RGS17 and ASCL2). Together, we introduce a powerful high-throughput pipeline for large-scale screening of functional SNPs at disease risk loci.Functional characterization of disease-causing variants at risk loci has been a significant challenge. Here we report a high-throughput single-nucleotide polymorphisms sequencing (SNPs-seq) technology to simultaneously screen hundreds to thousands of SNPs for their allele-dependent protein-binding differences. This technology takes advantage of higher retention rate of protein-bound DNA oligos in protein purification column to quantitatively sequence these SNP-containing oligos. We apply this technology to test prostate cancer-risk loci and observe differential allelic protein binding in a significant number of selected SNPs. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq) in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci  for large scale screening of functional SNPs at disease risk loci. With an eventual goal of finding new therapies and preventative measures for these diseases, efforts are now focused on determining the functional underpinnings of these associations. For example, prostate cancer GWASs have been extremely productive, yielding over 100 single-nucleotide polymorphisms (SNPs) with risk association4. Importantly, a significant number of these have subsequently been validated in well-powered, large case–control studies. Despite exceptional success, however, we are faced with the tremendous challenge of how to interpret these emerging results. There is currently a substantial knowledge gap between SNP–disease associations derived from GWASs and an understanding of how these risk SNPs contribute to the biology underpinning human diseases5.Since 2005, more than 2000 genome-wide association studies (GWASs) have been published, identifying loci associated with susceptibility to over 1000 unique traits and common diseases4. Due to lack of more effective screening approaches, identification and functional characterization of disease-causal SNPs remains as a significant challenge. For a given GWAS locus, the SNP with the lowest P value is not necessarily causal. Any SNP in linkage disequilibrium (LD) with a reported risk SNP may be causal, and the number of such LD SNPs are often from dozens to thousands6. To associate GWAS variants with regulatory elements in the genome, epigenomic profiling such as ChIP-seq, DNase-seq, and their derivatives (ChIP-exo and ChIP-nexus) have been developed11. Several computational programs have also been developed to integrate epigenomic landscapes with GWAS SNPs16. These profiling analyses and computational programs have been widely used and help facilitate discovery of candidate regulatory SNPs.As most risk SNPs have been found in non-coding regions of the genome, with many residing some distance from nearby annotated genes, it is believed that many of these  will be located in regulatory domains of the genome that control gene expression rather than in coding regions that directly affect protein function18. Although these current methods have enabled functional characterization of regulatory variants at some GWAS loci, the progress is extremely slow. Given the huge number of disease-associated regulatory variants, high-throughput methods are urgently needed to overcome limitations of these one-assay-one-SNP approaches.However, there remains a significant challenge from the knowledge-based prediction to functional validation. Currently, to experimentally validate putative SNPs for regulatory potential, the commonly used assays include electrophoretic mobility shift assays (EMSA) and reporter assays. EMSA can test whether a given SNP influences binding ability of a transcription factor (TF) to the regulatory element while a gene reporter assay can test the effect of a SNP on promoter or enhancer activity. More recently, CRISPR/Cas9-based gene editing technology has emerged as important tool to evaluate the effect of a single-nucleotide variant21 in characterizing allele-dependent transcriptional regulation and provide detailed functional analysis at two risk loci (RGS17 and ASCL2). This study introduces a powerful experimental pipeline to functionally screen for regulatory SNPs at GWAS-defined risk loci.One existing high-throughput method is to calculate allele-specific read counts from available ChIP-seq data. Significant deviation of read counts between two alleles indicates allelic binding preference for the unique TF. However, to be informative, the SNPs of interest need to be heterozygous in the tested cell line. For a large group of SNPs, it is difficult to find cell lines with heterozygous status in all (or most) candidate SNPs. In this study, we report a new massively parallel sequencing technology to distinguish potentially functional from nonfunctional SNPs. We apply the sequencing technology (named as single-nucleotide polymorphisms sequencing or SNPs-seq) to examine potential functional SNPs at prostate cancer-risk loci. We also test a unique application of self-transcribing active regulatory region sequencing (STARR-seq)21. This assay inserts candidate DNA sequences downstream of a super core promoter and allows the active enhancers to transcribe themselves. Such a direct coupling of candidate sequences to enhancer activity enables the parallel evaluation of millions of DNA sequences from arbitrary sources. We hypothesize that STARR-seq can quantitatively detect the enhancer activity of allele-specific sequences by measuring the RNA abundance among cellular RNAs. The principle and workflow of STARR-seq are shown in Supplementary Fig. To precisely determine allelic protein-binding differences with high-throughput capacity, we developed the massively parallel sequencing technology based on the principal that protein-bound DNA oligos will be retained in a protein purification column after extensive washes to remove free oligos. Following recovery from the column, SNP-containing oligos that are bound to nuclear proteins can be sequenced to determine allele-dependent protein binding Fig. . Because22 and functional annotation databases to determine candidate functional SNPs at prostate cancer GWAS loci. To further screen for candidate functional SNPs, we then performed SNPs-seq to determine allele-dependent protein-binding differences at these SNP sites simultaneously. To evaluate if these SNPs also showed differential transcriptional regulation, we performed STARR-seq and determined allelic read count of SNP-containing RNA transcripts. Finally, we applied a series of functional assays to validate the sequencing results. The overall study design is depicted in Fig. We first examined our prostate-specific expression quantitative trait loci (eQTL) datasetcis-eQTL signals , which were involved in a total of 2208 SNPs and 88 individual genes22. To select candidate functional SNPs from this reported eQTL SNPs, we examined prostate-specific ChIP-seq data23 and searched HaploReg database14. These analyses identified 255 potential functional SNPs involving 35 genes with eQTL P ≤ 1.96E−07. To expand the candidate SNP list, we selected an additional 51 SNPs involving 10 genes with eQTL P value between 0.05 and 1.96E−07. Finally, we selected 68 SNPs that were either reported risk SNPs or in LD with these SNPs but did not show any association with any reference genes in ±1 Mb regions. Overall, we selected 374 SNPs at 33 separate risk loci including 755 unique sequences . Most of the candidate SNPs were located at introns or intergenic regions .We previously performed eQTL analysis at 100 prostate cancer-risk intervals covering 146 risk SNPs in 471 normal prostate tissues and identified 51 loci showing significant To determine allele-dependent protein binding, we mixed 755 unique ds-oligos equally (5.05 nM each oligo) and used 264 ng of the oligo pool (25.23 fmols each oligo) for protein-binding assay. After extensive washes, the average yield of the eluted protein-bound oligos was 6–10 ng, accounting for 2–4% of original input    Fig.  and betwR2 = 0.6234), with low GC content toward low read counts. Among 755 unique oligo sequences, 49 sequences had low GC content (25% GC as cutoff) and 50% of these low GC oligos had low read counts (read counts 50 as cutoff) 24 to determine thermostability of each oligo and observed a significant association  Fig. . AccordiWe then tested the effect of nucleotide compositions at 5′/3′ ends on read counts. We grouped the 755 oligos into different groups, based on their nucleotide compositions. When compared to G/C, single-nucleotide A/T at either 5′ or 3′ end significantly reduced read counts of corresponding oligos. When comparing 16 possible two nucleotide combinations at either 5′ or 3′ ends, the combinations of AA, AT, TA, and TT showed the lowest read counts Fig. . ClearlyP value ≤ 1.00E−05 as significant cutoffs, 101 of the 374 candidate SNPs met the selection criteria  scores for all 374 candidate SNP sites. Example of the score calculation for SNP rs7123418 is illustrated in Supplementary Fig. R2 ≥ 0.95) , NOL10 (1 SNP), RAB17 (4 SNPs), RGS17 (1 SNP), HCG4B (1 SNP), PCAT1 (3 SNPs), CTBP2 (2 SNPs), NCOA4 (1 SNPs), ASCL2 (4 SNPs), C14orf39 (1 SNP), and FAM57A (1 SNP).For the 202 alleles (101 SNPs), the median read depth per allele was 20,961, ranging from 0 to 622,402. To select sequences with regulatory potential, we first transformed the read counts to RPM (read count per million mapped sequences) and then compared allele-specific read counts between test samples and input control samples. This analysis identified 56 SNPs with at least one allele showing 1.5-fold difference between test samples and input controls. To determine allele-dependent enhancer activity of the 56 SNPs, we calculated biased allelic enhancer  score using allele-specific read counts and observed 20 SNPs with absolute BAE score ≥ 0.58. The overall distribution of the BAE score is shown in Supplementary Fig. P value and ChIP-seq evidence, we selected one SNP (rs13215402) in the RGS17 region, and three SNPs  in ASCL2 region for further analysis. The SNP rs13215402 is located at 4.84 kb upstream of RGS17 transcription start site. ChIP-seq analysis shows that rs13215402 is at the center of multiple TF-binding sites including AR, FOXA1, and HOXB13. The SNP site is enriched with active enhancer epigenetic marks, H3K4me1/2 and H3K27ac -treated cell line and P = 6.78E−03 in cell line without DHT treatment)  when replacing pGL4.28 minimal promoter with RGS17 promoter regardless DHT treatment  Fig. . The lucent Fig. . This reP rs1321502 in theRGS17 and observed its downregulation by either allele G or allele A interference. Compared to non-target control (NTC), the downregulation of RGS17 expression was statistically significant in allele G (P = 0.028) but not allele A (P = 0.633)  when compared to parental cell line with heterozygous G/A   technology to evaluate the repression effect by interfering the SNP region on the RGS17 expression. We designed small guide RNAs to target either rs13215402 A or rs13215402 G allele in LNCaP cell line (heterozygous G/A for rs13215402). We then quantified the expression of 33) Fig. . We alsoG/A Fig. . Corresp31) Fig. . AdditioASCL2 transcription start site. ChIP-seq data show various degrees of overlapping with TF-binding sites including AR, FOXA1, and HOXB13  are located at ~59.8 kb upstream of B13 Fig. . BecauseB13 Fig. . InteresASCL2 expression with two copies of A–A–A showing the highest gene expression (P = 2.65E−06) . This analysis showed significant enrichment of haplotype A–A–A with an average BAE score = 3.13 (range: 2.98–3.37) in DHT-treated group, and 2.46 (range: 1.91–2.99) in non-DHT group. Clearly, A–A–A haplotype showed an increased enhancer activity when compared to C–G–C haplotype. To further confirm the effect of the haplotype on gene expression, we examined the haplotype-based eQTL in a collection of 462 benign prostate tissues and observed haplotype-dependent dosage effect on 06) Fig. . The can25. Because the allele G is in complete LD with variant allele A of the functional SNP rs13215402 reported in this study, we reasoned that the allele A would also be associated with reduced prostate cancer risk. Based on our prostate tissue eQTL analysis, allele A significantly downregulated target gene RGS17, consistent with the protective effect of GWAS risk SNP on prostate cancer. To test whether downregulation of RGS17 inhibited prostate cancer cell growth, we performed cell proliferation assays in the prostate cancer cells using siRNA against RGS17 and observed greatly reduced cell growth and viability when compared to the cells with control siRNAs NAs Fig. . Additiooma Fig. . Higher sis Fig. . Strikin4. To address this challenge, we report here, for the first time, the development of the SNPs-seq technology, a high-throughput approach based on massively parallel sequencing strategy to examine SNPs for their protein-binding differences. By applying the technology in prostate cancer-risk regions, we successfully identified over a hundred of SNPs showing allele-dependent protein binding. To evaluate the regulatory potential of these selected SNPs, we further tested the SNP-containing fragments using STARR-seq, a high-throughput enhancer assay, which further defined 20 SNPs with differential transcriptional regulation. Sequential use of the two sequencing technologies may enable comprehensive survey of regulatory SNPs to better understand biological roles of GWAS signals and their functional consequences.One of major challenges in the post-GWAS era is the lack of high-throughput assays to screen a large number of candidate SNPs for their potential functional consequences9. Although useful for mapping regulatory genomic regions, none of these datasets provide direct access to allelic binding preferences. One potential solution is to extract read counts at SNP sites from these sequencing data. To date, ENCODE project has collected over six thousands of epigenomic profiling data including ChIP-seq and DNase-seq. Systematic examination of these sequencing data may reveal allele-specific transcriptional control at each SNP site. However, for a specific disease type, there may be no or few epigenomic profiling data available. Importantly, most current epigenomic profiling datasets are based on a handful of cell lines. If a cell line used is not heterozygous for a specific SNP, it will not generate any allelic information. For example, only 96 (25.67%) of 374 candidate SNPs selected in this study are heterozygous in LNCaP cell line. Furthermore, most current epigenomic profiling datasets have relative low coverage at a certain SNP sites, which makes determination of allele-specific binding difficult.Currently, to determine whether a given SNP is functional, a common approach is to map the SNP to a regulatory element defined by ENCODE and other epigenomic projects32. However, both EMSA and reporter assay are not practical if a large number of candidate SNPs are tested. To overcome this limitation, we developed the SNPs-seq by taking advantage of higher retention rate of protein-bound DNAs and high capacity of sequencing technology. This technology applies easy-to-use protein purification columns and determines allele-dependent protein–DNA binding by counting sequence reads. Due to high-throughput nature, this sequencing technology can examine hundreds or even thousands of protein-bound DNA oligos simultaneously, significantly increasing screening capacity for candidate functional SNPs. It is worth mentioning that regulation of gene expression is cell and tissue type-specific, so is allelic binding preference detected by SNPs-seq. When applying the SNPs-seq to detect allele-specific binding difference, cell/tissue type and surrounding environments that are involved in the diseases/phenotypes of interest should be taken into consideration.Additionally, allele-specific EMSA and luciferase reporter assays are commonly used in laboratory validation of candidate regulatory SNPs35, MPFD36, CRE-seq39, STARR-seq21, TRIP40, FIREWACh41, and SIF-seq42 have been developed to evaluate regulatory potential of target sequences, these technologies were initially designed to determine transcriptional regulation at selected genomic regions. So far, no report has been published to test their applications in allele-dependent transcription controls. Due to its simplicity, STARR-seq was selected to examine this unique application. Our result strongly suggests that STARR-seq is capable of characterizing functional SNPs in disease risk regions. Furthermore, we performed in depth evaluation on the regulatory role of two prostate cancer-risk loci and provided strong evidence showing essential role of RGS17 for the maintenance of the proliferative potential of tumor cells44. Further characterization of the gene and its regulatory variants will have important implications for developing potential screening strategies to assess prostate cancer predisposition.Although several high-throughput technologies including MPRAMain advantage of SNPs-seq is its high capacity to screen a large number of candidate SNPs in a flexible, easy-to-use and low-cost procedure  for a total of 6324 SNPs22. We first excluded any SNPs with eQTL P value ≥ 1.96E−07. We then examined these eQTL SNPs for potential overlap with prostate-specific ChIP-seq signals. The ChIP-seq data were previously collected23, including TFs of FOXA1, AR, CTCF, ETS, EZH2, GR, JUND, NKX3_1, NR3C1, RUNX2, TCF7L2, and epigenomic marks of H3Ac, H3K4me2, H3K4me3, H3K27ac, and H4K5ac. Based on number of colocalization with ChIP-seq signals, we assigned a ChIP-seq score for each SNP and selected the candidate SNPs if ChIP-seq score was ≥ 1, meaning at least one overlap between ChIP-seq signal and a SNP. We also examined HaploReg database  for additional predicted regulatory signals to prioritize the SNP selection.To select candidate functional SNPs at prostate cancer-risk loci, we systematically examine 146 risk SNPs and their LD SNPs . Before androgen treatment the culture medium was replaced with phenol red free RPMI supplemented by 10% charcoal-dextran-treated FBS  and 1% penicillin/streptomycin for 3 days. Final concentration of androgen in the form of DHT  was 10 nM for treatment cells and 0.1% ethanol (ETH) for control cells. After 24 h treatment, we extracted the nuclear proteins using Ne-Per nuclear and cytoplasmic extraction reagents . The protein concentrations were determined using BCA protein assay kit (Pierce Biotechnology). Aliquots at 25 µl each were stored at −80 ℃ until use.For each selected SNP site, we synthesized four single-stranded oligos  with each allele having two complementary oligos. For each oligo (21 nt), SNP site was in the middle (11th nucleotide). Concentration of each oligo was 20 µM in 25 µl duplex buffer. To make ds-oligos, we mixed 5 µl of each forward and reverse strand. After denaturation for 3 min at 95 ℃, the oligo mix was subjected to annealing by gradually reducing temperature from 95 to 25 ℃ in 70 min. The ds-oligos were then combined in equal molar concentration to generate a large oligo pool containing all selected SNP sequences.oC for 30 min before transferred to the center of the Spin Columns. After extensive washing (×6 times), the protein-bound ds-oligos were eluted in 1× Column Elution Buffer and further purified using Oligo Clean & Concentrator . Final concentrations of purified oligos were quantified using Qubit dsDNA HS Assay Kits (Life Technologies). We ran each assay in duplicate to test reproducibility.To facilitate DNA–protein binding, we mixed the ds-oligo pool with 10 µg nuclear extract in 1× incubation buffer  at 15 variant/RCreference)/input(RCvariant/RCreference)]. The test and input represented tested samples and input control samples, respectively. RCvariant/RCreference represents ratio between read count from variant allele and read count from reference allele. We defined absolute BAB score ≥ 0.58 as significant cutoff for allelic binding difference, which represents 1.50-fold difference between variant and reference alleles.2 ng of purified ds-oligo pool were subjected to sequencing library preparation using ThruPLEX DNA-seq kit . The sequencing libraries were purified by Agencourt AMPure XP Beads  and quantified by Qubit. A pool of indexed sequencing libraries was sequenced in an Illumina HiSeq 2500 sequencer with 50 bp single read. To count sequence reads, we mapped sequencing data (fastq files) directly to 755 allele-specific sequence templates . Only 100% match to each oligo was allowed during the mapping. Because all sequences are unique, the perfect match will ensure complete separation of 755 oligo sequences, even for oligos with only one nucleotide difference . We counted sequence reads for variant and reference alleles separately. To determine allele-specific binding difference, we developed a BAB score using following formula: log2 [test.Human STARR-seq vector was kindly provided by Dr. Stark . The plasmid vector was linearized by AgeI-HF and SalI-HF digestion , followed by agarose gel purification  and then 1.0 × AMPure XP DNA bead purification. To prepare inserts (candidate SNP-containing sequences), we performed PCR to amplify DNA fragments covering all SNPs selected from SNPs-seq. The template was a mixed DNA from 111 prostate cancer patients to generate DNA fragments with heterozygous alleles. To facilitate cloning of the pooled PCR products, the PCR primers were designed to have an additional 15 nt recombination arms at 5′ end of each forward primer (TAGAGCATGCACCGG) and reverse primer (GGCCGAATTCGTCGA). We used highly efficient recombination-based cloning  to construct the expression vector by mixing 50 ng pooled SNP-containing amplicons with the STARR-seq vector (100 ng). To avoid biases during the cloning, we performed a total of 8 separate recombination reactions and pooled every 4 reactions. After further purification (Agencourt AMPure XP DNA beads), we used the SNP-containing vectors (2.5 µl) to transform MegaX DH10B T1 electrocompetent bacteria . The electroporation was carried out using Gene Pulser II Electroporation system . Again, we performed 8 separate transformation reactions and pooled every 4 transformations. The transformed bacteria were grown in two 500 ml LB6 LNCaP cells using 20 µg plasmid DNA library and 40 µl Lipofectamine 3000 (Life Technologies). 24 h after transfection, we extracted total RNA from 3.2 × 107 cells with on-column DNase treatment . We also extracted the plasmid DNA (as input control) from 1.6 × 107 cells using Qiagen plasmid plus midi kit. By further 10-unit DNase treatment using TURBO DNase (Life Technologies) and RNA purification using RNeasy MinElute clean up kit (Qiagen), we isolated the polyA + mRNA using Ambion Dynabeads Oligo-dT25 (Life Technologies) from the total RNA. We ran each assay with technical repeat to estimate its reproducibility.We transfected per 8.0 × 10We used 150 ng polyA+ mRNA and performed first strand cDNA synthesis  with a reporter RNA specific primer (GTCCAAACTCATCAATGTATC) in 16 separate reactions. After pooling every 4 reactions, we split each of the pooled cDNAs into five separate aliquots for a total of 20 PCR reactions  using 2 reporter-specific primers (TGCTGGGATTACACATGGCAT and CTTATCATGTCTGCTCGAAGC)  analysis, we first defined haplotypes for each patient using MACH146 and minimac program48 in 1000 Genomes Project Phase I V3 EUR reference (n = 379) and then performed linear regression analysis, regressing normalized expression levels on the number of minor alleles of each SNP/haplotype genotype. R (version 3.2.2) was used to perform the statistical tests and graphically visualize the association between SNP genotypes and gene expression levels. The RGS17 and ASCL2 mRNA levels were assessed by RNA-seq in TCGA, Illumina Expression BeadChip-based transcriptional profiling in Camcap and Stockholm cohorts of human prostate tissue samples.To validate the selected candidate SNPs, we performed additional eQTL analysis in three independent prostate datasets including The Cancer Genome Atlas (TCGA), Camcap and Stockholm cohorts which comprised of 389, 119, and 94 prostate samples, respectively2. Specifically, LNCaP and 22Rv1 cells were grown in RPMI-1640 , VCaP cells were grown in DMEM . Ten percent FBS and antibiotics  were added to the base media. To study AR activity and stimulate androgen signaling in these cell lines, we cultured cells in charcoal-stripped medium for up to 48 h, then the cells were treated with 100 nM DHT (dissolved in ethanol).The LNCaP, 22Rv1, and VCaP cells were originally obtained from the American Type Culture Collection  and confirmed to be mycoplasma-free during the experiments. The cell culture condition was at 37 °C with 5% CORGS17 promoter  to evaluate direct effect of a candidate SNP on its target gene. All reading measurements were obtained from at least three replicates. Statistical analysis of significance was determined by two-tailed student’s t-test using IBM SPSS statistics software version 24.To validate allele-dependent regulatory differences in selected candidate SNPs, we applied dual-luciferase reporter assay system by cloning SNP-containing sequences into vector pGL4.28 . We used the pGL4.74 as an internal control and measured the luciferase activity of the transfected cells according to the manufacturer’s protocol on a bio-luminometer. We also replaced minimal promoter in pGL4.28 with target gene To test for TF-binding status at selected SNP sites, we fixed the LNCaP and VCaP cells using 1% formaldehyde for 10 min and stopped the fixation using 125 mM glycine at room temperature. To isolate nuclei, we suspended the cell pellet in hypotonic lysis buffer (Sigma-Aldrich) for 45 min. The nuclei were washed twice by cold PBS and then suspended in SDS lysis buffer. The nuclei chromatin was sonicated to an average of 400 bp. To make dynabeads–antibody complex, we first washed dynabeads  twice by blocking buffer, and then incubated the beads with 7 µg antibodies for 10 h at 4 °C. 250 µg of sonicated chromatin was diluted in IP buffer to final volume of 1.35 ml, then added to 70 µl of Dynabeads–antibody complex (~1:400 dilution for the antibodies with 2 µg/µl concentration in stock). After 12 h incubation at 4 °C, the complex was washed once with wash buffer I and buffer II, followed by two more washings with buffer III and buffer IV. The DNA–protein complex was separated from beads by extraction buffer, then DNA and protein were reverse cross-link with Proteinase K and NaCl overnight at 65 °C. The DNA was purified by MinElute PCR Purification Kit (Qiagen). The buffers and antibodies are listed in Supplementary Tables To confirm TF-binding at selected SNP sites, we performed ChIP-AS-qPCRs to quantify TF-binding differences between variant and reference alleles. We designed PCR primers to amplify the DNA fragments harboring the different alleles at these SNP sites. We performed the quantitative PCRs at each SNP site in triplicates. We determined the relative enrichment of each candidate TF at target DNA fragments by comparing to IgG controls. The qPCR and allele-specific qPCR primers are listed in Supplementary Table RGS17 mRNA expression, we applied online tool (http://crispr.mit.edu/) and designed small guide RNAs (sgRNAs) targeting 20 bp at SNP site of interest49. We cloned the sgRNAs into the pLV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro plasmid . To confirm genotype of individual clones, we performed sequencing analysis with hU6 primer. The constructed plasmids were used to transfect LNCaP cell lines with Lipofectamine 3000 . 48 h after transfection, we isolated total RNA and performed RT-qPCR using SuperScript VILO cDNA Synthesis Kit (Thermo Fisher Scientific). We performed each transfection in triplicates and each RT-qPCR in triplicates. We determined relative gene expression using the ∆∆Ct method with ACTB as a control. sgRNA and PCR primer sequences are listed in Supplementary Table To test direct effect of a candidate SNP on RGS17 expression, we inserted sgRNAs into pSpCas9(BB)-2A-Puro (PX459) V2.0 50. The single-stranded oligodeoxynucleotides (ssODNs) that were centered at rs13215402 (either A or G) were used as repair templates  with control, cell death, RGS17 siRNAs using HiPerFect transfection reagent (Qiagen). We changed the medium after 24 h and collected the cells after 48 h to test siRNA-mediated knockdown efficiency. To determine the cell viability and proliferation, we applied XTT  reagent and measured the absorbance at 450 nm at a designated time point following manufacturer’s instruction. Two-tailed t-test was used to calculate the significances. siRNAs are listed in Supplementary Table To evaluate functional consequence of selected target gene, we performed siRNA-mediated knockdown assay in prostate cancer cell lines and determined the effect of the gene knockdown on cancer cell proliferation. 24 h before transfection, we seeded 50–60% confluent LNCaP or 22RV1 cells. We reversely transfected LNCaP or 22RV1 cells . We applied the non-parametric Mann–Whitney U test to evaluate the significance of gene expression levels between 52 normal and 497 tumor tissues. R (version 3.2.2) was used to perform statistical analyses and box plot was used to graphically display gene expression intensities (log base 2) between different groups. In addition, we evaluated the association of target gene expression with tumor stage, Gleason score, prostate-specific antigen (PSA), and the severity in TCGA dataset. To assess the potential association between target gene expression levels and prostate cancer survival, we applied the non-parametric statistic Kaplan–Meier estimator and tested 162 stage 1 patients in TCGA data and 79 patients from the Glinsky52 in Oncomine database53. Samples were stratified into two groups based on the mean values of RGS17 expression levels. R (version 3.2.2) and R package “Survival” were used for the analysis.To estimate clinical relevance of All relevant data are available within the article and supplementary files, or available from the authors upon request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5


Ziziphus jujuba Mill) is an economically important fruit species native to China with high nutritious and medicinal value. Genotyping-by-sequencing was used to detect and genotype single nucleotide polymorphisms (SNPs) in a core collection of 150 Chinese jujube accessions and further to characterize their genetic diversity, population structure, and linkage disequilibrium (LD). A total of 4,680 high-quality SNPs were identified, of which 38 sets of tri-allelic SNPs were detected. The average polymorphism information content (PIC) values based on bi-allelic SNPs and tri-allelic SNPs were 0.27 and 0.38, respectively. STRUCTURE and principal coordinate analyses based on SNPs revealed that the 150 accessions could be clustered into two groups. However, neighbor-joining trees indicated the accessions should be grouped into three major clusters. Our data confirm that the resolving power for genetic diversity was similar for the SSRs and SNPs. In contrast, regarding population structure, the resolving power was higher for SSRs than for SNPs. The LD pattern in Chinese jujube was investigated for the first time. We observed a relatively rapid LD decay with a short range (∼10 kb) for all pseudo-chromosomes and for individual pseudo-chromosomes. Our findings provide important information for future genome-wide association analyses and marker-assisted selective breeding of Chinese jujube.Chinese jujube ( Ziziphus jujuba Mill) is a diploid species , and is one of the most important fruit tree species native to China  and SNP  markers are useful for investigations of plant population genetics . Given tThe results of large scale studies revealed that genotyping-by-sequencing (GBS) is useful for identifying high density SNP markers and genotypes . It is aIn the present study, we used GBS technology to study a core collection of 150 Chinese jujube accessions. Our objectives were to (1) detect and genotype SNPs at a genome-wide scale, (2) compare the performance of SSR and SNP markers by estimating the genetic diversity and population structure, and (3) characterize the LD pattern. The data presented herein may be useful for selecting appropriate SSRs and SNPs for different types of jujube analyses. Additionally, our findings may facilitate future genome-wide association studies and marker-assisted selective breeding of Chinese jujube.We used a core collection of 150 Chinese jujube accessions previously characterized by Total genomic DNA was extracted from fresh leaves using plant genomic DNA rapid extraction kit developed by Biomed Gene Technology Co., Ltd., Beijing, China. The integrity, purity, and concentration of the extracted DNA were determined by 1% agarose gel electrophoresis and a Qubit Fluorometer (Invitrogen).ApeKI. The resulting samples were ligated to common and barcode adapters. The ligated products were pooled in equal volumes and then purified with the QIAquick PCR Purification Kit (Qiagen). Polymerase chain reaction (PCR) amplifications were conducted using the PCR Primer Cocktail and PCR Master Mix to enrich the adapter-ligated DNA fragments. The amplicons corresponding to target fragments were purified using the QIAquick Gel Extraction Kit (Qiagen) following 2% agarose gel electrophoresis. The average DNA fragment length for the final library was determined using the Agilent DNA 12,000 kit and 2100 Bioanalyzer system (Agilent). Quantitative real-time PCR with a TaqMan probe was used to quantify the final library. We amplified specific DNA fragments (180–480 bp) on the cBot instrument to generate DNA clusters on a flowcell, after which 100-bp paired-end sequencing was completed using the HiSeq 4000.We digested 100 ng genomic DNA with Raw Illumina DNA sequence reads were de-multiplexed according to the barcodes, and the adapter/barcode sequences were trimmed using a custom C script. Reads in which more than half of the bases had quality values ≤5 and those able to be mapped to multiple locations were discarded. The generated clean data were then aligned against the jujube reference genome sequence  were calculated using the VCFtools program . For SSRFor the SSR genotyping data, a Bayesian clustering analysis was implemented in the STRUCTURE program (version 2.3.3)  to evaluFor the genotyping data of 4,680 high-quality SNPs , we used the method described by Genetic distances between pairs of accessions were calculated and a principal coordinate analysis (PCoA) was completed for the SSRs and SNPs using the GenAlEx program (version 6.5) .r2) using the PopLDdecay program1. The LD decay was calculated when the r2 value decreased below a threshold level . Mean r2 values were used to calculate the LD using a 100-kb sliding window-based approach.The pairwise LD between 4,680 genome-wide SNPs for all pseudo-chromosomes and individual pseudo-chromosomes in 150 Chinese jujube accessions was calculated based on the allele frequency correlations , with an average of 0.99 Gb reads (range: 0.208–3.32 Gb reads) per accession. BJFU-435 was excluded because of a lack of sufficient clean data (Supplementary Table A GBS assay of the sequencing of 96-plex Table 1). Transitions  were more frequent than transversions , with a ratio of 1.59. The A/G transitions and G/C transversions occurred at the highest and lowest frequencies, respectively. The frequencies of the two types of transitions were similar , as were the frequencies of the four types of transversions  (Table 2).Among the 4,680 high-quality SNPs, 4,005 (85.6%) were physically mapped across 12 jujube pseudo-chromosomes, with an average map density of 81.79 kb. A genome-wide SNP density plot revealed that most SNPs were physically mapped on jujube pseudo-chromosome 1 . The average marker density was 81.79 kb. The highest and lowest marker densities were observed on pseudo-chromosome 7 (62.54 kb) and pseudo-chromosome 3 (99.91 kb), respectively. The remaining 675 SNPs were physically mapped on unanchored scaffolds of the jujube genome with a marker density of 169.27 kb . The allele frequency spectra were very different for the three data sets . We determined that 78.95% of the SSR alleles were present in the population at a low frequency  . In contrast, 53.71% of the bi-allelic SNPs and 48.25% of the tri-allelic SNPs were present in the population at an intermediate frequency  . Among the alleles present in the population at a high frequency , the bi-allelic SNPs were the most abundant, followed by the tri-allelic SNPs and then the SSRs (Table 3).A total of 209 alleles were generated from the 24 SSRs, with an average of 8.92 per locus. The highest average PIC value (0.59) was associated with the SSRs, followed by the tri-allelic SNPs (0.38), and the bi-allelic SNP markers (0.27)  when the accessions were roughly divided into two major groups. Furthermore, based on the results of the STRUCTURE analysis, the accession with a score higher than 0.80 was considered to be a pure one, while it with a score lower than 0.80 was considered to be admixture one. For the SSRs, 33 accessions (19 pure and 14 admixture) were grouped into the green cluster and 117 accessions (90 pure and 27 admixture) were grouped into the red cluster . For the SNPs, 37 accessions (23 pure and 14 admixture) were grouped into the green cluster, while 113 accessions (96 pure and 17 admixture) were grouped into the red cluster . The classification of accessions into the two groups was consistent for the two marker types, with only 18 accessions classified into different groups (data not shown).For the data sets of 4,680 SNPs and 24 SSRs, we observed a clear delta-K peak at K = 2 . The proportion of genotypic variance explained by the first two principal coordinates was higher for the SSRs  than for the SNPs . The first three SSR axes explained 63.06% of the cumulative variation, while the SNP datasets explained 20.83% of the variation (Table 5).A PCoA based on the SSR and SNP markers revealed that the 150 Chinese jujube accessions were clearly separated into two broad groups across the first two axes . The clusters labeled in green, blue, and red contained 51, 72, and 27 accessions, respectively. The jujube accessions were also grouped into three major clusters in the phylogenetic tree based on SNP data . The clusters labeled in green, blue, and red included 50, 59, and 41 accessions, respectively. In both trees, 23, 41, and 15 accessions were consistently classified into the green, blue, and red clusters, respectively. These clustering results provide evidence of the close genetic relationships among the 150 jujube accessions.Neighbor-joining trees were constructed based on the SSR and SNP markers. In the tree constructed using SSR data, the 150 Chinese jujube accessions were grouped into three major clusters , but not for individual pseudo-chromosomes (data not shown). All of the LD decays  were estimated to a physical distance of 10 kb .Linkage disequilibrium is an important consideration for marker-assisted selective breeding and genome-wide association studies. However, there are currently no reports regarding LD in Chinese jujube. In this study, the distributions of Genotyping-By-Sequencing has many key advantages including low cost, reduced sample handling, few purification steps, no size fractionation, efficient barcoding and easiness to scale up . These a1 population segregating for skin color were studied using GBS (6-fold sequencing depth). A total of 3,967 SNPs were finally identified  when preparing a GBS library. They ultimately identified 2,540 high-quality SNP markers, which were used to construct an integrated genetic linkage map. ApeKI. Additionally, we identified tri-allelic (0.80%) SNPs. Definitely, reducing DNA treatments and applying stricter filter conditions during a GBS assay will decrease the number of multi-allelic SNPs. Thirty-eight tri-allelic SNPs were identified using the default parameters of the GATK program. We detected a single position for each tri-allelic SNP, which confirmed their validity  for all pseudo-chromosomes and individual pseudo-chromosomes. The LD level in plants can be affected by reproductive systems . The LD um study . Low valDifferent mutational processes govern allelic variations at SSR and SNP loci, with lower mutation rates for SNPs than for SSRs . AdditioIn terms of genetic diversity, the highest PIC value among the three data sets was associated with the SSRs. The PIC values based on SSRs and bi-allelic SNPs were higher in this study than in a previous study involving rice , while tAccording to theoretical expectations, the distribution of allele frequencies differed between the SNP and SSR markers. The SSR loci were more common in the population at low frequencies, whereas the tri-allelic and bi-allelic SNP loci were more abundant at intermediate frequencies in this study. These results are consistent with those of previous studies . SSRs arThe cost per polymorphic locus for the SSR procedure was higher than that for the GBS-SNP procedure. However, fewer SSR markers were required for examining the genetic diversity and population structure of the jujube core collection. Thus, the total cost of the SSR procedure was much lower than the corresponding cost for the GBS-SNP procedure. The cost per polymorphic locus for the SSR procedure was higher than that for the GBS-SNP procedure. However, fewer SSR markers were required for examining the genetic diversity and population structure of the jujube core collection. Thus, the total cost of the SSR procedure was much lower than the corresponding cost for the GBS-SNP procedure.In terms of population structure, based on the analysis of PCoA and neighbor-joining trees for the two marker types, the 150 Chinese jujube accessions were grouped into same numbers of clusters with similar broad patterns, however, the number of accessions grouped into the clusters differed. These findings were not surprising because a broad grouping pattern is expected regardless of the marker types used in investigations of genetic relatedness. Similar findings were reported in rice . The lowAlthough the number of clusters differed among the PCoA, STRUCTURE, and neighbor-joining tree analyses, the same clusters were obtained using SNP- and SSR-based methods for each analysis. This indicted the clusters generated in this study for the 150 accessions were real. Similar results were reported by Several studies concluded that many SNPs are required to obtain the same information as SSR markers . In thisGenome-wide SNPs for diverse jujube germplasm were identified in this study. They were subsequently applied to assess the genetic diversity, population structure, and LD of Chinese jujube accessions. This is the first report describing the LD pattern in Chinese jujube. We revealed that GBS technology is a powerful tool for identifying and genotyping SNPs at a genome-wide scale. The PIC values calculated based on the SSRs and SNPs suggest the 150 Chinese jujube accessions are highly genetically diverse. The two markers exhibited similar resolving power regarding genetic diversity. However, the resolving power of the SSRs was higher than that of the SNPs in terms of population structure. Our findings may help researchers select suitable SSRs and/or SNPs for various analyses of Chinese jujube. They may also serve as a useful source of genetic information relevant for future genome-wide association studies and/or marker-assisted selective breeding.YL designed the conception and experiment; WC performed the experiments; WC and LH collected and analyzed the data; WC wrote the manuscript; XP and LH provided valuable suggestions on the manuscript; ZZ, LH, XP, and YL revised the manuscript; YL obtained funding and is responsible for this article. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Litopenaeus vannamei, is the most farmed aquaculture species worldwide with global production exceeding 3 million tonnes annually. Litopenaeus vannamei has been the focus of many selective breeding programs aiming to improve growth and disease resistance. However, these have been based primarily on phenotypic measurements and omit potential gains by integrating genetic selection into existing breeding programs. Such integration of genetic information has been hindered by the limited available genomic resources, background genetic parameters and knowledge on the genetic architecture of commercial traits for L. vannamei. This study describes the development of a comprehensive set of genomic gene-based resources including the identification and validation of 234,452 putative single nucleotide polymorphisms in-silico, of which 8,967 high value SNPs were incorporated into a commercially available Illumina Infinium ShrimpLD-24 v1.0 genotyping array. A framework genetic linkage map was constructed and combined with locus ordering by disequilibrium methodology to generate an integrated genetic map containing 4,817 SNPs, which spanned a total of 4552.5 cM and covered an estimated 98.12% of the genome. These gene-based genomic resources will not only be valuable for identifying regions underlying important L. vannamei traits, but also as a foundational resource in comparative and genome assembly activities.The Pacific whiteleg shrimp,  As with other realms of biology, animal production science is currently in the midst of a genomics revolution and there has been an increasing global focus on the development of genomic resources and subsequent identification of markers linked to genes of economic importance. Although still in its infancy as a production industry, aquaculture is perfectly situated to uptake recent advances in quantitative genetics and to integrate new genomic technologies into future breeding program designsLitopenaeus vannamei, is an aquaculture species that would benefit substantially from the integration of genomic information into traditional breeding programs, particularly for disease resistance and other difficult to measure or low heritability traits. Unfortunately, even though several genetic linkage maps have been produced3, comprehensive genomic information available for L. vannamei is still very limited and there is currently a poor understanding of fine scale genome structure and the genetic basis underlying complex commercially important traits. For example, current breeding programs for L. vannamei use traditional phenotypic selection to produce shrimp with improved growth and resistance to various viral pathogens like Taura syndrome virus (TSV)5. While this traditional approach has been moderately successful in producing more productive shrimp strains, genetic progress using multi-trait phenotypic selection in L. vannamei has been significantly impeded by unfavourable genetic correlations between growth and disease resistance6, as well as a poor correlated response in susceptibility to multiple diseases9. In light of these unfavourable genetic correlations between traits of interest in L. vannamei (i.e. growth and disease), breeding strategies would benefit from the integration of genetic markers tightly associated with trait variation (i.e. quantitative trait loci - QTL). The development of single nucleotide polymorphism (SNP) marker panels with the power to simultaneously identify genome-wide QTLs for complex and/or correlated traits would assist shrimp breeding strategies, as it would allow for the improved identification of selection candidates possessing advantageous genes. This would negate the current requirement for multiple selection lines and allow selection decisions for traits to be made directly on candidates, thereby increasing the accuracy of selection and resultant genetic gains. Despite recent increased research effort into L. vannamei genomics which have yielded SNP markers and moderate density linkage maps14, limited gene-based (Type-I) genomic resources are publically available, and production trait architecture and localisations are based on low density maps containing either AFLPs or microsatellites15. Therefore, there is still a need to develop comprehensive genome-wide SNP marker panels and dense genomic maps that allow the simultaneous detection of genome-wide QTLs for commercially important traits.The globally important whiteleg shrimp or Pacific white shrimp, L. vannamei by developing a gene-based commercial SNP array and genomic linkage map, demonstrate the placement of additional markers using novel locus ordering by linkage disequilibrium (LODE) methods17, and describe the genome synteny between two commercially valuable penaeid species. The gene-based Type-I SNP marker panel and comparative genomic maps will be valuable resources for investigating genome-wide genetic trait associations, creating optimal marker sets for selective breeding and genomic prediction, understanding functional biology and genome evolution, and assisting in genome assembly.SNPs derived within expressed sequence tags (ESTs) which originate from gene coding and 3′-UTR regions are considered a valuable resource useful in linking genotype to phenotype. Anchoring EST-derived SNPs and their associated transcript sequences to a high density genomic map not only allows insights into genome structure and marker spacing across the genome, but also helps identify the biochemical pathways underlying traits of interest. This study aimed to extend on the current genomic resources available for L. vannamei individuals representing prominent domesticated industry lines , using TRIZOL® Reagent (Life Technologies). RNA from each individual were pooled together in equimolar amounts before being converted to double stranded cDNA using the Mint cDNA synthesis kit (Evrogen), and normalised using the Trimmer cDNA normalisation kit (Evrogen). Normalised cDNA was then sequenced using an Illumina GA-IIX genome analyser, which produced approximately 25 gigabases of 76 bp paired-end EST sequence data (~10× genome coverage). Illumina sequence adaptors and primers were screened and removed using the software Seqclean (https://sourceforge.net/projects/seqclean/). MOTHUR was used to remove sequences with an average quality score (Phred score) less than 15 (window size = 10 bp) and/or shorter than 50 bp in length18. The cleaned sequence data was assembled using Velvet V1.019 and OASES20. Assembly parameters consisted of no extra gap penalty with all other options at default or recommended settings. Transcript assemblies were conducted at kmer lengths of k39, k41, k43, k45, k47, k49, k51 and k53 before being clustered together at a 90% sequence identify threshold using the software CD-HIT21. Where multiple transcript sequences were identified, only the longest sequence was retained. Transcript redundancy removal was undertaken, since it is a requirement for SNP discovery.To enable the identification and development of genome-wide Type I SNPs, total RNA was extracted from the tail muscle tissue of 30 22 and the NCBI non-redundant protein database conducted through the software package Blast2GO23. Where multiple annotations were returned, the one with the best bit score was retained. For each successfully annotated contig, gene ontology (GO) terms InterPro scan results were retrieved using Blast2GO.Annotation of the assembled sequence database was achieved using a Blastx search algorithm24 and custom scripts. NOVACRAFT  was used to align the cleaned sequence reads to the full sequence assembly. The SAMTOOLs pileup command was used to produce mapping qualities. The varFilter option in SAMTOOLs was employed to filter SNPs, keeping only the most informative . Any SNP identified within 50 bp of a candidate SNP was excluded to ensure a conservative flanking region for probe design. In addition, multi-allelic SNPs and SNPs requiring type I Illumina Infinium Probes (A/T or C/G) were removed and sequence repeat elements were masked. The resultant SNPs with the highest MAF and read depth were prioritised and submitted for assay development analysis using Illumina’s Assay Design Tool (ADT). Any SNP that returned an ADT score of less than 0.7 was excluded from the array. To ensure no unintentional duplicate SNPs were included on the array, probes for each SNP were mapped to the initial assembly using NOVOCRAFT  and only the probes that mapped uniquely were included in the array. Following this procedure, 8,967 SNPs  were incorporated into the Illumina Infinium ShrimpLD-24 v1.0 SNP genotyping array . For some nauplii pools, one of the two parents was either unknown or not sampled. Consequently for these families, the full unknown parental genotype was reconstructed using methods described in Supplementary Methods and Peiris, et al.25. All families were raised indoors in a Nucleus Breeding Centre under biosecure conditions from founding individuals representing most of the prominent industry domesticated/selected lines. To ensure all genotypes calls were genuine and to identify aberrant SNPs and DNA samples, strict genotypic data integrity was undertaken in GenomeStudio V2011.1 following methods outlined in Jones, et al.26. Family groups were reconstructed using SNP genotypic data (as described below) to enable the assessment of Mendelian inheritance (MI) of alleles. Genotype reproducibility between batches and across arrays was tested using 52 replicate samples and 26 replicate SNPs.To validate the performance of the L. vannamei samples or pools using a modified CTAB protocol27. DNA was standardised to 50 ng/µl using PicoGreen dsDNA quantification (Invitrogen), while DNA quality was inspected by agarose gel electrophoresis. All array genotyping was undertaken at PathWest Medical Laboratories, Perth, Western Australia, following manufacturer instructions28. Genotypes were calculated within the genotyping module of Genome Studio V2011.1 (Illumina Inc.) using the GenTrain genotype clustering algorithm. A minimum GenCall (GC) score cut-off  of 0.15 was used in SNP genotype clustering. The proportion of loci that produced a genotype for a sample is the sample genotyping rate. The SNP conversion rate is defined as the number of SNPs that produced a genotype divided by the number of total SNPs included. SNP validation rates were calculated as the number of SNPs with a heterozygous call divided by the number of SNPs that produced a genotype. SNPs with a minor allele frequency of greater than 0.01 were considered polymorphic. Mendelian errors for each SNP were reported as in Mendelian agreement whereby;Genomic DNA was extracted either from the 2,004 th percentile of the GC scores (GC10 scores). All SNPs were investigated for conformation to expected Hardy-Weinberg Equilibrium (HWE) and Mendelian Inheritance (MI) patterns. All recorded pedigree information was validated in a number of subsequent iterations using the 1,800 highly reliable “first class” SNPs produced from the array and the parentage programs Cervus 3.030 and COLONY31. Briefly, all individually genotyped females and male family relationships were confirmed using this integrated approach, whereby all maternal assignments were verified in COLONY , before being used to verify paternal assignments (750). Then using all validated parental relationships, COLONY was used to cluster pedigrees as an extra level of validation and to estimate unknown parents by inferring genotypes (N = 30). Any disagreements or pedigree alterations were resolved.All GenCall scores are reported as the 1033. Markers were segregated into linkage groups by the group function at a logarithm of odds (LOD) threshold of 10 and a distance threshold of 30 cM. Linkage groups were defined as groups of at least three markers ordered on a map at a LOD 3 threshold, and having agreement with independent linkage disequilibrium (LD) and LODE mapping assignment (as described below). The remaining 1,447 markers which did not have three markers ordered at a LOD 3 threshold and/or were not confirmed by LD and LODE analysis were designated as orphan markers. The defined linkage groups were subsequently constructed using a hierarchical approach whereby ordering was determined using consecutive thresholds of LOD3, LOD2 and the most likely marker position. For each consecutive threshold, maps were created using the buildfw function, followed by annealing, flips 6 and polish, until the best sex average map was produced. After all linkage groups were ordered, orphan markers were tested again using two-point to determine whether they could be inserted into any ordered linkage groups. In addition, the five distal markers from both ends of each linkage group were compared by two-point to identify if any linkage groups could be merged together. Sex specific maps were also produced by locking in the sex average marker order and re-calculating interval distances based on separate male and female informative recombination events. The Kosambi34 mapping function was used for all centi-Morgan (cM) calculations.After parental relationship validation and genotype reconstruction, a total of 631 progeny from 30 grandmaternal and 19 grandpaternal traced families were selected for linkage map construction  for each interval and linkage group as well as over the entire map. To ensure any observed sex-specific recombination was truly due to differences between the sexes, and not affected by variation in individual F1 parents, family specific heterogeneity was investigated for each F1 parent independently. LINKMFEX version 2.438 was used to calculate the recombination fraction, number of co-informative meiotic events (N) and the number of recombinations (r) for all mapped locus intervals for the maternal and paternal lines of each family separately. The Zmax score (LOD) was calculated for the mother and father in each family, and combined across all mothers and fathers respectively using methods outlined in Ott35. The following M-test was employed to investigate individual F1 recombination heterogeneity within each mapping family Ott35.ith F1 reference family for a pair of markers, with ith reference families.To detect any differences in sex-specific recombination rates, ratios of female-to-male map distances were calculated  patterns. This was investigated using log-likelihood ratio tests for goodness of fit to Mendelian expectations on manually phased genotypic data across all markers from all dams and sires as described in Sokal and Rohlf 41. The LODE procedure used in this study is an adaptation of the two step procedure described in Khatkar, et al.16. Firstly, SNPs are assigned to a chromosome or linkage group, then subsequently its position within this linkage group is estimated. Both of these steps rely on pair-wise estimates of linkage disequilibrium (LD). LD estimates (r2 and D′) were computed among 6,379 SNPs and 1,963 individuals  using GOLD software42. The extent of LD among SNPs, within and across the linkage groups, was estimated using position of SNPs on the current linkage map. Placements of orphan SNPs using the LODE method were defined based on at least three pairs on a chromosome with r-squared 0.1 or more, but also looking at the maximum LD score.Locus ordering by disequilibrium (LODE) is a novel methodology that allows the utilisation of additional linkage disequilibrium data to place unpositioned or orphan SNPs within genetic maps or scaffoldsm + 1)/(m − 1), where m is the number of loci in each linkage group43. The total expected genome length was then the sum of Ge from all linkage groups. Genome coverage (Coa), was calculated by dividing Goa by Ge44.Genome coverage of the integrated linkage and LODE sex-average map was calculated using observed and expected genome lengths. The observed genome length (Goa) was calculated by adding the observed linkage group lengths. The expected genome length (Ge) was produced by multiplying the length (cM) of each linkage group by . Comparisons to Yu, et al.2 were undertaken using their contiguous sequences generated from their genome survey sequencing, bacterial artificial chromosomes (BACs) and marker sequences, whereas comparisons to Baranski, et al.45 were undertaken with the contig sequences associated with their mapped SNPs. The primary hit was retained in each case. In addition to sequence similarity search of the marker sequences published in Du, et al.3, 159 SNPs from a previously published low density SNP map3 were included on our genotyping array to allow the direct comparison of their linkage map to ours. Comparison of genome synteny in this case was undertaken by matching marker IDs of all SNPs from this current study that were directly genotyped and mapped with our integrated map. BLAST annotations to Daphnia pulex and Drosophila melanogaster for SNPs with common IDs were also carried across from Du, et al.3. Oxford Grids46 of the integrated map presented here versus Yu, et al.2, Du, et al.3 and Baranski, et al.45 were plotted using custom R scripts to confirm mapping position and illustrate genome synteny. An example linkage group (LG4) was drawn using ArkMap47 to illustrate genome conservation.Syntenic relationships were explored for the integrated linkage and LODE map against three previously published maps, a et al.3; and rs142459135–rs142459627 developed in Ciobanu et al.11]. The Illumina Infinium ShrimpLD-24 v1.0 array is available from https://www.illumina.com/products/by-type/microarray-kits/infinium-shrimp-ld.html. All remaining data used and/or analysed during the current study are available from the corresponding author on reasonable request.The assembled contig sequences and mapped raw reads generated within the current study have been submitted to GenBank as a SRA database (Accession number: SRP094129). All SNPs included on the Illumina Infinium ShrimpLD-24 v1.0 array have been submitted to dbSNP on NCBI , however, comparisons between a higher density of markers is required before inferences of chromosomal rearrangements can be made reliably. Overall, the robustness of the marker orders in all maps is demonstrated by the high correlations between marker orders across most linkage groups calculated from independent analysis. The sporadic marker disagreements may be due to either genotyping errors, or differences in the mapping algorithms used during map construction1.70. These novel techniques may help increase disease resistance to specific emerging diseases which is a major priority for current shrimp breeding programs. It is predicted from simulated genetic advancement using genomic information in selection programs for survival and disease resistance was up to 2.6 times as effective than that of phenotypic sib-selection alone70. Furthermore, considering vaccination is not an option and management interventions to curve disease are usually unfeasible, several shrimp breeding programs have already been implemented in a number of countries to improve disease resistance as reviewed in Neira71, Rye72, and Castillo-Juárez, et al.70. With the continuing development of genomic resources in penaeids, incorporation of genomic information into breeding programs is a viable option promising to increase the accuracy of selection and therefore response compared to conventional selection70.Penaeid genomics has come a long way in the last 15 years. Many species now have significant genomic resources which will enable more advanced methods of breeding such as marker assisted and genomic selectionL. vannamei is a fundamental step towards further understanding the genomic structure and genetic contribution to commercially important traits. The development and validation of a large EST-derived SNP database and commercial genotyping array as described within this study will expedite the development and incorporation of genomic information into advanced selective breeding programs by enabling researchers to cost effectively genotype a large number of individuals within breeding programs. In addition, this study provides an integrated linkage and LODE map for L. vannamei, which revealed high macrosynteny between L. vannamei and P. monodon with only a small number of occurrences of inter chromosomal rearrangements. Combined, these data provide an important resource for genetic association studies, comparative genomics, and assisting in genome assemblies across several related shrimp taxa.Developing a large set of type I genome-wide molecular markers and genomic maps for Supplementary InformationSupplementary Table S1Supplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S7Supplementary Table S8Supplementary Table S9


PstI restriction enzyme) on Genome-Wide Association Studies (GWAS) in an outbred F2 chicken population. We have validated 91.7% of these 134,528 SNPs after imputation of missed genotypes. Out of those, 20 SNPs were associated with feed conversion, one was associated with body weight at 35 days of age (P < 7.86E-07) and 93 were suggestively associated with a variety of performance traits (P < 1.57E-05). The majority of these SNPs (86.2%) overlapped with previously mapped QTL for the same performance traits and some of the SNPs also showed novel potential QTL regions. The results obtained in this study suggests future searches for candidate genes and QTL refinements as well as potential use of the SNPs described here in breeding programs.Performance traits are economically important and are targets for selection in breeding programs, especially in the poultry industry. To identify regions on the chicken genome associated with performance traits, different genomic approaches have been applied in the last years. The aim of this study was the application of CornellGBS approach (134,528 SNPs generated from a  Production efficiency in the poultry industry is constantly improving as a result of selection for growth rate, feed efficiency and carcass traits for broilers, and egg production and quality traits for layers1Several studies have been conducted in chickens using markers randomly distributed in the genome (microsatellites), which have allowed the identification of several QTLs for production traits3456789101112141516171820212223242527282930PstI-derived SNPs dataset from CornellGBS optimized approach. This dataset was originated from a SNP call from the reduced representation of the sequenced genome (~5%) through PstI restriction enzyme36Despite the high-throughput data generation by NGS, which have facilitated the identification of SNPs in several populations, the use of this method for GWAS is still a limitation. This is caused by the high cost involved with the generation of data to be applied in a large number of individuals. To solve this high cost problem, SNP panels were designed to be applied in GWAS32PstI-derived SNPs dataset, and further use that information to conduct a GWAS with performance traits in chickens. In addition, we have performed a linkage disequilibrium (LD) analyses in the parental, F1 and F2 generations, to better understand the segregation of haplotype blocks, and the population structure, from the associated and suggestively associated SNPs identified. Finally, we have compared the location of these mentioned SNPs with known QTLs, with the objective to validate and to refine the regions of known QTLs.The aim of this study was to identify genetic markers using 2 population.The descriptive statistics for the eight performance traits analyzed can be observed in 7PstI-SNPs present in all animals.In our previously work2-7810 family). The genotype concordance of the SNPs with concordant chromosomal positions detected between the two methods is shown in The dataset of 134,528 SNP chromosomal positions obtained with the CornellGBS before and after the imputation analysis was compared with the 600 K Affymetrix® HD genotyping array dataset in order to perform a method validation, since both sets were obtained from the same animals , the average frequency of heterozygous SNPs was 25.32% (±5.6%) before the imputation and after the imputation, it increased to 27.70% (±5.2%). The average heterozygosity observed per chickens before imputation ranged from 8.30–44.69% and after the imputation between 11.38–44.67%. The proportion of heterozygous SNPs remained virtually unchanged before and after imputation among the lines/generations .2 population structure. This plot was useful for visualizing internal structure explained by the variance from PstI-derived SNPs dataset of 134,528 SNPs using eigenvector-based multivariate analyses. Each individual lies in its proper group consistent with our F2 population structure composed by five F2 dame-based families , based on covariance, using Tassel v.5.2.26families .The genetic and residual variance for each trait and their genomic heritability are shown in 40Twenty significant SNPs (P < 7.86E-07) were associated with feed conversion adjusted to body weight at 35 days (adj35) and one significant SNP associated with body weight at 35 days of age . In addi2 population  depending on the number of sequenced individuals of the reduced genome by restriction enzyme cleavage providing a specific SNP profilePstI restriction enzyme to cleave the cattle genomeCornellGBS is a widely employed method to genotype large genomes of model and non-model species exploring important regions in the genome362 crosses resulted in 20 SNPs associated (P < 7.86E-07) with feed conversion adj.35, one SNP associated with body weight at 35 days (BW35) and other 93 SNPs suggestively associated (P < 1.57E-05) with different performance traits  and were within introns, upstream and downstream of these genes  and often encompassing several genes, making difficult the selection of candidate genesPstI-derived SNPs to a GWAS for the first time in chickens. We showed a strategy, changing filtering criteria and subsequent genotype imputation, to increase the number of reliable SNPs to be analyzed. We found 13 SNPs indicating new regions associated with performance traits, mainly in microchromosomes, that have not been previously reported. We improved the available information about loci controlling performance traits and we refined these regions to discover novel candidate regions to be explored. Finally, by demonstrating that GBS is a valid strategy for QTL mapping in a species that has genome sequence and SNP panel available, we can argue the validity of GBS in species without genome resources.To the best of our knowledge, we showed the application of the CornellGBS All experimental protocols employed in the present study that relate to animal experimentation were performed in accordance with the resolution number 010/2012 approved by the Embrapa Swine and Poultry Ethics Committee on Animal Utilization to ensure compliance with international guidelines for animal welfare.2 populations originated from a reciprocal cross-experimental population from Embrapa Swine and Poultry National Research Center, Concórdia, SC, Brazil. We also included in the analysis, 10 chickens from their parental lines TT and CC (5 from each one), and eigth from the F1 generation. This F2 population was developed for QTL mapping studies, and was originated from the crossing of seven males from a broiler line (TT) and seven females from a layer line (CC), resulting in seven full-sib families (F1 generation). Then, twenty-one F1 females were artificially inseminated with seven F1 males (3:1 ratio) to generate the F2. The F2 population comprised seven paternal half-sib families composed of three full-sib families of approximately 100 individuals each, produced in 17 hatches, totaling 2,063 F2 chickens416This study was conducted using 464 chickens from a FThe TT broiler line was selected over six generation to improve body weight, feed conversion, carcass and breast yields, viability, fertility, eclodibility, reduction of abdominal fat and metabolic syndromes2 chickens were reared as broilers with free access to corn and soybean meal-based diet and water up to 42 days of age. From 35 to 41 days, they were transferred to cages to collect feed intake, and to compute the conversion and efficiency. Body weight was recorded at 1 (birth weight), 35, 41 and 42 (after fasting) days of age. The body weight (BW) at 41 days of age was collected at the end of the conversion measurement and, BW42 days was collected after 6-h fasting period and transportation to the slaughterhouse. More details were previously provided5162 animals selectect to be evaluated in this study.The FPstI enzyme, ligated to adapters with barcodes identifying individual animals, and then sequenced on Illumina platform. After filtering parameters were applied, 134,528 SNPs were identified from 462 individuals in our experimental population of chickens using minimum minor allele frequency (mnMAF) of 1%; minimum taxon coverage (mntCov) of 20% and minimum site coverage (mnScov) of 70% filter parameters. All procedures to obtain the data were previously describedPstI-SNPs × 462 individuals using 90% of loci call rate and 134,528 derived PstI-SNPs x 462 individuals using 70% of loci call rate). The imputation of missing genotypes was performed using Beagle 4.1 softwareGenomic DNA was cleaved with the PstI-derived SNPs dataset with the 600 K Affymetrix® HD genotyping array SNP dataset using five individuals from F2-7810 family. The SNPs in both datasets were located on GGA1-28, 32, W and Z chromosomes on the Gallus-gallus-4.0 reference genome. The validation standards used in this study were based on a methodology previously proposedThe validation was performed by the comparison of the filtered 134,528 http://www.r-project.org/).The principal component analyses (PCA) of imputed genotype data were performed using Tassel v.5.2.26 The compressed mixed linear model (MLM) implemented in TASSEL v.5.2.26 softwareβ is the vector containing fixed effects, including the sex , hatch (1–17) and SNP  effects, and the covariate body weight at 35 days for traits measured from 35 to 41 days of age ; u is the vector of random additive genetic effects from background QTL for individuals, X and Z are design matrices, and e is the vector of random residuals. We assumed that u and e vectors are normally distributed with null mean and variance ofwhere y is the vector of the dependent variables, K is the kinship (co-ancestry) matrix calculated from SNPs and provided by the same software using centered identity by state (IBS) method. For the residual effect, homogeneous variance was assumed, with I is an identity matrix and 2) was calculated as the ratio of the additive genetic variance 54Quantile-quantile (Q-Q) plots for each trait and Manhattan plots of genome-wide association analyses were performed in R using ggd.qqplot and Manhattan functions. The thresholds were corrected for multiple testing (Bonferroni) established by the estimated number of independent SNPs and LD blocks (pairwise r1 and F2 generations (The linkage disequilibrium (LD) analysis was performed with Haploview 4.2erations  using Hahttp://www.r-project.org/). For the data presentation, we designed a figure using ggplot2 package karyogram layout from R 3.3.1 software.The QTL data was obtained from QTLdbHow to cite this article: Pértille, F. et al. Genome-wide association study for performance traits in chickens using genotype by sequencing approach. Sci. Rep.7, 41748; doi: 10.1038/srep41748 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


P value of 9.01E-18 that was close to the potential causative gene of NR6A1. The result of conditioning on the top SNP association test indicated that only one QTL was responsible for this trait in SSC1. The linkage disequilibrium (LD) drop test result for the condition of the reported potential causative mutation (c.575T > C missense mutation of NR6A1) indicated that this mutation was probably not the underlying mutation that affected lumbar number in our study. As the first trial of imputed whole-genome sequence GWAS in swine, this approach can be also powerful to investigate complex traits in pig like in human and cattle.Resequencing a number of individuals of various breeds as reference population and imputing the whole-genome sequences of individuals that were genotyped with medium-density chips to perform an association study is a very efficient strategy. Previously, we performed a genome-wide association study (GWAS) of lumbar number using 60K SNPs from the porcine Illumina chips in 418 Sutai pigs and did not detect any significant signals. Therefore, we imputed the whole-genome sequences of 418 Sutai individuals from 403 deeply resequenced reference individuals and performed association tests. We identified a quantitative trait locus (QTL) for lumbar number in SSC1 with a  Sus scrofa) approximately 10,000 years ago1. Thus, a large number of traits have changed dramatically, including more docile behavior, larger litter size and increased carcass length. The number of vertebrae associated with carcass length varies among breeds. Compared to the wild boar, European commercial pigs have 2–4 more vertebrae2. Because of its importance, this trait has received considerable attention. A quantitative trait locus (QTL) on chromosome 1 that affects the carcass in swine was identified in 1998 using a Meishan × White reciprocal backcross population3. In addition, a QTL significantly affecting vertebral number that is located extremely close to the QTL affecting carcass length on SSC1 was discovered in the Meishan × Gottingen cross population4. Furthermore, two additive quantitative trait loci (QTLs) on chromosome 1 and chromosome 7 were identified for the number of vertebrae using nine F2 families, including European breeds, Asian breeds, and miniature pigs5. Subsequently, to further investigate these two QTLs, fine mapping was carried out and the NR6A1 gene was found to be a potential gene controlling the number of lumbar vertebrate; later, the c.575T > C missense mutation of this gene was suggested to be the potential mutation affecting the number of lumbar6. However, the QTL located on SSC1 was not detected in the Sutai population.Pigs were first domesticated from wild boars  were recently uncovered for economically important traits in pig breeds11. However, the power of GWASs is limited by the current density of SNP chips. The average density of the porcine SNP chips is much lower than the linkage disequilibrium (LD) block of most native breeds12, and as a result, several QTLs are missing from GWASs based on Illumina 60K porcine SNP chips. To improve the reliability and accuracy of GWASs, the use of high-density SNPs or even whole-genome sequence data to reperform the GWAS based on low-density SNPs is needed to identify missing QTLs. With the rapidly decreasing costs of next-generation sequence technology and the increasing accuracy of sequencing, numerous researchers have employed sequencing or resequencing to understand the demography, diversity and selection sweep of the investigated animals15. However, resequencing thousands of individuals and then determining associations for economically important traits is still an inefficient strategy. A more efficient approach is to impute the whole-genome sequence genotypes of individuals genotyped with medium-density chips using a previously sequenced reference population, and then determine associations between imputed genotypes and traits of interest using well-developed GWAS software. This approach is very popular for human disease studies, such as HapMap16 and the 1000 Genomes Project17, which provided standard reference panels. This approach has also worked very well in cattle, such as the 1000 bull genomes project (Run 2.0)19. To the best of our knowledge, there are still no GWASs using whole-genome resequenced data in pigs.With the rapid development of SNP genotyping technology, genome-wide association studies (GWASs) have become a very effective and widely used approach to identify genetic variants associated with complex diseases or traits across the entire genome20. Unexpectedly, no association signals were identified in Sutai pigs for lumbar number, which was different from the results of most published QTL mapping studies. Therefore, we hypothesized that the non-significant result may have arisen because of the low LD between causal mutation and nearby SNPs. To increase the detection power and decrease the cost of the GWAS, we first imputed the genotypes of 60K chips to the genotypes of whole-genome sequence variants in Sutai pigs using a reference panel containing 403 deep-sequenced individuals. Then, we used the imputed genotypes to reperform GWAS for the same phenotypes with the objective of determining whether there was a genetic variation in NR6A1 associated with lumbar number in this breed. As noted above, the c.575T > C missense mutation of NR6A1 was the strongest potential candidate for lumbar number. However, the causality of this SNP in Sutai pigs was unknown. In this study, we genotyped this mutation to estimate the imputation accuracy and its causality in Sutai pigs.Previously, we performed a GWAS using 60K porcine Illumina chips in Sutai pigs to detect the association loci for lumbar number. We expected to identify significant loci for this trait in Sutai pigs because this breed originated from Duroc and Erhualian pigs, which have similar paternal and maternal structures of an advanced intercross resource familyAll the experiments that involved animals were performed in accordance with the guidelines approved by the Ministry of Agriculture of China. Approval was obtained from the ethics committee of Jiangxi Agricultural University before this study.21.The target population of Sutai pig is a synthesized swine breed produced by crossing the Western Duroc and Chinese Erhualian breeds with continued selection for 19 generations. For the present study, we genotyped and phenotyped 526 individuals. The pigs were raised with the same fodder under uniform circumstances and slaughtered at 240 days of age in a commercial slaughterhouse. After the harvest, the carcasses were cut into halves and the numbers of lumbar vertebrae were counted and recorded. The lumbar number was either 5 or 6 in 436 pigs, including 206 gilts and 230 barrows, and the lumbar number was not available for 90 animals. More detailed information on the pigs’ environment and other phenotype data for these experimental animals were provided in our previous study22, and the samples were diluted to a standardized concentration of 50 ng/µl after the quality was checked. A total of 526 samples were genotyped using Illumina PorcineSNP60 Beadchips, including 62,163 SNPs, on an iScan System 23. Quality control (QC) was conducted using PLINK (v1.90 beta) to detect and exclude unreliable genotypes24. SNPs with a missing rate of each marker (geno) >0.1 or with minor allele frequency (MAF) <0.05 were excluded. Individuals with a call rate <0.9 were also removed. To maintain consistency with the sequencing data, the primer sequences of each SNP were aligned to the reference porcine genome assembly Sus-scrofa 10.2 using BLAST to detect their positions and forward (reverse) strand information. SNPs without positions were excluded, and the genotypes of reversed SNP strands were flipped using PLINK software.Genomic DNA samples were extracted from ear tissue using the standard phenol/chloroform method27 was used as a reference and each breed contained 9 to 86 pigs. More details on the breeds, origins and sample size are listed in Table 28. Variants were identified following the GATK 29 best practice protocol. PCR duplications were first marked by Picard MarkDuplicates (http://broadinstitute.github.io/picard/), and local realignments were performed with GATK IndelRealigner. Individual GVCF files were produced using GATK Haplotypecaller. Variants were called and filtered with GATK Genotype GVCFs and VariantFiltration options. Structural variants were removed with VCFTOOLS30. With cleaned SNP data, the haplotypes of 403 individuals were constructed using Beagle (v4.1)31.In this study, a wide collection of 403 whole-genome sequence data from 10 different pig populations32 using the default parameter settings, and the size of each sliding window was set to 7,000,000 bp. This software is based on a hidden Markov Chain Monte Carlo algorithm for imputation that first constructed local haplotypes using the MCMC algorithm and then resampled new estimated haplotypes for each individual using the HMM model.Imputation from 60K SNPs to whole-genome sequences for Sutai pigs was conducted with Beagle (v4.1)35. Ninety individuals were selected randomly from the sequenced reference population as a target population for each fold , and the genotypes in this target population were reduced to the variants that were included in the 60K genotyping array. The remaining individuals (313) were included in the reference panel. Two validation actions were taken to calculate the accuracy of imputation. One action was allelic correct rate (CR), which calculated as the number of alleles imputed correctly divided by total alleles at each locus, and the more detailed formula (see equation (ij) and Imp (nij) are the observed and imputed numbers of allele “1” for individuals i at marker j, respectively. The other action was the correlation coefficient between true and imputed SNPs. To investigate the imputation accuracy impacted by MAF, we classified CR and correlation into 10 classes with regard to the MAF of imputed SNPs. The accuracy of imputation was the mean CR or correlation across 15 folds for each class.Because of the very low density and common variants (MAF > 0.05) in 60K , imputation accuracy should be investigated in whole-genome sequence data. We used a 15-fold cross-validation strategy described in several previous studies36. This method implements a mixed model37  or whole-genome sequence variants. To make the results comparable, the values of the 60K marker from the results of the whole-sequence association study were extracted for comparison.The associations between lumbar number and imputed genotypes were tested using GEMMA (v.0.93)equation ) includi40. Haplotype blocks were then estimated with a confidence intervals algorithm in Haploview.To detect the linkage disequilibrium (LD) of SNPs near the most significant SNPs in the GWAS results, the 3 Mb region near the top SNPs in the whole-sequence association results was used to conduct LD analysis by extracting genotypes from the 60K data set using Haploview (v.4.2) softwareNR6A1 in Sutai pigs was detected following the methods of Yang et al.41. Briefly, a 360 bp segment was amplified and cut into two pieces of 183 and 177 bp for allele C at the position of 299,084,752 bp on SSC1. Genotypes of this locus were then identified through agarose gel electrophoresis.Variation of the c.575T > C (rs326780270) of To elucidate whether there are additional QTLs for lumbar number on SSC1, we performed a conditional test by including the genotypes of the top SNPs as a covariance to the mixed model and retested the association between SNPs and phenotypes. If no additional signal was detected, then there was only one QTL that affected lumbar number. Otherwise, there were multiple QTLs that cooperated to control lumbar number.NR6A1 c.575T > C was the mutation that determined lumbar number in Sutai pigs, we performed an LD drop test by including the genotypes of NR6A1 c.575T > C in the mixed model framework to determine how rapidly the association with the signal decreased.To determine whether After QC, 11,338 variants were excluded for the lack of chromosome position information, 42 pigs were removed due to a low genotype call rate, 3,229 variants were removed due to a low call rate and 9,804 variants were excluded for low minor allele threshold(s). Finally, a total of 37,792 SNPs and 484 pigs were introduced to perform further analyses.Imputation was produced using Beagle software. The summarization of imputation results is presented in Table P values positioned on the 60K original data were extracted from sequencing GWAS (which included the 60K imputed data). To further compare array based result to sequences based result underlying the same kinship matrix, we extracted P values positioned on the 60K chips from result of sequencing GWAS. The Manhattan plots of the 60K imputed data results are shown as Supplementary Fig. P values of the top SNP in the 60K imputed data and the 60K original data were 1.27E-06 and 2.99E-06, and the position of the top SNP in both results was 298972575 (rs81352477) in chromosome 1. In the scenario with the sequence data, 105 genome-wide significant SNPs were uncovered , which indicated much lower significance than the top SNP  at a position of 299,627,873 bp.We conducted a GWAS on the Sutai population in two scenarios, i.e., the target panel data before and after imputation. In the scenario for before imputation, as noted above, no significant loci were detected in Sutai pigs using 60K chips  in the 60K data that were used to conduct LD analysis. The LD block was shown as follows  density SNP chips. Whole-genome sequences of the target panel were imputed based on shared haplotype blocks between reference and target individuals and then were used to test associations of complex disease (traits) or to predict the genetic potential of economically important traits. Imputation accuracy ranged from 0.90 to 0.95 in cattle from the genotypes of an Illumina BovineHD genotyping array to whole-genome sequence data35. High correlations (0.64) were observed in humans with MAF = 0.1% when imputing an Illumina 1M SNP array to whole-genome sequences using a reference panel of 64,976 haplotypes45. In our study, a total of 403 individuals were included on the reference panel, including 32 Duroc and 29 Erhualian pigs, which are ancestors of the Sutai population. CR decreased and correlation increased along with an increase in MAF. CRs are highly sensitive to allelic frequencies and are not appropriate for comparing SNPs with different values of MAF47. Correlation is a more popular approach that is used to evaluate imputation accuracy. The correlation values ranged from 0.74 to 0.86 with an average of 0.80, which was lower than the results for cattle and human studies. Both were imputed from high-density chips (600K in cattle and 1M in human) to sequenced data, and the reference panels were very large. In pigs, the vast majority of studies were based on genotypes from the 60K porcine Illumina BeadChip because a high-density panel  that provides high-quality imputed genotypes in pig populations is currently impractical. Therefore, increasing the number of sequenced populations and individuals in the reference panel to improve imputation accuracy is necessary. Our GWAS results demonstrated that this was a powerful method to identify QTLs in agricultural animals, and this method will help researchers find new loci or rediscover QTLs associated with complex traits.Imputation-based association studies have achieved great success in humanset al.48, GWAS has become an effective method for identifying genetic variations associated with economically important traits in agricultural animals. A recent GWAS study showed that the QTL for the number of vertebrae on chromosomes 1 and 7 independently influenced the numbers of thoracic and lumbar vertebrae49. Potentially significant signals could be missed in a GWAS analysis if low-density SNPs were applied to a population that held a low LD characteristic, such as the results of our GWAS when only 60K SNPs were used before imputation and a highly significant QTL was uncovered for lumbar number after imputation. The LD between top SNPs in the 60K original association results (rs81352477) and top SNPs (rs334252332) in the sequence association result was 0.75, indicating a medium correlation. The increased detection power was probably due to causal mutations being in the data by imputation. This result was further confirmed by displaying the LD profiles of markers near the top loci. The top SNP was located in the smallest haplotype block, and the r2 values among these SNPs in this region were very low, which hampered the discovery of association signals. Furthermore, no haplotype block was found near the NR6A1 gene, which implicated the low LD station in that region in the Sutai population. The Sutai breed was intercrossed from Erhualian female and Duroc male for approximately 19 generations. Thus, the Sutai genome is a mosaic mixture of these two breeds. As a result, the LD block is smaller than the LD block in either of the two founder breeds. In this study, we identified 105 significant SNPs located on chromosome 1 across a region of 4.6 Mb  associated with lumbar number, and the highest signal was located on 299,627,873 bp of chromosome 1. This region contains the NR6A1 gene, which was reported to be associated with lumbar number6. The results also showed that using whole-genome resequencing data to perform genotype imputation can be an effective method to identify the QTLs that were missed in low-density SNP GWAS analysis. The imputation method can also narrow the QTL region or improve the power when GWAS analysis is performed. To determine whether population stratification was corrected in this study, we exploited quantile-quantile plots 6 showed only a weak association with lumbar number in our study . The possible reason for this result is that the QTN in the position of 299,084,752 bp may not be the causative mutation in the Sutai population. To confirm that NR6A1 c.575T > C was the causative mutation in our population, we performed an LD drop test by fitting genotypes of this locus into a mixed model. Normally, all significant signals nearby would disappear after correcting for causative mutation. The minimum P value increased from 9.01E-18 to 1.93E-08, which still indicated genome-wide significance. This result indicated that NR6A1 c.575T > C was not the causative mutation in our study. The results also indicated that we should recognize that the accuracy of imputation also affects the GWAS result. Several imputation studies in different species have shown that as the minor allele frequency in the target panel decreased, the imputation error rate increased50. As shown in previous studies, the fundamental aspect of imputation is the identical DNA segments in the target and reference panels, and increasing the number of parents or male parents in a reference panel can increase the imputation accuracy53. In other words, if we can increase the number of individuals in the target panel and reference panel, the imputation accuracy will be increased. Mixing different breeds in a reference panel would thus improve imputation accuracy54. In this study, we mixed different pig breeds in the reference panel and executed strict quality control, such as MAF and call rate, in the target and reference panels. We achieved a high CR with an average of 90% and real genotypes of c.575T > C, which confirmed the high imputation accuracy (98.43%). Therefore, this factor may not be very critical in this study, but we also should pay more attention to exploring the factors that affect the imputation results in the future. In addition, a reassociation study using real genotypes at c.575T > C achieved a P value of only 3.89E-07, which further indicates that it was not associated with lumbar number in the Sutai population. To determine whether there are several causative mutations responsible for lumbar number, we performed a conditional test by adjusting the top SNP on SSC1 and conducted GWAS again. Additional significant signals would stand out if the multiple causative mutation hypothesis was true. In our analysis, there were no other QTLs associated with lumbar number, which means there is only one QTL that controls lumbar number on SSC1, whereas the causative mutation is not the same as that previously reported. Further functional studies, such as gene expression and site-specific editing technology, are necessary to confirm the possibility of causality for the top SNP in the Sutai population.Although we identified the same QTL as that identified in a previous studyNR6A1, that was previously reported, while the top SNP differed from the previously reported potential causative mutation. This study illustrates the importance and effectiveness of uncovering the traits in agricultural animals using a whole-genome imputation approach and provides a solution that combines second-generation sequence data with GWAS. Our results also show that this approach can be a powerful strategy to analyze economically important complex traits in livestock. Along with developing good imputation software, exploiting more public database systems will contribute to genotype imputation in the future.In this study, we rediscovered the missing QTL for lumbar number in Sutai pigs using GWAS based on a whole-genome imputation strategy. This QTL includes the same potential causative gene, Supplementary info