In addition to additive genetic effects, epistatic interactions can play key roles in the control of phenotypic variation of traits of interest. In the current study, 475 male birds from lean and fat chicken lines were utilized as a resource population to detect significant epistatic effects associated with growth and carcass traits.SDHB, PARK7 and VAMP3) and nine genes  on GGAZ that might be potentially important candidate genes for testis growth and development were detected based on the annotated gene function. In Sub-network2, there was a SNP on GGA19 that interacted with 8 SNPs located on GGA10. The SNP (Gga_rs15834332) on GGA19 was located between C-C motif chemokine ligand 5 (CCL5) and MIR142. There were 32 Refgenes on GGA10, including TCF12 which is predicted to be a target gene of miR-142-5p. We hypothesize that miR-142-5p and TCF12 may interact with one another to regulate testis growth and development. Two genes (CDH12 and WNT8A) in the same cadherin signaling pathway were implicated as potentially important genes in the control of metatarsus circumference (MeC). There were no significant epistatic effects identified for the other carcass and growth traits, e.g. heart weight (HW), liver weight (LW), spleen weight (SW), muscular and glandular stomach weight (MGSW), carcass weight (CW), body weight , chest width (ChWi), metatarsus length (MeL).A total of 421 significant epistatic effects were associated with testis weight (TeW), from which 11 sub-networks (Sub-network1 to Sub-network11) were constructed. In Sub-network1, which was the biggest network, there was an interaction between GGA21 and GGAZ. Three genes on GGA21  contains supplementary material, which is available to authorized users. Epistasis arises due to interactions, either between single nucleotide polymorphisms (SNPs), genes or quantitative trait loci (QTLs), which result in non-linear effects that control variation in phenotypes. Epistasis can have a large influence on phenotypic variation of traits such as starvation resistance, startle response, and chill coma recovery in Drosophila . IdentifWith the advent of SNP arrays and genomic re-sequencing, it is relatively easy to genotype a wide array of individuals. As a result, many genome–wide association studies (GWAS) have been carried out in the past several years. Most of these studies have focused on single locus additive genetic tests. However, this is not that only type of genetic association. A genome wide SNP-SNP interaction analysis should provide new insights into the genetic architecture that underlies variation in complex traits.Gallus gallus), it has been suggested that epistatic interactions between genes (or QTLs) are important for variation in quantitative traits [Recently, genome wide SNP-SNP interaction analysis have been conducted in humans , 9 and de traits , 13, 14.e traits .Two Northeast Agricultural University broiler lines that have been divergently selected for abdominal fat content (named as NEAUHLF) for more than 10 years were used in the current study. Previously, we reported that 52 pairs of SNPs had significant epistatic interactions that were associated with abdominal fat weight . In the Two Northeast Agricultural University broiler lines that have been divergently selected for abdominal fat content (NEAUHLF) were used to identify epistatic interactions. The NEAUHLF lines have been selected since 1996 using abdominal fat percentage  and plasma very low-density lipoprotein (VLDL) concentration as selection criteria. The G0 generation of NEAUHLF came from the same grandsire line, which originated from the Arbor Acres broiler, which was then divided into two lines according to their plasma VLDL concentration at 7 weeks of age. The G0 birds were mated (one sire: four dams) to produce 25 half-sib families for each line, with an average of 70 G1 offspring per family in two hatches. From G1 to G11, the birds of each line were raised in two hatches with five birds per cage. Plasma VLDL concentrations were measured for all male birds, which had free access to feed and water at 7 weeks, and the AFP of the male birds in the first hatch was measured after slaughter at 7 weeks. Sib birds from the families with lower (lean line) or higher (fat line) AFP than the average value for the population were selected as candidates for breeding, considering the plasma VLDL concentration and the body weights of male birds in the second hatch and the egg production of female birds in both hatches. These birds were kept under the same environmental conditions and had free access to feed and water. Commercial corn-soybean-based diets that met all National Research Council (NRC) requirements were provided. From hatch to 3 weeks of age, the birds received a starter feed  and from 4 weeks of age to slaughter the birds were fed a grower diet . The birds used in the current study included 475 male individuals from the 11th generation of NEAUHLF . The birGenotyping was carried out using the Illumina Inc.  chicken 60 K SNP chip, which contained 57,636 SNPs. After quality control, 48,824 SNPs in 475 individuals were used in the epistatic interaction analyses. The quality control of the SNP genotypes was described previously by Zhang et al. .The EPISNP3 module in epiSNP_v4.2_Windows software package was used to identify significant epistatic effects . The staP-values of the epistatic effects were Bonferroni corrected for multiple testing , which resulted in P < 8.39 × 10−12 as a significance threshold. Significant interactions, including additive by additive (AA), additive by dominance (AD) or dominance by additive (DA) and dominance by dominance (DD), between two SNPs on the same chromosome were deleted because these interactions may potentially be markers for a haplotype effect that contains a single QTL [The The figures that illustrate the SNP-SNP networks with the significant epistatic effects for carcass and growth traits were drawn using the epiNet option within the epiSNP_v4.2_Windows software package .The linkage disequilibrium (LD) between SNPs was calculated using Haploview software (version 4.2). The solid spine method within the package was used to define the LD block.https://genome.ucsc.edu/) . Functional annotation of genes was performed using DAVID bioinformatics resources 6.8 (http://david.abcc.ncifcrf.gov/summary.jsp) for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Statistical significance was set at the nominal P-value < 0.05.Genes within 1 Mb (upstream and downstream) of the SNPs that had significant interactions with another SNP for carcass and growth traits were retrieved from UCSC (P < 0.01) in Chwi, MeL, MeC, LW, SW, TeW and significant difference (P < 0.05) in HW between the lean and fat lines. After quality control, 48,824 SNPs were utilized for epistatic interaction analyses (Table Phenotypic summary statistics for carcass and growth traits are shown in Table P < 8.39 × 10−12). Of these 421 significant SNP by SNP interactions, 403 (95.72%) exhibited an additive by additive interaction, 18 (4.28%) exhibited an additive by dominance (or dominance by additive) interaction, and no dominance by dominance interactions were detected  enriched. No significant KEGG pathways were detected. Three genes on GGA21  and nine genes  on GGAZ might be important for testis growth and development based on their basic functions.To investigate the complex mechanism of epistatic effects on TeW, networks were constructed using the 421 significant SNP by SNP interactions. The epistatic interaction sub-networks that contained more than three nodes are shown in Additional file CCL5) and MIR142, which is related to two miRNAs, miR-142-5p and miR-142-3p. Interestingly, five genes in the 4.3 Mb region on GGA10 are predicted to be target genes of miR-142-5p and miR-142-3p (Table TCF12) was the only gene that was predicted to be the target gene of miR-142-5p by three different packages, including TargetScan (http://www.targetscan.org), miRDB (http://mirdb.org) and PicTar (http://pictar.mdc-berlin.de). Unfortunately, it was impossible to predict which genes in the other regions of the genome may be good candidates to control testis growth and development.Sub-networks 2, 3, 4, 5, 8 and 11 each had several SNPs in the same LD block on one chromosome, which interacted with a single SNP on another chromosome. For example, in Sub-network2 eight SNPs on GGA10 interacted with the SNP (Gga_rs15834332) on GGA19  in the region on GGA2. There were fifteen Refgenes  located in the region on GGA13.For BW1, BW3, BW5, BW7, ChWi and MeL, no significant epistatic interactions were detected. Fifteen pairs of SNPs with significant interaction effects on MeC were detected  method to minimize false positives. Utilization of the Bonferroni correction method will however also decrease the discovery of true interactions compared to the use of a false discovery rate correction. Therefore, the SNP X SNP interaction detected in this study may be fraction of the interactions that affect these. Second, when considering the linkage disequilibrium (LD) between the SNPs and the QTL for traits of interest, an interaction between two SNPs on the same chromosome may detect a haplotype effect but not an interaction. In other words, it is not possible to separate true interaction effects from haplotype effects if the two SNPs are located on the same chromosome. Therefore, in order to increase the power to detect the true interaction effects for the growth and carcass traits, we deleted interactions that occurred on the same chromosome . Third, For testis weight, a total of 421 pairs of SNP-SNP epistatic interactions were detected. These pairs of SNPs comprised 211 single SNPs, and none of these individual SNPs were identified in our previous GWAS analysis for testis weight . A similSDHB, PARK7 and VAMP3) and nine genes  on GGAZ might be important for testis growth and development based on their annotated functions. The previous result indicated that the motility and viability of sperm were positively correlated with mitochondrial SDHB [SDHB may serve as a marker of sperm quality and male fertility [PARK7 (DJ1) is highly expressed in human testes and has been shown to be essential for sperm maturation and fertilization [VAMP3 has been shown to play an important role in the process of fertilization of sperms in pig [AGTPBP1 was important for spermatogenesis, moreover, it was important for the survival of germ cells from the spermatocyte stage onward [2+/calmodulin-dependent protein kinase IV (CaMKIV) and calspermin (CaS) [CDC14B mutant mice were less fertile than the wild-type control [−/− mice [d/d male mice were subfertile [W749FLAG/W749FLAG were fertile, however, Ror2W749FLAG/W749FLAG male mice showed a decreased in fertility [CCL5 and MIR142. A total of 32 Refgenes were located in the 4.3 Mb region of GGA10. Among these genes, TCF12 was predicted as a target gene for miR-142-5p [TCF12 was the only gene that was predicted to be the target of miR-142-5p using three packages online. Therefore, it is proposed that miR-142-5p and TCF12 might work together to regulate the reproductive function of male broilers. Furthermore, TCF12 was a partner of TCF21, which was detected as an important gene for testis growth and development in our previous GWAS result [There were 11 sub-networks with at least three nodes that were identify by using the 421 pairs of interaction effects. Sub-network1 was the biggest one and most of the interactions in Sub-network1 occurred between GGAZ and GGA21. The two regions on GGA21 and GGAZ that may harbor genes important for testis growth and development spanned 572 kb and 9.5 Mb, respectively. Three genes on GGA21  –38. CaMKin (CaS) . CDC14B  control . Reduced−/− mice . The resbfertile . The desbfertile –47. In mertility . StarD6 R-142-5p . We alsoS result .CDH12 on GGA2 and WNT8A on GGA13, which are both located in the same cadherin signaling pathway, may be important for bone growth. It had been shown that the pathway was involved in many biological processes, such as development, neurogenesis, cell adhesion, and inflammation, and also involved in many disease, such as cancer [For MeC, 15 pairs of SNPs with significant epistatic effects were detected, which indicated an interaction between GGA2 and GGA13. It is proposed that s cancer .TCF12, and some other genes in GGA21 and GGAZ  might be important for testis growth and development. In contrast, very few significant epistatic interactions were identified for other carcass and growth traits. These results indicate that epistatic interaction may play very different roles in the control of phenotypic variation for different traits in chickens.In the current study, a large number of epistatic interactions were found to be significantly associated with testis weight in chicken. It appears that miR-142-5p along with its target gene Additional file 1: Figure S1.Phenotypic distribution of the carcass and growth traits in the lean and fat lines, respectively. The red and light blue bars represent the traits in the lean and fat lines, respectively. The dark blue bars represent the overlap of the traits between the two lines. (PDF 322 kb)Additional file 2: Table S1.The 421 pairs of SNPs with significant interaction effects on testis weight (TeW). (XLSX 67 kb)Additional file 3: Figure S2.The phenotypic distributions of the four different genotype classes for the most significant AA effect between GGaluGA22768 on GGA3 and Gga_rs14722408 on GGA10. (PDF 122 kb)Additional file 4: Figure S3.P-value of an interaction . The color of the edge indicates the type of epistatic effect . (PDF 1222 kb)Epistatic network among SNPs that affect testis weight (TeW). Each node represents a SNP. The chromosome in which a given SNP is located is shown within the circle. A pair of SNPs connected by an edge had a significant interaction. The colors of the nodes represent the Additional file 5: Figure S4.The LD blocks of the SNPs on every chromosome in Sub-network1 for testis weight (TeW). This information was used to simply Sub-network1. (PDF 619 kb)


Quality deficiencies in single nucleotide polymorphism (SNP) analyses have important implications. We used missingness rates to investigate the quality of a recently published dataset containing 424 mitochondrial, 211 Y chromosomal, and 160 432 autosomal SNPs generated by a semicustom Illumina SNP array from 5 392 dogs and 14 grey wolves. Overall, the individual missingness rate for mitochondrial SNPs was ~43.8%, with 980 (18.1%) individuals completely missing mitochondrial SNP genotyping (missingness rate=1). In males, the genotype missingness rate was ~28.8% for Y chromosomal SNPs, with 374 males recording rates above 0.96. These 374 males also exhibited completely failed mitochondrial SNPs genotyping, indicative of a batch effect. Individual missingness rates for autosomal markers were greater than zero, but less than 0.5. Neither mitochondrial nor Y chromosomal SNPs achieved complete genotyping (locus missingness rate=0), whereas 5.9% of autosomal SNPs had a locus missingness rate=1. The high missingness rates and possible batch effect show that caution and rigorous measures are vital when genotyping and analyzing SNP array data for domestic animals. Further improvements of these arrays will be helpful to future studies. Single-nucleotide polymorphism (SNP) arrays have received wide recognition for detecting DNA polymorphisms in domestic animals . The avaWe retrieved dog SNP datasets from Dryad (doi: 10.5061/dryad.v9t5h) . DetaileFull iMR and lMR results are shown in Supplementary Tables S2 and S3, respectively. As summarized in Overall genotype missingness rates (MR>0) for mitochondrial and Y chromosomal SNPs were realized in 2 367 (43.8%) and 766 (28.8%) individuals, respectively, with the missing genotyping proportions in each breed summarized in Supplementary Table S4. Of the 980 individuals with mitochondrial MR=1, 374 were males, which all had Y chromosomal MR>0.96 . FurtherAssessment of lMR showed that none of the mitochondrial or Y chromosomal SNPs achieved complete genotyping (MR=0). While 5.9% of the autosomal SNPs were completely genotyped, 0.5% of the autosomal SNPs together with 0.7% of the mitochondrial SNPs had a ≥20% MR among the study individuals . OverallThe missingness rate can be used to clarify overall quality of genotyping. Problems at any stage of the genotyping process can adversely impact data analyses, including the definition of haplotypes and calculation of genetic diversities. Missingness rates can inform decisions on how to account for possible errors to support the genotyping process, and possibly inform technological advancements in SNP arrays . The obsBatch effects commonly occur in high-throughput technologies, where a subgroup of observations show qualitatively different behaviors across conditions, which might not be related to biological variability . Batch eIn the present study, MRs tended to be higher for pure breed dogs than for other dogs, suggesting potential breed-based differential SNP array missingness, contrary to more robust technologies such as next-generation sequencing. Missing genotype calls are widespread in high-throughput genotyping, but their effect on subsequent analyses has been largely ignored . In SNP Due to the diverse, complex, and cryptic nature of genotyping issues in high-throughput technologies, such as batch effects, a thorough understanding and awareness of potential causal avenues, consequences, and mitigation strategies are serious concerns among researchers . SNP arrN.O.O. thanks the support of the Chinese Academy of Sciences-The World Academy of Sciences (CAS-TWAS) President's Fellowship Program for Doctoral Candidates. G.-D.W. and M.-S.P. are grateful for support from the Youth Innovation Promotion Association, CAS.All data and software used in this paper are freely available. The SNP dataset for the 5406 dog samples has been published previously , and is


Myospalax baileyi) in the high-altitude Qinghai-Tibetan Plateau. Transcriptomes of zokors from three populations with distinct altitudes and ecologies  were sequenced and compared. Phylogenetic and principal component analyses classified them into three divergent altitudinal population clusters. Genetic polymorphisms showed that the population at H, approaching the uppermost species boundary, harbors the highest genetic polymorphism. Moreover, 1056 highly up-regulated UniGenes were identified from M to H. Gene ontologies reveal genes like EPAS1 and COX1 were overexpressed under hypoxia conditions. EPAS1, EGLN1, and COX1 were convergent in high-altitude adaptation against stresses in other species. The fixation indices (FST and GST)-based outlier analysis identified 191 and 211 genes, highly differentiated among L, M, and H. We observed adaptive transcriptome changes in Myospalax baileyi, across a few hundred meters, near the uppermost species boundary, regardless of their relatively stable underground burrows’ microclimate. The highly variant genes identified in Myospalax were involved in hypoxia tolerance, hypercapnia tolerance, ATP-pathway energetics, and temperature changes.Animals living at high altitudes have evolved distinct phenotypic and genotypic adaptations against stressful environments. We studied the adaptive patterns of altitudinal stresses on transcriptome turnover in subterranean plateau zokors ( Myospalax, are burrowing rodents that resemble mole rats, Spalax. In many subterranean mammals, like the Myospalax baileyi in Tibet, environmental stresses play a major role in their adaptive evolution1. Though they reside underground, Tibetan plateau zokors may have transcriptomic changes that might correlate with altitude. Notably, extinction ensues if organisms can’t adapt to the changing environmental stresses2. In addition, high-altitude stresses like solar radiation, hypoxia, hypercapnia, low temperature, and food shortage work in concert imposing interactive physiological challenges on these organisms. In regions such as the Tibetan plateau, solar radiation increases 8% ± 2% per 1000 m with elevation3, whereas temperature decreases by approximately 6 °C/km as the altitude increases5. The temperature and humidity within the burrow system of the plateau zokors are positively correlated with the air temperature and humidity above ground, despite that the burrow atmosphere is relatively stable6. Likewise, hypoxia in high altitudes coupled with the hypoxic subterranean environment reduces the oxygen supply to the respiratory tissues and cells, and influences the rate of metabolism9. Many animals adapt to such hypoxia by suppressing the rate of metabolism, or reducing oxygen demand10, including torpor or dormancy. However, homeothermic subterranean mammals which inhabit cold environments must sustain heat production to compensate for the cold stress. This results in producing sufficient energy to perform heavy daily digging activity, which may increase the metabolism rate and oxygen consumption11. Thus, deciphering the molecular genomic and transcriptomic mechanisms for adaptation of subterranean mammals to high altitude, such as on the Qinghai-Tibetan Plateau, will contribute to a better understanding of adaptive evolution.Zokors, genus 14. Much rain occurs during summer on the Tibetan Plateau, thereby blocking the pores within the soil. Likewise, the gas permeability of the frozen soil in winter is also limited. The combination of all these factors limits the gas exchange between the underground burrow and atmospheric air. Furthermore, hypoxia increases with altitude and burrow depth15. Heavy soil on the Tibetan plateau significantly blocks air permeability16. Adapting to all of these severe stresses, organisms evolve diverse strategies including molecular genomic changes18 and alternative splicing19. Examples of such adaptations include deer mice in high altitudes that enhance their thermogenic capacity by oxidizing lipids as a primary metabolic fuel source10. Specific adaptive genes associated with altitudinal stresses have also been highlighted in humans27, dogs29, and leopards30. Most of the Qinghai-Tibetan Plateau animals studied live above-ground, but there is insufficient information on subterranean animals, on their altitudinal stress response despite their underground relative atmospheric shelter, primarily regarding humidity and temperature.The gas composition in underground burrows is affected by many factors including seasonal change, soil type, rain, depth of burrows, and metabolism of the animalMyospalax baileyi) are blind subterranean mammals living from 2,600 m to 4,600 m31 on the Tibetan Plateau, spending nearly all their lives underground33. Plateau zokors burrow down to a depth of 70–250 cm underground, due to the frozen soil during the winter from November to March34. Similar to other subterranean mammals37, plateau zokors are also subjected to stresses such as hypoxia, hypercapnia and darkness, constant energy requirement during digging and exposure to pathogens, as in Spalax38. Noteworthy, the brain size of Spalax increases with higher environmental stresses39. Likewise, the brain is the most sensitive organ to hypoxia. Thus, we have chosen to use brain tissue in our Myospalax experiments.Plateau zokors . This is especially critical in the upper level of 4,000+ m approaching the species uppermost boundary in Tibet. Moreover, gene ontology analyses revealed that putatively selected genes, like EPAS1 and COX1, were overexpressed under hypoxia conditions24. We used the comparative SNPs frequencies analysis with other Tibetan animals and meta-analysis to verify our findings at the different altitudes for zokors. Figure H altitude for G −> A, A −> G, T −> C, C −> T, and G −> C, comparing the data with altitudes. Moreover, we showed that natural selection, which was substantiated statistically, has adaptively molded zokor’s transcriptome between distinctly divergent populations of the same subterranean rodent species, Myospalax baileyi, and across a few hundred meters, to adaptively and effectively cope with Qinghai-Tibetan Plateau altitudinal stresses.In this study, we identified 1,056 differentially expressed genes whose expression was upregulated upward across the short distance of 432 m from L, 2,846 m), middle , and high , in the Tibetan Plateau . However, the SNPs density was significantly different for the comparisons of Lvs. H (P = 0.005) and Mvs. H populations   and middle (M) altitudinal populations are similar but they were very different from the high-altitude population H , and produced the lower variants in G−> A, and the higher variants in G−> C  Table . Three d4) Table  and Prin4) Table  based onely Fig. . All togn H Fig.  due to iH, displays the largest proportion of polymorphic loci. The genetic diversity of the three populations was estimated by both θ and л. The θ value was 3.88 × 10−6, 3.26 × 10−6, and 5.56 × 10−6 for L, M and H, while the л value was 3.90 × 10−6, 3.39 × 10−6, and 5.84 × 10−6, respectively  at H, but not at L and M. The average SNP numbers within genes, and across the whole data set of the H group are 1.18 and 1.5, respectively . The proportion of SNPs within genes unique to the L, M, and H were 0.004, 0.003, and 0.085, and SNPs across the whole data set were 0.027, 0.018, and 0.517, respectively. The proportion of unique SNPs in the H population was significantly higher than that in L and M . The difference in proportion between SNPs across the whole data set and SNPs within genes in H was significant   Fig. S. Remarkade Fig. S. The higry Fig. S. SNPs inry Fig. S. These rlly Fig. .L vs. M, M vs. H, and L vs. H show that 99, 1,056, and 862 UniGenes were significantly up-regulated, while 150, 488, and 357 UniGenes were down-regulated, respectively  |>1] of animals from L vs. M, M vs. H, and L vs. H pairs  was found in the L vs. M pair. DEGs from each pairwise altitudinal group were shown by means of volcano plots  and statistically significant changes for each pairwise comparison. Finally, the Gene Ontology (GO) analysis revealed that the potential adaptive genes were overexpressed in hypoxia and blood pressure regulation. Remarkably, EPAS1, EGLN1, and COX1 were identified with specific signature, which correlated with high-altitude convergent adaptation in other species. The GO enrichment was performed on the DEGs in L vs. M, M vs. H, and L vs. H. The functional enrichment of DEGs between L and H revealed that up-regulated genes were mainly related to energetics, neurogenetics, and nutrition . Comparisons of gene expression levels of ly Fig. S. A compars Fig. S show expts Fig. S, where ton Fig. S, which con Fig. S.FST and GST parameters display population differentiation. FST and GST outlier genetic analyses showed that there were 191 and 211 genes (Supplementary Data) that were highly differentiated between L, M, and H with 149 shared genes. Notably, the genes of APOPT1 and COX1 showed large divergences in both M vs. H, and L vs. H pairs. The differences between L and M were the smallest, while the differences between L and H, and M and H were distinctly larger  value higher than 0.5. Within the H group, 3,379 genes were considered putative signatures of selection. Remarkably, there were 1,039 genes with LSBL value equal to 1, which harbored some distinctive mutations. Some genes with large LSBL values, like COX1 (LSBLH)( = 1), EPAS1(LSBLH)( = 0.75), and EGLN1(LSBLH)( = 0.5), were identified with strong selection signature in H. Interestingly, regarding adaptive hypoxia tolerance, we found GO terms mainly involved in heme vascular endothelial (heme-copper) terminal oxidase activity, oxidoreductase activity, acting on a heme group of donors and vascular growth as well as development     Fig. S and EPAS6) Fig. S.Figure 444. ABC-based methods approximate the likelihood function by simulations, the outcomes of which are compared with the observed data. With the ABC rejection algorithm, a set of parameter points is first sampled from the prior distribution. The ABC model44 revealed that the gene expression level difference occurs due to genetic components , as environmental components were “fixed” during our computational implementation. Therefore, we claim, although we did not perform the common garden experiment, that gene expression level differences were strongly affected by genetic background and natural selection. The ABC model substantiates the conclusion that SNP density increases at the 432 m between the M population at 3,282 m and the H population at 3,714 m. To conclude, we showed that in Myospalax the gene expression level differences involve both genetic and environmental (plasticity) components. To separate the genetic and environmental (plasticity) effects on the transcriptome, the ABC model was implemented to show that there is a clear bifurcation in SNP density in the highest altitude, where the cluster-mean increases. The cluster-mean for SNP density in the middle altitude has not showed a significant increase  to H, i.e., towards the higher stresses; second, the ABC model clearly indicates that there is a certain genetic component that leads to the changes in the SNP density at the highest altitude.The Approximate Bayesian computation (ABC) algorithm has been widely used in the scientific community to analyze population demography, growth rate, and time of divergence in the last decadease Fig. . Next, wng Fig. S. In addid Tables –S12. To 46. The analysis included different species from each L, M, and H sites to support the genetic diversity and selection findings , we randomized them into 27 studies (nine for each set). We used the SNP data for plateau zokor and calculated the p-values for a 95% confidence interval. The main reason for the randomization was a way to test our hypothesis: whether the p-values would improve for SNPs from M to H altitude. Thus, Tables M to H altitudes45. Thus, taken together with the previously made comparison of SNPs of zokors with other organisms, we identified the same alterations in the SNPs frequency in other studies on Tibetan animals at similar altitudes, as we have observed in zokors.Meta-analysis was done to improve the sample selection and highlight the data divergence and convergencengs Fig. . Figure L, M, and H), have been compensated by the entire transcriptome analysis, also using a comparative and meta-analysis with other Tibetan animals42. The de novo assembly of RNA sequencing data was used to identify that the gene expression showed unique gender, and spatio-temporal and tissue-specific characteristics, as shown also in other organisms50. To eliminate abiotic and artificial effects caused by gender and tissue differences, only males were collected, and brains were harvested for transcriptome analysis, since the brain tissue is remarkably sensitive to oxygen levels and requires high oxygen for its function. All the animals were above 230 g  to analyze the effect of the altitude on their transcriptome and SNPs density property. The relative small number of samples, three in each of the three populations , environmental stress appears to play a key role in population differentiation, particularly between M  and H , approaching the biological threshold of the plateau zokors’ survival threshold. Mutations occurring in coding regions, especially those that are non-synonymous in nature, alter amino acids and hence influence the function or activity of proteins. This leads to the adaptations described as resisting multiple environmental stresses. In the present study, 643 non-synonymous SNPs were found in H as compared to 284 found in L and 323 found in M. As the altitude rises, especially from M to H, the environmental stresses are significantly accentuated, and genetic polymorphism dramatically increases. The association between genetic polymorphism and environmental stress was shown at local, regional, and global scales by allozyme, and DNA markers39, and at the SNP level52. This is most likely due to the increasing innovative adaptations and recombination to cope with climaxing environmental stresses. Particularly, it is the higher genetic polymorphism that enhances fitness and facilitates adaptations to the more severe stresses at a higher altitude2. Moreover, SNPs located in 3′ and 5′ UTR are important because they affect miRNA binding54. In this study, we found more SNPs in UTRs of the H population leading to high efficiency of gene expression and associated with the highest stress site in our samples , which is the uppermost boundary of the species Myospalax baileyi.The ses Fig. . This reM and H, and L and H populations are only 2.46% and 4.68%, respectively. However, the shared SNPs between L and M populations are substantial: 44.53% for M and 47.54% for the L population. This suggests similar stresses between L and M but different due to severe stresses at high altitudes. This is quite remarkable as H is only 432 m above M. This 432 m inflicts higher stresses near the upper species range hence generating dramatic transcriptome responses  were strongly selected and harbored many unique mutations. These three genes were shown to be associated with high-altitude evolutionary convergent adaptation in many other species61. APOPT1 plays an important role in the regulation of apoptosis, promoting programmed cell death62, which is cardinal in coping with hypoxia and avoiding cancer. In the present study, the APOPT1 gene showed a large differentiation at H, most probably due to the upward increasing hypoxic stress at the higher altitude, thus decreasing the risk of hypoxia-induced damage as well as the increasing potential of cancer. Oxidative/reductive stresses occur at high altitudes where there is lower oxygen pressure63. APOPT1 could suppress the level of reactive oxygen species64 to protect tissues from damage. The COX1 gene is another outlier gene in both genomic variants and gene expression analysis, and is known to be associated with hypoxia adaptation65. EPAS1 and EGLN1 have also been reported as playing a significant role in the adaptation to Tibetan Plateau stresses66. Mutations unique to plateau zokors may also facilitate the adaptation to high-altitude Qinghai-Tibetan Plateau stresses, but further functional verification should be done to substantiate this in plateau zokors, and show experimentally that the H population is better in resisting hypoxia than other populations.The allele number of SNPs across the whole data set is significantly higher in ns Fig. S, highligde Fig. S. Locus-sM. baileyi, at three altitudinal populations in the Tibetan Plateau, 2,846 m (L), 3,282 m (M), and 3,714 m (H) demonstrated interesting transcriptome evolutionary adaptations. These adaptations positively correlate with altitude at a short distance of 868 m, but primarily at an even shorter distance of 432 m at the higher altitude, approaching the uppermost boundary of the species survival. Adaptive complexes generated by natural selection, as shown by the Tajima’s D test, involved SNP genetic polymorphisms, and GO elements including hypoxia, energetics, neurogenetics and nutrition, which correlated with the following environmental stresses: low-oxygen tension, cold temperature, and drought as well as food shortage. This is highlighted by stress genes displaying an increase in SNP density at the higher altitude, near ecological extremes of the species boundary. Therefore, our results uncovered the adaptive evolution of the subterranean zokor because of the highly stressful environment at different high altitudes in the Tibet Mountains. What is next? Epigenetic studies, changes in the methylation, microRNAs, and other regulators may highlight the regulatory mechanisms evolved at the high-altitude adaptation. The analysis of the noncoding regulatory elements and extended experimental lab work will probably identify the physiological increase in hypoxia resistance from L to H altitudes.Stressful environments, such as the Tibetan Plateau, are excellent biological- evolutionary laboratories to decipher transcriptome evolution across very short distances. This is especially true if tests are conducted intra-specifically in populations at their extreme species ecological distribution ranges at higher altitudes. Our results in plateau zokors, H from L and M, at the p-value of 0.012, while the p-value for L and M was 0.2. Genetic polymorphism among the three altitudinal divergent populations was compared using SNP density. GO enrichment was performed on DEGs  that were identified among the three altitudinal populations. Natural selection was detected by FST and GST -based fixation indices and Tajima’s D test. Finally, using the ABC algorithm, we used the SNP density at L, M and H, to understand whether there is any dramatic change in their measurement across altitudes. In this method of model-based statistical inference, a likelihood function expresses the probability of SNP density data under the Null-hypothesis, stating that there is no dramatic increase in SNP density with respect to altitude. The outcome of our ABC rejection algorithm clearly showed that SNP polymorphism density with increasing altitude includes all the genetic components. The individual ancestry was estimated by FRAPPE v 1.1. Genetic polymorphism among the three altitudes was compared using SNP density. The GO enrichment analysis was performed on DEGs that were identified among the three altitudinal populations by Database for Annotation, Visualization and Integrated Discovery (DAVID), a knowledge base of functional annotation tools.All the operations on plateau zokors were approved by the Qinghai Forestry Bureau, and all animals were treated in accordance with Animal Care and Use Committee Rules of Northwest Institute of Plateau Biology, Chinese Academy of Sciences. Transcriptome sequencing was performed on animals from three altitudinally distinct populations of the Tibetan Plateau. Genetic divergence was estimated by the Neighbor-Joining (NJ) cluster method using TreeBeST v1.9.2, and by the principal component analysis (PCA) based on the SNPs across the transcriptome. The permutation test was conducted for PCA analysis to ecologically differentiate the Supplementary Information


Apis mellifera L.) has been changed by humans in recent decades to such an extent that the formerly widest-spread European subspecies, Apis mellifera mellifera, is threatened by extinction through introgression from highly divergent commercial strains in large tracts of its range. Conservation efforts for A. m. mellifera are underway in multiple European countries requiring reliable and cost-efficient molecular tools to identify purebred colonies. Here, we developed four ancestry-informative SNP assays for high sample throughput genotyping using the iPLEX Mass Array system. Our customized assays were tested on DNA from individual and pooled, haploid and diploid honeybee samples extracted from different tissues using a diverse range of protocols. The assays had a high genotyping success rate and yielded accurate genotypes. Performance assessed against whole-genome data showed that individual assays behaved well, although the most accurate introgression estimates were obtained for the four assays combined (117 SNPs). The best compromise between accuracy and genotyping costs was achieved when combining two assays (62 SNPs). We provide a ready-to-use cost-effective tool for accurate molecular identification and estimation of introgression levels to more effectively monitor and manage A. m. mellifera conservatories.The natural distribution of the honeybee ( Apis mellifera L.) is a blended ecosystem service of managed and unmanaged  colonies that is under threat from human-mediated environmental changes including climate change, habitat loss, habitat fragmentation, pesticides, and introduced parasites and pathogens2. There is growing evidence that management of locally adapted genetic diversity in honeybee subspecies and ecotypes is key to the long-term sustainability of this service5. Accordingly, actions towards preserving the large stores of genetic diversity held by the 31 honeybee subspecies9 are expected to counteract the trend of global colony losses.Pollination by the honeybee  and the South-eastern European (lineage C). Lineage M includes only two subspecies: the Dark honeybee Apis mellifera mellifera and the Iberian honeybee Apis mellifera iberiensis. Yet, these two subspecies cover the largest territory in Europe with A. m. iberiensis occupying the Iberian Peninsula and A. m. mellifera ranging from France in the south to Scandinavia in the north, and from Ireland and the UK in the west to the Ural Mountains in the east10. Lineage C occurs in a smaller geographical area composed of the Apennine and Balkan peninsulas and includes the most widely kept honeybee subspecies: the Italian Apis mellifera ligustica and the Carniolan Apis mellifera carnica. In spite of its wide distribution, A. m. mellifera is the subspecies most under threat as it is considered extinct in many parts of Europe not only because of the human-mediated environmental changes but more insidiously through replacement by and introgression from non-indigenous subspecies, particularly A. m. ligustica and A. m. carnica13.Of the 31 subspecies that have been identified in the natural distributional range of 14. However, this form of admixture reduces the frequency of locally adapted gene complexes, leading to an increased likelihood of reduced survival rates of colonies15. How to protect locally adapted gene complexes that are more suited to local environments is a growing problem, as the increased breeding and movement of C-lineage honeybees promotes sympatry and gene flow between A. m. mellifera and imported commercial breeds. Efforts to assist conservation of A. m. mellifera are gathering momentum in multiple European countries (www.sicamm.org) and with the knowledge that reduced adapted genetic diversity threatens both managed and unmanaged populations, the interests of commercial beekeeping and honeybee conservationists should be aligning, particularly in A. m. mellifera indigenous areas.It has been argued that, unlike with other domesticated stock organisms, management and selective breeding in honeybees increase genetic diversity through introgressionA. m. mellifera populations in official or unofficial conservatories is to give the stakeholders an accurate and cost-efficient tool to test for C-lineage introgression. Microsatellites have been extensively used to examine C-lineage introgression in A. m. mellifera16. Yet, the numerous advantages of SNPs over microsatellites promise to make them the tool of choice for population monitoring and conservation purposes. In addition to being more abundant and widespread in the genome17, SNPs display lower genotyping error, have higher quality data, are more amenable to automated analysis and data interpretation, and can be easily transferred between laboratories18. Moreover, SNPs proved to be more powerful than microsatellites at estimating C-lineage introgression in A. m. mellifera19. These properties make SNPs a powerful tool for testing the breeding stock in A. m. mellifera conservatories and SNP data can be readily incorporated in shared genetic databases, facilitating implementation of a conservation strategy at the European scale.An important first step in protecting A. m. mellifera breeding stocks. Also, while costs have dropped dramatically, WG sequencing is still unaffordable for most stakeholders committed to the long-term sustainability and conservation of honeybees. Costs are accrued as WG analysis requires considerable computing storage and processing power and trained bioinformatics personnel. However, encouragingly, Muñoz, et al.20 showed that reduced panels of highly informative SNPs can accurately identify honeybee stocks23. Genotyping using reduced SNP panels considerably decreases laboratory processing costs. Furthermore, analysis of the generated genotypes requires low computational power and conventional bioinformatics skills.Whilst SNP analysis on whole genome (WG) sequence data may be required in studies concerned with fine-scale relatedness, such deep sequencing is disproportionate when determining introgression levels for the discrimination of et al.20 developed reduced SNP panels for genetic identification and introgression analysis in A. m. mellifera. The authors used a combination of metrics to rank by information content over 1183 SNPs that had been genotyped in A. m. mellifera, A. m. ligustica and A. m. carnica using the 1536-plex GoldenGate® Assay of Illumina13. The top-ranked SNPs were combined into five nested panels whose sizes  fitted the plexes of the now discontinued GoldenGate® Assays formerly genotyped with the VeraCode® technology. Here, we built from the 144-SNP panel to propose four customized assays tailored for high sample throughput genotyping using the iPLEX MassARRAY system. By providing a ready-to-use molecular tool for accurately, rapidly, and cost-effectively genotyping large sample sizes of A. m. mellifera, we hope to bring affordable C-lineage introgression detection to stakeholders in the fight to safeguard remaining reservoirs of unique combinations of genes and adaptations in A. m. mellifera and to expand its reduced current distribution.Muñoz, et al.20, the Assay Design software was able to multiplex 127 into four assays (identified by letter M), each containing a variable number of SNPs ranging from 38 in M1 to 24 in M4  with those inferred from the genome-wide SNPs, which provides the best estimate of the admixture proportions  with those inferred from the WG dataset , although the combination of M1 + M3 (62 SNPs) and M1 + M2 + M3 (94 SNPs) with the highest individual correlations produced equally interesting statistics with mean accuracies >96.9%, absolute precision error <0.04, and with over 28 individuals (out of 32) with absolute accuracy error <0.05. Performance was also assessed by counting purebred A. m. mellifera individuals misclassified as admixed  and vice versa , two (M1 + M2) or three assays (M1 + M2 + M4) correctly identifying all 16 admixed individuals .The performance of the four assays was assessed by comparing their et Table . The laret Table . The bessa Table  purebredA. m. mellifera, 16 A. m. carnica, and 16 F1 hybrids. On average, Q-values inferred from the genotypes called using the four individual  and three combined assays  fit the thresholds defined for the two subspecies and hybrids  when the Q-values were inferred from called genotypes. However, when mixed combinations of the four assays were employed, the estimated Q-values matched the expectations with all A. m. mellifera and A. m. carnica correctly classified as purebred  at five dilution ratios. Of the 117 SNPs, only 103 were informative in this experiment . As expected, the sensitivity decreased as the dilution ratios increased, with only 29 unlinked SNPs being able to detect the A. m. ligustica alleles in every dilution and replicate , 1,977 (77%) were accurate, as determined by comparing the calls for single workers with those of the pools. The most common sources of mismatch were “the most frequent allele” and “higher DNA concentration” Table .Table 2IQ-values estimated for the 22 pools from the called genotypes were similar to those estimated from the expected genotypes using either the four assays, M1 + M3, or the 29 SNPs . Furthermore, the MassARRAY platform was able to detect low frequency alleles, either of M-lineage (pools containing A. m. mellifera) or C-lineage ancestry (pools containing A. m. carnica or Buckfast), even when the tissue dilution was as low as 1:7 , Scotland (0.006 ± 0.011) and Netherlands (0.046 ± 0.141) with over 80% of the individuals showing a Q-value < 0.05, although most individuals (91%) of the unprotected populations of Ireland were also very pure (0.021 ± 0.022). Populations of Denmark, France and Switzerland exhibited greater Q-values  in both protected and unprotected populations with ≤11% of pure individuals. Admixture proportions estimated for A. m. ligustica and A. m. carnica sampled from native and introduced ranges showed that they are very pure , excepting for some Swiss colonies (0.750 ± 0.296). The commercial breed Buckfast was mostly of C-derived ancestry (0.806 ± 0.055).The four assays were applied to 478 colonies of various ancestries, represented by single 431 colonies) or pooled individuals (47 colonies), collected in 13 European countries  and 0.118 ± 0.042 (unprotected), which were lower than those inferred from a single individual . These findings indicate that honeybee conservation managers can choose the software of their preference and, more importantly, can simultaneously analyse workers and drones without biasing estimates of C-lineage introgression in 20 into four multiplexes. While the iPLEX protocol allows multiplexing up to 40 SNPs, only assay M1 (38 SNPs) approached the maximum plexing capacity. This is in part due to the relatively small size of the baseline SNP set from which the Assay Design had to work. However, the plex level of each assay can be expanded any time. By using the Replex option of the software, additional high-graded nuclear SNPs or even mitochondrial SNPs can be added to the customized four assays for detecting C-derived genes at both genetic compartments.The Assay Design software was able to combine only 127 of the 144 high-graded SNPs27.The iPLEX MassARRAY system revealed highly accurate and delivered high-quality calls for 117 of the 127 SNPs. Quality assessment was greatly facilitated by the honeybee haplodiploid system. Using the SNP calls of the drone subset, problematic SNPs were easily detected by locating genotypes erroneously typed heterozygous. Three such SNPs were consistently identified in numerous drones. While the mechanism responsible for the false allele is unclear, it is possible that gene homology is the source of miscalling at least in locus 1379-est5929. Using the 120-bp flanking region of this SNP locus, a NCBI query found a second hit with 98% similarity in the honeybee genome. The 117 SNPs were successfully genotyped in over 96% of the samples, indicating that the customized four assays and the iPLEX MassARRAY system work well in DNAs obtained from a variety of tissues with the virtually full spectrum of extraction methods routinely employed in honeybee research29. Further assessment of the four assays (used singly or combined) at the individual level indicates that there is a greater chance of misclassifying purebred individuals as admixed than the reverse, viz. misclassifying admixed individuals as purebred. This result has practical implications in conservation management suggesting that it is more likely that A. m. mellifera genetic diversity is erroneously discarded from the breeding population than C-derived genes are maintained. At this point, simulation and empirical studies are needed to determine the best threshold criterion to separate purebreds from admixed individuals30. While the stringent Q-value threshold of <0.05 arbitrarily established here for defining purebred A. m. mellifera may assure a more efficient purging of C-derived alleles, it may also lead to erosion of A. m. mellifera diversity and loss of unique gene complexes. The problem is that low diversity is particularly detrimental for honeybees because it may decrease colony resistance to brood diseases31 and increase genetic load at the sex locus32. Therefore, managers of A. m. mellifera conservatories need to make a trade-off between purging foreign alleles from the breeding population while minimizing the effects of reduced diversity.The four combined SNP assays were able to estimate introgression with a high degree of accuracy. However, performance decreased to some extent when SNP assays were used singly and the 23-plex M4 showed the worst behaviour for most statistics. This finding is consistent with studies on other organisms which have also detected drops in accuracy when the number of SNPs is <25Q-values obtained for the F1 hybrids were in close proximity to the expected 0.50, although there was a bias towards C-derived genes as most Q-values were >0.50. When used singly, the SNP assays failed to correctly identify all purebred individuals and the Q-values were more dispersed around 0.50. However, when the assays were combined, the performance increased with all purebred individuals correctly classified and the Q-values showing a lower dispersion around 0.50. Interestingly, despite the lower number of SNPs contained in M1 + M3 (62 vs 117), this assay combination shows an overall performance similar to that of M1 + M2 + M3 + M4.Validation of the four SNP assays in an independent set of individuals, including F1 hybrids , further confirms the resolution power of our customized SNP assays. Interestingly, the Sustainable conservation management requires tools capable of reliably identifying breeding colonies in a time- and cost-efficient manner. The SNP assays tested herein have a high resolution power for accurately estimating introgression, and the iPLEX MassARRAY system offers an interesting option for rapid and cost-effective genotyping. This system is very flexible and scalable allowing a variety of options for sample and assay throughput at a variable cost, depending on the chip format  chosen. The 384 format, for example, allows genotyping 384 samples with a single assay at an approximate outsourced cost of 4.5€ per sample. Alternatively, this format could be used to genotype 192, 128, or 96 samples with two, three, or four assays, respectively. This option would incur in an increment of 4.5€ for any additional assay. Based on overall results, the best compromise between genotyping costs and assay accuracy is achieved when using M1 + M3.33 thereby doubling the per-sample cost. However, this charge does not include PCR and microsatellite fragment analysis. Contrary to microsatellites, outsourced SNP genotyping with the iPLEX MassArray system only requires DNA (instead of PCR products) to generate a table of genotypes ready to analyse, avoiding the hurdle of fragment analysis.Genotyping a single microsatellite multiplex in a 96-plate format costs approximately 2.5€ per sample. Introgression proportions using microsatellites has typically been estimated from over 11 loci, which requires genotyping a minimum of two multiplexes34. This means that in areas where A. m. mellifera and commercial colonies are sympatric, matings may occur with drones of C-lineage ancestry originating colonies made up of subfamilies with diverse genetic backgrounds. Although population-level studies typically require genotyping a single worker per colony16, colony-level introgression estimates may require genotyping several individuals to more effectively capture the colony structure. The problem is that genotyping several workers per colony is time consuming and costly. An economical way to circumvent this issue is to genotype pools instead of individuals35, provided that the genotyping system of choice is sensitive enough to detect low-frequency alleles.Honeybee queens mate in flight with up to 20 dronesHere, we assessed whether our customized SNPs assays and the iPLEX MassARRAY system offer a reliable alternative for pool genotyping. Both DNA and tissue pooling experiments show that the genotyping system is very sensitive as it was able to detect low frequency alleles. Despite the small number of SNPs showing consistent amplification across experiments, introgression analysis indicates that as few as 62 SNPs (M1 + M3) were able to detect highly diluted C-derived alleles. These results suggest that this system has the potential to detect C-lineage introgression in colonies with hybrid sub-families at low frequency, a scenario that might occur if drones of commercial colonies are able to accidentally enter congregation areas of conservatories.36. While pools constructed from equi-molar DNA concentrations would be the most correct approach to genotype a colony, pooling tissues is often the only option in conservation programs requiring screening of numerous colonies with a limited budget. Pooling tissue instead of DNA requires less time, effort and money during preparation in the laboratory and still enables detection of C-derived alleles even when most of the individuals in the pool are A. m. mellifera.Analysis of pool genotypes showed that miscalling was mainly due to the unequal contribution of each individual (different concentrations) and to the unbiased representation of allelic products that are present in a DNA pool, both common problems reported for DNA poolsA. m. mellifera. This SNP survey adds to Pinto, et al.13 by expanding the sampling in France, Switzerland, UK and by including de novo Wales and Ireland. Concordant with earlier microsatellite12 and SNP21 surveys, C-lineage introgression in A. m. mellifera is heterogeneous across Europe. Samples originating from conservatories were generally less introgressed than those from unprotected areas. Our previous and this SNP survey revealed that Scotland, Norway, Netherlands and now Ireland possess important pockets of pure A. m. mellifera. Ireland represents a particularly interesting case of A. m. mellifera diversity because, contrary to the other countries, the survey was performed in unprotected populations from a wide geographical area.The introgression analysis on the samples collected throughout Europe and genotyped using the four SNP assays and the iPLEX MassARRAY system provides a rough picture of the genetic integrity of 21 represent only partial, and in some cases biased, assessments on the status of the genetic integrity of A. m. mellifera across its distributional range, this novel tool now makes it possible to perform a comprehensive genetic survey in a time- and cost-efficient manner. We suggest that if the efficacy of this SNP tool is generally agreed among stakeholders the next step is for them to seek input from government agencies and/or research facilities and begin to describe the purity of their honeybee populations on as wide a geographic area as possible in order that conservation efforts correctly and efficiently target regions of greatest concern and greatest possible reward.As this and previous studieset al.20 identified 144 highly informative SNPs for estimating C-lineage introgression in A. m. mellifera. The flanking regions (60 bp of either side) of these SNPs were used to design multiplexed assays with the software Assay Design 4.0 (Agena BioScienceTM) for genotyping using the Agena BioScience iPLEX chemistry and the MassARRAY® MALDI-TOF platform (hereafter abbreviated to iPLEX MassARRAY). The software searched for optimal areas within the 120-bp flanking regions to design forward and reverse PCR primers while constructing the different multiplexes. The maximum multiplexing capacity (40 SNPs) allowed by the iPLEX chemistry was attempted whilst preventing hairpin and dimer formation. In addition to the PCR primers, the software designed the iPLEX extension primer placed immediately adjacent to each SNP. Of the 144 SNPs, the Assay Design was able to combine 127 SNPs distributed along four multiplexed assays  their accuracy when genotyped with the MassARRAY system, (ii) their performance when employed individually or combined, (iii) and their sensitivity when employed in pools of DNA and tissue. To that end, different combinations of samples, representing single and pooled, haploid and diploid individuals were used, as depicted in Fig. A. m. mellifera (N = 462) native range in Western and Northern Europe (protected and unprotected areas), (ii) A. m. ligustica (N = 10), and A. m. carnica native ranges (N = 10) in South-eastern Europe, (iii) introduced range of A. m. carnica in Switzerland (N = 8), Germany  and Scotland (N = 3), (iv) commercial strain Buckfast from Switzerland, Scotland, and Denmark (N = 11), and (v) F1 hybrid crosses performed in isolated mating stations in Denmark (N = 19). Nine samples of A. m. carnica and seven A. m. ligustica, previously genotyped using the GoldenGate® Assay in the BeadArray platform of Illumina13, were added to the dataset to have a better representation of C-lineage.A total of 464 colonies  for SNP genotyping.Genomic DNA was extracted from the head, antennae, thorax , legs, or abdomen of adults or immatures (larvae or pupae) of a single individual, multiple individuals , or a pool of individuals  per colony in 561 samples , A. m. ligustica (N = 10) and A. m. carnica (N = 15), by (i) identifying the heterozygous SNP loci  and with the HiSeq. 2500 platform of Illumina . The SNP loci that were called heterozygous by the MassARRAY system in >10% of the drones and showed inconsistent genotypes between at least two genotyping technologies in >5% of the drones were excluded from further analysis  were estimated by ADMIXTURE41 using datasets of varying ploidies , which produced similar Q-values (see Supplementary Information for details). Q-values were estimated for K = 2 using 10,000 iterations in 20 independent runs. The convergence between iterations was monitored by comparing log-likelihood scores (LLS) using the default termination criterion set to stop when LLS increases by <0.0001 between iterations. CLUMPAK42 was used to summarize and visualize the Q-plots.Introgression proportions . A total of 38 drones , for which there were WG sequence data available, was used in this comparison  was assessed by (i) Pearson’s correlation coefficient (r), (ii) similarity score obtained by CLUMPAK, (iii) absolute accuracy error calculated as the absolute difference between Q-values inferred from the SNP assays and the 2.399 million SNPs, (iv) mean accuracy calculated via percentage of absolute error, (v) absolute precision error calculated via standard deviation of the absolute differences, (vi) number of purebred individuals classified as admixed, and (vii) number of admixed individuals classified as purebred. Admixed individuals were defined by a threshold Q-value > 0.05. Any individual with Q-value between 0 and <0.05 or >0.95 and 1 was classified as purebred A. m. mellifera and C-lineage (A. m.carnica or A. m. ligustica), respectively.The performance of the SNP assays in estimating C-lineage introgression in A. m. mellifera , 16 A. m. carnica , and 16 F1 hybrids obtained from crosses between A. m. mellifera queens, from the conservatory in Læsø, and A. m. carnica drones from Mandø, Denmark  by ADMIXTURE and then compared with the defined thresholds of >0.95 for A. m. carnica, <0.05 for A. m. mellifera, and ~0.5 for the F1 hybrids.The four assays were validated and tested using an independent subset of 62 workers, including 30 ark Fig. . The croA. m. ligustica and one A. m. mellifera .The sensitivity of the genotyping system in detecting C-lineage ancestry in the pooled samples was also assessed via introgression analysis. The A. m. mellifera (N = 30), A. m. carnica (N = 16), Buckfast (N = 3), and F1 hybrids , as detailed in Fig. TM  SNP calls of the pools matched those of the worker with higher DNA concentration, (iii) SNP calls of the pools matched the most frequent allele, and (iv) the least frequent allele. The sensitivity of the genotyping system in detecting C-lineage ancestry in the different pools was also assessed via introgression analysis. The Q-values were estimated for the 22 pools from the expected and called genotypes, for a variable number of SNPs (four assays and best assay combination), using ADMIXTURE. The expected genotypes were inferred from the calls obtained for the single workers.The sensitivity of the genotyping system was first assessed by comparing the SNP calls obtained for the single workers with those obtained for the pools of workers. Mismatches were counted and the error identified among the following sources: (i) pools displayed alleles uncalled in single workers and A. m. mellifera (N = 425), A. m. ligustica (N = 10), A. m. carnica (N = 21), and Buckfast (N = 6) from 8 13 European countries  and unprotected (N = 300) areas. Of the 462 samples, 415 were represented by a single individual and 47 by pooled individuals  from Scotland and England was represented by both a pool of 16 workers and one individual worker. For a better C-lineage representation, nine samples of A. m. carnica and 7 of A. m. ligustica , previously genotyped using the GoldenGate® Assay13, were added to the dataset. Q-values were inferred from the genotypes of single and pooled samples using ADMIXTURE.The four assays were used to genotype in the MassARRAY platform 462 samples representing tica N = 0, A. m. A. m. carnica), 1 for heterozygous and 2 for homozygous different to reference, i.e. representing the A. m. mellifera allele. Where data was missing, the SNP was scored 1.01. For ease of interpretation, the total combined score for each SNP in each sample was calculated and the SNPs reordered from the smallest score to the largest. The SNP data and associated sample metadata was loaded into Graphia and a Pearson correlation matrix was calculated comparing the profile of SNP scores for each sample. A network graph was then constructed by connecting the nodes (samples) with edges . Utilising the overlay of metadata the graph was then explored and clustered using the Markov Cluster (MCL) algorithm43 at an inflation value (which determines cluster granularity) of 1.2.The genotype data were further examined by network analysis using the software Graphia Professional . For each sample, SNPs were scored 0 when same as reference (A. m. carnica and A. m. mellifera whole-genome sequence data is deposited at the ENA (www.ebi.ac.uk/ena) under study accession number PRJEB16533.Supplementary TablesSupplementary Information


Deciphering the genetic control of flowering and ripening periods in apple is essential for breeding cultivars adapted to their growing environments. We implemented a large Genome-Wide Association Study (GWAS) at the European level using an association panel of 1,168 different apple genotypes distributed over six locations and phenotyped for these phenological traits. The panel was genotyped at a high-density of SNPs using the Axiom®Apple 480 K SNP array. We ran GWAS with a multi-locus mixed model (MLMM), which handles the putatively confounding effect of significant SNPs elsewhere on the genome. Genomic regions were further investigated to reveal candidate genes responsible for the phenotypic variation. At the whole population level, GWAS retained two SNPs as cofactors on chromosome 9 for flowering period, and six for ripening period  which, together accounted for 8.9 and 17.2% of the phenotypic variance, respectively. For both traits, SNPs in weak linkage disequilibrium were detected nearby, thus suggesting the existence of allelic heterogeneity. The geographic origins and relationships of apple cultivars accounted for large parts of the phenotypic variation. Variation in genotypic frequency of the SNPs associated with the two traits was connected to the geographic origin of the genotypes , and indicated differential selection in different growing environments. Genes encoding transcription factors containing either NAC or MADS domains were identified as major candidates within the small confidence intervals computed for the associated genomic regions. A strong microsynteny between apple and peach was revealed in all the four confidence interval regions. This study shows how association genetics can unravel the genetic control of important horticultural traits in apple, as well as reduce the confidence intervals of the associated regions identified by linkage mapping approaches. Our findings can be used for the improvement of apple through marker-assisted breeding strategies that take advantage of the accumulating additive effects of the identified SNPs. Flowering time in temperate plants is influenced by multiple environmental factors related to temperature and day length at different periods of the year  present among individuals from natural populations or germplasm collections to dissect the genetic basis of complex trait variation  was used for the first time in the present study to perform GWAS. Here, we focused on the analysis of the genetic control of flowering and ripening periods in a panel of almost 1,200 different genotypes distributed over six apple collections managed by six European institutes. We identified one genomic region associated with flowering period and three with ripening period. Co-variation between the genotypic frequencies at the significant SNPs and three major geographic groupings of genotypes was explored, and candidate genes were identified in the detected genomic regions. To our knowledge, this is the largest association study ever performed in a fruit tree species considering both population size and SNP marker density.The high density Axiom®Apple 480 K SNP array  using the same scoring scales. Flowering period was assessed by recording dates of Fleckinger phenological flower stages F or F2 , while we considered the combined effect of site and year (Equation 2) for the whole analysis, i.e., all the collections were combined into a single analysis:Pik is the phenotypic value of the kth genotype in the ith year; μ is the mean value of the trait; Yi is the fixed effect of the ith year on the trait; gk is the random genotypic effect of genotype k; and eik is the residual term of the model. For (Equation 2), μ, Yi, and gk have the same meanings as in (Equation 1); Pijk refers to the phenotypic value of the kth genotype in the ith year in the jth site; Sj is the fixed effect of the jth site; and eijk is the residual term of the model. Heritability of genotypic means  was estimated for each individual collection as:where for (Equation 1), n is the mean number of observations per genotype. These analyses were performed using “R” software .The 1,168 apple genotypes were genotyped with the Axiom®Apple 480 K array containing 487,249 SNPs evenly distributed over the 17 apple chromosomes  between the genotypes at the whole population level and within each collection. A Principal Component Analysis (PCA) of the SNP data was performed using PLINK . Matrix Q was constructed for the whole population as well as for each collection separately.GEMMA software  and as r2 corrected for population structure and relatedness (r2) was assessed for chromosomal regions of 1 Mb surrounding the SNPs retained as cofactors in the GWAS (see next section for details) and displayed in LD maps and network plots using “LDheatmap” and “network” R-packages, respectively.LD was studied between sets of SNPs spanning regions of 10 kb randomly sampled along the genome. These sets were obtained by a random choice of 50 contigs larger than 10 kb on each chromosome, followed by the selection of SNPs spanning a random region of 10 kb in each of these contigs. LD was estimated as squared allele frequency correlations (Q) and modeling phenotypic covariance with the kinship matrix (K) implemented in a modified version of the multi-locus mixed model (MLMM) proposed by Segura et al. ) of the position of an underlying association was estimated as the median absolute deviation of the positions of the SNPs retained as cofactors on each chromosome over all subsets. Then, the 95% confidence interval was calculated as the position of the most significant SNPs retained as cofactors in the analysis of the whole population ±1.96 se(x).To establish 95% confidence intervals for the significant SNPs retained as cofactors in the whole population, we conducted a re-sampling approach as proposed by Hayes . The fuld) to additive (a) effects calculated from the mean of the genotypic means for each genotypic class. The dominance effect was calculated as the difference between the mean observed within the heterozygous class and the mean across both homozygous classes (d = GAB – 0.5 (GAA + GBB), where Gij is the trait mean in the ijth genotypic class). To classify the mode, we used the following ranges, similar to Wegrzyn et al.  for each of the three geographic groups using the ratio of dominance  defined by the SNPs identified for each trait, mean and statistical significance among the most frequent genetic variants were calculated by the Tukey-Kramer test (α = 0.05).Q) and modeling phenotypic covariance with the kinship matrix (K). Percentages of variance explained by additive plus dominance effects, and by additive plus dominance and epistatic effects were estimated in a hierarchical sequence, using a cumulative R2 metric.Dominance and epistatic effects among the identified SNPs were tested in a model including their additive effects with correction for population structure . Annotations regarding the biochemical function of genes (mainly provided by InterproScan) were enriched by the biological functions inferred from the putative orthologs identified in Arabidopsis thaliana, Solanum lycopersicum, and Prunus persica genomes. Furthermore, structures of predicted genes and intergenic regions were systematically investigated to detect eventual mis/not-annotated genes and pseudogenes (stop codons and/or frameshifts in their CDS) in the regions of interest.Chromosomal regions corresponding to approximate 95% confidence intervals for the position of SNPs retained as cofactors in the whole population for flowering and ripening periods were investigated for r = 0.44; p-value = 2.2e−16), see Figure Large phenotypic variation was observed at both whole population and collection level Table . In the The three geographical groups differed considerably for both traits Figure . For floPCA was applied to summarize global genetic marker variation in the association panel: the ten largest Eigenvalues used to describe the whole population structure explained 17% of the overall variation. In Figure r2) was very variable in the SNP sets of 10 kb randomly sampled along the genome, spanning the entire range from absence to complete LD  were computed: in the whole population, mean r2 values were 0.23 and 0.19 without correction, and 0.19 and 0.14 with correction.LD  values till around 3, indicating that the significant SNPs are unlikely to be biased by population structure and relatedness. A strong association signal was found on chromosome 9 (49 SNPs) with a Bonferroni correction threshold of 5% (–log10(p) > 6.74). The remaining SNP was located on the fictive chromosome 0 containing all unassigned scaffolds. The SNPs located on chromosome 9 spanned a distance of 3.24 Mb .Using a single-locus mixed model with control for population structure and relatedness, 50 SNPs were significantly associated with flowering period for the whole population Table . In a quAnalyses of the individual collections revealed significant associations for flowering period only at INRA (29 SNPs), NFC (2 SNPs) and RBIPH (1 SNP) Table . Twenty-For ripening period, 82 SNPs exhibited a significant association for the whole population with adjustment for population structure and relatedness Table . The Q-QWhen GWAS was performed for each collection separately, numbers of significant SNPs were 38, 12, and 8 for NFC, INRA and SLU, respectively, two for both RBIPH and UNIBO, and only one for CRA-W Table . Thirty-To further dissect the signal from the chromosomal regions containing the sets of significant SNPs for each trait, we performed a GWAS with MLMM using SNPs as cofactors , both located on chromosome 9, only 27 kb apart Table . These tGWAS of each single collection retained one, two and four SNPs Tables , 3 for tThe optimal model according to the EBIC criterion retained six SNPs for the whole population, four at the bottom of chromosome 3 , one at the bottom of chromosome 10 (FB_AFFY_3795860 “SNP.10-1”), and another on the top of chromosome 16 (FB_AFFY_6370928 “SNP.16-1”) Table . AltogetThe optimal model for the analysis of each individual collection retained at least one SNP per collection Tables , 3: fourFor each trait, Manhattan plots obtained with the single-locus mixed model and the multi-locus mixed model for the whole population and for the individual collections, are shown in Figures r2 = 0.27; r2 = 4E−04 and 0.06, respectively), while LD was much higher .Pairwise LD was assessed to test the independence of SNPs identified as cofactors for each trait in the whole population as well as in each geographic group. For flowering period, low LD  were more frequently observed for the above mentioned regions .LD interconnections between the eight SNPs retained as cofactors and other SNPs residing within their surrounding regions of 1 Mb Figure  showed tkb Table . None orD Figure . ConversFor each of the eight SNPs identified as cofactors in the analysis of the whole population, the minor  allele remained the same across the individual collections, the three geographic groups and the whole population, except for two cases: SLU collection and North+East group for SNP.3-7 associated with ripening period Tables . In the The phenotypic effects of the two SNPs identified for flowering period indicated a strong mean difference (>1.9) between genotypic means for genotypes homozygous for the alternative alleles Table . DominanThe joint effect associated with the two SNPs identified for flowering period was assessed by comparing the average values for genotypes with different combinations of alleles in the whole population. Among the five genetic variants with a frequency above 1% Table , variantFor flowering period, we considered the interval 451,830–635,974 bp  on chromosome 9, corresponding to the fusion of the 95% confidence intervals of the two SNPs selected as cofactors. In this interval, we found 28 gene models Table  includinFor ripening period, we considered two intervals on chromosome 3, one corresponding to the fusion of the 95% confidence intervals of SNP.3-6 and SNP.3-7 which overlapped , and the second for the confidence interval of SNP.3-3 and SNP.3-4 . Only eleven gene models were found in the first interval and 24 in the second, with 6 additional gene models in between Table . Two sucA nearly perfect microsynteny (with some minor re-arrangement) was revealed between apple and peach in all the four confidence interval genomic regions estimated in our study, as shown by the numerous conserved homologs between the two species in those regions Tables –S12.p-values below a defined threshold of Bonferroni correction at 5%. Implementation of those stringent parameters was essential to eliminate false positives, but have probably sacrificed some true associations with small effects.The SNPs retained as cofactors in the GWAS on the whole population defined one genomic region controlling flowering period and three controlling ripening period. Additional regions were identified when conducting GWAS for individual collections. The associations found accounted for varying levels of trait variation  across the whole population and individual collections. We applied a conservative approach in identifying SNPs as cofactors for Rosaceae , whereas Allard et al.  which corresponds to a gene model (MD03G1222600) at position ~30,697,000 bp of GDDH13 genome. Interestingly, this position fits perfectly within the 95% confidence interval of SNP.3-6/SNP.3-7 . Since this genomic region has been identified in various environments and genetic backgrounds, it therefore appears to potentially be a major factor in the genetic control of ripening period.Chromosomes 3, 10, and 16 have shown associations with ripening period in previous linkage mapping studies  are probably explained by adaptive selection to different environments. Adaptive traits are frequently filtered by environmental gradients that coincide with patterns of population structure due to the differential fixation of alleles among groups of cultivars, following diversifying selection and/or genetic drift . NAC-domain proteins are transcription factors involved in the genetic control of flowering time in FLC (FLOWERING LOCUS C) gene involved with the FRIGIDA gene in vernalization response of Arabidopsis  highlighted by Porto et al.  was identified in a major locus on chromosome 4 controlling maturity date in peach, and a 9 bp DNA insertion in its last exon was described as a variant putatively linked to early ripening  with candidate genes belonging to the NAC family, surrounded by other genes putatively involved in apple ripening. In the genomic region of chromosome 3, two NAC transcription factors (MD03G1222600 and MD03G1222700) are strongly indicated as candidate genes for the control of this trait. A member of this gene family  and two Apetala2 (MD10G1290400 and MD10G1290900) transcription factors. Members of the plant-specific APETALA2/ethylene response factor (AP2/ERF) superfamily of transcription factors act downstream of the ethylene signaling pathway and are strongly conserved throughout the plant kingdom  or a TIFY domain (MD16G1127400) were identified as well as one gene encoding an auxin responsive protein (MD16G1124300) and one gene for a sugar bidirectional transporter (MD16G1125300) with high homology with a senescence associated protein (SAG 29) of Altogether, candidate genes identified after GWAS highlight the probable role of transcription factors, controlling the ethylene biosynthesis or regulatory pathway, for ripening in apple. Other candidates such as the gene encoding for an auxin responsive protein may also be considered since an ethylene–auxin interplay at a late ripening stage has been proposed in apple (Tadiello et al., GWAS mapping is a powerful tool for the identification of genomic regions associated with important traits, but results can be restricted by too much genetic heterogeneity, insufficient marker density and an overly strong impact of population structure. In the present study, narrow genomic regions controlling two phenological traits and a rather low number of candidate genes were identified, while other regions remained unidentified because of the relationship between traits and geographic structure. Enlarging the diversity panel with more genotypes, especially from Southern and Northern+Eastern groups, might improve detection of loci associated with those traits in each geographic group. Also, a combination of linkage and association analyses may achieve higher statistical power and resolution (Jansen et al., JU, HM, and DL carried out the statistical analyses under the supervision of C-ED and ST. CD and ER managed the leaf sample collection and part of DNA extraction. AT performed the remaining DNA extraction and the whole runs of the Axiom® plates on the GeneTitan® under the supervision of CP. CD performed the whole SNP genotyping analysis and validation process with the help of HM and C-ED. Selection of germplasm and acquiring phenotypic data were performed by C-ED, AG, RG, LF, ML, PH, MO, FP, JS, HN, RGr, LD, and ST. MB and SM brought expertise about GWAS methodology and results interpretation. SA performed the candidate gene analysis thanks to the new apple genome sequence developed by ND and J-MC. C-ED conceived and coordinated the study. FL coordinated the EU FruitBreedomics project including this study. JU, HM, and C-ED wrote the manuscript with decisive contributions of SA and LD. HN, MO, MT, LB, RV, MB, LG-G, and SM critically reviewed the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer AMA and handling editor declared their shared affiliation.