Phenotypes were recorded following field exposure to local isolates of Phytophthora infestans. QTL explaining resistance in four experiments conducted across the three countries were identified in chromosome IX, and environment-specific QTL were found in chromosomes III, V, and X. Different genetic models were evaluated for prediction ability to identify best performing germplasm in each and all environments. The best prediction ability (0.868) was identified with the genomic best linear unbiased predictors (GBLUPs) when using the diploid marker data and QTL-linked markers as fixed effects. Genotypes with high levels of resistance in all environments were identified from the B3, LBHT, and B3-LTVR populations. The results show that many of the advanced clones bred in Peru for high levels of late blight resistance maintain their resistance in Ethiopia and China, suggesting that the centralized selection strategy has been largely successful.The identification of environmentally stable and globally predictable resistance to potato late blight is challenged by the clonal and polyploid nature of the crop and the rapid evolution of the pathogen. A diversity panel of tetraploid potato germplasm bred for multiple resistance and quality traits was genotyped by genotyping by sequencing (GBS) and evaluated for late blight resistance in three countries where the International Potato Center (CIP) has established breeding work. Health-indexed,  Previous studies have identified genomic regions in CIP-bred germplasm to explain resistance to late blight focusing on phenotypic data collected from field trials in Peru or using local pathogen strains in greenhouse conditions  of collaborators and assembled a diversity panel that consists of representative advanced clones including elite materials from each of CIP’s breeding populations. This so-called TON panel was then distributed from Peru to China and Ethiopia, where it was included in a series of trait evaluation experiments by national research institutions and CIP. The specific aims of this study were (i) to identify QTL underlying late blight resistance in and across environments; and (ii) to test prediction models to support the global deployment and use of promising resistance sources in local breeding and variety development programs.We report the genotyping, estimation of linkage disequilibrium, and population structure of the TON panel and identify QTL for late blight resistance via genome-wide association (GWA). In addition, we present a case for genomics-assisted breeding for foliar late blight resistance and show how the use of genomics and pedigree information can be used to select “best bet” clones for breeding and variety development in diverse target environments.S. demissum-derived resistance from breeding programs around the world, including native Andean cultivars  and wild species (S. acaule and S. bulbocastanum). “Population B3” genotypes were derived from “Population A” with emphasis on increasing frequencies and levels of quantitative resistance to late blight. The “B1 population” is derived from the S. tuberosum group, Andigena. The “LTVR population” is characterized primarily for its resistance to the most important virus diseases , short crop duration, and adaptability to warm environments. The “LB-HT” population combines late blight resistance from the “B3 population” and heat tolerance from North American and European-bred varieties and the LTVR population. The “B3-LTVR” population contains hybrid genotypes originating from crosses between “B3” and “LTVR populations.” The “pre-Bred” population has genotypes with late blight resistance introduced from wild Solanum species into the tetraploid background of “B3” or “LTVR.” The varieties group consists of several potato varieties or key breeding lines: “Desiree, “Atlantic,” “Spunta,” “Granola,” “Yungay,” “Tomasa Condemayta,” “DTO-33,” “Kufri Yoti,” and “Chucmarina.” The CIP numbers and the parentage of the 380 genotypes are given in Supplementary Table S1.The TON panel consisted of 380 genotypes representing seven CIP breeding populations and a group of varieties with variable origins . “PopulaS. demissum R genes  and relative AUDPC (rAUDPC). AUDPC is expressed in infection percentage times days  to filter the variants for a minimum read depth (RD) of 17, a minimum genotype quality (GQ) of 30, and a minor allele frequency (MAF) of 0.03. The SNPs that did not pass these criteria were changed to missing calls. Only the SNP sites that contained less than 30% missing data were selected.In total, 380 potato clones were genotyped. Library construction and GBS were outsourced to the Genomics Facility at Cornell University in 2015. The DNA was digested with https://github.com/FelixKrueger/TrimGalore) to trim the ends of the reads. The reads were aligned to the reference genome version S. tuberosum_448_v4.03  and for each K, the potato genotypes (clones) were assigned in the K groups based on the highest probability. To estimate the components of covariance among and within the clusters Analysis of molecular variance (AMOVA) was conducted using a farthest-neighbor algorithm. K-means clustering and AMOVA were conducted using Poppr v 2.9.0  based on the SNP dosage scores (0–4) and plotted against the physical distance of the marker pairs. A spline was fitted on the 90th percentile using quantile regression  in R were the following: no additional fixed effects; four principal components included as covariates to account for the population substructuring; a minimum MAF of 0.03; a maximum genotype frequency (after applying dominance relations) of 0.95; and P3D approximation. To detect statistical significance, the Bonferroni correction method was used, ensuring the genome-wide type I error would not be greater than 0.05. Manhattan plots were generated to display significant SNP in the different genetic models. In addition, Quantile–Quantile (Q–Q) plots of the observed vs expected –log10(p) values were used to evaluate that the population structure had been adequately controlled.Marker trait associations were modeled for all trials independently with the GWASpoly package using both diploid and tetraploid marker sets  and then translated to an approximate physical position in Mbp using the information provided in The genomic positions of the QTL, known genes, and the SNPs associated with plausible QTL for pathogen resistance in relation to other loci were determined using the genome browser available at 2) were estimated for rAUDPC using the methodology proposed by From the weekly observations of the disease incidence in the plots, the AUDPC was calculated and then rescaled to the relative AUDPC (rAUDPC) to facilitate comparison among the different environments. Because the experimental design types varied across environments, the raw data were analyzed using a stage-wise approach as elaborated in µij = µ + Gij + jE + ɛij where µ is the overall mean (intercept), jE the fixed effect of the th environment  standard normally distributed and independent random error effects T of the th genotype follows a multivariate normal distribution Gi ∼ NT was assumed to follow a multivariate normal distribution with mean 0 and variance-covariance matrix In the second stage, the genotype-by-environment table of BLUEs and their standard errors were used as starting point for the multi-environment trial (MET) analysis. The BLUEs were weighted according to Method 2, as described in s manner . The conG allows for heterogeneity of genetic variances and covariances across environments through In a first simplified approach, it was assumed that Gi are assumed independent for Alternative—and more realistic—approaches recognize that genetic relationships between the genotypes exist. In the following models, genetic relationships were incorporated, first by using genetic theory (pedigree-based relationship or kinship matrix) and then by using the available molecular information (molecular-based relationship or kinship matrix). In the standard approach described above, the random genetic effects i.e., the Pearson correlation between the adjusted phenotypic values and the validation predictions of the genetic values). The mean and standard deviation of this empirical distribution were calculated to characterize the expected value and variability of the prediction ability, respectively.A fivefold cross-validation was performed to compare the different prediction models for genetic rAUDPC predictions per environment. For this validation procedure, the set of available adjusted phenotypic values  was randomly subdivided into five disjoint subsets. The environment-specific genetic rAUDPC values of each of the five subsets were predicted while the other four subsets were used as training data set to fit the prediction model. This cross-validation procedure was repeated 100 times to obtain an empirical sampling distribution of the prediction ability  used, and the sequencing depth. Although GBS methodology was initially developed using a single restriction enzyme  in a collection of tetraploid potato cultivars representative of the cultivated gene pool in Europe and North America. As suggested by r21/2max, 90. This estimate in CIP germplasm was 0.55 Mb, which corroborates values reported in recent European potato varieties  reported for European varieties . Therefore, to increase the chances of finding significant associations, the GWA was done using both tetraploid and the diplodized data. Three markers  map in the same region of chromosome IX previously found associated with late blight resistance in Peru  . The resA biplot for predicted GBLUP values using the diploid marker-based kinship matrix shows the performance of the genotyped clones in all environments . In thisP. infestans in the Peruvian Andes, supplemented by progeny tests to identify parents with good general combining ability and to eliminate those resulting in segregation for hypersensitive response against test isolates. The pathogen population in this area is currently dominated by the A1 mating type and EC-1 clonal lineage, which is highly aggressive and complex in its virulence  effects. The results show that many of the advanced clones bred in Peru for high levels of late blight resistance maintain their resistance in Ethiopia and China, suggesting that the centralized selection strategy has been largely successful.


Genomic prediction is now an essential technology for genetic improvement in animal and plant breeding. Whereas emphasis has been placed on predicting the breeding values, the prediction of non-additive genetic effects has also been of interest. In this study, we assessed the potential of genomic prediction using non-additive effects for phenotypic prediction in Japanese Black, a beef cattle breed. In addition, we examined the stability of variance component and genetic effect estimates against population size by subsampling with different sample sizes.Records of six carcass traits, namely, carcass weight, rib eye area, rib thickness, subcutaneous fat thickness, yield rate and beef marbling score, for 9850 animals were used for analyses. As the non-additive genetic effects, dominance, additive-by-additive, additive-by-dominance and dominance-by-dominance effects were considered. The covariance structures of these genetic effects were defined using genome-wide SNPs. Using single-trait animal models with different combinations of genetic effects, it was found that 12.6–19.5 % of phenotypic variance were occupied by the additive-by-additive variance, whereas little dominance variance was observed. In cross-validation, adding the additive-by-additive effects had little influence on predictive accuracy and bias. Subsampling analyses showed that estimation of the additive-by-additive effects was highly variable when phenotypes were not available. On the other hand, the estimates of the additive-by-additive variance components were less affected by reduction of the population size.The six carcass traits of Japanese Black cattle showed moderate or relatively high levels of additive-by-additive variance components, although incorporating the additive-by-additive effects did not improve the predictive accuracy. Subsampling analysis suggested that estimation of the additive-by-additive effects was highly reliant on the phenotypic values of the animals to be estimated, as supported by low off-diagonal values of the relationship matrix. On the other hand, estimates of the additive-by-additive variance components were relatively stable against reduction of the population size compared with the estimates of the corresponding genetic effects.The online version contains supplementary material available at 10.1186/s12864-021-07792-y. Genomic prediction is a technology using whole-genome information to predict the genetic merits of genotypes . GenomicDrosophila [The contributions of the non-additive effects to the prediction of phenotypes appear to be case-dependent. For example, dominance effects slightly increased accuracy in several pig studies , 7, wherosophila , whereasosophila , regardlJapanese Black is a major beef cattle breed unique to Japan, which is now known as Wagyu and associated with an abundance of fat and flavour. Genomic prediction of the breed was assessed using the single-step approach, which combines SNP information with pedigree information in a single genetic relationship matrix . Now, ge2), rib thickness , subcutaneous fat thickness , yield rate  and beef marbling score (BMS). A summary of the phenotypic values is presented in Table To assess the possibility of using non-additive genetic effects for predicting cattle phenotypes in farms for Japanese Black cattle, we estimated the non-additive genetic variance components for six carcass traits using 9850 animals. The traits analysed were carcass weight , rib eye area , on the other hand, dominance variances were estimated for this breed using pedigree records . The pror2 values between contiguous SNPs in our study are 0.126, 0.293, and 0.771 at 50 %, 70 %, and 90 % quantiles, respectively. These values largely correspond with the Arabidopsis data with 105 and 106 markers in Ref. [AA generated from the reduced numbers of SNPs; the SNP densities were reduced to 0.125-, 0.25-, 0.5- and 0.75-fold of the original density by sampling the corresponding number of SNPs from the consecutive eight SNPs. As a result, the AA variance increased with the reduction in the density  of traits except for RT, we adopted Model AA2, in which the dominance effect from Model AA was removed. For RT, we adopted Model AD because of its relatively large AD component. Although Akaike information criterion (AIC) tended to support complex models such as Model Full and Model AD Table , our choAA were much closer to zero than those of A and D because of the Hadamard product of A  and models with non-additive genetic effects (Model AD for RT and Model AA2 for the others) showed little gain of accuracy. The bias of prediction was also similar between the models . The number of animals with phenotypes was 9850, of which 4142 were heifers and 5708 were steers. These animals had been fattened in 65 farms, of which two were experimental stations of LIAJ, one was a national experimental station, and the rest were commercial farms. Slaughter dates ranged from April 2012 to December 2018, and the mean ± SD ages at slaughter were 30.1 ± 1.5 (heifer) and 28.8 ± 1.2 months (steer). These animals were the offspring of a total of 487 sires. The number of offspring per sire was a mean of 20.2 ± 42.3, ranging from 1 to 554.All traits  were measured by the Japan Meat Grading Association, which is an official grader in Japan. The value of BMS was an integer between 1 and 12, with higher values indicating more abundant intramuscular fat. YI was estimated from CW, REA, RT and SFT by the grader.The animals with phenotypes were genotyped for genome-wide SNPs using Illumina BovineLD Genotyping BeadChip ver. 1, 1.1 or 2 . The call rate of these animals was 0.998 ± 0.003. Using Beagle ver. 4.0 , the BovA and D, respectively) were calculated using the imputed SNP genotypes following the natural and orthogonal interactions model [AA, AD and DD, respectively) were calculated as the Hadamard products between the corresponding matrices [AA avoiding these issues and compared it with AA calculated with the Hadamard product of A. However, the off-diagonal elements were almost the same between the two matrices (the Pearson correlation coefficient > 0.999), thus, we used the AA generated by the Hadamard product.The realised genetic relationship matrices for the additive and dominance effects  between the phenotypic values adjusted with the fixed effects estimated from the full data and the summation of predicted genetic effects . The fixed effects used for this adjustment were estimated with the same non-additive models as prediction. Prediction bias was evaluated using the coefficient obtained by regressing the predicted genetic effects on the adjusted phenotypic values.The predictive ability of the models was examined by 10-fold CV. For each trait, one model with non-additive effects was empirically selected from Models D, AA, AA2, AD and Full based on the full data analyses, as described in the Results section. Because genetic population structure was negligible in this population Figure , the anir was calculated between the estimates from the subsampled and full data sets. This calculation was performed for the sampled animals  and unsampled animals  separately. The latter corresponds to comparing the predicted genetic effects with the estimates from the full data.To investigate the stability of variance component and genetic effect estimates against the population size, we reduced the population size by subsampling and fitted the models. The sizes considered were 500, 1000, 5000 and 8865. The last of these corresponds to the training population size of CV (9850 − 985). We used the phenotypes of CW adjusted with the fixed effects estimated with Model AA2 from the full data. For each population size, animals were randomly sampled without replacement. Then, the models that had the same random effects as Model AA2, but only had the intercept as the fixed effect, were fitted. The estimates of variance components and genetic effects (additive and AA effects) were compared with those from the full data. For the genetic effects, Additional file 1: Figure S1. Genetic population structure estimated with eigen decomposition of the additive relationship matrix. (A) Cumulative proportions of variances of principal components (PCs). (B) Plot for PC 1 and PC 2. (C) Plot for PC 3 and PC4. Figure S2. The additive-by-additive (AA) genetic variance estimated from reduced SNP densities. Model AA2 was used. The y-axes indicate the proportion of phenotypic variance explained by the AA variance. The x-axes indicate the SNP density represented as the proportion of the original density. Bars indicate the standard errors. The broken horizontal lines indicate the estimates at the original density. CW, carcass weight; REA, rib eye area; RT, rib thickness; SFT, subcutaneous fat thickness; YI, yield rate; BMS, beef marbling score. Table S1. Akaike information criteria (AIC). The best models are highlighted in bold. Table S2. Comparison of predictive accuracies between models with the lowest AIC and chosen models. Table S3. Predictive accuracy and bias of phenotypes using additive genetic effects in cross-validation.


OsPTR9 for root weight, R/S and root length was provided. The results of given moderate correlation between traits and their corresponding sub-traits, and moderate additive effects between a trait and the accumulation of excellent alleles corresponding to its sub-traits supported a bottom-to-top regulation model for SBA. This model depicted each lowest-order trait  was determined by its own regulation loci, and competition among different traits, as well as the pleiotropy and LD. All above ensure the coordinated development of each trait and the accumulation of the total biomass, although the predominant genetic basis of SBA is still indistinguishable. The presentation of the above two models and evidence of this study shed light on dissecting the genetic architecture of SBA.Genetic basis and network studies underlying synergistic biomass accumulation of roots and shoots (SBA) are conducive for rational design of high-biomass rice breeding. In this study, association signals for root weight, shoot weight, and the ratio of root-to-shoot mass (R/S) were identified using 666 rice accessions by genome-wide association study, together with their sub-traits, root length, root thickness and shoot length. Most association signals for root weight and shoot weight did not show association with their sub-traits. Based on the results, we proposed a top-to-bottom model for SBA, i.e. root weight, shoot weight and R/S were determined by their highest priority in contributing to biomass in the regulatory pathway, followed by a lower priority pathway for their sub-traits. Owing to 37 enriched clusters with more than two association signals identified, the relationship among the six traits could be also involved in linkage and pleiotropy. Furthermore, a discrimination of pleiotropy and LD at sequencing level using the known gene  Oryza sativa L.) is the staple food of half the world’s population1. Faster biomass accumulation (shoot and root biomass) is a guarantee of high yield of rice while maintaining the current harvest index (HI)3. In the past, conventional rice breeding mainly focused on the selection of some agronomic traits in the aboveground part4. It has been challenging breeding for strong roots and phenotype-based selection of ideal proportion of aboveground and underground parts6. Generally, conventional breeding approaches are usually phenotype-oriented and selections are ineffective for improvement of root traits7. Recently, rational design breeding based on the extensively accumulated knowledge about the genes that regulate important agronomic traits has been successfully implemented in pyramiding multiple complex agronomic traits4. In light of the previous knowledge-based, insights into the genetic architecture underlying synergistic roots and shoots biomass accumulation (SBA) could provide valuable information for high-biomass rice breeding.Asian cultivated rice  enabled identification of numerous SNPs associated with targeted traits in diverse rice accessions, which could be more valuable with stable- and high-effect under diverse genomic background. Lately, a great number of loci for rice biomass traits have been detected by GWAS, which confirmed that variation exists both within and between rice subpopulations for these traits21. However, in-depth knowledge of the natural variation in genes underlying SBA is required with respect to identifying or designing superior cultivars with greater biomass, especially root biomass.Root weight, shoot weight and the ratio of root-to-shoot mass (R/S) are three main traits directly related to biomass accumulation of plants, and they are further determined by their sub-traits, such as root length, root thickness, shoot length etc. Recently, a large number of quantitative trait loci (QTLs)/genes were identified for the six traits/sub-traits above using mutant and bi-parental populations, and most of them had been verified in transgenic plants or introgression lines23. As a matter of fact, correlations amongst traits are common phenomenon in biology, which have been detected among traits in determination of panicle24, growth duration and yield25 and root and shoot traits in rice26. Breeders need to overcome disadvantageous correlations to meet production requirements while to improve target trait(s). Integrated intracellular and intercellular signaling networks through roots and shoots enable trait correlations and have been confirmed in previous studies29, but their role and genetic mechanism in SBA remain elucidated. Additionally, pleiotropy and linkage disequilibrium (LD) in natural population are usually considered as the main reasons for genetic trait correlations, although the predominant genetic basis of trait correlations is controversial30. Previous study discriminates between genic and true pleiotropy30. Genic pleiotropy refers to a gene affecting two or more traits simultaneously. Because in this case genes represent the smallest genetic unit, genic pleiotropy can be caused by intragenic linkage of QTNs, each affecting different traits, or true pleiotropy. True pleiotropy is due to a QTN affecting two or more traits. In the modern era, a lot of pleiotropy gene and pleiotropy genome regions have been detected in rice, such as Ghd731, OsSPL1632 and qRT912, but the research on discrimination of LD from pleiotropy is insufficient. Therefore, understanding of how roots and shoots coordinate development at molecular level is essential for the genetic improvement of roots and shoots biomass accumulation.Furthermore, dissection of a sub-trait is insufficient for molecular breeding for complex quantitative traits constrained by their multiple sub-traits because many traits correlate with one another and tend to be tightly integrated, resulting in heritable co-variationIn this study, we measured root weight, shoot weight, R/S and their sub-traits including root length, root thickness and shoot weight, followed a correlational analysis among the six traits related to biomass accumulation. To explore new or subpopulation-specific loci underlying SBA, GWAS were performed in whole association panel and its two subpopulations respectively. Most of the loci can be validated by comparison of the GWAS results and known QTLs/genes. Subsequently, we explored interactions among the loci and the genetic network across SBA based on these loci. Meanwhile, an intragenic linkage event of quantitative trait polymorphic-nucleotides (QTNs) was detected within a pleiotropic gene for root weight and root length, while each QTN was affecting a different trait. The results shed light on how roots and shoots develop synergistically as well as information that are useful for future molecular applications of these loci in breeding for high biomass.Oryza glaberrima L.) was used in the present study, which obtained from the 3000 Rice Genome Project (3KRGP)33. The set included 239 accessions from a mini core collection of Asian cultivated rice in China34 and 427 accessions in the International Rice Molecular Breeding Network35. Genome sequencing data  of 666 rice accessions were obtained from the 3KRGP16. Imputation was performed to infer missing genotypes using Beagle 4.0 software36. After removing SNPs with minor allele frequency (MAF) ≥ 0.05 and heterozygote rates ≤ 50%, a common SNP set was constructed for further analysis using an in-house Python script.A total of 665 Asian cultivated rice accessions and one African cultivated rice accession  was conducted twice as two replications at China Agricultural University in 2014 and obtained accurate reproducible phenotypic data of root and shoot biomass. Method for the experiment and determination method of six seedling traits were described in Zhao et al.R2 ≥ 0.3)38, the population structure, principle components (PC) and phylogenetic relationship of the associations were determined by Admixture, GAPIT and Tassel software42.It is usually confounding for population structure evaluation because of the non-independence of SNPs caused by strong LD. When independent SNP numbers were determined by PLINK , indica  and japonica  were calculated for an appropriate threshold, given that it is too stringent for significant association detection when the threshold is derived from the total number of markers45. The suggested thresholds  of associations were further calculated by Genetic Type I error calculator (GEC) software to control the genome-wide type I error rate at 0.05 by using Bonferroni-adjusted correction44. Finally, the threshold was defined at − log(p) = 6 for full population, indica and japonica together.GWAS on full population of 46, in which six traits and their association panel were represented by nodes, while the edge represented the LD between signals and link, and also traits and signals. The nodes for six traits were uniform, and the sizes of other nodes for signals were determined by –log(p) value. Meanwhile, edges between traits and signals were uniform, and edges between each two signals represented their LD value. Pairwise R2 values were calculated between all significant SNPs using PLINK38.We constructed association network using Cytoscape 3.5.119. The sequencing data of the 666 accessions were directly retrieved from 3KRGP16. A total of 4,504,569 SNPs were identified after imputation and removing the SNPs with minor allele frequencies (MAF) < 0.05 and heterozygote rates > 50% (Methods), and used for further GWAS. The population structure analysis showed that indica and japonica varietal groups appeared clearly at K = 2 based on the 295,881 independent SNPs (Methods), which was supported by neighbor-joining tree  and japonica (252) for further phenotypic analysis and GWAS on two subpopulations, though there were several typical indica and japonica at the junction between the clouds with PC plots and the phylogenetic tree  were higher than those of japonica (242.8 and 401.1 mg) with extreme significance at p value < 1.0 × 10−8 by Independent-sample T-test . Among these sub-traits, signal strength for root thickness of japonica was significantly greater than those in indica, while no obvious difference was detected in root length and shoot length between the subgroups  and one shoot weight sub-trait (shoot length) (method). The rice population showed significant numbers of variations for root weight (27.2%), shoot weight (24.8%) and R/S 11.4%), along with their sub-traits including root length (12.5%), root thickness (9.2%) and shoot length (15.2%) Fig. . Compari1.4%, alost Figs. . Howeverups Fig. . These rss Figs. . Interesly Figs. . The res43. As QQ plot showed that FarmCPU controlled efficiently population structure and relationships because there was no inflated p values and the majority (99%) of markers exhibited p values equal to or lower than the expected with accordance null hypothesis  = 6 was determined after Bonferroni-adjusted correction (Method). We identified 14, 13, 10, 17, 15 and 20 significant SNPs  for root weight, shoot weight, R/S, shoot length, root length and root thickness respectively in the full population  using FarmCPU modelsis Fig. . Thus, ton Figs. . Here weindica and japonica, respectively  indicated that the most of associations accounted for low phenotypic variation with average PVE at 2.99%, 3.98%, 3.10%, 4.68%, 3.01% and 3.46% for root weight, shoot weight, R/S, shoot length, root length and root thinkness, respectively Table . To valight Fig. . FurtherJMJ703 around Chr5_6020893 were identified in the full population and subpopulations  to explore causal gene for root weight and shoot weight. We estimated a candidate region from 25.2 to 25.5 Mb on chromosome 5 by using pairwise LD correlations (R2 ≥ 0.6)  is the most likely candidate gene for root weight and shoot weight, since it encodes a subunit of RNA polymerase II which is a prerequisite for normal development in plants49.Long distance pleiotropic signals play important roles in mediation of shoot–root communication.6) Fig. . There w.6) Fig. . Of the indica-specific and 2 japonica-specific alleles respectively were detected out of the 39 root weight association signals  which indicated that there was a similar trend for accumulation of high-root-weight alleles and accumulated high-shoot-weight alleles from their corresponding association signal in rice germplasm. These results suggested that there could be a delicate balance of high-root-weight alleles and high-shoot-weight allele, which was determined by pleiotropy, LD, selection and maybe other factors.As a complex trait, R/S was involved in root weight and shoot weight. However, no obvious linear relationships were detected between R/S and accumulation of high-root-weight alleles of root weight association signals, and between R/S and accumulation of high-shoot-weight alleles of shoot weight association signals in rice Fig. J,K. On tjaponica and two association signal (Chr1_41971596 and Chr7_27699272) for shoot weight in indica and full population  within five known genes showed significant association with corresponding traits under general linear model (p < 0.01)  showed association with root weight and R/S with extremely low p value, and one non-synonymous SNP (Chr6_29839102) was associated with root length  and were responsible for root weight and R/S. A forth site of SNPs (Chr6_29839102) was for root length. Taken together, this study supported that OsPTR9 was a pleiotropy gene for root weight, R/S and root length, and there were close intragenic linkage among the three possible functional SNPs underlying root weight.Four non-synonymous SNPs were identified within dy Table . The resGLR3.1 gene  were identified to associate with shoot length within d35, of which d35Tan-Ginbozu mutant was only 60–70% the height for the parental cultivar throughout the vegetative development11. Based on the three non-synonymous SNPs, two main haplotypes were identified in cultivated rice  were identified to be associated with root thickness within OsPTR9-3 allele are distributed and formed. There were 12 main subpopulation in cultivated rice panel   Fig. . Meanwhina Table .Figure 7OsPTR9 in japonica, using 34 SNPs of temperate japonica, tropical japonica, subtropical japonica and wild rice. Distinct differentiations were detected between japonica accessions carrying OsPTR9-3 and other haplotypes, based on phylogenetic tree from SNPs in OsPTR9-3  and Tajima’s D (− 2.56) were identified in temperate japonica accessions carrying OsPTR9-3 haplotype  and/or the GA sensitivity of shoots and roots plays a role in determining the shoot-to-root ratio of the plant59. Cytokinin and auxin are involved in meristem development and maintenance of amount for both roots and shoots62. However, the top-to-bottom regulation model is inadequate to explain the synergistic biomass accumulation of roots and shoots in rice to full extent, due to large variations in a lowest-order single trait  which are sufficient to affect final biomass in rice64.The complex traits (biomass) are determined by multiple sub-traits  and each sub-trait is further determined by a second-order trait  through the utilization of two main genetic regulation approaches. One is the top-to-bottom regulation model which determined the highest priority biomass regulation pathway of root weight, shoot weight and R/S, followed by lower priority pathway for root weight, shoot weight. Based on the present study, we also proposed a bottom-to-top regulation model which determined the lowest-order traits  its own regulation loci and competition among different traits, as well as the multiple validity and LD of control loci and also ensured the coordinated development of each trait and the accumulation of total biomass. Integrated intracellular and intercellular signaling networks through roots and shoots are considered as top-to-bottom regulation model for plant growthIn this study, the highest correlations were detected between root weight and shoot weight in all three populations Figs. D, S4. Me25, positive correlation between heading date and grain yield per plant, and between heading date and grain number per panicle65 and positive correlation between root weight and shoot weight in this study. Pleiotropy and LD are two considerable factors responsible for trait correlations, and it is hard to distinguish them in a short physical interval. There are two types of genes with genic pleiotropy: one is a gene with a single function, but involved in multiple biological processes, and another is a gene with multiple functions that contribute to different traits30. At present, it is still more difficult to utilize effectively the pleiotropic gene, because it usually cause the un-decomposable correlation between favorable traits and disadvantageous traits25. By comparison, physical linkage and association of non-linked genes can be cut off by modern biotechnology to achieve effective utilization of favorable genotypes.The consequences of trait correlations are the most important issues to be considered in breeding, such as the negative correlation between grain number per panicle and panicle number, and between grain number per panicle and kilo-grain weight30. In fact, some studies have shown that gene pleiotropy is caused by the different allele combinations of intragenic QTNs, such as SD1 for plant height and internode growth of stems to keep above the water in rice66, and Dwarf8 for plant height and flowering time in maize67. In this study, a total of 37 enrich genome region related to at least two traits, and one common association signal for root and shoot weight were identified  within OsPTR9. Importantly, deep analysis of the role of pleiotropy and LD in correlation among traits provides guidance for fine tuning of complex traits in crop improvement by careful manipulation of cloned QTL genes at the sequence level using various molecular technologies.It is feasible to solve gene pleiotropy at the level of sequence polymorphisms, because a gene with single function is most likely due to a pleiotropic QTN, whereas intragenic linkage of QTNs seems more likely the underlying cause of genic pleiotropy in the case of a gene with multiple functionsed Table , which cSupplementary Figures.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.Supplementary Table 5.Supplementary Table 6.Supplementary Table 7.Supplementary Table 8.


The present research has estimated the additive and dominance genetic variances of genic and intergenic segments for average daily gain (ADG), backfat thickness (BFT) and pH of the semimembranosus dorsi muscle (PHS). Further, the predictive performance using additive and additive dominance models in a purebred Piétrain (PB) and a crossbred  pig population was assessed. All genomic regions contributed equally to the additive and dominance genetic variations and lead to the same predictive ability that did not improve with the inclusion of dominance genetic effect and inbreeding in the models. Using all SNPs available, additive genotypic correlations between PB and CB performances for the three traits were high and positive (> 0.83) and dominance genotypic correlation was very inaccurate. Estimates of dominance genotypic correlations between all pairs of traits in both populations were imprecise but positive for ADG-BFT in CB and BFT-PHS in PB and CB with a high probability (> 0.98). Additive and dominance genotypic correlations between BFT and PHS were of different sign in both populations, which could indicate that genes contributing to the additive genetic progress in both traits would have an antagonistic effect when used for exploiting dominance effects in planned matings. With crossbreeding, specific alleles of parental purebreds are inherited by the offspring generating many heterozygous loci contributing to increase the favorable heterosis effects on the traits of interest. Dominant gene action has a major role on heterosis3. Predominantly, genetic selection in pigs is performed on purebred lines using purebred data and accounting solely for additive genetic effects, whereas crossbred performance is the ultimate product to improve as a correlated response4. If the genetic correlation between purebred and crossbred performances substantially differs from unity, a combined crossbred and purebred selection method is advised5. In addition to selection strategies, if dominance effects are important, assortative mating strategies might enhance the total genetic values of the crossbred offspring at a given generation10. Thus, estimates of additive and dominance genetic parameters for traits of interest and for purebred and crossbred performances are required to determine the best breeding and management strategies in crossbreeding schemes. With technological developments in the recent years, these strategies can now be based on marker information and several genomic models to account for dominance effects are available for purebred and crossbred genetic evaluations (e.g.15).Crossbreeding schemes have largely driven genetic progress in pigs during the last 40–50 years18 to provide a better understanding of the genetic architecture and improve predictive ability of models for complex traits. A further step is to investigate the contribution of these genome regions to dominance genetic variance across purebred and crossbred populations.In addition, separately estimating and accounting for additive and dominance effects of genic and non-genic regions could provide a better understanding of the genetic architecture and potentially improve predictive ability of models for complex traits in pigs. The contribution of genic and non-genic regions of the genome to additive genetic variance has been investigated in humans and some livestock speciesThe present research has estimated the additive and dominance genetic variances due to genic and non-genic segments for average daily gain (ADG), backfat thickness (BF) and pH of the semimembranosus dorsi muscle (PHS) and their predictive performance in a purebred Piétrain (PB) and a crossbred  pig population. Estimates of additive and dominance genotypic correlations between purebred and crossbred performances for these traits as well as between traits within PB and CB populations are also provided.https://www.recherche-animale.org/sites/default/files/c_rural_2013.pdf) updated by the 2013–118 decree and five orders from February 1st 2013, published on February 7th, according to the 2010/63 directive from the EU. This regulation is under the responsibility of the French Ministry of Agriculture.Data used for this study was originated in the UtOpIGe project ANR-10-GENOM_BTV-015. All experimental protocols of the project were approved on the 01/23/2013 (R-2012-NM-01) by the local ethic committee . The Comité Rennais d’Ethique en matière d’Expérimentation Animale is registered in the French National Committee of ethical reflexion on animal experimentation number 7 . All methods were carried out in accordance with guidelines and the French regulation in Animal research .The study was carried out in compliance with the ARRIVE guidelines (https://doi.org/10.15454/1.5573932732039927E12). Both, PB and CB animals were descendants of 90 Piétrain boars. They entered the test station facilities in Le Rheu (France) at approximately 5 weeks of age and were slaughtered at a fixed weight of 110 kg (at 5–6 months of age).Animals were provided by the three French breeding companies of the Alliance R&D association  involved in the UtOpIGe project ANR-10-GENOM_BTV-015. We used 636 purebred Piétrain (PB) and 720 crossbred Piétrain × Large White (CB) entire “intact” male piglets produced on selection and multiplication farms and tested at a single test station INRA UE3P France Génétique Porc phenotyping station  was measured using a Xerolyt electrode  and a Sydel pH meter  at 24 h post- mortem. Further details regarding complete data collection and experimental design can be found in Tusell et al.10.Animals were misspelled weighed at the beginning  and at the end of the test period (when they reached 110 kg). Average daily gain  was calculated as the body weight gained during the test period divided by the duration of the period. Rib back fat thickness  was measured on carcass with the Capteur Gras Maigre methodAnimals were genotyped using the Illumina Porcine SNP60 Bead Chip . Single nucleotide polymorphisms (SNPs) with a call rate lower than 0.90 and a minor allele frequency lower than 0.05 were removed. For the remaining SNPs. Missing genotypes were imputed using a naïve method that sampled genotypes with probability weights of the allele frequencies at each locus. The missing genotypes were then replaced with these sampled genotypes. Animals that presented Mendelian inconsistencies with their parents were discarded. After quality control, 46,816 SNPs were included in the analyses. Due to separate data edition, the number of animals with records and number of SNPs retained for the analyses slightly differed for each trait. Summary statistics of the three analyzed phenotypes in the two populations are presented in Table https://www.animalgenome.org/repository/pig/). The SNPs that did not have RS number or a non-unique RS number were discarded leading to 39,727 SNPs available for the analysis. The physical positions of the SNPs obtained through Ensembl database (https://useast.ensembl.org/Sus_scrofa/Tools/VEP) for pig  allowed to locate each SNP into a genic or an intergenic region.Chromosome information of SNPs (i.e. the map file containing SNP-ID and RS-Numbers) was downloaded from the Animal Genome Database  encompassed the SNPs annotated into the following categories: introns , synonymous (321 SNPs), upstream the gene , downstream the gene , 5′ (77 SNPs) and 3′ untranslated regions (UTR) (382 SNPs), missense (108 SNPs), other exon mutations (27 SNPs). Intergenic region category  encompassed all SNPs annotated outside these genic regions. All region category (All) included the 39,727 SNPs.14:14. Terms AA, Aa and aa genotypes, respectively. Additive and dominance genotypic (co)variances were modelled as 12, where To explore the additive and dominance variance explained by the SNPs of the categories  in ADG, BF and PHS in the two populations, the following general univariate model was fitted separately for each trait, population and genomic regionA Bayesian framework was adopted for inference. The prior distributions for the parameters of the model were The variance components were estimated either using all the 39,727 available SNPs (All), the 19,672 SNPs located in the genic regions (Genic) or the 20,055 SNPs located in the intergenic regions (Intergenic). Thus, for each genomic region, the SNPs included in 12, the variance components Following20. Predictive abilities were assessed via Pearson’s correlation between pre-adjusted phenotypes and predicted phenotypes in the testing sets.Predictive ability of a model including only additive genetic effects and an inbreeding coefficient was compared to a model including additive and dominance effects and an inbreeding coefficient. These two models (additive model or additive and dominance model) were run separately for each combination of population (PB or CB), trait  and genomic region  leading to 36 different models. All models included the same systematic and non-genetic random effects described above for each of the three traits. Predictive ability of the models was evaluated by cross-validation (CV). Specifically, a four-fold CV scheme was used by attributing animals randomly to one of four separate subsets. From these four subsets, three folds were combined to create a training set and the remaining fold was used as testing set. Each of the four subsets was applied as a testing set only once. Because of the small size of the sample, the four-fold CV was replicated 10 times at random, and results were averaged over replications12 and a genomic inbreeding coefficient14. The following general two-trait model was applied:Additive and dominance genotypic correlations between PB and CB performances were estimated using all SNPs available and separately for each trait. In this case, PB and CB performances of the trait  were considered to be different traits in PB and CB populations and were jointly analyzed with a bivariate genotypic model accounting for additive and dominance effects, ,Index 12. The prior distributions for the parameters of the model were A tri-trait genotypic model including additive and dominance effects and a genomic inbreeding coefficient was used to estimate additive and dominance genotypic correlations between BFT, ADG and PHS within each PB and CB population. This approach allowed estimating (co)variances of additive and dominance genotypic effects between these three traits within purebred and crossbred populations. To achieve that, the following tri-trait model was applied separately in each population:,Index The (co)variance matrix for the additive genotypic effects isTerms Terms https://github.com/gdlc/BGLR-R)21, and the multiple-trait models were later run with GIBBS2f90 software (http://nce.ads.uga.edu/wiki/doku.php?id=start)22. For each proposed model, single chains of 200,000 iterations were run by discarding the first 20,000 iterations. The burn-in was determined by visual inspection of the chains and by the procedures of Raftery and Lewis23 and Geweke24. Samples of the parameters of interest were saved every 10 rounds.A Bayesian framework was adopted for inference. Flat prior distributions were assumed for the elements of the matrices of the variance components of all the models. Marginal posterior distributions of the parameters of interest were estimated via Gibbs’s sampling algorithm. Mean, highest density interval at 95% (HPD95%) and probability of the parameter of being higher or lesser that certain value, were obtained from these marginal posterior distributions. Univariate models were initially implemented using BGLR software . This indicates that all genomic regions have nearly the same contribution to the captured genetic variance, either in PB or CB individuals.Our results are in accordance with Do et al.26 reported that this part of the genome is responsible for genetic variation in 11 human diseases. However, also in humans, Yang et al.18 indicated that genic regions described more genetic variation than intergenic regions for height and body mass index. Given the precision of the pig genome annotation and quantity of data available in our study, the hypothesis of different shares of variance for these categories could not be further tested in our dataset. In this study, as a limitation, we used the commercial SNP panel for pigs that was the only one available to us. So, we supposed that the SNPs from genic and non-genic regions from a 60 K commercial SNP chip explain the same amount of genetic variance. However, with availability of the whole genome sequence data it might be a clearer result would be achievable.Regulatory functions such as promoter, enhancer or transcription factor binding sites are located the upstream and downstream regions of genes, and Gusev et al.28. Heritabilities for the three analyzed traits were higher in CB than in PB populations, however the difference was never significant. This suggests that the gamete effect of the PB population could not be the same when mated within the PB than mated to gamete of another population to produce the crossbreds. Dominance ratio remained of equal magnitude across populations and represented about 7 to 14% of phenotypic variance depending on the trait, suggesting that, for example, assortative matings could slightly enhance both purebred and crossbred performances. In a previous study using the same dataset, different contributions of ratios of dominance deviation variance with respect to the total phenotypic variance were reported across a wide variety of traits related to growth and feed efficiency, carcass composition, meat quality, behavior, boar taint and puberty . It would be interesting for further research to investigate in pig populations what would be the contribution to additive and dominance genetic variance of SNPs located in different genomic regions when using a high-density SNP chip panel or whole genome sequence data.This was in accordance with Abdollahi Arpanahi et al.14 showed that accounting for dominance did not increase the predictive ability of litter size in crossbred pigs but inbreeding depression did.In addition, inclusion of dominance genetic effect and inbreeding in the model did not improve predictive ability already attained with an additive genetic model in the two populations. Xiang et al.Table 33, genetic correlation between purebred and crossbred was estimated to be of 0.50 for ADG and 0.94 for PHS. Dugué et al. (2020), used bivariate models but did not account for dominance genetic effects. We believed that incorporating dominance effects and inbreeding coefficient simultaneously can improve phenotypic prediction accuracy. Wientjes and Calus5 reviewed 201 purebred-crossbred correlation estimates reported from 27 studies in pigs and the average estimate was 0.63, with 50% of the estimates between 0.45 and 0.87. These values indicate that for those correlations departing from unity, accounting for crossbred performance in genetic evaluations of purebred candidates could be advisable for boosting crossbred performance. In a further step, Xiang et al.14 estimated for the first time the correlations of allele substitution effects between purebred and crossbred performance for Landrace and Yorkshire breeds and proposed these correlations as consistency measurements of SNP substitution effects across breeds. For purebred performance, it was estimated to be 0.19 with a high imprecision indicating that SNP effects estimated in one breed cannot be readily applied to the other breed. For crossbred performances, this figure was estimated to be 0.98 indicating that SNP effects estimated in a crossbred population can be used to estimate crossbred breeding values in the two purebred parental populations14.Correlation between purebred and crossbred additive genotypic effects were high and positive for the three analyzed traits , dominance genetic variance and pen nested within batch variance with respect to the phenotypic variance estimated via the bivariate models of PB and CB performances for the three traits are shown in supplementary file Table 37 . Additive genetic correlation between ADG and PHS was estimated to be highly negative and moderately positive in PB and CB populations, respectively, the latter being in accordance with the estimate obtained by Lo et al. in Duroc pigs (0.24 ± 0.11)38. Although estimates are highly imprecise, this is a contradictory result, as one would expect to encounter an estimate given that genetic correlations for ADG and pH between PB and CB populations were high. A clearly negative genetic correlation between BFT and PHS was estimated in PB and CB populations used in the present study, whereas it was estimated moderate and positive in Duroc pigs (0.47 ± 0.10)38. Genetic relationships between growth performance and meat quality traits are scarce and not clear in pigs35 but the reported estimates suggest that genetic correlations are highly influenced by the breed type.Additive genetic correlation between ADG and BFT was estimated to be high in PB and moderate in CB, being the latter more in the range of previous reported estimatesEstimates of dominance genotypic correlations were very imprecise between all pair of traits in both populations. This is not surprising since it is more difficult to estimate accurately covariance between effects if the variances of these effects are low. However, there is some evidence of positive value for some of the estimated dominance genotypic correlations because the probability of a positive dominance genotypic correlation was high between ADG-BFT and BFT-PHS. Additive and dominance genotypic correlations between BFT and PHS were of different sign. This makes it difficult to discuss its implications, but one can hypothesize that genes contributing to the additive genetic progress in both traits would have an antagonistic effect when used for exploiting dominance effects in planned matings. to our knowledge, there are no reported estimates of dominance correlations in the literature for these traits, which precludes us from making further discussion about these estimates. Nonetheless, the different values of dominance genotypic correlations between traits indicate that maximizing total genetic gain of several traits simultaneously might not be straightforward.In this study, genic and intergenic regions were able to capture the same genomic relationships among either PB or CB populations. Correlations between purebred and crossbred additive genotypic effects were high and positive whereas estimates of the correlations between purebred and crossbred dominance genotypic effects were highly imprecise for the three traits analyzed. The later precludes us being conclusive about if genetic mechanisms responsible for the dominance effects differ between PB and CB populations. Estimates of dominance genotypic correlations were imprecise but with strong evidence of positive values for ADG-BFT and BFT-PHS. Additive and dominance genotypic correlations between BFT and PHS were of different sign in both populations indicating that genes contributing to the additive genetic progress in both traits would have an antagonistic effect when used for exploiting dominance effects in planned matings. The different values of dominance genotypic correlations between traits indicate that maximizing total genetic gain of several traits simultaneously would not be straightforward. Finally, all genomic regions lead to the same predictive ability, that did not improve with the inclusion of dominance genetic effects and inbreeding in the models.Supplementary Table S1.


We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GWAS) of an external large cohort has a prediction power for AF in Korean population through a convolutional neural network (CNN).This study included 6358 subjects  from the Korean population GWAS data. We extracted the lists of SNPs at each p value threshold of the association statistics from three different previously reported ethnical-specific GWASs. The Korean GWAS data were divided into training (64%), validation (16%) and test (20%) sets, and a stratified K-fold cross-validation was performed and repeated five times after data shuffling.PITX2 obtained the highest saliency score. The CNN-GWAS did not show AF prediction power by SNPs with non-significant p value subset (AUC 0.56±0.01) despite larger numbers of SNPs. The CNN-GWAS had no prediction power for odd–even registration numbers (AUC 0.51±0.01).The CNN-GWAS predictive power for AF had an area under the curve (AUC) of 0.78±0.01 based on the Japanese GWAS, AUC of 0.79±0.01 based on the European GWAS, and AUC of 0.82±0.01 based on the multiethnic GWAS, respectively. Gradient-weighted class activation mapping assigned high saliency scores for AF associated SNPs, and the AF can be predicted by genetic information alone with moderate accuracy. The CNN-GWAS can be a robust and useful tool for detecting polygenic diseases by capturing the cumulative effects and genetic interactions of moderately associated but statistically significant SNPs.NCT02138695. Atrial fibrillation (AF) is known to be a heritable disease, and multiple genetic loci associated with AF have been reported by genome-wide association study (GWAS) studies.The collaborative method incorporating a convolutional neural network (CNN) and GWAS could classify the AF vs non-AF with genetic information alone.CNN-GWAS with explainable artificial intelligence technique provides a new perspective for GWAS by identifying the positive and negative interactions of each single-nucleotide polymorphism (SNP).CNN-GWAS can be a robust method to predict AF patients by highlighting the cumulative effects and genetic interactions of moderately associated, but statistically significant SNPs. Further studies of comparison and validation with other predictive models are needed to standardised testing.Atrial fibrillation (AF) is a major cardiovascular disease with a prevalence of 1.6% in the total population and is the cause of 20%–25% of ischaemic strokes and about 30% of heart failure.5This study protocol adhered to the principles of the Declaration of Helsinki. We included 6358 subjects from four independent cohorts and their GWAS data . The cas10.1136/openhrt-2021-001898.supp1Supplementary dataSamples in the genetic dataset were used in our previously published early-onset AF GWAS.−8 to p<0.001, was considered as a feature selection prior to model training , we coded each SNP with homozygous aa 2, heterozygous Aa 1, and AA 0 for the minor allele as an additive model. The missing genotypes were represented as −1.h sample .We developed a CNN-based model of a hierarchical network so that it can be classified by a locus associated with AF . The appEarly stop and drop-out methods were used to avoid overfitting. Further description is available in online supplemental material.To verify our model, four validation processes were conducted. First, we repeated the training, validation and test processes five times to demonstrate the reproducibility of the AF prediction and each sample was randomly constructed. Second, to examine whether SNPs of statistically non-significant p values by a logistic regression did not really affect the AF prediction, an SNP list was constructed and verified based on a p≥0.99. Third, in order to identify that there was no predictive power for a phenotype without heritability (here are odd–even registration numbers) other than AF, the validity was verified by replacing the AF label with an odd–even registration number. Fourth, the saliency score of each SNP for AF prediction was analysed in all AF patients (n=872) using a model of best-performance among the model . A Grad-Further description is available in 2r >0.1 for linkage disequilibrium-based clumping within 250 kb range of the index SNP.To verify the robustness of CNN-GWAS in determining the AF risk, we evaluated the polygenic risk score (PRS), which is a conventional quantitative metric for the genetic risk.The data set consisted of mutually exclusive samples with training (64%), validation 16%) and test (20%) sets, each set was selected at random and directly proportional to the number of cases/controls in the population. The final output probability 0 to 1 of a model designed as a binary classifier was evaluated by the phenotype label Y = (control: 0 or AF patient: 1). The evaluation metrics used the area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, Gini coefficient,% and tesPatients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.−5, respectively  were selected in each ethnic-specific GWAS . Results using these statistically non-significant associated SNPs showed a poor predictive power for AF  and the proportion of males  did not significantly differ between the two groups. The ROC curve for odd–even registration numbers did not show any predicted values regardless of the p value cut-off, and the variation was also small .PITX2, which has been reported as the top first AF associated gene, exhibited a reproducibly with the highest saliency scores in all three independent cohorts of different ethnicities. The other proven AF associated SNPs, such as KCNN3, METTL11B, PPFIA4, HAND2 and TUBA8, were also included in the top 10 highest saliency scores. The Pearson correlation coefficient was 0.472 when comparing the Manhattan plot and Saliency score plot, and those displayed the selected SNPs set by the multiethnic GWAS at a p<1.0×10−5 (−8) were ranked in the top 5% of the saliency score plot. Conversely, 75 out of 78 SNPs ranked in the top 5% of the saliency score plot  in the Korean population by utilising moderate AF-associated SNPs proven in three independent cohorts with different ethnicities. We verified the CNN-GWAS model by randomly shuffling the dataset five times, demonstrating no AF predictive power using SNPs with non-significant The use of AI, which enables a fast, sophisticated diagnosis, treatment and improved patient care workflow, and precision medical care, is increasing in clinical practice. AI is particularly useful for analysing data-rich technology-based objectives, such as omics, mobile device biometrics and electronic health records to obtain clinically useful information.4 5 22PITX2. It is expected that the prudent monitoring of one AI algorithm by another AI algorithm will be used in the future.In this study, we used AI algorithms of supervised learning techniques and the CNN, which is a deep learning method. Because a large well-curated clinical dataset is essential to properly train the deep learning,There are several potential limitations to our study. First, the results of this study cannot be generalised due to the nature of AI, which is greatly influenced by the training dataset. Second, this study included a highly selected group of patients (60 years old and younger) who were referred for AF ablation. This select patient population represents symptomatic antiarrhythmic drug-resistant early-onset AF. Third, the outcome of this study based on the Korean AF cohort data may not be generalised to other cohorts with different ethnicities and races. Fourth, the reason why a p≥0.99 was used for the SNP analysis of the non-significant p values used for validation of this study is due to the limitation of the computing power when it is executed at a p≥0.05. Fifth, the sample size of this study is relatively small compared with other large-scale GWAS studies. However, it satisfies the research purpose of evaluating the reproducibility of AF-associated SNPs after CNN application in the same patient group as our previous study proven by conventional statistical methods.In summary, the CNN-GWAS algorithm can be used to predict the AF, but comparison and verification with other models will be further warranted. The CNN-GWAS algorithms capture the cumulative effects and genetic interactions of moderately associated but statistically significant genes that determine the manifestation of the AF phenotype. AF can be predicted by genetic information alone with moderate accuracy.


Quinoa’s germplasm evaluation is the first step towards determining its suitability under new environmental conditions. The aim of this study was to introduce suitable germplasm to the lowland areas of the Faisalabad Plain that could then be used to introduce quinoa more effectively to that region. A set of 117 quinoa genotypes belonging to the USDA quinoa collection was evaluated for 11 phenotypic quantitative traits (grain yield (Y), its biological and numerical components plus phenological variables) in a RCBD during two consecutive growing seasons at the University of Agriculture, Faisalabad, Pakistan under mid-autumn sowings. Genotypic performance changed across the years, however most phenotypic traits showed high heritability, from 0.75 for Harvest Index (HI) to 0.97 for aerial biomass (B) and Y. Ordination and cluster analyses differentiated four groups dominated by genotypes from: Peru and the Bolivian Highlands (G1); the Bolivian Highlands (G2); the Ballón collection (regarded as a cross between Bolivian and Sea Level (Chilean) genotypes) plus Bolivian Highlands (G3); and Ballón plus Sea Level (G4), this latter group being the most differentiated one. This genetic structure shared similarities with previous groups identified using SSR markers and G×E data from an international quinoa test. G4 genotypes showed the highest Y associated with higher B and seed numbers (SN), while HI made a significant contribution to yield determination in G2 and seed weight (SW) in G3. G1 and G2 showed the lowest Y associated with a lower B and SN. Moreover, SW showed a strongly negative association with SN in G2. Accordingly, G4 followed by G3 are better suited to the lowland areas of Faisalabad plain and the physiological traits underlying yield determination among genotypic groups should be considered in future breeding programs. The growing popularity of quinoa in the recent years made the crop require little introduction ,2. This Germplasm evaluation, ideally involving contrasting origins when performed for the first time, is central to guide breeding and agronomic management ,12. For Besides these agronomic evaluations, quinoa genetic studies were approached using molecular markers. The first researcher to complete this work, , using aGiven the current legal restrictions to access germplasm from the Andean Region , the USDThe aim of this study was to screen a collection of quinoa genotypes for adaptation to yield under the low altitude environments of the Faisalabad region, Northern Pakistan. Specifically, we measured yield, its biological determinants and components plus phenology in 117 quinoa genotypes from the USDA germplasm collection to determine: (i) the relative contribution of genotype, environment and genotype-by-environment interaction effect to the phenotypic variation of those traits; (ii) the role of these traits in capturing the patterns of genotypic adaptation; (iii) the association between these patterns and the origin of materials; and (iv) the physiological trait combinations underlying yield-formation among genetic groups. Our working hypothesis is that the structure of phenotypic variation expressed in the patterns of performance of quinoa in Pakistan reflects the geographical origin of the materials.−1, respectively, during both years. Fertilizers used were urea (46% N), diammonium phosphate  and sulfate of potassium (50% K2O). A half dose of N and a full dose of P and K were applied as a basal dose at sowing and the remaining N was added 75 days after sowing. During the entire crop period, four irrigations of 330 mm total were applied including the pre-sowing irrigation, recommended as optimal for the cultivation of quinoa, for the conditions of Pakistan [One hundred-seventeen quinoa genotypes belonging to the USDA-NPGS (United States Department of Agriculture) collection were utilized in this study . A two-yPakistan . The fie−2) and its biological determinants aerial biomass , harvest index (HI) plus terminal panicle grain yield , and the numerical components seed number  and weight  were obtained from five uniform tagged plants from the middle of each row, avoiding border plants. Plants were harvested with a sickle, sundried for three to five days and later on threshed manually to measure yield attributes using a digital scale. HI was estimated as the Y/B ratio. SN was estimated considering the final harvest data as the ratio of Y to the average individual SW. Individual SW was estimated by manually counting and weighing 1000 grains in each replicate plot. In addition, for the five plants tagged in each plot, the plant height  and terminal panicle length  were measured with a ruler. Ht was measured from the soil to the top of the terminal panicle, whereas the Pan Ht was measured between the basal and the last node below the main panicle.A set of 11 phenotypic traits encompassing grain yield-related attributes and crop phenological stages were obtained for each plot. Grain yield  were determined as emergence, first anthesis (extrusion of anthers completed), and physiological maturity (when grain could not be nail dented). Thus, we defined the main phenological phases as the emergence–anthesis (E–Ant) and anthesis-physiological maturity (Ant–PM) periods. Besides that, the crop duration (Cycle) period was recorded as the time between emergence and harvest.i in block k of environment j was modelled following the expression:i~NG  is the random main effect of the i-th genotype, j-th environment (years), ij~NGE  is the random interaction effect of the i-th genotype and the j-th environment, k(j)~NB > is the random effect of the k-th block nested within the environment j-th, and Linear mixed models were set up to examine the relative contribution of genotype and genotype-by-environment interaction effects to the phenotypic variation of all phenotypic traits across experiments. The phenotypic observation nalysis) . This fuTo determine the role of phenotypic traits in capturing similarities in genotypic response, Pattern Analysis (PA), defined as the combined application of ordination and classification multivariate analyses  and Cluster Analysis (CA)), was used. This set of analyses was based on predictions computed from random terms obtained from REML analyses (see above). First, we computed the Best Linear Unbiased Predictors (BLUPs) for genotype, and genotype-by environment interaction terms, and then the predictions adjusted by the BLUPs’ effects were used to build an array of 117 genotypes × 11 traits. Previous to PA, the array was trait-standardized by removing the traits’ grand mean and dividing the remainder by the within-trait standard deviation. For classification (CA), the hierarchical agglomerative method, with the incremental sum of squares as fusion criteria , was choFor ordination, a PCA was performed using the singular value decomposition algorithm on the Euclidean standardized distance of the two-way array of genotypes × traits, using the FactoMineR package . In ordehttps://power.larc.nasa.gov/data-access-viewer/, accessed on 15 October 2021). The seasonal photoperiod varied from 10.4 to 12.9 h day−1 for the crop cycle, with most genotypes flowering during spring and maturing in summer . The environmental means for phenotypic traits, i.e., the average across genotypes, differed between growing seasons for Pan Ht, B, Pan Y, SW, E–Ant and Cycle, with a trend towards higher values in the second growing season  explained 44.5% of the total variation and ordered the genotypes according to Y, its related traits  and time to anthesis. As indicated by The second principal component (PC2) explained 20.1% of the total variation and accounted for the effects of the contrasting traits Ant–PM, Pan Ht, Cycle, and HI plus SW, emphasizing the differences between groups G1 and G2 . G1 tendThe results of this study show the first phenotyping assessment carried out on a comprehensive set of quinoa accessions from the USDA germplasm collection accounting for yield and its components. This collection was the basis of several adaptations and breeding programs internationally and, as such, made a highly significant contribution to the global expansion of this crop ,40,41,42The high heritability observed for all traits implies that phenotypic variation reflects the patterns of genotypic adaptation. The genotypic groups found in the present study clearly showed differences in their performance . PhenotyThe hierarchical agglomerative groupings identified four genotypic groups associated with the genotype’s environments of origin . This grThe genotypes originating from the Ballón collection have been little evaluated in other studies ,15,17,49The physiological traits’ combinations underlying yield-formation differed among the four genetic groups and resembled results from other studies ,14. HighThe performance (average yields) of quinoa under Pakistan’s Mediterranean conditions was poorer than evaluations carried out in locations with similar climate regimes ,27,58,59


TMEM57, DOCK7/ANGPTL3, LPL, and APOA on lipid traits, the latter replicated in HTB. Sex-specific parental effects were also observed; variants at ANGPTL3/DOCK7 showed POE on lipid traits and obesity in daughters only, while those at LPL and TMEM57 showed POE on lipid traits in sons. Variants at LPL and DOCK7/ANGPTL3 showed POE on obesity-related traits in Botnia and HTB, and POE effects on obesity were seen to a higher degree in daughters. This highlights the need to include analysis of POEs in genetic studies of complex traits.Parent-of-origin effects (POE) and sex-specific parental effects have been reported for plasma lipid levels, and a strong relationship exists between dyslipidemia and obesity. We aim to explore whether genetic variants previously reported to have an association to lipid traits also show POE on blood lipid levels and obesity. Families from the Botnia cohort and the Hungarian Transdanubian Biobank (HTB) were genotyped for 12 SNPs, parental origin of alleles were inferred, and generalized estimating equations were modeled to assess parental-specific associations with lipid traits and obesity. POE were observed for the variants at the  Dyslipidemia is one of the modifiable risk factors for cardiovascular disease (CVD) ,2, whichPlasma cholesterol levels are mediated by a complex interplay of genetic and environmental factors. There is also a strong genetic component, and >350 SNPs corresponding to >200 loci associated with circulating plasma lipids have been reported so far . CurrentSex of the parent also influences the offspring’s lipid levels, and these effects could also be different depending on the sex of the offspring. For instance, female offspring of diabetic mothers showing lower HDL-C than male offspring  and sex-Some of these findings could be due to genetic parent-of-origin effects (POE), where an allele might have a different effect on a certain phenotype when inherited from one parent but be neutral or having opposite effect when inherited from the other. Some of this could be attributed to epigenetic mechanisms, such as preferential DNA methylation of one of the alleles even though other mechanisms are possible . POE couGiven the previous observations of parent-of-origin effects on lipid traits, we selected 12 loci associated with lipid-related traits from genome-wide association studies (GWAS) published before 2012 to investigate if any of them could account for the parent of origin (POE) or sex-specific effects  in families from the Botnia study. Dyslipidemia is strongly correlated with obesity. Obese individuals manifest elevated serum triglyceride, VLDL, apolipoprotein B, and non-HDL-C levels and are at an increased risk of developing cardiovascular disease . Treatmen = 3552) from 2257 families  in 2264 families (The Hungarian Transdanubian Biobank (HTB) was initiated in 1992 at the Hungarian Heart Center in Balatonfüred. For this study, families with at least 1 parent and at least 1 offspring were selected, giving a total of 7884 individuals  ; the Pre2). Waist and hip circumference were measured with a non-elastic soft tape to the nearest cm, the waist halfway between the lowest rib and the iliac crest and hip as the widest part of the gluteal region. Blood samples were drawn after overnight fast for measurements of TC, HDL-C, triglycerides (TG) ), and ApoA1, ApoA2 and ApoB ), as previously described [Height and weight  were measured to the nearest cm and BMI calculated as (weight in kg)/  for 172 The genotyping success rate was >90% for all the SNPs genotyped. A total of 4.8% of our samples were replicated and showed concordance with previous genotyping results, and the known genotyping controls showed concordance with expected results. All SNPs were in Hardy–Weinberg Equilibrium as tested in the individuals from the families.2, and BMI of the offspring as covariates. In the first model, Spearman rank correlation and partial correlation between father–offspring (son/daughter), mother–offspring (son/daughter), and father–mother was calculated including only the oldest child from each family. In the second model, all offspring from the families were included in a linear mixed model where family ID was used as random effects. A third model was computed similar to the second but with the addition of sex (when applicable), age, ageTo assess whether the difference between the paternal and maternal correlation coefficients were significant, Fisher r-to-z transformation was implemented. To assess the difference between the slopes from the linear mixed models, Wald’s test was applied.Parental origin of alleles was inferred for all offspring. For heterozygous genotypes, parental origin of the allele was inferred by comparing with parental genotypes, i.e., ApBm or AmBp, where m was maternal and p was paternal. For the genetic analysis concerning lipid-related traits, families from the Botnia study were split into 2 random subsets, for discovery and replication, respectively . For a s2, and diabetes affection status were included as covariates for all analyses. For replication, p-values of <0.05 with the same direction of effect were considered significant. Bonferroni corrected p-Values were indicated in the result tables.To test for POE of genetic variants on the traits in the family-based cohorts, we performed a generalized estimating equation analysis using IBM SPSS Statistics 22 , grouping by nuclear family ID. To determine if the allele manifested a paternal or maternal effect, we compared the effect of the minor allele inherited from the paternal side and from the maternal side independently, respectively. For the parent-of-origin effect analysis, we compared the effects of the paternal against the maternal minor allele in offspring carrying the heterozygous genotype. BMI, sex, age, ageFor POE analyses in unrelated individuals, POE was implemented in QUICKTEST v0.98  software onto mean genotypes, as described by Hoggart et al. . The metComparisons between generations in the Botnia study showed a statistically significant correlation between the father–offspring and mother–offspring lipid trait values . For allDOCK7/ANGPLT3 locus on ApoA2 levels was replicated in the replication subset.To determine the genetic basis of these parental effects, locus-by-locus paternal, maternal, and parent-of-origin effects on plasma lipid levels were assessed for the selected loci in the discovery and replication sub-cohorts of the Botnia study. Seven SNPs showed nominal POE in the discovery sub-cohort . Out of DOCK7/ANGPLT3 locus, wherein the G allele was associated with decreased ApoA1 and ApoA2 when paternally inherited. However, the maternally inherited G allele was significantly associated with lower ApoB/ApoA1 ratio: the differences in the effect of the paternal and maternal G on ApoA1 and APoB/ApoA1 ratio were nominally significant, whereas the POE on ApoA2 was significant after correction for multiple testing. In addition to the rs2131925 SNP, the results included variants at TMEM57 (rs12027135) wherein the A allele were associated with decreased ApoA2 levels when paternally inherited but increased ApoA2 levels when maternally inherited. The A allele of rs10503669 at the LPL locus was associated with decreased ApoA2 levels when maternally inherited. The rs12272004 SNP near the APOA gene also showed POE, with the A allele associated with decreasing ApoB and ApoB/ApoA1 ratio when paternally inherited but with increasing trait values when maternally inherited. The maternal A allele near the APOA gene was also associated with increased and the paternal allele with decreased TG levels; while the latter effect was not statistically significant, the POE was nominally significant. This POE was robustly replicated with the same direction of effect for the maternal and paternal alleles in the HTB families.In the combined analyses, a total of four SNPs showed a POE on at least one trait . These iDOCK7/ANGPTL3 locus was seen only in daughters but not in sons. The same paternal G allele was associated with decreased ApoA2 levels in sons as well as daughters; however, the POE was significant only in daughters. The paternal G allele also associated with ApoB/ApoA1 ratio in daughters in a POE manner; however, this association was not observed in sons. The POE of the variants at the TMEM57 and LPL loci on ApoA2 levels, APOB and KLF14 on ApoB/ApoA1 ratio, and APOA variant on TG levels were only seen in sons.Levels of total cholesterol, HDL-C, TG, ApoA1, ApoB, and ApoB/Apo1 ratio showed stronger correlations between mothers and daughters compared to fathers and daughters, but no significant differences were seen between father–son and mother–son correlations . TherefoDOCK7/ANGPTL3 locus showed POE on WHR as well as WHRadjBMI in the families from the Botnia study, with the paternally inherited G allele associated with increased whereas maternally inherited G with decreased WHR and WHRadjBMI. The POE was also replicated in a second cohort of families from the HTB with directional consistency for the maternal and paternal allelic effects.Given the strong relationship between obesity and lipid levels, the selected SNPs were assessed for their POE on obesity traits, including body mass index (BMI), waist–hip ratio (WHR), waist–hip ratio adjusted for BMI (WHRadjBMI), and waist–height ratio (WHtR) . The rs2LPL locus was associated with decreased WHR, WHRadjBMI, and WHtR when maternally inherited and showed the opposite effect for the paternal allele. Significant POE was seen for this variant for all the aforementioned obesity traits in the Botnia families. The meta-analysis POE p-values were significant for all the traits with directional consistency except for rs10503669 for WH when adjusting for BMI , further supporting our findings .PABPC4 variant rs4660293 showed POE on WHR and WHRadjBMI only in the daughters, with the paternal allele associated with lower obesity measures. The LPL variant rs10503669 showed POE on BMI and WHR and WHtR in daughters, with the maternal A allele associated with lower BMI, WHR, and WHtR than its paternal counterpart in the Botnia and HTB families. A similar effect was also observed in sons in the Botnia study but not in HTB. The rs2131925 SNP at the DOCK7/ANGPTL3 locus also showed POE on WHR and WHRadjBMI in daughters with the paternal G allele associated with increasing WHR and WHRadjBMI to a higher degree than the maternal G in both family cohorts. A similar trend was also seen in the Botnia sons before adjusting for BMI but not in HTB.Given the sex-specific parental effects of lipid levels, the genetic sex-specific parental effects on obesity were next assessed. After meta-analysis of POE on obesity in daughters/sons from both studies, SNPs at three loci showed significant POE on obesity . The PABSex-specific parental effects of plasma lipid levels have been reported in multiple family studies, with the lipid levels showing a certain degree of heritability in the families. However, very few genetic variants have been shown to associate with lipid traits in a POE manner thus far. In the present study, we investigated the genetic basis of sex-specific parental effects of plasma lipid levels by examining if common variants associated with lipid levels from previous GWAS studies also associate with lipid traits in a POE-specific manner. Given the strong relationship between lipid traits and obesity, we assessed if these same variants showed POE on obesity.LPL and DOCK7/ANGPTL3 loci on obesity measures in both family cohorts, and the same variants also showed POE on lipid traits in the Botnia family study. The variant at the LPL loci also showed potential POE in unrelated individuals.We discovered genetic parental-specific associations for seven SNPs to at least one trait, including sex-specific POE. Our key findings were the robust POE of variants at the In previous GWAS, the minor allele G of the rs2131925 SNP (ANGPTL3/DOCK7) associated with a decrease in TC, TG, and LDL-C levels , which wLPL) showed a POE on ApoA2, with the maternally inherited A allele associated with decreased ApoA2. The A allele of this SNP has previously shown association to increased HDL and decreased TG and TC in previous GWAS. This SNP also showed POE for obesity traits, with the maternally inherited A allele associated with low-er WH, WHadjBMI, and WHtR in Botnia, while the paternally inherited A allele was associated to increased BMI and WHtR. The POE tests in HTB families did not reach significance, but the direction of effect showed the same trend, and the meta-analysis was significant for POE for BMI, WH, and WHtR. This was seen for both Botnia and HTB daughters  while only in Botnia sons and not for HTB sons. The maternal effect seen on ApoA2 would be expected to be negatively associated with cardiovascular, while the effect on obesity would be expected to be beneficial.The SNP rs10503669  and assTEx data , whereasTEx data . ANGPTL3ctivated ,42,43. Aomicrons ,45,46. GThe association to higher obesity measures of the paternal G allele of rs2131925 and the paternal A allele of rs10503669 was consistent in both the Botnia and the Hungarian cohorts. It is possible that this could be a representation of the kinship theory , with thPrevious studies have demonstrated the sexual dimorphism in lipid traits with some evidence for a genetic basis ,49. ConsAnalyses of gender-specific effect of genetic variants are possible to perform in unrelated groups. However, we also show that the picture is even more complex, with variants showing not only different effects in males vs. females but also that the parent-of-origin effect may differ between sons and daughters. This is something that would not be seen in unrelated individuals and may provide valuable clues to both the missing heritability of lipid traits as well as an explanation of some of the differences seen in lipid levels among the sexes.Studies on POE are limited since family-based studies and especially investigation of POE require substantial study power. To address this, we selected a limited number of candidate genes to assess if they could explain some of the parental effects. Given the lack of replication for the lipid measurements in family data, we further subdivided the Botnia study into two random, family-based cohorts. Another limitation of our study regarding the variants showing sex-specific POE, with SNPs showing POE in daughters but not in sons, could be attributed to low study power or population specific effects. Another limitation of our study is that, due to low study power, very few SNPs showed genome-wide significant effects; however, the replication in another large family-based cohort (HTB) provides a robust validation of the findings. It is worth pointing out that our study does not investigate whether any of these SNPs is the functional variant driving these results, and we cannot prove a causal effect. Given the strong correlation between lipid levels and obesity, it could very well be that POE on obesity could be mediated by the POE on lipids or vice versa. However, large-scale studies are required to explore the causality through approaches like Mendelian randomization. Given the results, it is expected there could be a large number of genetic loci showing POE on lipid traits and obesity, and further studies in larger family cohorts will be necessary to determine the genetic basis of the sex-specific parental effects. Our results highlight the need to consider the possibility of POE in analyses of lipid traits and obesity in order to gain a correct understanding of their effects. A deeper knowledge of the genetic basis of dyslipidemia could help us understand the genes and pathways involved and lead to better ways of prevention, prediction, and treatment of CVD.