Ae. umbellulata is one of the species within the genus Aegilops that have been successfully used as sources of resistance genes to leaf rust, stem rust and powdery mildew. The objectives of the current work was to validate the map position of a major QTL that confers resistance to the stem rust pathogen races Ug99 (TTKSK) and TTTTF with an independent bi-parental mapping population and to refine the QTL region with a bulk segregant analysis approach. Two F2 bi-parental mapping populations were developed from stem rust resistant Ae. umbellulata accessions (PI 298905 and PI 5422375) and stem rust susceptible accessions (PI 542369 and PI 554395). Firstly, one of the two populations was used to map the chromosome location of the resistance gene. Later on, the 2nd population was used to validate the chromosome location in combination with a bulk segregant analysis approach. For the bulk segregant analysis, RNA was extracted from a bulk of leaf tissues of 12 homozygous resistant F3 families, and a separate bulk of 11 susceptible homozygous F3 families derived from the PI 5422375 and PI 554395 cross. The RNA samples of the two bulks and the two parents were sequenced for SNPs identification. Stem rust resistance QTL was validated on chromosome 2U of Ae. umbellulata in the same region in both populations. With bulk segregant analysis, the QTL position was delimited within 3.2 Mbp. Although there were a large number of genes in the orthologous region of the detected QTL on chromosome 2D of Ae. tauschii, we detected only two Ae. umbellulata NLR genes which can be considered as a potential candidate genes.Many disease resistance genes that have been transferred from wild relatives to cultivated wheat have played a significant role in wheat production worldwide.  Triticum aestivum L.) is used as a main source of protein and starch for human consumption. However, its production is threatened by many diseases including stem rust caused by Puccinia graminis f. sp. tritici (Pgt), which is known to cause severe yield losses in susceptible cultivars. Ug99, the stem rust race group discovered in Uganda in 1998, was found to be virulent on widely deployed resistance genes, and the majority of the world’s wheat germplasm is susceptible to this race group [Ae. tauschii Coss., Ae. searsii Feldman & Kislev ex K. Hammer, and Ae. geniculata into wheat  [Wheat  , 5, 6.Aegilops umbellulata has been successfully used as a source of resistance genes to leaf rust, stem rust and powdery mildew [Pgt races TTKSK (Ug99) and TTTTF was mapped on chromosome 2U of Ae. umbellulata using genotyping-by-sequencing (GBS) SNP markers [Many wild relatives of cultivated wheats have been used as sources of disease resistance genes over the last century, and those deployed genes have also played a significant role in wheat production worldwide . Among wy mildew –10. Most markers .Pgt races TTKSK (Ug99) and TTTTF was mapped on chromosome 2U of Ae. umbellulata using genotyping-by-sequencing (GBS) SNP markers [Ae. umbellulata, two approaches were followed in the current work; independent mapping in another bi-parental population and BSR-Seq. Therefore, the objectives of the current work are to: 1) Confirm the position of previously detected major QTL from Ae. umbellulata 2) map the QTL identified on chromosome 2U to a shorted genetic region 3) identify candidate resistance genes, and 4) to generate the transcriptome assembly for Ae. umbellulata.RNA-sequencing is an efficient and cost-effective method of identifying SNPs in transcribed genomic regions especially for non-model species with a little or no available genomic resources –14. The  markers . As part2Ae. umbellulata populations were developed from crossing of resistant and susceptible accessions. The 1st population comprised 140 F2 individuals that were obtained from crossing stem rust resistant accession PI 298905 and susceptible accession PI 542369. PI 298905 was resistant to both Pgt races TTTTF and TTKSK with seedling infection type ‘2-’ whereas PI 542369 was susceptible to both races with seedling infection type ‘3+’. Similarly, the 2nd population consisted of a total of 154 F2 individuals derived from a cross made between accessions PI 542375 (resistant to races TTTTF and TTKSK) and PI 554395 (susceptible to races TTTTF and TTKSK). F3 families of both populations were also screened with race TTKSK from the Ug99 stem rust race group in a Biosafety level 3 (BSL-3) facility.Two F2 individuals and F3 families of both populations were assessed for reaction to both races TTTTF (isolate 01MN84A-1-2) and TTKSK (04KEN156/04). Experimental procedures for inoculation, incubation, and disease assessment were conducted according to previously described methods [Both F methods . The see methods  12 to 14st population using GBS SNPs. In the current work, however, the QTL mapping was carried out with GBS SNPs common between the two populations using composite interval mapping (CIM) in the RQTL package. Both F2 individuals and F3 families that were derived from F2s of both mapping populations were used for QTL analysis. To compare the similarity of the QTL mapped with the two populations, we inferred the QTL region from the GBS marker-based consensus map previously developed from these two populations [QTL mapping was carried out for the two populations described above. QTL mapping was facilitated by GBS SNP markers identified from sequence data from the two populations following the same procedures mentioned in . In our ulations .nd population (derived from PI 5422375 and PI 554395 cross) was used for bulk segregant analysis. F3 families were grown to differentiate heterozygous from homozygous F2 plants. A total of twenty plants from each F3 family were assessed at the seedling stage with Pgt isolates 01MN84A-1-2 (TTTTF) according to previously described methods [Pgt isolates tested. For bulk segregant analysis (BSA), 12 and 11 homozygous F3 families were selected to make resistant bulk and susceptible bulks based on their response to race TTTTF, respectively. Leaf tissues were collected from about 4 weeks old seedlings of F3 families for RNA extraction.The 2 methods . FamilieTotal RNA was extracted from leaf tissues collected from homozygous resistant and susceptible families and the two parents using the RNeasy Plant Mini Kit (QIAGEN) according to the manufacture’s instruction. At least 500ng RNA of four samples (two bulks and the two parents PI 5422375 and PI 554395) was submitted to the University of Minnesota Genomics Center  for quality assessment, standard cDNA library preparation and RNA sequencing on Illumina HiSeq 2500 to generate 125 bp paired-end (PE) sequence reads for all four samples. Libraries were created using dual indexed TruSeq-stranded RNA library preparation kits. All libraries were pooled and sequenced across 3 lanes. The pooled libraries were Caliper XT size-selected to have inserts of approximately 200 bp. The reads were concatenated across lanes, and a single fastq file per sample for each read was generated. The average quality scores were above Q30 for all libraries.http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The Trimmomatic program [de novo assembling the reads from the two parents. SNPs identification was done using two approaches: de novo assembly of the parents as a pseudo-reference, and a reference-based approach using the Ae. tauschii reference sequence [Sequence read quality was evaluated using the FastQC program  using the sort, merge, and pileup programs in combination with VarScan software [In the software . We usedAe. tauschii v4.0 as a reference. SNP calling was done for the bulks and the parents separately. For the two bulks, the short reads were aligned against the Ae. tauschii reference sequence using BWA and the SNPs were called using samtools [Ae. tauschii v4.0 reference sequence using BWA and SNPs were called with samtools software with default set. VCFtools was used to filter out false SNPs with the following filtering criteria: minor allele frequency of >0.20, phered-quality score of 20, minimum read depth of 6 and average mapping quality (MQ) of 30.SNP calling was also completed using samtools  and Varsde novo assemblies of Ae. umbellulata accessions were aligned against local genomic and cDNA databases of hexaploid wheat, Ae. tauschii and barley. Transcripts were also annotated using BLAST homology searches against the UniProt databases within the Trinotate functional annotation site (https://trinotate.github.io/). Briefly, the likely coding regions were predicted using Transcoder software and then the predicted proteins were searched in the protein database by BLAST. In addition, protein domains were searched using “hmmer” software [Both software . The preAe. tauschii through blast approach were used to identify contigs potentially containing NLR domains in this region. Motif Alignment and Search Tool (MAST) in MEME suite [Ae. tauschii.We also assessed if there were genes that contained a nucleotide-binding site and leucine-rich repeat (NLR) domain in the QTL region. For this purpose, all contigs from the resistant parent assembly that were mapped on chromosome 2D of ME suite  and NLR-ME suite , were usME suite , and sixAe. umbellulata at the same region with both mapping populations (P. graminis f. sp. tritici races TTTTF and TTKSK significantly deviated from a 1:2:1 ratio in the 1st population (data not shown), the segregation pattern within segregating F2:3 families was in agreement with a 3:1 ratio for a single gene model . Majority of the SNPs linked to stem rust resistance in both populations also explained over 50% of the phenotypic variation suggesting that the resistance to each race is conferred by a single gene.GBS markers common across the two mapping populations were used to identify stem rust resistance QTL. Detailed procedures of GBS marker development have been reported previously for these bi-parental populations . QTL mapulations , Table 1st population (derived from accession PI 298905 and PI 542369 cross) was larger  than that of the 2nd population (accession PI 5422375 and PI 554395 cross) which was estimated to be 1.68 cM. GBS marker TP34301 (consensus position: 87.89 cM) that was linked with the QTL in the 1st population was only 1.35 cM away from another GBS marker TP44300 (consensus position: 89.24 cM) linked with the QTL (based on race TTTTF) in the 2nd population, and both markers are shown in bold face in Comparing QTL interval size between the two populations, the interval size in the 1Ae. tauschii reference, and all markers linked with resistance against Pgt races TTTTF and TTKSK in both populations were within 7.89 Mbp (566677717–574572533 bp) on chromosome 2D of Ae. tauschii  whereas the lowest number of SNPs was found on chromosome 4D  SNPs were on chromosome 2D of omosomes . Moreoveomosomes .Ae. umbellulata bi-parental mapping populations is in the orthologous region of 566.68–569.96 Mbp on the physical map of chromosome 2D of Ae. tauschii. The size of this refined region is approximately 3.2 Mbp. All genes in this region were extracted from the Ae. tauschii transcribed region, and there were a total of 111 predicted genes in the region . Out of these, only four contigs are in the predicted gene region, of which DN39311_c0_g2_i1, DN39311_c0_g2_i4 and DN39311_c0_g2_i5 are isoforms of one gene and mapped on the same physical position (567.98 Mbp) on chromosome 2D. The remaining NLR contig, DN16173_c0_g1_i1, was mapped 0.94 Mbp away from the former . Interestingly, from BSR-Seq analysis, two SNPs with the highest BFR (97.08 and 89.29) for the two bulks (resistant and susceptible) were identified at 567.94 Mbp on chromosome 2D suggesting that these three isoforms represent potential candidate genes for the QTL that we mapped with the bi-parental populations (Brachypodium distachyon disease resistance protein RGA2-like (LOC100839373) but none of the hits was NBS-LRR protein.For the purpose of assessing the presence of genes that contain a nucleotide-binding site and leucine-rich repeat (NLR) domain in the QTL region, contigs of the resistant parent assembly that mapped on chromosome 2D of ulations . The simAe. tauschii reference sequence and 98.8%  of the SNPs had known physical positions. The remaining 1% mapped to unmapped scaffolds. Chromosome 2U had the highest number of SNPs whereas chromosome 4U had the least number of SNPs, and the number of markers were between the two extremes for the remaining six chromosomes  whereas chromosomes 1D, 4D and 6D possess lower numbers of contigs   . Similar–26,000) .de novo assembly approach. A total of 25,931 unique SNPs were obtained with the default setting. In order to identify the parental SNPs in the two pools, we merged the parental SNP dataset with that of the bulks’ SNP data sets using the parental SNP positions to link the two SNP data sets. A total of 12,311 parental SNPs were identified from the bulks and merged with output from blasting the susceptible parent assembly with the Ae. tauschii reference sequence. Of these, a total of 11, 764 had significant hits in the reference sequence of Ae. tauschii.SNPs were also called for the two pools using the Ae. tauchii genome reference sequence, and a total of 36, 973 unique contigs had hits on chromosome 2D, and then these unique contigs were again merged with the 25, 931 SNPs identified using the susceptible parent as a pseudo reference. Out of these, a total of 6,346 SNPs were from chromosome 2D, of these, 2,222 SNPs had BFR values of four and above (nd SNP DN19324_c0_g1_i2 (position on chromosome 2D: 566490221 pb) with BFR value of 119 was mapped at position 566.49 which is also in the QTL region. The 3rd contig DN2794_c0_g1_i1 (574564921 bp) with a SNP associated with stem rust resistance had a BFR value of 6.77.The susceptible parent assembly was aligned against the nd above . The numAe. umbellulata, the two assemblies were aligned against the high confidence gene sets of hexaploid wheat, Ae. tauschii and barley. Contigs from Ae umbellulta that shared a high confidence CDS with hexaploid wheat were considered as isoforms of a gene. Out of the total 204,037 contigs from the resistant parent assembly, 59%  transcripts had hits in the hexaploid wheat gene model IWGSC RefSeq Annotation v1.0. These hits were grouped into the gene model identification numbers they matched. The resistant parent hits matched a total 43,276 genes in the 137,914 high confidence genes of hexaploid wheat. Out of these, a total of 42,807 genes had known chromosome information in the hexaploid wheat gene annotation file. Similarly, out of 239,727 transcripts in the susceptible parent assembly, 52%  transcripts were assigned into 44,027 unique high confidence genes in hexaploid wheat. The database comprised a total of 137,056 high confidence genes of hexaploid wheat. The similarity search in the 39, 622 high confidence gene set of Ae. tauschii retrieved a total of 22,685 and 23,111 unique genes for resistant and susceptible accession assemblies. Similarly, integrating the resistant parent into the 26,159 high confidence gene model of barley, Ae. umbellulata transcripts matched a total of 18,669 unique genes.To predict the number of genes in For the two assemblies, 40% and 39% of all transcripts matched records in Uniprot for the resistant parent and susceptible parents, respectively . Gene OnAe. umbellulata is one of those wild species that has been a useful source of resistance genes. Leaf rust resistance Lr9 was the 1st gene that was transferred to wheat 60 years ago from Ae. umbellulata, the secondary gene pool of wheat [Ae. umbellulata has also been identified as a source of resistance to stem rust [Ae. umbellulata was mapped on chromosome 2U, and its map position was validated with independent mapping bi-population, and finally the region was fine-mapped via integration of bi-parental QTL mapping method with the bulk segregant RNA-Seq analysis (BSR-Seq) approach.Wild relatives of wheat have been used as sources of resistance genes in wheat improvement programs for many decades. of wheat . Ae. umbtem rust , 25, 11,tem rust . In the Ae umbellulata bi-parental populations was validated and refined with the BSR-Seq approach in this report.BSR-Seq has been recently applied for fine-mapping gene region in cereal crops such as wheat , 19 and Ae tauschii reference sequence v.1.0, and de novo assemblies of Ae. umbellulata. From the integration of the SNP context sequences from RNA-Seq and GBS data into a pseudomolecule of Ae. tauschii, the QTL-associated GBS markers were mapped in the same region with the RNA-Seq SNPs that showed the highest BFR ratio, suggesting the suitability of BSR-Seq for validating and identifying candidate genes detected originally as QTL in classical mapping. Once the gene region was confidently known, by combining information from the physical positions of all GBS markers that were associated with the resistance gene in two bi-parental populations and RNA-Seq SNPs with high BFR) the size of gene interval was approximately 3.2 Mbp based on physical map of chromosome 2D of Ae. tauschii. Given stem rust resistance genes are known to encode nucleotide-binding site and leucine rich repeat (NLR) proteins [Ae. umbellulata bi-parental populations fits into features of a qualitative gene, exploring the presence of NLR genes in the predicted region is useful to identify short lists of potential candidate genes. In this way, five contigs with NLR domains were discovered from the resistant parent assembly that had hits within this 3.2 Mbp gene region, of which four NLR contigs are variants of a single gene. The similarity search of the four variants of this gene indicated that the gene encodes disease resistance NLR proteins in barley and hexaploid wheat suggesting the NLR contigs represent a potential candidate gene for the major stem resistance QTL we mapped on chromosome 2U of Ae umbelluata. Moreover, this result agrees with our previous hypothesis that the gene identified from Ae umbellulata is a novel gene as none of the candidate NLR contigs had similarity to the known major disease resistance genes in barley and hexaploid wheat.We used comparative mapping to infer the map positions of QTL-linked GBS markers and RNA-Seq derived SNPs using recently released proteins –38, and de novo method and orthologous reference-based approach. Although SNPs were effectively identified with the de novo approach where de novo assemblies were used as a pseudo-references to call SNPs from RNA-Seq data, orthologous reference sequence-based approach enabled us to precisely identify SNPs that are closer to the region of the gene mapped with GBS markers with two bi-parental mapping populations. This indicated that with the absence of a species reference sequence the use of closely related species reference sequence is an advantageous alternative for SNPs identification. SNPs with high BFR from both methods mapped at the same region, but the resolution was better in the cross-species reference-based approach as we identified more SNPs with higher BFR that mapped closer to the GBS marker position.Two approaches were followed for the identification of SNPs that showed differences between the resistant and susceptible bulks to fine map the QTL region: In summary, our study demonstrated that BSR-Seq is cheap and efficient approach not only precisely validating the map position of previously identified QTL but also identification of potential candidate genes is possible as the approach provides additional SNPs in the mapping interval. BSR-Seq could also be utilized for other wild relatives of crop species that are difficult to propagate and self-fertilize.S1 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Use of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS.2) higher than 0.6, and the dosage scores of all iWGS SNPs.Phenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660 K and 80 K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80 K and 660 K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R2 of 0.39 (0.49). After quality control, 2.5 × 106 (3.5 × 106) SNPs had a Beagle R2 higher than 0.6, resulting in an average Beagle R2 of 0.83 (0.93). Compared to the 80 K and 660 K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis.For the DL-line (LW-line), imputation of 80 K genotypes to iWGS resulted in an average Beagle RImputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80 K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping.The online version of this article (10.1186/s12711-019-0445-y) contains supplementary material, which is available to authorized users. Use of whole-genome sequence (WGS) data is expected to improve the detection of quantitative trait loci (QTL) because such data are expected to contain most of the causal single nucleotide polymorphisms (SNPs), as was shown in dairy cattle populations by using WGS data of 234 bulls . ImproveTo benefit from WGS data, a large population of animals with such data is needed. In spite of the decreasing costs of WGS , it is sInaccurate imputation can influence the results of follow-up analyses such as genome-wide association studies (GWAS), especially when the accuracy of imputation is ignored in those analyses. Two approaches to account for imputation errors are to filter SNPs based on imputation accuracy prior to analysis or to use dosage scores in the analyses. Using dosage scores means that all imputed SNPs are included in the analysis, although the power to detect associations with poorly imputed SNPs will be low compared to using accurately imputed SNPs. So far, there is little information on the accuracy of imputation to iWGS using data of a commercial pig breeding population and on the effect of using iWGS genotype or dosage scores in a GWAS. Therefore, the objectives of this study were (1) to investigate the accuracy of imputation to WGS in two pig lines using a multi-line reference population and a limited number of sequenced animals available for the target lines; and (2) to investigate the effect of using imputed WGS genotypes versus dosage scores in a GWAS on QTL detection.The dataset used in this study was provided by Topigs Norsvin. Phenotypes for the number of teats recorded after birth on 12,184 Large White (LW-line) and 4943 Dutch Landrace (DL-line) pigs were available. We investigated this trait because records were available for both sexes and its heritability is relatively high, i.e. 0.4 . The pheFor both the LW-line and the DL-line, Geneseek-Neogen GPPHD 80 K SNP genotypes were available for all animals with phenotypic records. In addition, genotypes from the Affymetrix Axiom porcine 660 K SNP chip were available for the 120 sires with the largest number of offspring in the set of genotyped animals of the LW-line. Using the 120 sires as reference population, within-line imputation from 80 K to 660 K SNP genotypes was performed for all phenotyped animals from the LW-line using FImpute v2.2 software in gene. This gene is important for vertebral development and is positively correlated with number of teats in pigs [casein kinase 1 gamma 3 gene, which plays a regulatory role in the Wnt signalling pathway, which is essential for early mammary gland formation [casein kinase 1 gamma gene (casein kinase 1 gamma 2).Iso-Touru et al.  and Daet in pigs , 39, 41.ormation –44. For Compared to the DL-line, more QTL regions were identified for the LW-line when 660 K genotypes and genotype scores were used but fewer QTL regions were identified when 80 K genotypes or dosage scores were used for GWAS. This was unexpected because the DL-line has a smaller population size and more DL-line iWGS genotype scores were removed because of low imputation accuracy. Therefore, the power to detect associations was expected to be lower in the DL-line than in the LW-line . The larOf the 94 QTL regions found for the DL-line with iWGS genotype scores, 24% overlapped with QTL regions identified for the LW-line with iWGS genotype scores. Although power was limited in each line and we did not expect to detect each QTL in each line, differences might also be caused by differences in genetic architecture between the lines, such as MAF and LD patterns . CompariAlthough a large number of new QTL were found using iWGS genotype, whether they are indeed new associations or artefacts of the definition of the QTL region can be questioned. Here, a QTL region was defined as the 0.5-Mb region to the left and right of the most significant SNP in a region because multiple studies have found that the average LD in commercial pig lines decreases below 0.3 when the SNPs are more than 0.5 Mb apart –30. ThisIn addition to the definition of the QTL region, the newly identified QTL regions could also be an artefact due to the significance threshold that we applied in this study. Here, the significance threshold was set by applying a Bonferroni correction using the number of independent chromosomal segments instead of the commonly used total number of SNPs, which does not take LD between SNPs into account. The number of independent chromosomal segments has often been used for Bonferroni correction for GWAS in human , 46, 47,Finally, even with low imputation accuracy, GWAS using iWGS can be beneficial in several cases. For example, the QTL regions identified in this study could help to pre-select SNPs to improve the accuracy of genomic predictions, as shown by –54. In a2, was relatively low and, therefore, many SNPs were inaccurately imputed. When using iWGS genotype scores, quality control measures removed around 50% of the SNPs from the analysis and we assumed that the remaining SNPs were called without imputation error. Using iWGS dosage scores in the GWAS model is another way of analysing poorly imputed data. Dosage scores are a posterior probability of having one of the three genotypes and, thus it accounts for the uncertainty of imputation to iWGS. In this study, we identified 56.7 and 26.9% more QTL regions for the DL-line and the LW-line, respectively, when dosage instead of genotype scores were used. Moreover, the most significant SNPs in the QTL regions explained more of the phenotypic variance when using dosage scores. Genotype scores may not capture as much phenotypic variation because some information is lost due to inaccurate imputation. For example, for the DL-line, a clear peak was detected on chromosome 2 at approximately 56 Mb with dosage scores but not with genotype scores ), and thus the SNPs in this regions had approximately the same imputation accuracy and dosage scores, and thus, the same significance level.In this study, imputation accuracy by SNP, measured with Beagle Rres Fig. . In addiIn this study, the genomic inflation factors were lower when using dosage scores compared to using 80 K and 660 K genotypes and iWGS genotype scores. The scenario using dosage scores included SNPs with a low imputation accuracy, and hence SNPs with a low MAF. In the other scenarios, there is ascertainment bias, which is caused by the preference of SNPs on a chip that are more likely to be common . In addi2 lower than 0.6. Imputation from 660 K to iWGS was more accurate than imputation from 80 K to iWGS, which suggests that step-wise imputation, i.e. first imputing from 80 to 660 K and then from 660 K to iWGS, could increase the accuracy of imputation. Although imputation accuracy was poor, using iWGS instead of genotypes from a lower density SNP chip increased the number of detected QTL regions and the expected proportion of phenotypic variance that they explained. When using iWGS dosage scores instead of genotype scores, even more QTL regions were detected because all SNPs were used in the analysis and the uncertainty of imputation was taken into account by the model.Use of a multi-line reference population resulted in relatively poor imputation accuracy to iWGS, with 52.7% of the SNPs on the 660 K array in the LW-line and 39.1% of the SNPs on the 80 K array in the DL-line having Beagle RAdditional file 1. The adapted subroutines in GCTA. Patches for the program GCTA (Version 1.26.0) to be able to run GWAS with dosage scores.Additional file 2. Figures S1 and S2 contain the Beagle R2 of imputation on chromosome 7 for the LW-line and DL-line, respectively, and Figure S3 shows the minor allele frequency of the most significant SNPs identified in the DL-line with iWGS genotype scores plotted against their minor allele frequency in the LW-line.


They used DNA methylation at CpG-sites with a variant...This study highlights dangers in over-interpreting fine-mapping results. Chundru  n = 1166). The choice of imputation reference panel had a strong effect on imputation accuracy, with the 1000 Genomes Project Phase 3 (1000G) reference panel (n = 2504 from 26 populations) giving a mean nonreference discordance rate between imputed and sequenced genotypes of 3.2% compared to 1.6% when using the Haplotype Reference Consortium (HRC) reference panel . These imputation errors had an impact on whether the CpG-SNP was included in the 95% credible set, with a difference of ∼23% and ∼7% between the WGS and the 1000G and HRC imputed datasets, respectively. All of the fine-mapping methods failed to reach the expected 95% coverage of the CpG-SNP. This is attributed to secondary cis genetic effects that are unable to be statistically separated from the CpG-SNP, and through a masking mechanism where the effect of the methylation disrupting allele at the CpG-SNP is hidden by the effect of a nearby SNP that has strong linkage disequilibrium with the CpG-SNP. The reduced accuracy in fine-mapping a known causal variant in a low-level biological trait with imputed genetic data has implications for the study of higher-order complex traits and disease.Genetic variants disrupting DNA methylation at CpG dinucleotides (CpG-SNP) provide a set of known causal variants to serve as models to test fine-mapping methodology. We use 1716 CpG-SNPs to test three fine-mapping approaches , assessing the impact of imputation errors and the choice of reference panel by using both whole-genome sequence (WGS), and genotype array data on the same individuals ( This asBecause of the small number of known causal variants, comparisons of fine-mapping methods need to be performed through simulation, and are often idealized and do not encompass the full range of experimental variation. However, high-throughput measurement of DNA methylation across the genome provides a potential model trait for testing fine-mapping methods. DNA methylation is an epigenetic modification that is influenced by both genetic and environmental factors, with an average heritability of 20%  , BayesiaThe Lothian birth cohorts of 1921 and 1936 (LBC)  or ScottFrom the LBC, 1370 individuals were whole-genome sequenced on a HiSeq X installation to an average coverage of 36× . All reads were mapped to the build 38 version of the reference genome using BWA  and variThe whole-genome sequence data were compared to array data for the same individuals using PLINK 1.90 , the SNPs with high missing rate (>5%), SNPs with Hardy–Weinberg exact test  Phase 3 . The impP < 0.01), and low-quality samples were removed. Individuals with low call rate  were removed. Probes on the X and Y chromosomes were removed, leaving 450,726 probes remaining. β Values were corrected for BeadChip, sample plate, hybridization date, white blood cell count, and sex.Details of the DNA methylation QC can be found elsewhere (We used the UK10K dataset [European Genome-phenome Archive (EGA) accession numbers: EGAS00001000108 and EGAS00001000090] for the simulations . The UK10K dataset (The Systems Genomics of Parkinson’s Disease (SGPD) cohort comprises 956 individuals with Parkinson’s disease, and 930 controls genotyped on the Illumina PsychArray-B.bpm. In our analyses we did not take disease status into account. The data were filtered to remove individuals with high missing rate, the SNPs with high missing rate (>5%), SNPs with Hardy–Weinberg exact test n server  and was n server . The impP < 0.01). The R meffil package was also used to perform sample QC using Illumina recommended thresholds. Samples were dropped if call rate was low , if predicted sex (based on XY probes) did not match reported sex, and if predicted median methylated signal was >3 SD from the expected. After these QC steps, methylation β values were quantile-normalized with respect to 20 principal components generated from the control matrix and the most variable probes. Additionally, normalization was adjusted for batch, slide, cohort, sentrix row/column, sex, and age. Of the 1716 probes in the LBC dataset, only 1678 remained after cleaning, thus the replication was only conducted on the respective probes.The DNA methylation data were measured using the Illumina HumanMethylation450 BeadChip array. Raw intensity data were background-corrected and normalized using internal controls, and methylation β values were generated using the R meffil package (n = 3781), and a subset of the UK10K dataset to match the sample size in the imputed LBC dataset (n = 1366). The causal variants were chosen at random from the genome, but restricted to have MAF > 0.05.Phenotypes similar to DNA methylation at CpG-SNPs were simulated using the GCTA software  because we use the coverage of the causal variant in the sets as a measure of fine-mapping accuracy. Both sets are designed so that 95% of the time the causal variant will be captured. For simplicity we will refer to both sets as credible sets.Rank the SNPs by strength of association, and add the most associated SNP to the credible set;Regress the most associated SNP against the phenotypeN = 2, regress the Nth best SNP against the phenotype with the fitted values from the regression in step 2 as a covariate:Starting at N, and repeat step 3. If If where y is the phenotype, i, and Nth best SNP. To construct a 95% credible set of causal variants, a set of SNPs with 95% probability to contain the causal variant, a Bonferroni-corrected significance of The J-test  is a simBIMBAM , which uBSLMM  method . The UK10K dataset is available from EGA (accession numbers: EGAS00001000108 and EGAS00001000090). The source code used to run the three fine-mapping methods is available on GitHub (https://github.com/chundruv/finemapping_GENETICS2019). Details of the simulation results, the discordance between sequence and genotyped data, and the SGPD consortium member list is provided in File S1. Supplemental material available at Figshare: https://doi.org/10.25386/genetics.7906109.The LBC methylation data are available at EGA under accession number EGAS00001000910. The LBC1921 and LBC36 genotype data are available on request for relevant research purposes (n = 2504 from 26 populations) and the HRC  reference panels . Fine-mapping was performed at 1716 DNA methylation sites previously identified to have a cis-mQTL We compare 95% credible sets  obtained from three fine-mapping approaches using DNA mQTL at a CpG-SNP in the 1166 individuals from the LBC , as well as the number of SNPs within each credible set. BIMBAM performed marginally better than both BSLMM and the J-test in terms of coverage of the CpG-SNP, with the trade-off of larger credible sets (Table S1). In the 672 cases where the CpG-SNP was not the most associated SNP (top SNP), the top SNP in the credible sets had a median distance of 2 kb to the CpG-SNP, with 95% of SNPs being within 34 kb. . While performing well on simulated data (see File S1), all three methods failed to reach the expected 95% coverage of the putatively causal CpG-SNP  using eivs. genotyped SNPs and overall coverage of 95% credible sets was replicated in an independent dataset of 1886 individuals imputed using the HRC reference panel  were included in 95% credible sets between 29 and 33% more often than imputed CpG-SNPs using the 1000 Genomes Project Phase 3 reference, and between 8 and 19% more often using the HRC reference dataset, with this being driven by differences in imputation accuracy (see File S1). The difference between imputed ce panel . The effce panel .cis-mQTL, with this being implicitly assumed in the construction of the 95% credible set for each of the methods. We performed two analyses to identify mQTL under the influence of multiple genetic variants: a standard forward selection approach and the CoJo stepwise selection model implemented in GCTA-CoJo . Only one independent signal was detected by both methods for 87% of the mQTL. However, when considering only those mQTL showing a single independent association for both methods, we see that the coverage is still below the expected 95% (The underlying assumption of our comparison of fine-mapping is the presence of a single causal variant underlying the cted 95% .For the mQTL with one independent association from the conditional analyses, and where the CpG-SNP was not the top SNP, we estimated LD between the top SNP and CpG-SNP. In all cases, the LD between the top SNP and CpG-SNP pairs had a hylation . As suchTo capture genuine biological complexity while assessing the performance of fine-mapping methodology, we examined the use of known genetic variation within DNA methylation CpG sites as a model trait. This identified limitations in fine-mapping with imputed sequence data and in statistically separating effects of closely linked variants.vs. sequencing. We have shown a dramatic reduction in the proportion of credible sets that actually contain the underlying causal variant when using imputed genotype data, particularly when using the 1000 Genomes Project Phase 3 reference panel for imputation. This imputation panel is still widely used, especially for GWAS meta-analysis combining populations with differing ancestry. In comparison, the more extensive HRC reference panel showed a great reduction in imputed genotype error rates, resulting in increased coverage of the causal variant. This highlights the need to continue the generation of large imputation reference panels across multiple ancestries. The HRC reference panel is ∼6.5 times larger than the African Genome Resource, which is currently by far the largest non-European imputation reference panel.Statistically minimizing the set of potential causal variants underlying the thousands of identified GWAS hits is essential for efficient experimental follow-up. However, we also need to ensure statistically derived sets of potential causal variants actually contain the underlying causal variant. While fine-mapping methods implicitly assume all potential causal variants are available, GWAS generally use imputed genotypes because of large sample size requirements and the relative cost of genotyping arrays cis region surrounding the CpG site for 11% of probes. It is likely that a much higher proportion of probes would be identified as having multiple genetic effects with a greater sample size. In addition, we identified SNPs that effectively masked the effect of the CpG-SNP; these variants had an effect on the methylation levels, and the methylation disrupting allele of these variants were in high LD Although common CpG-SNPs will have a very large effect on the DNA methylation, we were unable to reach the expected 95% coverage of the putatively causal CpG-SNP in our credible sets even when using whole-genome sequenced genotypes. We detected multiple statistically independent genetic associations in the The difficulties in fine-mapping a known causal variant in a low-level biological trait have implications for the study of higher-order complex traits and disease. For example,


Centrocercus and Tympanuchus genera). Two individuals were genotyped per species for a total of ten samples. A high proportion (91%) of the total 580 961 SNPs were genotyped in at least one individual (73–76% SNPs genotyped per species). Principal component analysis with autosomal SNPs separated the two genera, but failed to clearly distinguish species within genera. Gene ontology analysis identified a set of genes related to morphogenesis and development (including genes involved in feather development), which may be primarily responsible for large phenotypic differences between Centrocercus and Tympanuchus grouse. Our study provided evidence for successful cross-species application of the chicken SNP array in grouse which diverged ca. 37 mya from the chicken lineage. As far as we are aware, this is the first reported application of a SNP array in non-passerine birds, and it demonstrates the feasibility of using commercial SNP arrays in research on non-model bird species.The use of single nucleotide polymorphism (SNP) arrays to generate large SNP datasets for comparison purposes have recently become an attractive alternative to other genotyping methods. Although most SNP arrays were originally developed for domestic organisms, they can be effectively applied to wild relatives to obtain large panels of SNPs. In this study, we tested the cross-species application of the Affymetrix 600K Chicken SNP array in five species of North American prairie grouse ( Studies using RAD-seq compared to SNP arrays also involve more extensive processing requiring specialized expertise to filter and identify genetic markers. Thus, some researchers have recently tested commercial SNP arrays developed for model species, particularly mammals, on closely related non-model species to determine if they can provide easier access to larger numbers of SNPs4. In contrast to mammals and plants, relatively few studies have applied SNP arrays to studies of birds. Some studies of passerine birds have used custom Illumina beadchip arrays7, but they are not commercially available and have relatively fewer SNPs (10–50 K) than arrays for other model organisms. Also, the passerine arrays may not be readily applicable to non-passerine birds, as the two lineages are estimated to have split 60–75 million years ago (mya)9. The most recent SNP array manufactured by Affymetrix for a non-passerine poultry species, the domestic chicken Gallus gallus, however, has nearly 600 000 SNPs including a set of SNPs in exonic or coding regions10 and could be potentially useful for applications to non-passerine birds.DNA genotyping arrays with hundreds of thousands (or more) single nucleotide polymorphisms (SNPs) are commonly used in genetic studies of model species, but relatively few arrays have been developed in other species because of their high costs of development. In non-model species, alternative techniques such as Restriction Associated DNA sequencing (RAD-seq) are more often used to analyze SNPs, but for a variety of reasons studies using RAD-seq often have an order of magnitude fewer SNPs (tens of thousands of SNPs) than SNP arrays, which can be insufficient for some applications , which diverged ca. 54 mya from the equine lineage12. On the other hand, cross-species application of the domestic dog CanineHD BeadChip to seals (divergence time ~44 mya) amplified 19% of 173 K loci, but fewer than 200 loci were polymorphic13. Another issue with cross-species use of SNP arrays (and other markers) is ascertainment bias, in which the sample of SNPs does not represent the overall pattern of genetic diversity, in this case because the SNP array was developed in a different species. This difference is likely to lead to an over-representation of polymorphisms shared between species or polymorphisms resulting from artificial selection, when SNPs originally developed for domestic species are cross-amplified in wildlife. Ascertainment bias might also lead to spatial clustering of polymorphisms around selectively non-neutral SNPs, but most commercial SNP arrays developed for domestic animals are based on random selections of evenly spaced SNPs which may help to lessen this bias13. Also, most SNPs on commercial arrays are identified so researchers can screen markers depending on their objectives.One limitation of using SNP arrays is the level of cross-species amplification. In a study of 16 wild mammal species, the percentage of called SNPs declined 1.5% for each million years of evolutionary divergenceCentrocercus (sage-grouse) and Tympanuchus (sharp-tailed grouse and prairie-chickens) grouse to determine if it was suitable for genetic studies in these taxa. The five species, herein collectively referred to as prairie grouse, are estimated to have diverged from chickens about 37 mya14, while the two genera are thought to have split approximately 4.7–9.9 mya16. The domestic chicken SNP array is based on the Gallus gallus 4.0 reference genome and contains 580 954 SNPs, including 21 534 SNPs from coding regions. The SNPs on the array are evenly spaced (mean of 1 748 bases between markers) based on genetic map distance to equalize the density (per cM) of SNPs on micro- and macro-chromosomes10. Our cross-species application of chicken SNPs took advantage of the expected high synteny between the genomes of chicken and grouse (see Methods for details) and, to the best of our knowledge, this is the first study to test the Affymetrix 600K SNP array on a species other than Gallus gallus.In our study we tested the domestic chicken 600K SNP array on five North American species of We successfully genotyped 91% (530 287 of 580 961) of all chicken SNPs on the array (including sex chromosomes), and 53% (308 276 of 530 287) of genotyped SNPs were found in at least one of two sampled individuals of all five species  were polymorphic . Note that this includes SNPs with estimates from one (53% polymorphic) to ten (85%) samples. On average, 7.2 of the ten samples were genotyped across all polymorphic SNPs. Within each species, 48–54% of all SNPs were polymorphic, and 72–75% of them were genotyped for both individuals per species Table . Average2,133 = 159.6, P < 0.001). The largest inter-marker distances were found on macro-chromosomes, while the smallest inter-marker distances were found on micro-chromosomes, consistent with the equalization of marker densities across chromosomes , the mean genotyping success rate was 74% averaged across species, and it was similar between all chromosome categories, ranging from 73% for intermediate-chromosomes to 75% for micro-chromosomes . The proportion of SNPs among all genotyped loci was similar for all chromosome categories, ranging from 69% for macro-chromosomes to 71% for micro-chromosomes. By contrast, the spacing between adjacent markers differed significantly between autosomal chromosome categories  and 94.3 ± 0.2% (Tympanuchus) of SNPs  were recorded between individuals within species  to examine variation between species using 386 011 autosomal loci that were polymorphic across our samples. The first PC axis accounted for 21.5% of the variability in the SNP dataset and it distinguished between Centrocercus and Tympanuchus using the two genera as groups in the association test in PLINK  was below the minimum possible P value with our sample size (7.74 × 10−6) and, consequently, none of the tests was significant after Bonferroni correction. Thus, we used the P values as a relative index of importance in separating the two genera. First, we examined the distribution of 5 807 nominally significant (P < 0.05 unadjusted) SNPs that differed between genera. These SNPs were generally distributed evenly across chromosomes relative to the total number of SNPs on each chromosome . The only exceptions were small  and micro  chromosomes, which is likely an artefact of small numbers of SNPs (<585) on these chromosomes. Next we examined the genes encompassing the most significant SNPs  that were found in all 10 samples. In this sample of 53 SNPs, the maximum significance level was achieved because all of the individuals in one genus were homozygous for a different allele than individuals in the other genus , the Affymetrix 600K SNP array produced genotypes for 91% of SNPs in at least one of the five species of grouse and 53% of SNPs across all species of grouse. Even with the loss of about half of the SNPs, the 600K SNP array has potential to produce more SNPs than typical RAD-seq methods , particularly at known coding regions. For example, over 10 K coding SNPs were genotyped in each grouse species, which may encourage studies investigating functional divergence among species or populations. In fact, our cross-application of chicken SNP array in grouse allowed us to identify a set of candidate genes that may be responsible for large phenotypic differences between Centrocercus and Tympanuchus. Although the number of SNPs we genotyped here was fewer than might be gained through whole genome resequencing (millions), the loci on the array have already been characterized, and therefore less effort is required to filter and identify loci. Our results do suggest, however, that the loci included in the Affymetrix array may not be particularly suitable to infer phylogenetic relationships in grouse, as only 0.2% of all genotyped SNPs showed fixed differences between the species. While the PCA analysis conducted with autosomal loci could separate Centrocercus from Tympanuchus, it could not distinguish species within genera.Despite the relatively high divergence time between the domestic chicken and grouse  of SNPs, but they may not be effectively applicable in non-passerine birds due to the long evolutionary times since the divergence of these lineages. Thus, genome-wide SNP genotyping in non-passerines has often been based on RAD-seq methods21. However, RAD-seq applications in non-model bird species do not typically exceed 100 K SNPs24, and it has been shown that RAD-seq-based genome scans are likely to miss a considerable proportion of loci under selection, as the median density of markers is ca. 4 SNPs/Mb25. Thus, the usefulness of RAD-seq data in studies of local adaptation has been recently debated25. This problem can potentially be avoided with the Affymetrix SNP array, which has a density of 400–1300 SNPs/Mb at each autosome10. In our cross-species study of prairie grouse the Affymetrix array still yielded a density of 250–500 SNPs/Mb, depending on the size of the autosome. Over 21 K annotated SNPs from the Affymetrix array have been identified in exonic regions, while 190 K SNPs have been identified in intronic regions10. Thus, considering an even distribution of SNPs across the genome, it can be expected that the majority of the estimated 20 000–23 000 chicken genes26 will be covered with the array. The array also includes an additional 266 K SNPs that have been annotated from intergenic regions of the chicken genome10. Thus, cross-application of the Affymetrix array in wild avian species may provide researchers with easier means of studying population genetics, local adaptation and genome-wide selection. It is important to note, however, that our small-scale study was designed primarily to test the feasibility of the array, and these other applications may require using greater number of samples.The application of SNP arrays in research on non-model vertebrates has recently become an alternative tool in addition to whole-genome sequencing and RAD-sequencing methods. Although most SNP arrays were originally developed for model or domestic organisms, those same arrays can potentially be applied to wild relatives to obtain large panels of SNP markers11. In general, mammalian taxa, which diverged >5 mya shared less than 10% of polymorphic SNPs11. Unfortunately, we are aware of no previous attempts to apply SNP arrays across phylogenetically distant avian species, but our analyses suggest that some bird lineages may be much more conservative in the retention of shared polymorphism than mammals. Although this hypothesis certainly needs more empirical testing, it appears supported by the high similarity of draft genomic sequences of several grouse species to the reference chicken genome29. This high similarity between chicken and grouse also suggests that mispriming was not a likely bias in using the SNP array. At the same time, we acknowledge that genome synteny is unlikely to provide an exclusive explanation for high levels of shared polymorphism between grouse and chicken. In mammals, very high genome synteny between closely related taxa may not necessarily be associated with high levels of shared polymorphism. For example, cross-species application of SNPs developed for the domestic sheep Ovis aries in a recently diverged (ca. 2 mya) wild species, the bighorn sheep Ovis canadensis, showed very low level of retained polymorphism (less than 2%), despite high genome synteny between the two species17.Our application of the Affymetrix 600K SNP array in prairie grouse yielded surprisingly high levels of polymorphism . This is in stark contrast to previous research on mammals, which showed an exponential decline of shared polymorphism with the time to the last common ancestor11. Also, application of arrays originally developed for domestic animals in wildlife could introduce an over-representation of SNPs resulting from artificial selection. While these limitations have to be carefully considered while using SNP arrays, it should also be acknowledged that the problem of ascertainment bias is not restricted to SNP arrays, but is inherent in any marker development and can be compensated for during data analysis30. Cross-amplified loci should always be subject to the process of initial filtering, which may include removal of loci that show low allele frequencies , deviate from neutrality, or show strong linkage disequilibrium (LD) within a given basepair window. The latter approach (LD based SNP pruning) is particularly useful for reducing the effects of ascertainment bias, especially when estimating differentiation measures between populations (e.g. genetic distances), inbreeding coefficients or kinships31. The low sample size in this study prevented us from exploring these possibilities in any detail, but we recommend using these filtering steps in future studies that apply SNP arrays across species based on larger sample sizes.We also acknowledge that the application of the chicken SNP array in wild grouse could carry some risk of ascertainment bias, where the cross-amplified panel of SNPs is not representative of the overall pattern of genetic diversity in the study species. This could be caused by over-representation of evolutionarily old shared polymorphisms and under-representation of rare and novel variants32. Nearly 40% of all 187 extant phasianid species face some level of conservation risk and 12 species have been classified by the International Union for the Conservation of Nature (IUCN) as endangered (EN) or critically endangered (CR), and thus facing a very high risk of extinction in the near or immediate future33. All five taxa studied here are of conservation concern; Gunnison sage-grouse is classified as endangered and both species of prairie-chicken are classified as vulnerable by IUCN. As a consequence of habitat loss, these species have undergone long-term and rapid population declines, and have increasingly small and fragmented ranges34. All five of the study species are under active conservation programs, including translocations of birds to restore genetic variation35, or captive-breeding and reintroduction programs36. Thus, we suggest that the Affymetrix 600K SNP array may become a new and efficient tool to obtain genome-wide data that are increasingly important for the management of threatened populations of grouse and other phasianids38.Successful application of the Affymetrix 600K SNP array in North American prairie grouse suggests that the same method could be used for genotyping other galliform species. Chickens  and grouse (Tribe Tetraonini) are thought to have diverged relatively early in the radiation of the family Phasianidae. Thus, it seems likely that the Affymetrix array might be successfully applied across the entire Phasianidae clade. However, galliform species that are more distantly related to chickens, such as New World quail (Odontophoridae) or guineafowl (Numididae) would probably yield lower genotyping success than in the case of grouse. Cross-application of the array in phasianids may provide an effective tool for molecular studies of this group, including those focused on conservation related issues. In general, phasianids are one of the most threatened groups of birds, since they are often affected by direct exploitation, as well as challenged with severe habitat loss and degradationTympanuchus and Centrocercus. For example, the fossil-calibrated species tree using both autosomal and Z-linked nuclear loci estimated the time of divergence, within Tympanuchus, between the greater prairie-chicken and lesser prairie-chicken at less than 300 K years39. Also, autosomal and mtDNA loci showed extensive allele sharing among Tympanuchus species with minimal haplotype clustering corresponding with taxonomy31. A low degree of reciprocal monophyly between species within Centrocercus and Tympanuchus genera was also found based on the analysis of several innate immune genes40. This pattern was attributed to incomplete lineage sorting and female-biased introgression, as hybridization between Tympanuchus species has been widely reported in the areas of geographic overlap43 and female hybrids have been documented breeding in the wild39. Although our results are in general agreement with previous research, we explicitly acknowledge that the statistical power of our analyses was low due to small sample sizes . Consequently, analyses using larger sample sizes are needed to investigate whether the Affymetrix 600k SNP array could be used to investigate phylogenetic relationships within Centrocercus and Tympanuchus. Despite these limitations, our study suggests that even with hundreds of thousands of autosomal SNPs, there may be relatively few SNPs contributing to fixed differences between prairie grouse species, especially within Tympanuchus. This view is reinforced by several recent studies that find relatively small regions of the genome are responsible for the large phenotypic differences between some closely related species of birds46. At the same time, we acknowledge that having de novo SNPs discovered directly in prairie grouse could change this pattern and reveal more fixed difference between our study species.Our study provided support for high levels of shared polymorphisms between grouse species, especially within genera. This pattern seems to be consistent with previous phylogenetic studies on prairie grouse that suggest a recent divergence among species within the genera 47, they may contribute disproportionately to maintaining species boundaries, as previously reported for prairie grouse48. Since the chicken SNP array includes over 25 K Z-linked loci, future studies with larger sample sizes and a more balanced representation of sexes among taxa could use the array to investigate phylogenetic relationships among grouse .The sex chromosomes were excluded from our clustering analysis because we had an unbalanced mix of both sexes among species in our small sample set that impacted our ability to investigate phylogenetic relationships  and prairie-chickens  have found associations between those traits and SNPs at the Ephrin receptor B2 (EphB2) gene, which is a receptor for tyrosine kinase. The Eph family of molecules has 14 members, which play a role in tissue patterning and morphogenesis50 and, in birds, influence the development of the feather cytoskeleton52. In our study Ephrin type-A receptor 7 (EphA7) showed strong differences between the two genera of grouse. The A and B type receptors are generally thought to be activated by different types of ligands, but there appears to be high levels of interaction between them , suggesting that other Eph receptors could also be involved in feather development. We also found differences between the two genera of grouse at several other genes that have been associated with feather morphology , melanin pigmentation (FZD4 and LIMK155) and body mass (miR-1656) in birds. GO analysis revealed that these genes were primarily enriched in processes that influenced morphology and development, consistent with the large phenotypic differences between these two genera of grouse.We identified differences between sage-grouse . Over time, however, these costs may decrease. One of the advantages of the array is that it enables much easier access to an annotated genome and the traits associated with variable SNPs. Such information is becoming critically important for population genetic studies of animals in the wild and endangered species, in particular. Our results should provide some encouragement for other researchers willing to investigate the utility of using commercial SNP arrays in other non-model species.Centrocercus  and Tympanuchus . Blood samples were taken for DNA extraction from two individuals per species. Samples were collected from wild populations between 1996 and 2006 in Colorado (greater sage-grouse and Gunnison sage-grouse), Kansas (lesser prairie-chicken), Minnesota (greater-prairie chicken), and North Dakota (sharp-tailed grouse). The sampling of sexes was unbalanced among species, resulting in a total of six females  and four males . Genomic DNA was extracted using protocols described elsewhere58. Sample collection complied with the current laws of the USA and with the approval of IACUC committees at Colorado Department of Parks and Wildlife, Kansas State University, and the Univ. of Wisconsin-Milwaukee.We collected samples from all five species of North American prairie grouse in the genera 10. The Affymetrix 600K array was originally developed to contain a large catalogue of SNPs that are segregating within diverse populations of the domestic chicken and to address a variety of purposes, including the detection of genetic associations with complex traits, fine mapping of quantitative trait loci, detecting signatures of selection, exploitation of the linkage disequilibrium structure of the genome, and implementation of genomic selection10. The array contains exactly 580 961 biallelic SNPs across 28 autosomes, two sex chromosomes, and two linkage groups (LGE64 and LGE22C19W28_E50C23) that have not yet been assigned to a chromosome, but are expected to reside on micro-chromosomes59. Genotyping was performed by GeneSeek  using Genotyping Console Software v. 4.2 (Affymetrix). A minimum quality control score >0.82 (default), which is based on the signal to noise ratio, was used to filter each sample. The ten grouse samples were run on a single Axiom array  that also included domestic chicken samples. The grouse samples were analysed separately from the chicken samples following Affymetrix guidelines, because otherwise the statistical thresholds used to call SNPs would be based on the much larger chicken sample and this would lead to the exclusion of many SNPs among the grouse samples.All ten individuals from the five species were genotyped using the Axiom Genome-Wide Chicken Genotyping array  that was originally developed for the domestic chicken61. Traditionally, genomic analyses with phasianids often take advantage of their close relationship with the domestic chicken because it is well characterized in terms of genomic organization. To date, draft genomes of various grouse species29, including sage-grouse27, have been assembled and mapped to the reference chicken genome. For example, the draft genome of black grouse Tetrao tetrix had high synteny with the main chicken autosomal chromosomes 1–28, as well as sex chromosomes28. No major genomic rearrangements have been reported for grouse, when compared to the chicken genome28. Thus, based on the previous research, we assumed that genomes of our study grouse species shared high synteny with the chicken genome.Although we have no direct information on the genome organization in our study grouse species, birds in general are notable for possessing high level of karyotypic conservation and synteny across chromosomessnpgdsPCA function in the SNPRelate package62 developed for the R statistical environment63. The gdsfmt package62 in R was used for managing the genomic data structure (GDS) files. To avoid confounding sex and species differences in our small sample, we removed the sex chromosomes (Z and W) from the analyses comparing different taxa (n = 21 787 SNPs removed), but we included a separate analysis of the sex chromosomes in the Electronic Supplementary Material. Previous studies have indicated that the sex chromosomes are likely important in speciation and tend to differentiate faster than autosomes between species64. Indeed, a previous study of Centrocercus showed that the Gunnison and greater sage-grouse showed much stronger differentiation at SNPs on the Z chromosome than on the autosomes48 . Allele sharing rates were calculated as average identity-by-state (IBS) pairwise identities using the snpgdsIBS function in SNPRelate.We performed a principal component analysis (PCA) of the SNP genotypes to examine patterns of genetic diversity in the 10 samples. This analysis used SNPs that were successfully genotyped in at least one individual (see Results). PCA was conducted using the 65 to test individual SNPs for an association with taxonomy, here specifically between genera because of the PCA results (see Results). To understand the biological function of SNPs that differed between genera, we performed gene ontology (GO) analysis as implemented in Agrigo (http://bioinfo.cau.edu.cn/agriGO/index.php66) using the chicken Affymetrix array data for the genetic background. Fisher’s exact test with a false discovery rate (FDR) <0.05 was used to test for significant enrichment in each GO term. All values are presented as means ± SE.Following the PCA, we used PLINK v. 1.9Electronic Supplementary Material


Olea europaea L.) is one of the most economically and historically important fruit crops worldwide. Genetic progress for valuable agronomic traits has been slow in olive despite its importance and benefits. Advances in next generation sequencing technologies provide inexpensive and highly reproducible genotyping approaches such as Genotyping by Sequencing, enabling genome wide association study (GWAS). Here we present the first comprehensive GWAS study on olive using GBS. A total of 183 accessions (FULL panel) were genotyped using GBS, 94 from the Turkish Olive GenBank Resource (TOGR panel) and 89 from the USDA-ARS National Clonal Germplasm Repository (NCGR panel) in the USA. After filtering low quality and redundant markers, GWAS was conducted using 24,977 SNPs in FULL, TOGR and NCGR panels. In total, 52 significant associations were detected for leaf length, fruit weight, stone weight and fruit flesh to pit ratio using the MLM_K. Significant GWAS hits were mapped to their positions and 19 candidate genes were identified within a 10-kb distance of the most significant SNP. Our findings provide a framework for the development of markers and identification of candidate genes that could be used in olive breeding programs.Olive ( Olive tree products are some of the main components of the Mediterranean diet that contribute to good health3. Olive fruits and leaves contain various functional compounds, such as hydroxytyrosol and oleuropein, beneficial for human health5. Olive trees are predominantly located in Mediterranean and Asian countries though there is increasing cultivation in Argentina, the United States, Chile and Australia1 due to high consumption of olive products. It is thought that olive domestication began in the region closest to the border between Turkey and Syria about 6000 years ago, before olive cultivars spread throughout the Mediterranean countries via different routes6. Turkey has seen continuous cultivation of olive trees since then. In Turkey, olive growing regions occupy a large area including western and southern coastal strips of the country. This has led to the rich variety of cultivars in Turkey. In the long-term conservation efforts of plant genetic resources, olive germplasm collections play an important role7. There are more than 100 olive germplasm collections at international, national and regional levels in mostly Mediterranean countries for conservation and breeding purposes8. These collections have been extensively used in molecular studies including identification, molecular characterization and also mapping studies11.Olive is among the most important trees worldwide. Current world production of table olive and olive oil is over 19.212, however, these efforts are slowed down by olive’s juvenile period and complex genome13. As a result, very few mapping studies have been reported for dissecting agronomic traits in olive and limited numbers of markers have been identified as related to fruit traits15, flower traits, tree growth traits such as trunk diameter14 and olive oil quality traits16.In olive breeding programs, clonal selection and cross-breeding have been conducted for developing novel cultivars17, utilizes historical recombination in a diverse population18. Compared with QTL mapping, GWAS mostly provides a higher mapping resolution19 and does not need to use an experimentally developed segregating population. GWAS has evolved as a powerful tool to dissect the genetic architecture of complex traits in large germplasm sets. It does, however, require a large number of markers for whole genome scans in crops with low linkage disequilibrium (LD) and high haplotype diversity20.Genome-wide association studies (GWAS), which emerged as an alternative to classical linkage mapping21. Single-nucleotide polymorphisms (SNPs) have become popular in QTL mapping and GWAS in plants22. They are co-dominant and bi-allelic markers that are distributed along the whole genome23. Genotyping by Sequencing (GBS) is a simple and inexpensive technique originally developed for high-resolution association studies in maize24, which involves reducing genome complexity25 by using restriction enzymes. GBS has been implemented in many crops such as maize24, barley26, wheat27, soybean28, rice29, oat30 and cassava32 for purposes of genetic characterization, GWAS, linkage analysis and genomic selection. The GBS technique, which does not require prior knowledge of the genome, is preferred for species that do not have reference sequence information30. Genetic mapping with SNPs generated by GBS has been extensively used in tree species including peach33, grapevine34, sweet cheery35, eucalyptus36, oil palm37, and apple38, and found to be effective to identify marker traits associations. In olive, GBS technology was used to assess the genetic diversity in Italian cultivars39 and to construct linkage maps in F142 and F243 populations. However, there are no reports of using GBS based SNP markers for association mapping study in diverse olive accessions.Next generation sequencing (NGS) technologies have allowed discovering and genotyping thousands of markers in large and diverse germplasm collections44. GWAS in olive has been used by our group in Turkish olive genotypes using AFLP, SSR and SNP markers45. Here, we report the development and application of GBS in a diverse set of olive germplasm from Turkey and the USA. Our objectives were to (1) identify SNPs within olive genotypes based on GBS analyses and (2) perform a comprehensive GWAS to identify significant marker trait associations. Successful application of GBS in olive would suggest that the method can be used in other tree species.Most genetic studies in olive genotypes have focused on characterization of this species and QTL mappingH2 was calculated for FW and SW, 0.73 and 0.74, respectively (Supplementary Table H2 estimates of LL (0.36) and FFPR (0.43) were low compared with other traits. The BLUPs of phenotypic values exhibited a near normal distribution for FULL, TOGR and NCGR panels , leaf width (LW), fruit weight (FW), stone weight (SW) and fruit flesh pit ratio (FFPR) showed substantial variation was observed in all traits  than for PstI and there was no highly repetitive DNA amplified . The PstI library, however, contained a large amount of repetitive DNA (discrete peaks/bands). EcoT22I was chosen for reducing genome complexity in olive. The sequencing produced a total of 469,721,669 raw reads, an average of 2.56 million reads per sample, and those reads produced 3,415,115 tags. A set of 61,892 unfiltered SNPs were obtained from the FULL panel. The percentage of missing data and minor allele frequencies for all accessions are shown in Supplementary Fig. The fragment size distributions of GBS libraries from olive genomic DNA digested with The genetic structure of the FULL panel was estimated using two complementary approaches. We estimated the marker-based kinship and found 67.2% of the kinship coefficients ranged from 0 to 0.2, indicating that most accessions have weak genetic relationship with the other accessions. This wide genetic diversity among olive accessions was also supported by their broader Euclidean genetic distance   in the population was inferred to be six based on maximum likelihood and delta K (ΔK) values . The lowest degree of differentiation was found between Group 2 and Group 4 (Fst = 0.170). Fst values between the groups suggested that there was significant divergence across all groups.To investigate the extent of population differentiation between groups from STRUCTURE analysis, Fst values were calculated using the filtered markers  and r2 ≥ 0.1 (right) for all marker pairs.A total of 20,799 (4.81%) pairs of markers showed a significant LD value (D’) at P < 0.01 while 2,678 pairs of markers showed a significant LD at 46 at a 5% rate.Of the 3 three statistical models tested, the MLM_PCs + K model and the MLM_K model had similar power and showed a significant improvement in goodness of fit compared with the MLM_Q + K model. Increasing the number of PCs in the models did not decrease the type I error inflation . FF exhibited 19 significant SNPs that explained 5.27–27.8% of the phenotypic variance with an average 15.08%. A total of 18 significant SNPs was identified for SW, which explained 6.29–27.4% of the phenotypic variance with an average 13.18%. Variance explained by significant SNPs for FFPR ranged from 11.6 to 18.6% with an average of 14.7%.41 while 40 SNPs (75%) were mapped in the genome of Olea europaea L. subsp. europaea var. europaea cv. ‘Farga’47  explaining 19.8% of the phenotypic variance was located on chromosome 16  were mapped in the wild olive reference genomer2 = 0.88) associated with FW which were not aligned to the wild olive reference genome.To assess the extent of association mapping, triangle plots for pairwise LD between significant markers were created for each trait Fig. . The pat41 while 14 SNPs (93%) were mapped in genome of Olea europaea L. subsp. europaea var. europaea cv. ‘Farga’  was the most significant SNP with a P-value of 7.15e-07 and explained 18% of the phenotypic variation. Twelve (52%) and 11 (48%) significant SNPs in NCGR panel were mapped in wild olive reference genome41  to identify significant markers associated with LL, LW, FW, SW and FFPR. The large variation in traits observed among accessions as well as the significant correlations between some traits indicates the large phenotypic trait diversity among accessions. Three traits  showed a larger than 3-fold difference between minimum and maximum values Table . PreviouH2 for SW, FW, LW, FFPR, and LL were high to low, ranging from 0.74 to 0.36. SW and FW were among the highly heritable (0.74 and 0.73 respectively) traits in this study  while Fanizza et al.58 reported a moderate heritability estimate (0.6) for FW. Zeinanloo et al.59 obtained higher H2 estimates (0.85) for FFPR than we did, and they also obtained H2 estimates for FW (0.42) and SW (0.31). Contrary to our study, Padula et al.55 reported higher H2 estimates for FFPR than FW. These findings imply that accessions had abundant genetic variation and were suitable for marker-trait association mapping. Previous studies indicated that core collections of different numbers of olive genotypes from Genbank collections showed abundant phenotypic and genetic variation11. To investigate suitability of the olive core collections for association mapping studies, different sampling approaches and different numbers of genotypes from the World Olive Germplasm Bank (WOGB) in Cordoba, Spain10 and the WOGB in Marrakech, Morocco11 were analyzed. The studies reported that both core collections contained mostly Western Mediterranean cultivars10 but core collections with cultivars that reflect the full geographic distribution of olive11 are suitable for association mapping.The estimates of dy Table . High he1 and F2 individuals43 and Italian cultivars39. SNP calling in highly heterozygous species such as olive is more difficult than inbred lines38. The GBS protocol we implemented enabled the discovery of thousands of SNPs. The EcoT22I restriction enzyme was used for the reduction of genome sequence complexity. Ipek et al.40 used ApeKI while Unver et al.41 selected a combination of PstI-MseI restriction enzymes to perform GBS in olive. D’Agostino et al.39 used EcoT22I restriction enzyme in GBS of Italian cultivars. A total of 24,977 SNPs were obtained after filtering which is higher than the number of SNPs detected in other GBS studies in olive41. The average number of sequence reads per sample we obtained (2.56 million) was similar to what was reported in other olive studies by Ipek et al.40 (2.1 million) and D’Agostino et al.39 (2.6 million).We present the first application of GBS in diverse olive accessions from two different Genbank resources. The few GBS studies in olive to date have focused on genotyping F20. The PCA visualization did not show separation of accessions into subpopulations on the basis of geographic origin . Predictably, most Turkish genotypes were clustered together however, some fell into clusters comprised of European genotypes from NCGR. Diez et al.9. reported an indistinct geographical pattern of distribution among olive accessions from WOGB in Cordoba. Contrary our study, Belaj et al.10 and El Bakkali et al.11 showed that PCA analyses clustered olive accessions based on their geographic origin .Analysis of the population structure and genetic relatedness between accessions in a GWAS has critical importance for elimination of spurious marker-trait associations63. Differentiation between groups due to genetic structure was measured with Fst values and an Fst value greater than 0.15 can be considered significant64. The pairwise Fst values between all groups were higher than 0.15, indicating high genetic differentiation in our FULL panel. We observed concordance between distance-based cluster analysis  located these accessions close to each other. Frantoio (Dole 181 from Albany) and Oblonga (Dole 3 from France) accessions were closer to each other than any other pair of accessions. Also, the other Frantoio accession (Dole 2 from Albany) was located in same cluster with those accessions. Finally, some accessions that had the same accession name but different code numbers  collected from different locations were located side by side in the heatmap . GWAS was also conducted for TOGR and NCGR panels separately. Fifteen and 23 significant markers were detected in TOGR and NCGR panels, respectively. None of these were common with each other or with significant markers found in FULL panel.We found 53 significant markers associated with four traits in the FULL panel, including 12 associations with LL, 19 associations with FW, 18 associations with SW and 4 associations with FFPR. Two significant markers (S1_6412238 and S1_11607122) associated with FW were in high LD  compared to wild olive reference genome (42%). However, this reference genome47 does not have chromosome assignments so mapped significant SNPs could not be assigned to a particular chromosome using this reference genome  associated with FW were found on chromosome 11 and 15, respectively. Also, 6 significant markers associated with SW were found on chromosomes 12 (S1_11074838 and S1_13555831), 18 (S1_984251 and S1_2244037) and 23 (S1_7495520 and S1_8195416). Although these pairs of markers were located on same chromosome, they were distant from each other. Comparison of chromosomal locations between this study and previously published studies could not be done due to the use of different molecular marker techniques. It is also important to explore whether significant markers we found are located in the same regions as in the previously reported QTL studies. Limited information exists in olive identifying QTLs linked to the traits analyzed. In a QTL mapping study published by Sadok et al.15, 8 QTLs linked to fruit weight were identified on different 7 linkage groups using ISSR, SSR and AFLP markers. In another QTL mapping study14, one QTL was identified for fruit weight on linkage group 17, while 3 QTLs were identified linked to pulp/stone ratio on linkage groups 10 and 17 using DArT-SSR markers.We identified significant SNP marker locations using two recently published olive reference genomes. Among the 53 significant SNPs, 27 SNPs (51%) and 40 SNPs (75%) were mapped in the wild olive reference genome86. The analysis of transcriptomes provides genomic resources for functional annotation to discover genes for olive breeding87. To date, several transcriptome studies have been performed for olive using different organs at different developmental stages89. There have been attempts to identify candidate genes associated with important traits such as plant architecture90 and juvenility91 in olive, but no putative candidate genes underlying QTL have been reported. More studies are still required to facilitate validation of these results in different olive populations.We found 19 candidate genes close to significant markers in the FULL panel within a 10-kb region window in either direction of a significant SNP. The most significant SNP marker, S1_13767032, was located on 0.497 kb upstream of the Oeu046142.1 gene. This gene is annotated as a PHD finger transcription factor and the family to which this gene belongs, plays a key role in regulating plant growth and development92 in R version 3.4.293. Fresh leaf tissue was harvested from the youngest leaves of each tree in the leaf shooting stage. Leaf tissue samples were stored at −80 °C until DNA was extracted.We used 94 accessions from Turkish Olive GenBank Resources (TOGR) panel in Izmir, Turkey and 89 accessions from the USDA, ARS, National Clonal Germplasm Repository (NCGR) panel in Davis, CA, USA. Detailed information of these accessions (FULL panel) is provided in Fig. et al.45. LL, LW and FFPR measurements were carried out according to the methodology proposed by the International Olive Oil Council (IOOC). Phenotypic data for 89 accessions from NCGR in Davis was downloaded from USDA-GRIN (Germplasm Resources Information Network system) website for four seasons from 2005 to 2008 (Available at www.ars-grin.gov/npgs and accessed May 2017). To show the statistical distribution of traits divided over geographical origin of accessions and year, box plots were generated using R93. Phenotypic data from different years was averaged and used for descriptive statistical analysis using the pastecs package in R.93.Phenotypic data for the following five traits was evaluated: leaf length (LL), leaf width (LW), fruit weight (FW), stone weight (SW) and fruit flesh to pit ratio (FFPR). For 94 accessions from TOGR, phenotypic data measurements (FW and SW) were carried out during 2011 and 2013 and this data was obtained from a previously published study by Kaya b is the vector of fixed effect for country and p and a are vectors of random effects for genotype and year, respectively. The random effects and residual errors are assumed to be normally distributed independent of each other and each of them have covariance structure proportional to an identity matrix. We fitted the model using the lmer function from the lme4 R package94. The predictions for the random effects for GIDs (BLUPs) from this model were used as phenotypic data for the GWAS.To eliminate the effect of environment variation, the best linear unbiased prediction (BLUP) values of lines were calculated for each trait using mixed linear model (1):(H2), based on clone means, was estimated according to Hallauer et al.95. The H2 for each trait was estimated by the Eq.  extraction step was applied twice to improve removal of phenolic compounds. DNA was quantified with Qubit dsDNA BR Assay Kit (Invitrogen) on a Qubit 2.0 Fluorometer (Invitrogen) according to the manufacturer’s instructions. DNA samples were diluted to 20 ng/μl and subsequently used for GBS library preparation.Genomic DNA was extracted by the CTAB method of DoyleEcoT22I (ATGCAT) and PstI (CTGCAG) (both 6-base cutters), were tested to make GBS libraries and EcoT22I was selected. PCR amplification was performed to generate the GBS libraries and DNA was sequenced on an Illumina HiSeq. 2000 . GBS was carried out at the Institute of Genomic Diversity  as described by Elshire et al.24.The GBS libraries were constructed in 96-plex where each plate included a single random blank well as control. For choosing appropriate restriction enzymes for olive genotypes, two different restriction enzymes, 97 in the TASSEL 3 bioinformatics analysis package. SNPs were filtered to remove markers with more than 80% missing data and genotypes with more than 80% missing SNP calls in using R version 3.4.293. After filtering, the SNP dataset was converted to numeric coding  from nucleotide coding for statistical analysis in R version 3.4.293. The A.mat function from rrBLUP package in R was used to remove markers with minor allele frequency (MAF < 0.05) and impute the missing marker data based on expectation maximization (EM) algorithm.SNPs were identified using the TASSEL UNEAK (non-reference) GBS pipeline98. Each simulation included 10,000 burn-in and 50,000 iterations. Ten independent runs were performed for each K value ranging from 1 to 10 with an admixture model and correlated allele frequencies. The optimal K was chosen based on Evanno’s methods99 using the STRUCTURE HARVESTER software100. To visualize the population structure, a bar plot was obtained with sort by Q option based on the optimum K value. Genotypes with membership probabilities higher than 0.7 were assigned to one of the subpopulations. Otherwise, they were considered to be admixed. The membership coefficient matrix (Q matrix) that shows the percentages of admixture of each accession given by the STRUCTURE software was used as cofactors in the association analyses. Calculation of pairwise genetic differentiation (Fst) between the groups from STRUCTURE was performed using VCFtools101. Principal component analysis (PCA) was also carried out to study the structure of the genotypes using the function prcomp in R93. First two principal components (PCs) were plotted using the ggplot2 R package102. To apply the distance-based approach, a Euclidean marker distance matrix was obtained using the dist function and a graphical representation of distance matrix was created using the heatmap function in R.To assess genetic structure, we applied both model-based and distance-based approaches. The Admixture-based clustering model we applied was STRUCTURE v.2.3.4r2 using TASSEL. Permutation testing was applied to examine the significance level of LD between loci. Pairwise LD analysis between significant markers for the best GWAS model based on marker score correlations (r2) was performed separately on the FULL panel and was visualized as a heatmap using heatmap function in R.The LD between marker pairs was calculated based on D’ and 103. Mixed Linear Model (MLM) that accounts only for relative kinship (MLM_K model), MLM that accounts for both relative kinship and model-based population structure (MLM_Q + K) and MLM with first two PCs and K-matrix as correction for population structure (MLM_PCs + K) were compared. Q is the matrix of sub-population membership probabilities obtained from STRUCTURE98, K is the kinship matrix calculated using the A.mat function in the rrBLUP R package103 and the number of PCs was selected based on the scree plot of the variance explained by the first 10 PCs. We also examined the effect of including different numbers of PCs  as covariates in MLM_PCs + K model.Genome wide association analyses were performed using the SNP dataset consisting of 24,977 SNPs in FULL, TOGR and NCGR panels. For association analysis, three different models were tested for controlling population structure with the R package rrBLUP46. Markers with FDR < 0.05 were considered significant. The proportion of phenotypic variance explained by each significant marker was estimated via R2 by fitting a regression between phenotypes and marker profiles using R93. The quantile–quantile (QQ) plots were used for selecting the best GWAS model. The QQ plots were produced using the R qqman package104 and Manhattan plots were visualized using R93.Correction for multiple testing was carried out using the false discovery rate (FDR) values according to the procedure by Benjamini and HochbergOlea europaea var. sylvestris,41 includes ~1.1 Gb of sequence and is available at https://phytozome.jgi.doe.gov. Only ~573 Mb of this assembly is mapped to its chromosomal position (n = 23). The second olive genome, Olea europaea L. subsp. europaea var. europaea cv. ‘Farga’47 has a total length of 1.31 Gb which represents 95% of the genome’s estimated size (1.38 Gb). This genome has no chromosome assignments and is divided in more than 50k scaffolds with an N50 of 443Kb.The significant GWAS hits were mapped to their positions in the two recently published olive genomes. The first genome, 97. A multi-fasta was created using the marker identifier and the 64mer sequence. We then used Blast +105 to map each GWAS hit with its most probable location in both genomes. The best blast hit was chosen based on the percentage of alignment and E-value.Briefly, the significant GWAS hits were linked to their 64mer sequence tag using the TOPM file produced by the UNEAK GBS calling pipelinehttp://phytozome.jgi.doe.gov/pz/portal.html)106 was used to browse the wild olive reference genome41. Candidate genes were searched within 10 kb upstream and downstream of each significant SNP region in the genome browser.To find candidate genes associated with significant SNPs, the Jbrowse feature of Phytozome v.12.1 (Supplementary InformationSupplementary Table S2Supplementary Table S3Supplementary Table S13