The size, shape and orientation of the CaCO3 crystals influence the microstructural properties of chicken eggshells. However, the genetic architecture underlying eggshell crystal polymorphism remains to be elucidated.Eggshell is a bio-ceramic material comprising columnar calcite (CaCOPLCZ1, ABCC9, ITPR2, KCNJ8, CACNA1C and IAPP, which are involved in the biological process of regulating cytosolic calcium ion concentration, can be suggested as key genes regulating the total integral intensity.The integral intensity of the nine major diffraction peaks, total integral intensity and degree of orientation of the crystals were measured followed by a genome-wide association study in 839 F2 hens. The results showed that the total integral intensity was positively correlated with the eggshell strength, eggshell thickness, eggshell weight, mammillary layer thickness and effective layer thickness. The SNP-based heritabilities of total integral intensity and degree of orientation were 0.23 and 0.06, respectively. The 621 SNPs located in the range from 55.6 to 69.1 Mb in GGA1 were significantly associated with TA. The findings greatly advance the understanding of the genetic basis underlying the crystal ultrastructure of eggshell quality and thus will have practical significance in breeding programs for improving eggshell quality.The online version contains supplementary material available at 10.1186/s12864-021-08103-1. The shells must be strong enough to prevent failure during packing and transportation . The eggeggshell . The degeggshell . All theLTF) markers were found to be associated with crystal size, while ovocleidin-116 and ovocalyxin-32 (RARRES1) markers were associated with crystal orientation [Eggshell texture and ultrastructure are regulated by organic matrix proteins, which control the mineralization process and influence the biomechanical properties of the eggshell . Associaentation . Becauseentation , 18, 21.In the present study, the crystal structure of chicken eggshells was determined using X-ray crystallography. To study the genetic basis of biomineralization, a genome-wide association study (GWAS) was performed in the designated F2 population by using a high-density 600k SNP chip for chickens. We aimed to explore the genetic architecture of eggshell crystal structure to identify candidate genes, which may be valuable for the genetic improvement of eggshell quality traits.2snp) of TA was 0.23. The phenotypic correlations between every individual integral intensity (A1-A9) were low (r=-0.15–0.35). The absolute values of the genetic correlations were all higher than those of the phenotypic correlations. The variation in OD was higher (23.72 %) than that of the other traits and the value of h2snp was low (0.06).The descriptive statistics for nine reflection peaks A1-A9), total integral intensity (TA) and degree of orientation (OD) were shown in Table -A9, totar = 0.14–0.50), but OD was not significantly correlated with these eggshell quality traits (p < 0.05). There was a high genetic correlation between the total peak area intensity and eggshell ultrastructure (r = 0.52), and a medium genetic correlation between the OD and the eggshell ultrastructure (r = 0.31) .The association analyses revealed a total of 621 genome-wide significant SNPs associated with TA , IAPP (islet amyloid polypeptide), ITPR2 , PLCZ1 , ABCC9 (ATP-binding cassette sub-family C member 9) and KCNJ8 (ATP-sensitive inward rectifier potassium channel 8). Among them, the CACNA1C, IAPP, ITPR2 and PLCZ1 also played considerable roles in the biological process of regulating cytosolic calcium ion concentration. We also found 26 genes that were regulated by the octamer transcription factor 1 (Oct-1). However, no information on the interaction network is available for these candidate genes.The annotation of significant SNPs using the Variant Effect Predictor (VEP) tool supplied by Ensembl could help identify promising genes associated with eggshell crystal structure. Detailed information about the genes is summarized in Additional file PLCZ1, ABCC9, ITPR2, KCNJ8, CACNA1C and IAPP, respectively. These six SNPs exerted contributions of between 3.93 % and 5.90 % to the phenotypic variation in TA. Notably, rs314759160 exhibited the largest allelic substitution effect on TA with the substitution of one copy of the effect allele at the rs314759160 site causing a 0.345 SD/allele decrease in TA. The phenotypic differences between the genotypes of those SNPs were shown in boxplots . However, this SNP located in the intergenic region, explained 6.45 % of the phenotypic variation  crystals and organic proteinaceous matrix [r = − 0.48, Table h2 = 0.06) was also low with a large estimation error, but was smaller than previous estimates [r = 0.48) (Table r = 0.32) between OD and ET[The eggshell of chicken is a bio-ceramic material comprising columnar calcite  between the ET and TA , a family of endoplasmic reticulum Ca2+ channels, are essential for controlling intracellular Ca2+ levels in virtually every type of mammalian cell [ITPR2 has been found to be highly expressed in cardiomyocytes and to be associated with human heart failure [The formation of eggshells is mainly achieved by biomineralization in the chicken uterus , 28, 29. and EST , and all and EST , 21. Gen and EST . In the he heart . Inositoian cell . ITPR2 h failure , 36. As  failure . It has  failure . Our resITPR2 was also directly involved in the process of Ca2+ signal transduction and released during bone growth [2+ oscillation is mediated by the inositol 1,4,5-trisphosphate receptors 2 and 3 (ITPR2 and ITPR3) in the osteoclast differentiation [CACNA1C, IAPP and PLCZ1, which influence the function of regulating cytosolic calcium ion concentration. CACNA1C encoded the protein, voltage-dependent L-type calcium channel subunit alpha-1 C, which is essential for normal blood pressure regulation through the contraction of arterial smooth muscle cells [CACNA1C is highly expressed in the brain, heart, jejunum, ovary, pancreatic beta-cells and vascular smooth muscle [2+ influx through CACNA1C has also been reported to activate osteogenic transcriptional programs and promote mineralization [CACNA1C might play an important role in regulating the concentration of calcium in uterine fluid. Amylin is encoded by IAPP and is a member of the calcitonin family of hormones that is co-secreted with insulin by the pancreatic beta cells [PLCZ1 participated in the release of calcium but it has been reported that it indirectly affected the transmembrane transport of calcium [ITPR2, PLCZ1 and CACNA1C, were regulated by the transcription factor, Oct-1, which acted as downstream of notch signaling during radial glia formation. Oct-1 has been reported as forming a novel complex with the transcription factor (Runx2) to regulate the expression of the mammary gland-specific gene, beta-casein. Runx2 is essential for expressing several bone-specific genes and is primarily considered as a master regulator of bone development [-17 have not yet been annotated in the reference genome. It will thus be necessary to obtain more information on the crystallization system for eggshells.Several genes related to intracellular calcium concentration were also identified. e growth . Ca2+ osntiation . Except le cells . CACNA1Ch muscle –43. Ca2+lization . We therta cells . Amylin ta cells . There w calcium , 47. Intelopment . This suelopment . Some egelopment , which iPLCZ1, ABCC9, ITPR2, KCNJ8, CACNA1C and IAPP, were considered as promising genes that were implicated in the integral intensity of eggshell crystals and could improve eggshell quality. The present study has revealed the genetic basis of the crystallographic ultrastructure of eggshells and improved our understanding of its mechanical properties. These new findings will also provide a useful theoretical basis on biomineralization in chicken eggshells.The integral intensity and degree of orientation of eggshell crystal were determined, then used for a GWA analysis with a 600 K high-density SNP array. The result showed that 621 genome-wide significant SNPs ranging from 55.6 to 69.1 Mb in GGA1 were associated with TA. Ultimately, six genes, White Leghorn (WL) and Dongxiang (DX) chickens, representing a standard breed and a Chinese indigenous strain, respectively, were used to construct an F2 resource population for the present study. Six WL and six DX males were mated with 133 DX and 80 WL females, respectively. Then, 25 cocks and 407 hens from the WL/DX cross and 24 cocks and 235 hens from DX/WL cross in the F1 generation were selected to produce the F2 generation. A total of 3749 chickens from the F2 population were reared in individual cages with ad libitum access to feed. Finally, 839 hens from 49 half-sib families and 365 full-sib families with sufficient phenotypic and pedigree information were chosen for SNP genotyping. All the experimental animals were hens and came from the same batch.Eggs were collected from hens at 66 weeks of age on three successive days to ensure at least one egg per hen. The egg contents were removed then the eggshells were cleaned with tap water and dried naturally at room temperature. Pieces (approximately 3 × 5 mm) of eggshell were taken at the equator. The shells were measured using X-ray diffraction . The measurement conditions were as follows: Mo Kα (λ = 0.71075 Å), 50 KV, 80 mA, a pin-hole collimator (0.8 mm in diameter) and an exposure time of 30 s per frame. The samples were mounted with the inner surface of the eggshell facing the incident X-ray beam. The beam passed through the sample and the transmission diffraction pattern was recorded on the detector. The software THCLXPD attached to the XRD was used to analyze the diffraction patterns by measuring the intensity of the reflection spots.std) of the calcite powder standard sample (A113/A104) was 0.18. Thus, the OD of eggshell crystals was calculated using Eq. (The integrated intensities were determined from each eggshell sample using nine different rings. These rings were oriented with a set of (hkl) crystallographic planes: 113, 104, 012, 110, 202, 018, 122, 208 and 300. The data from the nine reflection peaks A1-A9) were added to give the TA to lower the data variability. The crystal size was estimated from the intensity of the reflection spots . To dete were addThe value of OD represented the degree of the preferential orientation of the crystals. For a value of one, the sample consists of randomly oriented crystals and for a value > 1, the sample consists of highly-oriented crystals .https://www.rproject.org/).The data on eggshell quality traits  from our previous publications was used in the correlation analysis, 52. TheP < 1 × 10− 6 were removed using the PLINK version 1.90 package. The BEAGLE version 4.0 procedure [R2 > 0.5 were retained for the next analysis step. A total of 839 samples and 435,867 SNPs finally remained for the subsequent GWAS.All blood samples were collected from brachial veins of chickens by standard venipuncture. Genomic DNA was extracted from whole blood samples using standard phenol-chloroform method and the 839 chickens were genotyped using the Affymetrix 600 K chicken SNP chip . Array Power Tools (APT)  software was used for quality control and genotype calling. SNPs with a minor allele frequency of < 5 % and a Hardy-Weinberg equilibrium test with rocedure  was usedThe loci-based analysis was performed using the generalized linear mixed model implemented in GEMMA , where tg), with G the genomic kinship matrix, and the additive effect variance Vg; S the design matrix containing the corresponding SNP sites; α the substitution effect size corresponding to each site; and e the vector of random residual effects ), with I the identity matrix and the residual variance (Ve).Where: Y is the vector phenotypes of eggshell; µ the overall mean; X the covariance matrix ; b the estimator vector of fixed effects; Z is an incidence matrix associating u with y. u the additive polygenic effect , with a suggestive significance level of 1.71 × 10− 5 . Principal component analysis was performed using the EIGENSOFT software (v.2.04) with the results showing that there was stratification in the experimental population. In order to correct the population stratification, we selected the top ten PCA [The genomic inflation factor (λ) was calculated using the R package, qqman (Version 0.1.2) . Multipl ten PCA  componenThe SNP-based heritability of eggshell crystal traits, calculated using the residual maximum likelihood (REML) method in GCTA (version 1.24) , was estvariance .We performed a series of linkage disequilibrium (LD) analyses to characterize the causative SNPs within strong LD regions by applying the solid spine algorithm in Haploview software version 4.2 . The sigGene Ontology annotation and KEGG pathway were completed using the Metascape website to assign genes with corresponding terms , 66. TheAdditional file 1.Additional file 2.


Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction.Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.The online version contains supplementary material available at 10.1007/s00122-021-03946-4. Oat (Avena sativa L.) ranks sixth in world cereal production and has increasingly been consumed as a human food USDA . Oat hasSeveral multi-omics prediction studies have been reported in cereal and animal species  in a core set of a worldwide oat collection (termed Diversity panel) planted in one environment and advanced breeding lines adapted to the upper Midwest region in the USA (termed Elite panel) planted in three environments. Our efforts included (i) comparing the accuracy of multi-omics prediction against genomic prediction in a single-environment trial; (ii) evaluating the efficiency of multi-omics prediction in multi-environment trials; and (iii) exploring the potential of using multi-omics data to predict distantly related individuals.The Diversity and Elite panels consisted of 378 and 252 lines (Table S2), respectively. The Diversity panel originally included 500 lines described by Carlson et al.  that wasPlant height was evaluated for five randomly selected plants in each plot after anthesis. Days to heading was defined by the days from seeding to heading in > 50% of total plants. 100 randomly selected seeds from each plot were dehulled with a hand dehuller for evaluation of hundred kernel weight, hundred hull weight and groat percentage. After dehulling, 50 randomly selected seeds were delivered to the Proteomics and Metabolomics Facility at Colorado State University for metabolite analysis, and the other 50 seeds were used for measuring seed length, width and height with an electronic micrometer. Fatty acids were identified and quantified with targeted GC-MS, then normalized to concentration (mg/g of oats) against the internal standard (C17:0) .https://triticeaetoolbox.org/oat/). SNPs were filtered using the following criteria (i) minor allele frequency (MAF) > 2%; (ii) site missingness < 60%; and (iii) site heterozygosity < 10%. After initial SNP filtering, lines were selected if (i) call rate > 80% and (ii) heterozygosity < 10%. A total of 73,014 markers and 568 lines  met these criteria and were used for further analyses. Subsequently, missing genotypes were imputed using the linear regression method glmnet described by Chan et al. . The 23 DAA was chosen based on our pilot study  Correlation network adjacency was calculated using the soft thresholding power 4, which was selected based on the scale independence chart as described in the WGCNA tutorial; (ii) To minimize effects of noise and spurious associations, we transformed the adjacency matrix into Topological Overlap Matrix (TOM), and calculated the corresponding dissimilarity (1-TOM); (iii) We then used hierarchical clustering to produce a hierarchical clustering tree of metabolite features based on TOM dissimilarity matrix with method = "average"; (iv) Modules were identified using the cutreeDynamic function with the following parameters: method = "hybrid", distM = dissTOM, deepSplit = 2, pamRespectsDendro = FALSE, minClusterSize = 20.Metabolite analysis was based on physiologically mature seeds because they have the highest level of health-promoting compounds and those compounds are stable at room temperature until germination. GC-MS non-targeted analysis and LC-MS phenyl–hexyl analysis were done at the Proteomics and Metabolomics Facility at Colorado State University. Details of chemical analysis, raw mass spectrometry data processing, metabolite annotation, and normalization were described in Supplemental Methods. The normalized metabolomics data were used for network analysis with the WGCNA package , metabolomic BLUP (M), G + T, G + M, and G + T + M models were fitted with the BGLR package  factors that affected both omics features and phenotypic traits may induce non-genetic correlations among traits. Therefore, we estimated prediction accuracy as nd Cheng , and usey = ’, g = ’, ε = ’. y1, y2, and y3 are the column vectors of phenotypic data in each environment. g1, g2, and g3 are the column vectors of random genetic effects in each environment. ε1, ε2, and ε3 are the column vectors of random error terms associated with each environment. X and Z are design matrices relating the fixed and random effects to each genotype. Vectors containing the random effects in Eq.  = G0K, Cov = IR0, and Cov = 0, where K is the additive genomic relationship matrix, I is an identity matrix, G0 is a 3 × 3 genetic covariance matrix, R0 is a 3 × 3 residual covariance for the three locations. There are various covariance structures for R0 or G0  from the second and third environments, respectively, and calculated their prediction accuracies following the same procedure as that applied to the first environment. Finally, we averaged the three prediction accuracies across environments to represent the prediction accuracy of a single run. This procedure was repeated for 50 times with different random partitions.We applied a single-environment cross-validation method originally designed for genomic prediction described by Mathew et al.  and exteSeed fatty acid concentrations were used as target traits for predicting distantly related individuals, which included two steps: likely causal loci prioritization in the Diversity panel (training population) and multiple-kernel prediction in the Elite panel (test population).r2 = 0.1 was used as it is frequently recommended for SNP pruning , and identified twenty-six network modules. Based on the metabolites annotation, we performed Fisher's exact test to identify a subset of network modules enriched with lipids and lipid-like molecules. We then performed hierarchical clustering (using correlation based dissimilarity matrix with method = "average") and GWAS on eigenvectors of the twenty-six network modules and PC1 of fatty acids. GWAS was performed based on the linear mixed model  and perform a multiple-kernel prediction. The two genomic relationship matrices were calculated with the A.mat function implemented in the rrBLUP package Endelman . GenomicAfter filtering out lines with low-quality genetic markers, the Diversity and Elite panels consisted of 368 and 232 lines (Table S2), respectively, with 32 lines in common. A reconstructed phylogenetic tree revealed that most of clusters were primarily comprised of either the Diversity or the Elite panel members, although a couple of clusters had approximately equal representation from both sets Fig. . This isUsing GBLUP (G) as a baseline, there were 5, 10, 11, 17, and 17 traits out of the 17 total traits with improved prediction accuracy from transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models, respectively  and 6.5–55.6% (median = 16.3%), respectively . In contrast, when adding the M kernel first (G + M model) then followed by the T kernel (G + T + M model), percent changes in prediction accuracy ranged from 2.5 to 67.3% (median = 41.7%) and − 3.3 to 3.5% (median = − 0.03%), respectively . These results indicated that seed metabolites generally contributed more than transcripts to improving prediction accuracy of both agronomic and seed nutritional traits when combined with SNPs.In addition to playing important roles in improving prediction accuracy when combined with other kernels, metabolites alone from mature seeds (M model) greatly outperformed SNPs (G model) and transcripts (T model) in predicting fatty acids  on three different kernels/combinations  with various genetic and residual covariance structures . However, the QTL-6A was not detected from other network modules. We further prioritized 140 markers including significant markers and the markers in LD with them based on the 'darkred' module GWAS hits on chromosome 6A.To explore this scientific question, we first attempted to prioritize likely causal loci from the Diversity panel (training population) based on the eight network modules enriched with lipids and lipid-like molecules (Table S6). Among the eight network modules, only one ('darkred') strongly correlated with fatty acids . We then performed hierarchical clustering and GWAS on eigenvectors of all the 26 network modules and PC1 of fatty acids. The eigenvector of the 'darkred' module was clustered together with PC1 of fatty acids  and had significant GWAS hits on chromosome 6A , which co-located with the fatty acids major-effect QTL . In contrast, C14:0 had significant GWAS hits on chromosomes 6D and 7A, and C18:3 showed a complex genetic architecture with several visible GWAS peaks on several different chromosomes.The primary use of locus prioritization is to split markers in the test population into two sets for a multi-kernel model prediction, in which the two genomic relationship kernels were constructed from the two marker sets. We termed our method multi-kernel network-based prediction (MK-Network) and found it improved prediction accuracy over GBLUP and BayesB for all fatty acid traits except C14:0 and C18:3  and similar LD relationships  with the surrounding loci between the Diversity and Elite panels promoted the success of our likely causal loci prioritization. The network-based prioritization strategy takes advantages of pleiotropy, in which one or a few genes influence both target traits and other metabolites from related network modules. In the 'darkred' module, 23 of 32 metabolites showed clear peaks at the QTL-6A, although only five of them were significant at FDR < 0.05 . This indicated that QTL-6A was likely a causal locus and influenced both fatty acids and the 'darkred' module. The relationships between fatty acids and the 'darkred' module are expected to be conserved between populations. However, we were unable to test this because there is currently no robust method to map all untargeted metabolites from one panel to another and quantify them accurately.In the prediction of distantly related individuals, the universal QTL of fatty acids Below is the link to the electronic supplementary material.


MED13L and TBX3, which could influence breed standard height. Integration of the CNVs with chromatin interactions, long noncoding RNA expression, and single nucleotide variation highlights a subset of specific loci and genes with potential functional relevance and the prospect to explain trait variation between dog breeds.Extreme phenotypic diversity, a history of artificial selection, and socioeconomic value make domestic dog breeds a compelling subject for genomic research. Copy number variation (CNV) is known to account for a significant part of inter-individual genomic diversity in other systems. However, a comprehensive genome-wide study of structural variation as it relates to breed-specific phenotypes is lacking. We have generated whole genome CNV maps for more than 300 canids. Our data set extends the canine structural variation landscape to more than 100 dog breeds, including novel variants that cannot be assessed using microarray technologies. We have taken advantage of this data set to perform the first CNV-based genome-wide association study (GWAS) in canids. We identify 96 loci that display copy number differences across breeds, which are statistically associated with a previously compiled set of breed-specific morphometrics and disease susceptibilities. Among these, we highlight the discovery of a long-range interaction involving a CNV near  Dogs have been the subject of intense study over many decades , providiAs a result of the extensive history of genetic studies in dogs, remarkable advances have been made toward the resolution of the canine phylogeny  and the https://omia.org/). Particularly, genome-wide association studies (GWASs) involving modest size cohorts of dogs have led to the identification of variants controlling a variety of morphological, behavioral, and disease traits . All the samples were previously sequenced at moderate coverage   , a phenomenon previously described in other species  with an average size of 25.53 kb are gains, and 66,254 events (126.94 Mb) with an average size of 2.36 kb are losses relative to the CanFam3.1 dog genome reference build . Gains,  species . In term species , which sSupplemental Figs. S4, S5). Altogether, we are able to separate breeds resulting from the first domestication bottleneck   . Howeverthounds) . Some pothounds) , nonneutthounds) , and a pthounds) . This obSupplemental Fig. S6). This is in stark contrast to the SNV decline reported using whole genome sequencing data in numerous domesticated organisms  analysis or by pairwise Euclidean distance A. Most drganisms . In factST statistic  or MAGI2  genes . We observed a large proportion of CNVs overlapping genes involved in fatty acid biosynthesis , some of which have been previously reported   genes . Howeverreported . We alsoreported B and SLI wolves) C genes w wolves) . We noteproblems , and a dSupplemental Table S3), controlling for population stratification only when an excess of significant P-values was observed after accounting for inflation , one order of magnitude below the Bonferroni correction. The most frequently represented phenotypes were body height, hair length, and tail-to-body ratio. In contrast, our analyses were not able to assess some commonly studied phenotypes such as eye pigmentation or fur color and density, for which phenotype-driving CNVs are known, for instance, those affecting the ASIP, RALY, RSPO2, FOXI3, and ALX4 genes , previously reported as a SNP result . Haploinsufficiency of the syntenic region containing TBX3 and MED13L   located ∼10 kb downstream from the use ESCs . HOXA10 elopment , as wellelopment .In this study, we sought to determine if genetic associations could be found for established breed propensities for cardiac, thyroid, orthopedic, and eye diseases  using a similar approach as that described for morphological traits. A minimum threshold of one affected individual in 2000 (0.05%) reported in the OFA database was required for a breed to be included in the analysis of any disease. Also, we selected as “cases” breeds with the highest risk of developing the disorder, whereas breeds with the lowest risk served as “controls.” We report a minimum of 21 “case” breeds for any of the eight diseases analyzed here.P-value deflation. However, our approach found a few noteworthy CNV candidates overlapping provocative candidate genes . We detected an association for generalized-progressive retinal atrophy (gPRA) risk in CNVs covering more than 10 exons of the DMBT1 multicopy gene   D,E. Of n260,415) . The low260,415) .To investigate the relationship between CNV- and SNP-driven associations, we gathered data on genomic variants from different studies to test whether our secondary-threshold GWAS associations followed any discernible patterns. In particular, we focused on signals that were close to significance, as concordance between multiple noncoding or intergenic regions and their previous, independent annotations could both serve as a validation and potentially point to polygenic effects.P-values than expected (Methods). Even if we were able to identify this trend in a few cases, most prominently deletions, the majority of CNV associations were independent of SNP associations . For instance, one of our most significant GWAS results, the SMAD2 locus, segregates together with a previously reported SNP at frequencies of 0.6 ± 0.29 depending on the breed . This means that associated CNVs behave the same as the rest of CN events in terms of sequence conservation. Indeed, there seems to be an overall depletion in highly conserved motifs in the canine structural variation space, in part due to the poor alignment within complex regions when constructing conservation scores. This depletion is consistent with previous findings involving ultraconserved elements in mammals . Although these results should be interpreted cautiously due to the low robustness of sparse and low contingency table counts, the consistency between traits with no major protein-coding associations and lncRNA expression encourages further exploration.A substantial part of the CNV-GWAS associations reported here either overlapped or were close to long noncoding RNA (lncRNA) genes. Therefore, we assessed the concordance between the lncRNA tissue of expression and the CNV-GWAS trait as a possible indicator of a nonspurious distribution of these association results. We used the dog lncRNA database  to annotTBX3 mentioned above, displayed any distal Hi-C contacts  involving two adjoining genes involved in innate immune response, NLRP13 and NLRP8 , respectively . Finally, we also found a homozygous deletion in many German Shepherds  encompassing two SLC7A orthologs (ENSCAFT00000025818 and ENSCAFT00000025829)  C.This study represents the first comprehensive and cohesive whole genome analysis specific to CNVs in the dog. We herein take advantage of our newly created map of structural variation in domestic dogs and other canids to explore the association between CNVs and phenotypes, showing clear and reproducible differences between established breed clades. GWASs with absolute CN as the testable variable are not common in the literature, especially outside of the field of human genetics , and eveSupplemental Fig. S7), provide proof of concept for this global approach. Our exploration of previously published chromatin contact maps for dogs and other mammals has provided additional insight into a distal association involving the candidate gene MED13L and has revealed candidate genes for hair length associations. Additionally, the CNV-GWAS for breed disease risks highlights some compelling associations, providing testable hypotheses in future studies.Using a curated panel of breed standards as a proxy for individual phenotypes, we discover new associations between phenotypes of interest and CNVs within or adjacent to excellent candidate genes. The replication of previous findings, especially those for achondroplasia, height, and body mass  of CNVs that are at an increased frequency in a subset of modern dog clades. These CNVs are candidates for recent selection or could have been swept along in the process of clade origination. The much lower incidence of overall CNV events compared to other kinds of genomic variation, such as SNVs, suggests that CNVs may drive stronger phenotypic effects, and their evolution through domestication is a relevant topic in several organisms  under accession numbers PRJNA232497, PRJNA448733, PRJNA186960, PRJNA176193, PRJNA192935, PRJNA233638, PRJNA247491, PRJNA263947, PRJNA261736, PRJEB6079, PRJEB6076, PRJEB2162, PRJNA188158, PRJNA208087, and PRJEB5500. The aCGH data used for validation  are available in the NCBI GEO DataSets database (https://www.ncbi.nlm.nih.gov/gds/) data set under accession numbers GSE26170, GSE40210, and GSE58195. The processed CNV files, the phenotype tables, the structural variants file, the differentiated CNV file, and the sample accessions file are available as Supplemental Data S1–S5. All accessions for the whole genome sequencing samples used in this study are detailed in the Methods section and Supplemental Data S5.The sequencing data used in this study can be downloaded from the NCBI BioProject database . The breed status of each sample was used to infer its phenotype.We analyzed a panel of 431 canid samples containing purebred dogs, free-ranging (village) dogs, and wolves. Four wolves  were used to train the Hidden Markov Model transition matrix (explained below) and discarded from the final panel. After quality control (described below), a total of 263 dog genomes, 59 village dogs, and 17 wolves were kept. The purebred dog samples classify into more than 130 breeds, which altogether can be divided into more than 30 breed clades (http://www.fci.be/en/Nomenclature/) and the AKC (https://www.akc.org/dog-breeds/) published by https://datarepository.wolframcloud.com/resources/C-BARQ-Survey). Temperament and intelligence data were available from the ATTS (http://atts.org/breed-statistics/) database and “The Intelligence of Dogs” book (https://www.ofa.org/diseases/breed-statistics#detail). Data for purebred dog litter size were obtained from a comprehensive study on the litters from a variety of dog breeds , qseq2fastq (https://github.com/ahcm/qseq2fastq), and fastq dump (https://github.com/ncbi/sra-tools), and all sequencing qualities were standardized to Phred 33 encoding. Adapters were trimmed with TrimGalore  was imposed on the CR standard deviation to avoid excessive scatter of the HMM emissions.Local  control region copy number correlations were assessed using Pearson's coefficient. An excess of nonindependent and nonhomoscedastic CR windows was detected in a few samples, which were discarded.Three primary parameters were assessed to decide which samples to include in these analyses:We sought to discretize the copy number estimations to enhance comparability and produce a more biologically consistent CN measure. In order to do so, a similar setup to the one described in https://github.com/jmschrei/pomegranate) package. Then, the forward-backward probability of each state for each 1-kb window was predicted in every sample.We implemented a Hidden Markov Model in which the observed read depth (emissions) was linked to a certain integer CN value (hidden state) via a Gaussian distribution. Briefly, a set of hidden states ranging from 0 to 20 (plus Gaussian mixtures of states with CN above 20) with variance proportional to the empirical diploid dispersion and the hidden CN was declared. The transition matrix was trained using the Baum-Welch algorithm coded in the Python pomegranate  compared to the modal CN was defined. The rest of the CN ranges were then assigned to any of the defined intervals based on the overlap, with the option to define intermediate, nonoverlapping intervals. The process was repeated until no range was reclassified.Additionally, we aimed to emit definite, unranged genotypes for the set of deletions (defined via HMM) by refitting the empirical observations with a Gaussian Mixture Model. The R mixtools package was used  to fit tST statistic . The column values of the resulting matrices were collapsed by breed and propagated across the diagonal to create a symmetric, synthetic distance matrix which retains the topological properties of the original matrix. The resulting distance matrices were thus ordered, filtered, and comparable under common scaling conditions. In our case, we applied simple correlation and 2-norm comparisons.In order to avoid the effect of possible excessive haplotype sharing across seemingly unrelated breeds on the tree topologies, these potentially confounding loci were omitted. For this, the positions of the pairwise shared haplotype locations in P-value calculations. The tests were chosen to match the requirements of each phenotype distribution, favoring linear regression for continuous, potentially additive traits, and χ2 independence tests for tabulated, categorical data. Stratification on categorical data was only applied if inflation was detected in the P-values .Generalizations of three widely used statistical tests were used to accommodate population stratification into 10P-value = 6.417) was established by dividing the significance value (α = 0.05) by the total number of CNV windows. However, real CNV events are generally composed of many successive windows, and therefore, their P-values will be statistically nonindependent. As the total number of independent tests should be lower than the total number of windows, a “secondary” and more permissive threshold  was also defined to be one order of magnitude below the primary. We considered P-values above either threshold to be of interest, prioritizing those above the primary threshold.A “primary” Bonferroni multiple-testing correction threshold  test by Richard Landis , as explThe phenotype data were split into the top 70 and bottom 30 percentiles (two groups). Copy number was also classified into categories, as previously described. When necessary, population stratification was accounted for by dividing the data into two similarly sized substrata based on the breed tree proposed by Assessment of specific copy number and phenotype trends was carried out using linear regression (R base software). The four first principal components of the scaled copy number data matrix (see below) were used as covariates to correct for population stratification. Regression analyses and any further GWAS recalculations were restricted to the duplications/deletions space.All genome arithmetics were performed using the BEDTools suite , enforciWe proposed the independent joint distribution of all copy number lncRNA tissues and the proportion of association signals across traits as the null hypothesis to test for deviations in the associated copy number variant (ptissue ⊗ passociation). Multinomial distributions over the association table were assumed, and excessive cell counts were reported in terms of standard deviations. lncRNA data were downloaded from Highly conserved regions were defined by binning GERP scores according to their 95th quantile value (∼3). All nonexonic structural variants were used as a background to test whether nonexonic associated variants were enriched in highly conserved elements. We tested the null hypothesis of variable independence using Fisher's test (variables: association and conservation). GERP scores were downloaded from Supplemental Fig. S10). Hi-C data were downloaded from The ratio of main contact read support (region against itself) and every other region involving that same contact was computed in CNV regions. A threshold was set at the 95th quantile of the distribution to call significant contacts (https://www.ebi.ac.uk/research/flicek/publications/FOG03) and the software PWMTools (All putative significant contacts were verified by assessing both that they contained at least one CTCF motif on each side  and thatPWMTools .P-values within 1 Mb surrounding the leading SNP GWAS signals proposed by P-values than the rest.We gathered all structural variation GWAS


ADH1B rs1229984 and ALDH2 rs671. Two-stage least-squares estimates of the county-level percentage of cultivated land devoted to paddy rice on the PGS of age at first birth  and ALDH2 rs671  are both statistically significant and robust to a wide range of potential confounds and alternative explanations. These findings imply that rice farming may influence human evolution in relatively recent human history.Following domestication in the lower Yangtze River valley 9400 years ago, rice farming spread throughout China and changed lifestyle patterns among Neolithic populations. Here, we report evidence that the advent of rice domestication and cultivation may have shaped humans not only culturally but also genetically. Leveraging recent findings from molecular genetics, we construct a number of polygenic scores (PGSs) of behavioural traits and examine their associations with rice cultivation based on a sample of 4101 individuals recently collected from mainland China. A total of nine polygenic traits and genotypes are investigated in this study, including PGSs of height, body mass index, depression, time discounting, reproduction, educational attainment, risk preference,  It fundamentally changed the way people live . Yet, noet al. [There is some observational evidence that rice cultures differ from nearby cultures that farm other crops. For example, Davidson  found thet al.  reportedet al. . Around et al. . Low-mob. 1.1Although the idea that behavioural differences resulting from rice cultivation history may be partly genetic seems controversial, three lines of findings support the possibility. First is the idea that human evolution is not limited to the distant past. Recent research in evolutionary biology has found evidence that natural selection is still operating in contemporary humans –13. To net al. [Second, there is accumulating and converging biological and genetic evidence to show that the transition from hunter–gatherer to agricultural societies exerted selective pressures on human evolution ,18–21. Aet al. ,23. ThesThe third line of thought is the accumulating evidence linking behavioural and personality traits to genes ,25. Much. 1.2The current study builds on existing GWAS evidence and leverages a unique Chinese dataset to test for selection by rice domestication and cultivation. China presents a unique test case for the theory for three reasons. First, China has a long history of rice cultivation, giving it enough time for genetic selection to play out . Second,. 1.3ADH1B rs1229984, and aldehyde dehydrogenase 2 or ALDH2 rs671), economic preferences  and socioeconomic outcome .Out of all the possible behaviours and genes that rice might have affected, where to start? We start with a few plausible candidates for phenotypes that might be connected to rice. The advent of agriculture reshaped diet, patterns of labour, population density and settled living ,23. BecaWe began by constructing several polygenic scores (PGSs) to measure these genome-wide complex traits. The sample consisted of 4101 adult participants from WeGene who consented to participate in research (see Methods). For each individual, we summed the weights of all related alleles. These PGSs are aggregated effects of hundreds or thousands of trait-associated DNA variants identified in GWAS studies, and can be used to predict propensities towards certain traits and outcomes ,17. Tabl. 1.4r = 0.95, p < 0.001 [rprov = −0.69, p < 0.001.The main explanatory variable is defined as the percentage of cultivated land per county devoted to paddy rice .To counteract this problem, we run multivariate regressions testing the effect of rice on polygenic traits while accounting for an extensive range of additional factors. First, we take into account participants' age and the size of the city they grew up in . Then, based on their birthplace, we control for regional characteristics that might confound the relationship between rice and genes: latitude, longitude, temperature, GDP, distance to coast, contact with herding cultures, regional education, history of rebellion and length of rivers (ALDH2 rs671).ALDH2 rs671 and people's age of reproduction (i.e. age at first birth PGS). These results fit the idea that selection from rice farming has been operating on genetic variants associated with these two traits. By contrast, the PGSs or genotypes of the other traits were not robustly associated with rice farming. After controlling for other regional differences, rice was not significantly linked to educational attainment, time discounting or ADH1B rs1229984.1We then sequentially add regional control variables: average temperature (Model 2), distance to coast (Model 3), GDP (Model 4), contact with herding cultures (Model 5) and history of rebellion (Model 6). In almost all models, rice significantly predicts . 2.3A potential threat to this analysis is reverse causality. In other words, perhaps certain people had genetic traits that were more suited to rice farming, so they chose to grow rice. For example, if the sensitivity to social norms helps solve the free-rider problem in collective irrigation systems , perhapsTo test for reverse causality, we exploit exogenous variations that determine regional differences in rice farming. We select a natural instrumental variable (IV) that measures environmental suitability for wetland rice modelled by the United Nations Food Agriculture Organization's Global Agro-ecological Zones database . Then weF-test and the overidentification test. In the first stage, the F-statistic is 84.74, far exceeding the traditional cut-off of 10 for weak instruments. The p-values of the Sargan statistics are all higher than 0.1, indicating that the environmental suitability variable and its quadratic form are strong and valid instruments for the percentage of paddy rice field.ALDH2 rs671 genotype  and age at first birth. Hence, our 2SLS estimation results imply that the selective pressures from paddy rice farming seem to favour individuals with a lower alcohol tolerance and people with polygenetic scores for having children at an earlier age. Furthermore, reverse causality is not likely to be driving the results.2Compared with the earlier rice farming results A, the re. 2.4We then extend our analysis by performing a number of checks to test whether the findings are robust. First, we test historical disease rates in order to test the pathogen prevalence theory. The pathogen prevalence theory argues that, in areas with more communicable diseases, humans developed behaviours that helped protect them against disease . For exaTo measure historical pathogen prevalence, we use rates of epidemic diseases in the Ming and Qing Dynasty at the province level (AD 1368–1911) . These d3Second, we test whether farming in general (as opposed to rice farming in particular) can explain genetic differences. To measure farming, we use the percentage of cultivated land per province. The multivariate regression results show that farming does not predict genetic differences, except for genetic variants associated with educational attainment B. Why mi. 2.5Overall, the data suggest that rice cultivation exerted selective pressures in favour of earlier reproduction and alcohol intolerance. This leaves the question of why rice farming might have selected for these specific traits. Although we cannot prove any particular theory from our data, we offer initial hypotheses based on the ecology of rice.. 2.5.1 (1)Rice is far more labour-intensive than wheat and other dryland crops –5. Resea (2)Rice farming is more productive than crops like wheat and millet. Historically, paddy rice produced three to five times the output per acre as wheat . The rel (3)There is also evidence that China's rice areas had shorter life expectancy than wheat areas historically . There iWhy might rice farming have selected for earlier childbirth? Three features of rice areas are consistent with this idea:. 2.5.2Why might rice have selected for genotypes of aldehyde dehydrogenase deficiency? This gene leads to excessive accumulation of acetaldehyde in the liver and the alcohol-flushing reaction, sometimes called the ‘Asian flush’ . Grain wWe offer initial theories for why rice might have selected for these genes, but these are early steps since there is still a lack of direct archaeological evidence (such as from ancient DNA). As historians and anthropologists uncover more about the history of rice farming, we can refine our understanding of how it might have selected for particular genes. Furthermore, these results await future replication in samples from China and other rice-farming populations around the world, from East Asia, to India, and West Africa.. 2.6PGSs are not destiny. The relationship between genes and behaviour can differ entirely in a different environment, such as the same country in an earlier era versus a later era. Thus, it is important to treat implications of genetic differences with caution.However, these differences may be a starting point for researchers investigating regional differences in China or between China and other countries. One relatively straightforward implication is that the aldehyde dehydrogenase difference in southern China would lead to less problematic drinking and perhaps less drinking overall. This hypothesis would be fairly straightforward to test.The genetic scores linked to earlier childbirth could lead to the prediction of higher fertility or earlier birth of the first child in rice-farming parts of China. However, birth rates have been falling across China in the last century, and modern forms of contraception may disrupt the link between gene-related fertility and actual childbirth. However, it is possible, for example, that rates of unintended pregnancies may be higher in rice-farming parts of China.. 3In sum, genetic data from over 4000 people across China produced evidence that genes for earlier reproduction and alcohol flush response were more common among people from areas with more historical rice farming. Rice farming was negatively associated with PGSs for educational attainment, although this relationship became marginal after controlling for the history of herding.The effect of rice remained robust after controlling for individual demographic characteristics, ethnic make-up, a range of regional characteristics and potential self-selection into rice farming. Moreover, the large sample size of counties substantially increases statistical power and allows for greater control over confounding factors in the analysis. The results of this study suggest that a major cultural transition in human history had small but detectable effects on genes.sped up in the last 40 000 years [Researchers used to believe that evolution worked so slowly that meaningful changes were unlikely to have happened in the last 10 000 years of human history. But more recently, researchers have concluded that ‘evolutionary change typically occurs much faster than people used to think’. There is also evidence that human evolution actually 00 years . If rice4 (iii) Identifying regional ancestry through the place of birth is not perfect. This method may misidentify people whose recent ancestors moved large distances. (iv) We analysed genetic differences but not phenotypes or actual behaviour. Genetic propensities are not destiny. (v) We do not have DNA samples from historical periods (e.g. ancient DNA). If future researchers gain access to historical DNA samples, this will allow for a directly test or completely rule out of the reverse causality issue.We should note several limitations in our data that point to possible future improvements. (i) The current study is based on a sample of 4101 observations, which may lack statistical power due to the small sample size. (ii) The GWAS summary statistics used to construct the PGSs in this study were mostly based on samples of European ancestry, which may lead to a Euro-centric bias and limit the predictive power constructed PGSs .4 (iii) It is worth remembering that environment is not destiny, either. It would be overly simplistic to expect that exact same pattern of results everywhere people grow rice. There is ample evidence that the same type of environment does not always lead to the same culture. As one small example, how farmers dealt with peak labour demands in rice differed across cultures. While Chinese farmers preferred to trade labour with family members, West African rice farmers sometimes relied on groups of youths, who would move from farm to farm. Rice presents common challenges, but cultures' solutions to those challenges  may differ.Finally, the finding of rice–wheat genetic differences presents a hint about a puzzle of modernization. As fewer and fewer people are farming in China, how is it that rice–wheat differences persist in modern China? Studies have found rice–wheat differences among people who do not farm ,9. Genet. 4. 4.1Through WeGene, we conducted an online survey with its customer base. WeGene is a leading private genetic testing company based in Shenzhen, which provides direct-to-consumer genetic testing and personalized healthcare information. After providing informed consent, approximately 4700 participants took our online survey between July 2018 and October 2019. The survey collected information on participants’ demographic and socioeconomic characteristics such as gender, birth year, birthplace and parental birthplaces. Excluding individuals who did not finish enough questions or were under the age of 16 at the time of the survey yielded a total of 4101 observations from the original sample. An important feature about our dataset is that all respondents were genotyped on a WeGene custom genotyping array (Illumina). Imputation and quality control were performed using PLINK (1.90 Beta), SHAPEIT (v. 2.17) and IMPUTE2 (v. 2.3.1). For each individual, we obtained a total of 10 670 107 SNPs, which we then used to construct PGSs for a number of behavioural and psychological traits.j dosages carried by a respondent i  multiplied by the estimated effect size (jβ) reported by related GWAS, i.e. We built PGSs  for all 4101 individuals using effect estimates from recently published GWASs on height, BMI, depression, time discounting, age at first birth, educational attainments and risk preference –37. We cADH1B rs1229984 gene and ALDH2 rs671 gene. ADH1B encodes alcohol dehydrogenase, and ALDH2 encodes aldehyde dehydrogenase. ADH1B is solely determined by SNP rs1229984, and ALDH2 is determined by SNP rs671 alone, rather than multiple SNPs. Therefore, we directly measure ADH1B rs1229984 and ALDH2 rs671 as the number of effect alleles, resulting in three possible values .Two exceptions are the . 4.2i denotes individuals and j denotes county of birthi (born in county j), who has one of the nine PGSs or genotypes ; j that i was born in, and λ is the coefficient of primary interest; ijX contains a set of control variables, including individual characteristics, ancestral compositions and measures of county/province differences depending on the specific model described in the main text.We estimate the following multivariate regression model using ordinary least squares (OLS), where To mitigate the concern of rice farming endogeneity, we estimate two-stage least squares (2SLS) models using the following equations:X as described earlier. In the second stage (equation (4.3)), different from in equation (4.1), each polygenic trait or genotype is then regressed on the fitted value of county-level rice farming (In the first stage (equation (4.2)), the endogenous variable of county-level rice farming percentage (Click here for additional data file.


GWS enables gains of 108 to 346% over the phenotypic selection, with a 50% reduction in the selection cycle. This technique has potential for the Jatropha breeding since it allows the accurate obtaining of GEBV and higher efficiency compared to the phenotypic selection by reducing the time necessary to complete the selection cycle. In order to apply GWS in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to perform a larger number of harvests.Genome-wide selection (GWS) has been becoming an essential tool in the genetic breeding of long-life species, as it increases the gain per time unit. This study had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Our objective was to compare GWS with phenotypic selection in terms of accuracy and efficiency over three harvests. Models were developed throughout the harvests to evaluate their applicability in predicting genetic values in later harvests. For this purpose, 386 individuals of the breeding population obtained from crossings between 42 parents were evaluated. The population was evaluated in random block design, with six replicates over three harvests. The genetic effects of markers were predicted in the population using 811 SNP's markers with  This scenario has imposed challenges on society, as pertinent questions arise: How to increase the production of food to meet society's demand and still meet the environmental sustainability goals? In this context, science is the main ally, since it is possible to develop innovation and technologies to improve yields and restore natural balances throughout the food system simultaneously2.In recent decades, there has been exponential growth in demand for energy sources, which is linked to population expansion. It is estimated that the world population in 2050 will be 9.7 billion people, compared to approximately 7.3 billion people in 2015, i.e., the population will increase by about 32%3. Renewable energy has grown fast in recent years, driven by policy support, advances in technology, and sharp reductions in production costs, and is at the heart of the transition to a less carbon-intensive and more sustainable energy system. In this context, using renewable energy is imperative in the world energy matrix. It is worth mentioning the use of biofuels, which has shown economic viability and some advantages compared to fossil fuels since they are non-toxic, biodegradable, and do not pollute the environment, have flash point and can be added to diesel due to similar properties4.Energy from fossil fuels is still crucial in the energy sector, but it is also known to be the primary source of greenhouse gas emissions and a finite sourceGlycine max L. Merril) presents highlight and supremacy as feedstock for biodiesel production, representing 69.8% of the Brazilian energy matrix5. Thus, there is a limitation in the number of raw materials composing the energy matrix. However, Brazil has the potential to expand the production of biofuels and other vegetable oil derivatives to meet both the domestic and global markets. One of the effective ways to increase the limited quantity of traditional raw materials and their high prices is to invest in the improvement of biodiesel production from inedible vegetable oil6, such as Jatropha (Jatropha curcas L.).In Brazil, there are several potential sources of oilseeds for biodiesel production. Given the vast diversity of the national ecosystem, soybean 10, rusticity12 and simple spread10. Due to these characteristics, Jatropha qualifies as a potential candidate for the sustainable production of biofuels13. Despite the enormous potential, this species is in a domestication stage in Brazil, and because it is perennial, it needs several years to complete a breeding cycle. One of the objectives of the perennial plant breeders is to shorten the selection cycle. For this purpose, an alternative that has been used with considerable success is Genome-Wide Selection (GWS), which is one of the promising tools to increase selection efficiency, reduce costs in the launching of cultivars, reduce the breeding cycle through early selection, and increase the genetic gain between breeding generations15. For crops like Jatropha, it is estimated that GWS could shorten the breeding cycle14, which would have a high impact on the release of new cultivars for planting.16. However, these authors recommended that the study should be validated over the years and by progeny evaluation. This research had a hypothesis that GWS is a tool that can decrease the breeding cycle in Jatropha. Thus, the objectives of this study were (1) to use the Jatropha training population evaluated in multiple harvests, and (2) to develop models for predicting breeding values between harvests. This paper presents unprecedented results in validating GWS in Jatropha for biofuel production.However, few studies have reported the use of GWS in Jatropha worldwide. A pilot evaluation of predictive model accuracy using only one harvest and demonstrated the potential of GWS in Jatropha breeding2a) for grain yield in the three harvests, to assess the extent to which phenotypic variation is genetically controlled and genomic selectable. Heritabilities ranged from 0.18 to 0.20 for the first and third harvests, respectively, and 0.25, when the average grain yield was considered  on all harvests. Even using the mean of harvests, high magnitude accuracy values were not obtained17. The values varied from 0.17 to 0.37 for the first and third harvests, respectively.Low selective accuracy was observed based on phenotypic information  ranged from 0.27 to 0.57 for the first harvest and for average grain yield, respectively , it can be observed that in the validation of the second harvest data the accuracy reduced more than 50% Fig. . The sam21. Thus, more accurate methodologies must be used to predict genetic effects. Therefore, GWS becomes more appropriate to select superior Jatropha genotypes than conventional methods based only on phenotypic data because this technique can efficiently capture small genetic differences between families.Based on narrow-sense heritability estimates Table , the sel24, in which increased trait heritability has resulted in increased GWS accuracy.However, even if the GWS allows adequate prediction of genetic effects, the heritability has importance in the model, because the lower the trait heritability the lower the phenotypic data accuracy and, therefore, lower the heritability of marker effects. Consequently, the lower will be the ability to reliably predict the phenotypes of individuals not sampled to compute the model. Several authors have demonstrated this theory through simulations22, where accuracy increased only by 10–20% as heritability increased from 0.2 to 0.6, regardless of population size. Thus, unlike marker-assisted selection, GWS is efficient in selecting superior individuals even for low heritability traits as reported by25.However, even with the impact of the low grain yield heritability throughout the harvests on selection, we can note that the predictive models performed well. This result corroborates those obtained by26. Similar findings were obtained by27, who found low accuracy for grain yield in Jatropha.The observed values of accuracy based exclusively on phenotypic data showed low magnitude. This reveals that selection based only on phenotypic data has low accuracy in the first and second harvests, and moderate accuracy in the third harvests26.As for the GWS accuracy, low values were found in the first and second harvests, but the accuracy value was high in the third harvest. We highlight that these estimates were also obtained using genomic heritability as a proportion of phenotypic heritability, considering the efficiency of markers in capturing QTLs, i.e., considering the degree of imperfection of the linkage disequilibrium26. There was an increase in this correlation throughout the harvests, suggesting a higher additive genetic variation for this trait in this reproductive population. These findings show that rgy values were higher for the harvests with the highest h2a estimates. This indicates that phenotypes for low heritability traits contain higher environmental noise and hence will be less predictable by genomic models.The genomic selection efficiency depends on the correlation between the predicted and the actual genotypic value, i.e., the predictive capacity28. However, the use of 811 SNPs allowed a satisfactory predictive ability. This result is consistent with those found by29 when assessing a Jatropha breeding population by GWS. These authors concluded that training models with 800 and 1000 markers are sufficient to capture the maximum genetic variation and hence the maximum predictive ability for grain yield in Jatropha families. In addition, not just the number of SNPs, but high LD with QTLs is the important factor in GWS success.One of the principles of genomic selection is using a large number of markers able to cover the entire genome of the species to maximize the number of QTLs in linkage disequilibrium (LD) with at least one marker, allowing the maximization of the genetic variance explained by the QTLs20, reveal the possibility of using a small set of SNP markers in Jatropha breeding will enable significant gains in the short-term and at relatively low cost. The predictive ability of genomic selection (ryg) shows the potential of molecular information to consistently predict a phenotype30. The magnitudes obtained here were similar to those reported in forest species24 and coffee crop31.In genomic selection, the genotyping step is the costliest and often makes it impossible to apply genomic selection. However, given the results found here, as previously reported by32, by using canola as a model, reported that low-density marker sets comprising only a few hundred markers allow high accuracy of genomic prediction in breeding populations with strong linkage disequilibrium. The authors also mentioned that the breeder could obtain a significant advantage in selection by using reduced marker density, even when the prediction accuracy is lower than a high-density chip. This strategy provides substantial cost savings and thus enables phenotyping resources to be focused on pre-selected genotypes that, even with lower selection accuracy, will still allow significant gains for the breeding program.LikewiseThroughout the evaluation of the Jatropha harvests, many alleles will be acting with more or less expression on the expression of phenotypes. However, we do not know if the major effect alleles for grain yield in the first harvest will keep pronounced effect over the next harvests, i.e., over advanced ages. In this sense, considering that genomic selection requires phenotypic data for its calibration, a high correlation between the marker effects representing the alleles at different ages of the plant will allow the phenotype to be predicted earlier for prediction at future harvests. This would enable the early selection of superior genotypes by GWS.Thus, grain yield was evaluated over three harvests, allowing the development of prediction models for each harvest. We tested how the models developed on the first harvest act in the prediction of phenotypes on the second and third harvests. However, the models developed for the first harvest showed limited accuracy in phenotype prediction in the second and third harvests. Given this result, we infer that there is a low genetic correlation between the first harvest and the second and third harvests. This low correlation is attributed to the lower yield stability of the Jatropha in the first harvest, since a significant part of the individuals in the population is still growing, making it impossible to express their productive potential.33, observed an increase in grain yield from 322 to 1972% from the first to the third harvest. On the other hand, the accuracy of the models developed based on the grain yield data of the second harvest was higher in the measurements of the third harvest, and there may be a higher genetic correlation between these harvests. Genotype by harvest interaction can affect the transferability of models between harvests when the first harvest is used. Therefore, the results obtained are essential to facilitate the ongoing use of genetic selection in Jatropha breeding programs.When evaluating the agronomic performance of jatrophaRegarding the efficiency of wide-genome selection, considering the 50% reduction in the selection cycle, we observed an increased gain with GWS compared to the phenotypic selection of 66.74 and 346.45% for the second and third harvests, respectively. Our findings reveal that the use of GWS in Jatropha in an early manner is a reality to be explored. This strategy makes the applicability of GWS even more significant since late phenotyping would reduce the gain by time unit, i.e., the selection gain already achieved simply by conventional breeding.By selecting superior individuals early through GWS analysis, the breeder will focus on potential genotypes and eliminating undesirable ones. For this reason, the costs of maintaining breeding populations in the field can be reduced. Furthermore, the early genomic selection makes it possible to carry out breeding populations with higher agronomic performance, which will maximize the genetic gains.15 and34, is due to the fact that genomic prediction and selection of superior genotypes can be performed at the seedling stage, and thus GWS becomes more time-efficient. Similar results were reported by several studies in pinus35, eucalyptus15, citrus34, and coffee31. An increase in breeding efficiency by using simulated data was also reported36.This possibility, as reported byDue to the significant population growth, the demand for energy sources, especially renewable energy, is intensified, since the use of energy from oil or coal is finite. Thus, encouraging the use of renewable energies, especially biofuels, has become one of the alternatives faced with the issue of global warming. Several studies have been carried out to consolidate new crops for biodiesel production.37.In this sense, the use of Jatropha as a renewable energy source becomes a great alternative. However, to consolidate Jatropha as a new source of biodiesel, it is necessary to implement techniques able to assist the breeders in obtaining new cultivars, since the species is poorly improved. Genetic breeding programs have been started in several countries using conventional approaches to increase the grain and oil yield in Jatropha. However, by using conventional breeding, Jatropha yields are still low to make profitable and sustainable its growingIn this sense, clearing and using GWS allows access to genetic information, which is potentially useful to Jatropha breeding programs. Our study showed one of the first worldwide applications of GWS in a Jatropha breeding program. As Jatropha is a perennial crop, this tool becomes one of the most promising ways to promote the development of the crop for biodiesel production.The wide-genome selection proved to be promising for Jatropha breeding since it allows the accurate obtention of GEBV and higher efficiency in relation to phenotypic selection, making it possible to reduce the time needed for selection cycle.In order to apply genomic selection in the first harvests, a large number of individuals in the breeding population are needed. In the case of few individuals in the population, it is recommended to carry out a higher number of harvests.-1) was evaluated in the years 2015, 2016, and 2017, corresponding to 1, 2, and 3 years old, respectively.A total of 386 individuals from the Jatropha breeding population, which comes from the crossing between 42 parents, were evaluated. The population was evaluated in random block design with six replicates, three plants per plot, and a spacing of 4 × 2 m. The parents come from the Active Germplasm Bank (BAG) of Embrapa Agroenergia, which was composed of genotypes from several regions. The grain yield (g plantNucleoSpin Plant II (Macherey–Nagel), with modifications. The quantification and quality of the samples were performed using the NanoDrop Aspectrophotometer to evaluate the A260/A280 wavelength ratio, which represents the amount of nucleic acids by the amount of protein in the sample. Samples with A260/A280 between 1.80 and 2.10 were considered adequate, indicating a low amount of protein and RNA in the samples. The samples were genotyped using the Axiom myDesign Genotyping Arrays platform, using a chip developed by Embrapa Agroenergia based on Affymetrix's Axiom technology (Axiom ENERCHIP) selected for species with bioenergetic potential. SNPs were filtered based on multiple criteria that included: (1) consensus sequence size, (2) minimum and maximum reading depth, (3) SNP quality score, (4) Minor allele frequency (MAF), (5) presence of other SNPs in the adjacencies, (6) SNPs present in various populations (if they were sampled), (7) SNPs present in coding regions, coverage of genes of interest  and (8) coverage at the genomic level (assessed by the distribution of SNPs in the reference genome).The DNA extraction was performed from fully expanded young leaves, according to the protocol of the manufacturer's manual 38. After the QC, 811 SNPs were obtained that met the exclusion criteria.Genomic data corresponding to 12,598 SNPs were submitted to an initial quality control (QC), where the marker exclusion criteria were: Call Rate = 0.95 and MAF = 0.04. The Call Rate is used to eliminate markers with a large amount of lost values, whereas MAF is related to the polymorphism of marker loci in the population. The critical level for the MAF parameter was obtained through the equation, 39. All statistical modeling was performed using software R. RR-BLUP was performed using the rrBLUP package (mixed.solve function). For estimating the marker effects by RR-BLUP methodology, the following mixed linear model was used:y is the vector of phenotypic observations; β is the vector of fixed effects; ε is the vector of random residuals; X and Z are the incidence matrices for β and a. The structure of means and variations in this model is described as defined by40:Genomic selection analyses were performed using the RR-BLUP random regression methodn: number of markers. The prediction of the individual's genomic breeding value (GBV) is given by:The genomic mixed model equations for predicting a by the RR-BLUP method is equivalent to:41.The Z matrix was constructed from the number of alleles observed in each SNP marker  and was standardized to have zero mean and variance 1, as described by1 = (9⁄10) × NT were used as the training population, while the remaining individuals in the population N2 = (1⁄10) × NT were used as the validation population, in which NT is the total number of individuals in the population. This process was repeated ten times (k = 10), using a different set of individuals as the validation population at a time. Thus, each fold did not overlap with the others, and at the end of the process (10 folds), all individuals had their phenotypes predicted by the genomic selection, as previously described by 43.The k-fold cross-validation method was used. The set of observations was randomly divided into groups. In the analysis process, random samples of N26, in which it considers genomic heritability as a proportion of phenotypic heritability, thus considering the efficiency of markers in capturing QTL, i.e., it considers the degree of imperfection of linkage disequilibrium:The accuracy of genomic selection was estimated according toqtl is the number of QTLs controlling each trait given by 2 genomic heritability40.The estimate of the number of individuals to be evaluated to achieve the desired accuracy was obtained by the expression:35.The selective efficiency of GWS compared to selection based on phenotypes only was calculated using the expression:44.The GWS models developed in each harvest were evaluated for accuracy in predicting breeding values, among other harvests. For this analysis, the accuracy was calculated by the correlation between GEBV derived from data collected in the first two harvests and EBV in the second and third harvests. As the same plant is compared between ages (harvests), there is a dependency between one plant on two different harvests. Therefore, tenfold cross-validation was performed as described previously. All analyses were performed in the R software The authors are allowed to do research with Jatropha curcas in Brazil.The handling of plants were carried out in accordance with relevant guidelines and regulations.


Zea mays L.) ZmCNR13 gene, encoding a protein of fw2.2-like (FWL) family, has been demonstrated to be involved in cell division, expansion, and differentiation. In the present study, the genomic sequences of the ZmCNR13 locus were re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes, and the nucleotide polymorphism and selection signature were estimated. A total of 501 variants, including 415 SNPs and 86 Indels, were detected. Among them, 51 SNPs and 4 Indels were located in the coding regions. Although neutrality tests revealed that this locus had escaped from artificial selection during the process of maize domestication, the population of inbred lines possesses lower nucleotide diversity and decay of linkage disequilibrium. To estimate the association between sequence variants of ZmCNR13 and maize ear characteristics, a total of ten ear-related traits were obtained from the selected inbred lines. Four variants were found to be significantly associated with six ear-related traits. Among them, SNP2305, a non-synonymous mutation in exon 2, was found to be associated with ear weight, ear grain weight, ear diameter and ear row number, and explained 4.59, 4.61, 4.31, and 8.42% of the phenotypic variations, respectively. These results revealed that natural variations of ZmCNR13 might be involved in ear development and can be used in genetic improvement of maize ear-related traits.The maize ( Zea mays L.), one of the most important cereal crops, is cultivated worldwide as sources of food, animal feed, and industrial materials. It was suggested that the total global maize production was 1,148.4 million tons in 2019, which was far greater than those of rice (Oryza sativa L.) and wheat (Triticum aestivum L.)fea2 (fea3 (KNR1 (KNR4 (td1 (bif2 (ba1 (Maize ( L.)fea2 , fea3  was detected as an essential locus in controlling fruit weight and size (fw2.2-like (FWL) genes, encoding proteins with a conserved PLAC8  domain, were suggested to play essential roles in cell division and organ size control  genes  genes . The ZmCng vigor . These oCNR gene ZmCNR13 was firstly identified through a narrow odd dwarf (nod) mutant  mutant . Furtherof maize . Howeverh2) for these ear-related traits. The observed values of these traits in three environments were used to calculate the best linear estimated prediction (BLUP)   using thZmCNR13 locus in the selected lines were resequenced using the target sequence capture sequencing technology on the NimbleGen platform (ZmCNR13 (GRMZM2G027821) locus in the inbred line B73 (AGPv3.21) were used as the references for target capture sequencing.A total of 224 inbred lines, 56 landraces and 30 teosintes  were colplatform  by BGI  in the ZmCNR13 gene, which is defined as the mean number of nucleotide differences per site between any two DNA sequences, was estimated using R package “PopGenome” (R2) for all pairs of SNPs using the program PopLDdecay v3.41  higher than 0.05 were used for association analysis. The p-value thresholds were empirically set to 1/398 and 0.05/398 for MLM and GLM, respectively, using the Bonferroni correction method (The method of genotyping-by-sequencing (GBS) was used to identify the genotypes of the selected lines . A totaln method .r) between any two traits were obtained . The frequency of variation sites in the exon region was the lowest (23.23 bp for SNP and 321.75 bp for InDels). For all the tested lines, the overall nucleotide diversity (π) of the ZmCNR13 locus was 0.01719, where the estimated π values of intron regions were relatively higher than exon regions. The nucleic acid polymorphisms of exon regions were low (θ = 0.00765), while that of other regions were relatively higher (θ = 0.01702 for intron regions and 0.01544 for downstream)  . In addin others .ZmCNR13 locus, the sequence variation parameters of this gene in three different populations were analyzed and compared. The sequence conservation (C) of the ZmCNR13 gene was similar in landraces (C = 0.903) and inbred lines (C = 0.914), and the lowest value of 0.813 in teosintes. Correspondingly, compared with teosintes, landraces and inbred lines showed lower nucleotide sequence polymorphisms . The neutrality of ZmCNR13 gene was tested by Tajima’s D, Fu and Li’s D*, and Fu and Li’s F*. The Tajima’s D values of the three populations didn’t achieve a significant level. Furthermore, the Tajima’s D was positive in the inbred line population, indicating that the gene was lack of rare alleles in this population  higher than 0.05 were used for association analysis. Both methods of GLM and MLM were used for marker-trait association analysis. Four polymorphic sites  were captured jointly by two models, which were found to be significantly associated with six ear traits . Among them, two sites (SNP2305 and SNP2337) located in exon 2 jointly controlled four ear traits . Furthermore, we noticed that SNP2305 belongs to non-synonymous mutation, where the transition of T to A will lead to the changes of amino acid serine to threonine. In addition, SNP2305 were found to be associated with EW, EGW, ED and ERN, and could explain 4.31–8.42% and 7.06–10.41% phenotypic variations under MLM and GLM, respectively   . We furtallele T . On thisZmCNR13 and ear-related traits. Association analysis, also known as linkage disequilibrium mapping or association mapping, is a method based on linkage disequilibrium to identify the association between genetic polymorphisms and phenotypic variations (Zmisa2 (ZmBT1 (ZmYS1 (ZmCKX5 (ZmMADS60 (ZmHKT1 (ZmPGP1 (In the present study, association analyses were employed to illustrate the relationship between the maize riations . Compareriations . As an eriations . Maize iriations . Many lo (Zmisa2  and ZmBT2 (ZmBT1  for star1 (ZmYS1  for kern (ZmCKX5  and ZmMAZmMADS60  for root (ZmHKT1  and ZmPG (ZmPGP1  for planZmCNR13 gene were analyzed in inbred lines through re-sequencing. A total of 501 variations including 415 SNPs and 86 InDels were detected, and most of them concentrated in the intron regions. The exon region had one SNP per 25.71 bp, while the intron region reached one SNP per 11.28 bp. The decrease of nucleotide polymorphism in exon region suggested that these regions might be influenced by greater selection pressure. Moreover, it is worth noted that the LD decays faster in landraces and teosintes than inbred lines, suggesting that there were genetic bottleneck effects (Abundant genetic diversity is the basis for crop improvement . In this effects  in the pZmCNR13 gene was found to be significantly associated with four ear-related traits, including EW, EGW, ED and ERN. In addition, we further noticed that the inbred lines carrying SNP2305A possess higher EW, EGW, ED and ERN than those carrying SNP2305T. The validity of candidate gene association analysis has been repeatedly confirmed (dwarf8 and flowering time was detected by some independent works (ZmCNR13 possess potential application values in maize genetic improvement.Ear-related traits are the most important components of kernel yield of maize. Illustrating the genetic background of the genes related to ear traits and digging their elite variations will be of great importance in high-yield breeding maize . In the onfirmed . For insnt works . To confnt works , suggestZmCNR13 gene was re-sequenced in 224 inbred lines, 56 landraces and 30 teosintes. Although this gene was escaped from artificial selection during maize genetic improvement, a non-synonymous mutation in exon2 was found to be associated with ear-related traits, including EW, EGW, ED and ERN. The superior allelic variations of ZmCNR13 has potential application values in maize genetic improvement.In conclusion, the maize