Beef cattle breeding programs in Brazil have placed greater emphasis on the genomic study of reproductive traits of males and females due to their economic importance. In this study, genome-wide associations were assessed for scrotal circumference at 210 d of age, scrotal circumference at 420 d of age, age at first calving, and age at second calving, in Canchim beef cattle. Data quality control was conducted resulting in 672,778 SNPs and 392 animals.Associated SNPs were observed for scrotal circumference at 420 d of age (435 SNPs), followed by scrotal circumference at 210 d of age (12 SNPs), age at first calving (six SNPs), and age at second calving (four SNPs). We investigated whether significant SNPs were within genic or surrounding regions. Biological processes of genes were associated with immune system, multicellular organismal process, response to stimulus, apoptotic process, cellular component organization or biogenesis, biological adhesion, and reproduction.Few associations were observed for scrotal circumference at 210 d of age, age at first calving, and age at second calving, reinforcing their polygenic inheritance and the complexity of understanding the genetic architecture of reproductive traits. Finding many associations for scrotal circumference at 420 d of age in various regions of the Canchim genome also reveals the difficulty of targeting specific candidate genes that could act on fertility; nonetheless, the high linkage disequilibrium between loci herein estimated could aid to overcome this issue. Therefore, all relevant information about genomic regions influencing reproductive traits may contribute to target candidate genes for further investigation of causal mutations and aid in future genomic studies in Canchim cattle to improve the breeding program.The online version of this article (doi:10.1186/s40104-017-0199-8) contains supplementary material, which is available to authorized users. Cattle breeding programs in Brazil have given greater emphasis to the study and selection of reproductive traits due to their economic importance for the production system. In males, scrotal circumference traits are related to the reproductive potential of bulls, because testis size is associated with the production and quality of sperm and the production of sex hormones .In general, female reproductive traits are difficult to measure and, in some cases, are strongly influenced by environmental factors. The reproductive performance of heifers depends on the age at which they calve for the first time; the ones that calve earlier have a more productive life . In addiMany achievements in animal breeding were obtained based on the classical approach, using information from phenotypes and pedigree. Nowadays, molecular data analyses are bringing new insights to the genetic architecture of species. Regarding reproductive traits of beef cattle, genome-wide association studies (GWAS) are useful tools for the identification of candidate genes that could be used to classify precocious or more fertile individuals , 7.The identification of candidate genes provides a better understanding of the distribution of genes that affect traits of economic interest, as well as a basis for further studies to identify causal mutations. Despite its potential, important observations are needed for this approach. According to Tabor et al. , the difThe Canchim is a composite breed (62.5% Charolais and 37.5% Zebu) developed in the 1940s by the Brazilian Agricultural Research Corporation (Embrapa), located in São Carlos city, SP, Brazil –13. One The Canchim cattle represents about 3% of the beef cattle produced in Brazil . IndicinThe estimated breeding values (EBVs) used in our study considered the following traits: scrotal circumference at weaning adjusted for 210 d of age (SC210), scrotal circumference adjusted for 420 d of age (SC420), age at first calving (AFC), and age at second calving (ASC). Linear interpolation was previously used to adjust the scrotal circumferences for 210 and 420 d of age.The EBVs were obtained from the genetic evaluation carried out by the Brazilian Agricultural Research Corporation (Embrapa) for the Canchim breed which considered 318,307 animals in the relationship matrix and 267,002 animals with valid records. In general, the traits analyzed by Embrapa for the genetic evaluation of Canchim that could be highlighted are body weight traits measured at various ages, reproductive traits of males and females, carcass quality, navel visual score of males and females, and hair coat. The EBVs were estimated by multi-trait analysis using the REMLF90 program , under aAs described by Mokry et al. , the genP < 10–5) from the Hardy-Weinberg equilibrium, SNPs with minor allele frequencies of less than 0.05, and call rate for SNPs and animals lower than 0.90. Only autosomal chromosomes and SNPs with known positions, according to the UMD_3.1 bovine genome assembly [The BovineHD BeadChip SNP panel  was used to genotype 194 males and 205 females: 285 animals of the Canchim breed and 114 animals of the MA genetic group, calves of 49 bulls and 355 cows. The animals were born between 1999 and 2005 and come from seven farms in the States of São Paulo and Goiás. A detailed description of these animals was previously presented by Buzanskas et al. . Genotypassembly , were usXi = ', in which Xi represents the EBVs for the ith animal. The genotype of the animals was coded as “0”, “1” or “2” and, as the genotypes were represented by Yi = ', in which Yi = ½ * , the respective proportions would be equal to 0, ½ and 1. The expected SNP allele frequency is represented by μ, in which μ = ' = E(X| Y), thus 0 < μi < 1.The Generalized Quasi-Likelihood Score (GQLS) method  was usedμi with Xi, the following logistic regression was defined:β0 is the constant term and β1 is the slope.To associate μi can be interpreted by i = 1 ,  …  , n. The mean vector of Yi no longer depends on Xi, which becomes μi = E(Y) = μ1, and “1” is a vector. The solution of the “quasi-likelihood score” results in an estimate for μ, such that − 1 is the inverse of the relationship matrix of the individuals.Under the null hypothesis, To obtain the GQLS, the statistic WG was calculated as in Feng et al. , as follP-values for each SNP. This method does not account for the genetic variance explained by the marker. The GQLS provides the advantage of accounting for the population structure by means of the pedigree-based relationship among animals (A − 1).Under the null hypothesis, WG follows a Chi-squared distribution with one degree of freedom , resultiP-values of each SNP were sorted in ascending order and the following formula applied:q is the desired level of significance, m is the total number of SNPs, and P is the P-value of the ith SNP. To account for multiple comparisons, a genome-wide and chromosome-wise FDR of 5% (significant association) and 10% (suggestive association) was applied. The main reason for considering a minimum FDR of 10% was to obtain a comprehensive number of SNPs, which might assist to comprehend the genetic architecture of the studied traits.The false discovery rate (FDR) was used for multiple testing corrections  to verif2 measure [2 and plot the results. Due to the high linkage phase consistency between Canchim and MA genetic group (above 0.80 up to 100 kb) [Pairwise linkage disequilibrium was estimated using the r measure  for the  measure  was used 100 kb) , analyseThe SNPs associated with the EBVs for SC210, SC420, AFC, and ASC traits were surveyed to their corresponding genes or to surrounding genes. The National Center for Biotechnology Information (NCBI) database  and EnseThe PANTHER tool  was usedA total of 672,778 SNPs and 392 animals were used for GWAS. The Manhattan plots for chromosome-wise (in blue) and genome-wide (in red) significantly associated SNPs after FDR correction for SC210, SC420, AFC, and ASC, respectively, are presented in Fig. In Table P ≤ 0.00001) were located on chromosomes 20 and 28. Five SNPs were located in the intergenic or intragenic regions of SMIM23, PAPD7, ICE1, and EDARADD genes, and non-coding RNA LOC101907249  for AFC (Table NXPH1 and EXOC4 genes) and 27 (ZMAT4 gene). For ASC, four SNPs were significantly associated (P ≤ 0.000001) and located on chromosomes 10 (FMN1 gene), 11 (TMEM182 gene and LOC790871 pseudogene), and 15 (UBQLN3 gene). Biological processes for SC210, AFC, and ASC are presented in Fig. Six significantly associated SNPs , cellular (22 genes), developmental (14 genes), biological regulation (13 genes), and localization (11 genes) processes. Biological processes were observed for the immune system (seven genes), multicellular organismal process (seven genes), response to stimulus (seven genes), apoptotic process (six genes), cellular component organization or biogenesis (six genes), biological adhesion (four genes), and reproduction (one gene). Pathway analysis showed that the RAP1B gene is involved in four pathways, while the IFNG, COL12A1, and SRGAP1 genes are involved in two pathways.For SC420, biological processes and pathway associations are presented in Figs. The average linkage disequilibrium for each trait by chromosome was equal to 0.70 (AFC - BTA27), 0.94 (SC210 – BTA20), 0.89 (SC210 – BTA28), 0.64 (SC420 – BTA5), 0.69 (SC420 – BTA9), 0.90 (SC420 – BTA13), 0.56 (SC420 – BTA14), 0.85 (SC420 – BTA18), and 0.99 (SC420 – BTA21). Some significant or suggestive regions presented r2 equal to zero. We highlighted the SC420 trait due to the higher number of regions presenting significant or suggestive associations . In some cases, we observed the lack of information regarding the SNP or gene identified.2 varying from 0.33 to 0.40 up to 2.5 kb).We observed high average linkage disequilibrium among significantly or suggestively associated SNPs for most of the traits studied; therefore, there is evidence of direct or indirect associations that could be affecting the traits. Mokry et al. , when stSMIM23 gene is located inside QTL regions associated with the rate of non-return to estrus in Swedish Red and Swedish Holstein breed animals [PAPD7, ICE1, and LOC101907249 gene regions associated with percentage abnormal sperm in Holstein bulls [EDARADD gene participates in cell differentiation and mutations in this gene can result in abnormal development of tissues and organs of ectodermal origin [SMIM23, ICE1, and LOC101907249 genes was observed in the literature.For SC210, we have found that the  animals . In this animals  found QTin bulls  and calvin bulls . The EDAl origin . In thisl origin . No specFew suggestive associations were observed for SC210 and none of the SNPs associated with SC210 presented pleiotropic effect with SNP associated with SC420 (discussed below) or other studied traits (discussed as follows), which could be due to low genetic correlation among EBVs for these traits in our dataset.Bos taurus indicus, Bos taurus taurus, and Bos taurus taurus x Bos taurus indicus. According to Ramayo-Caldas et al. [STAU2 gene was present in two main networks of PPARGC1A and HNF4G genes, which acts as transcription factors that activate a variety of hormone receptors [SRGAP1 and TOP1 genes, respectively.Regarding the SC420 trait, we have identified the STAU2 gene, which participates in multiple biological processes Table , highligs et al. , the STAeceptors  and bindeceptors . Peddinteceptors  observedRAP1B gene is involved in four pathways . This gene is involved in the protein processing in endoplasmic reticulum pathway. A QTL associated with weight and carcass traits has been described [It has been verified that the and rats  and is cescribed  in the rFew associations were observed for SC210, AFC, and ASC, reinforcing their polygenic inheritance and the complexity of understanding the genetic architecture of reproductive traits. Finding many associations for SC420 in various regions of the Canchim genome also reveals the difficulty of targeting specific candidate genes that could act on fertility; nonetheless, the high linkage disequilibrium between loci herein estimated could aid to overcome this issue. Therefore, all relevant information about genomic regions influencing reproductive traits may contribute to target candidate genes for further investigation of causal mutations and aid in future genomic studies in Canchim cattle to improve the breeding program.Additional file 1:Significantly associated single nucleotide polymorphism (SNP) with scrotal circumference at 420 days of age (SC420) after Bonferroni (B) and false discovery rate (FDR) correction at a chromosome-wise (CW) and genome-wide (GW) level. (DOCX 95 kb)Additional file 2:P-values are presented as the minimum (Min) and maximum (Max) significance obtained from the generalized quasi-likelihood method. (DOCX 34 kb)Genome-wide (in bold) and chromosome-wise associations for scrotal circumference at 420 days of age. Gene symbols, SNP reference number, and chromosomes (Chr) and positions  were obtained from NCBI website. Distances to gene (kilobase) are presented from 5′ to gene and 3′ to gene directions. If distance equals zero (0.00), the SNP is on intragenic region. Additional file 3:Candidate genes identified for scrotal circumference at 420 days of age. (DOCX 26 kb)Additional file 4: Fig. S1.Fig. S2. Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 9. Fig. S3. Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 13. Fig. S4. Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 14. Fig. S5. Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 18. Fig. S6. Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 21. (ZIP 1985 kb)Linkage disequilibrium for scrotal circumference at 420 d of age on chromosome 5.


Oryza sativa genome, using the 40 million single nucleotide polymorphisms (SNPs) dataset from the 3,000 Rice Genomes Project (http://snp-seek.irri.org), the largest and highest density SNP collection for any higher plant. We have shown that the DNA-binding transcription factors (TFs) are the most conserved group of genes, whereas kinases and membrane-localized transporters are the most variable ones. TFs may be conserved because they belong to some of the most connected regulatory hubs that modulate transcription of vast downstream gene networks, whereas signaling kinases and transporters need to adapt rapidly to changing environmental conditions. In general, the observed profound patterns of nucleotide variability reveal functionally important genomic regions. As expected, nucleotide diversity is much higher in intergenic regions than within gene bodies (regions spanning gene models), and protein-coding sequences are more conserved than untranslated gene regions. We have observed a sharp decline in nucleotide diversity that begins at about 250 nucleotides upstream of the transcription start and reaches minimal diversity exactly at the transcription start. We found the transcription termination sites to have remarkably symmetrical patterns of SNP density, implying presence of functional sites near transcription termination. Also, nucleotide diversity was significantly lower near 3′ UTRs, the area rich with regulatory regions.We analyzed functionality and relative distribution of genetic variants across the complete  E. coli strains912131412Understanding the relationship between genotype and phenotype is a key issue in life sciences with hugely important implications in biomedical R&D, healthcare and agriculture. Innate genetic variability is both the source and consequence of selection in populations of humans, crops and animals. There is a fine balance between the variability in DNA sequences and the evolutionary constraints for conservation of the original state or fixation of a new variant with a selective advantage. Most genetic variants occur as single nucleotide polymorphisms (SNPs) and small insertions and deletions (indels). In eukaryotic genomes, distribution of variants and the rate of spontaneous mutations are not uniform across individual sites1234et al.et al.et al.et al.Oryza sativa), Arai-Kichise et al.et al.Most studies on genomic variability have been conducted on mammals and yeast17182021222324Arabidopsis thaliana to grasses3637Understanding DNA variability in plants is hugely important, as crops are the core of world’s agriculture. Food consumption is predicted to double within the next 35 years, which translates in the demand for 2% annual yield growth rates for major crops. Yet, current breeding and selection methods, including transgenic technology, can only support on average a 1.1% annual growth rate3334Here, we present a large-scale study on genetic variability in the rice genome, based on high quality data from the SNP-seek database, with over 40 million SNPs from 3,000 rice genomesOryza sativa. We found that the minor allele frequency (MAF) distribution varies between parts of genome. The original 29M dataset of bi-allelic SNPs mapped to the Nipponbare genome contains many rare SNPs: 22% of them have MAF < 0.00035 , and 67% of them have MAF < 0.01 (present in less than 30 genomes). The genome-wide average MAF is 0.052 while the mean MAF for CDS is 0.028 and, contrastingly, 0.061 for introns. Exons contain more SNPs with lower MAF scores as compared to introns and UTRs. With an increase in the MAF cut-off (to control sequencing errors), a larger portion of SNPs in coding regions are excluded from the dataset. Rare exonic SNPs originated from five genomes of Oryza glaberrima, showing massive levels of genomic structural variation, with particularly high instability in defense-related genesO. sativa. Twelve other genomes were excluded since one had a very low coverage and others had excessive numbers of heterozygous singleton SNPs, which is highly unlikely for an inbred species. To reduce the number of possible false positive polymorphisms, we imposed two levels of constraints, as described in Materials and Methods, arriving at two datasets  for the analysis  and termination (TTS) sites are enriched in important regulatory elements4041The final stage of transcription is its termination, when the complete transcript dissociates and the RNA polymerase is released from the DNA template. The mechanism of termination is the least understood of the three transcription stages; two competing, yet not fully satisfactoryOverall, coding regions are more conserved than intergenic regions, promoters and UTRs . The freet al.A. thaliana and O. sativa genes with UGA stop codons had more nonsense codons in the first triplet position of 3′ UTR . Approximately 20–30 nucleotides (nt) upstream of the cleavage-polyadenylation site is the AAUAAA hexamer, which is the consensus signal for the transcript cleavageTranslation termination depends on codon-specific release factors that recognize the stop codon sequence in mRNA, as well as on GTP-binding release factors52The third position of the codon, as expected, is more variable than the first two, and that the first one is slightly more variable than the second, which follows from the redundancy of the genetic code and agrees with prior observations in human and mouseWe also analyzed sequence variability in introns . In bothThere are two classes of rice genes that differ in function, nucleotide composition, promoter organization, gene expression, and other features36375657596061Distribution of the AT-skew is shown in 64683759n repeat at 100 or more nucleotides away from the TSS is positively associated with promoter sequence variability  showed that transcription factors feature fewer SNPs than other families. Genes with DUF1618, Myb DNA-binding, zf-C3HC4, and AP2 domains were among the least variable genes. Importantly, we saw the same variability patterns at transcription regulation level. SNP density was the lowest in the promoter regions of the genes belonging to “sequence-specific DNA binding transcription factors”.Variability of DNA and protein sequences reflects the balance between adaptation and conservation of essential functionality. Analysis of conserved patterns enables “reverse engineering” of function from the sequence data. Active sites of enzymes can be predicted using conserved motifs in protein families, transcription factor binding sites may be identified using conserved elements in homologous promoter sequences, and gene structures can be predicted from conserved fragments of orthologous genes. In general, an unusually high conservation in a DNA or protein sequence implies existence of a biologically important function.Here, we analyzed the genetic variability within a unique collection of nearly 30M SNPs from the 3,000 Rice Genomes ProjectHere, we demonstrated patterns in genetic variability, established earlier for mammals and observed some novel trends. The rate of polymorphism in introns is known to be higher than in exons, which is consistent with evolutionary constraints in the mutability of protein-coding regionsWe identified promoter motifs associated with gene conservation using the cisExpress programIn terms of gene families, we have shown that transcription factors feature unusually low SNP density agreeing with their importance of as hubs modulating various downstream cascades. The most genetically variable genes tend to encode membrane-bound proteins, ATPases and serine/threonine protein kinases, key regulators of plant responses to abiotic stresses. Similar trends were highlighted in comparative analysis of human and mouse genomesFinally, CG-skew coupled with the associated transcriptional start sites, distributions of regulatory elements, expected lengths of UTR and other trends will be useful to refine gene prediction models in plants, especially the positions of TSSs and TSTs, the boundaries of gene models and the foundation for refinement of genotype to phenotype predictions.http://snp-seek.irri.org)We used a collection of SNPs obtained using the BWA-mem/GATK pipeline from the 3 K rice genome sequence data. SNP calls were made using the BWA-MEM alignment of short reads against the Nipponbare IRGSP-1.0 RefSeq and GATK pipeline. SNPs are available from the SNP-Seek database  of heterozygous singleton SNPs. (see obs) and computed the expected proportion of heterozygosity for its allele frequency (Hexp). The expected ratio Hobs/Hexp for rice is ~0.05, because of high level of inbreeding; however, many SNPs had Hobs/Hexp > 1. SNPs that exhibit high degree of heterozygosity could represent alignment errors due to paralogs that do not occur in the reference genome. We estimated inbreeding coefficients for the 3 К dataset (F3K = 0.9520), indica (Findica = 0.9251), and japonica (Fjaponica = 0.9689) as median value of 1 − Hobs/Hexp, excluding all SNPs of low frequency (<0.05) and high heterozygosity (Hobs > Hexp). The SNPs, which had more than 2 heterozygotes and violated the condition Hobs/Hexp < 10*(1 − F3K), were flagged for removal. The same procedure of flagging was repeated separately for the indica and japonica subsets, because paralogs are likely to be population specific (e.g. there are SNPs that are nearly 100% heterozygous in indica but not in the entire 3000 genomes dataset). All SNPs that have been flagged  were removed from the 29M dataset, arriving at the “Base” dataset consisting of 16,854,442 SNPs.We excluded SNPs detected in 5 genomes of It was obtained by removing SNPs with MAF < 0.01 and maximum per-SNP missing rate 0.1 (PLINK options –maf 0.01 –geno 0.1). “Filtered” dataset contains 4,817,175 SNPs.http://rice.plantbiology.msu.edu/) of rice contains 55,986 predicted genes and 66,338 gene modelsThe current MSUv7 annotation (Gramene database (http://ftp.gramene.org/CURRENT_RELEASE/data/ontology/go/go_ensembl_oryza_sativa_japonica.gaf.gz). The map between MSU and RAP-DB locus names was downloaded from the RAP-DB site (http://rapdb.dna.affrc.go.jp/download/archive/RAP-MSU.txt.gz). Enrichment analysis was performed using one-sample t-test implemented in oracle as function stats_t_test_one. We excluded GO classes containing less than ten genes.Gene Ontology assignments were downloaded from the http://rice.plantbiology.msu.edu/) database and validated using the NPEST algorithmMotifs in promoter were found using the cisExpressCustom scripts for statistical data analysis and visualization were written in R and C++. In order to calculate the SNP density per genomic region, we wrote an R script that counted the number of SNPs per position for each of the 20,367 selected genes. The number of SNPs was divided by the total number of genes , producing the number of SNPs at each position per 1000 genes.How to cite this article: Tatarinova, T. V. et al. Nucleotide diversity analysis highlights functionally important genomic regions. Sci. Rep.6, 35730; doi: 10.1038/srep35730 (2016).


IS >0) and low values of Hobs, which could be due to inbreeding and Wahlund effects. We also developed a collection of high-quality SNP probes that will likely be useful in a variety of applications in genotyping for cervid species in the future.Sika deer are an economically valuable species owing to their use in traditional Chinese medicine, particularly their velvet antlers. Sika deer in northeast China are mostly farmed in enclosure. Therefore, genetic management of farmed sika deer would benefit from detailed knowledge of their genetic diversity. In this study, we generated over 1.45 billion high-quality paired-end reads (288 Gbp) across 42 unrelated individuals using double-digest restriction site-associated DNA sequencing (ddRAD-seq). A total of 96,188 (29.63%) putative biallelic SNP loci were identified with an average sequencing depth of 23×. Based on the analysis, we found that the majority of the loci had a deficit of heterozygotes (F Sika deer are an economically valuable species, with some body parts, such as antler, blood, penis, and placenta, used in traditional Chinese medicine. In order to continue the tradition, people have captured sika deer from the wild on a large scale in Jilin province in the northeastern part of China since the Qing dynasty (c. 1733) . These aAlmost all sika deer raised on farms in northeast China are used for antler (velvet antler) production . In receThe genetic diversity of the sika deer wild population has been investigated using two major sources of genomic variation. Mitochondrial genes, such as cytochrome b and the control region, have proven highly informative for investigations into the classification and phylogeny of sika deer with maternal haplogroups . AutosomTheir genomic abundance and amenability to cost-effective, high-throughput genotyping, has meant that SNPs are now the most widely used class of genetic markers. Use of genome-wide SNPs constitutes an important genetic tool for investigating genetic diversity in livestock animals , SAMtoolIn this study, we employed the ddRAD-seq technology to achieve the first genome-wide SNP discovery for farmed sika deer that are widely distributed in northeast China. The STACKS pipeline was applied for SNP calling. Using newly developed markers, we investigated the genetic diversity of farmed populations. We also developed a collection of SNP probes for genotyping in the future.A total of 42 unrelated animals were selected from eight farms  in northeast China, with six from NM and XK farms and five from the other six farms. Whole-blood samples were extracted from the jugular vein using EDTA vacuum tubes and were stored at −20 °C until DNA extraction. The genomic DNA extractions from whole blood were performed using a blood DNA kit (Qiagen) according to the manufacturer’s instructions. Each DNA sample was evaluated by gel electrophoresis for the presence of high-molecular-weight DNA and then stored at −80°C until ddRAD-seq library construction.The procedure was performed as described in previous studies  with somhttp://www.repeatmasker.org) was 44.14% and the GC level was 42.11% .The available sika deer genome reference (SK-REF) included 387,939 contigs, comprising a total of 2.73 Gbp. All sequences were longer than 500 bp, with a N50 length of 38.4 kbp. The percentage of repeat sequences detected by Repeatmasker-4.0.6  of the process_radtags program in STACKS v.1.41 , raw reaobs), expected heterozygosity (Hexp) and inbreeding coefficient of an individual relative to the subpopulation (FIS) were calculated using the populations program in STACKS v.1.41. Deviation from the Hardy–Weinberg equilibrium (HWE) was assessed by performing a Fisher’s exact test with the HardyWeinberg R package (Observed heterozygosity (H package  for eachk) represented by the dataset. Three iterations were run per K (number of populations) for K = 2 or K = 3 using an admixture model. Each run consisted of a burn-in of 100,000 MCMC steps, followed by 500,000 replications. Population structure was also examined by carrying out principal component analysis using SMARTPCA within EIGENSOFT  no repetitive sequences; (b) SNP-free within flanking sequence; (c) one of two alleles in accordance with the base in the reference; (d) flanking sequence on the SK-REF ≥50bp.Table S1 and Table S2.Raw (adapter trimmed) Illumina ddRAD-seq sequences: NCBI Short Read Archive (project accession: SRP105008). Statistics describing different properties of each sequenced individual and all high-quality SNP probes are available in the Supplemental Material, Table S1).After trimming the barcodes and filtering low-quality bases, ddRAD sequencing generated a total of over 1.45 billion high-quality paired-end reads (288 Gbp) across the 42 animals. Of these clean reads, >0.93 billion (65.63%) were aligned uniquely to the SK-REF genome. As variation among animals is often observed in a pooled ddRAD sequencing library, the number of aligned reads per individual ranged from 0.55 to 5.3 million  (Table S1). With a read rate within individuals averaging 83%, this suggests that the rate could be ∼0.74 per kbp.Using the Stacks pipeline, we initially obtained 7,576,300 candidates of the ddRAD loci from all individuals. Quality filtering (see Methods) reduced this to a total of 324,564 (4.28%), which were shared by >75% of the individuals, corresponding to genomic size of 0.032 Gbp (∼1.1% of the genome) . Within P value = 0.056)  SNP frequency decreased from 5′ to 3′ end in all loci excluding both ends; however, transition/transversion (Ts/Tv) ratios were very stably distributed over the loci except for the last five positions at the 3′ end ; (b) fre= 0.056) ; (d) a m= 0.056) .obs, Hexp, FIS, and deviation from HWE for the studied population. The distribution of MAF showed that the SNPs with MAF <0.15 were overrepresented at nearly half of the SNPs (49.0%), and SNPs were evenly distributed in high-MAF bins ranging from 0.25 to 0.5 (29%)  . The aveP ≤0.05) were shown to deviate from HWE. Following application of the Benjamini–Hochberg correction, 8205 (5.53%) SNPs remained significant (P ≤0.05). The excess of Hobs values was observed in the frequency distribution from 0 to 0.15, and the deficit was from 0.4 to 0.5 (IS >0) is shown in obs, Hexp, and FIS calculated across all 96,188 SNPs were 0.24, 0.28, and 0.16, respectively.Of 96,188 SNPs, 11,876  with one SNP on each ddRAD loci across >38 animals (≥90%). Graphic displays of the consistent results from two SNP datasets provided a meaningful explanation of the genetic structure and levels of admixture for the farmed populations  were clearly distinguished and, of these, SP3 and SP4 represented an admixture pattern (two clusters of genetic background). At K = 3, another cluster of two animals (AD5 and DF4) was evident. Similar results were also observed using the PCA approach based on these two SNP datasets. Although PC1 and PC2 accounted for only 2.4% and 1.9% of total variation, respectively, four animals  were clearly distinguishable based on PC1, and two animals (AD5 and DF4) based on PC2  across >32 animals (≥75%). Since the STRUCTURE model assumes that the loci are independent and at HWE, we also performed STRUCTURE analysis on a filtered SNP dataset  at HWE . As expected, 96.24%  of these matched SNP probes produced one unique alignment to a bovine genome, with an average identity of 94.39%. Only 3.76%  probes hit multiple cattle genome positions .We selected 27,009 high-quality SNPs probes according to four filtration criteria. The Ts/Tv ratio was 3.72. The MAF frequency distribution was accordant between the 96,188 and 27,009 SNP datasets , suggestThe ddRAD-seq technique is an efficient and cost-effective means of SNP discovery, providing thousands of high-quality SNPs, even in the absence of an available genome sequence  and can also vary greatly in the CpG islands and GC content of the region  deviate from HWE, which may be caused by the different subpopulations sampled. The higher values of FIS and the lower-than-expected values of Hobs could be also explained by subpopulation structure (Wahlund effect). The STUCTURE and PCA results further provide evidence of subpopulation structure in farmed sika deer, despite the lack of overall variation and the fact that the PC1 and PC2 accounted for such a small proportion  of the total variation. The small-scale farming population and the application of the technique of artificial insemination over a long period have encouraged inbreeding and genetic drift for velvet antler production. Such population events also compromise the genetic structure of the original population. Hence, our results could help in investigating the structure of farmed populations so such disadvantages can be better managed.The average frequency of heterozygous SNPs per kilobase pair is 0.74 in the genome of sika deer individuals. As a comparison, this value is slightly higher than that of milu , but almost <2 times those of cow and panda . The relSNP markers are not only readily applied to population genetic analyses, but also suitable for parentage testing and assessment of breed composition. Genome-wide association analysis may offer the opportunity to identify the genomic regions and mutations that underpin production traits such as velvet antler weight. One attractive feature of association studies is that pedigrees are not necessary, so potentially a larger number of the farmed animals may be amenable to this type of analysis .−20), which could be an indication of the sharing of SNP genotypes between the genomes. Only 593 SNP probes of sika deer could be aligned multiple times to the bovine genome, which may represent a duplicated and/or expanded region on the cattle genome. In comparison, Overall, we have discovered ∼27,009 high-quality SNPs in 1% of the sika genome using ddRAD-seq technology. A total of 58%  probes were uniquely matched on to the bovine genome (BLASTN E value 1ewww.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300082/-/DC1.Supplemental material is available online at Click here for additional data file.Click here for additional data file.


Different horticultural types of lettuce exhibit tremendous morphological variation. However, the molecular basis for domestication and divergence among the different horticultural types of lettuce remains unknown. Here, we report the RNA sequencing of 240 lettuce accessions sampled from the major horticultural types and wild relatives, generating 1.1 million single-nucleotide polymorphisms (SNPs). Demographic modeling indicates that there was a single domestication event for lettuce. We identify a list of regions as putative selective sweeps that occurred during domestication and divergence, respectively. Genome-wide association studies (GWAS) identify 5311 expression quantitative trait loci (eQTL) regulating the expression of 4105 genes, including nine eQTLs regulating genes associated with flavonoid biosynthesis. GWAS for leaf color detects six candidate loci responsible for the variation of anthocyanins in lettuce leaves. Our study provides a comprehensive understanding of the domestication and the accumulation of anthocyanins in lettuce, and will facilitate the breeding of cultivars with improved nutritional value. Horticultural lettuce varieties vary considerably in phenotype. Here, via RNA-seq of 240 different lettuce accessions, the authors identify loci and expression patterns associated with flavonoid and anthocyanin content and show that cultivated lettuce likely arose via a single domestication event. Lactuca sativa) is one of the most important vegetables worldwide. It is a member of the Compositae (Asteraceae) family which contains an exceedingly large number of species of flowering plants1. The wild ancestor of lettuce is believed to be Lactuca serriola2. Lettuce is more distantly related but sexually compatible with Lactuca saligna and Lactuca virosa3. Cultivated lettuce was first recorded on the walls of Egyptian tombs ca. 2500 BCE, indicating that lettuce has been cultivated for at least 4500 years4. Based on their morphological characteristics, cultivars of lettuce are classified into six horticultural types: butterhead, crisphead, looseleaf, romaine, stem, and oilseed. The former four types are harvested and consumed for their leaves and they are collectively referred to as “leafy” types. In contrast, stems of stem lettuce (mainly cultivated in Asia) are harvested and consumed, while seeds from some cultivars are harvested for oil production in Egypt4. Leafy lettuce appeared in China at least 1700 years ago during the Jin dynasty, as recorded by an ancient Chinese book “Zhou Hou Bei Ji Fang” . Leafy lettuce was then developed into stem lettuce, which has thick succulent stem and reduced leaves. Stem lettuce was first reported ~900 years ago, as recorded by another ancient Chinese book “Dongjing Meng Hua Lu” .Lettuce  of lettuce, plant shape (including leaf and stem shapes) has changed dramatically. Besides the variations among different horticultural types, cultivars within a type may also exhibit considerable phenotypic variations, such as color variation. Most lettuce cultivars have green leaves, but others may have red leaves. The red color is caused by the accumulation of flavonoids, which contribute to both plant fitness and to the nutrition of consumers7, maize9, rice10, soybean11, and tomato12. NGS also makes linkage or association analyses possible for most  species, leading to the identification of many candidate genes responsible for important traits in crops, such as grain size13, drought tolerance14, and kernel oil15. However, the domestication of lettuce, its population structure, and the genetic and molecular mechanisms underlying the variations among different horticultural types remain to be investigated16.The development of next-generation sequencing (NGS) technology has revolutionized life science research, especially genetics and evolution. NGS has been used to study the evolution and domestication of important crops, such as cucumberIn this study, 240 wild and cultivated lettuce accessions were studied comprehensively using RNA-Seq. The origin and domestication of lettuce were analyzed using a large number of SNPs obtained for wild and cultivated lettuce. The population structure of cultivated lettuce and the relationships among different horticultural types were studied in detail. Artificial selection during domestication and subsequent cultivar differentiation were also analyzed. The SNPs were used to perform a GWAS analysis of leaf color in lettuce. Furthermore, the SNP and expression data were combined to investigate eQTLs for genes involved in anthocyanin biosynthesis. The results in this study not only shed light on lettuce evolution but also provide useful information for future lettuce breeding programs.Lactuca spp., which represented most of the phenotypic diversity in our collection, were selected for genome-wide analysis  and 6 intermediate accessions that have both wild and cultivated characteristics. The other 163 accessions are cultivars, including 28 butterhead, 19 crisphead, 17 looseleaf, 3 oilseed lettuce, 31 romaine, and 24 stem lettuce. Forty-one cultivars are atypical of any horticultural type and are referred to as “atypical type” hereafter. In addition to these 200 accessions, we also included 40 inbred lines derived from a cross between a looseleaf cultivar (S1) and a stem lettuce cultivar (Y37), which were only used for eQTL and GWAS analysis in this study. RNA was extracted from the young leaves of ~2.5-month-old plants from each accession. RNA sequencing using Illumina technology generated 4.51 billion paired-end reads of 125 bp (1.13 Tb of sequences), after filtering out the low-quality reads. On average, 18.8 million paired-end reads (4.70 Gb) were obtained for each sample. The filtered reads from each accession were mapped to the L. sativa cv. Salinas genome17. The mapping rate varied from 91.08 to 98.54% among different accessions, with an average of 97.28%.A total of 240 accessions of L. saligna and L. virosa relative to the L. serriola and L. sativa groups  are unique to As expected from RNA-Seq data, the majority of SNPs  were located within genes  as an out-group, a maximum-likelihood phylogenetic tree was constructed to show the phylogenetic relationships among wild and cultivated lettuce  were also distributed irregularly across the L. sativa clade.Using accession “W17”  Fig. . The PCA19 was used through gradually increasing the number of clusters (K). Different numbers of clusters were identified as K was increased  were assigned to independent clusters. However, cultivars of the looseleaf and the atypical types exhibited admixture proportions, consistent with the results from our phylogenetic and PCA analyses. Based on genome-wide SNP analysis and phenotypic variation, we suggest that looseleaf cultivars may have experienced frequent gene flow from other horticultural types and that the atypical types are probably derived from crosses between different horticultural types. The pairwise population differentiation level (Fst) between different horticultural types or groups was also calculated, indicating strong population differentiation among different horticultural types  between SNP loci, was calculated for L. serriola and cultivated lettuce groups  were analyzed, the LD decay was estimated to be 140 kb (r2 = 0.29). When different types were analyzed independently, their LD decay varied dramatically. For example, the genome-wide LD was 5 kb for L. serriola (r2 = 0.20) and 200 kb for all types of cultivars combined (r2 = 0.33). When different horticultural types were calculated individually, the LD decay varied from 215 kb to 2.0 Mb. The LD decay (r2 = 0.40) of the crisphead type was the largest (2.0 Mb) among all horticultural types.Linkage disequilibrium (LD), measured as the correlation coefficient . The looseleaf type exhibiting admixture proportions was excluded from further study due to its strong impact on the demographic analysis21.The demographic inference tool L. serriola and four individual horticultural types), two population , and four population  models were tested step by step  were analyzed to test whether there was single-founder or double-founder  event for lettuce. Gene flow among different populations was allowed in these models. Two symmetrical migration parameters were used, one for migration between different horticultural types , and the other for migration between L. serriola and each of the four horticultural types . The result showed that single-founder model , similar to the time of the first record of lettuce in China ~1700 y B.P. (likely first introduced into China through Silk Road). The split time of butterhead, crisphead, and romaine from the ancestral cultivated lettuce was estimated to be around 500 y B.P., which is consistent with a previous study4. The migration rate between horticultural types (MRHT) and between wild and cultivated lettuce (MRWC) were 7.7 and 5.5 per 100,000 alleles, respectively.To help guide the development of demographic models for lettuce, one population  for each group/type was calculated (Supplementary Table L. serriola (4.84 × 10−3) than in all cultivated lettuce combined (2.14 × 10−3), consistent with the genetic bottleneck that occurred during domestication. Among the five cultivated types, looseleaf type (2.11 × 10−3) has the highest while crisphead type (8.94 × 10−4) has the lowest nucleotide diversity.Using the SNP data, genome-wide nucleotide diversity . The CV of selected genes (51.72%) is considerably lower than that of the non-selected genes (71.26%) . We also found less local eQTLs  and more distant eQTLs  for selected genes than for non-selected genes. Our results suggest that the low expression variation of selected genes is due to less variation of their cis-acting regulatory regions.The coefficient of variation (CV) of genes was calculated to assess their expression diversity for each group and type 23, IKU1 24, PLDZ1 25, etc.  were detected Fig. . These p26, seed shattering, and stem spines27. These known QTLs overlap with the putative selective sweep regions detected in our study. For example, genetic analysis of an F2 population mapped a single gene controlling lobed leaf to the interval at 116.24–118.15 Mb on LG326; genetic analysis of RIL population detected a major QTL for seed shattering centered at 13.4 Mb on LG6 and stem spines centered at 302.4 Mb on LG5, respectively16. Several regions that have high XP-CLR values were identified in the corresponding regions. However, these regions were not grouped into a single region due to the low SNP density in this study 28, BOP2 29, ATH1 30, and TOAD2 31  was coincident with the selective signals for the crisphead group  of many important traits (like resistances to diseases and pests) in modern lettuce breeding programs32. We identified 173 potential regions of introgression (71.88 Mb) from wild species , followed by L. saligna and L. virosa. Our results are consistent with the fact that lettuce breeding has been mainly based on the utilization of L. serriola32. Seven group-specific introgressed regions were found in different horticultural types (Supplementary Table L. serriola on LG5 (235.5–236.5 Mb).Wild ssion 71.8 Mb fromies Fig. . Most of33 was used for association analysis of the transformed expression levels for each gene. A total of 331,706 SNPs were significantly associated with the expression of at least one gene over the Bonferroni-corrected threshold (−log10(P) = 6.31, α = 0.05). Linked SNPs that are associated with the same gene were grouped into unique eQTL blocks, which are represented by the most significant SNP in the block. After grouping associated SNPs, 5311 candidate eQTL regions were identified for 4105 genes  >5%. The EMMAX software10(P) value and explained expression variation of local eQTLs were higher than those of distant eQTLs, indicating that the local eQTLs tended to have larger effects on gene expression than distant eQTLs  and one on LG5  are the most significant distant eQTL hotspots, containing 58 and 45 distant eQTLs, respectively  located at a distant eQTL hotspot on chromosome 5 has 47 potential targets, and the Glutathione S-transferase gene  located at a distant eQTL hotspot on chromosome 3 has 29 potential targets. GO analysis of the target genes of these two master regulators showed enrichment in flavonoid metabolic process  = 6.31, α = 0.05). Among these 24 genes, 7 genes are regulated by only local eQTLs, 12 genes are regulated by only distant eQTLs, and 5 genes are regulated by both local and distant eQTLs. After merging the overlapping eQTLs into one region, nine candidate regions were obtained , CHS , CPC 36, GL2 37, ANL2 38, etc. Strikingly, the most significant (P = 1.65 × 10−10) regulator identified was GST , which facilitates the transport of anthocyanin from the cytosol to the tonoplast39. Both eQTL analysis and co-expression analysis indicated that these four candidate regulators are likely to be important regulators of flavonoid biosynthesis in lettuce.Several eQTLs for different flavonoid-related genes were located in the same region. These data are consistent with the same regulators controlling the expression of these flavonoid-related genes. The iterative group analysis (iGA) approachp < 0.01). Among these genes, 74 had no reported function related to flavonoid biosynthesis  = 5.02, α = 1)  Fig. . Among tLG1_162414) at locus 1 encodes a protein of unknown function. One local and two distant eQTLs were detected for this gene  at locus 2 encodes GST. Three eQTLs were detected for the GST gene, including one local and two distant eQTLs , encoding a cinnamyl-alcohol dehydrogenase that contributes to secondary metabolism41. Only one local eQTL was identified for this gene . As described above, MYB113 is one of the master regulators that we identified. One local eQTL and one distant eQTL were detected for this gene . One local eQTL and four distant eQTLs were detected for bHLH42  in the interval is associated with flavonoid biosynthesis. Three distant eQTLs of the ANS gene overlapped with the second, fourth, and fifth locus, respectively  in the Middle East and the Fertile Crescent. Interestingly, a L. serriola accession  is more similar to cultivated lettuce than the other wild accessions studied. According to the Centre for Genetic Resources (CGN), this accession was collected 20 km south of Al-Fallujah in Iraq, this is within the Fertile Crescent where lettuce was domesticated. The W44 is not different from other wild accessions in leaf shape, flowering time, shattering, although it has a lower density of spines on stems. The close relationship between the wild accession W44 and cultivated lettuce may reflect its location near the center of domestication. This hypothesis could be tested in the future by analysis of additional genotypes collected from Al-Fallujah and its surrounding regions.In this study, we performed RNA sequencing of 240 To successfully identify loci controlling traits in lettuce populations, the trait of interest should exhibit a broad variation within sub-populations. For example, the lettuce population in this study exhibited a broad variation in leaf color in all horticultural types. In this study, a model that considers population structure was used to successfully identify six loci associated with leaf color in lettuce, and four of them have been genetically verified, showing success of GWAS in lettuce. The high LD makes it challenging to identify a candidate gene in a detected locus. Our results showed that the application of eQTL and gene expression network analysis greatly improved the chances to identify a candidate gene in GWAS.eQTL analysis in this study identified nine loci in lettuce contributing to expression variation in genes associated with anthocyanin biosynthesis. However, only five of them were shown to contribute leaf color variation. In the future, it will be interesting to investigate the roles of the other four eQTLs in the quantity and diversity of flavonoids in lettuce. Expression network analysis may also identify novel genes in the anthocyanin biosynthesis. Our analysis yielded dozens of genes that are potentially associated with flavonoid synthesis, some of them have unknown functions or functions not related to flavonoid biosynthesis. Future experiments will test whether these genes contribute to the accumulation of flavonoids and may provide insight in flavonoid biosynthesis and its regulation in lettuce.Lactuca materials were ordered from the USDA GRIN website (http://www.ars-grin.gov/) and the Centre for Genetic Resources, the Netherlands (CGN) website (http://www.wageningenur.nl/); some were bought from commercial websites or Chinese seed market and some accessions of L. serriola were collected in natural populations in China by H. Kuang. All Lactuca materials (more than 1000 accessions) were sown in November, 2014 in greenhouses on the campus of Huazhong Agricultural University, Wuhan, China. Based on the phenotypic variation, 240 accessions were chosen for genetic analysis in this study.Most of Total RNA was extracted from the top fully expanded leaves from 2.5-month-old plants using the TRIzol reagent (Invitrogen) according to the manufacturer’s protocol. The non-directional paired-end RNA-Seq library of each accession was prepared following the Illumina TruSeq RNA sample preparation kit, version 2. The libraries were sequenced on the Illumina HiSeq 2500 platform to obtain 125-bp paired-end reads.42. The filtered fastq files were aligned to lettuce genome (version 8)17 using the STAR software (version 2.4.2a) in the 2-pass mapping mode43. After alignment, Picard tools  were used to remove PCR duplicates according to the mapping coordinates. Finally, a single BAM file was generated for each accession and was used for further analysis.Raw RNA-Seq data were filtered to remove sequencing adapters and low-quality bases using Trimmomatic (version 0.33)44. BCFtools was further used to filter potentially false SNPs using the following criteria: (1) mapping quality, total sequencing depth, and SNP quality each had to be equal to or greater than 30; (2) each SNP had to be more than three base pairs away from an InDel; (3) each SNP had to be biallelic; (4) for homozygous genotypes, supporting reads had to be greater than 3 and the SNP quality had to be greater than 20; (5) for heterozygous genotypes, supporting reads for the reference and alternative allele had to be equal to or greater than 2 and the genotype quality had to be greater than 20; and (6) for sites that failed to pass, based on the above criteria, the individual genotypes of these sites were assigned as missing.To detect SNPs in the population, raw SNPs were called using the mpileup command in the SAMtools package (version 1.2)45. The results were parsed using in-house Perl scripts. Gene Ontology (GO) annotations were assigned using the Trinotate pipeline46. Protein domain families were assigned by Pfam database47 with an e-value cutoff as 1e−5. GO term and protein domain enrichment analysis were performed using agriGO (v2.0)48 and FuncAssociate (v3.0)49 at P < 0.05.The effects of SNP on genes was predicted using SnpEff (version 4.1l)18 that is based on the k-nearest neighbor algorithm was used to impute missing genotypes. In order to get the optimal imputation accuracy and filling rate, the following steps were used: (1) SNP sites were divided into ten categories according to the missing rate, which ranged from 10 to 90%; (2) 1% SNP sites for each category were randomly masked as missing genotypes to measure the imputation accuracy and filling rate; (3) the imputation was done using the fillGenotype software using the following parameters: w , p , k , and r . These parameters yielded 320 sets of parameters for each category; (4) after testing 320 combinations of parameters, the best imputation accuracy and filling rate were obtained for each category under certain parameters; (5) the best results of each category were plotted and compared to each other. Certain missing rates with optimal imputation accuracies and filling rates were selected; and (6) SNP sites with decided missing rates and parameters were used to impute missing genotypes.The fillGenotype software50 was used to construct maximum-likelihood trees using SNPs without imputation, with the following parameters: -m ASC_GTRGAMMA --asc-corr = lewis -f a -p 23 -x 123 -# 100. A nonparametric bootstrap analysis was performed, with 100 bootstrap replicates. Accession “W17” which belongs to L. virosa was set as an outgroup. The final tree was visualized using iTOL software51.The RAxML software52 was used to perform PCA on individual genotypes. SNPs with missing rates < 20% were used to perform PCA analysis. The first two components were plotted for the lettuce accessions.The EIGENSOFT software package19. The “admixture model” implemented in STRUCTURE was used to estimate the ancestry and admixture proportions in each individual. SNPs with missing rates <10% and MAF >5% were used. For each K value that ranged from 1 to 20, STRUCTURE was run 20 times with an admixture model and 10,000 burn-in and MCMC replicates. The final results were imported into CLUMPAK53 to estimate the most likely number of sub-populations and to graphically represent the sub-population membership of each accession.The STRUCTURE program was used to infer population structurer2) was calculated for all pairs of SNPs after imputation within 5000 kb using PopLDdecay software . The following parameters were used: -MaxDist 5000 -MAF 0.05 -Het 0.88 -Miss 0.25. The maximum value of r2 was calculated based on SNP pairs within 1 kb. For each group, a LD decay curve was plotted based on r2 and the distance between pairs of SNPs.LD 20. To minimize the bias in demographic inferences due to selection, only neutral sites  were used for this analysis. Furthermore, SNPs that had missing data before imputation in populations were excluded, resulting in 46,002 segregating sites of the 2,073,047 total 4DTV sites without missing data in the final data. The folded SFS was created using a modified script from δaδi54. First, one-population models were run for all groups including the wild ancestor L. serriola and four individual horticultural types . Second, each horticultural type was analyzed together with L. serriola in two-population models. Third, three leafy horticultural types  were analyzed jointly with L. serriola in four-population models. Finally, stem lettuce was incorporated into five-population models. In four- and five-population models, sample sizes were projected down to 18, 14, 14, 14, and 14 for L. serriola, butterhead, crisphead, romaine, and stem, respectively, due to the one million entry limitation of the SFS files that fastsimcoal2 can process. For each model, at least 25 independent runs with varying starting points were conducted to determine the parameter estimates leading to the maximum likelihood. Each run consisted of 20–40 rounds  of parameter estimation using the expectation-conditional maximization algorithm with a length of 100,000 simulations per likelihood estimation . The best model was determined based on the maximum value of the likelihoods and the akaike information criterion55. To obtain parameter confidence intervals (CIs), 100 bootstrap data sets were created by sampling with replacements from the 4DTV sites. Parameters were estimated from 50 independent optimizations for each bootstrapped data set. To calculate absolute values of population size and divergence time, we assumed a mutation rate of 4 × 10−8 per bp per generation.To reconstruct the domestication history of lettuce, we used the joint site frequency spectrum (SFS) approach implemented in 56, based on the alignment to the reference genome.The expected read counts and fragments per kilobase per million reads (FPKM) for each gene were calculated using StringTieTo calculate the expression diversity in a population, the CV was calculated for each gene as a standard deviation (SD) of FPKM divided by mean FPKM in the population using an in-house Perl script.57 and π ratio methods was used to identify regions associated with domestication in lettuce. We first performed a genome scan using XP-CLR between L. serriola and cultivated lettuce. Genetic distances between adjacent SNPs were calculated using a previous genetic map by assuming a uniform recombination between mapped markers58. The program XP-CLR was run for each chromosome with parameters “-w1 0.005 100 2000 1 -p1 0.7”. Mean likelihood score was calculated using 20 kb sliding windows with a step size of 10 kb across the genome. Adjacent windows with XP-CLR values in the top 20% were grouped into a single region. Merged regions across the genome with XP-CLR values in the top 5% were identified. We further calculated π ratio between L. serriola and cultivated lettuce using 20 kb sliding windows with a step size of 10 kb. Windows with the top 50% of highest π ratios were selected and merged into regions. Finally, regions identified by both XP-CLR and π ratio methods were considered to be associated with domestication.The combination of cross-population composite likelihood ratio (XP-CLR)π ratio methods28 was used to identify regions under selection in different horticultural types. To detect the selective signals for a horticultural type, cultivated accessions belonging to that horticultural type were grouped as the object population and the remaining cultivated accessions were grouped as the reference population. We first calculated PiHS for each SNP across the genome between object and reference populations. The normalized (z-score) value of the PiHS was then calculated using 200-kb sliding windows with a step size of 100 kb. Regions with z-score larger than 2.33 (P ≤ 0.01) were merged. We further calculated π ratio between object and reference populations using 200 kb sliding windows with a step size of 100 kb. Windows with the top 50% of highest π ratios were selected and merged into regions. Finally, regions identified by both PiHS and π ratio methods were considered to be under selection.The combination of population-based integrated haplotype score (PiHS) and Lactuca species  to cultivated lettuce was performed using a combined approach of likelihood ratio test10 and phylogenetic analysis. Briefly, the cultivated group was compared with three wild Lactuca groups. SNPs with missing data and heterozygous genotypes were excluded from further analysis. For each cultivated lettuce, the ratio of shared genotype in cultivated group versus each wild Lactuca group was calculated in 200 kb sliding windows with a step size of 20 kb. Regions with ratio ≤0.5 and SNP number ≥20 were merged and further used for phylogenetic analysis. FastTree259 was used to build the maximum-likelihood phylogenetic bio-neighbor joining tree for each region, and the R package ape60 was used to display the tree file. Then each phylogenetic tree was manually checked whether the putative introgressed cultivars were located within the putative corresponding donor wild Lactuca group. Finally, regions with distances <200 kb were merged.Identification of introgressed regions from wild n = 180) for further analysis. To obtain a normal distribution of expression values for each gene, normal quantile transformation of expression values for each gene was performed using the qqnorm function in R62. Genome-wide associations of transformed expression were estimated using EMMAX33. The BN matrix of all SNPs calculated using the EMMAX was used to control population structure and kinship among individuals. The hidden and confounding factors that contributed to the variability of expression were identified using the PEER program63. Ten factors were treated as additional covariates to increase the detection power for eQTL. The Bonferroni test criterion at α = 0.05 was used as a threshold for significance of associations between SNPs and traits. In this study, the Bonferroni-corrected threshold for the P value was 0.05/ = 3.66 × 10−6, with a corresponding −log10(P) value of 6.31. An R script was used to generate quantile–quantile (q–q) plots and Manhattan plots from P values of each SNP.Only cultivated accessions (including RIL lines) were used for eQTL mapping. Genes with a median FPKM value equal to 0 were excluded from the eQTL mapping. To identify outlier samples, PCA was performed to investigate the first three principal components (PCs), which together explain 92.79% of the variance in the expression data. We removed seven samples  that were >2.5 SD from the mean in any of the first three PCs, yielding a total of 180 samples  values. To identify significant correlations, the threshold PCC value was set at either 0.39 or −0.28 (P < 0.05) based on the permutations.To identify the candidate master regulators, the following criteria were used: (1) the candidate regulator gene should be located in a distant eQTL region; (2) the candidate regulator should have a local eQTL; and (3) a correlation should exist between the target gene and the master regulator. The pair-wise Pearson’s correlation coefficient (PCC) for the target gene and the candidate master regulator was calculated using log66. Briefly, overlapping distant eQTLs for different genes of a pathway were merged. Each gene from the merged eQTL region was used to calculate the PCC with the relevant target genes . Based on the sorted PCC values, an iGA algorithm35 was used to define the probability of change (PC) value for each gene in the merged eQTL region. If a gene in the merged eQTL region had a PC value of <0.01/, we classified this gene as a candidate regulator that may regulate those genes contributing to the significant PC value.The genetic regulatory network was constructed using genes known to be involved in a pathway and their potential regulators, following previous studies with modificationsP < 0.01, based on 1000 permutations). If a gene was related with at least three seed genes, it was considered to be associated with the pathway of interest.To detect additional genes associated with a pathway, the regulators and target genes identified using the iGA approach were used as seed genes. Pair-wise PCC values were calculated between the seed genes and all of the other genes in the genome. Two genes were considered to be related to each other if their absolute PCC value was larger than 0.44 (n = 187) were planted in an experimental field using a randomized complete block design in Wuhan, Hubei Province, China in 2015. Leaf color was classified as either green or red. Association analysis was conducted using the FaST-LMM40 software. Suggestive (−log10(P) = 5.02, α = 1) and significant (−log10(P) = 6.32, α = 0.05) thresholds were used to identify significantly associated SNPs68. Highly linked significant SNPs may be considered as one single signal, as described above for eQTLs detection.Two replicates of cultivated accessions (The sequencing data for this project have been deposited at the NCBI Sequence Read Archive (SRA) under project PRJNA394784, accession SRP113265. The authors declare that all other data supporting the findings of this study are included in the main manuscript file or Supplementary Information or are available from the corresponding author upon request.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Supplementary Dataset 5Supplementary Dataset 6Supplementary Dataset 7Supplementary Dataset 8Supplementary Dataset 9Supplementary Dataset 10Supplementary Dataset 11Supplementary Dataset 12Supplementary Dataset 13Supplementary Dataset 14Supplementary Dataset 15


Lepista nuda is a wild edible fungus that is valued for its odor and taste. Recent studies identified intraspecific morphological and genetic differences in L. nuda. Although single-nucleotide polymorphisms (SNPs) are useful for revealing intraspecific differences, the traditional methods used for investigating SNPs are time consuming and expensive, and they only locate a limited number of SNPs. This study used a “restriction-site associated DNA” (RAD) method combined with high throughput sequencing to efficiently identify a large number of SNPs in two samples of L. nuda. A total of 7 and 9 billion bp of raw data were obtained from the two collections. A total of 712 SNPs were found. These SNPs will be useful for the further analysis of the genetic variation within L. nuda. The study also confirms that the RAD method can be used to identify SNPs in a nonmodel macrofungus for which a reference genome is unavailable. Lepista nuda (Bull.) Cooke, which is in the Agaricales (Basidiomycota)  refer to variations in a single base pair of a DNA sequence. SNPs, which can function as useful markers for the study on population genetic, consist of unlinked loci that occur throughout the genome and that have relatively low mutation rates  with high throughput sequencing  were obtained in this study. The sequences were submitted to GenBank: the GenBank accession numbers are KU215618 and KU215619. To assess the taxonomic status of these specimens, the ITS sequences obtained from this study were compared with the sequences in GenBank by a BLAST database search .In this study, the RAD method was used to find SNPs in L. bicolor samples using the RAD method, the samples included both ingroup and outgroup specimens. Therefore, the number of informative SNP markers obtained for L. bicolor ranged from about 322 to 1000. In this paper, 712 SNP loci were obtained from two L. nuda samples using the RAD method. This number of SNP loci is sufficient to support further study of the genetic variation of L. nuda. The results of this study support that the RAD method was useful for identifying SNP loci in species for which genomic information is lacking.The RAD-tag depth distribution is shown in Fig. L. nuda. These SNPs will be used to examine the population genetics of L. nuda. This paper also confirms that the RAD method can be used to identify SNPs in a nonmodel macrofungus for which a reference genome is unavailable. Furthermore, the SNPs could provide the theoretical support for further research about cultivation and breeding of L. nuda. It is important for the protection of resources and the conservation of the population genetics of L. nuda in the field.This study used RAD method combined with high throughput sequencing to identify a total of 712 SNPs in two samples of ® 3730 Genetic Analyzer . Nucleotide sequences of the ITS regions that were amplified from the collections (HMAS 254481 and HMAS 254482) were aligned with the sequences of L. nuda ITS regions retrieved from GenBank using BioEdit 5.0.6 . The specimens were dried with an electric air-ventilation drier and were deposited in the Mycological Herbarium of the Chinese Academy of Sciences (HMAS) with accession numbers of HMAS 254481 and HMAS 254482. Genomic DNA was extracted from the dried blocks of tissue of the herbarium specimens Table  using th0.6 Hall  and Clus0.6 Hall .Table 3CL. nuda is unavailable, the genomic DNA was first digested with EcoRI (NEB Company). The digestion was performed at 37 °C overnight; digestion ended with a 20-min deactivation step at 65 °C followed by cooling to 4 °C and the enzyme-digested product was pooled. The fragments of digested DNA were connected to the P1 adapter using T4 DNA ligase (NEB company). The sequences of the P1 adapter include the EcoRI restriction site, a reverse amplification site, and the Illumina sequencing primer sites. Subsequently, the fragments were physically broken using a Covaris ultrasonicator with 200 bursts of 90 s each on high power, after which the resulting fragments were confirmed to be 300–500 bp in length by agarose gel electrophoresis. Then, a P2 adapter was connected to the pieces (300–500 bp). The sequences of the P2 adapter include the forward amplification and Illumina sequencing primer sites. After PCR amplification, only the fragments that included the P1 and P2 adapters were screened. Single-end  sequencing was performed using the Illumina HiSeq2000 in a total throughput of 16 lanes .For construction of the RAD library, the DNA from the two samples was pooled. The sample indexing and pooling were followed by the published method  by sequence similarity using ustacks (Catchen et al.


In this context, from a total of 163,782 SNPs derived from DNA resequencing and RNA-sequencing of 41 groundnut accessions and wild diploid ancestors, a total of 58,233 unique and informative SNPs were selected for developing the array. In addition to cultivated groundnuts (Arachis hypogaea), fair representation was kept for other diploids . Genotyping of the groundnut ‘Reference Set’ containing 300 genotypes identified 44,424 polymorphic SNPs and genetic diversity analysis provided in-depth insights into the genetic architecture of this material. The availability of the high-density SNP array ‘Axiom_Arachis’ with 58 K SNPs will accelerate the process of high resolution trait genetics and molecular breeding in cultivated groundnut.Single nucleotide polymorphisms (SNPs) are the most abundant DNA sequence variation in the genomes which can be used to associate genotypic variation to the phenotype. Therefore, availability of a high-density SNP array with uniform genome coverage can advance genetic studies and breeding applications. Here we report the development of a high-density SNP array ‘Axiom Crop improvement programs in general are focused on enhancing productivity, improving quality and resilience to biotic and abiotic stress by creating and/or harnessing genetic diversity. Genomics-assisted breeding (GAB) has accelerated crop improvement programs for development of improved cultivars in several cropsSingle nucleotide polymorphisms (SNPs) are the most abundant DNA sequence variations among various types of structural/genetic/sequence variations in the genome. Until recently, it has been a tedious, labor-intensive and expensive task to develop even a limited number of SNPs. In the last decade, next-generation sequencing (NGS) technologies have evolved very rapidly and have become the cheapest and fastest method of identification of genome-wide SNPsHigh-density fixed SNP arrays, though expensive as compared to GBS, provide genotyping data for almost all SNPs for all the individuals in a population. There are several genetics and breeding methods e.g. genomic selection that require consistent genotyping data for the same SNP loci across different germplasm sets and over filial generations in breeding. Therefore, fixed SNP arrays with >40 K SNPs have been developed and used for a variety of genetics and breeding applications in several crops such as rice5713Arachis hypogaea) is a globally important crop that is cultivated in >100 countries and consumed in almost every country. The last decade has witnessed a steady increase in demand due to a tremendous population growth in Asia and Africa. In 2014, this crop produced 42.3 Mt from 25.7 Mha with the average productivity of 1649 Kg/ha (http://faostat.fao.org/) but there is an existential need to increase its productivity to meet the growing demand. Cultivated groundnut is an allotetraploid (2n = 4x = 40) species with two subgenomes (A and B) that complicates sharing of diversity between cultivated species and wild diploid species2A. duranensis (A subgenome) and A. ipaensis (B subgenome) in 201617Groundnut or peanut  and A. ipaensis (B subgenome)  against the genomes of two groundnut progenitors i.e., bgenome)  yielded bgenome) . Of the Arachis array  (AxiomICRISAT) . GenotypICRISAT) . SubsequA. stenosperma (87.7%) and A. batizocoi (82.1%) showed highest level of polymorphism while SNPs from A. magna (52.5%) showed minimum polymorphism in the ‘Reference Set’ (A. hypogaea (76.8%), A. cardenasii (74.9%) and A. duranensis (76.0%) showed similar level of polymorphism  to 0.99  with an average of 0.92 . Minor aA. hypogaea). Among the tetraploid groups, the Cluster-II had genotypes form hypogaea subspecies, Cluster-III had both the subspecies i.e., hypogaea and fastigiata while Cluster-IV had genotypes from fastigiata subspecies. The diverse lines with maximum genetic distance within cultivated groups can be used for developing genetic and breeding populations for both mapping traits as well as for developing improved varieties with desirable agronomic traits and enhanced genetic base.Generation of high throughput SNP genotyping data on the ‘Reference Set’ provided an opportunity to gain deeper insights into the genetic relatedness among the genotypes and also the genetic architecture of this important germplasm set . The genfastigiata and hypogaea of the A. hypogaea (cultivated tetraploid). For example, the genomic regions conserved to subspecies fastigiata were observed on pseudomolecules A02, A06, A07 and A10 of A subgenome while B01, B07 and B09 of B subgenome. Similarly, the genomic regions conserved to subspecies hypogaea were observed in pseudomolecules A04 of A subgenome while B04, B08 and B10 of B subgenome.This array has also shown significant loss of diversity in the cultivated gene pool and preferential selection of genomic regions in these subspecies . The losfastigiata and hypogaea  SNPs for subspecies hypogaea . Of the Arachis array with 58,233 SNPs to the Arachis community provides an opportunity to generate high throughput genotyping data on different types of genetic and breeding populations for accelerating genetic diversity, high resolution trait mapping and breeding applications. Similar arrays were developed recently in other crop species such as rice , 6.2% SNPs from A. duranensis, 4.7% SNPs from A. batizocoi, 4.5% SNPs from A. magna, 4.1% from A. stenosperma, and 3.8% SNPs from A. cardenasii. The 58,233 SNPs placed on the newly developed array represented mostly intronic region with 39.8% share followed by 38.1% synonymous_coding, 11.1% non-synonymous_coding, 6.9% UTR_3_prime and 2.7% UTR_5_prime. The remaining 1.2% SNPs included intergenic, stop_gained, stop_lost, non-synonymous_start, non-synonymous_stop, start_lost, and others. The 50 K SNP array developed by Singh et al.A. duranensis enriched with genes coding for extracellular, periplasmic space proteins and involved in antioxidant activity while A. ipaensis with genes involved in reproductive processes and riboflavin synthase complex.We also tried to make this array diverse by including informative SNPs from different sources i.e., 76.7% SNPs from _Arachis SNP array was deployed to study genetic diversity and genetic relatedness in the ‘Reference Set’ developed by ICRISATA. monticola. High quality SNP genotyping data was generated successfully for all except three genotypes of the panel mainly due to poor QC for allele call. The polymorphism rate was four times higher in the smaller set of wild genotypes than the larger set of cultivated genotypes with mere ~10% common SNPs between both the sets.The newly developed AxiomA. stenosperma (87.7%) and A. batizocoi (82.1%) showed highest polymorphism as compared to A. hypogaea (76.8%), A. duranensis (76.0%), A. cardenasii (74.9%) and A. magna (52.5%).After removing the common SNPs, 77.6% SNPs showed polymorphism in the ‘Reference Set’ with comparatively higher rate of polymorphism in A subgenome (53.0%) than the B subgenome (47%). Considering the genome size of A  and B  subgenomes, single SNP per 45 Kb polymorphism density has been achieved in A subgenome as compared to single SNP per 65 Kb in B subgenome. Further, SNPs selected from A. hypogaea) were clustered into three distinct groups. Majority of the genotypes from hypogaea subspecies were grouped together in Cluster-II, both the subspecies i.e., hypogaea and fastigiata genotypes in Cluster-III and genotypes of fastigiata subspecies in Cluster-IV. The grouping of different subspecies genotypes using the SNP array was found much better than the earlier studies conducted with SSR and DArT genotyping on the same germplasm set26Mean major allele frequency, minor allele frequency, heterozygosity and PIC was found to be 0.92, 0.08, 0.02 and 0.13, respectively in the ‘Reference Set’. Phylogenetic analysis clearly grouped all the genotypes of the ‘Reference Set’ in four groups. As expected, the wild genotypes were grouped together in Cluster-I while cultivated genotypes , selective genomic regions remained conserved. These genomic regions might be harboring genes that are responsible for maintaining subspecies specific features.The cultivated groundnut crop across the world can be divided into four market types from two subspecies. The subspecies fastigiata and hypogaea. Prediction of 198 common SNPs between subspecies hypogaea and wild accessions indicated their location in 133 genes including plant defense against biotic and abiotic stresses, cellular growth and development, seed and pollen development. More importantly genes related to domestication traits such as skotomorphogenesis, flowering time (Aradu.1A8NN.1), seed maturity and germination (Aradu.PZ509.1), lateral root development (Aradu.895HT.1), stem elongation (Aradu.Y6SZD.1) and self-incompatibility (Araip.D2CP3.1) have shown genomic variation between these two subspecies. Such variation for domestication related traits might be playing an important role in retaining the basic features of these two subspecies during the course of evolution.In order to get further insights, we identified the pseudomolecule-wise subspecies specific high frequency SNPs for Arachis array with 58 K informative SNPs and its successful deployment in understanding the genetic diversity of ICRISAT ‘Reference Set’ in groundnut. This array is an important genomic resource for the Arachis and especially the groundnut community that will be useful not only for accelerating genetics and breeding applications but also to understand evolutionary biology in Arachis species.In summary, the present study reports development of Axiom_Arachis spp. accessions  were used for generating the sequence data in earlier studies18A total of 41 A. duranensis , A. ipaensis (ICG 8206), A. batizocoi , A. magna (ICG 8960 and KG30097), A. stenosperma (V10309) and A. cardenasii. The ‘Reference Set’ that is comprised of 300 accessions coming from 48 countriesA total of 38 accessions were used for whole genome re-sequencing (WGRS) while 3 accessions  were used for transcriptome sequencing. 38 accessions used for WGRS included 23 tetraploids, 4 tetraploid pooled samples (resistant and susceptible for foliar disease resistance) and 11 diploid species genotypes representing et al.High quality DNA isolation using modified CTAB-based method followed by quantification and quality check of DNA was done as mentioned in Mace A. duranensis and A. ipaensis)A. duranensis and A. ipaensis annotated genesThe raw sequences obtained were filtered using various softwares to get high quality reads for downstream processing. Briefly, the adapter sequences were trimmed using Cutadapt v1.2.1in silico validation. The in silico validation of the assay involved preliminary screening of the designed array file for each selected SNP, including their p-convert values generated using Affymetrix power tool (APT) AxiomGTv1 algorithm to ensure a high-quality final array (http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/powertools.affx). Both forward and reverse probes of each SNP were assigned with p-convert values, derived from a random forest model to predict the probability of SNP conversion on the array. The model considers factors including the probe sequence, binding energy, and expected degree of non-specific hybridization to multiple genomic regions. SNP probes with high p-convert values are expected to convert on the SNP array with a high probability. Potential probes were designed for each SNP in both the forward and reverse direction, each of which was designated as ‘recommended’, ‘neutral’, or ‘not recommended’ based on p-convert values through which the SNP data sets were easily filtered. A SNP marker/strand is recommended if: p-convert >0.6, no wobbles, and poly count = 0. In other words a SNP marker/strand was not recommended if they had duplicate count >0 or poly count >0 or p-convert <0.4 or wobble distance <21, or wobble count > = 3. Therefore, if a marker has the same recommendation for each strand, then it was tiled on strand with the highest p-convert value. None of the [A/T] or [C/G] markers were selected as they take up twice as many features. Finally, probes for selected SNPs were designed and successfully synthesized on the array chip.The selected SNPs representing 10 pseudomolecules each for A subgenome and B subgenome following the above mentioned criteria were subjected to ®platform was used to genotype “Reference Set” with the SNP array. Initially the target probes were prepared using each DNA sample having minimum quantity of 20 μL of good quality DNA and 10 ng/μL concentration. This procedure is explained in detail in Affymetrix Axiom® 2.0 Assay Manual. These samples were then amplified, fragmented and hybridized on chip followed by single-base extension through DNA ligation and signal amplification. This procedure is explained in detail in Affymetrix Axiom® 2.0 Assay Manual Target Prep Protocol QRC. The GeneTitan® Multi-Channel Instrument was then used for staining and scanning the samples and the details are provided in .Affymetrix GeneTitanBest Practices, Sample QC, Genotyping and Summary Only . We used ‘Best Practices’ workflow to perform quality control (QC) analysis of samples to select only those samples which passed the QC test for further analysis. The ‘Sample QC’ workflow was then used to produce genotype calls for the samples which passed QC analysis using ‘Best Practices Workflow’. The ‘Genotyping’ workflow was used to perform genotyping on the imported CEL files regardless of the sample QC matrix. Before making the genotyping calls, samples not passing the QC were removed as their inclusion may reduce the quality of the analyzed results. Finally the ‘Summary Only’ workflow was used to produce a summary containing details on the intensities for the probe sets for use in copy number analysis tools. It also allows to export the SNP data after the analysis is completed for downstream analysis. We have analyzed the diploid and tetraploid genotypes separately keeping the DQC > 0.75 and call rates >90. The above criteria helped in removing the SNPs having low call rates and keeping only the high quality SNPs for the further analysis. NR database was used to annotate all SNP containing genes using BLASTx program (http://blast.ncbi.nlm.nih.gov/blast/Blast.cgi?PROGRAM=blastx&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) with cut off E value <1.0E-5. Further, these SNPs were annotated and their effect on gene function was predicted using SNPEff V4.2et al.Allele calling was done using Axiom™ Analysis Suite version 1.0 using its three workflows i.e., Called allelic data was used for studying genetic diversity and genetic relationship among individuals of the “Reference Set”. The polymorphic information content (PIC), major allele frequency, number of observations, availability and gene diversity were calculated using the software PowerMarker ver. 3.25fastigiata, hypogaea and wild accessions. The allele frequency at each SNP site within each category was calculated. Further, the allele frequencies at each SNP position were compared between three categories and SNPs with contrasting allele frequencies in all the three categories were identified. Additionally, the annotations of these SNPs and their effect on genes were predicted using SNPEff V4.2In order to identify subspecies specific SNPs, the genotypes from the ‘Reference Set’ were divided into three categories viz. How to cite this article: Pandey, M. K. et al. Development and Evaluation of a High Density Genotyping ‘Axiom_Arachis’ Array with 58 K SNPs for Accelerating Genetics and Breeding in Groundnut. Sci. Rep.7, 40577; doi: 10.1038/srep40577 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Crassostrea gigas) is an economically and ecologically important marine bivalve, and it possesses one of the highest levels of genomic DNA variation among animal species. Pacific oyster SNPs have been extensively investigated; however, the mechanisms by which these SNPs may be used in a high-throughput, transferable, and economical manner remain to be elucidated. Here, we constructed an oyster 190K SNP array using Affymetrix Axiom genotyping technology. We designed 190,420 SNPs on the chip; these SNPs were selected from 54 million SNPs identified through re-sequencing of 472 Pacific oysters collected in China, Japan, Korea, and Canada. Our genotyping results indicated that 133,984 (70.4%) SNPs were polymorphic and successfully converted on the chip. The SNPs were distributed evenly throughout the oyster genome, located in 3,595 scaffolds with a length of ~509.4 million; the average interval spacing was 4,210 bp. In addition, 111,158 SNPs were distributed in 21,050 coding genes, with an average of 5.3 SNPs per gene. In comparison with genotypes obtained through re-sequencing, ~69% of the converted SNPs had a concordance rate of >0.971; the mean concordance rate was 0.966. Evaluation based on genotypes of full-sib family individuals revealed that the average genotyping accuracy rate was 0.975. Carrying 133 K polymorphic SNPs, our oyster 190K SNP array is the first commercially available high-density SNP chip for mollusks, with the highest throughput. It represents a valuable tool for oyster genome-wide association studies, fine linkage mapping, and population genetics.Single nucleotide polymorphisms (SNPs) are widely used in genetics and genomics research. The Pacific oyster ( The Pacific cupped oyster Crassostrea gigas (Thunberg 1793) originated in northeastern Asia, and is natively distributed along the coasts of China, Japan, and Korea. It was introduced into Europe [Oysters  constitute an essential component of many aquatic ecosystems and are economically important in the fisheries and aquaculture industries . Betweeno Europe , Australo Europe  during to Europe , 6, in mC. gigas has been completed; this facilitates our understanding of the mechanisms of stress adaptation and shell formation in oysters [Sustainable factory farming of oysters requires the development of high-quality oyster strains with rapid growth rates and high disease and stress resistance. Recent studies of the Pacific oyster industry have primarily focused on growth and yield –10, summ oysters . NeverthSingle nucleotide polymorphisms (SNPs) are widespread nucleotide variations among individuals of a population, and they constitute the most abundant type of molecular marker in plant and animal genomes. Owing to their high abundance, co-dominant mode of inheritance, and ease of high-throughput detection, SNPs are widely used in biological research , 27. TheSince the release of the first commercial SNP array by Affymetrix  in 1996 , the useOwing to the increasing accessibility of next-generation sequencing (NGS) technologies, genotyping by sequencing (GBS) technologies—which usually detect SNPs through whole or reduced genome sequencing—have become a powerful genetic analysis tool . GBS metC. gigas) based on the Affymetrix Axiom platform, and to assess the potential of this array for future genetic and genomic research.Further to the completion of our oyster genome project, we are currently conducting an oyster genome-wide association studies (GWAS) project using a re-sequencing approach to search for genes related to certain complex and important traits. The re-sequencing data generated from wild oysters will provide extensive resources for SNP mining and array design. The aim of the present study was to develop a high-density SNP genotyping array for the Pacific oyster . DNA fragments were then treated according to the Illumina DNA sample preparation protocol. Fragments were end-repaired, A-tailed, ligated to paired-end adaptors, and PCR amplified with 300–500 bp inserts for library construction. Sequencing was performed to generate 100-bp paired-end reads with coverage of ≥20 folds on the HiSeq 2000 platform (Illumina) according to the manufacturer’s standard protocols.https://broadinstitute.github.io/picard/). The reads from SNPs around the insertions and deletions (INDELs) in the bam files were then realigned using the Genome Analysis Toolkit (GATK) module RealignerTargetCreator and IndelRealigner [Filtered reads from all individuals were aligned to the oyster reference genome using Burrows-Wheeler Aligner (BWA) software  with theealigner . To obtaealigner  were useCandidate SNPs were filtered in multiple steps using several criteria to eliminate possible false positive sites and distribute SNPs relatively evenly across the genome. For each oyster, if a SNP was of low and excessive read coverage (DP < 10 or DP > 100) and low genotype quality (GQ < 20), the SNP genotype call of the individual was considered to be missing or invalid. For a SNP, the missing rate was defined as the proportion of individuals with a missing SNP genotype in all individuals. The minor allele frequency (MAF) was calculated based on the allele information of individuals without a missing SNP genotype. In this step, SNPs within 20 bp around a predicted INDEL or with MAF < 0.05 or with a missing rate of > 0.1 were filtered out. The remaining SNPs, together with SNPs that were significantly related to important traits such as glycogen, amino acid, fatty acid, and heavy metals contents from our GWAS project (unpublished data), were submitted to Affymetrix for design score assessment using the Axiom myDesign GW bioinformatics pipeline. For each SNP, the sequence feather, binding energies, expected degree of nonspecific binding, possibility of hybridization to multiple genomic regions, and impacts originated from adjacent SNPs were taken into account; a p-convert value—which denotes the probability of the SNP converting to a reliable SNP assay on the Axiom array system—was assigned to each of the two probes flanking an SNP. SNPs with probes having high p-convert values were more likely to be convertible. In addition, a design proposal categorized as “recommended”, “neutral”, “not recommended” and “not possible” was assigned to each of the two probes. The SNP with at least a “recommended” or “neutral” proposal was retained and annotated with in-house custom Perl scripts. The GWAS SNPs and a small number of “large-effect” SNPs—which are predicted to cause stop or start codon loss, introduce a new stop codon, or cause a splice acceptor or donor variant—were initially selected and added into the final array SNP set. For the next selection, every SNP was assigned a priority level value, i.e., 4 for non-synonymous coding, 3 for synonymous coding, 2 for 2-kb upstream/downstream of a gene or in the intron region, and 1 for the intergenic region. The planned SNP capacity was ~200 K, and the assembled oyster genome size was 560 million; hence, the calculated average SNP interval was ~2,800 bp. A 3-kb sliding window was used to scan the oyster genome, and the SNP with the highest priority level value in the window was selected. Next, for genome scaffolds having an observed array SNP number lower than the expected SNP number, a 1-kb sliding window was used to fill in the remaining array probe space. The final list of SNPs was submitted to Affymetrix for production of the Axiom genotyping array.To assess the performance of the oyster SNP array, we genotyped 96 Pacific oysters. Among these oysters, 44 were randomly selected from those re-sequenced for SNP discovery. Each oyster was deeply sequenced; hence, the genotype obtained using the oyster SNP array could be compared and cross-checked with that obtained through the GBS approach. We included 24 off-springs of a full-sib family designed for linkage mapping; both parents of this full-sib family were among the 44 randomly selected oysters mentioned above. In addition, 28 oysters were collected from a wild population in Qingdao, China. Genomic DNA was extracted from mantle tissues using a standard phenol-chloroform method. Genomic DNA samples were placed in a 96-well microtiter plate, and adjusted to a final concentration of ~50 ng/μL with a final volume of 10 μL. Genotyping with the oyster SNP chip was conducted using the Affymetrix GeneTitan Multi-Channel (MC) Instrument according to the standard operating instructions. The raw data stored in the form of CEL files were imported into Affymetrix Axiom Analysis Suite software version 1.1.1.66 for QC analysis and genotype calling, using the Axiom GT1 cluster algorithm with default parameters. The genotyping results were processed using SNPolisher software version 1.5.2. To assess the usability of the array in population genetics, all samples were subjected to principal component analysis (PCA) using Eigensoft software [in silico analysis for reproducibility prediction by Affymetrix, 886 K SNPs passed the evaluation; based on the distribution and function of each SNP, we submitted a final list of 190,420 SNPs to Affymetrix for production of an oyster genotyping array . All the raw data stored in CEL format have been deposited in the NCBI GEO database with an accession number of GSE94633.We performed a series of QC steps to ensure the accuracy of the outcomes during genotype calling. The signals of all 96 samples possessed a dish QC (DQC) value of 0.92–1.0, which was much greater than the default Axiom DQC cutoff value of 0.82. However, one wild oyster sample did not pass the call rate (CR) QC because its CR was 0.954, which was slightly lower than the default cutoff value of 0.970; hence, the overall rate of passing samples was 0.99 (95/96). As predicted, the passing rates of the 44 re-sequencing samples and the 24 full-sib family samples were 1.0, and there was no significant difference between the passing rate of the 28 wild samples (27/28 = 0.96) and that of the 44 re-sequencing samples  had a concordance rate of 1.000, 31,181 SNPs  had a concordance rate of 0.971–0.977, and 14,777 SNPs  had a concordance rate of 0.950–0.955 . CumulatWe further evaluated the genotyping accuracy using data obtained from the two parents and 24 off-springs of a full-sib family. The numbers of genotype combinations of the two parents that could be used in linkage analysis for types “AA × AB”, “AB × AA” and “AB × AB” were 20,716, 20,814, and 10,271, respectively . No erroC. gigas [The overall genome similarity of any two samples and the population stratification has frequently been estimated in population genetics and GWAS analyses . Based oC. gigas , clearlyC. gigas.In this study, we have established the largest commercially available Pacific oyster SNP array, composed of 190 K SNPs. Genotyping of 96 oysters revealed that ~133 K SNPs (~70%) were polymorphic and successfully converted on the chip. The SNPs were distributed evenly throughout the oyster genome, located in 3,595 scaffolds; the average interval spacing was 4,210 bp. In addition, 111,158 SNPs were distributed in 21,050 coding genes, with an average of 5.3 SNPs per gene. Comparison of the array-derived genotypes with those obtained through re-sequencing revealed that the mean concordance rate was 0.966. Moreover, evaluation based on the genotypes of two parents and 24 off-springs of a full-sib family revealed that the average accuracy rate was 0.975. Our results indicate that the oyster SNP array constitutes an alternative platform for genome-wide SNP genotyping and represents a valuable tool for research into the genetics of S1 Dataset(GZ)Click here for additional data file.S2 Dataset(GZ)Click here for additional data file.S3 Dataset(GZ)Click here for additional data file.S4 Dataset(GZ)Click here for additional data file.S5 Dataset(GZ)Click here for additional data file.S1 FigSNPs could be classified into six categories according to the probeset clustering properties: (i) ‘PolyHighResolution’; (ii) ‘NoMinorHom’; (iii) ‘MonoHighResolution’; (iv) ‘OTV’; (v) ‘CallRateBelowThreshold’; and (vi) ‘Other’.(TIF)Click here for additional data file.