EcoRI-NlaIII based GBS protocol to teleost large yellow croaker, an important commercial fish in China and East-Asia, and reported the first whole-genome SNP development for the species. 69,845 high quality SNP markers that evenly distributed along genome were detected in at least 80% of 500 individuals. Nearly 95% randomly selected genotypes were successfully validated by Sequenom MassARRAY assay. The association studies with the muscle eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) content discovered 39 significant SNP markers, contributing as high up to ∼63% genetic variance that explained by all markers. Functional genes that involved in fat digestion and absorption pathway were identified, such as APOB, CRAT and OSBPL10. Notably, PPT2 Gene, previously identified in the association study of the plasma n-3 and n-6 polyunsaturated fatty acid level in human, was re-discovered in large yellow croaker. Our study verified that EcoRI-NlaIII based GBS could produce quality SNP markers in a cost-efficient manner in teleost genome. The developed SNP markers and the EPA and DHA associated SNP loci provided invaluable resources for the population structure, conservation genetics and genomic selection of large yellow croaker and other fish organisms.Whole-genome single-nucleotide polymorphism (SNP) markers are valuable genetic resources for the association and conservation studies. Genome-wide SNP development in many teleost species are still challenging because of the genome complexity and the cost of re-sequencing. Genotyping-By-Sequencing (GBS) provided an efficient reduced representative method to squeeze cost for SNP detection; however, most of recent GBS applications were reported on plant organisms. In this work, we used an  Whole-genome single nucleotide polymorphism (SNP) is one of the most important genomic resources for population diversity, conservation genetics and functional gene identification for biological traits . To obtaApeKI in maize and barley. The protocols for GBS are simple, extremely specific and highly reproducible. In recent years, the easy transferability of GBS to other species leads to many application in plants , belonging to the Sciaenidae family of teleost, is an important marine fish in China and East Asia  370 million years ago . The extast Asia . Due to ast Asia . The envast Asia . HoweverEcoRI and NlaIII based GBS protocol was developed and optimized. Massive whole-genome SNP markers were developed from the sequencing reads by bioinformatic pipelines, which were subsequently validated by Sequenom MassARRAY assay. The detected SNP markers in this work were then applied to the whole-genome association study of the muscle Eicosapentaenoic Acid (EPA) and Docosahexaenoic Acid (DHA) content in large yellow croaker. Our study confirmed the suitability of GBS on whole-genome SNP marker development in teleost genome. The developed whole-genome SNP markers and functional genes involved in muscle EPA and DHA contents offered valuable genetic resources for conservation genetics and genomic selection of large yellow croaker.GBS technique provides the potential cost-efficient way for whole-genome SNP marker development in complex teleost genome. In the present investigation, we used large yellow croaker to verify the applicability of GBS on teleost. Two restriction enzymes of The sample collection and experiments in the study was approved by the Animal Care and Use Committee of Fisheries College of Jimei University .The mixed reference population of 500 individuals was bred by a random fertilization of 30 males and 30 females at the large yellow croaker breeding base of Jimei University in Ningde, Fujian, China. All fish individuals were 1.5 year old with the total length and weight of 24.5–25.9 cm and 217.8–234.1 g , respectively. To extract genomic DNA respectively from 500 individuals, the dorsal fins (20–30 mg) of the fish individuals were collected, frozen in liquid nitrogen for the following DNA extraction. Total genomic DNA was prepared in 1.5 ml microcentrifuge tubes containing 550 µl TE buffer . The samples were incubated at 55 °C overnight and subsequently extracted twice using phenol and then phenol/chloroform (1:1) method. DNA was precipitated by adding two and a half volumes of ethanol, collected by brief centrifugation, washed twice with 70% ethanol, air dried, re-dissolved in TE buffer . DNA concentration and quality were estimated with an ND-1000 spectrophotometer  and by electrophoresis in 0.8% agarose gels with a lambda DNA standard.in silico digestion of large yellow croaker genome: ApeKI-PstI, EcoRI-BstNI, EcoRI-NlaIII and PstI-NlaIII . The activation and heat-inactivation temperature, location in chromosome and length distribution of all fragments were analyzed to evaluation the best performance of each enzyme combination.To assess how endonucleases influence the DNA fragment length distribution, four enzyme combinations were designed for EcoRI  and NlaIII . A pilot GBS experiment was performed before the library construction to optimize the temperature and time parameters for yield, size distribution. Based on the pilot experiment, the GBS libraries of large yellow croaker based on EcoRI and NlaIII were constructed following the similar method in previous report  experiments were performed in the reaction solutions (20 µL) containing the diluted restriction/ligation samples , dNTP , Taq DNA polymerase  , Illumina Primers  and Indexing Primers . The PCR procedure was: 95 °C 2 min; 15 cycle of 95 °C 30 s 60 °C 30 s, 72 °C 30 s; 72 °C 5 min and kept in 4 °C. The PCR products were run on a 8% polyacrylamide gel electrophoresis. Fragments of 200–300 bp were isolated using QIAGEN QIAquick® Gel Extraction Kit and diluted for pair-end sequencing on an Illumina HiSeq 2500 sequencing platform .The GBS libraries were constructed based on two DNA endonucleases: s report . BrieflyEcoRI and NlaIII were retained for the following quality control. Second, the overall base and read quality were accessed by FastQC. To avoid the negative influence of ambiguous bases for SNP detection, reads with more than 5% of N were removed. Then, the resulted reads were cleaned by the following steps: (1) discarding the reads that the quality lower than 20; (2) deleting 5 bp windows in reads end that the average quality smaller than 20; (3) removing read pairs if one end was shorter than 50 bp.The raw sequencing reads generated by Illumina HiSeq 2500 from the GBS libraries were treated and cleaned for SNP detection. First, the adaptors were removed and the resulted reads were split by sample-specific barcode sequences. Only reads begins with the digest site sequences of The cleaned reads were mapped to large yellow croaker genome by BWA 0.7.6a . The mapGenomic DNA was extracted from dorsal fin ray tissue as the method described before. PCR amplification was performed in the reaction system  containing 20 ng of genomic DNA, 0.5U HotstarTaq (Qiagen), 0.5 µl 10× PCR buffer, 0.1 µl dNTPs for each nucleotide and 0.5 pmol of each primer. All PCR experiments were carried out in a PTC-100 PCR instrument (Eppendorf) with the following program: 4 min denaturation at 94 °C, 35 cycles of 20 s at 94 °C, 30 s at 56 °C and 1 min at 72 °C and a final extension at 72 °C for 3 min. After the PCR products were cleaned using 2 µl SAP (SEQUENOM), the single base extension used 2 µl EXTEND Mix (SEQUENOM) contained 0.94 µl Extend primer Mix, 0.041 µl iPLEX enzyme and 0.2 µl iPLEX termination mix and performed with the following steps: initial denaturation at 94 °C for 30 s, followed by 40 cycles of 3-step amplification profile of 5 s at 94 °C, additional 5 cycles of 5 s at 52 °C and 5 s at 80 °C and a final extension at 72 °C for 3 min.The PCR product was cleaned by resin purification and then analyzed using MassARRAY Analyzer Compac (SEQUENOM) and software TYPER (SEQUENOM).To evaluate the accuracy of SNP detection in this study, the genotypes from GATK SNP calling were compared with those from MassARRAY assay. If the genotypes of one SNP locus from GATK calling were identical with that in MassARRAY, then the locus was called a correct genotype. As a result, 1,421 of 1,500 SNP loci were correctly genotyped by GATK and the success rate of SNP calling was ∼94.7%. The specificity and sensitivity of SNP calling in the study were also evaluated. The reference homozygous genotypes (AA) both from MassARRAY and GATK were called true negatives, and the heterozygous genotypes or allelic homozygous (AB and BB) both from MassARRAY and GATK were called true positives. Specificity was then calculated as the number of true positives divided by the number of true positives plus the number of false positives, and the sensitivity was estimated as the number of true positives divided by the number of true positives plus the number of false negatives as the following formula: From the 500 large yellow croaker population, 200 individuals were randomly selected for the muscle EPA and DHA content measurement for the following statistics and association analysis. The fat acid composition analysis followed the similar methods in previous reports . BrieflyPrior to the association study, pairwise clustering based on the alleles shared identical by state (IBS) between any two individuals was performed to assess the genetic relatedness. The population stratification was analyzed by multi dimensional scaling (MDS) clustering methods available in the PLINK software .p-values ≤ 1e–4 were considered significantly associated with muscle EPA and DHA contents. To identify the biological functions of nearby genes and whether the orthologs of these significantly associated loci were also associated with the EPA and DHA content in other species, we identified the protein-coding genes around 50 kb of the significant SNP markers. We aligned the genes against the NCBI nr database by Blastx (http://mirror.bjtu.edu.cn/cran/web/packages/EMMREML/index.html).With the developed SNP markers, the association analysis was performed between genotypes and measured muscle EPA and DHA content using Plink 1.07 A simple linear regression of phenotype on genotype was performed in the analysis. Markers with y Blastx . GO termy Blastx  by two-ty Blastx  against ApeKI-PstI, EcoRI-BstNI, EcoRI-NlaIII and PstI-NlaIII . To assess the fragment size distribution and the number of potential SNP marker developed, the public large yellow croaker draft genome sequences . By the assessment of the combination of EcoRI-NlaIII, roughly 1.5 million fragments would be collected in libraries.According to the principles of the enzyme combination design for GBS library construction, four enzyme combinations were designed for the GBS analysis of large yellow croaker genome: equences  were in GBS libraries were constructed by the two enzyme based digestion (see ‘Method’ for the details). The NGS sequencing of GBS libraries for 500 individuals generate roughly 314 Gb raw sequencing reads. To evaluate the raw data distribution among samples, we found that the majority of individuals (∼95%) had the raw sequencing reads ranged from 600 to 650 Mb, indicating the excellent sequencing uniformity among samples from library construction and sequencing. The raw reads were cleaned by HTSeq to trim low quality ends  and eliminate short reads (length < 50 bp). The cleaned reads were mapped to large yellow croaker reference genome sequences  by BWA . To asseTo develop molecular markers based on the GBS library sequencing, SNP variants markers were detected from the reads alignments by GATK  pipelineTo control SNP marker quality while maximizing the number of shared samples and to facilitate the following GWAS analysis, markers with the loci depth higher than 5 and the shared in at least 400 individuals  were used for the following analysis, resulting 69,845 SNP markers in large yellow croaker genome. To answer the question if our sequencing data was sufficient for the whole-genome SNP development, the numbers of the detected genomic SNP markers were plotted against the sequencing data for each sample. As shown in To assess the reliability of the SNP makers developed from the reduced representation libraries, 50 loci from 30 individuals were randomly selected to validate the marker polymorphism by the Sequenom MassARRAY assay. As shown in p-value of 0.94 with Kolmogorov–Smirnov test) with an average of 21.5 mg/g and a standard deviation of 4.1 mg/g . Notably, many associated markers were significant by clusters in linkage group 4, 5 and 11, suggesting the credibility of the association studies. The results might also imply that many genes contributed to the muscle EPA and DHA levels in large yellow croaker. With the variance estimation by Restricted Maximum Likelihood (REML) method  method , we founp-value <  1e–4). As a result, 122 genes were identified from the above association regions. The biological KEGG pathway and GO term annotations of the associated genes were enriched under the background of all protein-coding genes. The metabolic pathway of fat digestion and absorption was significant (FDR <  0.023) in the KEGG enrichment (APOB), Carnitine O-acetyltrasferase (CRAT) and oxysterol binding protein 10 (OSBPL10). APOB is a crucial lipid transport protein in organism. Previous nutriology studies confirmed the correlation of EPA and DHA contents with APOB genotypes and gene expression   as a potential functional gene contributing to muscle EPA and DHA contents. PPT2 gene was also identified by genome-wide association study on n-3 and n-6 polyunsaturated fatty acid levels in Chinese and European-ancestry populations  was almost two-fold of that in the lowest 20 ones (14.6 mg/g). Given that those fish were reared in the same cage and fed with the identical feed, there was a great potential to raise the muscle EPA and DHA content in large yellow croaker via genetic improvements. Using the developed quality SNP markers by GBS protocol, 39 SNP markers from 11 linkage groups were observed to be significantly associated with muscle EPA and DHA levels. From the coordinates of gene and SNP loci, 122 protein-coding genes were identified around those significant markers. The functional analysis by homological searching found that many genes were involved in fat metabolism and transport, such as contents  and S3. ulations . The PPTEcoRI-NlaIII based GBS protocol was used to develop the whole-genome SNP markers in teleost large yellow croaker. The study verified the applicability of GBS on teleost species and provided useful references for GBS applications in other fish species. For large yellow croaker, about 70,000 high quality SNP markers, supported by at least 400 individuals in population, were detected from the GBS libraries. Those SNP markers were further experimentally validated by Sequenom MassARRAY assay. The even distribution and diversified biological impacts of those molecular makers confirmed the effect and efficiency of the GBS-based SNP development in large yellow croaker. With muscle EPA and DHA contents from 176 individuals, a genome-wide association study between genotypes and EPA and DHA level were performed. 39 and 122 significantly associated SNP loci and related protein-coding genes were identified. The functional analysis of the related genes confirmed the results of the association study.Teleost were widely believed to undergo the third round of WGD during the natural evolution; therefore, genomes of many teleost species were characterized by the complexity of high heterozygosity and repeat contents. In this work, For the aspect of molecular resources, our developed SNP markers could be valuable genetic resources for large yellow croaker, and would be used in the following population structure, conservation genetics and the association studies for other important economic traits. The associated results for the muscle EPA and DHA content, namely the significant SNP loci and functional genes, provided us important guidance for the further investigation of genetic bases of the muscle EPA and DHA accumulation in large yellow croaker and would eventually aid the technological development towards the genetic improvement of meat quality via the molecular-aided selection of the species.10.7717/peerj.2664/supp-1Table S1Primers used for SNP validation in Sequenom MassARRAY assayClick here for additional data file.10.7717/peerj.2664/supp-2Table S2Click here for additional data file.10.7717/peerj.2664/supp-3Table S3Click here for additional data file.10.7717/peerj.2664/supp-4Table S4Click here for additional data file.10.7717/peerj.2664/supp-5Figure S1Click here for additional data file.10.7717/peerj.2664/supp-6Figure S2Click here for additional data file.10.7717/peerj.2664/supp-7Figure S3Click here for additional data file.10.7717/peerj.2664/supp-8Figure S4Click here for additional data file.


SNP-sites which can rapidly extract SNPs from a multi-FASTA alignment using modest resources and can output results in multiple formats for downstream analysis. SNPs can be extracted from a 8.3 GB alignment file  in 267 seconds using 59 MB of RAM and 1 CPU core, making it feasible to run on modest computers. It is easy to install through the Debian and Homebrew package managers, and has been successfully tested on more than 20 operating systems. SNP-sites is implemented in C and is available under the open source license GNU GPL version 3.Rapidly decreasing genome sequencing costs have led to a proportionate increase in the number of samples used in prokaryotic population studies. Extracting single nucleotide polymorphisms (SNPs) from a large whole genome alignment is now a routine task, but existing tools have failed to scale efficiently with the increased size of studies. These tools are slow, memory inefficient and are installed through non-standard procedures. We present  SNP-sites is available from GitHub under GNU GPL v3; (URL – https://github.com/sanger-pathogens/snp-sites)1. The source code for 2. The software is available from Homebrew using the recipe “brew install snp-sites” and from Debian using "apt-get install snp-sites”.Salmonella Typhi multi-FASTA alignment data has been deposited in Figshare: https://dx.doi.org/10.6084/m9.figshare.2067249.v13. SNP-sites was created with these challenges in mind and this paper demonstrates that it scales well, using modest desktop computers, to sample sizes far in excess of what is currently analysed in single population studies. The software has also been packaged to allow it to be easily installed on a wide variety of operating systems and hardware, something often neglected in bioinformatics.Rapidly extracting SNPs from increasingly large alignments, both in number of sites and number of taxa, is a problem that current tools struggle to deal with efficiently. As the cost of sequencing has rapidly decreased, the number of samples sequenced within a study has proportionately increased and now stands in the thousands , or slowA number of applications exist which can extract SNPs from a multi-FASTA alignment, such as JVarKit , TrimAl JVarKit is a Java toolkit which can output SNP positions in variant call format (VCF) . Standard installation methods are used, with the software prepackaged and available through the Debian and Homebrew package managers. The software has been successfully run on more than 20 architectures using Debian Linux, Redhat Enterprise Linux and on multiple versions of OS X. A Cython version of the SNP-sites algorithm called PySnpSites (https://github.com/bewt85/PySnpSites) is also presented for comparison purposes.Here we present The input to the software is a single multiple FASTA alignment of nucleotides, where all sequences are the same length and have already been aligned. The file can optionally be gzipped. This alignment may have been generated by overlaying SNPs on a consensus reference genome, or using a multiple alignment tool, such as MUSCLE , PRANK , MAFFT , or ClusBy default the output format is a multiple FASTA alignment. The output format can optionally be changed to PHYLIP format  or VCF  for creaEach sequence is read in sequentially. A consensus sequence is generated in the first pass and is iteratively compared to each sequence. The position of any difference is noted. A second pass of the input file extracts the bases at each SNP site and outputs them in the chosen format. Where a base is unknown or is a gap (n/N/?/-), the base is regarded as a non-variant.For example, given the input alignment:>sample1AG-CACAGTCAC>sample2AGACAC----AC>sample3AAACGCATTCANthe output is:>sample1GAG>sample2GA->sample3AGTA in all samples. As there is no variation this site is excluded from the output. The second site in the input contains bases A and G, and since there is variation at this site, it is outputted. The third site in the input contains a gap (-) and the base A. Since gaps are regarded as non-variants, this site is not outputted.The first site (column) of the input contains the base p, the number of samples is s and the number of bases in a single alignment is g, the maximum memory usage can be defined as:The maximum resource requirements of the algorithm are known. Given the number of SNP sites is p×s, g×2)max. These properties make the algorithm theoretically scalable and feasible on large datasets far beyond what is currently analysed within a single study.SNP-sites are validated automatically against a hand-generated set of example cases incorporated into unit tests. A continuous integration system (https://travis-ci.org/sanger-pathogens/snp-sites) ensures that modifications which change the output erroneously are publicly flagged.All changes to SNP-sites, we have compared it with JVarKit, TrimAl and PySnpSites (https://github.com/bewt85/PySnpSites). PySnpSites is a Cython-based partial reimplementation of the SNP-sites algorithm. A number of simulated datasets were generated to exercise the different parameters and to see their effect on memory usage and running time. All of the software to generate these datasets is contained within the SNP-sites source code repository.To test the performance of All experiments were performed using a single processor (2.1 Ghz AMD Opteron 6272) with a maximum of 16 GB of RAM available. The maximum run-time of an application was set as 12 h, after which time the experiment was halted.SNP-sites and PySnpSites is reasonable, however the memory usage of PySnpSites rapidly exceeds the maximum allowed memory (16 GB). Where 20 % of bases in the input alignment are SNPs, SNP-sites uses only 1 GB of RAM, or approximately 20 % of the file input size, scaling with the volume of variation rather than the size of the input file. In all experiments JVarKit exceeded the maximum allowed memory and was halted. All experiments using TrimAl exceeded the maximum running time of 12 h. As both of these applications did not successfully complete they are not present in the results.Alignments were generated with varying numbers of SNPs to show the effect of SNP density on the performance of each application. Each alignment had 1000 samples and a genome alignment length of 5 Mbp, with a total file size of 4.8 GB. This is a scale encountered in recent studies  of S. Typhi CT18. This gave a total alignment file size of 8.3 GB and incorporated SNPs at 22 618 sites. SNP-sites used 59 MB of RAM and took 267 seconds.The performance of SNP-sites performed consistently under a variety of conditions, using low amounts of RAM and had a low running time for even for the largest datasets we simulated to represent the scale of studies expected in the near future. This makes it feasible to run on standard desktop machines. SNP-sites uses standard installation methods with the software prepackaged and available through the Debian and Homebrew package managers. The software has been successfully tested and run on more than 20 architectures using Debian Linux and on multiple versions of OS X.Extracting variation from a multiple FASTA alignment is a common task, and whilst it is simple to define, existing tools fail to perform well. We showed that


One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD).th-99th CAC score percentile range, and age-matched controls  with no detectable CAC . These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC).Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and “vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs.We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders, hold promise for deriving predictive disease models and networks.The online version of this article (doi:10.1186/s12918-017-0474-5) contains supplementary material, which is available to authorized users. Informed medical decision making through the effective use of clinical and genomic data is one of the promising elements of personalized precision medicine  in which9p21, PHACTR, and PCSK9 D and MI –30 and tD and MI . StatistD and MI , 33.Recently, there has been increasing interest in the application of machine learning methods for predicting disease subphenotypes by utilizing genomic features . These mIn this study, we utilized machine learning tools for predictive modeling of the advanced CAC subphenotype by integrating clinical variables and genotype data. Our study focused on identifying predictors of the high-risk subgroup of CAD patients with advanced CAC among an intermediate risk sample of middle-aged Caucasian males. Previous studies have established that higher CAC scores are observed among men compared to women , 38, as We used the random forest (RF) algorithm, which is a decision tree based machine learning method  establisOur overall strategy was to use clinical data and genotype data for predicting advanced CAC in a discovery cohort, and to test if the observed predictive patterns can be confirmed in an independent cohort Fig. . We deveAfter assessing the RF based predictive patterns generated by the clinical variables, SNP Set-1 and SNP Set-2 in the ClinSeq®; discovery cohort, we focused on testing the most predictive set of SNPs in the FHS replication sample. Based on the analysis of predictive performance and replication in both sample sets, we identified the subset of SNPs that generated optimal performance in RF models in both cohorts. As an additional validation of the robustness of our findings, we trained and tested NN models with the genotypes of these SNPs in the ClinSeq®; and FHS cohorts, respectively. Data used in NN models came from advanced CAC cases and age-matched controls  in both cohorts.Upon verifying the high predictive performance under a wide range of NN topologies, we utilized GeneMANIA  to creatth-99th CAC score percentile range.The models we developed in this study aimed at predicting the binary case-control statuses of age-matched Caucasian male patients. Hence, we first transformed the CAC scores (measured by Agatston method ) of the th-99th CAC score percentile range. As an additional set of comparative control groups, 122 cases from FHS within 29th-88th CAC score range were split into two distinct sets of 61 cases within 29th-68th and 69th-88th percentile ranges and were age-matched with two sets of 61 subjects with no CAC. These two equal-sized subcohorts were then used to test whether the predictive patterns generated by the discovery  and replication (FHS) cohorts were specific to the 89th-99th percentile CAC score range and not replicable with lower levels of coronary calcium. Two classes of model variables were used in this study as predictors of coronary calcium, namely clinical variables and genotypic variables, as described below.The replication cohort from FHS comprised of 36 controls and 36 age-matched Caucasian male case subjects (including three diabetic cases) also within the 89p-values are listed in Additional file Nine clinical variables available from all subjects in both cohorts were utilized as predictors of CAC. These variables included body mass index (BMI), cholesterol levels (low-density lipoprotein (LDL), high-density lipoprotein (HDL), and total cholesterol), triglycerides, blood pressure (systolic and diastolic), fasting blood glucose level, and fibrinogen. All subjects were non-smoker Caucasian males in both ClinSeq®; and FHS cohorts. The detailed description of each clinical variable is given in Additional file p-values). From the the ClinSeq®; genotype data, we also generated a second set of 56 SNPs (“SNP Set-2”) as described above. All SNPs in SNP Set-2 are listed in Additional file We compiled two sets of SNPs using a feature selection strategy that relied on the existing CAC literature, as well as the ClinSeq®; discovery cohort. The first set of 57 SNPs were reported in previous association studies of CAC that focused on the presence of CAC rather than its extreme levels –25. We nTM of Matlab®; [p-value as the fraction of AUC values in 1000 runs (with randomly permuted case-control statuses) at or above the mean AUC value generated when the case-control statuses are not permuted . This approach has been previously used for computing the statistical significance of ROC-AUC values [We implemented the RF classification method using the Statistics and Machine Learning Toolbox Matlab®;  for predMatlab®; ). For eaC values , 52. ForC values ) correspeOOB) is computed as nm,OOB/, where nm,OOB and nc,OOB stand for the numbers of misclassified and correctly classified samples without permutation, respectively. Likewise, the prediction error for OOB samples with permuted input values  is computed as nm,OOB,perm/, where nm,OOB,perm and nc,OOB,perm stand for the numbers of misclassified and correctly classified samples with permutation, respectively. The difference between the two error terms  is computed for each tree and the average value of this difference  is divided by its standard deviation to identify the predictive importance of a feature. Features with positive predictive importance have higher eOOB,perm values in comparison with their eOOB values.For each decision tree, approximately two-thirds of the data (this ratio varied up to ±15% among different runs) is retained to be used for model training, whereas the remaining data is used for model testing. These test samples are referred to as “out-of-bag” (OOB) samples, whereas the training samples are expanded by bootstrapping  , we decreased the number of features gradually by leaving out weaker predictors to identify the optimal predictive performance and the corresponding optimal set of features. We repeated this procedure to compare the predictive performances of models trained and tested by combining clinical and genotype data, as well as using each layer data in isolation. The predictive patterns generated by data from the ClinSeq®; discovery cohort were also compared with the patterns generated by the independent FHS replication cohort. Finally, RF models were also used to identify a subset of SNPs in SNP Set-2 that generated the optimal predictive performance in both ClinSeq®; and FHS cohorts.TM of Matlab®; [Upon identifying the subset of SNPs in SNP Set-2 that generate RF models with optimal performance in both cohorts, we further validated our results by implementing a neural network (NN) based classification approach using the NN ToolboxMatlab®; . This alWe trained three-layer feedforward networks using backpropagation  with sigp-value based on the predictive performance obtained from 1000 runs with randomized case-control statuses.Predictive accuracy was once again assessed with ROC curves. For each NN configuration, we computed the median AUC value  among 1p-value=0.015) and 0.61  for ClinSeq®; and FHS cohorts, respectively  generated AUC ranges of 0.68-0.72 and 0.71-0.78 in ClinSeq®; and FHS cohorts , respectively. Only five of the 17 predictive SNPs (29%) from the ClinSeq®; discovery cohort were predictive in the FHS cohort pointing to a low degree of replication between the two cohorts. In order the test whether the combination of the nine clinical variables and SNP Set-1 resulted in improved predictive performance, we merged these two groups of model inputs with the ClinSeq®; discovery data set. We observed a significant improvement in the AUC range from 0.68-0.72 (only SNP Set-1) to 0.72-0.77 (combined set of inputs). In contrast, when we used the FHS replication data set in the same way, AUC range declined from 0.71-0.78 to 0.69-0.75. Hence, the improvement of predictive accuracy we observed within the ClinSeq®; discovery cohort, by adding clinical variables to SNP Set-1, was not confirmed in the FHS replication cohort.Although the literature-based SNP Set-1 provided a useful initial source of model inputs, we recognized that a potential limitation of this approach was the focus of past association studies on CAC as a broad and heterogeneous phenotype. In contrast, our study aims to derive an optimal set of predictors for the subset of CAC positive patients with the most advanced vascular lesions at the top decile of the broad CAC score range. Accordingly, we employed a standard feature selection approach to derive an alternative set of genotypes (SNP Set-2) from the ClinSeq®; data that were highly correlated with the advanced CAC subphenotype (described in Methods). This approach effectively leverages the capacity of RF algorithm to eliminate non-informative signals and sort out input SNPs of potential predictive utility without the multiple-testing penalty. The range of genotype-phenotype correlation among the SNPs in SNP Set-2 (no overlap with SNP Set-1) was 0.63-0.73 within the ClinSeq®; discovery cohort. Upon incorporating the genotypes of SNP Set-2 in this cohort into RF models, we obtained an AUC value of 0.9975. Given this high predictive performance, our subsequent analyses focused on further validation and refinement of this set of genotypes.FOXN3) were the same in both cohorts.In order to test whether the high predictive performance of SNP Set-2 was replicated in the FHS cohort, we trained and tested RF models using the genotypes of SNP Set-2 in the replication cohort. We identified that the positive predictive importance values of 30 of the 56 predictive SNPs (54%) were replicated. We also observed common patterns between the discovery and replication cohorts in terms of the predictive importance based rankings of the 30 SNPs with positive predictive importance in both cohorts. Nine of the top 18 SNPs overlapped between the two cohorts, whereas the top two SNPs . These results further extended the robustness of our findings and demonstrated that the high predictive performance of SNP Set-2 was only observed in the 89th-99th percentile CAC score range.One potential explanation of the high predictive performance of SNP Set-2, which does not include any SNPs previously associated with CAC, in both cohorts is the broad range of CAC levels. Given that SNP Set-2 was derived from cases with extreme levels of CAC, it remained to be determined whether the predictive power of SNP Set-2 was specific to this extreme phenotype or whether it could be generalized to a broader range of CAC levels. Hence, we tested the collective predictive performance of the 30 SNPs in SNP Set-2 that had positive predictive power in both cohorts with genotype data from cases with lower levels of CAC. Among the 61 cases within the 29p-values of less than 0.05, thereby utilizing a different machine learning approach to further validate the collective predictive ability of these SNPs in the FHS replication cohort. This result demonstrates the stable and consistent features of these 21 SNPs in predicting advanced CAC independent of the classifier strategy employed. The optimal NN topologies have 9-20 nodes in their first hidden layers and 6-20 nodes in their slightly less complex second hidden layers.Table We identified a total of 13 genes that included the 21 SNPs leading to optimal predictive performance in both cohorts. Using GeneMANIA, we derived a molecular network that included this group of 13 genes in addition to the 18 genes known to be linked to the first group based on coexpression and genetic interaction data from the literature . Figure p-value of 0.0025 (modified Fisher’s exact test) based on DAVID and the Genetic Association Database. Furthermore, through mouse and rat models, six genes in our network  have been previously associated with several CAC risk factors including diabetes, insulin resistance, LDL cholesterol, and triglycerides , that are enriched within our network. Several biological processes enriched among the network genes are associated with “vascular aging” (further discussion in the next section).In order to identify whether gene list was enriched in any biological functions or processes associated with CAD, we used two bioinformatics resources, namely Database for Annotation, Visualization and Integrated Discovery (DAVID)  and IngeA major goal in the cardiovascular disease field is identifying individuals who are at greatest risk of accelerated CAD pathogenesis and complications, such as stroke and MI. Recognizing that the utility of traditional risk factors (particularly those driven by age) is not sufficiently robust to identify all patient groups with accelerated CAD , incorpoIn a previous study , authorsEMR2 is a network gene that promotes the release of inflammatory cytokines from macrophages and has been reported to be highly expressed in foamy macrophages handling lipid overload in atherosclerotic vessels [NNT  and CYBar aging . NADPH oar aging .TLR5 is a member of the TLR (toll-like receptor) family, which is an established mediator of atherosclerosis [RETN is a biomarker for metabolic syndrome. Its overexpression has been shown to lead to increased atherosclerotic progression in mice [EGLN1 has been shown to provide protection against atherosclerosis in mice by improving glucose and lipid metabolism and reducing inflammation and decreasing the areas of atherosclerotic plaque [EGLN1, are established regulators of inflammation and atherosclerosis [Among our network genes previously associated with arterial aging, clerosis  due to iclerosis . RETN is in mice . Similarc plaque . HIF1-alclerosis .NRG3 is a network gene that is a member of the neuregulin family. Another member of this family is NRG1, which has been shown to inhibit atherogenesis and macrophage foam cell formation in a human study [FOXN3 has been associated with fasting blood glucose, serum cholesterol, and diabetes in past GWAS [FOXN3 has also been linked to carotid intima-media thickness  and plaque in recent fine mapping studies in humans [an study . It has an study . Anotherast GWAS –92. FOXNn humans , 94. Takn humans . In factn humans .th-99th percentile CAC score range and age-matched controls in two patient cohorts. The replication of highly predictive loci identified from the ClinSeq discovery cohort in the FHS cohort and the fact that we observe enrichment of several biological processes previously linked to cardiovascular disease at the network level demonstrates the effectiveness of our machine learning based approach. Our analysis provides a candidate list for conventional genotype-phenotype association studies of advanced CAC without the genome wide multiple testing penalty, thereby illustrating the complementary utility of machine learning and regression-based methods that can provide inputs to each other for follow-up studies.Dividing case subjects into subcategories based on the level of disease measured by different measures such as CAC scores, to pursue subphenotype-specific models  is a potWe used a combination of clinical and genotype data for predictive modeling of advanced coronary calcium. Machine learning models trained with SNP Set-2 (identified from the ClinSeq discovery cohort) produced high predictive performance in the FHS replication cohort. Upon identifying a subset of 21 SNPs from this set that led to optimal predictive performance in both cohorts, we developed NN models trained with the ClinSeq genotype data. We tested these models with the FHS genotype data and obtained high predictive accuracy values (AUC=0.80-0.85) under a wide range of network topologies, thereby replicating the collective predictive ability of these SNPs in FHS. At the gene network level, several biological processes previously linked to cardiovascular disease, including processes associated with accelerated “vascular aging”, were found to be enriched among the predictive loci.A potential extension of our modeling study is the expansion of the panel of SNPs, which are highly predictive of advanced CAC levels, around their loci for building more comprehensive models. Subsequently, we would like to test these potential predictors of rapid CAC progression and early onset of MI with longitudinal data in independent cohorts, especially for cases poorly predicted by traditional risk factors. To conclude, our study on CAC, a cardiovascular disease phenotype and a predictive marker of future cardiac events illustrates the potential of combining multiple machine learning methods as informative and accurate diagnostic tools. Our results also suggest that utilizing markers specific to a limited range of coronary calcium, rather than its complete spectrum, is an effective approach for building accurate predictive models for personalized medicine efforts that require disease-level specific risk prediction and prevention.Additional file 1Supplementary Tables. This pdf file includes supplementary tables referred to in the main text. (PDF 184 kb)Additional file 2This pdf file provides information about the genotype data from ClinSeq®; and FHS cohorts, rationale behind random forest and neural network implementation for modeling advanced CAC, and mouse and rat model-based associations between the predictive network genes and cardiovascular disease processes and risk factors. (PDF 165 kb)


A total of 142 functional variants, one novel, have been detected within these regions, in which 30 and 26 were classified as of zebu and African taurine origins, respectively. High density genome-wide SNP analysis of zebu × taurine admixed cattle populations from Uganda and Nigeria show that 25 of these regions are shared between KEASZ and Uganda cattle, and seven regions are shared across the KEASZ, Uganda, and Nigeria cattle. The identification of common candidate regions allows us to fine map 18 regions. These regions intersect with genes and QTL associated with reproduction and environmental stress  suggesting that the genome of the zebu × taurine admixed cattle has been uniquely selected to maximize hybrid fitness both in terms of reproduction and survivability.The East African Shorthorn Zebu (EASZ) cattle are ancient hybrid between Asian zebu × African taurine cattle preferred by local farmers due to their adaptability to the African environment. The genetic controls of these adaptabilities are not clearly understood yet. Here, we genotyped 92 EASZ samples from Kenya (KEASZ) with more than 770,000 SNPs and sequenced the genome of a pool of 10 KEASZ. We observe an even admixed autosomal zebu × taurine genomic structure in the population. A total of 101 and 165 candidate regions of positive selection, based on genome-wide SNP analyses ( Bos taurus) and humped zebu (B. indicus) cattle. They originated from different auroch ancestral populations B. primigenius primigenius and B. p. namadicus at two separate domestication centers; the Near East and the Indus Valley—Northern part of the Indian subcontinent, respectively  in Kenya, Adamawa Gudali in Nigeria, and Karamojong zebu in Uganda, African sanga, e.g., Ankole in Uganda, or African zenga e.g., Nganda in Uganda Epstein, . These cTheileria parva, Ehrlichia ruminantium, and Haemonchus placei; de Clare Bronsvoort et al., Rhipicephalus appendiculatus tick, the vector of East Coast Fever (ECF) protozoan parasite T. parva ,” using the Z-transformation method “Stouffer's method”  or call rate <95%  were filtered out from the entire dataset. These include 1,712 SNPs that failed both criteria. In total, 649,611 SNPs were therefore retained. The ancestral allelic state for 373,005 SNPs [mean gap size = 6.7 kb and standard deviation (SD) = 12.1 kb] has been reported previously by Utsunomiya et al. (Bovinae species: two B. gaurus (gaur), six Bubalus bubalis , and two B. grunniens (yak), with the fixed allele in the three species considered as ancestral. Only these SNPs were included in the downstream analyses.Quality control (QC) analyses for 735,297 autosomal SNPs were conducted through the a et al.  followinAdditional QC criteria included a minimum sample call rate of 95% and a maximum pairwise identity-by-state (IBS) of 95%, with the lower call rate animal eliminated from the high IBS pair. One ZS sample was excluded for having a low call rate, whilst 15 samples  were excluded following the IBS criterion.prcomp function implemented in GenABEL package for R software. These analyses were carried out in three levels; (i) all cattle populations, (ii) all cattle populations excluding the European taurine cattle, and (iii) only the KEASZ, Uganda (UGN) and Nigeria (NGR) cattle.PCA were conducted using the K = 3 was conducted on the whole dataset to determine the European taurine, Asian zebu, and African taurine ancestries at genome-wide level and for each autosome separately. The output files were graphically displayed by the ggplot2 package  from the Gaussian cumulative density function Φ. Candidate regions were defined as having five adjacent SNPs, not separated by more than 500 kb, passing the threshold of −log10P = 4.iHS analyses -0.5| from the Gaussian cumulative density function Φ. Candidate regions were defined as in Rsb.Rsb and iHS analyses, fastPHASE 1.4 . Likewise, the mean frequency of the first allele for each SNP was calculated for the combined reference cattle populations (AFpop2). The standardized values of the ΔAF (AFpop1–AFpop2) were normally distributed -0.5| from the Gaussian cumulative density function Φ. Candidate regions were defined as in iHS and Rsb.ΔP-values obtained from Rsb, iHS, and ΔAF, for each analyzed set of populations, i.e., KEASZ, UGN, and NGR, in meta-SS analyses , where Φ−1 is the inverse cumulative distribution function for a standard normal distribution. Then, the SNP-specific Z-scores were combined together according to the following equation: Zi = (ZRsb + ZiHS + ZΔAF)/k are numbers of SNPs and tests, respectively. The resulting Z-scores were referred back to the standard normal distribution to obtain combined P-values [P-value = 1- Φ(Zi)], where is the cumulative distribution function for a standard normal distribution. Candidate regions were defined as having five adjacent SNPs not separated by more than 500 kb passing the threshold of −log10P = 4.The Stouffer method was used to combine the http://www.lifetechnologies.com/lifescope). SNPs and indels (insertions/deletions) were called using the diBayes package implemented in LifeScope. A minimum coverage of two uniquely mapped reads and two non-reference allele counts were required to call a variant. Additionally, a minimum read mapping quality of 20 (MAPQ20) and base quality of 20 were also implemented.A single pool of 10 unrelated KEASZ DNA samples were sequenced using an ABI SOLiD 4 genetic analyser . Hp-values were calculated using the following formula: Hp = 2 ∑ nMAJ ∑ nMIN/(∑ nMAJ + ∑ nMIN)2  and a ZHp ≤ −4 was applied as a threshold to specify windows carrying selective sweep as in Liao et al.  was used for bovine exome sequence enrichment. It covers a total of ~45 Mb of the bovine sequence that composed of the coding regions in the UMD3.1 reference genome  and microRNA. The exomes of a further 10 KEASZ samples  functional terms . The intersectBed function from the BedTools software was used to overlap these QTL with the identified KEASZ candidate regions  in the genes within the genome-wide SNP and Hp candidate regions were selected based on their biological function, e.g., immunity, reproduction, fertility, heat tolerance and anatomical development. Variants (SNP and indels) within these genes were defined and the biological effect of the missense SNPs were predicted by the online tool PolyPhen-2 , while PC2 (2%) reveal a much higher genetic heterogeneity within and across East African cattle populations compared to the West African ones , African taurine (NDM and MT), East African (KEASZ and UGN) and West African (NGR) cattle populations Figure  shows geThe admixture analysis indicates a relatively even admixed genome of Asian zebu and African taurine ancestries across KEASZ animals with an estimated genetic proportion of 0.7 ± 0.01 SD and 0.30 ± 0.01 SD, respectively. The same even admixed proportion is present across the Karamajong zebu cattle from Uganda, while the Serere zebu has an estimated 0.74 ± 0.04 SD and 0.24 ± 0.04 SD zebu × taurine genetic proportions, with minor European taurine ancestry (0.02 ± 0.01 SD). The genome of the Ankole cattle has a larger African taurine genetic proportion (0.48 ± 0.01 SD). Nganda cattle has an admixed genome of 0.57 ± 0.04 SD Asian zebu and 0.36 ± 0.03 SD African taurine ancestries, it also carries low level of European taurine ancestry (0.07 ± 0.05 SD)  and West African (NGR) cattle populations  reveal six, 25 and three autosomal candidate regions, respectively , the three tests  and four in commercial breeds  mapped on the UMD3.1 bovine reference autosomes. These MAPQ20 reads covered ~97% of the reference autosomes with an average of 11 times depth of coverage. SNP calling using LifeScope diBayes package identified a total of 10,466,699 SNPs .Hp of 100 kb windows, as in Liao et al. . Out of the total 250,930 windows, 1,825 (~0.73%) have a ZHp score of ≤ −4 merged into 165 autosomal candidate sweep regions  genes within all KEASZ candidate regions from the SNPs and Hp analyses combined together. The first DAVID analysis identifies 57 functional term clusters with six significantly enriched clusters  and West African (NGR) cattle populations , or non-coding regions , they are likely of zebu origin in KEASZ. Whilst, 15 missense SNPs in 12 genes were also identified in the two African taurine cattle populations examined and therefore they may be of taurine origin. Among the 50 splice regions SNPs, 44 in 32 genes are identified in the KEASZ exome data, in which seven SNPs in six genes may be of zebu origin and ten SNPs in nine genes may be of taurine origin  and pooled heterozygosity (Hp) analysis. These regions were then further characterized to identify candidate causative variants and to assess their probable zebu or African taurine origins.In this study, we unravel the autosomal zebu × taurine admixed genome structure of African indigenous cattle populations from the eastern (Kenya and Uganda) and western (Nigeria) part of the African continent using genome-wide high density SNP data. Also, for the first time, both genome-wide SNP and full genome sequence data have been utilized to identify candidate signatures of positive selection in the genome of an African cattle, the EASZ from Kenya, based on meta-analysis of selection signals  separately, a composite statistical approach “meta-SS analysis” was conducted at the autosomal level as in Utsunomiya et al.  method proposed by Grossman et al. . Additionally, genes and QTL related to regulation of bovine immunity, fertility and reproduction, anatomical development, and heat stress have also been found within the identified KEASZ candidate regions (Supplementary Tables CCR7) and granulocyte macrophage-colony stimulating factor (CSF3). Upon binding to two chemoattractants: CCL19 and CCL21, CCR7 is involved in maturating dendritic cells and hence activate T lymphocytes , relaxin/insulin-like family peptide receptor 2 (RXFP2), spermatogenesis associated 24 (SPATA24), and sperm associated antigen 7 (SPAG7). The retinoic acid receptor, which is expressed in sertoli cells in the seminiferous tubules, plays a role in maintaining retinoic acid signal to induce spermatogonia differentiation  and sperm-associated antigen 7 (SPAG7) can also be classified into the cattle fertility and sexual reproduction category due to their role in spermatogenesis. Such signals may be the legacy of selection for fertility in hybrid populations between two cattle lineages (zebu and taurine), with a common ancestry perhaps as old as half a million years ago  and two members of the DnaJ family (DNAJC8 and DNAJC18) are mapped within three of the identified candidate regions . These genes are important to maintain optimum growth and development. LEMD3 and LOX are associated with the development of different organs, such as heart (LEMD3), lung and blood vessels (LOX)  and non-tropical-adapted  cattle to further support their role as causative variants under selection. Two of the missense variants are predicted to show non-benign effects on their genes calling for their effect to be validated, e.g., through gene editing approaches , we were able to infer the possible ancestral origin of some of these functional variants (Asian zebu or African taurine origin) indicating the role of the zebu × taurine admixture as a selective force shaping the genome of KEASZ. However, this origin assignment was only partially successful due to the low number of sequenced samples and the unavailability of a full indicine We reported here for the first time extensive exploring for candidate regions and putative causative variants under selection on the genome of an indigenous African cattle (East African shorthorn zebu) using both genome-wide SNP data and full genome sequence data. The possible ancestral origins of some of these variants have been inferred using exome data of EASZ from Kenya and full genome sequence of African taurine cattle. In this study we have defined three selective forces on the KEASZ genome; the external environmental pressures, the internal admixed genome pressure and possible ancient human selection for production traits. Our results can be considered as the first milestone in conserving the adaptive genetic resources of the indigenous African cattle and to further improve their breeding strategy. This will enhance the productivity of the indigenous African cattle populations and at the same time retain their African environment adaptability. Although in this study we have used two genomic tools and cattle populations from East and West of Africa, these results need to be further validated in larger number of cattle populations with different ancestries and environments.PRJNA386202. All identified SNPs are deposited in dbSNP under handel (UON_LAB_A100). The high density SNP genotyping data available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.38jp6.The pooled full genome sequence and the 10 exome sequences of the East African shorthorn zebu, in addition to the Muturu full genome sequences, are publicly available from GenBank with the Bioproject accession number Standard techniques were used to collect blood. The procedure was reviewed and approved by the University of Edinburgh Ethics Committee (reference number OS 03-06) and also by the Institute Animal Care and Use Committee of the International Livestock Research Institute, Nairobi.HB and OH conceived, designed the experiment. HB and OH performed the experiment. HB, AT, and FA analysed the data. MB and DW helped in the bioinformatic analysis. TS, MW, HH, ON, AT, GA, CM, MM, and CV contributed in data. HB and OH wrote the manuscript. All authors have agreed on the contents of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer MB and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.


Smallholder dairy farming in much of the developing world is based on the use of crossbred cows that combine local adaptation traits of indigenous breeds with high milk yield potential of exotic dairy breeds. Pedigree recording is rare in such systems which means that it is impossible to make informed breeding decisions. High-density single nucleotide polymorphism (SNP) assays allow accurate estimation of breed composition and parentage assignment but are too expensive for routine application. Our aim was to determine the level of accuracy achieved with low-density SNP assays.sv) between true and false assignments.We constructed subsets of 100 to 1500 SNPs from the 735k-SNP Illumina panel by selecting: (a) on high minor allele frequencies (MAF) in a crossbred population; (b) on large differences in allele frequency between ancestral breeds; (c) at random; or (d) with a differential evolution algorithm. These panels were tested on a dataset of 1933 crossbred dairy cattle from Kenya/Uganda and on crossbred populations from Ethiopia (N = 545) and Tanzania (N = 462). Dairy breed proportions were estimated by using the ADMIXTURE program, a regression approach, and SNP-best linear unbiased prediction, and tested against estimates obtained by ADMIXTURE based on the 735k-SNP panel. Performance for parentage assignment was based on opposing homozygotes which were used to calculate the separation value (2 = 0.962 to 0.994 for 100 to 1500 SNPs) with an average absolute bias of 0.026. Panels of SNPs based on the highest MAF in the crossbred population (Kenya/Uganda) gave the most accurate parentage assignments (sv = −1 to 15 for 100 to 1500 SNPs).Panels of SNPs based on the largest differences in allele frequency between European dairy breeds and a combined Nelore/N’Dama population gave the best predictions of dairy breed proportion  contains supplementary material, which is available to authorized users. Bos taurus)  and 1.7 Mb (SD = 0.7 Mb), respectively.Allele frequencies were calculated for the ancestral breeds. The weighted average allele frequency across breeds was calculated based on the number of animals in each breed sample. Weighted averages were calculated across the Nelore and N’Dama populations (NelNd) and across all European dairy breeds (EU). The differences in absolute frequencies were determined between Nelore and EU (NelEU), N’Dama and EU (NdEU), and NelNd and EU (NelNdEU). SNPs were sorted according to differences in absolute frequencies and subsets that had the largest differences were selected. As above, these subsets are not independent because larger subsets include all the SNPs in the smaller subsets. The average distance between SNPs per chromosome for the 100-SNP panel was 17.6 Mb (SD = 14.4 Mb), 17.4 Mb (SD = 16.3 Mb), and 15.6 Mb (SD = 14.2 Mb) for NelEU, NdEU, and NelNdEU, respectively. For the 1500-SNP panel, the average distance between SNPs per chromosome was 1.7 Mb (SD = 0.7 Mb), 1.7 Mb (SD 0.7 = Mb), and 1.7 Mb (SD = 0.6 Mb) for NelEU, NdEU, and NelNdEU, respectively.We selected 10 random samples for each subset and results were averaged across these random samples. These random panels were not restricted by SNP spacing (i.e. the 1 Mb pair restriction). The average distance between SNPs per chromosome ranged from 20.4 Mb (SD = 16 Mb) for the 100-SNP panel to 1.7 Mb (SD = 1.6 Mb) for the 1500-SNP panel.Bos indicus animals. We also tested 47,810 SNPs from the Illumina 50k-SNP bovine chip v2 .The official International Society for Animal Genetics (ISAG) panel for parentage assignment  consistsThe differential evolution (DE) algorithm is based on Storn and Price  and rankopH.In our study, the “all animals” set  was split into a training and a test population. The DE algorithm was initiated in the training population with 100 random samples of SNPs  for each panel size . From these 100 parental sets, two sets were randomly selected to create an ‘offspring set’ consisting of 50% randomly sampled SNPs from each parental set. If this offspring set performed better than the initial 100 parental sets (according to a fitness function), then this offspring set was retained and the worst parental set was discarded. The dairy proportions were estimated internally with a SNP-best linear unbiased prediction (BLUP) approach (see below), whereas the parentage test was based on number of 2) between the subsets of SNPs and the dairy breed proportions predicted with the 735k SNPs in ADMIXTURE. To optimize parentage assignments, the fitness function was the percentage of correctly assigned parentages according to the reconstructed pedigree. This process was run for 2000 iterations/generations. No spacing restriction between SNPs was applied since the DE algorithm should select best SNPs by default.The fitness function used to optimize prediction of dairy breed proportions was the coefficient of determination (rThe average distance between SNPs per chromosome for the panels to estimate breed proportions ranged from 23.6 Mb (SD = 12.8 Mb) for the 100-SNP panel to 1.6 Mb (SD = 1.6 Mb) for the 1500-SNP panel and for the panels to assign parentage, it ranged from 19.7 Mb (SD = 11.2 Mb) for the 100-SNP panel to 1.7 Mb (SD = 1.6 Mb) for the 1500-SNP panel.2) between observed  and predicted (based on subsets of SNPs) dairy proportions of the 1933 crossbred animals. The linear bias of breed proportions estimated from subsets of SNPs was assessed as the average deviation or average absolute difference estimated minus the observed (735k SNPs) values.Total dairy proportion for an animal was the sum of the estimated individual breed proportions for Ayrshire, Guernsey, Jersey, Holstein, and Friesian. Accuracy of the prediction of dairy proportions for all subsets of SNPs was assessed by the coefficient of determination  was used to quantify and visualize the performance of each SNP subset and was calculated as:FR is the number of opposing homozygotes in false parent–offspring relationships according to the reconstructed pedigree information; and TR is the number of opposing homozygotes in true parent–offspring relationships [The ionships , 29.Prediction of breed proportions was also made with a regression model and a SNP-BLUP approach to test the ability of the SNP panels to perform outside ADMIXTURE.The regression method was based on Kuehn et al.  and descThe SNP-BLUP approach required the replacement of missing genotypes (NA) with the average allele count across all animals. Only SNPs with a call rate higher than 95% were used in this analysis to limit potential bias due to SNPs with only a few recorded genotypes. SNP-BLUP was performed as follows:SNP effects .The linear regression and SNP-BLUP estimates of breed proportions are subject to ascertainment bias and thus require validation. Validation was achieved by using the SNP effects that were estimated in the Kenyan/Ugandan dataset to predict dairy proportions in the Ethiopian and Tanzanian dataset and vice versa. To determine whether population structure or random sampling caused bias in the estimates, we applied the SNP-BLUP approach to predict breed proportions for 50% of the animals in the Kenyan/Ugandan dataset by randomly selecting 50% of the animals in each breed (training dataset). Then, cross-validation of the estimates was performed on the other half of the Kenyan/Ugandan population as well as on the Ethiopian and Tanzanian crossbred animals (test datasets).sv was used for parentage assignment.We further validated our sets of SNPs in independent crossbred populations. The subsets of SNPs that were selected from the Kenyan/Ugandan dataset were used to predict breed proportions and parentage assignment in the Ethiopian and Tanzanian datasets. The coefficient of determination and the absolute linear bias between the full dataset and the subsets within the Ethiopian and Tanzanian datasets were used to determine the performance of each subset of SNPs to accurately assign dairy proportions, and the After merging the quality-controlled datasets for each breed, 4.8, 5.1, and 5.8% of genotypes were missing in the entire Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively. Within the crossbred animals, 4.9, 5.5, and 4.8% of genotypes were missing in the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively. There was no general pattern of where the missing genotypes occurred along the genome.FIS) did not show any substantial average inbreeding in any of the breeds; however, the SD was very large (Table Bos indicus breed and N’Dama represents a unique African Bos taurus breed. The largest proportion of SNPs on the 735k-Illumina chip was chosen based on high information content (high MAF) within non-African Bos taurus populations.The average inbreeding coefficient  than the difference between the European Bos taurus dairy breeds.A principal component (PC) analysis based on the GRM for the combined Kenyan and Ugandan dataset separated the European dairy breeds from the Nelore breed and then from the African pure breeds and the first PC explained 86.91% of the genetic variation. The second PC clearly separated the Nelore and N’Dama breeds from the European breeds and crossbreds, with the East African indigenous breeds being intermediate; it explained 1.75% of the genetic variation Fig. a. The seThe first two PC in the Ethiopian dataset explained 90.92 and 1.71% of the variation, respectively , 0.78 (SD = 0.20), and 0.78 (SD = 0.18) for the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively. This proportion was highest for Ayrshire in Kenyan crossbred animals and for Friesian in Ugandan crossbred animals, which was consistent with the PCA results Fig. . Based oIn Ethiopian crossbred animals, proportions of dairy breeds were highest for Holstein and Friesian Fig. , which wWe selected three SNP panels  based on the largest differences in allele frequency between ancestral breeds. Our hypothesis was that SNPs that display the largest difference in allele frequency between the indigenous ancestral breeds and the dairy breeds will provide the most accurate estimates of total dairy breed proportion. Total dairy breed proportion was defined as the sum of breed proportions across Ayrshire, Guernsey, Jersey, Holstein, and Friesian breeds. Panels of SNPs that were selected by applying other methods were included to investigate the factors that determine accuracy of prediction and whether it was possible to develop SNP panels that could estimate both breed proportion and parentage assignment.2 of 0.725 to 0.963 (SE = 0.004–0.012) for the smallest subsets of 100 SNPs, and 0.977–0.994 (SE = 0.002–0.003) for the largest subsets of 1500 SNPs  with just 200 SNPs. The next best panel was the NdEU for all panel sizes except 100 SNPs, for which the DE algorithm performed slightly better  and 0.898 (SE = 0.007) for 100 and 200 SNPs, respectively, because as shown by Table Bos taurus from Bos indicus but not for separating African from European Bos taurus.The various panels used in this study predicted dairy breed proportions in the Kenyan/Ugandan crossbreds with an r2 of 0.69 and 0.88 for 100 and 1000 SNPs, respectively. These coefficients of determination are lower than those obtained by using the same number of SNPs and the DE algorithm but the genetic differences between Yeonbyun and Hanwoo are much smaller than those between indigenous and European dairy breeds in our study.The performance of the panels selected with the DE algorithm did not improve much as the number of SNPs increased, and hence were outperformed by the NelNdEU and NdEU panels for more than 100 SNPs. The DE algorithm was designed to optimize a panel for the prediction of the 735k ADMIXTURE estimates of dairy proportions. However, SNP-BLUP was used to estimate dairy proportions, rather than ADMIXTURE as for all other panels. In addition, when we predicted dairy proportions by using a SNP-BLUP approach independently of the DE algorithm (see next section), the DE-based panels continued to perform less well than the NelNdEU and NdEU panels, which indicated that the DE algorithm failed to find the optimal solution with the number of iterations performed. Esquivelzeta-Rabell et al.  used theFigure When regressing 735k SNP predictions on either the 200 or 400 SNP predictions  than for the Ugandan crossbred animals . Kenyan crossbred animals are the result of crosses between European dairy breeds and Zebu whereas Ugandan crossbreds are crosses between European dairy breeds and Ankole and Nganda, which have much higher proportions of African Bos taurus ancestry than Zebu. This suggests that the bias observed for the crossbreds in these two countries is predominantly due to a tendency to over-predict the African Bos taurus proportion and under-predict the European Bos taurus proportion.The estimates of dairy proportions were slightly more accurate and less biased for the Kenyan crossbred animals  in Ethiopian and Tanzanian crossbreds for 100 SNPs, respectively, compared to an r2 of 0.783 (SE = 0.012) in the Kenyan/Ugandan population. Average absolute bias was smallest for the NelNdEU panel in both datasets , followed by the NdEU panel . In both countries, panels under-predicted dairy proportions except for some NelEU panel sizes in Tanzania , 0.830 (SE = 0.018), and 0.768 (SE = 0.022) in the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets. Average absolute bias of the ISAG panel was among the highest values for the 200-SNP panels, i.e. 0.069, 0.067, and 0.076 in the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively. This poor performance is not unexpected since the ISAG panel was selected for parentage assignment, predominantly in Bos taurus populations. SNPs on the 50k-SNP v2 Illumina chip predicted dairy proportions with r2 of 0.9987 (SE = 0.0008), 0.9989 (SE = 0.001), and 0.9985 (SE = 0.002) in the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively. Absolute bias of predicted dairy proportions was equal to 0.006, 0.005, and 0.009 for the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets, respectively.The full ISAG panel of 200 SNPs predicted dairy proportions with r2 = 0.442, SE = 0.017) followed by the NdEU SNP selection . The panels based on maximizing indigenous versus dairy allele frequencies including the Nelore breed  gave very poor predictions of individual dairy breed proportions. This is due to the selection method that preferentially selects alleles at extreme frequencies between Bos taurus and Bos indicus breeds and hence results in low variance between dairy breeds.When predicting the proportions of each of the seven ancestral breeds based on the various 200-SNP panels  followed by the N’Dama breed . Jersey breed proportions were poorly predicted with on average an r2 of 0.107  breeds that are most genetically distant from the others (such as the Nelore breed) are more likely to display allele frequencies that differ from those of other breeds with most methods used to select SNP panels.With subsets of 200 SNPs, individual breed proportions were on average best predicted for the Nelore breed (r2 of 0.944 (SE = 0.005), but gave poorer predictions of total dairy proportion  because of its poor prediction of N’Dama (African Bos taurus) versus European Bos taurus proportions. Although this panel was not as good at predicting total dairy proportion in these African crossbred populations, it should perform better in populations in which the indigenous population is pure Bos indicus, as is the case in much of India.The NelEU panel performed well for the prediction of Nelore proportion, with an rSeparating the crossbred animals according to their country of origin (Kenya vs. Uganda) improved the prediction of Nelore proportion in the Kenyan crossbred animals and of breed proportion for Holstein, Jersey, and N’Dama in the Ugandan crossbred animals with most panels. Ayrshire and Guernsey predictions were less accurate with most panels in both Kenyan and Ugandan crossbred animals.2 of 0.9914 (SE = 0.002) and an absolute bias of 0.014 (SD = 0.018). However, the selected SNP panels gave poor predictions when using the regression method  with 40,000 SNPs, with a partial least square regression approach performing best. Frkonja et al. [2 = 0.949), but observed a significant loss in accuracy when it dropped to 400 (r2 = 0.912). Our results show that the regression approach performs much worse than ADMIXTURE even with 1500 SNPs  decreased substantially   led to decreased accuracy [average reduction in rThe poor performance of the estimates obtained with the panels that were optimized by using the DE algorithm indicates that either it did not properly search the parameter space to find the optimum panel, or that the number of iterations was insufficient to evolve to the optimum. Figure S8  provides a measure of the difference in opH in true parent–offspring relationships and other forms of relationships and in unrelated individuals. A sv lower than 0 indicates that the panel cannot reliably separate parent–offspring status from other relationships. SNPs with a high MAF have the highest probability of having opH between two unrelated individuals within a population under Hardy–Weinberg equilibrium. Thus, panels of SNPs that were selected for a high MAF in the crossbred population should perform best in assigning parentage using opH criteria. Our hypothesis was confirmed since SNP panels based on MAF achieved the highest sv in the Kenyan/Ugandan, Ethiopian, and Tanzanian datasets  but it would still likely generate spuriously high sv values due to ascertainment bias.Since sv of 1 and 170, respectively, in the Kenyan/Ugandan dataset, 3 and 944, respectively, in the Ethiopian dataset, and 5 and 796, respectively, in the Tanzanian dataset.The ISAG panel and the 50k-SNP chip yielded sv of the randomly selected panels indicated that at least 300 SNPs are required to achieve a positive sv, which is in concordance with the findings of Strucken et al. [sv in a composite cattle population.The average n et al. , who repopH counts for parent–offspring pairs with dairy proportions higher than 0.5 and those with dairy proportions lower than 0.5 were equal to 293 (SD = 93) and 420 (SD = 84), respectively. Although statistically highly significant (P < 0.0001), this difference is small since only a proportion of SNPs were tested with the SNP panels. MAF for animals with dairy proportions lower than 0.5 versus higher than 0.5 were virtually identical (0.2738 vs. 0.2734), and when using the MAF-based panel with either 200 or 400 SNPs, there was no significant correlation between opH and dairy proportion. Therefore, parentage assignment of the MAF panel is not expected to be affected by dairy proportion.We investigated whether the accuracy of the MAF-based panel was affected by breed composition. When using all 735k SNPs, dairy proportions of the animals in parent–offspring pairs ranged from 11 to 99%. Average Bos taurus dairy breeds, and no information from crossbred animals was used to select the SNPs. Thus, there is no ascertainment bias in the estimated accuracies with the NelNdEU panels using ADMIXTURE in our datasets. The 200-SNP panel provides a good compromise between a small number of SNPs while achieving high accuracy and the lowest absolute bias . In practice, not all of the 200 SNPs are available due to genotyping errors or to failure of some SNPs to work on a given assay platform. Therefore, we randomly selected 75 to 95% of the SNPs by simulating a genotyping failure of 5 to 25%. Accuracies of predicted dairy proportions remained high with an r2 of 0.965 and 0.975 when 75 and 95% of the genotypes were available, respectively. Thus, the NelNdEU 200-SNP panel should perform well in the field for the prediction of dairy proportions.The NelNdEU panel was superior for the prediction of dairy proportions in all three populations: Kenyan/Ugandan, Ethiopian and Tanzanian. This panel was chosen based on allele frequencies in reference samples of Nelore, N’Dama, and sv of 1 with 200 SNPs. Similarly, we calculated the sv assuming a random genotyping failure of 5 to 25%. The sv was positive with 1 opH between true and false parentages. Thus, the MAF-based 200-SNP panel should perform well in the field for parentage assignment.The MAF-based panel performed best for parentage assignment and achieved a positive 2 of 0.978  for the prediction of breed proportion and a sv of −1. These results are slightly less good than those achieved by the NelNDEU and MAF-based 400-SNP panels  were chosen from each panel. Using all 200 SNPs from each panel (i.e. 400 SNPs) resulted in an rsv, even if the majority of SNPs were chosen from the MAF panel. This is due to the fact that the 200-SNP panel chosen for high MAF is not performing well enough, i.e. the positive sv is not sufficiently high, to counteract the negative sv value of the NelNdEU panel. The 300 or 400 SNPs from the NelNdEU and MAF panels resulted in positive sv values. However, combining these to create a panel of 600 or 800 SNPs means doubling the number of SNPs. Therefore, we recommend using the 200 SNPs of the NelNdEU panel for prediction of breed proportions or the 200 SNPs of the MAF panel for parentage assignment.The combined panels performed relatively well for prediction of breed proportions, especially if more than 50% of SNPs were chosen from the NelNdEU panel. The combined panels did not achieve a positive The 1500 SNPs in the NelNdEU and MAF panels are provided in rank order of selection in Table S1 [see Additional file Bos taurus and Bos indicus populations. Alternative panels based on SNPs that differentiate Bos indicus from European Bos taurus should perform well in countries where the indigenous base population is Bos indicus, such as in south Asia, but this needs to be tested.For East African crossbred dairy cattle populations, it is possible to create SNP panels with as few as 200 SNPs that will result in accurate estimates of dairy proportions and panels of similar size but with different SNPs to assign parentage accurately. A single combined panel of 400 SNPs achieved sufficient accuracies for breed proportion prediction but was not able to assign parentages correctly. Results of the 200-SNP panels chosen independently for breed proportion prediction and parentage assignment indicate that these panels should be reliable for animals that are crossbred to a wide range of African indigenous breeds. However, they are not expected to perform well outside of Africa where indigenous breeds do not originate from ancient crosses between African Additional file 1: Figure S1. 3D-plots of principal components for reference, indigenous, and crossbred populations from Kenya/Uganda, Ethiopia, and Tanzania.Additional file 2: Figure S2. Allele frequencies for SNP panels selected for the largest allele frequency differences of ancestral breeds, i.e. between Nelore or N’Dama and a weighted EU average in Nelore, N’Dama, or European populations.Additional file 3: Figure S3. Allele frequencies for SNP panels in a crossbred cattle population (Ethiopia). Bold horizontal lines indicate the median and + indicates the mean.Additional file 4: Figure S4. Allele frequencies for SNP panels in a crossbred cattle population (Tanzania). Bold horizontal lines indicate the median and + indicates the mean.Additional file 5: Figure S5. Regression of estimates of dairy proportions (735k) on predictions of the NelNdEU 200-SNP panel.Additional file 6: Figure S6. Regression of estimates of dairy proportions (735k) on predictions of the NelNdEU 400-SNP panel.Additional file 7: Figure S7. Validation of dairy proportion estimates from one half of the Kenyan/Ugandan crossbreds (NelNdEU panel). (a) Validated in the other half of the Kenya/Uganda dataset. (b) Validated in independent populations from Ethiopia and Tanzania plus the second half of the Kenyan/Ugandan dataset.Additional file 8: Figure S8. Accuracy (r2) of dairy proportion estimates and parentage assignment with increasing number of iterations.Additional file 9: Table S1. Best 1500 SNPs for breed proportion estimation (NelNdEU) and parentage assignment (MAF). They are presented in rank order, from best to worst SNP.


Here, we present a genome-wide association study (GWAS) to identify candidate loci conferring Mn toxicity tolerance in rice (Oryza sativa L.). A diversity panel of 288 genotypes was grown in hydroponic solutions in a greenhouse under optimal and toxic Mn concentrations. We applied a Mn toxicity treatment  at twelve days after transplanting. Mn toxicity caused moderate damage in rice in terms of biomass loss and symptom formation despite extremely high shoot Mn concentrations ranging from 2.4 to 17.4 mg g-1. The tropical japonica subpopulation was more sensitive to Mn toxicity than other subpopulations. Leaf damage symptoms were significantly correlated with Mn uptake into shoots. Association mapping was conducted for seven traits using 416741 single nucleotide polymorphism (SNP) markers using a mixed linear model, and detected six significant associations for the traits shoot manganese concentration and relative shoot length. Candidate regions contained genes coding for a heavy metal transporter, peroxidase precursor and Mn2+ ion binding proteins. The significant marker SNP-2.22465867 caused an amino acid change in a gene (LOC_Os02g37170) with unknown function. This study demonstrated significant natural variation in rice for Mn toxicity tolerance and the possibility of using GWAS to unravel genetic factors responsible for such complex traits.Manganese (Mn) is an essential micro-nutrient for plants, but flooded rice fields can accumulate high levels of Mn Plants were grown in hydroponic culture for 33 days. An optimum rice growing environment was maintained in the greenhouse under natural light supplemented with artificial lighting for 12 hours a day to ensure a minimum light intensity of 19.5 kilo lux, 29/22°C day/night temperature, and 49/65 percent day/night relative humidity. Twelve-day-old rice seedlings were first transplanted into 60 liter tanks filled with half strength modified Yoshida solution  in TASSEL 5.0 analysis. The latter results are presented as supplementary data  annotated for abiotic stress response were present in 66 kb window with significant SNP-2.17092835 (Table B in 10 (p) value were detected on chromosome 7 because the significant SNPs were in strong LD   . NotablyIn this study we employed a HDRA composed of 700 k SNPs  for a suDro1 [Pup1 [Among the candidate genes identified in this study, a SNP variant detected for RSL causing an amino acid substitution was located right in the coding sequence of LOC_Os02g37170 . AlthougDro1  or Pup1 o1 [Pup1 , were unOsNRAMP5 (LOC_Os07g15370) is known to be involved in root to shoot translocation of Mn in rice [OsNRAMP1 (LOC_Os07g15460), an orthologue of AtNRAMP1 which has been characterized as a high affinity Mn transporter regulating Mn uptake in Arabidopsis [Some of the candidate genes contained in candidate loci can be assigned to typical adaptive strategies employed by plants. The transport of Mn between plant organs plays an important role in Mn tolerance.  in rice ,56 and wbidopsis .indica) accumulated more Mn in rice shoots than the sensitive one [2+. These genes are highly expressed in both roots and shoots especially in early vegetative stages (http://ricexpro.dna.affrc.go.jp). Moreover, proteins with metal binding domains are known to confer heavy metal toxicity tolerance [2+ binding proteins  were present in the candidate loci for SMC in this study. Likewise, antioxidant activity is important for the scavenging ROS, which can arise from reactions involving transition metals such as Mn [OsGRX genes were constitutively expressed in shoots in vegetative growth stages (http://ricexpro.dna.affrc.go.jp). GRX is involved in maintaining glutathione dependent peroxidase activity as part of the antioxidant system during oxidative stress in plants [Shoot tolerance is generally referred to as the ability to tolerate high concentrations of elements in aboveground tissue through inclusion mechanisms. Mn shoot tolerance was illustrated in a bi-parental QTL mapping study, where the tolerant parent Click here for additional data file.S2 FileTable A–Table C.(XLSX)Click here for additional data file.


Nine known and new significant SNPs were identified on eight chromosomes. These lead SNPs explained 34.9% of the phenotypic variation, and four of them were designed as dCAPS markers in the hope of accelerating molecular breeding. Moreover, a total of 212 candidate genes was predicted and eight candidate genes showed plant tissue-specific expression in expression profile data from different public bioinformatics databases. In particular, LOC_Os03g10110, which had a maize homolog involved in embryo development, was identified as a candidate regulator for further biological function investigations. Additionally, a polymorphism information content ratio method was used to screen improvement footprints and 27 selective sweeps were identified, most of which harbored domestication-related genes. Further studies suggested that three significant SNPs were adjacent to the candidate selection signals, supporting the accuracy of our genome-wide association study (GWAS) results. These findings show that genome-wide screening for selective sweeps can be used to identify new improvement-related DNA regions, although the phenotypes are unknown. This study enhances our knowledge of the genetic variation in seed dormancy, and the new dormancy-associated SNPs will provide real benefits in molecular breeding.Seed dormancy is an important agronomic trait affecting grain yield and quality because of pre-harvest germination and is influenced by both environmental and genetic factors. However, our knowledge of the factors controlling seed dormancy remains limited. To better reveal the molecular mechanism underlying this trait, a genome-wide association study was conducted in an  Plant seeds, especially cereal grains, are vitally important sources of human nutrition, providing half of the global per capita energy intake. Consequently, various seed traits such as seed dormancy, which is regarded as the failure of an intact viable seed to complete germination under favorable conditions Bewley, , have beqSD12   to identify a substantial number of significantly associated SNPs and some putative genes potentially regulating seed dormancy in the whole panel; (2) to design dCAPS markers for different alleles of the associated SNPs for breeding applications; (3) to analyze the genetic diversity population structure and LD of the landraces and improved lines; and (4) to detect selective sweep regions using PIC ratio statistics between the landraces and improved lines.In this study, on the basis of our previous reports .html]   after heading, and then ~100 seeds were stored at 4°C to maintain seed freshness for germination evaluation on the next day and another ~100 seeds per line were air-dried in greenhouse (~35°C) for 7 days and then treated at 45°C for 3 days to break dormancy. To test the germination, the seeds were wrapped in doubled sheets of 20 × 20 cm wetted absorbent filter paper, placed vertically, and germinated at 30°C and 100% relative humidity in the dark for 10 days. Germinated seeds were defined as those in which the length of shoot exceeded half the length of the seed. Germination (%) = number of germinated seeds × 100/number of tested seeds. This experiment was repeated three times. The mean values of the three replications were used as the final phenotypic data. All seeds were intact grains.Means, standard errors (SEs), broad-sense heritability  were performed using the R package lme4 ij is the random effect of the jth replicate in the ith environment; (L × E)ik is the random interaction effect of the ith environment and the kth line, and εijk is a random error following N   and the Rice Genome Annotation Project database (http://rice.plantbiology.msu.edu/). SNPs located in the candidate genes and haplotype data were obtained from the RiceVarMap database (http://ricevarmap.ncpgr.cn/). Homologous gene identification was performed in the Rice Genome Annotation Project database, and then protein sequences were aligned using BLASTP in NCBI (https://www.ncbi.nlm.nih.gov/).The expression profiles of all candidate genes were analyzed according to the results of Davidson et al. . Previour2) dropped to half of its maximum value /PIC(improvedline). SNPs with PIC ratios > 3 were empirically considered improvement footprints.To detect the regions of improvement sweeps, the 266 landraces and 89 improved lines were used to analyze LD decay, population structure and PIC ratios. The LD decay of the two panels was investigated using TASSEL version 4.0  and a mixed subgroup (Mixed)  Figure , and rel) Figure , 2016. E) Figure . In part) Figure . This reTo evaluate the effects of the two elements and control false positive associations, four models  were compared using a quantile-quantile (Q − Q) plot Figure . CompareP = 1.19E-06) was used as the representative. As a result, nine peak SNPs were retained (Table P = 1.19E-06), on chromosome 7 was located ~164.2 kb downstream of the cloned pleiotropic gene qSD7-1 for seed dormancy, and another peak SNP, seq-rs3527 (P = 1.93E-04), resided ~3.7 kb downstream of Sdr4, which is involved in seed dormancy  underlying human diseases , was not detected in previous reports.In previous studies, many seed dormancy QTLs distributed on the 12 rice chromosomes have been identified using traditional linkage mapping  or highly significant (P ≤ 0.01 or 0.001) levels between the two alleles of each SNP marker , which was ~25.4%  and seq-rs3527 (P = 1.93E-04), two known seed dormancy-related genes, qSD7-1  of the lead SNPs were searched to identify candidate genes in the Rice Haplotype Map Project Database . By comparison, the Sdr4 had high expression levels in the embryo at 25 DAP, endosperm at 25 DAP, and seeds at 5 and 10 DAP  showed that a total of 34 SNPs were located within this gene    (Table P = 7.96E-05) was located just 1.1 kb downstream of the strongest selection SNPs    Table . Notably4) Table . More im4) Table –F. In pa) Figure . In addi) Figure . The pai) Figure , implyin) Figure .Taken together, our results suggested most of the selective sweep regions ~66.7%) were adjacent to both known domestication-related genes and SNPs we identified previously by GWAS . Any other datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.The SNP dataset used during this study is available in the in the Dryad digital repository (Conceived and designed the experiments: QL, XN, and XW. Performed the experiments: QL, XN, MZ, CW, QX, and YF. Analyzed the data: QL, YY, SW, XY, HY, and YW. Wrote the paper: QL. Revised the manuscript: XC, XL, and XW. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.