The genetic variants in lincRNA transcription factor binding sites (TFBSs) can change lincRNA expression, thereby affecting the susceptibility to human diseases. To identify and annotate these functional candidates, we have developed a database SNP@lincTFBS, which is devoted to the exploration and annotation of single nucleotide polymorphisms (SNPs) in potential TFBSs of human lincRNAs. We identified 6,665 SNPs in 6,614 conserved TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. We also performed comprehensive annotation for these SNPs using 1000 Genomes Project datasets across 11 populations. Moreover, one of the distinctive features of SNP@lincTFBS is the collection of disease-associated SNPs in the lincRNA TFBSs and SNPs in the TFBSs of disease-associated lincRNAs. The web interface enables both flexible data searches and downloads. Quick search can be query of lincRNA name, SNP identifier, or transcription factor name. SNP@lincTFBS provides significant advances in identification of disease-associated lincRNA variants and improved convenience to interpret the discrepant expression of lincRNAs. The SNP@lincTFBS database is available at  Large intergenic non-coding RNAs (lincRNAs) are recently emerging as a novel class of functional non-coding RNAs, which are more than 200 nucleotides in length, derive from the intervals between protein-coding genes, have similar exon-intro-exon structure, but lack of protein-coding capacity PCGEM1 was higher in prostate tumor specimens than in matched normal tissues HOTAIR (HOX antisense intergenic RNA) can be regard as an independent cancer prognostic marker due to its significantly overexpression in breast cancer, hepatocellular cancer, colorectal cancer and laryngeal squamous cell carcinoma MALAT1  is originally identified as a marker for lung cancer metastasis; its expression is strongly regulated in many tumor entities including lung adenocarcinoma and hepatocellular carcinoma HULC is highly associated with the incidence of hepatitis B virus (HBV) infection Recently, several profiling studies have revealed that dysregulated expression of lincRNAs was involved in several forms of human cancer Previous studies have shown that single nucleotide polymorphisms (SNPs) in transcription factor binding sites (TFBSs) of protein-coding genes could affect gene expression by altering transcription factor binding, and participated in human diseases Furthermore, with the advent of high-throughput technologies, large-scale lincRNA annotation data, SNP data, predicted and experimentally supported TFBSs data have been generated. This provides a great opportunity to systematically identify SNPs in the human lincRNA TFBSs. For example, in the new update of NONCODE database, the lincRNA data set were expanded by collection of newly identified lincRNAs from published literatures and integration of the latest version of RefSeq and Ensembl Therefore, to provide a beneficial annotation of these potential functional variants in human TFBSs, we developed a SNP@lincTFBS database for integrating and annotating functional SNPs in predicted lincRNA TFBSs. We identified 6,665 SNPs occurring in 6,614 TFBSs of 2,423 human lincRNAs, and provided a comprehensive and useful resource of candidate SNPs relevant to the aberrant expression of lincRNAs. The SNP@lincTFBS database will be helpful to identify functional SNPs of lincRNAs in the level of transcription and contribute to profound complex disease study.We obtained 6,631 human lincRNAs with genomic coordinates from the lincRNA list of GENCODE project (version 16) We downloaded the locations and scores of conserved TFBSs from the UCSC genome browser We downloaded 690 ChIP-Seq datasets for 169 human transcription factors in different cell lines and tissues from ENCODE project We downloaded SNPs (common and rare variants) in public dbSNP database (build ver. 137) and identified 6,665 SNPs within 6,614 putative TFBSs of 2,423 human lincRNAs. In addition, with ChIPSeq dataset, we identified 139,576 SNPs in 304,517 transcription factor peaks of 4,813 lincRNAs. Then, we downloaded the annotation information of minor allele frequencies and others from 1000 Genomes Project (release of July 2012) datasets across 11 populations We manually collected known disease-associated SNPs in lincRNAs TFBSs using PubMed to search the previous studies. We also annotated lincRNAs in SNP@lincTFBS that have been reported to be associated with diseases, and identified SNPs within their putative TFBSs. In addition, we integrated recently well-known disease-associated SNPs and disease lincRNAs into SNP@lincTFBS database.SNP@lincTFBS is an online query tool developed utilizing ECLIPSE platform as the frontend, and MySQL as the backend database. The web engine was implemented using JSP technology, Struts framework and the Java connection pool Proxool, and web server was built using Apache Tomcat.We developed a novel integrated database named SNP@lincTFBS that allows users to perform SNP and TFBS searches in human lincRNAs. In this database, we: 1) obtained human lincRNAs, 2) identified conversed TFBSs and transcription factor peaks in defined promoter regions of human lincRNAs, 3) identified SNPs in the TFBS of lincRNA and collected experimentally supported disease-associated SNPs in lincRNA TFBSs, 4) integrated annotation information of SNP, TFBS and lincRNA. The architecture of identifying SNPs in lincRNA TFBSs is shown in Currently, SNP@lincTFBS contains 8,290 entries of annotated SNP-TFBS-lincRNA associations, including 3,839 lincRNAs, 33,181 conserved TFBSs, 6,665 SNPs and 165 transcription factors. In addition, 19,878,236 entries of SNP-peak-lincRNA associations were stored in SNP@lincTFBS, including 4,831 lincRNAs, 323,256 transcription factor peaks, 139,576 SNPs and 169 transcription factors. We identified a large number of conserved TFBSs in the promoter regions of human lincRNAs and found that the distribution of SNPs in these lincRNA TFBSs was extensive . Previouhttp://bioinfo.hrbmu.edu.cn/SNP_lincTFBS). HOME page provides a brief description of the SNP@lincTFBS database, users can browse the high-resolution flowchart of this work to get the main idea of this database. SEARCH page provides a quick search by query of three kinds of entries: 1) a lincRNA name (Ensembl ID), 2) an SNP identifier (rs number from dbSNP), and 3) a transcription factor name. Statistic of dataset contained in the database is introduced. Search result shows lincRNA summary information and all identified TFBSs and TF peaks in promoter region of this lincRNA. SNPs in these TFBSs and TF peaks are listed below  susceptibility can be traced to more than a decade, and previous studies have found that NAG7 played a key role by means of both expression and interaction, it could inhibit proliferation and induce apoptosis in NPC cell but also stimulate NPC cell invasion NAG7 gene was provided as a long intergenic non-protein coding RNA 312 (LINC00312) in HGNC (HUGO Gene Nomenclature Committee) LINC00312 expression with NPC progression based on microarray technology has indicated that LINC00312 was significantly down-regulated in NPC tissues and it could represent a potential biomarker for metastasis, progression and prognosis in NPC LINC00312 gene (Ensembl ID: ENSG00000237697), and rs112175570 might be a potential risk SNP for nasopharyngeal carcinoma by regulating the expression of LINC00312.We also found several lincRNAs in SNP@lincTFBS that have been reported to be associated with human diseases, and these lincRNAs had SNPs within their putative TFBSs. For example, we found human lincRNAs NAG7, MEG3, PCAT1, CASC2 and LINC00032, which were involved in nasopharyngeal carcinoma −5) in a lincRNA TFBS (ENSG00000253583) Besides cancer, we also found several neurological or psychiatric disorder associated SNP in human lincRNA TFBSs. For example, we found three SNPs  in the TFBSs of a lincRNA, ENSG00000214548 , ENSG00000214548 has been demonstrated to be associated with multiple human diseases, including glioma and neuroblastoma Accumulating studies of dysregulated lincRNA expression in diverse cancers have suggested that lincRNAs might act as potential tumor suppressor genes and novel prospective therapeutic targets in cancer treatments. SNP@lincTFBS is designed to serve as a practical resource of SNPs in the TFBSs that dysregulate the expression of human lincRNAs. The database provides available genomic informations and annotations of SNPs in the TFBSs in putative promoter regions of human lincRNAs, and also a web-based interface allowed easy access to query and download flexibly. Most human lincRNAs have TFBSs in their promoter regions and the distribution of SNPs in these TFBSs of lincRNAs is widespread.Previous studies have demonstrated that the genetic variants in the TFBSs of human lincRNA regulatory regions may change lincRNA expression, and thereby affecting the susceptibility to human diseases Our database has the potential to become an available resource for further studies of lincRNA function and complex disease. For example, we found several disease-associated SNPs and lincRNAs in SNP@lincTFBS, suggested the potential application of the SNP@lincTFBS in the field of disease-associated lincRNA variants. We found multiple SNPs in the TFBSs of cancer-associated lincRNAs, further experimental verification of these disease candidates might yield novel insights into disease pathophysiology. In addition, we also found multiple SNPs in the TFBSs of neurological or psychiatric disorder associated lincRNAs, this finding was consistent with previous studies, which revealed that lincRNAs played important roles in brain In the future, we envisage the database to be available as a semantically linked interoperable data resource. We hope that SNP@lincTFBS will be a useful tool for researchers in pertinent fields, and will benefit the functional study of human lincRNAs. With the increasing availability of genome-wide transcriptome identification and functional annotation of human lincRNAs in the public domain, we would enrich the database with this information. We will update the disease-associated lincRNAs with their annotations and disease-associated SNPs mapped to the TFBSs of lincRNAs every 4 months. SNP@lincTFBS may act as an advance resource that can provide great convenience for the research on identification of disease-associated lincRNAs or risk SNPs and the discovery of responsibility for discrepant expression abundance of lincRNAs.


Mastitis is a major disease of dairy cattle occurring in response to environmental exposure to infective agents with a great economic impact on dairy industry. Somatic cell count (SCC) and its log transformation in somatic cell score (SCS) are traits that have been used as indirect measures of resistance to mastitis for decades in selective breeding. A selective DNA pooling (SDP) approach was applied to identify Quantitative Trait Loci (QTL) for SCS in Valdostana Red Pied cattle using the Illumina Bovine HD BeadChip.A total of 171 SNPs reached the genome-wide significance for association with SCS. Fifty-two SNPs were annotated within genes, some of those involved in the immune response to mastitis. On BTAs 1, 2, 3, 4, 9, 13, 15, 17, 21 and 22 the largest number of markers in association to the trait was found. These regions identified novel genomic regions related to mastitis (1-Mb SNP windows) and confirmed those already mapped. The largest number of significant SNPs exceeding the threshold for genome-wide significant signal was found on BTA 15, located at 50.43-51.63 Mb.The genomic regions identified in this study contribute to a better understanding of the genetic control of the mastitis immune response in cattle and may allow the inclusion of more detailed QTL information in selection programs.The online version of this article (doi:10.1186/s12863-014-0106-7) contains supplementary material, which is available to authorized users. Mastitis is one of the most frequent inflammatory disease with a significant economic implication for the dairy herds and the resistance to this pathology may be improved by breeding.The development of mastitis is the result of the interaction among three components: the individual genotype, the pathogens  and the environment  .The resistance to an infection disease or the absence of susceptibility may be defined as the immune response ability (immuno-competence capability) of an animal, to avoid the pathogens replication after the establishment of an infection. This implies that animals tend to vary in their genetic potential for immuno-competence . The genThe discovery of millions of SNP markers in animal genomes forming dense marker panels, and the concomitant decrease in genotyping costs have allowed the performing of genome-wide association studies (GWAS) . The avaThe high costs of screening large populations for marker allele frequencies can be decreased using the SDP approach, genotyping pooled DNA samples from selected individuals at each of the two phenotypic extremes of the trait distribution . Equal aThe aim of this study was to identify QTL associated with SCS as an indicator of mastitis. We performed a GWA study for SCS in the Valdostana Red Pied cattle, with a selective DNA pooling analysis, using the Illumina BovineHD Bead chip.Among the 2,417 bulls with DP-EBV values, 275 had semen samples available in the Valdostana Red Pied bio-bank that encompassed in total 373 sires samples spanning across generations.The Valdostana Red Pied population counting at present about 11,000 milking cows, did not undergo focused selection for milk production only and no gene introgression from other populations have ever occurred. The breed is strongly adapted to harsh alpine environment because breed natural adaptation and because has been selected to maintain pasture capability (summer pasture is the common farming system), longevity, functionality and fertility. Thus the population is somehow a unique genetic resource to map mastitis resistance, a trait related to adaptation, functionality and longevity. The study used all the sire samples available in the Valdostana Red Pied bio-bank thus highlighting the overall observable variability for productive and functional traits in this breed. The smaller number of sires available for the study respect to mapping, may limit the capacity to disclose QTL for mastitis resistance. Nevertheless the experimental design here used and the genetic makeup of the population allowed to identify several new QTL and confirm regions identified in the Italian and Swiss Brown population , anotherDescriptive statistics for the DP-EBVs and the size of the pools for each tail are reported in Table The initial dataset included 721,644 SNPs. After editing, the association analysis were performed with 655,665 SNPs for SCS DP-EBV.Figure http://www.animalgenome.org/cgi-bin/QTLdb/index) for clinical mastitis, SCC and SCS.A total of 171 significant SNPs in 24 chromosomes were identified above the Bonferroni genome-wide threshold of 0.05. The Additional file Among the 171 significant markers, 52 SNPs were annotated within 36 genes (Table BovineHD0900019961 (rs136413030) SNP was associated to the VNN1 (vanin 1) on BTA 9, the BovineHD1500008135 (rs134980659) SNP was associated to the THY1 (Thy-1 cell surface antigen) located on BTA 15 and the BovineHD2100001405 (rs133992914) SNP was associated to the IGF1R (insulin-like growth factor 1 receptor), located on BTA 21.The BovineHD1500008366 (rs41754552) and the BovineHD1500008367 (rs110269361) SNPs were located respectively at 594,104 bp and 601,630 bp from THY1 on BTA 15.Also the THY1 is one of the genes differentially expressed between control quarters from cows infected with E. coli and S. aureus pathogens [THY1 upregulation in S. uberis intramammary infections.athogens . Also Moathogens  reportedIGF1R is involved in innate immunity through autophagy  was associated to mastitis incidence, determining the inhibition of autophagy in response to S. Agalactiae invasion.Sugimoto and Sugimoto  providedsosomes, ) in boviBovineHD0900019716 (rs109049649), the BovineHD4100007550 (rs41662465) and the Hapmap49339-BTA-84110 (rs41662464) SNPs were mapped near the VNN1 (vanin 1) and the VNN2 (vanin 2) located on BTA 9 respectively at 73.37 Mb and 73.39 Mb. On the same BTA 9, the BovineHD0900019961 (rs136413030) SNP was close to VNN2. Jiang et al. [VNN1 and VNN2 are related to resistance to bovine mastitis, being ranked among the 160 most mastitis relevant genes.The g et al.  reportedSOCS3  was found at 673,863 bp upstream the BovineHD1900015066 (rs132720248) SNP. This gene, important for the mammary tissue homeostasis, encodes an intracellular inhibitor of cytokine signaling, thus playing an important role in the initial steps of the recognition of pathogen-associated molecular pattern (PAMP) of the innate immune cells. This leads to the activation and initiation of the innate and the adaptive immune responses. Heeg and Dalpke [SOCS3 gene among the 39 differentially expressed genes in milk fat globules of goats in response to an experimental intramammary infection with S. aureus.On BTA 19, at 55 Mb, d Dalpke  and Brend Dalpke  found thserine dehydratase (SDS) on BTA 17 was located 416,619 bp upstream of the BovineHD1700018352 (rs135157738) SNP. This gene is included in the glycine, serine and threonine metabolism, as reported by [The gene encoding for the BovineHD0900019961 (rs136413030), BovineHD0900019716 (rs109049649), BovineHD4100007550 (rs41662465) and Hapmap49339-BTA-84110 (rs41662464)) mapped near CTGF (connective tissue growth factor). The ZNFX1 (X1-type zinc finger-containing) on BTA 13 was close to four SNPs (BovineHD4100010442 (rs41634068), BovineHD1300022626 (rs137320993), BovineHD1300022630 (rs109123247) and BovineHD1300022672 (rs41710487)). The TRIM21 (tripartite motif containing 21) was located 444,354 Mb upstream the strongest association chromosome region identified in BTA 15 (Table CXCL2 (Chemokine (C-X-C motif) ligand 2) and the CXCL10 (Chemokine (C-X-C motif) ligand 10) on BTA6 were significantly associated to the BovineHD0600025253 (rs42615160) SNP.Four SNPs on BTA 9  are significantly differentially expressed by the bovine mammary epithelial cells stimulated with E. coli crude lipopolysaccharide [The genes above mentioned near to significant SNPs  SNP on BTA 22 mapped close to the MYD88 (myeloid differentiation primary-response gene 88) at 11.72 Mb, which plays a functional role in transducing pro-inflammatory molecule lipopolysaccharide (LPS) that are responsible for the majority of acute clinical cases of mastitis [The http://www.animalgenome.org/cgi-bin/QTLdb/index), thus identifying a supposed candidate chromosome region associated to SCS. The chromosome region on BTA 9 (72.78-72.80 Mb) mapped in a QTL region previously identified for the general disease resistance  and for SCS [Table  for SCS .Lund et al.  found a BovineHD0600023179 (rs133319155) and BovineHD0600023185 (rs136907262)) were found respectively at 84.25 and 84.26 Mb on BTA 6 , being mapped within the QTL region described by the authors previously cited , as showed in Additional file PLXNA4 (plexin A4) on BTA 4, the THY1 (Thy-1 cell surface antigen) on BTA 15 and the SHISA9  on BTA 25, the FAM19A1 (family with sequence similarity 19 (chemokine (C-C motif)-like), member A1) on BTA 22 associated with SCS. This study thus highlighted possible QTL related to mastitis resistance in the other 19 genes annotated and considered in the GO analysis.The literature brings evidence that some of the genes reported in Table This is the first mapping for SCS in Valdostana Red Pied population, an autochthonous alpine dual purpose cattle breed whose selection is mainly focused on milk quality, meat production and functionality.This study brings evidence of significant associations between SCS and SNP markers on several chromosomes in known and newly disclosed QTL regions. Some genes involved in mastitis resistance or variation of SCS content were in QTL on BTAs 9, 13, 15, 17, 19, 21, 22. In particular, the strongest associations were highlighted on BTA 15 with a total of 24 significant SNPs distributed in three regions.The detection of genomic regions will help to understand which potential candidate genes may be responsible for the genetic variation in mastitis resistance/susceptibility, a trait of primary importance in dairy cattle breeding and farming.The Valdostana Red Pied cattle is the most common autochthonous dual purpose breed in the region Val d’Aosta , coming from the red pied cattle and dating back to the end of the fifth century. The National Association of Valdostana Breeders (A.N.A.Bo.Ra.Va.) provided commercial semen samples for 373 bulls and 725,337 test day records from milk routine recording from 45,410 cows. No animals were involved in the work.The daily SCC were transformed into SCS . GeneticThe bull families structure was verified in terms of number of sons per bull, in order to avoid overrepresentation of a single sire. Only 1 bull had 6 sons, 4 bulls had 5 sons, 3 bulls had 4 sons and the rest of bulls had 3 or less sons. The sires were ranked according to DP-EBVs for SCS: the top 20% and bottom 20% sires were identified for the constitution of independent pools within tail of the DP-EBV distribution. In order to obtain two independent groups of different animals within tail with comparable phenotypic value, the selected samples for each tail were clustered (even and odds numbers) into 2 sub-pools.A total of 79 samples were selected for the pools constitution as follows: 2 independent pools of 20 individuals each in the high tail and 2 independent pools of 20 and 19 individuals each in the low tail. Furthermore, for each pool, 2 DNA duplicate-pools were independently constructed from identical samples. Thus, a total of 4 pools per tail were produced.Bulls DNA was extracted from semen samples using the ZR Genomic DNA™ Tissue MiniPrep (Zymo). The quality control was performed on each sample to verify the DNA integrity on Invitrogen E-Gel 1% Agarose Gel. The GloMax®-Multi Detection System instrument using the Quant-iT™ dsDNA Broad-Range (BR) Assay Kit (Life Technologies), determined the initial DNA concentrations. The DNA concentration for a single sample was evaluated three times and each read was verified twice (e.g. 2 instrument runs). Samples having concentration diverging ±1 SD from the mean value were not included in the pools. Samples of DNA were normalized to a concentration of 10 ng/ul, which was reconfirmed with the same methods above described. DNA pools were constructed by taking equivalent amounts of DNA from each sample.The final pools were concentrated to 50 ng/ul, as required for the Illumina array protocol. Each sub-pool was genotyped 3 times on different chips (array replicates). In all, 24 different chip positions on 3 microarrays were used for the pooled genotyping. Genotyping was performed using the Illumina BovineHD BeadChip  according to the Infinium protocol. SNPs positions were accordingly to the UMB 3.1 bovine assembly.Pools were analysed according to the SDP approach. The B-allele frequencies being a good estimator of the allele frequency of the individuals in a pool for each array replicate , were ushttp://www.r-project.org/) was adapted from [A pipeline in R software  plot was examined to estimate the number and the magnitude of the observed associations between genotyped SNPs and DP-EBVs, compared to the statistics expected under the null hypothesis of no association.Using the -log10 of the linkage test p-values for each SNP, a Manhattan plot was created. Manhattan plot is a SNP set out across the chromosomes for left to the right, and the heights correspond to the strength of the associations of the trait.−8.Bonferroni correction for multiple testing was applied in the analysis. The genome-wide significance threshold was set as a corrected p-value ≤ 0.05, which equated to a nominal p-value of approximately 7.62 × 10The annotation analysis of significant SNPs was performed using UCSC, NCBI ENSEMBL and the Bovine SNP Annotation Tool (Snat), integrating the information from a variety of public bioinformatics databases , KEGG PATHWAY and AnimalQTLdb ). The Il


Maize kernel row number might be dominated by a set of large additive or partially dominant loci and several small dominant loci and can be accurately predicted by fewer than 300 top KRN-associated SNPs.2:3 populations identified 33 KRN quantitative trait loci (QTLs) representing 21 QTLs common to several population/environments. The majority of these common QTLs that displayed a large effect were additive or partially dominant. We found 70 % KRN-associated genomic loci were mapped in KRN QTLs identified in this study, KRN-associated SNP hotspots detected in NAM population and/or previous identified KRN QTL hotspots. Furthermore, the KRN of inbred lines and hybrids could be predicted by the additive effect of the SNPs, which was estimated using inbred lines as a training set. The prediction accuracy using the top KRN-associated tag SNPs was obviously higher than that of the randomly selected SNPs, and approximately 300 top KRN-associated tag SNPs were sufficient for predicting the KRN of the inbred lines and hybrids. The results suggest that the KRN-associated loci and QTLs that were detected in this study show great potential for improving the KRN with genomic selection in maize breeding.Kernel row number (KRN) is an important yield component in maize and directly affects grain yield. In this study, we combined linkage and association mapping to uncover the genetic architecture of maize KRN and to evaluate the phenotypic predictability using these detected loci. A genome-wide association study revealed 31 associated single nucleotide polymorphisms (SNPs) representing 17 genomic loci with an effect in at least one of five individual environments and the best linear unbiased prediction (BLUP) over all environments. Linkage mapping in three FThe online version of this article (doi:10.1007/s00122-015-2581-2) contains supplementary material, which is available to authorized users. A better knowledge of the genetic architecture of KRN is required to establish breeding programs. However, current knowledge about the genetic control of the maize KRN was mainly obtained from genetic assays of inflorescence mutants. For example, However, KRN in natural population displays a quantitative variation that is controlled by numerous QTLs. In the last 20 years, more than one hundred QTLs have been identified by linkage mapping  and training sets (TSs) to predict the KRN of maize inbred lines and hybrids. Our objectives were to dissect the genetic architecture of the KRN by GWAS and linkage mapping and to evaluate the predictability of WGP for the KRN of maize.Genome-wide genotyping also permits the improvement of the trait by genomic selection or whole-genome prediction (WGP), similar to that achieved in cattle breeding. This approach shows a great potential for crop improvement (Lorenzana and Bernardo http://www.maizego.org/Resources.html), was evaluated for the KRN in five environments, Ya’an , Sanya  and Kunming  in 2009 and Wuhan  and Kunming  in 2010, under a randomized block design with two replicates. At harvesting stage, 8–10 ears in each replicate were phenotyped for KRN. The average value of the two replicates was considered as phenotype of a given genotype, and average KRN in each environment was used to calculate the best linear unbiased prediction (BLUP) of KRN by the linear mixed models of the SAS software (SAS Institute Inc.). The average KRN in each environment and the BLUP results were then used for further research. The repeatability was calculated by H2 = δg2/(δg2 + δe2/nr) .Panel 1, which is composed of 513 inbred lines  for the 48962 SNPs of Panel 1 . All of kinship coefficient (K), population structure (Q) and principal components analysis (PCA) used in GWAS were estimated in a previous study , TY6 × Mo17 (TM) and TY6 × W138 (TW) with 200, 188 and 284 families, respectively. NX531 and TY6 are two inbred lines with a high KRN, while SIL8, Mo17 and W138 have a low KRN. TY6, Mo17 and W138 were selected from Panel 1 based on the genotype at significant association sites. The number of favorable alleles at the significant association sites in TY6, Mo17 and W138 is listed in Table S3. Genome-wide SSR markers, with 170 for NS, 199 for TM and 184 for TW, were used to genotype the three linkage mapping populations, respectively. The F2:3 families of the NS were phenotyped at Xingtai  in 2011; both the TM and TW were phenotyped at Xingtai and Wuhan  in 2012 using a randomized block design with three replicates. The broad-sense heritability was calculated by Hb2 = δg2/(δg2 + δge2/n + δe2/nr) . The linkage maps were constructed by MAPMAKER/EXP V3 . Then, we performed a WGP using the ridge-regression best linear unbiased prediction (RR-BLUP) using various MSs, training sets (TSs) and validation populations (VPs) ]. In Strategy 2, five to 24 K tagSNPs were randomly sampled from the marker bank by automatically incrementing 5 SNPs at the next sampling  between the predicted and observed KRN was calculated to evaluate the predictability.To predict the KRN of the inbred lines and hybrids, we estimated the predictability by whole-genome prediction (WGP). First, the LD among 48,962 SNPs was estimated and was used to classify all of the SNPs to LD blocks based on the threshold ) Piepho . To comp) Piepho a. These ing Fig. a. The pring Fig. b. Moreoving Fig. c. Each or = 0.69–0.85) among the five environments (Table S4). As in the study of Yang et al. . Importantly, the observed KRN values indicated high repeatability (93.87 %) (Table S3) with significant correlations  for the 48962 SNPs of Panel 1 was 48498.7, similar to total SNPs number. This result was different from the finding of Rincent et al., who found a larger decrease in Meff compared to total SNPs number (2014). Genetic diversity in an association population likely influences the number of independent tests. Thus, the estimation of independent tests for various genetic materials deserves further investigation. The 0.05/Meff level of Bonferroni corrected −log was 5.99  thresholds, corresponding to 0.1/Meff, 0.05/0.1N and 0.10/0.1N, were 5.69, 4.99 and 4.69, respectively  4.69 was used to identify a larger number of KRN-associated SNPs, thereby the 31 KRN-associated SNPs were considered for further investigation. The proportion of phenotype variance explained (PVE) by individual SNP ranged from 2.45 to 10 %, and 24 SNPs had a PVE >5 % (Table S5). In Panel 1, frequency of the allele with positive effect at these 31 SNP sites ranged from 0.09 to 0.79 and was less than 0.3 for 68 % of the sites. Interestingly, frequency of the allele with positive effect at the KRN-associated SNPs was negatively correlated with phenotypic variation explained by these sites . Considering the criterion of LD with r2 > 0.2, the 31 SNPs belonged to 17 LD blocks which represented 17 KRN-associated genomic loci  were developed by crossing high KRN lines with low KRN lines to map the KRN QTLs. In each of the F2:3 mapping populations, ANOVA analysis also revealed significant genetic variation in the KRN among the F2:3 families. The KRN ranged from 10.2 to 17.4 in NS, 10.7 to 18.1 in TM and 10.0 to 19.2 in TW . Additionally, the KRN in the TM and TW populations at Wuhan were highly correlated with that at Xingtai (Table S3), and exhibited high broad-sense heritability with 88.39 % in TM and 78.03 % in TW (Table S3). The SSR markers  were used for the linkage and QTL mapping. A total of 1836.7 cM-, 2277.7 cM- and 2258.9 cM-length genetic maps were constructed for the NS, TM and TW, with an average distance among the markers of 10.8, 11.4 and 12.3 in NS, TM and TW, respectively. A total of 33 QTLs were detected in the three linkage populations, 7 in the NS, 13 in the TM and 13 in the TW  were detected across two environments, and four QTLs  were detected in two or more populations  co-localized with 5 common QTLs  of the KRN-associated SNPs or 35 % (6/17) of the KRN-associated genomic loci (r = 0.78) , and rapidly increased again when the randomly selected tagSNPs increased from 1 K to 24 K  was observed when ~40 top tagSNPs were used and was maintained at a high level along with increasing the top tagSNPs . Using the MS comprising of 17 SNPs, which represented 31 KRN-associated SNPs in the GWAS , the prediction accuracies increased sharply with the increasing number of the top tagSNPs from 5 to 300 and decreased with the increasing number of the top tagSNPs from 1 K to 24 K. For the temp-VP or trop-VP, the prediction accuracies were almost the same when the temp-TS and trop-TS were used, respectively. Moreover, no matter whether the temp-TS or trop-TS was used, the prediction accuracies were always higher for the temp-VP than for the trop-VP. Second, with the increasing TS size, the accuracies increased slowly in the prediction of the inbred lines but increased quickly in the prediction of the hybrids Figs. .Since the first application of molecular marker technology in QTL mapping in the 1980s, numerous QTLs for agriculturally important traits have been identified in diverse populations. The QTLs for KRN have been widely assayed, and hundreds of QTLs have been identified , QTLs that were detected in the linkage populations were divided into three types: additive, partial dominant, and dominant  than in the NAM population  and 22 KRN QTL hotspots identified in previous studies. Although none of variations in these loci were confirmed to be the causal variations for KRN, those Top tagSNPs from GWAS were successfully employed to predict KRN of inbred lines and hybrids using RR-BLUP. Moreover, we found a high predictability can be achieved through selecting hundreds of the Top tagSNPs in inbred lines and hybrids, suggesting the Top tagSNPs should be potential target loci for KRN improvement with genomic selection in maize breeding.LL, JY, and ZZ carried out the GWAS of the KRN in Panel 1 and Panel 2 and drafted the manuscript. LL, YD, DH, MW, XC and XS participated in the linkage mapping. LL, ZZ and YZ participated in the design of the study and the interpretation of the results and wrote and edited the manuscript.Supplementary material 1 (TIFF 456 kb)Supplementary material 2 (TIFF 173 kb)Supplementary material 3 (TIFF 692 kb)Supplementary material 4 (XLSX 23 kb)Supplementary material 5 (XLSX 10 kb)Supplementary material 6 (XLS 27 kb)Supplementary material 7 (XLS 23 kb)Supplementary material 8 (XLSX 16 kb)Supplementary material 9 (XLSX 13 kb)


Germplasm collections are an important source for plant breeding, especially in fruit trees which have a long duration of juvenile period. Thus, efforts have been made to study the diversity of fruit tree collections. Even though mango is an economically important crop, most of the studies on diversity in mango collections have been conducted with a small number of genetic markers.de novo transcriptome assembly from mango cultivar ‘Keitt’. Variation discovery was performed using Illumina resequencing of ‘Keitt’ and ‘Tommy Atkins’ cultivars identified 332,016 single-nucleotide polymorphisms (SNPs) and 1903 simple-sequence repeats (SSRs). Most of the SSRs (70.1 %) were of trinucleotide with the preponderance of motif (GGA/AAG)n and only 23.5 % were di-nucleotide SSRs with the mostly of (AT/AT)n motif. Further investigation of the diversity in the Israeli mango collection was performed based on a subset of 293 SNPs. Those markers have divided the Israeli mango collection into two major groups: one group included mostly mango accessions from Southeast Asia  and India and the other with mainly of Floridian and Israeli mango cultivars. The latter group was more polymorphic (FS = −0.1 on the average) and was more of an admixture than the former group. A slight population differentiation was detected (FST = 0.03), suggesting that if the mango accessions of the western world apparently was originated from Southeast Asia, as has been previously suggested, the duration of cultivation was not long enough to develop a distinct genetic background.We describe a Whole-transcriptome reconstruction was used to significantly broaden the mango’s genetic variation resources, i.e., SNPs and SSRs. The set of SNP markers described in this study is novel. A subset of SNPs was sampled to explore the Israeli mango collection and most of them were polymorphic in many mango accessions. Therefore, we believe that these SNPs will be valuable as they recapitulate and strengthen the history of mango diversity.The online version of this article (doi:10.1186/s12870-015-0663-6) contains supplementary material, which is available to authorized users. Mangifera indica L. species which includes all commercial cultivars is still undetermined. The genus Mangifera has approximately 70 members which are located mostly on the Malay peninsula, in the Indonesian archipelago, in Thailand and in the Philippines [The origin of lippines , 2. Somelippines . These clippines .Germplasm collections are important for genotypic and phenotypic analyses, and as a genetic resource in breeding programs. Knowledge of the diversity and the genetic structure of these collections is a fundamental for association studies and controlled breeding . DespiteRecently, the genetic diversity of mangoes originated from Andhra Pradesh, the major mango breeding area in India, was studied based on 106 polymorphic SSRs. Accessions of the same ideotype  were more related to each other but did not show any significant differentiation . FurtherDe novo transcriptome sequencing is one solution for marker discovery, gene expression analysis and exposing genetic variability in organisms with no genomic infrastructure such as olives, Chinese chestnut, carrot and pomegranate [de novo genome assemblies. Moreover, studying the genetic variation of the germplasm collection can give insights into the historical basis of the diversity and can additionally be used for genome wide association studies in order to identify markers linked with important horticultural traits for plant breeding [Molecular efforts to create wide genomic and genetic data for mango are in their initial stages. These efforts have included establishment of a leaf transcriptome  and fruiegranate –31. Largbreeding .In the present work we describe our effort to broaden the transcriptome resources for mango by sequencing RNA from various tissues and fruit stages. Using 454-GS FLX Titanium technology we reconstructed a large portion of the ‘Keitt’ mango transcriptome and used it as a reference for aligning resequencing results. Resequencing of the ‘Keitt’ transcriptome itself as well as another mango accession,‘Tommy Atkins’, by Illumina HiSeq 2000 was used to discover a large set of genetic variation. A subset of that variation was utilized in order to explore the Israeli mango collection which comprises cultivars from several world regions.de novo assembly of the whole transcriptome was performed by 454-GS FLX Titanium sequencing of ‘Keitt’. Second, resequencing of both mango cultivars, ‘Tommy Atkins’ and ‘Keitt’, was aligned to ‘Keitt’ de novo assembly contigs to obtain high coverage and therefore high accuracy of allele identification [Genic variation is a very useful resource for marker assisted selection (MAS) and association studies. Therefore RNA samples of two mango accessions, ‘Tommy Atkins’ and ‘Keitt’, were obtained from a pool of tissues  as a representative transcriptome (hereafter Pool transcriptome). By pooling we expected to compensate for tissue-specific gene expression. Variation discovery in the transcriptome was performed in two steps. First, fication .de novo assembly was performed on 1,113,875 reads resulting in 60,997 contigs. These contigs were then reassembled into super-contigs using the CAP3 program [de novo assembly of short reads. Ninety percent of the contigs were 412, 219, and 223 bp or longer and half were at least 757, 321, 438 bp long for Pool, Leaf and Peel transcriptome assemblies respectively  as a result of similarity searches against ‘Gene Bank’, ‘TAIR’, and ‘UniProt’ protein databases (Table SSRs and single-nucleotide polymorphisms (SNPs) are highly useful in plant genetics and breeding for the construction of linkage maps and MAS , 36. Thede-novo assembled transcriptome as a reference. The analysis resulted in the discovery of 332,016 SNPs  than 2.18 as a result of a slight increase of inORF SNPs. Herein we described the first set of SNP markers for mango. The closest fruit tree relative of mango with a published genome, Citrus sinensis, is as polymorphic as mango [In the recent years, with the evolution of next generation sequencing, many studies have developed SNP markers for marker-assisted breeding , 42–45. as mango . The genas mango . Like oras mango , apple [as mango , and eucas mango , respectas mango , 12, 53.χ2-test; df = 1; p-value = 0.74) and was therefore representative. As reported in previous studies of collections, mangoes are highly polymorphic [An overwhelming number of SNPs derived from the genic region of the genome may be useful in the future for genome-wide association studies (GWAS). However as a preliminary step to such studies, a survey of the structure and diversity of the mango collection is required . A subseymorphic , 19. TheThe Israeli germplasm collection comprises cultivars that were originated from India, Southeast Asia, South America and the Pacific islands, Florida, Australia, and from elite local hybrids. A dendrogram based on the proportion of shared alleles distance classified the accessions in the mango collection into two genetic subgroups. The dendrogram Fig.  split thp-value = 0.47) was observed between the proportions of poly- and monoembryonic accessions in the two clusters.In contrast to the mango accessions’ origin, there was no clear division observed between poly- and monoembryonic accessions in the SP1 and SP2 clusters. SP1 comprised 13 and five mono- and polyembryonic accessions respectively (one was undefined). SP2 comprised 40 and 11 mono- and polyembryonic accessions respectively (four were undefined). No significant difference  than the expected heterozygosity of SP2 (median = 0.43). The accessions in SP1 are in Hardy-Weinberg equilibrium (HWE) with a median FS of -0.05 . In contrast, slight outbreeding was estimated for the accessions in SP2 with a median of FS = -0.1 . Both FS values were close to zero and slightly negative, suggesting that mango accessions in these clusters are not prone to inbreeding. The SP1 cluster that was enriched in accessions from Southeast Asia and India, i.e., suggested mango’s origin [ST value that is only slightly greater than zero  which suggests that SP2 is only in the beginning of its differentiation. Small FST values, such as the one shown in this study, were previously suggested by three other studies [The two major clusters in the dendrogram were compared for their genetic diversity. The expected heterozygosity of SP1 (median = 0.28) is significantly smaller ; they clustered together with SP1 subpopulation that contained mainly accessions that are cultivated in Southeast Asia and India. This supports the claim that the Southeast Asia and India is the origin of Mangifera indica [Finally, two non-indica species of the genus a indica  and thatWe have established a sequence for the mango transcriptome from a pool of tissues. This transcriptome was not reconstructed to study expression but rather served as a reduction in complexity for variation discovery. It was used as a reference to align resequencing of two commercially important mango accessions, ‘Keitt’ and ‘Tommy Atkins’, constituting a resource for genetic variation discovery. The annotation and the SSR motifs were congruent with the existing knowledge in the literature. The discovered SNPs established a large pool genetic variation in mango. A subset of this pool was shown to be applicable for studying diversity in the Israeli mango collection and for dividing it into two subpopulations, i.e., two genetic groups. The SP1 cluster comprised a Southeast Asian and Indian accessions and was suggested to arise from a narrow genetic background. Yet it was found to be in HWE, probably due to a long duration of cultivation. In contrast the SP2 cluster comprised mainly accessions cultivated in the western world except for three Indian accessions, one of which had been reported to be the ancestor of many Floridian mangoes. The structure analysis based on the SNP markers suggested that the three Indian mango accessions are an admixture. Consequently, most of the descendent cultivars are admixtures as well. In contrast to SP1 accessions, those in SP2 were not in HWE. We suggest that the different results are probably due to difference in duration of cultivation, although this was not strongly supported by the results. We believe that the novel set of SNPs is valuable for mango because that they have been polymorphic in the Israeli mango collection and they enabled us to recapitulate the mango’s diversity.Mango accessions from the Israeli mango germplasm collection were used in this study. The collection is comprised of accessions from different regions of the world as well as promising lines identified through the Israeli breeding program. A list of the accessions that were included in this study is provided in . Total RNA was isolated using a hexadecyltrimethyl ammonium bromide (CTAB)-based method . Tissue Genomic DNA was isolated from young mango leaves (2 g) ground in liquid nitrogen and extracted with 15 ml of extraction buffer  and 15 ml of chloroform: isoamyl alcohol. Following a second extraction with chloroform: isoamyl alcohol, DNA was ethanol-precipitated, treated with 20 units of ribonuclease A (Sigma), precipitated and resuspended in water. The DNA was quantified in a NanoDrop spectrometer and by separation on a 0.8 % agarose gel.6 molecules/μl and pooled. Emulsion-based clonal amplification and sequencing on a full plate of the ‘454 Genome Sequencer FLX+’ system were performed according to the manufacturer’s instructions . Signal processing and base calling were performed using the bundled 454 Data Analysis Software v2.6. The read outcome was used to create a mango transcriptome as a reference for alignment of resequenced ‘Keitt’ and ‘Tommy Atkins’ total RNA mixture isolated from several tissues in equal amounts. Those RNA samples were prepared with Illumina’s ‘TruSeq RNAseq Sample Prep kit’, quantified by qPCR, and sequenced for 100 cycles on a HiSeq 2000 using a ‘TruSeq’ SBS sequencing kit version 3. To get a lane-independent yields, ‘Keitt’ and ‘Tommy Atkins’ RNA samples were initially tagged and then mixed evenly and were run on two separate lanes. The sequence reads from those lanes were used for discovery of genetic variation.‘Keitt’ total RNA samples from the different tissues were mixed evenly and ran on one plate of the 454-Titanium platform. Construction of two cDNA libraries and 454 pyrosequencing were carried out at the W.M. Keck Center for Comparative and Functional Genomics, Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign. Briefly, mRNA was isolated from 20 μg of total RNA with the Oligotex kit . The mRNA-enriched fraction was converted to a primary cDNA library with adaptors compatible with the 454 system as previously published . After lhttp://bioinf.comav.upv.es/sff_extract/) and arguments for removing the adaptors and clipping the poly-A were applied. Reads were assembled by a stable version of MIRA, v3.2 [Raw sequence reads of the 454-FLX GS Titanium platform were pre-processed by “SFF_extract”  by the “getorf” program of the EMBOSS package . The lon−5. Best hits were further mapped to GO-slim by Blast2GO [A sequence-similarity search of contigs was run against the non-redundant (nr) protein database using blastx with a filter of e-value <10Blast2GO  and onlyBlast2GO  to the tBlast2GO .http://bowtie-bio.sourceforge.net/bowtie2/index.shtml). SNPs were discovered using Varscan [Read results from ‘Keitt’ and ‘Tommy Atkins’ mRNA resequencing using Illumina HiSeq 2000 were mapped to the ‘Keitt’ reference-transcriptome contigs using bowtie2 ( Varscan .http://pgrc.ipk-gatersleben.de/misa/) and SciRoKo [SSR scanning was performed on the 47,956 reference contigs. MIcroSAtellite (MISA) identification tools ( SciRoKo  were runhttp://www.fluidigm.com) with a minor modification of four no-template control (NTC) samples instead of one. The SNP assays were used to screen the 74 accessions’ DNA samples by running on ‘FR96.96’ arrays of the EP1 Fluidigm platform according to the manufacturer’s instructions (http://www.fluidigm.com).A subset of 472 SNPs was chosen for further analysis by maximizing sequence coverage of 1 SNP per contig. SNP assays for all 472 SNPs were developed by Fluidigm based on the genetic variation that was found between ‘Keitt’ and ‘Tommy Atkins’. The assays were run according to the manufacture’s instructions on an EP1 platform using ‘96 × 96’ chips following standard Fluidigm protocols . PSA was calculated as of loci .Hierarchical clustering was performed on a pairwise D distance matrix and the “ward” agglomerative method  was applThe subpopulation structure underlying the germplasm collection was estimated by running a simulation of STRUCTURE software v2.3.3  with 500S and FST [S is the fixation index of each subpopulation, Hobs is the observed heterozygous types and Hexp is the estimated heteozygosity under HWE,ST is the genetic differentiation of a subpopulation due to genetic drift, HS is the weighted average of all subpopulations’ expected heterozygozity, and HT is the expected heterozygosity in the entire population (germplasm collection).The fixation indices F and FST  were calhttp://www.ncbi.nlm.nih.gov/genbank/tsa) repository under the accession numbers of BioProject: PRJNA254771, BioSample: SAMN02947194, and BioSample: SAMN02905156. These data can be found under a search in the Nucleotide repository at the NCBI site.The dataset supporting the results of this article is available in the NCBI TSA (Transcriptome Shotgun Assembly Sequence Database,


Progress in genetics and breeding in pea still suffers from the limited availability of molecular resources. SNP markers that can be identified through affordable sequencing processes, without the need for prior genome reduction or a reference genome to assemble sequencing data would allow the discovery and genetic mapping of thousands of molecular markers. Such an approach could significantly speed up genetic studies and marker assisted breeding for non-model species.A total of 419,024 SNPs were discovered using HiSeq whole genome sequencing of four pea lines, followed by direct identification of SNP markers without assembly using the discoSnp tool. Subsequent filtering led to the identification of 131,850 highly designable SNPs, polymorphic between at least two of the four pea lines.Medicago truncatula genome. Sequencing of four additional pea lines showed that 33 % to 64 % of the mapped SNPs, depending on the pairs of lines considered, are polymorphic and can therefore be useful in other crosses.A subset of 64,754 SNPs was called and genotyped by short read sequencing on a subpopulation of 48 RILs from the cross ‘Baccara’ x ‘PI180693’. This data was used to construct a WGGBS-derived pea genetic map comprising 64,263 markers. This map is collinear with previous pea consensus maps and therefore with the The subsequent genotyping of a subset of 1000 SNPs, chosen for their mapping positions using a KASP™ assay, showed that almost all generated SNPs are highly designable and that most (95 %) deliver highly qualitative genotyping results. Using rather low sequencing coverages in SNP discovery and in SNP inferring did not hinder the identification of hundreds of thousands of high quality SNPs.The development and optimization of appropriate tools in SNP discovery and genetic mapping have allowed us to make available a massive new genomic resource in pea. It will be useful for both fine mapping within chosen QTL confidence intervals and marker assisted breeding for important traits in pea improvement.The online version of this article (doi:10.1186/s12864-016-2447-2) contains supplementary material, which is available to authorized users. Drosophila melanogaster [SNPs (Single Nucleotide Polymorphisms) are genetic markers of choice for both linkage and association mapping and for population structure and evolution analysis. They are virtually unlimited, evenly distributed along the genome, bi-allelic and co-dominant. Massive SNP discovery was first limited to the few species with an available reference genome. Recently, with the advances in cheaper next generation sequencing (NGS) technologies, various accessions within species even with complex genomes could be sequenced . The chanogaster , fish annogaster . In plannogaster  and rye-nogaster . In legunogaster  first renogaster , where 2nogaster  first usAphanomyces euteiches Drechs. Resistance Quantitative Trait Loci (QTL) have been described, but the QTL confidence intervals are still large, especially due to the lack of markers and low resolution of existing genetic maps. It remains a challenge to reduce QTL confidence intervals, to discover underlying candidate genes and develop breeding programs using molecular markers strongly associated with phenotypes.Pea is the third production in the world among temperate grain legume crops after soybean and common bean and is a major source of protein for humans and livestock. Pea is particularly relevant in temperate cropping systems due to its capacity to fix nitrogen through symbiosis. Nevertheless, the species suffers from significant yield instability due to its high susceptibility to abiotic and biotic stresses, among which Aphanomyces root rot disease, due to the oomycete M.truncatula [http://www.coolseasonfoodlegume.org/pea_genome), however no full genome sequence is available yet. Large numbers of new molecular markers are still needed to saturate pea maps and significantly improve QTL mapping both for research and breeding objectives. Although transcriptome sequencing has recently been used in pea for SNP discovery [Although pea has actually entered the genomic era , it stiluncatula . This inuncatula . Recent uncatula  and thatiscovery , 15, 16 iscovery , 17, 18,iscovery  and on aiscovery , 21 up tiscovery , 18, 22 A.euteiches resistance. The quality of the SNPs was then validated through genotyping using a benchmark technology [To complement the existing resources, our objective was to develop a comprehensive SNP resource in pea using genotyping by HiSeq sequencing of whole genome DNA and then to apply it for substantial genetic mapping. To our knowledge, this is the first report, in a species lacking a sequenced reference genome, of a whole genome genomic DNA sequencing strategy for high-throughput SNP discovery, genotyping  and genetic mapping. This novel approach was carried out at low sequence coverage, without prior genome complexity reduction and without sequence read assembly, on a RIL population segregating for chnology . This wachnology , and devchnology .P. sativum genotypes were selected for genomic DNA preparation and HiSeq sequencing. These were the parental lines of the ‘Champagne’ x ‘Terese’ and ‘Baccara’ x ‘PI180693’ RIL mapping populations. Raw and pre-processed sequencing data analysis across the four samples showed low levels of contamination, and unexpectedly low levels of sequence repeats. In addition, considering that the nuclei were not isolated prior to DNA extraction, there were also rather low levels of organelle contaminants . The final clean sequences represented 69 to 80 % of the raw sequence data depending on the genotype  were polymorphic between the ‘Baccara’ and PI180693’ parental lines. The kissreads module  of the dhttp://lipm-bioinfo.toulouse.inra.fr/download/glint/) showed that only 482 SNPs are 100 % identical between the 35,455 SNP set developed by Duarte et al. [A strict alignment of SNP context sequences with the Glint tool , was constructed from the ‘Baccara’ x ‘PI180693’ polymorphic markers used in Hamon et al.  and DuarA second high-density ‘Baccara’ x ‘PI180693’ WGGBS-derived map (below referred to as the BP-WGGBS map), including 64,263 markers and covering 1027 cM, was constructed from genotyping data used for the BP-Duarte genetic map by adding the data for the selection of 64,754 SNPs “genotyped-by-sequencing” on the 48 ‘Baccara’ x ‘PI180693’ RILs (Table P. sativum LGs (PsLGs), and ranged from 52 markers/cM (PsLGI) to 74 markers/cM (PsLGIV)  among the 48 ‘Baccara’ x ‘PI180693’ derived RILs , in a KASP™ 1,536-well plate format assay see .Among these 1000 SNPs selected, 47 (5 %) failed due to missing or non-readable signals. Successful genotyping results were obtained with the other 953 SNPs (95 %), among which 949 (99.6 %) revealed the bi-allelic codominant polymorphism expected in the ‘Baccara’ x ‘PI180693’ RIL population. The remaining four markers (0.4 %) revealed a dominant polymorphism probably due to a failed probe design.The 1,439,983 genotyping data points consisted of 47.3 % homozygous loci for alleles from ‘Baccara’, 48.1 % homozygous loci for alleles from ‘PI180693’, 1.3 % heterozygous loci, and 3.3 % unassigned data . The entire polymorphism data set generated by the KASP™ genotyping assay in the eight sequenced pea parental lines and 4 F1s from crosses between pairs of these lines is described in Additional file Forty-five thousand seven hundred forty-four comparisons were made between KASP™ and WGGBS genotyping data obtained with 953 SNPs on 48 RILs. Although there were higher levels of unassigned data generated with WGGBS than KASP™ genotyping, both methods gave very similar results for the 48 RILs genotypes  and thought to contain a large proportion of repeated sequences, we chose not to go through this genome reduction step and to work on direct whole genome DNA sequencing, associated with reduced coverage thresholds to validate the SNPs. This strategy generated more than ten billion 100 bp high quality reads from eight pea lines and 48 RILs, with a high level of polymorphism validation in other sequenced parental lines or in derived RILs. Results of the cleaning process were consistent for all lines, except ‘Baccara’ which was not etiolated prior to tissue sampling. The unexpectedly low level of repeated sequences (10 to 15 %) could be because the mapping parameters in the cleaning method were very stringent or some pea genome repeated sequences are under-represented in the REPbase and Genbank databases. Regardless, the remaining repeated sequences were taken into consideration during SNP discovery with the discoSnp tool and post discoSnp filtering processes. Thus we did not observe high SNP coverage that could have corresponded to organelle or repeated sequence regions  due to low coverage and low depth of whole genome sequencing data. Another filter, based on the coverage of the minor allele for SNPs detected on four pea lines, was used to remove questionable SNPs for complexity, multilocus status, copy number variations, or potential location in genome repeats. After this stringent filtering step, around 50 % of the 419,024 SNPs for all four pea parental lines were retained. This is a much higher figure than currently reported for GBS strategies which usually result in high levels of missing sequence data and a large percentage of uncalled genotypes , 28. Indpe” SNPs .Only 482 of the 88,851 newly discovered polymorphic SNPs (between ‘Baccara’ and ‘PI180693’) were in common with the 35,455 gene-based SNPs developed by Duarte et al. .Despite the rather low average sequencing coverage in SNP discovery (around 7x) and in further SNP inferring around 3.5x), several biological and technical observations confirmed the robustness of our strategy and showed its full efficiency in revealing reliable and designable SNPs. The polymorphism levels revealed between pairs of lines were consistent with previously described genetic distances between these lines [.5x, seveFinally, genotyping data obtained with both KASP™ and WGGBS methods for the 953 SNPs on the 48 RILs were very similar. The level of missing data , as well as the 1.5 % inconsistent data between methods, suggests that WGGBS is less robust in heterozygote discrimination, probably due to the low sequencing depth. However, WGGBS was highly efficient and robust in revealing homozygous genotypes. Thus we confirmed that the low average sequencing coverage (around 3.5X) coupled with the use of the kissreads module of discoSnp inferred robust and reliable genotyping data.HTA GENE tool: about 10 h were needed to map 64,000 SNPs on the seven PsLGs, whereas annealing commands in CARHTA GENE would probably have taken months or years. We are currently working on parallelizing the code for multicore computers to further reduce this computation time.Most developments in genotyping techniques in the last ten years have been clearly associated with multiplexing large numbers of markers (up to millions for example in Illumina or Affymetrix genotyping arrays) for each plant analyzed. WGGBS approaches tend to also provide huge quantities of SNP allele information for each plant. This leads to new challenges when building linkage maps. The main problem is that the number of plants in the mapping population is always far too small for a robust ordering of all the polymorphic markers. Thus, two distinct objectives have to be considered to produce: (i) a robust map with the largest possible number of markers (but still moderate due to limited population size) for which the order is statistically supported for a given LOD threshold; and (ii) a “complete map” in which all polymorphic markers are given a position, but without the order between markers being statistically supported (this is the principle of “bin-mapping” ). AnotheP. sativum composite [This pipeline allowed us to generate the first high-density pea genetic map mainly based on SNPs developed and genotyped by sequencing whole genome genomic DNA from a RIL population. The map size obtained was significantly smaller than those observed in previous reference composite maps , 18, 32,omposite , BP-DuarP < 0.01) was observed for the newly developed SNPs among the 48 ‘Baccara’x‘PI180693’ derived RILs. This figure is very close to the 3 % identified for 224 markers in the ‘Baccara’/‘PI180693’/‘552’ consensus map [A 3.2 % segregation distortion  and includes 2,477 markers on the ‘BP-WGGBS’ map. The upper section of this region  can also be linked to a 4.2 Mb region of M.truncatula chromosome 4 using around 30 gene-derived bridge markers  aimed at specific regions when generating large segregating populations. Finally, we generated a large choice of polymorphic markers internal to and bordering QTL confidence intervals. These provide an unprecedented tool for Marker Assisted Selection.P. sativum genotypes was selected for (i) being parental lines of mapping populations (ii) including sources of partial resistance or tolerance to biotic and abiotic stresses and (iii) examining polymorphisms within or between P. sativum cultivated types, i.e. fodder, field (winter sown and spring sown), and garden pea types  selected from a 178 RIL mapping population developed by Single Seed Descent from the cross between the cultivar ‘Baccara’ and the ecotype ‘PI180693’ , 33. TheP. sativum genotypes were grown in a climate-controlled chamber . At least five plants per genotype were collected 15 days after sowing including a final etiolation period of five days (except for the Baccara sample). Tissues were flash frozen in liquid nitrogen and stored at −80 °C until further use.The 56 Genomic DNA was extracted from leaf tissue using a CTAB method as described by Rogers and Bendich . The quaDNA libraries were prepared using the Illumina TruSeq DNA protocol following manufacturer’s guidelines. Briefly, DNA was fragmented using a Covaris M220 sonicator. DNA was then end repaired and A-tailed, followed by the ligation of adapters and 12 cycles of PCR. Library profiles were controlled on a BioAnalyzer High Sensitivity chip. Quantities of usable material for each of the libraries were estimated by qPCR  and then normalized and pooled. The quality of the pools was then checked using qPCR and immediately followed by sequencing on the HiSeq2000 platform , using TruSeq PE Cluster Kit v3 (2 x 100 pb) and TruSeq SBS Kit v3. Eight lanes, each generating on average 36 Gb of sequences (360 M reads x 100 pb length) were necessary to sequence the set of eight lines. Twenty-four lanes, each generating on average 38 Gb of sequence (190 M reads x 100 pb length), were necessary to sequence the 48 RILs. Raw data were produced as sff files.ftp://ftp.plantbiology.msu.edu/pub/data/TIGR_Plant_Repeats/TIGR_Medicago_Repeats.v2), and available pea repeat sequences from Genbank. BWA was used subsequently to remove mitochondrial  and chloroplastic (from pea NCBI RefSeq NC_014057.1) contamination. Low complexity sequences were finally removed using a custom perl script.Raw sequence data were processed with a dedicated pipeline enchaining different cleaning steps: after excluding contaminants, Flexbar  was usedThe discoSnp tool  calls SNRobust SNPs (highly or very highly designable for genotyping assay designs) polymorphic between the ‘Baccara’ and ‘PI180693’ parental lines were inferred using the kissreads module of the discoSnp tool  on cleanTo identify potential polymorphisms in a larger set of parental lines, SNPs were inferred in the same SNP set as used for the 48 RILs using the kissreads module. It was applied to the cleaned reads generated by sequencing four additional parental lines of mapping populations . A 5x coverage threshold was set in order to select reliable data.For each SNP, the allele was coded “A” when identical to the ‘Baccara’ parent, or “B” when identical to the ‘PI180693’ parent.HTA GENE software [HTA GENE. The resulting map was called BP-Duarte.To first build a robust individual ‘Baccara’ x ‘PI180693’ map, we used genotyping data from the Duarte et al.  and SNP software , with a HTA GENE called from custom R scripts (http://www.r-project.org/foundation). In the first step, statistically robust scaffold maps were constructed by elongating the map from one seed marker in both directions with the most strongly linked marker in the data set, located at a distance greater than 5 cM. This procedure ensured an extremely robust order of regularly-spaced markers. To avoid potential detrimental consequences due to the choice of the seed marker, for instance on incomplete map coverage, 10 independent replicates were performed using ten randomly drawn seed markers, and a consensus scaffold was built by merging all the ten scaffolds. In a second step, marker density of the consensus scaffolds was increased to produce framework maps containing as many markers as possible, while keeping a LOD score >3.0 for the robustness of marker orders. Finally, the complete map was obtained by individual placement of additional markers on the framework map using bin mapping [To build the new map including the WGGBS data, we added to the previous matrix the “genotyping by sequencing” data obtained in this study on 48 ‘Baccara’ x ‘PI180693’ RILs. The 1:1 allelic segregation ratio at each marker was verified and the LGs established as for the previous genetic map. Then each LG was constructed individually as described earlier  using CA mapping . MarkersFor both the BP-Duarte and BP-WGGBS genetic maps, the Haldane function was used to calculate cM distances between markers  and MapChttp://www.lgcgenomics.com) for sample normalization and genotyping using KASP™ technology [A subset of 1000 SNPs was chosen among the mapped SNPs on the BP-WGGBS genetic map to design a 1536 well plate KASP™  assay. Schnology . A totalchnology . The homhttp://www.ebi.ac.uk/ena/data/view/PRJEB9689) under the accessions ERS762237-ERS762244 for the eight sequenced pea lines and ERS762253-ERS762300 for the 48 sequenced pea RILs.SNP markers were directly identified using the discoSnp tool without sequence assembly. The entire set of cleaned reads that was generated in this project has been deposited in FASTQ format at ENA/PRJEB9689 (


NRC) consists of animls belonging to the Danish Red, Finnish Ayrshire, and Swedish Red breeds. Compared to the Holstein breed, NRC animals are smaller, have a shorter calving interval, lower mastitis incidence and lower rates of stillborn calves, however they produce less milk, fat and protein. Female fertility is an important trait for the dairy cattle farmer. Selection decisions in female fertilty in NRC are based on the female fertility index (FTI). FTI is a composite index including a number of sub-indices describing aspects of female fertility in dairy cattle. The sub-traits of FTI are: number of inseminations per conception (AIS) in cows (C) and heifers (H), the length in days of the interval from calving to first insemination (ICF) in cows, days from first to last insemination (IFL) in cows and heifers, and 56-day non-return rate (NRR) in cows and heifers. The aim of this study was first to identify QTL for FTI by conducting a genome scan for variants associated with fertility index using imputed whole genome sequence data based on 4207 Nordic Red sires, and subsequently analyzing which of the sub-traits were affected by each FTI QTL by associating them with the sub-traits.The Nordic Red Cattle (10(P) > 8.25) were detected for FTI distributed over 25 chromosomes. The chromosomes with the most significant markers were tested for associations with the underlying sub-traits: BTA1 (822 SNP), BTA2 (220 SNP), BTA3 (83 SNP), BTA5 (195 SNP), two regions on BTA6 (503 SNP), BTA13 (980 SNP), BTA15 (23 SNP), BTA20 (345 SNP), and BTA24 (104 SNP). The fertility traits underlying the FTI peak area were: BTA1 , BTA2 , BTA3 , BTA5 , BTA6 , BTA13 , BTA15 , and BTA24 . For BTA20 all sub-traits had SNP markers with a –log10(P) > 10. Furthermore the genes assigned to the most significant SNP for FTI were located on BTA6 (GPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521).A total 17,388 significant SNP markers (−logGPR125), BTA13 (ANKRD60), BTA15 (GRAMD1B), and BTA24 (ZNF521). It is not known how the genes/variants identified in this study regulate female fertility, however the majority of these genes were involved in protein binding, 3) a SNP in a QTL region for FTI on BTA20 was previously validated in three cattle breeds.This study 1) shows that many markers within FTI QTL regions were significantly associated with both AISH and IFLH, and 2) identified candidate genes for FTI located on BTA6 (The online version of this article (doi:10.1186/s12863-015-0269-x) contains supplementary material, which is available to authorized users. NRC) consists of animals belonging to the Danish Red, Finnish Ayrshire, and Swedish Red breeds. Compared to the Holstein which is the dominating dairy cattle breed in the Denmark, NRC animals are smaller, have a shorter calving interval, lower mastitis incidence and lower rates of stillborn calves. However, they also produce less milk, fat and protein [FTI) in the breeding goal. FTI reflects how easily a cow or a heifer conceives given the index is composed of four different female fertility traits namely: number of inseminations per conception, interval from calving to first insemination, days from first to last insemination, and 56-day non-return rate.The Nordic Red Cattle . The trait definitions have been standardized across the three countries and the cattle industry has made the large NRC dataset available for cattle genetic research.Genetic evaluation for three NRC populations from Denmark, Sweden and Finland are carried out jointly by Nordic Cattle Genetic Evaluation , a joint analysis could improve the power to identify the causal mutation [Previous studies on the identification of DNA markers have been focusing on the individual red breeds , 4, howemutation . The powmutation  can potemutation .The joint association analysis of the NRC population with imputed WGS data allows the detection of QTL for FTI and the determination of which sub-traits are affected by these QTL for FTI. Therefore our first objective of this study was to identify QTL for FTI by conducting a genome scan for variants associated with FTI in the joint NRC population using imputed WGS. The second objective was to re-analyze the identified QTL region for FTI to ascertain which sub-trait(s) of FTI are influenced by these QTL.The analysis was based on a two-step approach. In the first step the genome was screened for the fertility index only based on a sire model without considering relationship among sires. In the second step targeted regions were selected based on the genome scan and re-analyzed for the individual female fertility traits using an animal model considering relationship among all animals in the study.EBVs) were predicted by the Nordic Cattle Genetic Evaluation (NAV) for each animal as part of routine genetic evaluation of Nordic dairy cattle breeds. Predictions were based on first to third parity records. BLUP EBVs were predicted using multi-trait sire models (SM). For details regarding the phenotypes recorded and models used in routine breeding value prediction, see http://www.nordicebv.info. The reliability of the breeding value is in the range of 40 to 99 % , Sweden (RDCSWE) and Finland (RDCFIN) with official breeding values for female fertility traits were used for the association study. Estimated breeding values (AIS) in cows and heifers, the length in days of the interval from calving to first insemination (ICF) in cows, and days from first to last insemination (IFL) in cows and heifers. In addition 56-day non-return rate (NRR) in cows and heifers were analyzed even though it had no weight in the FTI. The reason to include NRR in our analyses was that NRR is a trait of biological importance understanding female fertility. For AIS, IFL and NRR EBVs were predicted separately for 1st parity animals  and 2nd and 3rd parity animals . For more information about the heritability and correlations between the traits please see http://www.nordicebv.info/NR/rdonlyres/5CD2E4DC-F82A-4809-A770-3022E270E205/0/PrinciplesNyeste.pdf.Female fertility index reflects the ease with which a heifer or cow was able to conceive. FTI is a compound index and its sub-traits are: number of inseminations per conception  the 50 K genotypes for 12,322 Nordic bulls  were imputed to HD genotypes using the software IMPUTE2 [Imputation of 50 K SNP to the full sequence was done in two steps. First, in another study  family j from population i, μ was the general mean, pi is fixed effect of i‘th population , b was the allelic substitution effect, xijk was the number of copies of an allele (with an arbitrary labeling) of the SNP count in the individual k , and sj was the random effect of the j‘th half-sib family assumed to follow a normal distribution s ~ N where σs2 was the sire variance, and eijk was a random residual of individual k assumed to follow a normal distribution e ~ N. All statistical analyses were conducted using DMU [0: b = 0 was tested with a t-test. A SNP was considered significant if the –log10(P) value for the SNP exceeded a genome wide Bonferroni corrected threshold of 8.25 ) corresponding to a nominal type I error rate of 5 % corrected for 8,938,927 simultaneous tests.Where sing DMU . The nul10(P) value in that peak. QTL regions were defined as a section of chromosome 0.5 Mb on both sides of the most significant SNPs for FTI in the genome. The selected regions were screened for association with the sub-indices. An animal model considering all relationships among individuals was used.The results from the step I analysis were plotted in a Manhattan plot Fig. . QTL peayij was the EBV for j‘th bull from i‘th population, μ was the overall mean, pi was a fixed effect of i‘th population , b was the allelic substitution effect, xij was the number of copies of an allele (with an arbitrary labeling) of the SNP count in the individual j , uij was the random polygenic effects. The vector u, whose elements were the uij was assumed to follow a multivariate normal distribution u ~ N  where A was the additive relationship matrix between sires and σu2 was the polygenic variance, and ej was the random residual of individual j with a normal distribution N. The model was fitted by REML using the software DMU [t-test against a null hypothesis of H0: b = 0. A SNP was considered significant if the –log(p) value for the SNP exceeded a genome wide Bonferroni corrected threshold of 8.25 ) corresponding to a nominal type I error rate of 5 % corrected for 8,938,927 simultaneous tests.The association between the SNP and the phenotype was assessed by a single-locus regression analysis for each SNP separately, using a linear mixed model . The modware DMU . The esthttp://www.ensembl.org, [Bos taurus 75.31, UMD3.1.All positions provided in this text refer to the UMD3.1 assembly. The annotations of the most significant SNP markers in the selected regions were extracted from the ENSEMBL data base : ENSEMB10(P) > 8.25) were detected for FTI distributed over 25 different chromosomes. The most significant QTL was identified on BTA12 with 12,860 significantly associated SNP. For this QTL the causal variant, a 660-Kb deletion around 20.5 Mb at BTA12, has previously been described [The results of the step I analysis for FTI are presented Fig. The regions selected based on association results for FTI Table  were reaTRPC1 gene, while the most significant SNP marker for IFLH was annotated as an upstream gene variant of the GRK7 gene. On BTA2 the associations with the highest test statistic were found for AISH, IFLH, and NRRH. The SNP BTA2:132321329, (rs209242168) was the SNP with the highest test statistics for all three traits. This SNP is located within the EIF4G3 gene. On BTA3 the associations with the highest test statistic were found for AISH and NRRH. For the QTL for AISH the most significant SNP was located within the RHOC gene, while for the QTL for NRRH the most significant SNP was l annotated as an upstream gene variant of the WDR77 gene. On BTA5 the SNP with the highest test statistic for FTI was BTA5:62781359 (rs135099682) with a –log10(P) of 10.79. The same marker was also the most significant marker for the traits AISC, AISH and IFLH with –log10(P) values of 9.99, 13.87, and 13.45 respectively.On BTA1 the associations with the highest test statistic were found for IFLC and IFLH. The most significant SNP marker for IFLC was located in the GPR125 gene. A second QTL on BTA6 was detected and showed the strongest association with AISH and IFLH. The SNP BTA6:99400480 (rs379908987) was the most significant SNP for both AISH and IFLH and were located within the SCD5 gene. The SNP with the highest test statistics for NRRH  was located within the C4orf22 gene. Furthermore, FTI was most strongly associated with the SNP BTA6:98115824 (rs208894094). This SNP was also the most significant marker for AISC and IFLC.On BTA6 the associations with the highest test statistic were found for AISH and NRRH. Both SNPs  were located in an intergenic region  above 10. All SNP markers detected were located in an intergenic region. On BTA24 the associations with the highest test statistic were found for AISH and IFLH. The significant SNPs were located within the ZNF521 gene.On BTA13 the associations with the highest test statistic were found for IFLC and IFLH, whereas the marker with the highest test statistic for FTI was located in the In this study we have applied a two-step approach to screen the cattle genome for QTL for the female fertility index. In the first step the cattle genome was screened for QTL for the FTI based on the whole genome sequence variants using a sire model without considering the relationship among all animals in the study except the sire-son relationship. This was done to pre-select markers to analyze with a linear mixed model. Based on the results in step I, targeted QTL regions for the FTI were reanalyzed for the underlying female fertility traits using sequence data and an animal model taking the relationship among sires included in the study. Below we discuss the findings from individual targeted regions.TRPC1. TRPC1 has functions related to protein binding (GO:0005515). In mice it has been shown that osteoblast formation and function is regulated by a TRPC1 protein-dependent pathway [GRK7. Höglund et al. [In this region for the FTI QTL AISC, IFLC and NRRC have their most significant SNP  in common. AISC and IFLC are phenotypes which are highly connected as they measure the same i.e., AIS reflects the number of inseminations, whereas IFL measures the time this has taken. BTA1:127295226 is located in intronic region of  pathway . Furtherd et al. .EIF4G3. The function of EIF4G3 is related to protein binding (GO:0005515). Previously a SNP on BTA2 has been detected for NRRC at 12.4 Mb in Finnish Ayrshire [The most significant marker  was in common for the traits AISH, IFLH and NRRH and located in the intron of SLC6A17 gene functions as a vesicular transporter selective for proline, glycine, leucine, and alanine [CVM), which is characterized by misshapen and fused vertebrae around the cervico-thoracic junction is caused by a mutation in the Golgi-resident nucleotide-sugar transporter encoded by SLC35A3 located a 43.4 Mb on BTA3 [SLC6A17 is a strong candidate gene for this QTL. Sahana et al. [The most significant SNP for FTI  was also the most significant SNP for AISC and IFLC. This SNP is intergenic close to SLC6A17 gene .  alanine . Complex on BTA3 . This QTa et al.  detectedThe most significant SNP for FTI  was also the most significant SNP for AISC, AISH, and IFLC. This SNP was not assigned to any gene. Sahana et al.  detectedGPR125. GPR125 has functions related to protein binding (GO:0005515). Even though many female fertility traits were highly significant, none of the traits shared their most significant SNP (Table KCNIP4. The function of KCNIP4 is among others related to protein binding (GO:0005515). In the region from 97.6 to 98.6 Mb the most significant SNP marker  for FTI was also the most significant marker for AISC and IFLC. Furthermore AISH and IFLH had their most significant marker  located in SDS5 gene. The function of SDS5 is related to stearoyl-CoA desaturase activity (GO:0004768). Sahana et al. [IGFBP7) located at BTA6 influencing NRRH and IFLH. However this gene is not located within any of the QTL areas.BTA6 harbors 2 QTL for fertility traits 54 Mb apart. In the first QTL defined in the region 43.0 to 43.9 Mb the most significant marker  was located in an intron in a et al.  identifiANKRD60. This gene is related to protein binding (GO:0005515). In a window of 1 Mb around BTA13:58664049 the traits AISH, AISC, ICF, IFLH, IFLC and NRRH showed highly significant markers  was located in the gene GRAMD1B. This gene’s involvement with female fertility is unknown. Höglund et al. [In the region 34.1 to 35.1 Mb FTI, AISC, ICF, and IFLC did not have their most significant SNP in common, but each of their significant SNP was located in the intron region of d et al.  confirme10(P) of over 10 in the region of 500 kbp around SNP marker BTA20:67116858 (Table The most significant SNP for FTI is BTA20:67116858 rs133488500). All the female fertility traits have high –log88500. AlZNF521. ZNF521 is among others related to protein domain specific binding (GO:0019904). This gene has not been reported to be associated with fertility before, but has been reported as a candidate gene for the trait, rear side view, in Chinese Holstein cattle [The most significant SNP for FTI  coincided with the most significant SNP for AISC and IFLC. In the same region ranging from 31.1 to 32.1 Mb AISH, IFLH and NRRH shared their most significant SNP . The two SNPs are close to each other in the genome and are located in an intron in the gene All the significant SNPs on BTA5 and BTA20 were located in intergenic regions. With the improvement of the Bovine annotation of the genes and localization of the set sites of gene transcription, initiation, termination as well as differential splicing one would expect the identification of the causal mutation to improve. To date information on genomic structure of organisms which are better annotated like mouse and human are used as an information source. However it has been shown that areas which contain mainly regulatory elements and non-protein coding regions the annotation are more challenging , 26.Though we have used the “full” sequence level variants in our analysis half of the total genetic variants identified in the WGS were filtered out for various quality control reasons. All the variants which were not bi-allelic were dropped due to limitations in the imputation software. Therefore the actual causal polymorphism may be missing from the data analyzed here.The genetic correlation between cow and heifer fertility is generally low , 28. ThiThe results of this study showed that 1) the traits AISH and IFLH had in many cases the same significant SNP marker in the QTL detected for FTI, 2) the majority of the candidate genes assigned to the most significant SNP regulating female fertility were involved in protein binding, and 3) a SNP marker on BTA20 located in the QTL region for FTI has previously been validated for female fertility in three different breeds.ftp://ftp.ncbi.nlm.nih.gov/genomes/Bos_taurus/). All whole genome sequencing data for RDC animals are now part of the 1000 Bull Genomes Project. All variations detected by the 1000 Bull Genomes Project have been or will be submitted by the 1000 Bull Genomes Project to dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). Whole genome sequence data for the 253 individuals included in Run2 of the 1000 Bull Genomes Project are available at NCBI using SRA no. SRP039339 (http://www.ncbi.nlm.nih.gov/bioproject/PRJNA238491). All annotation information was obtained from a publicly available source (http://www.ensembl.org).Genome assembly data were taken from publicly available sources. The assembly is available for download (