The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits . Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect. G was proposed [In the last decade, the two-step genomic best linear unbiased prediction (GBLUP) method  has beenproposed . Many stproposed ,11,12,13proposed ,13,14. Iproposed ,16,17,18proposed ,20,21. Iproposed , termed proposed  and Ogetproposed  depictedIn Hanwoo beef cattle, carcass traits (backfat thickness (BFT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS)), and yearling weight (YW) are economically important traits used to select young and proven bulls . Since thttp://www.limc.co.kr (accessed on 11 February 2021)), and data for carcass traits were recorded by special inspectors, from the Institute of Korean Animal Products Evaluation (http://www.ekape.or.kr (accessed on 11 February 2021)), at the slaughterhouse through the progeny testing program in Korea.Genomic data, pedigree and phenotypic data related to growth and carcass traits, were generated following the protocol for the progeny test program, as notified by the Ministry of Agriculture, Food and Rural Affairs based on livestock law in Korea. Hanwoo Improvement Center (HIC) of the National Agricultural Cooperative Federation, as an enforcement institution for the testing program for selecting proven Hanwoo bulls, is obligated to maintain data and ownership of enrolled animals under notice. DNA samples were obtained from blood samples collected by veterinarians. Pedigree data were recorded by the Korean Animal Improvement Association, data for growth traits were obtained from the Hanwoo Improvement Center (t) at the termination (t) of the test (body weight at ~12 months) and the previous weight (Wt − 1) recorded at a time point (t−1) before (body weight at ~6 months) the termination, according to the equation described by Park et al. [The records of YW for 15,796 animals  and carcass traits of 5622 steers were born between 1997 and 2017 and raised in Hanwoo Improvement Center of the National Agricultural Cooperative Federation were used for this study . The pedk et al. :YW= [(Wtn = 3720), version 3 (n = 4121) and customized Hanwoo version 1 (n = 4923). The genotyped animals with Illumina BovineSNP50K version 2 were considered as reference populations to impute target animals  with FImpute V3 [The dataset consisted of 12,764 animals  genotyped using Illumina BovineSNP50K version 2  and birth place (109 levels) for YW; slaughter date (274 levels), and slaughter age (days from birth to slaughter) was considered as covariates for carcass traits; u is the vector of random genetic additive effects; e is the vector of random residual effects; X and Z are incidence matrices related to fixed and random genetic additive effects, respectively. Var (u) = G⊗A and Var(e) = R⊗I were assumed, where A is the numerator relationship matrix, I is the identity matrix, and G and R are additive genetic and residual covariance, respectively, for the five traits  as proposed by Lee et al.  through e traits .Therefore, the adjusted phenotype for each animal was calculated as the sum of the estimated breeding values (EBVs), and the residual ,30.1 is a vector of ones; µ is the overall mean; a is the vector of random genetic additive effects; e is the vector of random residual effects; Z is incidence matrix related to random genetic additive effects. Var (a) = Ae) = IA is the numerator relationship matrix, I is the identity matrix, and The single-trait pedigree-based evaluations (PBLUP) were performed using the following animal modelH as a combination of A (numerator relationship matrix) and G matrices. The inverse of the H matrix was obtained as the following equation [A22 is the numerator relationship matrix for genotyped animals. In the ssGBLUP method, the statistical model was the same as that for the traditional evaluation; however, the non-genotyped and the genotyped animals were simultaneously included in the hybrid relationship matrix of equation ,31 emploequation (3)H−1=AG) was constructed as described by VanRaden [i is the allelic frequency of ith marker, M is the matrix of centered genotypes and D is the identity matrix. The genomic relationship matrix (VanRaden (4)G= MDD is a diagonal matrix of weights for markers which was obtained with back-solved markers effect (û) from genomic estimated breeding values (GEBVs) (â) as follows [For the WssGBLUP method,  follows :(5)u^=λDij is the elements in G−1 corresponding to relationships between animal i and j. It must be noted that DGV is a more relevant starting point than GEBV for calculating SNP effects because genotyped populations may include animals with different levels of accuracy [In this stage, GEBV was replaced by direct genomic values (DGV), which was obtained as follows (6)DGVi=accuracy .D was replaced through the normalization of weighting strategies [G matrix was created based on the new D matrix and combined with A to constitute a new H matrix used to estimate GEBVs and DGVs in a single-step procedure. Updating the DGV and marker effects were continued for 10 iterations in each trait. As proposed by Zhang et al. [D matrix (dii) as follows:In the next step, each diagonal element of rategies  to highlg et al. , weightiii =(1) Default: SNP weights are calculated based on the individual genetic variance of SNP (d(2) Constant: the maximum genetic variance of SNP effect in the first iteration (D matrix (dii) for the nonlinear A was defined as3) Nonlinear A: the diagonal elements of  Nonlineas effect ,34.ii = max((4) Largest window: using the concept of SNP-window weights, with weights for a group of (n) SNPs as dii =(5) Mean window: the weight for a group of SNPs was calculated as dii(6) Summed window: the summation of the individual genetic variance of markers effect for a group of SNPs was calculated as dTo optimize window size, four groups of SNPs  were considered for the largest, mean, and summed window weighting strategies. The explained genetic variance for the group of SNPs (k) was obtained as Breeding values, direct genomic values (DGV), and markers effect were estimated utilizing the BLUPF90 programs . A singlThe variance components for traditional and genomic prediction were estimated using the pedigree-based single-trait animal model ; becauseAccuracies of all the methods were evaluated by splitting the whole data into training and validation dataset by a cut-off point of birth year. The training set included the pedigree, the adjusted phenotype (obtained from Equation (1)) of non-genotyped animals, and the genotype of 4960 animals for YW (4331 for carcass trait) born between 1997 and 2015. The validation set contained 803 and 1656 animals for carcass traits and YW, respectively born between 2016 and 2017 in which the adjusted phenotypes were assumed to be unknown, and only the genotypes and pedigree information was retained. The predicted ability of different methods was calculated as the correlation between adjusted phenotypes and EBV/GEBV of animals in the validation populations. Accuracy was determined as the predicted ability divided by the root of heritability, which was achieved through the single-trait pedigree-based animal model. The bias was defined as a regression coefficient of the adjusted phenotypes on EBV/GEBV. The root of mean square error (RMSE) was predicted as the mean of the squared differences between adjusted phenotype and EBV/GEBV.The accuracies of genomic prediction for YW and carcass traits using linear (default and constant) and different nonlinear A weighting approaches over ten iterations are presented in The biases of genomic prediction for the traits under study using different single SNP weighting methods over ten iterations are shown in In terms of the RMSE ratio, the trend toward increased in all over iterations using the default method was obtained for BFT, EMA, and MS . However, we observed an increase or a decrease of the RMSE ratio by this method for CW and YW in which the lowest ratio was at the second iteration. Different trends using the constant method depending on the trait were observed so that the RMSE ratio for BFT, EMA, and MS gradually increased after iteration 5, 7, and 4, respectively. Nonetheless, the lowest RMSE ratio was obtained at iteration 6 for CW and iteration 4 for YW and then remained constant over the succeeding iterations. The results also showed that the RMSE ratio using the nonlinear A methods remained constant over ten iterations for all traits, except for CW and YW in which a slight drop between iterations 1 and 2 was observed.The accuracies obtained using SNPs window weighting methods  with different window sizes over ten iterations for all traits are illustrated in For BFT and MS, increasing trends in this ratio were observed along with iterations, while for other traits, the lowest magnitudes of ratio occurred at iterations 2, 3, and 4 depending on windows size and weighting strategies .The accuracies of genomic prediction for the five traits obtained with pedigree-based BLUP, ssGBLUP, and the best weighting procedure of WssGBLUP are shown in The regressions of adjusted phenotype on the estimated breeding value ranged from 0.71 to 1.17 for PBLUP, 0.83 to 1.51 for ssGBLUP, 0.79 to 1.04 for WssGBLUP_single, and 0.82 to 1.16 for WssGBLUP_window methods . PredictExcept for EMA, RMSEs were decreased by switching PBLUP to ssGBLUP or WssGBLUP . The impThis study aimed to assess the accuracy of genomic predictions for yearling weight and carcass traits using WssGBLUP alternatives in Hanwoo beef cattle. A previous study was performed for genomic evaluation using WssGBLUP with only default weighting in this breed . RecentlStreptococcus agalactiae in GIFT strain of Oreochromis niloticus.According to the observed results , the higOverall, the genomic prediction deteriorated with increasing iteration, except for constant and particular nonlinear A weighting, which were more persistent than other weighting procedures. The explanation of the observed decline in accuracy with iteration for most of the methods could be because of the overweighting or underweighting of the SNPs across iterations ,35. In oThe degree of superiority of the models used for genomic prediction depends on the genetic architecture of the traits of interest ,35. The Our findings were supported by results of ssGWAS which reveals major QTLs on chromosomes 6 and 14 for EMA, YW, and particularly for CW , in whicS1-casein gene is well known to segregate in the Alpine and Saanen breeds. In a recent study, Oget et al. [Previously, Lee et al.  identifit et al.  also shot et al.  concludet et al.  indicateIn the current study, the impact of the single weighting and window weighting with different window sizes  on the accuracy of genomic prediction was investigated. Our findings showed that the window weighting performed better than single ones for CW, EMA, and YW . Also, tG matrix in models seems to be a reasonable approach to attain the greatest accuracy, especially in the presence of QTL with large effects.Consequently, the proper choice of weighting This study aimed to compare pedigree-based BLUP and different genomic evaluation methods for yearling weight and carcass traits. Our results demonstrate that the accuracy of GEBV/EBV estimated with WssGBLUP and its alternatives are more than or as accurate as those from ssGBLUP, followed by PBLUP. The window weighting procedures performed better than single SNP weighting for CW, EMA, MS, and YW, whereas no gain in accuracy was observed for BFT. Also, considering window methods for estimation SNPs weights in WssGBLUP improved the accuracy of genomic predictions for EMA, YW, and particular CW over ssGBLUP, whereas the gain was trivial for BT and MS.


Associative analysis was performed using the GLM procedure in R version 3.5.1. The analysis of reproductive traits was carried out according to the results of the first parity, the second and subsequent parities, and totals for lifetime of sows. The significant effect on litter traits in LW was determined for SNP rs80956812, SNP rs81471381, SNP rs81421148, and SNP rs81399474. The significant effect on litter traits in LN was determined for SNP rs81421148 and SNP rs81319839. AKT3 gene was identified as perspective candidate gene, whose biological functions, as well as the results obtained in our work and in other studies, indicate its potential role in the reproductive process regulation in pigs. In general, the data obtained help to explain the genetic mechanisms of reproductive traits.The reproductive ability of sows is the principle of continuous and efficient production, based on such traits as the number of piglets, the total number of parities, and the period of economic use. Currently, SNPs associated with the TNB and NBA are presented in the PigQTLdb. The aim of this work was the assessment of the SNP effects on the litter traits in Large White (LW,  Selection of specific individuals with desirable traits is fundamental for animal breeding. The scope and complexity of selection, as well as the number and size of populations, in traditional breeding programs require new tools based on recent advances in molecular biology and genetics. Therefore, the interest of scientists is focused on the study of the molecular genetic basis of the farm animal productive traits. Nowadays, genome-wide association study (GWAS) is the most powerful tool for studying polygenic traits genetic architecture , 2. SNP The aim of this work was to assess the effect of SNPs on litter traits in LW and LN pigs in a Russian breeding farm. To achieve this goal, it was necessary to select SNPs associated with TNB and NBA from the PigQTLdb and to develop techniques identifying these SNPs. Another goal was to identify prospective candidate genes to improve the reproductive qualities of pigs.https://www.vij.ru/infrastruktura/ckp). Samples from the Unique Scientific Installation (UNU) “Bank of Genetic Materials of Animals and Birds”  were used for the work. For this collection, samples (tissue samples from the ear) were given by the owners of the breeding farms (according to their will). All methods were performed in accordance with the guidelines approved by the L.K. Ernst Federal Research Center for Animal Husbandry, Russia.The research was carried out on the basis of the Center for Collective Use of Scientific Equipment “Bioresources and Bioengineering of Agricultural Animals” of the L.K. Ernst Federal Research Center for Animal Husbandry (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) and NEBcutter V2.0 (http://nc2.neb.com/NEBcutter2/index.php). In case of the restriction site absence, the pyrosequencing (PSQ) method was used. Selected primers, annealing temperature, and restriction endonuclease for PCR-RFLP are presented in SNPs presented in the works of Sell-Kubiak et al. ; Bergfeln = 502) and LN (n = 432) sow breeds in a farm in Russia. All the animals had the same conditions of housing and feeding. Genomic DNA was extracted from porcine tissue samples (ear aperture) using the DNA kit Extran-2  according to the manufacturer's instructions. The quantity, quality, and integrity of the DNA were assessed by Qubit 2.0 fluorimeter  and a NanoDrop 8000 spectrophotometer .The studies were carried out on LW , total number born alive (NBA_1), litter weight of piglets born alive (BALWT_1); the results of the second and subsequent parities: total number born (TNB), total number born alive (NBA), litter weight of piglets born alive (BALWT); and totals for lifetime of sows: total number born (TNB_All), total number born alive (NBA_All), litter weight of piglets born alive (BALWT_All), and all parities (P_All). In accounting for P_All, the sows with at least three parities were selected.Yij = μ  +  Gi + Bj + eji, where Yij is the ijth trait observation value; μ is the mean; Gi is the effect of the ith genotypes; Bj is the effect of jth breeding; and eji is the random residual that corresponds to the trait observation value. To determine if the SNP had a significant additive effect, dominance effect, or both, contrasts for additive and dominance effects were tested for the most significant SNP. Additive effects were declared when the contrast between the effects of the two homozygous genotypes was significantly (P ≤ 0.01 and P ≤ 0.05) or suggestively different (P ≤ 0.15). Dominance effects were declared when the contrast between the average effect of the two homozygous genotypes (AA and BB) and the effect of the heterozygous genotype was significantly (P ≤ 0.01 and P ≤ 0.05) or suggestively different (P ≤ 0.15). To adjust P values, the Bonferroni method was used.Associative analysis was performed using the GLM procedure in R version 3.5.1. The mixed model with the fixed effects was as follows: а = (BB−AA)/2 and d = AB−(BB + AA)/2, where a is the additive effect; d is the dominant effect; and АА, АВ, and ВВ are the genotypes of SNP.Additive and dominant SNP effects were calculated using the following formulas : а =  and 1.1 kg (P ≤ 0.05) compared to the sows with the AA and AB genotypes, respectively. The negative dominant effect of SNP_2 on TNB and BALWT has been found. In the sows with the genotype SNP_2_AB, compared to homozygous sows, TNB and BALWT there were lesser by 0.7 (P ≤ 0.05) and 1.0 (P ≤ 0.05), respectively. In assessing the SNP_3 genotypes, additive effects on litter traits in first parity and the dominant effect on P_All were revealed. The sows with the genotype SNP_3_BB had 3.1 more TNB_1 and NBA_1 (P ≤ 0.05) compared to the sows of the genotype SNP_3_AA. For the entire productive period, sows with the genotype SNP_3_AB had 0.5 more P_All than sows with homozygous genotypes. According to the results of parities, the suggestive dominant effect on TNB_All, NBA_All, and BALWT_All is observed during the entire period of keeping. Analysis of the SNP_5 genotypes showed dominant effects on NBA_1 and BALWT_1, as well as additive effects on TNB, NBA, and BALWT. In the sows with the genotype SNP_5_AB, NBA_1 was smaller by 1.0 (P ≤ 0.05) and BALWT_1 was smaller by 1.6 (P ≤ 0.05) than these traits in the sows with homozygous genotype. Sows with the genotype SNP_5_BB had 1.1 more TNB (P ≤ 0.1), 0.9 more NBA (P ≤ 0.05), and 1.3 more BALWT (P ≤ 0.05).In general, the additive and dominant effects on litter traits in LW sow are determined for SNP_1, SNP_2, SNP_3, and SNP_5. The significant effect of the SNP_1 genotypes on BALWT is determined. The BALWT in the sows with the genotype SNP_1_BB was higher by 1.8 kg (P ≤ 0.05), 7.4 more NBA_All (P ≤ 0.05), and 0.5 more BALWT_All (P ≤ 0.1) and P_All (P ≤ 0.1) compared to homozygous sows. The additive effects of the genotypes SNP_5 were found for TNB, NBA, and BALWT. The sows with the genotype SNP_5_AA had 1.0 more TNB (P ≤ 0.05), 1.3 more NBA (P < 0.01), and 1.8 more BALWT (P < 0.01), compared to sows with the genotype SNP_5_BB. An investigation of the first parity showed significant effects of the SNP_7 genotypes on NBA_1 and BALWT_1. The sows with the genotype SNP_7_AB had 1.8 more NBA_1 than (P < 0.01), 1.1 more BALWT_1 (P < 0.01), and 1.4 more TNB_1 (P ≤ 0.1) compared to sows with the SNP_7_BB genotype. Significant effects of SNP_7 on subsequent parities were not observed statistically.We have determined the additive and dominant effects of SNP_2, SNP_5, and SNP_7 on litter traits of LN sows. The analysis of SNP_2 genotypes showed significant dominant effects on TNB_All and NBA_All and suggestive dominant effects on BALWT_All and P_All. Sows with genotype SNP_2_AB had 8.9 more TNB_All  gene . The protein encoded by this gene belongs to the SMAD family of proteins, which have been identified as signaling mediators of the transforming growth factor beta superfamily (TGF-beta). They are involved in a number of biological processes, including cell growth, morphogenesis, development, and immune responses [SMAD1, SMAD2, SMAD3, and SMAD5 are ligand-specific: SMAD1 and SMAD5 transform signals from bone morphogenetic proteins (BMPs); SMAD2 and SMAD3 mediate TGF-beta signaling, and SMAD4 acts as a common signaling component. SMAD6 is completely different in structure from other SMAD proteins; it forms stable associations with type I receptors and works as an inhibitor [SMAD6 gene polymorphism studies are focused on identification of associations with human ovarian cancer [SMAD6 gene can lead to changes in gene expression or regulation of the signaling function involved in the development of the reproductive process in pigs too.It is interesting to note that SNP_1 is localized in the intron of the small mothers against decapentaplegic 6 . Sherman et al. [SUGCT (C7orf10) is a member of the coenzyme A family of class III transferases, based on a missense mutation (p.Arg336Trp) found in a homozygous state in several patients with type III glutaric aciduria. Further functional studies of catalytic activity and subcellular localization carried out by Marlaire et al. [SUGCT (C7orf10) corresponds to succinyl-CoA: a mitochondrial enzyme glutarate CoA-transferase, involved in the metabolism of glutarate and possibly in the metabolism of longer dicarboxylic acids. Homologs of this enzyme are found in numerous bacterial operons, which also include the putative glutaryl CoA dehydrogenase, indicating that an enzyme with similar specificity exists in prokaryotes [Significant effects of SNP_2 on the analyzed traits were identified in LW and LN. It should be noted that these effects resulted from the heterozygous genotype (dominant effects). In LW, the effect of this SNP on BALWT only was determined, but in LN, effects were determined for all traits evaluated for the productive period . SNP_2 is localized in the gene encoding hydroxymethylglutarate CoA-transferase succinate . Brady et al. [MTBP gene (MDM2 binding protein) in the mechanisms ensuring the destruction of the p53 protein by the MDM2 ubiquitinated ligase. The p53 protein is a critical coordinator of a wide range of stress reactions in the body. The p53 gene is expressed with constant activity, but the protein has a very short period of life, regulated by E3 ubiquitinated ligases. The most studied and probably significant of them is the ubiquitinated ligase MDM2. The p53 regulation is provided by the negative feedback implemented by MDM2 using MTBP. It is very important for the organism to regulate p53 activity firmly and accurately to prevent inappropriate activation leading to the death of cell and possibly the death of organism [MDM2. Expression of MDM2 is observed in a wide range of values and varies in different tissues, but the highest level is found in the testes and ovaries [MTBP mRNA also has the highest level in these tissues [MTBP in inhibiting the cis- and stimulating the trans-reactions of ubiquitin ligase E3 MDM2 suggests that MTBP can also provide the functions which are necessary to regulate the reproductive activity of sows, especially during the first parity.According to the LW pig research conducted by Wang et al. , we havey et al.  showed torganism . This re ovaries . Interes tissues . In geneAKT3 gene . This gene codes serine/threonine protein kinase. Three subtypes of protein kinases, AKT1, AKT2, and AKT3, have been identified in mammals; they play a key role in metabolism of glucose and angiogenesis as well as in PI3K/AKT signaling pathway, which regulates cell cycle and apoptosis. Despite the differences between the specific functions of AKT isoforms, all 3 subtypes are potential candidate genes associated with phenotype variation in humans and animals. The single-nucleotide polymorphism of AKT3 (rs4590656) was found to be associated with three physiological parameters  in people with chronic altitude sickness, indicating a strong association of this gene with angiogenesis [ATP in the fibroblasts cell line in mice significantly depends on the AKT family. Bionaz and Loor [AKT1 and AKT3 levels of expression increase considerably during lactation periods in cattle. Wang et al. [AKT3 gene polymorphism on the early maturity in rabbits. Chen et al. [AKT3 gene associated with Xiang pig fattening traits.Among the studied SNPs, the most significant effect on variability of studied traits in the investigated population has been obtained for SNP_5 (SNP rs81421148). This result was described by Bergfelder-Drüing et al.  in theirogenesis . A studyogenesis  showed tand Loor  determing et al.  revealedn et al.  demonstrn et al.  found thAKT3 functional role is realized through the PI3K/AKT pathway. In mammals, an inhibition of the PI3K/AKT pathway blocks almost all insulin metabolic actions, including stimulation of glucose transport [PI3K/AKT pathway is involved in the regulation of folliculogenesis and oogenesis. The role of AKT in mammalian ovaries was evaluated by gene knockout. Female mice with AKT1 deficiency have demonstrated that the fertility decreases, the estrus delays for about 5 days, the age of the first litter increases, and the average litter size decreases. Primary follicles oocytes in females with AKT1 deficiency are larger than those in wild-type animals, and sometimes the follicles contain several oocytes. The effects of AKT2 and AKT3 on fertility are still unclear. The AKT controls the activity of some transcription factors of the forkhead box protein O1 or FOXO family . The transcription factors FOXO1, FOXO3, and FOXO4 are also involved in the control of folliculogenesis [AKT3 is a perspective gene candidate for reproductive traits of pigs.The ransport . It is eogenesis . On the AKT3 was identified as a promising candidate gene, whose biological functions, as well as the results obtained in our work and in other studies, indicate its potential role in the reproductive process regulation in pigs. In general, the data obtained help to explain the genetic mechanisms of reproductive traits.The results obtained showed the pleiotropic effect of the selected SNPs on the traits related to the litter size, the litter weight, and the amount of parities. Here, we demonstrated the local genotyping systems of some SNPs from PigQTLdb, which can be used in further studies on the genetic architecture and its connection with the pig productivity traits. The analysis of the SNPs alleles and genotypes frequency distribution revealed features associated with the pigs' breed, which must be considered when evaluating the effects of SNPs. The results showed significant effects of SNPs on litter traits in LW and LN.


N = 22,229). Our results demonstrated a significant population genetic heterogeneity for behavioral traits such as age at first sexual intercourse and academic qualification. Our approach may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different populations.Heterogeneity in the phenotypic mean and variance across populations is often observed for complex traits. One way to understand heterogeneous phenotypes lies in uncovering heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations were typically based on discrete groups in populations stratified by different countries or cohorts, which ignored the difference of population characteristics for the individuals within each group and resulted in loss of information. Here, we introduce a novel concept of genotype-by-population (G × P) interaction where population is defined by the first and second ancestry principal components (PCs), which are less likely to be confounded with country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex traits with significant SNP-heritability and the PCs as covariates to examine G × P interactions across diverse populations including white British and other white Europeans from the UK Biobank ( Most human traits are polygenic and their phenotypes are typically influenced by numerous genes and environmental factors, and possibly by their interactions, e.g., genotype-environment (G × E) interaction . These tOne way to understand such phenotypic heterogeneity lies in uncovering genetic differentiation for the traits captured by common variants across populations . Some stPrincipal component (PC) analysis provides a powerful tool to characterize populations and the first few PCs are typically used to control population stratifications in large-scale GWAS . PCs allThe aim of the study is to explore if there exists significant G × P interaction, which is also referred as genetic heterogeneity (heterogeneous genetic effects) across populations, for a wide range of complex traits. To do so, we applied the whole-genome RNM with PCs as continuous covariates to investigate G × P interactions for more than one hundred phenotypes using the UKBB data. The significant G × P interaction detected in this study may shed light on the latent genetic architecture of complex traits that underlies the modulation of genetic effects across different population backgrounds.N = 17,000). As shown in N = 9,809). The rest of the other white Europeans except POP3 were named as POP2 .Our study was based on the UKBB data which contains approximately 500,000 individuals sampled across the United Kingdom . UKBB’s Our primary interest was to investigate G × P interaction where population was classified by ancestry PCs. For this purpose, we used three designs of combinations of the three groups, i.e., POP1 + POP2 , POP2 + p-value < 0.0001, (6) SNPs with minor allele frequency <0.01, and (7) ambiguous SNPs with A/T or G/C alleles. We also retained HapMap3 SNPs only as they are reliable and robust to bias in estimating SNP-heritability and genetic correlation  all duplicated and non-autosomal SNPs, (2) SNPs with INFO score <0.6, (3) SNPs with call rate <0.95, (4) individuals with missing rate >0.05, (5) SNPs with Hardy-Weinberg equilibrium relation . HereaftFor current UKBB resource, we have access to 496 variables whose data types are categorical (multiple), categorical (single), continuous, integer, date, text and time. Here, we focused on the variables of categorical (multiple), categorical (single), continuous and integer types, and categorized each variable as one of four value types: continuous, binary, ordered categorical and unordered categorical variable . Where aAmong the 199 variables, we selected 128 variables as the main phenotypes  in our pA standard linear mixed model assuming no G × P interaction can be written as:y is an n × 1 vector of phenotypes with n being the sample size, μ is an n × 1 vector for fixed effects, g is an n × 1 vector of total genetic effects of the individuals with e is an n × 1 vector of residual effects with A is a genomic relationship matrix (GRM) and I is an identity matrix. GRM can be estimated based on common SNPs across the genome and the elements of GRM can be defined as , xil denotes the number of copies of the reference allele for the lth SNP of the ith individual, xl denotes all the numbers of copies of the reference allele across all the individuals, andpl denotes the reference allele frequency of the lth SNP.where (V) is:The variance-covariance matrix of the observed phenotypes The SNP-based heritability, the proportion of the additive genetic variance explained by the genome-wide SNPs over the total phenotypic variance, is then referred as:The phenotypes with significant SNP-based heritability from this baseline model will subsequently be investigated for G × P interaction.In cases where G × P interaction exists across populations, the baseline model cannot account for heterogeneous genetic effects. We therefore applied RNM methods to detect heterogeneity across populations using the UKBB data. RNM and multivariate RNM (MRNM) have been demonstrated to perform better than the current state-of-the-art methods when detecting genotype-covariate and residual-covariate interactions in terms of simulation studies on type I error rate and power analyses . Here, wy, μ, g and e are the same defined in the baseline model above, g0 and g1 are n × 1 vectors of the zero- and first-order random regression coefficients, respectively, c is an n × 1 vector of covariate values of the n individuals . In the RNM, the random genetic effects, g, are regressed on the covariate gradient (reaction norm), which can be modeled with random regression coefficients, g0 and g1. This G × P RNM accounts for phenotypic plasticity and norms of reaction in response to different populations  among samples.where g0 and g1 allow us to verify whether estimates of the parameters are reasonable or not. Specifically, estimated values should be within a valid parameter space:The mathematical properties of variance-covariance structure between (1)(2)(3)The estimates which violated one of above criteria were excluded for follow-up analyses. We obtained a p-value to detect G × P interaction using a likelihood ratio test (LRT) that compared the goodness of fitness of two models (GREML and G × P RNM), penalizing the difference in the number of parameters between them.We further tested if the significant G × P interactions were orthogonal (independent without confounding) to residual-population (R × P) interactions, i.e., residual heterogeneity across populations . Similar0 and e1 are n × 1 vectors of the zero- and first-order random regression coefficients when residual effects, e, are regressed on the covariate, c, i.e., an n vector of PC1 or PC2. Furthermore, a full RNM model with both G × P and R × P interactions can be expressed as:where eSince the G × P and R × P models are nested within the full model, LRT comparing the full and R × P or G × P model with an appropriate degree of freedom can determine the significance of orthogonal G × P or R × P interaction . More deFor the analyses showing a significant G × P interaction, we used rank-based INT phenotypes to check explicitly if the significance was due to phenotypic heteroscedasticity or normality assumption violation . The biaWe used the UKBB data that have only a 5.5% response rate, i.e., selection. Consequently, the resulting sample may not be representative of the UK population as a whole and the selection may be associated with some of the phenotypes in the UKBB, causing selection or collider bias . To testWe first applied the standard GREML model to estimate nt hSNP2 –S5 were y) and environmental covariates (c) are not always uncorrelated, for which multivariate RNM accounting for  correlations between y and c should be used , we further considered additional trait-specific confounders that might be relevant to some of traits , e.g., TWe examined the distribution of phenotypic values after controlling additional confounders of the six traits with significant G × P interactions  and coulp-value = 0.88 for PC1 and 0.92 for PC2 in p-value = 4.15E−05 for PC1 and 0.003 for PC2 in For age first had sexual intercourse and qualifications that were shown to have significant G × P interactions, we further tested if the G × P interactions were orthogonal to R × P interactions, i.e., residual heterogeneity . Using the rank-based INT phenotypes adjusted for basic and additional confounders, we carried out an R × P model and a full model in which both G × P and R × P were fitted jointly. Subsequently, we conducted LRT to obtain p-values, comparing the full and nested models. A significant p-value from LRT between the full and R × P model indicates that G × P interaction is orthogonal to R × P interaction  and qualifications  have demonstrated strong genetic heterogeneity signals . For several traits showing significant heterogeneous signals with covariate PC1 or PC2 after Bonferroni correction see , S19, weFor the categorical phenotype qualifications, there were various ways to convert the seven UKBB categories into a continuous or a binary measure . FollowiWe examined the distribution of phenotypic values for age first had sexual intercourse and qualifications in which G × P interactions were consistently detected from both POP1 + POP2 and POP2 + POP3 , S26. ThOur simulation studies testing for detecting spurious heterogeneity across POP1 and POP2 with multiple scenarios varying the level of selection odds ratios see ; (2) onlFor age first had sexual intercourse and qualifications, we also confirmed our findings using bivariate GREML, a robust approach against selection bias . As confPrevious results  were morSE = 0.2044, p-value = 0.3249). This might be due to the fact that there was no genuine interaction effects or that the bivariate GREML was simply underpowered. For the two binary measuring ways of qualifications , we also used bivariate GREML to examine genetic correlations between POP1, POP2 and POP3  and between POP2 and POP3  but no significant signal between POP1 and POP3 , which were consistent with those obtained from the main analyses. For the binary data of “college or university degree” versus other six categories, the bivariate GREML indicated a marginally significant heterogeneity between POP1 and POP2  and no significant signal between POP2 and POP3 , and POP1 and POP3 . The reason that the genetic heterogeneity became weaker or disappeared is probably due to the fact that the bivariate GREML has less power compared to the RNM approach, and the phenotype categories reduced from four to two levels.From the POP2 + POP3 analyses, we also found a significant G × P interaction for diagnosis of diabetes that is a binary response variable. As the RNM has not been explicitly verified for binary traits, we also used bivariate GREML to estimate the genetic correlation between POP2 and POP3 for this disease trait and found no significant signal for genetic heterogeneity  interactions, e.g., random regression (RR)-GREML  and GCI-Residual-covariate interaction may result in heterogeneous residual variances across different covariate values, thus it is necessary to examine and distinguish genotype-covariate and residual-covariate interactions . Our resThere are several limitations in this study. Firstly, we examined G × P interaction across populations using three data designs , in which population is referred to the first and second ancestry PCs. As POP1 and POP3 are very close in terms of PCs, the individuals in the two primary groups POP1 + POP2 and POP2 + POP3 have common population structures . But botIn conclusion, the main findings in our study demonstrated a significant population genetic heterogeneity for behavioral traits . Our study provided a paradigm shift tool in investigating genetic heterogeneity across populations. The new concept of G × P interaction with the use of ancestry PC is more plausible in explaining the genetic architecture of complex traits across heterogeneous populations. The G × P interaction effects on age first had sexual intercourse and qualifications were found by a powerful approach based on technically homogeneous data (free of genetic measurement errors and cohort/country confounding factors), and these findings were validated in both data designs POP1 + POP2 and POP2 + POP3. The analyses performed in this study can be applied to dissect the genetic architecture of complex traits and diseases across populations, and the results from these analyses will provide important information and suggestion for studies of polygenic risk prediction across Europeans.http://www.ukbiobank.ac.uk) Research Ethics Committee (REC) approval number is 11/NW/0382. Our reference number approved by UK Biobank is 14575.Publicly available datasets were analyzed in this study. This research has been conducted using the UK Biobank Resource. UK Biobank (CY and SL conceived and initiated the project. SL supervised and directed the study. CY and GN quality-controlled the data. CY analyzed the data and drafted the manuscript. JW provided key statistical advice. All authors revised the manuscript and approved the final version of the manuscript.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.


Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep. In classical models of quantitative or complex trait genetics, the phenotypic value of each trait is controlled by a large number of loci; moreover, the interaction and alternative splicing of genes also play an extremely essential role . The pheGS has improved modern breeding programs and has made a great contribution to improving the accuracy of BVs , 7, espeWith the publication of the whole-genome sequence of dairy cows and the continuous upgrading of commercial SNP microarrays, GP and GS have been adopted on a large scale in conventional breeding programs of dairy cows , 18. ThiFor this study, components of the genetic variance of the Alpine Merino sheep dataset were estimated, including additive and dominant genetic variance, then the accuracy of the BVs estimated by two models compared using cross validation. The main objective of this study was to explore the impact of additive and dominant genetic effects on the accuracy of GP and the optimization methods of GP for the Alpine Merino sheep population.All animal work conducted in the present study was performed in accordance with the guidelines for the care and use of laboratory animals promulgated by the State Council of the People's Republic of China. This research was approved by the Animal Management and Ethics Committee of Lanzhou Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences (license number: 2019-008).The initial phenotypic dataset was derived from animals on the HuangCheng pasture in Gansu Province, China, part of the Sheep Breeding Technology Extension Station. The dataset consisted of 11,200 individuals and included their dates of birth, region (defined by herd), and sex. In the present study, the phenotypes of 498 offspring for 26 breeding rams were collected from 7 different herds, consisting of 295 rams and 203 ewes. Most of them were half-sibs, and very few (<12%) were full-sibs, the age of which were around 14 months. A blood sample (5 mL) from each sheep was collected from the jugular vein and immediately transferred to a vacuum blood collection tube . A standard set of red blood cell data was recorded using an H-100IV diff whole blood analyzer  within 24 h of sample collection, after which the remaining blood sample was stored at −20°C  Array Plate Processing Workflow from Thermo Fisher (Affymetrix). Prior to statistical analysis, SNPs were pre-processed using PLINK v1.9b4 software . Samplesanalysis . FollowiIn this study, we explored the possibility of combining both additive and dominant genetic effects in the genomic evaluation and further compared the accuracy of GP via different models. Nine traits of 498 samples were used in (1) estimating the variance of components in the genotype dataset of Alpine Merino sheep, including additive and dominant variance, and (2) analyzing the accuracy of GP using 5-fold cross validation and then comparing the accuracy of estimation of BVs between the prediction model using additive effects and that when combining both additive and dominant effects. Both models were evaluated within a GBLUP framework. Replicate measurements were not available for the sheep; thus, permanent environmental effects were not modeled. Samples were of different genders and from different herds, factors that altered phenotypes in a fixed manner, so system environmental effects were added to the framework.The components of variance of additive genetic effect  of each individual and the different herds (H1–H7). u is the vector of breeding values of the individual, e is the vector of residual effects, X is the design matrix corresponding to fixed effects, and Z is the design matrix corresponding to breeding values. Covariance matrices of additive effects were G is the matrix of the genomic relationship, calculated based on the approach of VanRaden (https://github.com/xiaolei-lab/hiblup):where VanRaden  and usinW1 is the matrix of additive genetic effect markers, with dimensions of the number of individuals (n) by the number of loci (m), and pi is the minor allele frequency (MAF) value of locus i.where In addition, the components of variance of the additive genetic effect , while v refers to the vector of the dominant effect of an individual. The covariance matrices of dominant effects were where D represents the matrix of the genomic dominant relationship (https://github.com/xiaolei-lab/hiblup):As with the MAG model, in Equation (3), tionship , also caW2 represents the matrix of dominant genetic effect markers and where, n, m, and pi are the same as those defined in Equation (2).where The accuracy of GP was evaluated by 5-fold cross validation. The dataset was randomly divided into 5 approximately equally sized subgroups , for each cross validation, of which 4 were considered as training groups (reference population) to estimate parameters, and the remaining group (candidate population) was used to validate samples. The population was generally randomly divided when performing 5-fold cross validation. A number of validation samples had offspring in the training population, causing in that case the cross validation to be based on offspring, which would exaggerate the accuracy of the prediction . In the Based on the MAG and MADG models, the breeding values of the validation group were predicted and their respective components of variance estimated. In addition, we performed the 5-fold cross validation described above twice to ensure randomness of the verification group. Finally, 10 different values of accuracy were calculated for each trait, the mean value recorded as the final accuracy.Estimated components of variance for the MAG and MADG models are presented in Genome-wide dense markers have replaced pedigree information and have provided a new methodology for estimating additive and dominant genetic effects, anticipated to improve the accuracy of genomic predictions . The tecThe use of an additive and dominant genetic relationship matrix in a typical linear mixed model (such as the GBLUP model) is capable of estimating additive and dominant components of variance. Goddard and Hayes et al. established that the GBLUP model is equivalent to a linear random regression model, assuming that each SNP causes an effect, and the effects of all SNP effects follow a normal distribution with equal variances , 40. TheAdditive and dominant variance components were estimated for 9 traits of interest in the Alpine Merino sheep population, including 5 wool traits and 4 blood traits. Due to complex genetic mechanisms and differences between species, to date, few studies have estimated genetic variance components in sheep blood traits. Safari and Fogarty et al. collected and summarized a number of studies on the genetic parameters of important traits in sheep. Their reported results suggested that the weighted average heritability of FD 0.51–0.59) was based on 41 and 43 estimates, respectively; the weighted average heritability of SL 0.46–0.48) was based on 21 estimates; the weighted average heritability of FBS (0.34) was based on 11 estimates; and the weighted average heritability of CFWR (0.34–0.51) was based on 36 and 43 estimates, respectively , 45. In –0.48 was–0.59 wasTo the best of our knowledge, studies of GP or GS about high-altitude hypoxia adaptation in sheep were rarely reported, so we did not find reference statistics related to sheep blood traits. However, for wool traits, the statistical averages of FD and FBS calculated by Daetwyler et al. in their study on genetic parameter estimation of Australian Merino sheep were 17.3 and 33.4, respectively . The staIn the present study, the accuracy of GP was evaluated by 5-fold cross validation, the results of which are presented in For these two models (MAG and MADG), the accuracy of prediction of the MAG model was slightly higher than that of MADG, for both wool and blood traits. The proportion of full-sib relatives and the relationship coefficient of the dominant effect between the training and verification datasets are very small because samples were selected from the same generation with different dams. The MADG model, which combined additive and dominant genetic effects, did not exhibit a clear advantage over the MAG model with only the additive genetic effect, possibly a reflection of only a small proportion of the dominant variance effect information being transferred from the training group to the verification group through 5-fold cross validation. This has also been found in previous studies: Ertl and his colleagues found by genomic analysis of Fleckvieh cattle that when the whole sib relationship between training and validation datasets is small, the accuracy of prediction of the total genetic value by cross validation is not higher in the dominant model than in the additive model . In the For the accuracy of GP, the MADG model containing dominant genetic effects does not show a significant advantage over the MA model with only additive effects. Although the advantage of the MA model over MAD in these traits involved in the current study was not obvious, it could not be ignored. It could be expected that when a larger reference dataset was adopted, especially when it contains more offspring animal records, comparing the predictive ability of models with additive effects and models with additive and dominant effects will get greater benefits for the GP; meanwhile, it is not excluded that genetic models containing additive and dominant effects may be beneficial to the development of specific capabilities of integration for other important traits . FurtherIn summary, the present study estimated the additive and dominant variances of 9 traits of Alpine Merino sheep based on two different GBLUP models and used 5-fold cross validation to evaluate the accuracy of prediction of breeding values for these traits. This was the first time GP has been applied to the domesticated Alpine Merino sheep population. Dominant genetic effects account for a large proportion of total phenotypic variation in particular traits . Both additive and dominant variances play a vital role in the accuracy of prediction, while for some traits the latter may have a greater impact on the accuracy of prediction than the former. In addition, this study indicates that the predictive capability was not improved when dominant effects were included in the model if the proportion of full-sib relatives in the population was small.Based on the current results, we will expand the scale of the dataset in the subsequent study and continue to research GP by adding other important traits of the Alpine Merino sheep population, in order to provide more theoretical references for the breeding of this sheep population. Moreover, this study adopts genome-wide SNP information to construct additive and dominant relationship matrices; compared with the relationship matrix based on pedigree, the former is obviously more reliable, which suggests the great contribution of genome-wide SNP information in genome selection. Although the individuals involved in this study only included the Alpine Merino sheep population, the heritability estimation results of SL and FD and the accuracy of GP were very close to those reported in the previous literature. Therefore, it is not excluded to extend these study methods and apply them to other breeds of sheep.https://figshare.com/, https://figshare.com/articles/GS_sample/12497993, https://figshare.com/articles/GSQC_bed/12497963.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: The animal study was reviewed and approved by Animal Management and Ethics Committee of Lanzhou Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences. Written informed consent was obtained from the owners for the participation of their animals in this study.SZ: conceptualization, formal analysis, and writing of the original draft. SZ and CY: data curation. BY: funding acquisition. HZ and MH: investigation. YY and TG: methodology. SG and BY: project administration and writing of the review and editing. TW and FL: resources. JL and CY: software. HZ: validation. GQ: visualization. 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.


Effective matching of genotypes and environments is required for the species to reach optimal productivity and act effectively for carbon sequestration. A common garden experiment across five different environments was undertaken to assess genotype x environment interaction (GxE) of coast redwood in order to understand the performance of genotypes across environments.The quantitative genetic analysis discovered no GxE between investigated environments for diameter at breast height (DBH). However, no genetic component was detected at one environment possibly due to stressful conditions. The implementation of universal response function allowed for the identification of important environmental factors affecting species productivity. Additionally, this approach enabled us to predict the performance of species across the New Zealand environmental conditions.In combination with quantitative genetic analysis which identified genetically superior material, the URF model can directly identify the optimal geographical regions to maximize productivity. However, the finding of ideally uncorrelated climatic variables for species with narrow ecological amplitude is rather challenging, which complicates construction of informative URF model. This, along with a small number of tested environments, tended to overfit a prediction model which resulted in extreme predictions in untested environments. Understanding the effects on plant productivity, not only of the sequestration of carbon from elevated COesources . Forest esources .Changes in climate observed globally over a number of years are expected to continue and are likely to affect the types of trees that can be grown in particular environments . EffectiSequoia sempervirens (Lamb. Ex D. Don) Endl.) is a forest tree species that can sequester large amounts of carbon depending on the silvicultural regime used and the proportion of heartwood present [Coast redwood  J.Buchholz) has instead invested into more robust root system on account of reduces above-ground biomass production as a strategy to cope with drought. Coast redwood has few pest and diseases. Parts of redwood trees such as leaves, branchlets, roots and wood are rarely damaged by non-human animals presumably due to the volatile essential oils and tannins content. While there is a number of insects infesting coast redwood trees, none of them is capable of causing the death of mature trees [Coast redwood naturally occurs along a narrow coastal strip (∼50 km width) of western North America ranging from Southern Oregon (OR) in the north to Southern California (CA) in the south . Many sire trees . Coast rre trees .The objective of this study was to examine the genotype x environment interaction (GxE) of coast redwood by comparing tree growth among a number of different environments across New Zealand. We examined GxE interaction within a genetic framework using a range-wide collection of clones planted across 11 environments . We usedA cluster analysis based on geographical covariates and climatic variables generated only two significant clusters of genotypes, which were separated by location from the northern and southern parts of the US geographical distribution (data not shown). The second-best scenario defined by critical score detected 23 clusters Fig. , which rAn analysis of each of the five sites  . Since tin Table . The higin Table . The URFin Table  extractein Table .Fig. 5CST (0.0031) [ST) and genetic differentiation between populations based on selectively neutral markers (F ST) [Extensive natural distributions of forest trees over highly variable environments predispose the adaptive traits of forest trees to exhibit the effects of strong local natural selection. This effect can result in different phenotypic responses from different populations when compared using common garden experiments –30. This(0.0031) . These f(0.0031) . Signifi(0.0031) , 10 and s (F ST) , 35. In s (F ST) –39 whichs (F ST) .Genetic variance and interaction of the genetic and environmental components were investigated at both the population and genotype level. The population-level broad-sense genetic correlations between sites for DBH exceeded 0.7, so the PxE interaction was considered to be non-significant . Since tST versus F ST).Some individuals were found to have large changes in ranking across environments compared with others Figs.  and 4. WThe appearance of epicormic sprouts on trees was found to be strongly influenced by genotype, but this was less obvious when fitting the population component. This pattern was the opposite of that observed for DBH and could imply that the EPI trait is not under natural selection. The production of epicormic sprouts presents a means of regenerating the understorey after wildfires  which arThis investigation of the PxE interaction found high but non-significant broad-sense genetic correlations across environments at the population level, which is assumed to be connected to a negligible variance component estimate for the effect of populations. The GxE interaction generally showed stable expression in terms of broad-sense genetic correlations between environments at a genotype level. A strong GxE interaction was found only between the Awaho and Ngapuke farms sites Table . The difSequoia genus [The analysis of individual sites showed significant genetic components at four of the five sites examined. Virtually no genetic component was found in either of the studied traits at the Taupō site  to achieve this goal was rather limited due to the fact that coast redwood occupies ecologically relatively uniform environment and most of the investigated climate variables were highly correlated  at planted sites as the most significant factors affecting productivity in New Zealand along with the quadratic term of mean daily maximum temperature at the origin. In contrary, an only a weak signal was detected in climate variables at origin Table . The nonl growth .Additionally, total annual precipitation is coarse environmental parameter as it does not reflect the variability in precipitation distribution and intensity throughout the year, which may be more critical for plant adaptation to cope with summer drought. In coast redwood’s natural range, fog water is a significant water source, especially for coastal populations . It has The universal response function , 65 has The URF model was implemented to predict the productivity of genetically superior material identified in quantitative genetic analysis (Cluster 19) across New Zealand. The results show the best performance at the north of North Island (Northland) as well as the coastal line along the east coast of North Island. These regions were identified in the previous study using data from permanent sample plots analyse by multiple regression model . HoweverThe current study provides evidence of the ability to detect a heritable component of phenotypic variation in productivity, as measured with DBH, and epicormic sprouts in a sample of coast redwood outside its natural conditions. However, also environmental conditions under which genetic component was suppressed were detected. The investigation of genotype x environment interaction did not discover any serious interaction of population/genotype with the environment. However, some combinations of sites showed slight effects in genotype ranking, specifically for particular genotypes which were attributed to possible selfing. Nevertheless, such hypothesis will need to be approved by the implementation of genetic markers. The implementation of universal response function allowed for the identification of important environmental factors affecting species productivity. Additionally, this approach enabled us to predict the performance of species across the New Zealand environmental conditions. In combination with quantitative genetic analysis which identified genetically superior material, the URF model can directly identify the optimal geographical regions to maximize productivity. However, the finding of ideally uncorrelated climatic variables for species with narrow ecological amplitude is rather challenging, which complicates construction of informative URF model. This, along with a small number of tested environments, tended to overfit a prediction model which resulted in extreme predictions in untested environments.Coast redwood material was collected in 1984 from 98 locations along the west coast of USA representing most of its natural distribution, i.e. from Curry County (OR) to Monterey County (CA)  [Climatic variables such as mean daily minimum temperature [C ersion2)  as monthY is a matrix of phenotypes, β is the vector of fixed effects (intercept), p is the vector of random population effects following var(p) ∼N, where G1 is variance-covariance structure of population effects following G1= I, where st and nth environment, st and nth environment, I is the identity matrix. The vector g is vector of random genotype within population effects following var(g) ∼N, where G2 is variance-covariance structure of genotype within population effects following G2=I, where st and nth environment. The vector r is vector of random replication effects following var(r) ∼N, where G3 is variance-covariance structure of replication effect following G3=I, where r(b) is vector of random block nested within replication effects following var(r(b)) ∼N, where G4 is the variance-covariance structure for block nested within replication effects following G4=I, where st and nth environment. The vector e is vector of random residuals (effect of ramet within genotype) following var(e) ∼N, where R is residual variance-covariance structure following R=I, where st and nth environments, X and Z are the incidence matrices assigning fixed and random effects to measurements in matrix Y. Broad-sense heritability at population level was estimated as follows:th and jth environment, th and jth environment, Each location was represented by 1 - 6 trees (Additional file  package  with imp package . The phe package . The numThe graphical investigation of GxE at the individual level was investigated through the construction of boxplots. The individuals were ranked in each environment regarding their performance, and average clonal ranking was estimated as arithmetical mean of genotype ranks across investigated environments. Box of each genotype then represents the dispersion of pair-wise change in rankings between environments. In this way, we can investigate behaving of individuals generally ranked as the best, poor or average.The universal response function was constructed by using climatic variables at origin as well as at planted sites following :\documeAdditional file 1 Supplemental tables. The Additional file 1 contains Table S1 summarizing variance components and broad-sense heritability from single site analysis and Table S2 describing climatic and geographical variables related to each sampling site in US.Additional file 2 Phenotypic data. The Additional file 2 contains all phenotypic data used in this study.


A defining challenge of the 21st century is meeting the nutritional demands of the growing human population, under a scenario of limited land and water resources and under the specter of climate change. The Vavilov seed bank contains numerous landraces collected nearly a hundred years ago, and thus may contain ‘genetic gems’ with the potential to enhance modern breeding efforts. Here, we analyze 407 landraces, sampled from major historic centers of chickpea cultivation and secondary diversification. Genome-Wide Association Studies (GWAS) conducted on both phenotypic traits and bioclimatic variables at landraces sampling sites as extended phenotypes resulted in 84 GWAS hits associated to various regions. The novel haploblock-based test identified haploblocks enriched for single nucleotide polymorphisms (SNPs) associated with phenotypes and bioclimatic variables. Subsequent bi-clustering of traits sharing enriched haploblocks underscored both non-random distribution of SNPs among several haploblocks and their association with multiple traits. We hypothesize that these clusters of pleiotropic SNPs represent co-adapted genetic complexes to a range of environmental conditions that chickpea experienced during domestication and subsequent geographic radiation. Linking genetic variation to phenotypic data and a wealth of historic information preserved in historic seed banks are the keys for genome-based and environment-informed breeding intensification. Landraces dominated agriculture for millennia, until the advent of intensive modern breeding in the mid 20th century, when reduced sets of elite cultivated varieties largely displaced the wider diversity of local genotypes . AlthougCicer arietinum, chickpea, which is among the world’s most widely grown grain legumes and provides a vital source of dietary protein for ~15% of the world’s population. Chickpea was first domesticated ~10 KYA, initially in southeastern Turkey, and then spread regionally throughout the Fertile Crescent. Although exact dates are unknown, archeological evidence suggests chickpea moved to India ~6000 years ago and to Ethiopia and North Africa ~3000 years ago [Here, we focus on biodiversity of Chickpea domestication and breeding imposed a severe genetic bottleneck on the crop, with an estimated >95% of diversity lost between the crop wild progenitor and modern elite varieties . LandracHere, we combine genomics, phenotyping, and computational biology to understand chickpea’s agricultural variation one century ago, and from that analysis to infer the breadth and genetic bases of trait variation in the pre-modern era. Such knowledge can prioritize landrace haplotypes that contributed to diversification of chickpea as a crop, particularly haplotypes missing from modern breeding programs, thereby facilitating their use for crop improvement.To fully cover the biogeographic range of historic chickpea cultivation, we assembled 407 accessions collected between 1911 and 1940. Text descriptions of sampling locations, which were often local markets in small towns, were converted to geographic coordinates a. This s2, BIO19, DEM) were not strongly correlated to other variables. The first, third, and fifth groups  was used to survey polymorphism within the genomes of 407 accessions. SNPs were filtered to retain polymorphisms present in at least 90% of genotypes with a minor allele frequency of at least 3%. The resulting 2579 polymorphisms are distributed among all chromosomes, but with variable density that is especially elevated on chromosome 4 a. The elThe sufficiency of this marker set for genetic tests depends in part on the scale of linkage disequilibrium (LD), because the relationship between physical distance and recombination frequency determines the precision of genetic association tests. LD is the non-random association between polymorphisms and can originate from demographic processes  or from selection . In smaller populations of predominantly selfing organisms (including those that are the product of breeding), drift and selection typically have stronger effects than recombination, and thus LD extends to large genomic regions. Landraces are expected to exhibit especially extended LD. In line with these expectations, LD in chickpea landraces is very slow to decay . MoreovePatterns of population differentiation were analyzed using principle components (PCA) and visualized with unrooted trees. These observations are consistent with the deduced pattern of molecular evolution. Maximum likelihood phylogenetic trees constructed with genome-wide SNP a supportGenetic and phenotypic data were strongly concordant, as described in 3) and mean temperature of the warmest quarter (BIO10)  and BIO3, which are themselves weakly positively correlated , and the group of phenotypes characterizing plant and organ weights. Three additional SNPs on chromosome 7  are associated with three bioclimatic variables, BIO3, BIO6, and BIO11, which are part of a larger group of correlated variables  and BIO3 are all located within intron of gene Ca_16015. This gene encodes phosphoenolpyruvate carboxylase, enzyme involved in carbon fixation, and citric acid cycle biosynthesis flux [Clematis terniflora DC, decreasing activity of this enzyme elevates the plant photosynthesis by activating the glycolysis process, regulating Calvin cycle, and providing adenosine triphosphate (ATP) for energy metabolism. Besides, polyphenol oxidase is involved in the formation of brown melanin pigment in fruits and vegetables, plays a crucial role in the biosynthesis of secondary metabolites, and has a role in plant defense against biotic and abiotic stresses [N-acetylhexoamidase that catalyzes the hydrolysis of N-acetylglucosamine or N-acetylgalactosamine from the non-reducing terminal of oligosaccharides, glycoproteins, glycolipids, and other glycoconjugates. b-N-acetylhexosaminidase is highly active in dry or germinating seeds, where it participates in the degradation of reserve glycoproteins. Moreover, its activity is induced in the period of ripening in tomato and peaches [Pisum sativum (protein A) and Medicago truncatula (bHLH-A), which are flower color associated genes [Because of extended LD, we cannot identify causal relationships between SNPs and phenotypes. Nevertheless, we explored the potential nature of the associated genes and found several important genes that have been reported in previous studies. For example, genes Ca_10410, Ca_10426, and Ca_10428 are present within haploblock Ca6:2541669….Ca6:3024335, to which several SNPs associated with the beginning to flowering to the beginning to maturation phenotype and temperature related variables map see . Ca_1041sis flux . The firsis flux . SNP Ca1 peaches . The Ca_6,8,11,12,13,16). Pod-related traits form a subgroup with three temperature-related bioclimatic factors: mean temperature (BIO1), mean temperature of coldest month (BIO6), and temperature annual range (BIO7); this subgroup is similar in a set of enriched haploblocks with the group containing two additional heat-related bioclimatic factors, max temperature of warmest month (BIO5), and mean temperature of warmest quarter (BIO10). This grouping is consistent with a well-known relationship between high temperature and pod shattering/retention. A third group includes color-related traits, flower color (FloCol), peduncle color (FlowstemColo), seed color (SCO), and stem color (StemColo), which is expected, because genes in the phenylpropanoid pathway are implicated in the production of pigments in different plant organs. A fourth group aggregates Ascochyta blight resistance (AscoRes) and precipitation of the coldest quarter (BIO19), which reflects a well understood relationship between Ascochyta incidence and rainfall during periods of reduced temperatures. Also of note is a group containing moisture stress-related covariates  and plant height (Ptht), which is expected to depend on moisture availability; interestingly, this group clusters with a group that contains phenotypic traits related to plant size , which are traits related to the duration of vegetative growth and that are limited by moisture availability.The total number of the Haploview-inferred  haplobloFor many millennia, farmers and breeders have focused on selecting crops with desirable phenotypes . With thChickpea landraces represent an expanded source of genetic and phenotypic variation that has not been systematically explored and has been used only in an ad hoc manner for modern breeding. The Vavilov Institute of Plant Genetic Resources is one of the world’s primary libraries of lost genetic variation in food crops, capturing the genetic and functional diversity of regionally stratified agriculture typical of one century ago. It contains tens of thousands of legume accessions, including approximately one thousand chickpea accessions collected prior to intensive international breeding efforts . The re-Our observations contribute to an increasing understanding of genetic variation of quantitative and categorical traits in chickpea ,20,21. Tp-values. This analysis identified eleven haploblocks . This haploblock covers two SNPs on chromosome 3, reported by Varshney et al. [3 and BIO5. This haploblock intersects with a QTL for days to 50% flowering time . This haploblock covers SNP on chromosome 4 associated with days to 50% flowering  GBS protocol from von Wettberg et al.  was usedPrincipal component analysis (PCA) was conducted using the “SNPRelate” R library . Custom 2) between genotypes. VCFtools [2 values. LD decay was assessed by plotting intra-chromosomal r2 values against the physical distance (bp) between markers. The parametric 95th percentile of unlinked r2 values distribution was taken as a critical value. The threshold beyond which the LD was accepted as real physical linkage was estimated to be r2 = 0.16. The intersection of the smothering second degree local regression (LOESS) curve of intra-chromosomal r2 values with this threshold was considered to be an estimate of the range of LD. Linkage disequilibrium (LD) was estimated using the squared correlation coefficient  was estimated from polymorphic sites and separately for each chromosome and geographical group using VCFtools . By consThe Genome-wide complex trait analysis (GCTA) program  was usedBioclimatic analysis was performed as described in Plekhanova et al. ; for detShapiro–Wilk test for normality  was implGC) and a false discovery rate (FDR) [GWAS analysis was performed using a single-locus linear mixed model, implemented in FaST-LMM toolset  . Principte (FDR)  of 0.05 te (FDR)  in R.Annotation of significant associated markers was performed using the SNPEff program , as wellIn total, 407 accessions were split into six distinct groups reflecting geographic locations : Ethiopip-values and the list of haploblocks. The method returns an enrichment score and FDR corrected p-value [p-values (<0.05). The outcome of this analysis was that each phenotype or bioclimatic variable was characterized by a set of haploblocks significantly enriched with associated SNPs. To obtain groups of phenotypes and variables sharing sets of enriched haploblocks, we applied bi-clustering on the matrix of pairwise similarities between traits. To estimate the degree of overlap between haploblocks enriched for SNPs associated with different traits, we calculated the haploblock simalarity score as a sum of common haploblocks  divided by the sum of all haploblocks significantly enriched for SNPs associated with these two traits.To divide the genome into haplotype blocks (haploblocks) based on linkage disequilibrium, Haploview tools  were app p-value  for eachThe Vavilov seed bank contains numerous landraces collected nearly one hundred years ago, and thus may contain ‘genetic gems’ with the potential to enhance modern breeding efforts. Here, we analyze 407 landraces, sampled from major historic centers of chickpea cultivation and secondary diversification. The collection was grown in the southern European part of Russia in 2016 with climatic conditions well suited for chickpea cultivation. GWAS conducted on both phenotypic traits and bioclimatic variables at landraces sampling sites as extended phenotypes resulted in 84 GWAS hits associated to various regions, most of which were not discovered in prior GWAS and QTL analyses. The novel haploblock-based test identified haploblocks enriched for SNPs associated with phenotypes and bioclimatic variables, of which eleven haploblocks intersect with previously reported GWAS hits on chromosomes Ca1, Ca3, Ca4, Ca5, Ca6, Ca7, and Ca8. Subsequent bi-clustering of traits sharing enriched haploblocks underscored both non-random distribution of SNPs among several haploblocks and their association with multiple traits. We suggest that these clusters of pleiotropic SNPs represent co-adapted genetic complexes to a range of environmental conditions that chickpea experienced during domestication and subsequent geographic radiation. We observed significant genomic diversity in Central Asia, which may have been a bridge for subsequent radiation in India and nearby areas. Linking genetic variation to phenotypic data and a wealth of historic information preserved in historic seed banks are the keys for genome-based and environment-informed breeding intensification.