P = 5.5E-06) was located on SSC14 with 45.3 homozygous individuals expected but none observed. This SNP was significant for mummified fetuses. One hundred fifty two haplotypes were also found to potentially harbor recessive lethal mutations. Twenty-one haplotypes had a significant harmful effect on at least one trait. Two regions, located on SSC8 (144.9–145.5 Mb) and SSC9 (19–19.4 Mb) had significant effects on fertility traits in both datasets. Additionally, regions on SSC1 (82.0–82.8 Mb), SSC3 (73.3–73.7 and 87.1–87.5 Mb) and SSC12 (35.8–36.2 and 50.0–50.5 Mb) had significant deleterious effects on TNB or BA or L24 in dataset 1. Finally, a region on SSC17 (28.7–29.3 Mb) had significant effects on TNB, BA and L24 in dataset 2. A few candidate genes identified within these regions were described as being involved in spermatogenesis and male fertility , or displayed recessive lethality  in other species. The putative loci detected in this study provide valuable information to potentially increase Duroc litter size by avoiding carrier-by-carrier matings in breeding programs. Further study of the identified candidate genes responsible for such lethal effects may lead to new insights into functions regulating pig fertility.Lethal recessive alleles that act prenatally may be detected from the absence of homozygous individuals in a population. However, these alleles may be maintained at relatively low frequencies in populations as heterozygotes. In pigs, they may reduce litter size. This study aimed to detect putative lethal variants in the Duroc breed. Phenotypes for the numbers of piglets born (TNB), born live (BA), alive at 24 h (L24), stillborn (SB), and born as mummified fetuses (MM) were available from 5340 recorded litters which resulted from mating of 192 genotyped boars with sows of unknown genotype (dataset 1). An additional 50 litters were produced from parents that were both genotyped (dataset 2). Imputed genotypes of 650K SNPs for 1359 Duroc boars were used in this study. One significant SNP (Bonferroni corrected  Comparatively small litters are characteristic of the Duroc breed . Yet, us1. The pedigree of these boars was used to identify 171 of their sires and grandsires. DNA from these ancestors was genotyped using the Affymetrix Axiom® 650K SNP Array. DNA extraction and genotyping were conducted at the Delta Genomics laboratory . The genotypes and details of genotyping and imputation were described by Sscrofa10.2), minor allele frequency (MAF) ≥ 5%, and Chi square of Hardy–Weinberg equilibrium test less than 600. After editing, a total of 416,392 SNPs were used for this study.DNA from 1359 Duroc boars born between 2012 and 2013 was genotyped using the Geneseek-Neogen GPPHD 80K SNP chipP < 0.01.For each SNP, the expected number of animals with a homozygous genotype was calculated assuming random mating and determined as the number of genotyped animals multiplied by the square of the MAF. Those SNPs with greater than five individuals expected to be homozygous for the minor allele, but where none were observed in the data, were considered to be necessary for life. Further, the difference between the observed and expected number of homozygotes was tested using a Chi-squared test (df = 1). The Bonferroni-corrected significance threshold was set as Genotypes were phased on each chromosome using SHAPEIT2 , 2014 wiP-value from the Chi-square test was adjusted for whole genome wide significance by Bonferroni correction. A haplotype allele was defined as a putative recessive lethal if the observed number of homozygotes was zero and the expected number of homozygotes was greater than 6 as suggested by previous studies . Five traits: numbers born (TNB), born alive (BA), alive at 24 h (L24), stillborn piglets (SB), and mummified fetuses (MM) were used in association analyses to evaluate the effect of the detected haplotypes on fertility. Although they were count data, the distributions of TNB, BA and L24 were approximately normal and they were analyzed in linear animal models in ASREML . The disy is the observed phenotype, X is an incidence matrix associated with fixed effects of parity  (not included for MM) and farrowing year-season (37 levels), b is the vector of those fixed effects, M is the design matrix of haplotype alleles , i is the vector of corresponding haplotype allele effect on observation (linear regression) or log of observation (Poisson regression), Z is an incidence matrix indicating random additive genetic effects (a) due to boars with mean 0 and (co)variance structure of AA is the pedigree-based additive relationship matrix and W is an incidence matrix indicating random effects (v) due to sows with (co)variance structure of II is identical matrix and e is a vector of random residual errors.In this model, iM) to test their effect and significance. Due to the small sample size, the population mean was the only fixed effect that was included in the model. The significant threshold was set as P < 0.10.Additionally, there were 50 litters with known mating types which were generated from 27 genotyped boars mated with 22 genotyped sows (dataset 2). This information was suitable to estimate the effect of the lethal haplotype combination on TNB, BA an L24, but not for SB or MM due to small numbers of observations. Mating type for each detected lethal haplotype allele, including carrier mated with carrier (11), non-carrier mated with non-carrier (00) and carrier mated with non-carrier (10 or 01), was fitted as a fixed effect in the above linear model . It had a MAF of 0.17 and thus it was expected that 45.3 homozygous recessive individuals would have been found. This SNP showed an effect on MM that litters sired by the boars carrying such allele had an average of 0.15 more log of mummified fetuses than the litters sired by non-carrier boars (P = 0.09). This SNP was on SSC14 at 59.5 Mb. Only one SNP was found to have a sufficient expected number of homozygous recessive individuals with none being observed to attain statistical significance  and were presented in Table A total of 152 haplotypes from 129 blocks were detected as potentially harboring recessive lethal mutations with Bonferroni-corrected A few recent studies report potential harmful recessive haplotypes in pigs. P < 0.05) and either BA or L24 (P < 0.10). In each case, litters sired by carrier boars had approximately 0.3 fewer piglets than those sired by boars that were not carriers of the recessive haplotype. The two overlapping haplotypes on SSC 8 spanning the interval from 144.9–145.5 both affected TNB. A total of 192 boars were assumed to be randomly mated with 3460 sows with unknown genotypes to generate 5340 litters (dataset 1). Nineteen haplotypes were significant for at least one fertility trait  at least one fertility trait (Table Fifty litters were generated from matings between boars and sows with known genotypes (dataset 2). In this smaller dataset, four haplotypes affected  close to haplotype 3 on SSC3 is expressed in the testis of embryonic mice and plays a central role in germ cell development, but not in ovarian tissue of like-aged female embryos  is within haplotype 13 on SSC9. The mutations in PCF11 exhibit recessive lethality in Drosophila (TLX3 (T Cell Leukemia Homeobox 3) is related to the pathways of sudden infant death syndrome (SIDS) susceptibility and transcriptional misregulation in cancer (GABRP (Gamma-Aminobutyric Acid Type A Receptor Pi Subunit) is expressed in several non-neuronal tissues including the uterus and ovaries  and SEPT4 (Septin 4) showed significant effect on spermatogenesis and male fertility. Diseases associated with TEX14 include Spermatogenic Failure 23. Formation of intercellular bridges in the testis of mice requires expression of TEX14  may act as a transcriptional factor. Testes of adult HSF5-/- male zebrafish have relatively abundant spermatocytes and very few spermatozoa resulting from miss-regulation of the cell cycle and meiosis (GTSF1 (Gametocyte Specific Factor 1) within haplotype 8 on SSC5 is required for spermatogenesis and is involved in the suppression of retrotransposon transcription in male germ cells.Several genes within or around the significant haplotypes are involved in spermiogenesis related pathways which may affect prenatal survival. Haplotype 16 around 35.8 – 36.3 Mb on SSC12 is relatively gene rich. of TEX14 . These intiation . SEPT4-n survive . HSF5 (H meiosis . GTSF1 (FASLG (Fas Ligand), TNFSF4 (TNF Superfamily Member 4) and TNFSF18 (TNF Superfamily Member 18), or tumor suppression, e.g., RNF43 (Ring Finger Protein 43) and PCSK2 (Proprotein Convertase Subtilisin/Kexin Type 2). These genes are involved in important signaling pathways that are essential for immune system regulation, organ development, tumor development and progression. Mutations in these genes may affect essential functions resulting in more stillborn piglets. The protein encoded by FGF18 (Fibroblast Growth Factor 18) is a member of the fibroblast growth factor (FGF) family which are involved in a variety of biological processes including embryonic development (SCD5 (Stearoyl-CoA Desaturase 5) and ENOPH1 (Enolase-Phosphatase 1) within Haplotypes 10-11 on SSC8 are related to the disease of Chromosome 4Q21 Deletion Syndrome. Mouse pups that do not express SCD5 are stillborn, with reduced growth rate in utero (ITGB1 (Integrin Subunit Beta 1) include Gallbladder Cancer and Breast Cancer. Integrin family members are membrane receptors involved in a variety of processes including embryogenesis, hemostasis, tissue repair, immune response and metastatic diffusion of tumor cells.A few genes in Haplotype 14 and Haplotype 17 are involved in tumor necrosis, e.g., elopment . A few gin utero . DiseaseFSHR is the receptor for follicle stimulating hormone and functions in gonad development  plays a role in embryonic stem cell proliferation and differentiation. Mutations in CALM2  or another member of the calmodulin gene family seem to result in patients with severe forms of long-QT syndrome (LQTS) who displayed life-threatening ventricular arrhythmias, a rare disorder thought to be the cause of a significant fraction of sudden cardiac deaths in young individuals, and together with delayed neurodevelopment and epilepsy  as part of routine commercial operation prior to the research. The proposed work was reviewed by the University of Alberta Animal Care and Use Committee. No other specific permissions were required for the work, since the animals were cared for according to the Canadian Quality Assurance Program, which includes attention to animal health and well-being and is in line with the Canadian Council on Animal Care guidelines.GP, MM, and MD conceived and designed the experiments. CZ performed the statistical analysis. GP and MM provided valuable advice to refine the statistical analyses. RK contributed materials and application advice. CZ, GP, and MM were major contributors in writing the manuscript. All authors read and approved the final manuscript.RK is employed by Genesus Inc. who provided data and genotypes as an in kind contribution to the research. RK was involved in the sample and data collection and contributed to the paper by advising on the potential application of the results. The potential conflict of interest did not interfere with the outcome of this paper. The remaining 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.


Genetic surveys of the population structure of species can be used as resources for exploring their genomic architecture. By adjusting filtering assumptions, genome‐wide single‐nucleotide polymorphism (SNP) datasets can be reused to give new insights into the genetic basis of divergence and speciation without targeted resampling of specimens. Filtering only for missing data and minor allele frequency, we used a combination of principal components analysis and linkage disequilibrium network analysis to distinguish three cohorts of variable SNPs in the mountain pine beetle in western Canada, including one that was sex‐linked and one that was geographically associated. These marker cohorts indicate genomically localized differentiation, and their detection demonstrates an accessible and intuitive method for discovering potential islands of genomic divergence without a priori knowledge of a species’ genomic architecture. Thus, this method has utility for directly addressing the genomic architecture of species and generating new hypotheses for functional research. Wild‐collected specimens were not sexed prior to DNA extraction. An additional 13 adults from north–south controlled crosses were captive‐reared. Further details concerning wild and laboratory‐bred specimens are given in Trevoy et al. , while LD filtering used a cutoff of r2 = 0.5. LDHeatmap was chosen because it can calculate LD without known positions for markers; thus, it can detect LD even among high numbers of potentially unlinked scaffolds. The default assumption of 1 kbp separation between markers was used as per the LD Heatmap manual.First, to retain a reliable dataset for further analysis, we removed low‐quality individuals using VCFtools v0.1.12b  filtered for high‐quality samples only (referred to as unfiltered); (b) the filtered dataset with 5% MM and 5% MAF filtering applied to loci (referred to as 5%‐only); and (c) the 5% filtered dataset with both HWE and LD filtering applied  Table . For sub2.5Principal component analysis (PCA) is a widely used multivariate technique for compressing and distilling complex observations into intercorrelated orthogonal variables, called principal components (PC)  = 2). LDna was not applied to the FF dataset since it had already been filtered for LD using LD Heatmap. The SNP compositions of the cohorts from LDna analyses were then compared to the SNP groups that were identified by high PC loading values.The 5%‐only dataset was used in LDna  for further analysis. After trimming barcodes and adapters, we obtained 255 million reads of 62 bp in length from 188 samples. On average, 85% of reads were successfully mapped to the reference genome. Quality scores for Run 1 (HiSeq) and Run 2 (NextSeq) were similar, with average phred scores of 36 and 34, respectively. On average, Run 2 had 47% more unique read locations per sample than Run 1, but average read depths in Run 2 were 39% lower. The consistency and reproducibility of GBS across both genotyping platforms is supported by Campbell et al.  of sampling sites, with a central cluster comprised of samples from Jasper National Park and the majority of laboratory‐bred north–south crosses Figure a Trevoy. All PCAIn contrast, the 5%‐only dataset aligned to the female MPB genome showed the effect of including SNPs that violated the LD and HWE assumptions. In this PCA plot, the north–south division was reflected in the PC1 axis, but the PC2 axis showed strong nongeographic clustering Figure a, PC2 lo2A2) and lowest (A1A1) clusters  dataset revealed six SNP cohorts . However, three SNPs were removed because the variant was too close to the edge of a reference scaffold to extract a flanking sequence of more than 50 bps. Thus, a total of 387 SNPs from the 5%‐only dataset were used for gene ontology analyses.T. castaneum and Anoplophora glabripennis Motsch , PCA results support a north–south geographic division among the sampling locations Figure a, in agr4.3Datasets that were not filtered based on LD  showed additional clustering that did not clearly correspond to sampling locality. The PC2 axis sharply segregated individuals by percent heterozygosity based on 217 SNP loci that had high loadings. The homozygous group contained all the female individuals from the sexed, laboratory‐bred specimens Figures b and incThe neo‐X chromosome in MPB is thought to be a fusion of the largest ancestral autosome and the ancestral X chromosome, leaving the daughter autosomal chromatid to become the neo‐Y after the loss of the ancestral Yp chromosome Lanier, . This fuT. castaneum, another beetle species, are located predominantly on autosomes. Of the 78 BLAST matches between the PC2 cohort and the T. castaneum genome, 80% were found on autosomes 2 and 4  partitioning genetic variance among individuals in a PCA and examining the distribution of PC loadings, and (b) discrimination of SNP cohorts with LD network analysis to verify that correlated SNP cohorts are due to linkage disequilibrium, rather than population structuring. However, studies using more conventional approaches to detect divergence between populations are required to verify the efficacy of this method (Lindtke & Yeaman, et al.While useful as a means of ensuring independence of loci in classical population genetics surveys, LD analysis can also offer insights into genomic architecture and differentiation, even within non‐model species Baird, . Recent 5Our geographic survey of GBS SNP variation in the mountain pine beetle in western Canada has allowed us to determine both population structure and genomic architecture, as well as to explore functional aspects of population divergence. In addition to replicating previously documented population structure, we uncovered at least three cohorts of genomically linked loci when we dispensed with the traditional approach to filtering for HWE and LD.The largest cohort of linked SNPs is hypothesized to be composed of paralogous loci from the neo‐X and neo‐Y regions of the sex chromosomes. This provides a means to determine the sex of individuals. The second SNP cohort is composed of geographically associated loci in tight LD. This SNP cohort yielded several candidate genes for further study of adaptive radiation and selective pressures facing MPB as it expands eastward in Canada. A third cohort of SNPs is independent of the other two and represents further opportunities for research. Using a procedure related to that of Li et al.  to integNone declared.The experiments were conceived and designed by F.A.H.S. and J.K.J. The laboratory work was performed by S.A.L.T. and J.K.J. The data were analyzed by S.A.L.T and K.M., including analytical tools developed by K.M. The data were interpreted by S.A.L.T. and F.A.H.S. The paper was written by S.A.L.T., F.A.H.S., and J.K.J. Click here for additional data file. Click here for additional data file.


Myodes glareolus, the specific asymptomatic reservoir host of Puumala hantavirus (PUUV), which causes nephropathia epidemica (NE) in humans. Despite the continuous spatial distribution of M. glareolus in Sweden, NE is endemic to the northern part of the country. Northern bank vole populations in Sweden might exhibit tolerance strategies as a result of coadaptation with PUUV. This may favor the circulation and maintenance of PUUV and lead to high spatial risk of NE in northern Sweden. We performed a genome‐scan study to detect signatures of selection potentially correlated with spatial variations in tolerance to PUUV. We analyzed six bank vole populations from Sweden, sampled from northern NE‐endemic to southern NE‐free areas. We combined candidate gene analyses  and high‐throughput sequencing of restriction site‐associated DNA (RAD) markers. Outlier loci showed high levels of genetic differentiation and significant associations with environmental data including variations in the regional number of NE human cases. Among the 108 outliers that matched to mouse protein‐coding genes, 14 corresponded to immune‐related genes. The main biological pathways found to be significantly enriched corresponded to immune processes and responses to hantavirus, including the regulation of cytokine productions, TLR cascades, and IL‐7, VEGF, and JAK–STAT signaling. In the future, genome‐scan replicates and functional experimentations should enable to assess the role of these biological pathways in M. glareolus tolerance to PUUV.Natural reservoirs of zoonotic pathogens generally seem to be capable of tolerating infections. Tolerance and its underlying mechanisms remain difficult to assess using experiments or wildlife surveys. High‐throughput sequencing technologies give the opportunity to investigate the genetic bases of tolerance, and the variability of its mechanisms in natural populations. In particular, population genomics may provide preliminary insights into the genes shaping tolerance and potentially influencing epidemiological dynamics. Here, we addressed these questions in the bank vole   For each locality, we built four independent libraries to avoid methodological biases. Digested DNA pools were ligated to a modified Illumina P1 adapter containing locality‐specific, 5‐ to 6‐bp‐long multiplex identifiers (MIDs). All MIDs differed by at least three nucleotides to limit erroneous sample assignment due to sequencing error  were discarded from further analyses. Sequences were trimmed to 85 nucleotides .Sequence reads from Illumina runs were demultiplexed and quality‐filtered using the M. glareolus, we needed to build a de novo RAD assembly. To that end we first assembled reads 1 per sample with the ustacks program from the Stacks package and default options, except for (a) the minimum depth of coverage required to create a stack (‐m option) that was set to 2; (b) the maximum distance in nucleotides allowed between stacks (‐M option) that was set to 3; and (c) the maximum distance allowed to align secondary reads to primary stacks (‐N option) that was set to 2. The resulting set of loci were then merged into a catalog of loci by the cstacks program from the Stacks package run with default options except for the number of mismatches allowed between sample tags to form stacks (‐n option) that was set to 2. For each of the obtained read 1 contigs , we further assembled the associated reads 2 using CAP3  to try to assemble all contigs into a single one, which was in this case retained for further analyses. If read 2 contig overlapped with their corresponding read 1 contig, as assessed with the blastn program from the BLAST+ v2.2.26 suite , both contigs were concatenated. Otherwise, fifteen “Ns” were inserted between both contigs.Because no reference genome assembly was available for aln and sampe implemented in bwa 0.5.9 and ran with default options. The resulting bam files were then jointly analyzed with the mpileup program from the Samtools v0.1.19 suite. We used default options except for the minimum mapping quality for alignment (‐q option) that was set to 20. The mpileup file was further processed using a custom awk script to perform SNP calling and derive read counts for each alternative base  as previously described  and among (He) individuals, as well as the inbreeding coefficient FIS, were estimated using Genepop v4.2 , and the logarithm of geographic distances using a Mantel test implemented in R  that indicates which allele is selected for in the ith deme, at the jth locus. If the reference  allele is selected for in one deme, then the posterior mean of κij is shifted toward 0 (resp. 1) , computed from the posterior means of σij and κij. This composite parameter tends to 0 when κij tends to 0.5 ; it tends to −σij when κij tends to 0 ; it tends to σij when κij tends to 1 . Broadly positive or negative values of ζij are therefore expected when selection is strong at one locus in one population. Then, we examined the correlation of ζij with some environmental variables.There are two locus‐ and population‐specific parameters in SelEstim: the coefficient of selection σXTX, which might be interpreted as a locus‐specific analogue of FST, explicitly corrected for the scaled covariance of population allele frequencies. To define a significance threshold for the XTX statistic, we estimated the posterior predictive distribution of this statistic under the core model, by generating and analyzing a POD made of 20,000 SNPs . Roughly speaking, for a given SNP, the empirical Bayesian p‐value measures to which extent the posterior distribution of the regression coefficient excludes 0  to estimate the moments of the posterior distribution of the regression coefficients. We calibrated eBPis by generating and analyzing a POD.We used BayPass to test for associations between allele frequencies and environmental variables presumably related to PUUV epidemiology, while controlling for demography. The standard covariate model (STD) in BayPass assumes a linear effect of the environmental variable on allele frequencies. Association of SNPs with environmental variables was assessed using empirical Bayesian Gautier, . In ordehttps://www.umass.edu/landeco/research/fragstats/fragstats.html) were averaged in the 3‐km buffer and included the contiguity index, the shape index , and the perimeter. Finally, the soil water index (SWI) representing the soil moisture conditions was also included.Environmental variables related to PUUV prevalence in human (number of NE cases), climate, forest composition and shape, and soil water content, were selected with regard to PUUV ecology in Europe as they should reflect PUUV distribution in Sweden  from a PCA that included all 15 variables Figure . These n2.62.6.1M. glareolus, we compared the RAD contigs to the genome of Mus musculus (build GRCm38/mm10). We downloaded (date: 19/01/2018) the GFF3 gene annotation and the sequences of the 22 mouse chromosomes, 111,462 cDNAs and 64,553 proteins from ENSEMBL  and blasted the RAD contigs against these sequences. We used blastn for genome and cDNA , and blastx for proteins (with the same parameters as blastn but without identity criterion). Regarding the whole‐genome alignment, all RAD outliers being aligned on a mouse gene were identified by considering the gene coordinates being present in the GFF3 gene structure file.In the absence of a published high‐quality genome assembly and gene annotation for 2.6.2Tlr7 (ENSMUSG00000044583). As background set the BioMart annotations for 53,946 mouse genes were obtained online via the Blast2GO interface. The overrepresentation of outlier GO terms was tested using Fisher's exact test and a p‐value threshold of 0.05 corrected using FDR , and the Chinese hamster, Cricetulus griseus (ENSEMBL assembly CriGri_1.0). Given the low quality of annotation for these two genomes, we could not identify any gene enrichment, and we therefore present the results based on the M. musculus genome assembly only.The above analyses were also conducted using genome assemblies of two alternative species: the prairie vole, 33.1CAP3 assembling run of the associated reads 2 produced 46,474 unique contigs represented by more than 40 sequences, and among which 46,471 had <5% singleton. The second CAP3 assembling run enabled to provide 69,777 read 2 contigs. We found a single significant alignment between read 1 and read 2 contigs in 59,242 cases and no significant overlap in 10,495 cases. The other 38 cases corresponded to a complete alignment of read one contig with read two contig (one case), an alignment of read one contig within read two contig (two cases) and multiple significant alignment (35 cases). The resulting assembly finally consisted of 69,777 contigs spanning 38,482 Mb . Reads were aligned to this reference contig dataset and 485,182 SNPs were detected . Among them, 95,988 SNPs distributed on 70,699 contigs were kept according to the more stringent criteria described before.Sequencing of the 24 RAD libraries  generated 340,692,418 reads, with an average of ca. 13.5 million reads per multiplex identifiers (MIDs). The number of sequences generated per locality and MID ranged between ca. 10.1 and ca. 15.1 million reads. After trimming sequences to 85 bp and after filtering for quality, an average of ca. 12.6 million reads per MID  were retained. Read 1 assembling produced 151,522 contigs. The first 3.23.2.1Tlr4, Tlr7, and Mx2 genes were successfully genotyped in 250 Swedish bank voles using the KASP genotyping. Not surprisingly, significant linkage disequilibrium (LD) was observed between most SNPs located within genes (p < 0.05): Tlr4‐exon3 776 and Tlr4‐exon3 1146, Tlr4‐exon3 1662, Tlr4‐exon3 1687; Tlr4‐exon3 1146 and Tlr4‐exon3 1662, Tlr4‐exon3 1687; Tlr4‐exon3 1662 and Tlr4‐exon3 1687. We did not detect any linkage disequilibrium between SNPs located in different genes.The eight SNPs within Tnf promoter and Tnf promoter . Moreover, significant departures from Hardy–Weinberg expectations were observed in Gimo for two SNPs after correcting for multiple tests.Estimates of diversity indices per SNP and per sampling locality are given in Table 3.2.2FST between pairs of populations ranged from 0.091 to 0.361, and the overall differentiation among populations was estimated as FST = 0.212 (see details in Supporting Information Table FST/(1 − FST), and the logarithm of geographic distance . The PCA performed on the covariance matrix of population allele frequencies revealed a strong differentiation between the two northernmost populations, Hörnefors and Härnösand, and all other populations on the first axis. The second axis differentiated the southern population of Gimo from all other populations . All these outliers showed high FST estimates compared with the background FST  equaled 2.61. We found a total of 86 SNPs, representing 78 unique contigs and the candidate gene 3.3.2XTX > 14.88, 99.9% quantile). Six SNPs corresponding to five unique contigs were common with the SelEstim analysis , we found 10 outlier SNPs, belonging to nine unique contigs . A total of 395 SNPs — corresponding to 339 unique contigs — showed significant association with the first principal component of the environmental variables , we found 483 SNPs (belonging to 413 unique contigs) at the 99.90% threshold, with strong association signals eBPis > 1.35. A tos Figure .3.53.5.1n = 30,973) of the 69,777 bank vole contigs using the M. musculus genome sequence from ENSEMBL. These contigs covered all 22 mouse chromosomes. Moreover, about 21%  of the 69,777 bank vole contigs were located in 5,977 mouse protein‐coding genes (cDNAs). Out of these, 9,794 (14%) bank vole RAD contigs were actually located in the coding sequence of 4,853 mouse proteins (the other RAD contigs were more likely located in UTR termini of the cDNA).We could annotate 44.4% . They were either detected either by the algorithm of SelEstim or by that of the STD model in BayPass , in line with the strong pattern of isolation by distance that we found.The phylogeography of bank voles is characterized by a bidirectional colonization of Sweden, resulting in a contact zone between two genetically differentiated mitochondrial lineages from southern and northeastern Sweden, respectively  that are rarely completely met in natural conditions. Moreover, spatial autocorrelations in allele frequencies due to isolation by distance can result in spurious correlations between such frequencies and environmental variables  so that potentially important adaptive SNPs might have been missed  are challenging.Because of the limits listed above, the results from the genome scans have to be considered cautiously. The combination of three statistical analyses based on population differentiation and ecological associations to detect outlier loci aimed at reducing the rate of false positives and at enhancing our chances to detect genuine signatures of selection . However, this comparison has to be taken cautiously, as the number of immune‐related genes is known to be highly variable across species , showing high levels of genetic differentiation consistent with divergent selection acting along our latitudinal transect in Sweden. A significant fraction of these SNPS were related to immunity and responses to hantavirus infections. In particular, one third of the enriched biological processes associated with these SNPs corresponded to immune processes and “positive regulation of cytokine production” was one of the three most represented enriched pathways. These results strengthen the hypothesis that infectious pathogens are among the strongest selective forces acting in natural populations  for hantaviruses, being involved in early recognition of infections  and from the Animal Ethics Committee in Umeå (latest permission: Dnr A‐61‐11). Click here for additional data file. Click here for additional data file. Click here for additional data file.


Eucalyptus polybractea, a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration , than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height . Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species.Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in  Picea glauca) (Pinus taeda)  animal model  is relias taeda) , resultis taeda) . Genomice.g., SNPs) are jointly estimated so that an individual’s breeding value can be predicted solely from their genotype  is the primary species grown for commercial production of Eucalyptus oil in Australia due to its high foliar oil concentration of up to 13% of dry weight and desirable oil composition , a simple and robust GS approach that is commonly used in forestry for calculating GEBVs . GBLUP iBLUP given Genetic Architecture”), which allows for the use of locus-specific weightings within the computationally efficient GBLUP framework  in a progeny trial at the property of GR Davis Ltd, West Wyalong, Australia. The mother trees for 37 of the families are located in natural stands of -1 of n-tetradecane for extraction of essential oil and subsequent GC-MS analysis to determine the concentration of individual terpenes 1,8-cineole (CIN) and α-pinene (APIN), the total concentration of all monoterpenes (MONO), the total concentration of all sesquiterpenes (SESQ), and the total essential oil concentration (OC). SESQ and APIN were square-root and log transformed, respectively. The proportion of total oil that is 1,8-cineole (PCIN) was also calculated as 1,8-cineole is the dominant terpene in E. polybractea and PCIN is a key trait for the quality determination of pharmaceutical-grade Eucalyptus oil. Tree height (HT) was measured in the field when the trees were one year post-coppice (March 2013) and two years post-coppice (March 2014). The growth trait (dHT) represents the one year absolute change in height. Due to within-family environmental variation, a postblocking factor was introduced which assigned individuals to one of four blocks in the East-West direction. Phenotypes were adjusted for the fixed effect of postblock.We sampled fresh mature leaf in ethanol containing 0.25 g LWe used genotypes obtained from low coverage whole genome re-sequencing (WGS) in a genome-wide association study (GWAS) of the same population . Briefly, to obtain genotypes we extracted DNA from frozen leaf collected from all 480 trees using the Qiagen DNeasy Plant kit  and then prepared barcoded libraries for Illumina sequencing using a modified version of the protocol by Eucalyptus grandis reference genome  we used the Thunder variant calling pipeline . SNPs with very low MAF may introduce errors in genomic prediction since there is an increased chance that they do not segregate in both the training and validation population. Therefore we filtered out SNPs with MAF < 0.05. To evaluate the effect of marker density on predictive ability, we produced three SNP sets with progressively fewer markers. The first set, 500K, contains approximately 502,000 SNPs with MAF > 0.05 after removing SNPs with pairwise LD E. polybractea to provide reliable approximations of TBVs, we instead calculated the predictive ability for each CV iteration as the Pearson’s correlation between the GEBVs and the phenotype adjusted for postblock fixed effects, r, of the individuals of the validation set . Each fold of cross-validation used 390 individuals as the training set while the remaining 78 had their phenotypes masked for use as the validation set. Accuracy of genomic selection is ideally measured as the correlation between genomic estimated breeding value (GEBV) and true breeding value (TBV) (adj) across the six cross-validations within that rep, and we present the result for each model in each scenario as the mean predictive ability for the 10 reps.To test the effect of SNP density on predictive ability we performed GS using each of the three SNP datasets . For every trait and SNP density (8 traits × 3 densities = 24 scenarios), we performed 10 replicates (reps) of sixfold cross-validation per model. Within each replicate, every individual was placed in a validation set exactly once so that every individual had its GEBV estimated once per rep. The predictive ability of a rep is the mean r, X is the design matrix relating individuals to fixed effects, Z is the design matrix relating individuals to additive genetic effects, and e is the vector of individual residual error random effects. The distribution of a ∼ N where A is the pedigree relationship matrix and σa is the total additive genetic variance, while e ∼ N where I is the identity matrix and σe is the residual error variance. Equation 1 was solved using REML by the mixed.solve function in the R package rrBLUP v4.4  but with the pedigree matrix  package . We thenWe used the R package VIGoR 1.0  to estimWhereas GBLUP disregards trait architecture by treating every marker equally, both BayesB and BLUP|GA attempt to pre-select and up-weight those markers that explain a non-negligible proportion of the trait variance, though BLUP|GA does so within the efficient GBLUP framework.SM, is selected from the full genotype matrix M, where SM contains SNPs that are assumed to define the genetic architecture of the trait . SM will be weighted so that the genotypes of the selected SNPs influence the calculation of genetic covariance differently to all SNPs in M. The selection of SNPs in SM can be informed by different information sources, such as estimated marker effects from a GWAS, or by a-priori candidate gene and functional biology studies. SM and M are then used to construct two GRMs, S and G respectively, which are recombined into one trait-specific GRM, known as the T-matrix, for use in a standard GBLUP model:G is a standard GRM constructed from all SNPs per equation 2; S is a GRM constructed from only the SM SNPs, per equation 4, with the identity matrix (I) used in equation 2 replaced by D, a diagonal matrix, which contains SNP-specific weightings:S during the construction of the T matrix. The impact of S (and hence the SM SNPs) can vary from ω = 0 which is the equivalent of standard unweighted GBLUP, to ω = 1 which applies maximum weighting to the S matrix and is the equivalent to using only the SM SNPs in the construction of the GRM. We tested the predictive ability of BLUP|GA for 0.0 < ω < 1.0 in increments of 0.1, noting that ω = 0 is the equivalent of GBLUP; ii) D provides a fine-scaled weighting mechanism for the SNPs within SM by allowing for varying importance of individual SNPs during the construction of the S matrix, and ultimately the T matrix. To keep S on the same scale as G, diag(D) is normalized to have a mean of one.With BLUP|GA a subset of markers, SM and defined D by estimating SNP effects with BayesB using only the genotypes and phenotypes of the individuals from the training set in each cross validation fold. Hyperparameters were chosen to shrink the vast majority of SNP effects toward zero, thus exaggerating the effect of SNPs that are likely to truly be a part of the genetic architecture. We set π = 0.999, implying only 0.1% of SNPs have an effect. SNP effects were drawn from an inverse chi-squared distribution with 4 degrees of freedom and a scale parameter of 0.01, which implies that the vast majority SNP effect sizes are very small with a few being considerably larger. We then selected the SNPs comprising SM based on whether their squared estimated effect (µ2) was in the largest 0.1% of effect sizes  using the squared estimated effects of those selected SNPs.We selected ct sizes . We alsohttps://figshare.com/s/be57a3a4d49742dd6fdf. We implemented BLUP|GA in R  produced from 100K SNPs shows that the assumed pedigree of 40 half-sib families was mostly recovered from the genotype data . That isadj) of the sixfold CV. In For each trait we performed 10 reps of sixfold cross-validation (CV) in each of the three SNP density scenarios . In each CV we calculated the predictive ability of ABLUP, GBLUP, BayesB and BLUP|GA. The mean predictive ability for a rep is the mean r were poor compared to the predictive ability of GEBVs. For some traits, such as CIN, PCIN and HT, predictive ability was as low as half that of the genomic models. The only trait where ABLUP was competitive was SESQ, though the genomic models were still considerably better when high SNP densities were available to them.E. polybractea foliar oil, had both its foliar concentration (CIN) and its proportion of total oil (PCIN) very accurately predicted by all models tested. Mean predictive ability was as high as 0.727 for CIN and 0.787 for PCIN using BayesB.The predictive ability of GEBVs was generally higher for oil traits than for biomass traits, as expected due to the generally higher heritability of oil traits . AggregaBayesB was superior to GBLUP in 19/24 scenarios tested, and was particularly strong for prediction in oil traits rather than biomass traits . BayesB Increasing the SNP density from 10K to 100K resulted in improved predictive ability for all oil-related traits regardless of the model used . FurtherIncreasing SNP density occasionally had a stronger effect for BLUP|GA than it did on GBLUP or BayesB. In the most drastic instance, BLUP|GA predictive ability for SESQ increased from 0.439 to 0.539 when NSNP increased from 10K to 100K .i.e., at ω = 0).The ability for BLUP|GA to improve predictive ability over GBLUP by weighting selected SNPs appears to be very dependent on the trait and SNP density available. The predictive ability of BLUP|GA for a given trait and SNP density can be summarized based on three possible outcomes from cross-validation: i) maximum predictive ability was achieved at ω = 0. This occurs when the weighting of selected SNPs provides no benefit to the model so the model falls back to standard GBLUP. Black bars in For OC, BLUP|GA very rarely achieved its maximum predictive ability at ω > 0, which means that the process of weighting the top 0.1% of SNPs according to their estimated effect sizes was usually detrimental to predictive ability compared to standard GBLUP. For CIN and SESQ, on the other hand, BLUP|GA very often achieved maximum predictive ability at ω > 0, indicating that the weighting of SNPs was more useful for these traits if the appropriate SNPs were available. In fact, in 14/60 CV iterations run for SESQ using 500K SNPs, the maximum BLUP|GA predictive ability achieved was at ω = 1 .st century, forestry may hold some solutions to current modern world problems and may mitigate climate change. Trees may provide traditional building products as well as renewable feedstocks to replace proportions of fossil fuel-based feedstocks for new products, such as cellulose-based polymers and advanced biofuels. Many tree species exhibit large phenotypic variation of desirable traits and while this is promising to tree improvement, long maturation and generation times have slowed traditional breeding processes. The use of molecular markers and genomic information has already begun to increase yield gains per unit time in forest species. Here we utilize, for the first time, genomic prediction as a tool for improvement of essential oil yield in the oil producing tree species Eucalyptus polybractea.Forest products have been utilized by human civilizations for millennia, but unlike many agricultural products such as corn and wheat, tree species have not undergone similar extensive domestication processes. In the 21e.g., during the initial establishment of family plots) or is partly due to mis-labeling in the sample handling and processing for this study. Nevertheless, the pedigree error is probably the primary cause of the poor performance of traditional ABLUP for the prediction of breeding values seen here. The genomic prediction models, which either correct the pedigree errors using SNP data or estimate GEBVs from marker effects, generally perform much better than ABLUP and, if implemented in a breeding program, would result in far greater gain per cycle for key oil-yield traits such as 1,8-cineole concentration (CIN) and proportion (PCIN).Traditional breeding in forest species uses known pedigree data to calculate estimated breeding values with the best linear unbiased prediction model (equation 1). However, pedigree information in forestry species is often shallow, incomplete or partially incorrect due to human error or unintended cross-pollination. Here the importance of using genomic data for the estimation of breeding values in forest tree systems is immediately apparent from the kinship analysis which reveals considerable pedigree error within this progeny trial population. It is somewhat difficult to pinpoint whether the pedigree error occurred in the silvicultural operation , proportion of 1,8-cineole (PCIN) and 1,8-cineole concentration (CIN). The ability for coppiced mallees to regrow vigorously is also important. Our results show moderate predictive ability of 0.38 for OC, and very high predictive ability up to 0.73 and 0.79 for CIN and PCIN, respectively. This is highly promising for breeders looking to develop high 1,8-cineole producing lines. Post-coppice height growth (dHT), though less predictable (0.24), may still be a useful trait for multi-trait selection. However, one must be wary of selecting strongly for height since rapid vertical growth can result from biomass accumulation in the stems rather than leaf, which is detrimental to oil yield . At low SNP density (10K), the maximum predictive ability achieved for SESQ by BLUP|GA was 0.439. However, with a 10-fold increase in SNP density BLUP|GA showed greatly increased predictive ability of 0.539. On investigating why increasing the SNP density resulted in such a major improvement of BLUP|GA for SESQ, we noted that a specific SNP of large effect on chromosome 10 was present in the 100K SNP set but was not present in the 10K SNP set. We then explicitly added this SNP to the 10K set, forming the 10K_EXTRA SNP set. Without the extra SNP, BLUP|GA achieved its maximum predictive ability at ω > 0 in only 6/60 cross-validations, indicating almost no positive effect from weighting selected SNPs. However, with the extra SNP, the maximum predictive ability achieved now occurred at ω > 0 in 55/60 cross-validations, showing that weighting this single SNP in the GRM had a major impact . This ini.e., as ω approaches 1.0), the outcome is accuracy below that of GBLUP. Indeed, Given the presence of a large-effect locus for SESQ on chromosome 10, it is perhaps surprising that BayesB performed only marginally better than GBLUP and considerably worse than BLUP|GA when given the same 100K SNP data. After all, the strength of BayesB is in selecting SNPs with moderate to large effect. Eucalyptus EUChip60k SNP chip (The utility of whole genome sequencing (WGS) for genomic prediction has been debated in recent years. The question reflects a balance between the extra cost of sequencing the entire genome of each individual (as opposed to employing GBS or SNP chip) and the potential improvement in accuracy due to higher marker density. While simulations (E. polybractea. Genotyping approach and model selection are key factors that can influence the predictive ability, sometimes by a considerable amount. In turn these are subject to the genetic architecture of the traits of interest. Greater understanding of trait architecture through GWAS methods may help to improve predictive ability further, with positive implications for both essential oil and terpene production.Genomic prediction can be used to accurately guide selection for high essential oil producing individuals, and in particular for those with high concentrations and proportions of major terpenes, such as 1,8-cineole. Compared to traditional BLUP, genomic models provide greater predictive ability due to their ability to avoid the pitfalls of incorrect pedigree specification, which is a problem in undomesticated species such as


Coffea canephora P. (Robusta) and C. arabica L. (Arabica) are important cash crops in tropical regions around the world. C. arabica is an allotetraploid (2n = 4x = 44) originating from a hybridization event of the two diploid species C. canephora and C. eugenioides (2n = 2x = 22). Interestingly, these progenitor species harbour a greater level of genetic variability and are an important source of genes to broaden the narrow Arabica genetic base. Here, we describe the development, evaluation and use of a single‐nucleotide polymorphism (SNP) array for coffee trees. A total of 8580 unique and informative SNPs were selected from C. canephora and C. arabica sequencing data, with 40% of the SNP located in annotated genes. In particular, this array contains 227 markers associated to 149 genes and traits of agronomic importance. Among these, 7065 SNPs (~82.3%) were scorable and evenly distributed over the genome with a mean distance of 54.4 Kb between markers. With this array, we improved the Robusta high‐density genetic map by adding 1307 SNP markers, whereas 945 SNPs were found segregating in the Arabica mapping progeny. A panel of C. canephora accessions was successfully discriminated and over 70% of the SNP markers were transferable across the three species. Furthermore, the canephora‐derived subgenome of C. arabica was shown to be more closely related to C. canephora accessions from northern Uganda than to other current populations. These validated SNP markers and high‐density genetic maps will be useful to molecular genetics and for innovative approaches in coffee breeding.Coffee species such as  Coffea genus (Rubiaceae family) is a large genus that currently comprises 125 accepted species (http://www.theplantlist.org/1.1/browse/A/Rubiaceae/Coffea/). Despite the large number of species in the genus, only two are economically important and cultivated on a large scale: Coffea canephora Pierre ex Froehner (commonly known as Robusta) and Coffea arabica L. (Arabica). Both species occur in the inter‐tropical region of Africa with C. arabica being mainly restricted to Ethiopia and C. canephora native to West and Equatorial Africa  resulting from a natural hybridization event estimated to have taken place 0.665 million years ago at the most, between the ancestors of present‐day C. canephora (Ca subgenome donor) and Coffea eugenioides S. Moore (Ea subgenome donor) (each with 2n = 2x = 22)  from its Ethiopian origin during the 14th century  of et al., et al., et al., et al., The development of new genomic tools can help us explore, more deeply and more precisely, the genomic diversity at intra‐ and inter‐specific levels. Two examples of high‐throughput platforms include next‐generation sequencing (NGS) (Davey Coffea canephora is highly compliant with genomic‐based breeding approaches using Genome Wide Association Studies (GWAS). Thanks to the rapid development of genomic resources and the publication of the reference genome  of the SNPs allowed to generate a high‐quality array. This high‐density SNP array has greatly improved the high‐density genetic maps of C. canephora. This array also provides a valuable resource for genetic diversity analyses and for the investigation of genetic relatedness between Coffea arabica CWRs.Here, we are reporting on the development of the Coffee8.5K SNPs array that contains 8580 unique and informative SNPs, covering the whole Coffea, with two considerations: SNPs had to (i) provide an adequate representation of the genome diversity based on genotyped mapping populations and diverse germplasm collections and (ii) additionally harbour sufficient allelic variations at the level of the functional genes that control important breeding traits. We define the ‘Discovery Panel’ as the set of samples used to design the Coffee8.5K array; the ‘Diversity Panel’ is the set of samples used to validate the array and perform subsequent genetic analyses; the ‘Mapping Populations’ are the Robusta and Arabica mapping progenies used to construct linkage maps.The whole‐genome coffee SNP array, Coffee8.5K, was primarily designed to provide an efficient screening of the wild genetic germplasm resources in C. arabica (five genotypes) and C. canephora (twelve genotypes) mapped against the C. canephora reference genome  SNPs selected from the C. canephora Discovery Panel had to be found in at least two genotypes in order to avoid rare and individual‐specific alleles; (iii) both genic and intergenic SNPs, in equivalent numbers, were selected with a minimum distance of 40 kb between SNPs, to provide an accurate representation of the genome without redundancy. The final SNP set also included informative markers associated with 149 genes related to traits of agronomic or organoleptic importance (Table The selection of SNPs was based on three main criteria: (i) SNPs from the ce Table .C. canephora BP409, Q121, and C. arabica Ar8 and Ar36B) and also between the two SNPs duplicated on the SNP array: the signal and genotyping qualities were efficient and reproducible. Moreover, 42 of the 50 SNPs previously targeted by KASPar assays were successfully synthetized on the Coffee8.5K and used for validation purposes. These 42 SNP‐generated genotypes were 100% concordant with those obtained from the KASPar assay, thereby validating the presence of these SNPs in the coffee genome as well as the SNP detection process itself.In the end, our Coffee8.5K array comprised 8580 SNPs that met Illumina‐quality criteria during the manufacturing process and were finally successfully synthesized Table , represeC. canephora genome was evaluated over the whole‐genome resequencing dataset of the C. canephora Discovery Panel (12 genomes). The linkage disequilibrium declined rapidly across all accessions to half its maximum value at a distance of 8.1 kb  (data not shown). When filtering SNPs with a distance of over 40 kb  occurs when alleles at two or more loci appear together in the same individual more often than would be expected by chance. Two SNPs that are in strong LD provide redundant genotyping information. The requisite knowledge of the LD in the b Figure . Since try Panel 2 genomesC. canephora reference genome .In addition, we assessed the quality of coverage by studying the physical distribution of selected Coffee8.5K SNPs along the pseudo‐chromosomes of the C. canephora reference genome  and Q121 (Conilon‐type derived accession) comprising 93 individuals. After the removal of low‐quality SNPs from the 8580 scorable SNPs, 1307 (15.2%) SNPs were polymorphic between the two parents (BP409 and Q121) and their segregation scored in the Robusta mapping population  were observed on 11.4% of the SNPs mapped. The majority (69%) of the strong segregation distortions  were grouped in two regions located on the LG C (22 SNPs) and LG H (27 SNPs). The final high‐density Robusta genetic map consisted of 3039 loci distributed over 11 linkage groups covering 1370 cM, with a mean distance between markers of 2.2 cM  was derived from a cross between two wild Ethiopian Arabicas: Ar8 and Ar36B. We successfully identified 945 polymorphic SNPs between the two parents (Ar8 and Ar36B) . Only 1.3% of the 945 SNPs selected for genetic mapping presented significant segregation distortion  in the progeny. Consequently, this pool of 945 new SNPs markers provides a promising tool for constructing our Arabica genetic map.The Arabica FC. canephora genome and on the Robusta linkage map.Among the 1307 and 945 SNPs segregating in the Robusta and Arabica mapping populations, respectively, only 46 SNPs (2%) were common and would thus be used for co‐linearity analyses. The mapped SNP markers of the Coffee8.5K SNP array are available in the newly implemented MoccaDB v 2.0 database Figure . This daCoffea species—C. canephora, C. arabica and C. eugenioides Diversity Panels— C. arabica and 6183 (72.1%) C. eugenioides. As expected, we observed a slow decline in the call rate as a function of divergence from the species of the Discovery Panel together with a decrease in polymorphic SNPs. However, the cross‐species application of our array, and particularly the transferability of C. canephora‐discovered SNPs to C. eugenioides, was shown to be efficient, with a success rate of 85.8% . A lower diversity within the allotetraploid, self‐compatible, and recent C. arabica species was expected. Because of its alloploidy, homeologous SNPs, that is polymorphism between the two subgenomes Ca/Ea, were estimated based on the heterozygous SNPs scored in the dihaploid Et39  and represented 24.2% of the scored markers. After filtering for these homeologous markers, we estimated the allelic SNPs  on the 16 tretraploid individuals of the Diversity Panel, which accounted for 10.6% of polymorphic SNPs.The percentage of polymorphic SNPs was much higher for C. arabica neighbour‐joining tree  is probably due to differences in global genetic diversities of these two species, which present a very different geographical extension (very large for C. canephora vs. reduced for C. eugenioides). Even if differences in the sampling size (n = 24 vs. n = 6) may be evoked to explain such a difference, Diversity Panels were chosen on purpose to represent a maximum of the known species diversity.While both C. canephora diversity, this may induce an ascertainment bias when other species, such as C. eugenioides, are concerned.As a large proportion of the SNPs on the array are Robusta‐derived, filtered to maximize the within C. canephora Diversity Panel  , corresponding to different geographic origins  and R (southern DRC). Three individuals showed mixed ancestry: AG‐Q121, BE‐110, OE‐KL.1.2, and ER‐BP409.Genotyping of this Diversity Panel with the Coffee8.5K SNP array resulted in the generation of 4095 polymorphic SNPs without missing data. The population structure and relatedness among accessions was examined using population clustering with sNMF. The genetic structure analysis identified the most likely number of genetic clusters as These results were further supported by the neighbour‐joining tree, which distinguished the six known and the two new groups with high bootstrap supports of 100% Figure c. The adCoffea source populations of each C. arabica subgenome, we assigned the C. arabica haplotypes to current sampled individuals of the two progenitor species C. canephora and C. eugenioides. However, because of standard deviation overlaps, the assignation of the Arabica Ea subgenome to sampled C. eugenioides individuals could not discriminate a specific individual or an origin (Kenya or Uganda)  or B (Northern DRC) and in particular to an individual of the Zoka forest   is geographically the closest to the current distribution of C. arabica  Figure d. On the) Figure c. Group a Figure a.C. canephora and C. arabica and also in assessing the diversity of C. arabica, C. canephora and other C. arabica‐related species (CWRs). The genotypes of the Discovery Panels were chosen to represent a range of common C. arabica and C. canephora types.Coffee8.5K was designed and developed to assist in mapping C. canephora is reported to be about 710 Mbp  of the effective SNPs were selected to be located in genic regions in the Coffee8.5K array.C. canephora map (BP409 X Q121) demonstrates the usefulness of the Coffee8.5K for developing high‐density genetic maps for this species. Combined with previously developed markers  of the Coffee8.5K SNPs on the C. arabica accessions. Despite this situation, some mapping efforts have been previously undertaken with AFLP markers , suggests that a high percentage of loci represented on our Coffee8.5K array would also be transferable to other species of the Coffea genus, at least from EC‐Afr and W/C‐Afr clades, and represent a substantial improvement in the identification potential of the genetic diversity available within the genetic resources of the Coffea wild species.Genotyping data was generated successfully for all three species of the panel with 7065 (82.3%), 6824 (79.5%) and 6183 (72.1%) high‐quality SNP markers in C. canephora Diversity set. They were also shown to be very efficient for diversity analyses by their application: they allowed to assign each C. canephora individual to its a priori genetic group—as previously defined by the SSR markers—as well as to discriminate between closely related accessions and to estimate admixture levels. Thanks to these markers, two additional, well‐differentiated genetic groups have been identified and characterized, one from Angola (group G) and the other from DRC (group R). This yielded a whole updated genetic structure of C. canephora within eight well‐differentiated and geographically localized groups  were polymorphic in the C. arabica, from the 6824 scored SNPs, 24.2% (1653) turned out to differentiate Ca and Ea subgenomes in the dihaploid Et39, corresponding to fixed heterozygosity, whereas an estimated 10.6% (724) SNPs represented the within subgenomes allelic polymorphism varying across the C. arabica species. Fixed heterozygosity has been suggested to act as a buffer against the reduced population‐level diversity that resulted from the genetic bottleneck associated with polyploid speciation as well as to facilitate the transition to inbreeding .For C. eugenioides Diversity set, although sharing more than 90% of the called SNP markers with the C. canephora set, the polymorphism rate was 19 times lower than for C. canephora, certainly due to the lower global genetic diversity of that species. A reduced number of tested C. eugenioides individuals can also be evoked, but our sampling covers the available accessions of the species in worldwide collections  and C. eugenioides (Ea genome donor)  and those of its progenitor species, demonstrating that the nuclear genomes have remained essentially unaltered since the formation of the hybrid.Yu et al.  providedC. arabica accessions, we took advantage of the fact that they exhibited close genetic relationships in order to apply a haploid‐genotype‐based assignation procedure to discover its closest current relatives in our sampling. Using our Coffee8.5K genotyping, we could not preferentially associate the Ea subgenome to any of the sampled individuals  Diversity group  Upon hybridization, C. arabica could have followed one of the typical patterns of the polyploid distribution with peripheral expansion outside the range of the distribution of its diploid parental species. Indeed, it has been suggested that plants with double genomes—auto‐ or allo‐polyploids—have the potential to develop phenotypic novelties, increase their adaptability and obtain higher fitness features that would render them more tolerant towards changing conditions than their diploid counterparts . The climatic changes could have reduced the diploid distributions to their current locations. As a consequence, the birthplace of C. arabica could possibly be not only in Ethiopia but also in the entire region  followed either by migration to present‐day Ethiopia or by survival in that region alone.Two non‐exclusive scenarios could be suggested for the origin and geographical isolation of C. arabica in different environments and a reduction in diploid competitors would have increased the divergence of initially con‐specific populations and eventually would have given rise to the C. arabica speciation. C. arabica's self‐fertility would have further contributed to its genetic isolation.Due either to past habitat shift in Eastern Africa or to the colonization of new peripheral geographical areas, the presence of C. canephora and/or to C. arabica.The availability of our Coffee8.5K to the Coffee community provides an opportunity to generate high‐throughput genotyping data on different types of genetic and breeding populations for accelerating genetic diversity, high‐resolution trait mapping and breeding applications. It will help in identifying the allelic diversity present in the wild relatives of the two cultivated species, offering a clue to the transferability of beneficial alleles to a subgenome to its closest present‐day C. canephora relatives. Our array is definitely a powerful tool for quality control and traceability of all merchant coffees.Thanks to our stringent selection of genome‐wide distributed and informative SNPs, our array is an efficient tool for fingerprinting. As an application, detecting admixtures up to individual levels has allowed us to assign the Arabica CC. canephora and 5 C. arabica) were chosen to obtain an array for optimized use in mapping and genetic diversity analyses in coffee trees (Table Accessions used to design the Coffee8.5K array (12 es Table .C. arabica Discovery Panel included five genotypes whose genome sequences were kindly provided by the Arabica Coffee Genome Consortium (ACGC): the parents of the Arabica mapping population (Ar8 and Ar36B), two cultivated varieties (Mundo Novo and Bourbon) and Et39, which is a spontaneous C. arabica dihaploid, that is with only one set of chromosomes from each subgenome . SNP calling was performed by mapping the resequencing reads to the reference genome  and SNP positions with suitable flanking sequences (60 bp on both sides with no variation). Accessions used for SNP discovery and SNP selection criteria were chosen to design an array for optimized use in mapping and genetic diversity analyses in coffee trees.C. arabica SNPs was conducted on the Arabica Discovery Panel. Set 1, represented by the two wild Ethiopian parents of the Arabica mapping population, Ar8 and Ar36B, together with the dihaploid Et39. We identified 8 208 891 raw SNPs  we considered SNPs spaced with a minimum distance of 40 kb.Screening for C. canephora genotypes was used for SNP discovery. To build a reliable and balanced genotyping array, accessions belonging to the six genetic groups  a sTo minimize the impact of SNP ascertainment bias on downstream analyses, only SNPs found in at least two genotypes within the surveyed genotypes were chosen .® dsDNA Assay Kit  using the LightCycler® 480 . The DNA quality control was performed according to the Agilent Genomic DNA ScreenTape assay with the Agilent 2200 TapeStation system. Templates were diluted or concentrated to obtain a concentration of 50 ng/μL as required by Illumina for the amplification step.Total DNA was extracted from adult leaves using DNeasy Plant Maxi Kit  following the manufacturer's instructions. Final DNA concentration was determined with the Quant‐iT™ PicoGreen® is a competitive allele‐specific dual FRET‐based assay for SNP genotyping  and 8580 of them were successfully synthesized by Illumina manufacturing processes. The SNP genotyping assay was performed on an Illumina® Infinium HD iSelect Custom Genotyping Array according to the standard Illumina's protocol, using 200 ng of genomic DNA per sample. The extension and staining steps were operated on a Tecan Evo‐150 liquid‐handling robot. Fluorescence intensities were read with Illumina® iScan Control software and allele calling was carried out using the Genome Studio v2.0 software . All data were visually inspected and manually re‐scored if any errors were evident in the calling of the SNP clusters by the default algorithm. Reproducibility error rates were calculated between the control sample and SNP replicates.The 9827 selected SNPs were submitted to the Illumina Assay Design Tool for design score calculation  and Q121 (Conilon‐type derived accession) comprising 93 individuals and an Arabica F2 segregation population  originating from a cross between two wild Ethiopian Arabica: Ar8 and Ar36B  genetic linkage map was built using JoinMap, et al. . The Koset al. (http://gmod.org/wiki/CMap) and in a genome browser . The linkage disequilibrium decay curve was calculated as r2, the squared value of the correlation coefficient of the allelic states of two given polymorphic loci, against biallelic SNPs with MAF > 0.10 and with <10% missing genotypes  using the PopLDdecay software  was determined as the one minimizing the cross‐entropy criterion, for K ranging between 1 and 10 (with 20 estimation runs per K).Population structure within the C. arabica individuals (595 intra‐specific polymorphic markers without missing data). The stability of each node was evaluated using 100 bootstraps by blocks of 100 SNPs (R package ape)  and the other one for the Coffea arabica contains two diploid genomes Ca and Ea that originated from two different wild diploid ancestor species, C. canephora and C. eugenioides  distances were computed between each of the 17 C. arabica accessions with each individual without admixture of the two related species. We used haploid genotype downscaling to mitigate the impact of the polymorphic SNPs in C. arabica that cannot be precisely assigned to one or the other related species. Haploid genotypes were randomly drawn for each SNP and each accession over 200 runs. The IBS distances were averaged over all runs for each C. arabica accession. The average IBS distances and their standard deviations were then calculated over all 17 C. arabica accessions.The authors declare no conflicts of interest.Figure S1 Linkage disequilibriumClick here for additional data file.Figure S2 MoccaDB v2 database screenshotsClick here for additional data file.Figure S3 C. arabica neighbour‐joining treeClick here for additional data file.Table S1 List of the Candidate Genes with their corresponding SNP markers  (2014) annotation)Click here for additional data file.Table S2 List of accessions‐ List of accessions and their use: Discovery/Diversity/Mapping panel‐ Geographical location of the C. arabica samples used in this study overlaid on the Guillaumet (1978) map‐ Location of analysed C. eugenioides individuals on the microsatellite neighbour‐joining treeClick here for additional data file.Table S3 SNP information Click here for additional data file.Table S4 Summary table of the historical definition of C. canephora genetic groups with the references and the marker types in use.Click here for additional data file.


Preimplantation genetic testing (PGT) has already been applied in chromosomally balanced translocation carriers to improve the clinical outcome of assisted reproduction. However, traditional methods could not further distinguish embryos carrying a translocation from those with a normal karyotype prior to implantation.To solve this problem, we developed a method named “Chromosomal Phasing on Base level” (BasePhasing), which based on Infinium Asian Screening Array-24 v1.0 (ASA) and a specially phasing pipeline. Firstly, by comparing the number of single nucleotide polymorphism (SNP) loci in different minor allele frequencies (MAFs) and in 2Mbp continuous windows of ASA chip and karyomap-12 chip, we verified whether ASA could be adopted for genome-wide haplotype linkage analysis. Besides, the whole gene amplification (WGA) of 3–10 cells of GM16457 cell line was used to verify whether ASA chip could be used for testing of WGA products. Finally, two balanced translocation families were utilized to carry out BasePhasing and to validate the feasibility of its clinical application.The average number of SNP loci in each window of ASA (473.2) was twice of that of Karyomap-12 (201.2). The coincidence rate of SNP loci in genomic DNA and WGA products was about 97%. The 5.3Mbp deletion was detected positively in cell line GM16457 of both genomic DNA and WGA products, and haplotype linkage analysis was performed in genome wide successfully. In the two balanced translocation families, 18 blastocysts were analyzed, in which 8 were unbalanced and the other 10 were balanced or normal chromosomes. Two embryos were transferred back to the patients successfully, and prenatal cytogenetic analysis of amniotic fluid was performed in the second trimester. The results predicted by BasePhasing and prenatal diagnosis were totally consistent.Infinium ASA bead chip based BasePhasing pipeline shows good performance in balanced translocation carrier testing. With the characteristics of simple operation procedure and accurate results, we demonstrate that BasePhasing is one of the most suitable methods to distinguish between balanced and structurally normal chromosome embryos from translocation carriers in PGT at present.The online version of this article (10.1186/s12920-019-0495-6) contains supplementary material, which is available to authorized users. Balanced translocation is one of the most frequent indications for preimplantation genetic testing (PGT), which occurs at an incidence of 1/500 to 1/625 in the general population and even up to 1/20 in the patients who has a history of repeated IVF failure or recurrent miscarriages . The unbThe first PGT case for translocations reported in 1998 was used by fluorescence in situ hybridization (FISH) . With thIn our former study , we succhttps://www.illumina.com). As we known, the number of informative SNPs is more valuable to linkage analysis, which allows for directly determining the accuracy of haplotype classification. For a SNP to be informative, one parent must have a heterozygous genotype and the other one should have a homozygous genotype. This is limited by specific families and may also be affected by the distribution of SNP frequency. For the same family, SNPs with high MAF are more likely to generate the informative SNP.The basic Microarray technical data of ASA and Karyomap-12 was downloaded from Illumina official website . By comparing the number of total SNPs and effective SNPs in each widow, we can get analytical performance of ASA bead chip.Written informed consent was obtained from each family and the study protocol was approved by the Ethics Committee for Human Subject research of the Obstetrics and Gynecology Hospital, Fudan University.Cell line GM16457 was obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research, with karyotype 46,XX,del(18)(q22.3) and a known 5.3Mbp deletion in chr18. Two translocation carrier families that would undergo assisted reproductive were enrolled in Shanghai Ji Ai Genetics & IVF Institute in May 2018. Both families had a history of recurrent spontaneous abortion, infertility or pregnancies with chromosome anomalies. The translocation karyotypes were 46,XY,t(1:5) and 46,XY,t respectively. Five milliliter peripheral blood from each couple and family members was collected at recruitment.For cell line GM16457 and peripheral blood samples, the high molecular weight DNA was isolated as described in the manufacturers’ protocol .Three to ten cells of the GM16457 cell line were isolated by micromanipulating under a dissection microscope  using a finely pulled glass Pasteur pipette. For embryos at the blastocyst stage, three to ten cells were removed from the trophectoderm on day five of embryonic development. The biopsied cells were placed into 0 .2mL PCR tubes in a total volume of less than 4 .0μL. Whole genome amplification (WGA) was conducted by means of multiple displacement amplification (MDA) according to the manufacturer’s instructions . The isothermal amplification was carried out at 30 °C for 8 h and followed by enzyme inactivation at 65 °C for 3 min.The isolated DNA and WGA products were treated according to the manufacturer’s instructions , which were then scanned using an Illumina iScan Bead Array Reader. The microarray scanning results were processed using the B allele frequency and Log R Ratio of Genome Studio software (Illumina) and Karyo Studio software (Illumina) to analyze the copy number of the chromosomes. Genome wide preimplantation genetic haplotyping (PGH) analysis based on Illumina Human Karyomap-12 V1.0 microarray was performed as our previous description .Based on informative SNPs and chromosomal phasing principles we developed BasePhasing pipeline, which was programmed in Practical Extraction and Reporting Language (Perl), and was capable of obtaining the clear haplotypes of each family member in linkage analysis Fig. . The rawAdditionally, BasePhasing was a universal pipeline with compatibility for other bead chips such as Human Karyomap-12, Infinium Global Screening Array-24 (GSA), Human CytoSNP-12, and Infinium Omini series microarrays.ASA bead chip contained approximately 700 K SNPs, more than double what those for Karyomap-12 bead chips. Although half amount of ASA SNPs MAF was lower than 0.1, the total number of high frequency SNPs was more than Karyomap-12 . The CCS analysis mainly relied on the SNP allele frequency analysis in the whole genome. We gathered and compared 136 previous samples data of bead chip in our laboratory, including 43 samples by ASA and 93 samples by Karyomap-12. We found that there were no differences between ASA and Karyomap-12 on the LogRdev versus %Defects . BasePhasing analysis was performed in the 10 blastocysts, which verified that 5 were balanced carriers and 5 were normal embryos. Sample information and BasePhasing results of the two families are listed in Table In the meantime, the number of informative SNP loci within ±2Mbp regions flanking the breakpoints and the whole related chromosome was calculated Table . It was We also calculated the number of informative SNP loci of family 2 in the whole human genome. The average number of informative SNPs was 85.9 in ASA bead chip and 59.4 in Karyomap-12 bead chip within ±2Mbp region along. The number of informative SNPs on ASA bead chip was a little bit more  cost of Kayomap-12 bead chip Additional file 2:Table S2. The low cost of BasePhasing in PGT. (DOCX 16 kb)


Tyrosine kinase receptor erythropoietin-producing hepatocellular receptor A2 (EphA2) is abundant in the endometrium and plays a role in the establishment of eutopic implantation. A similar molecular mechanism may exist between uterine implantation and tubal implantation, therefore EphA2 involvement in tubal pregnancy is suspected. Due to the limited availability of human Fallopian tube specimens, EphA2 expression in human Fallopian tube epithelium remains largely unknown.n = 29) and women with tubal pregnancy (n = 17). The changes of EphA2 and its activated form, phosphorylated-EphA2 (Pho-EphA2), in the Fallopian tube epithelium from non-pregnant women (n = 12) and women with tubal pregnancy (n = 14) were compared by quantitative RT-PCR and western blot assay.A total of 31 women with tubal pregnancy and 41 non-pregnant women with benign uterine diseases were enrolled in this study. Immunohistochemistry was used to investigate the expression pattern of EphA2 in the Fallopian tube epithelium of non-pregnant women (P > 0.05). With respect to the protein level, a significantly higher ratio of EphA2 over Pho-EphA2 was shown in women with tubal pregnancy (P < 0.05).EphA2 was expressed throughout the Fallopian tube epithelium, including the isthmus, the ampulla and the infundibulum. EphA2 concentration remained unchanged throughout the whole menstrual cycle, irrespective of menstrual phases and tubal regions. EphA2 mRNA in the Fallopian tube epithelium did not differ between normal women and women with tubal pregnancy (EphA2 is widely expressed in human Fallopian tube epithelium in a temporospatial-independent manner. Dysregulated EphA2 and its phosphorylation-dependent regulatory mechanism may unexpectedly enhance the cell adhesion activity of the Fallopian tube epithelial cells, leading to a mis-contact between the Fallopian tube epithelium and the embryo. An ectopic pregnancy is a pathological event characterized by embryo implantation occurring outside the uterine cavity. Fallopian tube localization accounts for roughly 98% of all human ectopic pregnancies . Tubal dErythropoietin-producing hepatocellular receptor A2 (EphA2) is a member of the largest known tyrosine kinase receptor family, which is well known to generate multiple cellular responses including cell adhesion and migration –10. ActiWe have previously demonstrated in vitro study that EphrinA1 could induce EphA2 activation in human fallopian tubal epithelial cells, accompanied by the up-regulation of Pho-EphA2 . MoreoveThirty-one women who had been diagnosed as tubal pregnancy and undergone surgeries in the First Affiliated Hospital of Jinan University, PR China were collected, and 41 non-pregnant women who had undergone hysterectomies for benign uterine diseases were set as controls. Informed consent was attained from each patient and ethical approval for this study was obtained from the Local Research Ethics Committee. All the enrolled subjects should meet the following criteria: (1) a normal history of fertility and regular menses; (2) a definitive date of the last menstrual period; and (3) no use of exogenous hormonal drugs or intrauterine devices in the 6 months preceding the surgery. Women with tubal pregnancy who were determined to have morphological abnormalities in the Fallopian tubes or benign uterine diseases such as uterine leiomyoma, adenomyosis and endometriosis after surgical operations were excluded.n = 17) and non-implantation site (n = 17), according to the distance apart from the gestational sac. The implantation site was defined as the circumference originating 5 mm from the gestation sac, while the non-implantation site was 10 mm outside the gestation sac [n = 8), mid- and late-stages of the proliferative phase , early-stage of the secretory phase  and mid- and late-stages of the secretory phase . All of the paraffin blocks were cut and prepared for immunohistochemical staining.Among all the enrolled subjects, the Fallopian tube samples from 17 tubal pregnant women  and 29 non-pregnant women (control)  were paraffin embedded and used for immunohistochemistry. Each Fallopian tube sample with tubal pregnancy was further divided into 2 portions, implantation site  and 12 non-pregnant women  were used for qRT-PCR and western blot analysis. Each Fallopian tube tissue with tubal pregnancy was similarly classified into implantation site and non-implantation site. Samples used for mRNA analysis were immersed into Trizol solution, and all fresh specimens were frozen at − 70 °C until analysis.The paraffin-embedded materials were cut into 4-μm-thick sections. These sections were deparaffinized and boiled in citrate buffer solution for 15 min in an oven. After washing with phosphate-buffered saline (PBS), the sections were immersed into 3% hydrogen peroxide for 10 min to quench endogenous peroxidase activity. Afterwards, mouse anti-human EphA2 monoclonal antibody  was applied overnight at 4 °C according to the EnVision™ system . The sections were washed in PBS and incubated for 30 min with horse radish peroxidase (HRP)-conjugated goat anti-mouse IgG . Diaminobenzidine (DAB) substrate-chromogen system  was used as the color-developing substrate. The slides were applied in two 5-min incubations and then counter-stained with hematoxylin. Normal gastric mucosa with known presence of EphA2 served as a positive control. The primary antibody was replaced with non-immune mouse serum IgG to serve as a negative control. All slides were viewed under an E200 microscope . Sections were quantified over ten randomly selected fields of view. Positive unit (PU) assessed by the QW550 image analysis system  was used to describe the EphA2 expression , 20. TheEphA2− ΔCt18sRNA).Total RNA was extracted from the fresh Fallopian tube samples using Trizol total RNA extraction reagent . Agarose gel electrophoresis was used to ascertain the quality of the RNA products by the presence of 28S rRNA, 18S rRNA and 5S rRNA bands, with a 2:1 ratio of 28S rRNA to 18S rRNA. cDNA was reverse transcribed from the extracted RNA using RT kit . The primers used for EphA2 were 5’-AAGACCCTGGCTGACTTT-3′ (forward) and 5’-GTTCACCTGGTCCTTGAGT-3′ (reverse), while the primers for 18sRNA were 5’-CCTGGATACCGCAGCTAGGA-3′ (forward) and 5’-CCTGGATACCGCAGCTAGGA-3′ (reverse). Real time RT-PCR was carried out using an ABI 7300 real-time PCR system  with 5 μl diluted cDNA and 0.5 μl primers. The programmed thermocycler included 40 cycles of predenaturation at 95 °C for 10 min, followed by denaturation at 95 °C for 15 s, annealing at 60 °C for 15 s, and extension at 72 °C for 30 s. All reactions were performed in triplicate. Relative quantification of EphA2 mRNA was calculated using the comparative Ct method , after which the proteins were transferred to nitrocellulose membranes. The membranes were blocked for 2 h in 10% bovine serum albumin (BSA)  at room temperature, followed by incubation at 4 °C overnight with the primary antibodies, rabbit anti-human polyclonal antibodies against EphA2 and Pho-EphA2  and mouse anti-human monoclonal antibody against glyceraldehyde phosphate dehydrogenase (GAPDH) . After washing, the membranes were then incubated for 1 h with the appropriate HRP-conjugated secondary antibodies, goat anti-rabbit IgG  or rabbit anti-mouse IgG . Protein bands were identified using ECL . The bands were semi-quantified using Bio-Rad Quantity One software. EphA2 was normalized to GADPH, and Pho-EphA2 was normalized to EphA2.P values < 0.05 were considered statistically significant.All statistical analyses were carried out using SPSS V.14. . Differences among groups were determined by one-way analysis of variance (ANOVA) followed by Student–Newman–Keuls (SNK) multiple-comparison test. Results were presented as mean ± standard deviation (SD). P > 0.05)  Table .Fig. 1EpP < 0.05). With regard to the latter two groups, the EphA2 staining intensities were similar (P > 0.05)  Fig. .Fig. 2EpP < 0.05). As for Pho-EphA2, a remarkably decreased level was seen in the implantation group (0.51 ± 0.25) compared to that in the non-implantation group (0.79 ± 0.37) and the control group (0.78 ± 0.35) (P < 0.05). However, both EphA2 and Pho-EphA2 levels did not differ between the non-implantation group and the control group (P > 0.05)  Fig. .Fig. 3CoP > 0.05) , with the corresponding values referring to 1.94 ± 0.51, 1.85 ± 0.74 and 2.02 ± 0.55, respectively (To the best of our knowledge, this is the first study to compare the expressions of EphA2 in human Fallopian tube epithelia between non-pregnant women and women with tubal pregnancy. Acted like the endometrium, the morphology of the Fallopian tube epithelium could similarly respond to a menstrual cycle , 22. TheInvestigation of pathological events occurring within the Fallopian tube necessitates considerations of the tubal microenvironment, of which the homeostasis is largely regulated by the molecules expressed on the Fallopian tube epithelium. Tubal pregnancy is currently accepted to be a direct consequence of molecular causes, considering that molecules capable of mediating cell adhesion have been confirmed to play a critical role in the pathogenesis of tubal pregnancy . Eph recIt is well-recognized that overexpression of EphA2 is merely presented in malignant cells, but in non-transformed epithelial cells, EphA2 displays a relatively low level of expression –27. SimiAn increased level of EphA2 was shown in the implantation group compared with that in the non-implantation group and the control group by western blot. However, it seemed a paradox that we failed to find any discrepancy in EphA2 mRNA in the Fallopian tube samples among these three groups. Previous studies concerning the linkage between EphA2 and malignant tumors indicated that an elevated EphA2 found in malignant tumors was a result of deficient phosphorylation of EphA2 itself, leading to the consequent blockage of EphA2 degradation. In other words, insufficient Pho-EphA2 could accordingly give rise to an excessive storage of EphA2 , 29. ThiCiliary motion is closely associated with female fecundity by regulating the processes of ovum pickup and transport . AlteredThere are still certain limitations in the present study we need to take into consideration. Firstly, there were a limited number of samples included in this study which may weaken the strength of our evidence. Secondly, there were some age differences between non-pregnant women and women with tubal pregnancy since it is difficult to obtain Fallopian tube specimens from normal young non-pregnant women for ethical reasons. Lastly, Fallopian tube samples from women with tubal pregnancy can only be collected after an implantation event has occurred, making it difficult to directly identify the causal linkage between EphA2 and tubal pregnancy.Many efforts have been made to clarify the mechanisms responsible for EphA2 activation during pathological events, yet there is still largely unexplored –34. EphAIn conclusion, we have demonstrated that EphA2 is expressed in human Fallopian tube epithelium in a temporospatial-independent manner. EphA2 and its phosphorylation-dependent regulatory mechanism seem to be activated only under pathological conditions. The presence of a reduced Pho-EphA2 accompanied by an elevated EphA2, a state that may be recognized as EphA2 inactivation, maybe involved in the molecular pathogenesis of tubal pregnancy through up-regulating cell adhesion activity of the Fallopian tube epithelial cells. A better understanding of EphA2 and its signal pathways is of significance to illustrate the molecular pathogenesis of tubal pregnancy. Further investigations with larger samples are necessary to complement our present study.