FRMD8, SLC25A45 and LTBP3 as potential candidate genes for meat tenderness on the basis of functional annotation of these genes. The region SSC2_109 was close to a previously reported candidate gene CAST; however, the very weak LD between DRGA0003285 (the best marker representing region SSC2_109) and CAST indicated the potential for additional genes which are distinct from, or interact with, CAST to affect meat tenderness. Limited information of known genes in regions SSC2_109 and SSC17_20 restricts further analysis. Re-sequencing of these regions for informative animals may help to resolve the molecular architecture and identify new candidate genes and causative mutations affecting this trait. These findings contribute significantly to our knowledge of the genomic regions affecting pork shear force and will potentially lead to new insights into the molecular mechanisms regulating meat tenderness.Of all the meat quality traits, tenderness is considered the most important with regard to eating quality and market value. In this study we have utilised genome wide association studies (GWAS) for peak shear force (PSF) of loin muscle as a measure of tenderness for 1,976 crossbred commercial pigs, genotyped for 42,721 informative SNPs using the Illumina PorcineSNP60 Beadchip. Four 1 Mb genomic regions, three on SSC2  and one on SSC17 (at 20 Mb), were detected which collectively explained about 15.30% and 3.07% of the total genetic and phenotypic variance for PSF respectively. Markers ASGA0008566, ASGA0008695, DRGA0003285 and ASGA0075615 in the four regions were strongly associated with the effects. Analysis of the reference genome sequence in the region with the most important SNPs for SSC2_5 identified  Eating quality of pork is mostly linked with wholesomeness, freshness, leanness, juiciness, taste and tenderness, with tenderness being considered the most important feature of eating quality and the primary factor influencing consumer purchase decisions –3. For thttp://www.animalgenome.org/cgi-bin/QTLdb/SS/summary), but only a few gene variants, such as CAST   wer werCAST orted by . The DNAorted by .P < 0.05) for the trait. For the Genesus population, all animals within a slaughter batch were raised in the same pen. Preliminary analyses indicated that the interactions among these factors were not significant (P > 0.05) and therefore ignored in the model.yijkl is phenotypic observation, μ is overall mean, si is the effect of the ith sex , gj is the effect of the jth contemporary group (j = 1–57 for Genesus and 58–103 for Hypor), rk is effect of the kth room (k = 1–10 for Hypor), pl is the effect of the lth pen (l = 1–13 for Hypor), and eijkl is the random residual The original phenotypic records were adjusted within each population using the GLM procedure of SAS 9.3. A contemporary group effect was defined to include year and group during the test (~ 70–115 kg) and slaughter batch. There were 57 contemporary groups for Genesus and 46 for Hypor. Fixed effects of sex and contemporary group were fitted in the model below. For the Hypor population, the room and pen were fitted as fixed effects in the model as they were available and significant , zj = design matrix for the genotype covariate for SNP j (j = 1 to k) based on the copy number of the B alleles , αj = allele substitution effect for SNP j, and δj = indicator for whether SNP j was included (δj = 1) or excluded (δj = 0) in the model for a given iteration of the Markov chain Monte Carlo procedure. A total of 61000 iterations were run with the first 1000 iterations discarded as burn-in. The probability of δi = 0 was set equal to π = 0.99. The Bayesian model was implemented using method Bayes B. Genomic regions associated with the trait were identified using 1 Mb, non-overlapping windows using Ensembl build 10.2 of the swine genome.The adjusted phenotypes were used for the GWAS analysis, which were conducted in each (Hypor and Genesus) population separately and the combined data set. Population (Hypor and Genesus) was fitted as a fixed effect in the model for the combined data. The marker effects were estimated by fitting all SNPs simultaneously using Bayesian methods as implemented in the online software GenSel as follows:CAST genotyped with a small panel were conducted in a sub-population (n = 831). The method and statistical model were the same as described above.The revised GWAS which included the additional SNPs of Further analysis of marker effect estimation and significance test was conducted in the combined data set. Regional genomic estimated breeding values (GEBV) for the detected 1 Mb windows were calculated as the sum of the marker effects of the SNPs within the region. The GEBV was regressed on single SNP variant (0/1/2) within the region as most of the SNPs within the region were in very weak LD. The R squared (RSQ) for the regression model, defined as the percentage of the regional GEBV variance explained by the SNP, was used to determine the most important SNP within that region affecting the trait.CAST) were examined with a multi-marker association model implemented using ASReml [Y = Xb + Za + e, where Y is the vector of adjusted phenotypes, b is the design matrix for fixed effects including mean, population and fitted markers, a is the random polygenic effect excluding the examined marker effects, which was assumed with A is the relationship matrix constructed based on pedigree information, and e is the vector of residual errors with a distribution of I is the identity matrix and X and Z are the incidence matrices for b and a, respectively.The significant test and least square means (LSMs) for the interesting SNPs (both from GWAS and in g ASReml . The modhttp://uswest.ensembl.org/biomart). The functions and related information about the genes were summarized using the available database of GENECARDS (http://www.genecards.org/).Candidate gene identification and functional annotation for the interesting SNPs were obtained using Ensembl annotation of Sscrofa 10.2 genome version  relationships for these SNPs were very weak based on an overview of the haplotype LD block maps (2 = 0.96–1.00) captured an average 91.8%, 98.7% and 95.5% of the variance for the estimated GEBV in the window, indicating that any of these SNPs could be affecting the trait. SNP25 was chosen for further analysis due to it having the highest RSQ (0.987). For region SSC2_109, the 11th SNP (SNP11) captured more than 99% of the genetic variance and was considered to be the most important SNP for this region.In combined data set, three 1 Mb regions on SSC2 associated with PSF were located at 4 Mb, 5 Mb and 109 Mb, respectively. A total of 54 SNPs were included and the LD  found in the present study. Five SNPs in CAST were included in the Illumina PorcineSNP60 BeadChip. Two of them (ASGA0096522 and ASGA0011047), located in intron 3 and intron 30 of CAST, remained in the GWAS but did not show a significant effect on PSF at the genome wide level. The other three were discarded from the GWAS as one was uninformative and the other two (MARC0042944 and ALGA0014711) did not pass the call rate criterion (95%) but were included for further analysis in a sub-population of the samples (see below).To date, calpastatin has been regarded as the most important candidate gene for pork tenderness. CAST with the regions detected on SSC2, four informative SNPs of CAST in the panel  and three other important markers in CAST associated with meat tenderness, including Ser66Asn, Ser638Arg and 41642_408i, were genotyped in a sub-population of the samples (n = 831) using a custom SNP panel [CAST were estimated . A closer analysis of the SNPs within CAST found that MARC0042944 was in moderate LD (0.21–0.61) with Ser66Asn, ALGA0014711, ASGA0096522 and 41642_408i, but in very weak LD with Ser638Arg (r2 = 0.08) and ASGA0011047 (r2 = 0.06). The LD between two other interesting SNPs (2 and 25) on SSC2 and CAST was also very weak .To further investigate the relationships of NP panel . The LD stimated . Almost CAST effect, MARC0042944 (the only marker of CAST in LD with SNP11) was fitted in the model simultaneously to re-estimate the effect of SNP11, SNP2, SNP25 and SNP7  on PSF (CAST was significant for PSF (P < 0.01) with allele B showing a significant additive effect of -1.05 ± 0.40N (P < 0.05). GWAS within population 2 also showed that MARC0042944 was the best marker (RSQ = 0.99) in region SSC2-106 associated with the trait (data was not shown). After accounting for the CAST effect, SNP11 was still significant for PSF (P < 0.001) with AA identified as the favorable genotype with a significant additive effect of -1.94 ± 0.79 N (P < 0.01). In addition, SNP11 showed a trend for an effect on meat Minolta colour  and crude protein content at P < 0.10 (P < 0.001) and SNP25 (P < 0.01), the other two important SNPs detected on SSC2, also remained in the model and showed a significant effect for PSF after accounting for CAST and SNP11. At the genome wide level, the revised GWAS in a sub-population which included all above six SNPs of CAST in the model showed that SSC2_5 and SSC2_109 were still the top two regions explained the largest genetic variance of the trait (data was not shown). All these findings indicated that the regions on SSC2 detected by GWAS had very weak relationships with CAST. Therefore, the effect of the detected regions on PSF might be distinct from, or interacting, with CAST.In order to partition the ) on PSF . As expeP < 0.10 . SNP2  captured almost all of the GEBV variance of this region with near a perfect RSQ (0.966)  . A small (0.966)  consistiCAST. Therefore, the effect of region SSC17_20 on PSF is expected to be distinct from the regions detected on SSC2 and CAST. After accounting for the effect of CAST and SNPs on SSC2, SNP7 was still significant for PSF (P < 0.001), and BB was the favorable genotype with the lowest PSF (P < 0.05) for other meat quality traits, such as drip loss (%), rib trim crude moisture content, fresh loin L* and a*, and fresh ham a* and b* . For SNP26 and SNP20 which were in strong LD with SNP25, one was located in exon 1 of SLC25A45  and the other was located in intron 17 of LTBP3 (latent transforming growth factor beta binding protein 3). For regions SSC2_109 and SSC17_20, very few genes have been found in pig. The nearest gene to SNP11 and SNP7 were FAM174A and PLCB4, respectively.All the known genes in these detected regions are shown in Gene functional annotation  indicateMYOD1 (~ 44.4 Mb) and UBL5 (~ 68.9 Mb), which showed a significant effect on meat tenderness and other quality traits, such as fat deposition, pH 1h post-mortem and meatiness in Italian Large White pigs [Sus scrofa assemblies and maps. A recent GWAS in Landrace-Duroc-Yorkshire pigs detected four SNPs located on SSC1 (255 Mb), SSC2 (5 Mb and 74 Mb) and SSC9 (1 Mb) significantly affecting slice shear force [CAPN1) and was also identified in the present study which will be discussed later. On SSC17, four QTL associated with shear force have previously been mapped with peaks at 9 cM [In this study, three 1Mb regions on SSC2 and one region on SSC17 were found to explain a relatively large proportion of genetic variance for pork peak shear force. Up to now, most of the QTL mapped for pork shear force were detected using linkage analysis, which covered large regions of the genome. In these previous studies SSC2 was identified as an important chromosome for meat tenderness as a large number of the QTL mapped to the central part of the chromosome within 40–79 cM –24. A reite pigs . Other rite pigs ,22 and aite pigs ,26. The ite pigs ,19 due tar force . The reg at 9 cM , 37.81 c at 9 cM  whereas CAST as a candidate gene for meat tenderness [CAST region (SSC2_106) was also detected in population 2, and MARC0042944 located in intron 3 of CAST was considered the best marker in this region with an effect on the trait. In the combined data set, a region very close to CAST (SSC2_109) was significantly associated with PSF. Calpastatin is a specific inhibitor of the calpains, an enzyme family which is believed to be responsible for post-mortem tenderization of muscle through degradation of myofibrillar and associated proteins [CAST have been found to significantly affect meat tenderness and other meat quality traits [CAST for meat tenderness are the three missense mutations of Ser66Asn, Arg249Lys and Ser638Arg first reported by Ciobanu et al. [CAST seems to be the most important gene on SSC2 to affect pork shear force. However, because none of these three mutations were included in the 60K SNP panel, the contribution of the SNPs/genes to the effect of these detected regions on the trait still remains unknown. Therefore, further investigations of the genomic architecture of this region and its impact on meat tenderness are encouraged.Previous work identified nderness . In thisproteins . In recey traits ,19,32. TCAST were included in the 60K SNP panel and all of them were located in introns. Only two of them (ASGA0096522 and ALGA0014711) qualified for the final GWAS here based on genotype filtering, and neither of them showed a significant effect on PSF. Instead region SSC2_109 explained a large proportion of genetic variance of PSF, and SNP11 (DRGA0003285) was considered the best marker, as it explained more than 99% of the genetic variance of this region. Sequence analysis found that SNP11 was about 2.5 Mb downstream of CAST. After accounting for the effects of CAST and other SNPs , SNP11 was still significant for PSF (P < 0.05). Therefore, we hypothesized that the important effect of region SSC2_109 on shear force is due to SNP11 which might be distinct from the effect of CAST.Five SNPs of CAST, including the two non-synonymous mutations (Ser66Asn and Ser638Arg). Ciobanu et al. [CAST had different autolyzed μ-calpain activity 24 h and 72 h post-mortem, which affected post-mortem activation time of calpain and resulted in lower shear force and drip loss [CAST had moderate LD with SNP11, and showed an important effect on PSF in population 2. Although, no previous association of MARC0042944 on meat quality has been reported to date, in this study MARC0042944 was detected in population 2 and also showed a significant effect on peak shear force in the sub-population of samples tested. The remaining four SNPs in CAST were in very weak LD with SNP11. These findings indicated the possibility of other genes/markers in this region affecting shear force, which may be distinct from, or interacting, with CAST.Further analysis showed that almost no LD existed between SNP11 and SNPs in u et al.  found thu et al. ,28,33–35rip loss ,37. One CAST and no significant LD was observed between these regions and any SNPs in CAST. After accounting for the effect of CAST and SNP11 (SSC2_109), these two regions still had a significant effect on PSF. Region SSC2_4 has not previously been reported to be associated with QTL for shear force. A recent GWAS reported that one SNP (H3GA0005672) in region SSC2_5 was significantly associated with pork slice shear force [2 = 0.32) with H3GA0005672. Region SSC2_5 is close to the gene μ-calpain (CAPN1) which was reported to affect beef and pork tenderness through interaction with CAST [P < 0.05) effect of these two regions on other meat quality traits, such as a* values of ham and loin (Two other regions (4 Mb and 5 Mb) on SSC2 also contributed a large effect on PSF. These two regions were more than 100 Mb away from ar force , but it ith CAST –40. We aand loin . AnalysiP < 0.01) effect on PSF. However, only two genotypes (AA and AB) of SNP7 were observed in this study . Although four QTL have been mapped on SSC17 [P < 0.05 (The most important marker (SNP7) at 20 Mb on SSC17 had a significant . The best SNPs (2 and 25) along with two others in very strong LD with SNP25 were intragenic in FRMD8, SLC25A45, LTBP3 and a novel gene (ENSSSCG00000021760) in pig. We assume that these genes or others close to these regions may play important roles on meat tenderization post-mortem.A total of 56 known genes are found in the regions of SSC2_4 and SSC2_5 in the pig  which plays a key role in the maintenance of intracellular reactive oxygen species (ROS) homeostasis so that oxidative cellular damage is prevented through antioxidant pathways. Oxidation is one of the important processes that cause extensive modifications encountered by the pig muscle proteome post-mortem, which is significantly associated with post-mortem events, such as tenderization [FRMD8 on peak shear force might be related to its anti-oxidative activities that support maintenance of cytoskeleton integrity and intracellular ROS homeostasis and control the rate of apoptosis. A proteomics review of pork tenderization revealed that a sharp increase (57%) in oxidation levels in sarcoplasmic proteins takes place during the first four days after slaughter, and the oxidation levels of 70 myofibrillar proteins were observed to increase during a 48 h post-mortem period in meat with high water holding capacity [rization ,43. Studrization . It is pcapacity . Post-mocapacity .SLC25A45 which belongs to the SLC25 family of mitochondrial carrier proteins [SLC25A20) which mediates the transport of acylcarnitines into the mitochondrial matrix for their oxidation by the mitochondrial fatty acid oxidation pathway, which might be related to the energy homeostasis of the muscle cell post-mortem and thus affecting the meat tenderization.SNP26 was in complete LD with SNP25 and was intragenic with proteins . FunctioLTBP3 forms a complex with transforming growth factor beta (TGFB) proteins and plays critical roles in controlling and directing the activity of TGFB1. TGFB1 encodes a multifunctional protein which is essential for function of many cell types in controlling proliferation, differentiation, adhesion and migration. LTBP3 also plays a structural role in the extracellular matrix (ECM). The ECM is composed primarily of connective tissue [LTBP3 showed an effect on peak shear force, which might be due to the structural role of LTBP3 in ECM and the functional activities in the muscle cell through regulating TGFB1.The protein encoded by e tissue , and mane tissue ,48. The CAPN1). This region was also found to be significant for pork slice shear force [CAPN1 have been reported to influence the expression of the calpain-calpastatin enzyme complex, which regulates the rate of protein degradation in the live animals and post-mortem muscle [CAPN1 on the 60K SNP panel were not found to be associated with peak shear force in the present study.Additionally, it is noteworthy that SNP25 was about 173 Kb upstream from μ-calpain , which also lacks functional information. CAST, about 2.5 Mb upstream to SNP11, was one of the few genes known to be responsible for meat tenderization post-mortem through its role in muscle protein degradation [PLCB4 . Phospholipases are ubiquitously expressed and have diverse biological functions including roles in inflammation, cell growth, signaling and death and maintenance of membrane phospholipids. GO annotations related to PLCB4 include phosphatidylinositol phospholipase C activity and calcium ion binding. These functions may also play important roles in meat tenderization post-mortem.The known gene information for SSC2_109 and SSC17_20 was very limited, which restricted further candidate gene analysis. SNP11 representing SSC2_109 was about ~320 Kb downstream of an unknown gene in pig, and the orthologue of this gene in other species is radation ,32,45,50FRMD8, SLC25A45 and LTBP3 were potential candidate genes for association with meat tenderness, as they play important roles in muscle cell oxidative activities and the integrity of the ECM, which are two important determinants for meat tenderization post-mortem. The μ-calpain gene was also found close to this region but was not associated with PSF in this study. Few known genes have been mapped in regions SSC2_109 and SSC17_20. Very weak LD existed between SSC2_109 and CAST potentially indicating new genes affecting the trait in this region. Re-sequencing of these regions may help resolve the molecular architecture of the region. Study of the gene expression of these genes is encouraged to further identify the molecular mechanisms and potential causative mutations affecting this trait.Three 1 Mb genomic regions at 4 Mb, 5 Mb and 109 Mb on SSC2 and one region at 20 Mb on SSC17 have been found to explain a relatively large proportion of genetic variance for pork loin peak shear force. The SNPs of ASGA0008566, ASGA0008695, DRGA0003285 and ASGA0075615 were the four markers found to be most strongly associated with the effect and could be considered for marker assisted selection for meat tenderness improvement. Regions of SSC2_4 and SSC2_5 were enriched for known genes. The genes S1 FigThe numbers on the top indicated the SNP order in this region; SNP in the green circle was the most important marker representing this region affecting the trait; The SNPs grouped in each triangle box indicated they were grouped in one block based on LD information.(PNG)Click here for additional data file.S2 FigThe numbers on the top indicated the SNP order in this region; SNP in the green circle was the most important marker representing this region affecting the trait; The SNPs grouped in each triangle box indicated they were grouped in one block based on LD information.(PNG)Click here for additional data file.S3 FigThe numbers on the top indicated the SNP order in this region; SNP in the green circle was the most important marker representing this region affecting the trait; The SNPs grouped in each triangle box indicated they were grouped in one block based on LD information.(PNG)Click here for additional data file.S4 FigThe numbers on the top indicated the SNP order in this region; SNP in the green circle was the most important marker representing this region affecting the trait; The SNPs grouped in each triangle box indicated they were grouped in one block based on LD information.(PNG)Click here for additional data file.S1 TableThe full name of the SNPs in first row can be found in (DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Zfp106 gene, which encodes peptide epitopes known to mediate corneal graft rejection. The other three regions are novel minor histocompatibility loci. The results suggest that rejection can be predicted from SNP analysis prior to transplant in this model and that a similar GWA analysis is merited in humans.In rodents, immune responses to minor histocompatibility antigens are the most important drivers of corneal graft rejection. However, this has not been confirmed in humans or in a large animal model and the genetic loci are poorly characterised, even in mice. The gene sequence data now available for a range of relevant species permits the use of genome-wide association (GWA) techniques to identify minor antigens associated with transplant rejection. We have used this technique in a pre-clinical model of corneal transplantation in semi-inbred NIH minipigs and Babraham swine to search for novel minor histocompatibility loci and to determine whether rodent findings have wider applicability. DNA from a cohort of MHC-matched and MHC-mismatched donors and recipients was analysed for single nucleotide polymorphisms (SNPs). The level of SNP homozygosity for each line was assessed. Genome-wide analysis of the association of SNP disparities with rejection was performed using log-likelihood ratios. Four genomic blocks containing four or more SNPs significantly linked to rejection were identified , none at the location of the MHC. One block of 36 SNPs spanned a region that exhibits conservation of synteny with the mouse H-3 histocompatibility locus and contains the pig homologue of the mouse  The accumulation in recent years of increasingly detailed and accurate sequence data for the human genome and for the genomes of mammalian species used in medical and veterinary research has enabled powerful analytical methods to be employed to identify genetic loci accounting for pathological conditions. In the transplantation field such methods have, for example, identified minor histocompatibility antigens (mHags) governing the success of bone marrow transplantation , liver tZfp106 [Zfp106 gene have been identified in other species including human (ZNF106) and pig (ZNF106 aka ZFP106). Two CD8 T cell epitopes in the Zfp106 protein contribute to corneal graft rejection in the mouse [Although the importance of mHags in the immune response to corneas have long been established in rodent models , 6, theiZfp106 . Ortholohe mouse , each reWe have recently developed a pre-clinical model of corneal graft rejection in the semi-inbred NIH minipig , in whiccc and SLAdd lines of NIH minipig ([https://www.ebi.ac.uk/ipd/mhc/sla/haplotypes.html) and large White Babraham pigs [bb for the purposes of this paper. Each line is homozygous at the MHC locus, but retains intra-line minor locus incompatibilities [bb Babraham (n = 6) or SLAdd line minipigs (n = 10) were transplanted to SLAcc line minipig recipients, each combination providing both major and minor histocompatibility mismatches. A third group of SLAcc minipig intra-line transplants (n = 5) yielded minor histocompatibility mismatches alone. Recipients were of both sexes, but X-Y mismatches were excluded.Swine leukocyte antigen (SLA)minipig  as per manufacturer's instructions, then stored at -20°C.www.ark-genomics.org) (ARK-Genomics is now integrated into Edinburgh Genomics (http://genomics.ed.ac.uk). The locations of SNPs in the Sscrofa9 genome assembly were used in the analysis, but were updated during manuscript preparation to locations of the Sscrofa 10.2 assembly (Ensembl release 83).DNA samples were genotyped using the Illumina PorcineSNP60 chip  in the AA GWA analysis was performed to identify minor loci affecting outcome. The genotype match/mismatch between donor and recipients was defined assuming two different rejection models. Model 1, consistent with a T cell epitope model of rejection, took into account the direction of the mismatch at the particular locus: i.e. a heterozygous graft AB was mismatched with a recipient of homozygous AA or BB genotype, whereas a mismatch in the opposite direction, i.e. an AA or BB donor in an AB recipient was matched (graft versus host reactions being irrelevant in this model). Model 2 took no account of the direction of the mismatch, i.e. both AB into AA/BB and AA/BB into AB were considered mismatches.ijO and ijE are the observed and expected counts for each of the possible groups . Under the null hypothesis, the LRT follows a χ2 distribution with one degree of freedom [Hence, for a given SNP, genotypes of donor and recipient were classified as matched or mismatched and the outcome of the graft as accepted or rejected, thus yielding four possible graft outcome groups: matched-accepted, matched-rejected, mismatched-accepted, mismatched-rejected. The association of the SNP with corneal transplant outcome could be tested using the likelihood ratio test (LRT), equal to: freedom . Because freedom  where thbb, 27 SLAcc, and 11 SLAdd were genotyped. These pigs included donors, recipients and additional available non-transplanted pigs to increase the pool of samples for the homozygosity analysis. A total of 59,852 SNPs were successfully genotyped. The proportions of SNPs fixed (i.e. invariant) within each line were 0.86, 0.71 and 0.78 for the SLAbb, SLAcc and SLAdd lines respectively (cc line) to 0.98 (chromosomes 13 and 16 of the SLAbb line) (bb line) suggests that a chromosome is close to full homozygosity.A total of 5 SLAectively . These wectively  confirmsbb line) . The meabb to SLAcc, SLAdd to SLAcc and SLAcc to SLAcc line combinations respectively, 4/6, 6/10 and 0/5 grafts progressed to immunological rejection [We previously reported that in the SLArej) occurred in blocks on chromosome 4  and a further SNP 7Mbp downstream from these) and chromosome 9 (7 SNPs spanning 2.4Mbp). For rejection Model 2, SNPrej were located on chromosomes 1 (37 SNPs spanning 13.4Mbp and approximately 115 known genes), 4 , 6 (4 SNPs spanning 2.1Mbp) and 14 (1 SNP). The SNPrej in each closely linked block of SNPs were occasionally adjacent, but more frequently interspersed with SNPs that had non-significant LRT scores . LRT values for the full genome scan in Models 1 and 2 are shown in Sixty one SNPs were significantly associated with corneal graft outcome . For rejT scores . Intereser model . Chromosh models . There wrej the distribution of donor-recipient pairs between the four outcome groups  was identical, but that there was variation between genomic blocks . Regions on pig chromosomes 1 and 4 and the homologous regions in the human and mouse genomes have a particularly high density of genes. This is exemplified by ZNF106 gene, located 2.4Mb from the 3’ end of the 13.4Mbp block of SNPrej on chromosome 1, together with homologous human and mouse regions. The mouse homologue of ZNF106, Zfp106, codes for H-3a the minor antigen associated with corneal graft rejection in mice [rej also includes the beta-2-microglobulin (β2-M) gene, the mouse homologue of which also contains allograft-defined polymorphisms [The pig genome is still not fully annotated. However, the major blocks of SNPorphisms .Zfp106 ) and skin graft rejection (β-2M  respectively). Chr 9:9183235–11819832) contains at least 22 protein coding genes with close homology to human and mouse genes on chromosomes 11 and 7 respectively, none of which have been associated with transplant rejection in any species. The current Sus scrofa genome build (10.2) defines SNPs in the remaining two regions (Chr 4 and Chr 6) as intergenic.GWA analysis has revealed at least four independently segregating non-MHC regions of the pig genome containing genetic polymorphisms associated with corneal graft rejection, three of which have not been previously identified. Two of the four regions  bear close homology to regions in humans and mice containing a high density of protein coding genes. One of these (Chr 1) containing approximately 115 genes, includes homologues of genes in the H-3 region of the mouse which have already been associated with corneal graft rejection  or by altering the activity of genes that control immune responses (Model 2 hypothesis). The fact that there were few SNPs common to both models is not surprising since in Model 2 no account was taken of the direction of mismatch. Thus considerably more donor-recipient pairs would be considered mismatched in this category, resulting almost always in a different distribution of donor-recipient pairs in the four outcome groups for a given SNP.The minor locus SNPhttp://www.ensembl.org/Sus_scrofa/Info/Annotation), all the SNPs identified are likely to be markers through linkage for one or more nearby causative loci. It has been shown in the context of both mouse and human graft rejection that MHags usually comprise both CD4 and CD8 epitopes that are not necessarily in the same gene [rej on chromosome 1 (Model 2) homologous to the mouse H-3 region, (with linkage accounting for the remaining SNPrej), due to the block size (encompassing approximately 115 genes). The mouse H-3 region contains at least 3 minor loci: H-3a (a cytotoxic lymphocyte (CTL) epitope within Zfp106 ); H-3b . The 430 putative SNPs in the pig ZNF106 gene include 11 putative missense SNP variants. The extent to which graft rejection in different species is cause by polymorphisms in homologous genes is uncertain and identifying whether rejection in the pig could be accounted for by polymorphisms in the swine ZNF106 and β2-M genes would require sequencing of these genes in our donors and recipients and functional studies. However, the close homology between the SNPrej region on chromosome 1 and a well-documented minor histocompatibility region in the mouse containing Zfp106, previously implicated in mouse corneal graft rejection [ZNF106 gene and/or other genes in this region (such as β2-M) may contribute to corneal graft rejection in all three species.Based on our current understanding of the mechanisms of rejection, we can offer no immunological explanation for the association of 4 SNP mismatches with graft acceptance. As the SNP chip only assays a small fraction of the >28 million putative SNPs currently annotated in the pig genome (ame gene . Therefosk2 gene ); and β2rej on chromosome 1 were revealed only in the Model 2 analysis, which did not conform to a T cell epitope hypothesis. We thus anticipate that loci highlighted by SNPrej in Model 2 may be immune response genes, such as micro RNAs, transcription factors or other immunoregulatory proteins, or ligands of NK receptors outside the MHC region. In the context of the Model 2 hypothesis, it is noteworthy that polymorphisms in mouse β2-M can modulate immune responses by influencing indirectly the effectiveness of peptide binding and therefore of both self and allogeneic peptide selection for presentation on MHC class I molecules [Irrespective of whether critical polymorphisms are located in these genes in swine, it is noteworthy that SNPolecules , 26. In cc to SLAcc (or other intra-line) grafts to be mismatched at some or all of these loci would test whether this rejection on account of minor mismatches was truly independent of MHC disparity. Furthermore, we have previously shown that some allografts can contain significantly elevated numbers of T cells without showing clinical sign of rejection [The evidence from this study justifies further testing of the model, whereby donors and recipients are typed before transplantation and outcome is predicted according to SNP disparity. For example, our results suggest that pre-transplant typing and selection for matches or mismatches between donor and recipient at any of the three largest blocks of SNPs, i.e. chromosomes 4 or 9 (Model 1) or chromosome 1 (Model 2) would permit prediction of graft survival or rejection with 90% accuracy (18/20 cases). Similarly, typing of donors and recipients and deliberate selection of SLAejection . Thus, srej regions. This would determine whether there was true species overlap of genes controlling rejection and whether it would be of value to type and match at minor loci to improve outcome in human corneal transplantation.In summary, the study demonstrates for the first time the power of GWA to reveal independently segregating minor histocompatibility regions associated with corneal graft rejection, using a model that more closely resembles human genetic variability between donor and recipient than do fully inbred rodent models. The regions identified contained homologues of mouse genes associated with rejection, as well as genes that have not so far been associated with rejection in other species. The exact minor locus genes accounting for swine transplant outcome have yet to be identified, but we suggest that the level of precision we have achieved is sufficient to merit a comparable human study, focusing initially on regions of the genome that share homology with pig SNPS1 Fig(PPTX)Click here for additional data file.S2 Fig(XLSX)Click here for additional data file.S3 Fig(XLSX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.


Interlocus gene conversion (IGC) is a recombination-based mechanism that results in the unidirectional transfer of short stretches of sequence between paralogous loci. Although IGC is a well-established mechanism of human disease, the extent to which this mutagenic process has shaped overall patterns of segregating variation in multi-copy regions of the human genome remains unknown. One expected manifestation of IGC in population genomic data is the presence of one-to-one paralogous SNPs that segregate identical alleles.Here, I use SNP genotype calls from the low-coverage phase 3 release of the 1000 Genomes Project to identify 15,790 parallel, shared SNPs in duplicated regions of the human genome. My approach for identifying these sites accounts for the potential redundancy of short read mapping in multi-copy genomic regions, thereby effectively eliminating false positive SNP calls arising from paralogous sequence variation. I demonstrate that independent mutation events to identical nucleotides at paralogous sites are not a significant source of shared polymorphisms in the human genome, consistent with the interpretation that these sites are the outcome of historical IGC events. These putative signals of IGC are enriched in genomic contexts previously associated with non-allelic homologous recombination, including clear signals in gene families that form tandem intra-chromosomal clusters.Taken together, my analyses implicate IGC, not point mutation, as the mechanism generating at least 2.7 % of single nucleotide variants in duplicated regions of the human genome.The online version of this article (doi:10.1186/s12864-015-1681-3) contains supplementary material, which is available to authorized users. Thes alleles .Fig. 1Deet al. (2009) used a gene-tree species-tree reconciliation strategy to estimate that 2.16 % of duplicated sites in the human genome have been directly converted [et al. (2005) and Dumont and Eichler (2013) used a third approach based on the identification of fine-scale switches in the phylogenetic tree relating paralogs to derive estimated conversion rates of ~4-5 % [Although a handful of exceptional gene families show signals consistent with frequent IGC –24, overonverted . Jacksonf ~4-5 % , 28. TogNaively, the limited footprint of IGC on patterns of between paralog divergence in the human genome may also be predicted to extend to its effects on within paralog polymorphism. However, there are at least two reasons to speculate that IGC may have left a more pronounced stamp on levels of within paralog diversity in the human genome. First, the human population has experienced rapid, super-exponential population growth over the last ~100 generations . As a reRHCE gene are also polymorphic at corresponding positions in the paralogous RHD locus [One powerful approach for identifying IGC signals embedded in DNA diversity data is to mine population genetic datasets for one-to-one paralogous positions that are polymorphic for identical alleles , 30–33 . ApproxiHD locus , and oveHD locus  and the HD locus  are sharToward this aim, I leverage well-annotated segmental duplications in the human genome and SNP calls from population genomic sequences generated by the 1000 Genomes Consortium to systematically catalog 15,790 pairs of shared polymorphic positions at one-to-one paralogous sites. I show that these paralogous SNP pairs are most parsimoniously interpreted as the outcome of historical IGC events, rather than the consequence of parallel mutation events. Together, my analyses demonstrate that a small, albeit significant, fraction of variants in duplicated sectors of the human genome have arisen by the recombinogenic process of IGC rather than by conventional point mutation.IGC between duplicated loci can introduce shared polymorphisms at one-to-one paralogous positions Fig. . Using Sparallelism. I use the term “parallel SNP” to refer to either of the two constituent SNPs in a parallelism.For ease, I will refer to a pair of parallel polymorphic SNPs as a Short sequence reads derived from duplicated regions may map to multiple loci in the genome and lead to false positive genotype calls. In particular, redundant or incorrect mappings between highly identical duplicates could generate artificial parallelisms and lead to the spurious inference of IGC. To mitigate the effects of this important and likely source of error, I eliminated SNP calls associated with reads that map to multiple locations in the human reference genome see . This fiNot surprisingly, the majority of parallelisms that are eliminated by the unique read mapping filters occur in high-identity duplicated regions that cannot be reliably distinguished by short reads. Specifically, 55.9 % of redundantly mapping parallelisms lie in duplicated regions with >95 % sequence identity, and 10.7 % fall in regions with >99 % pairwise sequence identity (PSI). In contrast, only 42.8 % and 3.6 % of uniquely mapping parallelisms are located in paralogous sequences with >95 % and >99 % PSI, respectively  not included among the 1000 Genomes samples . Because5 = 0.0625, assuming no allele bias. These clone-based sequences validate ten of the eleven shared parallelisms . Second, many parallelisms concentrate within previously characterized IGC hotspots in the human genome, including the Rhesus blood group antigens RHD and RHCE on 1p36.11 [HLA locus [P < 0.001).The distribution of parallelisms in the human genome recapitulates several patterns that are consistent with IGC, providing additional evidence for their origin via this mechanism. First, IGC is more frequent between intra-chromosomal duplicates than between inter-chromosomal SDs , 27, 47. 1p36.11 , 48, 49, 1p36.11 , olfacto 1p36.11 , and theLA locus , 50, 51 DUOX1 and DUOX2 genes on 15q21.1. Over 20 % of all SNPs observed in regions of high quality alignment between these duplicons are involved in a parallelism, suggesting that ~10 % of variants in these genes have arisen from historical IGC events . Approximately 10 % of SNPs in these duplicated genes are also likely the direct by-product of IGC ((18 ÷ 2)/87 = 0.1034).I have uncovered evidence for historical IGC involving 5.2 % of uniquely mapping, non-CpG SNPs in SDs. Accounting for nested redundancies in the data due to sets of >2 paralogous SNPs see , I estimPSG genes are abundantly expressed in the placenta during pregnancy and are vital for safeguarding the developing fetus from infectious agents in the maternal bloodstream [PSG paralogs has played a role in the adaptive protein evolution of this gene family.In addition to these gene-level IGC hotspots, there are 606 SNPs  that have potentially seeded new SNPs at multiple paralogous acceptor loci .Despite this limitation, the sites I identify collectively comprise a catalog that will empower future investigations on IGC. This resource will be especially useful for deriving biologically relevant parameter values and fine-tuning evolutionary models to accurately reflect observed patterns of human polymorphism in duplicated genomic regions . In addThis analysis has focused exclusively on SNPs, but the approach taken here can be readily extended to include other variant types such as indels and multinucleotide variants. Moreover, this general method for identifying IGC signals can be applied to other species with population genomic data. This straightforward extension will facilitate comparative studies on the evolution of the mechanism of IGC, including comparisons of its impact on DNA sequence variation in diverse organisms.de novo rates of IGC [Although differences in power between divergence-based and polymorphism-based methods for detecting IGC signals make it difficult to directly compare estimates , resultss of IGC , 47, 63.de novo SNPs. If IGC occurred recently, the minor SNP allele will likely correspond to the fixed base at a paralogous position. Across the pairwise alignments analyzed here, there are 70,619 SNPs with minor allele frequency ≤0.01 at which the minor allele is fixed at a paralogous position. This corresponds to 5.8 % of all SNPs in duplicated sequence space and 9.0 % of all SNPs in segmental duplications (SDs) with minor allele frequency ≤0.01 . However, alternative evolutionary scenarios can generate an identical genetic signal, without a requirement for IGC. For example, positive directional selection or random drift could lead to the near fixation of a new allele in one duplicate. Complex population genetic simulations are needed to determine the relative fraction of these signals that is attributable to IGC, an effort that will ultimately yield a more accurate estimate of IGC’s impact on diversity in the human genome.Finally, this study has systematically mined the human genome for one specific signature associated with IGC: shared, parallel SNPs. IGC events that span fixed differences between paralogs can also lead to per se, but rather the mutagenic action of IGC bears directly on the continued study of these functionally significant regions of genome. First, as a consequence of variant input via IGC, SDs may harbor more diversity than expected under neutral evolutionary models. Elevated diversity relative to null expectations is commonly a signature of loci evolving under positive Darwinian selection [de novo assembly and accurate short-read mapping in genomic regions with recurrent IGC. The continued development of novel experimental methods with the precision to query variants within high copy genomic sequences will not only meet this challenge [Regardless of the precise number of segregating variants due to IGC, the finding that a non-negligible fraction of SNPs within human SDs are not due to mutation election –66 and ielection , 68. Thuelection , 21. Sucelection . Finallyhallenge , 70, butHere, I have used publicly available population genomic data from the 1000 Genomes Project in conjunction with well-annotated duplicated sequences to identify 15,790 shared, polymorphic SNPs between one-to-one aligned duplicated positions in the human reference genome. I have carefully shown that these parallelisms are not the consequence of redundant read mappings in multi-copy genomic regions and that they cannot be explained by parallel mutation events. I conservatively estimate that at least 2.7 % of SNPs in duplicated regions of the human genome have arisen as the consequence of IGC, not point mutation. These findings underscore the importance of an often over-looked mechanism of human genomic diversity – including possible disease alleles – and bear on the interpretation of polymorphism patterns across the ~5 % of the human genome that lies in segmental duplications.stretcher program implemented in the EMBOSS suite  were obtained from the genomicSuperDups table downloaded from the UCSC Table Browser . The corn 6.3.1; ). This det al. [Regions of low quality or uncertain alignment were identified using several complementary methods. First, I computed average PSI in 100 bp non-overlapping sliding windows across each alignment, and masked windows with PSI <2 standard deviations below the alignment-wide average PSI. Second, I used the method of Han et al.  to identMasked alignments were integrated with genotype calls from low-coverage short-read whole-genome sequencing of 1,058 unrelated individuals carried out by the 1000 Genomes Project Consortium  to ideni.e., the X0 field is >1) or (ii) >5 suboptimal hit positions . I further required that the alignment between a read and any suboptimal hit contain at least two mismatches more than the number of mismatches in the alignment of the read to its optimal placement. These filtering steps leverage information from mate pairs, such that if one read in the pair maps redundantly and the other maps uniquely, the paired read unit is considered to map to a single locus in the reference genome. Note also that these quality control steps are performed on a per-sample basis. Reads overlapping a given SNP may map redundantly within the genome of one sample, but map uniquely in other sequenced individuals. In these instances, only SNP calls from the latter individuals are retained for downstream analysis.To identify the subset of Phase 3 1000 Genomes SNP calls that are derived from reads that map to single positions in the hg19 reference genome, I first extracted all sequence reads overlapping SNPs in SDs from the bam alignment files. I then eliminated any SNP calls associated with reads mapping to (i) multiple best hit loci (bwa [http://krishna.gs.washington.edu/indianGenome/).Fosmid clone pool sequences from a Gujarati Indian individual (GM20847) were downloaded from the NCBI short read archive as unaligned sam files (project accession SRP004325 ), converbwa . SNP calThe standard samtools/bcftools pipeline  was usems [i.e. the number of SNPs across the locus observed in the 1000 Genomes dataset). For example, if the sequence harbors 300 segregating sites, I simulated coalescent datasets with 300 variable sites. Second, I assume a single population with a static population size for most of its history , and model a strong recent population expansion that initiated 100 generations ago . Specifically, the executed commands are:S1 -eG 0.0025 0 -G 920./ms 2116 1000 -s S2 -eG 0.0025 0 -G 920./ms 2116 1000 -s Coalescent simulations were performed on a subset of 100 paralog alignments using Hudson’s ms . Simulatα = 920; ). I did α = 920; , 78. ForS1 and S2 are the observed number of segregating sites in the two paralogous sequences. For each simulation replicate, I determined the number of variable sites present at the same position in both paralogs. Assuming a Jukes-Cantor model of nucleotide substitution, one-third of these “two-hit” sites will segregate the same alleles and manifest in sequence data as mutation-derived parallelisms.where de novo mutation and that an IGC event subsequently transferred the new to the paralogous position. However, owing to the presence of higher order parallelisms in the human genome, the number of SNPs in human SDs due to IGC is not simply equivalent to the number of parallelisms. For an n-dimensional parallelism, (n-1) SNPs have presumably arisen via IGC following a single mutation event at one paralog. Therefore, the number of SNPs within duplicated regions of the genome that are due to IGC is given by: k is the observed number of n-dimensional parallelisms.At each parallelism, I assume that one SNP arose by All statistical analyses were implemented in the R Environment for statistical computing .i.e., the total number of high-confidence parallelisms identified after stringent filtering) unique one-to-one aligned positions from the set of 48,931 paralog alignments. Sampling was agnostic to the variant status of the underlying sites.To test whether observed parallelisms are enriched in specific genomic contexts, I generated 1000 null datasets by randomly sampling 15,790  with >10 parallelisms (n = 386). The observed number of parallelisms in 1 kb non-overlapping window across each paralog alignment was compared to the uniform expectation  using a Chi Square goodness-of-fit test. P-values were calculated from the empirical Chi Square distribution .I used an The data sets supporting the results of this article are included within the article and its additional files.


Apis mellifera) subspecies in Europe, and have resulted in extensive introductions of two eastern European C-lineage subspecies (A. m. ligustica and A. m. carnica) into the native range of the M-lineage A. m. mellifera subspecies in Western Europe. As a consequence, replacement and gene flow between native and commercial populations have occurred at varying levels across western European populations. Genetic identification and introgression analysis using molecular markers is an important tool for management and conservation of honey bee subspecies. Previous studies have monitored introgression by using microsatellite, PCR-RFLP markers and most recently, high density assays using single nucleotide polymorphism (SNP) markers. While the latter are almost prohibitively expensive, the information gained to date can be exploited to create a reduced panel containing the most ancestry-informative markers (AIMs) for those purposes with very little loss of information. The objective of this study was to design reduced panels of AIMs to verify the origin of A. m. mellifera individuals and to provide accurate estimates of the level of C-lineage introgression into their genome. The discriminant power of the SNPs using a variety of metrics and approaches including the Weir & Cockerham’s FST, an FST-based outlier test, Delta, informativeness (In), and PCA was evaluated. This study shows that reduced AIMs panels assign individuals to the correct origin and calculates the admixture level with a high degree of accuracy. These panels provide an essential tool in Europe for genetic stock identification and estimation of admixture levels which can assist management strategies and monitor honey bee conservation programs.Beekeeping activities, especially queen trading, have shaped the distribution of honey bee ( The role of introgression and admixture in conservation is a dilemma: While natural admixture may be an important evolutionary force in speciation and maintenance of genetic diversity –2, admixApis mellifera L., represents a valuable model to study human-mediated change. Beekeeping has been practiced in Europe for many centuries , a second European reserve was recently created in the United Kingdom. Other A. m. mellifera conservation efforts, although not enacted by law, are underway in France, Holland, Norway, Switzerland, Ireland, and Belgium, among others . The success or failure of all these efforts will be tightly linked to efforts that monitor the integrity of these protected populations.Increasing awareness that native honey bee diversity represents a valuable asset for sustainable beekeeping is fuelling local breeding and conservation efforts across Europe. One of the earliest, and until recently the single conservation program enacted by law, is that implemented by the Danish Beekeepers Association and the Læsø Beekeepers Association on behalf of the Danish Government in 1993 and the European Union in 1998  to creatA. m. mellifera and introduced C-lineage subspecies using microsatellite and PCR-RFLP markers  on a training dataset, in an effort to select AIMs and to rank them by decreasing level of informativeness. In the second stage, we tested the power of the reduced five designed panels and validated their performance on holdout and simulated sets, by comparing the admixture estimates produced by the panels with those produced by an initial dataset of 1183 SNPs.The ability of an AIMs panel to measure ancestry is generally evaluated empirically, by examining its performance on a given set of samples for which ancestry is known . In thisA. m. mellifera, A. m. ligustica and A. m. carnica in Europe  were collected from apiaries located in England (N = 8), France (N = 15), Belgium (N = 3), Denmark (N = 10), Holland (N = 15), Switzerland (N = 6), Scotland (N = 10), and Norway (N = 10) from protected and unprotected populations [leu-cox2 and microsatellites; [A. m. mellifera and have therefore been integrated into conservation programs. To prevent C-lineage introgression and assure pure breeding, these colonies have been maintained in islands or in isolated mating stations. Despite careful management to protect the threatened A. m. mellifera from C-lineage introgression, a recent SNP survey detected variable, although generally low, levels of introgression in these protected populations  and Croatia (N = 11) and from the natural range A. m. ligustica in Italy (N = 17). The owners of all the sampled apiaries gave permission to collect honey bee individuals from the hives. In each location, samples were taken from the inner part of hives, placed into absolute ethanol and stored at −20°C until molecular analysis.A total of 113 honey bee haploid males were collected in 2010 and 2011 across the native range of o et al. ). The saulations . Colonieellites; , 50–51) o et al.  for detaA. m. mellifera sampled across Western Europe [Using a phenol/chloroform isoamyl alcohol (25:24:1) protocol , total Dn Europe  and to dn Europe . Genotypn Europe ) across n Europe .ST of Weir & Cockerham [ST–based outlier test developed by Foll & Gaggiotti [pAi—pAj|, where pAi and pAj are the frequencies of allele A in the ith and jth populations, respectively. When more than two populations were analyzed, the Delta value for each SNP locus was estimated as the mean across all pair-wise comparisons. The fourth method was the informativeness for assignment  proposed by Rosenberg et al. [n provides the amount of information gained about population assignment from observation of a single randomly chosen allele at a locus. This method assumes a uniform prior across K potential source populations for the origin of the allele. For a given set of populations, the minimum value of In (0) occurs when all alleles have equal frequencies in all populations whereas the maximum value (1) occurs when alleles are not shared among populations. In was calculated using the software Infocalc available at http://www.stanford.edu/group/rosenberglab/infocalc.html. Finally, the fifth selection method was principal component analysis (PCA), which was performed using the PAST software [Five different methods were employed on the initial 1183 SNP dataset for estimating marker information content. The first method, which has been one of the most popular for selecting informative loci, was the pairwise Fockerham  as calcuockerham . The secaggiotti , which eaggiotti  using 20g et al. . In provsoftware . The firsoftware . The loasensu Soland-Reckeweg et al. [A. m. mellifera, previously identified in Pinto et al. [A. m. ligustica and 19 A. m. carnica) were used for SNP ranking (training set = 70) and the remaining 43 individuals of A. m. mellifera were reserved for panel testing (holdout set = 113). To minimize the effect of clusters of populations on the selection of the AIMs [A. m. mellifera and 36 C-lineage individuals, with no distinction between the A. m. carnica and A. m. ligustica subspecies (dataset I). The second dataset consisted of 51 individuals: 34 pure A. m. mellifera and 17 A. m. ligustica (dataset II). The third dataset consisted of 53 individuals: 34 pure A. m. mellifera and 19 A. m. carnica (dataset III). Finally, the fourth dataset consisted of 70 individuals: 34 pure A. m. mellifera, 17 A. m. ligustica and 19 of A. m. carnica (dataset IV).SNPs were ranked and panels of SNPs tested using reference populations and the Anderson’s Simple Training and Holdout method to reduce the potential for upward bias, which is introduced when loci are ranked and assessed using the same individuals . To thatg et al. ) individo et al. , and allthe AIMs , 60–61, The five selection methods were implemented on the four training datasets producing a total of 20 information content values for each of the 1183 SNPs. These values were ranked and analyzed individually and then were averaged in two steps to obtain a single global value per SNP. In the first step the information content values were averaged across the four training datasets for each of the five selection methods. In the second step the information content values produced by each selection method were converted into a 0–1 scale and then averaged to obtain a global score for each of the 1183 SNPs. After standardizing the values produced by the five selection methods, the global ranking was obtained for the 1183 SNPs using the global score. Given that linked loci yield redundant information, having therefore similar resolving power, markers were excluded if they were within a predefined genetic distance (<1 cM) of higher ranking selected SNPs. The genetic distance of the remaining SNPs ranged from 1.01 to 24.25 cM with a mean of 4.64 cM. Prior to obtaining the global score for each SNP, pairwise associations between information content values produced by the five methods and between the four training datasets were calculated using the Spearman`s rank correlation coefficient, in order to compare the five selection methods and examine the effect of clusters of populations.A. m. mellifera and the reference A. m. ligustica  and A. m. carnica , as described above. The simulated set  was generated with the program ONCOR [Five panels of 48-, 96-, 144-, 192- and 384-SNPs  were designed from the top-ranked SNPs. These nested panels were tested against a holdout set and a simulated set to obtain the admixture proportions estimated by each SNP panel. The holdout set  consisted of 34 pure individuals plus 43 reserved individuals of am ONCOR  using thTwo approaches were used to validate the five reduced AIMs panels. First, a PCA was performed with SNPs in each AIMs panel on the holdout set using the software PAST to generate two-dimensional PCA and to visualize the stability of population assignment produced by the panels. Second, ancestry and admixture was analyzed. Admixture proportions were estimated with SNPs in each AIMs panel for the holdout and simulated sets using a model-based maximum likelihood estimation of individual ancestries implemented in the software Admixture v1.23 . Coances2) and the standard deviations of the differences between admixture proportions. Finally, the accuracy of the reduced panels was estimated via percentage of absolute error of admixture estimates obtained with the five panels in relation to the initial 1183 SNP dataset.The performance of each reduced panel was examined using different approaches. First, the pairwise differences between admixture proportions inferred from the initial 1183 SNP dataset and the five panels were tested using a Mann-Whitney test. Second, the precision of each panel was tested against the initial 1183 SNP dataset by calculating linear regression coefficients  contain high levels of information content  between the different estimates of genetic information content produced by the five selection methods across the four training datasets is shown in ST, Delta and In  whereas a moderate correlation was detected between the FST-based outlier test and Weir & Cockerham’s FST, Delta and In . The lowest correlations were observed between PCA and the other four methods . Regarding the four training datasets .The level of similarity  was first validated by using PCA to produce a visual summary of the observed genetic variation carried by the holdout set . The oveAncestry and admixture analyses based on admixture estimates confirm the overall pattern captured by the PCA . At the P<0.001) in unprotected  than in protected individuals  for any AIMs panel. The overall estimates of C-lineage introgression into A. m. mellifera varied with the panel , although the differences were not statistically significant . As for the analyses with the holdout set, the simulated set produced two clusters corresponding to M and C lineages with no significant differences in admixture proportions between the different AIMs panels and the initial 1183 SNP dataset  is stimulating implementation of conservation programs across Europe in an attempt to recover and protect al range , and at al range , 31. TheST, FST-based outlier test, Delta, In and PCA, in order to balance out the limitations of each individual method [ST, Delta, and In suggesting that they capture the same information. Nonetheless, the smaller panels  did not necessarily include all AIMs simultaneously detected by the five methods as the global ranking depended on the average score. High pairwise correlation values were obtained for Weir & Cockerham’s FST, Delta and In but not for PCA, as found by Wilkinson et al. [The AIMs included in the five reduced panels were simultaneously selected by pairwise Weir & Cockerham’s Fl method , 58, 65.l method –61, 65. The five reduced panels tested with the holdout and simulated sets performed virtually as well as the initial 1183 SNP dataset, as revealed by the strong correlations obtained between admixture estimates and low associated error rates. The assignment power was high across the five panels with average values of correct assignment varying between 98.2 and 99.4%, although the accuracy decreased slightly with panel size. Nonetheless, even the 48-AIMs panel exhibited high accuracy levels, which is not surprising as it includes the AIMs with the greatest resolution power. Studies on other organisms have also found good performances with panels of similar sizes , 60, 65,A. m. mellifera and the two most common sources of foreign genes, A. m. ligustica and A. m. carnica, revealed a negligible effect of population groupings on the AIMs ranking. These results suggest that the designed panels are suited for identifying and assessing introgression of A. m. ligustica, A. m. carnica or both into A. m. mellifera. While these panels will possibly perform well in the presence of other C-lineage subspecies, more complex combinations that include sources of different evolutionary lineages will require further testing and, most likely, new panels developed from broader baseline datasets. Additionally, it should be noted, that these reduced panels are not suitable for standard population genetic analyses, including determining allelic diversity or measuring isolation by distance, genetic drift or bottleneck effect. The bias introduced through selection for markers that segregate among target populations would seriously compromise these calculations [Evaluation of different combinations of the focal ulations –67.A. m. ligustica and A. m. carnica, for estimating C-lineage introgression into A. m. mellifera we recommend using the 96-AIMs panel because it is accurate; and high-throughput 96-plex genotyping assays can be outsourced at an affordable cost ($8 900 for 480 samples), representing a saving of 92.4% when compared with the 1536-plex assay ($116 800 for 480 samples).Ancestry identification of honey bee subspecies is undergoing steady development  from clA. m. mellifera populations that suffer from hybridization and introgression with the most commonly introduced and beekeepers’ preferred A. m. ligustica and A. m. carnica subspecies. This can be an important advance because the current European regulation on organic beekeeping states that “preference shall be given to the use of European breeds of Apis mellifera and their local ecotypes” and several conservation programs have been undertaken in Europe . The usS1 FigK = 2, which distinguishes the M (red) and C (cyan) evolutionary lineages of A. mellifera.Global estimates (y-axis), for the 113 individuals of the holdout set (x-axis), inferred from the five AIMs panels  and the initial 1183 SNP dataset using the model-based approach implemented in the ADMIXTURE software. Results are shown for the optimal (TIFF)Click here for additional data file.S2 FigPrecision estimates obtained using the SD of the differences between admixture proportions inferred from the initial 1183 SNP dataset and the five AIMs panels  using the holdout (blue line) and simulated (orange line) sets.(TIFF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableThe SNPs are ordered from high to low information content. The top 48, 96, 144, 192 and 384 SNPs were included in the five reduced panels. SNPs marked with an asterisk (*) were excluded from the reduced panels because they were within a genetic distance <1 cM of other informative SNPs.(DOCX)Click here for additional data file.S3 TableA. m. mellifera and all reference individuals of A. m. ligustica (17) and A. m. carnica (19). * Samples marked with an asterisk (*) are of A. m. mellifera from protected populations  and 43 reserved individuals of carnica 1. * Sampl(DOCX)Click here for additional data file.S4 TableValues obtained from comparing individual admixture proportions estimated with the five AIMs panels  and the initial 1183 SNP dataset using the holdout set.(DOCX)Click here for additional data file.S5 TableValues obtained from comparing admixture proportions inferred from the five AIMs panels and the 1183 initial SNP dataset using the simulated set. The simulated set was generated with the program ONCOR  using the function “simulate a single mixture”. Ten populations, each with 100 genotypes, were simulated using different levels of C-lineage introgression .(DOCX)Click here for additional data file.


To fill in this gap, we develop a new method MAFsnp, a Multiple-sample based Accurate and Flexible algorithm for calling SNPs with NGS data. MAFsnp is based on an estimated likelihood ratio test (eLRT) statistic. In practical situation, the involved parameter is very close to the boundary of the parametric space, so the standard large sample property is not suitable to evaluate the finite-sample distribution of the eLRT statistic. Observing that the distribution of the test statistic is a mixture of zero and a continuous part, we propose to model the test statistic with a novel two-parameter mixture distribution. Once the parameters in the mixture distribution are estimated, p-values can be easily calculated for detecting SNPs, and the multiple-testing corrected p-values can be used to control false discovery rate (FDR) at any pre-specified level. With simulated data, MAFsnp is shown to have much better control of FDR than the existing SNP callers. Through the application to two real datasets, MAFsnp is also shown to outperform the existing SNP callers in terms of calling accuracy. An R package “MAFsnp” implementing the new SNP caller is freely available at  The development of next-generation sequencing (NGS) technologies in the past few years has transformed today’s biological science . With chA variety of SNP calling tools have been developed to identify SNPs using single or multiple sample(s) –22. BaseIn each of the above SNP callers, either quality score or posterior probability is provided as a measure of confidence of being a SNP, and some threshold of the quality score or posterior probability is suggested for calling SNPs. Unfortunately, it is hard for users to determine their own threshold in these SNP callers to control required false discovery rate (FDR). It is thus desired to have a statistical method providing a p-value  for each candidate locus, which can be easily used to control FDR. MAFsnp, a Multi-sample Accurate and Flexible SNP caller, is designed in this article for this purpose.MAFsnp is based on a likelihood function for the NGS read counts from multiple samples, and the SNP calling issue is transformed into a hypothesis testing problem on the minor allele frequency (MAF) for each candidate locus, then an estimated likelihood ratio test (eLRT) statistic is used to detect SNPs. The null distribution of the eLRT statistic is essential in calculating a p-value. Note that the eLRT statistic is a function of mapping/sequencing error rate and minor allele frequency. Because the mapping/sequencing error rate is usually very small and it is close to the boundary of the parameter space, the finite sample distribution of the eLRT statistic could greatly deviate from the standard limiting distribution. Through extensive simulations, we find that the test statistic is a mixture distribution of zero and a continuous distribution. We propose to approximate the distribution of the continuous part with a scaled chi-square distribution. An algorithm is then developed to estimate the scale parameter and the proportion of zero part, and p-values for detecting SNPs follows immediately. Using these p-values, multiple-testing corrected p-values can be easily obtained to control FDR. One key feature of MAFsnp is that it only uses summarized read count data. As a result, MAFsnp is applicable to the NGS data generated from any sequencing platform.−5, 0.157, 0.003, 0.061, and 0.011, repectively . Third, the application to two real datasets further verifies that MAFsnp could outperform the competitors in terms of calling accuracy. For example, based on a fragment of public sequencing data, the accuracy of MAFsnp was 91.2%, compared with 31.7%, 59.0%, 90.3%, and 88.8% by seqEM, MAQ, GATK, and SAMtools, respectively. Finally, some concluding remarks are given in Discussion.The rest of this article is organized as follows. First, a new distribution family is used to model the read count data with mapping/sequencing error, and a rigorous statistical method is developed to call SNPs based on this model. Second, a simulation study is conducted to evaluate the performance of MAFsnp and several existing SNP callers, which demonstrates that MAFsnp could have much better control of FDR than the competitors. For example, in one of our simulation situations, the FDRs of SAMtools, GATK, MAQ, seqEM, and MAFsnp were 8.7 × 10R and r the reference allele and variant allele, respectively. Let p denote the frequency of the variant allele r in a general population, which is called minor allele frequency (MAF) throughout this article. By definition, a locus is a SNP if and only if p > 0. Therefore, the SNP calling issue can be transformed into the following hypothesis testing problem:We will transform the SNP calling issue into a hypothesis testing problem. In our method, we only consider diploid organisms, and we only use read counts mapped to a reference genome. For any nucleotide locus, denote by J nucleotide loci. For the jth locus with reference allele Rj and variant allele rj, let pj denote its MAF (the population frequency of rj). We assume that the Hardy-Weinberg equilibrium holds, so that the population frequencies of the genotypes RjRj, Rjrj, and rjrj are (1 − pj)2, 2pj(1 − pj), and n independent samples. For the jth  locus of the ith  sample, let Gij denote the unknown genotype, Nij the total number of reads, and Xij the number of variant reads. Let Nj = T and Xj = T. Because of mapping/sequencing error, the genotype rjrj or Rjrj could be observed even if the the nucleotide locus is not a SNP. As done in literature [ej denote the common mapping/sequencing error rate at the j locus, and let θj =  denote the unknown parameter vector for the jth locus. It is reasonable to assume that the observed number of variant reads follows a binomial distribution given the true genotype and the total number of reads,. Therefore, we have the following log-likelihood function for the observed read counts:Nij and successful probability ej. The likelihood ratio test (LRT) statistic for tesing H0 : pj = 0 isθj under H0 has a closed form: Suppose that we have terature , we assuTj is a centralized chi-square distribution (mixed chi-square distribution) provided that the true parameter vector under the null hypothesis is an inner point (on the boundary) of the parameter space and some other regularity conditions hold. In our model, the parameter vector under the null hypothesis is on the boundary of the parameter space, and the limiting null distribution of Tj is a mixture of 0 and the centralized chi-square distribution of 1 df [Tj could greatly deviate from the limiting distribution Tj = 0) could be very close to 1, and the non-zero part of Tj could also deviate from the chi-square distribution Tj by the following modified mixture distribution:k. Obviously, D0.5,1 is a special case of Da,k. The p-value based on the distribution Da,k is x-quantile of the chi-square distribution of 1 df. For convenience, hereafter, the methods based on D0.5,1 and Da,k are called MAFsnp0 and MAFsnp, respectively.The null distribution of the eLRT statistic is essential for calculating p-values. According to the standard large sample theory, the limiting null distribution of  of 1 df :D0.5,1≔a could be much bigger than 0.5 under some simple conditions. For example, with a constant coverage of N = 2, a mapping/sequencing error rate of e = 0.01, and a sample size of n = 100, the proportion of zero part of Tj  is approximatly 0.99. Refer to Theorems 1 and 2 in a and k using genewise information. To this end, we first identify J null loci that are not SNPs. In practice, some SNP information can be obtained from a public database like dbSNP [J0 be the number of null loci with Tj = 0, then we can estimate a byJ − J0 null loci be s = 1, …, J − J0), then we can estimate k withTs ≤ 15}{ = 1 if Ts ≤ 15 and 0 othewise. Here we use a trimmed sample mean instead of the ordinary sample mean. This can effectively reduce the impact of large outliers. The threshold 15 is around the upper 10−4 quantile of the chi-square distribution of 1 df, so that very few non-outliers would be excluded for calculating We can theoretically show that ke dbSNP , and theFDR represents the fraction of non-SNPs in the positive SNP list. Given a list of p-values for detecting SNPs, FDR corrected p-values can be obtained using the R function “p.adjust” for the purpose of controlling FDR. A good SNP caller should have its FDR controlled around the nominal level. In literature, precision is defined as 1 minus FDR. Among all SNP callers with the same precision, the one with highest recall (true positive rate) would be preferred.In our simulation studies, we will compare the performance of the considered SNP callers through FDR. Here F1 score is commonly used to balance precision and recall:F1 ≤ 1, and the F1 score serves as a measure of accuracy, that is, a larger value of F1 score suggests a higher accuracy.It is impossible to have a SNP caller with both precision and recall uniformly larger than other SNP callers. The following F1 score on the recision-recall curve is termed Fmax. For any real dataset, the true SNPs might be unknown, but we can use the SNP information in the dbSNP as a reference, and precision and recall can be correspondingly estimated.For simulated data, precision and recall at any given threshold of p-values can be calculated because the SNP information is known in advance. As a result, a precision-recall curve can be drawn for any SNP caller provided p-values are available. Hereafter, the maximal Transitions are interchanges of two-ring purines base (A ↔ G) or one-ring pyrimidines (C ↔ T), while transversions are interchanges of purine for pyrimidine bases. Recent human studies show that the Ti/Tv ratio for whole human genome is around 2.0∼2.1 [Transition/transversion (Ti/Tv) ratio is shown to be another useful index indicating false positive rate (FPR) in practice . Transit 2.0∼2.1 . Generalwgsim in SAMtools.The inputs of the SNP callers seqEM and MAFsnp are counts of the reads mapped to a reference, while SAMtools, GATK, and MAQ only accept BAM/SAM/cns files containing mapped sequence reads. Note that seqEM and MAFsnp also accept mapped sequence reads since the reads can be counted for each nucleotide locus. Accordingly, we considered generating both read count data and sequence data. The counts data were generated from binomial distributions, while the sequence data were generated using the module e, p, n, and N by assuming Hardy-Weinberg equilibrium. To mimic real situations, the setup of parameters we considered are as follows:eMapping/sequencing error rate . The mapping/sequencing error rate e across the genome was generated from a truncated normal distribution, i.e. e ∼ max{0, N}, where μ was either 10−2 or 10−3, and σ2 = 10−6.pMAF . To mimic rare variants, less rare variants, and common variants, we generated p across the genome from the uniform distributions U, U, and U, respectively. Futhermore, p = 0 for all non-SNPs.nSample size . We considered various sample sizes: 50, 100, 200, 500.NRead coverage . The read coverage N across n samples follows the generalized Poisson distribution with expectation μ and the dispersion parameter λ defined by the expectation divided by the variance. This distribution reduces to the Poisson distribution if λ = 1, and a variance overdisperion is present if λ < 1. We considered various expectations: μ = 5, 10, 20, and 30, and fixed λ at 0.4. Here the dispersion parameter 0.4 was evaluated through two real NGS datasets from the “1000 genomes project” [project” , 9.In order to assess the performance of MAFsnp, MAFsnp0, and seqEM, we generated read counts for various combinations of parameters 3 SNP loci for each of 2 × 3 × 4 × 4 = 96 parameter combinations (p ≠ 0) and read counts of 5 × 105 non-SNP loci for each of 2 × 1 × 4 × 4 = 32 parameter conbinations (p = 0).Since the proportion of SNPs on the human genome is approximately 1%, we simulated read counts of 5 × 10wgsim in SAMtools to compare the performance of all considered SNP callers.We used NGS reads generated by the module wgsim can generate pair-end reads in fastq format. We extracted a reference sequence with a length of 1.9 × 106 bp from the chr20q13.32 region of human reference genome . The pawgsim: average read length = 70 bp, outer distance between two ends = 500, mutation rate = 0.001, percentage of SNPs among mutations = 85%. For each combination of base error rate and read number, we generated reads for 50 and 100 samples, separately, which resulted in 18 multi-sample datasets.We adopted all default setting in BWA  was usedhttp://gsl.hudsonalpha.org/information/software/bam2fastq) and alignment was performed using MAQ. On the other hand, the multi-sample calling modes of GATK and SAMtools used bam files as input. For comparison, we utilized dbSNP build 137 as a standard reference.The 1000 Genomes Project (1KGP)  sequenceTj, i.e. Da,k. Accurate estimates of a (zero proportion of Tj) and k (expectation of non-zero part of Tj) are essential in calculating the p-value for SNP calling by MAFsnp. The distribution of the non-zero part of Tj is compared with the chi-square distribution Tj pretty well when the mapping/sequencing error rate e = 0.01 across all considered coverages and sample sizes. With a small mapping/sequencing error rate (e = 0.001) and a low coverage (N = 5), the fitting gets poorer. This might be due to the fact that the counts are too small in such situation. Actually, the distribution of Tj becomes rather dicrete as shown in the Q-Q plot. The estimated a and k, i.e., N, as shown in N increases, it is more likely that 0 is the maximizer of the pseudo likelihood function under the null hypothesis H0:p = 0, which results in a larger a. The correlation between e is not that evident  and seqEM. For comparison purpose, the maximal F1 value on the precision-recall curve, i.e., Fmax, is calculated. All F1 values are displayed in F1 values of MAFsnp are only slightly smaller than Fmax, and MAFsnp almost uniformly outperforms seqEM.Finally we compare the Da,k. a and k. It is seen that Tj very well with e = 0.005 or 0.01, while the fitting is slightly poor with e = 0.001. These results are in accordance with those for the read count data. As is seen in N gets larger.Similar to the analysis of simulated read count data, we first check the estimation results for Then we compare the empirical FDRs and powers of MAFsnp and MAFsnp0. As is shown in −5 and 3.1 × 10−3, respectively. On the other hand, GATK and seqEM have much larger FDRs, with median values 0.182 and 0.155, respectively. Next we compare the powers of the considered SNP callers. SeqEM ranks the first with a median power of 0.955, but it has much larger FDRs as a trade off. MAFsnp ranks the second, with a median power value of 0.885. MAQ and SAMtools comes the next due to smaller FDRs, with median powers 0.855 and 0.775, respectively. Surprisingly, GATK is least powerful with a median power of 0.775, though it has the largest FDRs in general. Finaly, we compare the F1 scores that balance precisions and recalls. MAFsnp has the highest median F1 score of 0.935, which is close to Fmax (= 0.945). SeqEM, SAMtools and GATK have lower F1 scores  due to either too small FDRs or too large FDRs. In summary, MAFsnp has empirical FDRs well controlled around the nominal level. Meanwhile, MAFsnp has the best balance of precisions and recalls.In what follows, we compare the results of the SNP callers SAMtools, GATK, MAQ, seqEM, and MAFsnp at nominal level 0.01. The empirical FDRs, powers, and F-scores are presented in calling accuracy from now. We fixed the nominal level at 0.01 in MAFsnp.For real data, we do not know which variants are true SNPs, so the comparison metrics would be different from those for simulation data. Following , we consThis dataset has an overall mean coverage of about 4.7×, which is a classic representation of low coverage data. The SNP calling results of seqEM, MAQ, GATK, SAMtools, and MAFsnp are reported in The average coverage is 2.4× for this targeted exon sequencing dataset. The SNP calling results are again presented in Sequencing a large sample of individuals is a trend in NGS studies. Most existing SNP callers are based on Bayes frameworks, which cannot control FDR at desired nominal levels. We propose a novel multiple-sample SNP caller “MAFsnp” based on a likelihood model. In MAFsnp, the SNP calling issue is transformed into a hypothesis testing problem in a frequentist framework, so that a list of p-values can be obtained, which can be used to call SNPs by controlling FDR at any given nominal level.Da,k. The simulation results of both read count data and sequence data demonstrate that the new distribution Da,k has a much better control of FDRs compared with the conventional limiting distribution D0.5,1. The performance of MAFsnp is compared with those of some existing SNP callers through both simulated data and two real datasets. For the simulated read count data, MAFsnp outperforms seqEM in almost all situations; for the simulated sequence data, MAFsnp has a better balance of precisions and recalls than SAMtools, GATK, seqEM, and MAQ. In the application to two real datasets with low coverage, MAFsnp is demonstrated to have a better performance compared with the other SNP callers and achieves a good balance between the number of called SNPs, calling accuracy, and Ti/Tv ratio.In this article, we propose a new SNP caller MAFsnp by using a eLRT statistic and approximating the null distribution of the statistic with a novel distribution Da,k is proposed to approximate the null distribution of the eLRT statistic. Forth, MAFsnp is based on read count data, making it applicable to all types of sequence data. Fifth, MAFsnp avoids a tedious filtering procedure used in Bayesian methods.MAFsnp has several features. First, MAFsnp is the first NGS data based SNP caller that provides p-values for calling SNPs. Second, a pseudo-likelihood function is adopted to greatly speed up calling speed. Third, a novel distribution Da,k has potentially wide application areas in many biologic research circumstances where associated parameters are on/near the boundary of parameter space. For example, in genetic association studies, the question of interest is to compare the allele frequencies between cases and controls. For rare variants, the allele frequencies are close to zero, i.e, the boundary of the parameter space. The distribution Da,k could searve as a good null distribution of the conventional test statistics for association testing. The Hardy-Weinberg equilibrium is assumed in MAFsnp, which uses a single parameter (i.e. MAF p) to characterize the genotype frequencies. This assumption could be relaxed by introducing two parameters, i.e., the frequencies of RR and Rr, denoted by pRR and pRr. The corresponding hypothesis testing problem can be modified as H0 : pRR = 1 versus H1 : pRR < 1.The proposed distribution http://homepage.fudan.edu.cn/zhangh/softwares/.The speed of MAFsnp depends on coverage, sample size, and number of nucleotide loci. For the whole genome dataset analyzed in this article , it took a 3.20GHz CPU laptop computer about 2.5 minutes to call SNPs from 500k nucleotide loci. It could take a longer time to call SNPs when the covarage gets lower. For the targeted exon dataset we analyzed , it took the computer 18 minutes to call SNPs from 500k nucleotide loci. An R package implementing MAFsnp is available publicly at S1 Method(PDF)Click here for additional data file.S2 Method(PDF)Click here for additional data file.S1 FigRed straight line has a slope (PDF)Click here for additional data file.S2 FigRed straight line has a slope (PDF)Click here for additional data file.S3 FigRed straight line has a slope (PDF)Click here for additional data file.S4 FigRed straight line has a slope (PDF)Click here for additional data file.S5 FigRed straight line has a slope (PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.


Solanum lycopersicum) illustrates this evolutive trend. Genes involved in flower meristem development are known to regulate also fruit morphology. To decipher the genetic variation underlying tomato fruit morphology, we assessed the nucleotide diversity and selection footprints of candidate genes involved in flower and fruit development and performed genome-wide association studies.Domestication and selection of crops have notably reshaped fruit morphology. With its large phenotypic diversity, tomato  maximizing the molecular diversity, using the Roche 454 technology. A total amount of 17 Mb was sequenced allowing the discovery of 6,106 single nucleotide polymorphisms (SNPs). The annotation of the 30 gene regions identified 231 exons carrying 517 SNPs. Subsequently, the nucleotide diversity (π) and the neutral evolution of each region were compared against genome-wide values within the collection, using a SNP array carrying 7,667 SNPs mainly distributed in coding sequences.Thirty candidate genes were selected according to their similarity with genes involved in meristem development or their known causal function in FW2.2 and BAM1 sequences revealed selection footprints within their promoter regions suggesting their potential involvement in their regulation. Two associations co-localized with previously identified loci: LC (locule number) and Ovate (fruit shape).About half of the genes revealed footprints of selection and polymorphisms putatively involved in fruit size variation by showing negative Tajima’s D and nucleotide diversity reduction in cultivated tomato compared to its wild relative. Among the candidates, Compared to whole genome genotypic data, a drastic reduction of nucleotide diversity was shown for several candidate genes. Strong selection patterns were identified in 15 candidates highlighting the critical role of meristem maintenance genes as well as the impact of domestication on candidates. The study highlighted a set of polymorphisms putatively important in the evolution of these genes.The online version of this article (doi:10.1186/s12870-014-0279-2) contains supplementary material, which is available to authorized users. Understanding the evolutionary basis of plant variation can be reached through the identification of the molecular mechanisms responsible for the large diversity in plant architecture ,2. EvoluArabidopsis thaliana is the standard reference for plant biology .A total of 96 accessions , see Additional file Genomic DNA was isolated from 100 mg of frozen leaves using the DNeasy Plant Mini Kit  according the manufacturer’s recommendations. DNA titration was performed using fluorescence. We used long range PCR (LR-PCR) to amplify DNA sequences (5-10 kb) and cover candidate genes and their potential regulatory regions. Amplification primers were designed in Primer3 .Each DNA library was fragmented by high pressure nitrogen flow to a 300-500 bp size range . Fragmenhttp://solgenomics.net/organism/Solanum_lycopersicum/genome). To improve mapping accuracy of wild accessions, a de novo assembly was performed using a BLAST-like Alignment Tool (BLAT) procedure [http://solgenomics.net/organism/Solanum_lycopersicum/genome) was used to predict gene sequence architecture. We used SnpEff [http://www.ncbi.nlm.nih.gov/projects/gorf/). The longest ORF were kept for subsequent analysis. Polymorphisms were selected with a minimal coverage of 10x and polymorphism occurrence higher than 90%.Checking for contaminants and quality trimming was performed using PyroCleaner software suite . Assemblrocedure . Genome d SnpEff  to classhttp://solcap.msu.edu). The SNP calling rate threshold per locus was set to 90%. Among the 8,784 SNPs from the SolCAP array, 7,667 SNPs passed the quality control. This SNP dataset –without Minor Allele Frequency filtering (MAF) was considered as a neutral dataset, a comparative basis for the candidate genes. To perform GWAS, filtering for low MAF (10% threshold) and missing data (10%), 5,795 SNPs were performed.Whole genome SNP genotyping was performed using Infinium assay (Illumina) developed by the Solanaceae Coordinated Agricultural Project -SolCAP- ,137 as dN/dS neutrality test was performed on the synonymous (dN) to non-synonymous (dS) substitution rates [S  or purifying selection (dN/dS <1). The pairwise-population fixation differentiation index, Fst [Sequencing and genotyping data on the collection of 96 accessions were subjected to genetic diversity indices calculation. Total nucleotide diversity (π) and Tajima’s D test  were comon rates ,143 usinon rates  with thedex, Fst , was asshttp://app3.titan.uio.no/) with parameters as described in [Population stratification (Q matrix) was defined with STRUCTURE  and Evanribed in  Pairwiseribed in  followinribed in .Associations between polymorphisms and adjusted means of fruit traits were screened with TASSEL v3.0 . For eacad hoc Q and K matrixes are deposited on the GNPis repository hosted https://urgi.versailles.inra.fr/association/association/viewer.do#panelCard/id=3 [The SNP genotype and phenotype datasets as well as ard/id=3 .