Moniliophthora roreri is the fungal pathogen that causes frosty pod rot (FPR) disease of Theobroma cacao L., the source of chocolate. FPR occurs in most of the cacao producing countries in the Western Hemisphere, causing yield losses up to 80%. Genetic diversity within the FPR pathogen population may allow the population to adapt to changing environmental conditions and adapt to enhanced resistance in the host plant. The present study developed single nucleotide polymorphism (SNP) markers from RNASeq results for 13 M. roreri isolates and validated the markers for their ability to reveal genetic diversity in an international M. roreri collection. The SNP resources reported herein represent the first study of RNA sequencing (RNASeq)-derived SNP validation in M. roreri and demonstrates the utility of RNASeq as an approach for de novo SNP identification in M. roreri. A total of 88 polymorphic SNPs were used to evaluate the genetic diversity of 172 M. roreri cacao isolates resulting in 37 distinct genotypes (including 14 synonymous groups). Absence of heterozygosity for the 88 SNP markers indicates reproduction in M. roreri is clonal and likely due to a homothallic life style. The upper Magdalena Valley of Colombia showed the highest levels of genetic diversity with 20 distinct genotypes of which 13 were limited to this region, and indicates this region as the possible center of origin for M. roreri. Moniliophthora roreri H. C. Evans, Stalpers, Samson, and Benny is the causal agent of frosty pod rot (FPR) of Theobroma cacao L.  represent the most copious source of genetic variation and are linked to heritable differences between individual strains of a pathogen . Since t isolate , discove isolate  is based isolate . SNP det isolate .M. roreri isolates and the reference genome sequence  that were isolated in 1978 and 1984, respectively, from Costa Rica. M. roreri cultures were isolated from pods showing the initial or intermediate stages of external necrosis. Pods were cut with a sterile knife and small internal tissue sections from the zone where necrotic and healthy tissues meet were removed to water agar plates. Cultures typical of M. roreri, slow growing white mycelia, were transferred to V8 medium . Isolates were shipped to USDA-APHIS-PPQ facility in Beltsville, MD and transferred to USDA-ARS Sustainable Perennial Crops Lab in Beltsville, MD after inspection.Most isolates used in this study belong to CATIE’s collection of pathogen . A consi2) were transferred from cultures grown on V8 media to 50 ml falcon tubes containing 20 ml liquid clarified V8 media  while shaking at 100 rpm. Cultures were allowed to grow for 10–15 days. Mycelia were washed with sterile water and collected by centrifuging at 20,000 n buffer . AdditioM. roreri (MCA2977) genome sequences . For the RNASeq analysis, cDNA was generated using a routine RNA library preparation TruSeq protocol developed by Illumina Technologies . Using the kit, mRNA was first isolated from total RNA by performing polyA selection step, followed by construction of single end sequencing libraries with an insert size of 160 bp. Single-end sequencing was performed on 13 samples using the Illumina HiSeq platform. Samples were multiplexed with unique six-mer barcodes generating 627,844,665 filtered  1 × 50 bp read pairs. Each library  ranging from 20 to 62 million reads in fastq format was aligned to the equences  using a equences . The filequences . Variantequences . The resequences . SNPs weequences . SNPs weequences . In ordein silico generated SNPs are not due to paralogous genes, we randomly selected 25 putative homozygous and heterozygous SNPs and performed BLASTn searches of the ∼100 bp flaking regions against the M. roreri MCA 2997 (taxid:221103) whole-genome shotgun contigs. As the homozygous SNPs showed single hits while the heterozygous SNPs showed multiple hits within the M. roreri genome  according to the manufacturers protocol. Then 9–12 clones from each amplicon were randomly selected to be commercially sequenced  from both ends using M13F and M13R primers. After removing the vector sequences, ClustalW2 . The Fluidigm SNPtypeTM Genotyping Reagent Kit was used according to the manufacturer’s instructions. Using these primers, the isolated DNAs were subjected to Specific Target Amplification (STA) in order to enrich the SNP sequences of interest. Genotyping was performed on a nanofluidic 96.96 Dynamic Array IFC . This chip automatically assembles PCR reactions, enabling simultaneous testing of up to 94 samples with 96 SNP markers. The use of a 96.96 Dynamic ArrayTM IFC for SNP genotyping of human samples was as described by TM imager . The data was analyzed with Fluidigm Genotyping Analysis Software .To evaluate the putative SNP markers for suitability of genotyping the M. roreri isolates using Sanger sequencing. The PCR-amplified products flanking putative SNP markers from different M. roreri isolates were recovered from agarose gels and purified using QIAGEN gel purification kits  according to the manufacturers protocol. Then 9–12 clones from each amplicon were randomly selected to be commercially sequenced  from both ends using M13F and M13R primers. After removing the vector sequences, ClustalW2 . After aligning the reads against the M. roreri reference genome sequence -based SNPs were obtained  ranged between 224 and 2,340 (Table 1). The ratios between homozygous and heterozygous SNPs ranged between 1.9 and 0.1 (Table 1). Among the 25 randomly selected homozygous and heterozygous SNPs tested, none of the homozygous SNPs containing flanking regions showed more than one hit (E > 2e–50) within the M. roreri genome while all but one heterozygous SNP containing flanking regions showed two or more hits (E > 2e–50) with the exception being SNP 485_1_1286  from RNASeq libraries ranged from 20 to 62 million reads . Therefore we selected 288 homozygous SNPs for validation by genotyping a test panel of 172 M. roreri isolates from the most important FPR affected areas of South and Central America. The overview of the SNP mining strategy from M. roreri RNASeq libraries shows the stringency involved at the various levels .Sanger sequencing of PCR-amplicons containing a putative homozygous SNP from three in silico variant calling, Fluidigm genotyping analysis software called 69 SNPs as heterozygous. To validate the heterozygous SNP calls by the Fluidigm system, two such putative SNPs were tested from three M. roreri isolates using Sanger sequencing. Sanger sequencing invalidated the presence of heterozygosity . Based on the PCoA results and aligning all the isolates according to the SNPs (See Supplementary Excel file Table 2). All 86 Costa Rican isolates along with two Colombian and one Ecuadorian isolates were synonymous (Syn Grp. 1). While all the Bolivian and Peruvian isolates along with one Ecuadorian isolate formed another synonymous group (Syn Grp. 2). Treating each synonymous group as an individual isolate allows all of the 37 distinct genotypes to be grouped into two major clusters. One comprised most of the variant Colombian isolates along with two Ecuadorian isolates , while the other cluster is mostly comprised of the synonymous groups derived from Costa Rican, Colombian, Venezuelan, Bolivian, and Ecuadorian isolates .The genetic relationships among the 172 a and b) supported by bootstrap values >50%, and another four phylogenetic sub-groups that were poorly supported, which were derived from Colombia isolates . Approximate geographical locations for all of the 172 isolates are shown in Figure 4.A dendrogram was generated based on the SNP profiles for the synonymous groups and isolates with unique genotypes. This analysis increased the resolution and the degree of statistical support for the groups identified, with two discrete phylogenetic groups of isolates  of RNA as a method of SNP detection using expressed genes of M. roreri. The developed SNP panel has already proven to be a powerful tool for studying the dispersion, evolution and epidemiology of this pathosystem, and will have a major impact on future studies designed to understand the interplay between pathogen genetic diversity and host resistance/tolerance.Due to the insufficient throughput and data standardization issues over time and between research facilities, the existing molecular marker-based genotyping technology for M. roreri isolates included in the RNASeq analysis were selected to represent all five of the genetic groups previously reported by M. roreri genome  and/or were located within regions presenting additional flanking SNPs (data not shown). A BLASTn search against the M. roreri genome has further confirmed the postulation that the majority of these heterozygous SNPs are actually associated with misalignment between paralogous sequences instead of unique genes. Moreover, Sanger sequencing of partial genomic DNA fragments associated with specific SNPs has validated homozygous SNPs which vary between isolates but not heterozygous SNPs. Therefore we adopted a very stringent approach for the final SNP calling .The SNP discovery process for ow trout , 70% in ow trout , 83% in ow trout , and 85%ow trout . Moreovetruction , and, astruction . Because meiosis  in which meiosis . This fuM. roreri isolates by performing a multiplex PCR reaction before genotyping, using primers for all loci of interest, but without targeting the specific alleles, thus proportionally increasing the copies of these loci. Results from the repeatedly genotyped isolates showed 100% concordance of allelic intensity for the 88 selected SNPs, and suggests that the nanofluidic system is a reliable platform for generating M. roreri isolate genotypes with high accuracy. In the few instances where the Fluidigm genotyping analysis software erroneously called heterozygous SNPs, it was observed that the allelic intensity of the non-template controls (NTC) for those SNPs were comparatively high. Therefore it is necessary to stringently check the NTC values for any SNP panel using the Fluidigm system.The present method of nanofluidic array of SNP markers can handle a large amount of samples in a short period of time and is highly robust and repeatable. The STA protocol  used in M. roreri reproduces sexually with any frequency as predicted previously by M. roreri isolates and would seem to make it highly unlikely that M. roreri reproduces by sexual recombination with any significant frequency. M. roreri population analysis using AFLPs and ISSRs suggesting M. roreri reproduction is clonal. The present study confirms in unambiguous terms that M. roreri reproduction is clonal but does not rule out meiosis as a common occurrence in M. roreri.If M. roreri infection, the fruit develops malformations and then progresses to the necrotrophic phase where rot occurs, followed by sporulation on the pod surface . Analysis of additional isolates should confirm this observation, although with time additional isolates may be introduced into these areas.The two Costa Rican isolates from late 1970s and early 1980s (ATCC42952 and ATCC64239) showed 100% sequence similarities with all the isolates collected in the country between 1999 and 2013, while the same genotype  was also isolated from infected cacao pods in Colombia and Ecuador during 1999 and 2004. It clearly suggests that these particular M. roreri sister taxon M. perniciosa, the presence of A and B mating type genes, normally associated with tetrapolar species, was used to demonstrate the evolution of self-compatibility in the presence of such genes  and HD2 (XP_007858883) along with nine potential pheromone receptors  indicates that it may also possess a tetrapolar mating system similar to M. perniciosa. Even though primarily homothallic Agaricomycetes have no obvious need for such mating type genes, they are widely present in homothallic species  proteins are up-regulated during the necrotrophic phase prior to spore formation which could be responsible for the homokaryotic nuclear state. These up-regulated proteins are het-c, het-e, nacht, and WD40 domain proteins and NWD2 proteins  and is most likely the center of origin of M. roreri. This was the area where FPR has been recorded on wild Theobroma by T. gileri. Moreover, the presence of ancient populations of wild Theobroma and Herrania species known to be susceptible to M. roreri in this region of the Magdalena Valley of Colombia  never escaped from the sub-montane forests, and is non-pathogenic to cacao. It is probable that cacao was originally brought from its center of origin in the Upper Amazon and has been cultivated by the indigenous peoples of Magdalena Valley, Colombia for several thousands of years before it moved to other regions of Colombia  while the other group (Group a) is mainly confined to Colombia, and has significant genetic diversity resulting in many unique isolates . The low bootstrap support for the four phylogenetic Sub-groups within Group a was due to small number of SNPs involved in those differentiations. As each SNP is a unique event that can be tracked with certainty, the genetic differentiations here in is of high confidence. The phylogenetic Sub-group I is centered around the upper Magdalena Valley and crossed the Central Andes through the northern side and then moved south along the Cauca Valley. Similarly, Sub-groups II and IV moved west from upper Magdalena Valley and crossed the Western Andes and the moved south along the coast reaching Ecuador . On the other hand phylogenetic Sub-group III was able to cross the Eastern Andes. Similarly, isolates of phylogenetic Group b were isolated from the upper Magdalena Valley. They moved North and West from the upper Magdalena Valley, while a few isolates were able to move south along the coast . In a previous finding by M. roreri maintains a clonal state, we expect to see less indication of genetic drift associated with the movement from the pathogens center of origin. Instead, this study suggested dispersal of M. roreri clones from the upper Magdalena Valley over time and the subsequent localized genetic drift associated with these unique isolates and events.Unlike the previously reported five genetic groups , the disTheobroma and Herrania hosts (M. roreri life cycle when it comes to dispersion in time and space. Though comparison of spore shape and size of these two groups suggest no obvious difference (See Supplementary Figure It may be that natural dispersal started out on the wild ia hosts  and thenM. roreri can be widely used for population genetics and evolutionary studies of the pathogen and would be a great asset for breeding cacao tolerant against to FPR. The fact that M. roreri propagation is clonal or homothallic and the generation of new recombinants through sexual propagation is a comparatively rare event, if it occurs at all, has a huge implication on the breeding and disease management strategy against FPR. The present study also gives credence to the need for strict quarantine even in areas where FPR is endemic to stop the introduction of new M. roreri genotypes which might make the disease management more complicated. Being the probable center of origin and representing almost half of the entire genetic diversity, the upper Magdalena Valley of Colombia becomes the most important area in terms of FPR research. The establishment of breeding and screening programs in this area would assure new cacao germplasm was selected for tolerance against the widest M. roreri genetic base.The current set of SNP markers developed for The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bison bonasus) males are affected by posthitis (necrotic inflammation of the prepuce) and die in the wild forest. Despite many years of study, pathogenesis of this disease has not yet been determined. The main aim of the study was to find SNP markers significantly associated with the incidence of posthitis and mine the genome for candidate genes potentially involved in the development of the disease.About 5–6% of the European bison (OR10A3 gene (olfactory receptor genes), known to be involved in atopic dermatitis in humans. It was also observed that five other significant SNP markers were located in the proximity of candidate genes involved in severe diseases of skin tissue and cancer/tumour development of epithelial or testicular germ cells, which suggest their potential participation in the posthitis. The 25 investigated SNPs showed marked differences in allelic and genotypic frequencies between the healthy and affected bison groups.It was shown that relatively small number of SNPs effects reached genome-wide significance after false discovery rate (FDR) correction. Among 25 significant markers, the highest effects were found for two SNPs (rs110456748 and rs136792896) located at the distance of 23846 bp and 37742 bp, respectively, from posthitis and should be closer examined to find causal mutations helpful in better understanding of the disease ethology and to control its incidence in the future.The 2 Mb region of the BTA15 chromosome is involved in genetic background of This article was reviewed by Prof. Lev Klebanov and Dr. Fyodor Kondrashov. Bison bonasus), (EB), after extinction in the wild before World War II, was restored based on progeny of just 12 individuals preserved in private collections and zoological gardens [Genome Wide Association Studies (GWAS) have become one of the major tools in explaining the ethology of human diseases of genetic origin . The tec gardens . The res gardens .posthitis affected EB males. This disease was discovered in the 1980s in the Bialowieza Forest [Posthitis is defined as necrotic inflammation of the prepuce. About 5–6% of the males are affected each year and die in the wild forest or are eliminated by officially approved hunting [posthitis in bison has not yet been determined. The affected bulls do not exhibit changes in the general physiological mechanism as indicated in the long-term studies of 30 physiological indices [Posthitis is thought to be a disease of an endemic character since it is only observed in males living in Bialowieza National Park or transported from it. Apparently, no environmental or genetic factors which would explain the susceptibility of some individuals to develop the disease have been identified [www.illumina.com) have opened new possibilities to improve the genetic analysis of posthitis.In the last four decades, a severe disease called a Forest ,12. At ta Forest . Posthit hunting . Despite indices . Posthitentified . New tooAmong the commercially available microarrays, the Illumina Bovine HD 777 K microarray is the most suitable tool for bison genome studies. Its usefulness was evaluated recently by showing that the Lowland line is a genetically homogenous population, with only a small amount of polymorphic loci, which can be helpful in pedigree analysis . It was posthitis and the non-affected group. The differences in allelic frequency found between these two groups were used to perform a GWAS to identify genes potentially involved in the posthitis disease.In this paper, we used high density bovine SNP microarray to investigate the entire bison genome in order to identify differences in SNP allele and genotype frequencies between the group of individuals affected by posthitis incidence is presented in the Manhattan plot in Figure A global view of all SNPs effects for posthitis. Eleven significant markers were located on chromosome 15 in the close vicinity of olfactory receptor genes.Detailed characterization of the top 25 markers are given in Table To better show how the number of SNP genotypes change between affected and unaffected bison, the allelic and genotypic frequencies of the 25 markers were estimated for the two bison groups and are listed in Table AA vs BB) for these SNPs were found . No discrepancies between the alternative genotypes  is extrposthitis [The inbreeding level of EB was also considered as a potential factor increasing the risk of disease, but the difference between the inbreeding coefficient of healthy (32 bison) and affected (12 bison) was not found to be significant . Anotherosthitis .posthitis. In Figure OLFML1 and PPFIBP2 and in the close vicinity of the ZNF214 gene. Most significant effects were found for two SNPs (rs110456748 and rs136792896) located at a distance of 23846 bp and 37742 bp from OR10A3 gene, known as being involved in atopic dermatitis, a common inflammatory skin disease [In this study, the utilization of an HD (High Density) microarray, allowed a detailed screening of 18,079 polymorphic markers which were used in a GWAS analysis to identify those significantly associated with the incidence of  disease .PPFIBP2 and OLFML1. PPFIBP2 encodes protein-tyrosine phosphatase-interacting protein, which is differentially expressed in endometrial cancer [OLFML1 (olfactomedin-like protein 1) encodes a secreted protein, is expressed in many tissues and may play a significant role in the regulation of cell proliferation in vitro [ZNF214) - a potential tumour suppressor gene whose expression is increased in tumours derived from testicular germ cells [COG6 and LHFP, were found to be associated with susceptibility to psoriasis [atopic dermatitis, psoriasis) or cancer/tumour development of epithelial or testicular germ cells, which suggest their potential participation in the posthitis.Several significant markers were mapped within two genes: l cancer  and is al cancer . OLFML1 in vitro . Anotherrm cells . The nexsoriasis ,26. Amonpothistis susceptibility. It is possible that their contribution is indirect, through immunological pathways, or has not yet been reported. It can be assumed that, as for any complex trait, many genes whose names are not “trait-specific” participate in the overall genetic variance of the trait [The fact that the remaining five candidate genes Table  do not she trait .COL17A1 encoding collagen type XVII, alpha 1  is involved in epidermolysis bullosa, causing separation of the basal keratinocyte from the underlying basement membrane and consequently skin atrophy [TRIM6 coding tripartite motif 6 located 3799 bp from the significant marker rs1986306 has been reported as playing a role in an IFN-induced antiviral state against retrovirus infection [ILK gene encoding integrin-linked kinase  interacts with pathogenic bacteria reinforcing host cell adherence [FERMT1 encoding fermitin family member 1 is located 34,7Kb from significant marker rs137458105 (BTA13). Its mutation causes Kindler syndrome, whose symptoms include skin blistering, skin atrophy, photosensitivity, colonic inflammation and mucosal stenosis [PCNA) is located about 787Kb from marker rs137458105. It was shown that the percentage of PCNA-positive cells in malignant and some non-malignant skin diseases  is from 2- to 5- times higher than in normal skin cells [Being aware that in typical GWAS, most of the significant markers are located outside the functional genes and are in linkage disequilibrium with other genes, we additionally used the MapViewer service to identify genes located in the broader neighborhood of the significant marker (at a distance of less than 1 Mb). The size of the inspected region is difficult to be justified objectively since the distribution of the functional gene along the chromosomes is very different and specific to each autosome . One Mb dherence . Anotherstenosis . Prolifein cells .CSF3R gene encoding colony stimulating factor 3 receptor is located. Its mutation causes chronic neutrophilia [POSTN gene exacerbates the pathogenesis of atopic dermatitis in mice [POSTN is located 618Kb from the significant marker rs137427505 on BTA12. Another gene located near the significant marker rs137024050 is forkhead box O1 transcription factor, whose loss of expression in lesional psoriatic tissues has a potential contribution to the development of psoriasis [In the close vicinity of significant marker rs134255411 (BTA 3)  in mice ,36. POSTposthitis indicate regions containing genes involved in the immune system and disorders of skin and epithelial cells. Unlike in Johnston [loci. Attempts to use standard microchips  failed as we could not find any marker associated with the disease symptoms (data not shown). In future research, GWAS should be validated for a larger population of posthitis-affected bison and candidate genes should be screened to find causal mutations enabling a better understanding of the disease and, prospectively, to control its incidence. The fact that the 25 SNPs investigated showed marked differences in allelic and genotypic frequencies between the healthy and affected bison groups is a clear indication of an excess of the genotype BB in the affected population together with a higher frequency of the B allele in the affected compared to the non-affected bison population. In addition, the fact that many alleles showed the same allelic frequency indicate a strong linkage between several SNPs on the same chromosome. This suggests that an appropriate breeding strategy based on identity by descent (IBD) could be developed for the European bison. Information from the SNP chip could be used as an accurate tool for guiding which individuals should mate, and which should be excluded from the mating as they are bearing the alleles associated with incidence of posthitis. Given the fact that many of the SNPs which showed associations are strongly linked, it would be enough to simply screen one of the SNPs which are linked to each other. At the same time it would also be desirable to optimize the best contribution of animals to the next generation, in order to reduce the pace at which the inbreeding coefficient is increasing every generation. This can be in the form of a specific list with suggested matings or guidelines on the number of matings that a given bison should be engaged in during a given number of generations. Developing a SNP chip with a subset of SNPs found to be associated with the posthitis incidence and with a subset of SNPs informative for paternity analysis and to estimate the kinship between individuals could be useful for this purpose and make these goals more realistic from an economic point of view.All of the above-mentioned reports support the main outcome of our work – that the most significant markers for Johnston , we usedposthitis-affected animals. Genes localized in the closest vicinity of these markers show connections with immunological processes. 18 out of 25 markers are localized on chromosome BTA15 within the space of 2 Mb, which suggests this region may play an important role in the ethology of the posthitis. This subregion should be closer examined to find causal mutations helpful in better understanding of the genetic background of the disease and to control its incidence in the future.Allele frequencies of 25 SNP markers are significantly different in health and posthitis (54). The presence of the disease and the degree of its advancement were ascertained post mortem by an expert veterinarian, on the basis of anatomical and pathological changes [The study included 90 adult males of the Lowland line of the EB living in the Bialowieza National Park. Bison DNA samples were provided by the Mammal Research Institute in Bialowieza (Polish Academy of Sciences). The animals were divided into two groups: healthy 36) and affected by  changes  . Initial data clean-up was performed to remove poorly performing SNPs. SNPs in the control population with significant deviations from the Hardy–Weinberg equilibrium (HWE) at the significance level of P < 0.0001 were removed prior to association analysis. Additionally, the SNP selection criteria were applied based on a minor allele frequency (MAF) of at least MAF > 0.005, due to highly monomorphic data and the technical quality of the chip – a minimum call rate of 98%. After filtering, 18,079 SNPs were used in the final analysis. The density of SNPs used in the analysis was 1 SNP per 138 kb. The average call rate obtained for our data set amounted to 98.86% (SD = 0.2%) for selected SNPs.posthitis incidence. SNP association analysis was performed on autosomal SNPs. Following this, the allelic and genotypic frequencies of the significant SNP markers were then estimated for the two bison groups.An additive model of the Linear Regression Analysis with a False Discovery Rate, kinship matrix  and heterozygosity rate as additional covariate was performed to estimate the effect of an SNP marker for http://www.ncbi.nlm.nih.gov/tools/primer-blast/):Since bison DNA was used on a bovine SNP array, two fragments of bison genomic DNA (containing two SNPs) were amplified and sequenced to check whether the SNP genotype was correct. The following SNPs were PCR-amplified and sequenced: BovineHD1500012672 (rs136792896) and BovineHD1200007116 (rs137427505). Primers were designed and blasted using the NCBI primer-blast website , 35 cycles of denaturation (94°C/30 s), annealing (61°C/30 s) and elongation (72°C/30 s), final synthesis (72°C/5 min).In the PCR reaction, 20 pM of each primer, 1 U of Taq DNA polymerase , 100 ng of genomic DNA, 2.5 μl of 10× PCR buffer, 2.5 mM of MgClPCR yielded two specific amplicons of expected 185 (BovineHD1500012672) and 537 bp (BovineHD1200007116). The PCR products were purified and sequenced by the GENOMED Ltd (Poland). The sequences were analyzed using BioEdit v. 7.2.0 software.www.ncbi.nlm.nih.gov) in order to check whether there are genes functionally associated with inflammation or diseases showing symptoms related to posthitis.Two approaches were used to locate candidate genes: statistical and functional. In the statistical approach, genes located in the closest vicinity of markers above the cut-off line were chosen . In the functional approach, 1 Mb region around the position of the significant SNP marker was inspected in the MapViewer service (www.illumina.com). Genomic positions of candidate genes were assigned based on the UMD 3.1 bovine genome assembly [www.ensembl.org).The genomic positions of SNPs included in the Illumina BovineHD 777 K were taken from the Illumina publication  and Regional Directorate for Environmental Protection  and released for Mammal Research Institute in Bialowieza. Reference numbers of the approvals: 2010: DOP-Pozgiz-4200/IV-57/4139/10/kp; 2011: DOP-OZGTZ.6401.05.5.2012.kp; 2012: WPN.6401.222.2012.EJ; 2013: WPN.6401.266.2013.EJ; 2014: WPN.6401.221.2014.EJ.We appreciate very much all remarks, suggestions and comments provided by reviewers. We considered them carefully trying to improve the manuscript.posthitis, which can now drive a more enlightened breeding effort to reduce its impact. While this study is a good demonstration of how tools developed for specific industries can be taken advantage of for environmental issues, it is unlikely that species without a closely related relative of economic impact can be studied in a similar manner.I do not see any issues in the design and implementation of the present study and the results are straightforward. The authors capitalized on the application of tools that have been developed for domestic species to one of conservation value. The study identifies several candidate genes for a specific disease, Quality of written English: AcceptableAuthor respond: We appreciate very much sharing our view on how new tools developed for domestic animals may be efficiently used for related species living in wild conditions.posthitis and mine the genome for candidate genes potentially involved in the development of the disease. The methods developed here are based on the use of high-density bovine SNP microarray to investigate the entire bison genome. The study shows that among the 25 top significant markers, 18 were located on chromosome 15. Eleven significant markers were located on chromosome 15 in the close vicinity of olfactory receptor genes. In my opinion, the manuscript on the review is very interesting and worth to be published in Biology Direct. However, I have some questions, and think that the answers them may make the presentation better. Namely, on page 10 there is written: “SNPs with significant deviations from the Hardy–Weinberg equilibrium (HWE) at the significance level of P < 0.0001 were removed prior to association analysis. Additionally, the SNP selection criteria were applied based on a minor allele frequency (MAF) of at least MAF > 0.005, due to highly monomorphic data and the technical quality of the chip – a minimum call rate of 98%.” I did not understood, why such levels of P and MAF were used here.The manuscript contains an interesting study of finding SNP markers significantly associated with the incidence of Bovidae species . The out-group species are only related with Bos species, therefore we have to be aware that bovine SNPs sometimes may generate spurious results when we use bison DNA. The only way to minimize that is to exclude all samples (bisons) giving low call rate. Moreover careful analysis of SNP cluster quality is also of key value. It was the reason why we used higher cal rate (>98%) than standard value (95%).Author respond: Illumina BovineHD BeadChip is designed especially for the domestic cattle and some out-group Another problem with bison SNPs is their limited number. The Lowland  line of the European bison was restored from 7 pure Lowland animals – one male and six females. The relationship and the inbred level of all living animals are extremely high. Obviously, such low heterozygosity reduces the number of SNPs available for association study. Our intention was to keep almost each polymorphic SNP to cover the whole genome with markers located with relatively short distance from each other. Therefore we decreased the typical level of MAF from 0.01 to 0.005.Summing up, both criteria were used to maximize the number of good-quality heterozygotic SNPs.Analyzing criteria for SNP selection we found that one sentence should be corrected “SNPs in the control population with significant deviations from the Hardy–Weinberg equilibrium (HWE) at the significance level of P < 0.0001 were removed prior to association analysis”.


Arabidopsis FLOWERING LOCUS C (FLC) plays a key role in controlling vernalization—the acceleration of flowering by prolonged cold. Li et al. identify five functionally distinct FLC haplotypes, defined by noncoding sequence variation, which vary in FLC expression level and silencing. Allelic heterogeneity at this single locus accounts for a large proportion of variation in Arabidopsis vernalization. This study advances our understanding of adaptation and provides a new paradigm for analysis of complex traits.A key question in evolutionary biology is how molecular variation relates to phenotypic diversity.  Arabidopsis thaliana, FLOWERING LOCUS C (FLC) is a major determinant of variation in vernalization—the acceleration of flowering by prolonged cold. Here, through analysis of 1307 A. thaliana accessions, we identify five predominant FLC haplotypes defined by noncoding sequence variation. Genetic and transgenic experiments show that they are functionally distinct, varying in FLC expression level and rate of epigenetic silencing. Allelic heterogeneity at this single locus accounts for a large proportion of natural variation in vernalization that contributes to adaptation of A. thaliana.Relating molecular variation to phenotypic diversity is a central goal in evolutionary biology. In  Arabidopsis thaliana, this process, called vernalization, involves expression and then subsequent epigenetic silencing of a floral repressor gene named FLOWERING LOCUS C (FLC)  . A. thallization . Relativlization . In somes itself , but theuestions . We explFLC haplotypes across the 1307 accessions (FLC region is characterized by high population structure and extensive haplotype sharing (Single-nucleotide polymorphism (SNP) data from the Regional Mapping panel project  identificessions , with ficessions . In the  sharing . Hap1, H sharing .A. thaliana accessions that represented the major haplotypes and generated a high-quality FLC sequence for each accession . Remarkably, the predicted amino acid sequences from these FLC alleles were identical, with very low polymorphism in the coding sequences but considerable nucleotide sequence polymorphism in noncoding regions . Additionally, 114 Swedish A. thaliana accessions , which had been genotyped using the 250K SNP array . In general, therefore, accessions in the different FLC haplotype groups show distinct vernalization profiles that involve changes in silencing of FLC and altered flowering time.We selected 47  regions . The funaplotype . Two hapNP array , were alFLC haplotypes is supported by previous quantitative trait locus (QTL) analyses  crossed to Lov-1 (SV1), Ull2–5 (SV2), or Var2–6 (SV4) .The functional diversification of the different –6 (SV4) . To furtenotypes . This asFLC variation is the major contributor to the QTL, we generated transgenic lines differing only in their FLC allele. Twelve-kilobase FLC genomic clones from the A. thaliana accessions Edi-0, Col-0, Lov-1, Ull2-5, and Var2-6, representing haplotypes RV1, RV2, SV1, SV2, and SV4, respectively, were transformed individually into FRI flc-2 , whereas the correlation of prevernalization FLC expression between accessions and transgenic lines was weaker (R2 = 0.29)  . The traFLC fusions previously showed that cis polymorphism quantitatively modulates a chromatin silencing mechanism underpinning the epigenetic regulation of FLC in one Northern Swedish accession . This region includes the nucleation region important for FLC silencing; the VRE , defined as important for maintenance of silencing  and Edi-0 (RV1) FLC alleles, which show slow and rapid silencing, respectively  and Col-0 (RV2) FLC alleles  FLC allele. After longer vernalization (12 wk), both alleles produced a similar seed yield , where spring-, summer-, and fall-germinating cohorts are observed  and 30 d (T30) after transfer from cold, along with nonvernalized plants (NV). Flowering time was assayed as days after bolting when stems reached 3 cm in height.Transgenic lines were obtained as described previously . Plants Controlled environment conditions were as follows: Plants were vernalized for 4 or 12 wk at 4°C under short-day conditions (8 h light) and moved to controlled environment conditions at 22°C under a long-day photoperiod (16 h light). Soil was from The Scotts Company Ltd., Sphagnum Moss peat (pH 5.5–6.0), with fertilizer added (milligram/liter): N150, P200, and K200. Twenty-five percent grit was added before use. Pot size was 5 × 5 cm. Plants were grown individually in each pot. In the greenhouse, total seed was collected from each plant after siliques had matured and dried naturally and was weighed.In the field, plants of 39 accessions were grown in common gardens at the John Innes Center in spring, summer, and fall plantings as described previously . FitnessFLC genome sequences were aligned using ClustalW with manual corrections in Mega5 . Genome Studio software (http://www.illumina.com/software/genomestudio_software.ilmn) and custom-made scripts in R software (http://www.R-project.org) were used for SNP calling.F2 populations were genotyped using a 384-SNP Illumina GoldenGate genotyping assay  explain ∼22.1% of the variation inT30/(T10 +T30) (η2 = 0.221), which is considered a large effect size.A general linear model (GLM) with T30/(T30 + T10) as response variable and SNP as the predictor was analyzed for each SNP separately. The F coefficient (adjusted MS/error variance) was calculated and plotted against the SNP position. The strength of the association of each SNP and the response variable was expressed by calculating the effect size estimator FLC were extracted from a preimputation version of the Regional Mapping Project SNP panel described in SNPs in a window ±50 kb around A standard approach for accounting for such genomic background effects is linear mixed models . We asse


Nested in the environment of the nucleus of the cell, the 23 sets of chromosomes that comprise the human genome function as one integrated whole system, orchestrating the expression of thousands of genes underlying the biological characteristics of the cell, individual and the species. The extraction of meaningful information from this complex data set depends crucially upon the lens through which the data are examined. We present a biophysical perspective on genomic information encoded in single nucleotide polymorphisms (SNPs), and introduce metrics for modeling information encoded in the genome. Information, like energy, is considered to be a conserved physical property of the universe. The information structured in SNPs describes the adaptation of a human population to a given environment. The maintained order measured by the information content is associated with entropies, energies, and other state variables for a dynamic system in homeostasis. “Genodynamics” characterizes the state variables for genomic populations that are stable under stochastic environmental stresses. The determination of allelic energies allows the parameterization of specific environmental influences upon individual alleles across populations. The environment drives population-based genome variation. From this vantage point, the genome is modeled as a complex, dynamic information system defined by patterns of SNP alleles and SNP haplotypes. In this short report on single nucleotide polymorphisms (SNPs), we explore a biophysical perspective on the genome as the most sophisticated living “information and communication system” known to mankind. As such, it is expressed in and through all levels of existence, from the invisible world of quantum physics through the visible biological hierarchy of molecules, cells, tissues, organs, and systems integrated in whole individuals, to the limitless, global diversity of all human populations that constitute humanity. The early recognition of SNPs as common variants widely distributed in the genome provoked broad interests in their utility as genomic markers. The rapid development of high throughput DNA sequencing accompanied by bioinformatics tools and technologies for analyzing and managing these massive datasets have revolutionized human genome studies, thrusting 21st century DNA sequence-based biology into the data-driven arena of “big science”. The abundance of easily ascertained SNPs, which occur approximately every 600 – 1000 bp across the genome, made them the DNA marker of choice in genetic epidemiology, genome-wide association studies (GWAS), DNA sequence variation research, pharmacogenomics, anthropological and population genetics. Although other types of genome variation  have been employed in disease studies, SNPs have been the most useful and widely applied markers in biomedical research and behavioral science. The advent of genome-wide population-based variation resources, such as the Haplotype Map, has changed the way we conceptualize human variation, categorize human populations, and conduct population studies. The distribution of genome variation in large continental population groups has posed major challenges to the authenticity and biological integrity of institutionalized social constructs of human races. Online public availability of SNP alleles and SNP haplotype data for global populations, defined geographically by continent of origin, have forged a new era in population genetics, offering unprecedented opportunities to investigate adaptive forces that have shaped patterns of common variation in natural populations.The completion of the Human Genome Project (HGP) in 2003 introduced a new 21S is a measure of the disorder (noisiness) of a distribution of occurrences, expressed as: jp is the probability of the occurrence of type j.Recently, the Howard University National Human Genome Center (NHGC) Biophysics Research and Development Group applied first principles of thermodynamics and statistical physics in studying the informatics of SNPs as dynamic sites in the genome . We intrI is a measure of maintained order, which can be parameterized by the difference between the entropy of a system of maximum disorder and that of the entropy of the given system,In contrast, information i.e. SNP haplotypes), which are maintained throughout generations.The information is contained both in individual SNPs, as well as, those combinations of SNPs in linkage disequilibrium (NIC) allows for comparison of SNP haploblocks across different sites in the genome and across different populations. NIC is defined as,Our normalization of this information metric  in the group. This definition poses difficulties in categorizing individuals in the population who do not fit the phenotype. Our work demonstrates the promise of defining populations by their SNP haplotype frequency distributions, a quantitative genomic population metric that incorporates the allelic contribution of all persons in the population rather than discrete alleles and/or most common traits in the population. NIC values range from 0 to 1. In NIC values in the human leukocyte antigen (HLA)—Disease Related (DR) region of the major histocompatibility complex shown below for African-Americans in Southwest, USA, we found that SNP haploblocks with low NIC values contained regulatory elements involved in innate immunity, which allows for rapid adaptation to environments, whereas SNP haploblocks with high NIC were in genic regions [The 21NIC that correlate with genomic energy units (GEUs) [i.e. haplotypes). The determination of individual allelic energies then allows the parameterization of specific environmental influences upon shared alleles across populations in varying environments. A stable genomic free energy trades off low genomic energy U (parameterizing genomic conservation and increased order) against high genomic entropy S (parameterizing genomic variation), where ET is an environmental potential which drives variation. The genomic free energy is given by the equation,Our biophysical perspective on genomic information facilitates the characterization of the dynamic impact of the environment on genome variation. We have translated SNP haplotype data into entropic measures of s (GEUs) . The latjp which is the probability of occurrence of allele or haplotype j. The metrics defining the dynamics should prove particularly relevant to characterizing individual genotypes in the emergent era of personalized/precision genomic medicine. Human migration has provided us with natural laboratories that map whole genome adaptation to environmental stresses , allowing the examination of individual genomes within their environment. Our use of GEUs as a biophysical metric in DNA sequence analysis has provided new insights into the foundations of population differences expressed in and through SNP allele and haplotype variation. Viewing genomic big data through the lens of theoretical physics has allowed us to develop energy measures for modeling genome-environment interactions.Individual allelic and haplotype energies are directly related to Nature in 2012, showed that individuals harbor considerably more regulatory sequences in non-coding compared to protein-coding variants. These data support the likelihood of a common regulatory mechanism for SNPs. From our perspective, SNPs are dynamic sites correlated with patterns of conserved versus variable sequences, with the environment as the driving force for variation. Our model allows us to directly study genome-environment interactions and the effect of the environment on a given genotype for an individual as well as the population. Genodynamic studies of SNP alleles and haplotypes in non-protein coding regions should be instructive for modeling and testing the impact of the environment on regulatory mechanisms.Endowed with the capacity for self recognition and unlimited self replication, the human genome, encodes and expresses principles of life and living systems, not unlike those displayed in stem cells and cancer cells. The non-random, wide distribution of SNPs provokes intriguing questions on the function of this system of natural variation. With the preponderance of SNPs in non-protein coding regions of the human genome, a series of papers from the Encyclopedia of DNA Elements (ENCODE) Project Consortium published in Despite phenomenal gains from the HGP in deciphering the pathophysiology of common diseases and significant improvements in biological knowledge-based therapies, disadvantaged and minority communities still carry a disproportionate burden from common complex diseases, characterized as health disparities.SNPs are powerful markers for probing and assessing aspects of human identity that are common to and shared by all individuals and peoples. We now have a mechanism to investigate the dynamics of how a whole genome (and its parts) adapts to its environment. We submit that parameterization of genomic energy associated with information structured in sequence variation can be insightful in testing models of health and disease. An analysis of SNPs as complex dynamical systems can provide tremendous insight on the extent to which genomes differ from cell to cell, person to person, family to family, and from population to population. Inquiring minds want to know: 1) What is the link between genes and behavior? 2) How does our genotype interact with our environment to produce behavior? 3) What are the societal and ethical implications of SNP discoveries in the non-protein coding regions of the genome? Towards this end, SNP databases may be queried for patterning in regions of comparable genomic energies for correlations with well-defined instruments for phenotyping psychological parameters, as well as variation in host response to well-defined psychological stressors.One of the first lessons learned from sequencing the human genome was that we are all uniquely the same. This means that all of humanity is the expression of one incredibly diverse genome. We recognize SNPs as common variants strategically enmeshed throughout the genome as dynamic sites involved in regulation and expression of whole genome homeostasis.


SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering.k  for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms  on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data.Here we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters http://www.bioconductor.org).Methods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs  offer improved accuracy over methods that do not . The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project ( Single-nucleotide polymorphism (SNP) microarrays with increased coverage of low frequency and rare variants are available from both major manufacturer’s Affymetrix and Illumina. Previous comparisons of genotyping methods have shown that many popular algorithms have reduced call accuracy for these SNPs [Microarray technology has revolutionized the study of the genetics of complex disease, with large-scale case-control studies completed in the past 7 years ,2 uncoveese SNPs -8. Optim3[Illumina SNP microarrays are currently the most widely used array-based platform in both large- and small-scale genetic studies. Illumina’s largest BeadChips contain between 2.5 million and 4.3 million SNPs Table  and proc3, zCall [3 and Opti3).k=1, 2 or 3) to be fitted to the between sample normalized intensity data. We analyze datasets from a number of platforms and highlight the benefit of careful signal adjustment between samples to optimize calling accuracy. We compare this approach to four existing algorithms  by analyzing data sets with increasing coverage of low frequency/rare variants, and compare the performance of these methods in terms of accuracy at varying minor allele frequency. KRLMM is shown to perform favourably in most comparisons, particularly at low MAF.In this paper, we introduce KRLMM, a new genotype calling method for Illumina BeadArray data that takes a novel approach to that of other methods by allowing a variable number of clusters (ftp://ftp.ncbi.nlm.nih.gov/hapmap/genotypes/2010-08_phaseII+III/forward/). The number of SNPs with at least 1 non-missing call varied by chip type . These data provide us with an independent truth that can be used to compare the call accuracy of the different genotyping methods.Data from HapMap samples run on 3 high density SNP platforms in-house at Illumina were analyzed Table  using 5 X and Y to refer to the intensities of the respective alleles . For well-behaved SNPs with normal copy number, log-ratios (M = log2X∗- log2Y∗) or other contrasts between the normalized intensities  tend to be well separated into distinct clusters corresponding to the major genotypes present  [In the description below, we use Illumina’s nomenclature of t Figure . This se. (2011)  for a reX and Y) separately to adjust for systematic differences between the signal from different arrays. Although this resulted in a more consistent distribution of log-ratios between samples .Two different preprocessing methods were considered for use in KRLMM. The first involved between sample quantile normalization of each channel  for a variable number of clusters, where the choice of k = 3 is first applied to normalized log-ratios from each SNP separately. A vector containing the median values for the AA, AB and BB clusters (μk) is used as a consensus value, and the variance-covariance matrix (i) predictors calculated across all ni samples (j is the sample index) used in the regression model include the residual sum of squares , Mahalanobis distance (xik is a vector of cluster centers from a given k-means clustering) and agreement with Hardy-Weinberg equilibrium (ri2 = 2 pi (1-pi), ri3 = (1-pi)2, Ni1 = number of AA calls made, Ni2 = number of AB calls made and k). Each variable is calculated for each SNP using the cluster assignments obtained via k-means clustering with k = 1,2,3  leaving 8 variables in the regression. Coefficients for each parameter were estimated from fitting this model to a training set of 10,000 randomly chosen SNPs from the HapMap data sets. The independent genotype calls provide us with the true k for these SNPs. Both ordered logistic regression  of the alternate allele), or regular logistic regression (no ordering assumed) as available in the polr (MASS R package) and vglm functions (VGAM R package) respectively were used. Once regression coefficients are available, and given a complete set of covariates , the best k is determined for each SNP by obtaining fitted values from the model and choosing the k with maximum probability. Next k-means clustering is applied using the predicted value of k to obtain genotype calls. This approach is applied to all SNPs from autosomes and pseudo-autosomal (XY) regions of the genome.To obtain an initial estimate of the cluster centers, k-means clustering with between cluster distance for the ijth observation and all other observations in a different cluster and within cluster distance for the ijth observation and all other observations from the same cluster) as calculated in PAM clustering [The silhouette width  of each cluster  was determined to obtain a SNP-level view of the effect of normalization Figure . Althougk = 1 or 2 clusters are permitted in males since they have 1 copy of this chromosome. X chromosome: k = 1,2,3 clusters are allowed for females, since they have normal copy number, and k = 1 or 2 is allowed for males since they are hemizygous). When gender  of the samples is not specified, the average intensities  and the males to a second high intensity group that corresponds to signal from one copy of the Y chromosome. Samples are then assigned as female or male by looking at which cluster  is closer on average by comparing the median cluster position determined by k-means and the median S-value for these SNPs within each sample. This simple approach was found to be 100% accurate on each of the 3 platforms upon comparison of the imputed gender with the information provided by the HapMap database.SNPs outside of pseudo-autosomal regions on the X and Y chromosomes represent special cases that are called separately for males and females to allow for the appropriate number of clusters . The KRLMM method does not currently support data from Affymetrix genotyping arrays.An overview of the KRLMM algorithm and the regression analysis used to determine et al. (2011) [Table . (2011) .3[The OptiCall algorithm is a hybrid between GenoSNP and Illuminus. It first makes use of data from 50,000 randomly chosen intensity values to do an initial clustering using a mixture model with 4 states . This step is akin to GenoSNP’s between SNP approach for genotyping. OptiCall then clusters SNP-by-SNP using a model similar to the one used in Illuminus, with added hierarchical structure including priors derived from the initial clustering. In clusters with few observations, the prior ensures the clusters are sensibly placed, thus overcoming one of the shortcomings in Illuminus. We chose OptiCall as a representative method from the newer calling methods tailored for low frequency/rare variants. M 3 was also3, which uFor performance comparison, concordance between the genotype calls made by each method and the independent calls obtained from the HapMap database were used to calculate accuracy. Figure We next zoom in and focus on the ability of different genotyping methods to call low frequency content (SNPs with MAF < 5%). Accuracy was stratified to look at calls involving either the major allele only or the minor allele. This is a key comparison, as methods with a systematically higher error rate for calls involving the minor allele will pay a negligible price in terms of overall accuracy since there are so few of them. Figure Overall, the KRLMM algorithm marginally outperforms the vendor’s own alternative  in terms of accuracy, even at low minor allele frequencies. For low frequency variants, our results show the merit of approaches that use the signal from other SNPs either directly in their models  or indirectly to help choose the appropriate number of clusters (KRLMM) to give more accurate calls. Such approaches offer modest improvements over GenCall and Illuminus that do not have a tailored approach for dealing with SNPs with low MAF.The KRLMM method uses k-means clustering of the log-ratios obtained after between sample quantile normalization of the raw intensities and logistic regression to determine the number of clusters to fit to each SNP. It is the only algorithm to date that allows flexibility in the number of clusters. Although the data we have used to benchmark our approach (generated in-house at Illumina) is known to be of very high quality, we are still able to detect small performance differences between our method and the vendor provided alternative. These performance differences are expected to be more pronounced in disease association studies, where samples are collected over an extended period and will be subject to additional sources of variation. The benefit of KRLMM’s between sample normalization is anticipated to deliver additional performance gains in such settings.k). Although this information was obtained from HapMap data in our case, the regression coefficients could just as easily be estimated using calls from another method if such data were more readily available, as may be the case for platforms from other species. Support for newer Illumina platforms can be easily added and future extensions of the method to handle data from the two-color Axiom platform from Affymetrix are also conceivable. Another adaptation would be to allow k > 3 so that KRLMM could be used on cancer samples where gains and losses in the genome introduce copy number variation.The KRLMM method requires a small subset of training data  [Future comparisons of the performance of different genotyping algorithms on data from a genome-wide association study (GWAS) or suitable non HapMap control data set, such as the large collection of reference samples in Wang . (2011)  would al. (2011) . This imGWAS: Genome-wide association study; KRLMM: The new genotype calling algorithm described in this article; MAF: Minor allele frequency; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.RL, MR and RI devised the algorithm and ZD, RL and MR implemented the method in public software. MY provided advice on algorithm development and data sets. RL and MR performed data analysis and MR wrote the manuscript with input from RL and RI. All authors read and approved the final manuscript.


Next-generation sequencing opens the way for genomic studies of diversity even for non-model crops and animals. Genome reduction techniques are becoming progressively more popular as they allow a fraction of the genome to be sequenced for multiple individuals and/or populations. These techniques are an efficient way to explore genome diversity in non-model crops and animals for which no reference genome is available. Genome reduction techniques emerged with the development of specific pipelines such as UNEAK  and Stacks. However, even for non-model crops and animals, transcriptomes are easier to obtain, thereby making it possible to directly map reads. We investigate the direct use of transcriptome as an alternative strategy. Our specific objective was to compare SNPs obtained from the UNEAK pipeline as well as SNPs obtained by directly mapping genotyping-by-sequencing reads on a transcriptome. We assessed the feasibility of both SNP datasets, UNEAK and transcriptome mapping, to investigate the diversity of 91 samples of wild pearl millet sampled across its distribution area. Both approaches produced several tens of thousands of single nucleotide variants, but differed in the way the variants were identified, leading to differences in the frequency spectrum associated with marked differences in the assessment of diversity. Difference in the frequency spectrum significantly biased a large set of diversity analyses as well as detection of selection approaches. However, whatever the approach, we found very similar inference of genetic structure, with three major genetic groups from West, Central, and East Africa. For non-model crops, using transcriptome data as a reference is thus a particularly promising way to obtain a more thorough analysis of datasets generated using genome reduction techniques. In the last two decades, next-generation sequencing (NGS) technologies , 2008 haTherefore, even though RAD-seq and GBS data can be analyzed using either pipeline, they are preferentially analyzed using their original corresponding pipeline. There is also a preference for each RRL approach that depends on the “scientific community” concerned. For instance, RAD-seq is widely used for evolutionary history and conservation studies on wild organisms , whereasAn alternative strategy would be to map genomic reads from RRL approaches directly on a transcriptome. Most non-model crops possess a transcriptome reference that was primarily built for transcriptome studies. While building a transcriptome was formerly challenging , new tooHowever, it is not easy to assess the bias arising from using the SNP calling pipeline, especially for population genetic studies . TherefoPennisetum glaucum (L.) R. Br. ssp. monodii] from a collection held at IRD . The 48 populations were chosen to cover the known distribution of wild pearl millet . Seeds were grown in the greenhouse until flowering, and inflorescences from 10 plants per population were collected for DNA extraction. DNA was extracted using the MATAB protocol .We selected 48 wild pearl millet populations , and filtering was performed for QUAL  and QD  parameters which derived from Illumina quality scores . The 121,279 remaining SNPs were then filtered for DP using the median value, and the percentage of missing data per SNP (≤10%). It is important to note that the additional quality filters cannot be applied in the UNEAK pipeline since Illumina quality scores are not used and not kept through the pipeline. All command lines are available in Supplementary Data File https://sites.google.com/site/africropproject/data.Raw sequences were first trimmed for low quality ends (<20) and reads of less 35 bp were removed using Cutadapt 1.2.1 . SecondlWe aligned the Hapmap file of TAG sequences on the transcriptome using BWA version 0.7.5  with –n In order to identify SNPs shared by the two datasets, we identified TAGs among the 21,913 final UNEAK SNPs that aligned to the transcriptome and extract the SNP position. We then compared the position and the alleles to identify homolog SNPs in the TM dataset.3). We performed a principal component analysis (PCA) using SMARTPCA .Both pipelines produced a similarly high number of SNPs. With the UNEAK pipeline, we were able to identify 262,928 biallelic SNPs. After filtering for depth  and missing data , we obtained 21,913 good quality SNPs. With the TM approach, a total of 16,399,078 cleaned reads with a mean size of 92 bp mapped on 36 918 contigs. The mean coverage was 41.33 ± 44.2 and the mean MQ was 24.5. We identified 238,897 biallelic SNPs with a median depth of 90×, after filtering we obtained a total of 22,262 good quality SNPs. Specific filters  from TM pipeline removed nearly 50% of SNPs, while subsequent filters for depth (DP ≤ 90) SNPs and missing data (NA ≥ 0.1) removed 25 and 13.5%, respectively (r = 0.95). However, we had an average of 70% inflate number of missing data with UNEAK, since the average missing rates UNEAK:TM ratio was 1.7 ± 1.8 sd.The final sets of SNPs revealed that the quality of the two approaches was equivalent. The UNEAK final set of 21,913 SNPs had a mean DP per site and per sample of 7.24 ± 3.63 sd and an average missing rate per sample of 0.04 ± 0.04 sd. The TM final set of 22,262 SNPs had a mean DP per site and per sample of 8.68 ± 12 sd and an average missing rate per sample of 0.03 ± 0.03 sd. Within the TM final set, 56% of SNPs were found within a distance of 64 bp. The missing rates per sample between the UNEAK and TM dataset were highly correlated . The correlation coefficient between allele frequencies estimated by both pipelines for shared SNPs was very strong (r = 0.98).In addition, we tested direct mapping of the 262,928 UNEAK TAG 64 bp on the transcriptome. A total of 21,410 TAG loci (8%) mapped on 13,177 transcriptome contigs (26%). The mapping was relatively good since 94% of the mapped TAGs had a unique hit, among which 96% had a perfect 64 bp match. The mean MQ of these unique hits was 34 ± 9 sd. Among the 21,943 good quality TAGs, we found 3,146 TAGS (14%) that had good alignment on 2,382 (5%) contigs. Among those, we retrieved 822 SNPs common to the two datasets. Nearly all UNEAK SNPs had a MAF > 0.05 . Correlations between admixture values from both approaches within each cluster were high with r > 0.99. The results of a PCA were similar . Both datasets showed the same three geographic clusters and the correlation between PCA coordinates was very high (r > 0.99). Comparing UNEAK and TM PCA, only one sample (sample 5726B1) was in a different position in the two plots. This individual had 17 times more missing data with the UNEAK dataset than with the TM dataset despite missing rates <0.05%. This very high ratio of missing data between datasets might explain its outlier status. More generally, the regression of PCA coordinates between the two pipelines showed that most individuals qualified as slight outliers had three times more missing data in the UNEAK pipeline than in the TM pipeline. However, overall, we observed very good individual quality and a very strong congruent inference of population structure irrespective which pipeline was used.The two datasets showed very similar inference of genetic structure. We identified Table 1). For F-statistics, FIS was slightly but significantly higher with the TM dataset and FST was significantly (two times) lower. When we compared observed SFS and expected SFS for a population at equilibrium, the UNEAK dataset clearly did not retrieve the expected amount of low frequency SNPs . On the other hand, TM SFS appeared to overestimate their number. As a result, Θπ was 2.2 times higher with the UNEAK dataset and Tajima’s D-values consequently differed considerably with a positive Tajima’s D-value of 2.74 for UNEAK and negative value of –0.65 for TM dataset.In contrast, genetic diversity assessment was affected differently depending on the pipeline. Heterozygosity values were almost two times higher with the UNEAK dataset than with the TM dataset  66% of SNPs were found in genic regions , an alternative allele could be missing (non-random draw) or mapping from two different but similar genes (not a single gene) on a single reference. Neither pipeline deals very easily with the occurrence of statistical non-independence of reads. Pipelines developed for RRL approaches were not able to handle allelic dropouts and mistake heterozygous presence/absence for homozygous presence/absence . A very The amount of SNPs allowed within a genomic windows is important since regions with too many SNPs are not reliable and may (i) contain many sequencing errors, (ii) be associated with paralogs. Within the TM pipeline, we applied a SNP clustering filter with no more than three SNPs per 10 bp. Nevertheless, it allowed quite a number of SNPs in a 100 bp read. For instance in the TM datasets, 56% of SNPs were less than 64 pb away. Since the UNEAK approach only allows 1 SNP per 64 pb, more than 50% of TM SNPs would be automatically discarded by the UNEAK pipeline.However, the two pipelines differ strongly in their rare variant calling rates. Even if base quality is higher than 30 with the ILLUMINA sequencing platform, i.e., one error every 103 bases, with the amount of data that was generated, it ended up creating numerous errors. Calling rare variants (or not) will depend on the SNP and genotype calling algorithm implemented in the software and on how error sequencing rates are considered . With soIn summary, we identified two main reasons for the low number of shared SNPs: (i) the constraint of no more than one SNP within 64 bp, and (ii) the uncovering of rare variants by UNEAK, which represent the majority of the polymorphism expected for a population at equilibrium. We ended up with relatively few shared SNPs. However, the allele frequency correlation between these SNP was very high.How the specific characteristics of the SNPs we identified will affect population genetics estimates is another important question. There is an increasing literature on how parameters such as the number of mismatches allowed to assemble reads in orthologous loci with RRL approaches will influence the number of SNPs identified and population results. Most available studies focus on the effect of Stacks pipeline parameters . For insD test (D but so are other tests such as the CLR test (The main difference we observed between pipelines concerned the identification of low frequency variants. We found that the UNEAK pipeline was not able to recover rare variants while the SFS pattern for frequent variants was similar between pipelines. Thus methods based on “more frequent alleles” such as population structure approaches led to similar results. On the other hand, several statistics using low frequency variants differed considerably depending on the dataset used. Tajima’s D test  is basedCLR test . With suFST is dependent on allele frequency, low FST is expected for low frequency variants. Consequently, integrating more rare variants ends up adding low FST value, and thus lowering the mean FST value. The first and most simple consequence will be to make it difficult to compare diversity estimates obtained with different pipelines, an important issue in comparative studies (FST outlier detection approach for discovery of genes under selection. A number of FST outlier tests have been developed and extensively used for the discovery of candidate genes (FST. Underestimating rare variants will affect the overall distribution of FST and might therefore have an impact on these selection tests.Differences in the number of rare variants detected will also influence F-statistics in addition to heterozygosities.  studies . Anotherte genes  and are APEKI biased SNP discovery toward coding regions (It is certain that some of the differences in the results of diversity estimates observed between datasets are due to the fact that the UNEAK pipeline interrogates coding and non-coding regions while the TM pipeline only interrogates coding regions. However, like other authors, we previously observed that using  regions . All in  regions . Nielsen regions  that doe regions  and admi regions . HoweverWe have demonstrated the possibilities and discussed the advantages and disadvantages of two pipelines used for SNP discovery when no genome reference is available. We found that the UNEAK pipeline, with little and simple bioinformatics work, can efficiently identify a large number of SNPs as well as highlight genetic clustering. However, we observed notable underestimation of rare variants that could impact the estimation of population genetics and the detection of selection. Therefore, we encourage researchers to pay more attention to SFS. The transcriptome mapping reference was less biased in that sense and, more importantly, such a strategy could be used in combination with ongoing approaches without genotype calling to further reduce bias on SFS. The alternative strategy has the further advantage of enabling access to sequences surrounding SNPs for further genomic exploration. Moreover, since few SNPs are shared, both datasets could be combined, thereby significantly increasing the SNPs used.CB-S, CM, and YV designed the project. CM, MC, and JP carried out the molecular laboratory work. CB-S and J-BF analyzed the data. CB-S and YV wrote the manuscript. All the authors discussed the results and commented on the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The database allows quick retrieving of SNP alleles for all varieties in a given genome region, finding different alleles from predefined varieties and querying basic passport and morphological phenotypic information about sequenced rice lines. SNPs can be visualized together with the gene structures in JBrowse genome browser. Evolutionary relationships between rice varieties can be explored using phylogenetic trees or multidimensional scaling plots.We have identified about 20 million rice SNPs by aligning reads from the 3000 rice genomes project with the Nipponbare genome. The SNPs and allele information are organized into a SNP-Seek system ( The current rate of increasing rice yield by traditional breeding is insufficient to feed the growing population in the near future . The obsFor the SNP-Seek database we have considered only SNPs, ignoring indels. A union of all SNPs extracted from 3000 vcf files consists of 23 M SNPs. To eliminate potentially false SNPs, we have collected only SNPs that have the minor allele in at least two different varieties. The number of such SNPs is 20 M. All the genotype calls at these positions were combined into one file of ∼20 M × 3 K SNP calls, and the data were loaded into an Oracle schema using three main tables: STOCK, SNP and SNP_GENOTYPE Figure . Some vaNot all SNPs have been called in all varieties. Actually, the distribution of the called SNPs among varieties is bimodal, with one mode at about 18 M SNP calls corresponding to japonica varieties which are close to the reference genome, and the second peak at about 14 M corresponding to the other varieties Figure .http://rice.plantbiology.msu.edu/) (We used CHADO database schema  to storesu.edu/) . To browsu.edu/)  as a feaMost of the 3000 varieties  are conserved in the International Rice genebank housed at IRRI . PassporWe deployed interfaces to facilitate the following major types of queries: (i) for two varieties find all SNPs from a gene or genomic region that differentiate them; (ii) for a gene or genome region, show all SNP calls for all varieties ; (iii) find all sequenced varieties from a certain country or a subpopulation, which can be viewed as a phylogenetic tree, built using TreeConstructor class from BioJava  and rendWe used Rice SNP-Seek database to quickly examine the diversity of the entire panel at a particular region of interest. We chose the sd-1 gene as test case due to its scientific importance in rice breeding. This semi-dwarf locus, causing a semi-dwarf stature of rice, was discovered by three different research groups to be a spontaneous mutation of GA 20-oxidase , originating from the Taiwanese indica variety Deo-woo-gen. Its incorporation into IR8 and other varieties by rice breeding programs spurred the First Green Revolution in rice production in the late 1960s . Sd-1 isWe have organized the largest collection of rice SNPs into the database data structures for convenient querying and provided user-friendly interfaces to find SNPs in certain genome regions. We have demonstrated that about 60 billion data points can be loaded into an Oracle database and queried with a reasonable (quick) response times. Most of the varieties in SNP-Seek database have passport and basic phenotypic data inherited from their source accession enabling genome-wide or gene-specific tests of association. The database is quickly developing and will be expanding in the near future to include short indels, larger structural variations, SNPs calls using other rice reference genomes.Supplementary Data are available at NAR Online.