Quantification of linkage disequilibrium (LD) patterns in the human genome is essential for genome-wide association studies, selection signature mapping and studies of recombination. Whole genome sequence (WGS) data provides optimal source data for this quantification as it is free from biases introduced by the design of array genotyping platforms. The Malécot-Morton model of LD allows the creation of a cumulative map for each choromosome, analogous to an LD form of a linkage map. Here we report LD maps generated from WGS data for a large population of European ancestry, as well as populations of Baganda, Ethiopian and Zulu ancestry. We achieve high average genetic marker densities of 2.3–4.6/kb. These maps show good agreement with prior, low resolution maps and are consistent between populations. Files are provided in BED format to allow researchers to readily utilise this resource. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9901838 The product generated utilising the Malécot-Morton model are maps in cumulative linkage disequilibrium units (LDU), which are broadly analogous to an LD-based form of centimorgans. Previous studies have reported maps generated from array based genotyping data in multiple populations (e.g.6), allowing for cross-population comparisons.One approach to the quantification of LD is the generation of LD maps applying the Malécot-Morton modelLDMAP has been previously described5. In brief, LDMAP generates a cumulative map of LD distances between markers, based upon the Malécot-Morton model of association by distance:L is the component of M is the association at 0 distance (approximately 1 for monophyletic haplotypes), d is the physical distance between the markers5. The final LDU map is built by cumulative addition of The mathematical basis of 7.The increasing availability of whole genome sequencing (WGS) data allows the investigation of LD patterns at the highest level, without the impact of issues such as ascertainment bias in the selection of single nucleotide polymorphism (SNP) markers. We have previously shown that WGS-based maps provide tangible benefits in their practical application. Arrays have been designed to give a reasonable coverage of LD information for a reduced set of SNPs, as such they have limited resolution and population-specific biases are introduced during SNP selection. Given that WGS variant identification is ‘hypothesis free’ (i.e. SNPs are not required to be pre-defined as in array genotyping), these data, and thus these maps, represent a maximally informative resource8. Higher resolution maps allow for analyses on a finer scale of the patterns of LD, such as structure within genes9.The lack of ascertainment bias for SNP data collection is particularly important for African populations, as they have the greatest population diversity and are often under-represented in genomic studies. Though they are often underrepresented, these populations are particularly informative for many studies, given the extended time since a population bottleneckHere, we report our generation of WGS based LD maps for four populations, one of European and three of African descent. These maps provide a valuable population genetic resource, providing a maximal resolution, selection bias free, dataset for studies which require the incorporation of LD statistics.10, utilising Illumina short read sequencing to an average depth of 4x. European ancestry individuals were sequenced by the Wellderly Study11, utilising Complete Genomics high depth sequencing. Multidimensional scaling as implemented in PLINK12 was applied to ensure genetic homogeneity within the sub-cohorts.Autosomal WGS data from two cohort sequencing studies was utilised. African populations were sequenced within the African Genome Diversity Project−3). All analyses were undertaken using the reference genome GRCh37 (hg19).SNPs were subject to quality control prior to map generation. Specifically, they were required to have a minor allele frequency ≥1%, <5% genotype missingness and not to significantly deviate from Hardy-Weinberg equilibrium  format, including the cumulative LDU position of every SNP marker within the generated maps. Additionally, these data are also made available as the kb/LDU ratio for each inter-SNP span providing a view of the regional ‘intensity’ of LD.LD maps reported here are freely available at 10.6084/m9.figshare.7850882 8, 95–100 individuals were utilised for each sub-population, yielding approximately 14 million SNP markers (Table 11, yielding approximately 7.5 million markers. The increased population diversity for the African compared to European population can be seen in the increase common SNP density, as well as the longer LDU length which corresponds to the longer total haplotypic diversity within a population.For the African populations14.For these data, we can determine that they are robust as they are consistent with prior, lower resolution maps, and that they are consistent between populations assessed Figs  and 2. A15, allowing annotation of regions with LD information for subsequent analysis such as determining whether a genomic feature has higher LD than background on average.Maps can be readily incorporated into genomic analyses using tools such as BEDTools16.Genome wide association studies using a composite likelihood model can be undertaken with LD information as provided here, allowing for additional power for signal detection and refinement


The Russian White is a gene pool breed, registered in 1953 after crossing White Leghorns with local populations and, for 50 years, selected for cold tolerance and high egg production (EL). The breed has great potential in meeting demands of local food producers, commercial farmers and biotechnology sector of specific pathogen-free (SPF) eggs, the former valuing the breed for its egg weight (EW), EL, age at first egg (AFE), body weight (BW), and the latter for its yield of extraembryonic fluid (YEF) in 12.5-day embryos, ratio of extraembryonic fluid to egg weight, and embryo mass. Moreover, its cold tolerance has been presumably associated with day-old chick down colour (DOCDC) – white rather than yellow, the genetic basis of these traits being however poorly understood.p < 5.16*10− 5) were found for YEF, AFE, BW and EW. Moreover, on chromosome 2, an association with the white DOCDC was found where there is an linkage disequilibrium block of SNPs including genes that are responsible not for colour, but for immune resistance.We undertook genome-wide association studies (GWASs) for eight performance traits using single nucleotide polymorphism (SNP) genotyping of 146 birds and an Illumina 60KBeadChip. Several suggestive associations (The obtained GWAS data can be used to explore the genetics of immunity and carry out selection for increasing YEF for SPF eggs production.The online version of this article (10.1186/s12864-019-5605-5) contains supplementary material, which is available to authorized users. Programmes for the conservation of genetic resources including local, rare and endangered breeds are gaining increasing importance for the preservation of biodiversity, especially in farm animals e. g., , 2). Suc. Suc2]).sw for snow-white down in the chick [The RW population maintained at the Russian Research Institute of Farm Animal Genetics and Breeding (RRIFAGB) was put under strong selection pressure for 50 years 1953 to 2003)  to 2003 , 8. DeriDespite the importance of this breed for all the above reasons, there is little or no information on the factors affecting (a) the yield of allanto-amniotic fluid that serves as a raw material for the vaccine production, and (b) as a consequence, the viral antibody titre in this fluid –12. To cRecently, with the development and application of high-throughput genotyping technologies, it has become possible to identify associations of genomic regions and loci with selected traits. Traditional approaches, such as microsatellites randomly distributed throughout the genome, previously found quantitative trait loci (QTLs) for many economically important traits –20. LocaIn particular, SNP genotyping using the Illumina Chicken 60 K SNP iSelect BeadChip , along with whole genomic sequencing, has found associations with traits related to poultry immunity and phenotypic characteristics , 36. Givp ≤ 4.2e-5) with DOCDC. The phenotypic variance explained by the set of SNPs was 11% (SE = 0.12). Three SNPs on GGA4 (rs16455118), GGA7 (rs317256404) and GGA28 (rs16209462) were also above suggestive line, with the phenotypic variance explained by them being 33% (SE = 0.18). The Manhattan and quantile-quantile (Q-Q) plots for DOCDC are presented in Fig. λg) was 1.019.As a result of the genotyping of 146 RW females from 12 sires  and SLC7A13 (solute carrier family 7 member 13). The second 180.3-kb block, with the suggestive SNP rs16116752 inside, contained the ZFHX4 (zinc finger homeobox 4) gene as well as two uncategorised non-coding RNA (ncRNA) loci, LOC107052753 and LOC101749450. One suggestive SNP each was also found in the genes DYSF (dysferlin) on GGA4 (rs16455118), PLEKHM3 (pleckstrin homology domain containing M3) on GGA7 (rs317256404) and SPPL2B  on GGA28 (rs16209462).The level of linkage disequilibrium (LD) on GGA2 was calculated for the region span ranging between 119.6 to 124.4 Mb , while rs316856766 passed the suggestive line (p = 1.4e-5). Estimated explained phenotypic variance for both markers was 17.9% (SE = 0.20). Suggestive association on GGA12 was detected for two markers, rs15630281 and rs315166929, with explained phenotypic variance being 10.6% (SE = 0.12). The respective Manhattan and Q-Q plots are shown in Fig. λg being equal to 0.93.Two regions locating on GGA2 between 121.4 and 121.8 Mb and on GGA12 between 0.9 and 1.2 Mb were detected to be associated with YEF (Table ZNF704 (zinc finger protein 704) gene, and the CA2 (carbonic anhydrase 2) gene neighboured the suggestive SNP rs316856766. On GGA12, LD analysis was performed for region 0.9 to 1.2 Mb , and the closest described gene, PRKCD (protein kinase C delta), situated 60 kb aside 25-kb LD block with high LD status (r2 > 0.75).On GGA2, the significant SNP rs13730111 was located in the p-value = 1.5e-5 and 13% (SE = 0.17) of phenotypic variance explained. Despite a low signal, we detected a quite remarkable peak below the suggestive marker line on GGA1  and PDGFD (platelet derived growth factor D). We also noted a region with a high LD, containing the SNP rs13558365 and coinciding with the DCUN1D5 (defective in cullin neddylation 1 domain containing 5) gene.Only one marker rs317931060) on GGA1 passed the suggestive line for AFE Table , with p-7931060 op = 2.9e-5; λg = 1.019; Table LCORL (ligand dependent nuclear receptor corepressor like) gene.For BW, no SNPs were detected to be significantly associated with this trait. A single SNP, rs15619223, on GGA4 passed the suggestive line (KIAA1468 (lisH domain and HEAT repeat-containing protein) and PHLPP1 (PH domain and leucine rich repeat protein phosphatase 1), respectively. Also, there was the ZCCHC2 (zinc finger CCHC-type containing 2) gene between two SNPs in a region 68.3 to 68.5 Mb. One SNP on GGA4 was located on the suggestive line with p-value = 5.42e-5 and within the TLL1 (tolloid-like protein 1 precursor) gene. The Manhattan and Q-Q plots for EW are presented in Fig. λg being equal to 1.0106.For EW, two suggestively associated SNPs were identified on GGA2  on GGA2 and rs317565390 (p = 9.6e-5) on GGA8, respectively. Both markers are located in intergenic regions and have no links with any known genes. For REFEW, we identified the markers rs14196507 and rs13730111 on GGA2, rs315166929 on GGA12, and rs15763249 on GGA15 (p = 6.3*10–5 to 7.9*10–5). The SNP rs13730111 was also described in the given study as a significant marker for YEF. The second marker on GGA2 (rs14196507) is located in an intronic region of the RREB1 (ras responsive element binding protein 1) gene. The markers on GGA12 and GGA15 are both located in intergenic regions with no link to known genes.We did not detect any significant or suggestive associations for EM, EL and REFEW. The markers for EM and EL that had the greatest p < 0.05).Also, we found genetic correlation between the pairs of traits EL and AFE, EL and EW, and EW and YEF that was respectively 0.48 ± 0.09, − 0.21 ± 0.05; and 0.82 ± 0.13  examination [RALYL and ZFHX4, respectively. The SNP rs14243963 is a synonymous variant meaning that has no impact on the IMPA1 gene function. The DYSF gene on GGA4 encodes a skeletal muscle protein that is involved in muscle contraction and binds caveolin-3, a skeletal muscle membrane protein, which is important in the formation of caveolae. The three other found SNPs, rs16455118, rs317256404 and rs16209462, are located within introns. The SPPL2B gene on GGA28 encodes a member of the GXGD proteases that are transmembrane proteins with two conserved catalytic motifs localized within the membrane-spanning regions. This enzyme localizes to endosomes, lysosomes, and the plasma membrane. It cleaves the transmembrane domain, which triggers cytokine expression in the innate and adaptive immunity pathways. The function of the protein encoded by the PLEKHM3 gene is characterized as metal ion binding and participation in skeletal muscle (myoblast) differentiation by acting as a scaffold protein for AKT1. None of the above genes may be directly related to pigmentation or DOCDC variants.Typically, the down of RW chicks at day old is coloured in different shades of yellow. The first chicks with the white down were noticed during long-term selection for low temperature tolerance. Chicks with the white down were more tolerant to low temperatures and diseases, so breeders used colour as marker during the selection process . Over thmination , 38, theSPPL2B) and muscle growth (LLRCC1 and ZFHX4) in chickens. In the study by Psifidi et al. [λg) was close to 1, suggesting no population stratification. The Q-Q plot revealed a slight deviation from the distribution under the null hypothesis, which indicated a moderate association between SNPs and DOCDC.Thus, using a case-control GWAS approach for analysing phenotypes, we detected association with the genes responsible for immune system  eggs having no antibodies or pathogens are widely used for producing vaccines for animals and humans. Increasing volume of extraembryonic fluid results in a higher virus titre meaning an essential economical effect for biological preparation producers. The observed average YEF . Because λg was close to 1, no population stratification should be assumed. The Q-Q plot showed a slight deviation from the expected value, which indicated a moderate association between SNPs and AFE.In the study conducted by Psifidi et al. , severalLCORL gene, polymorphisms in which are linked with measures of skeletal frame size and adult height. Some GWASs [LCORL gene as a possible candidate responsible for birds growing and body weight improvement. The observed values on the Q-Q plot had negative deviation from the expected values, and at the same time λg was close to 1. In this particular case, the GWAS led to an association that can be treated as underestimated.Previous studies showed a suggestive association of BW with a single SNP on GGA4 , 42. Theme GWASs , 41, 43 me GWASs , 41, 43.PHLPP1 gene encoding a protein, which promotes apoptosis by dephosphorylating and inactivating the serine/threonine kinase and conventional/novel protein kinase C (PKC) isoforms, and KIAA1468 that participates in intracellular cholesterol transport. A single suggestive SNP was also detected on GGA4, and, as compared to previously shown results, the marker was situated 2 Mb aside from the published rs313911044 but close to ZCCHC2, with annotations related to this gene including nucleic acid binding and phosphatidylinositol binding. A high population stratification was shown by λg in EW, whereas the Q-Q plot reflected a low deviation of expected and observed markers.Studies related to the association of EW with SNPs were done by Liao et al.  and Fan r = 0.48 ± 0.09, p < 0.05). The traits EW and YEF were highly correlated  and should be analysed together, as well. A similar correlation coefficient between EW and the absolute YEF value  was previously reported for the Russian White breed, while that between EW and the relative YEF value was 0.31 ± 0.06 (p < 0.01) [r = − 0.26 ± 0.05, p < 0.05) comparable to that in other studies  . At the Our study has represented the first GWAS analysis completed on Russian gene pool chickens. The significant and suggestive associations we found for YEF may serve as an important information for future allanto-amniotic fluid studies. The hypothetical association between the white DOCDC and cold tolerance in chicks will be investigated further and in more detail to provide a genetic source of adaptation in poultry. Studies on production traits provided essential information for future breed development and selection programme at the RRIFAGB Collective Use Centre ‘Genetic Collection of Rare and Endangered Chicken Breeds’ (CUC GCRECB). To assess potential of the found markers for marker-assisted selection (MAS), it would be desirable to perform a deeper analysis of suggestive regions using an expanded dataset and sequence information. The RW breed can be a valuable resource for local farmers and biotechnology sector, and the obtained data will further be used at the CUC GCRECB to select a breeding core for SPF eggs production.In future studies, collection of additional phenotypic data and genotypes will be done for the RW breed, and MAS will be explored using SNPs that would explain a larger proportion of phenotypic variance. In addition, an extended GWAS can be done by using multi-trait analysis  for the Blood samples were collected from the wing vein of chickens by the standard venepuncture procedure. Animals were kept alive after blood collection and were not culled after the experiment. Genomic DNA was isolated from blood samples using a phenol/chloroform method . DNA conThe experimental chickens were a resource population of the RW breed maintained at the RRIFAGB CUC GCRECB. They were kept under the same conditions in individual cages and fed a commercial diet that contained 17% raw protein and 270 kcal energy per 100 g. In the current GWAS, 146 individuals representing progeny of 12 sires were tested. According to the pedigree, all animals descending from one sire were presented by half sibs (i.e. produced from different dams) and full sibs (from the same dam). The progeny data for each sire including number of half sibs, number of full sibs groups, mean number and standard deviation are given in Table The GWASs were performed using seven phenotypic traits: EW, YEF, REFEW, EM, EL, AFE, BW and DOCDC. For the EW, YEF and EM traits six sequentially laid eggs were determined per hen at age of 34 weeks. Liquid volume as a measure of YEF, and EM were found in 12.5-days embryos  using Illumina Chicken 60KBeadChip. Quality control was carried out using the Plink1.9 programme . SNPs weY = Xb + u + e, where Y is a vector of phenotypes, b is a SNP effect, X is a design matrix of SNP genotypes, u is a vector of additive genetic effects assumed to be normally distributed with the mean equal to 0 and (co) variance G as the genomic relationship matrix, and e is a vector of random residual effects. The genome-wide significance was assessed using the simpleM method [Meff . The significance and suggestive levels were set as 2.58*10− 6  and 5.16*10− 5 , respectively. The Q-Q and Manhattan plots were derived from the GWAS results using the qqman package [λg) was computed based on p-values from the GWAS analysis by determining a ration between the median of the resulting chi-squared test statistics and the expected median of the chi-squared distribution in the R software. Estimation of phenotypic variance and heritability using the genomic-relatedness-based restricted maximum-likelihood (GREML) method in family data [A whole genome scan, with accounting of high relationship structure, was performed using a mixed model approach implemented in the Efficient Mixed-Model Association eXpedited (EMMAX) software . SNP effM method , 55 in RM method , for cal package  within tily data , threshoThe post GWAS analysis included the LD determination of the chromosomal regions with significant SNPs that was performed using PLINK and Big-LD R-package . EnsemblAdditional file 1:Table S1. Basic information for SNP markers per chromosome after quality control. (DOCX 29 kb)Additional file 2:Description of the YEF and EM traits recording. (DOCX 13 kb)Additional file 3:Figure S1. Day old chicks with the white (A) and yellow (B) down colour. (JPG 586 kb)


Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels.We found that the 1KJPN population was more similar to other Japanese populations, Nagahama  and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%.1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population’s genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago.The online version of this article (10.1186/s12864-018-4942-0) contains supplementary material, which is available to authorized users. Genotype imputation is an important step in current genome-wide association studies. Imputation accuracy, as well as genomic coverage of highly accurate imputed genotypes, confers elevated statistical power in association tests.  The choiThe Tohoku Medical Megabank (TMM) project was launched in 2011 to investigate effects in the aftermath of the Great East Japan Earthquake in the Miyagi and Iwate prefectures (Northeast Japan). The TMM project developed prospective cohorts in these two prefectures  with theThe 1KJPN panel consists of haplotypes derived from a cohort of participants in the Miyagi Prefecture, which has approximately 5% of Japan’s total population. However, it is not known how much region-specific sampling for a reference panel affects the performance of genotype imputation for samples collected nationwide.In this study, we evaluated the imputation performance of the 1KJPN panel for participants from three areas other than Miyagi, namely, Iwate, Nagahama , and Aki (Shikoku Island) Fig. . We alsoWe performed WGS for all the available samples from Iwate, Nagahama, and Aki to construct an extended haplotype reference panel  with the diversity of populations from elsewhere in East Asia to determine how 1KJPN might reflect these populations. Principal component analysis (PCA) plots with 35,596 SNP genotypes (not indels) on chromosome 1 (see methods) are shown in Fig. ST) and haplotype sharing. Although the PCA did not provide sufficient resolution to separate the samples into the four discrete areas  among all pairs of areas examined, which corresponds to the findings in a recent study. [ST values among the four regions exceeded the FST between the CHB (Han Chinese in Beijing) population in the 1000 Genome Project and our Japanese samples  values of imputed variants was compared among samples from different areas  for common SNPs , whereas the accuracies differed among samples from these areas for lower frequency variants. For example, for rare variants in 1KJPN , the r2 values were 0.7123, 0.7067, 0.6312, and 0.6486 for Miyagi, Iwate, Nagahama, and Aki, respectively.To evaluate the difference in imputation performance of the samples from different regions, the aggregate of Pearson’s correlation coefficient . These results indicate that 1KJPN is adequate for use as a reference panel for populations from mainland Japan.Genotype imputation is difficult for rare variants, so this decrease in aggregate r2 of rare variants with 1KJPN+ improved to 0.7269, 0.7485, 0.6639, and 0.7037 for Miyagi, Iwate, Nagahama, and Aki, respectively, compared with the r2 obtained with 1KJPN. For the three reference panels, the imputation coverage (proportion of variants with confidence: imputation r2 ≥ 0.8) across different non-reference allele frequency bins as well as the number of variants that were efficiently imputed are shown in Additional file To assess the improvement in imputation accuracy gained by adding haplotypes from a different population, genotype imputation accuracies were compared among different reference panels as follows: 1KJPN consisting of 1070 Miyagi samples; 1KJPN+ consisting of 1KJPN and the three other Japanese populations analyzed in this study; and 1KGPp3, the phase 3 panel of the 1000 Genome Project Fig. . Imputatp < 10− 7) in allele frequencies between Miyagi and Nagahama or Aki  of an individual genome can detect very rare variants that were not identified previously. We collected the SNPs not found in 1KJPN (Miyagi population) but found multiple times among the Iwate, Nagahama, and Aki populations  and individuals from the rest of Japan (Hondo), but genetic differentiation among local regions in the Hondo area has been reported to be very low and not to show distinct clusters. , 16 In tWe demonstrated that haplotypes specific to samples in a local area had substantial impact on imputation performance, especially for low-frequency and rare variants. As mentioned above, genetic differentiation within the Hondo population was small in terms of SNP frequency, but apparent when haplotype sharing between samples was considered. Because imputation algorithms essentially rely on haplotype sharing between the reference panel and the study samples, the genetic differentiation between 1KJPN and other Japanese regions might have been substantial. Our results show that the imputation accuracy for common variants was only marginally affected by the combination of the panel and the area Fig. , and by Our data suggest that 1KJPN can cover most of the variants found in the samples from other areas in the Japanese mainland outside of Miyagi and that 1KJPN can be used as a representative of the Japanese population’s genomes, making it is useful genome reference panel for other parts of the Japanese mainland. We also showed that the addition of samples not included in 1KJPN improved imputation efficiencies for SNPs with MAFs under 1% from most of the Japanese archipelago.The haplotype reference panel (1KJPN panel) was constructed from the whole-genome sequences of 1070 participants from the prospective cohort study of the TMM project.  All sampThe haplotype reference panel was constructed from WGS data.  We constp < 10− 6, or MAF < 0.5% were excluded from the downstream analysis.Genotype calling was conducted using the apt-probeset-genotype program in the Affymetrix Power Tools suite . Quality control (QC) criteria were set in accordance with the manufacturer’s recommendations  and were met by all the samples. SNP-based QC was conducted using the Ps classification function in the SNPolisher package . SNPs that were categorized as “recommended” by the Ps classification were retained. SNPs with call rate < 97.0%, Hardy–Weinberg equilibrium of 2 scores. Indels and SNPs with maximum detection fraction > 0.1, smallest MAF 0.05, and maximum r2 0.8 were filtered out. The calculation of principal components for the SNP genotype was performed using the PLINK package.The SNP genotype data of the TMM samples were obtained by whole-genome sequencing or using the Japonica array. We obtained the corresponding SNP genotype data from next-generation sequencing analysis for the cases that were not analyzed with the Japonica array. To analyze the population genetics structure compared with that of East Asian populations, we downloaded the SNP data of chromosome 1 of unrelated East Asian populations  from the 1000 Genomes Project . We seleST value estimators [ST matrix, a network was inferred among populations using the neighbor-net method [Weir and Cockerham’s Ftimators  were calt method  in the St method . Sample t method . Haplotyt method .2) between true genotypes, taking a value of 0, 1, or 2, and imputed genotype dosages with values between 0 and 2. The r2 values were estimated upon aggregating the variants in the reference panel that were stratified by non-reference allele frequency to visualize the imputation accuracies for rare SNPs. These evaluations were conducted on the SNPs that were identified in all the examined reference panels.We performed genotype imputation using the IMPUTE2 program.  VariantsAdditional file 1:Table S1. The imputation coverage (proportion of variants with an imputation r2 ≥ 0.8), with each reference panel across different MAF bins. (XLSX 8 kb)Additional file 2:Figure S1. Pairwise coincidence matrix of individuals from the four Japanese populations created by fineSTRUCTURE. Samples from Miyagi residents whose maternal grandmother was also born in Miyagi Prefecture (288 of the 1070 samples) were used to analyze haplotype sharing among the four Japanese populations. Color scale (left panel) represents the posterior confidence probability. Areas of each sample are shown by a colored box at the top of the figure. (PDF 312 kb)Additional file 3:Table S2.The imputation coverage (proportion of variants with confidence: imputation r2 ≥0.8) across different non-reference allele frequency bins and the number of variants that were efficiently imputed. (CSV 20 kb)


Glycine max (L.) Merr.), a complicated quantitative trait controlled by multiple genes, was positively associated with soybean seed yields. In the present study, a natural soybean population containing 185 diverse accessions primarily from China was used to analyze the genetic basis of SWPP via genome-wide association analysis (GWAS) based on high-throughput single-nucleotide polymorphisms (SNPs) generated by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) method. A total of 33,149 SNPs were finally identified with minor allele frequencies (MAF) > 5% which were present in 97% of all the genotypes. Twenty association signals associated with SWPP were detected via GWAS. Among these signals, eight SNPs were novel loci, and the other twelve SNPs were overlapped or located in the linked genomic regions of the reported QTL from SoyBase database. Several genes belonging to the categories of hormone pathways, RNA regulation of transcription in plant development, ubiquitin, transporting systems, and other metabolisms were considered as candidate genes associated with SWPP. Furthermore, nine genes from the flanking region of Gm07:19488264, Gm08:15768591, Gm08:15768603, or Gm18:23052511 were significantly associated with SWPP and were stable among multiple environments. Nine out of 18 haplotypes from nine genes showed the effect of increasing SWPP. The identified loci along with the beneficial alleles and candidate genes could be of great value for studying the molecular mechanisms underlying SWPP and for improving the potential seed yield of soybean in the future.Seed weight per plant (SWPP) of soybean ( Glycine max L. Merr.)  . SWPP is. Merr.) . The dev1. Of these identified QTL, some QTL, located on Chr. 3 (LG N), Chr.4 (LG C1), and Chr.6 (LG C2), were verified by many studies  3 , Chr.4 (LG C1), Chr.5 (LG A1), Chr.6 (LG C2), Chr.7 (LG M), Chr.8 (LG A2), Chr.9 (LG K), Chr.10 (LG O), Chr.18 (LG G), and Chr.19 (LG L), have been reported in the SoyBase databank studies . The genGenome-wide association studies (GWAS), based on high-density markers and natural populations, have been regarded as an efficient alternative to linkage analysis with more extensive recombination events and shorter LD segments. Thus, the resolution and accuracy of marker-phenotype associations could be further increased via GWAS compared with the conventional QTL mapping of segregating populations . To dateThe aims of the present study were to understand better the genetic architecture of SWPP via GWAS based on 185 tested accessions and 33,149 SNPs and to analyze the potential candidate genes that might regulate soybean SWPP in associated genomic regions with peak SNPs.Supplementary Table S1), including landraces and elite cultivars, were applied to evaluate the variation of SWPP and for the subsequent sequencing analysis. All samples were grown in Harbin in 2015 and 2016, Gongzhuling in 2015 and 2016, and Shenyang in 2015 and 2016. Field experiments were performed with single row plots of 3 m in length with 0.65 m between rows. A randomized complete block design was used with three replications in each tested environment. After reaching full maturity, a total of 10 randomly selected plants per row in each plot were weighed and used to evaluate SWPP.A total of 185 diverse soybean accessions (Mse I (EC 3.1.21.4) and Hae III (EC: 3.1.21.4)  were used to generate more than 50,000 sequencing tags (approximately 300–500 bp in length) of all tested samples. The obtained tags were evenly distributed among the unique genomic regions of the 20 soybean chromosomes. The sequencing libraries of each tested accession were built based on the sequencing tags. A barcode method combined with the Illumina Genome Analyzer II system  was used to generate the 45-bp sequence reads at both ends of the sequencing tags from each accession library. Short Oligonucleotide Alignment Program 2 (SOAP2) software was used to align raw paired-end reads to the soybean reference genome (Version:Glyma.Wm82.a2). The SLAF groups were designed based on the raw reads, which mapped to the same unique genomic positions. Approximately 58,000 high-quality SLAF tags were acquired from each tested accession. The SNPs were called as such based on MAF ≥ 0.05. If the minor allele depth or the total depth of the sample was larger than 1/3, then the genotype was considered heterozygous.Total DNA from the fresh leaves of a single test sample was extracted by the CTAB method according to For thirty lines, a genome resequencing with 10-fold in depth was conducted on an Illumina HiSeq 2000 sequencer. Paired-end resequencing reads were mapped to the soybean Williams 82 reference genome (Version:Glyma.Wm82.a2) with BWA (Version: 0.6.1-r104)  using th2) in TASSEL version 3.0  from 185 tested samples with CMLM in GAPIT  and was used as the threshold to declare whether a significant association signal existed , splicing sites (within 2 bp of a splicing junction), 5′UTRs and 3′UTRs, intronic regions (overlapping with an intron), upstream and downstream regions (within a 1 kb region upstream or downstream from the transcription start site), and intergenic regions. SNPs in coding exons were further grouped into synonymous SNPs (did not cause amino acid changes) and nonsynonymous SNPs (caused amino acid changes). The variation happened in these regions (except for intergenic regions) of candidate genes in thirty lines generated from genome re-sequencing data which were analyzed using the General Linear Model (GLM) method in TASSEL version 3.0  to identFigure 1). Therefore, the SWPP in the present study was suitable for the subsequent GWAS.The SWPP of the 185 tested soybean accessions, grown in multiple locations over years, was determined. The mean results of the SWPP analysis showed that the effects of genotype, environment, and genotype by environment interactions were significant. The skewness and kurtosis of SWPP across the six environments were both less than 1, indicating continuous variation and a near normal distribution .The selective population contained 185 diverse accessions, primarily collected from China. The genomic DNA from all tested accessions was extracted and partially sequenced based on the SLAF-seq approach. For each tested sample, a mean of 49,571 high-quality tags (or SLAFs) was scanned from 153 million paired-end reads with a 45-bp read length and 6.51-fold sequencing depth. A total of 33,149 SNPs with MAF ≥ 0.05 were generated from the high-quality tags and subsequently used to perform GWAS for SWPP. The obtained SNPs were evenly distributed among the 20 soybean chromosomes, resulting in a marker density of 28.7 kbp per SNP. Chr. 6 and Chr. 11 included the most and least numbers of SNPs, which was 3,556 and 638, respectively . To scan the population stratification of the association panel, principal component analysis and kinship analysis were conducted based on all 33,149 SNP markers. The first three PCs explained 13.83% of the genetic variation. A drastic decline in the genetic variation appeared at PC2 . However, analysis of the variation of the first 10 PCs revealed an inflection point at PC3 . Thus, these results suggested that mainly the first three PCs dominated the population structure. Additionally, the heatmap of kinship matrix revealed low levels of genetic relatedness among the 185 tested samples .The average distance of LD decay was analyzed to characterize the mapping resolution for GWAS, and LD decayed differently among all the 20 chromosomes. Accordingly, the mean LD decay of the panel was evaluated at 214 kbp when rFigure 4 and Table 1). Among these signals, only one QTN (Gm18:23052511 located on Chr.18) was identified in the linked region of a known SWPP QTL. However, as a specific member of seed weight, another 11 SNPs were overlapped or located in the linked region of a known seed weight QTL from the SoyBase databank, particularly for 100-seed weight (Table 2). The remaining 8 association signals were regarded as novel loci, which were first reported for SWPP in the present study (Table 2). The average SWPP for all tested samples with two different alleles were compared (Table 1), and the results demonstrated that the SWPP among these samples were so different that the appropriate alleles might be effectively applied in the marker assisted selection (MAS) of soybean cultivars with suitable SWPP.A total of 20 association signals, distributed on 12 of the 20 soybean chromosomes, were detected by CMLM in the present study .The genes located in the 200-kbp genomic region of each peak SNP of the identified loci were considered as candidate genes, consistent with a mean LD decay distance of 214 kbp for the entire association panel. Approximately 126 candidate genes were identified. Among these genes, 26 genes had no functional annotation, and one gene had unknown function domains. The remaining 99 genes were classified into different groups by MAPMAN  to deterTTG2, AP2, MINI3, and C2H2  were identified as candidate genes. Similarly, Glyma.10G130000, an auxin response factor gene located 44.84 kbp from SNP Gm10:34946492 on Chr.10, Glyma.15G240600, an ethylene factor gene located 31.54 kbp from SNP Gm15:45663581 on Chr.15, and Glyma.17G127500, a gibberellin factor gene located 15.58 kbp from SNP Gm17:10196755 on Chr.17, were all considered as the genes controlling SWPP. Three E3 ubiquitin ligase genes , which are associated with protein modification or degradation, might also affect SWPP. An additional 10 genes belonging to transport systems (such as ABC transporters) and other main metabolic processes , including Glyma.02G159900, Glyma.08G196200, Glyma.12G163700, Glyma.12G163800, Glyma.14G207200, Glyma.14G207300, Glyma.14G207700, Glyma.14G207800, Glyma.17G127800, and Glyma.18G143700, were also selected and might also contribute to SWPP.The factors that influenced SWPP were almost the same as those that influence seed weight, and seed size was the main component for determining seed weight. Thus, the factors that could regulate the mechanism of seed size might be important elements in directly or indirectly adjusting SWPP . In someand C2H2 , the genand C2H2 , and regand C2H2 . Among tFigure 5 and Supplementary Table S3). Glyma.07G157900, with only one SNP, was not shown in the figure . Two haplotypes were identified for each of the nine genes and the SWPP between each pair of haplotypes showed significant or highly significant difference . Glyma.07G157900, from Gm07:19488264, was detected under the environments of Shenyang in 2016 and the average at Shenyang in 2015 and 2016, which was located in the genomic region of the known loci, “seed weight 12-4”  through genome re-sequencing. Finally, 44 SNPs from nine genes were significantly associated with SWPP among multiple environments . Among these SNPs, one SNP (Gm18:23052511 located on Chr.18) was identified in the same region reported by In addition to the QTL associated with SWPP in the SoyBase databank, the genes associated with seed weight were referred for more accurately identifying the genomic region underlying the SWPP of soybean via the twenty SNPs used in the present study. Among the twenty SNPs, eight loci, including Gm02:27731352 located on Chr.2, Gm03:25789435 and Gm03:26388034 located on Chr.3, Gm12:24101071 located on Chr.12, Gm14:47326193 located on Chr.14, Gm15:45663581 located on Chr.15, and Gm19:19848537 and Gm19:19848932 located on Chr.19, were novel genes first reported in the present study. The genomic region of Chr.19, which included two close loci (Gm19:19848537 and Gm19:19848932), might be a primary region associated with SWPP. Additionally, another twelve SNPs were overlapped or located near known QTL , ubiquitin, transporting systems, and other metabolic processes associated with plant growth , were considered as the important factors in determining the regulation of SWPP. Therefore, fourteen genes  mainly belonging to transcription factor families of AP2 and C2H2 were proposed as responsible for the SWPP of soybean. Another five novel genes associated with the pathways of brassinosteroid, auxin, ethylene, and gibberellin were also regarded as the candidate genes, including Glyma.10G129700, Glyma.10G130000, Glyma.15G240600, Glyma.17G127100, and Glyma.17G127500. As E3 ubiquitin ligase genes catalyze the ubiquitination of proteins in regulating the growth of plants associated with SWPP  and other main metabolic processes , might be novel genes associated with SWPP. To further accurately detect the candidate genes controlling SWPP, haplotype analysis of candidate genes was performed. As a result, nine genes and 15 beneficial haplotypes were detected by gene-based association analysis. Definitive function of all candidate genes would be discussed and verified in further studies.Currently, GWAS had been an effective method to acquire and confirm genes with a suitable LD block . In the ith SWPP , Glyma.0turation . Thus, GYJ and XZ conceived the study and contributed to population development. JW contributed to phenotypic evaluation. WT and LQ contributed to genotyping. YH and WL contributed to the experimental design and drafting the manuscript. All authors contributed to and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bemisia tabaci, is a major threat to tomato Solanum lycopersicum and ranks as one of the world’s 100 most invasive pests. This is the first study of B. tabaci (Biotype B and Q) global distribution, focusing on risk levels of this invasive pest, in areas projected to be suitable for open field S. lycopersicum cultivation under climate change. This study aims to identify levels of risk of invasive B. tabaci for areas of suitability for open field S. lycopersicum cultivation for the present, 2050 and 2070 using MaxEnt and the Global Climate Model, HadGEM2_ES under RCP45. Our results show that 5% of areas optimal for open field S. lycopersicum cultivation are currently at high risk of B. tabaci. Among the optimal areas for S. lycopersicum, the projections for 2050 compared to the current time showed an extension of 180% in areas under high risk, and a shortening of 67 and 27% in areas under medium and low risk of B. tabaci, respectively, while projections for 2070 showed an extension of 164, and a shortening of 49 and 64% under high, medium and low risk, respectively. The basis of these projections is that predicted temperature increases could affect the pest, which has great adaptability to different climate conditions, but could also impose limitations on the growth of S. lycopersicum. These results may be used in designing strategies to prevent the introduction and establishment of B. tabaci for open-field tomato crops, and assist the implementation of pest management programs.The whitefly,  Plant pest expansion increased in the last century, mainly due to international travel and the trading of plants around the world . A pest Climate is a major factor impacting on the distribution and abundance of arthropod species ,7. StudiThe term ‘climate change’ refers to both global-scale and regional climate alterations over time. It is an important concept in organism distribution studies, especially in the case of insects, which are classified as ectotherms . As partA species distribution model (SDM) is a tool widely used in understanding the effects of climate change on a species, as well as planning further expansion of agricultural species and potential risks this may entail ,16. SuchBemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) has been shown to be one of the most invasive and devastating insect pests of agricultural and horticultural crops, causing enormous economic losses worldwide  curve) were used to compare performance of the models [cv was also used for discriminating presence from background data. An AUCcv value of 0.5 indicates that predictions do not exceed random; values below 0.5 are below random; 0.5–0.7 represents poor performance; 0.7–0.9 represents satisfactory to moderate performance; and values above 0.9 rate as high performance [cv [Test sensitivities of 0% and 10% training Omission Rates (OR) ,58 and te models . To calcformance . In the formance . We rankance [cv ,58,61.B. tabaci invasion of areas suitable for open field cultivation of tomatoes from the MaxEnt outputs.ArcGIS 10.3.1 software was used to extract the risk levels for B. tabaci and S. lycopersicum which areas optimal for open field cultivation of S. lycopersicum would be at risk for B. tabaci invasion, according to the categories of highest, medium and lowest risk. For overlaying, ‘optimal conditions’ were defined as areas of medium or high suitability. Projections for both species, as well as their overlaying to ascertain level of B. tabaci risk in areas of optimal risk, were made using the four suitability classes of unsuitable, low, medium and high.Maximum Test Sensitivity Plus Specificity (MTSPS) threshold was chosen to ascertain from projected future distributions of B. tabaci (Biotype B and Q) and host open-field S. lycopersicum are distributed globally , precipitation seasonality (CV) (bio15), mean annual precipitation , precipitation of driest month , mean diurnal range in temperature  and temperature annual range  (S. lycopersicum (B. tabaci (Biotype B and Q) occurs in areas with mean annual temperature of 23.8°C, and mean annual precipitation between 0–3516 mm  , and S. persicum . Based o–3516 mm  and S. l–3200 mm .cv test values higher than 0.5  features, regularization multiplier = 1.0, produced the best model for B. tabaci and exhibited the lowest omission rate at 10% and 0%  features, regularization multiplier = 1, and had lowest omission rate at 10% and 0%  for S. lycopersicum open-field cultivations for 2050 and 2070 are shown in B. tabaci risk. Currently, much of South and Central America has a low, medium or high risk levels for B. tabaci in areas with optimal conditions for open-field tomato. On the North American continent, Mexico and the USA states of California and Florida exhibit low, medium or high risk. In all the above regions, B. tabaci is already present. In Europe, Africa, Asia and Oceania all three risk categories for B. tabaci can be discerned. Many sites in Europe and southern China which are at high risk for B. tabaci in optimal conditions for tomato open-field require close attention , the current risk levels of and high . Howeverthe host .B. tabaci and S. lycopersicum)  . The hig 23–24°C , and 18–persicum . The prore range  and highpersicum . The propersicum  and 6D, . tabaci , and tempersicum .B. tabaci is distributed worldwide (orldwide  and has B. tabaci and S. lycopersicum models produced 90% (AUC) agreement with the current modeled global climate. Cross-validation indicated that all models performed much better than would be expected at random, and had a high validation statistic. The high percentage of accordance with the distribution of both species highlights the consistency of these models. Thus, the model outputs and their overlaying may be considered reliable as a foundation of research on B. tabaci occurrence and risk in areas suitable for open-field tomato, globally.All models presented in this study show great degree of reliability. Both B. tabaci and S. lycopersicum occurrence has been reported for regions on all continents ..B. tabacS. lycopersicum growth. Vegetable species generally show great sensitivity under extreme environmental conditions, particularly temperatures, high or low temperatures [S. lycopersicum globally, than in B. tabaci  and Brazil (South America), which are at high risk for B. tabaci in optimal conditions for open-field tomato.The current climate modeling showed predominantly medium and high risk in South, Central and North America for d tomato . Europe,B. tabaci in open field tomato in some regions. After the overlaying of the current models and future projections in areas with optimal conditions for open field tomato, we observed that in some regions with current high risk, B. tabaci risk will decrease in the years 2050 and 2070, mainly in tropical countries of South America and Africa. The main reason related to the predicted increase of temperature in the world, which will mainly affect the host. Conversely, in China , and Mediterranean Europe  a great increase in susceptibility to B. tabaci is predicted in regions with optimal climatic conditions for open field tomato crops. In some areas along the coastlines of Brazil  and Australia (south and east), the same levels of susceptibility to, or risk of, the pest are maintained  was one of the most important variables associated with the distribution of  was one ibutions ,78. ConsS. lycopersicum shows a wide climatic tolerance and is grown in both tropical and temperate regions around the world, and thus high or low temperatures can impact negatively on this species [Tomato production becomes unviable in places where temperatures reach values above 40°C. There are many studies that show that high temperature causes stress in tomato plants (for the most common tomato cultivars) such as reduced fruit set, reproductive number, pollen production and pollen viability ,79,80. S species . As showB. tabaci risk and optimal conditions for open-field S. lycopersicum, both whitefly and tomato are already present. It would still be logical to introduce preventive measures for the spread of toxins and viruses into areas in which these have not yet been reported. Strategies such as inspection of seeding plant trade and phytosanitary regulations would be valid in locations with high and medium risk of B. tabaci, to lessen the risk.Our overlaying results should contribute to warning agricultural authorities in many locations to employ management strategies to prevent a decreased viability for open-field tomato. In areas of high Despite the inherent uncertainty of correlative niche models such as MaxEnt in regard to the quality of occurrence data, resolution of spatial data layers, sampling bias, species characteristics, and spatial autocorrelation ,82,83,84B. tabaci and S. lycopersicum were taken into account.Our models were based only on climate parameters, executed using the currently available global broad-scale climate data, and thus only show broad-scale shifts. Only open field occurrence data for both B. tabaci in protected and semi-protected environments may be diverse in several locations around the world but not pointed out by our model.It should be noted that the predictions of suitable areas for tomato production were established based on the current climatic thresholds from current commercial cultivars. However, the production of new cultivars resistant to high temperatures, the production of tomatoes in protected crops with climatic control or the production of tomato in times of mild temperature in very hot places can enable the future production of tomato in other places. Although tomato cultivation is still mainly cultivated in the open field, the current trend in tomato cultivation is increasingly favouring nethouses (50-mesh nets to protect from insects) or closed glasshouses. In both cases, proper ventilation or air-conditioning can control inside temperatures. These systems control the microclimate of crops and modelling studies can not take this into account. For this reason, the problems with B. tabaci is already widely distributed and has other host species, modeling other hosts may have predictions that differ from our study, on the basis of specific host sensitivity to climatic changes. In this study, we did not take into account potential genetic progress as B. tabaci has shown high adaptability and numerous biotypes. Therefore, the results of these models can be used in other studies, including non-climatic factors such as differences of the existing biotypes, other pest-plant interactions, natural enemies, pest resistance, dispersal and adaptations.Although it is well known that the B. tabaci and the host S. lycopersicum. Our study provides important information on the risk of B. tabaci for open-field tomato crops using the MaxEnt model. Considering both species together (B. tabaci and S. lycopersicum), it seems large areas with optimal conditions for S. lycopersicum under current climate are already at medium and high risk of B. tabaci, areas with high risk of B. tabaci will increase and areas with medium risk will decrease in the future (2050 and 2070). The future projections in areas with optimal conditions for open field tomato shows that some regions (e.g. Brazil), where B. tabaci currently shows medium and high risk will become less favorable (risks will decrease) in the years 2050 and 2070. Conversely in some places such as China , and Europe  projections show large increases in susceptibility to B. tabaci in regions with optimal climatic conditions for tomato crops in open field. The main reason for this is related to the predicted increase of temperature in the world, which may affect not only the pest, but also the host. Our results can be used in designing strategies to prevent the introduction and establishment of B. tabaci in areas still B. tabaci free, such as Finland, Sweden, Republic of Ireland and the UK, as well as implementing pest management programs in areas of current occurrence, particularly at sites under high risk.Our study indicates that climate change may impact on the geographical distributions of the pest S1 TableB. tabaci (Biotype B and Q) and S. lycopersicum).(Models: (ZIP)Click here for additional data file.


Recombinant Congenic Strains (RCS) were developed from these two lines, which show varied susceptibility to MD.Marek’s disease (MD) is a highly contagious pathogenic and oncogenic disease primarily affecting chickens. However, the mechanisms of genetic resistance for MD are complex and not fully understood. MD-resistant line 63 and line 72, six RCSs, and two reciprocally crossed flocks between the lines 63 and 72 (F1 63 × 72 and F1 72 × 63) using Affymetrix® Axiom® HD 600 K genotyping array. We observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 63. Additionally, homozygosity analysis was employed to explore potential genetic components related to MD resistance, while runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. We found several genes including SIK, SOX1, LIG4, SIK1 and TNFSF13B were contained in ROH region identified in resistant group (line 63 and RCS), and these genes have been reported that are contribute to immunology and survival. Based on FST based population differential analysis, we also identified important genes related to cell death and anti-apoptosis, including AKT1, API5, CDH13, CFDP and USP15, which could be involved in divergent selection during inbreeding process.We investigated genetic structure and genomic signatures across the genome, including the line 6Our findings offer valuable insights for understanding the genetic mechanism of resistance to MD and the identified genes could be considered as candidate biomarkers in further evaluation.The online version of this article (10.1186/s40104-018-0281-x) contains supplementary material, which is available to authorized users. Poultry products are main components of our daily life. The complexity of food safety and quality issues facing the poultry industry are rapidly escalating, and many other critical issues  are emerging simultaneously. Marek’s disease (MD) is a T cell lymphoma induced by the widespread and readily transmissible Marek’s disease virus (MDV) . MD is oMDV transforms mainly CD4+ T cells and causes various clinical syndromes in chicken tissues, which include peripheral nerves, gonad, iris, muscle, viscera, and skin . MDV is Currently, control strategies for MD predominantly rely on vaccination of chickens. But the vaccination cannot provide complete protection because of the changing nature of the disease itself and evolution of MDV with escalated virulence . Recentl3 and 72. The percentage of chickens developing a tumor or tumors induced by a partially attenuated vv + strain of MDV differed significantly between the two lines of chickens. Genetic resistance to MD in chickens is commonly evaluated with MD incidence post-MDV challenge, which had been descripted in previous study  : IBS2 and IBS1 are the number of loci that share either 2 or 1 alleles identical by state (IBS), respectively, and the N is the number of loci [http://tree.bio.ed.ac.uk/software/figtree/). In addition, Reynolds’ distance between pairwise inbred lines was estimated using Arlequin version 3.5 [Neighbor-joining (NJ) tree was constructed based on the estimation of IBD. As line 7 of loci , 18. Thesion 3.5 .FST was calculated for each single locus using Genepop software across all lines [FST values for the adjacent SNPs in a special window size were estimated to exceed the significant threshold [FST values of each nonoverlap region with 50 SNPs windows size. The top 1% regions were considered as candidate regions. Furthermore, we performed gene annotation based on genome assembly  to identify the candidate genes involved in divergent selection during the inbred process.To investigate genome-wide patterns of genetic polymorphism related to susceptibility and resistance during the inbreeding process, the global ll lines , which mll lines . Genomichreshold . We esti3 and line 72, we performed ROH analysis with PLINK using a total of 527,021 SNPs [Runs of homozygosity (ROH) are regions of the genome where the identical haplotypes are inherited from each parent. The exploration of ROH can help to identify recessive disease variants and investigate the effects of genome-wide homozygosity on traits of biomedical importance. To investigate homozygosity distribution in six RCS lines as well as the parental line 6021 SNPs . A slidiGallus gallus, and GOTERM_BP_FAT, GOTERM_CC_FAT, GOTERM_MF_FAT were selected as the functional annotation category.To assess the function of the positive selected genes identified in top 1% windows regions and genes in the ROH segments, we performed gene enrichment annotation and gene functional classification with DAVID (version 6.7) . The defP < 1 × 10− 6) were excluded, leaving a total of 155,216 autosomal SNPs in population genetic structure analysis. Distributions in autosomes for total SNPs and filtered SNPs were presented in Additional file The quality of all SNPs in the array met the 99% call rate , line 63 (resistance), RCS lines and F1 (hybrid F1 generation with various level of resistance). Six F1 chickens clustered together, and they were distinguished from both susceptibility and resistance lines. We also observed 18 chickens from RCS lines were specifically clustered into resistance sub-groups distributed around line 63  and the second dimension C2) separated the lines into three broad non-overlapping clusters, which represent line 7 63 Fig. . Moreove 63 Fig. . Other l separate2, F1, line 63 and others, as shown in Fig. 3 (indicating unique resistance lines). In addition, the neighbor-joining (NJ) tree was constructed based on IBD calculated between unambiguously separated individuals, and line 72 was used as control group  with diverse signatures. To gain insight of the function enrichment of these regions, we annotated the top 106 candidate regions, and 51 genes were identified to be candidate genes for the resistance to MD. To better understand the biological processes and molecular function involved in MD genetic resistance, we performed GO term analysis using DAVID. We observed the most represented molecular function was arylamine N-acetyltransferase activity (GOTERM_MF_FAT), which included gene PNAT10 , PNAT3 , and NAT  , CDH13 , CFDP1 , API5 (apoptosis inhibitor 5), and ING4 . Furthermore, KEGG_PATHWAY analysis identified PNAT10, PNAT3, NAT involved pathway caffeine metabolism and drug metabolism . The overlapping positions of each region and QTLs related to MD disease were listed in Additional file To explore the difference of genetic polymorphism related to susceptibility and resistance during the inbreeding process, we carried out genome scan across genome using FST = 1) indicates two different homozygous genotypes existed in populations. To estimate the inbred level across lines, we also calculated the inbreeding coefficient (F) for all 30 samples using 155,216 high quality SNPs  in line 63, which is over 0.98. Next is line 72, nearly 0.95. In contrast, the average of F among the RCS is 0.88 ± 0.028. Among them, the average of F values in RCS-S line is up to 0.94, whereas for lines N and X, F values are both about 0.86, which suggested there existed slight different inbred levels among inbred chicken lines.In this study, we found many loci had been fixed to homozygous. It is impossible to use them to explain the difference of MD resistance. However, the completed differentiations  and SNPs within each ROH segment, was shown in Additional file To explore the question whether the inbred levels and homozygosity differences across the genome could contribute to the susceptibility/resistance for MD, we conducted homozygosity analysis across all 24 chickens . We obtained 2,027 ROH segments, and 1,596 of them were found in RCS lines. The genome wide results from the ROH analysis indicated obviously different ROH distributions across the genome within 24 inbred chickens. We identified 406 ROH segments in line 73, 72, C, J, M, N, S and X, respectively, while the relative more ROH regions were identified on chr1 (10 ROHs) in line 72 and chr2 (8 ROHs) in line L. The most probable reason was both chromosomes cover a larger proportion of genome size as compared with other chromosomes. To facilitate the comparison of difference of ROH regions among lines, genome wide ROH regions were presented in Additional file 3. The results indicated that ROH region caused by inbreeding may be involved in the different MD resistance. On the other hand, the unique region from line 72 (susceptibility line) comparing other lines may offer some clues of genomic homozygosity region related to susceptibility.To compare the ROH distributions among 8 diverse inbred lines, we first merged ROH segments identified from all individuals for each line into nonredundent ROH regions based on genomics coordinate. We finally obtained 58, 71, 59, 64, 65, 63, 58 and 67 ROH regions in the lines 63 with resistance, parental susceptibility line 72 group, and RCS group , respectively. Comparisons of ROH regions based on genomics coordinates among three groups showed that 98.6% ROH region (902.5/915.0 Mb) were shared by all three groups  lines were developed from these two lines showing varying resistance to MD, we observed RCS lines were similar to line 63 based on genetics relation estimation in present study . We obtained one region that is uniquely distributed in line 63 with 513 kb length; eight regions (987 kb in size) in line 72 groups, and 47 regions totaling 7,719 kb not in line 72 but in shared by both RCS lines and line 63  and cell death and anti-apoptosis . We also detected one region located at 51.5 Mb in chromosome 5 with high selection signal (FST = 0.574986) that contained gene AKT1. AKT1  is a serine/threonine kinase that plays a critical role in regulating cell survival, insulin signaling, angiogenesis and tumor formation [AKT1 as one of candidate gene for Salmonella response [Salmonella in a highly advanced intercross revealed the position containing both AKT1 and SIVA may work as heterophil function to explain the host-resistance properties [CDH13 was located at 15.6 Mb in chromosome 11. This gene encodes a member of the cadherin superfamily, which is hypermethylated in many types of cancer. Previous studies have showed that CDH13 may be a promising candidate gene for Attention Deficit/Hyperactivity Disorder (ADHD) [API5 located at 20 Mb in chromosome. This apoptosis inhibitory protein prevents apoptosis after growth factor deprivation and suppresses the transcription factor E2F1-induced apoptosis, and negatively regulating acinus, a nuclearfactor involved in apoptotic DNA fragmentation. Its depletion enhances the cytotoxic action of the chemotherapeutic drugs [Although the limited samples size per line were utilized in the current study, the global Formation –29. Prevoperties . Anotherr (ADHD) , 33 and r (ADHD) . Intriguic drugs .SIK, SOX1, LIG4, SIK1 and TNFSF13B) within ROH region were found in resistant group (line 63 and RCS), and these genes have been previously reported that may be related to immunology and survival. Previous studies demonstrated that SIK1 gene plays critical role in promoting survival of skeletal myocytes using mouse model [Sox1 as a part of the Sox-B1 group of transcriptional regulators in neural progenitor cells is sufficient to induce neuronal lineage commitment [LIG4 was found to be associated with higher IgE levels to Ascaris [TNFSF13B, as one of BAFF receptors, was proved to be a key survival factor during B-cell maturation [ROH regions can increase the recessive deleterious alleles to be co-contributed, and reduce the viability of the organism. Differential ROH segments across lines may contain potential candidate genes related to MD resistance. In this study, 5 genes  [PLAG1 (pleiomorphic adenoma gene 1) was found frequently rearranged and activated in human salivary gland pleomorphic adenomas. It encodes a developmentally regulated transcription factor [PLAG1 has been proposed to play a crucial role in tumorigenesis of salivary gland pleomorphic adenomas. It was reported that PLAG1 can activate the transcription of insulin-like growth factor 2 (IGF2), functioning as a proto-oncogene [PLAG1 is a proto-oncogene whose overexpression is a crucial oncogenic event in salivary gland pleomorphic adenomas (PA), and in carcinoma ex pleomorphic adenoma (CA-ex-PA). Our results provided some important insights for understanding the molecular mechanisms of MD resistance. Moreover, we identified gene USP15 (ubiquitin-specific protease 15) in line 63, which may indicate its potential role for MD resistance. USP15 have been reported that have an essential role for regulation of caspase-3 during Paclitaxel-induced apoptosis [Interestingly, we identified one unique ROH region (at 110 Mb in chromosome 2) in line 7or (BCR) , and CD4or (BCR) . PLAG1  rate. MD resistance in chickens is generally evaluated with MD incidence (induced gross tumors by MDV) and survival day post MDV challenge, and the extent of resistance is dependent of the virulence of challenge viruses and other factors. Figure S2. Barplot show the call rate for 30 chickens on Affymetrix chicken SNP genotyping array. X axis represent the call rate values, and Y axis represents the chicken individuals. Figure S3. Frequency distribution in autosomes for a total of 527,021 SNPs on the Affymetrix chicken SNP genotyping array. Figure S4. Frequency distribution in autosomes for 155,216 SNPs on the Affymetrix chicken SNP genotyping array. Figure S5. Comparison of ROH regions among three groups, line 63, line 72, lines RCS . The overlap length of ROH was indicated in kb. (DOCX 35 kb)Additional file 2:Table S1. GO analysis for 51 genes in 106 identified candidate regions. (XLSX 20 kb)Additional file 3:Table S2. The overlapping positions for 106 identified candidate regions with QTLs related to MD disease. (XLSX 12 kb)Additional file 4:Table S3. The inbreeding coefficient (F) for all 30 samples using 155,216 high quality SNPs. (XLSX 11 kb)Additional file 5:Table S4. Summary statistics and ROH distributions among 8 diverse inbred lines including lines 63, 72, C, J, M, N, S and X. (XLSX 31 kb)Additional file 6:Table S5. Unique and shared homozygous segments across all chickens included RCS lines and line 63 chickens were compared with the line 72. (XLSX 13 kb)


The sequencing of radish genome aids in the better understanding and tailoring of traits associated with economic importance. In order to accelerate the genomics assisted breeding and genetic selection, transcriptomes of 33 radish inbred lines with diverse traits were sequenced for the development of single nucleotide polymorphic (SNP) markers. The sequence reads ranged from 2,560,543,741 bp to 20,039,688,139 bp with the GC (%) of 47.80–49.34 and phred quality score (Q30) of 96.47–97.54%. A total of 4951 polymorphic SNPs were identified among the accessions after stringent filtering and 298 SNPs with efficient marker assisted backcross breeding (MAB) markers were generated from the polymorphic SNPs. Further, functional annotations of SNPs revealed the effects and importance of the SNPs identified in the flowering process. The SNPs were predominantly associated with the four major flowering related transcription factors such as MYB, MADS box (AG), AP2/EREB, and bHLH. In addition, SNPs in the vital flowering integrator gene (FT) and floral repressors  were identified among the radish inbred lines. Further, 50 SNPs were randomly selected from 298 SNPs and validated using Kompetitive Allele Specific PCR genotyping system (KASP) in 102 radish inbred lines. The homozygosity of the inbred lines varied from 56 to 96% and the phylogenetic analysis resulted in the clustering of inbred lines into three subgroups. Taken together, the SNP markers identified in the present study can be utilized for the discrimination, seed purity test, and adjusting parental combinations for breeding in radish. Raphanus sativus L., 2n = 18) is an important root vegetable in the Brassicaceae family. The tap root of radish has been widely consumed dietary part worldwide. Similarly, radish leaves, seedlings, siliques, and sprout also possess dietary and medicinal importance1. Due to the higher consumption rate, several breeding efforts were implemented as well as researched to introduce cultivars with diverse phenotypic and economic qualities. Especially, the qualities such as shape, size, color, flowering period, anthocyanin contents of roots, and higher secondary metabolites have become the interest of plant breeders2. Researches on the construction of linkage maps using various molecular markers such as simple sequence repeats (SSR), amplified fragment length polymorphism (AFLP), and single nucleotide polymorphism (SNPs) were conducted to boost up the molecular breeding of radish. Further, QTL maps were constructed for the analysis of disease resistance, pathogen resistance, and cadmium accumulation in radish6. In recent days, NGS approaches like transcriptome sequencing resulted in the generation of comprehensive sequence data in several horticultural plants such as pepper, cucumber, and eggplant,9. In addition, the transcriptomics information acts as vital resources for the development of molecular markers in numerous plant species11. Among the molecular markers, SNP is widely employed in the molecular breeding programs now a days due to its abundance, biallelic nature, and low mutation rate12. Moreover, the amenability of SNP markers for high-throughput assays makes it as a marker of choice for plant breeders. In addition, the SNP genotyping is easily applicable for large populations or germplasm collection especially for the construction of genetic maps, genome wide association studies, and marker assisted breeding in cost effective manner13. In radish, the SNP markers were utilized for the construction of expressed sequence tags based genetic maps which aided in the genomic comparison among Brassica species14.Radish  assay employs the advantages of both polymerase chain reaction and fluorescent SNP genotyping approach. The KASP approach is a flexible SNP genotyping system that can be performed on various set of markers19. Overall, the objective of the present study is to discover SNP markers from 33 inbred lines of radish by utilizing the information obtained from the transcriptome sequencing, functional annotation of the SNPs, and investigation of SNPs associated with flowering. Further, the SNP marker data were used for designing the probes for marker assisted backcrossing (MAB) and genotyped using KASPar assay. The outcomes of the current endeavor will benefit the radish breeding and screening of cultivars with high economic importance.Several approaches were used to authenticate the SNP in a population. However, the selection of genotyping technique depends on the size of the sample, number of markers, assay platform, cost effectiveness, and accuracyR. sativus lines were selected based on the economically important phenotypic traits such as skin color , leaf shape, root shape and length, root flesh color, maturity, and flowering pattern , cellular process (19%), response to stimulus (13%), and biological regulations (12%), etc. , low (19.01%), moderate (8.84%), and high (0.44%) impacts Fig. . The majThe identified SNPs were mostly associated with the four major transcription factors such as MYB, MADS box AGAMOUS (AG), APETELA2/ethylene responsive element binding (AP2/EREB), and basic Helix-Loop-Helix (bHLH) Fig. . These t3 pathway, vernalization pathway, and age pathway were identified. In addition, the SNPs were also identified in the vital floral integrators, repressors, and floral meristem identity genes in radish  were identified. Among the radish inbred lines, K31 (early flowering accession) consisted of majority of SNPs associated with flowering genes whereas K38 (late flowering accession) possessed the least SNPs related to flowering. The SNPs linked to the floral repressors such as polycomb EMBRYONIC FLOWER 1, 2, and FIRGIDA like protein were identified in the early, middle, and late flowering accessions. Among the floral repressors, the majority of the SNPs were associated with polycomb EMBRYONIC FLOWER 2 and 1 followed by FRIGIDA like protein. Moreover, the SNPs related to polycomb EMBRYONIC FLOWER 2 were observed in more number than the polycomb EMBRYONIC FLOWER 1 among the early, middle, and late flowering accessions. On the other hand, six early flowering accessions possessed SNPs linked to FRIGIDA however only one was observed in the middle flowering accession (K53) and none in the late flowering accessions. The floral integrator namely FT interacting protein 1 related SNPs have been identified in four early flowering accessions , two middle flowering accessions (K48 and K51), and two late flowering accessions (K48 and K51).Similarly SNPs related to flowering genes involved in the major flowering pathways were identified in all the accessions of radish employed in this study. In detail, SNPs in the genes involved in the flowering pathways such as photoperiod pathway, autonomous pathway, GAish Fig. . A highe3 flowering pathway were observed in three early flowering accessions , two middle flowering accessions (K53 and K54), and one late flowering accession (K52). In addition SNPs related to flowering time control protein FCA involved in the autonomous pathway of flowering has been identified in 12 early flowering accessions, two middle flowering accessions, and one late flowering accession of radish. Moreover, the interaction analysis of proteins associated with flowering related pathways have been classified into two clusters using K-mean clustering algorithm based on the distance matrix resulted from the STRING scores. The algorithm interprets the scores and clusters the interacting proteins with higher global score into same group. In the present study, the first group (red color spheres) consisted of vital proteins involved in the photoperiod pathway, vernalization pathway, and floral integrator, etc. The second group (green spheres) encompassed the proteins involved in the age pathway, floral meristem identity, and others , phytochrome E (PHY E), cryptochrome 1 (CRY 1), and cryptochrome 2 (CRY 2) have been identified in the radish inbred lines. However, three accessions such as K36 (early flowering), K41 (early flowering), and K57 (late flowering) lacked the SNPs related to photoperiod pathway of flowering. On the other hand, the accession K38 (late flowering) consisted of only one SNP related to flowering genes which corresponded to PHY E. In addition, age pathway of flowering related genes such as squamosa promoter binding like protein 13A and 15 were identified with SNPs. However, SNPs related to both squamosa promoter binding like protein13A and 15 were noted in the early accessions K12 and K15 whereas K31, K19, K32, and K44 encompassed SNP related to squamosa promoter binding like protein 15. On the contrary, the SNPs linked to squamosa promoter binding like proteins were absent in the middle flowering accession and K48 (late flowering) possessed one squamosa promoter binding like protein 15 related SNP. The SNPs associated with vernalization insensitive 3 gene involved in the vernalization mediated flowering pathway were identified in three radish accessions such as K14 (early flowering), K53 (middle flowering), and K52 (late flowering). Moreover, SNPs related to gibberellin 20 oxidase 1 a vital enzyme in GATo validate the SNPs, 50 SNPs were randomly selected from the 298 SNPs to develop Kompetitive Allele Specific PCR genotyping system (KASP) markers , growth rate (fast and slow), skin color , flesh color , root length , leaf shape (lyrate and undivided) were sequenced.High throughput transcriptome sequencing using Illumina technology aids in the generation of large scale transcript sequence information for molecular marker development. The Illumina based transcriptome sequencing has been employed in several economically important horticultural crops such as 24, in Chinese cabbage. Molecular markers are vital tools for the investigation of genetic diversity, construction of linkage maps, association and evolutionary studies25. Among the molecular markers, SNPs are considered as marker of choice because of its numerous benefits such as automation for high throughput assays, diallelic nature, and accuracy26. Recently, Wang et al.3, have developed SNP markers from transcriptome sequences for the germplasm identification in radish. However, in the present study the SNPs have been functionally annotated for obtaining the comprehensive view of the genes associated with the SNPs. The functional annotation of the filtered SNPs revealed the association of SNPs with genes involved in numerous vital biological processes and molecular functions , low , modifier  wherein in the present study the SNPs with modifier effects were identified predominantly as the upstream and downstream variants.Among the molecular functions annotated, majority of SNPs were linked to genes involved in binding function such as the binding of nucleotide, nucleoside, metal ions, organic cyclic compounds, heterocyclic compounds, proteins, lipids, carbohydrates, co-factors, and co-enzymes -like 10 were identified in radish. The CO acts as the positive regulator of FT and negative regulator of the floral repressor FLOWERING LOCUS (FLC)31. In addition, the CO is considered to link the plant circadian rhythm and flowering control. Moreover, the florigene FT perceives and converges the photoperiodic signals and transport to the shoot apex for the initiation of flowering15. However in radish inbred lines SNPs in FRIGIDA were observed along the accessions. In general, the flowering is delayed by the up regulation of the FLC which is activated by the FRIGIDA31. Other than FRIGIDA the SNPs related to EMBRYONIC FLOWER 1, 3 which repress flowering have been discovered in the radish inbred lines.The signals associated with flowering from the abovementioned pathways are integrated and regulated by floral integrators such as FLOWERING LOCUS T (FT), LEAFY (LFY), and SUPPRESSOR OF OVEREXPRESSION OF CO1 (SOC1)15. In the current study SNPs associated with VIN3 in radish inbred lines were observed. Likewise, the photoperiod pathway controls flowering in Arabidopsis by channeling a series of signaling cascade mediated by GIGANTEA (GI) and CO31. In addition, the gibberellin 20 oxidase, a rate limiting enzyme that catalyzes numerous reactions in the biosynthesis of gibberellins by oxidizing its precursors15. In general, the onsite of floral induction consists of higher levels of bioactive gibberellins which aids in the floral initiation. Hence, the mutation in the gibberellin 20 oxidase could influence the flux of gibberellins in the early, middle, and late flowering accessions of radish inbred lines. The squamosa promoter binding protein like (SPL) involved in the aging pathway promotes flowering by increasing the expression of LFY, FRUITFUL (FUL), and SOC115. Apart from the abovementioned pathways, the meristem response also significantly influence flowering. For instance, the induction of flowering requires the transition of shoot apical meristem from vegetative to inflorescence meristem to bear the flowers. This transition requires the higher expression of genes related to meristem response like APETELA 215. Moreover, the meristem response genes are also involved in the complex interaction of transcription factors such as AG, SPL, and LFY32. In addition differences in the SNPs associated with flowering related transcription factors  were identified among the early, middle, and late flowering accessions of radish. The importance of transcription factors related to flowering is inevitable for the regulation of flowering related genes in radish33. The occurrence of SNPs related to flowering in the radish inbred lines could be utilized for the construction of QTL maps and screening of radish cultivars for the late flowering and bolting varieties with commercial importance.Moreover, the vernalization pathway suppresses the FLC locus by the vernalization insensitive 3 (VIN3) mediated histone modification and promotes flowering19. The utilization of cost effective KASP assay demonstrated the relationship among the collection of radish inbred lines. Similarly, the SNP based KASP analysis rendered the selection of chickpea19, maize34, rice35, and tomato36 for the diversity analysis, genetic purity test, and quality control to enhance the molecular breeding. Further, the genetic relationship among the radish inbred lines using KASP assay classified the radish lines into three groups. The group A consisted of radish accessions with long root, skin color varying from green to pale green color which is largely used for the Kimchi production in Korea. Moreover, the second group consisted of red, purple, and green skinned radish. The third group encompassed radish lines with pure white skin and round shaped. Similarly, Cheon et al.37, utilized the SNP based KASP markers for the phylogenetic analysis of Japonica rice varieties. According to Kim et al.26, the SNP markers derived from the transcriptome sequence of Chinese cabbage accession facilitated the understanding of genetic relationship among the accessions. In addition, the outcomes of the present study can promote the cultivar discrimination, seed purity test, and adjusting parent combination for radish breeding.Moreover the platform for the genotyping of SNPs relies on various factors such as precision, reproducibility, and multiplexing, high throughput, time and cost effectivenessRaphanus sativus inbred lines were selected based on the economically important traits such as skin color, leaf shape, root shape and length, root flesh color, maturity, and flowering pattern. The flowering pattern varied from early (flowers after 100–160 days of sowing), middle (flowers after ~180 days of sowing), and late flowering (flowers after more than 200 days of sowing) accessions. The plant materials were provided by the Hankook Seed. Co., Ltd. and Neoseed Co., Ltd.  and all the plants were maintained under natural conditions. The inbred lines were produced after seven to 11 generations of selfing. For total RNA isolation, young leaves from 14 days old plants were collected and immediately homogenized using liquid nitrogen. Further, the RNA was extracted using GeneAll Hybrid-RTM kit  according to the manufacturer’s protocol. The quality and quantity of the extracted RNA has been determined using the Nano Drop analyzer  and employed for transcriptome sequencing.The Illumina Genomic mRNA shotgun library process was performed at the National Instrumentation Center for Environmental Management . An Agilent 2100 Bioanalyzer  was used for quality control of the RNA. We selected high-quality RNA samples showing RIN (RNA integrity number) values over 7 and 28S/18S rRNA ratios over 1. cDNA synthesis and RNA-seq library construction were performed at the National Instrumentation Center for Environmental Management based on the protocols of the Illumina HiSeq2500 . Sequencing was performed by 101 bp paired-end (PE) sequencing. The DNA fragment size was 300 to 600 bp. The sequencing data from three lanes were merged for further analysis.38. Initially, the adapter sequences were removed followed by the read end correction and trimming. The sliding window trimming approach with the window length of 4, mean quality of 15, and minimum length of 36 bp were employed for obtaining the clean trimmed reads. Data below a phred score of 20 was eliminated. Subsequently, the trimmed reads were mapped to the radish reference sequence retrieved from Radish Genome Database at http://www.radish-genome.org. TopHat 2.0.1339 and BWA 0.7.8-r455 (Burrows-Wheeler Aligner) were employed for the reference based mapping and alignment of the reads.The raw data generated from the Illumina Hiseq2500 was trimmed to remove low quality data using Trimmomatic 0.3240 along with in-house customized scripts were used with the following parameters seed length (-l) = 30, maximum differences in the seed (-k) = 1, number of threads (-t) = 16, maximum number of gap extensions (-e) = 50, mismatch penalty (-M) = 6, gap open penalty (-O) = 15, and gap extension penalty (-E) = 8. The merging and sorting procedures were performed using Picard 1.112 program and GATK3.1 was employed for genotyping and unifying the SNPs. The physical map of the selected SNPs was created using MapChart 2.3 41.For the discovery and filtering of SNPs, Samtools 0.1.827. Briefly, the SNPs bounded sequences were fetched from the reference genome using BEDTOOL and queried against BLAST SNP-associated scaffold sequences were retrieved from the reference genome and used as BLASTn queries against the non-redundant nucleotide database at the National Center for Biotechnology Information (NCBI). The following BLAST parameters were employed for the search: E-value cut-off, 1.0E-5; word size, 3; number hits, 5; and organism, radish. The hits with highest similarity score and lowest E value for each SNP-bounded sequence were selected based on multiple hits, and utilized for the Gene Ontology (GO) annotations and pathway details using Blast2GO standalone suite (http://www.blast2go.com/b2ghome). The GO annotations were acquired by implementing the following criteria: annotation cut-offs of _ 55; weight, 5; and e-value hit filter, <1.0E-6. The GO enrichment analysis of flowering related SNPs was performed using PANTHER classification system. The SNP-associated enzymes and pathway information were retrieved from the Kyoto Encyclopedia of Genes and Genomes database using the Blast2GO tool. The impact annotations of SNPs have been performed using the SnpEff tool42 according to Bhardwaj et al.28. SNPs associated with flowering related transcription factors and genes have been screened manually and the interaction analysis of SNPs was carried out with STRING interaction database.The functional annotations of the SNPs have been carried out according to Lee et al.37. Based on the fluorescence signal, the SNP allele call data are graphically illustrated for individual markers assayed using the SNPViewer. Further, SNP allele call data obtained have been utilized for calculating both pair-wise genetic distance and per cent dissimilarity matrix to construct a dendrogram using DARWIN V5.0.128 software (darwin.cirad.fr/darwin/ Home.php)43. Cluster analysis was carried out using the UPGMA method.For SNP genotyping using KASPar assays, 60 bp upstream and 60 bp downstream flanking sequences around the variant position (SNP) were extracted from the reference sequence (Supplementary Table The present study illustrated the transcriptome sequencing based SNP marker discovery in 33 inbred line of radish with diverse characteristics. The SNP markers identified were functionally annotated to gain knowledge on roles of SNPs and their effects in the functionality of genes. Moreover, SNPs identified were associated with flowering related genes and transcription factors which significantly varied among the early, middle, and late flowering accessions of radish inbred lines. In addition the KASP marker based genotyping of SNPs can be utilized for the genetic diversity analysis, germplasm discrimination, and seed purity test for quality control investigations which enhances the molecular breeding of radish. In future, SNP linked QTL analysis could be conducted for the identification of potential markers linked with flowering for the introduction of late bolting radish cultivars.Supplementary table filesSupplementary dataSupplementary data 2Supplementary data 3Supplementary data 4Supplementary Figure


In genome-wide association study (GWAS), conventional interaction detection methods such as BOOST are mostly based on SNP-SNP interactions. Although single nucleotides are the building blocks of human genome, single nucleotide polymorphisms (SNPs) are not necessarily the smallest functional unit for complex phenotypes. Region-based strategies have been proved to be successful in studies aiming at marginal effects.We propose a novel region-region interaction detection method named RRIntCC (region-region interaction detection for case-control studies). RRIntCC uses the correlations between individual SNP-SNP interactions based on linkage disequilibrium (LD) contrast test.http://bioinformatics.ust.hk/RRIntCC.html.Simulation experiments showed that our method can achieve a higher power than conventional SNP-based methods with similar type-I-error rates. When applied to two real datasets, RRIntCC was able to find several significant regions, while BOOST failed to identify any significant results. The source code and the sample data of RRIntCC are available at In this paper, a new region-based interaction detection method with better performance than SNP-based interaction detection methods has been proposed. Genome-wide association study (GWAS) has served as an important tool to investigate the relationship between genomic variants and human traits . The genp-value is at the order of 10−8 (assuming we use Bonferroni correction). In SNP-SNP interaction detection, the threshold has to go down further to the order of 10−14. As a result, interactions with weak or moderate effect sizes might remain undiscovered.An important problem in SNP-SNP interaction detection is the stringent threshold when considering multiple testing correction. For marginal effect detection, a SNP can only be considered as significant when its corresponding In this paper, we proposed a region-based interaction detection method to address this problem. Region-based methods have been successful in marginal effect detection , 13. TheTo group different SNP-SNP pairs together, the key is to quantitatively measure and account for the relationships between different SNP-SNP pairs. To the best of our knowledge, no existing method is available to test region-region interactions for case-control studies, where we only have two groups of people: healthy people (controls) and people with the investigated disease (cases). Although Ma et al.  proposedHere we propose a novel region-based interaction detection method for genome-wide case-control studies that utilizes SNP-based interaction test statistics and their covariances. LD contrast test is adopted to measure SNP-based interaction effects. We derive the covariance of LD contrast test statistics, which enables a robust aggregation of SNP-SNP interactions within a region pair. The determination of regions comes from gene definitions or BOOST results.There are two alleles for almost every base pair (bp) position in the human genome, one from the maternal chromosome and the other from the paternal chromosome. A combination of the two alleles is denoted as a genotype of this bp position. SNPs are defined as the base pairs that could exhibit different genotype values in different individuals. Normally a SNP only has two possible allele values in the population, one major allele with a higher probability (denoted as B), and one minor allele (denoted as b). Correspondingly, there exist three genoytpes for a typical SNP, i.e., BB, Bb and bb, where Bb is called a heterogeneous genotype and the rest two are called homogeneous genotypes. GWAS uses microarrays to generate SNP genotype data. In SNP data analysis, we use 0/1/2, 0/1/1, and 0/0/1 for BB/Bb/bb as the encoding scheme for additive, dominant, and recessive genetic models, respectively. A more flexible strategy is to estimate the effects of three genotypes independently, at the price of an increased degree of freedom. Allele data could also be used for analysis, with 0/1 as the numerical values of major/minor alleles. However, statistical inference needs to be performed in advance to retrieve allele information from original genotype data, which is called haplotype phasing in the GWAS community. In this paper, we focus on the analysis of genotype data.Current interaction detection methods are mainly based on the deviation from additive effect by assuming a linear or logistic regression model. Nevertheless, this approach is not necessarily the most powerful method due to the uncertainty of underpinning biochemical mechanisms. Linkage disequilibrium (LD) contrast test provides another valuable perspective to investigate this problem. Empirical studies have shown that LD contrast test can achieve higher power than logistic regression under certain disease models for case-control studies . In thisA and B)LD represents the statistical association between two genetic loci with allele values, defined as the deviation from the independence of two SNPs  which only requires genotype data is commonly used to approximate LD. CLD is defined as :2\documP denotes the probability of the specific gamete combination. CLD could be regarded as a simplified version of phasing to facilitate the analysis based on genotype data. The statistical properties of CLD have been well studied  with size 200 kbp and chr20:  with size 335.339 kbp, leading to a cFWER of 0.0536. Multiple genes, including CACNA2D1, DGKG, AK057298, TOP1, BC035080, PLCG1, ZHX3, LPIN3, and EMILIN3, are located in these two region pairs. CACNA2D1 has been found to be involved in cardiomyopathy pathway  and chr15: , with a cFWER of 0.00382. Two genes, PAH and FMN1, are involved in this region pair. Both PAH and FMN1 were reported to be related to kidney disorders [26], whiO(n2), which is unacceptable for whole genome analysis. Second, the genomic resolution has been sacrificed by replacing SNPs with regions. One potential remedy is to extend statistical fine mapping methods for interaction detection to determine the leading SNP pairs within the significant region pairs.There still remain several issues that could be improved in our method. First, the computation complexity of calculating the covariance matrix is p-value threshold. Simulation results support that our region-based strategy outperforms SNP-based method in terms of statistical power at similar type-I-error rates.In this paper, we proposed a region-based interaction detection method named RRIntCC. We derived the correlation coefficients between SNP-SNP interaction test statistics by using LD contrast test. We aggregated SNP-SNP interaction test statistics by assuming a multi-variate normal distribution with the estimated covariance matrix to account for the potential intensive LD pattern within the regions. By using region-based strategy, we reduced the total number of tests and were therefore able to use a less stringent Bonferroni-corrected