CLDN1 and TWIST1 genes. These discoveries provide useful genomic information for further study of the discrete genetic mechanisms associated with economically important traits in pigs.Pigs have been one of the most important sources of meat for humans, and their productivity has been substantially improved by recent strong selection. Here, we present whole-genome resequencing analyses of 55 pigs of five breeds representing Korean native pigs, wild boar and three European origin breeds. 1,673.1 Gb of sequence reads were mapped to the Swine reference assembly, covering ∼99.2% of the reference genome, at an average of ∼11.7-fold coverage. We detected 20,123,573 single-nucleotide polymorphisms (SNPs), of which 25.5% were novel. We extracted 35,458 of non-synonymous SNPs in 9,904 genes, which may contribute to traits of interest. The whole SNP sets were further used to access the population structures of the breeds, using multiple methodologies, including phylogenetic, similarity matrix, and population structure analysis. They showed clear population clusters with respect to each breed. Furthermore, we scanned the whole genomes to identify signatures of selection throughout the genome. The result revealed several promising loci that might underlie economically important traits in pigs, such as the  Since the last century, traditional selective breeding has contributed significantly to the genetic improvement of economically important traits in pigs. For example, substantial improvements were achieved in pigs per sow per year  and the feed conversion ratio  during recent decades.4 Furthermore, some pig breeds have been used as mammalian model animals for the biomedical research because of their physiological and anatomical similarities to humans, and for developing several swine lines, including highly inbred miniature pig breeds.6 However, most of the achievements were made in European origin breeds, whereas, there are fewer systematic genetic improvement programs for diverse Asian local breeds.Domestication of pigs originated from local wild boar populations in Europe and Asia circa 10,000 yrs ago7 However, they are generally termed as the Korean native pig because of their same origin until the middle of the last century. Korean native pigs are known for their peculiar characteristics including higher intramuscular fat contents and chewy texture, which are appealing palatability factors to Korean pork consumers. However, they also have poorer productivities, such as lower growth rates and litters per sow per year than typical imported breeds, such as Duroc and Yorkshire.9 Since the beginning of the last century, the native breeds have been endangered, especially because of the extensive crossbreeding with economically promising imported breeds. To conserve the native genetic resource, recent efforts have been made by the National Institute of Animal Science (NIAS) in Korea to restore and manage the Korean native population.10 The Korean native pig is genetically more closely related to other Asian native pigs than to most European origin pigs.11 Kim et al.12 further showed that it might have a distinct genetic distance even from some Chinese native pig breeds.There are two types of Korean indigenous pigs currently registered with the worldwide Domestic Animal Diversity Information System of the Food and Agriculture Organization of the United Nations: Chookjin Chamdon and Jeju native pig.1 The recent advances in next-generation sequencing (NGS) technologies have led to further cataloguing of SNPs by resequencing of diverse pig breeds or multiple animals in comparison with the reference sequence assembly. Furthermore, the SNPs derived from NGS have been used successfully to dissect genomic characteristics of pigs. For example, Rubin et al. revealed significant signatures of selection in pig genomes by scanning whole genomes using SNPs.13 In addition, three Berkshire pigs were resequenced recently to explore their genetic relationship with 38 pig genomes obtained from the public database, providing useful information on the breed's origin and domestication.14The completion of the swine sequencing project has resulted in many single-nucleotide polymorphisms (SNPs) being identified throughout the genome.Sus scrofa 10.2). SNP sets derived from the whole-genome sequencing were used to explore genomic characteristics among the diverse pig breeds and to detect genomic regions potentially implicated in the strong selective breeding applied to the pig populations in this study.In this study, we present whole-genome sequencing analyses of 55 pigs of five breeds, including Korean wild boar, Korean native, Duroc, Landrace, and Yorkshire. Substantial numbers of SNPs were identified across the genome using the Swine reference assembly , 6 Duroc pigs (3 boars and 3 sows), 14 Landrace pigs (7 boars and 7 sows), and 15 Yorkshire pigs (7 boars and 8 sows) for this study . The libraries were sequenced using the Illumina HiSeq 2000 platform (paired-end 101-bp reads) and Illumina GA IIx platform (paired-end 100-bp reads). We generated 24,932,652,290 reads  from 55 samples. Each sample was sequenced to have over >34 Gb initial reads to produce a minimum of 10-fold genomic coverage.2.3.15 The genome sequence was downloaded from the UCSC genome browser (http://hgdownload.soe.ucsc.edu/goldenPath/susScr3/bigZips/susScr3.fa.gz). We also screened the unmapped reads and the reads that were aligned to unplaced scaffolds. Of the aligned reads, removal of polymerase chain reaction duplicates and re-synchronization of the mate information were performed using MarkDuplicates and FixMateInformation in the Picard software package version 1.48 (http://picard.sourceforge.net/), respectively. Local alignment around indels was performed to the duplication-removed reads using RealignerTargetCreator and IndelRealigner in the Genome Analysis Toolkit16 . Furthermore, basepair quality scores were recalibrated using CountCovariates and TableRecalibration in the GATK.16 Multi-sample SNP genotyping was performed to identify SNPs using UnifiedGenotyper in GATK. To reduce the false discovery rate, the filtering steps followed these criteria: QUAL < 30.0, QD < 2.0, MQ < 40.0, FS > 60.0, HaplotypeScore > 13.0, MQRankSum < −12.5, and ReadPosRankSum < −8.0. All the filtered SNPs were annotated to belong to 12 functional categories (Table 18 and the Ensembl Sus scrofa gene set version 75 (Sscrofa10.2.75). For the gene set, the canonical genes were applied to the annotation with the ‘-canon’ option in the snpEff program. Non-synonymous SNPs that had a SIFT score <0.05 were classified as potentially damaging variants. SIFT scores for the Sus scrofa gene set were downloaded from SIFT 4G .The sequenced reads were mapped to the Swine reference genome assembly (Sscrofa10.2) using Burrows Wheeler Aligner version 0.5.9 with default options.2.4. Phylogeny19 The phased haplotypes based on the SNPs were used to conduct phylogenetic analysis. The phylogenetic tree was generated using neighbour-joining tree estimation with pairwise distance, and Kimura's two-parameter distance method implemented in Analysis of Phylogenetic and Evolution (version 3.0.6) in R language.20 The tree was plotted using MEGA5,21 and the population structures were accessed by admixture analysis using the STRUCTURE version 2.3.4, which is based on the maximum likelihood method.22 We used different values of K  and an admixture model with a correlated allele frequency to assign the K clusters.23 A 30,000 burn-in period of Chain Monte Carlo searches followed by 20,000 replicate runs were performed at each K from 2 to 5. The result was then plotted using DISTRUCT version 1.1.24 The pairwise similarity between each animal was computed by the number of the same SNP genotypes divided by the total number of SNPs. The order of the 55 animals for the STRUCTURE plot and the similarity matrix followed the same order used in the phylogenetic tree.To perform phylogenetic and admixture analyses, haplotypes of all the animals in this study were inferred by an imputation tool, BEAGLE, version 3.3.2.2.5. Identification25 The number of major and minor allele sequence reads were counted at each of the identified SNP in each breed. We filtered the SNP positions whose minor allele frequency was <0.05. We then scanned the genome using 50% overlapping windows of 150 kb in size, which was successfully adopted in the previous pig resequencing study.25 Among the 150 kb windows, we excluded windows with fewer than 10 of the number of heterozygous SNPs in each breed. Briefly, the Pooled heterozygosity (Hp) was initially computed by an equation: 2*sum_maj*sum_min/(sum_maj+ sum_min),2 where sum_maj and sum_min are the sum of major and minor allele frequency at the given 150 kb window, using the numbers of reference and alternative allele sequence reads observed in each of the breeds. The ZHp score was calculated from Z transformation of the Hp score as described by Rubin et al.25To identify putative selective sweeps, we searched genomic regions with higher degrees of fixation, following the method by Rubin et al.2.6. Validationhttp://gatkforums.broadinstitute.org/discussion/48/using-varianteval).To validate the accuracy of SNP calling from whole-genome sequencing of five pig breeds, we genotyped five animals per each breed using the Illumina PorcineSNP60 v2 Genotyping BeadChip. Of the 64,232 SNP probes in the chip, we selected 44,903 probes that were successfully located on the autosomal and sex chromosomes. The reference allele of a chip SNP was set to the allele that is equal to the base at the reference genome, Sus scrofa 10.2. The genotype concordance was measured for each animal by the number of concordant genotypes between the SNP chip and the whole-genome sequencing derived SNPs divided by the total number of the SNP probes. The method was referred from GATK online documentation for VariantEval (2.7. DataAll SNPs detected in this study have been submitted to the NCBI dbSNP with the accession numbers: ss1754731760–ss1774855430.3.3.1. SequencingWe extracted genomic DNA from 55 pigs of five breeds including Korean wild boar (KWB), Korean native (KNP), Duroc (DUR), Landrace (LAN), and Yorkshire (YOR). Approximately 24.9 billion reads were generated by short-read sequencing technology including the Illumina GA IIx and HiSeq 2000 sequencing platforms. This equated to ∼2.52 terabases and 897-fold coverage compared with the expected genome size of the Swine reference genome . To detect high-quality SNPs from all the samples, we sequenced at least 34 Gb for each of the samples. Of the 55 sequenced animals, the least amount of sequence reads was 12.1-fold for the ‘DUR5’ individual. After mapping the reads to the reference genome, we removed erroneous sequence reads that were caused by potential polymerase chain reaction duplication (17.5%). This yielded ∼16.6 billion reads , covering 99.2% of the reference assembly at an average of 11.7-fold coverage across the covered region . Furthermore, we genotyped a part of the same individuals sequenced in this study, using the Illumina PorcineSNP60K BeadChip to perform a concordance test between the SNP panel genotype and SNPs derived from this study. A total of 44,903 SNPs were used for the concordance test to show high concordance rates (98.8–99.4%), which is reliable enough for further investigations (Supplementary Table S1). These results well suggested that most of the SNPs identified in this study were reasonably accurate.We identified a total of 20,123,573 SNPs throughout the genomes from all the 55 sequenced pigs of the five breeds in this study. The number of SNPs in each breed population varied from 6.6 to 14.0 million; the least number of the SNP set was observed in DUR, while the most values were found in KWB (Table 3.3. SNPSupplementary Table S2). To prioritize the nsSNPs, all the nsSNPs were further assigned a SIFT score to classify the effects of each nsSNP on phenotypic functions, based on the amino acid conservation.29 According to the typical interpretation of the score, a score of ≤0.05 classifies an nsSNP as ‘damaging’ and a score of >0.05 corresponds to ‘tolerant’. There were 6,849 damaging and 27,215 tolerant nsSNPs in our data (Supplementary Table S2). Furthermore, we extracted nsSNP sets that did not overlap among the breeds in this study (breed-specific nsSNP) and identified 126, 26, 11, 10, and 8 for KWB, KNP, DUR, LAN, and YOR, respectively (Supplementary Table S3).All the detected SNPs were annotated functionally using Ensembl gene annotation and dbSNP databases, assigning them to 12 functional classes. In agreement with previous studies, most of the SNPs were located in intergenic and intron regions (74.1 and 24.6% respectively), while fewer SNPs (1.3%) resided within exonic region, including exonic, splice sites, and untranslated regions . PLCB3 is involved in the mammalian oocyte at fertilization, spermatozoa activation, and embryonic lethality in sea urchin and mouse studies.32 Notably, the Yorkshire (YOR) has been widely used as a maternal line breed, particularly for its large litter size; thus, the SNP in PLCB3 is a candidate SNP to account for genetic variation in the reproduction trait, whose genetic effects have been difficult to predict. Little is known about which genes affect litter size, which has a low heritability and is a highly appreciated trait in the pig industry.34 A homozygous SNP was found in all 10 KWB individuals sequenced in this study . Interestingly, the same pattern was observed in the European wild boar samples, while the G allele frequency is around 33% in the Asian wild boars in a recent independent study . The gene affected by the SNP has homology to Neurexophilin and PC-esterase domain family, member 4 (NXPE4). Members of the NXPE family are neuropeptides that potentially signal via alpha-neurexins.35 It could be hypothesized that the NXPE4 homolog is involved in neurological mechanisms associated with temperament or fear, whose genetic mechanisms are poorly understood in pigs. Although it is beyond the scope of this study to conclude how each of the genes are affected by the nsSNPs, the many damaging nsSNPs identified provide ample information to identify promising candidates for further studies to dissect genetic mechanisms of diverse traits in pigs.Of the breed-specific nsSNP, we identified several interesting nsSNPs that may be implicated with economically important traits in the pig. For example, a YOR-specific novel nsSNP  was located in the phospholipase C, beta 3 gene  appeared to be genetically distant from the other four KNPs  that share other breed populations . The result was found consistently in the phylogenetic tree, similarity matrix, and STRUCTURE analyses in this study. KNPs can be classified as two subgroups with respect to their geographical location in Korea, and they have been bred separately for ∼50 yrs. Despite their same origin until the last century, this result may imply that the Chookjin Chamdon and Jeju native have discernable genetic differences that may be caused by their geographical isolation and different environments. In addition, we did not rule out the possibility that the native breed may have been affected by unrecorded crossbreeding with imported breeds before the systematic management of the native genetic resources.To examine the population structure and genetic relationships of the five breeds, we carried out multiple analyses, including phylogenetic analysis by neighbour-joining estimation, similarity estimation by comparison of genotypes, and admixture analysis using the STRUCTURE program. All five breeds were consistently separated by those three methods, as expected. The wild boar (KWB) was mostly distinguished from the four domesticated breeds , and a clear genetic distance was also observed within the domesticated breeds with the respect to each breed Fig. a. The re3.5. SelectiveSupplementary Figure S1), and we observed 1,277, 1,219, 656, 759, and 904 windows with ZHp values lower than −2 for KWB, KNP, DUR, LAN, and YOR, respectively (Supplementary Table S4–S8). An extremely low ZHp score is indicative of a selective sweep; therefore, we further accessed windows having low ZHp scores. Thus, we located several convincing loci that are potentially implicated with the selection applied on each breed. For example, the Claudin-1 (CLDN1) gene is located on chromosome 13 , and windows including the gene  exhibited a putative signature of selection in both LAN (ZHp = −1.81 and −2.96) and YOR (ZHp = −2.64 and −2.53) pigs (Supplementary Table S5 and S6). The CLDN1 genomic region was further examined by accessing pooled heterozygosity (Hp) calculated from each allele of the 142 SNPs that were identified in the region . The genomic region showed a distinctly lower ZHp of −3.02 in KNP (Supplementary Table S5) compared with the other four breed populations . TWIST1 is implicated in cell lineage differentiation and is associated with breast cancer and Saethre–Chotzen syndrome in humans.40 Furthermore, its expression was low in obese subjects and increased after weight loss, suggesting a prospective role in obesity.42 Despite its lower growth rate and litter size, KNP is known to have the higher intramuscular fat content (marbling) than typical imported breeds. The marbling is an important palatability factor appealing to many Korean pork consumers; however, limited numbers of genes have been identified to account for genetic variations in the trait in KNP. These examples show the potential of our analysis to identify candidate genes to dissect genetic mechanisms implicated in economically important traits in diverse pig breeds , which includes the twist family bHLH transcription factor 1 gene , of which substantial numbers were novel. Furthermore, we located numerous nsSNPs using a deep annotation process, which could be candidate genetic markers for predicting genetic variations in traits of interest. Second, multiple methodologies were applied to the detected SNPs to dissect genomic features of the pig breeds, revealing obvious signals of genetic differentiation among the breeds. Furthermore, the whole genomes were scanned to detect signatures of selection by accessing excess homozygosity throughout the genome. We observed many higher homozygosity regions, which are unevenly distributed across the genome. The results allowed us to retrieve several interesting genomic regions that may be associated with economically important traits in pigs.J.W.C., W.H.C., and K.T.L. wrote the article. N.K. and T.H.K. conceived and designed the experiments. J.W.C., W.H.C., K.T.L., S.H.L., W.L., D.L., Y.G.L., J.K., N.K., and T.H.K. carried out the analysis of the genome sequences. K.T.L., E.S.C., and T.H.K. performed the experiments.Supplementary data are available at www.dnaresearch.oxfordjournals.orgPJ00670701) from the National Institute of Animal Science; a grant (PJ008068) from the Next-generation BioGreen 21 Program, Rural Development Administration, Republic of Korea; and the 2014 Post-doctoral Fellowship Program of the Rural Development Administration, Republic of Korea. Funding to pay the Open Access publication charges for this article was provided by the National Institute of Animal Science.This work was supported by 2-7-10 Agenda Research (


However, few data are available regarding the clinical outcome, which provides vital reference for further application of the methodology. Here, we present a clinical evaluation of NGS-based preimplantation genetic diagnosis/screening (PGD/PGS) compared with single nucleotide polymorphism (SNP) array-based PGD/PGS as a control.Next generation sequencing (NGS) is now being used for detecting chromosomal abnormalities in blastocyst trophectoderm (TE) cells from A total of 395 couples participated. They were carriers of either translocation or inversion mutations, or were patients with recurrent miscarriage and/or advanced maternal age. A total of 1,512 blastocysts were biopsied on D5 after fertilization, with 1,058 blastocysts set aside for SNP array testing and 454 blastocysts for NGS testing. In the NGS cycles group, the implantation, clinical pregnancy and miscarriage rates were 52.6% (60/114), 61.3% (49/80) and 14.3% (7/49), respectively. In the SNP array cycles group, the implantation, clinical pregnancy and miscarriage rates were 47.6% (139/292), 56.7% (115/203) and 14.8% (17/115), respectively. The outcome measures of both the NGS and SNP array cycles were the same with insignificant differences. There were 150 blastocysts that underwent both NGS and SNP array analysis, of which seven blastocysts were found with inconsistent signals. All other signals obtained from NGS analysis were confirmed to be accurate by validation with qPCR. The relative copy number of mitochondrial DNA (mtDNA) for each blastocyst that underwent NGS testing was evaluated, and a significant difference was found between the copy number of mtDNA for the euploid and the chromosomally abnormal blastocysts. So far, out of 42 ongoing pregnancies, 24 babies were born in NGS cycles; all of these babies are healthy and free of any developmental problems.This study provides the first evaluation of the clinical outcomes of NGS-based pre-implantation genetic diagnosis/screening, and shows the reliability of this method in a clinical and array-based laboratory setting. NGS provides an accurate approach to detect embryonic imbalanced segmental rearrangements, to avoid the potential risks of false signals from SNP array in this study.The online version of this article (doi:10.1186/2047-217X-3-30) contains supplementary material, which is available to authorized users. Chromin vitro, 6, and  embryos . Further embryos , 9 are a embryos , 11 or r embryos , and the embryos . Thus, P embryos  and eithn (FISH) , comparan (FISH) –18 or arn (FISH) , 19, 20 n (FISH) –24 for sNext generation sequencing (NGS) has now become cost-effective, and this precise and comprehensive genetic analysis tool is being increasingly used in human medicine including noninvasive prenatal diagnosis , and theHere, we present the first clinical evaluation for NGS-based PGD/PGS combined with trophectoderm (TE) biopsy and cryopreserved embryo transfer (CET). We also employed SNP array as control for comparison in this study. The clinical outcomes including implantation, clinical pregnancy and miscarriage were recorded. In addition, the accuracy of NGS and SNP array were also evaluated in this study.in vitro fertilization-preimplantation genetic diagnosis (IVF-PGD) treatment, including 129 couples with a NGS-based test and 266 couples with a SNP array-based test, for the detection of embryonic chromosomal abnormalities. The NGS test was performed using low coverage whole-genome sequencing with a HiSeq 2000 platform. The SNP array test was performed using Affymetrix Gene Chip Mapping Nsp I 262 K. The average age of the patients was 32.1 years (with an age range of 20–44 years). In the NGS cycles group, 84 patients were confirmed to have a chromosomally abnormal karyotype; 18 of which had a Robertsonian translocation, 59 had a reciprocal translocation, and 7 patients had a inversion. Another 45 couples were included because of an advanced maternal age  and/or recurrent miscarriage . In the SNP array cycles group, 213 patients  and another 57 couples with AMA and/or RM were included. A total of 1,512 blastocysts were obtained, with 454 blastocysts for NGS testing and 1,058 blastocysts for SNP array analysis. In addition, 150 blastocysts from the 454 blastocysts were subjected to both NGS and SNP array tests, and blastocysts with inconsistent results would be further validated by qPCR.A total of 395 couples were subjected to Of the 454 blastocysts with NGS testing, an average of 8.2 million reads was obtained for each blastocyst, covering 5.5% ± 1.2% of the whole human genome and 98.7% ± 3.1% of the mitochondrial DNA (mtDNA). The ratio between the mean depth of the mitochondrial DNA and the genome for each blastocyst with NGS testing was calculated, which represented the relative copy number of mtDNA. It is displayed in Additional file A total of 198 (43.6%) euploid and 256 (56.4%) chromosomally abnormal blastocysts were identified by NGS. Among the abnormalities, 85 blastocysts had numerical chromosome aberrations, 127 contained imbalanced structural aberrations, and 44 blastocysts were detected with both numerical and imbalanced structural aberrations. Among the diagnosed blastocysts, the median number of normal/balanced embryos per couple was 1.53 (range from 0 to 6). The group of reciprocal translocation carriers obtained 43.0% (105/244) embryos with imbalanced aberrations, most of the imbalances were translocation related. Normal/balanced embryos constituted 54.6% (65/119) of the cohort in couples with AMA and/or RM  euploid and 590 (55.8%) chromosomally abnormal blastocysts were identified, 189 blastocysts had numerical chromosome aberrations, 298 contained imbalanced structural aberrations and 103 blastocysts were detected with both numerical and imbalanced structural aberrations. The median number of normal/balanced embryos per couple was 1.76 (range from 0 to 8). The group of reciprocal translocation carriers obtained 40.8% (247/606) embryos with imbalanced aberrations. Normal/balanced embryos constituted 60.2% (106/176) of the cohort in couples with AMA and/or RM  and were transferred in 283 cycles. The numbers of blastocysts transferred per cycle were 1.425 (114/80) and 1.438 (292/203) for NGS and SNP array, respectively. The proportion of transferred embryos that successfully implanted was evaluated by ultrasound 6–7 weeks after embryo transfer, indicating that 60 and 139 embryos resulted in a fetal sac, giving implantation rates of 52.6% (60/114) and 47.6% (139/292) for NGS and SNP array, respectively. Prenatal diagnosis with karyotyping of amniocentesis fluid samples did not find any fetus with chromosomal abnormalities. A total of 164 pregnancies were detected, with 129 singletons and 35 twins. The clinical pregnancy rate per transfer cycle was 61.3% (49/80) and 56.7% (115/203) for NGS and SNP array, respectively  of the euploid and the chromosomally abnormal blastocysts with NGS testing in Additional file Among the NGS-tested 460 blastocysts, 150 blastocysts were selected randomly for Affymetrix SNP array analysis as well, including 68 normal/balanced blastocysts and 82 blastocysts with chromosomal abnormalities. All of the normal/balanced blastocysts in the NGS test group were confirmed by SNP array. Among the chromosomally abnormal blastocysts, 7 blastocysts gave inconsistent results, which were further validated by qPCR. The detailed qPCR data for the 7 blastocysts can be seen in Additional file In this study, we provide the first evaluation of clinical outcome of NGS-based PGD/PGS. According to the NGS results of 454 blastocysts, we transferred 114 normal/balanced embryos into the uterus, with implantation, clinical pregnancy and ongoing pregnancy rates of 52.6% (60/114), 61.3% (49/80) and 52.5% (42/80), respectively. Twenty-four babies were born, so far, all of them healthy, indicating the accuracy of NGS as well as the safety of blastocyst biopsy and vitrification. It illustrates that NGS-PGD is applicable for the genetically high-risk populations, such as carriers of Robertsonian translocations and reciprocal translocations. The feasibility of NGS-PGS application for the patients with AMA or RM was evaluated as well, though the benefits of aneuploidy screening for this population remain hypothetical , 29.Array based methods (including aCGH and SNP arrays) provide an approach to screen all the 24 human chromosomes simultaneously, and are well established when applied to PGD/PGS – SNP array is widely used in clinics. Furthermore, an initial study [NGS is a rapidly developing method for whole genome testing. Our previous study  indicateThe outcome measures of NGS cycles were the same as SNP array, that the differences of the outcome measures were insignificant between these two methods. The numbers of normal/balanced embryos per couple and transferred blastocysts per cycle for the two methods were also insignificantly different. From the 150 blastocysts with both NGS and SNP array testing, we can find that the same normal/balanced embryos were detected by NGS and SNP array. Thus, the efficiency and accuracy for embryo selection would be similar between NGS and SNP array cycles, as well as the clinical outcome measures.There were 6 embryos with signals to be inconsistent with NGS and qPCR results in SNP array data. These signals may due to the fact that only a maternal or a paternal allele is present in SNP array results and so forth, which can easily lead to false-negatives and false-positives. A case of a Y chromosome gain was detected by NGS, but omitted by array for the lack of a Y chromosome marker in SNP array. False-negatives in PGD/PGS clinical practice may lead to a higher rate of spontaneous abortion and/or birth defect . False-pNGS is with a bright prospect. A case report described the use of NGS for PGD recently . SeveralIn conclusion, NGS-based PGD/PGS combined with blastocyst biopsy and vitrification can be efficiently applied in clinical practice. In this study, the clinical outcome measures of the NGS cycles were the same as SNP array, and NGS was able to detect some segmental imbalances that may be omitted by SNP array, which may avoid the potential risks of false signals. NGS-based PGD/PGS should be suitable for more extensive applications and might even provide services for the populations with genetic risks.Patients with various conditions including Robertsonian translocation, reciprocal translocation, inversion, advanced maternal age (AMA) and/or with recurrent miscarriage (RM) were recruited in the PGD/PGS program at Reproductive & Genetic Hospital of CITIC Xiangya, China from August 2011 to the current date. SNP array-based PGD/PGS cycles were performed earlier than NGS-based PGD/PGS cycles in this study. The karyotype of these patients was determined by G-banding by karyotyping peripheral blood. Blastocyst samples from all couples were obtained with Institutional Review Board (IRB) approval from BGI-Shenzhen, China and Reproductive & Genetic Hospital of CITIC Xiangya, China with reference number of LL-SC-RG-2012-001 for the IRB approval, and all couples signed a written informed consent form  agonist protocol or an antagonist protocol , 44 baseAll embryos were cultured in sequential media  to the blastocyst stage. On D4 after fertilization, an 18 μm hole was made in the zona pellucida of all embryos. On D5 or D6, blastocysts in which trophectoderm (TE) cells had herniated out of the zona pellucida were chosen for biopsy. Approximately 3–8 TE cells were aspirated using a biopsy pipette with 30 μm internal diameter and dissected with a Zilos TK laser . Biopsied TE cells were washed in G-MOPS medium . They were either used directly for Whole Genome Amplification (WGA) or stored at -20°C until WGA.The biopsied TE cells were used for WGA with WGA4 Kit  following the manufacturer’s instructions. 1-2ug of the WGA product was used for library construction according to the guidelines of TruSeq DNA Sample Prep Kits  with a 350 bp insert size. The libraries were processed for single-end 50 bp read length sequencing using the Illumina HiSeq2000 platform.The raw sequencing data were processed and quality controlled. The initial 20 bp sequence of each read was omitted, and the rest of the read sequence was aligned to a Human reference genome  using the Short Oligonucleotide Analysis Package 2 (SOAP2) . After aThe relative copy number of mtDNA for each blastocyst with NGS testing was calculated as the ratio between the mean depth of the mitochondrial DNA and the genome, which was displayed in Additional file WGA products were also processed for the SNP array analysis according to previous reports , 24. TheqPCR was used to validate the results of NGS and SNP array. It was performed according to previous protocols . The WGABlastocysts were vitrified after the biopsy using Kitazato vitrification solution (Kitazato Biopharma Co. Ltd. Shizuoka. Japan) and closed High Security Vitrification straws . The vitrification and warming procedure was carried out according to the protocol recommended in the Kitazato vitrification kit. Each blastocyst was stored in an individual straw. After warming and dilution, blastocysts were cultured in blastocyst medium for 1–2 hours. We selected the chromosomally normal/balanced blastocysts for warming, and the surviving re-expanded blastocysts with high morphology grade were selected for transfer.Luteal support was applied in cryopreserved embryo transfer (CET) cycles. Warmed blastocysts were transferred either 5 days after ovulation of a natural cycle or 5 days after the initiation by progesterone. Briefly, 6 mg Estradiol Valerate was started from Day 3 for 10–15 days, then luteal support was applied when a satisfactory endometrial development (thickness ≥8 mm) was confirmed with ultrasound. No more than two blastocysts were transferred, and single blastocyst transfer to each patient with well cryopreserved embryos was recommended.The main outcome measures included clinical pregnancy, implantation, miscarriage and ongoing pregnancy rates. These four outcome measures of NGS and SNP array cycles were used for Pearson’s chi-squared test (-test) to evaluate the difference between the two groups. Clinical pregnancy was confirmed when an intrauterine gestational sac with heart-beat was observed by ultrasound examination 30–40 days after embryo transfer. The amniocentesis fluid samples from fetuses or the peripheral blood samples from delivered babies were used for karyotyping to confirm the PGD/PGS results.The data sets supporting the results of this article are available in the European Genome-Phenome Archive repository at accession EGAD00001001037. Further details on data access are available from the GigaScience database .Supplementary data of sequencing, array and qPCR tests for the 7 embryos as well as the mitochondrial DNA analysis in sequencing.(XLSX 177 KB)Additional file 1:Supplementary data of chromosomal abnormalities for each embryo by sequencing and array tests as well as clinical outcome for each couple.(DOCX 1 MB)Additional file 2:Informed consent for preimplantation genetic diagnosis/screening.(DOCX 33 KB)Additional file 3:


Plasmodium biology and malaria epidemiology is required. An improved understanding of the genomic variation of malaria parasites, especially the most virulent Plasmodium falciparum (Pf) species, has the potential to yield new insights in these areas. High-throughput sequencing and genotyping is generating large amounts of genomic data across multiple parasite populations. The resulting ability to identify informative variants, particularly single-nucleotide polymorphisms (SNPs), will lead to the discovery of intra- and inter-population differences and thus enable the development of genetic barcodes for diagnostic assays and clinical studies. Knowledge of genetic variability underlying drug resistance and other differential phenotypes will also facilitate the identification of novel mutations and contribute to surveillance and stratified medicine applications. The PlasmoView interactive web-browsing tool enables the research community to visualise genomic variation and annotation  in a geographic setting. The first release contains over 600 000 high-quality SNPs in 631 Pf isolates from laboratory strains and four malaria-endemic regions .Malaria is a global public health challenge, with drug resistance a major barrier to disease control and elimination. To meet the urgent need for better treatments and vaccines, a deeper knowledge of  Plasmodium falciparum (Pf) in particular kills up to 1 million people each year [Pf parasites from almost all malaria-endemic countries show modest levels of drug resistance, especially to chloroquine [Pf parasites in Cambodia and Thailand are developing resistance to artemisinin – currently the most effective anti-malaria intervention [Pf genetics has provided new insights into the molecular mechanisms of drug resistance [Malaria parasites cause disease in approximately 650 million people and ach year . Pf paraoroquine . Recent rvention –7. An imsistance –10 and msistance .Pf data on an unprecedented scale. This means that it is now possible to densely map genomic variation  and assess global diversity. Knowledge of the diversity of variants across populations will enable the biological interrogation of novel mutations and identification of candidate vaccines. Other potential applications include SNP-based assays to barcode parasites over time and space for epidemiological, diagnostic and clinical studies. However there are several roadblocks to the translation of genomic variation into useful public health tools. These include difficulties in obtaining robust phenotypic data  on samples and the lack of web-based informatics tools to access robust genomic data, which are needed to launch further experiments and translational activities.High-throughput sequencing technologies and large-scale genotyping chips are generating genome-wide plasmodDB [genedb [Pf isolates. We are making available to the malaria community the PlasmoView web-browser (http://pathogenseq.lshtm.ac.uk/plasmoview) to facilitate the investigation of genome-wide polymorphisms in parasites from different malaria-endemic regions.Whilst the lasmodDB  and gene [genedb  web-browPf samples from four regions: West Africa , East Africa , Southeast Asia  and Oceania . After quality control procedures, 631 samples  were retained. The alignments and high coverage (>10-fold) enabled the identification of 593 579  high-quality SNPs across the nuclear genome . These SNPs include the 86 158 SNPs identified in 227 samples [Supplementary Figure 1B).We aligned raw sequence and genotyping data  from 971 Supplementary Figure 1C). This decay is higher in the African regions (WAF and EAF) than in SEA or OCE. As expected, a principal component analysis reveals that the SNP data differentiate the samples by geographic region  and population (not shown). To identify the polymorphisms driving the population differentiation, we applied a SNP-wise STF approach where values between 0 and 1, higher values imply more differentiation [STF > 0.1 indicating a high level of regional differentiation. As expected, the STF values of intra-region comparisons are substantially lower  than inter-region comparisons (% SNPs with STF > 0.1: 27.9%) in common SNPs (MAF > 15%).The linkage disequilibrium between non-rare markers reveals decay with physical genetic distance . PlasmoView provides real-time visualisation and summary statistics and is a timely tool for the high-level interrogation of large genomic datasets. Pf data are presented in two views: (a) the matrix view provides a colour-coded indication of mutation locations ordered by genetic and geographical location; and (b) the map view shows a global view of SNP prevalence by country. The matrix view includes graphical and real-time textual SNP-by-SNP genomic annotation  as well as MAF and STF graphs plotted for the selected data. Both matrix and map views are interactive enabling researchers to easily navigate the data via the menu at the top of the application or using mouse buttons. The PlasmoView launch page includes a facility to search by common gene name , previous id (PFD1145c or MAL7P1.27) and the latest gene nomenclature (PF3D7_0424100 or PF3D7_0709000). The tool is scalable for the significant increase in data that is anticipated over the next few years. In the next sections we demonstrate the utility of PlasmoView using specific loci in the Pf genome.The web-based Pf is commonly due to SNPs in genes involved in the biological pathways that antimalarials target. The matrix view in PlasmoView is ideally suited to display all observed mutations on a gene-by-gene basis. Figure PfCRT (PF3D7_0709000), a gene on chromosome 7 associated with chloroquine resistance [STF  involved in drug resistance are presented in Supplementary Figures 2A–C and also provided as part of the set of pre-loaded examples of interest for the web-browser.Drug resistance in sistance . Sixty-outation, , 18). PlPf genome was identified to be undergoing a selective sweep due to artemisinin resistance in Southeast Asia [STF) and those showing large geographical differences (high STF). For example, the SNP at position 1 793 121 in Pf3D7_1344700  has been identified as having the highest association with parasite clearance rate [[PlasmoView shows 46 SNPs in this region whose STF is greater than 0.1 including 14 non-synonymous SNPs and one synonymous in PF3D7_1343800  and four non-synonymous SNPs and one synonymous SNP in PF3D7_1344300 .Whilst a number of SNPs within drug resistance loci have been well characterised, other more recent and important regions may require investigation. For example, a major region of chromosome 13 (1.73 Mb to 1.82 Mb) in the Pf malaria [Pf Reticulocyte Binding Protein Homologue 5  is considered a promising candidate antigen, as it seems to be essential for the invasion of multiple laboratory-adapted Pf lines and clinical Pf isolates into red blood cells [PfRH5 across 227 clinical Pf isolates was low, with only 5 non-synonymous SNPs (out of 12) reaching a frequency greater than 10% in at least 1 population [PlasmoView to analyze more than double the number of samples and 5 additional populations, we identify 22 non-synonymous SNPs (out of 28) and confirm the locus has low diversity  and low STF (indicating little population differentiation). These signatures are shown in vaccine targets previously identified using methods to detect balancing selection, including the MSP3.8  and AMA1  genes [A is a recombinant protein (FMP2.1) based on AMA1 and has been tested in clinical trials [Detecting balancing selection is one method to identify signatures of acquired immunity and therefore potential targets for vaccines. As immunity to the commonest alleles rises in malaria-endemic areas, parasites expressing rarer alleles experience a selective advantage. This process maintains a balance of alleles in the population, with neither the common alleles moving to fixation nor the rare alleles moving to extinction. When multiple alleles are maintained within populations and none of them achieves fixation, balancing selection forces are believed to be present. In 0) genes , 21. Thel trials , see FigPf mitochondrial genome , GC content 31.6%) is uniparentally inherited and does not undergo recombination. Therefore mitochondrial DNA (mt) sequence divergence and variation data have been used to study the evolutionary history and migration of Pf [mt sequence variation have revealed geographic differentiation [PlasmoView shows 85 SNPs, including the three SNPs with MAF greater than 5% and STF in excess of 0.05  that had been correlated to atovaquone resistance following treatment failure [The on of Pf –25, partntiation –26. PlasPf biology and malaria epidemiology, in part deriving from analysis of parasite genetic variation. New genomic approaches, using massively parallelisable sequencing and genotyping technologies, are generating vast amounts of Pf genetic data. However, to fully achieve their scientific potential, including the initiation of further experiments and translational activities, web-based informatics tools are needed to help researchers to access this genomic information. The PlasmoView web-browser condenses and summarises SNP information derived from these technologies into a shared and interpretable visual form. The first release contains nearly 600 k SNPs in over 600 samples that have gone through the same high-quality control procedures as previous studies [PlasmoView.The continued public health burden of malaria and emergence of drug resistance requires the development of new treatments, vaccines and control measures. These efforts are likely to benefit from a deeper understanding of  studies , 27, 28,PlasmoView to support informed decision-making processes, whether they take place in the clinic, laboratory or public policy arena. The genomic variation identified in drug resistance  and vaccine candidate  genes will help to define the potential repertoire of polymorphisms for follow-up experiments. Similarly, the identification of polymorphisms that drive genetic differentiation at the continental, regional and village level  will facilitate the barcoding of parasites for use in surveillance applications. PlasmoView has the functionality to display the variation with annotations, geographical distribution and frequency data and population differentiation metrics in real time.The case studies above demonstrate the utility of PlasmoView is presented as a tool to visualise and summarise Pf variation, it will be extended to other Plasmodium species as the data becomes available. The next phase of the work is to characterise and display variation other than SNPs, leveraging off the high-coverage, paired-end nature of sequence data [Tajima's D [Whilst nce data  and resunce data ). Functijima's D ).PlasmoView is a powerful, scalable tool for the interactive geographic visualisation of Pf mutations. Using a high-quality set of polymorphisms, this study shows that PlasmoView is useful in confirming existing results, identifying potential avenues for further research and presenting complex genetic data to a broad audience.In summary, Supplementary materials are available at The Journal of Infectious Diseases online . Supplementary materials consist of data provided by the author that are published to benefit the reader. The posted materials are not copyedited. The contents of all supplementary data are the sole responsibility of the authors. Questions or messages regarding errors should be addressed to the author.


Cucumis melo L.) accessions  from 50 countries was selected to study the phenotypic variability for ripening behavior and sugar accumulation. The variability of single nucleotide polymorphisms (SNPs) at 53 selected candidate genes involved in sugar accumulation and fruit ripening processes was studied, as well as their association with phenotypic variation of related traits.A collection of 175 melon , which fitted well with the botanical classification of melon varieties. The variability in candidate genes for ethylene, cell wall and sugar-related traits was high and similar to SNPs located in reference genes. Variability at ripening candidate genes had an important weight on the genetic stratification of melon germplasm, indicating that traditional farmers might have selected for ripening traits during cultivar diversification. A strong relationship was also found between the genetic structure and phenotypic diversity, which could hamper genetic association studies. Accessions belonging to the The most remarkable association was found between sugar content and SNPs in LG III, where a hotspot of sugar content QTLs has previously been defined. By studying the differences in allelic variation of SNPs within horticultural groups with specific phenotypic features, we also detected differential variation in sugar-related candidates located in LGIX and LGX, and in ripening-related candidates located in LGII and X, all in regions with previously mapped QTLs for the corresponding traits.In the current study we have found an important variability at both the phenotypic and candidate gene levels for ripening behavior and sugar accumulation in melon fruit. By combination of differences in allelic diversity and association analysis, we have identified several candidate genes that may be involved in the melon phenotypic diversity.The online version of this article (doi:10.1186/s12863-015-0183-2) contains supplementary material, which is available to authorized users. Cucumis melo L.) is one of the most important crops within the Cucurbitaceae family, presenting a high variability in fruit traits among different cultivars, ranging from non-sweet fruits that are harvested before maturity and consumed as vegetables, to sweet fruits with high sugar concentrations that are eaten in salads or as dessert. Melon has been proposed to have an African and/or Asian origin [Melon .agrestis . Recentlagrestis  split th et al., ), and ssflexuosus group to the sweet and aromatic cantalupensis melons [Melon fruits display a broad range of phenotypic variation. Melon fruit weight varies from a few grams to several kilograms (fruits up to 35 kg have been reported), and the shape may be round, oblate, ovate, elliptical or extremely elongated . A huge s melons .Differences in sugar content among cultivars mainly reflect differences in sucrose accumulation . Sucrosecantalupensis group. These exhibit a distinct peak in respiration and ethylene production at maturity, and generally have a short ripening time and rapidly deteriorate in quality after harvest [inodorus group are unable to produce autocatalytic ethylene [loci (Al-3 and Al-4) in linkage groups (LG) VIII and IX, and four further QTLs in LGs I, II, III and XI. In a collection of near isogenic lines (NILs) derived from two non-climacteric genotypes [ETHQB3.5 and ETHQV6.3, respectively), which interacted epistatically [ETHQV6.3 to a 4.5-Mbp physical region of the melon genome [Melon also comprises broad genetic variation for ripening behavior, with climacteric and non-climacteric varieties. Typical climacteric melons are found within the  harvest . In contethylene  and, in ethylene . This diethylene , investienotypes -22, one enotypes . This NIatically . Fine man genome .), which encode a Raf-like kinase that negatively regulates the downstream ethylene response pathway. Also transcription factors such as ERF (ETHYLENE RESPONSIVE FACTOR), EIN (ETHYLENE INSENSITIVE), EIL (ETHYLENE-INSENSITIVE LIKE), and EBF (EIN3-BINDING F-BOX) are involved in ethylene responses [Ethylene affects the expression of many ripening related genes, in both climacteric and non-climacteric fruits, but expression of other genes is ethylene independent even in climacteric fruits -26. Mostesponses .ripening inhibitor , non-ripening , colorless non-ripening  and NR (never ripe) revealed that all the underlying lesions were in transcription factor genes [In tomato, mapping of mutants showing defects in fruit ripening, such as or genes -31. Otheor genes . ETO1 .The huge genetic diversity of the species has been studied with different molecular markers . HoweverIn an attempt to study the variation of genes involved in sugar metabolism and the ripening process, we searched the Melogene database for SNPs located in a set of candidate genes involved in these processes. We used this set of SNPs, along with reference SNPs evenly distributed in the genome, to genotype a set of 175 melon accessions, including commercial varieties, landraces, and wild or feral melons from over 50 countries, representing the wide diversity within the species. We report the variability of a set of genes involved in ripening behavior and sugar accumulation in melon fruit, providing a framework for studying the putative role of those genes in the diversification of the species, and to associate allelic variants with the phenotypic differences within the germplasm.melo and agrestis , a group of Spanish inodorus landraces (located in the upper-right part of the plot) is clearly differentiated from a group that includes other Spanish landraces and inodorus and ameri cultivars from Eastern Europe, Asia and North Africa (located in the center/lower-right part of the plot). The cantalupensis types could also be split into two groups: modern ‘Charentais’ and reticulatus cultivars in the upper-left area, and older cantaloup landraces in the central part of the plot. The non-sweet flexuosus types were grouped together, but clearly separate from all the sweet melons.The genetic diversity of the whole germplasm collection , the Indian momordica accessions could be distinguished from the rest. This distribution of the genetic variability in the PCA space supports previous observations, indicating that Far East conomon varieties represent one extreme within the overall genetic distribution of melons [For ssp. s Figure c, two grf melons ,47, and agrestis and melo  seem to be a mixture of Charentais and reticulatus populations. Some other cultivars from different origins were also included within one of these groups. Thus, the Japanese ‘Yamato Purinsu’ and the Chinese ‘China 151’, both considered makuwa types, were included with the French group although they showed some commonality with the conomon population , ‘Amarillo’ , ‘Tendral’ , ‘Rochet’ (e.g. ‘Mochuelo’), and ‘Blanco’ . Other singular Spanish landraces that show some traits of climacteric ripening including aroma and flesh softening , are not classified in the common market classes. These formed a separate population . This population also included some Turkish, Russian, Israeli and Egyptian varieties that belong to the highly variable ameri group .Population 3 , which mostly belong to the agrestis subspecies, two clear populations could be distinguished, in agreement with the PCA. Accessions belonging to different types of the conomon group  from Far Eastern countries  were grouped in population 6  and momordica varieties . Momordica has been traditionally assigned to the agrestis subspecies, but our results reflect the proximity of these agrestis varieties to the melo group. The subgroups demarcated by the PCA coincide with the sub-populations identified by STRUCTURE analysis, corroborating division of the overall population into the designated groups.None of the remaining accessions belonged to any specific population, but exhibited a high degree of admixture between populations of both subspecies. For example, the cantalupensis and reticulatus cultivars of the structure populations 1 and 2 had fruits with medium size (average fruit weight ± sd = 816 ± 348 g), mostly with orange flesh, with medium to high sugar content . These accessions also clearly show strong climacteric behavior, most with a fully formed abscission layer and fruit detachment, whereas the Spanish group of inodorus landraces (population 3) had bigger fruits , with green, white or cream flesh, with higher sugar content (°Brix = 11.1 ± 1.1 and 10 ± 1.7 and average sucrose = 134 ± 44 μg/g fresh weight), and were mostly non-climacteric with no abscission layer or fruit detachment.All accessions have been characterized for different fruits traits: fruit weight, flesh color, sugar and malate content, and also for traits related to climacteric behavior, such as abscission layer formation, fruit detachment, and flesh firmness .A higher variability in most traits was observed among accessions assigned to sub-populations 4 and 5. These developed medium to large sized fruits , with green, white, yellow, cream or light orange flesh, and variable sugar content . The ripening behavior was also variable, including some Spanish landraces with certain climacteric behavior, typical non-climacteric melo accessions  that show admixture of two or more of these sub-populations (populations 1 to 5) were also variable for fruit size and flesh color. Most had medium to high sugar content, but exhibited different degrees of climacteric behavior, ranging from clearly non-climacteric cultivars to some fully climacteric cantaloups and ameri accessions, with a wide range of intermediate behaviors , mostly with green or white flesh, and wide variation in sugar levels . They also showed different ripening behaviors, ranging from non-climacteric to weakly climacteric. Similar variation in ripening behavior was also observed in some wild African agrestis melons (population 7) that turn yellow during ripening and show signs of forming an abscission layer. However, this population was quite uniform for fruit size, flesh color and sugar content, developing very small, green-fleshed, non-sweet fruits . Accessions included in the admixture melo-agrestis group, consisting of momordica, dudaim, flexuosus and chate cultivar types, generally had little or no sugar, and showed weak to strong climacteric behavior  A126V amino acid substitution in invertase inhibitor 1 (CmINH1.1), appears only in the French ‘Vedrantais’ cultivar and some closely related ‘Charentais’ melons, and in the makuwa ‘Yamato Purinsu’ and ‘China51’ genotypes that are known to have been used in cantaloup breeding [CmINH1 is the mostly highly expressed of the three invertase inhibitors at the onset of sucrose accumulation in the reticulatus ‘Dulce’ genotype [agrestis types and tibish from Sudan. Similar SNPs in other invertase inhibitors, such as a C/T mutation that causes a tolerated P31S substitution in CmINHLIKE2.1, have a slightly less restricted distribution, occurring in other non-sweet, wild African agrestis types as well as agrestis and tibish from Sudan  , in a non-tolerated C/T (F9L) mutation in the vacuolar processing enzyme CmVPELIKE2.3, and in a synonymous C/T (Q721Q) change in CmSUS3 encoding one isoform of sucrose synthase (CmSUS3.1). Similarly, the non-tolerated C/A (L267I) mutation in CmSPP1 (CmSPP1.1) is present only in a few African wild types, tibish cultivars and in some African acidulus. CmSUS3 is the mostly highly expressed SUS gene during the sucrose accumulating period in reticulatus melons, and CmSPP1 increases its expression during ripening [agrestis from other melons. Some of these genes also make a substantial contribution to the genetic differentiation among groups (high Fst and R2 from AMOVA)  had a non-tolerated T/C (S173P) mutation in CmAIN2 (CmAIN2.3), which also appears in some exotic medium sugar ameri and cantaloups , but is absent in the other sweet genotypes. Most cantaloups and all inodorus had the alternative allele , although also present in a few low sugar momordica, dudaim and chate types. Also the CmINHLIKE2.4 SNP (C/T giving rise to a tolerated S137A substitution), shows a variation pattern similar to that of CmAIN2, with most of the non-sweet agrestis and melo genotypes  sharing the same allele as a few cantaloups. One synonymous mutation in the coding region of CmVPELIKE3 (CmVPELIKE3.2) and one 3´-UTR change in CmAAG2 (CmAAG2.1) could also be related with sugar content. The expression studies by Dai et al [AAG2 gene plays a role in hexose production only in the early stages of fruit development in the ‘Dulce’ reticulatus genotype. This genotype has the C allele, more common in the sweet genotypes. Most of these mutations are located in the sugar candidate genes with the highest contributions to the PC1 in Figure CmAIN2 and CmVPE-LIKE3 (LG IX), and CmAAG2 (LG X) [There are SNPs in the 3´-UTRs of two invertase inhibitor genes (CmINH1.3 and CmINH3.1), and in each case, one allele tends to occur more frequently in sweet genotypes  .CNR, AtEIN3, CmACO3 andCmERF3 genes linked to ethylene metabolism, and the cell wall related gene CmEXP3)  versus ssp. agrestis, (sub-populations 6 and 7), and are almost fixed within the respective subspecies , mostly composed of clearly non-climacteric genotypes. The cantalupensis and reticulatus populations, which are both highly climacteric, have the alternative allele, which is also more frequent in all of the remaining populations composed of genotypes with different degrees of climacteric behavior.The ripening-related gene candidates that contribute most strongly to differentiation between the subspecies  and non-climateric inodorus (sub-population 3). However, in this case the allele associated with climacteric behavior is also fixed in the conomon population (sub-population 6), while the allele that is more commonly associated with non-climacteric behavior appears in populations 4, 5 and 7. The latter three populations show variable levels of climacteric behavior. These SNPs include a tolerated C/G mutation (H121D) in the coding region of the cell wall related gene CmXTH5, a SNP in the 3´-UTR of CmACO3, and a synonymous T/G mutation (G171G) in CmERF2. All these SNPs contributed to the genetic differentiation of STRUCTURE populations  was studied in order to assess the possible degree of linkage between SNPs associated with the studied traits (see below) and real causal SNPs. Wild melon accessions were excluded because the fruits from these accessions differed in too many respects from fruits of cultivated accessions, making direct comparisons difficult if not impossible. Intra-chromosomal LD showed a rapid decay within a physical distance of less than 0.5 kbp, but increases from 0.5 to 1 kbp and then decreases rapidly with larger distances Figure . This isAssociation analysis was investigated using either a general linear model (GLM) or mixed linear model (MLM) approach, with the latter method being used to correct for the effect of genetic structure. GLM analysis gave a high number of significant associations , except in the Apelsinaja cultivar from Russia, the accession with the lowest brix degree in sub-population 2. Sub-populations 4 and 5 and the admixture group, with variable sugar content, contained both alleles, with the C allele occurring more frequently in the less sweet genotypes , and frequent in the low sugar genotypes of the admixture group. The alternative G allele was fixed in the remaining populations, as defined by the STRUCTURE analysis, and correlates with higher °Brix content in the admixture group. This SNP is located in LG XII in a region in which QTLs for sugar content have been reported previously [SlERF1, located in LG III was consistently associated with soluble solids content in both trials and to sucrose content. The C allele is more frequent in conomon, cantalupensis groups (both Charentais and reticulatus) and Spanish inodorus, while European and Asiatic inodorus and ameri share the alternative allele with African agrestis and most of the low sugar types of the admixture group   mutation. The C allele found in all conomon was also present in flexuosus, chate, dudaim, momordica, acidulus and some wild American and Indian agrestis, all accessions with acid pulp rich in malic acid. The 3´-UTR mutation in CMPSNP677  was initially selected as a reference SNP, but was also found to be related to malic acid content. The allelic distribution was similar to that of CmINH-LIKE2.2, except that the conomon allele is shared not only with flexuosus, dudaim, momordica, acidulus and wild American and Indian agrestis, but also with African agrestis. QTLs for glucose, fructose and soluble solids have previously been located in the chromosomal region of LG IV where this gene is located [AtEIN3 was also associated with malic acid content. For this SNP, the conomon allele is shared with acidulus and Indian and American wild types. In Arabidopsis, the AtEIN3 transcription factor plays a role in ethylene signaling downstream of the ETR1 ethylene receptor, and is involved in fine tuning of the ethylene response [Fruit flavor is also affected by acidity. Malic acid content was significantly associated with CmINHLIKE2.2, CMPSNP677 and AtEIN3ex4 Table . The SNP located . Finallyresponse . The assCmXTH5 located in LG X, was associated with formation of an abscission layer and fruit detachment  in  on LG X .MLO-like gene [inodorus types of Spanish, Turkish or Portuguese origin, which are mostly non-climacteric, whereas most other accessions have a homozygous TT genotype, including both climacteric and non-climacteric types. No QTLs related to climacteric ripening or fruit abscission have been reported before in this chromosome region [MLM showed a significant association with the same trait of MLO65044.1, a synonymous C/T mutation (L454L) in an ike gene  located e region ,17.conomon, African and Indian wild agrestis and acidulus, which had the highest values for firmness.Fruit firmness is also related to ripening behavior. MLM found the marker PSI_41-B07 SNP , on LG XI, significantly associated with the variation in this trait. In this region a QTL for flesh firmness was previously reported . The T agreen flesh (gf) and white flesh (wf), which interact epistatically [gf gene has been mapped to chromosome VIII [wf is located on chromosome IX [CmXTH5, the synonymous changes in CmERF2ex2, and MLO625760.1, a gene in LG XI that encodes an MLO-like protein (Table ZEAXANTHIN EPOXIDASE (ZEP) gene [Fruit flesh color is also related with both fruit quality and ripening behavior, as the flesh of most climacteric genotypes changes from green to orange during ripening. Flesh color is controlled by two major genes, atically  and giveome VIII ,47, whilosome IX . Neverthosome IX ,20. MLM EP) gene . The resPCA and STRUCTURE analyses carried out in the current study, based on SNPs located in reference and candidate genes, provided a germplasm stratification and classification very similar to previous works based on other marker systems or un-selected SNPs ,5,47. Thcantalupensis and inodorus group [cantalupensis Charentais, reticulatus or inodorus morphotypes completely, show variable admixture degrees of several sub-populations.Our results confirm that the highest genetic variability among cultivated melons occurs in Western and Central Asia and in the Middle East, where a high degree of genetic admixture was found. This admixture is not unexpected in melon, as no major geophysical barriers are found in the geographical distribution of traditional melon varieties, so gene flow among different regions is not impeded. Asian varieties are thought to be the likely ancestors of the us group ,55. In fmelo. In contrast, selection for strictly climacteric or non-climacteric ripening was probably less intense within the ssp. agrestis cultivars, which more frequently show an intermediate ripening behaviour. A number of consistent QTLs involved in climacteric ripening have been described previously [Our results indicate that SNPs in ripening related candidates, both ethylene and cell wall related, have a higher weight than SNPs in sugar candidates on both sub-species and sub-population stratification. This might reflect the effect of human selection in the development of strictly non-climacteric and high climacteric cultivars within ssp. eviously ,17,18,23eviously ,20,47, iAssociation of candidate SNPs with phenotypic variability has been investigated by two approaches: (i) by looking for alleles that are fixed or have a higher frequency within horticultural groups with characteristic phenotypic features, and (ii) by association analysis. Sugar content and ripening behavior were clearly related with genetic structure, so the use of MLM models to adjust for genetic structure would also reduce the power to detect associations. MLM analysis has resulted in a much smaller number of SNPs associated with the studied traits. This could be due to lack of linkage or to the association between genetic structure and trait variability, as alleles that are close to being fixed within groups could not be associated significantly after adjusting for population structure in the MLM analysis. Nevertheless, a consistent association of the SlERF1 SNP and sugar content has been found on LG III. This linkage group contains a sugar content QTL hotspot , and wasCmINH1 and CmINHLIKE2) and in other genes encoding enzymes or proteins involved in sugar metabolism . Also of interest are mutations with more balanced allele frequencies and those with differential patterns of variation within sugar accumulating and non-accumulating groups, such as the mutation in the acid invertase CmAIN2 and other sugar-related genes (CmVPELIKE3 or CmAAG2). The fact that these three genes co-localize with previously reported QTLs for sugar content in LG IX and X [Allelic differentiation analysis provided information that is complementary to the association results. Some SNPs in sugar-related candidate genes appeared in genotypes with common features in sugar content. These include SNPs that could be related with the mechanisms that prevent sugar accumulation in African melons, such as SNPs in invertase inhibitor genes , that map to regions in which QTLs for ripening in LG III, V and VII have been previously reported, were found to be associated with fruit firmness. However, further studies are needed to see if these mutations affect gene expression or gene function, as this differential allelic distribution might also be an effect of the population structure.Both GLM and MLM gave significant associations of traits related with ripening behavior, such as the formation of the abscission layer and flesh firmness, with reference SNPs in genes located in LG XII and XI, the latter being co-localized with a QTL for flesh firmness  Table 1. However in LG X  providesripening ,23. In aameri group showed both high phenotypic and genotypic diversity. Therefore, cultivars from this group, along with cultivars from related admixture groups from the Near-Middle East region, would provide a suitable genotype set for association analysis in melon.Our study shows that association and allelic differentiation analysis could be used as complementary approaches in highly structured populations, such as the current one, in order to define candidate genes. A highly variable germplasm collection with low genetic structure would be necessary to increase the power of association studies in melon. The The analysis of SNPs in sugar metabolism and ripening-related genes, and also in reference genes, has revealed differences in the amount of genetic diversity among the main groups. Ripening-related genes seem to contribute more to this structure than sugar metabolism related genes. We found specific alleles of candidate genes fixed in cultivar groups concomitant with differences in sugar or ripening behavior. This could be due to ancient selection of these alleles by early farmers, but given the strong genetic structure, it is not currently possible to distinguish between direct selection or genetic drift. Nevertheless, some SNPs were still found to be associated with ripening behavior or sugar accumulation even after taking the genetic structure of the collection into consideration. As LD is extremely low in melon germplasm, these SNPs should be tightly linked to the causal SNPs. Taking into consideration the low LD, the strong genetic structure of melon germplasm, and the relatively high phenotypic and genotypic diversity found among accessions of Near-Middle East origin within the admixture group, it should be feasible to define a panel of accessions derived from this group that has minimal genetic structure but retains sufficient genetic diversity for association analysis after more extensive genotyping.C. melo ssp. melo and C. melo ssp. agrestis, being represented by multiple accessions. This was based on a previously assembled core germplasm collection [www.comav.upv.es). Subspecies melo accessions included the commercially important groups inodorus, cantalupensis and reticulatus. The inodorus group contains commercial cultivars, Spanish landraces and traditional cultivars from Southern and Eastern Europe, North Africa and Asia. The cantalupensis group includes accessions widely grown in Europe, such as ‘Charentais’ types and other French, American, Israeli and Japanese cantaloupes, commercial reticulatus varieties and other cantalupensis types. The rest of the ssp. melo accessions belong to ameri, chandalak, adana, flexuosus, chate and dudaim groups from Southern and Eastern Europe, Northern Africa, Central Asia and India. The agrestis subspecies was represented mostly by accessions of Asian and African origin. Momordica from India, conomon, makuwa and chinensis from Far-East Asia, and Indian wild melons together represented the Asian melon diversity. Tibish cultivars from Sudan, considered as the most primitive melon known, cultivated acidulus and wild melons from Africa were also included.The panel of melon genotypes used in this study consisted of 175 accessions of diverse origins  reference 148 SNPs), (ii) sugar metabolism related (43 SNPs), and (iii) ripening related  (60 SNPs) .The SNPs of the candidate genes were selected from a SNP collection generated after resequencing eight pools of accessions representing the main melon botanical groups , most ofwww.melonomics.net) [Candidate SNPs were selected from the full SNP collection by using the BLAST algorithm to search the sequences for candidate genes against the Melogene database. The genome position of candidate gene SNPs was defined according to melon genome assembly v3.5, and their position in the genetic map was also established relative to the mapped refrence SNPs, using the anchorage of the genome sequence to the genetic map available in the Melonomics database (ics.net) ,45. The CmAAG1 and CmAAG2, sucrose synthases CmSUS2 and CmSUS3, sucrose P phosphatases CmSPP1 and CmSPP2, fructokinase CmFK3, soluble acid invertase CmAIN2, cell wall acid invertases CmCIN3 and CmCIN4, and invertase inhibitors CmINH1, CmINH2 and CmINH3). Dai et al. [reticulatus group, using publicly available data from the ICUGI melon EST database (www.icugi.org). Some of them were mapped by Harel-Beja et al [www.melonomics.net) ; (ii) VvETR2, VvRTE1 and VvCTR1 from Vitis vinifera; (iii) AtCTR1 from Arabidopsis thaliana; and (iv) SlETR3 from Solanum lycopersicum. SlETR3 (NR) was previously identified in the NR (never ripe) tomato mutant [AtEIN3, other genes annotated as EIN3 or EIL  in the Melonomics database, and the melon orthologue of the SlEBF gene of tomato [ [CmERF1, CmERF2, CmERF3, the melon othologue of the apple MdERF2, two melon orthologs of the tomato genes SlERF1 and SlERF3) [Cnr and rin [SlSBP and CmETHIND were also included in the SNP analysis, using information from melon genetic maps [CmEXP3), which has been hypothesized to contribute to hemicellulose depolymerization, and CmXTH5, which encodes a xyloglucan endotransglycolase/hydrolase that contributes to xyloglucan depolymerization in ripening fruit [Thirty four candidates involved in ethylene biosynthesis, signal reception and signaling, and cell wall disassembly were selected from the Melonomics database based on their genome annotation and information from previous studies in melon , tomato thaliana , apple [thaliana  and peacthaliana   ,36, ethy SlERF3) ,52,62, a and rin ,68. Genetic maps ,14,15,23ng fruit .http://sift.jcvi.org/), which predicts whether an amino acid substitution will significantly affect protein function. Scores below 0.05 indicate a strong probability that protein function will be affected by the mutation [The effect of the variants was analyzed with SIFT  (39°28′11″ N- 0°22′38″ W) at COMAV-UPV greenhouse facilities, and in VCO’s St Rémy station (France) (43°47′18″ N- 4°49′54″ E), during spring/summer 2008. The following fruit traits (one fruit per plant) were analyzed: (i) fruit weight; (ii) flesh color ; (iii) total soluble solids, °Brix (measured from drops of juice using a Milwaukee MR32ATC refractometer); and (iv) flesh firmness (measured using a fruit pressure tester FT327 with a plunger diameter of 8 mm). Flesh color, firmness, and total soluble solids were measured at four points in the equatorial region of the mesocarp. Samples of flesh tissue from the same region were harvested for metabolite analysis from fruits grown at COMAV, immediately frozen in liquid nitrogen and stored at -80°C until analysis. Soluble sugars  and malate were measured enzymatically in ethanolic extracts as described in Stitt et al. .From the classical markers of climacteric ripening , formation of an abscission layer and fruit detachment were selected as the most amenable for high-throughput phenotyping to classify the accessions as climacteric or non-climacteric. Formation of an abscission layer was scored visually in the assay performed at COMAV-UPV on a scale from zero (no abscission layer) to four (fully formed abscission layer). Fruit detachment was scored visually in the assay performed at VCO on the scale 1 = absent, 2 = no slip, 3 = half slip, 4 = full slip.www.r-project.org) [The genetic diversity of the germplasm collection as a whole was assessed by principal component analysis (PCA) using the ‘adegenet 1.4-0’ package in the R project for statistical computing  ,74. The ect.org) . Twenty ect.org) .Basic genetic variability parameters, MAF, gene diversity (expected heterozygosity), observed heterozygosity (Ho) and Fisher’s fixation index (Fis) were calculated for each locus with PowerMarker 3.5 . In ordehttp://www.maizegenetics.net) was used to estimate the LD parameter r2 and the comparison-wise significance was computed from 1000 permutations. LD decay was drawn as a smooth line from r2 against distance in kb, fitting the data using a second-degree, locally-weighted scatterplot-smoothing LOESS [2 was derived from a distribution of the unlinked r2. The parametric 95th and 99th percentiles of the unlinked-r2 distribution were taken as a population specific critical value of r2.TASSEL v 5.0   and mixed linear models (MLM) implemented in TASSEL v. 5.0. In order to control genetic structure effects, a kinship matrix was calculated using reference SNPs Associations were considered statistically significant for an experimental-wise threshold p < 0.05 after adjusting by the Bonferroni’s correction based on the total number of tested markers .


Chlorocebus aethiops sabaeus), one of the most widely used NHPs in biomedical research. We generated this resource by whole genome sequencing (WGS) of monkeys from the Vervet Research Colony (VRC), an NIH-supported research resource for which extensive phenotypic data are available.We report here the first genome-wide high-resolution polymorphism resource for non-human primate (NHP) association and linkage studies, constructed for the Caribbean-origin vervet monkey, or African green monkey (We identified genome-wide single nucleotide polymorphisms (SNPs) by WGS of 721 members of an extended pedigree from the VRC. From high-depth WGS data we identified more than 4 million polymorphic unequivocal segregating sites; by pruning these SNPs based on heterozygosity, quality control filters, and the degree of linkage disequilibrium (LD) between SNPs, we constructed genome-wide panels suitable for genetic association  and linkage analysis . To further enhance the utility of these resources for linkage analysis, we used a further pruned subset of the linkage panel to generate multipoint identity by descent matrices.The genetic and phenotypic resources now available for the VRC and other Caribbean-origin vervets enable their use for genetic investigation of traits relevant to human diseases.The online version of this article (doi:10.1186/s12915-015-0152-2) contains supplementary material, which is available to authorized users. Non-human primates (NHPs) are becoming increasingly valuable model species for biomedical research. NHPs share a greater degree of conservation with humans, compared to other animal models, across every level of biology from genome sequence and structure through physiology to behavior , 2. At tAs we describe here, genomic resources are now available in the vervet monkey that will enable genetic investigation of medically relevant traits, in phenotyped samples from highly abundant Caribbean-origin vervet populations. One such population is the Vervet Research Colony (VRC), an NIH-supported research resource founded in the 1970s and 1980s from 57 wild caught monkeys from the Caribbean islands of St. Kitts and Nevis , which hCommercially available arrays have provided an extremely inexpensive platform for genotyping genome-wide SNPs in humans and in a few model systems (such as the mouse). The insufficient market for such arrays in NHPs has necessitated the development of an alternative approach for inexpensively genotyping the vervet and other NHP models. Pasaniuc and colleagues have demonstrated that low-pass whole genome sequencing (WGS) provides a cost-effective alternative to SNP genotyping arrays for GWASs . We reasThe generation of common SNPs for linkage and association studies in the VRC started from a preliminary study, in which we systematically assessed the impact of coverage depth on genotyping accuracy depending on position in the pedigree, using sequence data from 105 monkeys. We used these data in a down-sampling experiment to inform our selection of coverage depth for sequence analysis of 620 additional monkeys. We then followed four main stages of analysis in the entire set of 725 monkeys: 1) identifying unequivocal segregating sites for common variants in 17 monkeys sequenced at high depth; 2) calling genotypes pedigree-wide at the SNPs identified in Stage 1 in all 725 monkeys; 3) performing sample-level quality control (QC) using SNPs called in Stage 2; 4) establishing SNP sets for genetic analyses by thinning the common SNPs from Stage 2, in monkeys that passed QC in Stage 3. We describe the results for each of these stages, indicating the number of SNPs that we removed, in each stage, by applying a series of QC filters.Down-sampling analysis showed that sequencing both parents at 4× (and their offspring at 1×) resulted in a much lower rate of Mendelian inconsistencies and a higher degree of genotype concordance with non down-sampled genotypes (in parents) than a strategy in which both parents are sequenced at 1X and their offspring at 4×  to rank the pedigree members based on the number of their direct descendants included in the set of 725 monkeys that comprised the WGS sample, and employed this ranking as the primary rationale for including them in either high coverage (>25× average), medium coverage (>4× average), or low coverage (>1× average) sequencing bins. Based on this algorithm (and including the 105 monkeys sequenced in our preliminary study) we assigned 16 monkeys to high coverage, 407 to medium coverage, and 302 to low coverage WGS.Given improvements in technology over the course of the project, our sequencing protocol resulted, for a substantial proportion of samples, in considerably deeper coverage than had been our target  in these 17 monkeys being the largest factor, leading to the removal of about 6M SNPs (Table In Stage 3 we used SNPs that passed through Stage 2 to perform sample-level QC and to refine the pedigree structure. Based on discrepancies between known pedigree relationships and identity by descent (IBD) estimated from linkage disequilibrium (LD)-pruned SNPs, and on concordance between WGS data and a set of SNP genotypes generated for another study with independent methods , we excl2 thresholds appropriate to either association or linkage mapping). We conducted the steps in Stage 4 to provide the community conducting vervet research in the VRC with well-vetted sets of polymorphisms that will facilitate their performance of genetic analyses; it is not our intent to utilize these sets to construct vervet-specific genotyping arrays. By LD pruning at r2 < 0.9 we obtained a set of almost 500,000 SNPs suitable for genome-wide association analysis, and by LD pruning at r2 < 0.4 obtained a less dense set of almost 150,000 SNPs suitable for linkage analysis. We then finalized these SNP sets by removing SNPs for which pedigree-wide checks revealed Mendelian inconsistencies . Both SNP sets  with redundant genetic information  files. We estimated a genetic map for these SNPs by interpolating from the 338 autosomal STRs  four monkeys for high coverage (about 30×) WGS from the surviving generation that was closest to the VRC founder generation, all having multiple (14–115) descendants available for WGS; and ii) 101 monkeys for medium coverage (about 4–6×) WGS from “middle” generations, emphasizing either those with multiple descendants available for WGS or those with extensive biological samples available for phenotypic investigations.We then selected for sequencing in the next phase of the project an additional 620 monkeys, chosen based either on i) their having multiple descendants or ii) their having been assessed for ≥ 7 phenotypes or having multiple biological samples available. Results from analysis of the preliminary study enabled us to devise an algorithm to rank these 620 monkeys for assignment to high coverage, medium coverage, and low coverage (about 1×) WGS, where monkeys with a higher rank would be sequenced at greater depth than monkeys with a lower rank.We identified segregating sites in 17 high coverage (30×) monkeys and then called genotypes at these sites in the full set of 725 monkeys (105 + 620). We then conducted a series of filtering steps to ensure that we were retaining high-quality, polymorphic sites, including genotype calling that incorporated linkage disequilibrium (LD) and Mendelian constraints. By thinning these filtered high-quality sites to remove markers with redundant genetic information (based on LD), we generated the final association and linkage SNP sets. The following sections provide details of all steps.We generated the WGS data for 725 monkeys from genomic DNA provided by the Systems Biology Sample Repository at UCLA. We have generated 100 base pair short read sequences on the Illumina HiSeq2000 instrument from short insert libraries (200–400 bp) for 725 vervets at The Genome Institute (TGI) at Washington University in St. Louis and the McGill University and Genome Quebec Innovation Centre. We have submitted all WGS sequences to the Sequence Read Archive (SRA) under the NCBI BioProject number PRJNA240242.We followed the published protocol of the 1000 Genomes Project  for readWith the pedigree broken into trios, duos, and singletons, we used Beagle to first obtain a pedigree-specific haplotype reference panel from the distantly related high coverage monkeys. We then used this reference panel to phase and impute genotypes of all sequenced members of the pedigree. Finally, we used TrioCaller to refine previous haplotypes by incorporating trio constraints and read depth information.After this step we derived two consensus haplotypes for each monkey, splitting replicated haplotypes from the same monkey into two clusters, corresponding to two chromosomes, based on the Hamming distance between haplotypes. Within each cluster we built a consensus haplotype by accepting the majority call at each locus. We applied the above procedure only to SNPs on autosomes, as the Mendelian transmission model of Beagle and TrioCaller is not applicable to chromosomes X and Y.in silico, to a prespecified level). We considered parent-offspring units for this analysis and determined the relative importance of selected parents, compared to their offspring, in influencing the accuracy of genotype assignments. We then extrapolated the results to the entire pedigree.We used the preliminary data to evaluate the impact of WGS coverage in monkeys, in relation to their position within the pedigree, by conducting analyses of down-sampling  to three different settings. To down-sample the level of coverage of one monkey in silico to the prespecified target coverage, we uniformly sampled a fraction (target-coverage/existing-coverage) of all its existing sequencing reads that had been aligned to a reference genome; this step generated a new alignment file with reduced coverage. We left the alignment files of other monkeys in the pedigree unchanged, and then applied the same calling procedure as that described above to this revised set of 105 alignment files. We then calculated and compared the Mendelian error rates and genotype concordance rates before and after down-sampling.The down-sampling analysis included three Construct the pedigree as a directed graph. Each node represents a monkey. Each edge goes from one parent (father/mother) to a child. Remove monkeys that will not be sequenced.Initialize an empty set (child-set). It will store the ID of monkeys for whom one parent has been already ranked. No identical IDs are stored in it.Start with the monkey that has the most direct descendants. Assign that monkey “rank 1” and add its direct descendants into the offspring-set.Select as “rank 2” the remaining monkey that would most increase the size of the offspring-set; if there are ties , older monkeys are given lower rank.Stop when there are no monkeys left to be ranked.The down-sampling experiment enabled us to evaluate the effect of sequencing coverage on SNP Mendelian error rates in the context of a pedigree, and results suggested that a ranking strategy, based on the number of a monkey’s direct descendants, would be useful to determine sequence coverage of the remaining 620 monkeys. The rank of a monkey would reflect its importance in holding down the error rate of SNPs of all sequenced monkeys, not just the error rate of the monkey with its offspring/parents. We devised a greedy algorithm to rank monkeys in terms of the impact that their sequencing would have on the frequency of errors in SNP calling, over the entire pedigree. We applied this algorithm to all 725 monkeys, assigning the 620 monkeys not yet sequenced to different sequencing coverage classes based on the ranking. This procedure involved the following steps:We conducted read mapping, as described above, of the entire set of 725 WGS monkeys; this step included re-mapping of the 105 monkeys from the preliminary study, described above, given updates to the reference genome assembly since we had produced the data from that study. We performed read mapping for the entire set using the final pre-submission version of the Vervet Reference Genome, consisting of 2,199 scaffolds, and followed the procedures described above for read trimming, alignment, and marking of duplicates.Additionally, we then performed a base quality score recalibration, and local indel realignment procedures . BecauseTo assemble SNP sets for linkage and association analyses, we wished to include only highly polymorphic and reliable variants. We therefore focused on identifying such polymorphic variants among the most deeply sequenced monkeys. We used a local-assembly-based haplotype caller, Platypus , to discWe used SAMtools to assign genotypes to all 725 sequenced monkeys, at the SNPs identified in Stage 1. As described for the preliminary study, we subsequently refined SAMtools calls with methods that used LD and Mendelian constraints . We then discarded loci whose coverage is outside the twofold range of global median coverage, loci whose minor allele frequency is below 10 % (as estimated in the full set of 725 monkeys), and loci with >=50 % missing calls.We lifted over the scaffold-based coordinates of VRC SNPs retained in Stage 2, from the pre-submission version of the reference assembly, to the chromosome coordinates of the NCBI-released version , using GATK’s LifeOverVariants procedure. We applied additional filters to remove SNPs with questionable positions . We then estimated the pairwise IBD sharing for all pairs of monkeys using this set of roughly independent SNPs. We then compared the PLINK IBD estimates with the kinship estimates that were entirely based on the pedigree structure, as estimated by SOLAR [Once we had obtained genotypes for all 725 monkeys at the variant sites identified in the 17 high coverage monkeys, we performed sample-level QC to identify possible sample mix-ups or contamination. Using PLINK , we LD-pby SOLAR ; for thiAt the start of the WGS studies, parentage was known for monkeys born prior to 2008; this information derived from microsatellite-based genotyping  togetherTo assign parentage we required: 1) identification of levels of IBD sharing expected between parent-offspring, with at most one monkey of each gender demonstrating such a level of sharing to monkeys of unknown parentage; 2) for mothers, concordance between the WGS IBD information with data from colony records on observed mother-infant behavior; 3) for fathers, concordance between WGS IBD information and data from colony records indicating a set of possible fathers (based on their sharing housing with the mothers and infants in question); 4) a plausible age difference between putative parents and offspring (within a range of 4–15 years).After removing data from samples suspected of contamination or mislabeling, and updating the pedigree based on newly identified parent-offspring relationships, we repeated the genotype call refining  step, described in Stage 2. We then applied the following filters to generate our final mapping sets: 1) removed SNPs with >=5 Mendel errors between parents and offspring; 2) removed SNPs with excess heterozygosity (heterozygous fraction>0.6); 3) marked the remaining (sporadic) parent-offspring Mendelian-error genotypes as missing; 4) removed SNPs with missing rate >5 %.2<=0.9) and one appropriate for linkage mapping (retaining SNPs with pairwise r2<=0.4). In conducting the pruning for both marker sets we used PLINK, with window size=50 and shift size =20. Lastly we used PedCheck [We then thinned the SNP dataset to generate two genetic mapping marker sets, one appropriate for association (retaining SNPs with pairwise rPedCheck  to detecPedCheck .To further enhance the utility of the SNP sets, we used LOKI  to const