Ovis aries) are still poorly understood. To explore genetic mechanisms underlying the variation in litter size, we conducted multiple independent genome-wide association studies in five sheep breeds of high prolificacy  and one low prolificacy (Texel) using the Ovine Infinium HD BeadChip, respectively. We identified different sets of candidate genes associated with litter size in different breeds: BMPR1B, FBN1, and MMP2 in Wadi; GRIA2, SMAD1, and CTNNB1 in Hu; NCOA1 in Icelandic; INHBB, NF1, FLT1, PTGS2, and PLCB3 in Finnsheep; ESR2 in Romanov and ESR1, GHR, ETS1, MMP15, FLI1, and SPP1 in Texel. Further annotation of genes and bioinformatics analyses revealed that different biological pathways could be involved in the variation in litter size of females: hormone secretion (FSH and LH) in Wadi and Hu, placenta and embryonic lethality in Icelandic, folliculogenesis and LH signaling in Finnsheep, ovulation and preovulatory follicle maturation in Romanov, and estrogen and follicular growth in Texel. Taken together, our results provide new insights into the genetic mechanisms underlying the prolificacy trait in sheep and other mammals, suggesting targets for selection where the aim is to increase prolificacy in breeding projects.Reproduction is an important trait in sheep breeding as well as in other livestock. However, despite its importance the genetic mechanisms of litter size in domestic sheep ( Ovis aries) has led to variable litter size (LS) within and among breeds. For example, individual litter size of 1 to 8 has been recorded in the Hu sheep and Finnsheep  using a genome-wide association analysis. Gr in Grivette sheep and FecXO in Olkuska sheep associated with the highly prolific phenotype by a genome-wide association analysis. Bar associated with the prolificacy in Tunisian Barbarine. Despite its great importance the genetic mechanisms of the high prolificacy trait in domestic sheep are still poorly understood, partly due to shortage of studies conducted across multiple prolific sheep breeds. To date, numerous fecundity-associated mutations have been identified in different sheep breeds, but very few mutations have been consistently detected across the breeds. Despite the reproduction of ewes can be affected by the complex interactions of environmental conditions   , previouFigure 1A) and genetic origins  of the world, respectively. Wadi sheep is a high-prolificacy native breed from the Shandong Province of China  on litter size in the sheep breeds of high  and low (Texel) prolificacy with a litter size ranging from 1 to 6 from different geographic regions  and one low  prolificacy were collected from farms in China, Iceland, Finland, and Russia . Animals included were as unrelated as possible based on analysis of pedigree records and farmers’ knowledge. Data for the phenotype of litter size and the total number of litters collected from farm records are shown in Figure 2. The litter size ranged from 1 to 6 based on parity from 1 to 11 in six sheep breeds. Genomic DNA was extracted from the ear marginal tissues following a standard phenol/chloroform method and was diluted to 50 ng/μl for the SNP BeadChip genotyping (TM Complete DNA Purification Kit (Epicentre Biotech) following the manufacturers protocol.A total of 522 ewes from five sheep breeds of high . We implemented quality control of these SNPs using PLINK v1.07 software  tree and principle component analysis (PCA). The global FST value was calculated using GENEPOP v4.2  analysis was performed based on the independent SNPs using PLINK v1.07. Firstly, we implemented the option of ‘indep-pairwise 50 5 0.05’ in PLINK v1.07, which calculated pairwise linkage disequilibrium (LD) in a 50-SNP-window shifted at a pace of five SNPs. If the LD estimate was  removed . The ind removed .p < 0.05) after the Bonferroni correction . However, the parity of individuals within breeds was different, and we mainly focused on the mean of litter size of individual  in per breed. Therefore, we excluded the effect of parity from the model. The Quantile–Quantile (Q–Q) plots were visualized by plotting the distribution of obtained vs. expected genome-wide p-values. For genotype effect of potential SNPs on litter size in each breed, differences between means were analyzed by the Student’s t-test. The p < 0.05 was considered statistically significant. All the results were presented as mean ± standard error (SE). We implemented pairwise tests of linkage disequilibrium (LD) between the most significant SNPs and their flanking SNPs within approximately 1 Mb upstream and downstream using PLINK v1.07. Regional association plots were generated using the R package v3.2.2.We performed genome-wide association studies within five sheep breeds of high prolificacy  and one low prolificacy (Texel) using the case/control design. We ranked all individuals within the breeds according to their litter size from the highest to lowest. Then, we selected individuals from two tails for each breed as ‘case’ and ‘control,’ respectively. Based on the distribution of phenotypes, 114 samples (LS ≥ 2) in Wadi, 66 samples (LS ≥ 2) in Hu, 20 samples (LS > 2) in Icelandic, 37 samples (LS ≥ 2.5) in Finnsheep, 40 samples (LS ≥ 2.5) in Romanov and 28 samples (LS ≥ 1.6) in Texel sheep were selected as ‘cases,’ while 28 samples (LS = 1) in Wadi, 15 samples (LS = 1) in Hu, 15 samples (LS ≤ 1.75) in Icelandic, 9 samples (LS ≤ 2) in Finnsheep, 26 samples (LS ≤ 2) in Romanov and 14 samples (LS ≤ 1.33) in Texel sheep were selected as ‘controls.’ In the GWAS, we used the function of “qtscore” in the GenABEL package. Associated SNPs were identified at both the genome-wide and chromosome-wise significance levels  database3 for gene ontology (GO) enrichment and pathways analyses . The NJ tree showed that these breeds were clustered into two major groups according to their Chinese and European origins . A similar geographic pattern was seen in the PCA analyses with the grouping of Wadi and Hu sheep separated from the other four European breeds .Pairwise Supplementary Figure S3). We have repeated the association analyses without these animals, and found the results are very similar. Thus, we did not exclude these animals in the association analyses due to the small sample size for the breeds. The resulting genomic inflation factors were equal to 1.07 in Wadi, 1.14 in Hu, 1.12 in Icelandic, 1.14 in Finnsheep, 1.10 in Romanov, and 1.05 in Texel sheep, suggesting well-controlled population stratifications .After the quality control, 508,444 SNPs and 114 individuals (91 cases vs. 23 controls) in Wadi, 506,031 SNPs and 80 individuals (66 cases vs. 14 controls) in Hu, 443,125 SNPs and 23 individuals (8 cases vs. 15 controls) in Icelandic, 492,165 SNPs and 37 individuals (28 cases vs. 9 controls) in Finnsheep, 465,794 SNPs and 38 individuals (29 cases vs. 9 controls) in Romanov, 475,955 SNPs and 39 individuals (28 cases vs. 11 controls) in Texel sheep were retained in the working dataset for the GWAS. We did find several animals outlying the clusters of cases, which might cause biases in the association analyses (p < 1.92 × 10-6) 5% significance after the Bonferroni correction, respectively . We observed a high level of LD between the top significant SNP rs416717560 and rs421635584 located in gene BMPR1B . For the SNP rs416717560, average litter size of individuals with the G/G genotype  was significantly (p < 0.01) higher than that of the ewes with the A/G  genotype . Also, we found three additional significant SNPs  neighboring genes BMPR1B, FBN1, and MMP2 (Table 2 and Supplementary Table S3).In Wadi sheep, we detected 59 and 8 SNPs at the chromosome-wise and genome-wide (p < 2.18 × 10-6) 5% significance after Bonferroni correction . The regional plot showed that the top significant SNPs rs429755189 and rs420460180 on chromosome 17 were in an LD block that contained gene GRIA2 . For the rs429755189, average litter size of individuals with the genotypes G/G  and A/G  were significantly (p < 0.001) higher than that of ewes with the genotype A/A  in the present population . Among these significant SNPs, 3  are located within the genes SMAD1 and CTNNB1 (Table 2 and Supplementary Table S3).In Hu sheep, we identified 98 and 9 SNPs at the chromosome-wise and genome-wide . The top significant SNP rs429836421 on chromosome 3 was located within gene NCOA1 . For rs429836421, average litter size of individuals with the A/G genotype  is significantly (p < 0.05) higher than that of the ewes with the genotype A/A  .In Icelandic sheep, we found 22 SNPs at the chromosome-wise 5% significance after the Bonferroni correction (p < 3.64 × 10-6) 5% significance after the Bonferroni correction, respectively . The regional plot revealed strong LD between the top significant SNP rs412280524 and its neighboring SNPs rs401960737 and rs407751830 harbored gene INHBB . For the SNP rs412280524, litter size of ewes with the genotype A/A  is significantly (p < 0.001) higher than that of the ewes with the genotype A/G  . Also, five additional significant SNPs  were found to be located near to genes FLT1, NF1, PTGS2, and PLCB3 (Table 2 and Supplementary Table S3).In Finnsheep, we detected 102 and 6 SNPs at the chromosome-wise and genome-wide (p < 4.56 × 10-6) 5% significance after the Bonferroni correction . The top significant SNP rs423810437 on chromosome 7 was in the gene ESR2 . For rs423810437, litter size of ewes with the genotype A/A  is significantly (p < 0.001) higher than that of the ewes with the genotype A/G  .In Romanov sheep, we identified 77 and 2 SNPs at the chromosome-wise and genome-wide . The regional plot showed that the top significant SNPs rs161146164 and rs413776054 on chromosome 16 were in a strong LD region containing one functional gene GHR . For rs161146164, litter size of ewes with the genotype A/A  is significantly (p < 0.01) higher than that of the ewes with the genotype A/C  . The two mutations  cause the amino acid change in coding region of the GHR gene. In addition, we found eight additional significant SNPs  surrounding genes ESR1, ETS1, FLI1, SPP1, and MMP15 (Table 2 and Supplementary Table S3).In Texel sheep, we observed 133 SNPs at the chromosome-wise 5% significance after the Bonferroni correction (Supplementary Table S7). Nevertheless, we observed some exceptions. For example, the genotype A/G of rs429836421, which was identified in Icelandic sheep, showed significant associations with increased litter size in both Icelandic and Hu sheep breeds. However, a lack of homozygotes for the SNPs such as the genotype G/G for rs412280524 in Finnsheep, G/G for rs423810437 in Romanov and C/C for rs161146164 in Texel sheep could be because of low frequency of the mutations and small sample size.In addition to the source breed where the target SNPs have been detected, we further assessed genotype effect of the most significant SNPs on litter size in the other five sheep breeds. In general, genotypes of the target SNPs did not show significant association with increased litter size in the breeds other than the source breed (p < 0.1) enriched GO terms associated with reproduction for the candidate genes. The GO clusters were primarily enriched in the categories of ovarian and oocyte development , phospholipase C activity (FLT1 and ESR1), SMAD protein (INHBB and SMAD1) and BMP signaling (SMAD1 and BMPR1B) and positive regulation of transcription , all of which are involved in the folliculogenesis, follicle growth and granulosa cell proliferation . Another relevant GO category was hindbrain development (SMAD1 and CTNNB1), which participated in regulating ovulation  involved in estrogen, thyroid hormone, TGF-beta, retrograde endocannabinoid and hippo signaling pathways, and these pathways played important roles in regulating follicle growth and ovulation in livestock (Supplementary Table S5). However, we observed different GO terms for the candidate genes in different sheep breeds. For example, I-SMAD binding were enriched in Hu sheep, and chromatin binding were enriched in Texel sheep (Supplementary Table S6). In the gene network analysis, we observed that 16 genes  showed protein–protein interactions in the network . Expression data further showed that the genes BMPR1B, FBN1, MMP2, GRIA2, SMAD1, CTNNB1, NCOA1, NF1, FLT1, PTGS2, PLCB3, ESR2, ESR1, GHR, ETS1, MMP15, FLI1, and SPP1 were either highly or moderately expressed in reproduction-related tissues such as ovary, uterine cervix, placenta, corpus luteum, cerebellum, pituitary gland or uterus in sheep . Also, gene INHBB showed a high expression in ovary and uterus of Mus musculus5.We found significantly . The three genes identified in Wadi sheep, BMPR1B, FBN1, and MMP2, all play a crucial role in regulating hormone secretion (BMPR1B gene can lead to an increased density of the follicle-stimulating hormone (FSH) and luteinizing hormone (LH) receptors with a concurrent reduction in apoptosis to increase the ovulation rate of ewes  , a known prerequisite of the subsequent hormonal cascade inducing the ovulation in mice  involved in the ovulation in bovine  . In Hu s element . For exa in mice . The gen of ewes . The CTNn female . In Icelethality . In Finnignaling . For exa atresia . The codelopment . The FLTogenesis . The PTGebrafish . The PLCignaling . In Roma element . In Texer growth . As a ke in ewes . The GHR in mice . The ETSn bovine . As a prt medaka . The FLIene ETS1 . The SPPs luteum . In addiFST, PCA, and NJ tree methods to obtain a refined picture of population genetic relationships. The result showed that the genetic groups were consistent with the geographic origins of the breeds. The different genetic mechanisms associated with physiological processes for the litter size among sheep breeds could be related to the various environments in different geographic regions.Also, we calculated genetic differentiation among populations using the global BMPR1B, BMP15, and GDF9 for the prolificacy in ewes (Table 1). Different from early investigations, we detected a set of novel genes for the litter size in ewes. The main reason could be that most of early studies are based on genome-wide selection tests between prolific and non-prolific breeds using a lower density of SNPs. Instead, here we implemented GWAS within specific sheep breeds of high or low prolificacy using a high density SNP BeadChip array, which should lead to more reliable associations. In addition, the difference in threshold value used to define the ‘case’ and ‘control’ groups for each breed was also another potentially influential factor. When we implemented the GWAS using a two-step approach via the general linear model and genome-wide efficient mixed-model analysis (GEMMA), we did not find interesting candidate genes associated with reproduction across the six breeds . The fact that no candidate genes associated with reproduction were detected could be due to that the power to detect such associations will be weak when treating the trait of interest as quantitative given the small sample size. Also, these populations could have been subjected to selection on litter size through environmental variables such as climate and diet. However, we did not obtain data for local environmental variables in our data. Thus, environmental variables as well as the age of reproduction for the ewes were not taken into account in the model of the GWAS, which would be essential for future study.We noticed that previous studies had identified several genes of major effect such as We revealed a set of novel functional genes for the litter size in different sheep breeds across the world. Our results suggested differentially genetic regulation mechanisms for the functional genes in the reproduction of sheep. The significant SNPs and genes identified here are useful for future molecular-based breeding for a higher fertility. Also, our results provide important insights into the regulation of reproduction in sheep and other mammals.M-HL conceived and designed the project. FW, Z-QS, Y-LR, MS, EE, JH, JK, and TK collected the samples. X-LX extracted the DNA. JK provided help in Beadchip genotyping. S-SX and LG analyzed the data. S-SX wrote the paper with contributions from M-HL. All authors reviewed 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.


Oryza sativa ssp. japonica cv. Nipponbare and the African wild rice species O. longistaminata. While most GBS pipelines aim to analyze mainly homozygous populations, we attempted to genotype a highly heterozygous F2 population. We show how species- and population-specific improvements of established protocols can drastically increase sample throughput and genotype quality. Using as few as 50,000 reads for some individuals , we were able to generate up to 8154 informative SNP markers in 1081 F2 individuals. Additionally, the effects of enzyme choice, read coverage, and data postprocessing are evaluated. Using GBS-derived markers, we were able to assemble a genetic map of 1536 cM. To demonstrate the usefulness of our GBS pipeline, we determined quantitative trait loci (QTL) for the number of tillers. We were able to map four QTL to chromosomes 1, 3, 4, and 8, and partially confirm their effects using introgression lines. We provide an example of how to successfully use GBS with heterozygous F2 populations. By using the comparatively low-cost MiSeq platform, we show that the GBS method is flexible and cost-effective, even for smaller laboratories.Rapid and cost-effective genotyping of large mapping populations can be achieved by sequencing a reduced representation of the genome of every individual in a given population, and using that information to generate genetic markers. A customized genotyping-by-sequencing (GBS) pipeline was developed to genotype a rice F2 population from a cross of  Advances in sequencing technology have drastically improved our ability to determine and simultaneously genotype genetic markers  discovery, high-resolution mapping, and genomic selection  and a West African wild rice . Several complex traits are found in OL but are absent in NB. For example, OL is capable of perennial growth, while NB is an annual plant. Furthermore, OL is capable of clonal propagation through the use of rhizomes. To identify the genetic basis of those traits, we wanted to perform linkage analysis in an F2 population. Since there are only few markers available for this cross in public datasets, and traditional marker development and genotyping can be laborious, we established a GBS pipeline.In most cases, GBS aims to detect and simultaneously genotype a large number of single nucleotide polymorphism (SNP) markers. In this study, we used GBS on a rice F2 population derived from a cross of an elite cultivar from East Asia , can be adapted easily to new populations, and increased in scale to larger sequencing platforms. The pipeline was designed to specifically address rice F2 populations, but it should be useful for any F2 population. We investigated the effects of two different REs and different levels of multiplexing on the number of detected SNP markers. Also, we provide an example of how relatively low-coverage data  can be sufficient to generate high density genetic maps. Our pipeline uses simple error correction and imputation methods that take advantage of the long, uniparental haplotype blocks found in F2 populations. To show that our GBS pipeline is producing useful genotypes, we mapped QTL for tiller number, and partially confirmed these QTL using introgression lines derived from the same parents as the F2 population.By a combination of the comparatively low-cost Illumina MiSeq platform (O. longistaminata Acc. IRGC110404 (OL) as male was crossed with the cultivar O. sativa japonica cv. Nipponbare (NB) as female to produce F1 plants, and subsequently F2 populations by self-pollination. Since NB and OL are rather distant relatives within the Oryza genus, there is some degree of incompatibility between both parents. Specifically, the cross between NB and OL led to a failure of endosperm development, resulting in embryonic death. Therefore embryo-rescue had to be performed to avoid embryonic death of F1 seeds. In total 301 and 813 F2 plants were grown in a paddy field enclosed by mesh (the screen house) to prevent insect damage at IRRI in the spring (Feb–May) and fall (Sep–Dec) seasons of 2014, respectively. The total number of tillers (primary and branched shoots of grass plants) was determined after digging up those F2 plants from the paddy field. Leaf blades of the F2s, and three replicate individual plants of each, NB and OL, were sampled for DNA extraction.The population used in this study was produced and cultivated in the International Rice Research Institute (IRRI), Los Baños, Philippines. An African wild rice, 4F7 and BC5F6 plants derived from a cross between OL as female, and NB as male, and successive backcrosses by NB followed by self-fertilization. Four ILs were selected based on the QTL regions found in this study. The ILs and the recurrent parent NB were germinated in a greenhouse, and cultivated for 30 d. The seedlings were then transplanted to paddy fields at the research station of Nagoya University, Togo, Aichi Prefecture, Japan. Ten plants per line were planted in each row. The number of tillers was counted at the flowering stage in the ILs and NB, excluding damaged plants and plants next to the border of the plot to avoid position effects.Previously, we developed a set of introgression lines (ILs) that harbor between one and three substituted genomic segments derived from OL in the NB genomic background ; for PstI and KpnI the High-Fidelity version was used]. The digestion was performed at 37° for 1 hr, followed by an inactivation step at 65° for 20 min. Ligation was conducted in CutSmart Buffer without any modifications to the original protocol. A set of 192 unique barcodes were selected from the list of 384 barcodes designed for PstI listed in PstI overhang and KpnI overhang. Then 32-multiplexed libraries for samples digested by PstI and MspI, or 96-multiplexed libraries for KpnI and MspI, were prepared by pooling samples and subsequent PCR-amplification. DNA qualities and fragment sizes in the prepared libraries were evaluated using a Microchip Electrophoresis System for DNA/RNA analysis . In total, 10 32-multiplexed libraries and nine 96-multiplexed libraries were prepared. The libraries were sequenced using a MiSeq instrument with the MiSeq reagents kit v3 for 150 cycles .Genomic DNA from plant material was extracted using the cetyltrimethylammonium bromide (CTAB) method . DNA intFile S1). This included creation of a collection of unique, 64 bp long sequences (tags) from the raw sequencing data, alignment of tags to the IRGSP V1.0 O. sativa Nipponbare reference genome  GBS pipeline  not variable within each set of triplicate parental samples, (2) not heterozygous in either parent, and (3) different between both parents. Filtering was performed using the hapmap-formatted files and awk. The resulting collection of SNPs was then thinned out using vcf-tools  that might have led to miscalled heterozygous genotypes. In the next step, a filter for different levels of missing data was implemented. Specifically, we generated  datasets, in which up to 5, 50 or 75% of all genotypes for any given site were missing (File S2).Preliminary analyses indicated that a large source of error would be undercalled heterozygous alleles . To counter this, we used vcf-tools to only allow genotypes that were supported by at least seven reads per site and sample. This limits the probability of undercalling a heterozygous site to a theoretical maximum of 1.6% (https://github.com/brwnj/fastq-multx), and aligned using bwa as described above. We then inspected read alignments from selected, error-prone markers using the R package Gviz (Results”), we found that raw read alignments supported the called parental genotype in all analyzed cases.To rule out errors in data processing, we also directly analyzed alignments of reads to the Nipponbare reference genome independently from our GBS pipeline. For this, reads were demultiplexed using fastq-multx . Imputation of missing genotypes was not restricted by the length of each imputed stretch. Post hoc analyses showed that the maximum length of a stretch of consecutive imputed genotypes was seven, but >99% of all imputations were restricted to stretches of one or two genotypes (Table S1). The median imputed distance between two known genotypes varied between 571 kb  and 1661 kb . This imputation resulted in an almost complete elimination of missing alleles. Next, we tried to address the undercalling of heterozygous sites. Empirically, we set a minimum haplotype length of four sites. In any given F2 individual, if a series of homozygous or missing sites of length ≤4 was flanked on both sites by a heterozygous allele, this stretch was replaced with heterozygous sites. The other main error type seemed to be single erroneous alleles interspersed in longer homozygous haplotypes. We assumed those errors are cause by structural differences in the genome of OL compared to the NB reference genome. To counter this, we used a similar strategy as the one used to correct undercalled heterozygous alleles, but used a minimum haplotype length of one. This procedure reduced the number of missing genotypes as a percentage of all genotypes from 2.07 to 0.18%, while it increased the number of heterozygous alleles from 46.27 to 54.57% . In the final step, data from both analyzed populations was combined based on the assumed physical position of SNP markers. Since two different enzymes were used for the spring 2014 and the fall 2014 population, no SNP marker was found in both datasets, as different enzymes generate different sets of reads. Thus, we imputed missing data again, using the rules devised above to fill in sites. This imputation step was characterized by a median length of consecutive imputed genotypes of six, and a median length of imputed distance between genotypes of 0.86 Mbp when the missing PstI-derived genotypes (more common) were imputed in the KpnI-derived genotypes (less common) in the datasets with up to 5% of missing data (Table S1).In the first step, the data are transformed from the nucleotide-based hapmap format to an ABH-based format, where A represents NB, B represents OL, and H represents heterozygous alleles. After conversion, we first imputed missing data. Stretches of missing genotypes were filled with the appropriate allele if both flanking, not missing alleles were of the same state  is also available in the “ABHgenotypeR” package for R, which is available at https://github.com/StefanReuscher/ABHgenotypeR, or via CRAN (Comprehensive R archive network).All TASSEL scripts, and the scripts used for post-TASSEL data processing, can be found in Table S3). PCR primer sets to amplify a 150–300 bp region around the selected SNPs were designed based on the IRGSP V1.0 Oryza sativa Nipponbare reference genome sequence (Table S4). Genomic DNA extracted from NB, OL and 10 F2 samples with a high error rate were subjected to PCR for the region of the selected SNP. The PCR products were purified by gel purification using the Wizard SV Gel and PCR Clean-Up System . After confirming the presence of a single PCR product using 1.5% agarose gels, Sanger sequencing was performed with either of the PCR primers as a sequencing primer using BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) and a 3130xl Genetic Analyzer . The sequence data, including the raw chromatograms, was analyzed using the sequence assembly software ATGC  to identify nucleotides at the SNP positions detected in GBS.Ten error-prone SNPs identified by the GBS pipeline were selected for Sanger sequencing (P ≤ 0.05) was detected around the simulated, true QTL position (±10 cM), we counted it as correctly detected. For each condition, 100 simulations were performed, and the probability to correctly detect all QTL was calculated.General data analysis was performed using the TASSEL graphical user interface, and R. QTL analyses and simulations were performed using the R package “qtl” (v1.37.11) . In the LOD score values for an obtained LOD profile and rescales it to have an area of one, followed by calculating the connected interval having 95% coverage of the area. The function “fitqtl” was used for calculating percentages of variance of the significant QTL by calculating the coefficient of determination for each single-QTL model obtained using “scanone.” Additive and dominant effects of the significant QTL were calculated from mean phenotypic values for each genotype at the QTL positions obtained by using the function “effectplot.”Genetic maps using real data were constructed using the “est.map” function with default parameters. QTL analyses for the number of tillers in 1081 F2 plants was performed using a linear regression model with the multiple imputation method by “scanone.” The threshold for significant LOD scores was calculated from 1000 permutation tests. The 95% confidence intervals of significant QTL were estimated using the function “bayesint,” which takes 10http://homes.cs.washington.edu/∼dcjones/fastq-tools/).Genome-wide analysis of restriction sites was performed using the “restric” tool from the emboss software suite , and are available under bioproject PRJDB5346. Figure S1). In fact, a few hundred markers gave sufficient detection power, while, at the same time, the number of F2 individuals appears to be the limiting factor. We therefore optimized our GBS pipeline to process more F2 individuals, at the expense of generating a lower number of SNP markers, by multiplexing more samples per library.A population of 268 F2 plants from a cross of NB and OL, including triplicate parental samples, from the spring 2014 season was sequenced first. From this population, libraries of 32 samples each were prepared, and processed with the GBS pipeline . This apPstI as the rare-cutting enzyme, we used KpnI. There are 107,953 PstI cut sites reported in the NB reference genome, while there are only 45,065 KpnI cut sites according to in silico digests. Thus, if all parameters were kept constant, in libraries prepared with KpnI, the resulting reads will be distributed among fewer sites, but reach a higher per-site coverage. (2) Taking advantage of the higher per-site coverage using KpnI, we increased the number of samples per library. Prior to library preparation, we examined the effects of decreased read coverage per F2 individual by randomly sampling a fraction of reads from each input fastq file. In these simulated multiplexing analyses, it became clear that the undercalling of heterozygous sites (50% in an F2 population) would become a large source of errors if multiplexing is increased . Based on those results, 96-fold multiplexing was deemed feasible, and was implemented with the fall 2014 population. This resulted in an average of 134,447 reads per F2, which yielded (with up to 5% missing data) 301 SNPs  instead of using 301 SNPs .KpnI, led to a lower number of detected SNP sites. When processed through our GBS pipeline, however, both datasets led to similar genotype patterns, the main difference being the number of sites that were reliably detected. As the final step of the GBS pipeline, both datasets were merged. To describe and evaluate the results of the GBS pipeline, we subsequently used data from the fall 2014 dataset. For results regarding the genetics of the NB × OL F2 population and linkage analysis, we used the combined datasets to maximize detection power and resolution.As expected, higher multiplexing, and a change to The unfiltered GBS dataset contained a high proportion of missing data  and onlyFigure S3). The 10% disagreeing markers are explained by single SSR markers, in which up to 1/3 of all genotypes disagree, probably by the SSR marker and the closest GBS marker being on different sides of a recombination event.To evaluate the fidelity of GBS genotypes, we independently genotyped 93 F2 plants using simple-sequence repeat (SSR) markers, and compared both sets of genotypes. It was found that the majority of parental genotypes (>90%) was identical when the results of both genotyping systems were compared (see KpnI recognition sites (data not shown). For example, a SNP density below the average was observed on the long arms of chromosome 4 and chromosome 9. The occurrence of such SNP deserts was observed before . However, a plot of allele states along the chromosomes revealed regions with distorted genotype ratios . As a geFile S2). This made it obvious that GBS data without imputation and error correction contains wrongly called genotypes  flanked by B genotypes (representing OL), as this is very unlikely to reflect the true allele state in an F2 population at the given marker density. Using the imputed, but not error-corrected, data we counted the occurrences of each possible error-type  in the dataset from fall 2014 (5% max missing) for each of the 301 SNPs, and for each of the 813 F2 individuals, respectively (Table S3).Although we were able to correct most assumed errors in the parental genotypes, and construct a reasonable genetic map, we further analyzed the types of errors that occurred. We first defined all haplotypes of length = 1 as errors; We first analyzed the distribution of the occurrence of each possible error-type per SNP . This diTable S3). For 27 sequences, no BLASTN hit was found, which is probably due to the lower coverage of the OL genome compared to the NB genome. For 68 sequences, however, the best BLASTN hit in the OL genome was on a different chromosome when NB and OL were compared. In addition, for 28 out of the 206 syntenic markers, BLASTN reported at least one additional hit. When we combined the data from BLASTN searches with the occurrences of overcalled AHA alleles per marker, we found that the top three most error-prone markers appeared to be nonsyntenic in NB and OL. However, from the top 10 most error-prone markers, only four appeared to be nonsyntenic, while six appeared to be syntenic. This indicated that nonsyntenic markers might explain some of the observed errors, but they cannot explain all observed errors.In the next step, we analyzed whether obvious genomic rearrangements might be the cause for error-prone SNPs. To this end, we isolated a 65 bp sequence from the NB reference genome centered on the location around the SNP. This sequence was then used as a query for a BLASTN search in the recently published OL genome , we also used Sanger sequencing to determine the nucleotides at SNP positions. We sequenced the genomic region around 10 particular error-prone SNP sites in 10 F2 plants and both parents. This enabled us to directly compare GBS- and Sanger sequencing derived genotypes (File S3). For the 10 analyzed F2 plants, 7 out of ten SNPs showed identical parental alleles between GBS and Sanger sequencing derived genotypes, despite many of those genotypes forming haplotypes of length = 1, and thus being flagged as errors as described above. This result rules out that mere technical errors  are a major source of seemingly erroneous alleles. Instead, it is possible that structural differences between the parental genomes are a major source of error in this population. To still allow the calculation of a genetic map for later QTL analysis, we assumed haplotypes of length = 1 to be errors and corrected them as described before.In addition to using SSR markers  against this real data, we performed linkage analyses for random subsets of varying numbers of F2 plants. As predicted in our simulations, we found that up to 1000 F2 plants are necessary to reliably detect all significant QTL . When we used all 1081 F2 plants for linkage analysis, but varied the amount of missing data allowed in the prefiltered datasets, we found very similar LOD score profiles . We thus used the dataset with up to 75% missing data per site before postprocessing to maximize marker resolution.To evaluate the results of our QTL simulation  and experimental  data suggested that our GBS approach resulted in a saturation of markers. The fact that our linkage analysis yields comparable results, even when up to 75% of missing data for each marker were acceptable in the raw data, shows that even simple imputation algorithms can reinforce the usefulness of GBS data tremendously. We speculate that, for certain applications, even fewer markers, and, in turn, fewer reads per individual would be sufficient, thus allowing even higher multiplexing and sample throughput. Of course, this might also depend on the genome size of the analyzed species, and the amount of repetitive elements in that genome.Our aim for this study was to utilize GBS for rapid genotyping of rice F2 populations. As expected, GBS proved to be a robust and efficient method to genotype large populations  are not a major source of errors. It would thus be worth investigating the source of errors, as this might lead to new insights into the population in question. In our case, where a wild species is crossed to a cultivated one, it can be assumed that there will be large-scale differences between the two parental genomes contributing to the F2 individuals (Although we were successful in using GBS, we noticed several shortcomings where no best practice seems yet to be established. There seems to be very little consensus about how GBS protocols should be adapted to different species, and to different populations. For variant calling, filtering, and exploration of our dataset we used the TASSEL4 (ividuals , indicatWhen we established our GBS pipeline, we noticed several irregularities in the genome-wide SNP statistics. For example, we noticed that several regions of the genome were sparsely covered with SNPs . Also, wIn summary, we show an application of GBS to perform linkage analysis in a rice F2 population. We also provide an example of how to plan and carry out adequate, cost effective, reduced-representation sequencing. With our dataset, we successfully detected QTL for tiller number on chromosomes 1, 3, 4, and 8, which we could partially confirm using ILs. For future GBS genotyping efforts, we suggest evaluating enzyme choice, multiplexing of libraries and post-processing to meet the requirements of the desired post-GBS analyses.www.g3journal.org/lookup/suppl/doi:10.1534/g3.116.038190/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required.In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs  and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study.Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.The online version of this article (10.1186/s12864-017-4390-2) contains supplementary material, which is available to authorized users. Bos taurus and 115 Bos indicus genomes [Different densities of genotypes can be derived from various SNP (single nucleotide polymorphism) chips , 2 or wh genomes . The ana genomes . For som genomes , 13.The accuracy of imputation has been evaluated for various livestock species  and genomic prediction may become more accurate , 19 becaIn this paper, we investigated the accuracy of imputation from HD to sequence-level genotypes in the BSW population. We used four imputation tools and different reference populations to determine the most accurate imputation approach for BSW. Subsequently, 1646 genotyped BSW bulls were imputed to sequence level to perform GWAS for lactation traits. Among the most associated SNPs we could identify variants that had been suggested to be causal in previous studies.Bos taurus animals from 34 breeds  plot Fig.  the genon = 49, Table n = 1528) were used as a validation set  was to exploit the full range of genetic diversity represented by all breeds of the full Run 5 dataset. Therefore, in S1 we randomly selected one animal of each breed and country for the reference set  , FImputeAccuracy of imputation from the Illumina bovineHD SNP chip panel to whole genome sequence variants was investigated for chromosome 25. The genotypes of the validation animals were masked on BTA25 to mimic animals genotyped with the HD SNP chip. Imputation was then carried out from 12,222 SNPs (HD SNP chip) to 642,911 SNPs (whole genome sequence).To determine imputation accuracy the sequence-derived genotypes were compared to the imputed genotypes in the validation animals. SNPs that were included on the HD SNP Chip were excluded for the assessment of imputation accuracy. Furthermore we did not consider SNPs that were monomorphic in both, the reference and the validation population. We assessed three accuracy measurements. The first was genotype concordance, which was calculated as the ratio of the number of genotypes that have the same alleles in the true and the imputed data set to the number of genotypes in total. For the correlation measurements, the imputed and the sequence derived genotypes were centered according to Calus et al. . We asseAccuracy of imputation was also evaluated according to the MAF of SNPs. The MAF for each SNP was calculated in the reference population for each scenario and were classified in 13 different frequency classes with a focus on low MAF classes using the following thresholds: 0, 0–0.025, 0.025–0.05, 0.05–0.075, 0.075–0.1, 0.1–0.15, 0.15–0.2, 0.2–0.25, 0.25–0.3, 0.3–0.35, 0.35–0.4, 0.4–0.45, 0.45–0.5. The boundaries of MAF classes were defined as greater than the lower boundary and less than or equal to the upper boundary  and high-density genotyping arrays , respectively. The chromosomal position of the SNPs corresponded to the UMD3.1 assembly of the bovine genome . Mitochoearly: lactation days 8–12; FClate: lactation days 298–302) of the first lactation. Estimated breeding values for milk (MY) and fat yield (FY) were obtained for the 5-day intervals from routine breeding value estimation for milk production traits [basis = 282.18463 and MYbasis = 7080.298. The mean accuracy of the EBVs was 0.95 (± 0.06). The correlation between breeding values for FCearly and FClate was 0.26.Response variables for association testing were daughter-derived phenotypes for milk fat content at two stages (FCn traits . Phenoty2 [early and FClate using the EMMAX software tool [Gσa2) where G is the realized genomic relationship matrix that was constructed based on genotypes of 573,650 autosomal SNPs (see above) [P < 3.84 × 10−9 were considered as significantly associated . Bonferroni-correction might result in a too stringent significance threshold in association studies with imputed sequence variant genotypes because it assumes that individual tests are independent and is thus likely prone to over-correction considering the small effective population size and long-range LD particularly in livestock  . Sequenc et al., ). HoweveWe evaluated imputation accuracy for the BSW individuals included in Run 5 of the 1000 Bull Genomes Project , 10 in sAs measures of accuracy we evaluated genotype concordance rate and dosage correlation, as well as genotype correlation between called and imputed sequence variants.Bos taurus breeds and breed groups from different countries included in the Run 5 data set  to selection of key ancestors S2 and S3). While S2 contains the same number of individuals as the random selection scenarios in S3 we selected 50 reference individuals. The idea of using key ancestors as reference animals according to  is that . While SComparing S2 to S4 we compared selection of key ancestors according to  to randoConcerning the program best suited we find with these scenarios that using genotype concordance and allele dosage correlation again using Minimac and IMPUTE2 generally lead to almost equal highest accuracies.Scenarios S5-S7 are designed to investigate the effect of a multi-breed reference population on accuracy. Due to the setting of the validation set, the reference sets are thus considerably larger than for S1-S4 (Table For S5-S7 we find accuracies from 0.960–0.985 (genotype concordance), 0.894–0.939 (genotype correlation) and 0.977–0.983 . This is indeed larger than the accuracies found in S2 and S4. Accuracy constantly increases when adding more individuals to the reference population. Furthermore, we find also for S5-S7 that the differences between the individuals are reduced  and late (FClate) lactation as response variables. The inflation factors of the association studies were 1.05 and 1.10 for FCearly and late FClate, respectively, indicating that population stratification was appropriately considered. Association testing revealed one and three QTL (P < 3.84 × 10−9) for FCearly  array-derived genotypes. To evaluate the precision of full-sequence data for quantitative trait loci (QTL) fine-mapping, we performed association studies with imputed sequence variants using daughter-derived phenotypes for milk fat percentage in early (FCP = 2.15 × 10−16) at a QTL for FClate on BTA20 was a missense mutation  in GHR encoding growth hormone receptor. The p.F279Y-substitution has been postulated to be a causal mutation for milk production traits in cattle [late, which agrees with previous findings [The top variant  in close neighborhood to a missense mutation in DGAT1  with a large effect on milk yield and milk composition in cattle [late in BSW cattle. The p.A232K-variant (rs109326954) in DGAT1 was not polymorphic among the sequenced BSW animals of the present study corroborating its low frequency in the BSW population [late QTL on BTA14 had a frequency of 26.23%. Thus the association of ss1947221089 with FClate does not result from linkage disequilibrium with the p.A232K-variant in DGAT1. Our findings indicate that a QTL other than DGAT1 at the proximal region of BTA14 affects milk production in BSW cattle [P < 3.84 × 10−9) with FClate. Among them, a missense mutation  in LOC506831. However, many sequence variants had nearly identical P-values and pinpointing a putatively causal mutation was not attempted in our study.Another QTL for FCn cattle , 50. Howpulation . HoweverW cattle , 52, 53.MGST1  had P-values markedly lower than all other variants  resided 1369 bp upstream of the translation start of MGST1. Four variants  that were associated with milk and fat content in Holstein [2 > 0.97) with the top variant. Their P-values (P < 1.48 × 10−49) were only slightly higher compared to the top variant indicating that a common variant at that QTL is likely to control milk production traits in several cattle breeds.On BTA5, 20 variants located in the first intron and in the promoter region, respectively, of nts Fig. . The topHolstein  and FlecHolstein , were inearly comprising 122 variants with P < 1 × 10−14 resided within a 127 kb segment on BTA27 . The top variant  was located 16 kb downstream of the translation end of AGPAT6 encoding 1-acylglycerol-3-phosphate O-acyltransferase 6  with rs384016750. Their P-values (P < 1.09 × 10−20) were only slightly above the P-value of the top variant.A QTL on BTA27 was associated with FCe 6 Fig. . The expf AGPAT6 . These vAchieving high accuracy of imputation to whole-genome sequence level is possible in Brown Swiss cattle using the 1000 Bull Genomes reference population. According to two of the three measurements (dosage correlation and genotype concordance) Minimac is the best-suited program for imputation of this data, although all methods were generally adequate. The final imputation and subsequent GWAS of 1646 BSW revealed top SNPs in the sequence data, which are not on the SNP chip, including a well-known true causal mutation for milk production traits. Further some causal variants were among the top SNPs. This underlines that GWAS using imputed whole-genome sequence might facilitate the identification of new causal variants.Additional file 1: Table S1.Table S2. Average number of SNPs per minor allele frequency class in S5, S6 and S7. Table S3. Imputation accuracy per breed in S1. Table S4. Imputation accuracy per minor allele frequency class in S5, S6, S7. Table S5.p-values and SNPs with Bonferroni corrected significant association for FCearly. Chr is the chromosome and Pos is the position of the SNP, they correspond to UMD3.1. R2 is an imputation quality measure provided by Minimac and P is the p-value from the genome-wide association study. Table S6. p-values and SNPs with Bonferroni corrected significant association for FClate. Chr is the chromosome and Pos is the position of the SNP, they correspond to UMD3.1. R2 is an imputation quality measure provided by Minimac and P is the p-value from the genome-wide association study. (XLSX 169 kb)Breed codes and number of individuals per breed within the fifth run of the 1000 Bull Genomes Project. Additional file 2: Fig. S1.Boxplots of individual animal accuracy of imputation for all scenarios measured as genotype concordance rate (A), genotype correlation (B), and allele dosage correlation (C) between called sequence variants and imputed variants. Straight lines represent the mean accuracy of imputation across all scenarios per program. (PNG 901 kb)


During evolution, humans colonized different ecological niches and adopted a variety of subsistence strategies that gave rise to diverse selective pressures acting across the genome. Environmentally induced selection of vitamin, mineral, or other cofactor transporters could influence micronutrient-requiring molecular reactions and contribute to inter-individual variability in response to foods and nutritional interventions.A comprehensive list of genes coding for transporters of cofactors or their precursors was built using data mining procedures from the HGDP dataset and then explored to detect evidence of positive genetic selection. This dataset was chosen since it comprises several genetically diverse worldwide populations whom ancestries have evolved in different environments and thus lived following various nutritional habits and lifestyles.SLC24A3 with one being a known eQTL of the NCKX3 protein. We identified 312 cofactor transporter (CT) genes involved in between-cell or sub-cellular compartment distribution of 28 cofactors derived from dietary intake. Twenty-four SNPs distributed across 14 CT genes separated populations into continental and intra-continental groups such as African hunter-gatherers and farmers, and between Native American sub-populations. Notably, four SNPs were located in These findings could support the importance of considering individual’s genetic makeup along with their metabolic profile when tailoring personalized dietary interventions for optimizing health.The online version of this article (10.1186/s12263-017-0579-x) contains supplementary material, which is available to authorized users. LCT) responsible for lactose metabolism in the small intestine [FADS2, which codes for an enzyme involved in long-chain polyunsaturated fatty acid synthesis. A variant of FADS2 was associated with higher mRNA expression in vegan individuals [Diet and food availability shaped genetic variation in humans and left distinct adaptation signals among geographically and culturally diverse populations –3. Lactantestine , 6. Othentestine –9. Positividuals  which haividuals , 12.The objective of this study was to identify variants showing signs of positive selection in genes coding for cofactor transporters , a dataset chosen because it includes multiple world populations representative of a variety of environments and ancestral nutritional habits , 13, 14.S-adenosyl-l-homocysteine (SAH), are synthesized in cells and used locally and as such do not require transporters. Fe-S complex, heme-thiolate, inositol hexaphosphate, and dipyrromethane circulate as part of hemoglobin in red blood cells. The gene coding for the pyridoxal phosphate (vitamin B6) transporter has not yet been identified [Public databases  were searched for proteins involved in the transport of cofactors (or their nutrient precursors) between cells or sub-cellular compartments. CTs are a subset of proteins that transport other nutrients such as essential fatty acids or amino acids. At least one transporter was identified for 28 of 43 nutrient-derived cofactors   and NIPAL1 (non-imprinted in Prader-Willi-like domain containing 1) that mediate the trans-membrane movement of five divalent cations—cobalt, copper, iron, magnesium, and manganese.Genotype data from HGDP was used to study the genetic differentiation in genes coding for CTs. The final sample set included 940 individuals from 53 populations using the quality control criteria described in the “q value < 0.05, with the exception of the SNPs related to PC1, PC2, and 1 SNP associated to PC6 (rs11682328) that did not exceed this threshold. Sixty-one of these 100 loci corresponded to genes previously described as being positively selected in the dbPSHP database [OCA2/HERC2, SLC24A5, and EDAR [CACNA1A, HPX, SLC11A2 SLCO1A2, and TRPM4, showed evidence of positive selection in at least one population or group of populations using this method  were identified as significant cis-eQTLs from the GTeX eQTL database [SLC24A3 (rs16980447) showed a nominal p value < 0.05 but was not significant after FDR correction. SLC24A3 SNPs were found to be associated with its expression level in blood cells while the CACNB4 variant was associated with its gene expression level in skin exposed to sun. SLC25A26 SNPs were cis-eQTL in the heart and adipose tissue. Two SNPs, rs3861656 in KCNK13 andrs16830593 in CACNB4, are likely to affect transcription factor binding  . However, two missense SNPs were identified as proxy SNPs (rs6757850 correlated with KCNH7 SNP rs6708255 and rs7588788 and rs114005357 correlated with SLC11A2 SNP rs12312876) when lowering the R-square threshold to 0.4. Similar analysis was not possible for Native American populations since no sequencing data from a different dataset was available to evaluate LD. For what concern PC5, we observed that the clustering of African populations in two groups corresponded to one of the two subsistence strategies traditionally adopted by these populations, namely being primarily farmers or hunter-gatherers. The best candidate gene related to PC5 is SLC24A3 since it contains four SNPs showing evidences of positive selection, one of which also being a strong eQTL in GTeX database. The African genetic variation in the SLC24A3 region was further examined by estimating haplotypes to better evaluate the difference in allele frequencies of SLC24A3 region between the previously identified groups of farmers and hunter-gatherers. The most common haplotype is characterized by the SNP alleles ACAG shared by both farmers and hunter-gatherers. Notably, some haplotypes were restricted to only one sub-group  with SNPs differentiated in African populations (related to PC5). No non-synonymous SNPs were found among those in LD with the differentiated SNPs . Cofactor transporters are of particular interest as they regulate the tissue and sub-cellular bioavailability of micronutrient-derived cofactors and are more likely to be influenced by different nutritional habits from ancient populations originating from regions with varying climates  and soilST statistic, a popular measure of positive selection based on population differentiation [The PCA-based approach followed here associated the population-specific alleles to a specific PC and thus a specific ancestry gradient. Contrarily to Fntiation , it doesntiation . We thusSLC24A3 as being positively selected. SLC24A3 encodes for the potassium-dependent Na+/Ca2+ exchanger type 3 protein (NCKX3), an important regulator of intracellular calcium homeostasis. This gene is expressed most abundantly in the brain but also found in the aorta, uterus, intestine, and skeletal muscle with low expression in other tissues [The signals of positive selection identified here were derived mainly from two PCs, namely PC3 and PC5. The gradient described is intra-continental and is due to the difference in allele frequencies across the Native Americans and Africans populations, respectively. PC5 separated African hunter-gatherers from farmers, two populations that traditionally based their subsistence on different diets and identified  tissues .SLC24A3 have been associated with salt-sensitive vasoconstriction and hypertension [Polymorphisms in rtension , while trtension ). Selectrtension . Indeed,rtension , 32. Namrtension , have dirtension ) comparertension ), which rtension . The oppp < 5 × 10−8, rs12312876 (p = 2.70 × 10−8). This issue could be overcome using 1000 Genomes dataset; however, the populations included in that project do not cover the spectrum of human genetic differentiation that would be necessary to study the selective pressure exerted by diet. In fact, even close populations such as the African farmers and hunter-gatherers, not present in 1000 Genomes, could have been affected by different environmental factors. Another important limitation is the lack of direct information on dietary habits of reference populations that prevent any conclusion about the driving force of the adaptation. For what concern the analysis, the method we chose allowed us not to split the dataset in separate populations and thus has been the methodology of choice. However, if we had the possibility of using larger sample sizes for the population of interest, it would have been interesting to apply other selection metrics such as the haplotype-based methods such as iHS [Limitations were inevitably present in the study and should be considered when interpreting observations. First, the HGDP dataset, obtained using the DNA chip technology, does not allow studying rare variants that would instead be detected using newer technology such as the next generation sequencing. Moreover, each population in the dataset is represented by a small sample and could be the reason of not having extremely significant results. In fact, even if all the SNPs reported in the manuscript were significant after FDR correction, only one met the genome-wide significance threshold of h as iHS  and XP-Eh as iHS , calculah as iHS , which uh as iHS .Genetic variation in cofactor transporters may be of use clinically to investigate and help explain inter-individual variability in response to dietary interventions . Indeed,http://www.ncbi.nlm.nih.gov/pubmed), UniProt (http://www.uniprot.org/), and OMIM (http://www.omim.org/) databases were searched for transporters of cofactors [NCBI PubMed  Tissue enriched: mRNA levels in one tissue at least five times higher than all other tissues, (ii) group enriched: mRNA levels of a group of 2 to 7 tissues at least five times those of all other tissues, (iii) tissue enhanced: mRNA levels in a particular tissue at least five times the average level in all tissues, (iv) expressed in all: mRNA detected in all tissues, (v) mixed: detected in fewer than 32 tissues but not elevated in any tissue, or (vi) not detected , and they have been previously used in Rosenberg et al. [The genotype data were obtained from the HGDP–CEPH panel, a resource that captures a significant proportion of human genetic diversity. The genotypes were obtained with the Illumina BeadStation technology for 1043 individuals, were downloaded from NK v1.07 . Before g et al. .Principal component analysis was performed with smartpca tool of the EIGENSOFT package v6.0.1  using thhttp://www.r-project.org/) unless otherwise specified. Our analysis was designed to identify SNPs with signal of positive selection on the basis of outlier detection from principal component analysis. Such PCA-based approaches were recently successful in identifying genetic loci under adaptive selection [ST statistic is that it assesses genetic differentiation along gradients without requiring a priori clustering of the individuals by population. Starting from the SNP weights (loadings) obtained from the smartpca output, the selection statistics D2 was calculated and it corresponds to the squared loading of each SNP [p value to each SNP. p values obtained were corrected for multiple testing using the R package q value, which controls for false discovery rate (FDR) [q value significance threshold of 0.05 was used. To evaluate the results obtained applying the selection statistic to the genome-wide HGDP dataset, the top ten SNPs were extracted for the first ten PCs . A genomic region spanning 200 kb around each SNP was identified and genes annotated using the Bioconductor annotation package TxDb.Hsapiens.UCSC.hg18.knownGene. The comparison of results with literature was done using the data from dbPSHP, a database which contains information about genes and genomic regions from curated publications about positive selection in different human populations [Statistical analyses were performed with R 3.1.2  ). q valuhttps://analysistools.nci.nih.gov/LDlink/?tab=home). We submitted the significant SNPs identified, and for each of them, we retrieved a list of proxy variants located −/+ 500 Kb of the query variant with a pairwise R2 value greater than 0.01.The identification of the proxy SNPs of each significant variant associated to PC5 was performed using the genotype data of 1000 genomes Yoruba population. The analysis was carried out using the online tool LDlink  and 200 as the number of the number of haplotypes sampled from the “posterior” distribution obtained from a particular random start of the EM algorithm (H parameter). To build the haplotype network, we used the indiv.out file which contains estimates which attempt to minimize individual error. The haplotype network was produced by Network 4.2.0.1 using the median-joining algorithm [The pattern of LD in lgorithm .http://www.ensembl.org/Homo_sapiens/Tools/VEP/), using the GRCh38.p7 human assembly. The regulatory potential of the SNPs was investigated using the RegulomeDB, Version 1.1 [http://www.gtexportal.org/home/) were used to investigate the presence of correlations between the SNPs and tissue-specific gene expression levels .The impact of SNPs on protein function was examined using the Ensembl Variant Effect Predictor tool Additional file 2: Figure S1.Scree plot from PCA. This chart shows the eigenvalues associated with each PC. (PDF 6 kb)Additional file 3: Figure S2.PCA analysis for PC3 and PC4. The two scatter plots show the grouping of individuals according to PC1/PC3 and PC1/PC4. (PDF 17 kb)Additional file 4: Figure S3.PCA analysis for PC5, 6, and 8. Principal components showing positive selection between African sub-populations of Hunter-gatherers and Farmers (PC5/6) and between Native Americans (PC5/8). (PDF 19 kb)Additional file 5: Figure S4.PCA analysis of entire HGDP dataset. The scatter plots show the grouping of individuals according to PC1 and PC2 using all the autosomal SNPs. (PDF 10 kb)Additional file 6: Table S2.Analysis of positive selection using the GWAS dataset and literature comparison. (XLSX 298 kb)Additional file 7: Table S3.SNP functional annotation. (XLSX 194 kb)Additional file 8: Table S4.Regulome DB annotation. (XLSX 59 kb)Additional file 9:Note 1. (DOCX 32 kb)Additional file 10: Table S5.SNP functional annotation (XLSX 13 kb)Additional file 11: Table S6.Regulome DB annotation. (XLSX 10 kb)


The pike-perch iridovirus (PPIV) was isolated in Finland from apparently healthy pike-perch fingerlings during routine disease surveillance. Our phylogenomic analysis revealed that PPIV is the first fish member of a clade of ranaviruses previously described from European and Chinese amphibians. Ranavirus (Pike-perch iridovirus (PPIV) was isolated in Finland in 1995 from apparently healthy pike-perch fingerlings during routine disease surveillance . A DNA library was prepared using the Nextera XT DNA kit (Illumina), and sequencing was performed using a version 3 chemistry 600-cycle kit on a MiSeq platform (Illumina). De novo assembly of 4,981,658 paired-end reads in SPAdes  with 10% fetal bovine serum (FBS) at 23°C. Inoculation of EPC cells at a high multiplicity of infection (MOI) provided sixth-passage material harvested after 48 h when CPE was extensive. Cell culture supernatant was clarified at 3,000 × A total of 109 putative open reading frames (ORFs) were predicted using GeneMarkS  and GATUIridoviridae core genes  amphibians. An analysis of locally collinear blocks in Mauve (Maximum likelihood phylogenetic analysis based on concatenated amino acid sequences of the 26 re genes  revealedin Mauve  revealedWild amphibians were observed in the same ponds that yielded PPIV from asymptomatic pike-perch, leaving open the possibility of interclass transmission , 12. ExpKX574341.The complete genome sequence of PPIV has been deposited in GenBank under the accession no.


Sorghum bicolor, including a pedigree segregating a recessive dwarf mutant. Comparing the sequences of the lines, we were able to identify and eliminate error-prone positions. One genomic region contained EMS mutant alleles in dwarfs that were homozygous reference sequences in wild-type siblings and heterozygous in segregating families. This region contained a single nonsynonymous change that cosegregated with dwarfism in a validation population and caused a premature stop codon in the Sorghum ortholog encoding the gibberellic acid (GA) biosynthetic enzyme ent-kaurene oxidase. Application of exogenous GA rescued the mutant phenotype. Our method for mapping did not require outcrossing and introduced no segregation variance. This enables work when line crossing is complicated by life history, permitting gene discovery outside of genetic models. This inverts the historical approach of first using recombination to define a locus and then sequencing genes. Our formally identical approach first sequences all the genes and then seeks cosegregation with the trait. Mutagenized lines lacking obvious phenotypic alterations are available for an extension of this approach: mapping with a known marker set in a line that is phenotypically identical to starting material for EMS mutant generation.In order to leverage novel sequencing techniques for cloning genes in eukaryotic organisms with complex genomes, the false positive rate of variant discovery must be controlled for by experimental design and informatics. We sequenced five lines from three pedigrees of ethyl methanesulfonate (EMS)-mutagenized  One of the most effective and convincing methods to demonstrate gene function is the molecular identification of allelic variation responsible for induced mutant phenotypes. This gene function discovery approach, originally known as genetics and now sometimes referred to as “forward genetics,” has dramatically expanded our understanding of development, physiology, and biochemistry over the last century . Much ofe.g., transposons or T-DNA) or natural variation, chemical mutagens induce changes in phenotype by modifying protein-coding capacity and only very rarely are noncoding sequence mutations in cis-regulatory sequences identified as causative mutations. Essentially, a genome’s coding capacity determines the phenotypically affective mutational target. For example, in a genome with 30 Mbp of protein-coding sequence, a 1/125,000 bp mutation rate would correspond to ∼240 genic mutations. As approximately one-third of all EMS alleles result in synonymous changes , rice, zebrafish, Sorghum, and Drosophila  for gene identification by forward genetics utilize large segregating populations to clone genes by bulked segregant analysis , encoding a putative enzyme in gibberellin biosynthesis. The mutation cosegregated with the dwarf phenotype, which was reversed by GA application, and confirmed the causative nature of this polymorphism. Using EMS-induced SNP variation as molecular markers permits any phenotypically unaffected line to act as an EMS Variation-Induced Lines (EVIL) twin for mapping crosses without introducing the segregation variation of a distinct and diverse genetic background. The use of preexisting data to remove error-prone positions to improve SNP calling and EVIL twins for mapping should improve both the accuracy and sensitivity of forward genetics.In this work, we compared the results of whole-genome, short-read sequencing of a dhr2-1 library was previously described and generated from a single individual , potting soil , and sand . For NGS, whole leaves were collected ∼1 wk after germination for a pool of 17 10-2 individuals, in which all individuals planted were of normal size, and 10-dwarf individuals, in which leaves from all dwarfs germinated  across all individuals were collected and pooled. DNA genotyping leaf tissue was collected ∼6 wk after germination from multiple individuals. DNA was isolated from leaves collected from 12 tall individuals in the 10-3 family, which segregated for tall and dwarf individuals. Leaves from normal-height 12-2 individuals were collected from a population segregating for a dominant tall variant (data not shown). The http://helix.wustl.edu/dcaps/). Primers for PCR amplification were: dCAPs_BstN1-F (5′-TGTGGAAGAGTATGGGAAGGTT-3′) and dCAPs_BstN1-R (5′-CCTCTCCACGTGCAATTCTT-3′). Post PCR-amplification, 10 μl of the PCR product was digested using five units of BstN1  and 1 × NEB Buffer 3.1 in a 25 μl total reaction. Digestions were carried out overnight at room temperature and examined using a 2% TAE agarose gel. BstN1 digestions only occurred if the PCR amplicon was wild-type for the causative SNP, i.e., were 44 bases smaller than the uncut amplicon for individuals with the mutation. Individuals were either wild-type (183 bp fragment), dwarf (227 bp fragment), or heterozygous (both 183 and 227 bp fragments).A dCAPS .SNP detection was performed using the dhr2-1) were obtained from seeds, which were independently treated with EMS, we subtracted all overlapping high-quality SNPs detected in the progenies of these individuals. To remove false positive shared SNPs, we searched for genomic position overlaps in the high-quality homozygous G to A, and C to T SNP positions for the five EMS-mutant individuals. For each of the 10-derived individuals  we subtracted the overlaps with the dhr2-1 and 12-2 individuals. Similarly, we subtracted SNPs in dhr2-1 with genomic positions overlapping SNP positions in 10-2, 10-3, 10-d, and 12-2. The process was repeated for 12-2 by subtracting SNPs with positions overlapping 10-2, 10-3, 10-d, and dhr2-1 SNP positions. We used the BEDTools intersect command (version 2.17.0) with options “-wa” to find common SNP variations detected in different samples. Similarly, we used the BEDTools subtract command with options “–A” to detect pedigree- and sample-specific SNPs for each of the samples.Since the three individual plants , maize B73 (version 5b.60), and Arabidopsis thaliana (TAIR release 10) from the Phytozome web portal. We created BLAST proteome databases for each of the three plant species using the makeblastdb program with parameter options “-input_type fasta -dbtype prot.” We used the blastp program (version 2.2.28+) in the BLAST package  to align the Sorghum proteome to maize and Arabidopsis. The following blastp nondefault parameter options were selected: “-evalue 1E-05 -num_threads 32 -max_target_seqs 5 -out -outfmt 6 -seg yes.” The sequence annotation for the detected maize and Arabidopsis homologs of the Sorghum genes were appended to the SNP call “.vcf” files, using a custom program .Functional information for the genes affected by these SNPs was obtained as follows. We downloaded the latest proteome sequences and the corresponding annotation files for 3 , 0.2% ethanol, and 0.005% silwet (“10 µM GA3 treatment”). Seedlings were harvested and photographed after 7 d total in the dark. Mesocotyl length was measured from the root-to-shoot transition zone to the proximal end of the coleoptile using ImageJ . Seedlings were planted in 72-cell germination flats in a 1:1 mixture of peat germination mix  and Turface (Profile Products LLC). Seedlings were allowed to germinate for 2 d (before emergence) and then were treated with 1.75 L per flat of 0.2% ethanol and 0.005% silwet (“no treatment”), or 10 µM GAet al. 2012).An M2 segregating population of mutant and wild-type siblings was grown in the light for 14 d under greenhouse conditions: 27° (day) and 21° (night) with 16 h supplemental lighting. Seedlings were grown in media as previously stated. Seedlings were allowed to germinate for 2 d then treated with 1 L per flat of 0.2% ethanol and 0.005% silwet (“no treatment”), or 10 µM GA3, 0.2% ethanol, and 0.005% silwet (“10 µM GA3 treatment”), every third day until harvest. Seedlings were harvested and photographed after 14 d in total. Plant height was measured from the distal end of the mesocotyl to the first leaf collar using ImageJ  EMS-induced Sorghum mutant, which was found in a different mutant population  of the paired-end reads were properly paired within the range of the above insert sizes. Sorghum genome assembly version 2.1 available at Phytozome (www.phytozome.net). Between 88 and 94% of the reads mapped to the reference genome. The coverage depths for the homozygous aphenotypic line 10-2, the previously described dhr2-1mutant, and the extreme dwarf 10-d were 45-fold, 43-fold, and 30-fold genome coverage, respectively , and a pool of homozygous wild-type siblings from line 10-2. The DNA from each sample was sequenced. In addition, we sequenced another line, 12-2, which showed no gross morphological defects and was independently derived from the same mutagenesis experiment as line 10  errors in the reference sequence, (2) differences between the mutagenized material and the originally sequenced line, and (3) positions in the genome that frequently return a likely SNP call due to structure or paralogy. Furthermore, only those SNPs present in the 10 lines and not in 12-2 or dhr2-1 can be causative for the extreme dwarf locus segregating in that lineage. dhr2-1 lines. In each case, a large number of SNPs were identified in multiple lineages, indicating that these SNPs are unlikely to be the result of EMS mutagenesis and cannot encode the causative alleles for the extreme dwarf phenotype in line 10. The majority of the shared SNP variation was detected in pairwise comparisons. Only a minority of differences was found when comparing the three lineages of line 10, which indicates that the majority of these shared SNPs are not the result of base calling errors in the reference sequence. SNP calling is a statistical likelihood procedure (Table S1). Detection and removal of the SNPs shared between pedigrees resulted in further enrichment, with 79% of all lineage-specific SNPs  encoding G:C to A:T transitions (Table S1). Thus, roughly one-fifth of SNPs in our stringent sequential SNP filtering procedure were due to false positives arising at reproducible, and therefore detectable, positions in the genome. This proportion was even greater for the coding sequence positions  of the high-quality SNPs were in intergenic regions and 2113 SNPs were within annotated introns (data not shown). In all five lines, a total of 967 SNPs were predicted to alter the coding capacity of an annotated protein-coding gene. Of these, 927 SNPs were missense mutations, 26 were nonsense (stop codon gain) mutations, and an additional 14 encoded stop codon loss or splice site mutations.Sorghum individuals, respectively .To confirm that disruption of GA biosynthesis was responsible for the dwarf phenotype, we tested whether the dwarf phenotype could be reversed by the exogenous application of GAhenotype . Similarotype , mutagenized material. The SNP calling approach and removal of false positives by comparing our mutant to other lines improved NGS processing for EMS-mutant cloning, increased our confidence in SNP calls, and permitted the use of EMS-induced variants as molecular markers for mapping  were selected because they had no visible morphological defects. As a result, we have discovered thousands of EMS-induced SNPs that can be used as molecular markers in mapping novel genetic variation without impacting whole-plant phenotypes. In addition, the While it would be tempting to conclude from these results that any mutant can be cloned just by sequencing a few lines, there are three essential components to our study that were combined to make it efficient and accurate. First, to limit the mapping procedure to actual polymorphisms, it is necessary to remove all error-prone positions in the genome. Second, we were able to impose logical expectations for SNPs linked to the phenotype that ruled out ∼98% of the genome using a pedigree containing a true wild-type (no dwarf progeny), a segregating sibling, and a dwarf mutant. If a segregating pedigree is not available, using the EMS-induced SNPs as markers still has many advantages for mapping. The largest advantage is that, due to their scarcity, far fewer recombinations will be necessary to reduce the possible mutation space to a manageable list. Finally, the nature of the mutagen used allowed us to narrow down the search space to the point where we had one and only one causal locus. The overwhelming majority of the mutations caused by EMS are G to A (with its complement C to T). Furthermore, SNPs affect phenotypic alterations in mutagenesis experiments via disruptions in protein-coding sequences, as very few single SNPs within noncoding portions of the genome can have large effects on phenotype. Thus, the causal SNP for a mutant phenotype in an EMS-mutagenesis experiment can be identified among G:C to A:T changes that alter protein sequence, as previously discussed . For large genomes, this depth may remain cost-prohibitive for whole-genome sequencing. The cost per lineage can be dramatically improved if exome-sequence capture is used instead, and the cost for sequence capture at similar depths of coverage demonstrates that this as a more cost-effective option for larger genomes  have great utility for future mapping studies. The lines have no obvious phenotypes and a defined set of real polymorphisms, which make them ideal crossing partners for mapping mutants from mutagenesis types that cannot use the methods described here. With reference to a common theme in literature and popular culture, we have named these lines EVIL twins to denote their status as phenotypically identical but genetically different to the wild-type. The advantage of crossing to an EVIL twin line is that the only phenotypes that will be segregating in the population are those that are present in the mutant. This could be especially important for cloning of mutants with small effects, in which the phenotypic diversity of the outcross lines can obscure the signal. In addition, radiation mutagenesis induces a wide variety of deletion/insertion polymorphisms along with SNPs, so many of the logical filters we applied here will not work to reduce the false positive calls, whereas crossing a radiation-derived mutant to a congenic EVIL twin can be used to introduce markers for mapping without causing additional segregation variation. Three of the lines from this study are candidate EVIL twins: 10-2, 12-2, and the  mapping .Sorghum genome. Further sequencing experiments by the community will continue to identify positions prone to false positive errors. Indeed, preliminary analysis of a larger set of sequenced lines indicate that additional moderate-frequency shared variants are present in sequence data sets derived from BTx623, and that their discovery was not completely saturated after sequencing 600 mutant lines .A number of called variants visible in Gibberellic Acid 3 dwarf mutant of Arabidopsis, the lh dwarf mutant of Pea, and the semidwarfing allele from Tan-Gibozou rice that contributed to the “green revolution” in Japan . Mutations of the ortholog of this gene are responsible for the Sorghum. Sorghum mutant, dwf1-1, that mapped to a frame-shift in a presumed GA20-oxidase. In addition to dwarfism, this also caused male sterility and impacted cellulose biosynthesis. This mutation occurs later in the GA biosynthetic pathway than the ent-kaurene oxidase mutation identified in this study. ent-copalyl diphosphate synthase , ent-kaurene synthase , ent-kaurene oxidase , and ent-kaurenoic acid oxidase ] early in the GA biosynthetic pathway that lead to severe dwarfism and culm bending in sweet Sorghum. Finally, Sorghum.Recently, other studies have found gene mutations in the GA and brassinosteroid biosynthesis pathways that impact not only plant height but other agronomically important traits in The application of EMS mutagenesis and cost-effective short-read sequencing is driving rapid progress in our understanding of the control of crop plant architecture. Additional improvements in experimental design can further accelerate this and extend the utility of these methods. If we can reliably detect all of the sequence polymorphisms in a given genome, whole-genome sequencing allows us to invert the typical process for mutant identification. When all possible causative SNPs are known, only recombinants distinguishing the putatively causative SNPs are needed. This reduces the number of individuals necessary to identify one and only one potentially causative polymorphism in a line. In addition, by using all of the EMS-induced alleles in a mutagenized line, even those in noncoding sequences, we can remove the need to cross a phenotypically selected mutant to a genetically distinct stock. No incidental segregating variance needs to be introduced and, thus, environmentally labile or adaptively consequential mutant phenotypes that would be masked by background variation are now amenable to cloning. Moreover, because there are few mutations of putative effect, and less segregation variance that might drown out the effects of induced alleles with small phenotypic consequences, we can use this approach to quickly and cost-effectively clone mutations that have moderate to small effects.Sorghum bicolor. By comparing the sequence of several lines within the pedigree, removing error-prone positions, and focusing on polymorphisms that EMS will induce, we were able to narrow the polymorphisms down to one and only one mutation, an induced stop codon in the cytochrome P450 gene Sobic.010G172700. We have demonstrated that this approach can change the current paradigm for mapping mutants. We have also produced three lines that can be used as EVIL twins for mapping in the BTx623 background without introducing additional phenotypic variation.Using a novel combination of full genome sequencing, experimental design, and bioinformatics algorithms, we have cloned the gene for an EMS-induced dwarf mutant in www.g3journal.org/lookup/suppl/doi:10.1534/g3.116.029660/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


KASP (KBioscience Competitive Allele Specific PCR) and Amplifluor (Amplification with fluorescence) SNP markers are two prominent technologies based upon a shared identical Allele-specific PCR platform.Amplifluor-like SNP and KASP analysis was carried out using published and own design of Universal probes (UPs) and Gene-specific primers (GSPs).Advantages of the Amplifluor-like system over KASP include the significantly lower costs and much greater flexibility in the adjustment and development of ‘self-designed’ dual fluorescently-labelled UPs and regular GSPs. The presented results include optimisation of ‘tail’ length in UPs and GSPs, protocol adjustment, and the use of various fluorophores in different qPCR instruments. The compatibility of the KASP Master-mix in both original and Amplifluor-like systems has been demonstrated in the presented results, proving their similar principles. Results of SNP scoring with rare alleles in addition to more frequently occurring alleles are shown.The Amplifluor-like system produces SNP genotyping results with a level of sensitivity and accuracy comparable to KASP but at a significantly cheaper cost and with much greater flexibility for UPs with self-designed GSPs.The online version of this article (10.1186/s12870-017-1197-x) contains supplementary material, which is available to authorized users. Low- and high-throughput technologies based on SNPs (Single nucleotide polymorphism) are a booming sector in the field of plant genotyping. There are several types of SNP analysis with very different principles and applications (Reviewed in –7). A reTwo well-established technologies are based upon a shared AS-PCR platform: (1) KASP or KASPar (Kompetitive Allele Specific PCR or KBiosience Competitive Allele Specific PCR), and (2) Amplifluor (Amplification with fluorescence) SNP markers.www.lgcgroup.com), who manufacture and apply the technology through their own proprietary high-throughput robotic system, starting from leaf samples until the finished genotyping results. The basic principles of KASP are similar to those in Amplifluor, but the underlying chemistry and probe/primer assay has been never disclosed, representing a ‘trade secret’ of the Company. A researcher has three options when conducting KASP marker analysis. Large Universities and Research Centres can buy whole or in-part, the instrumental modules for KASP analysis including the reagent assays from LGC Genomics; which obviously represents a substantial investment. The second option is to submit DNA samples for KASP analysis directly to LGC Genomics office, which is significantly cheaper, and costs about 10 times less than other approaches such as the TaqMan method [KASP markers are designed and fully operated by LGC Genomics (n method . The finn method .http://www.merckmillipore.com), and is available as reagent assays or a service. In contrast to KASP, the chemistry, design and sequences of all components in Amplifluor systems were openly disclosed in both an on-line booklet and published papers The basic design of UPs was published earlier , 10. EacThe fluorophore FAM is commonly used in all FRET-based technologies . The choThe size and length of the hairpin loop and stem of the UPs have been reported as quite variable for Amplifluor and self-designed Amplifluor-like SNP . HoweverInitially, only DABSYL quencher was used in early Amplifluor SNP marker development . With maBlack hole quenchers, BHQ1, BHQ2 and BHQ3, were developed and used in plant pathology with TaqMan PCR , 22 and On the 3′-end, each of the two UPs has a specific 21 bp-long tail. The first six nucleotides in the tail following the modified T carrying the quencher, are identical. The last 15 nucleotides in the 3′-end are unique for each tail. Initially, shorter tails were used for UPs labelled with FAM and DABSYL quencher . In lateA set of three non-labelled GSPs must be designed for each specific Gene of Interest (GOI), following principles that were published earlier , 14, 15.There are many different published protocols suitable for Amplifluor SNP and KASP markers, using various PCR thermal cyclers. Protocols for Amplifluor SNP markers contain either 35–40 identical cycles , 19, 20 As was mentioned above, researchers working with KASP markers can buy and use the entire robotic line for SNP genotyping various crops, as for example in the University of Adelaide, Australia. Others submit DNA samples to the LGC office and publish the received SNP genotyping results, as shown for barley  and peanHowever, many researchers have also ordered KASP Master-mix and carried out SNP genotyping in their own laboratory using regular thermal cyclers or qPCR instruments, as for example in wheat , 34, 35,The aims of this paper are: (1) to estimate the effects of modifications and optimise the protocol for reagents, instruments, Master-mix, PCR, fluorescent signal detection and SNP calls; (2) to evaluate the basic protocol of ‘self-made’ Amplifluor-like markers for SNP analysis in plants compared to those freely available in published articles; (3) to compare the results of genotyping using self-made Amplifluor-like markers and KASP markers targeting the same SNP.Plants of bread wheat and barley from a wide range of cultivars were grown in a field trial in the Akmola region, Kazakhstan. Leaves were collected and DNA extracted as described in our earlier paper  followinhttp://www.oligos.ru), and all gene-specific primers were synthesized by Biosan Company, Novosibirsk, Russia (http://www.biosan-nsk.ru).In our experiments, we used UPs with four types of specific tails Table . The firA Quant Studio-7 Real-Time PCR instrument, qPCR  and FLUOstar Omega Microplate Reader  were used at the Kazakh Agro-Technical University, Astana, Kazakhstan. A Real-Time qPCR system, Model CFX96  was used at Flinders University, Bedford Park, SA, Australia.2, 0.2 mM each of dNTPs, 0.25 μM each fluorescent label probe, 0.15 μM of each forward primer, 0.78 μM of reverse primer and 0.5 units of Taq DNA polymerase  or 0.1 units of Maxima Hot-Start Taq-polymerase . Half of the PCR volume was genomic DNA, adjusted for 10 ng/μl. The PCR program was optimised as presented in Additional file The PCR conditions were the same as described in our earlier paper  with altKASP analysis was carried out according to the manufacturers’ protocol in the LGC Genomics high-throughput genotyping line based at the Plant Genomics Centre, the University of Adelaide, SA, Australia. KASP Master-mix and consumable materials were supplied by LGC Genomics .n, reached 8 units for a single allele amplification, with very strong differentiation between alleles . Our self-designed protocol 4 . Clear allele discrimination was then observed in the studied wheat accessions, with heterozygotes completely absent and only a few undetermined genotypes  on the web-site of LGC Genomics . There iThe perfect optimisation of UPs and associated well-organised business and marketing makes KASP markers very popular among plant biologists and the wider range of researchers. A large and ever-growing number of publications with references to KASP analyses indicates a continued high interest in this technology Table . The cosOur results indicate that it is not complicated to order UPs based on published sequences or own design. The two UPs, shown in Fig. SNPs can be identified through the researcher’s own experiments, or sourced from published papers or publicly available databases. Depending on the targets of a researcher, a SNP may be required within a GOI or in the flanking regions. It is strongly recommended that the genetic fragment with putative SNPs is sequenced to verify it’s presence, and the sequenced genotypes showing SNPs can then be used as References for Amplifluor-like or KASP methods for SNP analysis. However, this is not always possible, due to timing or financial limitations, particularly in small laboratories. In this case, it is possible to make a preliminary analysis of the putative SNPs based on published results or SNP databases. The advantage of UPs provides extra-opportunity to order and screen as many putative SNPs as researchers wish, without sequencing requirements and with fewer expenses. However, technical calls for allele discrimination from the instruments used must be verified by later sequencing of genotypes showing the clearest SNP scoring results. After SNP verification, such Reference genotypes can be used for further genotyping.In Fig. At the same time, most current researchers, particularly when using KASP Master-mix, do not need to use any Real-time qPCR systems at all. Regular PCR can be used, with any ordinary PCR cycler, including older models. PCR products can be analysed using a spectrophotometer with absorption of the wavelength corresponding to the fluorophores used Additional file 2:Protocols used for Amplifluor and KASP markers and basic PCR cyclers. Protocols 1 and 2 were designed for Amplifluor SNP analysis, while Protocol 3 was used for KASP markers. Protocol 4 was designed as optimal and used in the current study. (PDF 152 kb)Additional file 3:Example of design for non-labelled Gene-specific primers (GSP), KATU37, in barley Contig ABC08184. SNP position in the sequence was coded ‘M’, designating mixed nucleotides ‘A’ and ‘C’, and highlighted in red. Two forward primers and one common reverse primer are shown in Bold and highlighted in blue and purple, respectively. Amplicon size is indicated. Two sets of forward primers with ‘standard’ and short tails Table , identic