the data were taken from an existing database of performance testing provided by the Czech Beef Cattle Association .The Czech Republic is situated in a moderate climate, in the middle of Europe, where herd management with spring calving (January to April) and a pasture system with natural mating is preferred. The genetic analysis was performed for two fertility traits, age at first calving, and first calving interval. n < 400 cows in breed) were excluded because of their poor connectedness within and between breeds. After this, the total number of primiparous cow records was 83,788 of 11 breeds  only AFC between 600 and 950 days was included. An AFC lower than 600 days was defined as implausible because of cows’ biological properties, and such data were removed. For Charolais, Limousine, Blonde d’Aquitaine, Gasconne and Salers only AFC higher than 880 days was used. For Galloway, Highland and Piemontese AFC between 750 and 1200 days were included. Other values were removed. The range of AFC values was chosen according to AFC distribution in population and breeding goals for these breeds in the Czech Republic. First calving interval was defined as the number of days between the first and second calving. Other calvings were not taken into account. For genetic analysis, the first calving interval had to be between 290 and 500 days. Lower and higher values were set as missing values.Data were edited before genetic analysis. Outlier or unreliable values, missing birth dates, and embryo transfer calves were removed from the database. Crossbreds with dairy cattle and dual-purpose breeds were also excluded. Then, three data sets were created. The first dataset contained all breeds  and herd-year-season of first calving (HYSc). Contemporary groups indirectly included the farm effect, but some farms were divided into more than one herd, which could be managed under different natural and management conditions, so the herd effects were preferred. Season was defined as a combination of three months of the year according to natural conditions: December to February, March to May, June to August, and September to November. Each HYS group included at least 5 cows fathered by at least 3 sires. For CH and AA, each HYS group included at least 5 cows fathered by at least 2 sires. These conditions were met by 33,533 cows of the whole beef population  and by 9For each data set, the relationship matrix with four generations of ancestors was created. If the ancestors were unknown, a phantom parent group was assigned according to the breed of the last known animal . FinallyPs and Pd are the proportions of genes of the primary breed in the sire and dam, respectively. The most prevalent breed across both sire and dam pairings was considered as the primary breed for the parental pair. Heterozygosity (H) was calculated as the function of the degree of heterozygosity of the cow, according to Hickey et al. :H = Pd(1p < 0.05) but also biological importance was considered. In the dataset representing the whole beef population, various breeds and their crosses were analyzed together, so the heterosis effect had been included. The effect of breed was considered in the phantom parent groups.The statistically significant environmental effects were determined by the procedure general linear model (GLM) in SAS 9.4 software  . Herd-yeAge at first calving was analyzed by the following model: ijklY = Age at first calving in days.First calving interval was analyzed with the following model: ijklY = First calving interval in days.−17. Variance components were estimated by the Average Information Restricted Maximum Likelihood method (AIREMLF90)  using a The assumption is that animal and random residuum were distributed as N  . Calvingp < 0.0001 for all three tested groups , except calving difficulty in AA probably because of a very low level of calving difficulties in this breed. Tested fixed effects explained approximately 81%, 42%, and 42% of all variability for the entire population, CH and AA, respectively. Most of the variability was explained by HYSb and the herd of first calving and thus the impact of herd management remained the most important effect in this case. The effect of heterosis was significant at p < 0.036. Cow’s heterosis reduced AFC by 7.37 days compared to purebred. The number of crossbred cows is constantly decreasing because of preferences for pure breed populations in the nucleus herds, but the proportion of crossbred cows with various levels of heterosis was still about 40%. Many crossbred cows are also included in the relationship matrix as historical ancestors.The tested statistical models and included fixed effects were statistically significant at The herd-year season of birth (HYSb) seems to be the most important effect because animals in these groups were under the same environmental, management, and nutritional conditions, which can explain a large part of the influence on fertility trait expression. Additionally, the herd of first calving was included too because in a third of cows it was different from the herd where the cow was born.Many authors classified the contemporary group herd-year or herd-year-season as the most important effect ,13. The Descriptive statistics for FCI of the entire population and populations of CH and AA are shown in The population mean of FCI in this analysis was 381 days. Grossi et al.  publisheThe longer FCI in CH than in the whole beef population could be caused by a longer postpartum rebreeding period due to a higher frequency of calving difficulties (CD) compared to other breeds in the population. Calving difficulty at first calving is divided into four levels: an easy calving with no assistance (1), calving with slight assistance (2), difficult calving with considerable assistance (3), or Caesarean section (veterinary assistance) (4). The proportions of levels of calving difficulty are shown in p < 0.0001 for all three datasets. The tested fixed effect HYS of first calving with significance p < 0.0001 for all tested groups. The fixed effect of calving difficulty during first calving was with significance p < 0.01 for the whole dataset and p = 0.01 for the CH breed, respectively. The effect of calving difficulty was not significant for the AA breed (p = 0.41). Heterosis effect was significant at p < 0.03, and heterosis of dam shortened first calving interval by 3.25 days.Models for FCI were significant at Finally, the model with fixed effect(s) explained 35%, 25%, and 25% of FCI variability for the whole population, Charolais and Aberdeen Angus, respectively. The importance of contemporary groups was stated by many authors ,19,21. TEstimated genetic parameters are presented in 2 of 0.085 [2 = 0.043. For the Aberdeen Angus, Simmental, and South Devon breed, the values were higher, h2 = 0.09, 0.125, and 0.10. For AA, we estimated a heritability 0.08. This was comparable to Roughsedge et al. [The estimated heritability for the FCI was 0.08. This is comparable value with other authors: hof 0.085 . Heritabof 0.085  and Grosof 0.085 , heritabof 0.085 , Van derof 0.085  and 0.05of 0.085 . A higheof 0.085  and the of 0.085 . Geneticof 0.085 . Gutierrof 0.085  publisheDifferences in estimated heritabilities could be caused by differences in genetic variations among breeds/populations or varying reactions to different environmental conditions. Low heritability is caused by a large unexplainable portion of residual variation  which coPhenotypic correlations between AFC and FCI were low, at 0.09, −0.26, and −0.23 for the whole population, CH and AA breeds, respectively. Some authors reported the phenotypic correlation to be not far from zero [For Charolais, the genetic correlation was −0.314, i.e., the lower AFC results in a longer FCI. The negative correlation may be caused by insufficient body weight and condition in the young CH cows, which results in a longer calving interval. The level of body fat composition affects the onset of early estrus , but allFor the early maturing breed AA, a genetic correlation of 0.357 was found between the traits. This may be a consequence of long-term breeding for sufficient body weight before mating/conception or a preference for calving at an older age because of higher body weight. Despite this, many breeders still prefer later calvings. The reason is that in low AFC, the cow´s own growth may not be fully complete yet. More energy and nutrients are required to continue their own growth and lactation, which could prolong the postpartum period and FCI as well. Other studies were done on the mixed populations or on populations with only one breed. A high correlation between AFC and calving intervals was also reported . A favorAge at first calving and calving interval are frequently used fertility indicators in beef cattle. Low to moderate heritability of these traits was found in this analysis. Additive genetic variance varied depending on the breed. For the first calving interval, most of the variability depended on nongenetic environmental factors. For age at first calving, the genetic variance was somewhat higher. Different genetic correlations between the age at first calving and the first calving interval were found for Charolais and Aberdeen Angus, as the later and early maturing breeds, and this should be considered and taken into account in future animal breeding. Differences between variance components suggest that differences between breeds should be considered before selection and breeding strategy should be developed within a breed.


PRELID2, GPER1, PDX1, TEX2, PLCL2, ICAM2) relevant for the four FE related traits. These genes are involved in the processes of fat synthesis and decomposition, lipid transport process, insulin metabolism, among others. Our results provide, new insights into the genetic mechanisms and candidate function genes of FE related traits in pigs. However, further investigations to validate these results are warranted.Feed efficiency (FE) traits in pigs are of utmost economic importance. Genetic improvement of FE related traits in pigs might significantly reduce production cost and energy consumption. Hence, our study aimed at identifying SNPs and candidate genes associated with FE related traits, including feed conversion ratio (FCR), average daily gain (ADG), average daily feed intake (ADFI), and residual feed intake (RFI). A genome-wide association study (GWAS) was performed for the four FE related traits in 296 Landrace pigs genotyped with PorcineSNP50 BeadChip. Two different single-trait methods, single SNP linear model GWAS (LM-GWAS) and single-step GWAS (ssGWAS), were implemented. Our results showed that the two methods showed high consistency with respect to SNP identification. A total of 32 common significant SNPs associated with the four FE related traits were identified. Bioinformatics analysis revealed eight common QTL regions, of which three QTL regions related to ADFI and RFI traits were overlapped. Gene ontology analysis revealed six common candidate genes ( Feed accounts for about 65% of the total cost in modern pig production and feed efficiency (FE) traits in pigs are critical . Better 1 respectively. However, their molecular regulation mechanism remains largely unknown.Therefore, it is essential to understand the molecular mechanism and genetic basis underlying FE related traits for the improvement of FE. In past decades, hundreds of quantitative trait loci (QTLs) affecting complex traits in pigs, including FE related traits, have been detected. Among these, 346, 618, 96, and 96 QTLs for FCR, ADG, ADFI, and RFI have been identified in different pig populations,1 four FE related traits  have been reported 371 QTLs in Duroc, 185 in Yorkshire and only 46 in Landrace pigs. Therefore, further investigation is needed to better understand FE related traits in Landrace population.In recent years, with the development of dense genomic markers and significant reduction in cost, genome-wide association study (GWAS) has become increasingly popular for detecting genetic variants associated with economic traits . StudiesIn this study, we performed a GWAS in a Landrace population to identify genomic regions and genes associated with four FE related traits: FCR, ADG, ADFI, and RFI.The whole recording procedure of ear tissue samples was carried out in strict accordance with the protocol approved by the Institutional Animal Care and Use Committee (IACUC) at the China Agricultural University. The IACUC of the China Agricultural University approved this study (permit number DK996).2 per pig). Each animal was labeled with a unique electronic identification tag on the ear and detected by the automatic feeding system. Once a pig visited the feeder, the date and exact start and stop feeding time, the animal number, and feed consumption of each visit were recorded.In this study 296 Landrace pigs were sampled. Phenotypic information of two batches comprising 156 and 140 pigs was recorded from April to July, and July to August in 2018, respectively. The first batch of 156 pigs was obtained from 46 litters born in April , and the 140 pigs in the second batch born in July were obtained from 41 L . The original feeding records were automatically generated by the pig automatic feeding system . The phenotypic data comprised individual ID, starting weight, daily feed intake, daily weight gain, final weight, feeding period, and feed conversion rate. Data were collected from each pig during the feeding period (approximately 11 weeks old), from 25 to 100 kg body weight (BW). The piglets were group-housed in half-open cement-floor pens  for each pig were calculated throughout the testing periods according to the information provided by the automatic feeding system. ADFI was calculated by the total amount of recorded feed intake divided by the length of the fattening period. ADG was calculated by total weight gain  during measure periods divided by the corresponding feeding days. FCR was calculated as the ratio of total feed intake to total weight gain. Finally, RFI was calculated following the formula .0 is the intercept, β1 represents the partial regression coefficient of ADFI on BW, β2 is the partial regression coefficient of ADFI on ADG, and e is the residual error.where βt-test showed that the data from the two batches of pigs were significantly different for FCR, ADFI, and RFI. The genetic correlations of FE related traits were calculated using ASREML software  and σa1, σa2 refer to the genetic covariance of two traits and genetic standard deviation of trait 1 and trait 2, respectively.where, rP < 10 × 10–6) were removed. After genotype quality control, no individuals were removed, and 41272 SNPs remained for further analysis.Genomic DNA was extracted from ear samples using a TIANamp Blood DNA Kit . Genotyping was performed on 50697 SNPs across the entire pig genome using a PorcineSNP50 BeadChip . Quality control was performed using PLINK 1.9 software . IndividIn this study, two different single-trait methods, single SNP linear model GWAS (LM-GWAS) and single-step GWAS (ssGWAS) were implemented to identify significant SNPs associated with FE related traits.A single SNP marker linear regression model was performed using the following single-trait animal model to detect the association of SNP with the four FE related traits, respectively. In order to control population stratification and to account for shared genetic effects of related individuals, a random polygenic effect was included in this model :batch is the fixed effect of the batch; b is the average effect of the gene substitution of a particular SNP; x is a vector of the SNP genotype ; g is a vector of random polygenic effects with a normal distribution g∼N, in which σ2 is the additive polygenic variance, and G is the genomic relationship matrix constructed using all markers following e is a vector of residual effects with a normal distribution e∼N, where I is the identity matrix of appropriate dimension and σe2 is the residual variance. For each SNP marker, the estimation of b and its sampling variance σb2 can be obtained through the mixed model equations.where, Compared to LM-GWAS, the following single-trait animal model in ssGWAS proposed by b is the fixed effect of batch; u is the vector of additive genetic effects with a normal distribution μ∼N, σu2 is the additive genetic variance, and G is same as in LM-GWAS. X and Z are incidence matrices of y related to b and u, respectively.where P-value of each SNP was calculated:The effect of each SNP can be estimated by ssGWAS, following t is the distribution function of t distribution, i2 is the genetic variance of ith SNP, n is the number of animals with ith SNP.where PP-values via postGSf90  method for multiple testing was used . FDR wasm is the number of times to be tested, n is the number of significant SNPs at assigned FDR level, e.g., 0.01. MaxP is the genome-wide significance level empirical P-value of FDR adjusted. Based on the P-values of SNPs obtained by two different methods, the empirical P-value of FDR adjusted at the genome-wide significance level of 0.01 was calculated on each trait for two methods in this study.where In order to assess the influence of population stratification on the GWAS, A quantile-quantile (Q-Q) plot was generated using PLINK 1.9 software.2 QTLdb3 was used to annotate significant SNPs located in previously mapped QTLs in pigs. To identify the related pathways and function annotation, KEGG4 and Gene Ontology analyses5 were performed.After identifying significant SNPs by GWAS, the genes located in or overlapping between the 0.25 Mb downstream and 0.25 Mb upstream region of the significant SNPs were determined using the Ensembl (Sus scrofa 11.1 genome version).The genetic correlations of FE related traits ranged from 0.12 to 0.90, while the standard errors of all genetic correlations were high. Among the four FE related traits, FCR had the highest genetic correlations of 0.90 with ADFI, while it had the lowest genetic correlation of 0.12 with ADG. The corresponding standard errors were 0.17 and 0.59. The genetic correlation between FCR and RFI was 0.72 with standard error of 0.18. Similarly, the genetic correlation of RFI with ADFI was 0.71 with standard error of 0.14. The genetic correlations of ADG with ADFI and RFI were 0.51 and 0.38 with standard errors of 0.34 and 0.44, respectively.False-positive results for significant SNPs are the most critical problem in GWAS. Therefore, it is essential to control population stratification and reduce the occurrence of false-positive results. The quantile-quantile plots (Q-Q plots) show that the influence of population stratification was negligible . MoreoveP-values of a multiple testing based on FDR adjusted at the genome-wide significance level of 0.01 for FCR were 7.48 × 10–4 and 7.17 × 10–4, respectively. For ADG, ADFI and RFI, the genome-wide empirical P-values obtained by LM-GWAS were 5.64 × 10–4, 6.53 × 10–4 and 7.58 × 10–4, and ssGWAS were 5.24 × 10–4, 6.16 × 10–4 and 5.89 × 10–4, respectively. A total of 55 and 50 genome-wide significant SNPs were found by LM-GWAS and ssGWAS methods, 32 SNPs out of them were common  identified by GWAS are illustrated in e common . Among te common . Among te common , and exp1 eight regions identified in our study overlapped or were close to the reported QTLs related to FCR, ADG, ADFI, and RFI. Among them, four regions overlapped and four regions were nearby the reported QTLs.Meanwhile, 13 regions were identified by two methods, as shown in All the significant SNPs identified by the two methods were annotated within the 0.25 Mb downstream and upstream region with reference to the Sus scrofa 11.1 genome assembly. GO analysis separately revealed 12 candidate genes for LM-GWAS and 7 candidate genes for ssGWAS . CombineSample size is a key factor for the efficiency of GWAS; one drawback of this study is that only 296 Landrace pigs were used to detect the genetic variants related to FE related traits. Compared to other traits, the measurements of FE related traits are usually difficult. Besides, it is not easy to acquire a large sample size. For instance, GWAS performed in pigs revealed significant associations for economically-relevant traits. In recent years, researches performed GWAS on FE related traits, unveiling high genetic correlations. For instance, Single SNP regression model is widely used in GWAS to identify the association of SNP with traits of interest, whereas it usually yields a high false-positive rate due to ignoring the linkage disequilibrium between adjacent SNPs. Some researchers investigated a haplotype-based sliding window strategy to reduce the false-positive by using multiple SNPs simultaneously. Some studies indicated that sliding window could result in different QTL regions, genes and SNPs with a single SNP method  while soAlignment of the genetic and physical maps on the Sus scrofa 11.1 genome assembly (Pig QTLdatabase) enabled comparison of the QTLs detected in our study with previously described QTL regions, and several of QTLs were selected for further analysis. The most prominent common regions for FCR were identified on SSC17, and 18. A QTL from this study located at 20.48–20.96 Mb on SSC18 coincides with a QTL identified for ADG in a Duroc sire population mapped across the region of 21.47–21.47 Mb . For ADFPLA2G1B), which can encode a secreted member of the phospholipase A2 (PLA2), is crucial for the biological functions of lipid metabolic and catabolic processes. PLA2G1B affects the inhibition of lysophospholipid absorption, and limits lipid catabolic process. Additionally, PLA2G1B also produces lysophospholipids that limit hepatic fat catabolism and reduce energy consumption , has a vital role in glutamine metabolism and negative regulation of insulin secretion. Some researchers found that this gene is a regulator of insulin secretion, and it can reduce pancreatic insulin secretion  which were reportedly related to FE traits in pigs, are similarly involved in fat synthesis and decomposition processes, as well as insulin and lipid metabolism, and lipid transport.Growth rate and feed intake were major influencing factors of FCR. One candidate gene Phospholipase A2 Group IB  gene. GPER1 is associated with positive regulation of insulin secretion, inhibition of fat cell differentiation , which is associated with phospholipid transport process and related pathway. In previous GWAS studies on pigs, the PRELID2 gene has been reported to be associated with reproduction traits in Yorkshire pigs  and Testis expressed 2 (TEX2) are associated with ADFI. ICAM2 has a key function in glucose stimulus and insulin metabolic process , is closely associated with lipid metabolism  was identified by the ssGWAS method. This gene was reportedly associated with glucose metabolism, insulin secretion, and glycolytic regulation . SNP, WU_10.2_11_4727497, associated with RFI was identified by ssGWAS method, it is also close to PDX1. Notably, the direct link between PDX1 and FE related traits has not been reported previously in pigs. Interestingly, PDX1 is associated with some classical pathways such as glucose/energy metabolism and insulin secretion pathway. In the adult endocrine pancreas, PDX1 is a pivotal factor for the up-regulation of insulin gene transcription that, in turn, regulates somatostatin, the expression of glucokinase, glucose transporter protein-2, and islet amyloid peptide , insulin] . Only on peptide . Do et a peptide , 2011. Melopment  and diabelopment . PancreaPLA2G1B and PRELID2). This study provides a better understanding of the genetic mechanisms underlying feed efficiency related traits, which offers an opportunity for increased feed efficiency using marker-assisted selection or genomic selection in pigs.In summary, the present study indicated that the result of LM-GWAS and ssGWAS methods are highly consistent. Combining LM-GWAS and ssGWAS improved not only the power of GWAS in a small population but also allowed screening of candidate genes with high reliability  at the China Agricultural University (permit number DK996). Written informed consent was obtained from the owners for the participation of their animals in this study.XD designed and supervised the study. LF and YaJ conducted the statistical analyses and interpretation of results. MM, ZZ, and WC conceived the experimental traits. YiJ and HS prepared the samples for DNA genotyping. LF and YaJ wrote the manuscript, which was critically remarked by XD. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Federal ex situ Genebank for Agricultural and Horticultural Crops with phenotypic data on complex traits including flowering time, plant height, thousand grain weight, as well as on growth habit and row type. We used previously published sequence data, providing information on 306,049 high-quality SNPs for 20,454 barley accessions. The prediction abilities of the two unordered categorical traits row type and growth type as well as the quantitative traits flowering time, plant height and thousand grain weight were investigated using different cross validation scenarios. Our results demonstrate that the unordered categorical traits can be predicted with high precision. In this way genome-wide prediction can be routinely deployed to extract information pertinent to the taxonomic status of gene bank accessions. In addition, the three quantitative traits were also predicted with high precision, thereby increasing the amount of information available for genotyped but not phenotyped accessions. Deeply phenotyped core collections, such as the barley 1,000 core set of the IPK Gatersleben, are a promising training population to calibrate genome-wide prediction models. Consequently, genome-wide predictions can substantially contribute to increase the attractiveness of gene bank collections and help evolve gene banks into bio-digital resource centers.Genome-wide predictions are a powerful tool for predicting trait performance. Against this backdrop we aimed to evaluate the potential and limitations of genome-wide predictions to inform the barley collection of the  This stimulated pioneering activities in biodiversity informatics to unlock historical phenotypic data from ex situ collections to assist the informed selection of gene bank accessions  (This study is based on previously published genomic  and phench (IPK) ,b. In toch (IPK) . DNA wasch (IPK) , 20,458 ch (IPK)  resultinch (IPK) .The phenotypic information includes data for flowering time (FT), plant height (PH), and thousand grain weight TGW; ,b. PhenoPairwise Rogers’ distances  among alAs the number of intermediate (128) and facultative (19) accessions is very small compared with the number of winter and spring accessions, we decided not to treat them as separate groups in genomic prediction. For simplicity, we merged the intermediate and facultative accessions with the winter accessions without deep biological rationale. Moreover, genomic data was used to cluster the 1,059 individuals that had no growth habit information into spring and winter barley populations. Then, we clustered the 7,648 individuals without row type information into five subpopulations . The latter was done for winter and spring barley separately.K groups and K > 2 , a “one-versus-all” strategy was recommended in i (1≤i≤K), the individuals with known group information were regrouped into two classes: those in the i-th group and those in the remaining groups. A GBLUP model with binary response is then applied to predict the probability pi of each individual belonging to the i-th group. Finally, the predicted group for each individual is given by argmaxi≤K1≤{pi}. The GBLUP model with binary response was implemented using the R package BGLR .The accuracy of the growth habit grouping was evaluated by performing 20-fold cross-validation in the population of accessions with known growth habit information. A balanced sampling strategy was applied to divide the entire population into 20 subsets, i.e., the proportion of accessions belonging to each growth habit group in each subset equals the proportion in the entire population. Similarly, the accuracy of row type grouping was assessed by Genome-wide prediction of FT, PH, and TKW was performed using the GBLUP model . Brieflyy is an n-dimensional vector of the observed phenotypic values (n is the number of genotypes), β is the vector of covariates including the common intercept term as well as (row type) subpopulation effects, X is the corresponding design matrix, g is the n-dimensional vector of the genotypic values and e is the residual term. In the model, we assume that β are fixed effects, g and e are random effects and G is the marker-derived additive genomic relationship matrix as in I is the identity matrix, 2-distribution.where age BGLR , in whicWe evaluated the prediction ability of GBLUP using the following scenarios:1.Five-fold cross-validation within each row type group.2.Five-fold cross-validation in the combined data set with all row type groups. To investigate the influence of row type subpopulation structure, we implemented two methods: 1) ignoring row types; 2) treating the row type as a fixed covariate. In addition to the overall prediction ability for all accessions, we also calculated the prediction ability for each row type subpopulation. In order to investigate the influence of the geographic origin, we implemented a third method in which the ancestry coefficients estimated by ADMIXTURE  were use3.Prediction across row type groups, i.e., using each row type group as the training set to predict the performance of accessions in each of the other groups.4.Using the accessions in a previously defined core collection comprising 1,000 accessions to predict the remaining individuals.5.Using all phenotyped accessions to predict the remaining individuals.6.Using the spring barley accessions to predict the performance of the winter accessions and vice versa.In scenarios 1 to 5, the genomic prediction was performed for the winter and spring subpopulation separately. In particular, accessions in the core collection were also divided into two subsets according to the growth habit. The prediction ability in scenarios 1 to 4 and 6 was estimated as the Pearson correlation coefficient of the predicted and observed values.gi be the true genotypic value of the i-th genotype  and gi, the reliability of For scenario 5, the prediction accuracy of each individual was assessed by the reliability, which is defined as the squared correlation between the true and predicted genotypic value . More prC. Then, the reliability can be calculated asbe the coefficient matrix of the mixed model equations MME, . Let [C1di is the diagonal element in C22 corresponding to the i-th genotype.where The principal coordinate analysis (PCoA) based on the Rogers’ distance matrix was performed separately for winter  and spriWe studied the possibility of using genome-wide predictions to cluster the accessions without prior information on the growth habit and the row type. First, we focused on growth habit and observed in the cross-validation study that on average 78.3% of the genotypes were clustered into the correct growth habit class. Therefore, the whole population with known information on growth habit was used and the 1,059 accessions without prior information were grouped into spring (999 accessions) and winter barley (60 accessions). In the following analyses, spring and winter barley populations were examined separately to classify the accessions into their row type. We first conducted a cross-validation study, which again showed a high prediction ability with on average 91.7% of the genotypes correctly clustered into their row type. Subsequently, the 7,648 individuals without prior information were grouped into row types.Details of the phenotypic data were outlined in a previous study  and we pWe performed genome-wide prediction within and across subpopulation of row types (Scenarios 1 and 3). Since only two accessions belong to the deficiens row type , they weWe then performed genomic prediction by pooling all accessions (Scenario 2), and observed that the overall prediction abilities for the three traits were high and very similar for the two different methods of treating row type subpopulation . The oveIn addition to the row types, the geographic origin of the accessions is also a clear confounding factor in the population structure . NeverthAmong the 3,898 genotyped winter barley accessions, there were 1,197, 1,216, and 1,791 accessions not phenotyped for flowering time, plant height, and thousand grain weight, respectively. Treating all phenotyped accessions as the training set, we predicted the performances of the non-phenotyped accessions (Scenario 5) using the GBLUP model and estimated the corresponding reliabilities, i.e., the squared correlation between the true and predicted genetic values . As expeAs in the winter barley population, we performed genome-wide prediction within and across subpopulations of row types (Scenarios 1 and 3). Similarly, we observed that the within-subpopulation prediction abilities were high, with values exceeding 0.6 for most trait-subpopulation combinations . The acrIn the prediction by pooling all accessions together (Scenario 2), we observed that the overall prediction abilities in the entire population were high for the three traits and very similar for the two different methods of treating row types , which iAmong the 16,556 genotyped spring barley accessions, there were 7,758, 7,783, and 9,645 accessions not phenotyped for flowering time, plant height, and thousand grain weight, respectively. As in the winter barley population, we predicted the performances of the non-phenotyped accessions (Scenario 5) using the GBLUP model and estimated the corresponding reliabilities . The meaWe observed that the ability of cross-growth-habit prediction (Scenario 6) was generally lower than within-growth-habit prediction for all three traits . For plaA 1,000 barley core collection was selected from a population consisting of 22,626 accessions with strictly filtered non-imputed marker data , which iA number of traits are routinely phenotyped during each multiplication cycle of gene bank accessions. This includes 25 phenotypic traits for barley, such as flowering time, plant height, and thousand grain weight, which are subject of this study. In addition, morphological traits are recorded to determine the taxonomic characteristics upon introduction into the gene bank collection and to verify the authenticity of the respective accession during later multiplication cycles. Such descriptor traits include row number, grain hull, awn length or ear color. These are unordered categorical values. The IPK uses the classification system according to versus row type. A more detailed investigation of the potential of genome-based classification of growth habit can benefit from phenotypic data that will facilitate a deeper description of the growth type. Summarizing, in the case of classification of traits that lead to a strong population differentiation, the description of accessions could be based on predictions alone, otherwise a two-stage strategy with genome-wide predictions followed by validation can be recommended.We used the traits growth habit and row type and investigated the potential of genome-wide predictions to fill the information gaps existing in the IPK barley collection. In line with the findings on subpopulation assignment in German Warmblood horses , we obseVrs1 and Vrs5 (for review see in silico determination of the taxonomic status of gene bank accessions will benefit from the ever-increasing number of genes known to be involved in the morphologic and phenologic differentiation of plants.An alternative to genome-wide prediction is classification based on functional markers. Key genes are known, which determine the row type such as Few studies have explored the potential of genome-wide predictions for gene bank collections with a focus on wheat , sorghumThe optimal strategy for compiling training populations for genome-wide prediction has been intensively discussed in animal and plant breeding e.g., . The preThe pros and cons of modeling the subpopulation as cofactor in genome-wide predictions were investigated in our study , 4, 5. WPrevious studies on the potential of genome-wide prediction in dairy cattle  and wheaDriven by the still considerable costs, core collections for gene bank collections are often defined for deep sequencing and phenotyping. This was also the case for the IPK barley collection : A core https://doi.org/10.1038/sdata.2018.278 and https://doi.org/10.1038/s41588-018-0266-x.Publicly available datasets were analyzed in this study. The datasets used for this study were published and are available at All authors declare that this study adheres to ethical standards including ethics committee approval and consent procedure. All experiments were performed under the current laws of Germany.YJ and JR designed the study. YJ performed the data analysis. YJ, SW, AG, and JR wrote the manuscript. All authors agreed with the current statement.The handling editor RP and the author AG declared that they are affiliated with the INCREASE consortium on genetic resources in legumes. The remaining 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.


The genomic era brought new opportunities to assess relationships between individuals. However, when pedigree and genomic information are used simultaneously, which is the case of single-step genomic BLUP (ssGBLUP), defining the genetic base is still a challenge. One alternative to overcome this challenge is to use metafounders, which are pseudo-individuals that describe the genetic relationship between the base population individuals. The purpose of this study was to evaluate the impact of metafounders on the estimation of breeding values for tick resistance under ssGBLUP for a multibreed population composed by Hereford, Braford, and Zebu animals. Three different scenarios were studied: pedigree-based model (BLUP), ssGBLUP, and ssGBLUP with metafounders (ssGBLUPm). In ssGBLUPm, a total of four different metafounders based on breed of origin  were included for the animals with missing parents. The relationship coefficient between metafounders was in average 0.54 (ranging from 0.34 to 0.96) suggesting an overlap between ancestor populations. The estimates of metafounder relationships indicate that Hereford and Zebu breeds have a possible common ancestral relationship. Inbreeding coefficients calculated following the metafounder approach had less negative values, suggesting that ancestral populations were large enough and that gametes inherited from the historical population were not identical. Variance components were estimated based on ssGBLUPm, ssGBLUP, and BLUP, but the values from ssGBLUPm were scaled to provide a fair comparison with estimates from the other two models. In general, additive, residual, and phenotypic variance components in the Hereford population were smaller than in Braford across different models. The addition of genomic information increased heritability for Hereford, possibly because of improved genetic relationships. As expected, genomic models had greater predictive ability, with an additional gain for ssGBLUPm over ssGBLUP. The increase in predictive ability was greater for Herefords. Our results show the potential of using metafounders to increase accuracy of GEBV, and therefore, the rate of genetic gain in beef cattle populations with partial levels of missing pedigree information.Pedigree information is incomplete by nature and commonly not well-established because many of the genetic ties are not known  For research purposes, the data requests should be forwarded along with the research proposal to fernando.cardoso@embrapa.br.Conexão Delta G Breeding Program . Hereford and Braford animals from eight herds had log-transformed tick counts recorded. Braford is a breed resultant of a crossing between Hereford and Zebu . A detailed descriptive statistic for the log-transformed tick count is in The data used for investigating the inclusion of metafounders in genomic evaluations were provided by p = 10–7), genotype call rate (CR) (<98%), minor allele frequency (MAF) (<3%), near-perfect collinearity with other SNPs (r > 0.98), and SNPs in the same physical position. The criteria adopted to reject samples were CR < 90%, heterozygosity deviation above three standard deviations, gender identification errors, and identical genotypes between different individuals . After quality control, a total of 3,591 samples  and 39,550 markers were retained for further analysis. Aiming to build a complete 39,550 marker panel, missing genotypes  were imputed across breeds according to the sliding window method using FImpute , whereas the BovineSNP50 Illumina panel  was used to genotype 3,591 animals. A total of 41,045 overlapping SNPs were selected for quality control. The quality control criteria adopted for SNP exclusion were the Hardy–Weinberg equilibrium chi-square test ( FImpute .Ne) individuals in the population. It is assumed that every individual from any population might have some degree of known or unknown relationship due to a common ancestor. From the perspective of founder individuals, their relationship can be derived by the use of metafounders, constructing a modified pedigree relationship matrix, A(Γ). The Γ matrix contains the relationship between metafounders (composed by at least one γ), and its simplest form is exhibited when the ancestral population is composed of only one breed, indicating that Γ is a scalar. In cases where the founder population is composed of several populations and eventually, with crossbred animals, it is possible to build an extended and more complex Γ. The latter is exactly the case of the population used in this study, which is composed of Hereford and Braford (an admixture between Hereford and Zebu) animals.The metafounder relationship used in this study was derived from the methodology proposed by A(Γ) followed usual rules  the matrix of pedigree-based relationships among individuals in population b. The residual term can be defined as i can be obtained and then the variance and covariance between means for markers within and across populations μ_b^ μ_b’ are the variance and covariance parameters for each Hereford (H), Braford (B), Zebu (Z), and unknown breed of origin (U).where Three different models were tested in this study, aiming to evaluate the gain in prediction accuracy due to the inclusion of metafounders in genetic evaluations. The first model contained only relationships based on pedigree information (BLUP); the second model was the single-step genomic BLUP (ssGBLUP), which combines pedigree and genomic information; the third model was the ssGBLUP with metafounders (ssGBLUPm). No restrictions were imposed on the approach to avoid or minimize inbreeding, and because of that, a total of 130 inbred individuals out of 72,755 were defined by non-zero inbreeding coefficient. The average inbreeding coefficient from inbred animals was 5.73%, with a maximum of 25%, and 0.06% for all 72,755 animals.To reduce the computational time for variance components estimation in average information REML (AIREML), the starting values were estimated through pedigree-based model via Gibbs sampling algorithm implemented in GIBBS2F90 . This soyijkl is the lth log-transformed phenotypic record for breed k  in the jth animal, from the ith contemporary group; β is a vector of systematic effects; ω = [ωkAωkk′Dωkk′AA] is a vector of Zebu breed proportion, heterozygosity, and recombination loss effects, represented respectively by ωA_k, ωD_kk’, and ωAA_kk’. Additionally, Go is the additive genetic (co)variance matrix and A is the numerator relationship matrix; xjk1, xjk2, xjk3 are known vectors, and zk is an incidence matrix where the elements of x2jk in column order are follows: (1) fk, defined as the proportion of alleles from the kth breed and corresponding to ωA_b; (2) fkk’ being the probability that a randomly chosen locus from an individual j, one allele is derived from breed k and the other allele is derived from breed k’, associated with ωD_bb’; and (3) bb′AA , G|ΣG,n∼IW, D|ΣD,n∼IW, R|ΣR,n∼IW, where Σq is the respective scale matrix for each q effect and degrees of belief parameter given by n. All effects were fitted using degree of belief equals 1 and Σq→∞ aiming to fit a flat distribution.where o ωAAbb′ . FinallyA total of 100,000 iterations were generated, with the first 30,000 discarded as burn-in, and 1 every 10th sample was stored for posterior analysis. Posterior means were then used as starting values in AIREMLF90  using thA two-trait repeatability animal model was used to estimate breeding values. The model can be seen as an incomplete version of the development proposed by yi is the vector of log-transformed tick counts in the ith breed – Hereford (H) and Braford (B); Xi, and Zi, are incidence matrices that relate phenotypes to its respective fixed, direct additive, and permanent environmental effect levels, respectively. The vector of fixed effect (βi) was composed by an overall mean and contemporary groups as cross-classified variables; zebu breed proportion, heterozygosity, recombination loss, and linear and quadratic effects of age at tick counting were considered as covariables. The vector of permanent environmental effect was defined as HB is the additive covariance between breeds.where A matrix was replaced by H and H(Γ), respectively, where H is the realized relationship matrix and Γ is a matrix of relationships among metafounders. The –1H can be defined as following:For ssGBLUP and ssGBLUPm, the –1H(Γ), was also constructed using the same approach, however, the pedigree-based relationship matrix was constructed as A(Γ) instead of A; likewise, A22 was replaced by A22(Γ). The genomic relationship matrix (G), was constructed as:where M is the matrix of SNP genotypes for each animal, P is a matrix of two times the frequency of the second allele p at locus j (pj), and s is the number of SNP markers. The denominator is a scaling factor for G. Under ssGBLUP, G was constructed using realized allele frequencies in the genotyped data, whereas 0.5 allele frequency was used for all loci in ssGBLUPm. G are required to ensure A, A22, and G compatibility in single-step approach (Γ computations were performed using a new software (GAMMAF90) being developed by BLUPF90 group1. The software was written in Fortran 95 and it is integrated in the new BLUPF90 software.where approach . The useThe BLUP model was fitted using the regular relationship matrix constructed based on aHB(1−γHB/2). Note the γH and γB represents the metafounder genetic relationship within Hereford and Braford, respectively, and γHB represents the across metafounder genetic relationship between Hereford and Braford. Heritabilities were calculated using these scaled (co)variance components. The genetic correlation between Hereford and Braford was calculated as To compare the estimated variance components and genetic parameters between models, ssGBLUPm parameters needed to be adjusted corresponding to (co)variances among the unrelated breeds  . More spIn this study, the within-breed predictive ability was used to measure the model ability to predict unknown phenotypes. For that, we used a forward validation approach. The selection of animals to compose the validation set in the forward validation was based on year of birth. Therefore, training animals were born from 2008 to 2010, and validation animals were born in 2011. A total of 198 and 766 animals were part of validation sets for Hereford and Braford, respectively.ith breed (Hereford and Braford) and fixed effects as defined in AIREML model. The predictive ability for Hereford was calculated as cor  and the last metafounder was assigned to the remaining base animals with unknown breed of origin. Self- and across- relationships (Γ) between Hereford, Braford, and Zebu breeds estimated by generalized least squares are also shown in r theory  where rer theory .A without metafourders are equal zero. Because of the inclusion of metafounders, an upward shift was observed in the inbreeding coefficients calculated based on A and H. Additionally, a few negative inbreeding coefficients were observed. This result suggests that parents were less related than the average in the base population . The classical quantitative genetics theory postulates that inbreeding for individuals with known parents is a function of parent’s relationships. Founder individuals are typically assumed to be drawn from a large, unrelated, ancestral population mated at random. Consequently, inbreeding coefficients for founder animals are usually defined as zero due to the lack of information. A different condition arises under the metafounder theory where base animals are assumed to be related due to a common ancestral population. In this case, the probability that identical gametes are shared between individuals may increase; thus, inbreeding coefficients are upward shifted  are shown in  shifted .H and H(Γ) matrices are available in A22 and G.Additional information about the diagonal and off-diagonal elements of all required matrices to create Variance components, heritability, and genetic correlations are available in In general, variance components and heritabilities were not considerably different between the genomic models. The most remarkable difference is seen in the heritability estimates on Hereford breed, where the inclusion of metafounders led to an increase of heritability. On the other hand, the inclusion of genomic information resulted in smaller heritability estimates on Braford breed. Both conditions can be attributed to improvements on additive genetic relationships, and consequently, on permanent environment effects estimation. The heritability shift observed between non-genomic and genomic models suggests that incomplete pedigree information may led to biased estimates on variance components, consequently, on heritability. This effect was already reported by ra) between Hereford and Braford were 0.67 (0.022), 0.45 (0.015), and 0.41 (0.017) for BLUP, ssGBLUP, and ssGBLUPm models, respectively. Note that under genomic models, the genetic correlation is lower than in BLUP. As stated by A and H can be different if the population is under genomic selection. In such a case, genomic information is part of the selection process, and if the genomic information is not included, variance components can be biased. The genetic correlation is useful when designing breeding schemes and defining breeding objectives. In the case of genetic correlation between different breeds, our results show that some genomic regions responsible for the control of tick resistance are being expressed in both purebreds and crossbreds. This result indicates that the selection of Hereford for tick count resistance may also account for a positive impact on Braford resistance, when the latter originates from selected Hereford parents.Genetic correlations .The degree of bias of the prediction methods is indicated by the coefficient of regression of phenotypes adjusted by fixed effects on (G)EBVs . The optΓ since the matrix is built based solely on SNPs. This study focused on evaluating the impact of metafounders on the estimation of breeding values, with Γ being computed based on all genotyped animals. However, only a fraction (28%) of the population is genotyped and the number of genotyped animals is limited; which is the reality in almost all livestock populations. Therefore, there is still a lack of knowledge on how large the number of phenotyped and genotyped animals connected to metafounders should be needed to obtain accurate Γ estimates. Future studies should investigate the impact of the number of genotyped animals from different breeds on the estimates.Our study shows the potential of the use of metafounders to increase the rate of genetic gain across generations due to a more acurate estimation of breeding values, in accordance to The inclusion of genomic information in a multibreed Hereford/Braford population provides greater predictive ability than pedigree-based models for both breeds because of a better estimation of genetic relationships. When the level of pedigree missingness is high, the inclusion of metafounders can help to further increase the ability to predict future performance in small multibreed populations.The datasets for this article are not publicly available because it is property of the Braford and Hereford producers, Embrapa, and GenSys Consultants and these information is commercially sensitive. For scientific research purposes, the data requests should be forwarded along with the research proposal to FC, fernando.cardoso@embrapa.br.VJ executed the analysis and wrote the manuscript. PL, DL, FS, and FC provided support during analysis execution and reviewed the manuscript. All authors contributed to the article and approved the submitted version.VJ was employed by the company Bayer and FC was employed by the company Embrapa Pecuária Sul. The remaining 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.


Genetic improvement of wool production and quality traits in fine-wool sheep is an appealing option for enhancing the market value of wool products. We estimated genetic parameters and the accuracies of estimated breeding values for various wool production and quality traits in fine-wool sheep using pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) strategies. ssGBLUP performed slightly better than PBLUP for the studied traits. Therefore, the single-step genetic evaluation method could be successfully implemented in genomic evaluations of fine-wool sheep and the prediction of future breeding values in young Merino sheep as part of an early preselection strategy in the near future.2 values were observed for FL (0.277) and MFD (0.290) with ssGBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. The accuracies of breeding values were low to moderate, ranging from 0.362 to 0.573 for the whole population and from 0.318 to 0.676 for the genotyped subpopulation. The correlation between the estimated breeding values (EBVs) and genomic EBVs (GEBVs) ranged from 0.717 to 0.862 for the whole population, and the relative increase in accuracy when comparing EBVs with GEBVs ranged from 0.372% to 7.486% for these traits. However, in the genotyped population, the rank correlation between the estimates obtained with PBLUP and ssGBLUP was reduced to 0.525 to 0.769, with increases in average accuracy of 3.016% to 11.736% for the GEBVs in relation to the EBVs. Thus, genomic information could allow us to more accurately estimate the relationships between animals and improve estimates of heritability and the accuracy of breeding values by ssGBLUP.Genomic evaluations are a method for improving the accuracy of breeding value estimation. This study aimed to compare estimates of genetic parameters and the accuracy of breeding values for wool traits in Merino sheep between pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP) using Bayesian inference. Data were collected from 28,391 yearlings of Chinese Merino sheep (classified in 1992–2018) at the Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm, China. Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The estimates of heritability for wool traits were low to moderate. The largest h Wool generally accounts for 20% of commercial return for wool producers. In 2017, China exported 407,000 tons of wool. However, over the last two decades, the wool clips in areas of major production have become progressively finer, while textile technology for processing superfine-wool types has improved. Consequently, the premium raw-wool prices set for fine- and superfine-wool categories have increased significantly compared with those for other types of wool. Wool quality and production traits are key features that reflect a fine-wool sheep’s economic value. Meanwhile, subjectively-assessed wool traits are widely used to select breeding ewes and rams in the sheep industry. Thus, these traits are often used together to determine the quality and grade of wool, as well as the main criteria for determining the price of wool. Breeding programs for fine-wool sheep have traditionally focused on improving wool production and wool quality. The genetic improvement of these traits is an appealing option for enhancing the market value of wool products.In China, sheep and lambs are raised mainly in large farm flocks in the northern border regions and northwestern regions and in small ranching operations in the northeast. Most of the 164,658 million sheep in China are wool or dual-purpose flocks  rams based on progeny testing ,2. The wH). Compared with pedigree-based best linear unbiased prediction(PBLUP) and genomic BLUP (GBLUP), ssGBLUP increases the accuracy of GEBVs in many livestock [Genomic studies of sheep have focused mostly on models, methods, and genomic estimated breeding value (GEBV) accuracy ,22,23. Sivestock ,28,29,30This study aims to explore the feasibility of breeding for wool traits in fine-wool sheep using genomic information. Therefore, the objective of this study was to compare estimates of genetic parameters for various wool production and quality traits in fine-wool sheep between relationship matrices. Moreover, the effects of the estimation methods on the accuracy of the EBVs were analyzed. Data from 28,391 yearlings animals (born in 1991–2017) were collected at Xinjiang Gonaisi Fine Wool Sheep-Breeding Farm , China. The pedigree file used for the analysis contained 41,830 animals, with each animal traced back three generations. In the full data set, one ram had a maximum of 385 offspring with data, and 573 rams had only one offspring. More than 3386 dams had 2 or more offspring with data available (156 dams had between 4 and 8 offspring). Furthermore, 663 full-sibling groups had 2 to 4 siblings. Only one shearing record was included per animal in the analysis. All animals were selected and eliminated on the basis of production performance classified from April to May each year. Shearing was carried out in June each year, and the first shearing took place at 13 to 15 months of age. The flock management of all animals was consistent with that reported by Zhao et al. . Subjectively-assessed wool traits, namely, spinning count (SC), crimp definition (CRIM), oil (OIL), and body size (BS), and objectively-measured traits, namely, fleece length (FL), greasy fleece weight (GFW), mean fiber diameter (MFD), crimp number (CN), and body weight pre-shearing (BWPS), were analyzed. The scales used for the subjectively-assessed wool traits in Merino sheep are described in p < 1 × 10−6). After quality control, 40,457 SNPs from 965 animals were reserved for analysis. A total of 965 ewes and 21 rams were genotyped with the Ovine 50K SNP BeadChip. The SNPs were obtained by common SNPs in the 50K bead chips as target files for analysis. Quality control of SNP genotypes was carried out with PLINK software . All genVariance components were estimated by Bayesian inference using threshold models for the subjectively-assessed wool traits and linear models for the objectively-measured traits that differed in the relationship matrix used.I and All wool traits were examined with the following model: A and H denotes the pedigree-genomic relationship matrix. H was derived given A and G via the following formula: A indicate subsets of ungenotyped and genotyped animals, respectively; M is an incidence matrix of SNP effects, with elements j th SNP. Thus, the inverse of the H isThe additive genetic effect was modeled using two kinds of genetic (co)variance structures: for PBLUP , a~N and MFD (0.290) with ssGBLUP. The smallest estimates were obtained for OIL (0.070 and 0.072) with both prediction models. Moreover, the heritability estimates of objectively-measured traits were moderate, except that for CN (0.089), which was lower with PBLUP. The heritabilities estimated for wool traits with ssGBLUP were slightly higher than those obtained with PBLUP. Furthermore, the ssGBLUP and PBLUP estimates of GFW were very similar. For all models, the standard errors of heritability estimates were low (between 0.013 and 0.071), as expected for such a small data set. Surprisingly, little improvement occurred in terms of the accuracy of the estimated heritability, as the standard errors were similar between models with the A or H. As shown in H of wool traits was skewed to the right compared with that of the A, indicating that the H improved the accuracy of GEBV predictions. Additionally, the figure shows that the number of individual animals with A estimated EBV accuracies equal to 0 for SC, CRIM, OIL, FL, GFW, and BWPS was larger than that with H estimated GEBV accuracies equal to 0 for these traits. Therefore, the H can be used to obtain accurate GEBVs for such individuals via genomic relationship information. The data in 2 values for GFW and BWPS in the present study were higher, but the heritabilities of MFD and FL were not different. In contrast, the genomic information-based h2 values for MFD and BWPS were larger than the pedigree-based ones. Thus, the gene frequency in the fine-wool sheep (Xinjiang type) population has changed over 10 years of breeding. Moreover, many investigations have used the restricted maximum likelihood (REML) method and reported high heritability estimates for GFW, MFD, FL, and BWPS in Merino sheep or dual-purpose breeds, ranging from 0.37 to 0.74 [The heritability estimates based on pedigree models for the objectively-measured traits were inconsistent with those reported in a previous study using a similar data set . Compare to 0.74 ,39,40. M to 0.74 ,42,43, w to 0.74 ,45,46. N to 0.74 . However to 0.74 . 2 estimates obtained with genomic methods mainly increased the additive genetic variance. However, there are several potential explanations for the difference in estimated genetic variances between the models based on pedigree and genomic relationships. First, the A and H were different, displaying a difference in scale for the diagonal elements, especially considering the information on the Mendelian sampling component in the H. Veerkamp et al. [H according to the eigenvalues of A slightly changed the genetic variances. Meanwhile, Forni et al. [A and H [The current study revealed that the heritability estimates of wool traits obtained using genomic information were slightly higher than those obtained with a pedigree, whereas the standard errors of the heritability estimates were generally equivalent. In particular, differences in hp et al.  demonstri et al.  reported[A and H . A secon[A and H  estimate[A and H  reported[A and H  and Lobe[A and H . In addi[A and H  found thIn this study, the breeding value accuracies for wool traits were low to moderate. In general, the relative increase in accuracy for PBLUP compared to ssGBLUP was larger for genotyped animals than for the whole population. However, there were many reasons for the above results. First, the reference population in this study consisted mainly of females , consistent with the conditions of by Molina et al. . HoweverAdditionally, Swan et al.  reportedGenomic evaluation in Merino sheep using ssGBLUP is feasible for both continuous and categorical traits. The first Merino sheep genomic evaluation carried out in China has shown that it is possible to improve heritability and breeding value accuracies for wool traits  by the ssGBLUP method in a sample of limited size. Our findings indicated that the estimated heritability and breeding value accuracies for wool traits were low to medium when using PBLUP and ssGBLUP. However, the estimated parameters provide a basis for calculating more accurate Merino sheep breeding values. It is likely that a method of this type will be implemented in fine-wool sheep genetic evaluations in the near future. The next challenge is to develop improved single-step analyses that can continue to accommodate larger numbers of genotyped animals in the future. Therefore, if the population of reference were increased beyond the size used in this study , the results would allow improving the breeding value accuracy for wool traits and the prediction of future breeding values of young Merino sheep as part of an early preselection strategy in the near future.