Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision.G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits.The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits.Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL. In animals, plants and humans, data on phenotypes and genome-wide genotypes are used for both genomic prediction and for mapping and identification of the causal variants that affect the phenotype. Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding, and in humans. This is due to cost-effective second-generation resequencing technologies, in combination with 1000 genomes projects . Populations with extensive LD (e.g. many livestock breeds) require a smaller training population than populations with less LD . To maxGenome-wide association data is also used to map and identify these causal variants. While mapping causal variants for complex traits to a chromosomal region is common, identification of the causal variants is less common because the causal variant is likely to be in high LD with many other variants. Thus, to identify them, first, the causal variants must be included in the data and second, statistical methods to identify them are required. Fine-scale mapping often considers genome sequence data within a small chromosomal region, but it would be advantageous to do this across the whole genome. Within a population, long-range LD causes SNPs that are located far from a causal variant to be associated with it, which implies the identification of a broad quantitative trait locus (QTL) region. Genomic selection models with variable selection fit all SNPs simultaneously and thus, they position more precisely and possibly identify the causal variants, especially when data from several populations are combined.A problem with the use of Bayesian variable selection methods is that they are computationally very intensive because they typically involve Monte Carlo Markov chain (MCMC) sampling. Fast iterative methods for Bayesian genomic prediction have been developed, but they are generally not quite as accurate as their MCMC counterparts . Since tIn this paper, we present a method for fine-scale mapping and genomic prediction across breeds of cattle using Bayesian variable selection and WGS data on large numbers of individuals. The developed method is called Bayes GC and the model was applied to the three dairy breeds: Holstein (H), Jersey (J), and Australian Red (AR). We will use Bayes GC to map some of the causal variants, to demonstrate a new method for calculating confidence intervals for causal variants, and to compare the accuracy of WGS-based genomic predictions to those obtained using dense 600 k SNP chip data and using the genomic best linear unbiased prediction (GBLUP) method.2 > 0.9) using PLINK [The dataset used for this analysis is a subset of the data that are described in detail by van den Berg et al. , since wng PLINK , 4,809,5ng PLINK .Table 1NThe phenotypes (YD and DYD) are modelled as the sum of fixed breed*sex effects, a polygenic genetic value fitted by a GBLUP term, and the effects of SNPs fitted by a Bayes C term , resultiMethod 2  that wasModel (1) is prone to over-parametrization since both the GBLUP and the Bayes C term alone can explain all the genetic variance. In addition to fitting all SNPs by the GBLUP term, we want the Bayes C term to fit the top d et al. , who foui is sampled within each MCMC cycle from its conditional posterior distribution [The fixed effects are sampled by Gibbs sampling. The effect of (breed*sex)ribution :\documenThe polygenic effects were updated in one block by a Gibbs sampling step from its conditional posterior distribution :\documens et al. :\documen, requires the calculation of , where and its inverse is:Within any cycle c of the MCMC algorithm, millions of SNPs are not in the model and almost all do not remain in the model when moving to the next cycle c + 1, i.e. their evaluation does not result in a move of the MCMC chain. Of course, some of these evaluations of SNPs do result in a SNP move, i.e. a change of the effect of a SNP. In order to direct computer efforts towards SNPs for which the estimates of their effects are expected to change, we will update in cycle c the SNPs that currently have an effect of zero (i.e. are not in the model) with a reduced probability of The If SNP he model :2\documemodel is :3\docume Bayes C ).If SNP Alternatively, if SNP In these acceptance probabilities, the term If SNP ribution :\documenFinally, we correct the data The variance of the SNPs with large effects and that of the polygenic effects are sampled in the same manner, in order to unbiasedly balance these two variances against each other. Assuming a flat prior distribution, ribution :\documenSimilarly, the polygenic variance ribution :\documenibits. This reduced the storage requirements of the genotypes by 16-fold at the computational cost of calling the ibits-function whenever genotypes were needed. In this way, all  would require 687 Gbytes, which exceeds the RAM of most computers. In PLINK , genotypA drawback of the binary storage of genotypes is that the stored genotypes are not centralized, whereas in genomic prediction random regression is typically on centralized genotypes (e.g. ). OtherwThe centralized genotypes are used to calculate right-hand-side Thus, the right-hand-side and the contribution to the diagonal of the mixed model equations for the centralized genotypes could be calculated from their uncentralised counterparts within every MCMC cycle, by calculating the weighted mean of the genotypes, Modern computers can run many processes simultaneously. In case of MCMC sampling, this suggests running many short MCMC chains simultaneously instead of a single long one. The latter also benefits convergence diagnostics: the variability of the MCMC outcomes across the chains are indicative of the standard errors due to MCMC sampling . RunningTo make more efficient use of computational resources, we developed a parallel Fortran90 computer program that simultaneously ran multiple MCMC chains but kept only a single genotype matrix in RAM storage. The latter was achieved by setting up a parallel loop that runs the MCMC cycling loop multiple times using the OpenMP directive. Moreover, we assumed that some hyper-parameters such as the error variance  were obtained from.Bayes GC can be used to map causal variants to regions (we used 250-kb regions) and to individual sequence variants. First, the importance of a region for harboring genetic effects was quantified by the variance of the local GEBV for this region as calculated based on the Bayes C term in model (1), i.e. excluding the GBLUP term which is considered to explain an equal amount of variance for all positions. Second, the mapping precision was further increased by examining the posterior probabilities of the SNPs in the 250-kb region, which are the proportion of MCMC cycles past burn-in where the SNP was included in the model 14 [Figure  (BTA)14 . This is (BTA)14 –29).Fig.The meta-analysis of van den Berg et al.  detectedGHR gene, which has major effects on milk yield and composition [To further fine-map the QTL on BTA20, Fig. position .Fig. 3FiDGAT1 gene [DGAT1. There were two more COJO SNPs within the first four Mb of BTA14, but their positions were not clear from the posterior probabilities provided in Additional file For each of the top-SNPs, a 95% posterior credibility interval was constructed by: (1) identifying within each MCMC cycle (excluding burn-in cycles) which SNP currently fitted in the model was nearest to the top-SNP in the 250-kb region  under the restriction that the nearest SNP was less than 500 SNPs away ; and (2) trimming-off the 2.5% SNPs that are the furthest away from either side of the region. The 95% credibility interval was between 30.046906 and 30.177482 Mb for the first top-SNP, and between 31.394136 and 32.121047 for the second top-SNP. Both 95%-credibility intervals contained the corresponding COJO SNPs detected by . PosteriAT1 gene , and hamAT1 gene  and the When extending the top 10 to the top 20 250-kb regions with largest variance of local GEBV, three more COJO SNPs  were detGHR [The QTL signals within the region between 30 and 35 Mb on BTA20 are more clearly depicted by considering milk production instead of fat percentage Fig. . The posGHR . FurtherThe QTL detected in the top 10 250-kb regions for fat and protein yield are provided together with their 95% credibility intervals in see Additional file Table Table entclass1pt{minimaTable Genomic prediction can be described in two equivalent ways: as using SNPs to estimate the genomic relationship between the animals or as estimating the effect of SNPs that are in LD with the causal variants. If the causal variants are numerous and some have very small effects, the data may not have the power or the resolution to identify them individually. In this case, the best we can do is to estimate the effect of chromosomal segments that are present in multiple animals. In Bayes GC, this is done by the polygenic component, Models that simultaneously fit a GBLUP and a BayesC term have been used before in the literature, e.g. , 32, 33,The following arguments justify the use of a fixed entclass1pt{minimaentclass1pt{minimaIt may seem that skipping the evaluation of the SNPs that are not in the model with probability entclass1pt{minimag et al.  found thMemory requirements were reduced by storing four genotypes per byte of memory, following the binary storage approach of PLINK . AlthougModern high-performance computers (HPC) can run many threads in parallel and can contain large amounts of memory. However, memory intensive tasks can occupy all this memory and thereby an entire computer node, even if they do not use parallelization, i.e. most threads on the node will be idle. The availability of many threads makes the running of several (short) MCMC chains efficient. However, the memory requirements for running several single-threaded programs are high . Here, multi-threaded parallel chains contain more burn-in cycles than a single-threaded long MCMC chain. Thus, the cost of this type of parallelization is related to the number of MCMC cycles needed to obtain the next  independent MCMC sample relative to the number of the burn-in cycles. Using good starting values reduces the number of burn-in cycles, and more research on how to obtain good starting values for the MCMC chains is needed. Computations per cycle are dominated by the evaluations of the SNPs, which increase approximately linearly with the number of individuals, and less than linearly with the number of SNPs, since as the number of SNPs increases a larger fraction of the SNPs will be out of the model. Computations for the within-cycle updating of the polygenic effects increase quadratically with the number of individuals. The eigen-decomposition of the DGAT1 [The Bayes GC model resembles the standard GWAS model, since GWAS models generally fit a polygenic component and a single SNP effect simultaneously. Hence, the Bayes GC model may be seen as an extension of the standard GWAS model towards fitting many SNPs simultaneously. This increases mapping precision, since a QTL effect will not yield mapping signals across long distances, because a closer SNP, which is in stronger LD with the QTL, will be fitted and pick-up the QTL’s effect. The latter is not the case for the typical GWAS methods that fit the SNPs one-by-one. However, the complex inheritance pattern at the beginning of BTA14, which suggests the presence of several causal mutations (e.g. ), seemedDGAT1  as a fixOur approach to fine-scale mapping was to first identify (250-kb) regions with a large variance of local GEBV. This gives clearer QTL signals than a genome-wide search for high posterior probability SNPs for two reasons. First, if there are multiple SNPs in high LD with the causal variant, no one SNP may have a high posterior probability. Second, a SNP may have its posterior probability overestimated, for instance, because the MCMC chain has not converged due to the SNP being stuck in the model for too many MCMC cycles. The latter is remedied substantially by running multiple chains. We constructed a 95% credibility interval surrounding the SNP with the highest posterior probability, by identifying within every MCMC sample the SNP that was closest to this position estimate.The construction of 95% posterior probability intervals is often performed by summing the posterior probabilities of individual SNPs in the region until they exceed 0.95 (e.g. ). HoweveDGAT1 and GHR mutations.Additional file GHR gene [The Manhattan plots in Fig. GHR gene , a QTL aGHR gene , and a nFor the Jersey and Australian Red data, the average increases in accuracy were (across the traits) 2.5 and 16.5%, respectively, when applying Bayes GC instead of GBLUP . In the case of Holsteins, which have a large reference population, this average increase in accuracy was only 1.2%. The relative large increase in accuracy of AR cows was probably because the GBLUP accuracy was low at an average of 0.218 (excluding protein percentage), i.e. there was a lot of room for improvement. The genomic prediction accuracies in Table The Jersey and, particularly, the Holstein bulls already had high prediction accuracies (0.6 to 0.8), and data/model improvements only resulted in moderate increases of their GEBV accuracies. Comparing Tables In spite of the implemented speed improvements in the Bayes GC software, it is too slow for practical evaluations of breeding values. When using the GBLUP approach for genetic evaluations, one could give extra weight to the SNPs according to their posterior probability of being included into the model when setting up the SNP-chip . This isAcross-breed variable selection based genomic prediction improved prediction accuracies relative to GBLUP, especially in the absence of a sizeable within-breed reference population. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. The combination of across-breed WGS data and a variable selection genomic prediction method proved remarkably effective for the fine-mapping of QTL.Additional file 1: Figure S1. Manhattan plots of the variance of the local GEBV within 250-kb regions for kg milk. Figure S2. Manhattan plots of the variance of the local GEBV within 250-kb regions for kg fat. Figure S3. Manhattan plots of the variance of the local GEBV within 250-kb regions for kg protein. Figure S4. Manhattan plots of the variance of the local GEBV within 250-kb regions for protein percentage.Additional file 2: Figure S5. Manhattan plot of the variance of the local GEBV within 250-kb regions for fat percentage on BTA2. Figure S6. Manhattan plot of the variance of the local GEBV within 250-kb regions for fat percentage on BTA5. Figure S7. Manhattan plot of the variance of the local GEBV within 250-kb regions for fat percentage on BTA11. Figure S8. Manhattan plot of the variance of the local GEBV within 250-kb regions for fat percentage on BTA14.Additional file 3: Figure S9. Fine scale map of the posterior probabilities of the SNPs that affect fat percentage in the neighborhood of the fat percentage QTL on BTA2 shown in Figure S1.1. The blue bar denotes the 95% credibility interval for the QTL, and the red dot the position of the COJO SNP detected by [Figure S10. Fine scale map of the posterior probabilities of the SNPs for affecting fat percentage in the neighborhood of the fat percentage QTL on BTA5 shown in Fig. Figure S11. Fine scale map of the posterior probabilities of the SNPs for affecting fat percentage in the neighborhood of the fat percentage QTL on BTA11 shown in Figure S12. The blue bar denotes the 95% credibility interval for the QTL, and the red dot the position of the COJO SNP detected by [Figure S12. Fine scale map of the posterior probabilities of the SNPs for affecting fat percentage in the neighborhood of DGAT1 on BTA14 shown in Figure S1.3. The blue bar denotes the 95% credibility interval for the QTL, and the red dots denote the positions of two COJO SNPs detected by [ected by . Figure ected by . Figure ected by .Additional file 4: Table S1. Positions (Mb) of QTL for fat yield and their 95% credibility interval in the top-10 of 250-kb regions explaining most variance of local EBV (excluding regions neighboring the main QTL on BTA14). Table S2. Positions of QTL (Mb) for protein yield and their 95% credibility interval in the top-9 of 250-kb regions explaining most variance of local EBV (excluding the regions neighboring the main QTL on BTA14).


Brassica carinata A. Braun) is an emerging sustainable source of vegetable oil, in particular for the biofuel industry. The present study exploited genome assemblies of the Brassica diploids, Brassica nigra and Brassica oleracea, to discover over 10,000 genome-wide SNPs using genotype by sequencing of 620 B. carinata lines. The analyses revealed a SNP frequency of one every 91.7 kb, a heterozygosity level of 0.30, nucleotide diversity levels of 1.31 × 10−05, and the first five principal components captured only 13% molecular variation, indicating low levels of genetic diversity among the B. carinata collection. Genome bias was observed, with greater SNP density found on the B subgenome. The 620 lines clustered into two distinct sub-populations (SP1 and SP2) with the majority of accessions (88%) clustered in SP1 with those from Ethiopia, the presumed centre of origin. SP2 was distinguished by a collection of breeding lines, implicating targeted selection in creating population structure. Two selective sweep regions on B3 and B8 were detected, which harbour genes involved in fatty acid and aliphatic glucosinolate biosynthesis, respectively. The assessment of genetic diversity, population structure, and LD in the global B. carinata collection provides critical information to assist future crop improvement.Ethiopian mustard ( Brassica carinata A. Braun, has been proposed as an industrial oilseed crop owing to its fatty acid profile with high levels of long and very long chain unsaturated fatty acids2. It is an allotetraploid formed through interspecific hybridization between ancestors of diploid Brassica nigra  and Brassica oleracea 3, with 2n = 4x = 34 chromosomes and a genome size of ~ 1,284 Mbp4. It is believed to have originated in the highlands of Ethiopia, and possibly adjoining parts of East Africa and the Mediterranean coast5. The crop is gaining importance in Western Canada, Southern Europe, Australia, South America and India because of its drought tolerance6, shatter resistance, large seed size7, and high level of resistance to blackleg8, and Alternaria leaf spot9. Although the oil is considered to be of lower nutritional value due to high levels of erucic acid (> 40%) it is being considered as an alternative source of biofuel feedstock, for example in the production of jet biofuel10.Ethiopian mustard, B. carinata crop improvement and low levels of molecular variation were identified using RAPD and AFLP technology for a relatively small number of lines12. However, progress has been made recently in the construction of genetic maps for the crop and quantitative trait locus (QTL) mapping using largely anonymous marker systems. The first linkage map was constructed using 212 SSR loci by Guo et al.13, while more recently 4,031 DArTseq loci were integrated into a map of the 17 chromosomes of B. carinata14. In addition, a gene conferring resistance to black rot was mapped to B7 using intron length polymorphism markers15. More recently, a diversity panel of 83 B. carinata accessions were evaluated using DArTseq, and QTLs associated with pod shatter resistance were mapped using an F2 population16. In addition, an independent panel of 81 accessions of B. carinata was genotyped using DArTseq to study population structure, and pattern of linkage disequilibrium and QTLs accounting for agronomic and seed quality traits were mapped using a doubled haploid (DH) mapping population17.There were limited genomic resources available that can be used for 18. Exploiting high throughput sequencing platforms in approaches such as genotyping by sequencing (GBS) has enabled large numbers of accessions to be genotyped in a relatively cost-effective fashion20. The GBS assay utilizes restriction enzyme digestion to reduce the genome area being sequenced, creating increased coverage for target sites, and the use of methylation sensitive enzymes limits the capture of repetitive DNA regions20. Although there are some limitations, the modified GBS method has been successfully implemented in multiple diploid and polyploid species23.With the rapid development of next-generation sequencing platforms single nucleotide polymorphisms (SNPs) have become the marker system of choice in plant genetic studies, particularly in the construction of high-density linkage maps, QTL mapping, association analysis, and genetic diversity studies24, thus characterisation of genetic diversity and population structure can unravel the evolutionary history and assist in maintaining and exploiting the available variation for a species. In addition, the characterised genetic variation can be employed in genetic mapping and genome-wide association studies (GWAS) for target trait analyses. Genetic analyses can also allow the calculation of local and genome-wide linkage disequilibrium (LD), the non-random association between alleles at loci across the genome, which is important in mapping studies as it dictates the resolution with which a trait can be determined25. Furthermore, haplotype analysis and selective sweep analyses can identify signatures of natural and artificial selection27. GWAS has proven to be a powerful tool to locate important genes underlying complex phenotypic traits in animal and plant studies29; however, it can be challenging in polyploid species such as those of the Brassicaceae due to the complexity and underlying redundancy of the genome.The nucleotide diversity of crop plants is influenced by both natural and artificial selectionB. nigra and B. oleracea, as a pseudo-reference for the B. carinata genome, the configuration of nucleotide polymorphism in B. carinata was explored. A worldwide collection of 620 lines was assayed, with thousands of high confidence SNPs distributed on the 17 chromosomes of B. carinata. These data revealed levels of genetic diversity, linkage disequilibrium, and haplotype patterns across the genome and identified genomic regions showing evidence of selective pressure. Conventional plant breeding has had limited success in B. carinata improvement30; however, the application of modern strategies such as marker assisted breeding has the potential to accelerate further development of this underutilised crop and the information and resources presented here should enable these goals.Using available genome sequences for the Brassica diploids, B. carinata accessions allowed detection of genome-wide SNP loci. For each accession, genome complexity reduction was carried out using two enzymes, PstI and MspI, followed by 96-fold multiplexed sequencing on an Illumina Hiseq2000, which yielded a total of ~ 660 million paired-end reads. The reads were aligned to a pseudo-reference genome for B. carinata composed of the concatenated genomes of the diploids B. nigra31 (https://Cruciferseq.ca) and B. oleracea32  > 0.05, heterozygosity ≤ 0.1, read depth ≥ 4 (per line), and up to 30% missing data, at any locus  compared to the C genome (one every 177.9 kb). The majority (43.9%) of the 10,199 high confidence SNPs were in coding regions, followed by intergenic regions (31.2%) and introns (23.9%) (Supplementary Table Genotyping by sequencing (GBS) of a global collection of 620 r2 = 0.1) genome-wide SNPs revealed two subpopulations  among the 620 B. carinata accessions. Using a membership probability threshold of 70%, 549 (88.5%) accessions were assigned to subpopulation 1 (SP1), 41 (6.6%) to subpopulation 2 (SP2), and the remaining 30 (4.8%) were retained in the admixture group (AG)  approaches were utilized to support the STRUCTURE analysis. A neighbour-joining (NJ) tree was constructed using an un-weighted pair group method with arithmetic mean (UPGMA). The phylogeny confirmed that SP1 and SP2 separated into distinct clusters Fig. . The firFST was calculated to assess the significance of genetic differentiation between subpopulations and revealed a modest difference (0.148) between SP1 and SP2. In addition, FST estimates were 0.048 between SP1 and AG, and 0.082 between SP2 and AG. High fixation indices were observed  within each subpopulation, suggesting high levels of inbreeding. Relative kinship between B. carinata lines is depicted in Supplementary Fig. Analysis of molecular variation was calculated to study genetic differentiation among STRUCTURE defined sub-populations. This analysis revealed low within sample  variance (14%) and between sub-population variance (23%), whereas a higher level of variance was observed within sub-populations (63%) , followed by coding (9.23 × 10–06) and intergenic SNPs (1.00 × 10−05). There was no significant difference in the level of nucleotide diversity and Watterson’s estimator among structure defined subpopulations. The overall Tajima D distribution at the whole genome level (− 1.9 to 5.70) significantly deviated from neutrality, evidence of a recent selective sweep.Measures of nucleotide diversity for various genomic contexts are presented in Table B. carinata accessions was evaluated using gene diversity (HE), observed heterozygosity (HO), and polymorphism information content (PIC) at individual genome (B and C), whole genome (BC) and subpopulation (SP1 and SP2) level. The B. carinata accessions showed a low level of HE (0.30) and HO (0.05) at the whole genome level , B subgenome (0.076), and C subgenome (0.081). The mean genome-wide r2 values suggested high levels of LD in B. carinata, extending up to 700 kb in the whole genome (BC), and across 475 kb  and 725 kb in Brassica B and C genomes, respectively  compared to the C genome   Table . The numb) Table . Genome-B. carinata population; namely co-incidence of biased principal component loadings  that appeared to be under selection within the ks Table . Utilisiks Table . Of thesfatty acid elongase1 and genes involved in the indole glucosinolate pathway, IQD1 and GSH1; and the ROI on chromosome B8 (spanning 4.4 Mbp) harboured regulatory genes involved in biosynthesis and accumulation of aliphatic glucosinolates namely, MAM1, AOP1, CYP79C1, BAT5, and LIF2  and modest estimates of nucleotide diversity (π = 1.31 × 10−05) compared to its sister species B. napus, which ranged between π = 1.89 × 10−3 and 2.30 × 10−3 across sub-populations34. SNP frequency and nucleotide diversity are affected by several factors, including selection, mutation rates, breeding behaviour, and effective population size during species formation and demography35. The lower levels of nucleotide diversity suggest stronger genetic bottlenecks during domestication of B. carinata than for B. napus. Although the two species followed a similar evolutionary path, each likely formed from a limited number of hybridization events between progenitor diploid species leading to small effective population sizes; however, B. napus benefited from introgression with extant diploid species37.A global collection of Ethiopian mustard was utilized to characterize molecular diversity, uncover population structure, and assess the potential of the available lines for GWAS. A large number of genome-wide SNPs were detected, which could be of value for future breeding efforts. B. carinata population was clustered into two distinct groups (SP1 and SP2) based on breeding history rather than natural selection. Raman et al.16 observed a weak population structure among 83 B. carinata accessions with four STRUCTURE defined populations and the majority of the accessions (83.3%) in one group. Although not reflected in the current STRUCTURE analyses, a similar weak grouping effect was observed in the current data when analysed using tree-based approaches. The Bayesian clustering, phylogeny, and PCA analyses indicated that most accessions irrespective of their assigned country of origin were derived from Ethiopian accessions; in accordance with previous suggestions that Ethiopia is the primary centre of origin for B. carinata and the species has spread across different continents through migration with early human civilization. The only accessions that clustered based on origin other than Ethiopia was a small number of Zambian accessions. Similar results were reported in Teklewold and Becker11, suggesting either these accessions separated earlier from the centre of origin or there has been selection for locally adaptive traits, but this needs further investigation and ideally additional germplasm. There has been limited traditional breeding applied to B. carinata improvement, so perhaps not surprisingly the greatest impact to the observed population structure was found with the inclusion of accessions from a select Agrisoma collection, which separated from the Ethiopian lines forming the basis of SP2. Even with this differentiation, there is a low level of pairwise population differentiation between SP1 and SP2, since SP2 almost certainly shares a common origin with SP1 and selective breeding seems to have targeted particular regions of the genome. Similarly, analysis of molecular variance revealed low differentiation between sub-populations, suggesting high rates of gene flow between populations.The 38 as observed in recently formed polyploid crops, such as cotton39, peanut40, and soybean41. The limited natural allopolyploidization event(s) that likely resulted in the origin of B. carinata and its geographic isolation appears to have had a dramatic impact on the level of genetic diversity42. A low level of heterozygosity was found among B. carinata accessions, indicating adverse effects of the small population size or loss of heterozygosity due to genetic drift43. Lower values of observed versus expected heterozygosity suggest a high degree of inbreeding among B. carinata accessions could have resulted in fixation of alleles, which almost certainly contributed to lower genetic variation in the B. carinata genome.Recently formed species might be expected to have lower levels of genetic diversity44. There were marginally higher non-synonymous substitution rates observed in captured genic regions for the B sub-genome (52.5%) compared to the C sub-genome (41.6%) (Supplementary Table B. nigra genome (8 Mya) than B. oleracea (4 Mya) from their shared common ancestor and thus could have accumulated higher levels of mutation45. It was noted that the effective recombination rate is lower across the C genome than the B genome  with a high mean pairwise r2 value (0.077) in B. carinata, consistent with previous work where an LD decay rate of ~ 525 kb was observed17. Also, similar rates of LD have been observed in closely related species, ~ 250 kb in A. thaliana53 and ~ 500 kb in B. napus46. Consistent with the lower rates of polymorphic loci the LD decay rate in the C sub-genome (~ 725 kb) was higher than the B sub-genome (~ 475 kb), which was comparable to a previous study, where the C sub-genome showed longer LD decay (~ 400 kb) than the B sub-genome (~ 250 kb)17. LD decay over the whole genome was used to predict the minimum number of SNPs required for genome-wide association studies in B. carinata. Considering LD at the whole genome level (700 kb) and a genome size of 1,284–1,544 Mbp, the number of SNPs required for successful implementation of LD mapping studies in B. carinata would be 1,834–2,206 SNPs. Nonetheless, higher LD will create resolution limits in mapping studies, thus a genome-wide association study may not yield strong positive signals due to an extensive haplotype pattern and the long range of LD in B. carinata54. There are various forces which affect the level of LD, including selection, tight linkage between genes, allele frequency, recombination rate, population size, and mating system28. In the studied lines the impact of small population size coupled with selection for specific traits in a breeding program led to the markedly higher level of LD observed in SP2 compared to that in SP1.LD blocks or haplotype blocks indicate regions where limited recombination has led to co-inheritance of loci, potentially from the ancestral genome, largely as a consequence of selection and they have often been found to harbour domestication-related genesFST values, which were suggestive of positive selection. The regions on B3 and B8 harboured well characterized fatty acid and glucosinolate biosynthesis genes, respectively, and both explained a significant amount of the variation between the SP1 and SP2 populations. These two regions bore all the hallmarks of selective breeding for seed quality traits, similar to observations made for selection of domestication traits56.Two ROI were identified with multiple lines of evidence, including low nucleotide diversity, deviation from neutrality (Tajima’s D), biased PC loadings and pairwise B. carinata germplasm available from resource centres worldwide, and identified thousands of genome-wide SNPs using GBS. The diversity observed suggests B. carinata originated from a very limited number, if not a single hybridization event, with little or no subsequent inter-specific crossing with the parental progenitors. Although as observed in the Agrisoma collection, specific traits can be targeted for selection, further use of related diploid species may be necessary to increase the levels of available genetic diversity. The long-range LD and extended haplotype blocks could hamper fine resolution of trait loci that might be detected through GWAS. The best approach to crop improvement might be a joint linkage/association mapping approach such as nested association mapping, which can break long LD blocks into smaller fragments using recombination and establish greater genotype–phenotype correlations57.The present study provides a comprehensive analysis of diversity among Brassica carinata lines potentially representing 15 different countries were obtained from eight different gene banks, including Plant Gene Resources (PGRC) Canada (https://pgrc3.agr.gc.ca/index_e.html), Australian Temperate Field Crops Collection (ATFCC), Australia (https://www2.dpi.qld.gov.au/extra/asp/AusPGRIS/Centres.asp), LEIBNIZ-INSTITUT FÜR PFLANZENGENETIK UND KULTURPFLANZENFORSCHUNG (IPK) Germany (https://www.ipk-gatersleben.de/en/gbisipk-gaterslebendegbis-i/), Crop Research Institute (CRI) Czech Republic (https://www.vurv.cz/index.php?p=index&site=default_en ), National Center for Genetic Resources Preservation (NCGRP) USA (https://www.ars.usda.gov/main/site_main.htm?modecode=30-12-05-00), Warwick Genetic Resources Unit (WGRU) UK (https://www2.warwick.ac.uk/fac/sci/lifesci/wcc/gru/), Centre for Genetic Resources (CGN), The Netherlands (https://www.wageningenur.nl/en/Expertise-Services/Statutory-research-tasks/Centre-for-Genetic-Resources-the-Netherlands-1.htm), and Agrisoma . Of the 631 accessions, 11 accessions were miss-identified B. juncea lines and were excluded from further analysis 31 and B. oleracea (C)32using Bowtie258. The parameters were set as: –local –sensitive –phred33 –minins 0 –maxins 1,000 –no-mixed –no-discordant –dovetail -k 50 –score-min L,0,0.8. Only unique matches to the genome were maintained for further analyses. SNPs were called using the genome analysis toolkit (GATK) version 3.2.259. The parameters were as follows: -T UnifiedGenotyper, -glm BOTH, -ploidy 2. The missing SNP genotype calls were imputed using BEAGLE 4.060 with the parameters as five initial burn-in iterations and ten iterations for estimating the genotype at missing data points. The imputation accuracy was assessed by altering known genotyping SNP calls as missing calls. The high-quality SNPs were named according to their base pair position within the diploid B. nigra and B. oleracea genome assemblies. Based on the genome annotation coordinates, the SNPs were categorised as coding, intergenic and intronic, the coding SNPs were further classified as synonymous or non-synonymous.Genomic DNA of 3–4 week old seedlings was isolated using Qiagen DNeasy plant mini kit according to the manufacturer’s instructions . DNA was quantified using PicoGreen dsDNA assay kit  and the Victor X2 Fluorimeter  and then normalized to 20 ng/µl. GBS libraries were constructed according to Poland et alr2 = 0.1) distributed on 17 chromosomes of B. carinata using STRUCTURE version 2.3.4 which employs a model-based clustering (Bayesian) approach61. The number of subgroups (K) was set from 1 to 10 using an admixture model with correlated allele frequency. For each K, five runs were performed separately using burn-in period of 100,000 iterations and 200,000 Markov Chain Monte Carlo repetitions (MCMC). The number of subpopulations were detected using an ad hoc statistic ∆K, the peak of ∆K value distribution, which is the rate of change of log probability of data between successive K values62. The probability of membership  equal or greater than 0.70 was taken as a threshold to assign genotypes to a particular subpopulation, those accessions with Q < 0.70 were retained as admixture. A neighbour joining (NJ) phylogenetic tree was performed with 10,000 bootstraps using DARwin 6.0.463 and visualized using ITOL64. Principal component analysis (PCA) was performed using genome wide association and prediction integrated tool (GAPIT) package in R65.Population structure analysis was performed based on 1,535 unlinked SNPs (D), expected heterozygosity (HE), observed heterozygosity (HO), and polymorphism information content (PIC) of SNPs across sub-genomes and subpopulations were estimated using Powermarker software version 3.066. Analysis of molecular variance (AMOVA) and pairwise FST was performed using GenAlex version 6.567. The estimates of nucleotide diversity (average pairwise nucleotide differences (π)), Watterson Theta (θw), and Tajima D were calculated using VariScan in 100 kb non-overlapping sliding windows69. To assess relatedness among genotypes, pairwise kinship analysis was performed using SPAGeDi v1.470. Negative values between two individuals were changed to 0 as it indicates less relationship than expected between two random individuals71.Genetic diversity parameters, including gene diversity  were plotted against physical distance to estimate the rate of LD decay for the whole genome, sub-genomes, and each chromosome separately. LD decay scatter plots were generated using R version 3 (R 3.4.0–3.6.0)73. LD blocks, also known as haplotype blocks, were identified using Haploview74, and the haplotype blocks were detected using all the SNPs by employing the block definition described in Gabriel et al.26. The B. nigra and B. oleracea genes residing in ≥ 200 kb of the LD blocks were identified, and putative functions were assigned based on those of their corresponding orthologues (best-BLAST-hit) in the related Brassicaceae Arabidopsis thaliana.The extent of LD across each chromosome was estimated using 10,199 high confidence SNPs. The pairwise ƿ) was estimated on the subset of 96 representative accessions using the Interval program in LDhat 2.275. In brief, 1,500,000 iterations were run, with sampling every 2000 iterations and a block penalty parameter of 5. The recombination rate was estimated for each chromosome separately and averaged across all the chromosomes to get an overall estimate.The genome-wide recombination rate rho (FST) was estimated in an 100 kb sliding window using VCFTools76. To identify genes residing in an ROI, BLASTN analysis was performed against Arabidopsis thaliana (TAIR10) database using an E value of 1e−10–6.Principal component analysis was undertaken using GAPIT and the PC loadings were plotted across the chromosomes. The SNPs with high PC loadings (> 0.02) were identified as region of interest. The population differentiation statistics (Weir and Cockerhams’s Pairwise Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by RFI group, iii) merged samples by RFI and tissue group. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR aligner. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed.Optimization of an RNA-Sequencing (RNA-Seq) pipeline is critical to maximize power and accuracy to identify genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study used RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures  associated with immune and metabolic pathways.On average, total reads detected for Approach i) non-merged samples for liver and muscle, were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), merging samples by RFI group, total reads detected for each merged group was 162,030,705, and for Approach iii), merging samples by RFI group and tissues, was 324,061,410, revealing the highest read depth for Approach iii). Additionally, Approach iii) merging samples by RFI group and tissues, revealed the highest read depth per variant coverage (572.59 ± 3993.11) and encompassed the majority of localized positional genes detected by each approach. This suggests Approach iii) had optimized detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive detection. Approach iii) was then used to detect unique SNPs fixed within low-  and high-RFI  groups. Functional annotation of SNPs revealed positional candidate genes, for each RFI group , which were significantly (The most optimized RNA-Seq pipeline allowed for more accurate identification of SNPs, associated positional candidate genes, and significantly associated metabolic pathways in muscle and liver tissues, providing insight on the underlying genetic architecture of feed efficiency in beef cattle. High-throughput RNA-Sequencing (RNA-Seq) technology is widely used to detect and quantify expressed transcripts, novel transcript discovery and analyze differential gene expression and alternative splicing in a biological sample –3. In adRNA-Seq experiments in livestock studies have identified significant SNPs in candidate genes associated with metabolic pathways that may play a role in the regulation of production traits , 8–12. TAlthough SNP identification for genetic markers has served as a powerful tool in genomics, the ability to better understand the relationship between genotype and phenotype relies on the accuracy of analysis to detect genomic variation. Studies have previously compared methods for genotype calling software such as GATK, Samtools, SNPiR, CLC Bio Genomics Workbench using RNA-Seq data , 17–22, Therefore, the evaluation of RNA-Seq pipelines to identify variants across different phenotypic or genotypic groups that include samples from multiple tissues has not been evaluated and strategies for the use and merging of RNA-Seq data from multiple samples and tissues for optimized power and accuracy remain limited. Optimized RNA-Seq analysis approaches can be applied for SNP discovery to detect SNPs that may serve as functional genetic markers and be used in selection strategies to improve economically relevant traits in livestock.The aim of this study was to compare three RNA-Seq sample merging pipelines for SNP identification to determine the most optimized and accurate pipeline based on study experimental design. The approach considered as the most optimized and accurate approach for SNP detection using RNA-Seq data was then used to identify functional SNPs associated with feed efficiency in Nellore beef cattle to improve the understanding of the biology and metabolic pathways underlying genetic markers that may influence the function and regulation of feed efficiency in beef cattle. The objectives of this study were to 1) compare three RNA-Seq pipelines using samples from two divergent groups for feed efficiency  and two tissues  including multi-sample calling from: i) non-merged samples, ii) merged samples for low-RFI and merged samples for high-RFI for each tissue (merged by RFI group), iii) merged samples for low- and high-RFI for both tissues (merged by RFI and tissue group), 2) determine the pipeline with maximized accuracy and power for SNP detection and apply it to identify unique SNPs, and associated functional information, fixed within high or low feed efficient Nellore beef steers.In this study, three RNA-Seq pipeline approaches and their variant calling results were compared. The most optimized approach was then applied to perform a more accurate SNP detection for genetic markers associated with feed efficiency in beef cattle. The number of total reads, total uniquely mapped reads, and percentage of uniquely mapped reads is reported in  may represent SNPs that are present in a small subset of animals and hence are not representative of a specific RFI group. For SNPs with a low non-reference allele frequency, merging reads from multiple samples could lead to dilution of reads supporting the variant and consequently be called as homozygous for reference . AlternaTo determine the most optimized approach with highest read depth, the total reads mapped across the whole transcriptome for each approach were compared  and Approach ii), due to the higher number of reads for muscle tissue seen in  , per detected variant between approaches. The Cohen’s d test suggests a large effect is ≥0.50 , but may vary across disciplines.As displayed in  and Approach iii) (0.151), which is much lower than the comparison between Approach i) v.s. Approach ii) (0.554), and Approach i) v.s. Approach iii) (0.457), which are expected to have much larger difference in coverage (read depth) due to the merging of samples  a) merged by RFI group (liver) with Approach iii) merged by RFI and tissue, and Approach ii) b) merged by RFI group (muscle) with Approach iii) merged by RFI and tissue, respectively  and ii) against Approach iii), based on alternative allele frequency of the variants among the samples, which is illustrated in Fig. It is also observed in each comparison Fig. , that thThe list of SNPs fixed within low- or high-RFI groups and filtered for Moderate, Modifier, and High functional impact were compared across each approach Fig. a. The moAs shown in the Venn Diagram in Fig. Total number of unique or shared SNPs identified across low- and high-RFI groups using Approach iii) are shown in  and gluconeogenesis [The three significant pathways associated with low-RFI (more feed efficient) cattle, included immune response, fertility, and metabolism pathways, including the BCR signaling pathway, oxytocin signaling pathway, and ay Table . The BCRfunction . It may ogenesis .EPHA2 forward signaling was also associated with more feed efficient cattle in this study. The EPHA2 is a gene known to be targeted by miR-26b, a microRNA (miRNA) in pituitary tissues of Zanbian cattle, and this miRNA is known to regulate cell proliferation, differentiation, apoptosis, and development [EPHA2 receptor is a key modulator for a wide variety of cellular functions, such as embryonic development, tissue boundary formation, central nervous system function, bone remodeling, vascular organization [The elopment . In addinization , suggestRAC1 was another significantly associated pathways with less feed efficient cattle, which is known to have a role in glucose transport and skeletal muscle [The three significant pathways associated with high-RFI (less feed efficient) cattle, also included BCR signaling pathway Table , which ml muscle , and couOverall, the most significant pathways associated with the extreme feed efficiency groups were both related to metabolic, reproductive, and immune functions Table . NotablyFurther studies using orthogonal strategies such as Sanger sequencing, target sequencing, and whole genome sequencing as truth sets could serve as further evidence to confirm the optimized approaches , as well as the functional positional variants associated with a specific trait.In conclusion, this study demonstrates the different results obtained in SNP detection from using different sample merging pipelines for RNA-Seq analysis. We suggest that the present optimized pipeline for SNP detection when analyzing multiple samples and tissues in divergent traits or phenotypic groups is to merge multiple sample and tissue data by group to increase aligned reads at each variant site which increases whole transcriptome read depth coverage, as well as read depth coverage per variant, and variant calling quality, leading to improved accuracy and power of SNP detection and reduction of false positive detection. This pipeline can be used to identify SNPs associated with extreme phenotypes of interest, which can be annotated to identify genes that may be important in the regulation of the trait. Overall, this work presents an optimized RNA-Seq pipeline to discover SNPs in coding regions to improve the detection of potential trait-associated variations using RNA-Seq data. Additionally, from using the proposed optimized RNA-Seq pipeline, this study successfully identified functional candidate SNPs within genes involved in major biological pathways associated with high and low feed efficient Nellore beef cattle. This suggests the relationship between immune, reproductive, and metabolic function with feed efficiency in beef cattle, and confirming the successful use of the most optimized RNA-Seq pipeline described in this study.n = 10 low-RFI and n = 10 high-RFI) at 21 months of age were used, which were selected from 585 steers that were calculated for feed efficiency through Best Linear Unbiased Prediction (BLUP) estimates for RFI [The datasets used in this study consist of RNA-Seq data obtained from liver and muscle tissue samples from Nellore beef steers deposited in the NCBI - Gene Expression Omnibus (GEO) public repository with PRJEB7696 and PRJEB15314 GEO accession numbers for both liver and muscle, respectively. Detailed animal management and sampling information was previously described by Tizioto et al.  and by T for RFI , 44. Tis for RFI . RNA was for RFI , 44.n = 6 low-RFI and n = 6 high-RFI) from both muscle and liver tissue  were removed from the study, and four corresponding individuals  from the high-RFI liver group were also removed. To maintain the same sample size across RFI groups, four animals were randomly selected to be removed from the low-RFI group for muscle  and liver . Therefore, this study used RNA-Seq data from 12 Nellore beef steers divergent for feed efficiency (Bos Taurus Assembly UMD3.1. release 94), using STAR (version 2.7.0 [http://broadinstitute.github.io/picard/). The RGs allowed for differentiation of samples by assigning the origin of the read (low- or high-RFI group) and assignment of SNPs to a specific genotype.Identification of SNPs was performed using the workflow shown in Fig. on 2.7.0 ;). with on 2.7.0  may reveon 2.7.0 . HoweverA diagram outlining the strategy used for merging BAM files is shown in . This The comparisons of approaches performed with VCF files of varying genotypes (GT) is summarized in [Additional file https://www.qiagenbioinformatics.com/) was used to determine the total number of unfiltered reads across the whole transcriptome or each individual sample in Approach i) and for merged sample by groups and tissues in Approach ii) and iii). This tool is used to identify regions in read mappings with unexpectedly low or high coverage, however, the summary of results of this analysis presents the ‘Total Mapped Reads’ across the whole transcriptome for each approach, counting both reads of a paired end sequence, while ignoring ‘non-specific matches’, or ‘broken pairs’ .The Whole Transcriptome Coverage Analysis tool of CLC Genomics Workbench 12.0.2  per variant distribution for each approach was calculated. The read depth per variant for each approach was plotted Fig.  and corrhttps://software.broadinstitute.org/software/igv/) was used to visualize the read mapping and coverage (depth of the reads displayed at each locus) between each approach. As an example, the IGV visualizations illustrates the read mapping , which compared the effect sizes of the quality (QUAL) in VCF files of detected variants in each approach. Quality (QUAL) value in VCF files represent the Phred-scaled probability that a reference/alternative polymorphism exists at the variant site, based on the sequencing data. Cohen’s d value was calculated using the lsr package in R assuming non equal variances between groups ; merged samples by RFI and tissue group) was performed by calculating the Cohen’s on 3.6.0 ;). All CAdditionally, the stats and plot-vcfstats options from bcftools software  were useUnique SNPs fixed within low- or high-RFI groups were identified using the VCF files containing filtered SNPs as previously described for each approach. These SNPs were filtered for moderate or high functional impact using Variant Effect Predictor (VeP) . VariantApproach iii) was then used to identify unique SNPs fixed within low- or high-RFI groups using the VCF files containing filtered SNPs as previously described Fig. . The VCFAdditional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.Additional file 8.Additional file 9.Additional file 10.Additional file 11.


P < 0.001) was observed among these lines for different RSA-related traits. This panel was genotyped with 2.18 million genome-wide single-nucleotide polymorphisms (SNPs) using a combination of genotyping-by-sequencing and whole-genome sequencing. A total of 10 quantitative trait locus (QTL) regions were detected for root total length and primary root diameter through a comprehensive genome-wide association study. These QTL regions explained from 15 to 25% of the phenotypic variation and contained two putative candidate genes with homology to genes previously reported to play a role in RSA in other species. These genes can serve to accelerate future efforts aimed to dissect genetic architecture of RSA and breed more resilient varieties.Increasing the understanding genetic basis of the variability in root system architecture (RSA) is essential to improve resource-use efficiency in agriculture systems and to develop climate-resilient crop cultivars. Roots being underground, their direct observation and detailed characterization are challenging. Here, were characterized twelve RSA-related traits in a panel of 137 early maturing soybean lines (Canadian soybean core collection) using rhizoboxes and two-dimensional imaging. Significant phenotypic variation ( The root system plays an important role in the acquisition of essential macro and micronutrients and water from the soil and ensures the anchorage of plants . Becausein situ have been developed.The RSA is essentially modulated by the growth inhibition of primary root and lateral roots. It can be also modulated by the formation of adventitious roots and root hairs . TherefoThese approaches known as non-destructive techniques are generally relied on rhizoboxes, transparent enclosures allowing the study of root system development in two-dimensional (2D) using different substrates such as soil or vermiculite . In contDEEPER ROOTING 1 (DRO1), an RSA-related gene that controls root growth angle was identified using a 2D phenotyping system in a rhizotron  provide a power tool for the identification of genes controlling the complex phenotypes such as RSA-related traits . In Arabhizotron . Introdur stress . A GWAS r stress  has alsoitecture . This riSoybean has many attributes that make it “super crop.” It constitutes an importance source of protein for food and feed. The content of the latter ranged between 38 and 44% of the total dry weight of the seed . SoybeanHere, a core set of 137 soybean lines that representative of Canadian short-season soybean was phenotyped for RSA-related traits in rhizoboxes. We carried out a GWAS using a catalog of 2.18M SNPs obtained from a combined dataset resulting from both genotyping by sequencing (GBS) and whole-genome sequencing (WGS) to dissect the genetic basis of RSA in soybean.1.A set of 137 lines representative of the extent of genetic diversity among short-session soybeans in Canada was used . AlthougApeKI (Genotyping of this population was performed through a hybrid approach combining genotyping by sequencing (GBS) and whole-genome sequencing (WGS). In brief, all 137 soybean lines were genotyped through a GBS protocol based on digestion with ApeKI . The SNPApeKI  and aligApeKI . GenotypApeKI . We thenApeKI  to imputApeKI . Among tApeKI  was usedr2 > 0.5) with PLINK  and a Kinship matrix (K) as covariates. A genome-wide significance threshold <0.05 was used to declare significant associations using the false discovery rate (FDR) test of GWAS analysis was performed on the full set of filtered WGS-derived markers (2.18M SNPs) using the FarmCPU algorithm  implemen2) for soybeans was used to visualize the expression in tissue related to RSA  [based on the transcriptomic data of We used a systematic analytical process to identify candidate genes for RSA-related traits. First, we measured LD (D’) between the peak SNP and all markers located within a 2-Mb window (1-Mb on each side) using PLINK . The regP < 0.001)  . In the first group, we observed that TLR was highly correlated with four other traits: LSR (r = 0.99), DTLR (r = 0.93), DRS (r = 0.84) and SAR (r = 0.78). In a second case, WRS was also found to be highly correlated with SRS (r = 0.97). In the last group, Med was also correlated with Max (r = 0.68). In all of these cases, the most frequently measured RSA-related trait  was kept as it was deemed redundant to perform GWAS on all highly correlated traits. Finally, the three remaining traits  were not highly correlated to another trait and were each retained for the GWAS.Wide and significant phenotypic variation was observed among the 137 lines for all RSA-related traits . Low coe< 0.001) . In gene = 0.32) . Many si = 0.32)  and threTo achieve a dense and exhaustive coverage of the genome, we used a dual genotyping approach combining both GBS and WGS. In a first step, the entire panel was characterized via 56K GBS-derived SNPs. A Canadian soybean reference panel  of 4.3M WGS-derived SNPs was used to perform imputation of untyped loci in the first catalog. This led to a final catalog of 2.18M SNPs (MAF ≥ 0.05) for a mean density of 1 SNP every 435 bp across the genome.To characterize population, a subset of 14K LD-pruned SNPs was used. The estimates of the optimum number of subpopulaions (K) ranged between 6 and 9 and trivial differences were observed between these estimates. A phylogenetic tree constructed with the same subset of markers showed seven main branches with bootstrap values ≥50% . Similarp-value < 1.2e–7; FDR ≤ 0.05) with two RSA-related traits: TLR and DR (qTLR1 to qTLR6) and 4 with DR (qDR1 to qDR4) (qTLR5) all the way to 2.2 × 10–10 (qTLR2). While the MAF for three QTLs  was below 0.1, for the seven other QTLs, the MAF ranged between 0.13 and 0.42, such that the estimation of allelic effects  of the latter QTLs is based on the phenotype of a good number of accessions (≥18). Finally, the phenotypic variance explained (PVE) by these genomic regions varied between 2 and 25% for both traits (TLR and DR). For the four traits  highly correlated with TLR, the same six genomic regions were detected in a majority of cases  and Kinship (K matrix) were incorporated as covariates. In total, 10 SNPs were detected as significantly associated (R and DR . Each ofto qDR4) . The FDRof cases , 9. No SqDR2) to as much as 425 kb (qDR4) (qTLR2 for which the haplotype block spanned 207 kb and yet contained a single candidate gene located 24 kb upstream of the peak SNP (Glyma.03g065700) and DR (Glyma.07g096000) (qTLR2), Glyma.07g096000 encodes a Scarecrow-like protein, a putative transcription factor thought to be involved in root radial patterning and root growth. In addition, transcriptomic data showed that Glyma.03g065700 was mainly expressed in roots. As for the qDR2 QTL, the haplotype block spanned 1.8 kb and contained a gene (Glyma.07g096000) located 155 bp downstream of the peak SNP  was identified as residing within the coding region and, in each case, was predicted as showing a “low impact” on the protein function.To establish a list of candidate genes, regions of interest for all 10 QTLs were defined as spanning from the leftmost to the rightmost marker in high LD (D′ ≥ 0.85) with the peak SNP. All genes residing in whole or in part within one of these ten regions of interest were extracted from SoyBase. These haplotype blocks differed markedly in size, ranging from as little as 1.8 kb (b (qDR4) . Surprispeak SNP . The Supg096000) . In the peak SNP . This geGlyma.03g065700 or Glyma.07g096000. As a result, the phenotypic variation in RSA observed among the 137 lines was not likely due to a loss of function of these candidate genes.As structural variants (mainly indels) are typically removed  or not called  when producing SNP catalogs for GWAS, we explored the possibility that structural variants located within these genes could be responsible for the observed association with these phenotypes. After examination of the WGS data for 56 of the lines, we did not identify any indel either within or overlapping with The existence of phenotypic variation within a germplasm pool is necessary for plant breeders to make progress through selection. In the work reported here, we used rhizoboxes to characterize root systems in 2D. In soybean, different phenotyping tools have been used for evaluation of RSA-related traits such as hydroponic system  or a conIn this study, the observed variation proved to be very large with the ratio of the maximum: minimum ranging from as little as 1.2:1 (DR) to over 150:1 (LSR)  and mostAnother characteristic of these phenotypes was their high degree of reproducibility. Even with as few as three replicates, coefficients of variation were <10% in all but one case . This sur > 0.65, P < 0.0001) traits  (r < 0.5 in most cases) between root diameter and other root traits  . This int traits , 2019.In this study, a GWAS was performed using an exhaustive genome-wide set of SNPs (2.18M). To our knowledge, this constitutes the largest marker dataset used to investigate RSA-related traits in soybean. In previous work encompassing both biparaental QTL mapping and GWAS, the number of markers varied between 232 and 38,052 , 2019. WHere, we uncovered a total of 10 genomic regions (QTLs) contributing to the length and diameter of the roots. Similarly, in a recent GWAS study on soybean landraces, the four QTL regions detected were for the number of lateral roots and the thickness of roots . We obseDespite extensive marker coverage (2.18 M SNPs), a broad and significant degree of phenotypic variation  and the In this work, we considered genes to be candidate causal genes if three conditions were met: (1) they were residing in the same haplotype block containing the peak SNP associated with the RSA-related trait (2) their GO annotation was suggestive of a possible role in root development and (3) they were expressed in at least one root-related tissue/organ such as roots, root hairs, nodule, root tip etc. These are admittedly strict definitions as these exclude, for example, cases where a causal gene is of unknown function or where the causal variant is associated with a regulatory region that need not be in close physical proximity to the gene it controls. A complete list of all genes located in the QTL regions surrounding the peak SNP have nonetheless been provided in qTLR2 locus, only one gene (Glyma.03g065700) was located within the 207 kb haplotype block containing the peak SNP associated with TLR. This gene is annotated as a putative ortholog of the Arabidopsis SCARECROW (SCR)/SHORT-ROOT (SHR) family of genes. The transcription factor SHR, in Arabidopsis root, plays a key role in the activity of stem cell and controls the transcription of SCR regulating the endodermal specification. The mutations of these genes can be manifested by phenotypes with short roots. Also, in rice, both OsSCR1 and OsSHR1 are known to control the division of the epidermis-endodermis initial cells  was located in qDR2 region associated with DR. This gene encodes a receptor-like protein kinase (RLK) known to regulate plant root growth and development in Arabidopsis (Glyma.07g096000 in Arabidopsis shows no direct effect on the diameter of the roots but plays in different aspects in the development of roots in particularly root development and root tip (A single gene (bidopsis . The homroot tip . Therefohttps://figshare.com/s/2eb426e0bb988b19e6ca/https://doi.org/10.6084/m9.figshare.12982886The original contributions presented in the study are publicly available. This data can be found here: DT and FB conceptualized the research project. WS conducted the phenotyping and analyzed data. WS, DT, and FB interpreted all phenotyping and genotyping data, wrote and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


SPY was found to be associated with 12 stable SNPs located in 11 putative candidate genes on chromosome 1, 6, and 12. Characterization of rice landraces and varieties in terms of stability for their grain Zn and yield identified promising donors and recipients along with genomic regions in the present study to be deployed rice Zn biofortification breeding program.Polished rice is widely consumed staple food across the globe, however, it contains limited nutrients especially iron (Fe) and zinc (Zn). To identify promising genotypes for grain Zn, a total of 40 genotypes consisting 20 rice landraces, and 20 released high yielding rice varieties were evaluated in three environments  for nine traits including days to 50% flowering (DFF), plant height (PH), panicle length (PL), total number of tillers (TNT), single plant yield (SPY), Fe and Zn in brown  and polished rice . Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype × Environment Interaction (GGE) analyses identified genotypes G22 , G17 (Taraori Basmati), G27  and G26  stable for ZPR and G8 (Savitri) stable for SPY across three environments. Significant negative correlation between yield and grain Zn was reaffirmed. Regression analysis indicated the contribution of traits toward ZPR and SPY and also desirable level of grain Zn in brown rice. A total of 39,137 polymorphic single nucleotide polymorphisms (SNPs) were obtained through double digest restriction site associated DNA (dd-RAD) sequencing of 40 genotypes. Association analyses with nine phenotypic traits revealed 188 stable SNPs with six traits across three environments. ZPR was associated with SNPs located in three putative candidate genes  on chromosomes 3 and 7. The genomic region of chromosome 7 co localized with reported genomic regions (rMQTL Rice, bpines1,2 . A rangepines1,2 . Traditipines1,2 . Increaspines1,2 1.The success of genetic improvement programs depends on the selection of productive and stable genotypes which depends on understanding of the interaction between genotypes and environments (G × E). Breeding for high grain Zn is slow because of low heritability and genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects . To studPisum sativum L.) were identified using dd RAD-seq in field pea . Using SNP markers, genomic regions for grain Zn along with other minerals have also been identified in rice using genome wide association studies (GWAS)  markers or candidate gene based markers . Either s (GWAS)  and alsos (GWAS) .The objectives of the present study were to identify stable donors for high grain Zn from shortlisted promising landraces in comparison to popular high yielding varieties based on evaluation for three years; correlate high grain Zn with yield and other traits; and identify polymorphic genome wide SNPs and their associations with high grain Zn, yield and other traits in the studied genotypes.In our earlier studies during 2013, > 5000 rice genotypes consisting landraces from various parts of India, breeding lines and high yielding varieties were evaluated for their grain Zn content . A few pKharif) considered as three environments viz., 2014 (E1), 2015 (E2) and 2016 (E3) (3 5.01 – 5.04%); CEC 44.1 – 45.2 C mol (p +)/kg soil and medium soil organic carbon (0.69 – 0.72%); low soil available nitrogen (228 – 230 kg ha–1); high available phosphorus (105 –108 kg P2O5 ha–1), high available potassium (530 – 540 kg K2O ha–1), and high available Zn (12.5 –14.0 ppm). One month old seedlings of 40 genotypes were transplanted in a randomized complete block design with three replications. In each replication, every genotype was grown in one m2 plot (33.3 plants) with 20 cm row spacing and 15 cm intra-row spacing. Recommended package of rice crop production and protection practices were followed3.Field experiments were conducted at research farm, Indian Council of Agricultural Research (ICAR)-Indian Institute of Rice Research (IIRR) (17°19′N and 78°29′E), Hyderabad, Telangana State, India during three consecutive wet seasons (016 (E3) . The ranThe genotypes were evaluated for days to 50% flowering (DFF), plant height (PH) (cm), number of tillers per plant (TNT), panicle length (PL) (cm), single plant yield (SPY) (g), grain Fe and Zn content in brown and polished rice  (ppm). The observations were recorded for three representative uniform plants from the center of the plot of each genotype. The seeds of each genotype and replication were dehusked using JLGJ4.5 testing rice husker  and polisher  with non-ferrous and non-zinc components. Each sample of brown and polished rice (5 g) was subjected to energy dispersive X-ray fluorescent spectrophotometer (ED-XRF) (OXFORD Instruments X-Supreme 8000) at ICAR-IIRR as per standardized protocols .viz, (1) Additive Main effects and Multiplicative Interaction (AMMI) , skewness, kurtosis and coefficient variations (%) were calculated to understand the characteristics, dispersion and heterogeneity of traits of the study. Graphical representation of summary statistics was depicted in frequency distribution plots and boxplots using R software . ANOVA wn (AMMI) , and (2)n (AMMI) .The AMMI model is a combination of ANOVA and principal component analysis (PCA), where the additive (main) effects were estimated using ANOVA and G × E interaction effects (multiplicative effects) using principal components. The AMMI model is expressed as follows;ijY is mean of ith genotype in jth environment, μ is the overall mean, δi is the ith genotypic effect, βj is the jth environmental effect, λk is the Eigen value for PC axis k, δik is the principal component score ithgenotype for kth PC axis, βjk is the principal component score jth environment for kth PC axis, ije is the error term. AMMI analysis and graphical representation through AMMI biplots was done using ‘agricolae’ r package  for recommending specific genotypes to specific environments or seasons. GGE biplots helps to determine stable genotype(s) across the locations or seasons and to understand discriminative power among genotypes in target locations or seasons. GGE biplots were plotted using ‘GGEBiplot’ r package .Correlation and stepwise regression analysis was carried out in SAS Version 9.3 software available at ICAR-IIRR. The regression model in terms of matrix notation is expressed as follows;Where Y is the response variable, X is the vector of exogenous variables, β is the regression coefficient vector and e is the residuals term assumed to be normally distributed with e∼N.SphI and MlucI  and the digested products were fractionated in 2% agarose gel electrophoresis to check product size in 250–400 bp. The Agencourt AMpure XP beads clean-up technology  was used to clean the digested products using standard protocols . Barcoded adapters were ligated to each DNA sample using T4 ligase  followed by indexing with the addition of Index-1 and Index-2 (8 nt long) for multiplexing sequencing library in NGS Illumina. To increase the concentration of sequencing libraries, PCR amplification (8–12 cycles) was performed using PhusionTM PolymeraseKit . Products of PCR amplification were analyzed in an Agilent Bio-analyzer  to quantify molarity and fragment size distribution. The size-selected library was sequenced on an Illumina HiSeq2500 . RAD tags were identified in the raw reads, processed for base trimming and removal of the Illumina adapter sequences. The high quality paired end reads were aligned to Oryza sativa L. cv. Nipponbare reference genome (MSU7) using.bowtie2 version 2.2.2.6 , allowing associated values of each SNP to be calculated . A valueThe positions of the associated SNPs of ZPR and SPY of the present study were compared to the genomic positions of the markers from the reported QTL to study the co-localization.5.Based on earlier studies, the linkage disequilibrium was reported to be between 100 and 300 kbp on average for different subpopulations of rice , thus a All the evaluated 40 genotypes have shown a wide range variation for the agro-morphological yield and mineral related traits within an environment and between the environments E1, E2, and E3. DFF and SPY were higher in E3 compared to E2 and E1. The trait values for PH, PL, TNT, IBR, ZBR, IPR, and ZPR were higher in E1 compared to E2 and E3. Wide range was observed for the traits mean of three environments as 84.72 to 114.17 days for DFF, 62.87 to 142.57 cm for PH, 19.24 to 28.10 cm for PL, 4.53 to 13.91 for TNT, 8.92 to 32.45 g for SPY, 4.71 to 13.78 ppm for IBR, 10.03 to 29.66 for ZBR, 1.06 to 4.74 for IPR and 5.20 to 22.65 for ZPR . The traviz., ZBR, ZPR, IBR and IPR. For the four traits of Zn and Fe in brown and polished grains, highly significant negative correlations with SPY were observed. Similarly, DFF has also showed significant negative correlation with ZBR, IBR and IPR and also with ZPR though not significantly. Interestingly, both IPR and IBR have shown positive correlation with PH  for nine traits of the study . As impoStability analysis of ZPR across all the environments has shown genotypic (G) effect was 66.66%, environment (E) effect was 3.93%, and genotype and environment (G × E) effect was 27.43% . Three evs stability G8 (Savitri) was found more stable among three environments, whereas G7 (NDR359) and G20 (Tilakkachari) found more unstable genotypes  effect was 73.75%, environment (E) effect was 1.30%, and genotype and environment (G × E) effect was 21.03% . All threnotypes . The graenotypes . The AMMenotypes .Stepwise regression analysis for grain Zn polished rice (ZPR) was carried out to identify the factors influencing ZPR and regression equation for ZPR is depicted as followed.Factors like ZBR (92.56%), TNT (1.31%) and IPR (0.4%) explain 94.3% variation in the model . For eveFor SPY, the resultant regression equation is as followedviz., DFF (19.52%) and ZPR (5.7%) explained 25% variation in the model and rest of the variations may be due to other factors which were not considered in this study  were aligned with 47,221,247 unique reads (94.7%) . A total((Considering variants only at read depth (RD) 10 chromosome wise of 40 genotypes, the range was from 20,531 (chromosome 9) to 43,143 (chromosome 1) for SNPs  and SNP . On pair; group 2 (124.9 cm) and group 3 (102.8 cm). Modest concurrence of clustering was also observed for ZPR as mean for group1 to be 10.8 ppm, group 2 to be 15 ppm and group 3 to be 15.8 ppm  were found and were classified based on their location in exonic, exonic UTR, intronic splice sites, intronic and intergenic regions . Maximumviz., G > A (GCG > ACG), a missense mutation changing alanine to threonine; G > A (GCG > GCA), a silent mutation and C > T (CTC > TTC), a missense mutation changing leucine into phenylalanine  LOC_Os01g64960 -chlorophyll A-B binding protein, putative, expressed gene has intronic mutation of C/A. (2) LOC_Os12g19090-metalloprotease ATP23, putative, expressed has intronic variation C/T. (3) LOC_Os12g19350-expressed protein has intronic mutation of A/G, (4) LOC_Os12g19590 - WD domain, G-beta repeat domain containing protein, has shown intronic mutation of A/T, (5) LOC_Os12g21500- exosome complex exonuclease, putative, expressed has change in intronic region with G/T, and (6) LOC_Os12g22300 - retrotransposon protein, putative, Ty3-gypsy subclass, expressed gene has intronic SNP A/G. Another gene LOC_Os12g19370 encoding DNA polymerase I family protein, has an intronic mutation with A/G and exonic-CD silent mutation of GGC > GGA. A gene LOC_Os01g64890 coding for CorA-like magnesium transporter protein, putative has a silent mutation TCT > TCA in exonic-CD. Three genes have shown missense mutation in exonic-CD region. LOC_Os06g30070 encoding retrotransposon protein, putative, Ty3-gypsy subclass gene has expressed a missense mutation (CCT > CTT) in exonic-CDS region, thus amino acid proline was replaced by leucine. The second gene LOC_Os12g19890 also encodes retrotransposon protein, putative, LINE subclass and has an Exonic-CD region missense mutation (GAG > AAG) changing glutamic acid by lysine. The third gene LOC_Os12g20420 also encoding transposon protein, putative, unclassified, ha also shown a missensed mutation (CGT > TGT) at exonic-CD region changing arginine to cysteine  on chromosome 7, ∼140 genes were noted and for LOC_Os03g47980, 90 genes were found. For SPY, 143 genes on chromosome 1, 82 genes on chromosome 6 and 328 genes on chromosome 12 were observed .6  to 16.5 ppm (G9 Pusa Basmati 1 and G8 Savitri). Most of the released rice varieties are known to be less in grain Zn content ranging from < 12 to 14 ppm in polished rice . Akut Ph1. Correlation analyses of the mean values of three environments in the present study corroborated the reported significant positive correlations among Zn and Fe of brown and polished grains and significant negative correlations of grain Zn and Fe with yield (R2 = 0.943) for enhanced ZPR and only ZBR on shown very close association (R2 = 0.92) , the developers may need to target ≥ 38.5 ppm increase in ZBR. Based on germplasm screening for grain Zn in brown and polished rice, our group suggested germplasm with zinc content ≥ 35 mg/kg in brown rice can be promising based on the threshold value of 28 mg/kg by HarvestPlus and 19.0% overall mean loss of zinc during polishing (R2 = 0.25) with DFF (R2 = 0.19) and ZPR (R2 = 0.5) of SPY suggesting ZPR has only limited negative effect on grain yield and the possibility of simultaneous improvement of yield and grain Zn content as suggested by earlier studies  ,b. The l = 0.92) . Previou = 0.92) . Two sigolishing . Thus, t studies . The imp studies .viz., G18 (Akut Phou), G22 , G17 (Taraori Basmati), G27  and G26 . For SPY, G8 (Savitri) was identified as stable genotype across environments. AMMI and GGE biplot models suggested the stable performers across the environments and partitioned the total phenotypic variance into individual factors (Kharif) and one dry season (Rabi) using AMMI, GGE biplot and Ysi stastics identified two stable lines with high yield , G17 (Taraori Basmati), G27  and G26  were found to be stable among the given three environments suggesting their utility as donors for high grain Zn. The stable genotype for SPY G8 (Savitri) is one of the mega varieties known for its high yield in several parts of India  was used for the discovery of unique SNPs following the standard procedures viz., raw reads de-multiplexing, sequence quality analysis, SNP calling and genotyping , G8 (Savitri), G16 (ARB-45), G18 (Akut Phou) and G23  in cluster 3 found to be promising for DFF, NT, SPY, IBR, ZBR and ZPR, thus they could be deployed in the future breeding program (viz. IPR (1), ZPR (10), ZBR (21), PL (23), SPY (37) and PH (96) across the three environments. Considering only the common SNPs across three environments increased the reliability of identified SNPs and compensated lesser number genotypes of study. While there were a total of 1952 associated SNPs for ZPR across three years , only stable 10 SNPs were selected based on their stability. Similarly, only 37 common stable SNPs were studied from a total of 1319 associated SNPs found . Similar studies of 17 marker-phenotype associations were reported for grain Fe and Zn in 102 rice genotypes with 25 gene specific and 75 SSRs , C7.28641194 (LOC_Os07g47950) and C7.28688976 (LOC_Os07g48050). While the association on chromosome 3 appears to be novel, the identified SNPs around ∼28 Mb on chromosome 7 have shown consistent co-localization with genomic regions grain Zn in rice reported through association and biparental mapping . Recentl7.1) and is a promising region for marker assisted introgression.Deploying AMMI model and GGE biplots for analyses of 20 landraces and 20 popular rice varieties across three environments, four stable landraces  were identified as donors for high grain Zn for rice biofortification breeding program. Using regression analysis, contributing factors for ZPR were identified and threshold levels for ZBR for 28 ppm of ZPR were estimated. Through GBS-dd-RAD analyses, 188 stable SNPs with their locations in the genes across three environments were identified for six traits. Functionality of identified SNPs in the three candidate genes identified for ZPR and 11 candidate genes identified for SPY has been analyzed through their location in the gene. The genomic region for ZPR on chromosome 7 has co-localized with reported metaQTL region (rMQTLPRJNA626560.The data has been submitted to NCBI and the accession number has been provided in the manuscript as submitted to the sequence read archive at NCBI under BioProject No: CN conceptualized the idea. PB, KS, and UC conducted the field experiments. CN, PB, SR, GU, NC, and VL carried out the data analysis. PB and CN prepared the manuscript. CN, LR, and SV edited the manuscript. All authors contributed to the article and approved the submitted version.VL was employed by company AgriGenome Labs Pvt. Ltd. The remaining 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.