Objective. To investigate the joint effects of the single nucleotide polymorphisms (SNPs) of genes in the folic acid pathway on homocysteine (Hcy) metabolism. Methods. Four hundred women with normal pregnancies were enrolled in this study. SNPs were identified by MassARRAY. Serum folic acid and Hcy concentration were measured. Analysis of variance (ANOVA) and support vector machine (SVM) regressions were used to analyze the joint effects of SNPs on the Hcy level. Results. SNPs of MTHFR (rs1801133 and rs3733965) were significantly associated with maternal serum Hcy level. In the different genotypes of MTHFR (rs1801133), SNPs of RFC1 (rs1051266), TCN2 (rs9606756), BHMT (rs3733890), and CBS (rs234713 and rs2851391) were linked with the Hcy level adjusted for folic acid concentration. The integrated SNPs scores were significantly associated with the residual Hcy concentration (RHC) (r = 0.247). The Hcy level was significantly higher in the group with high SNP scores than that in other groups with SNP scores of less than 0.2 (P = 0.000). Moreover, this difference was even more significant in moderate and high levels of folic acid. Conclusion. SNPs of genes in the folic acid pathway possibly affect the Hcy metabolism in the presence of moderate and high levels of folic acid. The folic acid pathway is essential for hundreds of intracellular transmethylation reactions including DNA methylation and DNA synthesis, processes that are closely related to homocysteine (Hcy) metabolism , 2. FoliVariations in genes that play key roles in the folic acid cycle have been widely investigated, where single nucleotide polymorphisms (SNPs) have been found to be associated with folic acid and Hcy metabolism. The variants of MTHFR and RFC1 were found to interact with Hcy levels , 12, whiWith the rapid development of ongoing high-throughput human gene sequencing and bioinformatics, abundant SNPs can be identified. Support vector machines (SVMs) are a classic supervised machine learning algorithm typically used for classification and regression analysis. SVM has been widely used in solving biological problems, including gene function prediction, gene expression data analysis, and even cancer diagnosis , 17, 18.The aim of the study is to identify the genotypes of 18 SNPs using MassARRAY and to investigate an association between the cumulative effects of 18 SNPs in the 9 genes of the folic acid pathway and Hcy metabolism.Four hundred pregnant women at 11–25 gestational weeks were enrolled in the study conducted at the Obstetrics and Gynecology Hospital of Fudan University, China, from April to May 2011. Women included in the study were not smokers, did not drink alcohol, had no chronic diseases, and were not taking prescription medications. Blood samples were taken from fasting subjects. The serum was separated for the measurement of the concentrations of folic acid and Hcy and the remaining blood clots were used for DNA extraction and genotyping. All individuals enrolled in this study signed the informed consent and the study was approved by the Ethics Committee of the Obstetrics and Gynecology hospital of Fudan University, China.Serum folic acid concentration was measured by a chemiluminescent microparticle immunoassay  using the ARCHITECT I Systems following the manufacturer's recommended protocols.+) according to the manufacturer's instructions.Serum Hcy measurements were carried out by Liquid Chromatography Coupled to Tandem Mass Spectrometry (LC/MS/MS) using an API 3000 LC/MS/MS system (Applied Biosystem) equipped with an electrospray ionization interface that was used in the positive ion mode ([M+H]18 SNPs in 9 folic acid pathway  related http://dx.doi.org/10.1155/2014/560183).Genomic DNA was prepared from peripheral leukocytes using Relax Gene blood DNA System . The genotypes were determined by the Sequenom MassARRAY MALDI-TOF system. Primer sequences of the 18 SNPs were shown in Supplementary Table 1  was used as the dependent variable. The predicted RHC was defined as the SNP scores. For the independent variables (features) of the SVM regression model, we coded each SNP as two independent variables. For example, if a SNP has three types, AA, Aa, and aa, its coding features would be only two independent variables, which are feature_AA and feature_Aa, following Basically, the SVM regression model was implemented using “SMOReg” algorithm of Weka software package with default parameters, where C equals 1.0; the kernel is polynomial kernel with exponent value equals 1.0. Using all of the 15 SNPs to construct a data model would result in suboptimal accuracy because the different variables may contain overlapping information that disturbs the model-constructing process and thus variable selection was needed. Variable selection out of the total 15 SNP variables was conducted using recursive addition and stepwise addition of the input variables. The basic idea of the method was that, beginning with two features, we tried to add features to the SVM regression model which would improve the correlation coefficient most and then add another feature until none of the added features could improve the model or the improvement was less than the 0.01 correlation coefficient  test. Pairwise linkage disequilibrium of SNPs was estimated using Haploview. The square of the correlation coefficient (r2) between markers was used to define linkage using the data from the study population. Linear regression was used for detecting the association between folic acid and Hcy concentration, while analysis of variance (ANOVA) was used for the association analysis between SNP and Hcy concentration with serum Hcy concentration as dependent variable, serum SNP genotypes as the fix factor, and folic acid concentration as covariance.Statistical analyses were performed using SPSS , version 16.0 for Windows. A r = −0.385, P < 0.01, Supplementary Figure 1).Genotyping results were available for 386 of the 400 subjects (96.5%). A total of 14 subjects were excluded: 5 samples due to sample hemolysis and 9 samples due to sample DNA degradation. Data on serum folic acid and Hcy were available for all subjects. The regression equation used wasP < 0.05) deviation from the Hardy-Weinberg proportions and CBS (rs5742905) and MTR (rs74767314) were monomorphic. The remaining 15 SNPs are shown in P > 0.05, Supplementary Figure 2). There was no significant difference in age, gestational weeks, parities, and pregnancies among each genotype of the 15 SNPs .All subjects were genotyped for 18 SNPs. Three SNPs were eliminated from further analysis: TCN2 (rs1801198) showed significant (MTHFR SNPs (rs1801133 and rs3737965) were associated with serum Hcy concentrations which were adjusted for folic acid concentration . There wIn the SVM regression, five SNPs were selected: MTHFR , CBS (rs234713), and BHMT (rs3733890). The weights of the five SNP variables are shown in P < 0.01, P < 0.05) . The Hcy concentration was significantly higher in the group with SNP scores of more than 0.2 than that in groups with SNP scores less than 0.2  and other genes related to Hcy metabolism. The results revealed that the integrate SNPs scores of SVM were significantly associated with Hcy concentration, especially at moderate and high levels of folic acid. This finding suggests that the variations in the genes of the folic acid pathway may be an important contributor to Hcy related diseases in women with moderate and high folic acid levels from folic acid supplementation.Our finding that the SNPs in MTHFR , RFC1 rs1051266), CBS (rs2851391 and rs234713), TCN2 (rs9606756), and BHMT (rs3733890) were associated with the Hcy level adjusted for folic acid level is partly consistent with previous studies. Moreover, it is worth noting that the variation of MHTFR (rs1801133) was included, along with other SNPs. The thermolabile protein MTHFR is of great importance for the regulation of available 5-MTHF, which serves as the main circulating folate necessary for Hcy remethylation. MTHFR (rs1801133) can result in 50–60% reduced enzyme activity, while MTHFR (rs1801131) can also decrease MTHFR activity , 25. MTH6, CBS (rHigh-throughput techniques have spawned a mass of complex biological data. However, analysis of these data creates a bottleneck seen in current studies .In our study, the joint effects of SNPs generated nonlinear, noisy, and complex data sets that also contained a great deal of irrelevant information. Despite the effects of serum folic acid concentration, we have successfully presented a SVM regression model that can evaluate the RHC by five SNPs of genes in the folic acid pathway as inputs. This model translated the complex SNP patterns into a simple output of SNP scores which was significantly related to the changes in Hcy concentration adjusted by serum folic acid concentration, and it was found that 5 out of 15 SNPs were useful as inputs. The RHC was constructed to eliminate the effects of folic acid, for folic acid itself can affect Hcy level. It was suggested that the SVM model could be a potential algorithm for predicting Hcy related diseases.Furthermore, we found that the Hcy concentration was significantly higher in the group with SNP scores of more than 0.2 than that in groups with SNP scores of less than 0.2, especially for those with folic acid level more than 25%. However, there were no changes in Hcy concentration detected for those subjects with folic acid levels less than 25%. Abnormal metabolism of Hcy is related to many diseases, such as congenital heart disease , and NTDIn conclusion, the joint effects of SNPs in genes that belong to the folic acid pathway can affect Hcy metabolism especially under normal and high levels of folic acid. Further research that includes a bigger sample size is needed to test this SVM model.The supplementary material includes the basic feather among different SNP Genotypes, primer sequence of 18 SNPs, the relationship between the SNP scores and residual Hcy, linear relationship between folic acid and Hcy concentration and the linkage disequilibrium among SNPs.Click here for additional data file.


The no reflow phenomenon can happen during elective or primary percutaneous coronary intervention. Thisphenomenon is thought to be a complex process involving multiple factors that eventually lead to microvascular obstructionand endothelial disruption. Key pathogenic components include distal atherothrombotic embolization, ischemic injury,reperfusion injury, and susceptibility of coronary microcirculation to injury. Thus, pharmacologic and mechanicalstrategies to prevent and treat no reflow target these mechanisms. Specifically, pharmacologic therapy consisting of vasodilatorsand antiplatelet agents have shown benefit in the treatment of no-reflow and mechanical therapies such as distalprotection and aspiration thrombectomy have also shown benefit. The “no reflow phenomenon” has various definitions. Classically, it is considered to be the lack of myocardial perfusion despite opening up the epicardial vessel in the setting of primary percutaneous coronary intervention (PCI). It can occur in up to 10% of cases of primary PCI and is associated with an increased 30 day mortality if not adequately treated  . When combined with adenosine compared to using adenosine alone for the treatment of no reflow, improvement of TIMI flow grades was higher in the combination group 1.5± 1 vs. 0.8 ± 0.6 (p<.05) . For prevention, a randomized trial of 98 patients with STEMI were evenly randomized to receive either 60 micrograms of nitroprusside or placebo delivered into the infarct related artery distal to the occlusion before any angioplasty was done. There was no difference in TIMI frame counts, myocardial blush score or ST segment resolution between the two groups. However, a combined 6 month clinical endpoint of target lesion revascularization, myocardial infarction or death occurred in just 6.3% of the nitroprusside treated group compared to 20% in the placebo (p=.05). This difference was primarily driven by myocardial infarction and TVR but not death. As this was not the primary endpoint of the trial, this outcome can only be considered hypothesis generating. As a whole, this randomized trial was taken to be a negative trial for prevention of no-reflow . Howevertreatment of no-reflow is not a guideline recommendation. However, a bulk of evidence suggests possible use for the prevention of no-reflow. Abciximab has been the most studied of the glycoprotein IIbIIIa inhibitors. Glycoprotein IIb/IIIa inhibitors are strong antiplatelet agents and should be effective in reducing both epicardial and microvascular thrombus burden. However, there are no convincing randomized trials in using Glycoprotein IIbIIIa inhibitors in the treatment of no-reflow, and therefore their use for There is convincing randomized trial data for hard clinical outcomes using IIbIIIa inhibitors in the setting of ST elevation MI  and its Furthermore, an intracoronary bolus of abciximab has shown decreased microvascular obstruction and infarct size by MRI measurement when compared to intravenous abciximab in a randomized trial of 154 patients. The median infarct size was 15.1% in the intracoronary versus 23.4% in the intravenous group (p=0.01). Similarly, the extent of microvascular obstruction was significantly smaller in intracoronary compared with intravenous abciximab patients (p=0.01) . A largeIntravenous Tirofiban has also showed a preventive effect versus placebo as measured by improved ST segment resolution  in the sIntracoronary nitroglycerin has been evaluated for the treatment of no reflow, but it has not shown benefit. This is not surprising due to the pharmacodynamics of nitroglycerin. Nitroglycerin has little impact on arteriolar tone and hence on no-reflow since physiologically it produces little effect in the microvasculature. This is due to the fact that it requires metabolism by the vascular wall to derive its nitric oxide. While the epicardial arteries are able to metabolize the nitroglycerin, the microvascular resistance arterioles are unable to metabolize the nitroglycerin. Therefore, unlike nitroprusside which is a direct nitric oxide donor, it is not thought to have much effect in the no-reflow phenomenon due to microvascular causes. et al gave intracoronary nitroglycerin injections prior to the verapamil injections in 82% of the patients. TIMI frame counts were measured before and after nitroglycerin was given. No significant difference was seen. Likewise, another small study [In the study previously described for Verapamil treatment of no-reflow , Werner ll study  looking Due to poor results in smaller observational studies and the known moot pathophysiology, there have been no large randomized studies evaluating the use of intracoronary nitroglycerin for no-reflow. As stated in the definitions sections, no reflow can also be considered an abrupt onset of TIMI zero flow during an elective percutaneous coronary intervention which could be due to epicardial spasm. This pathophysiology would benefit from intracoronary nitroglycerin. treat no reflow with any of the mechanical therapies. The largest randomized trial of mechanical aspiration in the setting of ST elevation MI was the TAPAS trial  [Besides pharmacotherapy, various mechanical therapies including thrombectomy and distal protection have been used as a method of preventing no reflow. There is no data to atheter) ,24. Thisatheter) , there watheter) -28 whereatheter) . Finallyatheter) , whereasatheter) . In the setting of a primary PCI, we recommend starting with manual aspiration thrombectomy and then proceeding direct stenting if possible. If no reflow persists in this setting, we recommend bolus injections of 100 micrograms of verapamil, adenosine or nitroprusside with frequent test angiography to look for resolution of no reflow. This can be given through the guiding catheter or more distally via an infusion catheter like the clearway infusion balloon  or throu3) for prevention and treatment algorithm). In the setting of elective PCI, the sudden absence of epicardial flow can represent microvascular phenomenon and the we recommend the same dosing of pharmacologic therapy as described. However, there is also the likely incidence of epicardial dissection, epicardial vasospasm or thrombus. Intravascular ultrasound can be helpful in discerning the exact cause of the decreased flow with specific management to follow depending on the cause. In accordance with the 2011 PCI guidelines, we do recommend prophylactic distal protection for elective saphenous vein graft interventions due to the high incidence of embolic debris which potentially could lead to the no-reflow phenomenon (See (Fig.


High density (HD) SNP genotyping arrays are an important tool for genetic analyses of animals and plants. Although the chicken is one of the most important farm animals, no HD array is yet available for high resolution genetic analysis of this species.Gallus_gallus_4.0) of which ~78 M appeared to be segregating in different lines. Using criteria such as high SNP-quality score, acceptable design scores predicting high conversion performance in the final array and uniformity of distribution across the genome, we selected ~1.8 M SNPs for validation through genotyping on an independent set of samples (n = 282). About 64% of the SNPs were polymorphic with high call rates (>98%), good cluster separation and stable Mendelian inheritance. Polymorphic SNPs were further analysed for their population characteristics and genomic effects. SNPs with extreme breach of Hardy-Weinberg equilibrium (P < 0.00001) were excluded from the panel. The final array, designed on the basis of these analyses, consists of 580,954 SNPs and includes 21,534 coding variants. SNPs were selected to achieve an essentially uniform distribution based on genetic map distance for both broiler and layer lines. Due to a lower extent of LD in broilers compared to layers, as reported in previous studies, the ratio of broiler and layer SNPs in the array was kept as 3:2. The final panel was shown to genotype a wide range of samples including broilers and layers with over 100 K to 450 K informative SNPs per line. A principal component analysis was used to demonstrate the ability of the array to detect the expected population structure which is an important pre-investigation step for many genome-wide analyses.We report here the development of a 600 K Affymetrix® Axiom® HD genotyping array designed using SNPs segregating in a wide variety of chicken populations. In order to generate a large catalogue of segregating SNPs, we re-sequenced 243 chickens from 24 chicken lines derived from diverse sources  by pooling 10–15 samples within each line. About 139 million (M) putative SNPs were detected by mapping sequence reads to the new reference genome (This Affymetrix® Axiom® array is the first SNP genotyping array for chicken that has been made commercially available to the public as a product. This array is expected to find widespread usage both in research and commercial application such as in genomic selection, genome-wide association studies, selection signature analyses, fine mapping of QTLs and detection of copy number variants. High density (HD) genotyping arrays have become extremely valuable tools for genomic analyses of human, model organisms and farm animal species due to their genome-wide coverage and high throughput nature. Such arrays are being widely used for many purposes including the detection of genetic associations with complex traits, fine mapping of quantitative trait loci (QTL), detecting signatures of selection, and increasingly for implementing genomic selection (GS) to farm animal species. In this paper, we describe the development of a HD genotyping array (600 K) for chicken, which is the first of this magnitude in chicken and has been commercially released for both public and proprietary use.http://www.animalgenome.org/cgi-bin/QTLdb/GG/index). In spite of all these resources no HD genotyping array is yet available for this species that can facilitate high throughput investigation of many individuals for research and commercial breeding. A number of studies, however, have reported the use of low to medium (3 K to 60 K) density panels for various purposes [The chicken is not only a major livestock animal but also an excellent model organism for genetic and evolutionary studies such as for detecting natural and artificial selection under domestication -3. A larpurposes -7 but thpurposes ,9.et al.[A major determinant of the utility of an array is the density of its marker panel. Our goal in developing the HD array for chicken was that the array should be versatile and suitable for addressing the different purposes described above in a diverse range for chicken breeds and populations. The effectiveness of an array will also depend on its ability to exploit the linkage disequilibrium (LD) structure of the genome of the target population. Previous studies have shown that the LD pattern in chicken varies not only across chromosomes and genomic regions but also varies widely among breeds -13. Moreet al. a whole-et al.[et al.[et al.[Developing a HD array, however, requires a large collection of SNPs segregating within populations to select from. Several studies have reported detection of a large number of SNPs from chicken, most notably, the detection of 2.8 M SNPs by Wong et al. by analyGallus_gallus_4.0, pre-published version), about 3.9 billion reads (~80%) mapped to the genome and about 3.6 billion reads (~74%) mapped with a mapping quality score ≥ 20. While the intended depth of coverage of sequencing was 10–20 folds, the actual coverage varied between 8 and 17 fold across different lines, with the potential loss appearing from failure of some reads to map at all or map with confidence . Although the current build of the reference genome  represents ~96% of the chicken genome, some of the smaller micro-chromosomes are not yet represented [The 243 sequenced individuals originated from 24 lines including four commercial broilers, six commercial white egg layers (WEL), five commercial brown egg layers (BEL), eight experimental inbred layers and one unselected layer line  the SNP position must be covered by at least five sequencing reads with a map-quality score ≥ 20; (2) relevant bases must have a base-quality score ≥ 20 and (3) the SNP quality score must be ≥ 20. To minimise the risk of detecting false positives from duplicated genome regions, any SNPs with coverage more than three standard deviations from the mean coverage were excluded. The number of segregating SNPs detected per line ranged between 448 K to over 11 M . Comparison suggests, about 65% of these SNPs are present in our 78 M list.In order to call SNPs from low coverage regions with greater confidence, we combined reads from all the 24 lines to perform a “global analysis”. The same SNP-calling criteria mentioned for the within-line analyses were used for this analysis resulting in the discovery of ~139 M SNPs. This analysis, however, detected not only segregating SNPs within lines but also the ones that are fixed in different lines for alternate alleles.Due to the very high number of SNPs detected in the previous steps, a multi-step filtering strategy was followed to reduce the number to a robust and tractable subset for HD array design. The filtering was done on the list of 78 M SNPs obtained from the within-line analyses as the goal was to use SNPs segregating within lines primarily to design an array of most practical use. The 139 M SNP list, however, was consulted for regions where few or no high quality SNPs were detected from within-line analysis. The filtering steps are summarised in Figure et al.[In the first step of filtration, an attempt was made to reduce further the occurrence of false positives from the detection stage. SNPs were accepted only if at least one of the following criteria was satisfied: (i) the SNPs with quality scores ≥ 60 and minor allele frequency (MAF) ≥ 0.05 from the within-line detected set; (ii) SNPs from global analysis meeting the same criteria as in (i), irrespective of their quality scores and MAFs in the within-line analyses; (iii) the SNP was present in previous panels or detected by Rubin et al.. FiltratThe above criteria were ignored for SNPs on Chr16 and ChrZ to maximize the retention of putative SNPs which are potentially linked to the MHC gene complex and sex-linked characters, whilst accepting the risk of having some sequencing artefacts.et al.[In this step, SNPs that were located very close to each other were removed. Very tightly spaced SNPs are less likely to be successfully assayed during genotyping, due to the interference from the neighbouring variant. Also SNP clustering may appear due to mis-alignment of reads due to the presence of InDels (insertions or deletions) at the beginning or end of reads . In deveet al. removed Figure A substantial proportion of the SNPs were shared amongst the different lines, indicating their potential utility to multiple lines, although those spanning across most of the lines are likely to be ancient variants. Venn diagrams in Figure in-silico analysis to predict their reproducibility in the Axiom® array. Two types of design scores were generated from this analysis: (1) 16-mer count, which is the number of times all 16 bp sequences in the 30 bp flanking region from either side of the SNP had a matched sequence in the genome and (2) p-convert value, which arises from a random forest model intended to predict the probability that the SNP will convert on the array. The model considers factors including probe sequence, binding energies, and the expected degree of non-specific binding and hybridization to multiple genomic regions. These scores were generated both for forward and reverse probes. Lower 16-mer counts and greater p-convert values are better for array design. The chance of a SNP converting with a 16-mer count above 100 is extremely low and the chance of conversion increases monotonically as the score decreases. Therefore the thresholds were set to be: 16-mer count ≤ 100 and the p-convert value ≥ 0.2.The 10 M SNPs selected in the above step were submitted to Affymetrix for an Of the 10 M SNPs tested, ~73% had a p-convert value ≥ 0.2 and ~93% had a 16-mer count ≤100 Figure  a and b.et al.[The fourth step was to prepare a list of ~1.8 M SNPs for validation through genotyping. The objective was to have an even distribution of segregating SNPs across the genome. To achieve this we spread out the SNPs evenly in terms of genetic map distance as provided by Groenen et al. instead Validation through genotyping of ~1.8 M SNPs was performed by manufacturing three Affymetrix® Axiom® arrays (each array contained ~ 600 K). The major purposes of validation were to assess for each SNP: (1) the conversion performance in the array in terms of genotype call rate, cluster separation and reproducibility, 2) polymorphism in different sets of commercial and wild chicken breeds, (3) stable Mendelian inheritance from parents to offspring, and (4) population characteristics in terms of allele frequency distribution, LD and Hardy-Weinberg equilibrium (HWE) probability. The ultimate goal was to use this information to select a robust set of ~600 K SNPs for the final genotyping array. The validation was carried out on an independent set of 282 individuals including 32 trio samples from three broiler lines, four WEL lines, five BEL lines and 26 individuals from a wide range of diverse traditional breeds of chicken  from the HWE were removed. While SNPs that show departure from HWE can actually be important as they might represent regions under selection, an extreme departure might indicate genotyping errors, presence of copy number variants (CNVs) or lethal recessive mutation and hence were removed.Population characteristics of the validated SNPs were explored to make informed decisions about selecting the final 600 K panel. Any SNPs that showed an extreme departure  for annotation purpose.To maximise the utility of the panel for QTL detection studies, all the validated SNPs from exonic or coding regions n = 21,534) were included in the panel of which 9,345 were predicted to be either non-synonymous or stop-loss/stop-gain types. Table 4 were inNo direct selection was performed based on MAF as the frequency estimates were not sufficiently precise due to small sample sizes. However, an attempt was made to include SNPs which are common to many lines to maximise the utility of the HD array. Figure denovo from unmapped reads in the current study. As mentioned earlier we used the pre-published version of the Gallus_gallus_4.0 for mapping our reads as this assembly was unpublished at the time of the work. However, recently the assembly has been published in the NCBI database and contains added features such as “unlocalized scaffolds” (for each chromosome these appear as Chr_random) and “unplaced” (Chr_Un). We mapped the denovo SNPs against this published reference. This mapping result showed that except 53 SNPs, the rest mapped to either some chromosomes (n = 968 SNPs) or unlocalized scaffolds (n = 22 SNPs) or Chr_Un (n = 92 SNPs). All the SNPs in the panel have been submitted to NCBI’s dbSNP and can be accessed via the link: http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1057286.The final 600 K array has 580,954 SNPs in total. Based on the genotyped samples used for validation, the number of polymorphic SNPs ranged between 127,958 to 473,077 per commercial line. This number is expected to rise for each line with screening of more samples. About 21%-53% of the SNPs in the final panel were also found to be polymorphic in the different traditional breeds of chicken that were included in the validation sample set even though only 1 or 2 individuals per breed were analysed. This confirms the versatility of the panel. The final panel includes 1,135 SNPs which were detected on contigs that were assembled The SNPs in the final array are distributed across the genome with a mean (± SD) inter-marker spacing of 1748 ± 5274 bases. About 98% of the markers are distributed across the genome with a gap size less than 6 kb, but there are some regions which have larger gap sizes and they cover about 10% of the genome  sequencing of pooled samples using a NGS method which has allowed screening of a large number of individuals rapidly and cost effectively leading to the detection of a large collection of SNPs segregating between and most importantly, within diverse populations; (2) applying stringent filtering criteria to minimize inclusion of false positives that appear as artifacts of NGS; (3) inclusion of validated SNPs in the final array that are proven to work in the Axiom® platform; (4) distributing the SNPs on the basis of map distance rather than physical distance to increase the ability to track genomic segments across generations; (5) using an iterative algorithm to select the SNPs to obtain a balanced representation from all the lines tested. The call rates from genotyping using the array are expected to be very high which is confirmed from the initial results of large-scale genotyping in broiler and layer lines (results not shown). We have also shown through validation that the panel will be usable on traditional chicken breeds, different Even though we have applied a very thorough approach in developing this array, future projects with a similar goal can consider a number of areas for improvement. One potential area of improvement is barcoding the individual samples before sequencing the pooled DNA as this would enable more information to be extracted from sequence data such as would allow better estimates of the allele frequencies. While this approach was not used in the current project to keep the cost down, future projects may consider this option as the cost of barcoding and library preparation are declining. Moreover, with the rapidly declining cost of sequencing, it would also be possible to increase the coverage and the number of birds for sequencing in future studies. Another area where the future arrays can attempt to improve is the inclusion of more SNPs from Chr16, ChrW and some of the micro-chromosomes, which was not possible in our case due to poor representation of these chromosomes in the current genome build.The 243 sequenced samples used for SNP detection were supplied by Aviagen (broilers), Hy-line International and Lohmann of Synbreed Consortium (white and brown egg layers), the Pirbright Institute  (inbred lines) and the Roslin Institute   obtained from Washington University [SNP detection was done by aligning the Illumina sequence reads to the chicken reference genome (iversity .Alignment was performed for paired end reads using Burrows-Wheeler Aligner (BWA) v0.5.7  using dede-novo into contigs using the package SOAPdenovo [Some of the sequence reads failed to map to the reference genome. These were assembled APdenovo  for broiSNP selection was done in multiple steps using several criteria. The filtration criteria were set with the aims to minimize the risk of choosing false positives, select SNPs with an even distribution across the genome in terms of cM distance, and obtain a balanced representation of broiler and layer SNPs. In order to achieve an even distribution of SNPs segregating in different lines, a custom-made algorithm was applied which is described below. To facilitate an efficient querying of the SNP data, a NoSQL database (in MongoDB) was created.An iterative divide-and-conquer algorithm was developed to select efficiently both the pre-screening 1.8 M panel and the final 600 K panel. Although the SNP selection was performed on each chromosome separately, the number of combinations of candidate panels was overwhelming even for the smallest chromosomes. To simplify and accelerate the process, the SNP selection was performed between “backbone” SNPs, which were common between lines. The implication was that the regions between backbone SNPs were far smaller and hence the number of candidate SNPs was dramatically smaller. The algorithm consisted of two major steps, the construction of the backbone panels and the filling of the gaps for each line.N SNPs that segregated between all lines and were more than t bases apart . This resulted in having N + 1 segments instead of the whole chromosome to fill. A second backbone was constructed consisting of I SNPs segregating in the inbred lines, which ensured that for these non-commercial lines there were N + I SNPs already in the candidate panel that were spread across the chromosomes providing a good coverage without big gaps. There were also N + (I-Iu) SNPs, (where Iu the number of SNPs unique to inbred lines), that were also segregating in at least one of the commercial lines. A third backbone was constructed, containing L SNPs that were segregating in all layers to fill the N + (I-Iu) + 1 gaps. The next backbone included B SNPs that were present in all broiler lines, and filled all the N + (I-Iu) + (L-Lu) + 1 segments, where Lu are the SNPs for L that do not segregate in any broiler lines. The final backbone consisted of X = N + I + L + B SNPs. For the final selection from 1.8 M to 600 K, where information about coding SNPs was available, all C SNPs in coding regions that were not already in the backbone were added to the backbone.The first step was to populate the baseline backbone panel by including the ith line, the gaps to evenly distribute SNPs were equal to X-Fi + 1, where Fi were the SNPs not segregating in the line of interest. Iteratively, every segment was filled by n SNPs, where n was a function of the target density for the ith line and the length of the segment. For the pre-screening panel the same density was used for all lines, but for the final panel, broilers had a higher density due to their shorter intra-marker LD. By calculating the n SNPs that had to be evenly distributed in the segment, the algorithm chose the SNPs that were closer to the ideal positions by minimising the sum of squared distances between consecutive SNPs in order to approximate the target density. If the gap was too small, meaning less than the target density, no SNP was placed.The second step was to start filling the regions for each line separately. For the i SNPs were placed for the ith line and the backbone was updated, now containing X + Si-F(i+1) + 1 for the next line i + 1, where F(i+1) was the number of backbone SNPs not present in the i + 1th line. We started with the white layers, followed by the brown and finished with the broiler lines, following the order in the extent of LD.In total SThe advantage of this strategy was to avoid having SNPs segregating in more than one line that were very closely located, which in turn meant a more even distribution was achieved in all lines and thus maximising the utility of the SNPs in the panel. No selection for MAF was enforced, as the estimates were biased, because either those were estimated solely on sequencing data on pools with variable depth coverage, or were available only for the broiler lines with sufficient samples in the pre-screening phase.About 1.8 M selected SNPs were validated on 282 individuals . The population characteristics of the SNPs such as allele frequency, HWE probability based on Fisher’s Exact Test and LD between adjacent pair of SNPs were explored using the software SNP & Variation Suite (SVS version7) of Golden Helix Inc. The genomic positions of the SNPs and their effect on protein coding were predicted by annotating them against the Ensembl gene annotation database for chicken based on Gallus_gallus_2.1 chicken reference genome. The software package ANNOVAR [Genotyping was done on three Axiom® arrays using the Affymetrix® GeneTitan® system according to the procedure described by Affymetrix  to assess the utility of the panel in detecting population structure. PCA was carried out using SVS (version 7) package of Golden Helix Inc. Each marker’s data was normalized by its theoretical standard deviation under HWE and an additive model was considered.BEL: Brown Egg Layer; CNV: Copy Number Variant; GS: Genomic Selection; GWAS: Genome Wide Association Study; HD: High Density; HWE: Hardy-Weinberg Equilibrium; InDel: Insertion or Deletion; LD: Linkage Disequilibrium; M: Million; MAF: Minor Allele Frequency; NGS: Next Generation Sequencing; PCA: Principal Component Analysis; QTL: Quantitative Trait Loci; SNP: Single Nucleotide Polymorphism; WEL: White Egg Layer.Four commercial organisations - Aviagen, Hy-line International, Affymetrix, and Lohmann- were involved in the development of the array and in preparation of the manuscript. Among the authors, AK and KAW work for Aviagen Ltd, JF works for Hy-line International, RP works for Lohmann, AP and FB work for Affymetrix and FB also holds shares in the Affymetrix. This project was funded by DEFRA/BBSRC LINK grant promoting industry-academia collaboration.de novo assembly of unmapped sequence reads. AP and FW performed the Affymetrix® Axiom® design and validation assays. PK, PMH, MF, NS, JF and KAW provided DNA samples and information on poultry lines. TMS, GH, SW, RP, MG, SQ and HS provided sequence data and DNA samples from the Synbreed consortium. DWB and JAW co-designed experiments, discussed analyses, interpretation, presentation and coordinated the project. All authors have contributed to, seen and approved the manuscript.The work presented here was carried out in collaboration between all authors. DWB, JAW, AK and KAW defined the research programme. AK, AAG, CB, KAW, JAW and DWB designed methods and experiments, analysed the data, interpreted the results and wrote the paper. FT, SS and RT collected and processed the raw sequence data and SNP detection. LY performed the An Excel file with the number of SNPs detected from within-line-analyses and selected at various stages across different chromosomes.Click here for fileAn Excel file providing description of the genotyped samples used for validation of the 1.8 M SNPs.Click here for file


Maize is an increasingly important food crop in southeast Asia. The elucidation of its genetic architecture, accomplished by exploring quantitative trait loci and useful alleles in various lines across numerous breeding programs, is therefore of great interest. The present study aimed to characterize subtropical maize lines using high-quality SNPs distributed throughout the genome.r2 ≤ 0.1 across all chromosomes explained the selection of favorable traits around low LD regions in different breeding programs. The association mapping panel was characterized by strong population substructure. Genotypes were grouped into three distinct clusters with a mean genetic dissimilarity coefficient of 0.36.We genotyped a panel of 240 subtropical elite maize inbred lines and carried out linkage disequilibrium, genetic diversity, population structure, and principal component analyses on the generated SNP data. The mean SNP distance across the genome was 70 Kb. The genome had both high and low linkage disequilibrium (LD) regions; the latter were dominant in areas near the gene-rich telomeric portions where recombination is frequent. A total of 252 haplotype blocks, ranging in size from 1 to 15.8 Mb, were identified. Slow LD decay (200–300 Kb) at The genotyped panel of subtropical maize lines characterized in this study should be useful for association mapping of agronomically important genes. The dissimilarity uncovered among genotypes provides an opportunity to exploit the heterotic potential of subtropical elite maize breeding lines. Zea mays L.) is one of the most important global food crops, and is of increasing agricultural importance in India [http://www.fas.usda.gov/psdonline/circulars/production.pdf), an area of 8.68 million hectares in India was used to produce 21.6 million tons of maize during 2011–2012. Maize is used in India for various applications, ranging from food and feed to industrial purposes. Although maize ranks third in terms of crop production, demand is expected to double by 2050, given the growth of the Indian population and the preference for maize over other cereals. Currently, maize productivity in India is 2.49 tons per hectare, which is far lower than the global average of 5.2 tons per hectare. This limited output can be explained by production constraints, which range from biotic and abiotic stresses to unexploited heterotic potential. Elucidating the genetic architecture of maize at the molecular level would aid the development of cultivars better suited to meet increasing demands.Maize (in India . AccordiZea mays ssp. parviglumis), which occurred in southwestern Mexico approximately 9000 years ago [Modern maize arose from the domestication of teosinte  is the non-random association of alleles at two or more loci in a population. An understanding of LD patterns in a population is useful for association mapping ,8. LD deY1 and in aY1. Many LDY1,7,18-20.Y1. SeveralY1,23, struY1, principY1, non-metY1, and a uY1, have beThe study of genetic relationships among breeding lines is essential not only for parental selection, but also for hybrid development and heterotic grouping . DiversiA comprehensive knowledge of the genetic architecture of maize populations is useful for exploiting germplasm for various breeding purposes. The present study was carried out to (1) characterize subtropical genotypes adapted to Indian conditions using genome-wide SNPs; (2) elucidate the LD and population structure of the genotype panel for use in association mapping; and (3) assess genotype genetic diversity to develop heterotic parental combinations.A panel of 240 subtropical or tropical genotypes, consisting of inbred lines adapted to subtropical climates and developed at different breeding stations in India or by the International Maize and Wheat Improvement Center (CIMMYT), were used for SNP genotyping. These elite inbreds had putative genes segregating for biotic and abiotic stress tolerances, nutritional traits, and agronomic traits . Quantity and quality of isolated DNA samples were checked with a NanoDrop ND-1000 spectrophotometer , followed by validation by 1% agarose gel electrophoresis. SNP detection was performed using the Infinium HD Assay Ultra protocol . DNA samples (50 ng in 4 μl) were hybridized to a Maize SNP50 BeadChip.GenomeStudio version 2010.3 was used to analyze the SNP genotyping data. GenomeStudio clusters genotype calls into AA, AB, and BB groups that are converted using TOP/BOTTOM rules into different allelic combinations: A/C, A/G, A/T, C/G, C/T, and G/T . In this study, every SNP was scored using GenTrain (GT) and Cluster Separation (CS)  selectioPolymorphism information content (PIC), minor allelic frequency (MAF), and genetic diversity (GD) were calculated using the Genetics package in R .r2 values < 0.1 distributed randomly across the genome. A subset was chosen to minimize the effects of LD, as the model employed by this software program does not explicitly take LD into consideration. The “Expectation Maximization” clustering algorithm was used with numerous clusters (K) ranging from 2 to 7. The algorithm was executed five times for each K value. To select the substructure level corresponding to the best partitioning, we also performed five-fold cross-validation.ADMIXTURE version 1.20  was usedPrincipal component analysis was performed using the R package SNPRelate . An LD-bA genetic dissimilarity matrix was calculated from 29,619 SNPs using Roger’s modified distance  with ther2 was determined for 29,619 high-quality SNPs. LD patterns without any MAF threshold and with thresholds of 5% and 10% were examined. Haploview 4.2 [The LD pattern across chromosomes was investigated using TASSEL 3.0.132 . Pairwisview 4.2  was usedEach SNP was assigned GT and CS scores across the 240 Infinium-assayed genotypes. Approximately 92.6% of GT scores and 80% of CS scores were in the range of 0.7–0.9 and 0.7–1, respectively  compared with chromosome 2 (0.21). Across the entire genome, 3,248 pairwise SNPs were classified as high LD (r2 ≥ 0.8), most of which (13%) were present on chromosome 8. Clusters of SNP pairs in high LD were found on chromosomes 3 and 8  and SS (chromosomes 2 and 3) models  was found to be more rapid than at r2 ≤ 0.1 (200–300 Kb). On chromosomes 1, 2, 3, 5, 8, 9, and 10, the LD distance dropped from 200–300 Kb at r2 ≤ 0.1 to 5–10 Kb at r2 ≤ 0.2  constituted the majority of group A, and were characterized by tolerance to acidic soil, lodging, and drought, and resistance against ear rot, tar spot, stalk rot, leaf blight, rust southwestern corn borer and fall armyworm. Yellow lines drawn from Karnal, Almora, and Ludhiana breeding programs constituted the majority of group B. These lines were drought and acid soil tolerant, and resistant to stalk rot and sorghum downy mildew. Group C included 37% of the yellow lines bred at Karnal and Udaipur, of which 63% were CML-derived, one a multiply-resistant genotype (CML 394).Subgroups A1, A2, A3, and A4 had mean dissimilarity coefficients of 0.37, 0.35, 0.35, and 0.34, respectively. A1 included 39% of A-group genotypes, A2 31%, A3 19%, and A4 11%. In the A1 subgroup, the breakdown of lines was as follows: 2% BAJIM, 5% CM, 64% CML, 5% HKI, 2% HPLET, 8% PANT, and 2% BML. Two of these were drought tolerant, and 41% were white lines. Group B contained two clusters, B1 (72%) and B2 (28%), with mean dissimilarity coefficients of 0.343 and 0.34, respectively. The B1 subpopulation comprised one white line and four drought-tolerant lines. Group C was subdivided into two clusters, C1 (69%) and C2 (31%); these were distinct clusters with genetic distances of 0.3 and 0.34, respectively. Group C1 included one HKI and 10 CML lines.When the groups uncovered in the ADMIXTURE analysis were compared with those based on genetic distances, group A, the largest group in the genetic dissimilarity dendrogram, contained 59%, 32%, 7%, and 2% of the genotypes from ADMIXTURE groups G1, G2, G3, and G4, respectively. The smallest group, group C, comprised 69% of the lines from G2 and the lines from G1 (at K = 4). Genetically distant lines V338 and CML 442 were included in G1 and G2 (at K = 4) in the ADMIXTURE analysis. Q-values of these genotypes were 0.53 and 0.65, respectively.A total of 240 genotypes were screened to identify genome-wide SNPs and to assess population allelic variation. Of 56,110 identified maize SNPs, 98% were detected in this screening, comparable to the number reported in other experiments ,46. We uThe set of genotypes screened in our study represents the most saturated panel to date of subtropical maize lines adapted to the Indian climate. As reported in other studies, tropical lines have more rare SNPs than temperate lines . In our r2 ≥ 0.8) were scattered throughout the genome. The high LD regions were mostly interspersed with low LD regions, indicative of maize genome complexity and the random nature of recombination events across the genome [We characterized genome-wide LD in subtropical elite breeding lines and found several low to high LD regions within and across chromosomes Figure . Approxie genome ,53. Howee genome . In almoe genome ,53.On the other hand, high LD regions were distributed uniformly along the chromosomes; this indicates that these loci were single or multiple genes of agronomic importance that were selected for by a number of breeding programs, thereby creating LD between linked and unlinked loci over time . These rHaplotypes are a function of population size, genetic diversity, and the extent of LD. The use of a large number of SNPs would increase their coverage, since most of the genomic variation would then be available for analysis. In the present study, genome-wide SNP genotyping revealed a total of 252 haplotype blocks varying in size from <1 Kb (2 SNPs) to 15.8 Mb 262 SNPs). The CI model identified fewer and shorter haplotype blocks than FGR and SS models (15.8 Mb), however; this difference may be due to blocks with strong LD in a high-confidence bound cut-off in the former model  [r2 ≤ 0.1 within 1,500 bp) in various maize lines [r2 ≤ 0.2 (5–10 Kb) than at r2 ≤ 0.1 (200–300 Kb), but we can assume that our panel still offers good resolution for gene mapping.When performing association mapping, an understanding of the LD decay pattern is important, because mapping resolution is correlated with LD decay . A low L~500 kb) . Based o~500 kb) ,13, poss~500 kb) , genetic~500 kb) , and popze lines . In our r2 across the 10 chromosomes. This implies that the allele frequency did not change drastically at the MAF ≥ 5% level, thereby increasing the frequency of common alleles. Another explanation for this result could be the occurrence of a domestication bottleneck leading to the elimination of rare alleles and hence shifting allele frequencies towards intermediate values [The removal of SNPs with a MAF < 0.05 facilitates high-power gene mapping of a population, as the inclusion of minor alleles may lead to inaccurate LD estimation. To analyze LD decay patterns at different MAF cut-off levels, we measured LD using SNPs with 0%, ≥ 5%, and ≥ 10% MAF cut-off levels. A change in the LD decay pattern was noticed between 0% and 5% cut-off levels, whereas an increase in the MAF threshold from 5% to 10% did not markedly affect the mean e values . Rare ale values . In addie values . Furthere values , furtherThe presence of structure in a selected population is due to various processes, such as population bottlenecking, genetic drift, and selection. Non-genetic factors, including genotyping error  and ascehttp://www.seedsofdiscovery.org) have also been used in several breeding programs in India. These lines have been selected based on adaptability as well as specific traits. Hence, CML lines integral to the Indian breeding program were included in our analysis along with the already adapted Indian lines.Indian maize breeding programs use both yellow and white lines, and the generation of lines derived by crossing these types is frequently carried out to maintain quality. In our study, few white lines appeared in groups containing mostly yellow lines, indicating the eventual outcome of inter-mating. CMLs developed by CIMMYT . Our study thus provides information for developing new hybrids possessing different maturity-trait combinations by performing selective crosses between and within maturity groups based on genetic distances.Genetic variability has been studied previously using SSRs and SNPs ,31,32. MSeveral parental pairs with high genetic dissimilarity were identified. Yellow lines NAI 147 (Group A) and CML 69 (Group B) from the late maturity group had a high dissimilarity coefficient (0.43). NAI 147 was also very dissimilar (0.43) to CML 193 from the medium maturity group. The genetic distance between such distant lines suggests that their crosses would show good heterosis. The selection of parental pairs based on genetic dissimilarity would be a good starting point to identify potential heterotic combinations. Before exploiting parental pairs in heterosis breeding programs, however, their agronomic traits should first be tested for combining ability.http://www.seedsofdiscovery.org), hybridization of CML lines with other lines would be desirable to impart disease resistance and to realize their heterotic potential. Genetically dissimilar, stress-tolerant parents can also be used for the development of QTL mapping populations for target traits. Biparental populations developed from individuals with contrasting traits, selected from within the association mapping panel, can serve as association mapping validation tools.Most of the CML-derived lines in our study clustered together with remaining Indian lines into groups 2A, 2B, and 3A. Because the CML lines are resistant to several diseases (in silico tools and maize gene prediction models (http://www.maizesequence.org) to identify putative genes for target traits. On the other hand, we observed very rapid LD decay across chromosomes when the cut-off was shifted from r2 ≤ 0.1 to 0.2. Consequently, the fine mapping potential of our subtropical maize panel should not be ignored. Our analysis uncovered strong population structure, which limits this panel’s use for GWAS; however, the structure could be corrected for through the use of statistical models based on ADMIXTURE and PCA results. We believe that our association mapping panel with genome-wide SNPs will provide an opportunity to map genes of agronomic importance.Our association mapping panel of Indian breeding lines, the most saturated panel currently reported with respect to marker density, not only contributes to an understanding of their genetic architecture, but also helps elucidate LD and population structure and may be useful for GWAS. The distribution of high and low LD regions across the genome provided an opportunity to identify target genes of agronomic interest. Haplotype blocks identified in the genome, such as the 74 blocks on chromosome 2, can be exploited for GWAS. Slow LD decay was observed, however, enabling only coarse mapping at a resolution of 200–300 Kb. Even at coarse resolution, it would still be possible with the help of We characterized subtropical elite maize breeding lines using a large number of high-quality SNPs. Assessment of marker-trait associations is facilitated by the availability of saturated SNPs across the genome. Genomes of these maize lines were found to have both low and high LD regions. The slow LD decay observed in the population was attributed to the inclusion of elite breeding lines in this study. Congruency between the ADMIXTURE and PCA results increases the confidence that the population structure can be corrected for during association mapping. The genetic diversity uncovered in the assayed population can be used to develop heterotic pools for exploitation of elite breeding line hybrid vigor.https://10.6070/H4BG2KX8) has been submitted to the website: http://www.labarchives.com/.The raw SNP data  and cluster separation (CS) scores for 56,110 SNPs. Each SNP had an individual GT and CS score across the subtropical panel.Click here for fileSNP coverage across all chromosomes. The average number of SNPs/chromosome across the whole genome was 2962.Click here for fileCharacteristics of 29,619 high-quality SNPs. Gene diversity (GD), polymorphic information content (PIC), and minor allelic frequency (MAF) averaged for 240 individuals.Click here for fileCharacteristics of haplotype blocks obtained from three different models using 10-Mb and 20-Mb windows.Click here for filer2 values at MAF levels of 0%, ≥ 5%, and ≥ 10% across the subtropical panel.Effects of minor allelic frequency (MAF) on LD decay. Comparison of mean Click here for fileGraphical representation of genotype grouping based on allele frequency at different K levels. Each of the 240 genotypes is represented by vertical lines partitioned into the respective clusters denoted by K .Click here for fileMembership value of the genotypes of the association mapping panel generated by ADMIXTURE.Click here for fileGenetic dissimilarity coefficient of all pairwise genotypes. The genetic dissimilarity matrix was calculated between 240 individuals using 29,619 SNPs and Roger’s modified distance.Click here for fileGenotype pairs selected on the basis of genetic dissimilarity (> 0.35) and other traits for various breeding purposes. Note: Maturity groups: E, early; M, medium; L, late. Kernel color: Y, yellow; W, white.Click here for file


PPFS identifies those SNPs that are non-randomly associated with a phenotype based on the χ2 probability, then uses those diagnostic SNPs for two distinct, but related, purposes: (1) to predict the phenotypes of strains whose phenotypes are unknown, and (2) to identify those diagnostic SNPs that are most likely to be causally related to the phenotype. In the example illustrated here, from a set of 68 E. coli genomes, for 67 of which the pathogenicity phenotype was known, there were 418,500 SNPs. Using the phenotypes of 36 of those strains, PPFS identified 207 diagnostic SNPs. The diagnostic SNPs predicted the phenotypes of all of the genomes with 97% accuracy. It then identified 97 SNPs whose probability of being causally related to the pathogenic phenotype was >0.999. In a second example, from a set of 116 E. coli genome sequences, using the phenotypes of 65 strains PPFS identified 101 SNPs that predicted the source host (human or non-human) with 90% accuracy.SNP-association studies are a starting point for identifying genes that may be responsible for specific phenotypes, such as disease traits. The vast bulk of tools for SNP-association studies are directed toward SNPs in the human genome, and I am unaware of any tools designed specifically for such studies in bacterial or viral genomes. The PPFS (Predict Phenotypes From SNPs) package described here is an add-on to  Several versions of the phylogenetic trees are provided, including those in which nodes are labeled with the number of SNPs that are found in all of the descendants of that node. kSNP automatically annotates the identified SNPs based upon the annotations in GenBank files. The number of SNPs that are identified can be enormous: over 100,000 SNPs from 288 genomes of the Rhabdoviridiae family, over 430,000 SNPS for 119 Escherichia coli genomes, over 1,388,000 SNPS in 207 Acinetobacter genomes Mycobacterium tuberculosis.SNP-association studies are the starting point for (1) identifying genes that may be responsible for specific phenotypes, such as disease traits, and (2) identifying potential gene-gene interactions. In bacteria and viruses they have the additional potential of identifying SNPs that might be incorporated into PCR probes for the purpose of quickly and inexpensively assessing phenotypes that are difficult, expensive or time consuming to determine by classical means, such as virulence or drug resistance in slow-growing organisms such as kSNP and has the following characteristics: (1) it runs seamlessly with kSNP, (2) not all SNPs have to be present in all genomes, (3) not all genomes need to have a known phenotype, (4) it takes advantage of the fact that kSNP can provide annotations of most SNPS and incorporates those annotations into its analysis, (5) it is fast, requiring less than 10 minutes to analyze over 418,500 SNPs. The PPFS package of executables for Mac OS X and Linux operating systems, source code, and a detailed User Guide are freely available at https://sourceforge.net/projects/ppfs.Here I describe the PPFS package that identifies SNPs that are non-randomly associated with a phenotype. It is an “add on” to Escherichia coli genomes. Pathogenic E. coli include EHEC (enterohemorrhagic), ETEC (enterotoxigenic) EPEC (enteropathogenic), ExPec , EAEC (entero-adherent), AIEC (adherent-invasive), UPEC (uropathogenic), and Shigella. Accession numbers, phenotypes and references for those phenotypes are included in E. coli genome sequences, using the phenotypes of 65 strains PPFS identified 101 SNPs that predicted the source host (human or non-human) with 90% accuracy. Accession numbers are included in As a proof-of-principle I illustrate the uses of the PPFS package by identifying 207 SNPs associated with the phenotype “pathogenic” for a set of 68 PPFS, PickPhenotypeSubset, GetSNPprobs, DiagnosticSNPs and CausalSNPs. It uses the output from kSNP, and must be run from with the kSNP output-files folder (directory). Using the full PPFS package requires that MEGA5 or MEGA 6 (available free at http://www.megasoftware.net/) be used to estimate ancestral states at internal nodes of an ML tree.The PPFS Package consists of five programs: The user must provide a manually constructed file (.pheno file) in which the phenotype of each genome is listed either as positive (1), negative (0), or unknown (?).PPFS serves as a “front end” that takes the command line arguments, then runs PickPhenotypeSubset, GetSNPprobs, and DiagnosticSNPs without direct interaction with the user. After running those programs PPBS collects the output files of those programs into a folder (directory) named PPFS.PickPhenotypeSubset is used to select, from those genomes whose phenotypes are known, a random subset of genomes that will be used by GetSNPprobs to calculate, for each SNP, the probability that the alleles of that SNP are randomly associated with the phenotype. Through PPFS the user chooses the approximate fraction of strains to include in that subset. If the user sets the fraction to be included at 0.5, then for each strain whose phenotype is known in the.pheno file there is a 50% probability that its phenotype will be changed to unknown in the output subset.pheno file. That file thus provides a set of “unknown phenotype” genomes whose phenotype can be predicted by the DiagnosticSNPs program, allowing those predicted phenotype to be compared with the phenotypes in the.pheno file in order to assess the accuracy with which phenotypes are predicted.GetSNPprobs uses the output file from PredictPhenotypeSubset to calculate, for each SNP, the probability that the alleles of that SNP are randomly associated with the phenotype. Not all SNP sites are equally informative. For some sites the SNP alleles are distributed randomly with respect to the phenotype, with others the SNP alleles are biased. An extreme bias would be one in which all of the positive strains had an A and all of the negative strains had a G. A χ2 test is used to determine the probability that a SNP is distributed randomly with respect to phenotype.2 probabilities of each SNP are written to an output file.Sites with low probabilities of the SNPs being randomly distributed with respect to phenotype are much more strongly correlated with the phenotype and have much more resolving power in predicting phenotypes than do sites with high probabilities. The χGetSNPprobs writes two output files: (1) a.SNPs file that is the same as the input.fasta file except in a different format, and (2) a.ppcs (posterior probabilities chi-square) file. The.ppcs file lists, for each SNP, the probability that the alleles is distributed randomly with respect to the phenotype, and for each possible character state (ACGT-) the probability that a genome is positive and the probability that it is negative given that character state for that SNP.DiagnosticSNPs uses the original.pheno file, the subset.pheno file written by PredictPhenotypeSubset, and the output files written by GetSNPprobs, together with several files written by kSNP to identify the set of SNPs that most accurately predicts the phenotypes. Those SNPs are the “diagnostic SNPs”.2 probability (p), then starting with the SNP with the lowest p, DiagnosticSNPs calculates the accuracy of the phenotype predictions. It then adds the next SNP in the sorted list and again calculates accuracy. A minimum of 50 SNPs are added, then SNP addition continues until one of several termination conditions is met: The accuracy with which positive strains are predicted (PPV) reaches a user-defined maximum (defaults to 0.98), or a user-defined number of SNPs is added (defaults to 1000), or the PPV  declines below a user-defined fraction of the maximum PPV value so far (defaults to 0.97).The SNPs are sorted according to the χTo understand the termination conditions it helps to understand the terms “accuracy”, “positive predictive value” (PPV) and “negative predictive value” (NPV). By comparing the known with predicted phenotypes the number of true positives (TP), True Negatives (TN), False Positive (FP) and False Negatives (FN) is determined. Accuracy is (TP+TN)/(TP+FP+TN+FN). Often we are actually more interested in knowing the probability that a strains that is predicted to be positive is actually positive. That probability is PPV, TP/(TP+FP). Similarly NPV, TN/(TN+FN), is the probability that a strain predicted to be negative is actually negative.p values are added the PPV at first increases, but as p increases the quality of the SNPs for predictions declines, so that eventually a point is reached where addition of more lower-quality SNPs reduces the PPV. PPV does fluctuate somewhat, so addition is not terminated until PPV declines enough below its maximum value to be confident that the decline is real.As SNPs with gradually increasing DiagnosticSNPs writes several output files: (1) a DiagnosticSNPs.summary file  named “CausalSNPs Output Files”:DiagnosticSNPs keeps track of the SNP number and the corresponding SNP ID), the p that allele changes were random over the branches along which the phenotype changed, and whether the SNP is synonymous or non-synonymous, the protein accession number, and a brief description of the protein function.CausalSNP.report . That file also included 31 strains whose phenotype were changed to unknown in the Eco68_patho_50%.pheno file and that were used to calculate accuracies of phenotype predictions.In the file Eco68_patho.pheno human pathogenic strains were listed as the positive phenotype (1), others were listed as the negative phenotype (0), and one strain had an unknown phenotype. GetSNPprobs was used to calculate, for each of the 418,500 SNPS the χ2 probability that the alleles were distributed randomly with respect to phenotype.DiagnosticSNPs identified 207 diagnostic SNPS that predicted the pathogenic phenotype for the 31 genomes whose phenotypes were unknown in the Eco68_patho_50%.pheno file. The accuracy of those predictions was 0.944, PPV was 0.931 and NPV was 1.0. Over all, of the 67 genomes whose phenotype is known, accuracy was 0.970, PPV was 0.96 and NPV was 1.0. Out of those 67 genomes there were two false positives  which is an updated version of the application reported in Escherichia fergusonii strain ATCC35459, and ancestral states of the diagnostic SNPS and the phenotype were estimated . Multifactor Dimensionality Reduction (MDR) is a statistical approach to detecting such interactions M. tuberculosis or for virulence phenotypes of viruses, PCR based assays can be designed to detect allele variants and thus to predict phenotypes. If a sufficient number of genome sequences are known to allow reliable genome predictions to be made, then those predictions can be applied to complete genomes, genome assemblies and raw-read genomes in the various databases. The cost of bacterial genome sequencing now approaches $100 US per genome and is likely to decrease significantly in the near future. It is quite feasible for a laboratory to characterize a phylogenetically diverse collection of a species with respect to a clinically important phenotype such as virulence, then to sequence 25–30 strains of each phenotype and to analyze those sequences with kSNP and the PPFS package. That analysis could then be used to predict the virulence of strains already in the database. If sequencing costs continue to decrease it may even become practical to routinely sequence genomes in epidemiological investigations. If the phenotype of interest is either expensive or time consuming to determine, the results of genome sequencing together with the information provided by a kSNP-PPFS analysis could provide a rapid assessment of the probability of exhibiting the clinically important phenotype.It is somewhat surprising that a phenotype as vague as “pathogenic” can be accurately predicted on the basis of a small set of diagnostic SNPs in a set of sequenced genomes. There may appear to be little point in predicting the phenotypes of completely sequenced genomes; after all, given the effort and expense of sequencing a microbial genome, the phenotype is likely to be known already. There are three reasons that phenotype prediction can be valuable today and is likely to become more valuable in the future. First, phenotypes are rarely reported in genome annotations and many genomes are submitted without reference to papers where those phenotypes might be available. The PPFS package permits prediction of the phenotypes of those genomes. Second, even when phenotypes are reported they are not necessarily the phenotype of interest to any particular investigator. Third, when phenotypes are difficult or time consuming to determine; e.g. for very slow growing organisms such as E. coli is often used as a surrogate to indicate the presence of human pathogens in water resources E. coli from human vs non-human sources would be valuable for distinguishing E. coli that indicate human waste contamination from those that are normally resident in soils near streams E. coli genomes in which the source host is specified in the annotations to determine whether there are SNPs that can predict human vs non-human origin.2 probability that the alleles were distributed randomly with respect to phenotype. DiagnosticSNPs identified 101 diagnostic SNPS that predicted the phenotype for the 51 genomes whose “source” phenotypes were unknown in the Human_50%.pheno file. The accuracy of those predictions was 0.845, PPV was 0.793 and NPV was 0.91. Over all, of the 116 genomes accuracy was 0.897, PPV was 0.877 and NPV was 0.923. Out of those 116 genomes there were seven false positives.In the Human.pheno file 61 strains were listed as the positive phenotype (1), i.e. coming from a human host, and 55 were listed as the negative (0) phenotype, i.e. coming from a non-human host. PickPhenotypeSubsets generated a subset of 65 strains whose phenotypes were given in the Human_50%.pheno file (see Methods). That file also included 51 strains whose phenotype were changed to unknown in theH_50%.pheno file and that were used to calculate accuracies of phenotype predictions. GetSNPprobs calculated, for each of the 470,806 SNPS the χAlthough accuracy, PPV and NPV are not as high as in data set 1, for environmental sampling purposes where assessments would be based on determinations on many individual isolates, accuracies of about 90% are probably high enough to be useful.kSNP for SNP identification and the PPFS package for SNP association studies, the major barrier to SNP association studies in microorganisms is no longer the availability of appropriate tools; it is the culture of Genomic science itself. There are very few microorganisms for which both complete genome sequences and biological information are available in the databases. There are a plethora of databases that include or point to microbial genome sequences; e.g. NCBI's Genome database, SRA database, Bioprojects database, bacterial genome assembly database, but it is rare for any phenotypic information to be included in any of those files and most do not even refer to published papers that might shed light on phenotypes. The Broad Institute E. coli antibiotic resistance database http://www.broadinstitute.org/annotation/genome/escherichia_antibiotic_resistance/MultiHome.html provides detailed information on genotypes of hundreds of strains, but does not release the antibiotic resistance phenotypes. On the other side of the coin, the Network on Antimicrobial Resistance in Staphylococcus aureus (NARSA) http://www.narsa.net/control/member/allapprovedisolates provides antibiotic resistance phenotypes for hundreds of S. aureus strains, but genome sequences are available for only about a dozen of those strains. Genome sequencing has become so fast and so inexpensive that we seem to have lost track of the reasons for which those individual strains were sequenced. Obviously a complete description of the biological properties of each sequenced genome is impossible, but at the very least there should be a paragraph explaining why the particular strain was chosen to be sequenced. Was it from a sick or healthy individual, from an environmental sample (what environment), etc?SNP association studies are not an end in themselves, but are a means to identify candidate genes for experimental studies related to the phenotype of interest. With the release of Ideally, an international repository would be created for maintenance of all strains  whose genomes are added to the genome sequence databases. Availability of the organisms would permit investigators to determine the phenotypes of interest of strains that have already been sequenced, and thus to conduct valuable SNP association studies. Failing that, the annotations of all genomes in the database should include contact information for an individual who is committed to providing a sample of the strain upon request.Table S1GenBank accession numbers and literature citations for phenotypes of genomes in this study.(DOCX)Click here for additional data file.Table S2DiagnosticSNPs.summary file written by DiagnosticSNPs.(TXT)Click here for additional data file.Table S3DiagnosticSNPs.call file written by DiagnosticSNPs.(TXT)Click here for additional data file.Table S4CausalSNPs.report file written by CausalSNPs.(TXT)Click here for additional data file.Table S5Chi-square details.txt file written by CausalSNPs.(TXT)Click here for additional data file.Table S6Changes.txt file written by CausalSNPs.(TXT)Click here for additional data file.Table S7Branches.txt file written by CausalSNPs.(TXT)Click here for additional data file.


In total, we identified 38 genome-wide significant regions containing 185 genome-wide significant SNPs by single-marker GWAS or LONG-GWAS. The significant regions are distributed on pig chromosomes (SSC) 1, 4, 5, 7, 8, 10, 11, 12, 13, 17 and 18, and most of significant SNPs reside on SSC7 and SSC8. Of the 38 significant regions, 7 show constant effects on hematological traits across the whole life stages, and 6 regions have time-specific effects on the measured traits at early or late stages. The most prominent locus is the genomic region between 32.36 and 84.49 Mb on SSC8 that is associated with multiple erythroid traits. The KIT gene in this region appears to be a promising candidate gene. The findings improve our understanding of the genetic architecture of hematological traits in pigs. Further investigations are warranted to characterize the responsible gene(s) and causal variant(s) especially for the major loci on SSC7 and SSC8.Hematological traits are important indicators of immune function and have been commonly examined as biomarkers of disease and disease severity in humans. Pig is an ideal biomedical model for human diseases due to its high degree of similarity with human physiological characteristics. Here, we conducted genome-wide association studies (GWAS) for 18 hematological traits at three growth stages  in a White Duroc × Erhualian F In the immune system, hematological traits include three components: leukocytes , erythrocytes  and platelets. All of these components represent important parameters of immune capacity of individuals The count and volume of cellular elements in circulating blood are highly heritable and vary considerably among individuals So far, 239 quantitative trait loci (QTL) for swine hematological traits have been reported by linkage mapping in the AnimalQTLdb database 2 resource population using 183 microsatellite markers, and identified a number of QTL affecting hematological traits measured at 3 growth stages 2 resource population using the PorcineSNP60 Genotyping BeadChip technology . The experimental data are available upon the readers’ request.In our previous studies, we conducted a whole genome linkage mapping in a White Duroc × Erhualian FAll the procedures involving animals are in compliance with the care and use guidelines of experimental animals established by the Ministry of Agriculture of China. The ethics committee of Jiangxi Agricultural University specifically approved this study.2 resource population and phenotype recording have been presented in our previous publications 2 individuals was developed by crossing 2 White Duroc boars and 17 Erhualian sows. All animals were kept under a consistent standard pigpen and were fed with same diet at the experimental farm of Jiangxi Agricultural University. Eighteen hematological parameters were measured for 1449 individuals at three age stages: days 18, 46 and 240. Blood samples of 5 ml were collected from each animal and were directly injected into eppendorf tubes containing 30 ul of 20% EDTA in polybutadiene-styrene. A standard set of hematological data were recorded using a CD1700 whole blood analyzer  at the First Affiliated Hospital of NanChang University, China. The 18 hematological parameters include 7 baseline erythroid traits (hematocrit (HCT), hemoglobin (HGB), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobina concentration (MCHC), mean corpuscular volume (MCV), red blood cell count (RBC), and red blood cell volume distribution width (RDW) ), 7 leukocyte traits (granulocyte count (GRAN), granulocyte count percentage (GRAR), monocyte count (MON), monocyte count percentage (MONR), lymphocyte count (LYM), lymphocyte count percentage (LYMA), and white blood cell count (WBC) ), and 4 platelet traits (plateletcrit (PCT), platelet distribution width (PDW), platelet count (PLT), and mean platelet volume (MPV) ).A detail description of the White Duroc × Erhualian F2 pedigree were genotyped for the Porcine SNP60 Beadchips on an iScan System  following the manufacturer’s protocol. Quality control was executed to exclude SNPs with call rate <95%, minor allele frequency (MAF) <5%, severely Hardy Weinberg disequilibrium (P<10E-5) and Mendelian inconsistency rate >10%. Moreover, individuals with missing genotypes >10% or Mendellian errors >5% were discarded from the data set.Genomic DNA was extracted from ear tissues using a standard phenol/chloroform . All DNA samples were qualified and standardized into a final concentration of 20 ng/ul. A total of 1020 individuals in the FGσα2), in here G is the genomic relationship matrix that was constructed based on SNP markers as described in Eding et al α2 is the polygenetic additive variance. X, Z are the incidence matrices for b and u, S is incidence vector for α, e is a vector of residual errors with a distribution of N . All single-marker GWAS were conducted by GenABEL packages The allelic difference of each SNP in phenotypic traits was tested using a general linear mixed model As all experimental individuals were recorded for hematological traits at three time stages . We assumed that measurements at different stages in the same individual would be more correlated than those obtained from different individuals. To conceptualize this assumption, phenotypic records on the three age stages were analyzed together using a mixed effect approach to distinct the correlations within and/or among individuals A haplotype-based association study Descriptive statistics of the measured traits in the current experimental population are presented in P<10E-5) and 208 markers exhibiting Mendelian inconsistency were excluded for further analyses. All individuals are qualified samples. A final set of 39622 SNPs on 1020 individuals were explored for the subsequent GWAS.After quality control, 3077 SNPs with call rate <90%, 15711 SNPs with MAF <0.05, 6502 SNPs showing Hardy Weinberg disequilibrium . The 13 regions harbor 185 significant SNPs and 91 annotated genes (data not shown). Of the 185 SNPs, 119 SNPs corresponding to 63 genes were evidenced by both association strategies. In the single-marker GWAS, 147 significant SNPs representing 78 genes were identified in comparison with 158 associated SNPs from 76 genes revealed by LONG-GWAS.For GWAS on FFigure S1.In total, we found 140 and 118 SNPs significantly associated with 7 erythrocyte traits by single-marker GWAS and LONG-GWAS, respectively. The corresponding Manhattan plots are shown in For HCT and HGB, we identified 3 genome-wide significant loci on SSC7. The loci cover the region from 33 to 56 Mb and had the effect exclusively on the measurements at day 240. The lead SNPs within the regions are identical for the two traits. No genome-wide significant SNP was found for these traits at days 18 and 46. For MCH, MCV and RBC, all significant SNPs were detected on SCC8 which appeared to harbor two independent associated regions (32.36–50.1 Mb and 66.03–84.49 Mb). The regions showed constant effect on MCV across the three age stages whereas exhibited time-specific influence on MCH and RBC as the association disappeared on early-stage samples at day 46. Notably, a cluster of SNPs (>100) on this chromosome exhibited signals of strong association with MCV and MCH at day 240 with the top SNP (ss131369293) at 50096834 bp. For MCHC, only ss131260759 at 4248936 bp on SSC4 achieved the genome-wide significant level for the trait measured at day 46.A total of 118 SNPs within 10 genomic regions showed strong association with erythrocyte-related traits by LONG-GWAS. The results confirmed the findings of single-marker GWAS for HCT on SSC7, and for MCV, MCH and RBC on SSC8. It indicates that these loci consistently regulate red blood cells at different stages. Moreover, we uncovered two novel loci for HGB on SSC1 and SSC12 with the lead SNPs at 65994430 (ss120021119) and 29107229 (ss131459230) bp on the two chromosomes, respectively. One new locus was identified for HCT at 85640695 bp on SSC11. Only one significant locus for MCHC was found at 4168738 bp on SSC10, which differed from the result from the single-marker GWAS.RAB44 gene on SSC7 were associated with WBC at day 240. Two significant SNPs (ss131368550 and ss131364780) for GRAR at days 18 and 46 were found at different positions (40.78 Mb and 26.31 Mb) on SSC8. Moreover, one SNP (ss131544979) at 2971217 bp on SSC17 was associated with LYM at the age of 18 days. A single SNP (ss478935524) at the position of 5860648 bp on SSC18 was associated with LYMA by LONG-GWAS.Only 6 genome-wide significant SNPs on 4 autosomes were identified for leukocyte traits by single-marker GWAS Figure 2P-values of 1.14 E-8 and 3.94 E-7 respectively. For LONG-GWAS, two significant loci for PDW were detected on SSC8. The lead SNPs at the two loci were SS131368505 at 40852645 bp and ss131371056 at 75662581 bp with a distance of 34.81 Mb, implying at least 2 loci controlling PDW on chromosome 8.Analysis of platelet traits revealed two significant loci on SSC13 and SSC5 by single-marker GWAS and 39 associated loci by LONG-GWAS Figure 22 population using 183 microsatellites. We detected 46 genome-wide significant QTL for baseline erythroid traits, 8 for leucocyte-related traits and 6 for platelet-related traits. These QTL are distributed on SSC1, 2, 7, 8, 10, 12, 13, 15 and X 2 populations were generally conducted by comparing the phenotypic difference between F2 individuals inheriting different alleles from the founder breeds under the assumption that QTL alleles were alternatively fixed in each founder breed of the F2 intercross. The advantage of this mapping strategy is that we can anchor genomic regions affecting phenotypic traits using sparse markers. However, it could result in false negative signals If QTL alleles are segregating within founder lines. Moreover, the confidence intervals of most QTL were larger than 20 cM. In our QTL mapping study, the smallest intervals were 3 and 4 cM for MCV and MCH at day 18 on SSC8, respectively We have previously performed genome scans on the Fet al. 2 intercross. All significant SNPs were found on SSC7 and SSC8 except one SNP associated with RBC on SSC1 and two SNPs for RDW on SSC12. Our results confirmed all of these findings. Wang et.al et al.Until now, there were only two very recent GWAS papers describing significant genomic loci for hematological parameters in pigs The single-marker GWAS revealed that the significant locus on SSC8 was consistently associated with MCV measured at days 18, 46, and 240, suggesting that a common variant regulates MCV at the whole life stage. The constant effect of this locus on MCV was further confirmed by LONG-GWAS that treated MCV data at the three ages together and obtained the same finding as the single-marker GWAS. In contrast, ss131544979 at 2971217 bp on SSC17 showed time specific effect on LYM at day 18. SNPs within two different regions on SSC8 were associated with GRAR at days 18 and 46, respectively, indicating that distinct genes are involved in development stages of granulocyte cells. Time specific loci were also evidenced for PLT and PDW on SSC5 and SSC13 as the association signals were observed only from the data at day 46. A high proportion of SNPs on SSC7 and SSC8 were identified for erythroid traits and leukocytes traits at day 240. Notably, the SNPs on SSC7 for HCT, HGB and WBC had a significant effect only at day 240 Table 2 Currently, standard GWAS (e.g. GenABEL) only utilized one time point for each individual. If QTL constantly control the traits during the whole life process, it is reasonable to assume that jointly analysis of data at all-time points may be more powerful than the single-time-point approach. We thus used LONG-GWAS that utilized multiple phenotype measurements for each individual as proposed by Furlotte et al. 2 pedigrees, in which high LD extents are expected. However, only one genome-wide significant SNP was associated with GRAR at days 18 and 46 (Table S2). None of suggestive SNP was detected in the neighboring region of the top SNP. To test if the signal was false positive result or real association, we conducted a haplotype based GWAS for this trait . We showed that dozens of genome-wide SNPs in a large interval were uncovered for GRAR at day 46, and the position of the top SNP was exactly the same to the lead SNP in the single-marker GWAS. For GRAR at day 18, the most significant SNPs were moved to another position (86.8 Mb), but the second top marker was identical to the top SNP identified in the single-marker GWAS. The findings support the reliability of the single significant marker for GRAR.In the standard GWAS, a prominent locus is usually featured by a lead SNP and a cluster of surrounding significant SNPs within a genomic region especially in the FFigure S3). For MCH at day 18, after controlling for the effect of ss131369293, the second significant region disappeared. Also, no SNP showed association with MCH and MCV at day 240 after correcting for the effect of the lead SNP (ss131369293). Moreover, the complete LD (D′ = 1) was observed for the two top SNPs (ss131369009 at 44.93 Mb and ss107827400 at 66.03 Mb) for MCV at day 240 in the two intervals within the ∼45 Mb region on SSC8. These observations support one major locus for the tested hematological traits on this chromosome. However, we can not rule out the possibility that two neighboring genes contribute to the phenotypic traits as the conditional analysis can not distinguish the effects of two adjacent loci due to LD.In the present study, the most interesting finding is the major locus for multiple hematological traits on SSC8. More than half of the detected regions (14 regions) were located on SSC8 that were associated with 9 hematological traits. Hundreds of significant SNPs in a large single region of more than 45 Mb on SSC8 were identified for MCH and MCV at day 240. In contrast, two segments in the ∼45 Mb region appeared to be independently associated with the two measurements at day 18 as no significant SNP was found in the inter-segment region Table 2.KIT) gene around 43.55 Mb on this chromosome stands out as a compelling candidate gene as it is essential for the development of hematopoietic stem cells and has been highly expressed in hematopoietic cells KIT have significant influence in RBC in the mouse et al.KIT variants with erythroid traits in piglets. Moreover, Fésüs et al.KIT on hematological parameters in adult pigs. Our observation of the KIT region associated with hematological traits reinforces the assumption KIT gene has a significant effect on peripheral blood cell measures in pigs.We noticed that SNPs associated with MCH, MCV, RBC and GRAR at the early stage mainly reside in the region of 36.31 to 50.10 Mb. The stem cell growth factor receptor  and causal variant(s) especially for the major loci on SSC7 and SSC8.In conclusion, a total of 185 genome-wide significant SNPs corresponding to 91 genes were identified for 18 hematological traits at the three growth ages in the White Duroc × Erhualian FFigure S1Manhattan plots for the LONG-GWAS analysis of hematological traits. log10(1/p) values are shown for all SNPs that passed quality control. The dotted line denotes the Bonferroni-corrected genome-wide significant threshold. SNPs surpassing the genome-wide threshold are highlighted in pink and SNPs reaching the suggestive threshold in green. HCT: hematocrit; HGB: hemoglobin; MCH: mean corpuscular hemoglobin; MCHC: mean corpuscular hemoglobin content; MCV: mean corpuscular volume; RBC: red blood cell; LYMA: lymphocyte count percentage; PDW: platelet distribution width.(TIF)Click here for additional data file.Figure S2Manhattan plots for the hidden haplotypes analysis of GRAR at 18 and 46 days on SSC8 where only one SNP was associated with each trait in the single-marker analysis. SNPs surpassing the genome-wide threshold are highlighted in pink and SNPs reaching the suggestive threshold in green.(TIF)Click here for additional data file.Figure S3Manhattans plots for conditional GWAS of MCV. (A-C) Results are shown for MCV at 18 (A), 46 (B) and 240 (C) days. Grey and blue dots denote the results for SNPs before and after controlling for the top SNP (ss131369293) at 50.10 Mb on SSC8, respectively. Grey lines represent the genome-wide significant threshold.(TIF)Click here for additional data file.Table S1Genome-wide significant SNPs associated with hematological traits by LONG-GWAS.(DOC)Click here for additional data file.Table S2Simple statistic results for GRAR at 18 and 46 days classified by the genotypes of the top SNPs.(DOC)Click here for additional data file.