We also showed that RY 7-33-97 and RY 7-20-59 were genetically closer to RRIM 600 and PR 107, respectively, in agreement with both their phenotypic similarities and gene expression profiles. After identifying ethylene- and jasmonic acid–responsive genes at the transcription level, we compared and analyzed the genetic variations underlying rubber biosynthesis and the jasmonic acid and ethylene pathways in detail. Our results suggest that genome-wide genetic variations play a substantive role in maintaining rubber tree heterosis.As an important industrial material, natural rubber is mainly harvested from the rubber tree. Rubber tree breeding is inefficient, expensive and time-consuming, whereas marker-assisted selection is a feasible method for early selection of high-yield hybrids. We thus sequenced and analyzed the transcriptomes of two parent rubber trees (RRIM 600 and PR 107) and their most productive hybrids (RY 7-33-97 and RY 7-20-59) to understand their gene expression patterns and genetic variations including single nucleotide polymorphisms (SNPs) and small insertions/deletions (InDels). We discovered >31,000 genetic variations in 112,702 assembled unigenes. Our results showed that the higher yield in F Hevea brasiliensis (the para rubber tree), of the Euphorbiaceae family, is perennial and native to Amazon rainforests, rubber trees are now mainly cropped in South and Southeast Asia. Similar to other Euphorbiaceae species, the rubber tree produces lateral inflorescences, and is a monoecious, self- and cross-pollinated3H. brasiliensis (the para rubber tree) is the only species cultivated commercially and is the primary source of NR. With the rapid development of the world economy, the global demand for NR will be increasing till 2020 and possibly beyond . Breeding new varieties and developing new tapping techniques are the most effective approaches to optimize latex regeneration and increase NR production from rubber trees. However, the yield genetic improvement of rubber tree is very inefficient and time-consuming mainly due to the difficulty to dissect yield component traits. Although great progress has been made in yield breeding of rubber tree since the 1920s, the yield of the most productive varieties currently available is still much inferior to the theoretical yield of rubber tree, which was predicted to be 7,000–12,000 kg/ha/yrNatural rubber (NR) is the critical raw material for more than 40,000 products, including 400 medical devicesd-erythritol 4-phosphate pathways are responsible for providing isopentenyl diphosphate for rubber biosynthesis910111415161819It is widely accepted that the three key factors that determine rubber yieldThe combination of conventional and modern breeding technologies is proving useful for improving the efficiency of rubber tree breeding252728New rubber tree varieties have been bred mainly through traditional cross-breeding programs in rubber-producing countries. The rubber tree clones PR 107 (a primary variety) and RRIM 600 (a secondary variety) are usually used as the backbone parents in cross-breeding owing to their favorable features. RRIM 600 was selected from the offspring of the primary varieties Tjir 1 and PB 86In this study, we used RNA-seq to investigate the genomes of the parents (RRIM 600 and PR 107) and their two elite hybrid offspring RY 7-33-97 and RY 7-20-59. Using assembled unigenes as reference, we identified the SNPs and small InDels in genes that are involved in latex metabolism and the JA and ET pathways. Furthermore, we randomly selected 33 genes for detailed analysis of their expression profiles in the two hybrids and their parents. Based on these data, the genetic makeup and variations between the parents and their elite hybrid progeny were compared and analyzed with respect to their transcriptomes and three major pathways related to rubber yield.The relationship of the four rubber tree varieties  is illustrated in Arabidopsis, we identified 73 and 29 genes, respectively, that putatively function in the ET and JA pathways in the rubber tree. Of the 29 genes involved in the JA pathway, 19 also are involved in the ET pathway . A total of 679 DEGs were identified as ET responsive, and of these 488 and 191 were up- or downregulated, respectively; 310 DEGs were JA responsive, and of these 173 and 137 were up- or downregulated, respectively. Among the ET- and JA-responsive DEGs, 198 were involved in both the ET and JA responses, suggesting that these DEGs are co-regulated by ET and JA in the rubber tree . As show pathway .The genetic variations  between PR 107 and RRIM 600 were individually identified by Samtools and the Genome Analysis Toolkit GATK (see Methods). To increase prediction accuracy, only the variations predicted by both methods were used for subsequent analyses. We detected 31,320 SNPs in 9,764 unigenes in total . As showWe further analyzed the genetic variations shared by all four elite rubber tree varieties. Among 9,043 unigenes expressed in all four, 7,975 (88.19%) contained 24,875 SNPs . We randWe next inspected the hybrid and parent sequences to determine the frequency of shared genetic variations. When compared, respectively, to the parental transcriptomes of RRIM 600 and PR 107, the hybrid RY 7-33-97 shared 9,128 and 7,366 SNP genotypes, and 30 and 38 small InDels; in the same comparison, the hybrid RY 7-20-59 shared 7,868 and 8,626 SNP genotypes and 38 and 30 small InDels . Among tTaken together, the results in et al.1 hybrids, there were fewer SNPs that were either the same as, or different from, both of their parents, than the number that was shared with only one parent.The pathways associated with ET, JA, and latex biosynthesis and flow (LBF) are strongly linked to rubber tree yield. Therefore, we further analyzed the genetic variations between the hybrids and their parents in genes that are involved in these three pathways. First, among 34 latex biosynthesis- and flow-related genes, 18 were found to contain 33 SNPs  premature stop codons; 2) alterations of the initiating methionine residue; 3) induction of frame-shift mutations; and 4) removal of annotated stop codons. We found that a majority (367) of the large-effect SNPs likely altered the initiating methionine residues. Because large-effect genetic variations in genic regions operate as deleterious variations, they have potentially disabling effects on gene function or the integrity of encoded proteins; as such, they have been suggested as a genetic basis contributing to heterosis49et al.3052Owing to the important roles of three pathways, namely ET, JA, and latex production/flow, in rubber tree yield, we targeted the genetic variations underlying these pathways between the hybrids and their parents as likely major causal factors influencing output. We identified 33 SNPs in 18 genes involved in LBF pathways. Mantello JA and its conjugate, methyl jasmonic acid, contribute to diverse biological functions in plants91 populations are still rich in genetic variations. Therefore, it is necessary for rubber tree breeders to use an effective selection method  to increase breeding efficiency as opposed to the traditional pure phenotype-based selection process. The SNP variations in genic regions  may play an important role in the manifestation of heterosis, which in turn may contribute to increased latex yield in F1 rubber tree hybrids. Therefore, our transcriptome-wide survey of SNPs and small InDels in four elite rubber tree varieties provides potential new strategies for the genetic-based improvement of rubber tree yield during breeding, i.e., increasing latex yield by selecting highly heterozygous offspring in the seedling stage from hybrids of elite parents via MAS. Our future work shall focus on the relationship between the SNPs/small InDels and corresponding gene expression changes that may lead to phenotypic variations. To further explore the relationship between genome heterozygosity and rubber yield, we will construct an F1 population using the four heterozygous elite varieties. The SNPs and small InDels discovered in this study will facilitate the construction of high-density genetic maps based on the F1 population, as shown by Pootakham et al.et al.Plant hybridization is a common process in nature and plays a vital role in plant breeding. As a perennial tree with a long growing and breeding cycle, conventional breeding of the rubber tree is labor-intensive and time-consuming. With heterozygous parents, it was expected, and in fact shown, that the hybrid progenies in FLatex was collected from four rubber tree varieties at the Guangba experimental farm in Hainan, China and dropped directly into liquid nitrogen for total RNA extraction and subsequent Illumina sequencingSequencing library construction and Illumina sequencing were performed at Beijing Genomics Institute–Shenzhen, China. Low-quality reads were filtered out prior to assembly based on the following standards: (1) percentage of low-quality bases  was >50% in a read; (2) the percentage of N bases was >10% in a read; (3) reads with the adaptor sequence; (4) reads with unmatched paired-ends after the above three steps.de novo transcriptome assembly, the Trinity method used default parameters: the parameter in cap3 was -p 98 -o 50, and the parameter in cdhit was -c 0.98 -G 0 -aS 0.90 -AS 200 -M 0 -g 1.The high-quality reads from RY 7-33-97 were assembled according to the Trinity method. The assembled sequences were further incorporated into distinct contigs using the programs Cap3 and cdhit59–5. When the annotation and open reading frame prediction for a unigene were in conflict among these four databases, the priority was given in the following order: NCBI nonredundant, Swiss-Prot, KEGG and KOG. All the assembled contigs were aligned with the NCBI nonredundant database with the sBLASTX program to predict the position of SNPs and small InDels in a target gene.The assembled unigenes were functionally annotated by BLAST against NCBI nonredundant, Swiss-Prot, KEGG, and KOG databases with an e-value cut-off of 10q-value cut-off of 0.05, and the absolute log2 (fold change) value was >1 after normalization. The ET- and JA-related genes were defined as the DEGs between the ET- and JA-treated materials and the controls, respectively. The enriched KEGG and GO pathways of the DEGs were separately determined using kobas softwarehttp://www.blast2go.org/), respectively. In this study, the KEGG and GO pathways showing a corrected p-value ≤ 0.05 were considered to be significantly enriched. To further analyze the genes involved in the ET and JA pathways, the sequences of genes that function in these pathways in Arabidopsis were downloaded (http://ahd.cbi.pku.edu.cn/) and aligned to the sequences of the DEGs from rubber tree with e-value < E-10. The aligned genes were considered as those underlying ET and JA pathway function in the rubber tree.With the assembled unigenes as a reference transcriptome, the DEGs between the controls (no treatments) and ET- or JA-treated materials were identified with TopHat and Cuffdiff 61With the assembled transcriptome as a reference, the latex data from PR 107, RRIM 600, RY 7-33-97, and RY 7-20-59 were aligned for SNP and small InDel identification. Two reliable and frequently used software programs, Samtools and GATK, were independently applied for the identification of SNPs and small InDels with the assembled contigs as a reference sequence. As for small InDels, the number of insertions and deletions ranged from 1 to 6 bp. The filtering thresholds were set as following: the consensus quality was no less than 50 and combined-sample read depth was no less than 5. The SNPs and small InDels identified in both Samtools and GATK outputs were manually corrected according to the genotype relationship between the hybrids and their parents based on Mendel’s laws, and only those that were identified in all the four varieties were regarded as the SNPs and small InDels. When the genotypes of genetic variations in the hybrids were abnormal, the corrected SNPs and small InDels were regarded as reliable genetic variations for the subsequent analyses. The degree of SNP polymorphism of 24 rubber tree varieties was calculated using polymorphism information contentTo validate the accuracy of SNP prediction, the unigenes containing SNPs were selected to design PCR primers and amplified with the designed primers. The PCR products were sequenced from both directions and the chromatograph flies were analyzed by the Chromas software; if the sequencing results of SNP sites contain one and two nucleotides with high-quality, the SNP sites are defined as homozygous and heterozygous ones, respectively. The sequencing results from four rubber tree varieties were aligned and validated the accuracy of predicted SNPs. In addition, 10 SNPs within 4 genes involved in LBF pathway were amplified, sequenced and analyzed in 24 rubber tree varieties. To validate the accuracy of predicted small InDels, the unigenes containing small InDels were selected to design CAPS or dCAPS primers and amplified. The PCR products were digested with their corresponding enzymes, and the digested products were detected by agrose gel electrophoresis. The primers designed for validating small InDels and SNPs were shown in Arabidopsis ET- and JA pathways as well as LBF were identified by aligning their sequences with the assembled unigenes containing genetic variations with e-value < 1.0E-95. The genes underlying ET, JA pathway were shown in The genetic variations  within the genes involved in For each real-time RT-PCR reaction, dif)The gene expression profiles were classified into 12 groups according to their differential expression patterns among the hybrids and their parents .


A 20K SNP array was developed and a comprehensive set of tetraploid cultivar was genotyped. This allowed us to identify footprints of the breeding history in contemporary breeding material such as identification of introgression segments, selection and founder signatures.A non-redundant subset of 15,138 previously identified SNPs and 4454 SNPs originating from the SolCAP project were combined into a 20k Infinium SNP array for genotyping a total of 569 potato genotypes. In this study we describe how this SNP array (encoded SolSTW array) was designed and analysed with fitTetra, software designed for autotetraploids. Genotypes from different countries and market segments, complemented with historic cultivars and important progenitors, were genotyped. This comprehensive set of genotypes combined with the deliberate inclusion of a large proportion of SNPs with a low minor allele frequency allowed us to distinguish genetic variation contributed by introgression breeding. This “new” (post 1945) genetic variation is located on specific chromosomal regions and enables the identification of SNP markers linked to R-genes. In addition, when the genetic composition of modern cultivars was compared with cultivars released before 1945, it appears that 96 % of the genetic variants present in those ancestral cultivars remains polymorphic in modern cultivars. Hence, genetic erosion is almost absent in potato. Finally, we studied population genetic processes shaping the genetic composition of the modern European potato including drift, selection and founder effects. This resulted in the identification of major founders contributing to contemporary germplasm.The online version of this article (doi:10.1007/s00122-015-2593-y) contains supplementary material, which is available to authorized users. The genetic diversity of cultivated potato, as studied today, is easier to interpret with insights from the past events that have shaped the gene pool. These past events include (1) the amount of genetic variation that was brought from South America to Europe since the late 16th century (Hawkes and Francisco-Ortega n = 4x = 48). Examples of historic cultivars are Russet Burbank (1908), a Burbank (1876) mutant, the most important cultivar in the USA and Bintje (1910) ranking 1st in Belgium and 6th in the Netherlands in 2014. Throughout the 100 years of breeding hardly any increase in yield has been achieved   whole genome resequencing  were considered as one cluster. As a next step the clusters were ordered per gene and subsequently two SNPs per cluster per gene were selected. For the clusters without two SNPs within a gene, one SNP per cluster was selected. Finally singletons were added which were genotyped in at least 67 cultivars, had a maximum of 2 flanking SNPs within 25 bp and no flanking SNPs within 10 bp. In this way SNPs are selected over the full length of large introgression segments and we tried to achieve a uniform distribution of SNPs across the length of the genome and across the depth of haplotypes. We did not filter SNPs according to allele frequency as calculated by Uitdewilligen et al.  was used as an internal standard. The tetraploid genotypes consisted of 192 genotypes of a representative subset of commercial potato germplasm available worldwide, selected for the study of  below 0.2. Subsequently two default settings of fitTetra were adjusted (1) p.threshold was lowered from 0.99 to 0.95, which implies that there is 95 % confidence of a sample belonging to a cluster, resulting in less missing calls as compared to the more strict 0.99 threshold. (2) The peak.threshold was increased from 0.85 to 0.99, which allows SNPs with a very low allele frequency  to be fitted by fitTetra. This was needed because the design of the array included a high number of low frequent SNPs. (3) the call.threshold was set to 0.60, resulting in the rejection of markers with more than 40 % missing values. Diploid samples are analysed along with the tetraploids to allow verification of the correct recognition of the nulliplex (AAAA), duplex (AABB) or quadruplex (BBBB) clusters.Simultaneous analysis of diploids and tetraploids may however compromise Hardy–Weinberg assumptions implemented in fitTetra and this may result in the rejection of markers that display deviations from Hardy–Weinberg equilibrium. Therefore two runs were performed, one with and one without the diploid samples. Genotype calls of both fitTetra runs were compared and inspected for markers having obvious genotyping errors such as (1) a heterozygous genotype call for the reference genome genotype DM; 2) diploid genotype calls assigned to simplex or triplex clusters and (3) deviating Mendelian segregation in a tetraploid mapping population from a matching project . Markers showing one of these unexpected results could be the result of a poor marker or a poor clustering by fitTetra. The poorly clustered markers along with the chloroplast markers were manually scored with GenomeStudio. Additionally SNP markers initially rejected by fitTetra were visually inspected using the graphical output of fitTetra  which reflects the Infinium assay design by the manufacturer, which uses the A nucleotide as a reference. Genotype calls were converted from the initial Illumina format into two derived datasets. The first dataset  is based on the DM reference genome, where the SNP alleles are indicated as REF (DM) and ALT (non-DM). SNP dosage values ranging from 0 to 4 reflects the observed number of non-DM SNP alleles. The second dataset  is based on the population minor allele frequency (MAF), where the SNP alleles are indicated as MIN  or MAJ . SNP dosage values in this second dataset ranging from 0 to 4 reflect the observed dosage of the minor allele. This MAF dataset is more convenient, because none of the many haplotypes in the potato gene pool assumes a haplotype frequency exceeding 50 %  the SNP dosage specifies the presence of specific SNP alleles. Subsequently we searched for the oldest cultivar that had this minor SNP allele. The age of a genotype is based on the year of market release as listed in the potato pedigree database  was compared with the allele frequency in cultivars released since 2005 (N = 81) and the allele frequency in the “starch” sub-populations (N = 59). Additionally the effect of allele dosage in important founders was compared with allele frequency changes. The subsets of genotypes are specified in supplementary file 2.Changes in allele frequency were calculated to study the effect of breeding on the genetic composition of the gene pool. For this purpose the allele frequencies in the group of cultivars released before 1945 .Several runs were performed with fitTetra for genotype calling using the signal ratios obtained from the Infinium array. Over sequential runs, the programme settings were optimized and minor errors of the software were corrected. Two properties of the Infinium data initially resulted in erroneous clustering by the software. Firstly it appeared that the five clusters are not evenly distributed over the X-axis, as shown in Fig. The improved version of fitTetra was used for the genotype calling of the SolSTW array. The genotype calling was performed twice, once using all genotypes and a second run without the diploid genotypes. The genotype calling without the diploid samples was used for further analysis, as inclusion of the diploid samples resulted in an additional rejection of 1184 markers, due to deviation from a Hardy–Weinberg test by fitTetra. The analysis of the tetraploid samples resulted in 15,271 fitted and 2716 rejected markers. Subsequently, a bi-parental tetraploid mapping population was used to identify SNPs where parental SNP dosage and offspring ratios were in disagreement. This is a putative indicator of poor SNP performance, and visual inspection of GenomeStudio output as shown in Fig. As shown in Tables The percentage of missing calls was very low for the final dataset of 14,530 markers and 537 genotypes, with only an average of 95 missing calls per genotype and 3.5 missing calls per marker (0.65 %). For genotypes having wild species in their pedigree and not used in the SNP discovery panel of Uitdewilligen et al. , the aveSeveral possible factors that could cause assay failure have been examined. In Table The high nucleotide diversity of potato implies that SNP assays may be frequently affected by flanking SNPs. Therefore we aimed to target SNPs without flanking SNPs for assays, this is however problematic in potato due to its high SNP density. Consequently for many 34.8 %) SNP assays . In specific market niches, the limited gene pool is easily affected by random genetic drift (genetic erosion). These processes  were studied by comparing SNP allele frequencies between two groups. Firstly, the pre-1945 cultivars were compared with the cultivars released after 2005. Also, the pre-1945 cultivars were compared against cultivars from the “starch” subpopulation. For post-1945 SNPs significant increases of the allele frequency can be observed. In this study we analysed 246 cultivars that were released between 1946 and 2005. In this group, 108 cultivars contributed post-1945 SNPs, ranging from 1 to 447 post-1945 SNPs per cultivar (Supplementary file 2). From these 108 cultivars 39 are shown in Fig. In contrast, 50 % of all post-1945 SNPs remain below an allele frequency of 1 % and 549 SNPs were not polymorphic anymore (nulliplex) in cultivars released after 2005. These 549 SNPs could be considered as lost, i.e. phased out soon after introduction. For the pre-1945 SNPs, 538 SNPs (4.9 %) were no longer polymorphic in contemporary cultivars. These SNPs are also assumed to be lost during breeding. This may be due to selection, but random genetic drift is also plausible, because the initial allele frequency of these SNPs in old germplasm was already very low (1.4 % on average).A comprehensive overview of the changes in allele frequency of all pre-1945 SNPs (in post-2005 and starch cultivars compared with old cultivars) is shown in Fig. The processes underlying allele frequency changes over time: introgression, substitution, selection, drift and founder effects (frequent use of parents) are highly confounded. Still we assume that SNP variants that show the greatest increase in frequency are linked to important alleles for agronomic performance and vice versa. The most striking observations are that (1) 95.1 % of the pre-1945 SNPs are still polymorphic after 50–150 years of breeding and (2) we do not observe any fixation of pre-1945 SNPs in cultivars released after 2005.Here, we describe the development and evaluation of a SNP array for potato. Another SNP array named SolCAP is already widely used by the scientific community . With fitTetra we were able to score the dosage for 80.5 % of the markers. This is relatively high compared to 45 % in Hirsch et al.  × (S. maglia × S. edinense) hybrid six meiosis back in the pedigree  resulted in candidate SNPs on chromosome 5 tagging the H1 resistance haplotype conferring resistance to Globodera rostochiensis.A comprehensive sampling of genotypes of different ages has enabled us to assign a date to each SNP and to differentiate between “pre-1945” and “post-1945” genetic variants based on the year of market release. We observed that new genetic variants, cluster together in specific chromosomal regions and reside in specific genotypes  was retained in more recent material. This suggests that other beneficial alleles linked to these R-genes are introduced in the potato gene pool that caused a positive selection on alleles in this region, which might be interpreted as linkage drag. In contrast, the majority of the post-1945 SNPs do not exceed an allele frequency higher than 1 %. Since this variation is not under positive selection we conclude that this variation is not adding anything to the potato genepool and it will be a matter of time that this variation gets extinct in the newly introduced cultivars.Few of the newly introgressed SNP alleles show a considerable increase in allele frequency within a subset of recent material Fig. . EspeciaIt is often thought that breeding will decrease the amount of genetic variation over time, also described as genetic erosion. However, this assumed trend of declining diversity is not supported by molecular data Supplementary material 2 (XLSX 36 kb)Supplementary material 3 (XLSX 12 kb)


A key challenge in analyzing high throughput Single Nucleotide Polymorphism (SNP) arrays is the accurate inference of genotypes for SNPs with low minor allele frequencies. A number of calling algorithms have been developed to infer genotypes for common SNPs, but they are limited in their performance in calling rare SNPs. The existing algorithms can be broadly classified into three categories, including: population-based methods, SNP-based methods, and a hybrid of the two approaches. Despite the relatively better performance of the hybrid approach, it is still challenging to analyze rare SNPs.3-S, for rare SNP calling. This new approach can improve genotyping accuracy through clearly defining the boundaries of genotype clusters from samples with known genotypes, and enlarge the call rate by combining the simulated data based on the inferred genotype clusters information with the study population.We propose to utilize information from samples with known genotypes to develop a two stage genotyping procedure, namely M3-S outperforms existing methods in calling rare SNPs.Applications to real data demonstrates that this new approach MThe online version of this article (doi:10.1186/s12859-015-0824-5) contains supplementary material, which is available to authorized users. Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants contributing to hundreds of human diseases in the past decade , 2. TheiA and B. The goal of genotype calling is to infer the genotype  carried by an individual across the SNPs in the genome. All the genotype calling algorithms share the common feature of first defining genotype clusters and then assigning individuals to these clusters. They differ in how the clusters are defined and how the samples are allocated to these clusters. One class of genotyping algorithms is population-based where all individuals are genotyped SNP-by-SNP. The genotype clusters are defined through the joint analysis of all samples at a given SNP separately. Although commonly used, its performance highly depends on the size of the study population. It may not perform well for SNPs with low minor allele frequencies (MAF) where there may be very few individuals for certain clusters. For example, GenCall [In this article, we focus on the analysis of Illumina arrays, which use two color single base extension (SBE) biochemistry technology  to infer GenCall , a repre GenCall . In cont GenCall . The goo3 that combines the population-based strategy with the SNP-based approach to improve calling accuracy for rare SNPs [3 borrows genotype cluster information from reference SNPs to improve the calling performance for rare SNPs. It also improves upon GenoSNP by utilizing the population-based calling scheme. However, the effectiveness of M3 depends on the quality of the reference SNP. If the reference SNP behaves very different from the target rare SNP, the inferred genotype will likely be incorrect. In this article, we consider using additional information to further improve the quality of the reference SNP. In particular, we consider the use of samples with known genotypes, e.g. HapMap samples, that are often included for quality control purposes. Because the HapMap samples have been extensively studied and their true genotypes can be considered known with almost certainty, the genotype calling results from these samples can provide a good metric for the performance of calling algorithms. Hence, we incorporate known genotype information from these quality control samples into the reference SNP selection procedure under the general framework for M3, and this new method is named M3-S where S denotes samples with known genotypes for calling. More specifically, M3-S utilizes known genotype information to construct three genotype clusters in the first stage, and the entire samples together with simulated data based on the inferred cluster information are then genotyped in the second stage.To address the limitation of these two approaches, we developed Mare SNPs . Compare3 to the samples with known genotypes. It can directly define boundaries of genotype clusters before genotyping other study subjects. In addition, our proposed method is computationally efficient and applicable to the large-scale intensity data , and then explain how this new method helps calling for rare SNPs. Finally we compare the performance of our method with existing methods to demonstrate its better performance.The rest of the paper is organized as follows. We first describe the two stages of our new method  denote the measured intensity values of the sth SNP for the ith individual where  with S being the total number of SNPs and n being the total number of subjects. For every SNP, we use ahs= to denote the raw intensity values of the hth subject with known genotype for the sth SNP, where h = 1, …, na and na is the total number of samples with known genotypes. In the study that motivated our work, HapMap samples were included and we obtained their “true” genotypes from the International HapMap Project [na is less than n, and each element in M = {1,…,na} can be matched to one unique element in a set N = {1,…,n}. To distinguish SNPs with study population and SNPs only containing individuals with known genotypes, the latter is named as featured SNPs. We propose the following five-step procedure to estimate the parameters of the genotype clusters.la samples, and the other is the testing set. Generally, the training samples are used to infer the parameters of genotype clusters, and the testing individuals are used to evaluate the performance of the method. In this study, we define different allocation ratios between the training samples and testing samples (ratio 1:1 or 2:1) to evaluate the impact of the allocation ratio on genotyping. For the data set that motivated this research, there are 141 samples with known genotypes. Under the allocation ratio 1:1, we consider 71 training samples, i.e. la=71, and 70 testing samples. For the allocation ratio 2:1, 94 subjects are assigned to the training group, i.e. la=94, and 47 individuals are in the testing set.(1) Step I: randomly assign samples with known genotypes into two groups. One will be called the training set with 1 collects those featured SNPs having three distinct genotypes, whereas G2 collects those featured SNPs with fewer than three distinct genotypes.(2) Step II: evaluate the quality of each featured SNP via analyzing the training samples. When the training samples are randomly selected, the number of distinct genotypes and the sample size of each genotype can be inferred from known genotypes, then all the featured SNPs with training subjects  Step III: select reference featured SNPs for target featured SNP in sth featured SNP is the target featured SNP, the two dimensional raw intensity vector bhs [sth featured SNP and all Ref featured SNPs are classified into three genotype clusters in terms of this univariate variable.(4) Step IV: calculate the Mahalanobis distance between the target featured SNP with training samples and each Ref featured SNP containing the same training samples. Here, the Mahalanobis distance measures the similarity between the target featured SNP and each Ref featured SNP, where the optimal Ref featured SNP is selected based on minimizing this distance. To simplify the calculation, when the ble bhs , and theds) is obtained by the following equation,SR is the set of Ref featured SNPs selected for the sth featured SNP; bhs and bhr are the projected intensities of the hth training sample for the sth featured SNP and the rth Ref featured SNP separately; and sh is the standard deviation of bhs and bhr. So, the best Ref featured SNP selected through ds may be most informative for the clustering of the sth featured SNP.The minimum Mahalanobis distance (sth featured SNP. If sth featured SNP and the best Ref featured SNP (such as: the rth Ref featured SNP). In the second case, the training samples in the sth featured SNP are not adequate to construct the three genotype clusters, and an appropriate Ref featured SNP could help the sth featured SNP estimate three cluster information. A new combined matrix  Step V: estimate parameters of three genotype clusters from the training individuals. When lask and lark denote the number of training samples at the kth cluster for the sth featured SNP and rth Ref featured SNP, respectively; lask+lark) x 1 column vector with all elements equal to 1; lask x 1 column vector with all elements equal to 1; and kth genotype group for the sth featured SNP.where μask is an 1 × 2 vector measuring the average intensity of training samples in the kth cluster for the sth featured SNP; Σask is a 2 × 2 covariance matrix of the kth cluster at the sth featured SNP.Note that In summary, the first stage focuses on selecting reference featured SNPs to better estimate the parameters of the three genotype clusters.In the first stage, samples with known genotypes are first analyzed separately to infer three genotype clusters for each SNP denoted by AA, AB, and BB. We use B to denote the less common allele, and all possible genotypes are denoted as: AA, AB, or BB. Let  Project , 4. From(1) Step I: simulate intensity data according to the inferred parameters of the three genotype clusters from the training samples with known genotypes.m additional individuals at each SNP from the above Gaussian mixture distribution. Parameters μask and Σask  could adequately provide the center and variability of the three genotype clusters for each SNP, and each cluster contains equal number of simulated subjects  Step II: genotype calling using both observed and simulated data.xis=, and the pair of simulated raw intensities are yjs=, then the combined raw intensity values at the sth SNP ts, where the number of components is fixed at three. Besides, we introduce a null component for those individuals whose genotypes are difficult to be assigned to one of the three clusters. In principle, this model assigns the wth pair of the combined raw intensities tws to one component with probability πsk where k measures three components corresponding to the three genotypes. The latent genotype class is denoted by the indicator variable zws generated from a multinomial distribution where zws takes the value of 1, 2, or 3. Then the three-component GMM can be expressed as:The pair of original raw intensities are mponents , to ts,n∗ is the total number of individuals collected at the sth SNP and simulated data where n∗=n+m, and S is the total number of SNPs. The normal density Φ has mean μsk and variance-covariance matrix Σsk in the kth cluster for the sth augmented SNP data; all pairs of raw intensity at the sth augmented data are measured by ts=  where πs= , μs=, and Σs=.where u+1)th iteration of the indicator variable zws=k  is inferred byThrough solving the score equation, the maximum likelihood estimates (MLEs) of the above parameters can be easily estimated . The (u+μsk and variance-covariance matrix Σsk areThe relevant iterative estimates for the mean qs), for the sth augmented data are calculated to quantify the quality of SNP calling [Two values, Posterior Rate , GenoSNP (a SNP-based approach), and M3 (a hybrid of the previous two approaches). In this data set, 38 HapMap samples were measured multiple times, using a total of 141 arrays. We focused on SNPs from chromosome 22 with a total of 15,020 SNPs. The performance of each genotyping method was evaluated by the comparison results between SNP calls inferred by each calling method and those from the International HapMap Project for these HapMap samples [M3 and M3-S. The effects of different thresholds on genotyping are summarized in Additional file We analyzed Illumina Omni 1M array data collected from 3258 samples to compare the performances of MM3-S (99.38 %) has the best call accuracy and high call rate, followed by M3, GenoSNP and GenCall. We can also see that M3 gives the highest call rate (99.77 %), followed by M3-S, GenoSNP, and GenCall.Because there were 141 HapMap subjects, we used their “true” genotypes to evaluate the accuracy of different calling algorithms. We varied the numbers of training and testing samples  to explore their impacts on genotyping. The effectiveness of two allocation designs is summarized in Table 3-S to see the impact of simulated data on the genotyping, especially for rare variants. 600, 1500, or 3000 simulated samples are roughly 1, 1/2 or 1/5 times the original study population. In brief, the performance of our proposed calling algorithm based on three different simulation designs is evaluated through the comparison between the genotypes of testing HapMap samples inferred from M3-S and the genotypes of these subjects inferred from the HapMap project. Table 3-S to infer genotypes. Because GenoSNP, M3 and M3-S have been developed to focus on calling less common SNPs, Table 3-S has the best call accuracy (99.22 %~ 99.28 %) and high call rate (99.59 %~ 99.81%) for rare SNPs with MAF < 0.1, 0.05, and 0.01, followed by M3, GenoSNP and GenCall.We simulated different sizes of samples  in the second stage of M3-S) depends on the accurate estimations of the three genotype clusters from the subjects with known genotypes in the first stage, and adequately simulated subjects from the Gaussian mixture distribution in the second stage. For parameter estimations of the three genotype clusters, the reference SNP selection method [The successful application of this proposed calling procedure   to infer genotypes at rare SNPs, but M3-S uses one SNP’s individuals plus 3000 simulated subjects (3258+3000) to infer genotypes at these rare SNPs. The change in sample size of two methods results in the difference of call rate between these two approaches. Moreover, Additional file 3-S, GenCall and GenoSNP.Here, we extend our analysis to the entire study population, not just testing HapMap samples. For all study subjects, the call rate and the concordance rate of the four calling algorithms are compared, where the call rate is the ratio of genotypes that can be inferred from each calling method to those that need to be genotyped, and the concordance rate is the genotype agreement between two algorithms. The overall comparison results are summarized in Table %) Table . Besides3 and M3-S fall in between in terms of the number of SNPs not meeting HWE. Specifically, we find that M3-S may make more SNPs fail the HWE test when this approach enlarges the number of samples for simulated data. It seems that improving call accuracy for rare SNPs is in conflict with guaranteeing the quality of SNPs via HWE test. People have to select the appropriate samples size for simulated data to balance the above two criteria.Hardy-Weinberg Equilibrium (HWE) test is an important criterion to examine genotyping quality as failing HWE test may indicate calling errors. We performed HWE tests for the four population groups: Hispanic African-American, non-Hispanic African-American, Hispanic European-American, and non-Hispanic European-American, separately. Table 3-S was motivated from a data set with HapMap samples genotyped as part of the study, but many studies do not have these valuable samples collected. In this case, we may use pairs of raw intensity of HapMap subjects provided by the International HapMap Project [M Project , but raw Project –21, we a3-S applies the reference SNP selection step to samples with known genotypes for improving the missing rate and call accuracy for rare SNPs, especially for SNPs with very low MAF. The successful application of M3-S is to select the appropriate Ref featured SNP from the whole genome. In practice, it is not practical to search the Ref featured SNPs from the entire genome due to plenty of tedious calculation involved, then the instrumental featured SNPs near the target featured SNP are picked out. The assumption about identical probe response of all SNPs allows each SNP to borrow information from other good quality SNPs. When some probes break this assumption, searching for the most optimal Ref featured SNP is still an open question. Besides, the reference SNP selection is based on maximizing the mathematical similarity between the target featured SNP and the Ref featured SNP. Because the probe intensity is highly correlated with 50 base probe sequence, incorporating the probe sequence information in the reference SNP selection procedure may greatly improve the quality of SNP calls.M3-S is based on the collection of samples with known genotypes. However, some SNP-array data may not contain individuals with known genotypes, which results in the restricted use in M3-S. Fortunately, M3-S is a supplement to our previous method M3 [3 method if their SNP array data do not contain any HapMap samples. If the data have samples with known genotypes, they could apply M3-S. Scientists could try these methods according to their requirements. Recently, a large amount of rare variants are widely captured in many SNP array data, some new powerful calling algorithms have been proposed for accurately calling rare SNPs, such as: optiCall [The prerequisite for running Method M3 . We strooptiCall  and zCaloptiCall . To bett3 to combine the benefits of both population-based and SNP-based strategies. Although M3 outperformed other methods, it is not able to use samples with known genotype information when they are available in a study, often as quality control samples. In this study, we propose M3-S to take advantage of genotype cluster information from the samples with known genotypes to further improve M3 on genotyping at rare SNPs. M3-S is a two-stage procedure where the reference SNP selection method is applied to samples with known genotypes at rare SNPs to estimate the parameters of the three genotype clusters, followed by simulating additional samples from the Gaussian mixture distribution, and fitting GMM to the augmented data for genotype calling. The superiority of this method rests on two aspects. First, samples with known genotypes help define the boundaries of three genotype clusters before finally genotyping. Second, adding simulated data in the original population greatly enlarges the sample size while genotyping. These two aspects help us improve genotyping technique for rare SNPs.Most genotyping approaches for microarray data are population-based methods, e.g. GenCall with GenTrain, that infer genotype clusters from a large number of samples to achieve a certain call accuracy. Although it may work well for common SNPs, it may perform poorly for rare SNPs where genotype clusters cannot be reliably established unless a very large number of samples are available. A SNP-based method, GenoSNP, was designed to address this problem, but many more SNPs inferred from GenoSNP tend to fail the HWE test which indicates the violation of the strong underlying assumption for GenoSNP to succeed. Recently, we proposed M


Plasmodium falciparum throughout West Africa are closely related, as gene flow is relatively unrestricted in this endemic region, but markedly varying ecology and transmission intensity should cause distinct local selective pressures. Genome-wide analysis of sequence variation was undertaken on a sample of 100 P. falciparum clinical isolates from a highly endemic region of the Republic of Guinea where transmission occurs for most of each year and compared with data from 52 clinical isolates from a previously sampled population from The Gambia, where there is relatively limited seasonal malaria transmission. Paired-end short-read sequences were mapped against the 3D7 P. falciparum reference genome sequence, and data on 136,144 single nucleotide polymorphisms (SNPs) were obtained. Within-population analyses identifying loci showing evidence of recent positive directional selection and balancing selection confirm that antimalarial drugs and host immunity have been major selective agents. Many of the signatures of recent directional selection reflected by standardized integrated haplotype scores were population specific, including differences at drug resistance loci due to historically different antimalarial use between the countries. In contrast, both populations showed a similar set of loci likely to be under balancing selection as indicated by very high Tajima’s D values, including a significant overrepresentation of genes expressed at the merozoite stage that invades erythrocytes and several previously validated targets of acquired immunity. Between-population FST analysis identified exceptional differentiation of allele frequencies at a small number of loci, most markedly for five SNPs covering a 15-kb region within and flanking the gdv1 gene that regulates the early stages of gametocyte development, which is likely related to the extreme differences in mosquito vector abundance and seasonality that determine the transmission opportunities for the sexual stage of the parasite.Locally varying selection on pathogens may be due to differences in drug pressure, host immunity, transmission opportunities between hosts, or the intensity of between-genotype competition within hosts. Highly recombining populations of the human malaria parasite  Plasmodium falciparum  determining the ability of parasites to survive inside mosquitoes , and comparison with a population sample previously taken from a lower transmission area in The Gambia  and crt (on chromosome 7); however, we observe only weak evidence of selection around the antifolate drug target dhps and none around dhfr (consistent with antifolates never having been first-line treatment in Guinea), contrasting with the history of drug use and selection in The Gambia. Further evidence of selective differences was provided by analysis of genome-wide patterns of FST divergence between these two closely related populations, identifying a small number of loci with extremely highly differentiated single nucleotide polymorphism (SNP) frequencies, the strongest being a cluster of SNPs on chromosome 9 within and flanking the gdv1 gene, which plays a key role in early-stage gametocytogenesis. Findings here reveal a similar subset of genes in each population with patterns of polymorphism consistent with balancing selection, whereas there were more differences in the loci implicated as under directional selection. For example, in Guinea there is evidence of recent selective sweeps on regions containing chloroquine resistance genes P. falciparum clinical isolates collected from the N’Zerekore area of Guinea  enabled identification of 99,305 SNPs that were polymorphic in the population. Allele calls for all isolates were present for 80,546 SNPs, with the remaining 18,759 positions missing data in <5% of the population sample. The vast majority of SNPs had a low minor allele frequency within the population, with 67,854 (68%) being observed in only a single isolate (A). Coding sequences had higher read coverage compared with intergenic regions, as expected, due to less extreme A+T nucleotide richness, and as a result, 68% of all SNPs called were located within genes. Four thousand seven hundred eighty six of the 5,188 genes analyzed  contained at least one SNP (B). To determine whether inferences from the analyses performed in this study were unique to the population sampled in Guinea or present across West Africa, we also reanalyzed previously sampled data from a Gambian population of lower endemicity  relative to that present over the whole sampled local population (s). In Guinea, Fws scores of individual infections ranged from 0.18 to 1.00  . All subsequent population-genetic analyses were undertaken with both the whole data set and also with the subset of predominantly single genotype infections. Results were very similar, so the analyses on the complete data set are presented in the following sections .Within each sampled infection, on index , which san 0.97) , whereasD values were mostly negative, with a mean of −1.76 , only 103 genes (2.5%) having positive Tajima’s D values. These predominantly negative values are consistent with previous analyses indicating a historical population expansion of P. falciparum in Africa  compared with Guinea, but there was a strong correlation in Tajima’s D values across all genes between the two populations . In terms of the top outlier genes, it is notable that 18 of the 26 genes with a Tajima’s D value >1 in Guinea also had a value >1 in The Gambia  or at each of the other stages individually (P < 0.05 for each comparison), with the exception of the late ring stage . This association between stage of expression and Tajima’s D values for the Guinea data is similar to those obtained in a previous analysis performed on the Gambian data , gene ontology (GO) term analysis was conducted. Genes associated with receptor activity (GO:0004872) and pathogenesis (GO:0009405) were found to be highly significantly enriched (P < 0.001) among genes with high Tajima’s D values in the population sample from Guinea or The Gambia. Genes annotated as having membrane-localized products were also significantly enriched among those with high Tajima’s D values in The Gambia (P = 3.9 × 10−3) or Guinea (P = 0.011) .To assess whether genes associated with putative functions were enriched among the group of genes with high Tajima’s supplementary table S5, Supplementary Material online). These identify windows containing genes that are likely to have been under exceptionally strong recent positive selection. There were strong signatures around the two major chloroquine resistance genes (crt on chromosome 7 and mdr1 on chromosome 5) but not surrounding dhfr on chromosome 4, which confers resistance to pyrimethamine. A weak signature, involving high |iHS| values for only two SNPs, was observed around the sulphadoxine resistance gene dhps on chromosome 8. These results contrast with those from The Gambia, where sulphadoxine–pyrimethamine was widely used for first-line treatment, and where strong signatures of recent selection were identified around dhfr and dhps  and identified 10 chromosomal loci that had two or more SNPs with a standardized |iHS| > 3.29 (top 1% of the expected distribution) and at least one SNP with an |iHS| > 5  and a cluster of different antigen genes , respectively. Although the window with elevated |iHS| containing msp1 spans a 293-kb region, 14 of the 16 supporting SNPs are located within msp1 itself, indicating that selection causing the signature on chromosome 9 is likely to have directly targeted MSP1.The genomic region containing the largest number of SNPs with a high |iHS| score in Guinea is located near one end of chromosome 6, for which a similar signature was previously observed in The Gambia  fig. 5)fig. 5) asupplementary fig. S2, Supplementary Material online).Using 112,089 SNPs genome-wide for which there were no missing data, principal components analysis could not separate most isolates from the two populations. Although a small number of isolates from each population appeared as slight outliers, these were not very divergent, and the first three principal components in combination accounted for only 8.6% of total variation , consistent with the minimal genetic divergence previously estimated between these sites in analysis of microsatellite polymorphisms with independent samples  that functions to initiate early gametocytogenesis (gdv1 and its neighboring gene PF3D7_0935500 but closer to gdv1. These five SNPs are in strong linkage disequilibrium (LD) with each other .The ulations . The ave samples , and onl samples . Eight Socus crt . The fivogenesis , whereasP. falciparum as the parasite has a high rate of recombination, large effective population size, and high rates of gene flow throughout the region, particularly in West Africa , while several other genes encoding vaccine candidate antigens had moderately positive values of Tajima’s D . Particular antigen genes have shown consistent evidence indicating balancing selection within different sampled populations  encoding the gametocyte development 1 protein (gdv1)  . It is pn (gdv1) . Inductin (gdv1) , but gencrt locus reflects differences in the intensity and timing of selection by chloroquine in the two populations, as the resistance allele frequency is highly labile and declines due to fitness costs after the use of chloroquine has ceased . Identification of genes under more moderate processes of differential selection locally is likely to be most effectively achieved by genome-wide analysis of additional populations to build up relevant data sets for pairwise and matrix analyses. This is warranted as the control of major infectious diseases such as malaria requires intensive efforts, which should be guided by a thorough understanding of adaptive processes occurring in pathogen populations in different endemic areas.The signature of differentiation of allele frequencies at the s ceased . Indeed,Permission to conduct the collection and analysis of clinical samples was granted by the Comite d’Ethique National Pour la Recherché en Santé, Republique de Guinee  following review of the proposed research. Written informed consent was obtained from a parent or guardian of each child included in the study, and locally authorized treatment for malaria with Artesunate-Amodiaquine was provided regardless of inclusion in the study.P. falciparum malaria parasites was performed by rapid diagnostic test , and a venous blood sample of up to 5 ml was requested from each patient that had a parasite positive test. Blood was collected in ethylenediaminetetraacetic acid vacutainers, depleted of leukocytes using a standard protocol of filtration through CF11 cellulose columns  and confirmation of P. falciparum parasitaemia.Malaria patients were sampled from local health facilities located within 25 km of the regional hospital in N’Zerekore, Republic of Guinea between March and May 2011. Patients were eligible for recruitment if they were children more than 1-year old presenting with an axillary temperature of > 37.5 °C or history of fever within the last 48 h. After consent, detection of  columns , and theP. falciparum underwent quality control screening before sequencing. For 132 (94%) of the samples, the amount and purity of P. falciparum DNA was above minimal levels recommended by the sequencing pipeline at the Wellcome Trust Sanger Institute, so sequencing proceeded on the Illumina HiSeq platform using previously developed protocols . Reads were mapped to the P. falciparum 3D7 reference sequence  using SMALT  with default parameters, and SNPs were called using SAMTOOLS as applied previously to a Gambian data set , if they were located within the hypervariable var, rifin, and stevor gene families, or were positioned within repetitive sequences as identified by Tandem Repeat Finder . Data were then filtered to exclude isolates and SNP positions with excessive missing calls . The filtered population data set for the N’Zerekore population consisted of 100 isolates and 99,305 biallelic SNPs, with allele calls for each isolate available for 80,546 SNPs.DNA preparations extracted from 140 leukocyte-depleted clinical samples confirmed to contain rotocols . Sequencdata set . For eacSequence data from the previously studied population from the Greater Banjul area of The Gambia  were reaFWS metric, calculated as previously described (Hw) were calculated from the relative allele frequencies and compared with the local population heterozygosity (Hs), to derive Fws = (Hs − Hw/Hs). For this analysis, individual alleles with a coverage of <5 reads and positions with a total coverage of <20 reads were classified as missing data. Isolates with >20% missing SNP data and SNPs with >10% missing isolate data were discarded, producing a final set of 54,175 Guinean and 33,290 Gambian SNPs. Isolates with Fws scores of >0.95 were classed as having a single predominant genotype due to limited genome-wide diversity, with this subset used to assess whether the whole population analysis was affected by the inclusion of diverse complex infections.Within-host diversity was assessed through the escribed . For allD indices . Signatures of positive directional selection in the Guinea population were identified using the standardized |iHS|, which was calculated for each SNP with no missing data and a minor allele frequency of >0.05 . Median values of Tajima’s D scores for the set of genes with an expression peak at each stage were calculated, and Mann–Whitney tests were used to assess the significance of pairwise differences between the Tajima’s D scores for genes grouped by stage of peak expression.Expression time-series query in PlasmoDB  was usedD value >1.0 were classed as genes of potential interest for GO analysis. Analysis was performed using TopGO . P values were calculated using Fisher’s exact test and adjusted to account for the GO graph topology using the weight algorithm proposed previously .


BADH1 and BADH2 are two homologous genes, encoding betaine aldehyde dehydrogenase in rice. In the present study, we scanned BADHs sequences of 295 rice cultivars, and 10 wild rice accessions to determine the polymorphisms, gene functions and domestication of these two genes. A total of 16 alleles for BADH1 and 10 alleles for BADH2 were detected in transcribed region of cultivars and wild species. Association study showed that BADH1 has significant correlation with salt tolerance in rice during germination stage, the SNP (T/A) in exon 4 is highly correlated with salt tolerance index (STI) (P<10−4). While, BADH2 was only responsible for rice fragrance, of which two BADH2 alleles (8 bp deletion in exon 7 and C/T SNP in exon 13) explain 97% of aroma variation in our germplasm. Theses indicate that there are no overlapping functions between the two homologous genes. In addition, a large LD block was detected in BADH2 region, however, there was no large LD blocks in a 4-Mb region of BADH1. We found that BADH2 region only showed significant bias in Tajima’s D value from the balance. Extended haplotype homozygosity study revealed fragrant accessions had a large LD block that extended around the mutation site (8 bp deletion in exon 7) of BADH2, while both of the BADH1 alleles (T/A in exon 4) did not show large extended LD block. All these results suggested that BADH2 was domesticated during rice evolution, while BADH1 was not selected by human beings. Spinacia oleracea L.), amaranth (Aramanthus spp.), barley (Hordeum vulgare L.) and sorghum (Sorghum bicolor L.), BADH plays a role in abiotic stress tolerance through the accumulation of GB from betaine aldehyde (BA). Conversely, plant species such as tobacco (Nicotiana tabacum L.), tomato (Solanum lycopersicum L.) and rice (Oryza sativa L.) reportedly do not accumulate GB due to insufficient BA sh4 [rc [c [prog1 , GS3 [233 [badh2 .The badhvolution . The evoBADH1 and BADH2 gene from 295 cultivated rice and 10 wild rice by re-sequencing and (2) investigate the function of BADHs on the salt tolerance at germination stage and rice fragrance, and their overlapping functions. Lastly, we (3) investigated evolutionary path of BADHs in Asian rice.The goal of this study were to: (1) identify sequence polymorphism in the Two hundred and ninety-five rice varieties used for the present study were collected from RDA gene bank of Korea. Among the 295 accessions, 137 accessions were selected from worldwide 4,046 accessions, based on 15 SSRs by a heuristic approach using PowerCore software ,25, the Seeds of each accession were sterilized in sodium hypochlorite (1%) solution for 10 min and then washed twice with deionized distilled water. Then, the seeds were placed in petri dishes with two-layer filter paper and soaked in 200 mM NaCl solution, which was previously determined as an the optimal concentration (data not shown) to distinguish salt stress tolerance and sensitivity. The salt tolerance index (STI) was used to evaluate salt stress tolerance, where STI (%) =  × 100.The aroma of rice grains and leaves were measured by a sensory evaluation according to the method of Amarawathi et al. (2008) with minor modifications. Ten milled rice grains were placed in a 15 ml tube containing 8 ml of 1.7% KOH and incubated at 30°C water bath for 10 min with lids on. The lids were then opened and samples were smelled. The aroma of young leaves was checked by the same method to confirm the results for rice grain.https://github.com/najoshi/sickle). High-quality reads were aligned to the rice reference genome IRGSP-1.0 (http://rapdb.dna.affrc.go.jp/download/irgsp1.html) using the Burrows-Wheeler Aligner (BWA) (version 0.7.5a) with default parameters [http://broadinstitute.github.io/picard/). By using Genome Analysis Toolkit (GATK) (version 2.3.9 Lite) [Oryza rufipogon and 5 Oryza nivara) were downloaded from the National Centre for Biotechnology Information Sequence Read Archive 023116 [BADH1 and BADH2 were purified from whole-genome resequencing data.HiSeq 2000 and HiSeq 2500 were employed for whole-genome resequencing of the 295 rice accessions. Raw sequences were first processed to obtain an average of quality score (QS) per read ≤ 20 by trimming 3'-end of reads using SICKLE (rameters . The rea.9 Lite) , regionae 023116 . The polBADHs without any missing data. To get reliable results, P-value ≤ 0.001 was considered to have a significant effect on each trait.Genotypic and phenotypic data files were prepared and imported to TASSEL4.0  for assoBADH genes. Sliding windows of 1 kb and 3k were used to estimate nucleotide diversity for the 4-Mb region of each gene. In each window, the π and Tajima’s D value was calculated using VCFtools [To identify LD blocks in both wild and cultivated rice populations, we used Haploview software with “-dprime–minMAF 0.05 –hwcutoff 0.001 –blockoutput GAB –pairwiseTagging” parameters . AverageVCFtools . The patVCFtools .BADH1 and BADH2, were evaluated for salt stress index and rice fragrance. The average STI is 1.14. Among these, RWG-140 had the highest stress tolerance . However, RWG-222 had the lowest STI (0.26), which is salt stress sensitive. In the rice aroma test, total 12 accessions were evaluated to be fragrant rice , seven SNPs were found in the exon region, three SNPs and one insertion were in the untranslated region (UTR) in BADH1  one G/A SNP (P1141) in exon 1 leading to substitution from arginine to histidine; 2) one T/A SNP (P11483) in exon 4 leading to asparagine to lysine substitution; 3) one C/A SNP (P13605) in exon 11 leading to substitution from glutamine to lysine; 4) one T/C SNP (P13612) in exon 12 leading to substitution from isoleucine to threonine; 5) one G/T SNP (P13883) in exon 12 leading to substitution from glutamic acid to aspartic acid. Two non-synonymous mutations were represented in wild type as heterozygous SNPs. One is T/A SNP (P11232) in exon 3 leading to a substitution from aspartic acid to glutamic acid, the other was a G/T SNP (P13672) leading to a substitution from arginine to leucine in exon 11  8-bp deletion in exon 7 (P23036) leading to premature transcription termination; 2) one C/A SNP (P24488) in exon 10 leading to amino acid substitution from alanine to glutamic acid; 3) three base-pair deletion (P25240) in exon 12 leading to loss of one amino acid; 4) one C/T SNP (P25390) in exon 13 leading to substitution from alanine to valine; and 5) one base pair insertion in exon 14 resulting in frameshift and premature transcription termination have been detected. One synonymous SNP (P24528) in exon 10 has been found in two varieties. Two non-synonymous SNP were detected in wild rice, 1) C/G SNP (P2205) in exon 1 resulting to alanine to glycine substitution; and 2) C/T SNP (P22685) in exon 5 resulting to alanine to valine substitution. Those two alleles were heterozygous and initially detected in BADH2. In the UTR, three InDels, P214 (1 bp insertion), P236 (1 bp deletion) and P241 (insertion in the 5’UTR) and two SNPs (P224 C/T in the 3’UTR and P26038 in the 3’UTR) were detected.Eighteen alleles were detected in BADH1 and BADH2 region, but some accessions had low coverage or missing sequence in these gene regions. After removing accessions with missing sequence data, a total of 205 accessions remained. Among these 205 accessions, totally 19 SNPs and InDels were detected in BADH1 and BADH2 transcribed regions  of 0.12 between P11483/P13605/P14811 and STI (P<10−4) in BADH1  explain 97% of aroma variation in our germplasm. However, no association existed between aroma and BADH1  is the major allele which is associated with rice fragrance . Here we follow the method which was used by Kovach et al. (2009) for domestication study. Firstly, we divided the whole 205 accessions into two subgroups: the one that have 8-bp deletion in exon 7 (badh2.1), and without 8-bp deletion (Wild). Then we investigated the extended haplotype homozygosity of BADH1 and BADH2 regions to find the evidence for artificial selections. We calculated the EHH for BAHD2 gene by comparing the haplotypes of fragrant (badh2.1) and wild type  group. The badh2.1 had a large LD block that extended around the mutation site, while LD decreased rapidly around the wild type alleles  allele was significantly correlated with salt tolerance, we separated the 205 individuals into two subgroups, one included “T” allele (Allele_T group), another one included “A” allele (Allele_A group). STI values of the “T” allele subgroup were significantly higher than the “A” allele subgroup  and wide type (P11483 T). However, these two groups carrying A or T alleles did not exhibit large difference, both of them did not show large extended LD block, reducing rapidly around P11483  was significantly biased from the balance  suggested that BADH2 is also responsible for salt stress tolerance. The relationship between these two BADH genes and salt stress tolerance or fragrance in rice is unclear. Various studies have reported conflicting results [ in rice . As the  results . These iBADH1 and BADH2 genes. For BADH1, we found a total of 10 genetic variants, seven SNPs were in the exon region and two SNPs and one insertion were in the untranslated region , P13605 (C/A) in translated region and P14811 in the 3’UTR were highly correlated with STI with, P < 10−4 and R2 around 0.12  did show a difference between fragrant (badh2) and non-fragrant (BADH2) rice for the ability to produce mature seed in the presence of salt. Tang et al. (2014) proved that BADH1 was associated with rice salt (NaCl) tolerance at seedling stage by using RNAi-directed knock down of BADH1 [BADH1 and BADH2 are both suggested to be responsible for salt stress tolerance in rice, but, possibly, at different growth stages. Since our STI was tested at germination stage, we extended the conclusion that BADH1 is response for rice salt stress at germination and seedling stage, while BADH2 may be accountable at the reproductive stage.Fitzgerald et al. (2008) reported that the expression level of of BADH1 . Thus, B3036 (8 bp deletions) and P25390 (C/T) were the main alleles that explained the aroma variation with 74% and 23% respectively , demonstrating that BADH1 (and not BADH2) contributes to salt stress tolerance. However, we cannot exclude the possibility that these contradictions may be resulted from the different conditions undertaken.Singh et al. (2010) reported that there’s no association between salt tolerance and common BADH2 led by Kovach et al. (2009) suggested that BADH2 is one of the domesticated genes responsible for rice fragrance. In this study, we attempted to determine if BADH1 is also a domesticated gene.Domestication of crops has historically been critical for human reproduction and development. The domestication of rice were explained by several models . The rec1483, P13605 and P14811) in BADH1 highly correlated with STI. These three alleles were consistently present together in the rice genome. We selected P11483 (T/A) as the key allele for an evolution study of BADH1. To validate our method, we tested the domestication signal of BADH2 using the same method (EHH) as Kovach et al. (2009). Our EHH test result was similar to the results of Kovach et al. (2009). The fragrant accessions which carrying 8 bp deletions in exon 7 showed a large LD block extended around the mutation site, while the wild type alleles relatively reduced rapidly  over 7 kb, while fewer LD blocks were found in wild type group  found that the genetic diversity is strongly reduced in the aberrima . O. glab. sativa . As a ge. sativa . Our res. sativa . HoweverS1 Figbadh1.3, badh1.5 and badh1.6 were detected by previous study [badh1.1, badh1.2, badh1.4 and badh1.7 were detected in this study.(DOCX)Click here for additional data file.S2 Figbadh2.1~badh2.2 [badh2.11~badh2.12, badh2.14 [badh2.13 [badh2.15 [badh2.16~badh2.18 were detected in this study.~badh2.2 ; badh2.1badh2.14 ; badh2.1badh2.13 ; badh2.1(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(Excel)(XLSX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Monozygotic twins arise from a single fertilized egg and, thus, for practical purposes are genetically identical. Therefore, in cases where one of the identical twins is associated with forensic biological evidence through DNA typing, the other twin cannot be excluded either. This conundrum of the ultimate “my brother did it” scenario has at times placed the legal community in a difficult position for solving the source of biological evidence in such cases. Indeed, identical twins are expected to have the same, for example, short tandem repeat loci, profile, no matter how highly individualizing the current forensic DNA diagnostic system is.Although the dogma is that monozygotic twins are genetically identical, it has been well-known for many years that there are genetic differences between such twins due to the accumulation of somatic mutations -8. UndenUntil recently, finding these few somatic mutations was not feasible routinely and very demanding technically. Over the last decade the advent of massively parallel sequencing (MPS) makes possible grand scale sequencing in which whole human genomes can be sequenced in a relatively rapid time frame and has ushered in a genetics revolution. Even forensic DNA analyses are being driven in this new direction with the promising capabilities provided by MPS. While much effort is being dedicated to the application of MPS in forensic DNA analysis, it is still considered by most in the development and validation testing phases and is not being used in casework analyses. However, recently Weber-Lehmann et al. published “Finding the needle in the haystack: Differentiating ’identical’ twins in paternity testing and forensics by ultra-deep next generation sequencing”  in whichThe authors sequenced reference DNA with the Illumina HiSeq 2000 platform using chemistry v3.0 and 2 × 100 bp paired-end read mode from sperm samples of two identical twins  and from a blood sample of the child of one twin. With such high throughput of approximately 600 gigabases, whole genome sequence data were generated resulting in about 90X coverage on average per twin. The child’s genome was sequenced on average to 56X. After bioinformatics processing to remove noisy data, five single nucleotide polymorphisms (SNPs) were observed in the father and child pair that were not detected in the other twin . Once these SNPs were identified, directed PCR assays were readily developed and standard Sanger sequencing verified the SNPs. Thus, the validity of the SNPs was bolstered by orthogonal testing. The mother of the child was tested for these SNPs using the directed assay, and she was excluded as the source of the child’s SNPs.This study demonstrated that it is possible and feasible to identify somatic differences between twins. Four of the five SNPs that the twin father carried were in both his buccal and sperm DNA . One of the SNPs was detected in both sperm and blood DNA. Lastly, one SNP was observed solely in sperm DNA. These findings support the hypothesis that the earlier in development of an embryo, rare SNPs may arise and be established not only in somatic tissues, but importantly in the germline.The implication of these findings for forensic investigations involving twins as the potential source is remarkable. Suppose there is a sexual assault case, semen is discovered, typed by standard DNA markers, and a “match” is observed between the evidence and an implicated twin. The primary question of differentiation of the twin who is the donor of the semen evidence can be resolved. Ideally, sequencing semen reference samples would be better for making comparisons by MPS; yet, obtaining such reference samples is unlikely. However, as long as the relevant somatic mutations that will be identified in a buccal or blood reference sample by MPS also reside in sperm DNA, the ability to differentiate the twins is highly probable. Using this investigative approach, the risk of false identification is infinitesimally low with the likely outcomes being differentiation or inconclusive. Indeed, only one or two SNPs would be needed to distinguish between the twins and there does not appear to be a need for additional statistical analyses of the outcome. Traditional STR typing already would have reduced the possible source of the biological evidence to be most likely that of one of the twins as opposed to other potential contributors.It is important to note that MPS is not being used to analyze the semen evidence with this approach. MPS is being used solely on reference samples to develop the investigative lead for identifying target SNPs. Once genetic variants are identified, typing of the evidence and the reference samples of the identical twins would be carried out using similar methodology which has been generally accepted for sequencing of mitochondrial DNA for almost two decades, i.e., targeted PCR and Sanger sequencing. Thus, the methodology that would be used for forensic analysis of the semen evidence and reference samples would be well-established and generally-accepted. Typing the evidence, and again the references samples, to detect those genetic variants using standard sequencing technology is an additional feature for supporting the reliability of the resultant SNPs. Lastly, using this approach, there is far less consumption of precious evidence than would be if MPS were used directly on the sperm DNA to identify SNPs. So, there will be, in at least some cases, sufficient DNA for retesting, if it were desired.Weber-Lehmann et al.  have shoThe author declares that he has no competing interests.


The number of SNPs per window also varied but was less dependent of known factors such as gene density, missing rate or recombination (R2=0.48). When we divided the samples in four groups, Asian wild boar (ASWB), Asian domestics (ASDM), European wild boar (EUWB) and European domestics (EUDM), we found a marked correlation in allele frequencies between domestics and wild boars within Asia and within Europe, but not across continents, due to the large evolutive distance between pigs of both continents (~1.2 MYA). In general, the porcine species showed a small percentage of SNPs exclusive of each population group. EUWB and EUDM were predicted to harbor a larger fraction of potentially deleterious mutations, according to the SIFT algorithm, than Asian samples, perhaps a result of background selection being less effective due to a lower effective population size in Europe.A comprehensive catalog of variability in a given species is useful for many important purposes, e.g., designing high density arrays or pinpointing potential mutations of economic or physiological interest. Here we provide a genomewide, worldwide catalog of single nucleotide variants by simultaneously analyzing the shotgun sequence of 128 pigs and five suid outgroups. Despite the high SNP missing rate of some individuals (up to 88%), we retrieved over 48 million high quality variants. Of them, we were able to assess the ancestral allele of more than 39M biallelic SNPs. We found SNPs in 21,455 out of the 25,322 annotated genes in pig assembly 10.2. The annotation showed that more than 40% of the variants were novel variants, not present in dbSNP. Surprisingly, we found a large variability in transition / transversion rate along the genome, which is very well explained (R In this new era of sequencing, it is feasible to routinely obtain whole genome sequence data from an increasing number of individuals, making it possible the analysis of populations at the genomic level. The availability of this large amount of data allows us to study any species variability to an unprecedented detail. An intriguing observation from these studies is that, despite intensive selection and small effective sizes, animal domestic species harbor much more variability than anticipated –4. In thThe availability of a reference genome makes it feasible the large scale variant discovery with new sequencing technologies or 'next' generation sequencing (NGS). In pigs, the last assembly of porcine reference sequence (assembly 10.2) was released in November 2012 . Althoughttp://www.ncbi.nlm.nih.gov/SNP/snp_viewBatch.cgi?sbid=1062009 and they are also available at the website http://bioinformatics.cragenomica.es/numgenomics/), obtained from analyzing 128 genomes from wild boar and domestic pig samples worldwide distributed. In addition, we report annotation, allele frequencies in four major pig groups and we infer the ancestral allele for the majority of the SNPs. Knowledge of the ancestral allele is required for many statistical tests of selection so this is an additional value of the catalog here presented.Here, we report such a catalog (data have been submitted to dbSNP at the following URL: Sus scrofa) and five outgroups . The 128 pig genomes were classified into four large groups: Asian Wild Boars , that comprise wild boars from China, Japan and East Russia; Asian Domestics , including 9 Chinese breeds like Meishan or Xian; European Wild Boars  from Spain, France, Switzerland, and the Netherlands; and European Domestics  from all major breeds Duroc, Large White, Landrace, Pietrain Hampshire and local breeds . European domestics include as well American village pigs, which have a predominant European, although hybrid, origin [We analyzed a total of 133 suid genomes , 128 piget al., submitted). Main sample details are in 9 reads, occupying around 2Tb of disk in bam format, were analyzed.All samples had been shotgun sequenced with Illumina’s technology but at different depths, ranging from ~3× in a Tibetan wild boar  to 22× iet al., [The detailed bioinformatics pipeline is in et al.,  (n = 50)et al.,  allowinget al., . For allet al.,  was ran Genotypes were called for each individual separately using samtools (v 0.1.19+) mpileup option and filtered with vcfutils.pl varFilter . We exclsamtools mpileup-Q 20-q 20-m2-Dugf Sus_scrofa10.2.fas PIG_NAME.realigned.bam | bcftools view-vcg—| vcfutils.pl varFilter-d 5-D (MEAN_DEPTH*2)+1-Q 10 > PIG_NAME.iSAM.flt.vcfIndividual vcf files were then merged using custom scripts. For each individual, missing variant positions were coded according to bcftools output without the “-v” flag to avoid variant calling; confident homozygous-reference calls were coded as ’0/0' , and the position was marked as missing ‘./.’ otherwise.VCFtools v0.1.12a  was usedThe SNP must be biallelic where at least one of the alleles is the reference allele.A)Sus spp. genomes, and be homozygous in all Sus spp. samples where the SNP is called;The SNP must be present in at least two B)P. africanus genome and be homozygous.Otherwise, it must be present in S. scrofa .The ancestral allele must be either the reference or the alternative allele in The variant calling and the merging steps were done including the five outgroup samples. An awk script was used to ascertain the ancestral allele, applying the following criteria:In sites not complying with these conditions, the ancestral allele was considered as unknown.Population allele frequencies were obtained with VCFtools . We defin where the highest number of variants was called in exactly n individuals). The count of derived alleles was performed and plotted with R package lattice .Sus scrofa reference genome assembly 10.2 [Sus scrofa 2.75 GTF annotation. Genes overlapping two or more windows were discarded. We used the recombination rate (cM/Mb) from Tortereau et al., [We evaluated whether the number of SNPs per window and the transitions / transversion rate (Ts/Tv) correlates with genome features knowing to affect variability: GC content (%GC), CpG count, gene density and recombination rate. GC percentage and CpG count were calculated based on the bly 10.2 , and gen et al.,  with the et al., :N_snps We used the logarithm transformation of recombination rate because the raw values were highly skewed.build 140). Among the terms used in the annotation, we focused on stop gain and stop lost (sequence variants which cause a premature stop codon or the stop codon is changed resulting in an elongation of the protein), missense variants (non synonymous variants) and synonymous variants (a variant in a coding region that does not change the aminoacid). The definition of all terms used is available at http://www.ensembl.org/info/genome/variation/predicted_data.html. Variant annotation was performed both on the whole data set and by group. When there was more than one alternative allele, all possible alternatives were retained. The effect of the aminoacid changes was predicted using SIFT [All variants were annotated with Ensembl variant effect predictor (VeP) pipeline v76  on Enseming SIFT ,25, a toSimulates, with the coalescence, genome data reflecting as much as possible the population analyzed.Maps simulated SNPs into a reference DNA sequence, this is done by replacing the reference base by an alternative base in the SNP position for each haplotype and produces a fasta file for each sequence; next, each individual genome is created by randomly choosing two sequences.Simulates the sequencing process producing reads that mimic Illumina’s technology; we used ART  to do sRuns BWA  to map tRecovery: percentage of original genotypes that are correctly identified.Sensitivity: percentage of callable genotypes, i.e. present in sites that pass the filters used, that are correctly identified.False Discovery Rate (FDR): percentage of genotype calls performed that are incorrectAnalyzes the output and reports several statistics of interest; among them:In this study, we used NGS data from different sources. These data are noisy and highly unbalanced, with highly variable depth across samples . MoreovePipeliner was fine tuned to duplicate as faithfully as possible the actual bioinformatics pipeline used to analyze the real data. First, to obtain the ‘real’ sequences in our sample, we simulated 256 sequences  with MaCS [NUMINDS = 128EUDM = 55ASDM = 23;EUWB = 9ASWB = 41macs NUMINDS*2 100000-t 0.0005-r 0.0005-I 4 EUWB*2 EUDM*2 ASWB*2 ASDM*2-n 1 0.2-n 2 0.5-n 3 2.5-n 4 3-em 0.049 2 4 5-eM 0.06 0-ej 0.07 2 1-en 0.09 1 5-en 0.2 1 7-en 0.21 4 10-ej 0.08 3 4-ej 10 1 4The command above simulates an older first split into two populations  and, a much more recent event, the split between domestic and wild populations in both continents (-ej 0.08 3 4 and-ej 0.07 2 1). This model is very similar to the model of . ParametFinally, alignment, variant calling and merging were done following the pipeline used for real data, which resulted in a simulated multi individual vcf file. We used mstatspop v.0.998982beta  to evaluFirst, we evaluated our pipeline by simulation as described. We need to distinguish two issues. The first one is how many SNPs out of those segregating can be recovered. This is the main target in the real data analysis and, in this case, uniformly high depth and coverage may not be so critical because a SNP position that is not covered in one individual may have been covered in another one (provided is not a singleton). The second issue is how reliably called is each individual genotype. Accurate genotype calling is important for allele frequency estimates but not so much for SNP detection; for instance, suppose a heterozygous Reference/Alternative (RA) genotype is actually called as ‘AA’, the SNP will be equally identified, but frequency estimate will be strongly biased. With Pipeliner , we evalAs for individual genotypes, we should expect according to the simulations, under the best case scenario, to recover ~70% of heterozygous genotypes, 73% of homozygotes for the alternative allele (AA) and 76% of the homozygotes for the reference allele RR, . The aveIn all, Pipeliner simulations predict that we could expect our bioinformatics protocol to be highly reliable, allowing us to uncover about 95% of all SNPs in the samples with rather low false discovery rate. In practice, the real situation is likely to be worse than simulated because we are simulating the best case scenario, without considering the true complexities of the genome, e.g., duplications, indels, repetitive sequences, unequal GC content and so on. It is nevertheless difficult to consider all these complicating factors in a simulation, and Pipeliner results should be taken as an upper limit, mainly valid for well aligned genome regions.et al, submitted). Logically, missing rate was highly correlated with average depth: the lowest depth individuals, most of them ASDM and ASWB samples, had the highest missing rate  to 4% oing rate . This hiing rate .The cumulative number of SNPs arranged by the times each SNP was called is in S. scrofa samples, a total of 48,119,476 SNPs that were called in at least one individual and passed all depth and quality filters. The majority (97.5%) were biallelic variants, whereas the rest presented 1, 3 or 4 alleles . For number of SNPs, Ts/Tv rate and recombination rate suffice to explain most of variability, whereas missing rate explains, marginally, only 3% increase in R2. This indicates that the increase in number of SNPs per window is partly explained by an increase in the number of transitions. Gene density in turn is almost irrelevant, in agreement with our previous results [In an attempt to explain how number of SNPs and Ts/Tv are interrelated, we fitted the linear models in equations  results . All var2, whereas the rest of factors are only marginally relevant. Note that GC% per se, once corrected by the other factors, is not important, nor is gene density. In all, variability in Ts/Tv rates is fairly well explained  is explained by CpG count ; this isxplained  by a difq can be obtained from Ewen’s sampling formula, N to choose is not clear. Taking N = 100 .The ancestral allele was determined for biallelic (RA only) and monoallelic (AA) SNPs. Following the rules detailed in methods, it was possible to determine the ancestral allele for 39,017,375 out of 47,266,056 such SNPs (82%). Of them, 31,939,953 (82%) had the reference allele as ancestral, whereas the opposite occurred in the remaining SNPs. The number of times the reference allele is expected to be the ancestral allele can be approximated by the frequency of the ancestral allele across SNPs in a population of size 2N, where N is the number of individuals analyzed. This frequency ing rate , the numqN θ L, where qN is Ewen’s sampling term, q is nucleotide diversity per base pair, and L, the length sequenced.We calculated the number of variants per group . The lowNext, we investigated for how many SNPs the derived allele was specific to one pig group or shared between two or more groups. To do so, we used the 34,500,122 variants where the ancestral allele was identified and called in at least 50% of the pigs in each group. n that contained the maximum number of SNPs genotyped in exactly n samples . Here w samples . Note thi.e., not present in dbSNP (build 140), variants. Most of the SNPs were annotated into intergenic (67.5%) or intronic (29.5%) regions, about 1%  were annotated into coding regions. On average we found 1,013 SNPs per gene, including coding and non coding variants, as well as upstream, downstream regions and intronic variants. The detailed number of variants per category and per chromosome is in Supplementary material The 48,119,476 variants called were analyzed by the VeP program . We foui.e., the reference allele was not found). The 174 stop lost SNPs found were located in 160 genes, 10 of them had the alternative allele fixed, suggesting a longer protein than that annotated or an error in the annotation. A summary description of these genes is in Stop gains overlapped 3,502 genes. Of them, 30 had the stop codon as homozygous in all samples  variants were found in 15,790 genes. SIFT predictions were obtained from 166,958 of these variants; 29% were predicted to have deleterious consequences (SIFT score < 0.05) on protein function . By popuet al., , we did  [et al.  pooled ATo our knowledge, we present the most comprehensive SNP catalog of any livestock species to date. Using primarily published sequences, we identified over 48 million variants in the autosomal pig genome, which is more than the 28.3M SNPs recently reported in cattle [Of the 48M identified SNPs, 46% were novel, indicating as well how incomplete are the porcine genomic resources available so far. These SNPs overlapped with 21,455 out of the 25,322 pig annotated genes, and we found an average of 1,013 variants per gene. Further, this catalog was obtained from worldwide samples, domestic and wild, making it an unbiased account of polymorphism in the species. Simulations suggest that the dataset generated should be highly reliable, at least for non complex regions where read mapping is not an issue. Simulation of the NGS pipeline with Pipeliner , using eAs in Drosophila and other species, including pigs ,37,38, wBut perhaps the most surprising observation is that this increased number of SNPs is largely explained by a correlated change in mutational bias Ts/Tv, and not by the percentage of missing values caused by shallow depth , Table 3th century onwards, which likely has introduced alleles that had been lost in the European wild boar [Ascertaining SNPs with extreme frequencies between groups is useful for traceability purposes, and to identify signatures of selection and of domestication. We looked for exclusive variants in all pairwise comparisons , and also between domestics and wild boar between and within continents, setting the minimum sample size to half the group size per each group. About 4M SNPs were segregating in all groups, suggesting that these SNPs are very old, prior to divergence between the European and Asian clades that occurred ca 1 MYA or that they were introgressed more recently in European breeds from Asia . Asian wild boar ,44.in silico automatized procedures. Therefore, the SNPs annotation provided here cannot be considered the definitive annotation; furthermore, about 8% of the pig genome is thought to be missing from current annotation [Annotation is one of the most critical and time consuming aspects of any genome, and that of the pig is still largely based on notation  score canotation . NeverthWith all these caveats in mind, it is nonetheless interesting to remark that we found a higher proportion of potentially deleterious variants in European Domestics (24%) and European wild boars (18%) than in Asian pigs 12%, . AlthougWe have carried out a large scale data mining effort of currently available pig genomes to uncover over 48M autosomal SNPs; a parallel simulation study suggests that false discovery rate should be very low, at least in genome regions with good 'mappability'. About 40% of the SNPs had not been reported, which shows how incomplete pig genome resources are. Intriguingly, we have found a large variability in mutational bias (transition / transversion rate) along the pig genome that is primarily explained by differences in CpG content and recombination rate. As for number of SNPs per kb, it is relatively insensitive to the rate of missing values and it depends mainly on Ts/Tv and recombination rates. The pig is a species with a very complex demographic history, where European and Asian branches isolated ~1 MYA only to be crossed in modern times to result in the most widely used pig breeds worldwide. As a result, there exists a relatively small percentage of SNPs that are exclusive of these European breeds compared to other populations. In contrast, the differences between Asian and European wild boars are much higher.DNA samples and genome analyses from all samples in this work have been published previously, and we refer to the original works for details ,13,36,37S1 Script(SH)Click here for additional data file.S1 FigRR = genotyped as homozygous for the reference; AA = genotyped as homozygous for the alternative; RA = genotyped as heterozygous.(TIFF)Click here for additional data file.S2 FigN SNPs, total number of SNPs per window; Ts/Tv, transition / transversion rate; CpG, number of CpG counts; log rec. rate, logarithm of recombination rate in cM/Mb from Tortereau et al.(TIFF)Click here for additional data file.S1 TableASDM, Asian Domestics; ASWB, Asian Wild Boar; EUDM, European Domestics; EUWB, European Wild Boar; N, number of samples; Average depth is calculated after filtering by base and map quality(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.