Cervus Nippon) and wapiti (Cervus elaphus) are closely related species and their hybridization can result in significant allele-shift of their gene pool. Additive genetic effects and putative heterotic effects of their hybridization on growth performance could confer considerable economic advantage in deer farming. Here, we used double-digest restriction site-associated DNA sequencing technology (ddRAD-seq) and detected ∼320,000 genome-wide SNPs from 30 captive individuals: 7 sika deer, 6 wapiti and 17 F1 hybrids . By screening observed heterozygosity of each SNP across four taxonomic groups, we report for the first time a resource of 2,015 putative diagnostic SNP markers (species-specific SNPs for sika deer and wapiti), which can be used to design tools for assessing or monitoring the degree of hybridization between sika deer and wapiti. These ddRAD-seq data and SNP datasets are also valuable resources for genome-wide studies, including trait discovery for breeders of domestic deer.Sika deer ( The use of SNPs as probes for determining the degree of hybridization has been applied to a wide variety of taxa (R. tarandus) (Single nucleotide polymorphic (SNPs) are abundant throughout the genome and mostly biallelic . Due to Here, we employed double-digest restriction-site associated DNA sequencing (ddRAD-seq) technology (Cervus nippon hortulorum) (SK), 6 wapiti (Cervus elaphus xanthopygus) (WT) and 17 F1 hybrid offspring  and wapiti ♀ × sika deer ♂ (8 WS)) ) . The SK Whole blood samples were extracted from the jugular vein using EDTA vacuum tubes and were stored at -20° until DNA extraction. Genomic DNA was purified from the collected blood samples using the Blood DNA kit (Qiagen). Each DNA sample was subjected to 0.7% gel electrophoresis for detecting the presence of high-molecular weight DNA and then stored at -80° until ddRAD-Seq library construction.i.e., PstI, NsiI, EcoRI and SacI) and two with 4 base recognition sites . Six digest reactions were carried out based on the protocols by manufacturers: MseI + Buffer 3.1, EcoRI + Cutsmart Buffer, PstI + Buffer 3.1, NsiI + Buffer 3.1, SacI + Cutsmart Buffer and MluCI + Cutsmart Buffer. Both PstI and MseI were selected to construct our ddRAD-Seq libraries because both could be activated in the same buffer and achieve nearly complete digestion . The reaction mixture was incubated at 37° for 2 hr and 65° for 30 min. Amplification and sequencing adapters with a unique barcode (6 bp or 8 bp) were ligated onto the digested DNA. Each sample was then amplified via PCR in a 50 μl reaction volume, containing 70-100 ng of adaptor-ligated DNA fragments and amplified with 22 cycles following the manufacturer’s protocol. Samples were electrophoresed on a 2% agarose gel, and DNA in the 300-450 bp size range (with indices and adaptors) was excised and purified using a Gel Extraction Kit (Qiagen). Each sample library was pooled in equal amounts and quantified using an Agilent 2100 bio analyzer (Agilent Technologies) and then paired-end 101 bp sequencing was performed using the Illumina HiSeq4000 platform at BGI .The double digest reactions were carried out in a volume of 25 μl containing ∼0.8 μg of genomic DNA, 5U of Low quality reads were filtered or corrected by using the process_radtags program (-c -q –r) in STACKs v2.0 .The SNP inference process used in this study was an expanded version of a recently published STACKs protocol , and is obs required to filter out a nucleotide site at a locus by using the–max_obs_het parameter in the populations program.The BAM files for the all individuals were used as the input of gstacks program in STACKs v2.0 with the marukihigh model , a type We selected species-specific SNPs as diagnostic SNP markers to meet two filtration criteria: 1) biallelic SNPs are fixed in each individual of the SK and WT groups respectively and 2) all corresponding homologous sites in each individual of the SW and WS groups are heterozygous . In order to design genotyping assays in the future, sufficient 40 bp flanking sequences with no repetitive sequences and no SNPs within them were also obtained by retrieving the sika deer reference genome.https://doi.org/10.6084/m9.figshare.5942554.The VCF file including SNP information is available from Figshare:10.6084/m9.figshare.5918464. The various genetic parameter statistics for each taxonomic group are available from Figshare: 10.6084/m9.figshare.5918467. The diagnostic SNP markers are available from Table S1. Code used to simulate and produce ddRAD-seq restriction fragments on the genome according to fragment size is available from Figshare: https://doi.org/10.6084/m9.figshare.6291740.The sika deer ddRAD-seq data can be retrieved from NCBI SRA under BioProject accession number PRJNA383774 . The ddRAD-seq data of the wapiti and F1 hybrids were stored under BioProject accession number PRJNA432874. Supplemental material available at Figshare: After performing quality control, we obtained a total of 1.11 billion clean paired-end reads (202 Gb), ranging from 2.75 to 10.34 Gb for each individual with an average of 6.76 Gb . The avePstI and MseI could correspond to a distinct electrophoresis band in the region between 750 bp and 1000 bp in the PstI lane (not digested by MseI)  produced from the gstacks program perfectly matched with those in the reference genome, we performed a simulation and produced a total of 3,049,956 restriction fragments from the reference genome. Consistent length distribution between the observed and the expected ddRAD loci indicates restriction digestion and sequencing are complete for our ddRAD-seq libraries . That isThe diagnostic power of SNP markers could be strongly reduced if any of the samples was unrepresentative. STRUCTURE analysis was performed to confirm that each sequenced individual was assigned to its respective population based on its SNP genotypes by using Structure v2.3.4 . Based oTheoretically, all biallelic SNPs should be shared among these groups and the private alleles  should not be detected if sampling number were large enough. We observed only a small subset of 2,086 (0.64%) SNPs that belonged to private alleles calculated by the populations program in STACKs, including 1,386, 352, 308 and 40 SNPs in the SK, WT, SW and WS groups respectively. More convincingly, the private alleles have lower allele frequencies (average MAF = 0.19). These results confirmed that our samples are suitable for the development of diagnostic SNP markers.obs), expected heterozygosity (Hexp) and inbreeding coefficients (FIS) for each SNP within each separate group were calculated by the populations program in STACKs. It is widely known that an observed excess of heterozygotes can be caused by hybridization. Substantial heterozygous SNPs with Hobs > 0.6 were observed in the WS and SW, but not in SK and WT (IS of each SNP was less than zero in WS and SW, further supporting that the F1 hybrids included an excess of SNP heterozygotes as expected (Observed heterozygosity (HK and WT . In addiexpected .The ddRAD-seq data and SNP datasets for the sika deer and wapiti are valuable resources to researchers who carry out genome-wide studies, including trait discovery for breeders of farmed deer. The developed diagnostic SNP markers will be useful for the development of hybridization monitoring tools. However, it is noticeable that these diagnostic SNP markers were only inferred from a small population, and therefore any diagnostic genotyping assay built from this dataset should be first tested with adequate sample sizes.


Breast cancer is the leading cause of cancer related deaths in women worldwide and is classified into subtypes based on the cancer’s receptor status. Of these subtypes, those expressing the human epidermal growth factor receptor 2 (HER2) receptor were traditionally associated with poor prognosis. Several advances have been made in the treatment of HER2-positive breast cancer, yet issues of resistance and poor response to therapy remains prevalent. In this study we explored the impact of HER-family and homologous recombination deficiency SNPs on response to patients who received TCH-based (docetaxel (T), carboplatin (C), and trastuzumab (H)) treatment versus those who received other treatment regimens. Using Cox regression analysis, we identified 6 SNPs that correlate with recurrence free survival in our patients and supported our findings using support vector machines. We also used reverse phase protein array analysis to examine the impact ERBB3 SNPs may have on both the PI3K/AKT and MAPK/ERK signaling pathways. Finally, using cell line models, we correlated SNP status with sensitivity to platinum based drugs and docetaxel. We found that patients on a TCH based regimen with the minor allele of the ERBB3 (rs2229046 and rs773123) and BARD1 (rs2070096) SNPs, were significantly more likely to relapse than those women who were not. Additionally, we observed that patients with these ERBB3 SNPs had shown elevated protein expression/phosphorylation of Src kinase, c-MET (Y1234/1235), GSK-3β (S9) and p27, indicating that these SNPs are associated with non-PI3K/AKT signaling. Finally, using cell line models, we demonstrate that the BARD1 SNP (rs2229571) is associated with greater sensitivity to both carboplatin and cisplatin. The BARD1 and ERBB3 SNPs can potentially be used to determine those patients that will have a worse response to TCH based treatment, an effect that may arise from the SNPs impact on altered cellular signaling. ERBB2) gene  and accounts for approximately 20% of all breast cancers. It is associated with a significantly worse survival [HER-2 positive breast cancer comprises cancers which exhibit the overexpression or amplification of the Erb-B2 Receptor Tyrosine Kinase 2 , ERBB2, ERBB3 or ERBB4 genes (HER-family genes) can be associated with either a worse relapse free survival (RFS) or worse overall survival (OS) rates in women with HER2-positive breast cancer who received adjuvant trastuzumab as part of their treatment regimen [Recent data has suggested that germline single nucleotide polymorphisms (SNPs) play a role in both sensitivity and resistance to targeted therapies ,8. In fa regimen ,10. Muta regimen .BRCA1/2 are the most common cause of HRD. However, any gene mutations which cause HRD can potentially result in a phenotype like that of BRCA1/2 mutated cancer. This phenomenon is called ‘BRCAness’ and is also characterized by HRD [Cancer cells with impairment in DNA repair mechanisms, such as homologous recombination deficiency (HRD), are sensitive to platinum based drugs, which directly damage a cell’s DNA. Mutations in the tumour suppressor genes d by HRD .Our current study aims to explore the effect on survival and functional impact of both HER-family and HRD related SNPs on response to TCH based treatment, versus patients who received a non-TCH based treatment.A total of 157 patients with operable primary BC were used in this study, which included 78 patient samples from the NCT01485926 phase II neo-adjuvant study of TCH/TCHL in women with early stage HER2-positive breast cancer. The remaining 79 patients came from the CTI-09-07 translational study, which enabled the collection of FFPE samples and the relevant clinicopathological data and treatment history. We selected an additional 32 samples from the CTI-09-07 translational study on which to perform high depth sequencing, as this discovery cohort had tumour blocks with sufficient tissue to perform NGS analysis. These studies were approved by the research ethics committees of all hospitals, were run by the All-Ireland Clinical Oncology Research Group  and included women who are confirmed as clinically HER2-positive by a 3+ HER2 immunohistochemistry score and or/ a FISH ratio of >2. Detailed clinical information is available in The patient data was analyzed anonymously, the study was approved by the ethics committee of all the individual hospitals in which the patients were treated, and approved the respective studies in accordance with the Declaration of Helsinki. CTI 09–07 is a translational study and enrolment on TCHL and CTI-0907 was possible. Central REC review for TCHL: University College Cork Clinical Research Ethics Committee, and Local REC reviews for 09–07: University College Cork Clinical Research Ethics Committee, Beaumont Hospital Ethics Committee, University Hospital Waterford Research Ethics Committee.In our initial SNP screen, we performed high depth sequencing on our discovery cohort, which contained 32 tumour formalin-fixed, paraffin-embedded (FFPE) samples, from patients with HER2-positive BC. Haematoxylin and eosin sections cut from the patient’s FFPE surgical blocks were analysed by a pathologist for tumour content and those with >50% tumour cellularity had a further 7*10μM sections cut, from which DNA was extracted using the Qiagen DDNA FFPE kit as outlined in a previous study [http://bio-bwa.sourceforge.net/) under default parameters. Duplicate reads were marked by Picard tools (http://broadinstitute.github.io/picard/) and local realignment and base recalibration were conducted with GATK [The reads from our discovery cohort were trimmed using Trimmomatic  and aligith GATK   was applied to confirm the allele calls of the 6 SNPs which are listed in http://cran.r-project.org/).Survival analysis was only performed using data from trastuzumab treated samples, with RFS as the survival endpoint. Survival curves are based on Kaplan-Meier estimates and the log-rank p-value is shown for difference in survival. The resultant p-values are adjusted for multiple testing using the Benjamini-Hochberg method . Cox reghttps://www.ncbi.nlm.nih.gov/geo/) in SRA format (accession number GSE48216). Samples were processed as per GATK [https://software.broadinstitute.org/gatk/best-practices/). Briefly, the SRA files were converted to FASTQ files using the SRA Toolkit (https://www.ncbi.nlm.nih.gov/sra/docs/toolkitsoft/), quality control was conducted using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/), the adapter sequences were trimmed using BBmap (http://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbmap-guide/) and the FASTQ files were converted to SAM format using BWA [http://broadinstitute.github.io/picard/) and reordered before being passed on to GATK for base recalibration, using known sites as provided by the Broad Institute best practices guide. The files were then run through GATK Haplotype caller using discovery mode and recommended settings and the files were run through the GATK Variant Recalibrator, using the recommended settings for SNP discovery and known sites as provided by the Broad Institute best practices guide. Finally, the files were annotated using Oncotator [Exome sequencing data for 70 breast cancer cell lines treated with 90 therapeutic agents  were dowper GATK  best prasing BWA . Next, tncotator .50 values of the three groups were then compared using Tukey Honest Significant Difference and an ANOVA model. The GD50 values and the HER2 status of the cell line were as per the original authors [For statistical analysis, the locus status of the mutations in each HER2-positive cell line was used to divide the cell lines into three groups . The GD authors . This te authors .To perform pattern recognition analysis, a predictive model was built and tested using a Support Vector Machine (SVM). The features used were our 6 SNPs and the time to last follow-up (in months) to predict RFS with (1) or without (0) event and a radial-kernel SVM was built using the e1071 package in R . 5-fold The accuracy was chosen to represent the predictive power of the model, the Phi score was utilized to determine how strong of a relationship existed between features and the classes, and the AUC was chosen to assess how efficient the features are at separating the classes, where a value of 0.5 represents the machine just ‘guessing’ the classes. The Phi score is a metric with a value between -1 and 1, where -1 represents an inverse correlation between the features and the classes, 1 represents a strong positive correlation .ERBB2 and ERBB3 genes; the ERBB2 rs1136201 SNP, the ERBB3 rs2229046 and rs773123 SNPs, which were found in approximately 15%, less than 5%, and 6% of the 1000 genome UK population, respectively. Our analysis also included 3 SNPs related to HRD; RNF8 rs2284922 SNP, found in approximately 44% of the 1000 genome UK population and BARD1’s rs2070096 and rs2229571 SNPs, which occur in approximately 19% and 45% of the 1000 genome population, respectively. All 6 SNPs are exonic and occur in protein coding domains, where 3  are nonsynonymous variations, with rs2229046 being implicated in alternative splicing for HER3 [We focused on 6 SNPs identified in our high depth sequencing study of the exome of 28 genes  from DNAfor HER3 . The freIn our study, we assessed the impact of HER-family and HRD SNPs on the response of patients who received TCH-based treatment versus those who received other standard treatment regimens which did not include TCH.ERBB2 rs1136201 SNP and the ERBB3 SNPs rs2229046 and rs773123, were significantly more likely to relapse on a TCH based regimen  than those women who received a non-TCH based treatment (We found that women who were heterozygous for the reatment . After mBARD1 rs2070096 SNP were significantly more likely to relapse on a TCH-based treatment than those patients (p = 0.05) who received a non-TCH based treatment (p = 0.89) (-4). Conversely, we found that women who had the SNP for the RNF8, rs2284922, were significantly more likely to have a higher chance of relapse on non-TCH based treatment (p = 0.01), than those women who received TCH based treatment (p = 0.90)  , which m = 0.90) , Fig 2. Using machine learning, we demonstrate an increase in metric scores for support vector machine (SVM) model predicting RFS events in patients who underwent TCH based treatment versus those that received non-TCH based treatment . A rise ERBB3 rs2229046 SNP had a trend towards lower P27 expression  but had significantly higher expression of SRC kinase . These patients also had a trend towards higher phosphorylation of EGFR (Y1068) and Shc (Y317) . These results indicate that the ERBB3 rs2229046 SNP may be associated with tumours which preferentially signal through the EGFR/MAPK/SRC kinase pathway (Our results indicated that patients who had the  pathway .ERBB3 rs773123 SNP were significantly more likely to have lower expression of P27 (-3), whilst also having significantly lower phosphorylation of c-MET (Y1234/1235)  but significantly higher phosphorylation of GSK-3β (S9)  , it would be important to validate these findings in a larger dataset.We also found that patients who had the n of P27   . These rBARD1 rs2229571 were significantly more likely to be sensitive to a platinum-based drug relative to those cell lines that had either the reference allele or were heterozygous for the SNP  (Using available sequencing data from the Genomics of Drug Sensitivity in Cancer (GDSC) database  we deter = 0.02) . However = 0.02) .Despite the improvement in response rates of women with HER2-positive breast cancer to trastuzumab based treatments, a significant population of women have innate resistance to the trastuzumab based treatments such as the standard of care, TCH based therapy . This inPreviously we and others demonstrated that the minor allele of the ERBB–I655V SNP was associated with a worse RFS in women with HER2-positive breast cancer ,10. A liERBB3 SNPs, (rs2229046 and rs77123) and who received TCH based treatment are significantly more likely to have a worse RFS than patients who received alternate therapies. To support our hypothesis that this is a TCH dependent effect, we previously demonstrated that patients who had the minor allele of the ERBB3 SNP rs2229046 and who received trastuzumab as part of their therapeutic regimen did not have a significantly worse RFS [We have shown that patients who have the minor allele of the orse RFS .ERBB3 SNPs identified in our study represent both a synonymous (rs2229046) and non-synonymous (rs77123) SNP. SNPs which are synonymous variants were until recently believed to be silent, resulting in little to no impact on the ensuing protein. However, recent studies have shown that synonymous SNPs can play an important role in the functionality of the cancer cell and how a patient responds to targeted therapies [The herapies ,10,28,29ERBB3 SNPs rs77123 and rs2229046 SNPs not having a described role in cancer susceptibility [ERBB3 SNP rs2229046 have significantly elevated expression of Src kinase, and a nearly significant increase in EGFR Y1068 phosphorylation, whilst patients with the ERBB3 SNP (rs77123) have a significantly higher level of GSK-3β (s9) phosphorylation relative to those with the reference allele. HER2-positive breast cancer is generally classified as being associated with activation of the PI3K/AKT signalling pathway [ERBB3 rs2229046 and rs77123 SNPs could potentially indicate why these patients do not respond as well to TCH based regimens relative to the patients who receive non-TCH based treatment.Previous studies have shown that despite the tibility ,31, bothtibility . To date pathway ,33. TrasBARD1 SNP rs2070096 were significantly more likely to have a worse RFS than those patients who received a non-TCH based treatment. Our hypothesis that impediment of the HRD pathway would result in altered sensitivity to platinum based drugs given as part of the TCH backbone is supported by our result that patients who were homozygous for the minor allele of RNF8 rs2284992 had a worse RFS when they were given non-TCH based treatment, compared to those receiving TCH based treatment. The confounding effects of differing HRD related SNPs in relation to response to treatment would require further functional interrogation to elucidate why this effect occurs.Our analysis also identified that patients with the minor allele of the RNF8 rs2284922 as playing a role in cancer susceptibility or drug response [BARD1 SNPs  in this study have been associated with nephroblastoma and neuroblastoma [The role of RNF8 in DNA damage repair has been extensively studied, however there are no studies which associate response . Howeverblastoma –38, no s50 to platinum (cisplatin and carboplatin) and docetaxel based drugs. We demonstrated that those cell lines with the minor allele of the BARD1 SNP rs2229571 had greater sensitivity to both carboplatin and cisplatin. Interestingly, the BARD1 SNP rs2229571 had no significant effect on a patient’s response to TCH or non-TCH treatment. These results may indicate that whilst SNPs in HRD related genes do impact on a drug’s sensitivity in vitro, that their impact on a patient’s response clinically may be overcome using multiple therapies, which can have confounding effects on how a patient responds.To further examine the impact of HRD related SNPs on platinum response we analysed the SNP status of a panel of 11 HER2-positive cell lines and correlated this against the cell line GDBARD1 SNP results from patients in cell line models. We demonstrated that patients with the minor allele of the ERBB3 and BARD1 SNPs were more likely to have a worse RFS than those who received non-TCH based treatment. For the first time we also demonstrated using NGS analyses of cell line data that SNPs which occur in and BARD1 correlate with carboplatin or cisplatin sensitivity in vitro. Finally, we have shown that patients with the minor allele of the ERBB3 rs77123 and rs2229046 SNPs have elevated expression and phosphorylation of proteins in pathways not associated with PI3K/AKT signalling, likely indicating why these patients do worse to TCH based treatment.Our study does have limitations that must be taken into consideration, as these may lead to residual confounding biases. These issues include a small sample size and, in some cases, incomplete clinicopathological information. Future studies building from our work should test these variables to see if there are any residual effects on the results we have observed. One of the ways we addressed the small sample size in our study, was to perform rigorous statistical analysis to ensure that our results remain valid after correcting for multiple testing. We also used machine learning as another statistical tool to support that our patient response results are robust. Finally using the publicly available GDSC database we could determine the SNP status of cell line models which could be used to demonstrate the validity of our This study has demonstrated the potential of SNPs, both synonymous and non-synonymous to be important markers in tumour response to therapy. These hereditary changes should be taken into account when clinicians decide on a suitable course of treatment for women who will receive adjuvant treatment of their HER2-positive breast cancer.S1 File(CSV)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.


Single Nucleotide Polymorphism (SNP) array and re-sequencing technologies have different properties  and drawbacks (e.g. ascertainment bias). This lead us to study their complementarity and the consequences of using them separately or combined in diversity analyses and Genome-Wide Association Studies (GWAS). We performed GWAS on three traits  measured in 22 environments on a panel of 247 F1 hybrids obtained by crossing 247 diverse dent maize inbred lines with a same flint line. The 247 lines were genotyped using three genotyping technologies .The effects of ascertainment bias of the 50 K and 600 K arrays were negligible for deciphering global genetic trends of diversity and for estimating relatedness in this panel. We developed an original approach based on linkage disequilibrium (LD) extent in order to determine whether SNPs significantly associated with a trait and that are physically linked should be considered as a single Quantitative Trait Locus (QTL) or several independent QTLs. Using this approach, we showed that the combination of the three technologies, which have different SNP distributions and densities, allowed us to detect more QTLs (gain in power) and potentially refine the localization of the causal polymorphisms (gain in resolution).Conceptually different technologies are complementary for detecting QTLs by tagging different haplotypes in association studies. Considering LD, marker density and the combination of different technologies (SNP-arrays and re-sequencing), the genotypic data available were most likely enough to well represent polymorphisms in the centromeric regions, whereas using more markers would be beneficial for telomeric regions.The online version of this article (10.1186/s12870-019-1926-4) contains supplementary material, which is available to authorized users. Recent progress in next generation sequencing and genotyping array technologies contribute to a better understanding of the genetic basis of quantitative trait variation by allowing Genome-Wide Association Studies (GWAS) on large diversity panels . Single s strong .Several causes may impact the power of Quantitative Trait Locus  detection in GWAS. Highly diverse panels have in general undergone multiple historical recombination, leading to a low extent of linkage disequilibrium (LD). However, these panels can present different average and local patterns of LD –11. A hiIn maize, an Illumina Infinium HD 50,000 SNP-array, named MaizeSNP50 array (hereafter 50 K) was developed by Ganal et al.  and has Few studies in plants have compared datasets from different high-throughput genotyping technologies –35. ElbaThe main drawback of the DNA arrays is that they do not allow the discovery of new SNPs. This possibly leads to some ascertainment bias in diversity analyses when the SNPs selected for building arrays come from (i) the sequencing of a set of individuals that did not represent well the diversity explored in the studied panel, (ii) a subset of SNPs that skews the allelic frequency profile towards the intermediate frequencies , 36. AscHere, we investigated the impact of using GBS and SNP-arrays on the quality of the genotyping data, together with the biological properties of data generated by each technology, and the potential complementarity of these approaches. In particular, we analyzed the impact of marker density and genotyping technologies (sequencing vs array) on (i) the estimates of relatedness and population structure, and (ii) the detection of QTLs (power). To address these issues, we performed a GWAS based on genotypic datasets obtained using either GBS or SNP-arrays with low (50 k) or high (600 k) densities on a diversity panel of maize hybrids obtained by crossing a panel of dent lines with a common flint tester line. Three traits were considered, namely grain yield, plant height and male flowering time (day to anthesis), measured in 22 different environments (sites × years × treatments) over Europe. We developed an original approach based on LD extent in order to determine whether SNPs significantly associated with a trait should be considered as a single QTL or several independent QTLs.1, Additional file 2), the concordance rate was 96.04% on the common markers with the 50 K and 11.91% of missing data remained for the whole GBS dataset. In GBS3, all missing data were imputed by Beagle and the remaining missing genotypes in GBS2 were excluded here to be comparable with TASSEL. This method yielded a lower concordance rate . In an attempt to increase the concordance rate of the genotyping while removing missing data, we tested two additional methods, namely GBS4 where the missing data and heterozygotes of Cornell imputed data (GBS2) were replaced by Beagle imputation, and GBS5 where Cornell homozygous genotypes (GBS2) were completed by imputations from GBS3  and predicted heterozygotes with a higher quality than GBS4. GBS5 was therefore used for all genetic analyses and named GBS hereafter.We estimated the genotyping and imputation concordance of the GBS based on common markers with the 50 K or 600 K arrays  was lower for GBS (0.27) than for arrays . The distribution of SNP residual heterozygosity of inbred lines was similar for the three technologies, with a mean of 0.80, 0.89 and 0.22% for the 50 K, 600 K and GBS, respectively. The residual heterozygosity of inbred lines was highly correlated between technologies with large coefficients of Spearman correlation: r50K–600K = 0.90, r50K-GBS = 0.76, r600K-GBS = 0.83. The distribution of the SNPs along the genome was denser in the telomeres for the GBS and in the peri-centromeric regions for the 600 K, whereas the 50 K exhibited a more uniform distribution , than for the GBS (37% for the direct reads). The MAF distribution differed between the technologies  was identified as the best for the three datasets. Considering a threshold of 0.5 for ancestral fraction, the assignation to the four groups was identical except for a few admixed inbred lines , calculated per technology. For IBS, pairs of individuals were on average more related using GBS than SNP-arrays . As expected, mean IBD was close for the three technologies (K_Freq: − 0.004). Relatedness estimates with the two SNP-arrays were highly correlated: r = 0.95 and 0.98 for IBS and K_Freq, respectively  and K_Freq, weights by allelic frequency) estimated from the three technologies  on IBD , structure (Number of groups = 4) or both  in spite of long physical distance (>30Mbp), were close genetically (< 3 cM), notably on chromosome 3, 5, 7 and to a lesser extent 9 and 10 (data not shown). These loci were located in centromeric and peri-centromeric regions that displayed low recombination rate, suggesting that this pattern was due to variation of recombination rate along the chromosome. Only very few pairs of loci in high LD were genetically distant (> 5 cM) but physically close (<2Mbp). Linkage disequilibrium (r2K and r2KS) was negligible beyond 1 cM since 99% of LD values were less than 0.12 in this case. Note that some unplaced SNPs remained in LD after taking into account the kinship and structure with some SNPs with known positions on chromosome 1, 3 and 4 , we studied genome-wide LD between 29,257 PANZEA markers from the 50 K within and between chromosomes before and after taking into account the kinship . This suggests that the estimation of LD extent did not strongly depend on our set of loci. LD extent varied significantly between chromosomes for both high recombinogenic (> 0.5 cM/Mbp) and low recombinogenic regions  than for DNA arrays . It can be explained by the higher genotyping error rate of GBS due to imputation and/or by its higher number of markers with a low MAF. Both reduce the power of GBS compared to DNA arrays and therefore lead to a higher false discovery rate. Proportionally to the SNP number, the 50 K and 600 K resulted in 1.5- and 1.7-fold more associated SNPs per situation (environment × trait) than GBS  and (ii) grouping significant SNPs that are adjacent on the physical map and are in LD . The QTLs defined by using the two approaches were globally consistent since significant SNPs within QTLs were in high LD whereas SNPs from different adjacent QTLs were not , plant height (240 vs 160) and grain yield (433 vs 237). The number of QTLs detected with the LD_adj approach increased strongly when the LD threshold was set above 0.5. Differences in QTL groupings between the two methods were observed for specific LD and recombination patterns. This occurred for instance on chromosome 6 for grain yield , LD_win grouped together associated SNPs that are genetically close but displayed a low LD . As these differences were specific to complex LD and recombination patterns, we used the LD_win approach for the rest of the analyses.We used two approaches based on LD for grouping significant SNPs Fig. : (i) con433 vs 23. The num433 vs 23. The numAlthough a large difference in number of associated SNPs was observed between 600 K and GBS, little difference was observed between QTL number after grouping SNPs Table , Table 4log10 increased by 2 and 21% by 1, potentially indicating a gain in accuracy of the position of the causal polymorphism  ranging from 5 to 7.6, whereas it was detected by only two 600 K markers in one environment (Ner12W) with –log slightly above the significance threshold  and 3 QTL95) detected in several environments  MAF distribution and (ii) SNP distribution along the genome, at different SNP densities. We detected more SNP associations, but less QTLs with a MAF distribution skewed towards low than high MAF. This difference increased as marker density increased  ≥ 8, Table −log10 = 10.03) on chromosome 3 , the QTL135 for GY  = 18.7) on chromosome 6  and QTL78 on chromosome 6  for plant height  = 17.31). The QTL95 for flowering time trait was the most stable QTLs across environments since it was detected in 19 environments . However, 6 out 7 most stable QTLs (Number of environments > 5) were found for flowering time. This was consistent with higher average correlations between environments observed for flowering time than for plant height and grain yield . We observed that QTLs that displayed a significant effect in more than one environment had larger effects and -log values than those significant in a single environment. This difference in -log values was stronger for grain yield and plant height than flowering time.After combining the three technologies, we identified 226, 160, 238 QTLs for flowering time, plant height and grain yield, respectively (Table ≥ 2 environments) in low than in high recombinogenic regions .The distribution of QTLs was not homogeneous along the genome since 82, 77 and 79% of flowering time, plant height and grain yield QTLs, respectively, were located in the high recombinogenic regions, whereas they represented 46% of the physical genome , missing data dropped to 11.9% i.e. only slightly more than in Romay et al. (10%) [The ascertainment bias of SNP-arrays due to the limited number of lines used for SNP discovery was reinforced by counter-selection of rare alleles during the design process of DNA arrays , 4. For l. (10%) . This coAlthough, we observed differences of allelic frequency spectrum between GBS and DNA arrays, these technologies revealed similar trends in the organization of population structure and relatedness :r2K that corrected r2 for kinship rather than the classical r2 since r2K reflected the LD addressed in our GWAS mixed models to map QTL [First, we used  map QTL .Second, we took advantage of the availability of both physical and genetic maps of maize to project the genetic LD extent on the physical map. This physical window was useful to retrieve the annotation from B73 reference genome, decipher local haplotype diversity Fig.  and estiThird, we considered an average LD extent estimated separately in the high and low recombinogenic genomic regions. This average was estimated by using several large random sets of pairs of loci in these regions rather than the local LD extent in the genomic regions around each associated peaks.In previous GWAS studies, the closest associated SNPs were grouped into QTLs according to either a fixed physical distance  or a fixWe preferred this approach rather than using local LD extent in order to limit the effect of (i) the strong variation of marker density along the chromosome  errors in marker order or position due to genome assembly errors or structural variations, which are important in maize [LD_win lead either to inflate the number of QTLs in high recombinogenic regions in which SNPs were too distant genetically to be grouped, or deflated their number by grouping associated SNPs in low recombinogenic regions. Since LD_win considered the average LD extent, this method could conduct either to separate or group abusively SNPs when local LD extent was different than the global LD extent. Simulations will be carried out in further research to better understand the properties of LD_win and LD_adj approaches.We compared in maize  (ii) genin maize , (iv) LDNote that LD windows should not be considered as confidence intervals since the relationship between LD and recombination is complex due to demography, drift and selection in association panels, contrary to linkage based QTL mapping . The magLD_win in our study correspond to regions previously identified: in particular, six regions associated with female flowering time [ZCN8, which showed significant effect in these two previous studies. One of the explanation is that we narrowed the flowering time range in our study, in particular by eliminating early lines. This reduced the representation of the early allele at the Zcn8 locus, leading to a MAF of 0.27 in our study vs 0.35 in Rincent et al. [Several QTLs identified by ing time  and 30 ring time . Converst et al. , which ct et al. . Also, tt et al.  for a reNumber of significant SNPs and QTLs increased with the increase in marker number  some haplotypes certainly remain not tagged (ii) the genome coverage was not complete, and (iii) the number of significant SNPs and QTLs continued to increase with marker density  from the Flint genetic groups to obtain 247 hybrids (hybrid panel). Dent and Flint genetic groups are known to be complementary to produce hybrids . Further− 1) was adjusted to 15% moisture. The adjusted mean  of the three traits were estimated per environment (site × year × water regime) using a mixed model based on fixed hybrid and replicate effects, random spatial effects (rows and columns), and spatially correlated errors in order to take into account spatial variation of micro-environment in each field trial , plant height (plantHT), and grain yield (GY) were previously described . While Dial  for a quality control of the genetic data before performing the diversity and association genetic analyses. For GBS data, the filters were applied after imputation using the method “Compilation of Cornell homozygous genotypes and Beagle genotypes” (GBS1) and four approaches for imputation  with the imputation of GBS based on their position. While SNP positions and orientation from GBS were called on the reference maize genome B73 AGP_v2 (release 5a) , flankineagle v3  of the m5 that passed the quality control were used to perform the diversity analyses (estimation of Q genetic groups and K kinships). For the 50 K, we used only the PANZEA markers  [G matrices with N rows and L columns, N and L being the panel size and number of markers, respectively. Genotype of individual i at marker l  was coded as 0 , 0.5 (heterozygote), or 1 (the other homozygote). Identity-By-Descent (IBD) was estimated according to Astle and Balding [After excluding the unplaced SNPs and applying the filtering criteria for the diversity analyses (MAF > 0.01), we obtained the final genotyping data of the 247 lines with 44,729 SNPs from the 50 K, 506,662 SNPs from the 600 K array, and 395,024 SNPs from the GBS   in order Balding :\documenpl is the frequency of the allele coded 1 of marker l in the panel of interest, i and j indicate the inbred lines for which the kinship was estimated. We also estimated the Identity-By-State (IBS) by estimating the proportion of shared alleles. For GWAS, we used K_Chr [K_Freq, but with the genotype data of all the chromosomes except the chromosome of the SNP tested. This formula provides an unbiased estimate of the kinship coefficient and weights by allelic frequency assuming Hardy-Weinberg equilibrium. Hence, relatedness is higher if two individuals share rare alleles than common alleles.where ed K_Chr  that areADMIXTURE v1.22 [ADMIXTURE of inbred lines between the three technologies by estimating the proportion of inbred lines consistently assigned between technologies two by two  using a threshold of 0.5 for admixture.Genetic structure was analysed using the sofware RE v1.22  with a n2pl(1 − pl) and was averaged on all the markers for a global characterization of the panel for the three technologies. Principal Coordinate Analyses (PCoA) were performed on the genetic distance matrices [1N,N − K_Freq, where 1N,N is a matrix of ones of the same size as K_Freq.Expected heterozygosity (He)  was estimatrices , estimatr2) between allelic dose at two markers [K_Freq estimator (r2K), the squared correlation taking into account population structure (r2S), and the squared correlation taking into account both (r2KS) [We first analyzed the effect of the genetic structure and kinship on linkage disequilibrium (LD) extent within and between chromosomes by estimating genome-wide linkage disequilibrium using the 29,257 PANZEA SNPs from the 50 K. Four estimates of LD were used: the squared correlation  .smooth.spline function R calibrated on the genetic consensus map of the Cornfed Dent Nested Association Mapping (NAM) design [r2 and r2K using 10 different kinships K_Chr. This last estimator was calculated because it corresponds exactly to the LD used to map QTL in our GWAS model. It determines the power of GWAS to detect QTL considering that causal polymorphisms were in LD with some polymorphisms genotyped in our panel [nls function in R-package nlme) against both physical and genetic position within each chromosome. Since recombination rate (cM/Mbp) varied strongly along the genome  design . We usedur panel . To studur panel  using noome Fig. . 500,000r2K = 0.1, r2K = 0.2 and r2K = 0.4.For all analyses, we estimated LD extent by calculating the genetic and physical distance for the fitted curve of Hill and Weir’s Model that reached r2K = 0.1 (negligible LD), r2K = 0.2 (intermediate LD) and r2K = 0.4 (high LD) estimated in low and high recombinogenic regions for each chromosome. We used the global LD decay estimated for these large chromosomal regions rather than local LD extent (i) to avoid bias due to SNP sampling within small genomic regions, (ii) to reduce computational time, and (iii) to limit the impact of possible local error in genome assembly. In low recombinogenic regions, we used the physical LD extent, hypothesizing that recombination rate is constant along physical distance in these regions. In high recombinogenic regions, we used the genetic LD extent since there is a strong variation of recombination rate by base pair along the physical position . In order to explore variation of genome coverage along the chromosome, we estimated the proportion of genome covered using a sliding-windows approach based on variable physical distances  considering LD extent for a r2K = 0.1.To estimate coverage of the three technologies to detect QTLs based on their SNP distribution and density, we calculated cumulative genetic and physical lengths that are covered by LD windows around the markers, considering different LD extents for each chromosome   in GWAS , leading to different -log10 thresholds: 5.49, 6.27, 6.34 and 6.58, respectively. Because of these differences, we used two thresholds of -log10 = 5 (less stringent) and 8 (hightly conservative and slightly above Bonferroni) for comparing GWAS to avoid the differences of identification of significant SNPs between the technologies due to the choice of the threshold.To take into account multiple tests in GWAS and their dependence, we applied the methods of Moskvina and Schmidt  and Gao LD_win) used LD windows, previously described, to group significant SNPs into QTLs considering that all significant SNPs with overlapping LD windows of r2K = 0.1 belong to the same QTL , significant SNPs were grouped into a same QTL if they were connected in terms of LD (r2K between adjacent significant SNPs superior to 0.5). We used LD heatmaps for comparing the SNP grouping produced by the two approaches on the three different traits across all environments  are available on request.We used two approaches based on LD for grouping significant SNPs. The first approach . In this resampling approach, we considered all markers together and that both associations and QTLs detected by the whole SNP sets are true. We selected only markers having MAF above 5%. To study the effect of MAF distribution on QTL detection, SNPs were classified in 5 MAF classes  and SNPs were randomly selected in each classes according to the MAF distributions: 1) similar to GBS (GBS_MAF), 2) similar to 600 K (600 K_MAF), 3) with equal frequency for the five MAF classes (Flat_MAF), 4) skewed towards high MAF (High_MAF) with SNP frequency of 0, 0, 0.2, 0.4, 0.4 in  skewed towards low MAF (Low_MAF) with SNP frequency of 0, 0, 0.2, 0.4, 0.4 in  evenly distributed according to the physical distance (Dens_Phys), 2) evenly distributed according to the genetic distance (Dens_Gen), 3) distributed like GBS (Dens_GBS), 4) distributed similarly to 600 K (Dens_600 K), and 5) distributed like 50 K (Dens_50 K). SNPs were sampled randomly according to the different densities in contiguous windows of 10 Mbp.Additional file 1:Figure S1. Different approaches used to impute missing data of the GBS. We considered the direct reads from GBS (GBS1) and four approaches for imputation (GBS2 to GBS5). GBS2 approach consisted in one imputation step from the direct read by Cornell University, using TASSEL software, but missing data was still present. GBS3 approach consisted in a genotype imputation of the whole missing data of the direct read by Beagle v3. In GBS4, genotype imputation by Beagle was performed on Cornell imputed data after replacing the heterozygous genotypes into missing data. GBS5, consisted in homozygous genotypes of GBS2 completed by values imputed in GBS3. (DOCX 14 kb)Additional file 2:Figure S2. Comparison of genotyping data between 50K and 600K arrays, and GBS. (a) Distribution of minor allele frequency per SNP before filtering (monomorphic SNPs removed). (b) Distribution of SNP missing data proportion for the 50K array, 600K array, GBS direct reads (GBS1) and GBS after imputation by Cornell Institute . (c) Relatedness distribution  after QC filtering with MAF≥1% . (DOCX 71 kb)Additional file 3:Figure S3. Variation of the markers density, the recombination rate and the genome coverage in non-overlapping 2 Mbp windows along each chromosome except chromosome 3  above 6 were shown. (PDF 152 kb)Additional file 4:Figure S4. Contribution of four ancestral populations to 247 inbred lines after ADMIXTURE analysis. Markers from the 50K (top), 600K (middle) and GBS (bottom) were used. One vertical bar corresponds to one individual. Lines were ordered according to contributions observed for the 50K. From left to right, we have Stiff Stalk lines type B73 and B14a (blue), Iodent lines type PH207 (red), Lancaster lines type Mo17 and Oh43 (turquoise), a group of lines assembling W117, F7057 type lines (green). (DOCX 239 kb)Additional file 5:Figure S5. Correlation between kinship matrix estimated by different technologies. Correlation (r) between the IBS and IBD (K_Freq) for each technology (A). Correlation of IBD (B) and IBS (D) between the three technologies (after imputation). (C) Correlation of IBD between the three technologies after removing the excess of rare alleles in the GBS to have the same distribution of MAF as in the 50K and the 600K. The red line is the bisector. (DOCX 255 kb)Additional file 6:Figure S6. Heatmaps of genome-wide linkage disequilibrium (LD) between all markers within and between chromosomes using PANZEA SNPs from the 50K. All SNPs were ordered according to their position on the genome. “Unpl” after chromosome 10 refers to unplaced SNPs, in an arbitrary order. Dots represented LD between two loci and were colored according to their strength. Classical LD measurement r2 between loci were represented within triangle below the diagonal. Linkage disequilibrium corrected for structure , relatedness  or both  were represented within triangle above the diagonal. (DOCX 442 kb)Additional file 7:Figure S7. QTL limits obtained by the LD_adj approach projected on heatmaps representing the level of LD between associated SNPs for each trait  and each chromosome. Upper and lower triangles on the heatmaps represent the r2 and r2K values between associated SNPs, respectively. Linkage disequilibrium between loci was colored according to values from weak LD (yellow) to high LD (red). The significant markers were ordered according to their physical positions on the chromosome and were represented by ticks on the four sides of the heatmaps. Limits of QTLs were displayed by gray dotted lines. QTL numbers were indicated in gray on the top and the right of each heatmap. (PDF 7373 kb)Additional file 8:Figure S8. QTL limits obtained by the LD_win approach projected on heatmaps representing the level of LD between associated SNPs for each trait  and each chromosome. Upper and lower triangles on the heatmaps represented the r2 and r2K values between associated SNPs, respectively. Linkage disequilibrium between loci was colored according to values from weak LD (yellow) to high LD (red). The significant markers were ordered according to their physical positions on the chromosome and were represented by ticks on the four sides of the heatmaps. Limits of QTLs were displayed by gray dotted lines. QTL numbers were indicated in gray on the top and the right of each heatmap. (PDF 7360 kb)Additional file 9:Figure S9. Number of significant SNPs (blue line) and QTLs (red line) identified as a function of SNP density (x-axis) for the male flowering time (DTA), plant height (PlantHT) and grain yield (GY). (DOCX 125 kb)Additional file 10:Table S1. Summary of all the QTLs identified for the male flowering time (DTA), plant height (plantHT) and grain yield (GY). “LowerLimit” and “UpperLimit” columns are the lower and upper physical limits for each QTL. The “Rec” column indicates if the QTL is located in a high or low region of recombination. “NbSNP50”, “LogPvaMax50”, “NbSNP600”, “LogPvaMax600”, “NbSNPGBS”, “LogPvaMaxGBS” are the number of significant SNPs and the most significant –log10 within the QTL for each technology across all environments. The physical position (“PosMax”), the proportion of the variance explained (“R2_LDMax”) and the effect (“EffectMax”) of the most significant SNP within the QTL is shown. “NbDiffEnv” gives the number of different situations that detected the QTL. (CSV 50 kb)Additional file 11:Figure S10. Examples of QTL detection on Chromosome 3, 6 and 8 for the different traits. The top panel represents the distribution of the QTLs along the chromosome of interest, for the different technologies. The vertical red line in this panel localizes the SNP chosen as reference for the QTL (marker with the strongest association). The middle panel is a zoom in the vicinity of the reference SNP, showing the Local distribution of the -log10. The bottom panel is the same zoom as the middle panel and shows the local linkage disequilibrium corrected by the kinship (r2k) of all SNPs, within this region, within the reference SNP. Ticks on different x-axes show the marker density of the three technologies . (DOCX 1632 kb)Additional File 12:Figure S11. Effect of minor allelic frequency distribution, SNP distributions along the genome and SNP densities on the number of associated SNP and QTL detected. Boxplot were drawn on 100 sets of 50 000 to 250 000 markers sampled according to different MAF distributions  and different SNP distributions along the genome . A, C: number of SNP associated; B, D: Number of QTL detected. In A and B, 600K_MAF (yellow), GBS_MAF (green), Low_MAF (cyan), Flat_MAF (blue), High_MAF (pink) on x axis indicate boxplots corresponding to MAF distribution similar to 600K, similar to GBS, skewed towards low MAF, flat MAF and skewed toward high MAF, respectively. In C and D, Dens_50K (red), Dens_600K (yellow), Dens_GBS (cyan), Dens_Gen (blue), Dens_Phys (pink) on x axis indicate distribution of SNPs along the genome corresponding to 50K, GBS, 600K, even genetic and physical distances, respectively. For A, B, C and D, modalities indicated as “Random” in x axis correspond to random sample of SNP. Number of markers for each boxplot are indicated after the point. (PDF 281 kb)Additional file 13:Figure S12. Distribution of markers, associations and QTLs according to the MAF classes for 50K, 600K GBS, and ALL technologies. A) Number of markers, B) Proportion of markers, C) Proportion of Association, D) Proportion of QTLs. (PDF 32 kb)Additional file 14:Figure S13. Colocalization of QTLs between the traits. Number of QTLs specific and shared by the three traits across all environments. Note that several QTLs from one trait were sometimes included in a single QTL of another trait. (DOCX 48 kb)Additional file 15:Table S2. Stability of QTLs across environments for the male flowering time (DTA), Plant Height (PlantHT), Grain Yield (GY) and all traits. “Env. Nb” indicates the number of environment in which a QTL was detected. Next four columns indicate the number of QTL corresponding to each category. (DOCX 14 kb)Additional file 16:Table S3. Proportion of low and high recombination regions, recombination rate and percentage of QTLs located in these regions for the three traits. “Chr” indicates the chromosome. Physical and genetic size columns indicated the size of each chromosome in bp and cM, respectively. Average recombination rate (“RecRate”) and proportion of the physical (“Phys”) and genetic (“Genetic) map in high recombination regions  for each chromosome are shown. Percentage of QTL in high recombination regions were displayed for three traits . (DOCX 16 kb)Additional file 17:Table S4. Description of inbred lines. Variety and accession along with the breeders, seeds providers and genetic groups obtained using ADMIXTURE for K=4 . (CSV 260 kb)Additional file 18:Table S5. Narrow sense heritability (h2) and variance components . The heritability and variance components were estimated for all traits  using the R package Heritability [tability . Additional file 20:Table S6. Number of SNPs called, after QC filtering (MAF>1%) and useful for GWAS (MAF≥5%). Note that GBS1 have SNPs with 100% missing genotypes which were removed while GBS2 used external haplotype library which allow to impute loci with 100% missing data. It conducted to a smaller number of SNPs for GBS1 than GBS2. (XLS 33 kb)Additional file 21:Notes S2. GWAS statistical models and effects of confounding factors on GWAS. (DOCX 14 kb)


Advances in high-throughput genotyping enable the generation of genome-scale data much more easily and at lower cost than ever before. However, small-scale and cost-effective high-throughput single-nucleotide polymorphism (SNP) genotyping technologies are still under development. In this study, we compared the performances of TaqMan, KASP and rhAmp SNP genotyping platforms in terms of their assay design flexibility, assay design success rate, allele call rate and quality, ease of experiment run and cost per sample. Fifty SNP markers linked to genes governing various agronomic traits of wheat were chosen to design SNP assays. Design success rates were 39/50, 49/50, and 49/50 for TaqMan, KASP, and rhAmp, respectively, and 30 SNP assays were manufactured for genotyping comparisons across the three platforms. rhAmp showed 97% of samples amplified while TaqMan and KASP showed 93% and 93.5% of amplifications, respectively. Allele call quality of rhAmp was 97%, while it was 98% for both TaqMan and KASP. rhAmp and KASP showed significantly better (p < 0.001) allele discrimination than TaqMan; however, TaqMan showed the most compact cluster. Based on the current market, rhAmp was the least expensive technology followed by KASP. In conclusion, rhAmp provides a reliable and cost-effective option for targeted genotyping and marker-assisted selection in crop genetic improvement. Plant breeding is one of the applied research areas presumed to benefit greatly from the current advances in molecular marker technologies. Genome-scale sequencing technologies such as genotyping by sequencing (GBS) enable the generation of millions of single-nucleotide polymorphisms (SNPs) that can be used in genome-wide association studies (GWAS) to identify genes or quantitative trait loci (QTL) governing traits of interest –3. ParalGenome-wide marker data have been used to identify many genes and QTL in wheat. The current challenge, however, is applying the identified markers in marker-assisted selection (MAS). Limited option of flexible and affordable small-scale high-throughput SNP genotyping has restricted MAS. Converting identified SNP regions into gel-based genotyping systems was reported as an option for small-scale genotyping, but it is obviously expensive and time-consuming , 12. GenConcerted research effort has developed small-scale high-throughput SNP genotyping platforms, including TaqMan and Kompetitive Allele Specific PCR (KASP) –16. TaqMEach system has its own merits and demerits. Some platforms are more comprehensive and relatively inexpensive, while others are highly precise for a specific purpose. An ideal genotyping platform should be flexible in assay design criteria, easy to run and cost-effective/affordable. So far, no single system fulfills all the requirements. The most competitive technologies in the current market are TaqMan, KASP and, as of recently, rhAmp. Therefore, this experiment was conducted to evaluate SNP genotyping performance of the three methods, TaqMan, KASP and rhAmp, in terms of assay design flexibility and success rates, allele calling and allele discriminating efficiency, ease of experiment run, and cost per sample.Genomic DNA was extracted from 94 diverse wheat genotypes. DNeasy Plant Mini Kit  was used following the provider’s protocol. Agarose gel (0.6 g/100 ml) electrophoresis was used to compare light intensity of DNA samples with the known concentration  of standard lambda DNA. DNA samples were normalized to 50 ng/μl for each sample. In addition to the 94 wheat samples, two non-template controls (NTCs) were included on each reaction plate.Assay probes were designed in the three platforms for 50 pre-validated SNPs or INDELs  that areOther genotyping reaction regents were purchased from the respective genotyping platform providers as well. TaqMan genotyping reactions were carried out in total volumes of 5 μl containing 1x TaqMan universal PCR master mix, 1x custom SNP genotyping assay mix and 5 ng DNA. KASP assay was also carried out in a 5 μl volume containing 1x KASP master mix, 1x custom KASP assay and 20 ng DNA. Similarly, rhAmp SNP genotyping was carried out in a 5 μl total volume containing 1x rhAmp genotyping master mix, 1x rhAmp reporter mix with reference dye and 5 ng genomic DNA. PCRs and fluorescent readings were taken in 384-well plates (Thermo Fisher Scientific) on QuantStudio 7 (QS7) Flex Real-Time PCR System (Applied Biosystems) following recommended thermal cycling conditions for each genotyping platform . All gen2, y2) with the NTC coordinate . The slope of the line connecting data points with the NTC coordinate gives us the tangent of the angle α , distance between allele clusters and non-template control (NTC) coordinates, and clusters compactness were compared among the three genotyping platforms. In an ideal case scenario of allele discrimination, homozygous allele 1 (Allele 11) lies along the horizontal axis , while homozygous allele 2 (Allele 22) is expected to be parallel with the vertical axis . Heterozygote alleles (Allele 12) are expected to cluster along the diagonal  . Using c angle α . Slope  of the Cartesian plane  one can order from the respective technologies and divided the total cost by the number of tests possible. rhAmp was the least expensive of the three technologies ($0.12 per 5 μl reaction), followed by KASP ($0.15 per 5 μl reaction), while TaqMan assays cost $0.41 per 5 μl reaction.Small-scale high-throughput genotyping systems are in high demand in current plant breeding programs. TaqMan and KASP chemistries are among the most popular technologies in this regard so far. However, TaqMan is more expensive and less flexible in assay design because it does not accept INDELs longer than 6 bps. KASP requires higher amount of DNA template and yet its allele cluster separation is lower than that of TaqMan and rhAmp due to its low fluorescence signal. rhAmp was developed to fill these gaps at lower cost with more flexibility in assay design compared with TaqMan and better allele discrimination efficiency with less DNA input compared to KASP. Comparative performance of these three technologies in genotyping hexaploid species has never been reported before. This research was conducted to objectively compare the three technologies in terms of ease of assay design, allele call rate and quality, allele cluster compactness and angle of separation between allele clusters (homozygotes from heterozygotes).Results in this study indicated that rhAmp was better in separating homozygous allele clusters from the heterozygotes  which waThe graphical view of allele clusters could be misleading in terms of the separation angle and cluster compactness, as the fluorescence of rhAmp was generally higher than that of the other two technologies. TaqMan had the most compact clusters due to the smaller projection of fluorescence away from NTC coordinate, while the opposite was true with rhAmp . Graphs In this study, rhAmp produced more amplified data points, only 3% failure compared with 7% and 6.5% unamplified samples in TaqMan and KASP, respectively. In a similar performance comparison between TaqMan and KASP, Braae and colleagues concluded that TaqMan showed higher failure rates than KASP . In addiThe three technologies varied in terms of the minimum amount of kit one needs to order and the number of tests possible in a given volume. The amount of assay was the limiting entity in the chemistry compared with the master mix in all three technologies. Price calculations were based on a 5 μl reaction volume for all technologies. TaqMan provided assay mix (primers and probes) enough for 2,000 reactions for $259 while KASP and rhAmp each provided assay mix enough for 5,000 reactions for $64.2 and $75.6, respectively. Similarly, KASP and rhAmp provided master mix of 25 ml at a cost of $1,382.5 and $1,038.6 enough for 10,000 reactions, respectively, while TaqMan provided 5 ml master mix enough for 2,000 reactions that cost $554. Cost estimations did not include price for DNA extraction. In addition to being more specific with high allele discrimination, rhAmp chemistry is highly affordable based on current market standards. rhAmp had the lowest cost per sample followed by KASP, while TaqMan was the most expensive.Generally, all three technologies provide reliable allele calling for small-scale applications in plant breeding. However, the differences in allele cluster separation and distance of clusters from the NTC coupled with lower genotyping cost make rhAmp a preferred technology. TaqMan and rhAmp chemistries are real-time PCR technologies, which are amenable for real-time data collection or post-read technologies, while KASP is a post-read technology, which collects data after completion of the PCR process. In conclusion, rhAmp has the potential to be a viable alternative to both TaqMan and KASP technologies for small-scale SNP genotyping. Data generated in the present study will help make informed decisions regarding choosing efficient genotyping platforms. The assay design flexibility and allele discrimination clarity of rhAmp coupled with the availability of complete genome sequence of wheat  will enaS1 Table(XLSX)Click here for additional data file.


Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in 435 unrelated individuals of Populus tomentosa. Tissue-specific expression analysis in eight tissues, including stem, leaf, root, and shoot apex, revealed negative correlations between Pto-MIR167a and lncRNA ARFRL and Pto-ARF8 , and a positive correlation between sponge lncRNA ARFRL and Pto-ARF8 , indicating their potential regulatory roles in tree growth and wood formation. Single nucleotide polymorphism (SNP)-based association studies detected 53 significant associations  representing 41 unique SNPs from the three genes and six traits, suggesting their potential roles in wood formation. Epistasis uncovered 88 pairwise interactions for 10 traits, which provided substantial evidence for genetic interactions among Pto-MIR167a, lncRNA ARFRL, and Pto-ARF8. Using gene expression-based association mapping, we also examined SNPs within the three genes that influence phenotypes by regulating the expression of Pto-ARF8. Interestingly, SNPs in the precursor region of Pto-MIR167a altered its secondary structure stability and transcription, thereby affecting the expression of its targets. In summary, we elucidated the genetic interactions between Pto-MIR167a and its targets, sponge lncRNA ARFRL, and Pto-ARF8, in tree growth and wood formation, and provide a feasible method for further investigation of multi-factor genetic networks influencing phenotypic variation in the population genetics of trees.MicroRNAs (miRNAs) play crucial regulatory roles in plant growth and development by interacting with RNA molecules, including messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs); however, the genetic networks of miRNAs and their targets influencing the phenotypes of perennial trees remain to be investigated. Here, we integrated expression profiling and association analysis of underlying physiology and expression traits to dissect the allelic variations and genetic interactions of  Arabidopsis . For example, nitrogen treatment increases the expression of ARF8 by reducing miR167 levels in the pericycle and root cap, initiating lateral root formation in Arabidopsis  are a class of non-coding RNAs (ncRNAs) that are derived from hairpin precursors and loaded into the RNA-induced silencing complex to post-transcriptionally regulate gene expression via cleavage or inhibitory mechanisms  in Arabidopsis contains a complementary motif to miR399, and IPS1 alters the stability of PHOSPHATE2 (PHO2) by sequestering miR399, the phosphate starvation induced miRNA. In Populus, few lncRNAs have been identified as miRNA sponges  gene, a key gene in the lignin biosynthesis pathway that is significantly associated with the microfibril angel (MFA) of wood in Eucalyptus nitens. SNPs in miRNA genes have also been identified in humans using association studies, and SNPs in the precursor miRNA (pre-miRNA) region were found to affect miRNA secondary structure and expression  for Pto-MIR167a-d. Sequence alignment detected nucleotide variants in the pre-miRNA region of Pto-MIR167a, thus we cloned Pto-MIR167a for further analysis. Degradome sequencing and psRNATarget prediction identified Pto-ARF8 as a target gene of Pto-MIR167a and lncRNA ARFRL. The regulatory roles of Pto-miR167a and its targets, lncRNA ARFRL and Pto-ARF8, were also supported by 5′ rapid amplification of cDNA ends (5′-RACE). Expression abundance analysis revealed a significant negative correlation between Pto-MIR167a and the targets lncRNA ARFRL and Pto-ARF8. Additionally, significant positive correlations were observed between lncRNA ARFRL and Pto-ARF8, indicating the potential role of lncRNA ARFRL as a Pto-miR167a sponge. Next, SNP-based association studies  were conducted in an association population of 435 unrelated individuals of P. tomentosa, which deciphered the genetic interactions of SNPs within Pto-MIR167a and its targets, lncRNA ARFRL, and Pto-ARF8, underlying tree growth and wood properties. The association mapping of underlying expression traits led to an alternative hypothesis that SNPs in regulators (Pto-MIR167a and lncRNA ARFRL) and Pto-ARF8 may affect phenotypic variation through regulating the expression of their target gene Pto-ARF8. Remarkably, we also found that SNPs in the pre-miRNA of Pto-MIR167a altered its secondary structure and expression. Our study provides a better understating of the genetic networks of Pto-MIR167a and its targets, lncRNA ARFRL, and Pto-ARF8, in tree growth and wood formation, and the association analysis of underlying physiology and expression traits proposed an alternative method for dissecting the genetic networks of ncRNAs and mRNAs in the population genetics of trees.Here, we first identified a potential sponge lncRNA P. tomentosa that represented almost the entire natural distribution of P. tomentosa , i.e., southern, northwestern, and northeastern regions of China. Plants in this population were randomly selected from the P. tomentosa collection  that was established in Guan Xian Country  in 1982, using a randomized complete block design approach with three clonal replications , diameter at breast height , and tree height . The wood property traits were MFA (°), fiber length , fiber width , lignin content , holocellulose content , α-cellulose content , and hemicellulose content . Detailed measurement methods and phenotypic variations of the 10 traits have been previously reported  was used to predict the target genes of Pto-miR167a using 3000 mature xylem cDNA sequences from P. tomentosa. This cDNA library was constructed using the Superscript λ System  according to manufacturer's instructions and consisted of 5.0 × 106 plaque-forming units, with an insert size of 1.0–4.0 kb , as previously reported  and RNA duplex energy calculated by RNAplex with E-value < −60  was performed using SMARTer RACE Kit  according to the manufacturer's instructions with some modifications. PCR was performed using 5′-RACE CDS Primer A [5′-(T)rs Table , using cP. tomentosa clone “LM50.” Total RNA was extracted from each tissue using a Plant Qiagen RNeasy kit  according to the instructions. Additional on-column DNase digestions were conducted using an RNase-Free DNase Set (Qiagen) during the RNA purification. The cDNAs of the eight tissues were reverse transcribed using a Reverse Transcription System  following the manufacturer's instructions, and were then used to test the tissue-specific expression profiles. Reverse transcription quantitative PCR (RT-qPCR) was conducted with a 7500 Fast Real-Time PCR System, using SYBR Premix Ex Taq (TaKaRa). All reactions were conducted with triplicate technical and triplicate biological repetitions, using gene-specific primers designed with Primer Express 5.0 software  with Actin (EF145577) as the internal control  under the accession numbers MG873890–MG873932 for Pto-MIR167a, MG873933–MG873975 for Pto-ARF8, and MG873976–MG874018 for ARFRL. Sequence alignment and SNP identification were conducted by MEGA 5.0 software . To identify SNPs, the full-length genomic DNA of candidate genes was sequenced using gene-specific primers, which were designed based on the cDNA of candidate genes. The PCR amplification procedure has been described previously  between each pair of common SNPs (MAF > 0.05) within the candidate genes was calculated by TASSEL 5.0 with 105 permutations , using the statistical model described by Evanno et al.  using the QVALUE package in R  in TASSEL 5.0  were evaluated for each gene, and their frequencies were determined using Haploview v.4.2  , and output the most significant SNPs for each trait after attribute selection, attribute construction, classification, and permutation testing. Entropy-based measures were performed for each SNP-SNP pair were performed and evaluated by information gain (IG)  algorithm was employed to detect the pairwise epistatic effects among the SNPs, for which high-dimensionality genetic data were processed into a single dimension to detect the non-additive interactions in a small set  was used to predict secondary structures, and RT-qPCR was performed to investigate the transcript abundance of different genotypes of Pto-MIR167a in 10 individuals randomly selected from each genotype class in the association population. Additionally, the corresponding expression of targets lncRNA ARFRL and Pto-ARF8 were measured in the background of different genotypes of the common SNPs in Pto-MIR167a. The differential expression of different genotypes was evaluated using analysis of variance (ANOVA).To investigate the effects of SNPs in the pre-miRNA region of P. tomentosa, we first cloned the pre-miRNA sequence of eight members of the Pto-MIR167 gene family based on Ptc-MIR167 pre-miRNA sequences in miRbase. Using the lncRNA database of cambium, developing xylem, and mature xylem of P. tomentosa, we identified lncRNA ARFRL (expectation = 2.5) as a potential target of Pto-miR167a-d, whose mature sequences were conserved in the Pto-miR167 family  and length (89–190 bp)  Figure . We alsop Figure .P. tomentosa, and Pto-ARF8 was identified as the target gene of Pto-miR167a  was 3854-bp in length with a 2490-bp open reading frame, 904-bp of 5′ untranslated region (UTR), and 406-bp of 3′ UTR. The encoded proteins displayed high protein similarity (98.92%) to Ptc-ARF8 (Potri.004G078200) in P. trichocarpa and contained the conserved domain of the Auxin response factor (at amino acid residues 216–338), B3 DNA binding domain (at amino acid residues 129–231), and the AUX/IAA family domain (at amino acid residues 719–791). Interestingly, Pto-ARF8 was also predicted as the potential trans-target of lncRNA ARFRL .Furthermore, psRNATarget and degradome sequencing were used to determine the targets of Pto-miR167a in 3000 cDNA sequences from the cDNA library of mature xylem of a Figure . The cDNARFRL and Pto-ARF8. The results revealed that both targets were supported by 5′-RACE. The cleavage site of Pto-miR167a in lncRNA ARFRL was at 468 nt, and the alignment range of Pto-miR167a and Pto-ARF8 was at 3196–3216 nt with the cleavage site at 3206 nt, which was consistent with the degradome sequencing results , suggesting that Pto-miR167a triggered the degradome of ARFRL and Pto-ARF8. Notably, lncRNA ARFRL and Pto-ARF8 was significantly positively correlated  in the tested tissues, suggesting the potential sponge role of lncRNA ARFRL for Pto-miR167a in regulating the expression of Pto-ARF8. These results supported the regulatory roles of the miR167a-ARFRL-ARF8 network during the process of growth and wood formation.To test the expression profiles of the three candidate genes, RT-qPCR was conducted to measure the transcript abundance in eight tissues and organs of s Figure . Pto-MIRPto-MIR167a primary transcript and the genes encoding lncRNA ARFRL and Pto-ARF8, including their 2000-bp promoters and 2000-bp flanking regions (downstream of the 3′ UTR), in 43 unrelated individuals from the association population , of which 93 SNPs were common SNPs (MAF ≥ 5%). Two common SNPs were identified in the pre-miRNA region of Pto-MIR167a, with no SNPs in mature sequence, indicating that the mature region was the most highly conserved. Remarkably, we predicted the secondary structure of Pto-MIR167a based on the two common SNPs in the pre-miRNA region (SNP48 and SNP49). The results revealed that Pto-MIR167a_SNP49 in the pre-miRNA of Pto-MIR167a significantly altered the stem-loop structure and the minimum free energy  of the predicted secondary structure of Pto-MIR167a , and five SNPs in the lncRNA transcribed sequences (π = 0.00052). For Pto-ARF8, 347 SNPs were detected across the entire gene with an average density of 1/39 bp (π = 0.00731), and the average non-synonymous (dN) nucleotide diversity was lower than that of synonymous (dS) nucleotide diversity, with the dN/dS ratio (0.83) < 1 for exons within Pto-ARF8, indicating purifying selection for non-synonymous sites in the exons , and 93.58% of them were found in non-coding regions, including the promoter (34), 5′/3′ UTRs (29), introns (90), and flanking regions (95). Only 17 common SNPs were found in coding regions, with seven non-synonymous mutations  were detected, representing 41 unique SNPs in the three candidate genes and six traits (Table R2) explained by each association ranged from 9.11% (Pto-MIR167a_SNP80) to 23.76% (Pto-ARF8_SNP13), with an average of 14.73%. Four associations, Pto-MIR167a_SNP66 for DBH, Pto-ARF8_SNP13 for DBH, Pto-ARF8_SNP225 for HC, and Pto-ARF8_SNP227 for HC, explained more than 20% of the phenotypic variation , including one SNP (ARFRL_SNP7) in lncRNA transcribed sequences, indicating the pleiotropic effect of lncRNA ARFRL on tree growth and wood properties  associations exhibited combined additive and dominant effects Table . The addr2 > 0.7, P < 0.01) within Pto-MIR167a and its targets lncRNA ARFRL and Pto-ARF8. Each gene contained 5–18 LD blocks and each block was composed of 2–4 common haplotypes  from 32 high-LD blocks , representing 33 unique SNPs from Pto-MIR167a (6), lncRNA gene ARFRL (9), and Pto-ARF8 (18), with 10 traits  and ARFRL (4) for the DBH, MFA, and V traits, with pairwise effects of 0.09–7.39% , and the expression of Pto-ARF8 was positively correlated with trait V , indicating that Pto-ARF8 may affect phenotypic variations of V via its expression to some extent, as an alternative pathway , with R2 of 8.58–15.68%, including two significant loci in SNP-based association mapping, ARFRL_SNP7 and Pto-ARF8_SNP13 , and low expression in the AA group (0.970 ± 0.036)  than in the CC group   Figure . Next, w) Figure .Pto-MIR160a changed the stem-loop number of Pto-MIR160a secondary structure and affected its stability, ultimately altering the transcript abundance of miRNA genes   and Pto-ARF8  in eight tested tissues, illustrating that the regulatory network of the three genes may affect tree growth and wood properties through a shared pathway.We also identified 91 common SNPs in the flanking region of n Figure , strongln Figure . Tissue-) Figure , indicats Figure . ExpressARFRL, only 23 SNPs were identified with a nucleotide diversity of π = 0.00119, which was lower than that of lncRNA UGTRL (π = 0.02607) previously reported in P. tomentosa . These results strongly support the sponge role of lncRNA ARFRL for Pto-miR167a in regulating the expression of Pto-ARF8. However, the detailed mechanisms of sponge lncRNA ARFRL will require further investigation in the future. Importantly, the expression and sequence characteristics of the three genes offer strong functional evidence of the miR167a-ARFRL-ARF8 network in tree growth and wood formation.For lncRNA L Figure , and preP < 0.01) according to SNP-based association studies and haplotype-based association studies, respectively, suggesting that the three candidate genes share common functions in tree growth and wood properties  were also identified in haplotype-based association studies for the same traits, which supported the SNP-based associations , indicating the functional roles of flanking regions for miRNA genes and suggesting that Pto-MIR167a may affect wood formation through multiple pathways. Correspondingly, nine associations were identified in lncRNA gene ARFRL, and only one significant SNP was found in the lncRNA transcribed sequences (ARFRL-SNP7), indicating that flanking regions in ncRNAs play a significant role in the regulation of gene expression and phenotypes , including two significant loci identified by association mapping, ARFRL_SNP7 and Pto-ARF8_SNP13  formed epistatic interaction networks for Pto-ARF8 expression, and the genotype combinations contributed differentially to the expression of Pto-ARF8, which provides a valuable resource for applications. Based on the findings of the association analysis for physiology and expression traits, the roles of lncRNA ARFRL should not be neglected, and ARFRL_SNP7 exhibited its functionality in the regulation of wood formation, which should be validated in the future. In general, epistasis illustrated the interaction networks of miR167a-ARFRL-ARF8 in trees, and gene expression-based association analysis aid in interpreting the genetic regulatory roles of the three genetic factors at the transcriptional level, providing an effective method for interpreting the genetic interactions of multiple genes for complex traits in trees.In addition to their interactions at the genomic level, the genetic variants also affected phenotypes at the transcriptional level , and gene expression-based association analysis to investigate the allelic interactions within MG873890–MG874018, and the transcriptome sequencing for lncRNAs and degradome sequencing data are available in SRA database under the accession number SRP073689 and SRX1447192, respectively.Sequence data in this article have been deposited with the GenBank Data Library under the accession numbers DZ designed the conception and experiment. MQ and LX performed the experiments. FS, WL, and JS helped to collect and analyze the data. MQ wrote the manuscript. QD, XL, and DZ provided valuable suggestions on the manuscript. DZ 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.


Picea abies (L.) Karst.] is ecologically and economically one of the most important conifer worldwide. Our main goal was to develop a large catalog of annotated high confidence gene SNPs that should sustain the development of genomic tools for the conservation of natural and domesticated genetic diversity resources, and hasten tree breeding efforts in this species.Norway spruce [P. abies exome with probes previously designed from the sequenced transcriptome of white spruce (Picea glauca (Moench) Voss). Capture efficiency was high (74.5%) given a high level of exome conservation between the two species. Using stringent criteria, we delimited a set of 61,771 high-confidence SNPs across 13,543 genes. To validate SNPs, a high-throughput genotyping array was developed for a subset of 5571 predicted SNPs representing as many different gene loci, and was used to genotype over 1000 trees. The estimated true positive rate of the resource was 84.2%, which was comparable with the genotyping success rate obtained for P. abies control SNPs recycled from previous genotyping efforts. We also analyzed SNP abundance across various gene functional categories. Several GO terms and gene families involved in stress response were found over-represented in highly polymorphic genes.Targeted sequencing was achieved by capturing P. abies genome. This resource should serve a variety of population genomics and breeding applications in Norway spruce.The annotated high-confidence SNP catalog developed herein represents a valuable genomic resource, being representative of over 13 K genes distributed across the The online version of this article (10.1186/s12864-018-5247-z) contains supplementary material, which is available to authorized users. Picea abies (Norway spruce) and Picea glauca (white spruce) )P. glauca from cDNA sequencing and expressed sequence tags (ESTs)  Karst.) grafted trees sampled in a 27-year old breeding orchard located north of Quebec City . All trees originated from central Europe, six of them being representative of distinct natural populations from Poland (3), Belorussia (1), and Latvia (2), and the four remaining ones being of unknown location. No permit was required to collect tissue in any location sampled in this study. DNA was isolated from needles using the Qiagen DNeasy Plant Mini Kit  and quantified using the PicoGreen fluorescent dye (Invitrogen). Afterward, DNA samples were assembled in two pools of five individuals with equimolar concentrations [Fresh needles were collected from 10 Norway spruce  and an average coverage between 25 and 800 were retained for subsequent steps.All paired megagametophyte reads were submitted to a de novo assembly by using the A5 assembler software  with defequences  which weTwo protocols were used for the alignment of pool reads and the SNP detection, producing two SNP datasets over the reference-guided alignment Fig. . In the https://www.python.org/) were developed to identify and retain SNPs common to both datasets, and to extract the 100 bases upstream and downstream of each SNP (or shorter when the SNP was too close to a contig end). Only SNPs satisfying the following in-house criteria were included in the SNP resource: MQ  ≥ 20, MMLQ  ≥ 10, QD  ≥ 10, PP (posterior probability) ≥ 20, SbPval  ≥ 0.01, hap score ≥ 15, max GOF  ≥ 20, SC (sequence context) ≥ 0.95, and a minimum of two reads for the alternative allele. Thus, singleton SNPs were de facto excluded from the resource in order to minimize the rate of false positives. Furthermore, only bi-allelic SNPs were retained since they are abundant and easier to genotype with common high-throughput genotyping platforms, and given that multi-allelic SNPs are more likely to result from variation at paralogous loci [Lastly, two Python scripts  was developed to estimate the true positive rate for a subset of newly identified SNPs, and to genotype trees for future population genomic applications. The array consisted of 6000 beads, with use of type II SNPs (one bead per SNP)  and one Newly predicted SNPs that were included in the array had to satisfy the following criteria: i) no SNP or indel within the 50 bp upstream or downstream of the predicted SNP (to ensure a good match of the Infinium probe); ii) a minimum distance of 20 bp from both contig ends  and an Illumina functionality score ≥ 0.60.P. abies breeding population from the Ministère des Forêts, de la Faune et des Parcs of Québec were genotyped with the SNP array. Two samples were used as positive controls and replicated on each 96-well plate to evaluate intra-assay genotyping reproducibility. DNA was isolated from needles and terminal buds by using the DNeasy 96 Plant Kit of Qiagen  and following the manufacturer’s instructions.To assess the true positive rate of newly predicted SNPs, a set of 1130 full-sib progenies resulting from various crosses among 35 parents of the Fe ≥ 0.80) were discarded as they usually result from probes annealing to paralogous loci.The SNP genotyping assay was manufactured and carried out at the Génome Québec Innovation Centre  according to Illumina’s protocols. A minimum of 80 ng of template gDNA per sample was used. Genotype calling was conducted using the GENOME STUDIO 2.0 software (Illumina). All SNPs with a GenTrain score ≥ 0.13 and a call rate ≥ 80%  were visually inspected in GENOME STUDIO, and manually cured to reject monomorphic and non-segregating polymorphisms. In addition, polymorphisms with large excess of heterozygotes (r = 0.30), we estimated the beta (β) parameter developed by Novaes et al. [S is the number of SNPs detected in the contig, L is the contig sequence length and D is the average depth for the contig (i.e. the average number of reads covering a nucleotide position). Given that the correlation between β and contig depth was low with r = 0.13, this parameter was deemed appropriate to compare SNP abundance across genes. When a gene was composed of multiple contigs, a weighted average β based on sequence length was computed for the gene   and the remaining genes for the following functional categories: GO terms, gene families, orphans (i.e. genes with unknown gene families), and conifer-specific genes.weight01 method, which is a mixture of the elim and the weight methods, both taking into account the GO hierarchy [p-values were used, as commonly done in similar studies (e.g. [http://bioconductor.org/packages/3.7/bioc/vignettes/topGO/inst/doc/topGO.pdf).We used the package topGO , availabierarchy . GO termes (e.g. ) and as We then assessed whether some gene families were over-represented within the 10% most SNPed genes, relative to the remaining dataset. The 69 gene families represented by at least two genes in the 10% most SNPed genes were tested for enrichment using Fisher’s exact tests. These tests were also used to determine if orphan genes and conifer-specific genes were over-represented within the 10% most SNPed genes.Additional file 1:Picea abies predicted SNP resource including quality parameters. (XLSX 13017 kb)Description of the Additional file 2:Picea abies SNPs successfully genotyped with the Infinium SNP array. (XLSX 1081 kb)Description of Additional file 3:Picea abies genes analysed. (XLSX 15 kb)The 30 most SNPed genes among the 16,516 Additional file 4:p-value and observed frequency. (ZIP 136 kb)The subgraph representing the most significant GO terms found by the weighted model produced by TopGO for scoring GO terms for enrichment. Boxes indicate significant terms and box color represents relative significance, ranging from dark red (most significant) to light yellow (least significant). Each shape provides GO term accession, definition, the raw Additional file 5:Picea abies genes according to GCAT and ConGenIE database with GO accessions. (XLSX 1731 kb)Annotation of the 16,516


First they note our earlier study in ref.2 overestimated SNP heritability (hereafter heritability) by excluding people in category two on the UK Biobank scale. We agree, heritability should have been reported as 0.64 not 0.94. This arose for a natural, if mistaken for this purpose, desire to categorize subjects clearly on a binary scale, and thus exclude indeterminate subjects. Their second criticism is that we overestimated the proportion of heritability explained by the 71 loci that have been identified. We stand by the broad conclusion that about one-third of the genetic effects  are explained by the 71-locus SNP score.Yap et al.In principle, the proportion of polygenic variance explained by the SNP score can be evaluated in the following three possible ways: (1) as the ratio of the phenotypic variance explained by the SNP score to the variance explained by polygenic effects; (2) as the ratio between the heritability due to the SNPs and the baseline heritability estimate; or (3) as ratio of the reduction in polygenic variance in a model that includes the SNP score to the polygenic variance in a model that does not include this fixed effect (see Supplementary Method for details). These three methods should give the same result if the residual variance and the phenotypic variance do not change between the models with and without the SNPs.2 and the correspondence1.Yap et al. have used the first method, and estimate that the 107 SNPs from 71 loci explain about 15–20% of variation in total liability. Our own estimate using the same method is 20% on the liability scale, close to theirs, implying that about 31% of the total heritability of 0.61 is explained by the SNP score. Our article, however, reported an estimate by method (2), in which the ratio of the difference in heritability in models including and excluding the SNP to the baseline heritability was 38%. Including category two did not change this estimate  are different. We thus applied method (3) which is not affected by the same issue as it works on the absolute and not relative scale (see Supplementary Method) and gives an even greater estimate of 45%. Given the limitations of fitting mixed linear models to a binary trait to estimate the parameter of interest, it is not easy to be certain which is the best one. However, irrespective of which is used, our conclusion that we can explain a relatively large proportion of heritability using SNPs from only 71 loci is still valid.Having now corrected the error in the estimation of heritability, we thus believe that the remainder of the results and conclusions are still valid, including in particular that we can explain a large proportion of the genetic variance using a relatively small number of SNPs. Furthermore, our identification and replication of several new loci for MBP remains accurate and increases the understanding and biological interpretation whilst highlighting the shared genetics with other traits.All data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary Information