Genomic selection and genome-wide association studies need thousands to millions of SNPs. However, many non-model species do not have reference chips for detecting variation. Our goal was to develop and validate an inexpensive but effective method for detecting SNP variation. Genotyping by sequencing (GBS) can be a highly efficient strategy for genome-wide SNP detection, as an alternative to microarray chips. Here, we developed a GBS protocol for ducks and tested it to genotype 49 Pekin ducks. A total of 169,209 SNPs were identified from all animals, with a mean of 55,920 SNPs per individual. The average SNP density reached 1156 SNPs/MB. In this study, the first application of GBS to ducks, we demonstrate the power and simplicity of this method. GBS can be used for genetic studies in to provide an effective method for genome-wide SNP discovery. The domestic duck is an economically important agriculture animal and is consumed worldwide, especially in Asia2567891011in silico digestion study. The results of simulated digestion are illustrated in MseI digestion was predicted to achieve more tags than other enzymes. The smooth tag size distribution curve for MseI supported its choice as a good candidate for this GBS study  were generated and 96.12% (523 million reads) of these were mapped to the duck genome with an average mapping rate of 96.25% . In totain silico study with 50 randomly selected SNPs. After considering chromosomal distribution of SNPs and suitable enzyme digestion loci, we chose 24 SNPs to perform PCR-RFLP analysis. These SNPs were randomly distributed in the duck genome with approximately one selected site per chromosome. The results of PCR-RFLP assay are illustrated in To validate results, we performed an 5 to 106 SNPs. However, many non-model species do not have reference chips for detecting variation. To solve this issue, one option is to generate more sequencing data as sequencing costs continue to fall quickly. The GBS method has great potential for application to the genomes of agricultural animals, as an alternatives to chip platforms. Pertille and coworkers sequenced 462 chicken using GBS method and identified 67,096 SNP with a 4.66% coverage of whole genome13Genomic selection and genome-wide association studies need 10We observed that the ratio of genome coverage was a mean of 13%, higher than the 6.9%, predicted coverage. Two possibilities might lead to this result. Firstly, the duck reference genome still has many gaps. In the current duck reference genome, there are more than 70,000 contigs/scaffold. Therefore, the real number of predicted fragments cannot represent the real data until the quality of the reference genome is improved. Another reason is that shorter fragments were used in sequencing, even though a narrow selection range was set using a Pippin system. In practice, higher genome coverage will obtain more SNPs, but with reduced sequencing depth in some loci with the same sequencing data. In summary, we genotyped 49 Pekin ducks using GBS and identified 169,209 confident SNPs. We have demonstrated that GBS is a highly effective method for accessible and low cost genome-wide genotyping.All experiments were performed according to regulations and guidelines established by the Animal Care and Use Committee of China Agricultural University (permit number: DK996). All protocols and procedures were approved by the Beijing Administration Committee of Laboratory Animals under the leadership of the Beijing Association for Science and Technology (permit number: SYXK 2007–0023). Blood was extracted from the wing-vein via vacuum piping, with 75% alcohol/cotton ball for disinfection. All efforts were made to minimize animal suffering during the study.ad libitum from 0 to 6 weeks. A blood sample was collected from each individual.Forty-nine Pekin ducks from the same flock were randomly selected at the Beijing Jinxing Golden Star Duck Centre. Birds were fed in silico digestion of the duck reference genome  was performed to choose the appropriate restriction endonuclease using R package SimRADMseI, which recognizes a 4-bp sequence (TTAA) and creates a 2-bp overhang (Genomics DNA was extracted from blood using the standard phenol/chloroform method. An overhang . Then a http://picard.sourceforge.net.). The data were deposited in the NCBI sequence read archive (SRP068685).The raw reads that had <20 sequence quality score and <50-bp of sequence length were removed, and then barcode sequences were eliminated. The clean sequences were aligned to the duck reference genome  using Burrows-Wheeler Aligner (BWA) with the default parameters-stand_call_conf 20 -stand_emit_conf 20, other parameters were default)Supplementary Table S3The genome analysis toolkit (GATK) was used to perform local realignment of reads to correct misalignments, and then to detect the SNPs and call the genotypes (http://rebase.neb.com)http://www.ncbi.nlm.nih.gov/tools/primer-blast/)Restriction enzymes for the PCR-RFLP assay were selected using information from REBASE .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Group B streptococcus (GBS) capsular polysaccharide is one of the major virulence factors underlying invasive GBS disease and a component of forthcoming vaccines. Serotype classification of GBS is based on the capsule polysaccharide of which ten variants are known to exist . Current methods for GBS serotype assignment rely on latex agglutination or PCR while more recently a whole genome sequencing method was reported. In this study, three distinct algorithms for serotype assignment from genomic data were assessed using a panel of 790 clinical isolates.cpsD-G region to differentiate serotypes Ia-VII and IX. Finally the third approach used the variable cpsG –K region coupled with a mapping methodology. All three approaches were assessed for typeability (percentage of isolates assigned a serotype) and concordance to the latex agglutination methodology.The first approach utilised the entire capsular locus coupled with a mapping methodology. The second approach continues from the first and utilised a SNP-based methodology across the conserved cpsG-K region demonstrated the best performance with 99.9% typeability and 86.7% concordance. Overall, of the 105 discordant isolates, 71 were resolved following retesting of latex agglutination and whole genome sequencing, 20 failed to assign a serotype using latex agglutination and only 14 were found to be truly discordant on re-testing. Comparison of this final approach with the previously described assembly-based approach returned identical results.Following comparisons, the third approach using the variable These results demonstrated that molecular capsular typing using whole genome sequencing and a mapping-based approach is a viable alternative to the traditional, latex agglutination-based serotyping method and can be implemented in a public health microbiology setting.The online version of this article (doi:10.1186/s12864-017-3820-5) contains supplementary material, which is available to authorized users. Streptococcus agalactiae  is a leading cause of neonatal sepsis and meningitis worldwide [orldwide . Increasorldwide . A rise orldwide . This isorldwide .Although no GBS vaccine is currently available conjugate polysaccharide vaccines covering the most common serotypes are in development . Serotypcps locus and is composed of 16-18 genes [cpsA to -F genes are located at one end distal to the cpsL, NeuB, −D, −A and –C genes at the other, and these genes are highly conserved across the ten serotypes. In the central region from cpsG to –K in serotypes Ia-VII and IX and from cpsR to -K for serotype VIII the presence of genes and/or the sequence similarity varies between the serotypes  and the rapid development of bioinformatic infrastructures to analyse and store the large amount of data generated, WGS can provide a feasible approach to perform GBS serotyping. A recent study has described an approach to successfully determine the GBS serotype from WGS data . The appA panel of 10 GBS reference strains  and incubated aerobically at 37 °C for 24 h. Serological classification based on capsular polysaccharide types Ia, Ib and II to IX was performed using latex agglutination according to the manufacturer’s recommendations .Strains were pre-lysed in a lysis buffer composed of 2 mg lysozyme , 120 U mutanolysin (Sigma-Aldrich), 400 μg RNase A , 20 μL proteinase K (>600 mAU/mL) and 15 μL of overnight culture. Cell suspensions were incubated for 1 h at 37 °C, 2 h at 56 °C followed by 1 h at 80 °C. DNA was extracted using the QIAsymphony SP system and the QIAsymphony DSP mini kit (Qiagen) according to the manufacturers’ recommendations. DNA concentrations were measured using the Quant-iT dsDNA Broad-Range Assay Kit  and GloMaxR 96 Microplate Luminometer . For sequencing preparation, a Nextera XT DNA Library Preparation Kit  was used followed by sequencing using a HiSeq 2500 System (Illumina) and the 2 × 100-bp paired-end mode.https://github.com/phe-bioinformatics/kmerid) was used to compare the sequence reads with a panel of curated NCBI Reference Sequence Database genomes to identify the species. FASTQ reads from all sequences in this study (n = 790) were submitted to the European Nucleotide Archive (ENA) using the ena_submission tool (https://github.com/phe-bioinformatics/ena_submission) and can be found at the PHE Pathogens BioProject PRJEB18093 at ENA  was used to deplex the samples and FASTQ reads were processed with Trimmomatic to remove bases from the trailing end that fall below a PHRED score of 30 . K-mer icps locus sequence of Streptococcus agalactiae strain A4 (accession number: DQ359707), a serotype V strain that covers the majority of the capsular genes [http://www.ebi.ac.uk/ena/data/view/LT671983-LT671992) for all reference strains are available in the PHE Pathogens BioProject PRJEB18093 at ENA  de novo assembly software  with thecpsD-G conserved regions were also aligned using MEGA6 [Capsular locus sequences were aligned using progressiveMauve  to visuang MEGA6  to invesReads from each readset were mapped to a multi-fasta file containing the reference sequences using bowtie2  . A thresSAMtools (version 0.1.19)  mpileup cpsD-G region and based upon their results were able to differentiate serotypes Ia to VII and IX. Serotype VIII was not included due to an inability to amplify the target region using the primers detailed in those studies.Serotype identification using PCR amplification followed by Sanger sequencing was previously described by Kong et al. for serotypes Ia to VII and later by Slotvet et al. to include serotype IX , 27. ThecpsD up to the 5′ end of cpsG for nine serotypes were aligned and all SNPs were recorded. Serotype VIII was excluded due to the lack of cpsF and cpsG genes; instead of the standard cpsD-cpsE-cpsF-cpsG-cpsH gene pattern, serotype VIII has cpsD-cpsE-cpsR-cpsH , as well as the repeat region, leaving a final set of 44 SNPs , IX and V (n = 71) and IX and VII (n = 17) , with the remaining two cases corresponding to samples assigned Ia and IX, respectively . Using this approach, a single serotype was assigned in 163 cases 20.6%) whereas in one case the highest percentage mapping coverage observed was below 90% resulting in a ‘Failed’ tag and in the remaining 629 cases the method was not able to distinguish between two serotypes that both exhibited >90% coverage. This result demonstrated higher than 90% similarity between the capsular sequences of serotypes Ia and III n = 35, IX an0.6% whercpsD-G region were investigated amongst serotypes Ia-VII and IX. The reads were first mapped to the full capsule and any isolates with serotype VIII capsule were precluded from further analysis (n = 2). Reads were mapped to the cpsD-G region of S. agalactiae strain 3139 (region 3166-5955: accession number AF332908.1) and a serotype was assigned based on the bases present in the 44 SNP positions described above (Table n = 17), a mixed base (variant present in 20-80% of the reads) in certain SNP positions (n = 2) or an incomplete SNP profile match (n = 20) , which covered 12 of the 44 SNP positions. Due to the low coverage (<90%) the SNP analysis was not performed. However, based on the 32 SNP positions that were covered this isolate matched (32/32) the serotype Ia SNP profile.Secondly, SNPs within the conserved cpsG-K  and the one isolate that failed with highest coverage of 66.97% was the same isolate that failed with the two previous methods.The variable region G-K Fig.  was the Concordance of the three molecular serotyping assays was observed compared to the latex agglutination method. Concordance was observed in 120 of the 162 typed isolates (74.1%) using the full capsular locus approach Table . The 42 The SNP-based approach resulted in concordance with serology for 653 of 749 typed isolates (87.2%). The remaining 96 discordant isolates were investigated by repeating serology and/or WGS. Discordance was resolved in 67 cases; in 29 cases repeat WGS was concordant with the laboratory serotype whereas in the remaining 37 cases repeating the serological analysis gave a result concordant with the WGS. Of the 96 discordant isolates, 37 were also discordant with the previous method; the remaining five were non-typeable by the SNP-based method (Table cpsG-K region concordance was observed in 684 of the 789 cases (86.7%). The remaining 105 discordant isolates, included all discordant isolates from the first method (n = 42) and 95/96 of the discordant isolates from the second method, in addition to 10 non-typeable by the SNP-based method. All discordant isolates were retested with latex agglutination by an investigator blinded to the previous results. One isolate discordant with the SNP-based method was correctly assigned serotype Ia with the cpsG-K method; using the full capsule method the serotype pair Ia/III was called and with the SNP-based method serotype III; following retesting in the laboratory serotype V was assigned which did not agree with either method. Further investigation into this isolate, using mapping analysis and de novo assembly followed by BLAST analysis, revealed that whereas the cpsD-G region corresponded to serotype III (44/44 SNP matches) this isolate contained a serotype Ia cpsH gene [n = 105) and in 40 cases the new result corresponded to the predicted serotype (by WGS) whereas in 20 cases no serotype was assigned due to lack of reaction or reaction with multiple sera. Of the remaining 45 discordant cases, 44 were re-sequenced and in 31 of these cases the new predicted serotype was concordant with the serological result. Overall following retesting only 14 cases remain truly discordant excluding the 20 non-typeable by sera  revealed that in all cases the serotype assigned by mapping was also the serotype with the highest coverage along the multiple contigs in the assembly/BLAST approach.The results of the third mapping-approach, that uses the variable by BLAST . All 790cpsD-G was previously used for molecular capsular typing in Group B streptococci using a SNP-based approach with PCR amplification and Sanger sequencing [cpsG-K was used to predict serotype from WGS data using a de novo assembly and BLAST analysis approach [cpsD-G region on a validation panel of 790 GBS isolates.The conserved region quencing , 27, wheapproach . In thiscpsH gene [cpsM based on the read mapping patterns for serotypes V and IX; that is in addition to the presence of cpsN in serotype V . For the VII/IX pair, the two serotypes differ in the presence of cpsO in serotype IX and cpsI in VII (VII: cpsG-cpsH-cpsM-cpsI-cpsJ-cpsK). Furthermore, fragmented mapping (reads mapping to some region but not others) between cpsM and cpsK suggests high sequence variability in cpsJ as well.The full capsular locus approach was able to predict 5/10 serotypes  with distinct capsular locus sequences but was unable to distinguish the remaining five, including serotype III which is the most prevalent serotype in the UK. These latter serotypes were resolved to one of two highly similar capsular loci  each sharing more than 90% similarity. Previous publications have reported the similarity of the serotype Ia and III capsular loci; the main difference lies in the psH gene . FurthercpsE and cpsF, including 10 previously published . However, based on previous studies [cpsD-G; three isolates have 43/44 SNPs matching the serotype II SNP profile and a T in position 437 suggesting that these isolates are indeed serotype III subtype 4 which also agrees with the serological result and the result from the cpsG-K method. Furthermore, in positions 486 and 1026 the presence of G and A instead of A and G, respectively are indicative of serotype III subtype 3 and based on this, three isolates with a partial 42/44 serotype III profile and G and A in 486 and 1026 respectively would be assigned serotype III subtype 3. This also agrees with the cpsG-K assignment and in one of the cases with the serological result. For the remaining 7 positions, the presence of these alternate variants, even though occurring rarely, suggests that variants at these positions are not conserved within a serotype.The SNP-based approach failed to predict a serotype in 39 cases due to low mapping coverage or a mixed base at certain SNP positions for approximately half the cases; this dependence on specific positions is one of the disadvantages of using a SNP-based methodology coupled with mapping and strict quality criteria . However, the remaining unassigned cases were of particular concern; in these cases failure to predict serotype was due to a mixed SNP profile, this is a 41-43/44 match to a serotype-specific SNP profile with one to three SNP positions matching the expected base of another serotype. The positions affected include 12 SNPs located in cpsG-K region produced the highest typeability (99.9%) with only a single isolate remaining non-typeable. However, this isolate was non-typeable with all methods and was shown to lack a large part of the capsular locus (cpsC-H) therefore the lack of serotype prediction was correct. Using this method 86.7% concordance was observed with only 105 isolates, which included all (but one) of the discordant isolates from the two previous methods and 10 non-typeable isolates by the SNP method. Therefore, this number represents the discordance between the molecular and serological approach. Following retesting using both serological and WGS methods only 14 isolates remained truly discordant.Finally, mapping the reads to the variable n = 18) being mis-identified as rarer types or non-typeables and was resolved when a new batch was used. Furthermore, isolates referred to the reference lab are inoculated onto an agar plate and following overnight incubation a sweep of material is usually tested for serotyping whereas for retesting subculture from a single colony was used. Therefore the presence of a mixed population in the initial culture could result in ambiguous serotyping results downstream in the investigation. However not all discordance can be attributed to the traditional methodology. Cases where the traditional re-serotyping was consistent but repeat WGS resulted in a different serotype could be attributed to a laboratory transcription error.The initially high discordance rate can mainly be attributed to the traditional methodology of latex agglutination which can be subjective; specifically re-serotyping using latex agglutination resulted in the same serotype in only 39/105 cases indicating latex agglutination or lab transcription error. This high ambiguity with the serological method may be in part due to a problem with a particular batch as some of the sera used were shown to agglutinate poorly when tested with controls. This included the Ia sera resulting in a number of serotype Ia isolates . (XLSX 11 kb)Additional file 2: Table S2.n = 790). (XLSX 36 kb)Metadata for all clinical isolates used in this study Additional file 4: Table S4.cps loci using a small a small dataset of clinical samples (n = 28). SNP positions highlighted in grey represent SNPs not found in all isolates of a particular serotype. These positions were removed and the remaining SNPs represent the final set used in this study. (XLSX 23 kb)Investigation of the SNPs (previously published and novel) identified using multiple alignment of the reference Additional file 5: Table S5.cps locus approach. Hit coverage corresponds to the mapping coverage observed during analysis whereas the BLAST coverage corresponds to the percent coverage seen during pairwise alignment using BLAST. (XLSX 8 kb)Sequence analysis for the serotype pairs identified using the full Additional file 6: Table S6.Investigation of the not-typeable (“Serotype undetermined”) isolates from the SNP-based approach. The pileup file created during the analysis was investigated in detail to determine the aetiology behind the incomplete SNP profiles. (XLSX 19 kb)


Based on the genetic map, one major QTL, named Up12.1, was detected for the fruit orientation trait. A total of 65 protein-coding genes were predicted within this QTL region based on the current annotation of the Zunla-1 genome. In summary, the thousands of well-validated SNP markers, high-density genetic map and genetic diversity information will be useful for molecular genetics and innovative breeding in pepper. Furthermore, the mapping results lay foundation for isolating the genes underlying variation in fruit orientation of Capsicum.The development and application of single nucleotide polymorphisms (SNPs) is in its infancy for pepper. Here, a set of 15,000 SNPs were chosen from the resequencing data to develop an array for pepper with 12,720 loci being ultimately synthesized. Of these, 8,199 (~64.46%) SNPs were found to be scorable and covered ~81.18% of the whole genome. With this array, a high-density interspecific genetic map with 5,569 SNPs was constructed using 297 F Capsicum spp.) are native to the tropics and subtropics of AmericasSolanum lycopersicum) and potato (Solanum tuberosum). Archeological microfossil evidence indicated that peppers were domesticated in the Americas and have been consumed in this region for more than 5,000 years2C. annuum, C. frutescens, C. chinense Jacq., C. baccatum and C. pubescens Ruiz & Pavon, are cultivated globally as fresh vegetables, seasoning, ornaments and medicinePeppers 51516182223up locus2628up locus, including A2C7469 (1.7 cM) and upCAPS (4.3 cM), have also been developedOver the course of domestication, several plant and yield traits in pepper were specifically selected for adaption to cultivation and human demands2 progenies of a pepper interspecific (C. annuum × C. frutescens) population, together with a panel of 399 pepper elite/landrace lines. Based on the genotype data, a genetic map was constructed and genetic diversity of the pepper panel was characterized. We expected that the thousands of well-validated SNP markers, genetic map and diversity information reported here would be useful for downstream applications in both basic and applied researches in pepper. In addition, we performed QTL analysis for the fruit orientation and hoped the related information would lay a foundation for further isolation of genes underlying the up locus of pepper.Thus, in the current study, we first describe the development of an Illumina Infinium iSelect SNP array for pepper. We then used this newly developed array to genotype 297 FCapSNP15K. A total of 12,720 bead types were ultimately synthesized. Of these, 8,199 (~64.46%) SNP loci were scored with normal signals among all 1,019 investigated samples . Based on the filtering criteria mentioned in the Methods, a final set of 15,000 SNPs was submitted to develop the Illumina Infinium iSelect SNP array, hereafter referred to as  samples , and the samples , the maj samples  based on samples . In addi samples  and S3, C. annuum and C. frutescens genomes18After the removal of 156 low quality loci from the 5,828 scorable SNPs within the mapping population, the retained 5,672 (97.32%) polymorphic SNPs were used for map construction. Finally, a high-density genetic map with 5,569 SNP markers forming 3,826 genetic bins was obtained and designated BY-SNP map . AssigniP < 0.01) in this interspecific population and 39 SDRs were identified on all chromosomes except for P6, P7 and P11  SNPs showed distorted segregation  fruit orientation phenotype  fruit phenotype like YNXML  and nearly matched the 1:2:1 expected ratio , respectively. These results suggested that the erect fruit phenotype was under single recessive gene control and that the pendant fruit was controlled by a single dominant gene with incomplete inheritance.In the present study, we found that all Fhenotype , which whenotype . As for ke YNXML , whereaske YNXML  and 30 vke YNXML . Chi-squUp12.1, was consistently detected on LG12 (chromosome P12) regardless of whether the EP or ELV classification method was used  to build the pseudo-chromosomes of the Zunla-1 genomeCapSNP15K being the lower ascertainment bias of the SNPs because the 15,000 SNPs were highly filtered and elaborately chosen from the resequencing reads of 20 diverse pepper lines, simultaneously considering the polymorphisms among three related parental lines .Although SNPs have become the marker of choice for the scientific and breeding community5CapSNP15K array, the overwhelming majority of the 12,720 synthesized bead types in fact produced genotyping signals. However, only 8,199 (~64.46%) SNP loci were ultimately found to be normally scorable among all 1,019 investigated samples after the manual removal of abnormal signals  and will substantially enrich the pepper marker repertoire. More importantly, the customized array will undoubtedly facilitate high-throughput genotyping and mapping in pepper.With regard to the  signals . The ratCapsicum species, C. annuum and C. frutescens exhibit a relatively close evolutionary relationship18332 population (n = 297) derived from BA3 (C. annuum) × YNXML (C. frutescens) using the newly developed CapSNP15K array and ultimately obtained a high-density SNP genetic map (BY-SNP) for pepper . PUP3 is a member of a family of proteins related to PUP1, a purine transporterIn flowering plants, fruit orientation is fundamentally determined by the adaxial-abaxial polarity of the lateral organ pedicel. Furthermore, a number of underlying molecular regulators governing alterations to pedicel orientation have been uncovered, including Arabidopsis C. annuum lines  and B702 (30.30×), to the Zunla-1 reference genome according to a previous studyhttp://www.illumina.com.cn/). The remaining set of SNP markers was analysed using GenomeStudio Genotyping software . Because of the large proportion of duplicated sequences and the complex genomic background in the pepper genome, several markers could not be correctly and automatically interpreted by GenomeStudio. To ensure the best cluster, the genotyping signals of each SNP locus of the mapping population and diversity panel lines were clustered and manually adjusted separately. To perform preliminary testing of the genotyping quality of the newly developed array, a set of 6 DNA samples from the parental lines (BA3 and B702) and their hybrids were used to investigate duplicate reproducibility and parent-parent-child (P-P-C) heritability.SNP genotyping experiments, including DNA amplification, fragmentation, precipitation, re-suspension, and hybridization, and BeadChip extension, staining and scanning were performed based on the Illumina genotyping platforms in BGI-Shenzhen  according to instructions from the Illumina company (C. annuum), is a cytoplasmic male sterility (CMS) line. Its maintainer line (near-isogenic line) shows mid-size pendant fruit. By contrast, the pollen donor, YNXML (C. frutescens), is an erect type with small fruit. An interspecific F2 genetic mapping population including 297 individuals was derived from crossing BA3 and YNXML. Of these, 142 and 155 individuals, together with the parental lines and their hybrid population, were grown successively at the Zengcheng Experimental Station, South China Agricultural University (SCAU), Guangzhou, China , in the autumn of 2011 and 2012. The sub-population from 2012 was previously utilized to identify QTLs for the number of leaves on the primary axis (Nle) trait based on InDel and SSR markersThe female parent, BA3  according to a previous studyA total of 5,828 SNPs were scorable among the parental samples and 297 FP-value of 0.01. A genetic confidence interval (CI) was defined as 1-LOD drop from a LOD peak supported regions, and was translated into a physical confidence interval  by projecting markers onto the Zunla-1 reference genome (http://peppersequence.genomics.cn/page/species/index.jsp).QTL analysis was performed using MapQTL6.0 softwareC. frutescens), all lines are belong to C. annuum species , were used for diversity assessment and population structure analysis. Except for the YNXML  replicates and 100,000 burn-ins. ΔKC. annuum lines based on 6470 SNPs with MAF ≥0.05 was performed using the software EIGENSOFTThe admixture model-based software STRUCTURE V2.3.4How to cite this article: Cheng, J. et al. Development of a SNP array and its application to genetic mapping and diversity assessment in pepper (Capsicum spp.). Sci. Rep.6, 33293; doi: 10.1038/srep33293 (2016).


Oryza sativa accessions grown in two environments. We identified more than 107 associations for five different traits, including the contents of albumin (Alb), globulin (Glo), prolamin (Pro), glutelin (Glu), and total SSP . A total of 28 associations were located at previously reported QTLs or intervals. A lead SNP sf0709447538, associated for Glu content in the indica subpopulation in 2015, was further validated in near isogenic lines NIL(Zhenshan97) and NIL(Delong208), and the Glu phenotype had significantly difference between two NILs. The association region could be target for map-based cloning of the candidate genes. There were 13 associations in regions close to grain-quality-related genes; five lead single nucleotide polymorphisms (SNPs) were located less than 20 kb upstream from grain-quality-related genes . Several starch-metabolism-related genes  were also associated with SSP content. We identified favorable alleles of functional candidate genes, such as RP6, RM1, Wx, and other four candidate genes by haplotype analysis and expression pattern. Genotypes of RP6 and RM1 with higher Pro were not identified in japonica and exhibited much higher expression levels in indica group. The lead SNP sf0601764762, repeatedly detected for Alb content in 2 years in the whole association population, was located in the Wx locus that controls the synthesis of amylose. And Alb content was significantly and negatively correlated with amylose content and the level of 2.3 kb Wx pre-mRNA examined in this study. The associations or candidate genes identified would provide new insights into the genetic basis of SSP content that will help in developing rice cultivars with improved grain nutritional quality through marker-assisted breeding.Rice seed storage protein (SSP) is an important source of nutrition and energy. Understanding the genetic basis of SSP content and mining favorable alleles that control it will be helpful for breeding new improved cultivars. An association analysis for SSP content was performed to identify underlying genes using 527 diverse  Rice is one of the major staple cereal foods and is an important source of total protein in human food. SSP account for approximately 8% of the dry grain weight and are the second most abundant ingredient after starch in rice. Rice has the lowest protein content among cereal grains, but net protein utilization is highest . High-prgpa3, Osvpe1, and OsRab5a have been successfully cloned , controlling the natural variation in SSP content, has been cloned using a map-based cloning strategy  mutation could increase free lysine levels and create the foundation for quality protein maize (QPM) breeding. Combining GWAS and linkage mapping, a gene duplication at the 27-kDa γ-zein locus qγ27 was identified  by means of single nucleotide polymorphism (SNP) has become the method of choice for investigation of the genetics of important traits in thaliana , rice  included 294 accessions, whereas the japonica subpopulation  included 155 accessions.A diverse worldwide collection of 527 rmediate  and are Lines were planted in two environments in Hubei province: Ezhou in 2014 (Env. 1) and Wuhan in 2015 (Env. 2). The sowing dates were 25 May in both years. Seedlings about 25 days old were transplanted to the field. There were three rows with 10 plants each in each plot. The planting density was 16.5 cm between plants within a row, and 26.4 cm between rows. Field management basically followed recommended practice of agriculture, with fertilizer applied (per hectare) as follows: 48.75 kg nitrogen, 58.5 kg phosphorous, and 93.75 kg potassium as the basal fertilizer; 86.25 kg nitrogen at the tilling stage; and 27.6 kg nitrogen at the booting stage.O. sativa accessions are listed in Supplementary Table At maturity, three plants in the middle of the second row of each accession were harvested and bulked. Dry seeds were threshed in bulk and the rough rice was air-dried, and stored at room temperature for 3 months, and then stored at 4°C. 50 g of rough rice were dehulled into brown rice using a TR 200 dehuller . The embryo and aleurone layer of brown rice were removed into milled rice through a Pearlest mill . The rice was ground into flour with a CT 410 Cyclotec mill , passed through an 80-mesh sieve and stored at -20°C until the Alb, Glo, Pro, and Glu contents were determined based on previously published previous methods . Brieflyindica subpopulation; and 1,857,845 SNPs in the japonica subpopulation  was used for GWAS  for each phenotype was estimated using repeatability between 2 years of phenotypic data, calculated as the variance among variety grand means divided by their total phenotypic variance.Based on the standardized disequilibrium coefficients (D’), linkage disequilibrium (LD) was investigated. LD heatmaps were constructed using the TASSEL5.0RP6, RM1, Wx, PROLM1, and other three candidate genes in the 527 rice accessions were obtained from the RiceVarMap database (see foot note text 1). The haplotypes were classified according to all SNPs (except sites in intron) including their intragenic region and 2 kb upstream with an MAF > 0.05 in a candidate gene. There were at least five rice accessions in the haplotypes for comparative analysis. One-way ANOVA and Student’s t-tests were applied to compare differences in SSP content among all possible haplotype pairs.Candidate genes within a 200 kb genomic region ( ± 100 kb from the lead SNP) in the associated loci were selected based on (i) biochemically related proteins or protein clusters; (ii) homologous genes with known function, and (iii) expression profiles. The genotypes of Actin was used as a reference gene in the qRT-PCR experiments. The experiment was designed with three biological replicates and three technical replicates per material. Error bars indicate standard error. The measurements were obtained using the relative quantification method. The significant difference was analyzed statistically by One-way ANOVA and Student’s t-tests. All primers for qRT-PCR analysis are listed in Supplementary Table According to the manufacturer’s instructions, the total RNA was extracted from rice different tissues using TRIzol reagent (Invitrogen). About 3 μg of RNA sample was processed by RNase-free DNaseI (Invitrogen) and reverse transcribed using M-MLV reverse transcriptase (Invitrogen) with Oligo(dT)15. Quantitative RT-PCR was carried out using Fast Start Universal SYBR Green Master (Rox) superMIX  in a ViiA 7 Real-Time PCR system (Applied Biosystems), according to the manufacturer’s introductions. Measurements were obtained using the relative quantification method. Figures E and Supplementary Table Figures E). Glu accounted for approximately 80% of total SSP; Alb and Pro each accounted for about 5%; and Glo accounted for about 10% . Compared with other three storage protein contents, the average content of Pro was the lowest, but the variation of Pro was largest in the whole population and each subpopulation in both environments. In four storage protein, Total SSP showed the lowest heritability (29.5%), whereas Pro showed the highest (76.8%) . Most of them were detected for Alb (21 associations) and Pro (12 associations). Lead SNPs for Alb were widely distributed in the rice genome: chromosome 1, 2, 3, 4, 5, 6, 7, 8, and 9, with chromosomes 6 and 7 having more associations. Associations explained phenotype variation of 10.4–20.9%, with the association on chromosome 6 (sf0601764762) making the largest effect. For Pro, associations accounting for 10.1–34.6% of the phenotypic variance were identified on chromosomes 2, 5, 7, 10, and 11, with chromosome 5 and 7 exhibiting more associations. Only one lead SNP sf0317000156 on chromosome 3 with a phenotype variation of 14.4% was detected for Glo. No lead SNP with phenotype variation of more than 10% was detected for Glu or total SSP.The association analysis for the whole population identified 34 loci (phenotypic variance >10%) associated with three traits with a suggestive threshold value at 5.0E-06 (indica (33 associations) and japonica (40 associations) subpopulations  and 9 (with phenotype variation of 15.5–22.6%) associations were identified in the indica and japonica subpopulations, respectively. Ten associations for Glu were detected on chromosomes 1, 4, 5, 6, 7, 8, 9, and 10, in both subpopulations. For total SSP, only three associations on chromosomes 10 and 11 with phenotype variation of 10.4–14.8% were detected in the indica subpopulation.A large number of peaks (phenotypic variance >10%) were also detected by GWAS in the indica and japonica subpopulations, 16 were detected in different populations and nine involved two or three different traits  in the whole population and one lead SNP (sf0515706446) in the indica subpopulation were detected in both environments. The significance levels of the associations ranged from P = 5.0E-06 to P = 8.6E-16, P = 4.9E-06 to P = 9.0E-08, P = 5.0E-06 to P = 1.3E-17, P = 2.1E-06 to P = 1.4E-08, and P = 4.5E-06 to P = 3.1E-08 in LMM for Alb, Glo, Pro, Glu, and total SSP, respectively, and the most significant association for sf0706363663 located in chromosome 7 (Table 1).Among the 107 associations detected in the whole population and in the Table 1 and Supplementary Table There were many overlaps between the present associations detected by GWAS and reported QTLs or intervals related to SSP content in rice. A total of 28 associations from this study were located at previously reported QTLs or intervals  and Delong208 (DL208) . NILs of QTL were developed by successive crossing and backcrossing ZS97 (high protein content) and DL208 (low protein content), three times (BC3) to ZS97. The QTL was selected by two molecular markers MRG186 and MRG4499 (Supplementary Table 3F1 plants heterozygous for this fragment produced NIL(ZS97) and NIL(DL208). Analysis of NIL(ZS97) and NIL(DL208) showed that NIL(ZS97) was significantly higher Glu than NIL(DL208), which was the same as the phenotype in two parents . The result indicated that the QTL was reliable, which helps further identify the underlying genes and their genetic basis.In the  (DL208) . To valiPG5a, Wx, RM1, RP6, and AGPS2a) were less than 20 kb from lead SNPs. PG5a, RM1, and RP6 were associated for Pro; Wx and AGPS2a was associated with Alb. Glutelin genes GluA1 and OsAAT2 were associated with total SSP in Env. 2. Additionally, several lead SNPs were located close to starch-metabolism-related genes, such as PUL, ISA2, Wx, GBSSII, OsACS6, and AGPS2a. SNPs located close to other reported grain quality-related genes  were also examined using a LMM  was identified in the whole population and indica subpopulation  with most polymorphic sites . We performed haplotype analyses for RP6 and RM1 and identified three main haplotypes (Hap1-3) at each locus . In the region contained coding region and 2 kb upstream of RP6 and RM1, 17 and 26 SNPs were found, respectively . Considering the complexity of population structure and genetic background, we checked the expressions of RP6 and RM1 with different haplotypes in 20 and 34 accessions randomly chosen from indica group, respectively. Using qRT-PCR analysis, The results indicated that in the endosperm at 7 days after pollination (d.a.p.), expression levels of RP6 in hap2 accessions were much higher than those in hap3 accessions   on chromosome 6 . The lead SNP, explaining 12.9 and 20.9% of the phenotypic variances in the whole population in Env. 1 and 2, respectively, was located in the first intron of Wx . Other SNPs in Wx showed different LD associations with sf0601764762 . Wx is the most important genetic determinant of amylose content . In contrast, quantity of 3.3 kb Wx RNA in hap7 indica accessions were much lower than those in hap2 indica accessions and in hap5 japonica accessions  than Minghui 63 (hap2), two indica cultivars . We further compared the quantity of 2.3 and 3.3 kb Wx RNA in various tissues between two varieties Zhonghua 11 (hap2) and ZS97 by quantitative RT-PCR . The results showed that ZS97 had lower 3.3 kb Wx RNA quantity and higher 2.3 kb Wx RNA quantity than Zhonghua 11 in endosperm of 7 and 14 d.a.p. However, Wx displayed very low-level expressions in stem, sheath, and flag leaf in both ZS97 and Zhonghua 11. Then we measured amylose content of the corresponding accessions in 2015 , suggesting that Wx may negatively regulate Alb. However, Alb and quantity of 3.3 kb Wx RNA had no significant correlations. We speculated that Wx might influence Alb content.We also found a highly significant association signal for Alb involving sf0601764762  and four starch-metabolism-related genes  were detected in both environments; however, GBSSII, GluA1, and RA16 were only detected in one environment. The order of magnitudes of the P-values for these genes was greatly reduced compared with that at the SNP level. To further verify the association possibility, we validated some of candidate genes detected via GWAS by haplotype analyses and expression profiles in public databases.Association analysis at the haplotype level greatly increases mapping power . We re-dP = 6.5E-07 in Env. 1 and P = 2.7E-11 in Env. 2), in Chr.5 (14.7-15.8 Mb) . This lead SNP was significantly associated with Pro and explained 8.5 and 13.7% of the phenotypic variance in the whole population in Env. 1 and 2, respectively (Table 1). SNPs in the region were grouped into two LD block, and sf0515211855 was in the first block. Within this block, there were a gene cluster of 18 prolamin precursors and a candidate gene encoding an expressed protein (LOC_Os05g25500) . Within the cluster we focused on PROLM1 (LOC_Os05g26240), located close to the lead SNP, and identified twelve major haplotypes (Hap1-12) . We obtained five major haplotypes (Hap1-5) for LOC_Os05g25500 . LOC_Os05g25500 exhibited higher expression levels in endosperm than other tissues of ZS97 . We focused on other two candidate genes LOC_Os03g29750 (encoding an expressed protein) and LOC_Os02g13130 (encoding a KH domain-containing protein) for Glo in indica group in 2015  and Glu in all group in 2014 , respectively. Hap5 of LOC_Os03g29750 had significantly higher Glo than other haplotypes, and LOC_Os03g29750 showed very high-level expression in endosperm of ZS97 . Hap4 of LOC_Os02g13130 had significantly higher phenotypes than other haplotypes. For LOC_Os02g13130, homologous with maize gene encoding high molecular weight glutenin subunit x, it had high-level expressions in most tissues and organs . These results indicated that these genes might be good candidates for the GWAS locus.To determine whether novel functional loci were implicated by GWAS, we investigated SNP sf0515211855 . The indica subpopulation showed wider variation in Pro than the japonica subpopulation, but narrower variation compared to japonica for Alb. These results indicated that there were probably different genetic bases underlying Alb and Pro in the two subpopulations. The contents and proportions of different components as well as total SSP in the present study were in agreement with those reported by The contents of the four components of SSP and the total SSP were normally distributed in two environments and influenced by environment in this study  and Glu (sf0602321094). Lead SNPs  were co-located in intervals 7–4 and 7–5. Although large numbers of QTLs for grain protein content were detected in the past; only one major QTL has been cloned (qγ27) affecting expression of 27-kDa γ-zein has been successfully cloned by GWAS and linkage mapping analysis , This region is considered a hotspot of major QTLs  for protein content in rice (Wx RNA showed that Wx might influence Alb content, that needs transgenic experiment to verify the function of Wx. Additionally, Starch-metabolism-related genes, such as PUL (ISA2 (GBSSII (Flo4 (AGPS2a (RAG2 significantly increased total protein content, prolamin, glutelin, and amylose content, but decreased total starch (We detected a lead SNP responsible for Alb in the  in rice . Analysih as PUL , ISA2 (UUL (ISA2 , GBSSII  (GBSSII , Flo4 (KII (Flo4 , and AGP (AGPS2a  were assl starch . Carbon Lgc1 mutation has been used to development new low easy-to-digest protein rice varieties with low glutelin content and high prolamin content, which is useful for patients with chronic renal failure (O2 mutation could increase free lysine and tryptophan levels by reducing the synthesis of zeins in maize, which is useful for QPM breeding (RM1 and RP6 are adjacent genes on chromosome 7, and with higher levels expression and higher Pro, hap2 of RP6 and hap3 of RM1 are distributed mainly in the indica subpopulations (Figure 4 and Supplementary Table RM1 and RP6 from japonica subpopulation can be a promising target for reducing Pro content and achieving better nutritional quality in indica cultivars. Wx may be a key gene for regulating the content of Alb and amylose content. The availability of the Wx gene sequence provides the possibility of improving the protein content via Wx gene modification. Different haplotype combinations of candidate genes for SSP would produce grains with different eating and nutritional quality. Genes and possible causative SNPs identified in the present study could be useful for breeding rice cultivars with favorable eating and nutritional quality.Grain with good quality could be developed by regulating the SSP content. SSP is a typical quantitative trait typically affected by environment . Combina failure . O2 mutabreeding . Genes abreeding . The leaWx pre-mRNA is positively correlated with Alb content, providing new insights into the genetic basis of rice quality. Overall, we provided useful information that could be used in future gene functional studies and rice quality improvement.In the present study many associations were identified for five SSP traits using GWAS in two environments. We detected novel associations, known SSP genes, and SNPs adjacent to known starch-metabolism-related genes. We also analyzed haplotypes of known grain-quality-related genes. Our results suggested that GWAS was an effective way to identify genes for rice SSP traits and the level of 3.3 kb YH designed and supervised the experiments. PC, ZS, GL, and BW performed all the phenotypic evaluations. LM, YiL, PC, WD, and BP performed analysis and interpretation of the data. PC wrote the paper. GW, YaL, and DZ provided rice germplasm samples. GG, QZ, JX, and XL participated in the field management and logistic work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Triploidy occurred about 2-3% in human pregnancies and contributed to approximately 15% of chromosomally caused human early miscarriage. It is essential for preimplantation genetic diagnosis and screen to distinct triploidy sensitively. Here, we performed comparative investigations between MALBAC-NGS and MDA-SNP array sensitivity on triploidy detection. Self-correction and reference-correction algorism were used to analyze the NGS data. We identified 5 triploid embryos in 1198 embryos of 218 PGD and PGS cycles using MDA-SNP array, the rate of tripoidy was 4.17‰ in PGS and PGD patients. Our results indicated that the MDA-SNP array was sensitive to digyny and diandry triploidy, MALBAC-NGS combined with self and reference genome correction strategies analyze were not sensitive to detect triploidy. Our study demonstrated that triploidy occurred at 4.17‰ in PGD and PGS, MDA-SNP array could successfully identify triploidy in PGD and PGS and genomic DNA. MALBAC-NGS combined with self and reference genome correction strategies were not sensitive to triploidy. Triploidy is an abnormal chromosome kayotype, which occurred about 2-3% in human pregnancies  and contThree pronucleis (3PN) embryo formation is common during in vitro fertilization (IVF), and is believed that polyspermic fertilization or oocyte-derived meiotic failure lead to triploid , 9. ThosPGD/S was used for chromosome structure abnormality carrier patients, single gene mutation carrier patients, advanced maternal age couples, recurrent implantation failure and recurrent miscarriage patients to screen embryos genetic condition prior to transfer , along wHere in our study, we systematically compared SNP array and NGS on detection chromosome deletion, duplication, uniparental disomy, mosaic and triploidy. We firstly identified triploid embryos using SNP array and compared it with NGS during PGD/S, our results indicated that present NGS based PGD/S procedures were unable to detect triploid embryos but SNP array can successfully distinguished triploidy.We have detected 5 triploid embryos in 1198 embryos of 218 PGD/S cycles using MDA-SNP array , the results of SNP array and NGS were highly in accordance Table . The NGSOur previous data detected triploidy in five PGD cycles, however, these only identified arr 1-22)×3,(X)×3, however, whether the arr (1-22) ×3, (XXY)×1 and arr (1-22) ×3, (XYY) ×1 could be detected or not in single cell lever using MDA based SNP array is still unknown. Here we system investigated the sensitivity of MDA based SNP array for the digyny and diandry triploidy on single cell lever. Our results showed MDA based SNP array could completely identify the arr (1-22)×3,(X)×3, arr (1-22) ×3, (XXY)×1 and arr (1-22) ×3, (XYY) ×1 ×3We selected 9 previous known triploids SNP array karotype to validate using NGS. The 9 triploid SNP array kayotype were shown in Triploidy is an abnormal chromosome kayotype, which occurred at very low percent in human pregnancies, this is the first time comprehensive analyzed the triploidy detection using SNP array and NGS in PGD/S and missed abortion chorionic tissues. Our results indicated that the rate of tripoidy was 4.17′ in PGD/S patients. In our study, we comprehensive compared the MDA based SNP array and MALBAC based NGS for triploid detection on single cell lever. And we concluded that SNP array could detect triploidy, while the present strategies of NGS are not sensitive to triploidy detection.PGD and PGS were used to detect the aneuploidy and pathological gene carrier embryos and has been used widely in clinical , which hIn our study, both self and reference genome correction were used to analyze the NGS data of triploid DNA of chorionic tissues, and we found these two methods could not identify triploidy. The present bioinformatics algorithms mainly calculate the reads falling into the “continues windows” usually ranging from 1K to 1M on each chromosome, it is able to identify the reads number of the chromosome deletion, duplication and mosaic as shown in our study, but it ignores the SNPs and could not detect triploidy. Triploidy usually leads to the SNPs three nucleotides heterozygosis and the homozygous/heterozygosis was 1:2, SNP array well distinguishes the SNPs and well detects the triploidy even after the single cell whole genome amplification, while NGS needs to sequence deeper and much powerful bioinformatics algorisms to analyze the data. Although the NGS is much more powerful on detection micro duplication and deletion, it is weaker than SNP on detection triplody. SNP-based approach detected the relative distributions of alleles at polymorphic loci and does not require a reference chromosome for comparison . AlthougThe controlled ovarian stimulation (COS) of all the patients underwent PGD and PGS were carried out in a long protocol, GnRH analogues was used for pituitary desensitisation, together with human menopausal gonadotrophins (hMG) or recombinant FSH. The starting dose of gonadotrophins for PGD and PGS patients was determined according to the patient's age, BMI and/or previous response to ovarian stimulation (range from 75 to 300 IU QD). Human chorionic gonadotrophin (hCG) was administered when at least 60% follicles above 16 mm mean diameter and the biggest under 22 mm mean diameter were seen when transvaginal ultrasound scan. Transvaginal ultrasound-guided and vacuum take-off oocytes collection was scheduled 36 h after hCG administration. Regardless of the sperm quality, Intra Cytoplasmic Sperm Injection (ICSI) was performed rather than IVF to prevent DNA contamination with sperm and cumulus cell's DNA during PGD and PGS. Fertilization was assessed 17–20 h after ICSI and embryo cleavage was recorded every 24 h. Embryo biopsy was performed on day 5 or 6 at blastocyst stage. All patients were informed consents in our study .Multiple Displacement Amplification (MDA) was used for single cell amplification in SNP array. Briefly, REPLI-g Single Cell Kit  was used for single cell amplification, single cell was seeding in 4.5μl phosphate-buffered saline, and then was lysised using 3μl DTT and DLB in incubator for 10 min at 65°C. After incubation, 3μl Stop Solution was added in the mix. The amplification mix was prepared and 40μl including 2μl REPLI-g sc DNA polymerase and 29μl REPLI-g sc Reaction Buffer was added to each tube. Then the mixture was incubated in incubator for 8h at 30°C following 65°C for 3 min to inactivate the REPLI-g sc DNA polymerase. The single cell amplification mix was stored at −20°C until for SNP array. The DNA of previous triploidy detected using SNP was diluted and amplification as described above. All the procedures were under the direction of Illumina human cyto12 microarray with minor modification in our center.Multiple Annealing and Looping Based Amplification Cycles (MALBAC) was used for whole genome amplification then high throughput sequencing. The DNA from previously SNP array results were quantified again with Qubit and then were diluted to 10pg for single cell amplification using YK015CHR (Yikon Genomics). Firstly the DNA was diluted into 4.5μl lysis buffer and lysis enzyme, the amplification products were fragmentation and ligated adapter, then PCR and magnetic purification according to the user manual. The barcoded DNA was sequenced using Hiseq 2500 Rapid 1×50 mode in our center.The SNP array data was analyzed using GenomeStudio Software . B allele frequency and Log R ratio was used to analyzed the genotype. The copy number variance was called using KaryoStudio Software v1.4. The uniparental disomy was reported by both KaryoStudio and GenomeStudio.The raw data (in.bcl format) was demultiplexed and converted to FASTQ format using a perl script configureBclToFastq.pl in CASAVA 1.8.4 package based on the sample-sheet information. Illumina adaptors, low quality bases  and MALBAC primers were removed from the FASTQ file using Trimmomatic .High quality reads were mapped to hg19 reference genome using BWA with default parameters . The map


Zea mays L.) genome, and suitability for high-throughput analysis. In this study, a maize 55 K SNP array with improved genome coverage for molecular breeding was developed on an Affymetrix® Axiom® platform with 55,229 SNPs evenly distributed across the genome, including 22,278 exonic and 19,425 intronic SNPs. This array contains 451 markers that are associated with 368 known genes and two traits of agronomic importance (drought tolerance and kernel oil biosynthesis), 4067 markers that are not covered by the current reference genome, 734 markers that are differentiated significantly between heterotic groups, and 132 markers that are tags for important transgenic events. To evaluate the performance of 55 K array, we genotyped 593 inbred lines with diverse genetic backgrounds. Compared with the widely-used Illumina® MaizeSNP50 BeadChip, our 55 K array has lower missing and heterozygous rates and more SNPs with lower minor allele frequency (MAF) in tropical maize, facilitating in-depth dissection of rare but possibly valuable variation in tropical germplasm resources. Population structure and genetic diversity analysis revealed that this 55 K array is also quite efficient in resolving heterotic groups and performing fine fingerprinting of germplasm. Therefore, this maize 55 K SNP array is a potentially powerful tool for germplasm evaluation , marker-assisted breeding, and primary quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) for both tropical and temperate maize.With the decrease of cost in genotyping, single nucleotide polymorphisms (SNPs) have gained wide acceptance because of their abundance, even distribution throughout the maize (The online version of this article (doi:10.1007/s11032-017-0622-z) contains supplementary material, which is available to authorized users. Over the past 20 years, diverse genetic markers, such as restriction fragment length polymorphism (RFLP), simple sequence repeat (SSR), and single nucleotide polymorphism (SNP), have been adopted for assessing the genetic characteristics of populations or germplasm, mapping quantitative trait loci (QTL), and facilitating the selection of breeding materials that bear desired genes/alleles or haplotypes in both plant genetics and breeding programs. Compared with other genetic markers, SNPs possess several prominent advantages and have become a trendy choice with the development of high-throughput sequencing and array-based technologies. First of all, SNPs, as the ultimate form of genetic markers, are more abundant than other markers throughout the genome. In plants, the SNP density ranges from 6 to 22 SNPs per 1 kb sequence  among tropical and temperate populations, were selected. These SNPs are all located in the genic regions of the maize genome. It has been shown that gene density in maize is correlated with recombination rate, and thus including the genetic variation located in genes will increase the genetic information for regions of high recombination rate as well. High SNP density in high recombination rate regions will improve the resolution in linkage analysis and GWAS.In a previous study . As transgenic breeding technology blossoms, more and more transgenic crops will be produced in the near future. Molecular markers should be explored as a powerful tool for facilitating the transformation process and detecting transgenic plants. Therefore, we also included 132 SNPs that are tags for published transgenic events on the maize 55 K SNP array.Maize is a species with a high level of nucleotide polymorphism. We have detected many dispensable sequences  based on resequencing 79 tropical and temperate inbred lines. A total of 4067 SNPs were selected based on the dispensable genome  (unpublished data) to capture the unique genetic diversity existing in tropical maize. A great number of genes that are related to maize development and desirable agronomic traits have been extensively studied. To cover these functional genetic loci, we included all available SNPs from 368 known genes in maize , the USA (96), and CIMMYT (189) were genotyped. Among them, the lines from China and the USA are mainly public inbred lines including 55 newly developed elite lines. The collection from CIMMYT is a widely used germplasm in the tropical regions. Detailed information about the 593 inbred lines is listed in Supplementary Table For each line, 15 seeds were grown in one pot and DNA was extracted from five uniform seedlings representing the inbred line with cetyltrimethylammonium bromide (CTAB) method , “MonoHighResolution” , “NoMinorHomozygote” (featured by two clusters with no examples of the minor homozygous genotypes), “OffTargetVariant” , “CallRateBelowThreshold” , and “Other” (where more than one cluster properties are below threshold)  were tested continuously with a default convergence criterion 20 times. The results from replicate runs came into integration by CLUMPP program with full search method.Population structure analysis was implemented with a non-parametric approach by fastSTRUCTURE . The remaining SNPs were aligned with MUSCLE, and the maximum likelihood tree was calculated by DNAML implemented in SNPhylo. Polymorphism information content (PIC) value . IBS was calculated using PLINK 1.07. Genomic divergence between different populations and pairwise nucleotide diversity within a population were calculated using VCFtools version 0.1.12.0 (Weir and Cockerham r2) was used to evaluate the level of LD decay.A total of 39,296 SNPs with MAF > 0.05 and missing data <20% were used for estimating the relative kinship of 593 inbred lines with the software TASSEL 5.2.23 . Secondly, the maize genome was divided into 2000 bins (defined as genetic bins). Then, we applied a genetic algorithm to identify the best sets of probes. We picked up a start set with one random probe in each genotyping bin, and the mutation was made to maximize the number of probes which located in different genetic bins. We picked two independent 1000 SNPs sets which start with two random sets of probes .To test the reproducibility of our design, concordant rates between five pairs of two biological replications were verified. A high level of concordant genotype calls in the range of 97.1 to 98.9% was observed . The unbiased design was further validated in the two-dimensional folded SFS. The most enriched pattern was derived from alleles with similar frequency in tropical and temperate germplasm  were examined Fig. b, c, d. asm Fig. d. A totaasm Fig. . Of themasm Fig. . Anotherasm Fig. . In partasm Fig.  between k from 2 to 12. The results indicate that when k = 2, two major groups, representing temperate and tropical ones could be claimed. Compared with the pedigrees of the tested lines, however, the model-based groups were largely consistent with known pedigrees when k = 8, so that the 593 tested lines could be largely divided into eight groups . This makes it difficult to compare and integrate samples from different batches  across the genome. If a probe located in regions with SVs, it might introduce artificial missing or heterozygote calls in the genotyping. In our analysis, the missing rate for probes was correlated with heterozygous rate, suggesting that these SNPs might be located in the dispensable genome. As more genotyping results are accumulated, we should be able to pick probes that are more likely located in the core genome.Fourthly, single reference genomes only represent a portion of the genetic variation for a specific crop species, and the development of pan-genomes (defined as the sum of the core genome and the dispensable genome) , 4067 markers that are not covered by the current reference genome, 734 markers that are differentiated significantly among heterotic groups, and 132 markers that are tags for important transgenic events  as well as RNA-seq data for tropical and temperate maize inbreds with great diversity, the maize 55 K SNP array can be used in primary GWAS and genetic mapping for both tropical and temperate maize germplasms, although markers with higher density are still needed for gene cloning.Compared with the currently primary marker system, SSRs, the maize 55 K SNP array contains numerous SNPs directly related to the crucial agronomic and quality traits and is more suitable for backcrossing breeding, recurrent selection, gene pyramiding, and genomic selection.Taking advantage of the long-service life and flexibly replaceable traits of Affymetrix platform, 1–3 K probes on the maize 55 K SNP array are reserved for a personalized design, which could be used to include the key markers for fingerprinting crucial genetic and breeding materials, novel gene identification and variety protection.Supplementary Fig. S1Distribution of 55 K variants on ten chromosomes. Window size is 1 Mbp. (JPEG 8 kb)High Resolution (TIFF 3321 kb)Supplementary Fig. S2Distribution of pairwise relative kinship values for 593 maize inbred lines. (JPEG 8 kb)High Resolution (TIFF 10884 kb)Supplementary Fig. S3Mean r2 of different physical distances for temperate, tropical and entire panels.(JPEG 10 kb)High Resolution (TIFF 8715 kb)Supplementary Fig. S4Venn diagram summarizing the probe sets that were common in three independent runs of Illumina® MaizeSNP50 BeadChip. (JPEG 15 kb)High Resolution (TIFF 13268 kb)Supplementary Table S1(XLSX 9 kb)Supplementary Table S2(XLSX 10 kb)Supplementary Table S3(XLSX 9 kb)Supplementary Table S4(XLSX 51 kb)Supplementary Table S5(XLSX 9 kb)Supplementary Table S6(XLSX 32 kb)Supplementary Table S7(XLSX 9 kb)Supplementary Table S8(XLSX 9 kb)Supplementary Table S9(XLSX 10 kb)Supplementary Table S10(XLSX 14 kb)


Cultivated chrysanthemum is an outcrossing hexaploid (2n = 6× = 54) with a disputed mode of inheritance. In this paper, we present a single nucleotide polymorphism (SNP) selection pipeline that was used to design an Affymetrix Axiom array with 183 k SNPs from RNA sequencing data (1). With this array, we genotyped four bi-parental populations , and a cultivar panel of 63 genotypes. Further, we present a method for dosage scoring in hexaploids from signal intensities of the array based on mixture models (2) and validation of selection steps in the SNP selection pipeline (3). The resulting genotypic data is used to draw conclusions on the mode of inheritance in chrysanthemum (4), and to make an inference on allelic expression bias (5).n = 405). Analysis of segregation of duplex x nulliplex SNPs resulted in evidence for genome-wide hexasomic inheritance. This evidence was substantiated by the absence of strong linkage between markers in repulsion, which indicated absence of full disomic inheritance. We present the success rate of SNP discovery out of RNA sequencing data as affected by different selection steps, among which SNP coverage over genotypes and use of different types of sequence read mapping software. Genomic dosage highly correlated with relative allele coverage from the RNA sequencing data, indicating that most alleles are expressed according to their genomic dosage.With use of the mixture model approach, we successfully called the dosage of 73,936 out of 183,130 SNPs (40.4%) that segregated in any of the bi-parental populations. To investigate the mode of inheritance, we analysed markers that segregated in the large bi-parental population  contains supplementary material, which is available to authorized users. The ability to genotype large numbers of polymorphisms is of major importance for breeding and genetic analysis. Costs for detection and genotyping of a large number of polymorphisms are still decreasing, and therefore become available to an increasing number of agriculturally important plant species, including polyploids. Genetic analysis in polyploids is less straightforward compared to diploids. An example is cultivated chrysanthemum, which is an outcrossing hexaploid (2n = 6× = 54) and has been classified as a segmental allopolyploid .Polymorphism detection in species without a reference genome is restricted to methods using a reduced representation of the genome, like restriction enzyme based selection methods , baiA disadvantage of the use of RNA-seq is possible discordance between the expression of an allele and the allele’s dosage in genomic DNA. Expression of certain alleles might even be completely absent. This feature is also referred to as allelic expression bias, and specifically occurs in allopolyploids . In hexaHigh throughput genotyping of SNP polymorphisms using SNP assays is common in diploids , 14. SevThe mode of inheritance has large implications for genetic analysis in polyploid organisms. In general, the mode of inheritance relates to the origin of ploidy of the organism: whether it is allopolyploid or autopolyploid. Disomic inheritance is usually a feature of allopolyploids and polysomic inheritance of autopolyploids. Ramsey and Schemske  defined The mode of inheritance in chrysanthemum has been under discussion , 29. CulIn addition to cytological observations, hexasomic inheritance is also suggested by the analysis of segregation of molecular markers. Two studies showed that alleles from a single multi-allelic SSR marker have independent assortment, which is only possible with hexasomic inheritance , 36. AnoMulti-allelic SSR markers are scarce, and self-compatibility is difficult to obtain in chrysanthemum. However, analysis of segregation of high numbers of SNP markers in large outcrossing F1 populations can also provide evidence for the mode of inheritance. One of such analyses involves segregation of markers that are duplex in one parent and nulliplex in the other. If inheritance is disomic and the duplex alleles are on the same subgenome, all progeny will be simplex (one). Existence of these non-segregating duplex x nulliplex (2 × 0) markers therefore indicates disomic inheritance. If the two alleles are on different subgenomes, disomic inheritance will lead to a 1:2:1 segregation of the dosages 0, 1 and 2. Hexasomic inheritance will lead to 1:3:1 segregation in all cases. Studies that analysed deviations from those types of segregation, in general found duplex markers both fitting hexasomic inheritance as well as disomic inheritance , 36, 38.r) assuming disomic (diploid-like) inheritance between markers in repulsion that approach zero indicate disomic inheritance. In the case of hexasomic inheritance, pairing should be random with all pairs of homologues chromosomes. In that case, the minimum diploid maximum likelihood estimator of r of markers in repulsion should be 0.4 [A third method for estimation of the mode of inheritance is analysis of repulsion linkage . Estimatd be 0.4 . In chryd be 0.4 .In this paper, we present a SNP selection pipeline for chrysanthemum from RNA-seq data (1), a method for dosage scoring in hexaploids from bi-allelic probe fluorescence (2) and validation of selection steps in the SNP selection pipeline (3). The resulting genotypic data is used to draw conclusions on the mode of inheritance in chrysanthemum (4) and allelic expression bias (5).RNA-seq resulted in an average of 100.4 M reads for the deep-sequenced parents of the large population  and on average 70.4 M reads for the 11 other sequenced cultivars . The other 76,286 SNPs were identified using data from only the parents of POP1 , which were selected using less stringent filtering. Most SNPs (65.8%) could be identified from the alignment files of both mappers , whereas none were linked in repulsion at that distance  could be called by fitPoly and had a missing value rate lower than 20%; those SNPs were considered as successfully discovered. Of those, 62,679 segregated as expected assuming hexasomic inheritance from the parental genotypes in at least one of the mapping populations and had a missing value rate lower than 10%  of those SNPs only one of both sides showed clear clustering  SNP, would have an expected segregation ratio of 1:6:11:6:1 resulting in five clusters for the dosages 0, 1, 2, 3 and 4 respectively. Markers with more than two clusters have a higher chance of missing values or wrongly assigned dosages, as more clusters usually have a higher chance to overlap each other.It is important to note that we probably invoked a bias towards SNPs with lower dosages. The reasons for this are the selection for SNPs for which there was at least one homozygous individual, and the difficulties with SNP dosage scoring of more complexly segregating SNPs. In line with this expected bias, we found that most of the SNPs we detected were of the 1 × 0 type in the parents of the F1 populations. However, relatively high numbers of 1 × 0 markers are also found in other studies of polysomic polyploids in which there was no such marker selection , 43–48. The use of two different types of sequence read mapping software resulted in three sets of SNPs: SNPs only identified with either bowtie2 or bwa-mem, and SNPs identified with both mappers. As there is some level of independence between the two SNP calls from both alignments, it would be expected that SNPs discovered with both mappers have a higher success rate . This waFrom the segregating SNP markers in our dataset, SNP allele read counts in the transcriptome conformed to their dosage estimated from the array data. Chrysanthemum therefore deviates from the allopolyploids wheat, cotton and camelina that show subgenome-specific expression patterns in a large number of expressed genes , 50–52. Variation of relative allele coverage in the chrysanthemum read data was larger than would be expected from a binomial distribution. The source of this extra variation could be both biological and technical. The biological reason would be allelic expression imbalance of some loci, which is also common in diploids –57. A teOur dataset gives evidence of complete or near-complete hexasomic inheritance in chrysanthemum. A first indication is the absence of non-segregating 2 × 0 markers. Presence of those type of markers would indicate disomic inheritance. Analysis of the segregation ratios of the 2597 segregating 2 × 0 markers pointed towards hexasomic segregation. Only 6.5% of 2 × 0 markers were rejected for hexasomic segregation and not rejected for disomic segregation. It is likely that a large number of the markers fitting only disomic segregation had genotyping errors or skewed segregation, as the number of markers not fitting any of the two types (18.8%) was much higher.r. We did find linkages between markers in repulsion in which r was lower than 0.4, the minimum expected r in repulsion for full hexasomic inheritance [r below the threshold. However, there were fewer, and minimum r was higher (0.29 compared to 0.15 and 0.08 for POP1 and POP3 respectively). The lower minimum r in the real datasets could be caused by incomplete disomic inheritance [Conforming to the analysis of 2 × 0 markers, comparison of linkages between 1 × 0 markers in coupling and repulsion phase also pointed towards absence of disomic inheritance in two populations (POP1 and POP3). There were no linkages in repulsion with a distance smaller than 8 cM, while a very large number of marker combinations in coupling were linked within this distance . This is contrary from what would be expected from disomic inheritance, since with disomic inheritance, the ratio between the number of linkages in coupling and in repulsion would be 1:1 , irrespeeritance . In the eritance . Howevereritance , 59 are In a number of studies, the expected distributions of marker dosage in hexaploid F1 populations were used to draw conclusions on the mode of inheritance , 47, 48.Our conclusions deviate from the most recent and elaborate paper on the mode of inheritance in chrysanthemum authored by Klie et al. . We concWe present an application of the use of next generation sequencing and high-throughput genotyping in hexaploid chrysanthemum. Development of these resources opens up many possibilities for plant improvement at the level of the genome. As a first step, we were able to find conclusive evidence for near-complete hexasomic inheritance on a genome-wide scale. With these resources and the information on the mode of inheritance we will now be able to progress in the development of genomic tools for genetic improvement in chrysanthemum, like linkage mapping and mapping of traits.A panel of thirteen genotypes was selected for RNA-seq . Library preparation was done using the strand-specific NEBNext Ultra Directional RNA Library Prep kit for Illumina sequencing . In short, messenger RNA was isolated from total RNA using oligo-dT magnetic beads. Then, mRNA was fragmented and reverse transcribed into cDNA. The cDNA was ligated to sequencing adapters and amplified using PCR. Fragment size selection was between 400 and 900 basepairs. Clustering and DNA sequencing was performed using the Illumina cBot and HiSeq 2500  according to manufacturers’ protocols, resulting in paired-end reads with a length of 126 basepairs at both ends.Trimmomatic  (v0.33) http://broadinstitute.github.io/picard/), and duplicates and reads with a map quality smaller than 2 were removed using samtools [The two parents of POP1 were assembled separately. Trinity  version samtools .Our aim was to identify both SNPs that can be used for a wide range of chrysanthemum genotypes and to identify SNPs for linkage mapping in POP1 specifically. To achieve this, we aligned reads of all 13 cultivars (ALL) and reads originating from only the two parents of POP1 (PAR) separately. In addition, we wanted to include SNPs that were called with two different types of mapping software (bowtie2 and bwa-mem). Therefore, four alignment files were created: the reads of the ALL set aligned with bowtie2 and bwa-mem and the reads of the PAR set aligned with bowtie2 and bwa-mem  sWe selected against SNPs that were detected as heterozygous in all genotypes, as they have a large chance of being false positives, or subgenome defining. False positive SNPs can originate from mapping reads originating from two or more recently duplicated genes to one locus, and would appear as a heterozygous SNP in all genotypes Fig. . SubgenoAfter selection against SNPs heterozygous in all genotypes, flanking sequences including the reference SNP allele were aligned to the reference transcriptome using BLAST with an e-value cut-off of 1e-5. Contigs assembled by Trinity are classified into different hierarchical categories . MarkersAn Axiom genotyping array was designed by Affymetrix. We expected resolution to be an issue for high confidence dosage estimation , since fMarker dosage was called from ratios of signal intensities of the genotyping array using a modified version of fitTetra , that al2 test assuming a 1:1 segregation. Probabilities were corrected for multiple testing  and markers with q < 0.01 were removed. After filtering, the dataset consisted of two parents and 398 F1 progeny genotyped with 28,485 markers.Two individuals of POP1 with more than 5% unexpected dosages based on parental dosages were removed. Then, marker dosages were converted to their most fundamental form, as described by Bourke et al. . After tAfter analysis of segregation of markers in POP1, it became clear that preferential pairing was absent. We re-ran the pipeline without filtering for duplicate markers on all biparental populations assuming hexasomic inheritance. In this case we also supplied fitPoly with information on the population each sample belonged to , which allowed fitPoly to use the expected segregation ratios to improve model fitting. To investigate repulsion linkage in POP3, and to investigate the marker distribution of POP1 compared to the first run, we repeated the post fitPoly processing of markers to those populations as described for the first run.In order to estimate the expected distribution of repulsion linkages with known modes of inheritance, we simulated F1 populations of 400 individuals each. We used PedigreeSim V2.0  with 900r) and logarithm of odds were calculated as described in Van Ooijen and Jansen [Recombination frequency Additional file 2:Statistics of assemblies of the two parents. (PDF 8 kb)Additional file 3:Venn diagram of the number of SNP markers called with bowtie2 and bwa-mem. (PDF 59 kb)Additional file 4:Distribution of marker types in POP1 after the second run of the pipeline see . Marker Additional file 5:Venn diagram of number of markers segregating in different biparental populations. (PDF 126 kb)Additional file 6:Pie chart of comparisons of both SNP assays of markers tiled from both sides segregating in POP1. Corresponding: both assays gave less than 4% conflicting dosages. One missing: one of the assays could not be called by fitPoly. Not corresponding: two assays gave different results. Total number of SNPs: 17,170. (PDF 9 kb)Additional file 7:Environment and tissue type of different samples. (PDF 6 kb)Additional file 8:Overview of SNP calling steps. For more information, see materials and methods. QSNP: QualitySNP. (PDF 22 kb)


Bos taurus cattle has been the ISAG SNP panels. While these ISAG panels provide an increased level of parentage accuracy over microsatellite markers (MS), they can validate the wrong parent at ≤1% misconcordance rate levels, indicating that more SNP are needed if a more accurate pedigree is required. With rapidly increasing numbers of cattle being genotyped in Ireland that represent 61 B. taurus breeds from a wide range of farm types: beef/dairy, AI/pedigree/commercial, purebred/crossbred, and large to small herd size the Irish Cattle Breeding Federation (ICBF) analyzed different SNP densities to determine that at a minimum ≥500 SNP are needed to consistently predict only one set of parents at a ≤1% misconcordance rate. For parentage validation and prediction ICBF uses 800 SNP (ICBF800) selected based on SNP clustering quality, ISAG200 inclusion, call rate (CR), and minor allele frequency (MAF) in the Irish cattle population. Large datasets require sample and SNP quality control (QC). Most publications only deal with SNP QC via CR, MAF, parent-progeny conflicts, and Hardy-Weinberg deviation, but not sample QC. We report here parentage, SNP QC, and a genomic sample QC pipelines to deal with the unique challenges of >1 million genotypes from a national herd such as SNP genotype errors from mis-tagging of animals, lab errors, farm errors, and multiple other issues that can arise. We divide the pipeline into two parts: a Genotype QC and an Animal QC pipeline. The Genotype QC identifies samples with low call rate, missing or mixed genotype classes , and low genotype frequencies. The Animal QC handles situations where the genotype might not belong to the listed individual by identifying: >1 non-matching genotypes per animal, SNP duplicates, sex and breed prediction mismatches, parentage and progeny validation results, and other situations. The Animal QC pipeline make use of ICBF800 SNP set where appropriate to identify errors in a computationally efficient yet still highly accurate method.A major use of genetic data is parentage verification and identification as inaccurate pedigrees negatively affect genetic gain. Since 2012 the international standard for single nucleotide polymorphism (SNP) verification in  Since the 1960's bovine pedigree verification has been performed with various DNA technology, initially performed with blood groups . Many groups world-wide primarily, or only, use the ISAG100 or ISAG200 panel for initial bovine parentage validation, and some groups use less. While the PE values for the ISAG SNP panels appear sufficient for accurate parentage, Vandeputte  recommended parentage SNP panel of 100 SNP (ISAG100) has a reported parental exclusion probability (PE) of >0.999 and the ISAG200 panel (200 SNP) has a PE >0.9999999 .These issues with PE values could be one of the reasons why we and others have reported  genotype database has rapidly increased since 2013, from ~25,000 to currently having >1,000,000 animals genotyped, data quality has become a higher concern, especially as “once in a million” and “rare” types of errors are encountered. Most publications only perform SNP and individual genotype QC based on SNP QC via call rate (CR), minor allele frequency (MAF), and Hardy-Weinberg deviation  is missing, or low frequencyFormat is wrong, e.g., mix of AB and ACTG formatThe main QC concerns for ICBF are (1) is this genotype ok and (2) does the genotype really belong to the listed animal. While most errors come about due to accidents or non-malicious actions, a very small amount could be due to intentional actions. We have tried to design a QC pipeline will identify both types of errors once enough data is collected. An example of potential errors includes:Animal Care and Use Committee approval was not obtained for this study because the data were obtained from the existing ICBF database, Bandon, Co. Cork, Ireland.B. taurus breeds are represented in Ireland, 58 beef and 27 dairy, with 44 breeds having at least 1 purebred animals genotyped in the ICBF database and 61 breeds being represented in a purebred or crossbred genotyped animal  chip  frequency below 20%. This last check came about as an analysis of 846,868 animals with ≥90% CR and no missing or mixed genotype classes revealed that <0.0001% of animals had a genotype  frequency lower than 20%  imputation . MSMs are where the calf was AB and both sire and dam were homozygous for the same allele. If MSM rates >1% for the ICBF800 are identified all animals in the trio are manually checked.Genotypes that have passed through the Genotype QC pipeline are then sent through the Animal QC pipeline Figure . All samExcluding identical twins, two animals should not have the same genotype across large numbers of SNP. Considering that only 1.6% of bovine births are of identical twins  and chromosome X (chrX) SNP that are located in the non-pseudoautosomal (nPAR) region. Sex prediction using only chrY SNP is logically simpler to use, but not all commercial SNP chips contain chrY SNP. Sex prediction only using the heterozygosity rates of chrX SNP can be more challenging as care must be taken to avoid using PAR SNP and highly inbred females such, as L1 Dominette 01449  they were homozygous in all males and not present in the females. Those chrY SNP were BOVINEHD3100000048, BOVINEHD3100000099, BOVINEHD3100000103, BOVINEHD3100000210, BOVINEHD3100000517, BOVINEHD3100001188, and BOVINEHD3100001406.SNP in the PAR of the X chromosome was determined by analyzing 467 SNP with unique locations on chrX (UMD3.1 assembly) in 606,122 IDBv3 genotyped animals. After filtering for MAF (<0.01) and CR (<0.90) the PAR region was determined via SNP with high male heterozygosity rates and genomic positions Figure . Male anPredict sex with nPAR chrY SNPCount nPAR chrY genotypesIf 0–1 genotypes = femaleIf 6–7 = maleIf 2–5 = ambiguous sexPredict sex with nPAR chrX SNPDetermine heterozygosity rate (# AB/ (#AA + #AB + #BB)) for nPAR SNPIf ≤5% het rate = maleIf ≥15% femaleIf between 5 and 15% = ambiguous sexDo chrX and chrY sex predictions match.Yes—report sex predictedNo—manual check genotype resultsIf step 1 = ambiguous and step 2 = male or female (or inverse) then report non-ambiguous predicted sexIf step 1 and 2 are both ambiguous = report ambiguous sex predictedThe current logic used by ICBF for sex prediction is as follows:Animals with non-matching chrX and chrY sex predictions could be Turner (X0) or Klinefelter's syndrome (XXY) animals  have their genotype invalidated and either the sample is regenotyped (if probable lab error) or a new tissue sample is requested (if probable farm error). For all non-Pedigree animals, a non-matching breed composition results in the genotype being flagged. Breed predictions are mainly used to help resolve SNP duplicate samples.National bovine pedigree errors of 4–13% have been reported  had 44 to 74 MSM.In May, 2017 ICBF had ~950,000 animals with a valid genotype, of them 195,299 trios existed with all individuals SNP genotyped and the offspring SNP validates to each parent separately. MSM, where the calf was AB and both sire and dam were homozygous for the same allele, were recorded. Of those 96.964% had 0 MSM, 3.000% had 1–3 MSM, 0.037% had 4–7 MSM, and 0.003%  the average number of these nPAR SNP is 225 and PAR SNP is 49 Table .Sex prediction can be used as an easy QC check to tell if the DNA genotyped truly came from the reported animal, e.g., if a docile cow was sampled instead of a dangerous stock bull. In 612,722 IDBv3 genotyped animals for 7 chrY SNP 568,841 animals had 0 chrY SNP genotypes; 3,109 (1 SNP); 216 (2 SNP); 171 (3 SNP); 138 (4 SNP); 168 (5 SNP); 1515 (6 SNP); and 38,564 (7 SNP). Some research groups have notices chrY SNP genotypes appearing in female Holsteins and this may be caused by an historic event of some part of the chrY transferring to chrX or an autosome . For females with 1–4 chrY genotypes, 940 have listed dams that have been genotyped on a chip with chrY SNP. Of those 940 animals, which represent a mixture of purebred and crossbred beef and dairy, only 1.81% of their dams also have 0–4 chrY SNP genotypes called, the remaining dams have 0 chrY SNP called Table . TherefoBreed composition prediction is performed for animals whose listed breed composition is comprised of one of 14 reference breeds. Even when >10,000 genotypes are received on a daily basis, ADMIXTURE runs fast enough to be used daily in a production setting. As ADMIXTURE provides breed composition percent, future work will consider if this can be applied to help improve genomic breeding value predictions. Correlations of 96.6% have been obtained for of the main breed percent between the ICBF database and 710,000 animals with breed composition predicted Table . AnimalsAs shown in Figure The accuracy correlation should be looked at with 3 notes: (1) The ICBF database breed composition is based on 32 discrete parts thus a purebred is 32/32 and each part (1/32) is a 3.125% step. ADMIXTURE's predictions are continuous, so it can predict an animal to be 87.4356% Angus; (2) The reported animal's breed composition in the database is not perfect; (3) The database calculation assumes that a ½ AA ½ HE bred to a ½ HO ½ LM would generate a ¼ AA ¼ HE ¼ HO ¼ LM animal, as we know that is not true due to recombination and Mendelian sampling.B. taurus population it is unknown how well they would perform in a Bos indicus or B. taurus x indicus population.While the ISAG100 and ISAG200 SNP panels do provide a good base for parentage validation via SNP they are not without their limitations, and only using them can result in parentage errors , the minimum MAF for the non-ISAG200 ICBF800 SNP within breed ranges from 0.027 to 0.144 and the average MAF ranges from 0.375 to 0.408. For the ISAG200 SNP the minimum MAF within these purebreds ranges from 0.017 to 0.145 and the average MAF ranges from 0.310 to 0.402 . FImpute (Sargolzaei et al., Only after samples have passed the full QC pipeline does ICBF use the data for parentage analysis, genetic disease/trait status, and SNP imputation. The IDBv3 has >200 diagnostic probes for genetic diseases and traits (In addition to using ≥500 SNP for parentage validation, we also strongly suggest each laboratory, breed society, or national valuation center put in place a QC pipeline. The QC pipeline we describe above can be used as an initial foundation for one to build a custom QC pipeline one. One advice we have for any QC pipeline is to include logic checks for any situation one can think of regardless of how “rare” you may think it could occur. It is far easier to deal with a problem genotype early than down the road after pedigrees have been validated, breeding values estimated, and breeding decisions made. The SNP and Animal QC process developed at ICBF has been extremely useful to improve and guarantee the accuracy of the data and any report based on it, from parentage to genomic breeding values. We hope that other can use our QC process to help improve their own systems as they increase their amount of genetic and pedigree data.MM and JK: developed the improved SNP parentage process; MM, JM, ED, JCM, DO, and JK: all identified aspects of the quality control pipeline; MM and JM: developed the quality control pipeline; MM and PF: identified SNP clustering issues; MM: wrote the manuscript with input from all authors.PF was employed by company Weatherbys Scientific. The other authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.