Tomato flavor has changed over the course of long-term domestication and intensive breeding. To understand the genetic control of flavor, we report the meta-analysis of genome-wide association studies (GWAS) using 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discover 305 significant associations for the contents of sugars, acids, amino acids, and flavor-related volatiles. We demonstrate that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. We suggest that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing unpleasant volatiles, by selection of the relevant allele combinations. Our results provide genetic insights into the influence of human selection on tomato flavor and demonstrate the benefits obtained from meta-analysis. Flavor is one of the most important traits for improving tomato sensory quality and consumer acceptability. Here, the authors report meta-analysis of genome-wide association studies of flavor related traits and show genetic insights into the influence of human selection during domestication and improvement. During long-term domestication and breeding history, flavor has not been a priority, in contrast to yield, disease resistance, and postharvest shelf life2. However, flavor is one of the most important traits for improving tomato sensory quality and consumer acceptability3. Flavor is centrally influenced by sugars, acids, amino acids and a diverse set of volatiles6. Most of these compounds are quantitatively inherited as shown by many QTL studies but only a few QTLs have been positionally cloned7. Genome-wide association studies (GWAS) have detected many significant associated loci for tomato flavor related traits12 However, reducing a QTL to a causative gene is difficult and only a few candidate genes have been functionally validated7. The underlying genetic control of tomato flavor is still incomplete and remains an important breeding target.The deterioration of tomato flavor has been a source of complaint from consumers for decades14. A recent meta-analysis in cattle stature also demonstrated its power in non-human species15. However, to the best of our knowledge, no GWAS meta-analysis has been reported in major crops, despite the increasing number of GWAS studies in major crops, such as rice. To date, the genomes of over 500 tomato accessions have been fully sequenced19, making it possible to perform genotype imputation21 and subsequent meta-analysis of GWAS using summary data14 to decipher the polygenic architecture of agronomic traits. In this study, we perform a meta-GWAS on 775 tomato accessions and 2,316,117 SNPs and discover 305 significant associations for diverse flavor-related traits. Our results provide genetic insights into tomato flavor.Meta-analysis of genome-wide associations is powerful in dissecting complex human diseases8, 291 accessions from panel B11, and 402 accessions from panel T6 . After quality control  was detected for most (83.3%) of the traits  using a random effect model. Manhattan plots and quantile–quantile (Q-Q) plots for all traits are shown in Supplementary Figs. P < 4 × 10−7 for sugars, acids, and volatiles; P < 2.99 × 10−6 for amino acids), among which 211 were new (Supplementary Data P < 10−9). Most of these loci passed the suggestive thresholds in at least one panel . We generated a local SQLite dataset for tomato (Online methods) and provided the LocusZoom plots for all the genome-wide significant associated loci  with functional annotations related to the pathways of flavor chemicals . We performed the meta-analysis with two software: METAL27 (http://bioinfo.cau.edu.cn/agriGO/index.php). Up to 10 biological processes were significantly enriched (P < 0.005) , TFM6 , and Phytoene synthase 1 28. In meta-analysis of GWAS, heterogeneity represents the genetic variations observed across combined studies13. In this study, strong heterogeneity occurred even for those loci with major effects, such as Lin5 . This could be due to population structure, linkage disequilibrium, phenotyping platforms, G × E interactions, etc.13. We then focused on loci in regions showing low LD, where one or a few candidate genes could be identified and regions with medium LD but with candidate genes near the peak SNPs.Previously reported flavor-related loci in the three panels were all strongly associated in the meta-analysis at a higher significance level, such as Lin5 and SSC11.1, we found four loci from the meta-analysis that were significantly associated both with fructose , fatty acid elongase 3-ketoacyl-CoA synthase , glucosyltransferase , and glyceraldehyde-3-phosphate dehydrogenase . Notably, near the region of FUCA (up to ten genes), there are two candidate genes , which are also promising candidate genes for association with fructose and glucose content. Notably, GTF (P = 7.55 × 10−34) and GAPDH (P = 7.84 × 10−17) also showed significant cis-eQTL in a related transcriptome-wide association study12.We looked into six candidate genes that were significantly associated both with fructose and glucose. In addition to ose Fig.  and glucose Fig. . These aLin5 , were not associated with any domestication or improvement sweep19. We compared the frequencies of different combinations of alleles of these candidate genes in relation to sugar content in wild, transitional, heirloom and modern accessions (more detailed explanations about group definition in Online Methods). All modern, heirloom, and transitional accessions lost most of the diversity of allele combinations that is present in the wild species group  than that of all reference allele combinations 28. We found a strong significant association for citrate , which was 1.54 kb away from Sl-ALTM9 . This candidate gene was also significantly associated with fructose (P = 4.28 × 10−09). For malate content, we found one association on chromosome 12  close (36 kb) to a gene coding a malic enzyme . We then took six candidate genes to analyze the relationships between different allele combinations and citrate and malate content, respectively , GTF , GS , AIMT , CS , and Rubisco . The six candidate genes for malate were GTF , PDHB , AIMT , SS , ME , and GAPB .Candidate genes directly involved in the biosynthesis of citrate and malate were also identified. For example, we identified an association with citrate on chromosome 7, 150 kb away from a gene coding a citrate synthase  close 6 kb to aGTF on chromosome 2 and AIMT on chromosome 6 were associated with both citrate and malate 19 and domestication sweeps (DS050 and DS175)19 were observed for malate on PDHB and ME. For citrate and malate, the modern tomato accessions presented very different allele combinations than those in wild species and cherry tomatoes  positive linear correlation (r = 0.16) between the number of alternative alleles and malate content   , which was also reported as a significant eQTL (P = 1.04 × 10−35)12. This gene is a serine incorporator, and directly regulates serine content. One locus corresponding to GDSL esterase/lipase (Solyc12g089350) was also significantly associated with four amino acids . For hexanal, we found the strongest association corresponding to the lipoxygenase gene LoxC , which encodes an enzyme that is essential for synthesis of C6 and C5 fatty acid-derived volatiles30. This candidate gene was also significantly associated with (Z)-3-hexen-1-ol (P = 3.94 × 10−07). For 2-methyl-1-butanol, the strongest association corresponded to a 3-methyl-2-oxobutanoate dehydrogenase gene , an enzyme associated with branched chain amino acid metabolism.Many candidate genes associated with amino acid and volatile contents were identified. For example, we found a significant association for serine on chromosome 3 (P = 6.73 × 10−26), hexanal , phenylacetaldehyde , and 6-methyl-5-hepten-2-one . The two unpleasant (or negative) volatiles were guaiacol  and methyl salicylate  . Interestingly, we also found that cherry tomatoes had the greatest diversity of allele combinations and some of them only appeared in this group  Fig. . Modern oup Fig. . The higoup Fig. . In contoup Fig. . These r31. We herein demonstrate the potential of meta-analysis of GWAS following the detailed protocols first proposed in human genetics33, which can be easily applied in other crops. Meta-analysis of GWAS is used when pooling raw data of separate panels  is not possible. It has been shown both theoretically and numerically that meta-analysis is statistically as efficient as mega-analysis35. Even when possible, it is thus not necessary to re-analyze the raw data to perform meta-analysis. Only summary data  from each panel is needed and should be provided with each GWAS result. For mega-analysis, genotypes and phenotypes from all panels should be first combined and then analyzed, which requires proper management of phenotypic structure  and genotypic structure (such as population structure and kinship). Compared to mega-analysis, meta-analysis can assess the heterogeneity (consistency) of studies, which can be caused by many factors, such as phenotypic structure, genetic structure, linkage disequilibrium, imputation accuracies or G × E interactions34.With the development of next-generation sequencing technology, GWAS has become a classical genetic approach to identify QTLs and causal genes in crops6. Here, we used imputation-driven meta-analysis of genome-wide association studies to greatly increase the number of SNPs linked to chemicals associated with flavor. Among the 305 significantly associated loci, 41% of the SNPs had a low frequency (MAF < 0.1). Very low-frequency (0.01 < MAF < 0.05) SNPs were also detected (3 significant associated loci)  as described in6 and collecting the summary statistics to run a meta-GWAS.The meta-GWASs approach is based on three different GWAS panels already published and genotyped using different technologies. Our approach consisted in imputing SNP data for panels S8, including 28S. lycopersicum (large tomato), 119S. lycopersicum var cerasiforme (cherry tomato), and 16S. pimpinellifolium (closest wild species). This panel was genotyped using the Solanaceae Coordinated Agricultural Project (SOLCAP) genotyping array57, generating 5995 high quality SNPs. The minimal success genotyping rate per accession was fixed at 90%. The minor allele frequency of SNPs ranged from 0.037 to 0.45. Tomato accessions in Panel S were grown in Avignon, France, following a randomized complete block design, in a greenhouse during the summers of 2007 and 200858.Panel S consists of 163 accessionsS. pimpinellifolium, 48S. lycopersicum, and 190S. l. cerasiforme accessions11. This panel was genotyped both with the SOLCAP57 and CBSG arrays59. After quality control, 9013 SNPs  and 291 accessions were kept. Accessions in Panel B were grown in Agadir, Morocco, France, under passive greenhouse irrigated conditions in 2011 and 201211. Each trial followed a randomized complete block design, with three and two blocks, in 2011 and 2012, respectively.Panel B consists of 300 accessions with 626. Panel T was genotyped by whole genome resequencing technology, generating a number of 2,014,488 SNPs passing quality control . This panel includes five tomato types, including modern (51), transitional (50), cherry (27), heirloom (243), and wild species (27)6.Panel T consists of 402 tomato accessions from two separate panelsA total of 31 flavor-related quality traits in tomato were analyzed for meta-analysis, including two sugars (fructose and glucose), two organic acids , 10 amino acids, and 17 flavor-related volatiles. The 10 amino acids were asparagine, aspartate, GABA, glutamine, lysine, methionine, phenylalanine, proline, serine, and threonine. The 17 volatiles were (E)-2-heptenal (E2HEP), (E)-2-hexenal (E2HEX), (E)-2-pentenal (E2PEN), -2,4-decadienal (EE24D), (Z)-3-hexen-1-ol (Z3H1X), (Z)-3-hexenal (Z3HEX), 1-octen-3-one (X1O3ON), 1-penten-3-one (X1P3ON), 2-methyl-1-butanol (X2M1BU), 3-methyl-1-butanol (X3M1BU), 6-methyl-5-hepten-2-one (X6MHON), beta-ionone (BIONO), geranylacetone (GRACE), guaiacol (GUAIA), hexanal (XEXAN), phenylacetaldehyde (PHEAC), and methylsalicylate (METHY).8. Data distribution was tested using the Shapiro–Wilk test and data with a non-normal distribution were Log10 transformed. In panel B, these metabolites were measured within the Product Metabolism and Analytical Sciences Endogenous Metabolite Profiling Platform at Syngenta Jealott’s Hill International Research Center, Bracknell, UK. Fructose and glucose were analyzed by high pH ion-exchange chromatography. Citrate and malate were analyzed using electrospray ionization-liquid chromatography (ESI-LC-MS/MS). Fructose and malate were transformed using the Boxcox method. Citrate was transformed using the Log10 method. In panel T, citrate and malate were measured using the citrate and malate analysis kits , according to the manufacturer’s instructions60. Measurements of amino acids and volatiles in panel S was measured using GC-MS by comparing with a database of authentic standards. Small organic acids and amino acids in panel B were analyzed using electrospray ionization-liquid chromatography (ESI-LC-MS/MS).Volatiles in panel T were first captured by headspace solid phase micro extraction (HS-SPME) coupled GC-MS.Sugars and organic acids were measured in all three panels. Amino acids were measured both in panel S and B, while flavor-related volatiles were measured both in panel B and T. Briefly, fructose and glucose in panel S were measured using the micro-method. Citrate and malate were measured by gas chromatography-mass spectrometry (GC-MS)19 to perform SNP imputation in panels S and B. Among this panel, only accessions with genome coverage ≥90% and mean sequencing depth ≥4.0 were kept. Wild tomato species were also removed, generating a total reference set of 221 accessions genotyped with 3,809,156 SNPs (Supplementary Table A reference panel was selected from the 360 re-sequenced tomato accessions57 have a limited genomic coverage (~3500 mapped SNPs). In order to use a much denser genetic map, we developed a Python script to infer the corresponding genetic positions of the 3,809,156 SNPs in the reference panel. Before calculating the recombination rate, we first compared the physical vs genetic distribution patterns for each chromosome 22 was adopted for imputation of panel S and B independently. The 221 filtered sequenced accessions passing quality control were used as the reference panel. The newly built recombination map was used instead of EXPIM 2012. The whole genome was then divided into genomic intervals of 5 Mb for imputation and the effective size of population (Ne) was set at 2000.One unphased reference panel from IMPUTE2  with the following command:./qctool -g GWAS.gen -snp-stats. We then filtered all imputed SNPs with Hardy-Weinberg equilibrium (HWE) ≥ 0.000001, MAF ≥ 0.037 (0.021 for panel B), missing rate ≤ 0.10 and missing call rate ≤ 0.10. After these primary control steps, a total of 224,097 and 327,436 SNPs were retained for panel S and B, respectively.After imputation, the minimum MAF for panel S and B was set at 0.037 and 0.021, respectively, according to the formula: [Number of chromosomes/]http://www.well.ox.ac.uk/~cfreeman/software/gwas/gtool.html). We then compared the imputed and genotyped values of the nine accessions , we compared the imputed and sequenced genotype data of the nine overlapping accessions in panel S that have been genotyped by SNP arrays and whole-genome sequencing. If the maximum of the three probabilities at a locus was higher than 0.9, we treated it as a certainty. This was done by converting the imputed data to ped/map format via GTOOL  ≥ 0.60, total of 209,152 and 252,414 SNPs were retained for panel S and B, respectively. The mean Info value at different MAF values for panel S and B were 0.929 and 0.922, respectively (Supplementary Data r2 < 0.2) were used. This was done in PLINK (https://www.cog-genomics.org/plink2) with: --indep-pairwise 50 5 0.2  –maf 0.05, generating a total of 3,602 and 4,294 independent SNPs for panel S and B, respectively.For population structure and kinship analyses, only independent SNPs (r2 < 0.2) in PLINK: --pca. Principal component analysis showed that genotype imputation did not lead to significant differences in genetic group composition and pairwise individual distances, for all three accession classes of panel S   for panels S and B, using all genotyped SNPs and independent imputed SNPs (63 (https://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.4/html/structure.html). So we first compared the structure following the same parameters, with 1 × 106 burn-in period and 5 × 106 MCMC steps. Based on the Evanno method63, the optimal number of ancestral populations was two. Only minor population assignment differences were found for both subpopulations, compared with structure from genotyped SNPs  using the independent 3,602 and 4,294 SNPs (r2 < 0.2) to infer the optimal population structure for panels S and B. This method partitioned the variance within and among groups without assumptions on LD or Hardy–Weinberg equilibrium65, which has shown a better performance in clustering individuals11. The optimal number of clusters was determined by Bayesian Information Criteria (BIC) with a minor increase or decrease. All PCs and all discriminant functions were retained to find the optimal number of clusters. In the following DAPC analyses, all discriminant functions and the first 50 PCs were retained in order to achieve 80% of cumulative variance for both panel S and B.We further used discriminant analysis of principal components (DAPC)6.For panel S, the optimal number of clusters was six  can use the imputed data from IMPUTE2 to detect associations directly, it cannot however handle too many cofactors in the model. For accessions from each panel used in this study, there is strong genetic structure. We first took one trait  in panel S as an example to choose the optimal association software to perform the association tests.Though SNPTEST v2.5.4 (http://ebe.ulb.ac.be/ebe/SPAGeDi.html) and structure in Structure v2.3.4. We then added the first 20 PCs as cofactors in the frequentist association test model in SNPTEST. In the next step, we used EMMAX (http://genetics.cs.ucla.edu/emmax/index.html) with the BN kinship matrix and DAPC results to conduct association analyses. For BN kinship calculation, the default command was used: emmax-kin -v -h -d 10. A uniform threshold  was used as the genome-wide significance threshold for all three panels. The effective number of independent SNPs was calculated in Genetic type 1 Error Calculator (GEC)66 (http://grass.cgs.hku.hk/gec/download.php). The suggestive p-value for the 224,097 SNPs of panel S was 9.63 × 10−5 and the significant p-value was 4.82 × 10−6. For the 327,436 SNPs of panel B, the suggestive and significant p-value was 5.99 × 10−5 and 2.99 × 10−6, respectively.In order to add kinship as a cofactor in SNPTEST, we performed a principal component analysis of the kinship calculated in SPAGeDi  and EMMAX were quite similar. So, in the following analyses, we only used SNPTEST to compute summary statistics, not for finding associations. For MLMM, this model adds the marker as co-factor using a window of 10. If too many markers are in full LD, the genetic variance calculation may be biased24. So, we used EMMAX for association analyses for all traits with the BN kinship matrix and DAPC results as covariance.After comparing the association results for malate of panel S, we found the strongest 61 (http://www.genabel.org/packages/GenABEL) in R. Genomic inflation can be caused by population structure, cryptic relatedness, genotyping errors, sample size, LD, trait heritability, number of causal variants and other technical artefacts67. Though no adjustment is necessary when λ is lower or equal to one, we still corrected the standard errors of beta coefficients by applying the formula SE × 68.A total of 788 tomato accessions and 2,316,117 SNPs from three GWAS panels were used for the final meta-analysis. Since each panel was stratified and a small number of individuals overlapped between panels , genomic inflation factor (λ) was corrected before meta-analysis using GenABEL25 (fixed-effect model) (https://genome.sph.umich.edu/wiki/METAL_Documentation) and METASOFT26 (random-effect model) (http://genetics.cs.ucla.edu/meta/) are two most commonly used meta-analysis software13. Meta-analysis was first performed using the inverse variance-weighted fixed-effect model in METAL25. The genome-wide significant p-value for meta-analysis was set as 4.0 × 10−7, except for SNPs that only appeared between panel S and B . For those SNPs where heterogeneity occurs , we used the Han and Eskin random-effects model (RE2) in METASOFT26. This model assumes no heterogeneity under the null hypothesis and offers greater power under heterogeneity, compared with conventional random-effect models26.METAL69 , a local SQLite database of tomato was required. We thus created a custom SQLite database in LocusZoom with the following steps. SNP positions in the 221 accessions of the reference panel were inserted by: dbmeister.py --db my_database.db --snp_pos my_snp_pos_file. For the gene information, we first downloaded the gene annotation file from Solgenomics (ftp://ftp.solgenomics.net/genomes/Solanum_lycopersicum/annotation/ITAG2.4_release/). We then converted it to genePred file format by gff3ToGenePred (http://hgdownload.cse.ucsc.edu/admin/exe/). Gene names were replaced with short codes instead of providing full names to avoid long names and overlapping. We then inserted the gene information by the following command line: dbmeister.py --db my_database.db --refflat my_refflat_file. For the recombination file, we used the recombination map previously inferred and inserted the data into our database by: dbmeister.py --db my_database.db --snp_set my_snpset_file. We used the 221 reference panel to calculate the linkage disequilibrium (LD) in PLINK by the following parameter: --ld-snp my.snp --ld-window-kb 100000 --ld-window 1000 --r2 --ld-window-r2 0 .In order to obtain a regional zoom plot of the candidate SNPs in LocusZoom70 (http://tea.solgenomics.net/expression_viewer/input).In order to define the window size of the candidate genes, we first calculated the LD around the significant associated SNP with the window size of 5 Mb in PLINK with the following command line: --ld-window-kb 500000 --ld-window 1000 --r2 --ld-window-r2 0 . We then chose LD higher than 0.5 as the threshold of LD decay for the candidate gene region sizes. Within the regions, we chose the candidate genes based on both the distance of the peak SNP as well as the closest genes with known functions related to the trait. If no gene fell in the candidate regions, we provided the closest gene. We further crosschecked the candidate gene expression patterns using the Tomato Expression AtlasS. lycopersicum var cerasiforme, heirloom, transitional, modern and the closest wild species S. pimpinellifolium tomato accessions6. However, there were up to 11 accessions with duplicated individual IDs , heirloom and modern , heirloom and transitional , and S. pimpinellifolium (27 members) .The relationship between allele combinations and flavor-related metabolites  was only based on panel T. For the accessions in panel T, they were previously defined as five clusters, namely Further information on experimental design is available in the Supplementary InformationPeer ReviewReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Source Data


AJAP1, TNFRSF9, C1ORF174, CAMTA1, and CEP104. Shell gland of chickens laying dark and light blue eggshell was chosen for detection of mRNA expression of the five candidate genes. The results showed differential expression levels of these genes in the two groups. The specific function of these genes has not yet been defined clearly in chickens and further in-depth studies are needed to explore the new functional role in chicken eggshell blueness.The eggshell blueness is an interesting object for chicken genetic studies and blue-shelled chicken industry, especially after the discovery of the causative mutation of chicken blue eggshell. In the present study, genome wide association study (GWAS) was conducted in Chinese Dongxiang blue-shelled chicken underlying four traits of blue eggshell pigments: quantity of biliverdin (QB), quantity of protoporphyrin (QP), quantity of total pigment (QT), and color density trait (CD). A total of 139 individuals were randomly collected for GWAS. We detected two SNPs in genome-wise significance and 35 in suggestive significance, 24 out of the 37 SNP were located either within intron/exon or near 15 genes in a range of ~1.17 Mb on GGA21. For further confirmation of the identified SNP loci by GWAS, the follow-up replication studies were performed in two populations. A total of 146 individuals of the second generation derived from the former GWAS population, as well as 280 individuals from an alternative independent population were employed for genotyping by MALDI-TOF MS in a genotype-phenotype association study. Eighteen SNPs evenly distributed on the GGA21 significant region were successfully genotyped in the two populations, of which 4 and 6 SNP loci were shown significantly associated with QB, QT and QP in the two repeat populations, respectively. Further, the SNPs were narrowed down to a region of ~ 653.819 Kb on GGA21 that harbors five candidate genes:  SLCO1B3 gene. This retrovirus was shown to be associated with the over-expression of the SLCO1B3 gene, proposing it to be the causative mutation of the oocyan phenotype in blue-shelled chickens [Eggshell color is considered one of the essential aspects of egg quality in many countries. Although there is little or no direct evidence to the relationship between eggshell color and the nutritional content of the egg, consumers tend to purchase eggs with particular colors . Researcchickens , 11.SLCO1B3 gene. Despite the fact that all of the hens lay blue-shelled eggs, the color intensity of the eggs varies from dark to light blue. Therefore, in the present study, a genome-wide association study on eggshell blueness was performed for the first time using a high-density 600K SNP array followed by two different replication association studies. We believe that identification of genes associated with the variations of blue eggshell color may provide a new insight into the genetic basis of these traits and benefit for designing an efficient selection strategy in laying hens breeding programs to produce eggs with uniform or desired colors.In China, Dongxiang chickens have been selected for blue eggshell and the population was fixed on the dominant homozygous genotype of All experimental procedures and animal use were approved by The Beijing Municipal Committee of Animal Management and The Ethics Committee of the China Agricultural University.SLCO1B3 gene were collected from China Agricultural University Research Farm. All birds were kept in single cages in one poultry house. Three eggs were collected from each hen on three consecutive days and analyzed while fresh. Eggshell color was measured using chemical and physical methods. In accordance with the chemical method, the experimental procedure to measure eggshell color traits, including quantity of biliverdin (QB), quantity of protoporphyrin (QP), quantity of total pigments biliverdin and protoporphyrin (QT) was followed as described by Wang et al. [in situ using an eggshell color analyzer  following the instruction. To minimize instrument error, dark and white standard reflectance calibration measures were taken regularly during the sampling. The color reflectance value is based on 100% scale and represents the brightness of eggshell surface, whereas the darkness of eggshell is desired for density of eggshell pigment. Therefore, we calculated 100-reflectance value as color density (CD) representing the mixed concentration of biliverdin and protoporphyrin. All phenotypic data were tested for normality, and any abnormal values highly deviating from normal distribution were deleted.For GWA study, blood and egg samples of 139 individuals aged 42 wk of blue eggshell resource population with dominant homozygous genotype of g et al. . The aveTwo ml blood samples were collected from brachial veins of chickens by venipuncture. The blood samples obtained by venipuncture were immediately injected into 5 ml tubes containing K3-ethylenediaminetetraacetic acid anticoagulant and stored at -20 pending the extraction of DNA or any other hematological parameters measurement. Genomic DNA was extracted using TIANamp Blood DNA Kit .The DNA concentration and its purity were examined using spectrophotometric analysis. A total of 139 individuals were quantified for DNA concentrations and genotyped using the 600 K Affymetrix Axiom Chicken Genotyping Array . Genotyping data were analyzed through the software package PLINK v1.09 . Quality2 threshold for the population was calculated for each chromosome using PLINK (v1.09) software and the population structure was assessed using MDS analysis available from the same software. As suggested by Wang et al. [2 threshold of 0.2, resulting in 30,885 independent SNP markers.Pairwise linkage disequilibrium (LD) measured by rg et al. , all autPairwise identity-by-state (IBS) distances were calculated between all individuals using the 30,885 independent SNP markers. Two dimension reduction routines provided by PLINK 1.09 were performed to assess population structure: the “pca” for principal components analysis (PCA) based on the variance-standardized relationship matrix , 16, andGenome wide association analyses were carried out in PLINK software (v1.09). Because pedigree of most individuals of the population under study was unknown, association tests were performed based on least squares (LS) regression test with the top 20 principal components as covariates for unrelated individuals and then Bonferroni correction was utilized to adjust multiple testing. The genomic inflation factor  was calchttps://www.genome.ucsc.edu/cgi-bin/hgGateway was used [http://www.ncbi.nlm.nih.gov) based on Gene ID.To analyze genes, their locations, and the position of SNP on the map, the UCSC Genome Browser on Chicken Nov. 2011  assembly implemented at was used . InformaFor the first replication study, blood and egg samples of 146 individuals (N146) aged 43 wk were collected from the second generation derived from the former GWAS population of China Agricultural University Research Farm. Eggs were collected on 3 successive days to quantify the eggshell color traits QB, QP, and QT by the chemical method as was described above. All phenotype data were checked for normality before conducting any further analysis.To test the marker effect on the eggshell blueness in a late stage of egg production, the second replication study was employed, in which blood and egg samples of 280 individuals (N280) aged 68 wk were collected from a separate population of Dongxiang chicken kept in Jiangxi Donghua Livestock & Poultry Breeding Co. Ltd. . Eggs were collected on 3 successive days to quantify the eggshell color traits with the same measurement procedure previously described. All phenotype data were checked for normality before performing any further analysis.The two repeat populations were fed the same type of the commercial diet, and the feeding protocol was provided by Zhengda Feed Co. Ltd. The main nutritional components are presented in Genomic DNA was extracted from the blood samples of the two populations using TIANamp Blood DNA Kit , and a NanoDrop spectrophotometer  was used to measure its quantity and quality. Concentrations of DNA were adjusted to 50ng/μl. A set of SNPs distributed on GGA21 that anchored by GWAS in genome and chromosome-wise significance associated with QB, QP and QT were carefully examined. Twenty-four different SNP loci were realized for genotyping using matrix assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF MS) on the Mass ARRAY iPLEX Platform .AJAP1), Tumor necrosis factor receptor superfamily, member 9 (TNFRSF9), Chromosome 1 open reading frame 174 (C1ORF174), Calmodulin binding transcription activator 1 (CAMTA1), and Centrosomal protein 104 (CEP104) were designed using Primer Premier 5.0 software  based on the predicted Gallus gallus sequence of these genes deposited in the GenBank database. The real-time PCR efficiency of each pair of primers was calculated using 5 points in a 5-fold dilution series of cDNA, which was used to construct a standard curve. The house-keeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was selected as internal standard. Primer sequences are listed in ΔCT method. Data were analyzed by Student’s t-test to compare the differences of mRNA expression level between the two groups of DB & LB.In the N146 population, a total of 10 hens laying dark blue eggs and 10 hens laying light blue eggs were selected according to eggshell color. We recorded the oviposition time for each hen every one hour from 6:00 am to 6:00 pm every day. Five chickens laying dark blue eggs (DB) and five chickens laying light blue eggs (LB), with fairly uniform oviposition time, were slaughtered 3–5 h before the next expected oviposition; shell gland of the uterus was collected and frozen immediately in liquid nitrogen pending the expression analysis. In case of N280 population, also five chickens laying dark blue eggs (DB) and five chickens laying light blue eggs (LB), followed the same treatment as described above. Total RNA was extracted from the shell gland sample using TRIZOL Reagent , following the manufacturer’s protocol. RNA quantity and quality were detected by a NanoDrop spectrophotometer at 260/280 nm and the integrity of RNA was monitored on 1% agarose gel. The first strand of cDNA was synthesized from 2μg of purified total RNA using the Promega ImProm-IITM Reverse Transcription System . The gene specific primers of five candidate genes Adherens junctions associated protein 1  and dominant effect (D). The additive (a), dominant (d) effects of each SNP were calculated based on the equations proposed by Falconer et al. [The SNPs were first analyzed for allele and genotype frequency within each population by the chi-squared test. The SNPs that deviated from Hardy-Weinberg equilibrium were removed from the analysis. The SNP with a genotype call rate <85% and a minor allele frequency <1% over all individuals in both populations were also discarded from further analysis. Linkage disequilibrium (LD) between SNPs and haplotype blocks were assessed using the software Haploview4.2 . The association analysis between the identified SNPs or haplotype and phenotypic traits was carried out using the general linear model procedure in SAS version 9.1.3  with the following fixed effect model: Yr et al. : A = (AAPhenotype data of four traits of quantities of biliverdin (QB), protoporphyrin (QP), quantity of the total pigments (QT) and color density (CD) were analyzed and presented in In this study, genomic inflation factors of the population for four traits were estimated and the data are presented in A total of 49 SNP effects (some of which are repeated within the traits) involving 35 different SNP markers and 29 genes were found to be associated with the four traits. Of the 49 SNP effects, three (2 SNP markers within or near 2 genes) on GGA21 reached 5% Bonferroni genome-wise significance in a P value threshold of 5.130E-07. All these significant SNPs reached 5% empirical genome-wise significance from 1,000,000 permutation tests. Moreover, forty-six SNP effects (35 SNP markers within or near 29 genes) reached the significance of suggestive linkage (P < 1.027E-05) distributing on seven chromosomes including GGA1, GGA2, GGA3, GGA8, GGA17, GGA21 and GGA26 under the LD condition. Of the 37 SNP markers, 24 SNP were located either within intron/exon or near 15 genes in a narrow ~1.17 Mb  region of GGA21. The quantile-quantile plots and the Manhattan plot of P values, in terms of -log (P), for all SNP markers and different traits are presented in Considering P value of 5.130E-07 as the genome-wise significance level with Bonferroni correction, it was revealed that two SNP markers on GGA21 showed significant association with QT and QB phenotypes of which, SNP rs315477097 significantly associated with QT and QB while SNP rs13602462 only related to QT. No SNPs were found to be associated with QP and CD traits at the genome-wise significance level (P < 5.130E-07). However, the number of SNP effects with suggestive significance (chromosome-wise significance) was much larger than those with genome-wise significance. The details of all genome-wise significant and suggestive significant SNPs, including their positions in the genome, P value, and candidate genes are summarized in AJAP1 gene. In chromosome-wise significance level (P < 1.027E-05), 20 SNPs were detected for QB phenotype, of which 16 SNPs were located within a ~793 Kb segment (between 135 Kb to 928 Kb) on GGA21, and four SNPs were distributed on three chromosomes including GGA2 (n = 1), GGA17 (n = 1), and GGA26 (n = 2). Of the 16 SNPs detected on GGA21 associated with QB phenotype, 11 were located in the intron or exon of five known genes , whilst five SNPs were positioned in the neighboring of six known genes   was located in the Intron 2 of 1ORF174) .For QP phenotype, the P values of association tests did not reach the 5% Bonferroni genome-wise significant level (P < 5.130E-07), but some SNP-trait combinations showed suggestive evidence with chromosome-wise significant level (P < 1.027E-05). In total, seven SNPs distributing on GGA1 (n = 1), GGA8 (n = 3) and GGA21 (n = 3) were detected to be associated with QP phenotype in chromosome-wise significance level .AJAP1 gene which was associated with QB trait in genome-wise significance level, also found to be significantly associated with QT phenotype (P = 2.09E-07), the other SNP rs13602462 was located at 60 Kb downstream region of AJAP1 gene and 30 Kb upstream of C1ORF174 gene (P = 4.400E-07)  .Similar to QP, the P values of the CD trait of association tests did not reach the 5% Bonferroni genome-wise significant level (P < 5.130E-07). However, the same SNP rs313176000 on GGA2 which showed suggestive significant association with QT phenotype, also was correlated with CD trait (P = 5.58E-06) .In the current work, 24 SNP markers distributed on GGA21 showing significance in genome and chromosome-wise levels of GWAS were selected for genotyping using (MALDI-TOF MS). Initially, six SNPs were excluded for lack of genotypic data and low signal intensity. Thereafter, 18 SNP markers were successfully genotyped, of which 6 SNPs with genotype call rate <85% and minor allele frequency <1% over all individuals were discarded from the N146 and N280 populations . In addiPhenotype data for the 3 traits included in this study were normally distributed. Descriptive statistics of phenotypic measurements QB, QP, and QT in N146 and N280 populations are given in In the N146 population, statistical analysis revealed that out of eleven SNPs, four SNPs  were significantly associated with QB and QT traits (P < 0.05), but none of the SNPs were related to QP (P > 0.05). Chickens with the homozygous AA genotype were associated with higher QB and QT at SNP rs313867043 and SNP rs16177126 than were those with the homozygous GG or heterozygous GA genotype. At locus rs16177219 the homozygous GG was associated with higher QB and QT than were the genotypes GT and TT (P < 0.05), whilst, the homozygous GG at SNP rs16177212 was associated with higher QB and QT than were GA and AA genotype (P < 0.05) .For N280 population, statistical analysis showed that out of ten SNPs, two SNPs (rs16177219 and rs16177126) were significantly associated with QB and QT traits. Polymorphism rs315477097 was strongly associated with QB, QP and QT traits, while Polymorphism rs13602462 was significantly correlated with the 3 phenotype traits. SNP rs315586328 and rs313199923 were strongly associated with QB (P < 0.01) and significantly with QT, while SNP rs16177212 significantly related only to QP (P < 0.05) . At locuThe genotypic effects of the SNPs in the two populations were further divided into additive and dominant effects. In the N146 population, SNP rs313867043, rs16177219 and rs16177212 showed significant dominant effects on QB and QT (P < 0.05) .For N280, the additive effect of the SNP rs315477097 was significant for QB and QT, whereas its dominant effect was significant for QP (P < 0.05). The additive genotype effect of SNP rs315586328, rs15180009, and rs313199923 was significant for QB (P < 0.05), whereas the dominant effect of SNP rs16177212 was significant for QP (P < 0.05) .For the N146 population, three haplotype blocks were inferred as shown in Associations of the haplotypes with 3 eggshell color intensity traits in two populations are presented in the GAPDH were within 95 to 105%, which was accepted for qRT-PCR. In this experiment, 5 candidate genes associated with the significant SNP loci were selected for detecting the mRNA expression levels between two groups of dark and light blue eggshell chickens. mRNA expression levels in all of the 5 candidate genes were shown down-regulated in dark blue than in light blue chickens in both of the N146 and N280 chicken populations and the differential expression was significantly different  region of GGA21. As quantity of biliverdin is an important criterion to determine the blue eggshell color intensity in blue-shelled chickens, 21 significant SNPs were detected to be associated with quantity of biliverdin. Of the 21 SNPs, one was in the genome-wise significance level and 20 SNPs showed suggestive significance, of which 16 SNPs were distributed on GGA21. The most significant SNP associated with quantity of biliverdin was found to be located in the intron 2 of AJAP1 gene, also known as MOT8, SHREW-1 and SHREW1, and this SNP is also observed in genome-wise significance level for QT trait.Genome wide association studies (GWASs) have been proven to be a powerful approach and efficient tool for genetic dissection of trait loci . In chicTNFRSF9 also known as , that is a protein-coding gene and the protein encoded by this gene is a member of the TNF-receptor superfamily. This receptor contributes to the clonal expansion, survival, and development of T cells. This gene plays a general potential role in immune function, and the expression of its receptor is induced by lymphocyte activation [AJAP1, a gene that is a novel transmembrane protein of adherent junctions in epithelial cells. AJAP1 may affect cell motility, migration, invasion and proliferation by unclear mechanisms [AJAP1 localizes and interacts with β-catenin in the E-cadherin-catenin complex and is found in cell-cell contacts in the human mammary gland, the uterus, and breast carcinoma cells [AJAP1 on human chromosome 1p36 was correlated with glioblastoma tumors and this loss of expression is due to promoter methylation [C1ORF174 gene. This gene is also found to be located on the deleted region of human chromosome 1p36 and associate with glioblastoma tumors and cell lines [CEP104 gene, this SNP was synonymous and caused no change in the amino acids of this gene. This gene is a protein coding gene that in humans encodes centrosomal protein 104 kDa protein. Like its Chlamydomonas ortholog, FAP256, it has been shown to localize to the distal ends of both centrioles in the absence of a cilium. During cilium formation, it is found at the tip of the elongating cilium [CAMTA1 gene. This gene localizes to 1p36 deletion region, it is a tumor suppressor candidate that inhibits key features of malignant cells and is involved in neuronal differentiation. Furthermore, The low expression of this gene is associated with neuroblastoma [To further narrow down the genomic region identified by our GWAS and verify the impact of the SNP marker on the eggshell color intensity traits, we have conducted a follow-up replication analysis in two different populations at two different age stages of Dongxiang chicken. In the first experiment, 4 SNP effects were found to be associated significantly with QB and QT of the eggshell at the age of 43 wk, but none of the SNPs were correlated with QP trait. The first SNP locus was located in Intron 2 of tivation , 31. Thechanisms . In polama cells . The epihylation . The thill lines . The foublastoma . Most ofAJAP1 is a putative negative regulator of angiogenesis in which the down regulation of AJAP1 leads to an increase in the cumulative sprout length during sprouting angiogenesis; they found that the number of sprouts was increased upon AJAP1 down regulation. In our qPCR analysis, AJAP1 gene was shown down-regulated in the dark blue-shelled chicken in the two populations. In this regard, we hypothesize that the low expression level of AJAP1 in endogenous cells of the shell gland in the dark group chicken has allowed more erythrocytes to flow through the wide blood vessels, and that led to a rise in the rate of heme synthesis resulted in an increased level in the production of biliverdin. It has been suggested that damaged and aging erythrocytes could be engulfed by macrophages in the shell gland [TNFRSF9 gene in the light group chicken induced the activation of macrophages in the cells of shell gland in which a little heme was released from the disintegrated erythrocytes and this little amount of heme was further catabolised to the biliverdin or protoporphyrin that stimulated and simultaneously deposited on the eggshell in different ratios giving a pale pigment of the shell [CAMTA1, C1ORF174, and CEP104 genes between the dark and light blue eggshell chicken, but little evidence is available regarding their role in biliverdin or protoporphyrin pigment pathway.To shed light on the potential regulatory role that these candidate genes might play in blue eggshell color intensity traits, we further performed quantitative expression analysis in the shell gland of samples from two different populations. In this experiment, the 5 genes were shown significantly down-regulated in the dark blue eggshell chicken than that of the light blue group. In a recent research, Hötte et al.  documentll gland , 8, and ll gland  stated the shell . FurtherNumerous studies attributed the cause of the eggshell color intensity to age, oxidative stress, diet contents, fear, and disease however; the potential regulatory genes and its related proteins and detailed molecular mechanisms regulating eggshell blueness have yet to be clearly defined , 40. AltAJAP1 gene in our GWAS, the AJAP1 gene might be a target gene for biliverdin quantity traits in blue eggshell chickens. Our follow-up association analysis results also showed that AJAP1 as well as CEP104, C1ORF174, TNFRSF9, and CAMTA1 genes might be used as genetic markers with significant effects on selection strategy to obtain chickens that lay eggs with uniform colors which would benefit the breeders to conserve the uniform color for future breeding programs.In light of the data presented here, as a significant signal was found for the AJAP1, C1ORF174, CAMTA1, CEP104, and TNFRSF9 in chickens, including blue-shelled chickens. To address these issues, we examined their mRNA expression profile in the shell gland in order to identify their potential regulatory mechanism, however further studies are warranted to explore the regulation sites that might be altered by DNA methylation status or loss of heterozygosity, epigenetic modifications and deletions in each of dark and light blue-shelled chicken.Specific function of these genes have not yet been determined clearly in chicken and to the best of our knowledge, there are no studies of the expression and regulatory mechanisms of AJAP1, C1ORF174, CAMTA1, CEP104, and TNFRSF9. Further expression profile analysis indicated that these five genes significantly associated with dark and light blue egg shelled chickens. The associations of these genes with blue eggshell color intensity traits are reported here for the first time, further in-depth studies including using of protein and cell experiments are required for better understand the biological mechanism underlying chicken eggshell blueness.Our GWA study revealed two genome-wise significant SNPs and 35 chromosome-wise significant SNPs besides 29 genes associated with eggshell color intensity traits, of which 24 SNPs and 15 genes were distributed in a relatively narrow region (~1.17Mb) of chicken chromosome 21. In the follow-up replication analysis, we verified the association of eight markers in a narrow region of 653.819 Kb with eggshell color intensity traits. This genomic region contained the candidate genes S1 Table1 Linkage group, 2 these SNPs are not assigned to any chromosomes.(DOCX)Click here for additional data file.S2 TableThe values are given in (%). Met: methionine.(DOCX)Click here for additional data file.S3 Table1 GenBank Accession No. relates to the transcript used for the design of the primers.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table1Linkage group. 2These SNPs are not assigned to any chromosomes.(DOCX)Click here for additional data file.S6 Table−8 mol/g). Values in bold indicate significant association with the trait.QB, QP, and QT (×10(DOCX)Click here for additional data file.S7 Table−8 mol/g). Values in bold indicate significant association with the trait.QB, QP, and QT (×10(DOCX)Click here for additional data file.S8 Table−8 mol/g). Means in the same column with different lowercase superscripts are different at P < 0.05.QB, QP, and QT (×10(DOCX)Click here for additional data file.S9 Table−8 mol/g). Means in the same column with different lowercase superscripts are different at P < 0.05. Values in bold indicate significant association with the trait.QB, QP, and QT (×10(DOCX)Click here for additional data file.


Formal statistical tools for genome-wide detection of mutation clusters under a microarray probe sampling system are yet to be established. A challenge in the development of statistical methods is that microarray detection of mutation clusters is constrained to select SNP loci captured by probes on the array. This paper develops a Monte Carlo framework for cluster testing and assesses test statistics for capturing potential deviations from spatial randomness which are motivated by, and incorporate, the array design. While null distributions of the test statistics are established under spatial randomness via the homogeneous Poisson process, power performance of the test statistics is evaluated under postulated types of Neyman-Scott clustering processes through Monte Carlo simulation. A new statistic is developed and recommended as a screening tool for mutation cluster detection. The statistic is demonstrated to be excellent in terms of its robustness and power performance, and useful for cluster analysis in settings of missing data. The test statistic can also be generalized to any one dimensional system where every site is observed, such as DNA sequencing data. The paper illustrates how the informal graphical tools for detecting clusters may be misleading. The statistic is used for finding clusters of putative SNP differences in a mixture of different mouse genetic backgrounds and clusters of de novo SNP differences arising between tissues with development and carcinogenesis.Mutation cluster analysis is critical for understanding certain mutational mechanisms relevant to genetic disease, diversity, and evolution. Yet, whole genome sequencing for detection of mutation clusters is prohibitive with high cost for most organisms and population surveys. Single nucleotide polymorphism (SNP) genotyping arrays, like the Mouse Diversity Genotyping Array, offer an alternative low-cost, screening for mutations at hundreds of thousands of loci across the genome using experimental designs that permit capture of  These signatures are identified by comparison of genomic sequences with a reference sequence and association with specific exogenous and/or endogenous conditions. Genome sequences can be viewed as a string in the genome alphabet, or equivalently as a time series or lattice sequence of a large length. For the mouse genomic experiments discussed here, the length of a single chromosome ranges from 6.14 × 10Current genomic technologies have broadened our perspective to mutation analysis, revealing a critically important phenomenon of non-random spacing of mutations as a new mutation signature . This siSince complete genome sequencing is expensive and generally impractical as a screening or survey method, genotyping microarrays are a low-cost alternative which are commonly used to detect mutations at loci with single nucleotide polymorphisms (SNPs). These loci are referred to as SNP sites. Differences in a single nucleotide, referred to as SNP genotype differences, can be interpreted as mutations when comparing samples. SNPs are genotyped using designed single-stranded short nucleotide probes affixed to a microarray platform. These probes complement specific locations within the genome and these locations are quite sparse in distribution across the genome relative to the genome length, yielding low cost for the array process relative to sequencing. Thus, a SNP genotype difference can be detectable or undetectable by a microarray platform, depending on whether the probes on the array are at that SNP locus. The objective we study in this paper is the development of a population, i.e., a large sample size, screening tool for a wide variety of tissues and cell types, using the low cost SNP array data for identifying clusters of putative mutations. The challenge is that arrays provide windows of observations along the genome, which depend on probe sites, in terms of both number of sites and distribution or spacing of the sites. Hence the screening tool would need to accommodate this constraint in the experimental design with microarray platforms.probe deserts, defined as consecutive probe sites spanning more than 1 million bp; the two largest gaps between consecutive probe sites are 7,268,520 bp and 7,033,330 bp on chromosomes 7 and X, respectively.The Mouse Diversity Genotyping Array (MDGA) is a single nucleotide polymorphism (SNP) microarray  that detWith the rapid development of genotyping and sequencing techniques in recent years, more genetic studies have begun to focus on assembling, visualizing and studying the spatial information of genomic events under different scenarios such as genome-wide association studies . For clude novo SNP differences between tissues with development and with carcinogenesis.From the discussion above we see that the observable microarray data depend on the probe design, that is, the locations of the probes. In this paper, we study several statistics for detecting mutation clusters: a set of non-parametric statistics based on neighbourhood measures, and a test statistic based on distances between SNP loci where mutations are detected, which is related to rainfall plots. These statistics are also studied in real-valued functional forms to summarize the cluster features. The microarray probe sampling system yields missing observations in the domain of interest. Numerical techniques have become increasingly important for the analysis of complex data structures, such as observed here. Such techniques are utilized in our analyses to incorporate the probe design constraints. The null process of complete randomness is a homogeneous Poisson process. For a natural alternative cluster process we consider the family of Neyman-Scott processes, which are a class of parent-child point processes. We evaluate the techniques through power studies which demonstrate that the tests proposed provide suitable tools for screening samples for clustering effects on the genome scale. We then apply the recommended statistical tools for finding clusters of putative SNP differences in a mixture of different mouse genetic backgrounds and for finding clusters of sf = mins∈Ss, and the location of the last probe target site on the chromosome as sl = maxs∈Ss. Denote the locations of SNP genotype differences detected by the probes as X: X ⊆ S.To detect mutation clusters genome-wide, chromosomes are studied individually as each chromosome consists of a linear space in itself. Define the set of the probe locations, determined by design, as d from the known SNP genotype difference, or by the number of SNP differences n, within the neighborhood. Each statistic can be considered as a function of a specific value of d or n, or alternatively, the behavior of each statistic over a range of d or n may be considered. The summary statistics for functional behaviors utilize the well-known frameworks of the Kolmogorov-Smirnov (KS) and Cramér-von Mises (CvM) tests, adapted for this missing data context. The test statistics proposed are:The test statistics proposed below consider SNP genotype differences within the neighborhood of a known SNP genotype difference, where neighbourhood is defined by either distance dA, fixed distance d, and site with a SNP genotype difference x, we haveI(E) is the indicator function for the event E.(I) Mean over all sites with SNP differences of the ratio of the number of sites with SNP genotype differences to the number of probes within fixed distance d of each SNP genotype difference, to the total, over all SNP genotype differences, of the number of probes within distance d of each SNP genotype difference(II) Pooled mean detection ratio: the ratio of the total, over all SNP genotype differences, of the number of SNP genotype differences within distance d from observed SNP genotype differences, while adjusting for varying probe sparsity over the chromosome. The index S is used to emphasize that the statistics depend on the design of the probe set S. While we focus on the above formulations, for comparison purposes, we also consider traditional neighbourhood formulations of test statistics:The two statistics above summarize properties in the neighborhood of distance D the minimum distance to include n SNP genotype differences around x0x0 ∈ X,n = 2, Dmin(n) becomes the minimum of the distances between any two SNP genotype differences. Algorithm 1, provided at the end of this section, describes an efficient procedure for the calculation of Dmin(n).(III) Consider d of any given SNP genotype difference(IV) Maximum of the number of SNP genotype differences within distance Another test statistic proposed is a count statistic related to the distances between SNP loci with genotype differences, which are features shown in the rainfall plots. The count statistic is defined as follows:dXi) Count of inter-SNP locus distances for those SNP loci with different genotypes under threshold G(y), may be viewed as function valued statistics with a fixed argument d or n. Instead of considering a fixed argument y, they may also be viewed as a functional form G(⋅), G(⋅) ≡ {G(y), y ∈ R(y)}, where R(y) is the range of y considered. Let G*(⋅) = E0(G(⋅)), the expectation of G(⋅) under an appropriate null hypothesis, e.g., homogeneous Poisson process, which is discussed in the next section. Two test statistics measuring the distance of G(⋅) from G*(⋅) as considered here are of the forms of Kolmogorov-Smirnov (KS) and Cramér-von Mises (CvM) tests . If the selected grid points are dense, KS and CvM; yet the selection of k should also account for feasible computational load.The parameter Algorithm 1: Calculation ofDmin(n)X = {xi, i = 1, ⋯, K} denote the set of ordered SNP genotype differences, where xi is the ith ordered SNP genotype difference on the chromosome. Then there are K − n + 1 clusters of consecutive SNP genotype differences of size n: {{xl, ⋯, xl+n−1};l = 1, ⋯ K − n + 1}.1: Let Dl ≡ minm∈max, l = 1, ⋯ K − n + 1. For the lth cluster of SNP genotype differences, consider the set of minimum distances to include ncluster SNP genotype differences around each SNP genotype difference in the cluster; then Dl is the minimum distance in the set. Note that cluster SNP genotype differences refer to SNP genotype difference in the lth cluster.2: Define Dmin(n) = minlDl; l = 1, ⋯ K − n + 1.3: Mutations may occur at any of the 2.8 billion base positions in the mouse genome. Among these mutations some exist at the genomic loci targeted by SNP probes and are thus detectable as SNP genotype differences by the SNP probe system, while the existence of the other mutations remains unknown. Both null and alternative hypotheses are established on underlying processes that generate all mutations, both detectable and undetectable. Since the target loci of the SNP probes are unique and non-random on each chromosome, the null and alternative distributions of the proposed test statistics are calculated conditional on the probe locations on the specific chromosome considered.η is linear. Numerical methods are adopted to obtain the null distributions of the test statistics for testing that Xs, the observed locations of SNP genotype differences from the sample, are randomly located along the chromosome. Algorithm 2 develops the Monte Carlo estimate of the null distribution of the test statistics, while algorithm 3 provides inferential procedure.Under the null hypothesis that SNP genotype differences are located at random locations along the chromosome, the underlying process generating SNP genotype differences can be assumed as a homogeneous Poisson process (hPP). Under such a process, every site on the chromosome, and in particular, every probe site, is independent and has an identical probability of having a SNP genotype difference. The relationship between the hPP rate parameter and the total expected number of detected SNP genotype differences Algorithm 2: Monte Carlo estimates of the null distributions of summary statisticsY = {yi, i = 1, ⋯, k}, which defines the scale of d or n as the evaluation range;2.1: Set a finite grid M replications of detected SNP genotype differences mth replication, 2.2: Simulate (a):Xs: η can be set as |Xs|, where |A| is the norm of set A, that is the count of the number of elements in A;Generate the total number of underlying SNP genotype differences (b):uj ∼ U, and U is the discrete uniform distribution on {sf, ⋯, sl};Generate the set of underlying (both observable and unobservable) locations with SNP genotype differences (c):Obtain the set of observed SNP genotype differences: m = 1, ⋯ M, obtain yi, i = 1, ⋯, k;2.3: For each G*(⋅) is 2.4: The Monte Carlo estimate of m = 1, ⋯ M, calculate the 2.5: For each (a):(b):2.6 The Monte Carlo estimates of the cumulative distribution functions of the test statistics (a):(b):Algorithm 3: Hypothesis testing procedureXs, calculate 3.1 Based on the observed sample (a):(b):3.2 Statistical inference:(a):α:For hypothesis testing at significance level (i):KS test: if (ii):CvM test: if (b):The p-values are calculated as:(i):KS test: (ii):CvM test: p-value calculation methods in step 3.2(b) of Algorithm 3 are based on the approaches for calculating p-values for Monte Carlo simulation provided in . Only parents within this range can yield offspring detectable by the probe set, because of the truncation range in offspring distribution.Parent sites with an expected number Dp = S. There are two important reasons to constrain parent sites to probe locations. First, probes are located where the corresponding SNP genotype differences have an occurrence of at least 1% in the population, so that the probe sites are selected based on their being favorable in terms of having SNP genotype differences. Secondly, under this constraint, all of the test statistics will attain the highest power compared to other parent site settings. Thus this setting is helpful for eliminating some candidate tests with sub-optimal performance.Parent sites are constrained to SNP probe locations: hp of a probe; Dp = ∪s∈S. This setting recognizes possible errors in identifying probe locations, so parents may not be exactly placed at favorable sites for SNP genotype differences.The parent sites are constrained to be within a certain distance η is set to equal the observed total SNP genotype differences |Xs|, which is achieved by adjusting the parameters in the alternative process. Algorithm 4 details the Monte Carlo estimates of the powers.In the simulation of each alternative hypothesis, as in the null hypothesis, the expected total number of detected SNP genotype differences Algorithm 4: Power StudyY = {yi, i = 1, ⋯, k} the same as in Algorithm 2;4.1: Set a finite grid M′ replications of detected SNP genotype differences mth replication, 4.2 Simulate (a):μp is the Poisson mean parameter.Generate the total number of unobservable parent points (b):U is the discrete uniform distribution on the domain Dp.Generate the set of parent points (c):For each parent point (d):Obtain the set of all generated offspring (e):Obtain the set of observed SNP genotype differences m = 1, ⋯ M′, obtain yi, i = 1, ⋯, k;4.3: For each m = 1, ⋯ M′, using 4.4: For each (a):(b):4.5 The Monte Carlo estimates of the power of the test statistics (a):(b):de novo SNP genotype differences between these two tissue samples on its chromosome 19. Based on this example, the total expected number of detected SNP genotype differences η is chosen as 50. Under the null hypothesis, the underlying rate parameter of the hPP −4 (See step 2.2(a) in Algorithm 2).Chromosome 19 is selected as an illustrative example to conduct simulation studies. Mouse 36.2 in our dataset, a mouse with a primary mammary tumor and lung metastasis, has about 50 putative d or n, which are selected to be scientifically meaningful. In sequencing data, having six or more consecutive mutations with an average distance of less or equal to 1 kb is considered as a mutation shower [di, i = 1, ⋯, 20 are set from 5000 bp to 100, 000 bp with an interval of 5000 bp, so di = 5000i; while a grid of cluster sizes ni, i = 1, ⋯, 7 is set from 2 to 8 with an interval of 1, so ni = i + 1.All of the statistics are evaluated using a grid of values for n shower . Anothern shower . In genoDmin(ni), i = 1, ⋯, 7; Nmax(di), i = 1, ⋯, 20, C(di), i = 1, ⋯, 20, and the 10 functional forms of these statistics based on KS or CvM frameworks. For each statistic, the null distribution is estimated from M = 104 replications generated under the null process. The critical values for all tests are based on α = 0.05.Thus there are, in total, 97 statistics formulated: σ and h jointly reflect the spread of clusters of the SNP genotype differences. Here, the truncation range h is set as h = 3σ, as there are very low probabilities associated with the normal distribution outside this range. In the definition of Dp in alternative hypothesis hp is set as hp = σ; note that hp = +∞ for alternative hypothesis hp = 0 for alternative hypothesis μo and σ. With μo and σ specified, the parameter μp is set to ensure that the expected number of detected SNP genotype differences η = 50. The experiment adopts a full factorial design with: (i) μo having two levels, 375 and 1125, denoting low and high levels of offspring within a cluster in order that powers of the statistics being evaluated are away from the extremes of 0 and 1, so that the performance of the test statistics can be differentiated; and (ii) σ having levels of grid distances of 500 bp, and from 1000 bp to 10000 bp with increment of 1000 bp. These values of σ are based on the definition of a mutation cluster by [σ ranges from 3kb to 60kb.For the alternative processes, the parameters uster by ; i.e., tμo = 375 under each of the three alternative hypotheses, while μo = 1125. In each table, for the test statistics with fixed argument of d or n, only the highest powers across all the arguments are displayed. These tables in the Supplementary Information provide the identical information as in σ are available in μo = 375, and μo = 1125, under each of the three alternative hypotheses respectively.In Supplementary Information, μo = 375, in general, the power of each test statistic decreases as σ increases. The test statistics based on C(d) generally have higher powers than those based on Nmax(d) and Dmin(n). C(d), including the statistics with fixed arguments as well as their function forms. Among these nine, C(d) in all the settings of σ. Among the six functional forms of statistics, σ increases.Under the alternative hypothesis μo = 375, available in C(d) outperform σ is not small. The comparison among the six functional forms of statistics shows similar results for alternative hypothesis The power performance under alternatives μo = 1125, the powers of the test statistics are higher than when μo = 375. The powers are closer to 1 and decrease less dramatically over σ than for the cases where μo = 375. The patterns of power comparisons are similar to the cases where μo = 375. Yet the powers of C(d) when σ is large and both are quite close to 1 under alternative hypotheses μo = 1125 is available in For C(d) seem to be best among the nine categories of statistics, yet they suffer the disadvantage that they require a choice of d. The optimal argument choices of d are usually unknown in application. Moreover, the optimal choices of d may change over parameter settings, particularly for σ, as seen in d can yield very low power.The power performance of d needs to be defined.In conclusion, the functional statistic ® Genomic DNA Purification Kit . Isolated DNA was submitted to the London Regional Genomics Centre to be processed  and hybridized to the Mouse Diversity Genotyping Array  [®, Santa Clara, CA) was used to call genotypes at the 493,290 SNP loci represented by the MDGA, using the fluorescence intensity data. The Genotyping Console software uses a clustering algorithm, Birdseed v2, and assigns each SNP locus as 1 of 4 possible calls: AA , AB , BB , or no call if the SNP genotype calls did not cluster well with any of the three possible genotypes. The resulting data for each biological sample used for further analysis consist of a list of SNP genotype calls, their locations in the genome (chromosome number and base pair number) and the genotyping call given by Genotyping Console for each sample. In the data sets utilized for testing for existence of clusters in this paper, the events are defined as SNP genotype differences, which are the binary indicators of differences at SNP loci when contrasting two biological samples. The genotyping call and the consequent SNP genotype differences are putative until the genotyping is confirmed by an alternate technology. All animal work was conducted according to relevant national and international guidelines. Western University’s Animal Use Subcommittee approved the study. All guidelines were followed including those approved standard operating procedures for euthanasia.DNA was extracted from mouse tissue samples using the Wizardara, CA) . GenotypThree specific examples are considered here.Detection of known clusters of putative SNP genotype differences in a mouse with a known mixed genetic background;Test for the existence of clusters of putative SNP genotype differences arising postzygotically between two healthy tissues from a C57BL/6J mouse;Test for the existence of clusters in comparison of two cancerous tissues from a MMTV-PyMT transgenic mouse .Rainfall plots portraying the mutation landscapes of the three samples are provided in As an example of a positive control for known clustered putative SNP genotype differences in a genome, the recommended mosome 6 , the tesIn example 2, the ed mouse . The SNPIn the third example, the ic mouse . The tesC(d) statistic based on the rainfall plot performs substantially poorer. Therefore In order to perform rigorous statistical testing to detect existence of clusters of putative SNP genotype differences identified by genotyping array probe systems, 97 candidate test statistics are proposed and evaluated. Conditional null distributions of test statistics are obtained by Monte Carlo simulations. The powers of all the test statistics are studied under three different types of Neyman-Scott processes, intended to mimic the unknown underlying mutation generation mechanisms. Various choices of parameters for alternative hypotheses are used to evaluate the power performance of the candidate statistics. Among all of the parameter settings, the Cramér-von Mises version of the pooled ratio estimate , statistical methods such as the Bonferroni correction or by  may be aThe methods developed in this article are designed for cluster detection under a genotyping array probe design. The probe design provides a cost-effective way for mutation detection compared to sequencing every base pair of the entire genome. Instead of a high resolution of mapping of mutations in the genome, the probe system usually only reveals a small proportion of information on a chromosome, leaving the regions outside of the probe sites unknown. As mutations in regions where probes are absent are undetectable by design, any mutation clusters occurring in such regions are correspondingly undetectable. The test statistics are established based on the information on the probe system, so they can only identify clustering when the probe system is capable of detecting potential clusters. The power evaluations in this study are conditional on the existence of the underlying clusters generated from a known clustering mechanism. This mechanism does not necessarily guarantee that clusters are detectable by the specific probe system. If all the samples evaluated in the power studies contained clusters detectable by the probe systems, the power performances of the tests would most likely be higher. One of the reasons for some low power performances in certain alternative parameter settings may be that clusters generated are not detected by the probe system. Designing an array with a larger number of probes or switching to an existing array system with a larger number of probes will augment the probability of detecting existing clusters.de novo mutations, such as in healthy somatic tissues and in cancer studies, the imputation based on LD or known haplotypes may not be appropriate; even so, other prior knowledge may become helpful. Extensions of the methods discussed in this paper could incorporate improvements based on such prior knowledge.In studies involving known genetic backgrounds, prior information on detected SNP differences may be utilized to improve the power of testing for mutation clusters. For example, information on SNP differences in high linkage disequilibrium (LD) with more unobserved SNP differences in their neighborhood may be given greater weight in the testing procedure. Alternatively, information on SNP genotypes undetectable by the microarray platform may be inputed based on other information such as known haplotypes . HoweverAfter mutation clusters have been detected, different downstream analyses are possible. The nature of the mutation types in clusters can be used to identify mutation signatures and to infer the underlying mutational mechanisms. Alternatively, the mutation clusters can be linked to functional annotations for the genome and inferences can be made about the functional impact of the mutation clusters.The method can be generalized to any one dimensional system where every site is observed, such as DNA or protein sequencing data, with probes designated as having length one at each site of the system. The method can be applied to cluster detection of any single site event along any one dimensional system, as for example, the distribution of DNA methylation locations detected by the CpG site probe system as described by .The arbitrary and informal graphical tools and definitions for portraying and detection mutation clusters can now be replaced with a formal statistic test for mutation cluster detection. The recommended test statistics in this study provide tools for genome-wide detection of mutation clusters under the genotyping probe system. Due to the cost-effectiveness of array systems, larger scales of experimental designs can be adopted compared to those possible with next generation sequencing techniques. Certain samples with putative mutation clusters can be further confirmed and investigated by sequencing techniques.S1 FigC(d) over values of d considered are displayed; dmax refers to the value of d yielding the largest power.Only maximum powers of (TIF)Click here for additional data file.S2 FigC(d) over values of d considered are displayed; dmax refers to the value of d yielding the largest power.Only maximum powers of (TIF)Click here for additional data file.S3 FigC(d) over values of d considered are displayed; dmax refers to the value of d yielding the largest power. σ.Only maximum powers of (TIF)Click here for additional data file.S4 FigC(d) over values of d considered are displayed; dmax refers to the value of d yielding the largest power. σ.Only maximum powers of (TIF)Click here for additional data file.S5 FigC(d) over values of d considered are displayed; dmax refers to the value of d yielding the largest power. σ.Only maximum powers of (TIF)Click here for additional data file.S1 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S2 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S3 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S4 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S5 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S6 Tableh is set as h = 3σ and μp is set to match with η = 50. For Dmin(n), Nmax(d) and C(d), only the maximum power across the values considered for d or n is shown. The significance level of the test is set as α = 0.05.Under each parameter setting, (PDF)Click here for additional data file.S7 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 375 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S8 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 375 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S9 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 375 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S10 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 1125 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S11 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 1125 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S12 Tabled or n for Neyman-Scott (NS) process under alternative hypothesis μo = 1125 under various σ choices. Under each parameter setting, h is set as h = 3σ and μp is set to match with η = 50.Optimal argument settings of (PDF)Click here for additional data file.S1 Dataset(CSV)Click here for additional data file.S2 DatasetChromosomal positions of SNP genotype differences in normal cerebellar tissue from chromosome 6 of a mouse with a known mixed genetic background of two common inbred mouse strains (75% C57BL/6J and 25% CBA/CaJ).(CSV)Click here for additional data file.S3 DatasetChromosomal positions of SNP genotype differences along chromosome 1 between cerebellar and splenic tissue from a healthy C57BL/6J inbred mouse.(CSV)Click here for additional data file.S4 DatasetChromosomal positions of SNP genotype differences observed along chromosome 1 for a comparison of primary mammary tumor and lung tissue with metastases from the same MMTV-PyMT transgenic mouse.(CSV)Click here for additional data file.


Copy Number Variation (CNV) is a common form of genetic variation underlying animal evolution and phenotypic diversity across a wide range of species. In the mammalian genome, high frequency of CNV differentiation between breeds may be candidates for population-specific selection. However, CNV differentiation, selection and its population genetics have been poorly explored in horses.N = 1755) belonging to eight European horse breeds, varying from draught horses to several warmblood populations. After quality control, 152,640 SNP CNVs , 18,800 segment CNVs (consecutive SNP CNVs of same gain/loss state or both) and 939 CNV regions  compared to the average signal of the reference (Belgian draught horse) were identified. Our analyses showed that Equus caballus chromosome 12 (ECA12) was the most enriched in segment CNV gains and losses (~ 3% average proportion of the genome covered), but the highest number of segment CNVs were detected on ECA1 and ECA20 (regardless of size). The Friesian horses showed private SNP CNV gains (> 20% of the samples) on ECA1 and Exmoor ponies displayed private SNP CNV losses on ECA25 (> 20% of the samples). The Warmblood cluster showed private SNP CNV gains located in ECA9 and Draught cluster showed private SNP CNV losses located in ECA7. The length of the CNVRs ranged from 1 kb to 21.3 Mb. A total of 10,612 genes were annotated within the CNVRs. The PANTHER annotation of these genes showed significantly under- and overrepresented gene ontology biological terms related to cellular processes and immunity . We identified 80 CNVRs overlapping with known QTL for fertility, coat colour, conformation and temperament. We also report 67 novel CNVRs.We investigated the patterns, population variation and gene annotation of CNV using the Axiom® Equine Genotyping Array  from a large cohort of individuals (This work revealed that CNV patterns, in the genome of some European horse breeds, occurred in specific genomic regions. The results provide support to the hypothesis that high frequency private CNVs residing in genes may potentially be responsible for the diverse phenotypes seen between horse breeds. With the development of genome-wide genotyping arrays, genetic diversity and the variation of complex phenotypes are often assessed via single nucleotide polymorphism (SNP). However, variation in SNPs only explains a fraction of the genetic component of phenotypic variance. Recently, increased attention has been drawn to copy number variations (CNVs), as they are widely considered to impact phenotypes. CNVs are typically defined as variation due to deletions, insertions, and duplication events (1 kilobase-pairs (kb) to several megabase-pairs (Mb)) when comparing the genomic sequence of individuals with a reference genome [STX17 on ECA25 has been linked to hair greying and melanoma [SHOX locus in a pseudoautosomal region are associated with Skeletal Atavism in Shetland Ponies [FGF genes, which also predispose to dermoid sinus disorder [The total number of CNVs could explain much of the heritability unaccounted for by SNPs as CNVs have been linked to genetic variation of complex traits influencing phenotypic diversity and disorders across a wide range of species –11. In hmelanoma , and lard Ponies . Furtherd Ponies . For insThere are also some mammalian gene super-families commonly known to underpin evolutionary changes driven by CNVs, where genes associated to CNV gains may be a likely instrument of adaptation . Consequhttp://bigd.big.ac.cn/gvm/home). Until now, CNVs identified in nearly 45 different horse breeds occupy about 1–3% of their genome and there are more CNVs residing in genes (~ 80%) than in intergenic regions (~ 20%) [The current publicly available database of genetic variants in the equine genome contains 25,756,212 SNP and 3,663,455 insertion/deletions polymorphisms “INDELs”, accessed from the Genome Variation Map ( ~ 20%) –19. In h (~ 20%) –17. The % –19. In (~ 20%) , 19.Previous studies in other mammals have hypothesized that CNVs with high frequency differences among breeds are involved in population-specific selection and adaptation to the environment –23, and N = 301) and the lowest variation in signal intensity among the studied breeds). After quality control, we identified 152,640 SNP CNVs , 18,800 segment CNVs , and 939 CNV regions .Genome-wide CNV analysis was conducted on 1755 horses from 8 different breeds (Table i) Warmblood horses, and ii) Draught horses including Exmoor ponies . First, the plot for the SNP B-allele frequency ratios  showed a clear separation of the individuals in three main breed groups on PC2, with the Friesian horses uniquely distinguished from the other horse breed populations, that clustered in two main subpopulations; ies Fig. a.Fig. 1Interestingly, the first principal component represented variation between individuals, across breeds , and the second principal component represented variation between breeds . In contrast, PCAs generated from segment CNVs showed less variation across breeds  and 56,033 (Friesian horses) , 5298 segment CNV losses (average length of 196.95 kb) and 324 segment CNV gains/losses (average length of 241.84 kb), compared to the reference. Overlapping segment CNVs by at least 1 bp were merged into 939 CNVRs . In general, low CNV diversity is expected  as the s%. In genOur results also support the hypothesis that high frequency private SNP CNVs in particular (e.g. on ECA25 in Exmoor ponies) may be responsible for population-specific selection and adaptation , 22. ThiThe large cohort of individuals analysed in this study (~ 195 horse on average per breed), in comparison with other studies where breeds are represented with one or two horses, is likely to provide a more accurate overview of single private CNVs. The PCA analysis of the SNP and segment CNV distribution according to the three breed clusters confirmed similar frequency levels within three breed clusters. This should not be surprising given that the largest number of shared CNVs was previously reported between closely related breeds such as Warmbloods .Equidae members have also been previously reported. As an example, Doan et al. [Equidae species, which has been attributed to larger divergences relative to the Thoroughbreds.Our results also showed that the proportion of breed cluster-specific SNP CNVs differed between groups. For instance, the Draught and Warmblood clusters displayed a relatively high proportion of unique private SNP CNVs (up to 30–50%). However, a smaller proportion of SNP CNVs may be attributable to breed-specific characteristics (less than 14%). Such differences in unique CNVs in ancestrally divergent n et al.  detected(RAD54B) and Reactive intermediate imine deaminase A (RIDA). The latest is regarded as a potential candidate gene for athletic performance since it is involved in metabolic processes and has been related to blood protein levels in humans [Interestingly, we also identified breed group-specific SNP CNVs located in particular genomic regions which may be attributable to breed-group features. For instance, specific SNP CNVs gains were detected in ECA1  in more than 20% of the individuals belonging to the Friesian horses’ cluster, and more than 20% of the individuals belonging to the Draught cluster showed specific SNP CNV losses in ECA7 . However, all these private SNP CNVs identified reside in intergenic regions or merge into CNVR which partially overlap with previously identified regions in the Quarter horse . Notwithn humans . CNV polThe evolutionary process of species formation (speciation) is complex and influenced by fast evolving changes in specific regions in the genome (i.e. CNVRs driving novel gene functions), which may affect regulatory key biological mechanisms and play a fundamental role in gradual adaptation to different environments , 22. In RIDA gene, involved in metabolic processes. Genes located within CNVRs demonstrated under- and overrepresentation of gene ontology biological terms related to cellular processes and immunity, suggesting the potential role of structural variants in driving phenotypic diversity and disease resistance in European horse populations. We identified 80 CNVRs overlapping with established fertility, coat colour, conformation and temperament trait QTLs. We also report 67 novel CNVRs, which contribute to the catalogue of known CNVs in the horse genome. Future research is needed in order to confirm if the observed CNVs across breeds are also linked to phenotypical differences.Our PCA analysis of SNP and segment CNVs showed that horse populations tend to group according to breed ancestral origins. Comparing breed and breed group clusters, we identified potentially unique private genomic regions displaying SNP CNVs. We found small percentages (less than 14%) of unique private intra-breed structural variants that may contribute to the equine breed diversity. Specifically, ECA9 displayed a high frequency of specific SNP CNVs gains in the Warmblood cluster in a region containing The studied cohort comprised 1755 horses representing eight breeds . Briefly, 100 ng of total genomic DNA was denatured and then amplified. The amplified DNA was fragmented, precipitated and then centrifuged. The pellets were dried and resuspended in a buffer and added to a hybridization master mix, followed by hybridization to the microarray plate (Axiom™ Equine Genotyping Array), in the GeneTitan™ Multi-Channel Instrument for 23.5 h. Moreover, the array plate was labelled with biotin and FAM, washed and further stained with streptavidin and αFAM antibody. Finally, scanning was performed in the Gene Titan™ Multi-Channel Instrument.Copy number calling was performed in the Axiom™ Analysis Suite 1.1 according to the Best Practices Analysis Workflow described in Axiom™ Genotyping Solution Data Analysis Guide  . The thrPrincipal component analysis (PCA) of the SNP B-allelic frequency ratios of the samples and signal intensity of the copy number changes of the sample segments was done using the prcomp function in R v13.1 .For the detection of breed specific SNP CNVs, we defined any SNP CNV identical between two or more horses across breeds as a shared SNP CNV. Similarly, for the detection of breed group-specific SNP CNVs, the eight breeds were grouped into three breed clusters according to the PCA analysis: Draught , Warmbloods (Belgian Warmblood and Swedish Warmblood), and Friesian horses. The Belgian draught horses showed the least variation in SNP CNVs and had a large sample size among the breeds studied. Therefore, the mean signal intensity of these individuals was used as a reference to discriminate between CNV gain  and CNV loss .The analysis of unique private SNP CNVs within breed groups was done using in house scripts, considering any SNP CNVs detected only in a single breed group (not shared with other breed groups) as a “unique private” breed group-specific SNP CNV.https://www.ebi.ac.uk/dgva) or published in recent scientific literature [http://www.ensembl.org/). Overrepresentation test was performed using the PANTHER  classification system for the classification of genes by their molecular function, biological process, cellular component and protein class with default Bonferroni correction and false discovery rate (FDR) [First, those segment CNVs that overlapped by at least 1 bp were summarized as a single CNVR using the BEDTools software (−merge Bed command) . Second,terature  on diffeterature ;) from tte (FDR) . The idete (FDR) ;) using te (FDR) .-ΔΔCt method that normalises the Ct values (cycle threshold) of the target gene with a reference gene (ΔCt), and compare the ΔCt to the ΔCt of the reference sample [GAPDH) gene on ECA6, according to previous CNVRs analyses in horses [Validation of four genomic regions containing CNVRs was performed by real-time quantitative PCR (RT-qPCR) on the StepOne™ Real-Time PCR System . These CNVRs were located within annotated genes. Primers and probes were designed using the Custom TaqMan® Assay Design Tool (Additional file e sample . We usedn horses . The refAdditional file 1: Table S1. CNVRs identified in this study and including information regarding genome position, size, status, segment CNV counts, gene counts, number of individuals for each breed, QTL overlap and novelty.Additional file 2. List of genes overlapping with the identified CNVRs.Additional file 3: Table S2. PANTHER gene ontology analysis of significantly over- and underrepresented genes.Additional file 4: Table S3. Genomic regions validated for CNVs by RT-qPCR. Primer sequences, position, product size, annealing temperature and TaqMan probes are shown. The CNV status in the SNP-array and in the qPCR and the CNV validation by qPCR are given. Table S4. Results of quantitative PCR. The results of the four CNVs per each horse and the outcome of the validation through qPCR are presented.