It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants . Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test ( sMiST) for testing the association of multiple genetically predicted mediators simultaneously and direct association of individual variants independent of mediators by using a random effects model. Extensive simulation and real data analyses demonstrate that sMiST recovers the results of MiST that is based on individual level data, but is computationally much faster. We applied our approach to a genome-wide association study of colorectal cancer and gene expression and identified several novel and secondary genetic loci.We developed summary statistics-based mixed effects score test statistics ( Single variant analysis in genome-wide association studies (GWAS) has been successful in identifying thousands of variants associated with various diseases and traits . HoweverResearch for integrating functional knowledge into GWAS has been active recently. This is in part due to success of large collaborative projects such as the Genotype-Tissue Expression (GTEx) project  and the These approaches could be considered as a type of set-based association test, in which the predictor is the weighted sum of a set of genetic variants with weights being the effect sizes on gene expression. However, these methods do not take into account the potential effects of genetic variants beyond their effects on expression of a specific gene. Complex trait loci typically map to regions of the genome clustered with regulatory elements, which in turn have combinatorial effects on the expression of several target genes , 10. VarWe recently proposed a Mixed effects Score Test (MiST), which formulates the association of mediators (fixed effects), while allowing for effects of individual variants on disease risk directly adjusting for mediators (random effects) , 12. ThusMiST. There are several novel contributions: 1) simultaneous testing of multiple mediators ; 2) testing of the variance component of the direct effects of genetic variants independent of the mediation effects; 3) combining the test statistics of both mediation and direct effects to form a single overall test that can capture information from both mediation and direct effects; 4) conditional testing of mediation and direct effects, adjusting for multiple other genetics variants. For example, one may perform conditional testing to examine whether a finding is novel conditional on known loci. When there is only one mediator, the test statistic for testing the association of the mediator under the assumption of no direct effects has the same form as PredXcan and TWAS  derThis study uses the summary statistics of genome-wide association studies for colorectal cancer, and the de-identified genotyping data from GECCO, CCFR and CORECT. The study was approved by the Institutional Review Board at the Fred Hutchinson Cancer Research Center in Seattle, WA under file numbers 3995 and 6501.n independent individuals. Let Y be a n × 1 vector of outcomes, G a n × P matrix of P variants for the n individuals, W a P × K matrix with Wpk being the regression coefficient of pth variant for the kth mediator, and D is a diagonal matrix of cov(G). Further, let G and Y so that the intercept is 0. It is easy to see thatβ* is the limit of n for GWAS typically is very large, under regularity conditions, by the continuous mapping theorem and the central limit theorem, τ2 = 0, the estimator for the mediation effect isAssume that there are no confounders. We first focus on linear regression model. Consider a study of We can obtain G) is the correlation matrix of G, which is the exact correlation of Here cor [For the variance component test, we derive the test statistic under l. 2018) . When col. 2018) , 31.P genetic variants Dp is the pth diagonal entry of D, and C is a (K + 1) × (K + 1) matrix with Wj and Wk the jth and kth columns of W, and Uτ2 follows a mixture of Dα*R*Dα*, where Dα* is a diagonal matrix with R* is the correlation matrix of The key to deriving the variance component test statistic conditioning on Δ is a diagonal matrix of μ(1 − μ) and μ = E(Y|G). Here G is centered. For simplicity of presentation, we omit any differences in the order of op(n−1/2) because for the γ ≈ {WT cov(G)W}−1WTDβ*. As a result, Under the logistic regression model, by the Taylor’s expansion, we have . (2013)  shows Δ XY and WTGY with the weight as the corresponding elements in the inverse of the covariance matrix of . If the effects of confounders are 0 or X and G are independent, then G and the above test statistics are the same. If the effects of confounders are not 0 and X and G are correlated, X; however, we observe our proposed test statistics hold very well based on our extensive simulations and real data analysis, suggesting that our test statistics are robust even in the presence of confounders.When there are confounders, the derivation for test statistics using summary statistics becomes complicated. Under the liner model, n = 26, 554). The details of study designs, genotyping QC, association and meta-analysis can be found elsewhere [Summary statistics (log-odds ratio estimates and standard errors of genome-wide genetic variants) were obtained from a meta-analysis of GWAS studies from three large consortia, including: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colon Cancer Family Registry (CCFR), and the Colorectal Cancer Transdisciplinary Study (CORECT) . In totalsewhere . A briefhttp://predictdb.org/). The regression coefficients were estimated from a regularized linear regression model with elastic-net penalty [R2 > 0.01 in the gene expression model, resulting in 8,893 genes. Using regulatory information derived from whole blood is relevant for studying susceptibility to CRC for two primary reasons. First, a subset of the immune-relevant cell types present in whole blood are relevant to CRC risk. In particular, T-cell populations of the intestine play a critical role in orchestrating the careful balance between immune activation and tolerance at the mucosal layer. Second, whole blood is the largest reference transcriptome dataset. As many tissues and cell types share common heritability in gene expression, in some cases whole blood models are preferred for building robust predictive models because of their large sample size.We downloaded the weights or regression coefficients of cis (< 1Mb from gene start or end) regulatory variants associated with gene expression for whole blood from the PredictDB Data Repository  from the eQTL database from the PredictDB Data Repository. Both CXCR1 and C18orf32 are of moderate size (∼40 genetic variants), while ARHGAP11A is larger set (92 variants). In terms of the LD structure, both C18orf32 and ARHGAP11A show largely independence or weak correlation among variants; however, CXCR1 contains several clusters of variants that are nearly perfectly correlated.We selected three genes , and generated the disease status under the generalized linear regression model . Here, c and σ2 were set such that variation of M explained by G is 0.05, 0.20, and 0.80, while keeping the variance of M constant, which we set to be 1.5. The weights {wp, p = 1, …, P} were obtained from the PredictDB Data Repository. The effect of the mediator M was set to be log(2). Further, we let the random effects δp ∼ N. To mimic the individual variant contributions that were not explained by predicted gene expression, we took the residuals from regressing the sum of direct effect B where Ip is 1 if pth variant has a direct effect and 0 otherwise, and added the residuals as direct effects to the model. The proportion of variants with direct effects was set to be 0.1, 0.2, 0.4, 0.6, 0.8, and 1.0. To save space, for most simulations presented in the main text, we set R2 = 0.05 and all variants have direct effects unless otherwise noted. Results for other parameter settings are provided in We used the GWAS genotyping data from GECCO as the template  forest plot of conditional association adjusting for predicted gene expression; (c) pairwise linkage disequilibrium (LD) R2. The p-values < 0.05 are labeled on the left margin in (a) and (b). The weights used in calculating gene expression are labeled on the right margin in (b).(a) forest plot of marginal association of genetic variants in (EPS)Click here for additional data file.S2 FigVPREB3; (b) forest plot of conditional association adjusting for predicted gene expression; (c) pairwise linkage disequilibrium (LD) R2. The p-values < 0.05 are labeled on the left margin in (a) and (b). The weights used in calculating gene expression are labeled on the right margin in (b).(a) forest plot of marginal association of genetic variants in (EPS)Click here for additional data file.S3 FigPLD6; (b) forest plot of conditional association adjusting for predicted gene expression; (c) pairwise linkage disequilibrium (LD) R2. The p-values < 0.05 are labeled on the left margin in (a) and (b). The weights used in calculating gene expression are labeled on the right margin in (b).(a) forest plot of marginal association of genetic variants in (EPS)Click here for additional data file.S4 FigANKRD10; (b) forest plot of conditional association adjusting for predicted gene expression; (c) pairwise linkage disequilibrium (LD) R2. The p-values < 0.05 are labeled on the left margin in (a) and (b). The weights used in calculating gene expression are labeled on the right margin in (b).(a) forest plot of marginal association of genetic variants in (EPS)Click here for additional data file.S5 Fig(EPS)Click here for additional data file.S6 Fig(EPS)Click here for additional data file.S7 Fig(EPS)Click here for additional data file.S8 Fig(EPS)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.


Prunus dulcis Miller (D.A. Webb)] is the main tree nut species worldwide. Here, genotyping-by-sequencing (GBS) was applied to 149 almond cultivars from the ex situ collections of the Italian Council for Agricultural Research (CREA) and the Spanish National Research Council (CSIC), leading to the detection of 93,119 single-nucleotide polymorphisms (SNPs). The study of population structure outlined four distinct genetic groups and highlighted diversification between the Mediterranean and Californian gene pools. Data on SNP diversity and runs of homozygosity (ROHs) allowed the definition of kinship, inbreeding, and linkage disequilibrium (LD) decay in almond cultivated germplasm. Four-year phenotypic observations, gathered on 98 cultivars of the CREA collection, were used to perform a genome-wide association study (GWAS) and, for the first time in a crop species, homozygosity mapping (HM), resulting in the identification of genomic associations with nut, shell, and seed weight. Both GWAS and HM suggested that loci controlling nut and seed weight are mostly independent. Overall, this study provides insights on the almond cultivation history and delivers information of major interest for almond genetics and breeding. In a broader perspective, our results encourage the use of ROHs in crop science to estimate inbreeding, choose parental combinations minimizing the risk of inbreeding depression, and identify genomic footprints of selection for specific traits.Almond [ Prunus dulcis Miller (D.A. Webb), syn. Prunus amygdalus L., 2n = 2x = 16) is one of the oldest domesticated tree species, presumably originating in the first half of Holocene1. Starting from the Fertile Crescent, almond cultivation rapidly spread westwards and eastwards through human migration and commercial routes. At present, almond is the main tree nut species worldwide, with an estimated production of 2.2 million tonnes (FAOSTAT data 2017). Approximately 80% of the global almond cultivated area is concentrated in California and the Mediterranean region (FAOSTAT data 2017). Clonal cultivars are widely grown in developed countries, whereas seedling populations mainly occur in developing countries.Almond  markers9. Nowadays, high-throughput genotyping methods, such as genotyping-by-sequencing (GBS), are routinely applied in agrigenomics research for the fine-scale characterization of genetic structure with thousands of single-nucleotide polymorphism (SNP) markers12.Information on the genetic structure of crop species is pivotal for the correct management of ex situ germplasm collections and the efficient implementation of breeding programs, which should be based on the hybridization of genetically diverse individuals13, has been widely documented in human, animal, and plant populations. In almond, inbreeding depression leads to declined vegetative vigor and dramatic reduction of flower and fruit set16. Therefore, information on kin relationships among cultivars, which can be reconstructed from pedigrees or inferred a posteriori from genotypic data18, is of great value for almond breeders to minimize the risk of inbreeding depression. In addition, it would be interesting to investigate whether some of the almond cultivars currently grown on a large scale are inbred, and thus may display deleterious phenotypes due to inbreeding depression.Inbreeding depression, i.e. the reduced fitness of offspring of related individuals19. Therefore, F can be estimated by the pedigree inbreeding coefficient (FP), which expresses IBD probabilities based on pedigree data. However, the use of FP has two major limitations, i.e., the strong assumption that the pedigree founders are unrelated, and the difficulty to retrieve pedigree information20. Another estimator of F  is based on the positive correlation occurring between IBD and the ratio between homozygosity observed and expected at Hardy–Weinberg equilibrium17.The level of inbreeding of an individual (F) depends on the extent of homologous chromosome segments displaying identity by descent (IBD), i.e., deriving from the same recent common ancestorP when genotyping with thousands of SNP markers19 and are therefore commonly used to estimate inbreeding in human and cattle population studies22. In contrast, only a few works used ROHs to estimate inbreeding in plants23.To date, high-throughput genotyping enables the assessment of inbreeding through the direct detection of IBD segments. These appear as long chromosomal stretches of homozygous marker loci, referred to as runs of homozygosity (ROHs). ROHs were proven to be more accurate than F24. Previous studies indicated that seed and nut weight are highly heritable and weakly correlated with the production density27. In addition, QTLs significantly associated with almond seed and nut weight were mapped on chromosomes 1, 2, and 724.Seed and nut weight are economically important traits associated with the almond cultivation and processing industry. Specifically, seed weight is a major determinant for the almond final market utilization, whereas nut weight, which is given by the sum of the seed and shell weight, is important to set up appropriate harvesting, dehulling, transportation, and storing strategies29 provides the opportunity to carry out genome-wide association studies (GWASs), identifying associations between phenotypes and markers with the known chromosomal locations. Information on the average linkage disequilibrium (LD) decay in the organism on which a GWAS is performed is of main importance, as this parameter influences, for a given number of markers, the chance to reveal significant associations30. In addition, rapid LD decay increases the possibility that GWAS experiments lead to the identification of marker loci residing within, or in the proximity of, genes causally related to the phenotype.The recent publication of the almond genome sequence31. Compared to GWAS, HM has the major advantage of using a lower number of covariates, thus reducing type II error associated with multiple correction tests. Practically, this means that HM may reveal genomic associations that escape GWAS detection. In addition, IBD identified by HM can highlight genomic regions associated with inbreeding depression or selective pressure32. Indeed, in medical genomics, HM is performed to map homozygosity derived from consanguinity and associated with recessive diseases33, whereas, in animal science, it is carried out to highlight genomic regions resulting from human selection for specific traits34. To the best of our knowledge, HM has been never applied in plant research.Besides GWAS, homozygosity mapping (HM) is another approach enabling the disclosure of genotype–phenotype relationships, which consists in testing the association between traits of interest and ROHsHere, we aimed to study genetic structure, kinship, inbreeding, and average LD decay in almond cultivated germplasm, through GBS-based identification of SNPs and ROHs. In addition, we addressed the detection of genomic regions associated with nut, shell, and seed weight, based on HM and GWAS.Sequencing of a GBS library based on 149 almond cultivars  distance among biological replicates of the same cultivar, 11 clonal groups (CG1–11) were identified  was the most suitable to describe genetic structure , 105 cultivars were assigned to one of the ancestral populations, whereas 26 cultivars were assigned to the admixed group. C1 and C2, composed by 19 and 27 cultivars, respectively, mainly include Italian germplasm; C3 encompasses 38 cultivars originating from several Mediterranean countries; finally, C4 is formed by 21 cultivars, all from U.S and Ukrainian origin, except for the French cultivar Sultana and the Greek cultivar Symmetrike  from C4 and C1, respectively, and the U.S. population could be mostly referable to C4 Fig. . The modasm Fig. , which, Kin relationships were predicted for known parent/offspring pairs present in the cultivar collection (“Cristomorto” (CG9)/”Ferragnès”, “Aï’/‘Ferragnès”, “Ferragnès”/”Antoñeta”, “Chino” (CG1)/“Antoñeta”, “Texas”/”Merced”, “Nonpareil”/”Merced”, “Nonpareil”/”Davey“, and “Nonpareil”/”Kapareil”) (Supplementary Table R2 = 0.16) was found between mean read depth per cultivar and ROH count per cultivar  was found between ROH count and FPLINK  to 0.147 (chromosome 2) . A high correlation was found between nut and shell weight (adjusted R2 = 0.96), whereas a weak correlation was found between nut and seed weight (adjusted R2 = 0.1). No significant correlation was found between shell and seed weight  calculated for nut, shell, and seed weight is reported in Supplementary Fig. 10  = 5.17; false discovery rate (FDR) P value = 6 × 10−3). In accordance with the correlation pattern found for phenotypic data, the same clusters of ROHs associated with nut weight were also associated with shell weight  = 11.05; FDR P value = 8.22 × 10−7) was found for the marker S1_30936643, residing in the putative promoter sequence (218 bp upstream the start codon) of the aspartyl protease gene Prudu_003450_v1.0. In accordance with the results of HM, SNP loci associated with nut weight were in most cases also associated with shell weight  = 6.19; FDR P value = 0.06) was detected for seed weight, for a marker (S1_2496687) located within the gene Prudu_000307_v1.0, encoding a putative pathogenesis-related thaumatin superfamily protein  might be due to the violation of the assumption of random mating used for PI_HAT estimationure Fig. . This re44. Several cultivars displayed kinship with CG1 and CG9, containing the Italian cultivars Tuono and Cristomorto, in accordance with the extensive use of these two cultivars in breeding as a source of self-compatibility. Surprisingly, the Italian cultivar Rachelina, which is not reported in main pedigree records, displayed the highest number of kin relationships (22), not only with Italian germplasm, but also with the French cultivars Rabasse and Tournefort, and the Ukrainian cultivar Picantili. The identification of kinship between “Sultana” and “Texas” further indicates the role of French introductions as founders of U.S. breeding programs. Remarkably, “Sultana” was previously indicated as one of the few commercial cultivars introduced to California from the Languedoc area of Southern France from 1850 to 1900, representing the basis of the U.S. almond industry45. Finally, the kinship between Ukrainian cultivars  and cultivars from Italy and U.S. is consistent with the use of foreign germplasm in breeding programs held at the Ukrainian Nikita Botanical Garden47.A large family group was composed by several U.S. cultivars, including “Nonpareil”. This is consistent with the recurrent use of “Nonpareil”, considered as standard for superior tree and nut characteristics, in U.S. breeding programs23, which focused on cultivated Citrus species, we searched for ROHs to estimate the level of inbreeding in individual cultivars , did not have a substantial effect on the almond edible part, i.e., the seed , is accompanied by an outstanding variation of protein degradation enzymes, including aspartyl proteases. It is thought that amino acids derived from the degradation of proteins stored in the early immature fruit act as substrates for the phenylpropanoid and lignin pathways activated during endocarp hardening53.In accordance with the results of HM, GWAS suggested that loci controlling nut weight and seed weight are mostly independent and that there is parallel control of nut and shell weight by several genomic loci Fig. . Many of54, thus it cannot be excluded they might also have a role in determining seed growth. With this respect, we highlight that some TLP proteins, referred to as permeatins, accumulate in high concentration in seeds of cereals55.Concerning seed weight, the association was found with a SNP residing in a gene putatively encoding a member of the thaumatin-like protein (TLP) superfamily , Murcia, Spain , Bari, Italy, and 17 from the 10, using the restriction enzyme ApeKI (The Elshire Group Ltd.). Paired-end sequencing was performed using the HiSeq2500 device (Illumina), including an empty negative control well. The TASSEL-GBS pipeline43 and the almond reference genome28 were used for SNP calling and mapping.Leaf tissue samples were collected from three biological replicates of the cultivar R1000, two biological replicates of the cultivars Ardechoise, Ferragnès, Filippo Ceo, Marcona, and Desmayo Largueta, and one individual of the remaining cultivars. DNA was extracted using the DNeasy Plant Mini Kit (Qiagen) and assayed for quality and concentration using agarose gel (0.8%) electrophoresis and the Qubit 3.0 fluorometer (Life Technologies). A GBS library was prepared as reported by Elshire et al.56. Specifically, marker quality control was performed by filtering for biallelic SNP loci with minor allele frequency >0.05 and call rate >0.7. As for genotype quality control, cultivars associated with an overall SNP call rate >0.6 were selected. In addition, mean and sd of the pairwise IBS distance17 between biological replicates were used to set up an IBS minimal threshold (mean−3 × sd) to declare clonal groups and select, within each group, the cultivar with the highest SNP call rate.Quality control was carried out using TASSEL v.517. This calculates pairwise R2 for all marker pairs in sliding windows with a size of 50 markers and an increment of 5 markers and removes the first marker of pairs, in which R2 < 0.5.Genetic structure was studied using SNPs in approximate linkage equilibrium, which were obtained using the LD pruning algorithm in PLINK v.1.90p35 was performed with a number of ancestral populations (K) ranging from 1 to 15. One thousand bootstrap replicates were run to estimate parameter standard errors. The most suitable number of K was selected in correspondence with the lowest cross‐validation (CV) error. Cultivars were assigned to one specific ancestral population when the membership coefficient qi for that cluster was >0.6. If not, they were considered admixed.Analysis with the ADMIXTURE parametric modelPCA on SNP data was performed using SVS v.8.8.3 (Golden Helix Inc.), and a three-dimensional plot was obtained using the top three components identified with default parameters of the additive model.36 was used to infer splits and mixtures among Italian, French, Spanish, and U.S. germplasm, testing a model with no migration, and models with all the three possible migration events among the four populations. The “get_f” R function was used to obtain the variance explained by each model.TreeMix (v1.12)P) of sharing 0, 1, or 2 IBD alleles were calculated at each locus, given IBS distances and allele frequencies, using the method-of-moments algorithm implemented in PLINK v.1.90p17. The matrix obtained with the PI_HAT parameter, given by P(IBD = 2) + 0.5 × P (IBD = 1) and providing an estimate of the proportion of IBD alleles, was used to draw a clustered heatmap, using the pheatmap v.1.0.12. R package57.In order to infer kinship among cultivars, pairwise genotype probabilities  was used to identify completely homozygous genomic stretches on chromosomes 1–8 with at least 15 SNP loci and with a minimal length of 100 Kb. As a measure of inbreeding, the ROH count and the ROH total length were computed for each individual. The ggplot2 R packagePLINK inbreeding coefficient, which was computed using the LD-pruned marker dataset as input. Regression analysis between ROH count, or ROH total length, and FPLINK coefficient, was performed using the ggplot2 R package58.Individual inbreeding was also estimated using the F17 was used to calculate pairwise linkage disequilibrium (LD) between SNPs59, expressed as the squared correlation coefficient R2. The R2 values were plotted against the marker physical distance, and the Hill and Weir formula was used to describe the decay of r260. The mean pairwise R2 for markers within and between chromosomes was used to define a lower threshold value for LD. Mean pairwise R2 was also calculated for markers on individual chromosomes.PLINK v.1.90pPhenotyping was carried out in 2006, 2007, 2009, and 2012, on 98 cultivars of the CREA-AA collection, grown at the experimental farm “La Piantata”   (Supplementary Table 61 and the following model: y = lmer (Trait ~ (1 | Genotype) + (1 | Year)). Normal distribution of BLUP data was verified using the Kolmogorov–Smirnov test implemented in the stats R package. Linear models to study correlation between BLUPs for nut, shell, and seed weight were generated using the ggplot2 R package58.BLUPs of phenotypic traits collected over 4 years were calculated using the Lme4 R package62 was used to trim boundaries of clusters of ROHs, in order to define homozygous regions highly overlapping among cultivars. Finally, a linear regression model was fit between clusters of ROHs and BLUPs, using the top five principal components as covariates to correct for population structure. The FDR correction was used to account for multiple testing and suggest an association for P < 0.1. BLUP means of cultivars either contributing or not contributing to clusters of ROHs associated with phenotypic traits were computed and compared using a two-tail Student’s t test. Genes included in clusters of ROHs identified by HM were retrieved by the Prunus dulcis (cv. Lauranne) v1.0 genome annotation available at the genomic database of Rosaceae63.HM was carried out using options available in SVS v.8.8.3 (Golden Helix Inc.). Clusters of ROHs, defined as genomic regions of at least 100 Kb whose loci occur in ROHs of at least ten cultivars characterized at the phenotypic level, were identified. Repeated binary spectral clustering64 was used for GWAS, using BLUPs as phenotypic values and the IBS matrix as a covariance matrix of random effects. The FDR correction was used to suggest the association for P < 0.1. Genes containing or flanking SNPs associated with phenotypic traits were retrieved by the Prunus dulcis (cv. Lauranne) v1.0 genome annotation available at the genomic database of Rosaceae63.The EMMAX linear mixed modelSupplementary FiguresSupplementary Tables


Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and  The metabolic phenotype (or “metabotype”) is a characteristic metabolite profile that depends on the interactions between genetic and environmental effects. Commonly, the metabolic phenotype of an individual is measured from easily accessible biofluids such as urine or blood . Additioglutamine synthase 2 gene (GLS2) were associated with glutamine in human serum, which provides a potential biological association, as the enzyme GLS2 catalyzes the hydrolysis of glutamine  with carnitine and validated that the hitherto uncharacterized protein was indeed a carnitine transporter in Xenopus oocytes. Additionally, as metabolites lie between genomic and external phenotypes, they could explain the variation of external phenotypes by revealing biological mechanisms underlying the associations between them. Recent application of GWAS have successfully uncovered genetic variants that contribute to variation in both the external phenotype  and the metabolic phenotype  (Metabolome-genome-wide association study (mGWAS) is a powerful tool for identifying genetic variants underlying metabolic phenotypes and provides new opportunities to decipher the genetic basis of metabolic phenotypes. Importantly, mGWAS detect genetic variants that are functionally associated with metabolic phenotype variation. For example, previous studies have reported that single nucleotide polymorphisms (SNPs) in the lutamine . Further levels) .Due to the rapidly growing number of candidate biomarkers and the increasing role of metabolites in genetic studies, the knowledge of the genetic basis of metabolites is therefore a prerequisite to evaluate new biomarkers and dissect the genetic architecture of metabolites. Until now, however, knowledge regarding the genetic level of metabolites in beef cattle has been limited. Thus, the objectives of this study were to estimate genomic heritability of 33 plasma metabolites in crossbred beef cattle, to identify genetic variants, genomic regions and candidate genes associated with metabolite variation, and to understand the biological functions and gene networks linked to these associations.All management and procedures involving live animals, where applicable, conformed to the guidelines outlined by the Canadian Council on Animal Care (1993); otherwise, existing data sets from the various Canadian research herds were used.n = 224) which is predominantly Charolais-based with infusion of Holstein, Maine Anjou, and Chianina1, Hereford-Angus (n = 181) crosses, Charolais (n = 68), and Angus (n = 2).The dataset used in this study was obtained from the Phenomic Gap Project . This prBlood samples were collected in EDTA tubes from each animal by jugular venipuncture on the first day of the feedlot feeding test and immediately frozen at −80°C which is considered appropriate for storage. Our assumption is that all samples were affected equally by the freezing process if at all. Therefore, although the metabolite profiles may not be the same as those obtained from fresh samples, the freezing process should not be a source of variation for this study since all samples were frozen the same way according to best practice. Frozen blood samples were sent to the Metabolomics Innovation Center at University of Alberta, AB, Canada in 2014 for analysis. The variation in time of sample collection is expected to be captured under the “contemporary group” variable applied in subsequent statistical analysis. Each frozen sample was thawed at room temperature then centrifuged at 10,000 rpm for 10 min to separate the plasma then filtered through 3 kDa molecular weight cut-off filters  to remove macromolecules, including lipids and proteins. As the filter tube manufacturer treats the filter membranes with glycerol as a preservative, filters were washed and centrifuged five times before use. Samples made up of less than 570 μl after filtration were diluted with HPLC water to ensure adequate volume for NMR acquisition. A total of 5 mm NMR tube  contained a total of 700 μl of total volume of 570 μl filtered serum, 60 μl DSS and 70 μl D2O. This mixture was vortexed and centrifuged shortly before it was transferred to an NMR tube for data acquisition. All metabolite concentrations obtained were adjusted by appropriate factors to account for the above dilutions, and represent the contents of the filtered samples, not the contents of the NMR tube.Spectra were acquired on a 500MHz VNMRS spectrometer equipped with a 5mm cold probe . The pulse sequence used was a 1D-noesy with a 990 ms presaturation on water and a 4 s acquisition period. Spectra were collected with 256 transients and four steady-state scans at 298K.Spectra were zero filled to 64k points and Fourier transformed. Spectral phasing was performed on the spectra along with baseline correction. In total, 33 metabolites were identified and quantified with a targeted profiling approach using the Profiler and Library Manager modules in the same software which contains a total of 304 metabolites. Each spectrum was peer reviewed by a separate analyst and a final review pass was done on all of the spectra before exporting concentration results. Concentration measurements were adjusted to report metabolite concentrations after the filtration of the samples.P < 10–5) were removed. Missing genotypes were imputed using Synbreed package. After quality control, 45,266 SNPs on 29 bovine autosomes for 475 individuals remained and were used for this study.Animals were genotyped using Illumina BovineSNP50 v2 BeadChip  at Delta Genomics, Edmonton, AB, Canada. The genotypes were coded as 0, 1, and 2 and quality control was performed using the Synbreed package  in R staK value). The value of K = 4 was chosen because it had the smallest cross-validation error and yielded the most accurate breed composition prediction based on prior knowledge. The four postulated ancestral breeds were Hereford, Angus, Charolais and Beefbooster TX line. The distribution of predicted genomic breed composition is shown in Genomic breed composition was predicted for all individuals using ADMIXTURE software . To predP <0.05) included contemporary groups (herd and birth year), animal type  and genomic breed composition. These factors were subsequently included in the mixed model used for estimating heritability and GWAS. Contemporary group and animal type were fitted in the model as fixed class effect whereas breed fractions were fitted as fixed covariates. Residual values of the linear regression model were checked and those residuals with more or less than 3 standard deviations from the mean of residuals were considered as outliers and the associated records were excluded. The distribution of residuals after excluding outliers was close to a normal distribution . The summary statistics of all metabolites after phenotypic quality control are given in L-methionine) to 5,024.04 μM (L-lactic acid), on average.Phenotypic records included 33 plasma metabolite concentrations quantified from blood samples of 475 animals. A linear regression model implemented in R statistical software was used to assess the significance of all systematic effects associated with variation in plasma metabolites. Fixed factors found to be significant (ZZ′/2∑pi(1−pi), where Z contains centered genotypes codes and pi is the minor allele frequency for locus i  and using one of VanRaden’s formulations  locus i . The fol locus i  was apply is a vector of phenotypic observation; X is the design matrix that relates the fixed effects to the observation and b is a vector of fixed effects of contemporary groups, animal type and genomic breed composition. W is a design matrix relating observations to random animal genetic effects; a is a vector of random additive polygenic effects that is assumed to be normally distributed as: G is genomic relationship matrix and e is a vector of random residual effects that is assumed to be normally distributed as I being an identity matrix and Where P values for 11 metabolites with non-zero heritability were determined using a single-step genomic BLUP (ssGBLUP) approach as described by a was assumed to follow H is the matrix that combines genomic and pedigree information (H for mixed model equations is:The genomic heritability obtained from model (1) was used to screen all metabolites for metabolome genome wide association analyses. Metabolites with zero or near zero heritability were excluded from mGWAS. Here, the SNP ormation . The invA is the pedigree-based numerator relationship matrix for all animals, A22 is the numerator relationship matrix for genotyped animals, and matrix G is the genomic relationship matrix, where G was weighted as described by P values for each SNP were used to compare observed distributions of −log  to the expected distribution under the null hypothesis for each metabolite. Manhattan plots of P values for each SNP were also used to illustrate significant associations at the level of each chromosome for the metabolites. All plots were completed using the R package qqman  was applied, which is equal to 5.96 in the −log10 scale. The quantile–quantile (Q–Q) plots of ge qqman .r2) between pairs of syntenic SNPs within this distance which is known to be 0.20 in a related beef cattle population  and implemented in R where applicable, to see how different allele combinations for these SNPs resulted in observed differences in the metabolite concentration.y,X,b, and e are the same as in model (1) and (2); SNP is a vector of genotype class 0, 1 and 2 fitted as a classification factor.Where 2 (IPA). Several lists including metabolites (PubChem CID) and candidate genes (Bovine Entrez gene IDs) in P value for the overlap comparison test between the input list and the knowledge base of IPA for a given biological function was less than 0.05. Identification of significant pathways in biological processes was described in detail by P value, as multiple testing for this sort of analysis is not feasible.The interpretation of mGWAS using metabolite concentrations as the target phenotype is a complicated task, because their concentrations are influenced indirectly by mRNA and protein expression as well as directly by several environmental effects. Pathway analysis using prior knowledge improves the interpretation of mGWAS data and provides insight from the genetics of biochemical conversions and biological functions. Functional analyses for the genes associated with each metabolite were performed using Ingenuity Pathway Analysis softwareEleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 (succinic acid) to 0.36 ± 0.15 (choline), while heritability estimates for 22 metabolites were zero or negligible. L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05) , while the other two SNPs were not mapped to any known candidate gene (Three significant SNP associations were detected for betaine (rs109862186), e <0.05) –4. The Sate gene .L-lactic acid. A total of 368 unique genes were identified within the selected SNP windows  for biological functions related to cellular, tissue, and organ development, cell-to-cell signaling and interaction, molecular transport, small molecule biochemistry, lipid metabolism, carbohydrate metabolism, and cellular growth and proliferation. All significant biological functions and their P values for each metabolite are provided in the The eleven heritable metabolites and their candidate genes were significantly enriched  greater in individuals that are homozygotes for the “A” allele of SNP rs81117935 while no significant differences existed for the other two genotypic classes. Our finding suggests that CTNNA2 gene may play a role in the regulation of plasma L-alanine which requires further investigation.Genetic profiling of plasma metabolites has been previously studied in other species to assess their value as biomarkers for disease prediction . Recentln cattle . The SNPskeleton , however results  showed tcholine kinase alpha gene (CHKA) which is associated with choline was mapped inside the SNP window  on chromosome 29. This gene encodes an enzyme called choline kinase alpha , and L-lactic acid (rs42009425) which suggest that the genetic effects may be largely polygenic. The SNP rs81117935 was found to be within CTNNA2 gene which is possibly associated with the regulation of L-alanine concentration in bovine blood. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and candidate genes and networks identified in this study will serve as baseline information for further research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.This study estimated heritability of 33 plasma metabolites for crossbred beef cattle and found low to moderate heritability for 11 metabolites, which provides evidence for the genetic basis underlying the variation of metabolite concentrations. Three significant SNP associations were detected for betaine (rs109862186), https://doi.org/10.7939/DVN/8X1YZC.The SNP data for this study have been submitted to the UAL Dataverse at All the animals were managed and cared for according to the guidelines of the Canadian Council on Animal Care, CCAC .JL carried out data analyses and initiated, drafted, and revised the manuscript with help from GP, EA, and TV. EA helped to manage quality control of data and construct statistic models. TV helped with single-step genomic BLUP analyses. MA-I helped with candidate gene mapping and functional enrichment analyses. BK designed the primary study, helped with data collection and provided information on animals, blood samples, and NMR spectra in the materials section. ZW contributed to the statistics and genetics background of the study. GP was the principal investigator of the project, participated in project management, and experimental design. All authors read and approved the final 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.


Capra genus. In this study, our main objective was to assess the use of sequence data to detect genomic regions associated with traits of interest in French Alpine and Saanen breeds.Goats were domesticated 10,500 years ago to supply humans with useful resources. Since then, specialized breeds that are adapted to their local environment have been developed and display specific genetic profiles. The VarGoats project is a 1000 genomes resequencing program designed to cover the genetic diversity of the Capra hircus sequenced individuals, European goats and French mainland goats. The best results were obtained with the French goat panel with allele and genotype concordance rates reaching 0.86 and 0.75 in the Alpine and 0.86 and 0.73 in the Saanen breed respectively. Mean correlations tended to be low in both breeds due to the high proportion of variants with low frequencies.Direct imputation from the GoatSNP50 BeadChip genotypes to sequence level was investigated in these breeds using FImpute and different reference panels: within-breed, all For association analysis, imputation was performed using FImpute for 1129 French Alpine and Saanen males using within-breed and French panels on 23,338,436 filtered variants. The association results of both imputation scenarios were then compared. In Saanen goats, a large region on chromosome 19 was significantly linked to semen volume and milk yield in both scenarios. Significant variants for milk yield were annotated for 91 genes on chromosome 19 in Saanen goats. For semen volume, the annotated genes include YBOX2 which is related to azoospermia or oligospermia in other species. New signals for milk yield were detected on chromosome 2 in Alpine goats and on chromosome 5 in Saanen goats when using a multi-breed panel.Even with very small reference populations, an acceptable imputation quality can be achieved in French dairy goats. GWAS on imputed sequences confirmed the existence of QTLs and identified new regions of interest in dairy goats. Adding identified candidates to a genotyping array and sequencing more individuals might corroborate the involvement of identified regions while removing potential imputation errors. Capra genus from 65 breeds including 44 French Alpine and 37 French Saanen animals. The sequenced individuals include widely used AI bucks and and enabled to perform a preliminary analysis of imputation for subsequent association analyses. They cover an appreciable part of the effective population sizes of both breeds, estimated to be 115 and 98 in Alpine and Saanen respectively .The recent decrease in sequencing costs has made it possible to sequence large numbers of individuals in key livestock species. The VarGoats resequencing program is the logical next step following the ADAPTmap initiative on 50 k genotyping data . The proImputation is a more cost-effective method for obtaining a large amount of sequence data for subsequent analysis. A high-density genotyped reference panel is used to predict high-density genotypes in a low-density genotyped population. When possible, imputation to a whole-genome sequence is performed in a stepwise manner starting with the lowest density panel, before moving on to a medium density chip, then a high-density chip and finally imputation to sequence level. In dairy cattle and sheep, this method has proved more efficient than direct imputation from lowest density to sequence , 3. In gGenome-wide association studies (GWAS) are commonly used to unravel the genetic architecture of complex traits. The GoatSNP50 BeadChip has led to the detection of a few Quantitative Trait Loci (QTL) regions for milk and type traits in French Alpine and Saanen breeds . One cauFew association studies have been conducted on semen production traits in goats. For example, Nickbin et al.  investigThis study is the first to investigate imputation in dairy goats. Our main objectives were to evaluate the quality of imputation, define the best imputation scenario and finally assess the usefulness of imputed sequence data to identify genome regions associated with semen production and milk yield traits in French Alpine and Saanen breeds.http://www.goatgenome.org/vargoats.html). The current data bank comprises 808 individuals from Capra hircus of various breeds and geographical origins, as well as 21 wild goat individuals. Forty-four French Alpine and thirty-seven French Saanen individuals were sequenced at Genoscope  with an average coverage of 12X. The individuals sequenced were selected to best represent the genetic structure of the current French population: AI bucks from the largest families, maximized haplotype coverage from the population by picking unrelated individuals following the approach described by Druet et al. [Gjk > 0.12) and 7 parent/descendant pairs (Gjk > 0.40). All selected animals except 3 had previously been genotyped using the Illumina GoatSNP50 BeadChip.No animal experiments were necessary for this study, therefore no ethics committee approval was required. Sequence data were obtained from the VarGoats project using Illumina HiSeq or Illumina NovaSeq technologies, the first step towards a 1000 goat genome project  remained on goat autosomes CHI 1 to CHI 29 and were used for subsequent analyses. Marker orders and positions were based on the ARS1 caprine Assembly [http://www.goatgenome.org/projects.html) and made publically available by the International Goat Genome Consortium.A total of 2455 French Alpine individuals  and 1570 French Saanen individuals  genotyped using the Illumina GoatSNP50 BeadChip were available for imputation. Pedigree information was available and used as the genotyped individuals were closely related to the reference panel of sequences. Data were cleaned using an in-house pipeline as described in Martin et al. (2018). In brief, all individuals with a call rate below 95% or showing pedigree inconsistency (i.e. having more than 10% parent/offspring conflicting SNPs) were discarded. SNP quality control was based on the following inclusion criteria: call rate above 99%, MAF above 1% and Hardy-Weinberg https://www.ncbi.nlm.nih.gov/assembly/GCF_001704415.1/) using the Burrows–Wheeler Alignment tool (BWA-MEM version 0.7.15) with default parameters [The sequenced reads were aligned to the goat reference genome assembly ARS1 .According to GATK best practices, BAM files were preprocessed: duplicates were removed, indels realigned and base quality score recalibrated with Picard tools version 2.1.1 and Genome Analysis Toolkit (GATK) version 3.7–0 . VariantVariant calling on the 829 individuals led to the identification of 110,193,942 variants on the 29 autosomal chromosomes: 97,889,899 SNPs and 12,304,043 indels. Among the 829 sequenced individuals, 16 had a mean coverage below 5 (4 French Alpine and 4 French Saanen) and were removed from the data set for subsequent analyses. The sequence dataset therefore consisted of 815 sequenced individuals including 40 French Alpine goats  and 33 French Saanen goats . Quality checks were applied to the sequence variants using the indicators listed in Fig. After the quality check, only variants with at least one observation of the alternative allele (ALT) in French Alpine or Saanen animals were retained in order to reduce computation time in subsequent analyses and only keep variants of interest in our breeds. Thus, 23,338,436 variants, including 40,491 GoatSNP50 BeadChip SNPs, were kept for imputation. Concordance with the 50 k genotypes was checked. After variant filtering, the individual mean concordance rate was 98.24% (± 1.12) and ranged from 94.00 to 99.96%.It should be emphasized that missing genotypes represented on average 4.63% of all the sequence variants for an individual of French Alpine and Saanen breeds. This percentage could attain 66% if sequencing was of low coverage. A within-breed imputation was therefore applied to fill in the gaps. Using a combination of AlphaImpute (v 1.9)  and FImpThree semen production traits were recorded on artificial insemination (AI) bucks Table : semen vMilk yield (MY) in kg was also measured as detailed by Martin et al. , 6 and aImputation of 50 k genotypes to sequence level was performed using FImpute software (v 3.0) which takes pedigree information into account . The accCapra hircus species. We also tried to impute sequences without pedigree while using all sequenced French goats available (excluding Angora and Creole breeds).Various reference populations were tested based on their proximity to French Alpine and Saanen goats. To accurately build the different reference panels, a Principal Component Analysis (PCA) was performed on chromosome 1 filtered data using PLINK software . Groups mlma option of GCTA software [y represents pre-adjusted phenotypes of the trait; μ is the overall mean; b is the additive fixed effect of the variant tested; x is the vector of imputed genotypes coded in 0, 1, 2 ; u is the vector of random additive polygenic effects, u~N with G the genomic relationship matrix; e is the vector of random residual effects normally distributed. The genomic relationship G matrix was calculated on 50 k genotypes using PLINK [The imputed sequences were subjected to single-trait association analysis for milk and semen production traits using mixed linear models with the software  and the ng PLINK .log10(0.05/(number of variants/29)).The four traits were subjected to within-breed association analysis Table . VariantThe results of the sequence data association analysis were then compared with 50 k-genotypes results, by performing a GWAS on the 40,491 SNPs found both in the filtered sequence data and the cleaned GoatSNP50 BeadChip SNPs.10 above the chromosomal threshold. The RumimiR database (http://rumimir.sigenae.org/) [Annotations were extracted from VCF files for variants with a -logae.org/)  was alsoAllele and genotype concordance rates (CR) and correlations (R) between true and imputed sequences were computed for all chromosomes separately. The mean allele CR, genotype CR and R were calculated per variant and per group of variants with the same MAF. Results per MAF are shown in Fig. p − value ≤ 1.22 ∗ 10−7) for milk yield   Table  and a clp − value ≤ 1.17 ∗ 10−7) for milk yield   were directly replaced in sequence data using information of 50 k genotypes. They, therefore, underwent the same analysis using the same model, method and phenotypes. The significance levels tended to be higher with sequence data for all traits in the QTL regions Fig. . Indeed,1 and X2 and rejects the pleiotropy if the squared correlation between a combination of effects at the variant level  test as developed by David et al.  was implWhere N is the number of animals included in the analysis, In our study, we obtained similar imputation results with both a within-breed reference panel and a multi-breed sequence reference panel, provided that the breeds included in the reference population are quite similar to the imputed breeds Fig. . We assuRemoving pedigree information before imputation strongly deteriorated the correlations (R), which decreased by 5.1 to 5.4% depending on the breed. CRs were less impacted, showing a decrease of 0.9 to 1.5% depending on the breed. In our conditions, a complete pedigree therefore seems useful to improve the accuracy of the imputed genotypes.Even though the CRs obtained in this study were similar to those involving equivalent reference population sizes in other species, the correlations were significantly lower than in dairy cattle  or poultOne reason explaining our low correlations could be a negative effect of the large number of variants with a low MAF in our dataset Fig. , for whiCapra hircus species [Another possible explanation is the considerable genetic diversity of the  species , 32. AccMoreover, the reference populations used for imputation were small and some individuals had initial low-depth sequences (coverage <10X). Parts of their sequence genotypes remain uncertain.Nonetheless, most of the QTL regions previously identified with GoatSNP50 BeadChip data were detected on the sequence data with refined signals and increased significance, which suggests that the imputed sequence could be suitable for association analysis.Nevertheless, the significance of the detected regions or the identification of new regions could be further increased by improving imputation quality. As only a limited number of animals in each breed were sequenced, some genotyping errors might be erased by increasing this number. According to Druet et al. , at a gip-values for sequence data were slightly higher than for 50 k data and clear distinct signals were identified when imputed sequence data were used for association analyses  discovered in blood samples, chr5_4536_mature (42.17 Mb) and chr5_4548_mature (45.82 Mb). The two latter are expressed at high levels in mammary tissue but are located further away from the signal and their exact involvement in milk yield is not known. As the signal only appeared when imputing using a French multi-breed reference panel, PCA was performed using PLINK for the region of chromosome 5 between 44.804 Mb and 44.816 Mb to try to understand where the imputed frequencies in the region came from. No significant breed group was found using PCA which implies that the QTL might actually be present in other French goat breeds.p-values between 2.96 ∗ 10−14 and 1.09 ∗ 10−15) are annotated for 2 genes (SCIMP and ZNF232). One of the 10 most significant variants  is situated in an intron of an unknown gene (GENE_id401516). Our study does not allow us to isolate functional candidate genes with certainty as it would require functional analysis. Nevertheless, the proximity of our signal to the ALOX genes cluster constitutes an interesting lead as the latter genes are implicated in lipid metabolism.In Saanen goats, a larger number of significant variants for milk production were detected on chromosome 19 and deeper investigation is required in this area that is also linked to udder health and conformation  and semeFor semen production and more particularly semen volume, using a multi-breed reference panel considerably increased the noise observed on Manhattan plots Fig.  making iA pleiotropic region for milk, type traits and udder health was previously identified on chromosome 19 for the Saanen breed by Martin et al. . Our stuCapra species would improve the quality of imputation to sequence level by proceeding in two steps. Furthermore, functional analyses are required to confirm the involvement of identified genes in the studied phenotypes.This study provides insights on how to implement a robust quality check and an imputation pipeline based on caprine sequence data that will ensure the quality of subsequent analyses. New signals for milk yield traits were detected in both Alpine and Saanen breeds. Signals for semen and milk production traits were detected in the Saanen breed on chromosome 19. The latter regions however require further investigation and annotation to determine the genes involved and determine more precisely their impact. Imputation using a within-breed scenario appears to be more efficient because it is less time consuming. Signals detected after within-breed imputation show less noise and are more significant. However, due to the small size of our sequenced panel, within-breed imputation might not be able to detect smaller weaker signals. Increasing the number of sequenced animals should therefore be considered. Densifying the current genotyping array in the identified regions could corroborate their involvement in functional and production traits while removing potential imputation errors. In the same way, developing a HD chip for


Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed. Accelerating genetic gain is needed to meet humanity's demand for plant products. Genomic selection, which has been largely used in livestock, can be used to enhance genetic gain in plants. The genomic selection strategies reviewed in this paper for plants include field and environment management, optimum model development, cost reduction, platform and network construction, open-source breeding, and transdisciplinary approaches. To meet the demand for plant-based products, plant breeding has been systematically evolving from art to science with the advent and development of genetics and genomics . Taking In early breeding stages, breeders intentionally selected plants and animals, mainly based on phenotypes that may include a few key yield-related traits, to achieve the genetic gain for target traits. As the development of quantitative genetics and statistics continued, best linear unbiased prediction (BLUP) was proposed  and usedIn GS, genome-wide markers are used to estimate their effects through optimum statistical models, then GEBVs are calculated for each individual to select potential elite lines. More precisely, two types of populations are required in GS, a training population (TP)  that is composed of a cohort of individuals with both genotypic and phenotypic data, and a breeding (or testing) population (BP) that consists of candidate breeding lines with genotypic data only. Data from TP are used to train a statistical model to estimate the effect of each assayed marker and then calculate the estimated breeding values for each genotyped individual in BP to rank the lines without phenotyping. Furthermore, these reserved individuals can be served as parental lines that may intermate with each other to pyramid favorable alleles for the next cycle of selection . AlthougG = i σArMG/t, where ΔG is the expected genetic gain, i is intensity of selection, σA is genetic SD, namely the square root of additive genetic variance, rMG is selection accuracy , and t is breeding cycle time. In the context of phenotypic selection, however, the rMG is equivalent to the square root of the narrow sense heritability (h), and thus ΔG = i σAh/t  can be expressed using the following formula:x1 is associated with marker density, x2 population size and structure, x3 TP–BP relationship, x4 heritability, and x5 genetic models; a to e are constants associated with the five corresponding variables x1 to x5, which may not be linearly regressed with rGM. As refining field management for improved heritability estimation is more related to breeding technologies; heritability-related issues will be discussed in the next section.Regarding the aforementioned strategies aimed at enhancing genetic gain, one of them aims to strengthen GS per se with improved predictive accuracy under cost-benefit balance  and 2. IMarker density has been shown to be an extremely important factor affecting prediction accuracy. Generally, high marker density can have benefit to augment prediction accuracy until prediction accuracy reaches a plateau and does not increase further as marker density increases . Moreoveh2 = 0.2 and h2 = 0.4, respectively  and grown in four field locations. Prediction accuracies of yield generally increased with training-set size. Higher prediction accuracies were obtained using related crosses in training and validation sets compared with using unrelated crosses, indicating the importance of training-panel design  as genomic features, the revised GBLUP led to further increases in prediction reliability (up to 13%–38% points across different populations) (Using a dataset consisting of 1500 Jersey bulls with sire conception rate (SCR) records and 95 000 SNPs, linear and Gaussian kernel-based models were used to fit both the entire SNPs and the subsets of SNPs either significantly associated with SCR or located within or close to annotated genes. The entire SNP set exhibited predictive correlations of around 0.30. SNPs marginally associated with SCR or genic SNPs both achieved higher predictive abilities than their counterparts using random SNPs . Predictlations) . In factlations) . TherefoVarious statistical models can be implemented in GS to train genotypic and phenotypic data to determine whether the marker effects are precisely estimated . Many reThe GS models developed on the basis of a non-linear kernel algorithm, such as reproducing kernel Hilbert space (RKHS), have specific capacity of capturing non-genetic effects and improving the estimated accuracy of marker effects . By collin silico based on 207 parental lines genotyped by 500 000 SNPs. Including the dominance effect increased the prediction accuracy for grain production by up to 30%, while the inclusion of interaction effects via multienvironment modeling increased the prediction accuracy overall  and interlocus (epistasis) non-additivity. With the presence of non-additive effects, breeding populations can have different allele substitution effects at the associated QTL. Both TPs and BPs should be examined to quantify the QTL allele substitution effects for their consistency across populations. With observed predominant consistency, core TPs can be developed to support broad GS application across multiple breeding populations . When no overall .The combined effects of epistasis and GEI can be tackled to enhance genomic prediction for complex traits. As one of the gene-to-phenotype (G2P) models, the crop growth model (CGM) is proposed to capture the combined effects to complement conventional GS . By coorWith the development of postgenomic tools and accumulation of omics data, integrating transcriptomic and metabolomic data into the GS models has improved prediction accuracy, because of efficiently capturing minor and non-additive effects especially when hybrid performance was predicted  and 2. MFrom the perspective of GS, there are two distinct strategies for enhancing genetic gain in plant breeding. One is to improve prediction accuracy as discussed in the previous section, and the other is to integrate GS with other breeding technologies such as MAS, marker-assisted recurrent selection (MARS), the transgenic approach, genome editing (GE), and doubled haploid (DH) technology, to shorten the breeding cycle time  and 3. IGenetic variation has been considered as the foundation for breeding selection, which provides genetic resources to accumulate favorable alleles or genes that are linked with targeted traits. There are two approaches that broaden genetic variation for GS: unlocking that which is hidden in genetic resources and creating that which does not exist in our target plants. Creating more genetic variation may increase both genetic diversity and novel germplasm that can offer more candidates and valuable lines for selection . In factTransgenic technology and GE are two powerful tools that can be used to improve plant species, the former transferring favorable genes from distant species into crop plants and the latter producing site-specific sequence changes that contribute to improved agronomic traits. Advanced transgene techniques can now stack multiple genes such as those for insect and disease resistance and herbicide tolerance  into theExotic germplasm that host hidden genetic variation can be harnessed, and relevant genes can be transferred into elite germplasm pools through prebreeding. Such a prebreeding process is more practical in plants than in animals (livestock) . Using hVg) in phenotypic variance (Vp), the latter being composed of genetic (Vg) and environmental (Ve) variances. Therefore, refining field management will reduce environmental effects and experimental errors, and thus improve heritability estimation and prediction accuracy , or some combinations thereof. Open-source breeding provides numerous opportunities of using existing and accumulated genotypic and phenotypic data created worldwide in the public sector e.g.,  or GS coGenotyping cost usually accounts for a large proportion of overall breeding cost, determining how GS could be implemented. By sharing genotyping platforms and running numerous samples in genotyping, multinational seed companies have achieved a significant cost advantage (up to 50%–70% savings) compared with individual breeding programs in developing countries. Establishing sharable genotyping platforms becomes one of the best options to reduce genotyping-related costs. Such sharable GS breeding platforms should be established for all GS-related components, including phenotyping, envirotyping, information management, and decision support tools . The plaReduced-representation GBS  or skim-The scale of breeding programs in developing countries and small- and medium-sized breeding companies is markedly smaller than that in developed countries and multinational incorporations, so that molecular breeding platforms such as those for genotyping, phenotyping, and envirotyping, once established, will be left unused for most of the time unless they are shared across companies, institutions, or countries. Therefore, an open-source GS breeding network, combined with shared molecular breeding platforms, needs to be built up, which has the capacity of providing national agricultural research organizations and small- and medium-sized breeding companies with advanced and comprehensive breeding technologies, including a high-throughput genotyping platform, improved phenotyping capacity, integrated germplasm resource management, and well-established modeling and prediction approaches that are now only available and functional in multinational breeding companies . The useAn open-source breeding network can be viewed as part of a high-efficiency breeding pipeline, and has at least four general advantages: (1) the ability to share phenotypic information between network members; (2) providing synthetic pipeline services and genotypic data between members with partnership; (3) low-cost genotyping platforms; (4) the capacity to obtain haplotype effects among environments and traits that cannot be analyzed individually . With thhttp://gobiiproject.org/) has been established for systematically applying high-density genotypic information to plant breeding. Open-source genomic data management and analysis tools have enabled breeders to integrate their breeding programs with GS and other MAS Strategies. As a support to the open-source breeding network, CGIAR established the Excellence in Breeding Platform (http://excellenceinbreeding.org), which consists of six components: breeding program excellence; trait discovery; breeding tools and services; genotyping and sequencing; phenotyping; and bioinformatics and data management. Another component that should have been included is envirotyping for collecting various sources of environmental data and their use in plant breeding (https://osseeds.org/), recruits a group of excellent plant breeders, industrious farmers, seed enterprises, nonprofit organizations, and policymakers for the purpose of maintaining and promoting an available open-source gene bank that can share the plant genetic resources and germplasm among participants around the world. OSSI can provide all kinds of and accessible opportunities for breeders to release newly developed lines or breeding populations compiled by the OSSI pledge, for which the cultivars or varieties should be unique and have been developed based on different heterotic groups  were selected based on GEBVs and recommended to breeders for further testing and validation. Selection intensity in GS was doubled compared with that used in phenotypic selection, around 20% in Stage-1 testing. The total genotyping cost for running this study is US$25 000 with a subsidy of $3.5 per sample from Bill and Melinda Gates Foundation, while the full genotyping cost without any subsidy is around $40 000, which is equivalent to the cost of phenotyping 1000 lines in three locations each with two replications, according to the current cost at CIMMYT, $7.00 per plot. Therefore, the tested population size in this GS study increased eightfold compared with the phenotypic selection . The coordination and communication to enable delivery against tight deadlines is critical in open-source breeding programs. This testing of GS at scale at CIMMYT has the potential to affect many more breeding programs through the Excellence in Breeding Platform. This was a sizeable, real-time GS application in public-sector breeding programs serving low- and middle-income countries, and breeders will be interested in what we learn from this process. Hence, a breeding program of integrating GS with other available approaches and tools should be established to assist developing countries, public sectors, and small- and medium-sized enterprises to augment efficiency and the level of breeding, and thus enhance the genetic gain in farmers' fields.The efficient and precise GS pipeline should be constructed for achieving and obtaining greater genetic gain and for improving the production of staple crops to meet the human demand from an increasing global population. In the era of molecular breeding, GS as a prominent and promising strategy will become an increasingly widespread application in plant breeding, as in livestock, with the evolution of key GS components and associated platforms. With the development of cost-effective genotyping platforms and high-efficiency breeding strategies, GS-assisted breeding will spread from livestock to plants and from case applications in few crops for some traits to wide applications in all major crop plants for all important traits, from individual regions to countries worldwide, and from isolated private sectors to associated partners through open-source breeding networks. The collaboration between GS and other technologies or transdisciplinary approaches is extremely important for developing a high-efficiency breeding pipeline in terms of rapidly pyramiding major genes identified by QTL mapping or GWAS into targeted lines. Besides, a set of closely linked genes within the chromosome can be inherited together as a haplotype and integrally transmitted from parental or ancestral lines to offspring. The concept of haplotype can be extended from the level of a single region within a chromosome to the whole genome to cover many functional markers. To enhance genetic gain by GS, genomics-assisted tools should be utilized to create a chimera that contains optimized combinations and haplotypes of two or more elite parental lines. GS-assisted breeding programs can be implemented with the reference of haplotypic effects, from which the accuracy of GS prediction can be improved and the associated breeding pipeline can be optimized to stack favorable genes into one elite line to create excellent varieties or hybrids. By incorporating more and more known genes and their favorable haplotypes, the blocks with pyramided favorable genes and networks for the target trait and trait combinations can be built up. Comparative analysis of GS results with long-term phenotypic selection for protein and oil contents that have been running for over 100 generations  and with10.13039/501100012166National Key Research and Development Program of China (2016YFD0101803), the 10.13039/501100012166National Key Basic Research Program of China (2014 CB138206), the 10.13039/501100012421Agricultural Science and Technology Innovation Program of 10.13039/501100005196CAAS, and Fundamental Research Funds for Central Non-Profit of Institute of Crop Sciences, CAAS (1610092016124). Research activities of CIMMYT staff have been supported by the 10.13039/100000865Bill and Melinda Gates Foundation and the CGIAR Research Program MAIZE.The research involved in this report was supported by the


Padi-Tuya, Songotra, and IT86D-610 were identified as good general combiners for grain yield, while Sanzi-Nya was identified as a general combiner for developing extra-early duration cowpea varieties. Crosses Songotra × Sanzi-Nya, SARC-1-57-2 × IT86D-610, Songotra × SARC-1-57-2, and Padi-Tuya × Songotra were identified as good specific combiners for days to 50% flowering, pod length, pods per plant, pod yield, grain yield, and seeds per pod. The findings from this study provide useful information on the inheritance of early maturity and yield traits in cowpea. This can be exploited to develop high yielding and early maturing cowpea varieties as climate smart strategy to mitigate climate change via breeding methods such as pedigree selection and marker assisted backcrossing (MABC). Pedigree selection method is being used to develop varieties from the hybrid with high and significant SCA for grain yield, whereas the development of extra-early duration varieties via MABC with Sanzi-Nya  as a donor parent is ongoing.Information on combining ability and reciprocal effects (REC) facilitates efficient utilization of genetic materials in a breeding program. This study was conducted  to determine general combining ability (GCA) and specific combining ability (SCA), heritability, genetic advance, GCA, and SCA effects as well as the relationship between parents per se performance and progenies for yield components and maturity traits in cowpea. The test populations were derived using a 5 × 5 complete diallel cross of parents with different yield attributes and maturity durations. The results indicated that GCA was predominant for number of days to 90% pod maturity, plant height at maturity, and hundred-seed weight. This showed that genes with additive effects conditioned these traits.  Vigna unguiculata (L.) Walp.) (2n = 2x = 22) is one of the most commonly cultivated grain legumes in sub-Saharan Africa, possibly because of its relatively wide adaptation to drought and ability to give appreciable yields on low-nutrient soils, where other crops would fail. It has the ability to fix up to 240 kg·N/ha, with an N benefit of 60–70 kg/ha to succeeding crops in rotation on infertile soils [Cowpea  to determine the combining abilities of selected cowpea genotypes for yield attributes and maturity and (2) to estimate heritability for maturity and yield components.Ferric Luvisols of the Tingoli series, with a brown color, moderately drained, and free from concretions [The research was conducted at the Council for Scientific and Industrial Research-Savanna Agricultural Research Institute (CSIR-SARI), Tamale. The CSIR-SARI is in the Guinea Savannah agroecological zone of Ghana . The area is characterized by a monomodal rainfall pattern, which normally begins in May and ends in early October, with an average annual rainfall of about 1200 mm. The cropping season commences in mid-June and ends in October, with the rest of the season being dry. The soils of the experimental site belong to cretions .Sanzi-Nya, Padi-Tuya, and Songotra, and their progenies. These genetic materials had phenotypic variation for grain yield, number of days to pod maturity, seed size, and color , with three replications at research field of CSIR-SARI during the 2016 cropping season at one location due to limited number of F1 seeds obtained from the crosses. Each plot was made up of two rows, each measuring 2 m. Field pests were controlled using K-Optimal  at the rate of 500 ml per ha at vegetative stage, flowering, and pod formation. Weeds were manually controlled as and when necessary.A 5 × 5 full diallel cross was made to generate 20 Data were collected on the following maturity traits: number of days to 50% flowering (DFF) (days were counted from the day of planting to the day 50% of the plants in each plot had at least one flower) and number of days to 90% pod maturity (DTM) (from the day of planting to the day 90% of the pods in each plot turned yellowish). The grain yield and its related traits were also measured from five randomly selected plants from each plot. Plant height (cm) at flowering (PHF) was measured from the base of each of the five selected plants to the terminal bud on the main stem, while plant height at maturity (PHM) was measured at 90% pod maturity. The number of branches at maturity (NBM) were counted from the five plants in each plot at 90% pod maturity, whereas in the case of number of pods per plant (NPPLT), pods were counted from each of the five selected plants. For number of seeds per pod (SPOD), 10 pods were randomly selected from each plot and the number of seeds was counted and averaged across the number of pods used. In the case of pod length (PODL), 10 pods were randomly selected from each plot and their lengths were measured in cm. The mean value was used to represent the plot. Hundred-seed weight (HSWT) was measured in grams from the weight of 100 randomly selected dried seeds. Pod yield (PYLD) and grain yield per plot (GYLD) were determined as average weight of pods and seeds harvested, respectively, from each plot and converted to t/ha.Yijk is the observed trait value from each experimental unit; μ is the overall mean; gi is the GCA effect of the ith parent; gj is the GCA effect of the jth parent; sij is the SCA effect of the ijth cross; rij is the reciprocal effect of the ijth cross; bk, is the replication effect; and eijk is the random residual effect .All data collected were subjected to analysis of variance using PROC GLM in SAS  to deterrij was further partitioned into maternal effect (mi) of the  ith parental line and nonmaternal effect (nmij) of the cross between the ith and jth parental lines [The reciprocal effect al lines .σGCA2), specific combining ability (σSCA2), reciprocal effects (σREC2), and error variance (σe2) were computed using mean squares for GCA, SCA, REC, and error extracted from the analysis of variance table. These were obtained by equating observed means squares to their expected means squares values and solving the resulting equations for the variance components [Variance components attributable to general combining ability (mponents .σGCA2), specific combining ability (σSCA2), reciprocal effects ( σREC2), and error variance (σError2) according to the procedures outlined by [The genetic variances were determined from the following equations using variance components for general combining ability (lined by –27.H) and narrow-sense heritability (h) were calculated from the following formulae [Both broad-sense (formulae :(3)broadThe relative importance of GCA and SCA effects for each trait was determined following the procedure described by :(4)Bakerμ = mean value of the particular trait; σp2 = phenotypic variance; and σG2 = genotypic variance.Phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were estimated according to  as folloihσp, where i = 1.76 (10% selection intensity), h = narrow − sense heritability, and σp = square root of phenotypic variance. Genetic advance was expressed as percentage of the mean.Genetic advance (GA) was computed per the formula proposed by  as follop < 0.0001) difference for all the measured traits  for the crosses except cross IT86D-610 × Sanzi-Nya, which had positive significant SCA effect. The number of seeds per pod had positive and significant SCA effect for cross Songotra × Sanzi-Nya and IT86D-610 × Sanzi-Nya, while cross Padi-Tuya × Sanzi-Nya and SARC 1-57-2 × Sanzi-Nya had negative and significant SCA effect for number of seed per pod. Hundred-seed weight exhibited positive and significant SCA effect for only cross Songotra × Sanzi-Nya, while only cross IT86D-610 × Sanzi-Nya had negative and significant SCA effect.Positive and significant SCA effect for grain yield (GYLD) were detected from crosses of A effect . CrossesThe variance components due to SCA were predominant over GCA and REC variances for most of the measured traits . The magBroad-sense heritability for all measured traits ranged from 40.06% to 97.99% for pod yield and hundred-seed weight, respectively, whereas narrow-sense heritability varied from 2.01% to 97.30% for pods per plant and hundred-seed weight, respectively . Again, Traits such as grain yield, pod yield, number of branches at maturity, pod length, pods per plant, and seeds per plant had very low narrow-sense heritability estimates between 2.01 and 25.49 . The magGenetic advance as percentage of mean ranged from 1.64 to 61.62% for pods per plant and hundred-seed weight. Number of days to 50% flowering (5.49%), pod length (4.97%), pod yield (2.82%), seeds per pod (2.51%), grain yield (2.30%), and pods per plant (1.64%) exhibited low magnitude of genetic advance as a percentage of mean (<10%). On the other hand, number of branches at maturity had moderate estimates of genetic advance greater than 10%. High genetic advance as percentage of mean (>20%) was recorded for plant height at flowering, plant height at maturity and hundred-seed weight .Sanzi-Nya for days to 50% flowering and days to 90% pod maturity also suggests that these parents could contribute favorable alleles for breeding early maturity in cowpea. Similarly, IT86D-610 and Sanzi-Nya are good general combiners and sources of genes for early and extra-early maturity in cowpea, respectively. Negative GCA for days to 50% flowering and days to 90% maturity are indication of favorable dominant or partial dominant alleles, which contribute to earliness [Sanzi-Nya into elite varieties [Padi-Tuya, Songotra, and IT86D-610 imply that crosses involving these varieties had superior mean performance than the general mean and as such indicate evidence of desirable gene flow from the parents to the offsprings at high intensity [Griffing's analysis of variance revealed significant GCA and SCA effects among diallel crosses with both additive and nonadditive gene effects being important for the inheritance of the pod yield and maturity traits studied in cowpea. The significant positive GCA for yield and yield-related traits observed for IT86D-610 indicates that this parental line could be useful for contributing favorable alleles for improving grain yield. Significant GCA effect is a manifestation of additive and additive × additive gene interactions, which represents a heritable portion of genetic variation which is transmittable from the parent to its progeny . This tharliness , 31. Thearieties . This rearieties , 21, 33 ntensity . They coPadi-Tuya × Songotra, Songotra × SARC 1-57-2, Padi-Tuya × IT86D-610, SARC 1-57-2 × IT86D-610, Songotra × Sanzi-Nya, and IT86D-610 × Sanzi-Nya were reported to be high combiners for yield. On the other hand, Padi-Tuya × Songotra and Padi-Tuya × Sanzi were good specific combiners for days to 50% flowering and days to 90% pod maturity. These crosses could be exploited for producing desirable transgressive segregants in breeding for earliness [The positive and negative significant SCA observed for grain yield and pod maturity, respectively, provide opportunity for improving yield and earliness in the cowpea genotypes evaluated. This was evident in the small SCA values observed for days to 50% flowering and days to pod maturity. Crosses arliness .The higher values of SCA variance for yield components compared with the GCA variance and REC variance reveal the importance of genes with nonadditive effects, whereas GCA variance for maturity traits compared with the SCA variance and reciprocal variance is an evidence for additive gene effects .F1 crosses and their reciprocals were significantly expressed for grain weight, pod weight, days to 50% flowering, and days to first flower appearances. This implies that the success or progress made on these traits was dependent on the direction of the cross. The high magnitude of reciprocal effect indicates that maternal effects or cytoplasmic inheritance were influential [Baker's ratio close to unity (≤1) for the maturity traits  also confirms the importance of additive gene action in modulating the expression of these traits. On the other hand, Baker's ratio close to zero for grain and pod yields implies that SCA estimates were more important. The estimates of Baker's ratio for these traits (grain and pod yields) confirmed the importance of nonadditive gene action. Thus, predicting hybrid performance based on GCA values alone will be ineffective. The predominance of nonadditive gene effects for these traits also suggests that genetic gain could only be achieved through hybridization followed by selection at advanced generations, where the genes are fully fixed and expressed, dominance is dispersed, and undesirable linkage is broken . The difluential  for graiHigh broad-sense heritability observed for number of days to 50% flowering (87.95%) and days to 90% pod maturity (89.22) indicates that additive or genetic factors influenced the expression of the traits and that the traits were less influenced by environmental factors. Similar results have been reported by . The higInformation on narrow-sense heritability is of prime importance to the breeder as a measure of efficiency in selection and as an index of transmissibility of favorable additive genes from parents to their offspring's . Narrow-The high genetic advance observed for plant height at flowering, plant height at maturity, and hundred-seed weight shows that additive genes controlled the expression of the traits . This inGenerally, there was higher phenotypic coefficient of variation than the genotypic coefficient of variation for both maturity traits and that of the yield components. However, the yield components recorded a higher PCV: GCV ratios than those of the maturity traits. This implies that the large ratio recorded for yield components was mainly due to environmental factors rather than the genetic factors controlling the traits. On the other hand, the maturity traits which had lower PCV: GCV ratios indicate that there was less environmental influence on maturity traits than grain yield. Hence, low PCV: GCV ratios are desirable in cultivar development. Similar finding has been reported by  for cowpPadi-Tua, Songotra, and IT86D-610 were identified as general combiners for grain yield. IT86D-610 and Sanzi-Nya were also identified as general combiners for early and extra-early maturity traits, respectively. IT86D-610 and Sanzi-Nya have small grain size and undesirable seed coat colors (brown and mottled); however, they could be used to improve low yielding, medium to late maturing varieties preferred by farmers, depending on the region. High broad- and narrow-sense heritability estimates were observed for number of days to 50% flowering, days to 90% pod maturity, hundred-seed weight, days to maturity, and plant height at maturity suggesting potential for improved genetic gain for these traits. Strong positive association between parental performance and GCA effects for the yield components and maturity traits studied indicated that selection can be made based on per se performance of parents for these traits. Though these observations were based on the results of single experiment, they provide basic insight into the inheritance of yield and maturity in cowpea which can be further tested in multilocational trials to validate their reliability in cowpea breeding programs for yield and maturity.