We extend a powerful individual-level data Bayesian multiple regression model (BayesR) to one that utilises summary statistics from genome-wide association studies (GWAS), SBayesR. In simulation and cross-validation using 12 real traits and 1.1 million variants on 350,000 individuals from the UK Biobank, SBayesR improves prediction accuracy relative to commonly used state-of-the-art summary statistics methods at a fraction of the computational resources. Furthermore, using summary statistics for variants from the largest GWAS meta-analysis  and show that it outperforms other summary statistic-based methods. Through large consortia and biobank initiatives, sample sizes for genome-wide association studies (GWAS) are reaching a critical point, now for some traits greater than a million individuals, at which, and under optimal modelling conditions, the genetic predictors generated could approach their maximum (from theory) prediction accuracy for some traits10.Accurate prediction of an individual’s phenotype from their DNA sequence is one of the great promises of genomics and precision medicinep value thresholding14. Although simple to implement and useful, this method leads to suboptimal genetic predictions16. Linear mixed model (LMM) methodologies have been shown to improve prediction accuracy21, and jointly analyse all SNPs, which accounts for linkage disequilibrium (LD) between markers, capturing the maximum amount of variation at a genetic locus, especially if multiple causal variants colocalise. Bayesian multiple regression (BMR) methods extend the standard LMM to include alternative genetic effect prior distributions and have been shown to further improve genomic predictions27. Recent BMR implementations require access to the individual-level data26 and currently do not scale well computationally to data with greater than half a million individuals and millions of genetic variants. Restricted access to individual-level genetic and phenotypic data has motivated methodological frameworks that only require publicly available summary data28. Summary statistics methodology now covers the gamut of statistical genetics analyses, including effect size distribution estimation30, joint SNP association analysis and fine mapping32, allele frequency and association statistic imputation35, heritability and genetic correlation estimation41 and polygenic prediction44. These methods require GWAS summary data, which typically include the estimated marginal effect, standard error and an estimate of LD among genetic markers, which are easily accessed via public databases.One approach for generating polygenic predictions uses a linear combination of simple linear regression effect size estimates and allele counts at single-nucleotide polymorphisms (SNPs) that are selected via marker pruning coupled with 39) and a finite mixture of normal distributions prior on the marker effects. We focus on optimising prediction accuracy, but the methodology is capable of simultaneously estimating SNP-based heritability In this work, we derive a BMR summary statistic methodology, SBayesR, that performs Bayesian posterior inference through the combination of a likelihood that connects the multiple regression coefficients with summary statistics from GWAS 39, LDpred42, summary best linear unbiased prediction (SBLUP)43 implemented in the GCTA software45 and clumping and p value thresholding (P + T) implemented in the PLINK 1.9 software46. For 36, individual-level data Haseman–Elston regression (HEreg) method47 and additionally for case–control phenotypes S-PCGC41. In fivefold cross-validation with 1.1 million HapMap 3 (HM3) variants and 12 real traits in the UKB, we show that SBayesR obtains similar prediction accuracies to BayesR and increases prediction accuracy over other summary statistics-based methods. We further perform large-scale analyses for height and body mass index (BMI) by using 1.1 million HM3 variants and an extended set of 2.8 million pruned common variants from the full UKB European ancestry  data set and predict into the independent Health and Retirement Study (HRS) and the Estonian Biobank (ESTB) data sets. In these across-biobank analyses, we show that by exploiting summary statistics from the largest GWAS meta-analysis to date  on height and BMI48 that on average across traits SBayesR improves prediction accuracy by 6.4% relative to an individual-level data BayesR analysis from the UKB . We show that SBayesR improves the prediction R2 by 5.2% relative to LDpred and by 26.5% relative to clumping and p value thresholding and gives comparable prediction accuracy to the RSS method, but at a computational time that is two orders of magnitude smaller. SBayesR achieves a maximum prediction accuracy for height of R2 = 0.352 in the ESTB when we use 2.8 million pruned common variants from the full UKB European ancestry cohort.We establish that SBayesR improves prediction accuracy over other state-of-the-art summary statistics methods in a wide range of simulations by using real genotype data from 350,000 unrelated individuals of European ancestry from the UK Biobank (UKB). We use the following state-of-the-art methods for comparison: individual-level data BayesR27, freely available at http://cnsgenomics.com/software/gctb/.A detailed method description is provided in the ‘Methods’ section. In brief, we express the multiple linear regression likelihood such that it is a function of GWAS summary statistics and a reference LD correlation matrix. We couple this likelihood with a flexible finite mixture of normal distributions prior on the genetic effects that incorporates sparsity, to perform Bayesian posterior inference on the model parameters, which include the genetic effects, variance components and mixing proportions. A right-hand side updating scheme is combined with the use of sparse matrix operations on the reference LD correlation matrix to maximise the computational efficiency of the implemented Gibbs sampling algorithm. The resultant methodology is capable of generating powerful polygenic predictors from the massive GWAS results now available at a fraction of the time and memory of individual data methods. Our method is implemented in the GCTB softwareBefore performing simulations using genome-wide variants, we thoroughly tested individual-level and summary statistics-based methods using a simulation study on chromosomes 21 and 22 and 100,000 individuals from the UKB  and removal of long-range LD regions . For the same set of variants, we generated two independent tuning and validation genotype sets, each containing 10,000 individuals.To investigate the performance of the methodology at a genome-wide scale, we simulated quantitative and case–control phenotypes using 1,094,841 genome-wide HM3 variants and a random subset of 100,000 individuals from the 348,580 unrelated European ancestry individuals in the UKB data set. We chose the HM3 variant set because it captures common variation well, has precedence in the literature as a widely used set, variants are imputed to high accuracy and 1 million variants were within the computational scope of the methods applied. The 1,094,841 variant subset was formed from the 1,365,446 HM3 SNPs further filtered on minor allele frequency (MAF) > 0.01, strand ambiguous SNPs . For methods requiring summary statistics (except S-PCGC), simple linear regression was run in the PLINK 1.9 software for each of the 10 simulation replicates in the eight simulation scenarios. To assess prediction accuracy, we generated polygenic risk score (PRS) (using the PLINK 1.9 software) for each individual by using the genotypes from the 10,000 individual tuning and validation data sets and the genetic effects estimated from each method. Parameter tuning was performed for LDpred and P + T, where for each simulation replicate the prediction accuracy was assessed for each of the prespecified fraction of nonzero effect parameters for LDpred and the pruning R2 and p value thresholds for P + T. The parameters that gave the maximum prediction accuracy in the tuning data set were then used for PRS calculation in the validation data set. The prediction R2 was calculated via linear regression of the true simulated phenotype on that predicted from each method. For the case–control scenarios, prediction accuracy is summarised by using the area under the receiver-operating characteristic curve (AUC) and the 51, except for S-PCGC.For each simulation scenario, the following methods were used to estimate the genetic effects and heritability: BayesR, SBayesR, RSS, LDpred, SBLUP and P + T. For further R2 across the 10 replicates  improvement in mean prediction R2 over other summary statistics methodologies in the 10,000 causal variant scenario and Across the simulation scenarios, BayesR or SBayesR gave the highest or equal highest mean validation prediction Across all scenarios, all methods except RSS showed minimal bias in 39. Across the simulation scenarios, SBayesR had the shortest mean runtime (~2–4 h) with up to a fourfold improvement over the second quickest LDpred  was chosen as mean prediction accuracy did not improve for further iterations , male-pattern baldness , basal metabolic rate , heel bone mineral density T-score , forced vital capacity , type 2 diabetes , body mass index , body fat percentage , forced expiratory volume in 1 s , hip circumference , waist-to-hip ratio  and birth weight . The primary SNP set used for method comparison was the same set of 1,094,841 HM3 variants described in the genome-wide simulation study. To investigate the capacity of SBayesR to perform analyses at large scale, we generated a set of 2,865,810 common (MAF > 0.01) variants by pruning (R2 > 0.99) a larger set of 8 million variants from the UKB that were of good quality, overlapped with previous large GWAS8 and were present in the 1000G genetic map. A set of 5000 individuals was kept separate for LDpred and P + T parameter tuning. To perform the cross-validation, the remaining individuals were partitioned into five equal-sized disjoint subsamples. For each fold analysis, a single subsample was retained for validation with the remaining four subsamples used as the training data. This process was repeated five times. We generated summary statistics for each pre-adjusted trait (see Methods) in the training sample in each fold by running simple linear regression in PLINK 1.9. We performed cross-validation for all traits by using the following methods: BayesR, SBayesR, RSS, LDpred, SBLUP and P + T. For R2 was calculated via linear regression of the true phenotype on the PRS from each method or the AUC for type 2 diabetes. We assessed the adequacy of four mixture distributions for use in SBayesR by performing the cross-validation analyses using 2–6 mixture distributions. The use of four mixture distributions was a good compromise between prediction accuracy and computational speed across all traits  for height, 15.1% (from 0.199 to 0.229) for MPB and 5.1% (from 0.188 to 0.197) for heel BMD. For height, MPB, BMR, hBMD, FVC, T2D and BMR, SBayesR showed significant mean increases  in prediction accuracy over RSS with a relative improvement in mean prediction accuracy of 2.1% (from 0.342 to 0.349), 4.7% (from 0.190 to 0.199), 1.5% (from 0.171 to 0.174), 2.8% (from 0.182 to 0.188), 3.0% (from 0.124 to 0.127) and 1.6% (from 0.646 to 0.657 AUC), respectively  by 9.6% from 0.349 to 0.383 for height and by 2.3% from 0.124 to 0.126  for BMI, when the expanded set of 2.8 million common variants were used over the HM3 set.For the HM3 variant set, SBayesR improved or equalled the mean prediction accuracy of all other methods, including the individual-level BayesR method, across the five folds for 9/12 traits Fig. . BayesR For all traits except height, SBayesR on mean took ~1.5–4.5 h and required 40 GB of memory to complete a genome-wide analysis  with variability in run time depending on the number of nonzero variants in the model  and used summary statistics from the largest meta-analysis of BMI and height48. For these analyses, the same set of 1,094,841 genome-wide HM3 SNPs and expanded 2.8 million common variant sets described in the cross-validation analyses were used. The set of traits was limited to BMI and height as these traits were present in the UKB and in accessible large independent validation sets, which included the HRS and ESTB.To investigate how the proposed methods scale and perform in very large data sets, we analysed the full set of unrelated and related UKB European ancestry individuals  in the cross-validation. We subsetted the set of 1,094,841 HM3 variants to 982,074 variants that overlapped with those in both the BMI and height summary statistics sets from Yengo et al.48. After per-SNP sample size quality control (see Methods), 932,969 and 909,293 variants with summary information remained for height and BMI, respectively. These sets of variants were also used in the LDpred and RSS analyses.Motivated by the hypothesis that summary statistics methodologies can increase prediction accuracy over large-scale individual-level analyses by utilising publicly available summary statistics from very large GWAS, we took the summary statistics from the largest meta-analysis of BMI and heightR2 values than BayesR for both BMI and height in both the HRS and ESTB samples . When the summary statistics from Yengo et al.48 were used, a further improvement in prediction R2 over BayesR was observed for SBayesR, RSS and LDpred for BMI, and for SBayesR and RSS for height  and 3.5% (from 0.315 to 0.326) for height in the ESTB sample when the summary statistics from Yengo et al.48 were used. For height, we observed a maximum relative increase of 19.9% (from 0.272 to 0.326) in SBayesR prediction R2 over the P + T predictor of Yengo et al.48 in the ESTB sample when the summary statistics from Yengo et al.48 were used. The expansion of the variant set to 2.8 million variants did not improve the prediction R2 for BMI. However, the maximum prediction accuracy for height was achieved when SBayesR was used to analyse the summary statistics generated from the full UKB for the 2.8 million variant set, with a prediction R2 value of 0.352 in the ESTB.Overall, SBayesR gave similar but consistently higher prediction ght Fig. . SBayesRClinically relevant genetic predictors for complex traits and disorders will require the analysis of data from large consortia and biobank initiatives, with efficient methods that produce powerful polygenic predictors being critical to this goal. We have presented one solution, SBayesR, that rests on an extension of the established summary statistics methodological framework to include a class of point-normal mixture prior Bayesian regression models and shown it to be a powerful method for polygenic prediction.The observation that SBayesR improves on the BayesR prediction accuracy in real data cross-validation and independent out-of-sample prediction is contrary to expectation. The major difference between these two methods is that interchromosomal LD is ignored in the SBayesR method. Such between-chromosome LD may result from genetic sampling in finite population sizes, population structure and nonrandom mating . The incorporation of this information appears only advantageous for predictions performed within an independent subset from the same population, for example, the partitioning of the UKB in the simulation studies and in cross-validation. The HRS and ESTB data are unlikely to contain the same interchromosomal LD correlation structure and thus its inclusion in the BayesR analysis may be partially detrimental as it comes into the model as informative within the data set (UKB) but as noise across data sets (UKB to HRS/ESTB). One hypothesis for this is that the HRS and ESTB populations have different patterns of assortative mating for specific traits than in the UKB, or individuals in HRS or ESTB are more randomly mated than those in the UKB.SBayesR is implemented in an efficient and user-friendly software tool that maximises computational efficiency via precomputing and efficiently storing sparse LD matrices that account for the variation in the number of LD friends for each variant. Currently, the LD matrix construction can only be performed with PLINK hard-call genotypes. The use of imputation dosage values may provide an improvement and is interesting future research. In simulation and cross-validation, we showed large fold improvements in time and memory over current state-of-the-art individual and summary data methods. The improvements in efficiency are not just a result of the computational implementation, but are a partial result of the faster convergence of the Gibbs sampling algorithm. This is evidenced by the comparison with RSS, which requires a much longer chain length to arrive at maximum prediction accuracy. Importantly, once the GWAS effect size estimates have been generated, the method’s runtime is independent of the sample size, making it applicable to an arbitrarily large number of individuals.33 can assist with more stable model convergence. Through simulation, we observed that the SBayesR 21), where the SBayesR model is derived under the assumption that the summary statistics have been generated from a least-squares analysis. The use of summary statistics from a LMM will affect the reconstruction of X′y with a potential remedy for this discrepancy being the use of the reported effective sample size from the BOLT-LMM analysis9. We recommend careful interpretation of 53. We have not assessed SBayesR’s effectiveness for mapping causal variants, but we expect it to be capable of performing this task, which is inherited from the individual-level BayesR method55. The capacity to fit millions of variants in one model also makes it a potential tool for fine mapping. SBayesR estimates all parameters from the data and does not require any post hoc tuning of prediction-relevant parameters in a test data subset (as in the tuning parameters of LDpred or P + T), which relieves the analytical burden of tuning these parameters in an external data set. Furthermore, SBayesR generates more generalisable predictors as the model parameters have been optimised over all possible values rather than selected from a finite grid.We found that model convergence is sensitive to inconsistencies in summary statistics generated from external consortia and meta-analyses. We observed that the shrinkage estimator of the LD matrix39, as well as minimal imputation error and data-processing errors such as allele coding and frequency mismatch. Summary data in the public domain often substantially deviate from these ideals and can contain residual population stratification, which is not accounted for in this model. Practical solutions include the use of imputed data and the restriction of analyses to variants that are known to be imputed with high accuracy as in Bulik-Sullivan et al.36 and Zhu and Stephens39. We found that the simple filtering of SNPs with reported per-SNP sample sizes that deviate substantially from the median across all variants from an analysis, as in Pickrell56 substantially improved SBayesR model convergence. Removal of high LD regions, such as the MHC region, further improved model convergence for real traits. Future research into efficient diagnostic tools and methods that can assist analysts with the assessment of sources of bias and error in summary data quality would be highly beneficial. The SBayesR implementation and model are very flexible and can easily incorporate other model formalisations such as understanding the contributions of genomic annotations to prediction and 57 or understanding genetic architecture via summary statistics versions of models such as those presented in Gazal et al.58 and Zeng et al.27.Formally, the model assumes certain ideal data constraints such as summary data computed from the same set of individuals at fully observed genotypes49. The UKB was approved by the National Research Ethics Service Committee and all participants provided written informed consent to participate in the study. The UKB data contain genotypes for 488,377 individuals  that passed sample quality control . A subset of 456,426 European ancestry individuals was selected. Ancestry was inferred by using the protocol in Yengo et al.48 that initially projects each study participant onto the first two genotypic PCs calculated from HM3 SNPs genotyped in 2504 participants of 1000 genomes project. Five superpopulations  are used as a reference and each participant is assigned to the closest population. The posterior probability under a bivariate Gaussian distribution is calculated for each participant to belong to one of the five superpopulations. Vectors of means and 2 × 2 variance–covariance matrices were calculated for each superpopulation, by using a uniform prior. To exclude related individuals, a genomic relationship matrix (GRM) was constructed with 1,123,943 HM3 variants further filtered for MAF > 0.01, pHWE < 10−6 and missingness <0.05 in the European subset, resulting in a final set of 348,580 unrelated  Europeans. Genotype data were imputed to the Haplotype Reference Consortium and UK10K panel, which was provided as part of the data release and described in Bycroft et al.49, and contained SNPs, short indels and large structural variants. Variant quality control included removal of multi-allelic variants, SNPs with imputation info score < 0.3, retained SNPs with hard-call genotypes with > 0.9 probability, removed variants with minor allele count (MAC) ≤ 5, Hardy–Weinberg p value (χ2 (df = 1) test statistic pHWE) < 10−6 and removed variants with missingness > 0.05, which resulted in 46,500,935 SNPs for the 456,426 individuals available for potential analysis.We used real genotype and phenotype data from the full release of the UKB. The UKB is a prospective community cohort of over 500,000 individuals from across the United Kingdom and contains extensive phenotypic and genotypic information about its participants59 and GENEVA Diabetes study obtained via dbGaP. The ARIC study protocol was approved by the institutional review boards of each participating centre, and informed consent was obtained from each study participant. The ARIC + GENEVA data consisted of 12,942 unrelated individuals determined by an absolute GRM off-diagonal relatedness cutoff of <0.05. After imputation to the Phase 3 of the 1000 Genomes Project (1000G)60, 1,182,558 HM3 SNPs (MAF > 0.01) were selected and available for analysis after quality control. Whole-genome sequencing data from the 1000G project were used for LD matrix reference calculation. These data were subsetted to a set of 378 individuals with European ancestry to be consistent with the LD reference used in Zhu and Stephens39. Whole-genome sequencing data from the UK10K project61 were also used for analysis. Ethics approval for the UK10K project was performed under the framework of the Ethical Advisory Group of the UK10K project with contributing studies ethical approval detailed in UK10K Consortium61 with individuals providing informed consent. The initial UK10K data set comprised 3781 individuals and ~45.5 million genetic variants called from whole-genome sequencing after quality control. Additional quality control  steps were performed, excluding SNPs with missingness > 0.05, Hardy–Weinberg equilibrium test (χ2 (df = 1)) p value < 1 × 10−6, or MAC < 3  by using PLINK. Individuals with genotype missingness rate > 0.05 and one of each pair of individuals with estimated genetic relatedness > 0.05 by using variants on HapMap 2 reference panels after quality control. The final UK10K contains 17.6 million genetic variants in 3642 unrelated individuals.The implemented summary statistics methodology requires an estimate of LD among genetic markers. In addition to the UKB, three data sets were used to calculate LD reference matrices. We used the genotype data from the Atherosclerosis Risk in Communities (ARIC)63. HRS obtained ethics approval from the University of Michigan Institutional Review Board, and the study has been conducted according to the principles expressed in the Declaration of Helsinki and all participants provided written informed consent to participate in the study. After imputation and restricting variants with an imputation quality score > 0.3, MAF > 0.01 and a pHWE > 10−6 there were 24,777,992 SNPs available for prediction. The ESTB64 is a cohort study of over 50,000 individuals over 18 years of age with phenotypic and genotypic data. The ESTB project obtained approval from the Ethics Review Committee on Human Research of the University of Tartu and all participants provided written informed consent to participate in the study. For the prediction analysis we used data from 32,594 individuals genotyped on the Global Screening Array. These data were imputed to the Estonian reference65, created from the whole-genome sequence data of 2244 participants. Markers with imputation quality score > 0.3 were selected, leaving a total of 11,130,313 SNPs available for potential prediction.For out-of-sample validation of genetic predictors we used two cohorts that are independent of the UKB. We used genotypes imputed to the 1000G reference panel and phenotypes from 8552 unrelated  participants of the HRSy is an n × 1 vector of trait phenotypes, which has been centred, X is an n × p matrix of genotypes coded as 0, 1 or 2 representing the number of copies of the reference allele at each marker and we consider that the columns of X have either been centred or scaled, β is a p × 1 vector of multiple regression coefficients (marker effects) and ε is the error term (n × 1). We can relate the multiple regression model to the estimates of the regression coefficients from p simple linear regression analyses b, by multiplying Eq.  of least-squares marginal regression effect estimates and the correlation matrix between all genetic markers Noting that ε1, …, εn are independent βµ and covariance matrix Σ for ξ. If individual-level data are available then inference about β can be obtained by replacing D and B with estimates D with jth variant, respectively.Assuming D with B, the LD correlation matrix between the genotypes at all markers in the population, in which the genotypes in the sample are assumed to be a random sample, with 39 discuss further the theoretical properties of a similar likelihood. We assess the limits of replacing D and B with these approximations through simulation and real data analysis.This reconstruction of We perform Bayesian posterior inference by assuming a prior on the multiple regression genetic effects and the posterior26, which assumes thatC denotes the maximum number of components in the finite mixture model, which is prespecified. The γc coefficients are prespecified and constrain how the common marker effect variance γ were with respect to the genetic variance C = 4 such that γ = ′ = ′. This requires the genotypes to be centred and scaled and equates the genetic variance m is the number of variants. We relax this assumption to disentangle the relationship between these parameters and to maintain the flexibility of the model to assume scaled or unscaled genotypes. In this implementation, we let the weights be with respect to γ = ′, which maintains the relative magnitude of the variance classes as in the original model. Supplementary Notes χ2 distribution with densityνβ are the scale parameter and degrees of freedom, respectively. The residual variance χ2 distribution prior with distributionIn this paper we implement the BayesR modelγ1, γ2, γ, γ4′ = ( for each observed βi) = . The full conditional distribution of βj under this assumption , the sample size of the genetic map reference, which corresponds to the 183 individuals from the CEU cohort of the 1000G and the hard threshold on the shrinkage value, which we set to 10−3. This threshold gave a good balance between computational efficiency and accuracy with, on average, each SNP having a window width of 10.6 Mb (SD = 5.6 Mb) across the autosomes . The calculation of the shrunk LD matrix requires the effective population sample size, which we set to be 11,400 ′. BayesR was run for 10,000 iterations with 4000 taken as burn-in and a thinning rate of 1 in 10. For SBayesR, the MCMC chain was run for 10,000 iterations with 4000 taken as burn-in and a thinning rate of 1 in 10 and run with four distributions and variance weights γ = ′. The posterior mean of the effects and the proportion of variance explained over the 600 posterior samples was taken as the parameter estimate for each scenario replicate for both methods. We ran S-PCGC chromosome wise and computed the cross-product of r2 values and allele frequencies by using the set of 50,000 random individuals from the UKB and a 1-Mb window. Summary statistics were computed by using the S-PCGC software and the prevalence set to the true value of 0.05. The marginal or conditional S-PCGC estimates were the same as no covariates were included.HEreg was performed using the GCTA software and requires a genetic relatedness matrix (GRM), which was built from the 1,094,841 genome-wide HM3 variants in the GCTA software. LDpred was run genome-wide and we specified 67. Principal components were calculated by using high-quality genotyped variants as defined in Bycroft et al.49 that passed additional quality control filters (as applied in the European unrelated UKB data) that were LD pruned (R2 < 0.1) and had long-range LD regions removed  and allele frequencies. Summary statistics were computed by using the S-PCGC software and the prevalence set at 0.06. S-PCGC was run chromosome wise and the sum of the per-chromosome marginal For LDpred, all parameters were as per the genome-wide simulation with the optimal parameter chosen by predicting into the independent subset of 5000 individuals initially partitioned off and choosing that which had the highest prediction 21 for the 1,094,841 HM3 variants in the full UKB data set. By using these summary statistics, we ran SBayesR as in the genome-wide and cross-validation analyses. For comparison in the full UKB data set, we ran the individual-level BayesR method by using the same parameters as per the genome-wide and cross-validation analyses. With the summary statistics from Yengo et al.48, SBayesR was run as above with the default γ for BMI and γ = ′ for height. Empirically, we observed that this constraint on the elements of γ was a further requirement for SBayesR model convergence when using these height summary statistics. LDpred was run genome wide for the same set of parameters in the genome-wide and cross-validation analyses. The optimal parameter was chosen by predicting into the HRS data set and choosing the parameter that had the highest prediction R2 when the predicted phenotype was regressed on the true phenotype. This optimal parameter was then used for prediction into the ESTB. For RSS, analyses were performed chromosome-wise by using the RSS-BSLMM model, which was run for 2 million MCMC iterations with 1 million as burn-in and a thinning rate of 1 in 100.BMI and height phenotypes were pre-adjusted for age, sex and the first ten principal components by using the R programming language and standardised as per the cross-validation. We generated summary statistics for SBayesR analysis for height and BMI by using a linear mixed model to account for sample relatedness in the BOLT-LMM v2.3 software39. Empirically we observed that the methodology can tolerate deviations from this assumption up to a limit. To improve method convergence, we removed variants from the Yengo et al.48 summary statistics that had a per-variant sample size that deviated substantially from the median of the sample size distribution over all variants, which was also performed by Pickrell56 and recommended by Zhu and Stephens39. To minimise the variants removed, we interrogated the distributions of per-variant sample size in each of the BMI and height summary statistics sets and removed variants in the lower 2.5th percentile and upper 5th percentile of the per-variant sample size distribution for BMI and in the lower 5th percentile for height ; true phenotype on lower-plus higher-ranked PRS . Analysis of variance (ANOVA) is used to compare the null versus alternative models, and the F-statistic and associated p value are reported from the ANOVA analysis. We further report the partial R2 for the null versus alternative. To assess prediction accuracy, we calculated PRSs by using the genotype data from the independent test data sets using the PLINK 1.9 software for all methods. Prediction R2 was calculated via linear regression of the true phenotype on that estimated from each method, which was used as a measure of prediction accuracy for each trait.Genotype data from the HRS and ESTB independent data sets were used to generated PRSs for each method and the prediction accuracy assessed as in the simulation and cross-validation analyses. The optimal LDpred polygenicity parameter was tuned in the HRS sample and applied in the ESTB sample. P + T predictors were generated by using the provided summary statistics from the GCTA–COJO analysis performed in Yengo et al.Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File


African livestock breeds are numerous and diverse, and typically well adapted to the harsh environment conditions under which they perform. They have been used over centuries to provide livelihoods as well as food and nutritional security. However, African livestock systems are dynamic, with many small- and medium-scale systems transforming, to varying degrees, to become more profitable. In these systems the women and men livestock keepers are often seeking new livestock breeds or genotypes – typically those that increase household income through having enhanced productivity in comparison to traditional breeds while maintaining adaptedness. In recent years genomic approaches have started to be utilized in the identification and development of such breeds, and in this article we describe a number of examples to this end from sub-Saharan Africa. These comprise case studies on: (a) dairy cattle in Kenya and Senegal, as well as sheep in Ethiopia, where genomic approaches aided the identification of the most appropriate breed-type for the local productions systems; (b) a cross-breeding program for dairy cattle in East Africa incorporating genomic selection as well as other applications of genomics; (c) ongoing work toward creating a new cattle breed for East Africa that is both productive and resistant to trypanosomiasis; and (d) the use of African cattle as resource populations to identify genomic variants of economic or ecological significance, including a specific case where the discovery data was from a community based breeding program for small ruminants in Ethiopia. Lessons learnt from the various case studies are highlighted, and the concluding section of the paper gives recommendations for African livestock systems to increasingly capitalize on genomic technologies. In developing countries, the livestock sector plays a key role in the provision of livelihoods as well as food and nutrition security. The majority of livestock are kept by the rural poor, where they serve multiple functions. These include: savings and insurance, food security (meat and milk), income, livelihood diversification and thus risk reduction (such as in mixed crop-livestock systems), inputs to crop production , transportation, various uses of hides and skin (such as for housing), allowing households to benefit from common-property resources , and fulfilling social obligations , amongst other . The livThe demand for animal source foods is rapidly increasing in developing countries: for example, in low income countries the demand in 2030 for beef, milk, poultry and eggs is predicted to be a 124, 136, 301, and 208% increase over that in 2000, respectively . This deIncreasing livestock productivity in developing countries generally requires simultaneous interventions in the areas of animal feed, health and genetics. In many livestock development programs these interventions take the form of capacity building of the livestock keepers and other value chain actors, ensuring the availability and accessibility of inputs, provision of new technologies or customization of existing technologies, support to private and/or public sector involvement, and advocacy for supportive policies. The provision of incentives for increased productivity can also be important, such as in some small-hold and pastoral sectors where livestock are primarily kept for savings and insurance purposes, so maintaining a livestock asset base is more important to the household than improving livestock productivity. Such incentives could be provided by, for example, increasing livestock income through facilitating access to strong and stable markets, or ensuring that intra-household benefit from the livestock enterprise is equitable. In addition, attention to other issues which can be affected through increased livestock productivity, such as equality, food safety and environmental sustainability, are also commonly part of livestock development programs. As livestock systems within developing countries are both diverse and dynamic, intervention packages typically need to be customized for each livestock sector.To date, the majority of African livestock systems have not benefited from livestock technologies to the extent that developed countries have, including in relation to genetic improvement strategies . Currentin situ comparison studies, or for the application of genomic selection in breed improvement programs – is just beginning to emerge, often overcoming a constraint that would otherwise exist, such as lack of recorded pedigree.The types of structured genetic improvement programs being implemented in Africa vary by system. These include: breed-substitution with other African breeds, breeds from other tropical countries such as India and Brazil, as well as breeds from elsewhere; cross-breeding, most commonly where a highly adapted but lowly productive indigenous breed is crossed with a poorly adapted but highly productive exotic breed; and less commonly within-breed improvement . IncreasIn this article we describe several examples of the use of genomics in sub-Saharan African livestock systems, draw lessons learnt from these, and giving recommendations for African livestock systems to increasingly capitalize on genomic technologies. The paper proceeds as follows. The subsequent section ‘case studies’ describes the case studies grouped by application, namely the use of genomic information to: (1) to identify the most appropriate breed or cross-breed type for different livestock production systems; (2) to enable or enhance breeding programs; (3) create new breed-types; and (4) discover genetic variants of economic and ecological significance. A discussion follows, first addressing current developments on livestock genomics in Africa, drawing on the case studies, and secondly describing the future outlook for livestock genomics in Africa.in situ comparisons  can do well in very good environments. The likelihood is that the intermediate grades do better in poorer production environments but given the lack of evidence most advice provided to farmers is that they should upgrade to higher grade exotic animals by using AI.in situ performance data to determine what breed composition worked best in different smallholder environments. By comparing farmer and enumerator (field staff) assessments of breed composition, based mostly on phenotypic appearance and farmer recollections on cows origins, with the admixture determinations of actual breed composition it was confirmed that phenotype-based assessments were very poor predictors of actual breed composition (R2 = 0.16). The results showed that intermediate to low grade (<50% exotic breed ancestry) cows performed best in the majority of the smallholder farms, while animals with higher grades (>50%) only performed better than lower grades in the best environments  worked best for different production environments. The project worked with farmers to collect performance data, including on milk yields, reproduction events, and disease incidence, for 18–24 months. Further the recorded animals were genotyped using the Illumina bovine high density  single nucleotide polymorphism (SNP) assay with the HD SNP data used to perform admixture analyses, using the ADMIXTURE software , to geneA surprising result of this study was that average production levels  of the cows in the study, which were randomly sampled based on location in order to achieve a representative sample, was much lower than the 3,000–5,000 l/cow/annum typically assumed in most development projects and extension programs. The highest yielding cow in the study only achieved around 2,400 l/cow/annum. The result meant that it could not be inferred at what level would high grade exotic crossbreds or purebred exotics become the best performing breed type. The results also mean that most development and extension programs are making unrealistic assumptions about smallholder production environments and are likely, therefore, to be offering suboptimal or unrealistic interventions and advice. This is mentioned here because studies such as Dairy Genetics East Africa have multiple objectives in studying what are highly complex systems. As such, genomics is a powerful tool that assists better understanding of system function that should be incorporated into multidisciplinary studies rather than used to tackle isolated (genetic) issues. In the case of Dairy Genetics East Africa the results that were enabled by the use of genomic testing provided much of the baseline information that demonstrated the value and feasibility of establishing long-term genetic improvement programs, beyond the provision of the most appropriate breed cross, which led to the establishment of the Africa Dairy Genetic Gains (ADGG) program, which appears as another case study later in this review.In Senegal, dairy production is mainly from cattle kept in low input systems, with domestic production unable to meet national demand. To increase national dairy production the Senegalese government has implemented a number of initiatives, including the introduction of exotic high-yielding dairy breeds through public artificial insemination campaigns. However, at the time of these campaigns there was no evidence base for Senegalese cattle keepers and other stakeholders to make informed decisions on which dairy breed or cross-breed to use. This knowledge gap was addressed by a project termed “Senegal Dairy Genetics” which aimed to identify the most-appropriate dairy cattle breed/cross-breed for Senegalese production systems.Project data was obtained by monitoring 220 rural or peri-urban dairy cattle keeping households, with collectively more than 3,200 cattle, over an almost 2 year period. Data collected included that on animal performance, economics of the household dairy enterprise, social issues including on gender, and dairy cattle feed and milk safety, amongst other. The aim was to collect a range of data such that different household dairy systems  could be compared from multiple perspectives including milk-yields, household profit and cost:benefit ratio, and food safety .Bos taurus breeds (such as Montbéliard and Jersey) and pure  Bos Taurus breeds. With the exception of the indigenous Zebu, the breed-type of individual animals was not able to be determined based on phenotype, and none of the cattle keepers kept pedigree records. Thus breed composition of a subset of the study animals, those with the most informative records, was determined using a genomic approach. Specifically, genotyping was performed using the Bovine 50 K SNP assay and admixture analysis performed. using the Bayesian Analysis of Population Structure software  identified the breed proportion of reference samples , as did sub-sets of 65, 55, and 45 SNPs selected on high or low FST values . The AIM were selected from Ovine SNP50K data from the Menz, Wollo, and Awassi breeds. A total of 74 SNP that showed large differentiation between the local Menz and Wollo breeds to the Awassi breed were selected based on their More than 700 animals, presumed to have a wide range of breed compositions, were genotyped using selected AIMs. Breed proportion of individual animals was then determined and related to ewe productivity expressed as 8 months lamb weight per year . The most productive breed compositions were then identified as 37.5–50% Awassi in the first study site, and12.5–25% Awassi in the second study site where ewes produced (on average) 26.5 and 19.5 kg lamb, respectively, at 8 months . FindingThe AIM is considered a great opportunity to estimate the level of admixture (breed proportion) in a cost-effective way. Currently, the cost per SNP is in the range of about €0.04–0.15 for low density panels, highly dependent on the method and number of samples to be genotyped at a time. It is of note that information on ram breed composition  is currently used in ram marketing, and that many farmers within the study site showed interest to pay for breed composition information. If an affordable tool (based on a low-cost SNP chip) was available for this purpose, ram sellers would be better placed to take advantage of the market opportunity for rams of known breed-type.In intensive livestock systems, genomic data enhances existing genetic improvement programs by increasing the accuracy of estimates of relationships among animals, and hence increasing accuracy of estimated breeding value (EBV), and in some cases also revealing functional variants which can be selected for directly using genotype data. The big immediate advantage of genomic data in Africa is to enable rapid implementation of genetic improvement where pedigree information is lacking, which is commonly the case. In such cases genomic data can be used to build a genetic relationship matrix among animals in a new recording program, so that EBV can be generated almost immediately. Where genetic relationships are based on pedigree recording, EBV cannot be generated until the next generation of animals have been born and recorded. Similarly, once phenotype and pedigree recording programs are in place, genomic data allows rapid expansion of recording to include animals with no previous pedigree information. Where genetic improvement systems are well established in Africa, genomic data potentially offers the same technical benefits as in intensive livestock systems. An additional advantage in crossbred populations is that genomic data can be used to accurately determine breed composition of individual animals and this information can be used to increase the accuracy of genetic evaluations and breed effects, in addition to being used directly to select animals of desired breed composition. In the case of pure breed populations, estimates of breed composition can also be used to ensure the purity of the breed. The case study below is an excellent example highlighting how genomics has facilitated a breeding program in an African livestock system.In the smallholder, crossbred dairy system that dominate sub-Saharan milk production, the lack of performance and pedigree recording means that there are no conventional genetic evaluation systems for these systems . In addiGenotype data from high-density SNP assays can offer quick wins in smallholder systems. SNP data can be used to assign parentage where pedigree data is not available. Knowledge of breed composition of bulls allows farmers to use bulls of the breed composition they desire, and having cows with known breed composition allows farmers to determine what breed composition of bulls is required to produce progeny of the desired breed type. Further, knowing the breed composition of cows and bulls allows purchasers of animals to obtain animals with the breed composition required for their production environment. As illustrated in the case studies described above, the same approach can be used to determine breed composition in studies that determine the optimum breed composition for different production environments, thereby informing farmers what breed composition of cow or bull they should be aiming to purchase or to produce through breeding.Commercially available SNP assays are currently too expensive to allow their routine commercial use in parentage assignment and determination of breed composition in East African dairy systems. However, using the Dairy Genetics East Africa high-density genotype data on 2940 crossbred cattle in East Africa , The availability of genotypic data has enabled the estimation of genetic parameters and the estimation of genomic breeding values for milk yield in these populations using the G matrix obtained from SNP genotype data . Using mTo improve the cost-effectiveness of applying genomic selection in East Africa, the feasibility of developing a reduced  chip for genomic prediction was examined using the 3,513 animals with high density genotypes in the Dairy Genetics East Africa data . VariousThe most appropriate breed-type for African livestock systems are typically considered those which are both productive and adapted/resilient. Genomics and its associated technologies/techniques  offer opportunities for creating such breed-types. The below case study is one example of this.Glossina spp.) is a major constraint to livestock production across much of the African continent with massive economic consequences  called trypanosome lytic factors (TLFs) which kill many trypanosome species . The acted APOL1 . There iunderway .African livestock populations are rich resources for discovery of genetic variants, and many efforts are underway to this end. The first case study below describes a breeding program for small ruminants (sheep and goats) which, whilst currently not using genomics as part of the breeding program itself, is using the breeding program data for genetic variant discovery purposes. Following this a second ‘case study’ illustrates other efforts toward genetic variant discovery: unlike the other cases described here which are specific initiatives/projects, this draws on numerous studies to showcase the various types of activities occurring in this space.In small ruminants, centralized breeding schemes, entirely managed and controlled by governments – with minimal, if any, participation by farmers – were developed and implemented in many developing countries. Such programs have generally failed to sustainably provide the desired genetic improvements to smallholder livestock keepers. Community-based breeding programs have been suggested as an alternative and are being implemented in a few pilot countries. Programs that adopt this strategy consider the farmers’ needs, views, decisions, and active participation, from inception through to implementation, and their success is based upon proper consideration of farmers’ breeding objectives, infrastructure, participation, and ownership . CommuniFST analysis revealed two candidate regions, one on chromosome 5 and the other on the X. The latter was the most significant. hapFLK identified the region on the X only. The candidate region on chromosome 5 was adjacent to GDF9 and the region on the X spanned the BMP15 (GDF9B) gene. These two genes are expressed in oocytes and have been shown to be essential for ovulation rate, normal follicular growth and maturation of preovulatory follicles  were taken as controls while those giving multiple birth (64) formed the cases. ollicles . From exPost domestication, livestock genomes have continuously been modified through selective breeding for economically or otherwise important traits, and natural selection for adaptation to local agro-environments. Africa has diverse agro-environments and a predominantly tropical environment that is characterized by harsh and extreme climatic conditions, seasonal feed and water scarcity, heat stress, high solar radiation, widespread pathogens, parasitic infections and disease epidemics. These present the main evolutionary forces shaping Africa’s livestock genomes. Accordingly, African livestock display unique adaptive traits including enhanced disease resistance, superior innate immunity and greater ability to thrive, produce and reproduce in unfavorable environments. Some of the adaptive traits in African livestock, such as resistance to gastro-intestinal parasites in small ruminants, are of global significance.There are numerous African livestock populations already identified as of interest for gene-discovery studies. These include, as examples: breeds that are highly resistant/tolerant to gastro-nematodes, such as the Red Maasai sheep and Small East African Goats of East Africa, West African Dwarf sheep and Goat ; breeds There are an increasing number of examples of African livestock populations being used in studies aimed at identifying the genes or gene-pathways and genomic variants underpinning economically or ecologically important traits. These include a number of studies that have detected putative signatures of selection for a variety of traits including feeding/drinking behavior, heat tolerance/thermoregulation, tick resistance, milk production under harsh environments, immune response, meat quality, and reproductive performance, amongst others . There aAn exciting possibility in crossbred dairy cattle populations such as those in the Dairy Genetics East Africa project is that as data increases it will be possible to undertake GWAS to identify genetic regions, and potentially the genes, controlling genetic variation in milk production and adaptation traits. The differences between exotic dairy breeds and indigenous breeds in their genetic potential for milk production and in adaptation traits are larger than for any other crosses of livestock. For example the genetic potential for milk production of Holstein cattle is about 10-fold higher than that of indigenous breeds such as the Small East African Zebu. GWAS may be able to identify the genetic regions that control these massive genetic differences between breeds. However, GWAS in crossbred cattle presents some challenges. In a purebred population GWAS is based on population-wide linkage disequilibrium (LD) between SNP and functional genetic variants. In a crossbred population there are at least three forms of LD: the LD coming from within the indigenous ancestors; the LD from within the exotic ancestors; the between population LD, which is the LD generated within the crossbreds due to segregation of loci that were fixed for opposite alleles in the exotic vs. indigenous ancestors. In practice the problem is even more complicated because most LD is not conserved between exotic or between indigenous breeds, so each of the various ancestral dairy breeds and indigenous breeds injects different amounts and phases of LD, reducing further the average LD observed in crossbred populations. It is not yet clear whether existing SNP assays provide sufficient information to track inheritance of segments of the genome back to their diverse origins with sufficient accuracy to undertake GWAS that separates the different forms of LD in the population. Assuming that it will prove possible, the between population LD is potentially of greatest interest given the very large genetic differences between ancestral breeds and the potential to apply gene-based selection for suitable combinations of productivity and adaptation traits. Very low density, and hence potentially cheap, assays of a few hundred SNP might be developed and applied to widely test animals and select those with optimum combinations of productivity and production variants, even if genotyping with commercial assays proves too expensive for routine use in genetic improvement in these systems.An additional body of work has focused on characterizing genetic diversity, population structure and relationships in African livestock . Such stThe case studies presented show-case a number of livestock genomic technologies currently being applied or piloted in livestock systems of sub-Saharan Africa. These included those aimed at identifying the most appropriate breed-type for particular production systems/environments, a breeding program incorporating genomic selection as well as parentage and breed composition determination, an initiative aimed at creating a new breed-type, and efforts toward discovery of genetic variants. Other examples outside of those presented here also exist within sub-Saharan Africa, with, in particular, major efforts in South Africa to incorporate genomic selection into established breeding programs for a number of species . These ein situ  has been transformational to this end, as it has removed the high error of assigning breed-type of admixed animals based on observation (phenotype) or farmer recall. Genomic selection is now common-place is many developed country livestock breeding programs, whereas in Africa it is in its infancy. This principally stems from the lack of breeding programs into which genomic selection can be implemented, with some notable exceptions including the African Diary Genetics Gains initiative described here and various breeding program in South Africa, many of which are working on developing sufficiently sized reference populations to incorporate genomic selection  have led to different emphasis on how genomics is currently being applied. In developed countries the most suitable animal genetic resources for a particular production systems is usually well established, whereas in many African production systems, and particularly those undergoing change such as through intensification, there is generally little evidence to make such recommendations . The useelection . In the Using genomics to aid the development of new breed-types for African livestock systems has received limited attention to date. Given the high interest in developing new breeds that have the adaptation and resilience of indigenous breeds combined with the productivity of exotic breeds, and the difficulty in many systems of maintaining a structured cross-breeding program, the cost:benefit of using genomic approaches to create an adapted and productive synthetic breed, in comparison to traditional approaches, is worth exploring in the African context. On the creation of new breed-types via transgenic or gene-edited approaches, few validated genes of interest currently exist. One notable exception to this is the gene conferring resistance to trypanosomiasis as described in the case study presented here. Other variants of potential interest are those underpinning the slick hair phenotype, given this phenotypes association with heat tolerance and tropical adaptation . As withAs also described significant efforts are ongoing aimed at discovering genetic variants of economic and ecological significance, primarily using a signature of selection approach. Given the current emphasis on incorporating traits conferring adaptation to harsh (including hotter) environments into breeding programs, both within developed and developing countries, this body of work may gain momentum. In one of the case studies presented the signature of selection study utilized data availed from an African breeding program, which adds value to the performance data collected. Whilst GWAS studies are currently few, additional studies using this approach are also expected as data-sets build up, such as what will be available via the African Dairy Genetics Gain project. Besides feeding into the discovery of genomic variants, GWAS studies can provide useful QTL information for use in genetic improvement programs. The evolutionary history of Africa indigenous livestock species, make African populations a particularly powerful resource for gene discovery  is the critical guiding document to this end. This protocol is a 2010 supplementary agreement to the 1992 Convention on Biological Diversity, which entered into force October 2014. The protocol defines obligations of the providers and users of the all genetic resources in relation to access, benefit sharing and monitoring the compliance of users with legal ABS requirements of the provider country. By default and due to the lack of any specialized international instrument, access to animal genetic resources for food and agriculture for R&D activities would fall under national ABS regulation, if the country did not determine this otherwise. According to information provided in the Access and Benefit Sharing (ABS) Clearing House, a web platform aimed at supporting implementation of the Nagoya Protocol2 43 African countries are currently parties the Nagoya Protocol, though many of these are still developing the related policies and laws as well as implementing procedures and practices. The implementation of the Nagoya Protocol provides both opportunities and challenges for African countries, as discussed in An important issue related to the use of African animal genetics resources is the fair and equitable sharing of benefits arising from their utilization. The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilization to the Convention on Biological Diversity . For the majority of countries, significant further investment in identifying and establishing context-specific and sustainable genetic improvement strategies are required, which genomic technologies can help enable or into which the genomic application can be embedded. Excellent guidelines to this end are given in In initiatives where both phenotypes and genotypes are required, the phenotypic information is usually more expensive and difficult to obtain than the genotypic information, particularly as the cost of genotyping declines . To thisPhenomic tools extend beyond recording into methods of analysis. Production systems, population structures and data quality of many African livestock populations differ markedly from the intensive systems in which most existing phenotype and genetic data analysis methods have been developed and tested. It can be expected that this will, at the very least, often lead to very different phenotype and genetic parameter values than typically seen in intensive systems. In many cases, statistical models will need to be developed that are appropriate for the population. For example, for smallholder dairy systems, typical herd-year-season effects cannot be applied , methods of fitting lactation curves may not be appropriate to lactations that do not exhibit a classical lactation curve and/or the shape of the curve is highly dependent on production level, and variation across the lactation may be high due to short-term fluctuations in feed availability. Additionally, factors such as genotype by environment (GxE) interaction that typically have modest effects in intensive systems, where environmental differences between farms are typically relatively small, may be much more imported in African livestock systems. For example, smallholder crossbred dairy farms in East Africa range from under 1,000 l milk/cow/annum to over 5,000 l milk/cow/annum. There is massive GxE in terms of breed composition (high grade or pure exotics do best in the best environments while low-grade exotics perform best in the poorest environments) and hence it should be expected there will be large GxE when undertaking genetic evaluations in such populations. It will be important to ensure that existing methods of analysis drawn from the global literature are properly tested and adapted where needed to provide appropriate analyses for African livestock populations.African livestock genetic research, development and application has a huge advantage in being able to utilize the wide range of genomic tools that have been developed for use globally. Most notably the existing genome sequence assemblies and associated annotations coupled with the various commercially available SNP genotyping assays provide immediate tools for analyses of genetic diversity, genetic evaluations, signatures of selection, GWAS and gene discovery. However, all of these tools were developed with little or no information from African livestock populations. It is not yet known whether updated or customized assays will be required to obtain the maximum utility in African populations, though in the case of cattle work is being undertaken to this end . As a prBuilding human capacity in animal breeding, genetics and genomics within Africa, such that appropriate expertise exists to design and support implementation of the genetic improvement strategies and linked genomic technologies, is required. Suggestions on how to strengthen developing country higher education systems in animal breeding are given by In conclusion, genomic applications are currently benefiting African livestock systems in a variety of ways, including on genetic improvement and more broadly, such as assisting in system characterization. This has emerged relatively recently, largely within the last 5 years. The expectation for the future is that African livestock systems will increasingly benefit from genomics, particularly if the various issues constraining this (as discussed in this paper) are addressed. The rate at which this will occur will large depend on the level of investment in African livestock genetic improvement.KM and JG were the main authors of the manuscript, with information on the case studies supplied by JG, OM, RM: Kenya dairy cattle and East Africa dairy cattle, KM: Senegal dairy cattle, TG: Ethiopia sheep, SK: trypanosome resistant cattle, AH: Ethiopia small ruminants, and JM: other initiatives on genomic variant discovery.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. The reviewer GM declared a past co-authorship with several of the authors TG, AH, and OM to the handling Editor.


Triticum durum (Desf).] is mostly used to produce pasta, couscous, and bulgur. The quality of the grain and end-use products determine its market value. However, quality tests are highly resource intensive and almost impossible to conduct in the early generations in the breeding program. Modern genomics-based tools provide an excellent opportunity to genetically dissect complex quality traits to expedite cultivar development using molecular breeding approaches. This study used a panel of 243 cultivars and advanced breeding lines developed during the last 20 years to identify SNPs associated with 24 traits related to nutritional value and quality. Genome-wide association study (GWAS) identified a total of 179 marker–trait associations (MTAs), located in 95 genomic regions belonging to all 14 durum wheat chromosomes. Major and stable QTLs were identified for gluten strength on chromosomes 1A and 1B, and for PPO activity on chromosomes 1A, 2B, 3A, and 3B. As a large amount of unbalance phenotypic data are generated every year on advanced lines in all the breeding programs, the applicability of such a dataset for identification of MTAs remains unclear. We observed that ∼84% of the MTAs identified using a historic unbalanced dataset  were also identified in a balanced dataset. This suggests the suitability of historic unbalanced phenotypic data to identify beneficial MTAs to facilitate local-knowledge-based breeding. In addition to providing extensive knowledge about the genetics of quality traits, association mapping identified several candidate markers to assist durum wheat quality improvement through molecular breeding. The molecular markers associated with important traits could be extremely useful in the development of improved quality durum wheat cultivars using marker-assisted selection (MAS).Durum wheat [ Durum wheat, the hardest of all wheats , is one The phenotypic evaluation of many important quality traits requires large amounts of grain per line, which is not feasible in early generations of breeding due to the large number of lines and limited resources in most breeding programs. Marker-assisted selection provides an opportunity for breeders to identify superior lines in early generations, thus saving significant resources and speeding up the process of cultivar development. However, most of the quality traits in wheat are quantitatively inherited and influenced by multiple QTL, QTL × QTL, the environment, and the interaction between QTL and environment . TherefoDuring the process of developing germplasm and cultivars in a breeding program, a large amount of phenotypic data are routinely collected on breeding lines in advanced generations. However, each year, only a small number of advanced breeding lines are evaluated. A few of those are replaced with new lines over the next few years, resulting in data from a large collection of advanced breeding lines composed of genotypes that are evaluated in different years and locations, generating unbalanced data. With genotyping cost decreasing day by day, the AM approach for identifying QTL could be even more cost-effective if a large amount of phenotypic data routinely collected by breeding programs could be used to gain insight of the genetics of quantitative traits and to identify MTAs for molecular breeding. However, information about the application of such unbalanced data for genetic studies is mostly missing. Therefore, in this study, a panel of advanced breeding lines and cultivars of durum wheat were used to identify 1) genomic regions associated with 24 quality traits, 2) associated markers suitable for MAS, and 3) whether historic unbalanced data are suitable for AM analysis and QTL identification. To our knowledge, this is the first such comprehensive study demonstrating the application of historic unbalanced phenotypic data for genome-wide association studies of complex, low, and moderate heritability traits in wheat.5:9) entered into the Uniform Regional Durum Nursery (URDN) from 1997 to 2014 (except in 2010 and 2011 due to severe weather conditions). These cultivars and inbred lines were chosen based on the phenotypic data available for the agronomic and quality traits routinely collected over the years. The historic unbalanced data were collected on these lines from 1997 until 2014. Each year, a subset of those lines was evaluated at five locations in North Dakota  in a randomized complete block design (RCBD) with four replications. Some of the lines were replaced each year with new inbred lines and evaluated in the same manner. So the unbalanced phenotypic data for the panel were obtained for the 80 environments belonging to 16 growing seasons. In 2015, a panel of 256 lines  was also evaluated together at two locations (Prosper and Langdon) to collect balanced data for the traits under study. Genotypes were planted in a 16 × 16 simple lattice design with two replicates. For both datasets, individual plots consisted of four 2-m-long rows spaced 0.3 m apart. Plots were harvested with a plot combine (HEGE 140), and the grain was collected in individual sacks. The grain was dried to approximately 12% moisture content and stored at 16°C until further processing. Quality tests were performed in the Durum Wheat and Pasta Processing Laboratory at North Dakota State University (NDSU).The AM panel consisted of 243 durum wheat cultivars and inbred lines  configured with a number 25 riddle and a number 2 top sieve and number 2 bottom sieve. The samples were cleaned again using the Carter-Day Dockage Tester configured with a number 25 riddle, a number 8 top sieve, and a number 2 bottom sieve. For quality traits analyses, subsamples from the total amount of seed available were taken and ground into whole-wheat flour using a Udy Cyclone Mill  fitted with a 60-mesh sieve and stored in plastic bags at 4°C until tests were performed. Ash content, protein content, sedimentation volume, falling number, and total yellow pigment content tests were performed using the whole-wheat samples.2, and a jacket temperature at 46–48°C. Room temperature and relative humidity were maintained at 25°C and 40–45%, respectively.Subsamples for milling were taken from the available grain. Prior to milling, all subsamples were tempered in two stages based on grain moisture. In the first stage, they were tempered to 12.5% moisture for at least 72 h; and in the second stage, they were tempered to 15% moisture for 24 h before milling. The samples were milled into semolina using a Quadramat Jr. Mill  according to the American Association of Cereal Chemists International (AACCI) method 26-50.01 . SemolinFor the 2015 balanced dataset, 300 g of semolina was hydrated and mixed in a KitchenAid commercial mixer. The mixer was set on low speed for 10 s while distilled water was added and then on high speed for 50 s. The semolina was mixed for an additional 2 min on high to ensure it reached a complete premix stage. Processing was done using a commercial tabletop electric pasta machine  and extruded through a 35-strand 1.09-mm Teflon spaghetti die.Due to the large number of samples evaluated in 2015 and the amount of time needed for drying, the drying process was omitted for the 2015 balanced dataset. For the historic unbalanced dataset, the extruded spaghetti was dried in a laboratory pilot-scale dryer  on the low-temperature cycle with a total drying time of 18 h. The low-temperature cycle typically has an 18-h total drying time at 40°C .Two different spaghetti cooking times were used for the two datasets. In the historic unbalanced dataset, dry spaghetti (10 g) was broken into lengths of approximately 5 cm and placed in 300 ml of boiling water for 12 min. However, based on preliminary results, the cooking time for the fresh spaghetti in the 2015 balanced dataset was reduced to 4 min. Fresh spaghetti (10 g) was cut into lengths of approximately 5 cm and placed in 300 ml of boiling water for 4 min. The optimum cooking time was determined using AACC method 66-50 .Phenotypic data for following quality traits were recorded:ProteinGrain protein content (WPROT): Protein content was determined using an Infratec 1226 Whole Grain Analyzer . The data on protein content were adjusted to 14% moisture content.Semolina protein content (SPROT): Semolina protein content was determined using AACC method 39-25.01  according to AACC method 26-50.01 . Total eTotal extraction (TEXT): Total extraction was expressed as a percentage weight per weight (w/w) of semolina and flour from tempered durum wheat samples.Semolina extraction (SEXT): Semolina extraction was expressed as a percentage weight per weight (w/w) of semolina from tempered durum wheat samples.Semolina ash content (SASH), moisture content: Approved methods ( methods  were useVitreousness (VIT): The percentage of vitreous kernels was determined by cutting 100 kernels taken at random transversally with a farinator (grain splitter) and identifying the grains that were not fully vitreous according to the appearance of the sectional areas of the endosperm. Vitreous grains are translucent and transparent when cut, while starchy grains are white and opaque due to the existence of air pockets in the endosperm (ndosperm .Gluten-Related TraitsSedimentation volume (SDS): The sedimentation volume was measured using sodium dodecyl sulfate (SDS) micro-sedimentation test as described by Mixogram score (MIXO): Ten grams of semolina, based on 14% moisture, was weighed. Water was added based on the grain protein content using a formula (Y = 1.5X + 43.6) described in AACC method 54-40A (2008), where Y = amount of water (ml) added to the sample and X = protein content at 14% mb. The 10-g bowl mixograph  was used to measure the dough mixing strength of semolina. Mixing tolerance was scored using a scale of 1 (weak) to 8 (strong).Wet gluten (WG) and gluten index (GI): Wet gluten and gluten index were determined with the glutomatic instrument  using AACC method 38-12.02 (38-12.02 .Glutork (GLUT): Water binding capacity (water bound in wet gluten) was determined with Glutork 2020  using AACC method 38-12.02 : Semolina color was determined using the Minolta colorimeter CIEL CR410 . Value “L” indicates lightness or brightness, value “b” indicates yellowness, and value “a” indicates “greenness.”Difference in color a (dif_a), difference in color b (dif_b), and difference in color L (dif_L):L, a, b). Differences in color a, color b, and color L were measured between the time intervals.A semolina dough sheet was made using a modified method described by Dry pasta color (color): Visual color was determined under the constant light source and assigned a numerical visual color score from 1 to 12, with 12 as the best score. The scores were generated according to the color map designed by Total yellow pigment (TYP): Total yellow pigment (TYP) content was determined using the water-saturated n-butanol AACC method 14-50.01 (n-butanol (10 ml) was added to 2 g of whole meal and shaken for 2 min. After a 30-min rest, the extracts were centrifuged at 12,000 rpm for 10 min, and the supernatant was carefully transferred to cuvettes. Absorbance was measured using a spectrophotometer  at a wavelength of 435.8 nm. Measurements per extracted sample were recorded, and values averaged and converted to yellow pigment concentration (μg/g) using the extinction coefficient (1.6632) for β-carotene (14-50.01  as modifcarotene .Polyphenol oxidase (PPO): Polyphenol oxidase activity was determined using intact kernels as described by N-morpholino)propane sulfonic acid] buffer with a pH of 6.5 was added to five undamaged seeds in a 2-ml microcentrifuge tube. The tubes were placed on an orbital shaker  and rotated for 1 h at room temperature to allow the reaction to occur. Polyphenol oxidase activity was measured as the change in absorbance at 475 nm using a Beckman Coulter spectrophotometer . The l-DOPA solution was made fresh daily. Each sample was measured in duplicate.Cooking-Related TraitsCooked weight (CWT): After being cooked, samples were rinsed off with distilled water in a Buchner funnel and drained. Spaghetti strips were weighed and reported in grams.Cooking loss (CLOSS): Cooking loss (% weight of solids) was measured by evaporating the cooking water to dryness in a forced-air oven at 110°C overnight. The residue was weighed and reported as percentage of the dry spaghetti.Cooked spaghetti firmness (FIRM): Cooked firmness was measured using a plexiglass blade probe attached to a Texture Analyzer  as described by Work to shear (WS): Five strands of cooked spaghetti were placed on a plexiglass plate and sheared at a 90° angle with a plexiglass tooth probe. A TA-XT2 texture analyzer was used to calculate the area under the curve (g·cm), indicating the amount of work required to shear the cooked spaghetti (the CF score). The average of three CF scores was used to report CF.The analysis of variance was conducted using the Statistical Analysis System (SAS) computer package version 9.3 . The unbP ≤ 0.05.The entry means plot-based heritability for all the traits were estimated using the method described by Four seeds from each genotype were planted into potting mix in the greenhouse in the fall of 2014. Three young leaf tissues from each genotype were harvested and sent to the USDA-ARS Cereal Crops Genotyping Laboratory in Fargo, ND, for DNA isolation. The extracted DNA samples were genotyped using the Illumina 90k iSelect BeadChip platform, and the markers were called using the diploid version of GenomeStudio software . FastPHAR2) for each of the marker pairs. Genome-wide LD decay was estimated by plotting LD estimates (R2) from all 14 chromosomes against the corresponding pairwise genetic distances (cM). Smoothing spline Fit (lambda = 338064.8) was applied to the estimate of LD decay.Linkage disequilibrium for all pairwise comparisons between intra-chromosomal SNP was computed, and the genome-wide LD decay was estimated using JMP Genomics 8.1 software . The LD P values of the selected models were later adjusted by calculating the corresponding false discovery rate (FDR)  . Marker–There were significant differences among genotypes for most of the traits in both balanced and unbalanced datasets Table 1.R2) ≥ 0.7 were considered a single QTL.A total of 6,492 SNP markers showed polymorphism among durum lines . MarkersPopulation structure was inferred using principal component analysis (PCA). Principal component analysis showed that two, three, and 10 PCs explain a cumulative 11.3%, 15.4%, and 26.8% of the genotype variation, respectively. The first three PCs clustered the collection into three sub-populations Figure 3Most quality traits were interrelated Figure 1Protein content: Both whole grain protein and semolina protein were genetically dissected in this study. Association mapping identified three loci for grain protein and five loci for semolina protein, including two common loci, located one each on 5B and 7A (R2 = 3.2–5.2%) on protein content.Milling-related traits: Milling-related traits are represented by total extraction (TEXT), semolina extraction (SEXT), semolina ash (SASH), and kernel vitreousness (VIT). All these traits except VIT were evaluated in the unbalanced dataset only. A total of four, six, eight, and 11 loci were associated with TEXT, SEXT, SASH, and VIT, respectively. These 29 QTL were located in 24 genomic regions belonging to all durum wheat chromosomes except 7B. All the four loci associated with TEXT  were also associated with SEXT (R2 > 10%) for kernel vitreousness were identified, one each on chromosomes 4B (17.7–22.2 cM) and 5B (146.14–149 cM). This genomic region on 4B was also found associated with Dif_b, CLOSS, Color, Color_a, and WG. Another QTL on 7A was also associated with WPROT, SPROT, and WG. The phenotypic variation contributed by loci associated with milling-related traits varied from 3% to 16.3%.Gluten-related traits: Five gluten-related traits were genetically dissected in this study. These were sedimentation volume (SDS), gluten index (GI), wet gluten (WG), mixogram score (MIXO), and glutork (GLUT). The number of loci identified for these traits ranged from five (MIXO) to 11 (SDS). The 39 QTLs identified for these five gluten-related traits were located in 25 genomic regions, belonging to 12 different chromosomes  , dried pasta color (“color”), total yellow pigment (TYP) in whole-wheat flour, and polyphenol oxidase (PPO) in whole wheat flour. “Color” represents spaghetti color and was only measured in the unbalanced dataset. A total of 58 QTLs, located on 41 different genomic regions, belonging to all 14 chromosomes were identified for nine color-related traits measured in both datasets. Ten genomic regions harbored QTL for two to four different color-related traits. For individual color-related traits, the number of loci ranged from four (color_L) to 13 (PPO). Five regions, located one each on chromosomes 3B, 4A, 4B, 6A, and 7A, could be considered important as they were associated with three to four colored-related traits. The QTL on 3B (86.4–89.4 cM) was associated with color_a, dif_b, and TYP. The QTL on 4AL was associated with pasta color, color_a, color_b, dif_b, and TYP. The locus on 4B was associated with pasta color, color_a, pigment loss defined by dif_b, and VIT (Markers strongly associated with PPO were identified on chromosomes 1A (IWA5150), 2B (IWA1488), 3A (IWB69399), and 3B (IWB23604) , cooking loss (CLOSS), work to shear (WTS), and cooked weight (CWT), were scored on fresh pasta in the balanced dataset from both Langdon and Prosper locations. In the unbalanced dataset, the same cooking parameters were taken on dry pasta. A total of 45 QTLs located in 32 genomic regions belonging to 13 different chromosomes (except 5B) were identified for four cooking-related traits : Co-localized or closely linked QTLs may help in improving several traits simultaneously when desirable alleles for each trait are contributed by the same parent. In this study, a total of 179 MTAs were identified for 24 different quality traits. The number of MTAs per chromosome ranged from 3 to 19. The highest number of MTAs were observed for chromosomes 2B and 6A (19 each), followed by 7A (18) and 1B (17). The lowest number of MTAs was observed for chromosomes 1A and 4B (three each). MTAs for different traits were considered in the same genomic region if they were identified at the same position or very close to each other (<1 cM apart) on the durum consensus map from Comparison of GWAS results from balanced and unbalanced datasets: To the best of our knowledge, there is no study on durum wheat that compares results from GWAS based on unbalanced and balanced phenotypic data. In this study, a total of nine traits, including grain protein (WPROT), semolina protein (SPROT), sedimentation volume (SDS), wet gluten (WG), gluten index (GI), kernel vitreousness (VIT), pasta firmness (FIRM), cooking loss (CLOSS), and cooked weight (CWT), were evaluated in both balanced and unbalanced trials. However, because FIRM, CLOSS, and CWT were measured on dry pasta in the historic unbalanced data and on fresh pasta in the balanced dataset, we decided not to include their comparison here. For the remaining six traits that were measured in both balanced and unbalanced datasets, we observed 40 and 31 marker–trait associations (MTAs), respectively  and their combination are for MAS. Two haplotypes for gluten strength and five for polyphenol oxidase activity (PPO) were identified , semolina protein (SPROT), gluten index (GI), wet gluten (WG), sedimentation volume (SDS), mixogram score (MIXO), and glutograph (GLUT) are considered the most important parameters of pasta quality and thus represent major breeding targets in the durum cultivar development program. QTLs for grain protein were previously identified on all wheat chromosomes (Gpc-B1 (Gpc-B1) into the adapted germplasm, through MAS  is also an important characteristic associated with many grain, flour, semolina, and pasta quality traits, including milling and pasta firmness. A high percentage of vitreous kernels maximizes semolina yield . VitreouPrevious studies have also shown that the high-molecular-weight glutenin subunits (HMW-GS) are particularly important for determining dough elasticity and correlated positively with dough baking quality . Group 1Milling quality: The aim of the durum grain milling process is to maximize semolina and minimize flour production through successive steps of grinding and sieving ( sieving . The pro sieving . The ide sieving . All QTL sieving  and wereSemolina ash (SASH), another important milling trait, has complex genetics. A decrease in grain weight always results in higher ash content or lower extraction rates . The QTLColor-related traits: Essentially, all the QTLs for dough color b, a, and total yellow pigment (TYP) had been reported earlier  ( earlier . Major Q earlier . In duruocessing . EarlierLpx-B1.1 . Similarta color . Like outa color . QTLs fo(PSY-B1) .† (green-red chromaticity), detection of four loci suggests complex genetic control of this trait. Considering the association of chromosome 3B, 4A, and 4B regions with color, col_b, and dif_b, as well as the negative correlation between dough colors a† and b† (r = −0.26) and pigment loss as measured by colors a† and b† (r = −0.63), a genetic linkage between these two traits could be suggested. This study’s findings and those of r = 0.31), as well as the negative correlations between dif_L and dif_a (r = −0.41) and dif_b and Dif_a (r = −0.63), and a single QTL on chromosome 6A associated with col_b and dif_b, may suggest an indirect masking effect of col_a on col_L by directly influencing col_b, especially in semolina dough over time.For pasta color aPreviously reported QTLs for polyphenol oxidase (PPO) activity were independent from other color-related traits, suggesting that their effect on pasta quality is mostly due to a browning reaction rather than the influence of semolina color components . The pheCooking-related traits: Many QTLs for four cooking-related traits  shared common regions, suggesting a close association between those traits. Two major regions associated with cooking-related traits were also associated with gluten strength on chromosome 1B and whole grain protein on chromosome 7B. The collection of phenotypic data requires extensive efforts and resources. Breeding programs collect large amounts of phenotypic data from advanced breeding lines every year for selection purposes. In the yield trial stages, the number of such advanced breeding lines tested each year is relatively small, and some of these lines are replaced by other breeding lines, resulting in an unbalanced dataset of advanced breeding lines developed over time. If these historic unbalanced data could be effectively used for genetic studies in crops, they could save significant amounts of resources and provide useful information for molecular breeding of crops. However, not many studies have been conducted to show the utility of such unbalanced historical data for genetic studies in plants. This could be addressed by comparing the results of genetic analysis using structured balanced data and unbalanced datasets. In barley, In the last few years, genotyping has become inexpensive, and a large amount of genotypic data can now be generated quickly. Also, an annotated reference sequence of whole wheat genome is now available. In this scenario, our ability to genetically dissect complex traits using routinely collected phenotypic data by the breeders will be extremely promising. This could help us save resources spent on genetic studies and may enable us to speed up genetic gain through molecular breeding tools.Multi-environment phenotypic data and high-density SNP platform were used to identify markers associated with 24 nutritional value enhancement and quality traits for genomics-assisted durum wheat breeding. The study also showed the application of historic unbalanced phenotypic data for genetic studies.The study aimed to dissect the genetics of durum wheat quality and identify useful marker–trait association for 24 different traits using high-density 90k Infinium SNP marker data. Genome-wide association studies revealed that MTAs for durum quality traits are distributed across the whole genome. Markers associated with some major QTL for gluten strength on chromosomes 1A and 1B and PPO activity on chromosomes 1A, 2B, 3A, and 3B could also be excellent easy candidates for MAS in durum wheat breeding programs. The information gained on extensive genetic dissection of durum wheat quality traits and the resources developed in this study may prove extremely useful to assess quality in early generations by incorporating molecular breeding tools in the breeding program. Another main objective of this study was to explore the possibilities of using a large amount of multi-year, multi-location unbalanced historical data generated by the breeding programs for genetic studies. A large number of common SNPs detected in both the unbalanced historic and balanced datasets suggest that the unbalanced data collected by the plant breeding programs over space and time could be used to gain knowledge about the genetics of important traits and identify MTAs for molecular breeding. This would save huge amounts of resources invested on conducting genetic studies using specifically designed populations. Also, as this study was based on advanced breeding lines, the MTAs identified in this study are easily accessible and should provide more directly useful information for local-knowledge-based breeding.MJ, FM, and EE conceived and designed the experiments. MJ, AO-A, and ES performed the experiment. MJ, AK, and MA analyzed the data. MJ and AK wrote the manuscript with inputs from all the co-authors.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.