Milk production is an economically important sector of global agriculture. Much attention has been paid to the identification of quantitative trait loci (QTL) associated with milk, fat, and protein yield and the genetic and molecular mechanisms underlying them. Copy number variation (CNV) is an emerging class of variants which may be associated with complex traits.In this study, we performed a genome-wide association between CNVs and milk production traits in 26,362 Holstein bulls and cows. A total of 99 candidate CNVs were identified using Illumina BovineSNP50 array data, and association tests for each production trait were performed using a linear regression analysis with PCA correlation. A total of 34 CNVs on 22 chromosomes were significantly associated with at least one milk production trait after false discovery rate (FDR) correction. Some of those CNVs were located within or near known QTL for milk production traits. We further investigated the relationship between associated CNVs with neighboring SNPs. For all 82 combinations of traits and CNVs (less than 400 kb in length), we found 17 cases where CNVs directly overlapped with tag SNPs and 40 cases where CNVs were adjacent to tag SNPs. In 5 cases, CNVs located were in strong linkage disequilibrium with tag SNPs, either within or adjacent to the same haplotype block. There were an additional 20 cases where CNVs did not have a significant association with SNPs, suggesting that the effects of those CNVs were probably not captured by tag SNPs.We conclude that combining CNV with SNP analyses reveals more genetic variations underlying milk production traits than those revealed by SNPs alone.The online version of this article (doi:10.1186/1471-2164-15-683) contains supplementary material, which is available to authorized users. DGAT. The dPTA were used as the phenotypes for genome-wide association studies.Traditional predicted transmitting abilities (PTAs) for five production traits, including milk yield (MY), fat yield (FY), protein yield (PY), fat percentage (FP), and protein percentage (PP), were calculated by USDA ARS AIPL . Those PTA are predicted additive genetic effects after removing fixed non-genetic effects, and the reliabilities of the PTA were used to quantify the amount of information available for different individuals, 40. De-The intensity data of 56,947 SNP probes were generated by Illumina BovineSNP50 arrays. We imported Log R Ratios (LRR) from the GenomeStudio software into Golden Helix SNP & Variation Suite (SVS) 7.7  using its DSF Export Plug-In 4.1.Bos taurus genome assembly UMD 3.1 (https://ccb.jhu.edu/bos_taurus_assembly.shtml) within 29 autosomes.A total of 48,669 SNPs were mapped onto the To normalize the LRR, we used the default GC correlation file (GC Reference bos_taurus_UMD3.1.gc_digest.dsf) to correct for the waviness contributed by GC content. We then utilized the copy number analysis module (CNAM) under the multivariate option to segment chromosomes with a maximum of 20 segments per window, a minimum of 3 markers per segment, and a significance level of p = 0.01 for pairwise permutations  as described previously.In Golden Helix SVS 7.7, a linear regression under the additive genetic model was employed to identify CNVs associated with each production trait with the option of Full Scan Permutations . We used the principal component analysis (PCA) option to correct batch effects/stratification of the test input data. Significant CNVs were counted at the significance level of  after FDR correction.-8 after FDR correction.Association tests were carried out using the linear model plugin of SVS 7.7 software with PCA correction. Significant SNPs were detected when their adjusted p-values passed the threshold of p < 1 × 102 were calculated using Haploview (Version 4.2)[To investigate linkage disequilibrium (LD) patterns in the regions containing associated CNVs, pairwise LD statistics D’ and rion 4.2) and the ion 4.2). Qanbariion 4.2) presenteWe classified all 82 significant combinations of traits and CNVs , fat yield (FY), protein yield (PY), fat percentage (FP), protein percentage (PP). The descriptive statistics of PTA, including reliability and heritability, are given in Additional fileIn contrast to conventional CNV discovery studies, which try to detect as many variable regions as possible, this CNV-based GWAS is intended to identify the common CNVs shared among samples in order to detect associations with common diseases or traits. Using the multivariate method of CNAM in SVS, a total of 2,626,669 distinct segments were detected in the 26,362 samples. After merging across samples, 99 nonredundant CNVs were left for subsequent association test . We also found other CNVs like CNV12, CNV18, CNV28 and CNV34. However, CNV28 overlaps with only one QTL previously reported on chr7[We also identified 11 and 29 CNVs that were significantly associated with FP and PP, respectively. The most significant CNV associated with FP was CNV33  with a p-value of 7.00 × 10 on chr7, CNV24[ on chr7, CNV26[ on chr7, 54 CNV3 on chr7–58.To further explore the relationship between SNPs and CNVs, we carried out association tests based on SNPs using the same 26,362 Holstein cattle SNP array data. Additional fileSome cases were complex situations involving both IN and LD. We generally assigned them to the IN, IN* and IN** instead of LD or LD* classes. However, no matter which class was used, CNVs in both the IN and LD classes were apparently well-captured by neighboring tag SNPs. It is interesting to note that although 9 out of 31 CNVs were located within breakpoints of haploblocks  with significantly associated SNPs Table , suggestPrevious and current genome wide association studies have been investigated to identify significant genes or linked markers based on SNPs. In this CNV-based study, our results indicate that CNV are associated with, and likely contribute to, differences in milk production. Our study provides a systematic estimate that approximately one-quarter of CNVs are not captured by LD with nearby SNPs. This provides an alternative framework to explain the missing heritability of complex traits. This study helps to fill gaps left by current SNP-based genome wide association and selection studies. Therefore, we conclude that combining CNV with SNP analyses reveals more genetic variations underlying milk production traits than can be revealed by SNPs alone. Interrogation of the genome for both CNVs and SNPs, including common and rare variations, could be an effective way to identify the causes of complex diseases and traits, 59. A mSupporting information is available in the additional files and further supporting data is available from the authors on request.Additional file 1: Figure S1: Boxplot of reliability of five production traits. (PDF 76 KB)Additional file 2: Figure S2: Heritability of five production traits. (PDF 75 KB)Additional file 3: Table S1: List of CNV segments. (XLSX 25 KB)Additional file 4: Table S2: Cattle QTL and RefSeq genes of 34 CNVs based on the UMD 3.1 assembly. (XLSX 20 KB)Additional file 5: Figure S3: Overlapping relationships of 34 significantly associated CNV segments among five milk production traits. (PDF 107 KB)Additional file 6: Table S3: SNPs near CNV regions. (XLSX 210 KB)Additional file 7: Figure S4: Haplotype block views. (PDF 1 MB)


MBL2) polymorphisms are reported to be associated with various diseases. In spite of being exhaustively studied molecule, no attempt has been made till date to comprehensively and systematically analyze the SNPs of MBL2 gene. The present study was carried out to identify and prioritize the SNPs of MBL2 gene for further genotyping and functional studies. To predict the possible impact of SNPs on MBL structure and function SNP data obtained from dbSNP database were analyzed using various bioinformatics tools. Out of total 661 SNPs, only 37 validated SNPs having minor allele frequency ≥0.10 were considered for the present study. These 37 SNPs includes one in 3′ near gene, nine in 3′ UTR, one non-synonymous SNP (nsSNP), thirteen intronic SNPs and thirteen in 5′ near gene. From these 37 SNPs, 11 non-coding SNPs were identified to be of functional significance and evolutionary conserved. Out of these, 4 SNPs from 3′ UTR were found to play role in miRNA binding, 7 SNPs from 5′ near and intronic region were predicted to involve in transcription factor binding and expression of MBL2 gene. One nsSNP Gly54Asp (rs1800450) was found to be deleterious and damaging by both SIFT and Polyphen-2 servers and thus affecting MBL2 protein stability and expression. Protein structural analysis with this amino acid variant was performed by using I-TASSER, RAMPAGE, Swiss-PdbViewer, Chimera and I-mutant. Information regarding solvent accessibility, molecular dynamics and energy minimization calculations showed that this variant causes clashes with neighboring amino acids residues that must interfere in the normal triple helix formation of trimeric subunit and further with the normal assembly of MBL oligomeric form, hence decrease in stability. Thus, findings of the present study indicated 12 SNPs of MBL2 gene to be functionally important. Exploration of these variants may provide novel remedial markers for various diseases.Mannose binding lectin (MBL) is a liver derived protein which plays an important role in innate immunity. Mannose binding lectin gene 2 ( MBL1 and MBL2, but in human the first of them is pseudogene leaving one functional gene MBL2 . The gene contains four exons and three introns. It encodes a 248 amino acid residue transmembrane protein, MBL (NCBI accession number XP_011538118.1) which is encoded via a 3570 bp long mRNA (NCBI accession number NM_000242.21). It belongs to a family of proteins called collectins, which consists of collagenous region and a carbohydrate recognition domain  is a liver derived acute phase protein. It binds to carbohydrates on the surface of mannose-rich pathogens and mediates clearing by phagocytosis or complement activation , also called as missense variants are particularly important as they result to changes in the translated amino acid residue sequence. nsSNPs may affect the protein function by reducing protein solubility or by destabilizing protein structure (Chasman and Adams A single-nucleotide polymorphism (SNP) is the most common type of genetic variation. Several studies have shown MBL2 gene of Homo sapiens as of October, 2015. To date the functional significance has not been established for the majority of them. In the absence of any experimental information on their functional effects, the potential functional consequences of a SNP can be predicted using various bioinformatics tools  and were subjected to various computational analyses. The strategy followed to select SNPs having structural and functional impact is shown in Fig. The SNPs and their related protein sequence for the mbl Genome browser release 48 (http://www.ensembl.org/). MBL2 gene from seventeen eutherian mammals was selected for the comparative analysis with the human MBL2 gene. These includes Homo sapiens (Human), Pan troglodytes (Chimpanzee), Gorilla gorilla gorilla (Gorilla), Pongo abelii (Orangutan), Chlorocebus sabaeus (Vervet-AGM), Macaca mulatta (Macaque), Papio Anubis (Olivebaboon), Bos Taurus (Cow), Ovis aries (Sheep), Sus scrofa (Pig), Equus caballus (Horse) Felis catus (Cat), Canis lupus familiaris (Dog), Callithrix jacchus (Marmoset), Mus musculus (Mouse), Oryctolagus cuniculus (Rabbit) and Rattus norvegicus (Rat). For a detailed comparison, base pair view was selected from the database and the SNPs were verified manually.SNPs located in evolutionary conserved regions (cSNPs) were identified using the EnseTo identify the effect of SNPs in non-coding regions, tools predicting potential functional effect of SNPs at Transcription factor binding sites (TFBS), Intron/exon border consensus sequences (splice sites), Exonic splicing enhancers (ESEs), and miRNA binding were used.SNPinfo (FuncPred) and RegulomeDB provide a collection of functional information using variety of tools and resources. The SNPs functionality was primarily determined by SNPinfo (FuncPred) (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) web server in Asian population (Xu and Taylor http://regulomedb.org/). It is an online composite database and prediction tool to annotate as well as prioritize potential regulatory variants from human genome  is an integrated database that systematically identifies DNA polymorphisms in miRNAs and miRNA target sites (PolymiRTS). It also elucidates the potential links of SNPs to molecular, physiological, behavioral and disease phenotypes  (http://compbio.uthsc.edu/miRSNP/). A list of all 3′ UTR SNPs of MBL2 gene, creating or destructing miRNA site was obtained. These 3′ UTR SNPs were further looked for possible miRNA IDs for which they create and abolish site for.Deleterious and damaging effect of nsSNP was predicted using web based tools SIFT (Sorting Intolerant from Tolerant) and Polyphen-2 (Polymorphism Phenotyping v2).SIFT (http://sift.bii.a-star.edu.sg/) performs analysis based on different algorithms and it interprets the homologous sequences using the Swiss-Prot (version 51.3) and TrEMBL (version 34.3)  was used to predict the possible impact of an amino acid substitution on both structure and function of protein by analysis of multiple sequence alignment and protein 3D structure  was used to find the proteins related to the MBL2. To obtain a list of the closest matches it performs a FASTA search against every macromolecular structure deposited in protein data bank (PDB) (http://www.rcsb.org/pdb/home/).Protein blast (Muster v1.0 (http://zhanglab.ccmb.med.umich.edu/MUSTER/) provides the Z-score and complete full length models by using Modeller v8.2 (Wu and Zhang I-Tasser (http://zhanglab.ccmb.med.umich.edu/I-TASSER/) server provides accurate structural and functional predictions using state-of-art algorithms   was used to generate the mutated models of the selected protein struture for the corresponding amino acid substitutions (Guex and Peitsch Chimera (http://www.cgl.ucsf.edu/chimera) is a extensible molecular modeling system used for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories and conformational analysis , a neural network based tool, predicts the change in the stability of the protein upon mutation . One sSNP was excluded from the study leaving 36. However no non-synonymous SNPs were found to have MAF even ≥0.05. So, SNPs falling in the list of pathogenic significance were also checked for validation status and MAF ≥ 0.1. Only one SNP Gly54Asp (rs1800450) have MAF = 0.1220 was found and hence included in the study. Thus, our investigation accounted for 37 SNPs including 3′ UTR, intronic, 5′/3′ near gene and nsSNP. A map of the MBL2 gene region highlighting the positions of each of these SNPs is shown in Fig. The dbSNP database contains both validated and non-validated polymorphisms. In spite of this drawback, dbSNP was availed because it is the most extensive SNP database   was performed by SIFT and Polyphen-2 softwares. It was predicted to be damaging by both the servers. The SIFT prediction was deleterious with a score of 0.006. This deleterious nsSNP was submitted to Polyphen-2 as query protein sequences in FASTA format with prediction outcomes to be probably damaging score closer to 1.Similarly, intronic and 5′ near gene SNPs identified as having putative functional effects, by the three tools were screened as shown in Table MBL2 gene related protein structures were searched. Although different mutations in MBL2 gene are reported, the native primary structure (with complete 248 amino acid sequence) of MBL monomeric protein is not yet available. Only partial protein structure, with PDB ID 1HUP was found  with Z score 13.966 and alignment length 141 aa residues with coverage of 0.568. However, the rs1800450 nsSNP lies in collagenous domain of human MBL protein , the To get variant modeled structure at position 54 i.e. Gly → Asp conversion Swiss-PdbViewer and chimera was used Fig. . It was MBL2 gene with susceptibility to various diseases as well as were shown to affect serum MBL levels  is a pattern recognition molecule of the innate immune system. It plays an important role in the first line of defense and provides primary immune response to pathogens and thus is important when adaptive immune response will not operate due to some reasons . Native protein structure is important for the in silico analysis of functional polymorphisms. As already explained in results, only partial structure, with PDB ID 1HUP corresponding to neck and carbohydrate recognition domains of human MBL is available (Sheriff et al. Although The comparison of specific properties of native and variant structures revealed difference in stability. This variation leads to slight deviation in energy and decrease in stability. This change in stability can be explained by the reason that the variant residue has bulkier R group than the wild type and cannot fit within the available space. The free carboxylic acid group on Asp can disturb the ionic interaction of nearby residues and hence causing vulnerable effects. The wild-type residue is non-polar while the variant residue is negatively charged and hydrophilic. Furthermore, there is difference in solvent accessibility of both residues with native being buried and variant being fully exposed, hence slight deviation in energy and decrease in stability.This variation which lies in the fifth Gly-Xaa-Yaa repeat distort the collagen-like region had been shown to interfere with normal assembly of the MBL oligomeric form and it’s interactions with MBL associated serine proteases (Wallis and Cheng 10 peptide with a Gly to Ala substitution near the center indicated a local loss of direct hydrogen bonding and a slight untwisting at the mutation site when the three collagen like peptide chains form a triple helix in solution (Bella et al. In the present study, we have used the relatively simple structure i.e. monomeric polypeptide of MBL to examine and understand the effect of this variation on its more complex homolog using theoretical modeling. Extensive studies had been carried out to investigate the effect of Gly substitution in the collagen like domain using synthetic peptides which offer an approach to characterizing the effect of mutations on the triple-helix (Baum and Brodsky There are some essential features that are used in this study to avoid the common pitfalls while conducting an in silico analysis. Feature one, looking for validation status to check the reliability of a given SNP as all SNPs in dbSNP are not real. Some polymorphisms might have arisen solely due to sequencing errors and others may be sole to the individuals (Fredman et al. MBL2 were found to be functionally important for candidate gene studies.Third and very important feature to prioritize functional SNPs identified with in silico tools is the use of MAF. Because MAF is linked to statistical power of the study i.e. MAF and sample size to detect variant allele of a SNP in a given sample population have inverse relationship (Grover et al. MBL2 gene are functionally important as well as deleterious to its structure and expression. These conserved SNPs may broaden our understanding of genotype phenotype relationship. Out of these 12 SNPs only 4 SNPs i.e. rs11003125, rs7095891, rs7096206 and rs1800450 are widely studied. But the rest of 8 functional SNPs rs2099902, rs10082466, rs11003123, rs36014597, rs7084554, rs11003124, rs2165813, rs2099903 reported by the present study haven’t been explored much till date. Thus, the findings of the present study provide a guideline to the fellow researchers to know important role of these SNPs in the etiology of complex diseases.The present study demonstrated that 12 SNPs in the


CSN1S1, CSN2, CSN3, and KIT whose products are likely involved in the yield and proteins of milk and their distinctive black-and-white markings. In addition, genes indicative of positive selection were associated with cardiovascular disease, which is related to simultaneous propagation of genetic defects, also known as inbreeding depression in Holstein.Holstein is known to provide higher milk yields than most other cattle breeds, and the dominant position of Holstein today is the result of various selection pressures. Holstein cattle have undergone intensive selection for milk production in recent decades, which has left genome-wide footprints of domestication. To further characterize the bovine genome, we performed whole-genome resequencing analysis of 10 Holstein and 11 Hanwoo cattle to identify regions containing genes as outliers in Holstein, including  Understanding the forces that govern changes in populations or species over time is important for population genetics. Elucidating the relative contributions of genetic drift and natural selection to extant patterns of genetic variation is also important. As opposed to the neutral theory, a significant proportion of variation is believed to affect the ability of an organism to survive and reproduce, and will therefore be subject to natural selection .The rapid development of large-scale catalogs of genetic variation has increased interest in identifying targets of positive selection, which will ultimately increase our understanding of the roles of drift and selection during adaptation. Furthermore, signatures of positive selection delimit regions of the genome that are (or have been) functionally crucial. Therefore, identifying such regions will allow us to detect genetic variation that contributes to phenotypic diversity and facilitate functional annotation of the genome. In addition to these benefits, each target of positive selection can be used to characterize the historical forces and events that have shaped the history of a population .LCT for lactose tolerance or intolerance; and malaria resistance , which was designed to detect ongoing or nearly fixed selective sweeps by comparing haplotypes from two populations . The extAll animals were handled in strict accordance with good animal practice, as defined by the relevant national and/or local welfare bodies, and all animal work was approved by the Institutional Animal Care and Use Committee of the National Institute of Animal Science (No. 2012-C-005).Whole-blood samples (10 ml) were collected from 10 Holstein and 11 Hanwoo cattle. Holstein samples originated from different heifers inseminated with semen imported from Canada. We generated pair-end reads using Illumina HiSeq2000. DNA was isolated from whole blood using a G-DEXTMIIb Genomic DNA Extraction Kit  according to the manufacturer’s protocol. We randomly sheared 3 µg of genomic DNA using the Covaris System to generate inserts of approximately 300 bp. The fragments of sheared DNA were end-repaired, A-tailed, adaptor ligated, and amplified using a TruSeq DNA Sample Prep. Kit . Paired-end sequencing was conducted with NICEM  using the Illumina HiSeq2000 platform with TruSeq SBS Kit v3-HS (Illumina). Finally, sequence data were generated using the Illumina HiSeq system.http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/, last accessed October 28, 2013). The pair-end sequence reads were then mapped against the reference bovine genome (UMD 3.1) using Bowtie2 .We performed a per-base sequence quality check using the fastQC software , potential PCR duplicates were filtered. We then used SAMtools  >200 were filtered as FS represents variation on either the forward or the reverse strand, which are indicative of false-positive calls.The “UnifiedGenotyper” and “SelectVariants” arguments of GATK were used for calling candidate single nucleotide polymorphisms (SNPs). To filter variants and avoid possible false positives, argument “VariantFiltration” of the same software was adopted with the following options: 1) SNPs with a phred-scaled quality score of less than 30 were filtered; 2) SNPs with MQ0  >4 and quality depth  less than 5 were filtered; and 3) SNPs with FS  and PRJNA210523 (Hanwoo).We used BEAGLE  to inferP value < 10−6, common loci of SNP chip and DNA resequencing data were extracted and examined to assess concordance. SNPs were pruned using PLINK .We additionally genotyped all cattle samples using BovineSNP50 Genotyping BeadChip . After filtering out SNPs based on a missingness rate >1%, minor allele frequency < 0.05, and Hardy–Weinberg equilibrium test ng PLINK . We obsehttp://hgdp.uchicago.edu/Software/, last accessed June 3, 2014). For each SNP loci, we calculated EHH and the log-ratio iHH (integrated EHH) for the pairwise test of the Holstein and Hanwoo populations. An XP-EHH score is directional: An extreme positive score implies selection in Holstein, whereas a negative score suggests selection in the Hanwoo population. The log ratios were standardized to have a mean of 0 and variance of 1. An XP-EHH raw score distribution plot is provided in supplementary figure S3, Supplementary Material online. We then split the genome into nonoverlapping segments of 50 kb to use the maximum (positive) XP-EHH score of all SNPs within a window as a summary statistic for each window. To take into account the SNP frequency, we binned genomic windows according to their numbers of SNPs in increments of 200 SNPs . A histogram of SNP density in each window is provided in supplementary figure S4, Supplementary Material online. Within each bin, for each window i, the fraction of windows with a value of the statistic greater than that in i is defined as the empirical P value, following the method previously reported  were considered strong signals in Holstein. In this report, the “P values” denote empirical P values; a low P value indicates that a locus is an outlier with respect to the rest of the genome. This approach is suitable, especially when the demographic parameters are unreliable and an explicit demographic model cannot be defined (as is the case for cattle) (The method cross-population extended haplotype homozygosity (XP-EHH) was first used to detect selective sweeps using the software xpehh  (http://reported . The reg cattle) . However cattle) .http://genepath.med.harvard.edu/∼reich/, last accessed June 3, 2014. We used the parameters as the following: Nonoverlapping sliding windows of 50 kb, maximum number of SNPs within each window as 400, and correlation level from which the SNPs contribution to XP-CLR result was down weighted 0.95. The regions with the XP-CLR values in the top 1% of the empirical distribution (XP-CLR > 282.3) were designated candidate sweeps.We additionally performed the cross-population composite likelihood ratio test (XP-CLR) for detecting selective sweeps that involves jointly modeling the multilocus allele frequency between two populations . XP-CLR “Significant” genomic regions identified from XP-EHH and XP-CLR were annotated to the closest genes (UMD 3.1). Genes that span  the window regions were defined as candidate genes.supplementary fig. S5, Supplementary Material online). The Holstein population has been subjected to more than 50 years of intense selection for milk production traits  version 6.7 to analyze the KEGG pathway . Milk prsupplementary table S1, Supplementary Material online). After filtering the potential PCR duplicates and correcting for misalignments due to the presence of INDELs, we detected autosomal SNPs using GATK . The same samples were additionally genotyped with Illumina BovineSNP50 BeadChip to cross-validate the genotype calls from resequencing data. We observed 97.57% overall concordance between SNPs detected by BovineSNP 50 and resequencing .The genomes of 10 Holstein and 11 Hanwoo were sequenced to 12.0× coverage on average, with a total of 4.75 billion reads comprising approximately 665 Gbp. Using Bowtie 2 , reads wing GATK . SeveralA) using GCTA . The same trends can be found across breeds. Samples from breeds tend to cluster on the plot with regard to both the total length and the number of ROH. In addition, we observed that Holstein (compared with Hanwoo) had longer and more frequent runs of homozygosity as a response to the rapid artificial selection.When an individual’s parents share a relatively recent common ancestor, large portions of their genomes will be identical-by-descent. If both parents transmit the same segment to the child, the child will be homozygous for that segment, thus creating a run of homozygosity (ROH) . There ifig. 1supplementary figure S6, Supplementary Material online, admixture between Holstein and Hanwoo was merely shown. In addition, the Treemix analysis detected no potential migration events between any pairs of populations . Migration events can be expressed as edges in the tree and are colored according to their weight; yet there was no evidence of migration between Hanwoo and Holstein in our analysis.To understand the degree of admixture in the populations more deeply, we used STRUCTURE  on a ranTo pinpoint loci under positive selection, we calculated the XP-EHH statistic between Holstein and Hanwoo population groups. This statistic assesses haplotype differences between two populations and is designed to detect alleles that have increased in frequency to the point of fixation or near-fixation in one of the populations . A compasupplementary fig. S8, Supplementary Material online). Based on this, and from previous studies . The genes included CSN1S1 and CSN2 . These results suggested that milk-related traits of Holstein breeds resulted from local positive selection on several distinct genes. The milk protein genes alphas1-casein (CSN1S1) and beta-casein (CSN2) are relevant to milk production parameters and milk protein quality  associated with coat color. As opposed to the brown coat color of Hanwoo  were also detected. Based on the database of cattle candidate genes and genetic markers for milk production and mastitis developed by CSN1S1 and CSN2 have been frequently reported to be associated with milk production performance and mastitis; putative miRNA target sites in candidate genes expressed in mammary gland include GLI3, ITGAV, and CSN2. From the work of RAB10 and RAB1A were identified as milk protein gene set along with CSN1 and CSN2.We next compared our results with those of previous studies. We first observed that the majority of genes  resided in QTL for milk production traits. In a previous study, Ron et al. generated the database of candidate genes for milk production traits in cattle (cgQTL) . We idensupplementary tables S6 and S7, Supplementary Material online). We observed 62 genes in the intersection with XP-EHH selection candidates. The candidate regions included additional milk protein gene, kappa-casein . Previous studies found a favorable effect of the CSN3 variant on protein yield and protein content . The reduced level of nucleotide diversity at the whole-genome level in Holstein is indicative of genetic drift followed by a unique demographic history.Nucleotide diversity measures the degree of polymorphism within a population, and is defined as the average number of nucleotide differences per site between any two DNA sequences chosen randomly from the sample population . Numerousupplementary table S8, Supplementary Material online). Although indiscriminate reduction of nucleotide diversity in Holstein at a genome-wide level was expected, the diversity between two populations was not always differentiable at the window size of 10 kb . To distinguish between selection signatures and demographic effects from nucleotide diversity measure, we searched for genes that showed extreme reduction under diversity, especially in the gene region compared with the neighboring regions  were positively selected in Holstein. Although the increased frequency of alleles and the increase in milk yield are beneficial for animal production, the simultaneous association with economic traits, such as the propagation of genetic defects, is not .


Several QTLs have been identified for major economically relevant traits in livestock, such as growth and meat quality, revealing the complex genetic architecture of these traits. The use of network approaches considering the interactions of multiple molecules and traits provides useful insights into the molecular underpinnings of complex traits. Here, a network based methodology, named Association Weight Matrix, was applied to study gene interactions and pathways affecting pig conformation, growth and fatness traits.PPARγ, ELF1, and PRDM16 involved in mesoderm tissue differentiation. Fifty-four genes in the network belonged to growth-related ontologies and 46 of them were common with a similar study for growth in cattle supporting our results. The functional analysis uncovered the lipid metabolism and the corticotrophin and gonadotrophin release hormone pathways among the most important pathways influencing these traits. Our results suggest that the genes and pathways here identified are important determining either the total body weight of the animal and the fat content. For instance, a switch in the mesoderm tissue differentiation may determinate the age-related preferred pathways being in the puberty stage those related with the miogenic and osteogenic lineages; on the contrary, in the maturity stage cells may be more prone to the adipocyte fate. Hence, our results demonstrate that an integrative genomic co-association analysis is a powerful approach for identifying new connections and interactions among genes.The co-association network analysis underpinned three transcription factors, This work provides insights about pathways and key regulators which may be important determining the animal growth, conformation and body proportions and fatness traits. Molecular information concerning genes and pathways here described may be crucial for the improvement of genetic breeding programs applied to pork meat production. About 43% of the meat consumed worldwide proceeds from pigs, thus representing the major source of meat for human food intake http://www.animalgenome.org/cgi-bin/QTLdb/SS/index]. Moreover, a genome wide linkage analysis for growth and body composition carried out in an Iberian × Landrace cross (IBMAP) confirmed previous QTL regions and identified new ones in 10 of the 18 autosomes Over 553 quantitative trait loci (QTLs) for growth-related traits have been reported in pigs . We also excluded markers which did not map in the Sscrofa10.2 version assembly. Pedstats program A total of 364 pigs, including their F0, F1 and F2 founder generations , were genotyped with the Porcine SNP60K BeadChip Genome-wide association analysis (GWAS) for the twelve phenotypic growth traits were performed using a mixed model accounting for additive effects with Qxpak 5.0 software ijlkm was the i-th individual record, sex (two levels) and batch (nine levels) were fixed effects, ß was a covariate coefficient with c being the covariate used in each case (described below), λl was a −1, 0, +1 indicator variable depending on the l-th individual genotype for the k-th SNP, ak represented the additive effect associated with the k-th SNP, ul represented the infinitesimal genetic effect with random distribution N were used for the analysis. Carcass weight was used as a covariate for CL, IMF, BFTS, HW, SW, and BLW. For BFT155 and BFT180 the covariates used were the body weight at their respective days. Meanwhile, for the body and the carcass weights the covariate used was the animal age.Different covariates (http://www.r-project.org/). Those SNPs associated  with the ham weight or with 3 or more traits were selected for further analysis. We included in the analysis the SNPs with a distance of minor than 2.5 kb (SNPs close) and major than 1,000 kb (SNPs far) from a gene. We also included SNPs located at less than 10 kb of miRNA. Finally, to facilitate the analysis, for SNPs clustering at less than 1 Mb of distance from each other, the SNP associated with the major number of characters was selected. The hierarchical clustering option of PermutMatrix software et al.The association weight matrix (AWM) was built from the GWAS results. First, the SNP additive effects were normalized with a z-score method using a R script and a matrix was constructed with these values, being SNPs in rows and traits in columns. Another matrix with the same format was generated for the p-values obtained in the GWAS. For the analysis, the ham weight was selected as the key phenotype. Subsequently, the AWM script www.ingenuity.com) was used to identify the most relevant biological functions and pathways in which the genes associated with the phenotypic traits were involved. IPA, which uses its own databases, allowed the identification of overrepresented pathways using the BH multiple testing correction http://www.informatics.jax.org) was used in order to identify how mutant alleles driven in mice for the identified growth-related genes present in the network affected the phenotype.The Ingenuity Pathways Analysis software , backfat thickness measured at 155 and 180 days (BFT155 and BFT180) and measured at slaughter (BFTS), carcass length and weight (CL and CW), weight of the hams, shoulders and belly  and intramuscular fat (IMF) content. The horizontal green line represents the statistical significance  calculated with the q-value library http://www.r-project.org/).(DOCX)Click here for additional data file.S2 FigureHierarchical cluster analysis among 12 phenotypic traits: body weight measured at 125, 155 and 180 days , backfat thickness measured at 155 and 180 days (BFT155 and BFT180) and measured at slaughter (BFTS), carcass length and weight (CL and CW), weight of the hams, shoulders and belly  and the intramuscular fat (IMF) content.(TIF)Click here for additional data file.S3 FigureLinkage disequilibrium among the PPARG and NR2C2 SNPs. Pattern of linkage disequilibrium analysis around ±2Mb of the SNPs in PPARG and NR2C2. Figure colored from blue to red according to LD strength between consecutive markers. The green diamond-shape corresponds to the SNP in PPARG gene and the blue diamond-shape the SNP in NR2C2 gene.(DOCX)Click here for additional data file.S1 TableList of 54 growth-related genes in the network.(XLSX)Click here for additional data file.S2 TableList of 142 regulators (transcription factors and miRNAs) identified within the list of associated-genes.(XLSX)Click here for additional data file.S3 TableTop networks of molecular functions identified with IPA for the 513 genes.(XLS)Click here for additional data file.S4 TableTop pathways identified with IPA for the 513 genes.(XLS)Click here for additional data file.


Population differentiation is the result of demographic and evolutionary forces. Whole genome datasets from the 1000 Genomes Project (October 2012) provide an unbiased view of genetic variation across populations from Europe, Asia, Africa and the Americas. Common population-specific SNPs (MAF > 0.05) reflect a deep history and may have important consequences for health and wellbeing. Their interpretation is contextualised by currently available genome data.The identification of common population-specific (CPS) variants (SNPs and SSV) is influenced by admixture and the sample size under investigation. Nine of the populations in the 1000 Genomes Project  and 5 European) revealed that the African populations (LWK and YRI), followed by the Japanese (JPT) have the highest number of CPS SNPs, in concordance with their histories and given the populations studied. Using two methods, sliding 50-SNP and 5-kb windows, the CPS SNPs showed distinct clustering across large genome segments and little overlap of clusters between populations. iHS enrichment score and the population branch statistic (PBS) analyses suggest that selective sweeps are unlikely to account for the clustering and population specificity. Of interest is the association of clusters close to recombination hotspots. Functional analysis of genes associated with the CPS SNPs revealed over-representation of genes in pathways associated with neuronal development, including axonal guidance signalling and CREB signalling in neurones.Common population-specific SNPs are non-randomly distributed throughout the genome and are significantly associated with recombination hotspots. Since the variant alleles of most CPS SNPs are the derived allele, they likely arose in the specific population after a split from a common ancestor. Their proximity to genes involved in specific pathways, including neuronal development, suggests evolutionary plasticity of selected genomic regions. Contrary to expectation, selective sweeps did not play a large role in the persistence of population-specific variation. This suggests a stochastic process towards population-specific variation which reflects demographic histories and may have some interesting implications for health and susceptibility to disease.The online version of this article (doi:10.1186/1471-2164-15-437) contains supplementary material, which is available to authorized users. The global diversity of human genomes is the outcome of a series of demographic and evolutionary events including migration, bottlenecks, admixture, population isolation, natural selection and genetic drift which occurred in different parts of the world at various time points in history –3. GenomData from these studies on genetic diversity have been instrumental in estimating the origin and history of different contemporary populations as well as shedding light on the evolutionary relationship between them . MoreoveThe next major wave of information about genetic and genomic diversity in human populations came from studies based on exome and whole genome sequencing –19. The SNPs and structural variants are broadly classified into common and rare based on minor allele frequencies (MAF). A widely used cut-off for defining rare SNPs being a MAF of less than 0.05 . HoweverSNPs and structural variants are often classified into ‘private’ and ‘shared’ based on their distribution in a single population or a range of populations. The term private however might imply different things based on the context, for example, a SNP might be private to an individual or a family, or to a population  or to an ancestral group. Therefore, we will use the term ‘population-specific’ for the SNPs that have been found to occur only in a single population. Although private SNPs have not been shown to be involved in major phenotypic traits or common diseases, population-specific SNPs might well be important in ascribing characteristic phenotypes and disease susceptibility/protection to a population , 30.Population specificity of genetic variants, if the population-specific allele is the derived allele, might originate from two different scenarios: in the first scenario, a variant allele originates in a single population and remains restricted to the population of its origin. The second scenario is that the variant originated before differentiation of populations, survives in only a single population, and gets eliminated from other populations. In cases where the population-specific allele is the ancestral allele, both the alleles are estimated to have evolved far back in evolutionary history and the derived allele replaces the ancestral allele in all but one of the populations, probably through selective sweeps. Alternatively, in some cases, the assignment of ancestral state may be incorrect. The other possible scenario by which population-specific SNPs might originate is by admixture with populations which are not included in the study or even populations which are no longer extant. Therefore, in addition to the functional role of these SNPs, the population-specific SNPs might also play an important role in characterizing ancestry and understanding demographic histories , 32. ForDespite their potential significance, population-specific SNPs have not been studied extensively. Previous HapMap data based studies on population-specific SNPs have been able to identify only a small number of population-specific SNPs due to ascertainment bias of the genotyping platform , 33–35. We have used 1000 Genomes sequence data (release October 2012), including more than one thousand individuals from 14 populations spanning Europe, Asia, Africa and America, to identify SNPs and structural variants that are private or specific to each population and to study their genomic distribution and potential functional relevance , 18. HowOne of the major achievements of the 1000 Genomes project has been the identification of numerous novel SNPs across different populations , 18. The, respectively. Although the numbers of common and rare SNP differ by many folds, there are some broad similarities in the distribution patterns of the CPS SNPs and RPS SNPs.The present 1000 Genomes data contain two African , LWK ), three Asian , CHB  and CHS (Han Chinese South)), three American , PUR (Puerto Ricans in Puerto Rico) and CLM ), 5 European (IBS (Iberian Populations in Spain), GBR (British from England and Scotland), CEU (Utah residents with ancestry from northern and western Europe), FIN (Finnish in Finland) and TSI ) and one admixed African (ASW (African Ancestry in SW USA)) population. The frequencies of common and rare population-specific SNPs in these populations have been summarized in Figure For example, the highest number for both CPS SNPs and the RPS SNPs was observed in the LWK population followed by the Japanese (JPT) population. Interestingly, in contrast to the large number of RPS SNPs observed, just a few CPS SNPs were found to occur in the Chinese populations (CHB and CHS). This observation is consistent with the fact that these populations have a similar geographic origin, and the differentiation between them probably started little more than a thousand years ago with the Southward migration of the Northern Han population –39. In sOne of the concerns with using all the current populations of the 1000 Genomes data for identifying population-specific SNPs is the inclusion of populations with known recent admixture, such as ASW and MXL . The inclusion of these admixed populations may mask the true population specificity of SNPs. In order to identify SNPs which are truly unique to populations, ASW and the three American populations , which are known to have undergone a significant amount of admixture in the recent past, were removed from the dataset [The trimming and rearrangement of the population datasets resulted in 9 potentially independent and essentially non-admixed populations for further investigation in the current study. The distribution of the CPS SNPs and RPS SNPs for each population was recalculated considering these 9 populations only, and has been summarized in Figure The relatively high frequency of CPS SNPs as well as RPS SNPs in the Japanese population is notable. It is well known that the modern Japanese population contains admixtures of at least two distinct genetic components; the old migrants who migrated to the Japanese Archipelago approximately 30,000 years ago and the new migrants that reached Japan only about a couple of thousand years ago –47. It wWe found that three of the European populations  have only a handful of common SNPs unique to them in contrast to a few hundred thousand rare SNPs. While this makes sense in terms of demographics , 49 and To classify the CPS SNP variant alleles into ancestral and derived  the ancestral/derived information for alleles in the 1000 Genomes vcf file was used . As expeThe relative prevalence of the CPS SNPs (as well as RPS SNPs and SSVs) across populations, therefore, shows high concordance with what can be expected on the basis of the generally accepted model of population divergence and the relationships between populations. However, as has been demonstrated, the number of population-specific SNPs observed in any population, in addition to population histories, is also influenced by factors like sample size and number of related and/or admixed populations included in the study. The removal of the admixed African and American populations almost doubled the number of common SNPs which were detected to be population-specific in the other 9 populations, indicating how important the detection and control of admixture is for identifying what is truly population-specific. While the lack of CPS SNPs in most European populations is not very surprising considering their population histories, as well as the number of populations (5 European populations in contrast to only 2 African and 3 Asian population) included in the dataset, it would be interesting to see how strongly the inclusion of other populations from Asia and Africa change the number of population-specific SNPs as new data pour in.p-value cut-off for delineating clusters of CPS SNPs in each population.The distribution of SNPs has for long been known to be non-random across the genome –55. Recep-values calculated for each window. The results clearly identified specific regions of the genome to be enriched with CPS SNPs in each population. We detected 655 CPS SNP-enriched windows/regions in the 6 populations  separately for each population. In each step the fraction of CPS SNPs in each window was recorded and compared to an expected value, based on the occurrence of CPS SNPs on the corresponding chromosome for the particular population. The statistical significance of the observations was estimated using cumulative hyper geometric ns Table . The popThe second approach was to use a sliding window of 5 kilobases (kb). This approach, in addition to identifying CPS SNP-enriched regions, provides a more direct way to identify possible overlap within CPS SNP-enriched windows across populations. Using this scan, 565 5-kb regions were found to be significantly enriched for CPS SNPs in the 6 populations Table . For eacSLCO1B1) gene in the YRI population. Sequence variants identified in the SLCO1B1 gene have been associated with altered transport activity and it has been shown that genetic polymorphisms in the gene have an impact on the inter-individual variability of the pharmacokinetics and pharmacodynamics of specific drugs [SLCO1B1 gene between populations with the greatest diversity among African populations [RIF1) gene in the CHINESE population. Additional files Interestingly, the longest blocks and the highest scoring windows show significant overlap in some populations Tables  and 3. Fic drugs , 63. Preulations , 63. SimClusters of SNPs with highly differentiated allele frequencies, within and between species, have been observed in numerous previous studies –66. The p-value for each observation was then estimated using bootstrap resampling. The results show that although some of the CPS SNP-enriched windows show significant iHS score enrichment, the overall distribution does not indicate any significant association of selection with these windows  statistic, which in principle involves the detection of unusually long haplotypes of low diversity as signatures of selection . iHS scos Figure A.Figure One of the concerns about using a centi-morgan (cM) based physical map, such as the one used in this study, is that the signals for signatures of selection might get underestimated as the threshold of iHS > 2 used by Voight and colleagues  might beTo test for selective sweeps on the basis of allele frequency differentiation rather than haplotype lengths we used the population branch statistic (PBS); which has been found to be very useful in detecting high altitude adaptation-related SNPs in Tibetans relative to Han Chinese and Danish populations, as an alternative approach for detecting signatures of selective sweeps in CPS SNP-enriched windows . PBS canFor each population, the PBS statistic for each CPS SNP-enriched 50-SNP window was calculated using the method used by Yi et al. . For theRegions of high recombination have been shown to be related to higher SNP densities , 75. As Although this analysis shows a very clear trend, as the maps used in this study are LD based, further evidence in terms of experimentally derived data for at least some of these regions will be required to reliably establish the relationship between recombination hotspots and CPS SNP-enriched windows. Nevertheless, the observed enrichment of recombination hotspots in CPS SNP-enriched genomic regions hints that high recombination might be one of the factors contributing to the generation of CPS SNP clusters. The presence of recombination hotspot(s) in a short genomic region (5 kb or 50 SNP), especially in case of a genotype based recombination map like the one used here, clearly indicates the LD architecture to be complex and the LD blocks to be short within that particular region. Moreover, as the width of a recombination hotspot (1–2 kb) is significant with respect to length of the sliding windows (5 kb or 50 SNP) used in the analysis, the presence of even a single hotspot can lower the LD of the region covered within the window considerably. The enrichment of recombination hotspots, therefore suggests that LD blocks are probably shorter and that LD is probably lower in the CPS SNP-enriched regions compared to average genomic regions. Moreover, in addition to recombination rate associated SNP density variations, the high recombination rates also suggest that the effects of population admixtures will be more prominent in these regions, which might also be an important source of the observed CPS SNP clusters. Furthermore, as recombination hotspots have been found to vary significantly among populations , 78. TheTo study the functional relevance of the CPS SNPs we analysed their localization with respect to known genes. As seen in the case of most novel variants identified by the 1000 Genomes project , as wellHLCS (holocarboxylase synthetase deficiency), TGM1 , DIAPh1 (deafness), and PAWR (which induces apoptosis in certain cancer cells). Moreover, a functional SNP was detected in TRIM5 which is a capsid-specific restriction factor involved in blocking viral replication early in the life cycle. Additionally, two coding non-synonymous SNPs were detected in the UPK3B gene which plays an important role in AUM-cytoskeleton interaction in terminally differentiated urothelial cells.Eleven coding non-synonymous CPS SNPs were observed in the YRI mapping to10 different genes, 8 of them were predicted to be functional by at least one of the tools. Four of the 10 CPS SNPs containing genes were detected to have known association with a disease, includin , 80g HLCABCA4, linked to Stargardt disease 1, hereditary macular degeneration and retinitis pigmentosa; ATP8B1, associated with various forms of cholestasis, GHR, which is linked to Laron syndrome, resulting in growth impairment; MCCC1, involved in methylcrotonoyl-CoA carboxylase 1 deficiency, and two SNPs in NLRP12 gene, which is associated with familial cold autoinflammatory syndrome. In the JPT population 8 non-synonymous CPS SNPs, all of which were predicted to be functional, were observed in 8 genes. Some of these genes were found to be involved in melatonin activity, melanogenesis, olfaction and hair formation. Only a single non-synonymous CPS SNP was detected in the MRP35 gene in the IBS population, whereas none was found to occur in the CHINESE and the FIN populations.In the LWK population 12 coding non-synonymous CPS SNPs in 11 genes were observed, 5 of which are linked to disease phenotypes. These include Additionally, a total of 520 CPS SNPs with probable consequences for gene regulation, all from RegulomeDB category 2, which demonstrates direct evidence of a binding through ChIP-seq and DNase data with either a matched position weight matrix to the ChIP-seq factor or a DNase footprint, were identified   and DAVIUsing DAVID, we identified a number of CPS SNP-enriched disease, pathway, and gene ontology (GO) classes for each population. As observed for the pathways detected using IPA, the CPS SNP-enriched disease, pathway and GO classes identified using DAVID overlapped between the different populations . Secondly, the definition of a population in terms of origin and admixture is at times vague and increased mobility worldwide leads to elevated levels of admixture. Moreover, the numbers of variants analysed is only a small subset (<1%) of all population-specific variants since rare variants (MAF < 0.05) have not been included. Genome sequencing of global populations is providing data which will assist in teasing out ancestral populations and will shed further light on population differentiation and adaptation. The availability of more extensive data along with an increased depth of sequencing, which permits the reliable study of rare genetic variants and structural variants, is therefore required for a better understanding of the relationship between unique genotypic variations and their geographical contexts.The recent version  of the 1000 Genomes vcf files containing phased genotypes for 36.7 million autosomal SNPs and 1.38 Million autosomal SSVs were downloaded from 1000 Genomes Project ftp server . The ancp-value for enrichment of CPS SNPs in each window was estimated. To correct for multiple hypothesis testing we used a conservative p-value cut-off of <5 × 10 -8 for the identification of windows enriched with CPS SNPs. In the second approach we employed a similar scan using 5-kb non-overlapping windows.Identification of enrichment of CPS SNPs in genomic regions was performed using custom Perl scripts. We used two sliding window based approaches. In the first approach, each chromosome was scanned using sliding and non-overlapping 50-SNP windows and the frequency of CPS SNPs in each window was computed. Based on the overall occurrence of CPS SNPs in the entire chromosome the cumulative hypergeometric p-value calculated by a bootstrap resampling analysis. A similar analysis was also performed for CPS SNP-enriched 5-kb windows in each population. In addition, a separate set of analyses were performed for both 50-SNP and 5-kb windows, considering only SNPs with a minimum MAF of 0.05.Signatures of selection were evaluated using two different approaches. The haplotype homozygosity based iHS score was calculated using the WHAMM package . As calcp-values were estimated using a bootstrap analysis.The calculation of PBS was carried out following the methods proposed by Yi and colleagues . For calWe retrieved the deCODE recombination map and the HapMap related recombination maps (hapMapRelease24YRIRecombMap and hapMapRelease24CombinedRecombMap) using the UCSC table browser . The disp-value was used to estimate the statistical significance of the over and underrepresentation for recombination hotspots and coldspots in the CPS SNP-enriched 50-SNP windows and the CPS SNP enriched 5 kb-windows in YRI. Similar analyses were conducted for all other populations, individually as well as combined together, using the HapMap combined recombination map (hapMapRelease24CombinedRecombMap).The HapMap YRI recombination map (hapMapRelease24YRIRecombMap) was used to identify recombination hotspots and coldspots in YRI and the combined dataset. The distribution of recombination rates was studied to select genomic regions showing the top 1% recombination rate scores and these regions were designated as recombination hotspots. We also used the top 5% recombination rate scores to select a second set of hotspots. Similarly, the two sets of coldspots likewise were defined by the lowest 1% and 5% recombination rates. Based on the genomic distribution of recombination rates in YRI (hapMapRelease24YRIRecombMap) we estimated the number of hotspot sites expected to occur in CPS SNP-enriched windows for YRI. The expected value was compared to the observed value and a cumulative hypergeometric , LRT [The genomic contexts of all CPS SNPs were determined using ANNOVAR , which w, LRT  and Muta, LRT . Non-synIn order to identify non-coding CPS SNPs that may have an effect on the binding of regulatory factors, intronic variants and those flanking genes were searched against the RegulomeDB database , which eFor each population, two gene lists were generated from the CPS SNP set. The first contained only genes that included selected variants, identified by rs IDs . The secp-value shows the significance and adjusted p-values are also provided, after correction for multiple testing. The gene lists for each population that contained CPS SNPs were run through DAVID to identify overrepresented pathways and other functional labels.The Database for Annotation, Visualisation and Integrated Discovery (DAVID)  is an onPotential functions of CPS-SNP containing genes and their role in various diseases were inferred from the GeneCards database .List of CPS SNPs for each population according to the 9 population model.(XLSX 7 MB)Additional file 1:50-SNP windows detected to be enriched with CPS SNPs.(XLSX 80 KB)Additional file 2:5-kb windows detected to be enriched with CPS SNPs.(XLSX 72 KB)Additional file 3:Potentially regulatory CPS SNPs.(XLSX 33 KB)Additional file 4:IPA pathways enriched in CPS SNP associated genes.(XLSX 12 KB)Additional file 5:Regulatory networks enriched in CPS SNP associated genes.(XLSX 11 KB)Additional file 6:Functional classes enriched with CPS SNP associated genes identified using DAVID.(XLSX 393 KB)Additional file 7:


Fusarium oxysporum is the most important disease in cape gooseberry  in Colombia. The development of resistant cultivars is considered one of the most cost-effective means to reduce the impact of this disease. In order to do so, it is necessary to provide breeders with molecular markers and promising germplasm for introgression of different resistance loci as part of breeding schemes. Here we described an association mapping study in cape gooseberry with the goal to: (i) select promising materials for use in plant breeding and (ii) identify SNPs associated with the cape gooseberry resistance response to the F. oxysporum pathogen under greenhouse conditions, as potential markers for cape gooseberry breeding.Vascular wilt caused by F. oxysporum. The identification of markers was based on common tags using the reference genomes of tomato and potato as well as the root/stem transcriptome of cape gooseberry. By comparing their location with the tomato genome, 16 SNPs were found in genes involved in defense/resistance response to pathogens, likewise when compared with the genome of potato, 12 markers were related.We found a total of 21 accessions with different resistance responses within a diversity panel of 100 cape gooseberry accessions. A total of 60,663 SNPs were also identified within the same panel by means of GBS (Genotyping By Sequencing). Model-based population structure and neighbor-joining analyses showed three populations comprising the cape gooseberry panel. After correction for population structure and kinship, we identified SNPs markers associated with the resistance response against P. peruviana – F. oxysporum pathosystem as a foundation for further validation in marker-assisted selection. The results have important implications for conservation and breeding strategies in cape gooseberry.The work presented herein provides the first association mapping study in cape gooseberry showing both the identification of promising accessions with resistance response phenotypes and the identification of a set of SNP markers mapped to defense/resistance response genes of reference genomes. Thus, the work also provides new knowledge on candidate genes involved in the The online version of this article (doi:10.1186/s12864-016-2568-7) contains supplementary material, which is available to authorized users. Physalis peruviana L.) is a species within the Solanaceae family widely used for medicinal and commercial purposes. It is native in the Andean region, primarily Colombia, Peru and Ecuador [Fusarium oxysporum, which is an important phytosanitary problem that is still unmanageable [The cape gooseberry , carnation (Dianthus caryophyllus L.), tomato (Solanum lycopersicum L.) and potato (Solanum tuberosum L.) causing yield losses between 20 and 70 % [The fungus and 70 % , 6. The Fusarium infection. This alternative will reduce the dependency on chemical protection, resulting in safer, more affordable and less environmentally detrimental cultivation of these crops [F. oxysporum have been reported in various species including lettuce [Several disease control methods have been attempted to reduce the incidence of vascular wilt. Crop rotation does not offer an effective solution because of the presence of chlamydospores in the soil. Another option is soil fumigation; however, it is not a long-term solution because recolonization frequently occurs. Finally, soil treatments with compost or enriched compost using selected microorganisms represent a possible management alternative , 9. Howese crops . Success lettuce , cucumbe lettuce , tomato  lettuce , among oI-1, I-2 and I-3 in chromosomes 11 and 7 from tomato which confer resistance to F. oxysporum f. sp. lycopersici [The development of resistant cultivars can-be-accelerated by using markers associated with resistance/defense Quantitative Trait Loci (QTLs). The continuing advances in QTL identification including Association Mapping (AM) studies are accelerating the identification of genes related with disease resistance, like the loci opersici , 14. Moropersici .F. oxysporum f. sp niveum race 1 on chromosome 1 of the watermelon genetic map was identified. In addition, a minor QTLs were identified on chromosomes 1, 3, 4, 9 and 10.Single Nucleotide Polymorphisms (SNPs) are usually the markers of choice for QTL identification as well as for studies of genetic diversity and population structure required for association studies . GenotypF. oxysporum in greenhouse conditions; 2) identify marker-trait associations for the resistance response to F. oxysporum based on GBS as a foundation for future GWAS/GS studies in cape gooseberry. The SNPs associated with resistance/defense regions will increase the knowledge of mechanisms underlying disease resistance, providing tools for Marker-Assisted Selection (MAS) or other new molecular selection methods to accumulate desirable genes in breeding programs.The present study aims to: 1) assess the resistance phenotype of a diversity panel of 100 cape gooseberry accessions to F. oxysporum under greenhouse conditions based on an initial screening conducted by Enciso-Rodríguez et al. [The cape gooseberry association mapping population used in this study comprised a diversity panel of 100 accessions from the germplasm collection managed by the Colombian Corporation for Agricultural Research (CORPOICA)  isolated from P. peruviana plants in field [2 of agar with mycelium growth were cut and was grown in liquid Potato Dextrose Broth (PDB) for 8 days at 27 °C in constant shaking and then adjusted to a final concentration of 1×106 conidia/ml according to Namiki et al. [The highly virulent monosporic strain of in field  and presi et al. . A volumi et al. , to inocP. peruviana accessions were clonally multiplied in vitro from node cuttings. Once a pair of true leaves appeared and plantlets were 5 to 7 cm tall (3 months old), eight plants per accession were transplanted individually into plastic pots with 600 g of inoculated substrate and two plants were mock-inoculated as negative control. The screening for resistance to the pathogen was carried out in a greenhouse at 26 + 2 °C, with light/dark photoperiods of 12/12 h, with a relative humidity of 70–80 % in the Corpoica’s facilities in Mosquera, Colombia. The symptoms were scored 3 times a week over 47 days using a nine-grade severity scale, with 0 denoting high tolerance and nine high susceptibility. This scale was described by Enciso-Rodríguez et al. [P. peruviana and related taxa inoculated with the Map5 F. oxysporum pathogenic strain. The percentage of incidence was calculated as the number of new cases of disease during specified period divided by size of population at start of period. The Area Under the Disease Progress Curve (AUDPC) based on the severity scale was calculated using the formula proposed by Shanner and Finner [The plantlets of z et al. , using 1d Finner . AccessiThe phenotypic data were statistically analyzed through Shapiro-Wilks normality test using the software package SAS v9.1.3   and normHindIII size/mass (Invitrogen) and the quality was inspected using restriction enzyme digestions with HindIII enzyme, and visualized by electrophoresis using 2 % agarose gels. The GBS libraries were constructed at Cornell Genomic Diversity Facility (USA), library duplicates were used as technical replicates in 95-plex using the restriction enzyme ApekI (GCWGC) and barcoded adapters were ligated to individual samples. Genotyping was performed following the GBS protocol by Elshire et al. [Total DNA was isolated from young leaves collected from each accession using DNeasy Plant Mini Kit  according to manufacturer’s instructions. The final elution volume was adjusted to 70 μl with TE solution buffer. Total DNA was quantified using λ e et al.  and multr2 ≥ 0.1.FASTQ files obtained from sequencing were processed using the GBS pipeline implemented on TASSEL standalone v4.3.5 , 28. TheHo), expected heterozygosity (He) and Polymorphism Information Content (PIC).Population estimates of genetic diversity were analyzed using the SNPs of cape gooseberry transcriptome and then separately for common tags. The program POWERMARKER v3.25  was usedgdsfmt and SNPRelate [Distance matrix based on Identity By State (IBS) similarity, defined as the probability that alleles drawn at random from two individuals at the same locus are the same, was calculated from HapMap files using TASSEL v4.3.5 and the resulting matrix was clustered by the Neighbor-Joining algorithm, and visualized by FIGTREE v1.4.0 . The SNPNPRelate  packagesNPRelate .In order to estimate the number of sub-populations in the sampled plant accessions, a bayesian model clustering analysis was carried out on the best set of SNPs for optimizing the run, applying the admixture model for the ancestry of individuals using the software STRUCTURE v2.3.4  with the2  across each one of the reference genomes using the software TASSEL v4.3.5 with a sliding windows of 50 markers for exploring variation patterns of LD.The linkage disequilibrium (LD) between two SNPs was measured and visualized using ry = Xa + Qb + Zu + e; where y is vector for phenotypes; a is the vector of marker fixed effects, b is a vector of fixed effects, u is the vector of random effects (the kinship matrix), and e is the vector of residuals. X denotes the genotypes at the marker; Q is the Q-matrix and Z is an identity matrix [p-values and plotting them against the expected values, under the null hypothesis of no association.The analysis was conducted using the Genome Association and Prediction Integrated Tool GAPIT , an R pay matrix . The sofy matrix , and they matrix , was usehttps://solgenomics.net/jbrowse/current/). For tomato (Solanum lycopersicum), the genomic annotation v2.3 realized by ITAG from SL2.40 genome construction was used. Additionally, for the potato genome (Solanum tuberosum group Phureja) the genomic annotation v3.4 realized by PGSC from DM3.40 genome construction was used.The HapMap archives were used to infer the potential molecular function and the possible underlying biological process of the associated markers, using the Sol Genomics Network (SGN) 1), the c anthers .Fig. 1WaF. oxysporum that made the population suitable for association mapping. The first and the second group presented the highest values for resistance response with mean disease severity scales of 3.138 (ranged from 1.571 to 5) and of 5.725 (ranged from 3.571 to 7.200), respectively  for severity with a negative tendency (−0.753) and 0.974  for AUDPC with a positive tendency (0.556), leading to rejection of the null hypothesis of normal distribution of the data. According to Mauricio [p-values of associated markers  because it generates large number of fragments necessary to cover a maximum number of recombination events [Glycine max L.) validated ApeKI restriction enzyme as appropriate for plants because constructed libraries were rich in gene regions [GBS can be used for de novo discovery of SNPs, making it particularly powerful in germplasm collections and uncharacterized species . GBS wasn events . Similar regions .A total of 453,005,454 good quality reads were obtained from 505,347,672 raw reads that were generated from two Illumina HiSeq lanes. The TASSEL-GBS pipeline  clustereA set of 60,663 (19 % missing data) SNPs was identified using cape gooseberry root/stem transcriptome as reference using the default parameter filter implemented in the TASSEL-GBS pipeline. Other pipelines to discover SNPs in species without a reference genome have found 88,217 SNPs in switchgrass  and 45,1The percentage of missing data from GBS may be a serious problem, especially for association analysis. Better coverage can be achieved by two approaches: 1) repeated sequencing runs of the samples, although this solution increases costs, and quickly reaches a point where there is little reduction in missing data; and 2) imputation procedures based on identifying the most similar haplotype that can be used to supply some of those missing data . The secThe genetic analysis shows that the cape gooseberry population used in the present study have a high level of genetic diversity with a mean value of He = 0.655, Ho = 0.431 and PIC = 0.344 using as reference the two Solanaceae genomes or the cape gooseberry root/stem transcriptome Table . Our resThe NJ and PCA analysis were carried out using SNPs obtained after alignment against the two reference genomes and the cape gooseberry transcriptome for confirmation of groups or sub-populations with the two analyses ) approach fluctuated continuously and never reached a plateau (data not shown). In contrast, the ΔK analysis provided by the Evanno method [ons Fig. . These w2) from pairs of all SNPs markers without a LD filter using the cape gooseberry transcriptome as a reference. About 4 % of total comparisons were significantly in LD with P ≤ 0.01 with an average r2 of 0.040 and a maximum value of 0.69. Similar results were found when we estimated r2 using tomato and potato as reference genomes  ≥ 3, with phenotypic data from the AUDPCs and severity scales as well as high quality SNP data obtained when using the two reference genomes we did not identify any significant association after the FDR correction. This could be the result of the reduced sample size of the association mapping population along with its large heterozygosity, as it has been reported in a study conducted by Kahn et al. [Malus genus. Besides, It is well known that a rapid LD decay occurs in cross-pollinated species needing large sample and marker density for association studies [The association analysis was conducted by the MLM approach with Q and K matrices fitted in the model to control spurious associations due to population structure and relatedness, respectively . Using an et al.  in Malus studies . As ment studies , large p studies  and unre studies , demonstF. oxysporum is not influenced by large effect QTLs or oligogenic trait variables that can be detected when using small sample sizes, which has been demonstrated for some traits in other species [Besides sample size and heterozygosity, it is also possible that the resistance response phenotype of cape gooseberry against  species . Nonethe species .p-values (p ≤ 0.005) before the FDR correction. In the Q-Q plots, observed p-values for each SNP are plotted against the values expected under the null hypothesis of no SNP associated with the trait, thus, deviations from the diagonal line suggest the SNPs markers contains values arising by a true association. Besides, the early separation of the expected p-values from the observed, it is due to population stratification [p-values ≤ 0.005 p-values fication . In orde p ≤ 0.00 before tSolyc08g081990.2 that is related to WD-40 repeats which are involved in protein–protein interactions and with functional roles in signal transduction, regulation of transcription to cell cycle control and hypersensitive response in the defense of plants against pathogen attack [Solyc12g049500.1 that possesses the legume lectin beta domain, probably involved in the protection against pathogens by producing lipoxygenase (LOX) that catalyzes dioxygenation reactions of polyunsaturated fatty acids (PUFAs) and the secondary conversion of hydroperoxy lipids according to Roopashree et al. [Solyc05g051900.2 gene located on chromosome 5 that is related with proteins of the Major Facilitator Superfamily (MFS). This superfamily of proteins represents the largest known group of active secondary carriers transporting a diverse range of small solutes across membranes and obtaining the energy by chemiosmotic gradients. According to Peng et al. [Solyc02g084920.2 gene that is related with a proteasome subunit beta type. Suty et al. [For the severity trait, one of the SNPs was mapped to the tomato gene n attack . The sece et al. . LOX proe et al. , 72. Theg et al. , the tray et al.  suggest y et al. , shows tSolyc05g054260.2 gene which has a kinesin motor activity, responsible for transport into the cell [Solyc07g006030.2 that is related with the protein TIF31 responsible for protein-protein interactions [F. oxysporum f. sp lycopersici are located on chromosomes 7 and 11 of the tomato genome. Another SNP marker mapped to the Solyc09g091070.1 gene on chromosome 9, that has a function in malate dehydrogenase processes involved in plant photosynthesis and C3 and C4 in the Calvin cycle [Solyc02g021620.2 gene located in the chromosome 2 which is related to the transporter superfamily of Na/K/Cl that facilitates the transport of sodium, potassium and chloride ions from the extracellular space into the interior cell [The remaining five SNPs were not specifically related to pathogen defense/resistance genes that may suggest new roles in defense for homologous gene regions in cape gooseberry or no roles in defense/resistance at all. One mapped on chromosome 5 at the the cell . Two mapractions . Howeverractions , reportein cycle ; finallyior cell .p-value ≤ 0.005  in fungi and metazoans, which receive the signal and are translated through heterotrimeric G proteins. According to Liu et al. [F. oxysporum, Botrytis cinerea and Alternaria brassicicola [For the AUDPC variable which is related with the time to the onset of disease symptoms, six SNPs were associated with a 01 Table  and mappu et al. , the presicicola . A recensicicola  shows thSolyc09g098450.2 of chromosome 9 related to lipase class 3 family protein involved in plant stress responses. The alpha/beta hydrolase family contains domains of lipase class 3 and has three proteins  forming a systemic signal that functions as the main barrier against pathogens [Solyc11g069690.1 that relates to proteins called thioredoxins, which act as antioxidants, facilitating the reduction of other proteins through thiol-disulfide exchange cysteine; also maintaining the redox homeostasis. According to Vieira Dos Santos & King [NtTRXh3) is overexpressed, reduces multiplication and pathogenicity in plants. Also the overexpression of the protein enhanced the resistance to oxidative stress [The third SNP was located at the gene athogens –91. The s & King , thioreds & King , determie stress .Solyc09g090940.2 gene on chromosome 9 whose function is to encode the nuclear transport factor 2 (NTF2). The last marker was located on chromosome 11 at the gene Solyc11g069690.1 with unknown function. Using the potato genome as reference, six SNPs were found associated with p-value ≤ 0.006 based on the AUDPC variable (Table The remaining two SNPs were related specifically to defense/resistance genes in tomato. The first of them is located at the Further research is focusing on verifying the differential expression of these candidate genes by quantitative PCR in cape gooseberry. Subsequently, the genes that correlate with the resistance response by qPCR analysis will be used to create functional variants using genetic transformation or gene silencing to validate if these genes confer resistance to vascular wilt disease in cape gooseberry.F. oxysporum. We also identified several SNPs associated with two resistance response phenotype variables that mapped to genes directly or indirectly related to pathogen resistance/defense responses involved in protein–protein interactions, signaling pathways, oxidative stress tolerance and hypersensitive response to pathogen attack. Additionally, some SNPs were found on chromosomes 7 and 11 of tomato, where QTLs associated with disease resistance have been reported previously, thus, these QTLs need validation of the homologous regions in the cape gooseberry genome. The work provides new knowledge on candidate genes involved in the P. peruviana – F. oxysporum pathosystem as a foundation for further validation in marker-assisted selection for breeding.The present work represents the first association mapping study in cape gooseberry. We found high heterozygosity and population structure in the diversity panel used for association and identified promising accessions to use in breeding for resistance against All the supporting data are included as additional files (Additional file


A total of 41,754 high-quality SNPs were obtained through re-sequencing of the IB population. Phenotyping was conducted under field conditions in two years and three locations. Association analysis identified six QTLs for CT on the chromosomes 3, 4 and 12. QTL qCT-3-2 that showed stable CT across years and locations was fine-mapped to an approximately 192.9 kb region. Our results suggested that GWAS applied to an IB population allows better integration of gene discovery and breeding. QTLs can be mapped in high resolution and quickly utilized in breeding.Cold stress is one of the major abiotic stresses that impede rice production. A interconnected breeding (IB) population consisted of 497 advanced lines developed using HHZ as the recurrent parent and eight diverse elite  Oryza sativa L.) is one of the most important food crops and provides staple foods for about half of the world population. Rice is more sensitive to cold stress than other cereal crops such as wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) because of its origin in tropical and subtropical regions [Cold stress is one of the major adverse environmental factors that limit the growth, productivity, and geographical distribution of crops . Rice , doubled haploid lines (DH), F mapping –10. Link mapping . More th mapping . Fifty-n mapping . Out of ng [qCT8 , qCT7 [1T8 [qCT7  and qLTB7 [qLTB3  were finB3 [Ctb1  was clonB3 [Ctb1 .To overcome the limitation of single bi-parental population in allelic diversity covered, mapping power and resolution the use of multiple crosses (MC) has recently become popular in QTL mapping. QTL analyses across populations revealed a substantially higher number of QTLs compared to the analyses of single bi-parental populations –17. The indica variety, Huanghuazhan (HHZ), was evaluated for CT at the booting stage to identify stably-expressed QTLs under natural low air temperature condition. A stably-expressed QTL was fine-mapped to a narrow genomic region using NILs identified from the IB population and high density SNP markers generated by re-sequencing. The QTLs identified and the elite lines with CT provided essential information and materials for developing CT rice cultivars by marker-assisted selection (MAS).The development of MC population is time consuming and labor-intensive. It is important to use diverse parental lines of good performance in many traits to allow effective integration of gene discovery and practical breeding. In recent years, our team has adopted a large-scale backcross (BC) breeding strategy to improve multiple abiotic stress tolerance, many interconnected (i.e common recurrent parent) populations were developed and highly selected for one or more agronomic traits –34. Manyindica variety in South China with high yielding and quality (http://www.ricedata.cn/variety/), was used as the recurrent parent to cross with eight high yielding donor parents including IR64, PSBRC28, PSBRC66, and IR50 from Philippines, Phalguna from India, OM1723 from Vietnam, and CDR22 and Teqing from China. Eight F1s were then backcrossed once with HHZ, later the BC1F1 selfing seeds were bulk-harvested to produce eight BC1F2 bulk populations (2000 plants per BC1F2). Two rounds of screenings for yield and tolerances to various abiotic stresses such as drought, salinity, and submergence were performed at the International Rice Research Institute (IRRI). In all such screenings, surviving plants or plants with higher yield were selected  with outstanding performance for at least one target trait were finally selected and used as the IB population for phenotypic evaluation of CT and re-sequencing.In 2006, Huanghuazhan (HHZ), a mega selected , which sEvaluation of CT at the booting stage was performed at three experimental stations of the Yunnan Academy of Agricultural Sciences over two consecutive years, i.e. in Yuxi  and Xundian  in 2013, and in Xundian and Songming  in 2014 under natural low air- temperature condition in Yunnan province, China. The three locations have low temperature air of 15.29–21.42°C during the growth periods starting from booting to milky stages, and are ideal for evaluation of CT for rice . Based ojaponica Nipponbare reference genome (MSU Release 7.0) using soap version 2.22. The consensus sequences for each sample were generated using SOAPsnp (version1.01) from uniquely aligned reads. These consensus sequences across the whole genome were used to obtain the single-base pair genotypes using SAMtools (version 0.1.18).Genomic DNA was extracted from the leaf tissues of a single plant from each line using the DNeasy Plant Mini Kit (Qiagen). The library construction and sequencing were performed as described by Huang et al . The 73-Q score <20) were removed, and the successfully called sites with conflicting genotypes among different reads were further excluded. Moreover, the overall depth was required to be <15 in each site to avoid mapping to regions with copy-number variation. Next, single-base pair genotypes of the 497 lines were integrated to screen for single nucleotide polymorphism (SNP) markers across the genome. Discrepancies with the reference genome were called as candidate SNPs. Unreliable sites were then filtered according to the following criteria: 1) candidate SNP loci must be more than 10 bp away from each other; and 2) all the singleton SNPs were excluded. Sites passing these criteria were retained and referred as common SNPs (MAF>0.05). Finally, 41,754 high-quality SNPs were selected out from the common SNPs for QTL mapping.The low-quality bases  was evaluated by computing the rSSEL 5.2 . The win−4 [Association analyses were conducted using the weighted Mixed Linear Model (Weighted MLM) implemented in TASSEL 5.2. The kinship matrix and principal components described previously  were use−4 –42. LD bFine mapping was carried out using a physical mapping method as described by Zhu et al. . Near-isA total of 497 ILs and their nine parents were genotyped. These parental lines were sequenced on average depth of 25X, while the ILs were sequenced in average of 3.16X. SNP calling was conducted based on discrepancies between the consensus sequence and the reference genome. After exclusion of singleton SNPs, a total of 2,256,146 non-redundant SNPs were identified, resulting in an average of 5.4 SNPs per kb, with 86.5% of the SNPs located within 0.2 kb of the nearest SNP. Finally, 41,754 high-quality SNPs with missing rate lower than 10% and minimal MAF higher than 5% were screened out to carry out GWAS.The inheritance of parental segments across the genomes of the 497 lines was characterized through genotyping 41,754 informative SNP markers. Marker saturation was high with an average genetic distance of 9.3 kb. We didn't obtain the LD among parents due to the limited number of samples. The genetic similarities between the nine parents were evaluated. The SNP data revealed that an intermediate genetic similarity between HHZ and donors, ranging from 0.42 to 0.65 . ParentsDiversity was also phenotypically visible . SF rang2 value, LD value was approximately 400 kb  evaluation and to execute GWAS by removing the lines that were seven days earlier or later than the population mean flowering date. According to ry 400 kb . Fig 4 gy 400 kb . The peay 400 kb , Fig 5. y 400 kb .qCT-3-1, qCT-3-2 and qCT-3-3, were indentified on the short arm of chromosome 3  by qCT-3-2 varied from 4.2% to 9.5% , having the PVE of 6.7% and 5.1%, respectively. On the short arm of chromosome 12, two QTLs were identified in XD2014. The qCT-12-1 was associated with SNP Chr122995458 with a PVE of 8.5% and qCT-12-2 was associated with SNP Chr125804934 with a PVE of 4.2%.Three QTLs, namely mosome 3 . The qCTronments . In 2013 to 9.5% . Thus, qectively . The qCTqCT-3-2. Then, we screened again the raw sequencing data and obtained 0.4 M SNPs with MAF>0.03 and miss rate <10%. Using these 0.4 M SNPs, we analyzed these 13 selected ILs. The results showed that the RGP varied from 66.8% to 99.2%. Five ILs had RPG higher than 95.0% and were retained as NILs  harbored gene Os03g0103300, which had been cloned and showed positive effect on rice seed germination under low temperature environment [qCT-3-2 identified in all the four environments in the present study partially overlapped with qPSST-3 for CT at booting stage under cold-water treatment identified by Suh et al. [qCT-3-3 located in the short arm of chromosome 3 is closely adjacent to the CT QTL identified at seedling stage by Zhang et al. [qCT-4 was in the region adjacent to the qCT4-1, a QTL contributing to CT at the booting stage [qCTS-4, a QTL contributing to CT at the seedling stage [qCT-12-1 was in the region flanked by markers RM3747 and RM6292, where qLTG-12 had been reported for CT at germination and seedling stages [qCT-12-2 on chromosome 12 was mapped to the region containing qCTS-12 identified previously for CT at seedling stage [qCT-3-2, the favorable allele for CT was from the recurrent parent HHZ that has moderate CT. This result provided further support to the clam that even though modern varieties do not show strong stress tolerances, they do harbor some excellent genes for drought tolerance [In this study, six independent QTLs for CT were identified on chromosomes 3, 4 and 12. Except for 600.9 kb . Compariironment . qCT-3-2g et al. . The qCTng stage , 48, andng stage . The qCTng stage –53. CT Qolerance , 54, salqCT-3-1~6 kb harboolerance , and cololerance , 57. Sucolerance , 58. It qCT-3-2 was repeatedly identified under four natural low temperature environments, suggesting it was a stable QTL for CT at the booting stage. The qCT-3-2 was fine mapped to a credible interval of 192.9 kb using high-density SNPs and NILs Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Figr2 was 0.50.Genome-wide LD decay rates were estimated at ~400 kb, where the (TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.