Solanum lycopersicum has undergone various genetic ‘bottlenecks’ and extreme inbreeding of limited genotypes. In Europe the tomato found a secondary centre for diversification, which resulted in a wide array of fruit shape variation given rise to a range of landraces that have been cultivated for centuries. Landraces represent a reservoir of genetic diversity especially for traits such as abiotic stress resistance and high fruit quality. Information about the variation present among tomato landrace populations is still limited. A collection of 123 genotypes from different geographical areas was established with the aim of capturing a wide diversity. Eighteen morphological traits were evaluated, mainly related to the fruit. About 45% of morphological variation was attributed to fruit shape, as estimated by the principal component analysis, and the dendrogram of relatedness divided the population in subgroups mainly on the basis of fruit weight and locule number. Genotyping was carried out using the tomato array platform SolCAP able to interrogate 7,720 SNPs. In the whole collection 87.1% markers were polymorphic but they decreased to 44–54% when considering groups of genotypes with different origin. The neighbour-joining tree analysis clustered the 123 genotypes into two main branches. The STRUCTURE analysis with K = 3 also divided the population on the basis of fruit size. A genomic-wide association strategy revealed 36 novel markers associated to the variation of 15 traits. The markers were mapped on the tomato chromosomes together with 98 candidate genes for the traits analyzed. Six regions were evidenced in which candidate genes co-localized with 19 associated SNPs. In addition, 17 associated SNPs were localized in genomic regions lacking candidate genes. The identification of these markers demonstrated that novel variability was captured in our germoplasm collection. They might also provide a viable indirect selection tool in future practical breeding programs.During its evolution and domestication  Solanum lycopersicum L.) is a major vegetable crop grown worldwide, from the tropics to within a few degrees of the Arctic Circle [The cultivated tomato  spread as a weed from the Andean region to Mexico, where it was domesticated [S. lycopersicum has undergone various genetic ‘bottlenecks’ imposed by self-pollination, founder effects, artificial and natural selection, in addition to extreme inbreeding of limited genotypes, particularly in Europe and North America [S. lycopersicum found a secondary centre for diversification [A commonly accepted hypothesis for the domestication of cultivated tomato is that the sticated . The domsticated  and it wsticated . During  America . In Euro America . In thesfication , which rfication . Charactfication . For thefication ,10–11. Ufication . Howeverfication . In addification –21, a Sofication ,23 to inIn the present study, in order to investigate genetic variation in the tomato genome to find new favourable alleles for tomato breeding, a tomato collection of 123 genotypes was analyzed using the SolCAP SNP array. The main goal of our work was to characterise at morphological and molecular level the selected tomato collection with special emphasis on fruit morphology. Specifically we wanted to 1) explore the genetic diversity available in our wide germplasm collection, 2) test the established collection for association mapping analysis.The study evidenceda wide genetic variability in our tomato collection,whichturned out to be suitable forsuccessfully GWAS approaches. We were able to identify 36 novel markers to target the phenotypic variability of 15 traits, mostly relating to fruit morphology.Solanum pimpinellifolium accession LA1579 and Solanum. lycopersicum var. cerasiforme accession LA1310. The material is listed in per each accession, then stored at -80°C for DNA extraction.A germplasm collection of 123 genotypes was established by selecting tomato accessions worldwide with the aim of capturing a wide diversity in our panel. This collection enhances the variability of one population made of 90 genotypesand previouslyused in our laboratory for GWAS for fruit quality related traits .The germper replica per genotype were evaluated for fruit colour (FC), fruit flesh colour (FLC), polar diameter (PD), equatorial diameter (ED), stem-end shape (SES), blossom-end shape (BES), number of fruit locules (LN), pericarp thickness (PT), puffiness (PUF), fruit-shape index (FS = PD/ED), fruit weight (FW), fruit-shape longitudinal section (FSL), and fruit-shape cross section (FSC). In addition, the fruit-shape (FS = PD/ED) and pericarp-thickness index (PI = PT/((PD+ED)/2) were calculated from raw data.For each accession, 18 morphological traits were evaluated, mainly related to the fruit . At 80 dThe morphological evaluations were carried out following the descriptors indicated by  for 12 tTM(Implen) at 260/280 and 260/230 OD ratios. Genotyping was carried out using the tomato array platform SolCAP developed in the framework of the Solanaceae Coordinated Agricultural Project from NIFA/USDA and based on the ILLUMINA Infinium Technology [o) and expected (He) heterozygosity (Gene diversity) and Polymorphism Information Content (PIC). All these calculations were performed using PowerMarker software 3.25 [Genomic DNA was extracted from100 mg of frozen leaves following a modified protocol of the cetyltriethylammoniumbromide (CTAB) extraction method described by . DNA quachnology . Data wechnology . The genare 3.25 .a priori information regarding population origin. The degree of admixture was estimated by setting for both burn-in period and Markov Chain Monte Carlo iterations a value of 100.000 for each run. Ten independent runs across a range of K values (K = 1–12) were made. The best number of clusters (K) was obtained using STRUCTURE HARVESTER program [To assess the genetic relationships of the investigated genotypes the population structure was determined by using STRUCTURE 2.3.3 software , with no program  based on program .P value of 4.1x10-4 after the Bonferroni correction. To ascertain the effectiveness of our association analysis, genes controlling fruit morphology traits and plant architecture in tomato were selected as candidate genes (CGs). CGs were identified both from the SOL genomics network [Associations between genotypes and phenotypes were calculated for all 18 morphological traits, excluding the two wild accessions given their high phenotypic differences compared to the whole collection. Associations were detected using the mixed linear model (MLM) implemented in TASSEL program, which accounts for kinship (K matrix) and population structure (Q matrix) matrices. Significant levels of association (p = 0.05) were estimated considering an adjusted  network  and from network .Various traits were evaluated to phenotype our collection, detailed data are reported in the vs. ED (0.795), SES vs. ED (0.719), LN vs. SES (0.749), FW vs. LN (0.678), and FS vs. BES (0.675). Extremely high correlation values were observed for FC and FLC (0.98), as expected, and between FW and ED (0.913). Significant negative correlations were observed for FS vs. LN (-0.714), and FS vs. SES (-0.666). The KMO (>0.7) and Bartlett’s tests (p = 0.000) indicated the suitability of our data for structure detection. Principal component analysis (PCA) reduced the 18 morphological characters to two principal components, which accounted for 46% of the total variation. The first component (PC1) explained 29.95% of the variation and was mainly associated to LN, SES, ED, FSC, FW and IT. The second component (PC2) explained 16.29% of the total variation and was basically defined by PD and PT  had red fruit, 26 genotypes had fruit that varied from yellow, to orange or pink, and five had brownish fruit. Some genotypes exhibited an absent (20) or very week (31) green shoulder, whereas a medium intensity or a strong/very strong intensity was recorded for 46 and 26 genotypes, respectively. Several traits were measured to determine the fruit shape, such as polar (PD) and equatorial (ED) diameter, stem- and blossom-end shape , longitudinal and cross section , puffiness (PUF). Altogether, including the number of fruit locules (LN), these traits contributed to determine six different fruit-shapes . Many geD and PT . The majD and PT , which iThe 123 genotypes were screened by 7,720 SNPs, among which 7,672 were successfully scored. As a whole, 4,763 SNPs exhibited minor allele frequency lower than 0.05. a priori classification. The neighbour-joining tree analysis clustered the 123 genotypes into two main branches (A and B) (The population stratification of our tomato collection was investigated without introducing any A and B) . Branch A and B) , accordiAssociations between SNP alleles and morphological traits were obtained on the basis of a genome-wide association analysis (GWAS) approach using the mixed linear model (MLM) analysis and taking into account the kinship matrix (K) and the population structure matrix Q = 3. As result of GWAS we found a total of 79 significant associations with 15 out of 18 morphological traits evaluated, with the number of associations per trait ranging from one (for FC and PH) to 12 (for FSC and LN) . As a whhttp://solgenomics.net) and from the literature. As a result, 98 genes were selected by merging the two research methods (In order to match the associations with previously identified candidate genes (CGs) for the corresponding traits, a list of CGs was retrieved both from the SOL genomics network( methods . Eighteefasciated (fas) gene (Solyc11g071810). In addition, in the same cluster mapped the SNP marker 1081 that is located into a sun gene (Solyc11g071840), validating our methodological approach for mapping. The SNP 1081 represent a transition from C to T in position 55196715 bp on chr 11. The SNP falls in the sixth exon of the gene Solyc11g071840 annotated as Calmodulin binding protein being a member of SUN gene family (SlSUN31). The polymorphism is synonymous, resulting in no changes in the corresponding protein sequence.On the upper arm of the same chromosome, also a cluster harbouring six SNPs associated to PT and the j gene (Solyc11g010570) was found. The cluster on chromosome 1 included SNP markers for FC and FLC together with one CG (Solyc01g079620) annotated as colorless fruit epidermis, while on chromosome 2 three SNPs were found associated to fruit shape traits that were in LD with the lc gene (Solyc02g083950). On chromosome 3 a co-localization between a marker for FSL (783) and the Solyc03g026110 coding for a SUN protein was found. The last co-localization cluster was identified on chromosome 8 and consisted of marker 7034 associated to FS and Solyc08g079100, a gene annotated as a YABBY family member. Besides these SNP-CG co-localizations, 17 new SNPs associated mostly to fruit morphology were found, pointing out the involvement of new regions of the genome in controlling this trait in tomato in the lower regions of chromosomes 2 and 10.Considering the LD-decay distance chromosome by chromosome , it was Phenotypic and genomic data can be used to compare individual genotypes and/or populations with the aim of optimizing characterization, discovery and use of functional allelic variations. In this study, a collection of 123 genotypes was analyzed, which were selected to represent a wide range of phenotypic diversity in tomato. A morphologically based classification mainly regarding fruit traits was carried out, as well as SNP-based genotyping. As expected, the phenotypic clustering did not completely overlap the genetic structure of the population; in fact, it has been previously demonstrated that major phenotypic differences can often occur with only minor genotypic changes ,35. A diTo evaluate the level of genetic diversity in the entire collection and within different groups of germplasm the observed and expected heterozygosity, and polymorphism information content were measured. We found that the level of heterozygosity was low, as expected in tomato, but variable among the sub-populations. The analysis of nucleotide diversity pattern showed that CVs maintained the largest amount of diversity within the collection, as also revealed by the STRUCTURE model-based clustering (for K = 3), in which most of the ILs showed one or two ancestors, while CVs derived from three ancestors. Indeed, genetic diversity was lower in the landraces compared to the contemporary cultivars, probably due to the different breeding programs that these two categories underwent. The long history of crossing cultivars to wild relatives has broadened the genetic diversity in contemporary germplasm with respect to vintage and landrace germplasm ,41, despIn the present study, we used a high-throughput genotyping platform to characterize our collection of genotypes and to verify that the genetic variability available in this collection was suitable to perform an informative association mapping approach. To this purpose, we used as case of study traits related to plant architecture and fruit morphology, which are reportedly stable and not highly influenced by environmental conditions ,18. A feIn addition to the 36 SNP markers found associated to traits in our GWAS analysis, we mapped on the 12 tomato chromosomes 98 CGs previously identified for the traits analyzed. Altogether, we detected 16 chromosomal regions where at least 4 genes and/or markers clustered. In some cases, these regions consisted of only CGs or associated SNPs. We first focused our attention on six regions where the co-localization of CGs and SNPs was evidenced, since these validated the trait-SNP association detected in our study. Among these, five co-localizations related to fruit morphology determinants  mapped to the upper regions of chromosomes 3 and 11, and to the lower regions of chromosomes 2, 8, 11, where also QTLs for these traits were previously located. The remaining co-localization of CGs and associated SNPs was related to the fruit colour (FC and FLC), and is located to the lower part of chromosome 1.sun, ovate, lc and fas [sun and ovate confer elongated fruit shape, whereas lc and fas control locule number, and if mutated, confer fruit fasciation and flat shape. These genes map to chromosomes 2 (lc and ovate), 10 (sun) and 11 (fas). The tomato fruit shape genes sun, o and fas belong to IOD/SUN, Ovate Family Protein (OFP) and YABBY gene family, respectively. Huang and co-workers [elf1) influencing elongation of tomato fruit was mapped on the lower arm of chromosome 8 between SlSUN23 and SlSUN24 [lc gene (Solyc02g083950) and three SNP markers  associated to various traits. LC is a WUSCHEL homeodomain protein that controls the number of carpel primordia and its mutation results in a fruit with more than the typical two or three locules [p = 8.02E-5) was evidenced for SNP 7034 in LD with the SlYABBY1b gene (Solyc08g079100).YABBY family proteins are involved in the control of locule number and also in the number of all floral organs [SlYABBY1b is expressed in young floral buds [fas was hypothesized to control the final fruit size. The position of our associated marker laid down between the two markers flanking the elf1 gene reported by [SUN31 (Solyc11g071840). The former gene was already aforementioned for its involvement in fruit shape and size. SUN31, as other SUN family members, is expressed during flower and fruit development supporting a possible role in the definition of the final fruit shape. Previously described mutations of SUN change fruit shape by redistributing fruit mass; an increase in cells in the proximal-distal direction is accompanied by a decrease in cell number in the columella and septum in the medio-lateral direction throughout the entire fruit [Despite the tremendous diversity of fruit shape in tomato, these are explained to a large extent by mutations in four genes, which are  and fas . Among t-workers  identifi SlSUN24 . The ass locules ,53. Incr locules ,53. On cl organs . SlYABBYral buds  showed torted by  thus reiJOINTLESS (J) gene (Solyc11g010570) that encodes for a MADS box transcription factor. It might play a role in floral meristem identity rather than fruit development leading to heavier fruit with less seed [J indicates a role for J in pericarp development or the existence of a different linked causal gene will require further investigation. The possibility of a spurious association also exists because the j trait is generally introgressed in modern processing cultivars that, on the other hand, have also been bred for improved pericarp thickness.Finally, six SNP markers associated to the PT trait  were found on chromosome 11, in LD with the sov1 and sov2) suppressing the ovate effect of fruit shape [fw2.2 and fw3.2. Our analysis also confirmed the involvement of this chromosomal region in fruit weight determination, where two markers associated to FW mapped. Similarly, beside the action of the major gene SELF-PRUNING (SP), a minor effect on growth habit might be due to genes located on the lower arm of chromosome 1, as well as other putative regions affecting growth habit were previously mapped by [Since it is widely ascertained that the effect of major genes on fruit size and shape also depends on the genetic background in which they are active, the identification of modifier genes on these traits is still a challenge, as demonstrated by the discovery of two genes Click here for additional data file.S1 File(XLS)Click here for additional data file.S1 TableFor each genotype, the source providing seeds, the accession number from the source, the common name (if available) and the geographical origin are reported. Acronym and description of morphological trait measures are reported in the legend below.(XLSX)Click here for additional data file.S2 Table(PPTX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLS)Click here for additional data file.


Quality control (QC) of germplasm identity and purity is a critical component of breeding and conservation activities. SNP genotyping technologies and increased availability of markers provide the opportunity to employ genotyping as a low-cost and robust component of this QC. In the public sector available low-cost SNP QC genotyping methods have been developed from a very limited panel of markers of 1,000 to 1,500 markers without broad selection of the most informative SNPs. Selection of optimal SNPs and definition of appropriate germplasm sampling in addition to platform section impact on logistical and resource-use considerations for breeding and conservation applications when mainstreaming QC. In order to address these issues, we evaluated the selection and use of SNPs for QC applications from large DArTSeq data sets generated from CIMMYT maize inbred lines (CMLs). Two QC genotyping strategies were developed, the first is a “rapid QC”, employing a small number of SNPs to identify potential mislabeling of seed packages or plots, the second is a “broad QC”, employing a larger number of SNP, used to identify each germplasm entry and to measure heterogeneity. The optimal marker selection strategies combined the selection of markers with high minor allele frequency, sampling of clustered SNP in proportion to marker cluster distance and selecting markers that maintain a uniform genomic distribution. The rapid and broad QC SNP panels selected using this approach were further validated using blind test assessments of related re-generation samples. The influence of sampling within each line was evaluated. Sampling 192 individuals would result in close to 100% possibility of detecting a 5% contamination in the entry, and approximately a 98% probability to detect a 2% contamination of the line. These results provide a framework for the establishment of QC genotyping. A comparison of financial and time costs for use of these approaches across different platforms is discussed providing a framework for institutions involved in maize conservation and breeding to assess the resource use effectiveness of QC genotyping. Application of these research findings, in combination with existing QC approaches, will ensure the regeneration, distribution and use in breeding of true to type inbred germplasm. These findings also provide an effective approach to optimize SNP selection for QC genotyping in other species. The CIMMYT  Maize Lines (CMLs) are a set of 577 elite inbred lines, which have been developed over the last 25 years. The CMLs represent one of the most widely distributed sources of publically generated elite lines, which are freely available to both public and private sector breeders, research and growers, worldwide. Distributed under the Standard Material Transfer Agreement (SMTA) of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA), these lines have become the most important public tropical maize germplasm source globally . Since 2Control and understanding of germplasm identity and purity are two fundamentals of germplasm management, be it for breeding or conservation –4. InabiA Single nucleotide polymorphism (SNP) is a single base difference within otherwise identical sequences of DNA found at one position in the genome. In recent years, the adoption of next generation DNA sequencing technologies has significantly increased the number of SNP markers available for use in crops ,17. Genoet al. used a total of 28 maize inbred lines to study genetic identity among different seed sources, genotyping them with 532 and 1,065 SNPs using the KASP and Golden-Gate platforms, respectively . These seed samples were delivered from the original seed lot provided to the CGB in 2005. In addition, seed samples of 22 CMLs from multiple regenerations were also obtained (2) of leaf tissue from each individual plant using a modified CTAB method [www.diversityarrays.com/).Seed samples and the passport data of all of the 561 CMLs (available in 2014) were obtained from the CGB [To order seed, go to: obtained . SeedlinB method . DNA waswww.illumina.com). SNPs were called using the DArTsoft analytical pipeline (http://www.diversityarrays.com/software.html#dartsoft). Sequence analysis was conducted to align reads with the sequence tag based maize meta-genome representation. A total of 88,600 unimputed SNPs were successfully called within the CML dataset. Data derived from this analysis included both SNP calls and the number of sequence reads per allele.A High-throughput genotyping method was conducted in 96 plex using DArTseqtechnology ,27. A ge2 of LD (linkage disequilibrium) between two markers was calculated in R, using the method described by Devlin [http://tree.bio.ed.ac.uk/software/figtree/). Principal Coordinate Analysis (PCoA) of all CMLs was carried out using cmdscale function in R, and the PCoAs and categorical data were plotted by CurlyWhirly v1.15 (http://ics.hutton.ac.uk/curlywhirly/). Principal component analysis (PCA) and K-means were used to group the SNP markers using the prcomp and kmeans functions in R [http://hdl.handle.net/11529/10246). The allele case control test was then performed using PROC CASECONTROL function in SAS/Genetics V12.3 to evaluate all markers’ p-value for QPM and imidazolinone resistance [The characteristics of each SNP marker, such as allele frequency , heterogy Devlin . The “coons in R ,36. Somesistance ,38. The n, where n is the sample size and p is the percentage of off-type expressed as a probability. Bayesian estimation of the proportion of off-types in the population was used with a Beta prior distribution by the “binom” library in R (R-project) [To determine the influence of sample number on the accuracy of detecting off-types in QC genotyping, we calculated the probability of finding at least one off-type from different sample sizes for a set of defined levels of off-types . The model assumed that the marker detection power was hundred percent . Using a binomial distribution, probability of detection at least one off-type (P) was calculated using the following formula: P = 1 –(1-p)project) . To estiproject) .http://hdl.handle.net/11529/10431. Per chromosome, the average marker heterogeneity was approximately 0.05, the proportion of missing values was close to 0.18, and the minor allele frequency (MAF) and polymorphic information content (PIC) were around 0.16 and 0.25 respectively  < 5% and heterogeneity >10%, resulting in 18,082 markers used for subsequent analyses , these gectively . The SNPPCoA of CMLs using selected SNPs produced clear separation of groups based on adaptation  and graiIn order to select the most informative markers for QC, a number of different marker parameters were evaluated, including MAF, marker group, coverage, and chromosome position . The marIn order to evaluate the effect of marker group, markers were clustered into groups using PCA and K-means methods ,36 S4 F. Random Assessment of marker coverage (sequencing depth at a marker locus) showed that coverage was proportional to heterogeneity and inversely proportional to missing data measures, as would be expected. The increase in measured heterogeneity with increasing coverage reflects the improved probability of sampling multiple variants at a locus if they exist , while tMarker distribution effects were studied by comparing randomly distributed markers versus markers with uniform distribute across chromosomes. Uniform marker selection demonstrated a better separation of CMLs than random selection .In the CML panel, there were two simple traits, quality protein maize (QPM), controlled by the o2 gene ,41 and nBased on the analysis of the effects of marker characteristics on the accuracy of QC genotyping for CMLs, the following marker selection rules were implemented to define markers for evaluation: 1) Marker coverage between 2 and 15; 2) Missing value less than 20%; 3) Remove SNPs without chromosome information; 4) Marker heterogeneity less than 6%; 5) Inclusion of QPM and IR specific markers. Using these stringent marker selection criteria, 193 markers across the whole genome were chosen for re-sampling analysis.A series of between 40 to 100 markers were randomly selected from the defined panel of markers, based on uniform chromosome distribution and PGD. This selection, compared with random selection, provided a better definition of CMLs . AnalysiIn order to define the best 80 SNPs for broad QC, replicated randomized re-sampling of SNPs from the panel of 193 SNPs was conducted, including the QPM and IR trait-specific markers . Of the 2,000 re-sampled sets, five subsets could successfully distinguish all CMLs. In order to identify the best subset of the five, the ability of each subset to differentiate pair-wise CML comparisons was assessed . Subset CMLs are frequently requested by users both internal and external of CIMMYT, and therefore must be frequently re-generated for distribution. For 22 CMLs, a series of regenerations, including the original source, were genotyped using DArTSeq platform. The genotypic similarity of the 22 original CMLs ranges from 0.44 to 0.76 . GenotypThe number of individuals sampled from a discrete entity is a key factor for implementation of routine QC genotyping, as it directly influences the cost, time and accuracy of detection of off-types within the entity. The influence of sample number was evaluated using different off-type levels  assuming the marker detection power was 100% . As antiThe main purpose of routine QC genotyping is to identify contamination or mislabeling of germplasm during regeneration, seed increase or seed distribution. In order to achieve this, a balance needs to be maintained between accuracy of detection and efficiency (both in terms of cost and time). It is often assumed that more markers result in higher accuracy, this accuracy being achieved at a higher cost. Optimization of the balance between accuracy and cost is the main concern of the QC marker selection. Semagn et al.  suggesteSample number is also a key factor for QC genotyping because it directly influences the cost, time and accuracy of detection of off-types. One hundred individuals have been used for hybrid purity testing . In thishttp://www.lgcgroup.com) and TaqMan™ from Applied Biosystems [http://www.lifetechnologies.com), to larger fixed array based systems, including BeadXpress™ and GoldenGate™ from Illumina [http://www.illumina.com/) and Axiom® Genotyping Array from Affymetrixs (http://www.affymetrix.com), and finally to the next-generation sequence based genotyping platforms, such as KeyGene® SNPSelect (http://www.keygene.com) GbS from Cornell and DArTSeq, developed by Diversity Arrays Technology (http://www.diversityarrays.com) [There are a number of widely used SNP genotyping platforms, ranging from single SNP methods such as KASP™ from LGC Genomics   ,47. The Here we have based our analysis on the use of the lowest number of markers to employ in QC genotyping to enable generation or selection of markers across multiple platforms. The analysis framework used to select markers is applicable across the genotyping system(s) used in analysis. The cost-benefit ratio may well swing in favor of sequence- and chip-based platforms and away from single plex systems in the near future as; the ability to increasingly multiplex in sequence and array-based platforms improves, the ability to better define specific sets of markers in sequence based systems (e.g. DArTcap) is commercialized by service providers and as the potential to use composite sample based genotyping to assess homogeneity is further realized. A summary of the current genotyping approaches used at CIMMYT, the approaches being reviewed and cost analysis is presented in The stage at which QC genotyping is conducted is also a critical consideration. A wide range of potential intervention points for QC genotyping are available during the breeding and conservation cycles, including vegetative plants in the field prior to pollination, seed or seedlings after harvest, and seed or seedlings prior to distribution. Each offers a different insight into the genetic status of germplasm. In the broader context, when sampling seed directly, one needs to consider the potential confounding effects of polyploid endosperm in non-homozygous germplasm. Different genotyping platforms and SNP markers respond in different ways to increased maternal effect  and detection of contamination can be confounded by this in some cases . Careful empirical assessment of this is required if using seed derived DNA across the different genotyping platforms available.In conclusion, the work presented here outlines the framework and output for the selection of the minimum number of high quality, robust markers for two complimentary QC genotyping approaches for maize lines. The methods and analysis presented offer a roadmap for the selection of markers for other germplasm resources while providing some reflection on the consideration of genotyping platforms.S1 Dataset(TXT)Click here for additional data file.S2 Dataset(TXT)Click here for additional data file.S3 Dataset(XLSX)Click here for additional data file.S1 Fighttps://ics.hutton.ac.uk/curlywhirly/download-curlywhirly/).The 3-D video of this figure can be found in “(TIF)Click here for additional data file.S2 Fighttps://ics.hutton.ac.uk/curlywhirly/download-curlywhirly/).The 3-D video of this figure can be found in “(TIF)Click here for additional data file.S3 FigExserohilum turcicum resistant fonder; SintAmTSR = synthetic yellow and tar spot complex resistant founder, SA = acid soil tolerance founder. The breeding program and source population information of all CMLs can be found: http://hdl.handle.net/11529/10246.The CIMMYT Breeding Program and the source population are indicated for each cluster. In the source materials, “P” indicates the CIMMYT synthetic population founder; “G” indicates the synthetic gene pool founder. MA/ST = mid-altitude/sub-tropical adaptation; MBRET = multiple borer resistance and (TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigHeterogeneity, missing values and number of markers are shown.(TIF)Click here for additional data file.S6 Fig(TIF)Click here for additional data file.S7 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 Video(AVI)Click here for additional data file.S2 Video(AVI)Click here for additional data file.


A set of 2,485 SNPs show significantly different allele frequencies when compared to populations from other continents. Two notable variants having risk alleles in high frequencies in this subgroup are: a nonsynonymous deleterious SNP (rs2108622 [19:g.15990431C>T] from CYP4F2 gene [MIM:*604426]) associated with warfarin dosage levels [MIM:#122700] required to elicit normal anticoagulant response; and a 3′ UTR SNP (rs6151429 [22:g.51063477T>C]) from ARSA gene [MIM:*607574]) associated with Metachromatic Leukodystrophy [MIM:#250100]. Hemoglobin Riyadh variant (identified for the first time in a Saudi Arabian woman) is observed in the exome data. The mitochondrial haplogroup profiles of the 15 individuals are consistent with the haplogroup diversity seen in Saudi Arabian natives, who are believed to have received substantial gene flow from Africa and eastern provenance. We present the first genome resource imperative for designing future genetic studies in Saudi Arabian tribe subgroup. The full-length genome sequences and the identified variants are available at ftp://dgr.dasmaninstitute.org and http://dgr.dasmaninstitute.org/DGR/gb.html.Population of the State of Kuwait is composed of three genetic subgroups of inferred Persian, Saudi Arabian tribe and Bedouin ancestry. The Saudi Arabian tribe subgroup traces its origin to the Najd region of Saudi Arabia. By sequencing two whole genomes and thirteen exomes from this subgroup at high coverage (>40X), we identify 4,950,724 Single Nucleotide Polymorphisms (SNPs), 515,802 indels and 39,762 structural variations. Of the identified variants, 10,098 (8.3%) exomic SNPs, 139,923 (2.9%) non-exomic SNPs, 5,256 (54.3%) exomic indels, and 374,959 (74.08%) non-exomic indels are ‘novel’. Up to 8,070 (79.9%) of the reported novel biallelic exomic SNPs are seen in low frequency . We observe 5,462 known and 1,004 novel potentially deleterious nonsynonymous SNPs. Allele frequencies of common SNPs from the 15 exomes is significantly correlated with those from genotype data of a larger cohort of 48 individuals (Pearson correlation coefficient, 0.91; p <2.2×10 Genetic approaches, like Whole Genome Sequencing (WGS), Exome Sequencing and Genome Wide Association (GWA) Studies, have helped identify causal variants associated with various recessive and complex disorders in many populations Individual populations can exhibit different profiles of rare and common variants Substantial drop in sequencing costs have enabled large scale sequencing of populations, such as Southeast Asian Malay population  Arabian Peninsula is at the nexus of Africa, Europe, and Asia and has been implied in early human migration route out of Africa, Kuwait, like many other countries in the Peninsula, is constituted of settlements from three ancient population groups of (i) Persian origin; (ii) “city-dwelling” Saudi Arabian tribe origin; and (iii) “tent-dwelling” Bedouin origin from Middle East and North Africa. The subgroups in Kuwaiti population resulting from these three distinct ancestries have been characterized in earlier studies using data on mitochondrial DNA genetic variations, Y-STRs, and genome-wide genotypes The Kuwait Genome Project (KGP) aims to sequence (at high resolution) genomes from different ethnic groups inhabiting Kuwait. In this work, we report for the first time a genome sequence resource of the Kuwaiti population subgroup of inferred Saudi Arabian tribe ancestry. This subgroup traces origin to the tribes of Najd region of Saudi Arabia. Najd is a plateau in the central region of the Arabian Peninsula spanning from west to east; the eastern sections  are marked by oasis settlements engaged in farming and trading activities, while the rest has been sparsely occupied by nomadic Bedouins). A group of families and tribes from the Najd region began to arrive into the Kuwait region in the year 1613 AD and this marked the formation of modern-day Kuwait. The Bani Khalid tribe established a fishing village  on the site of present day Kuwait City and maintained ties with members of their tribe who had settled and wielded political influence in the region of Najd during their earlier migration eastwards. Since 1710 AD, several other tribes left Najd region (because of drought) and started to arrive (after wandering around the Arabian Peninsula) into Kuwait in several waves until 1756 AD. Bani Utub tribe , one of the new arrivals, took over power from the Bani Khalid rulers The genome resource is developed by sequencing two whole genomes (referred as KWS1 and KWS2) at >40× coverage, and thirteen exomes (referred as KWS3-15) at >60× coverage. In total, we catalog 4,950,724 SNPs, 515,802 short indels and 39,762 structural variations. We further identify a set of 2,485 exomic markers having significantly different allele frequencies compared to other global populations. This study further provides genetic evidence that the HVS1  segments of the 15 samples cluster with the observed segments from native Saudi Arabian population A group of 15 Kuwaiti natives of Saudi Arabian tribe origin, from the Kuwait S subgroup as confirmed in our previous study UE, union set of exomic variants from the two whole genomes and thirteen exomes) and the rest . The UE data set includes 121,470 SNPs and 9,675 indels; the UW data set includes 4,829,254 SNPs and 506,127 indels. Of the total reported SNPs, 1,174  are found to be triallelic. Biallelic SNPs in the UE data set show a higher genome-wide transition-to-transversion (Ti:Tv) ratio of 2.58, as compared to SNPs in the UW data set, which gives a value of 2.09.A total of 4,950,724 SNPs and 515,802 indels were identified. These variants were sub-divided into two categories viz. variants that are in exome regions .When assessed against dbSNP 137 −4 for 9999 replicates).We characterized the biallelic and triallelic variations from the UE and UW data sets onto thirteen classes based on their genome annotation  for 25,974 common exomic markers (from UE data set) between the two sample sets. Further narrowing down the comparison to only deleterious SNPs , the correlation coefficient is still high at a value of 0.91  (We considered the remaining set of 48 participants from our previous study o 0.919) . These vIn order to assess the extent of variability exhibited by genomes of Saudi Arabian tribe subgroup, we compare each of the two KWS1 and KWS2 genomes with 20 genomes see  across fThe overall pattern of neighbor-joining of the genomes using OMIM variants is similar to that seen in the tree deriving using shared genome-wide variants – African genomes are clustered together, the Asian genomes are clustered together, and the KWS genomes are clustered along with European genomes. The one exception observed is that GIH (Gujarati Indians in Houston), that was appearing as a separate branch in the tree based on genome-wide shared variants is now seen clustered along with KWS and Europeans in the tree based on OMIM shared variants. The distribution of known disease-causing and predisposing variants in the Saudi Arabian tribe substructure of the Kuwaiti population is quite distinct from the African and Asian populations, but is similar to those of Europeans and Gujarati Indians (in Houston).We further compare the allele frequencies of the exomic SNPs with those seen in populations from four continents  using data from the 1000 Genomes Project. The Pearson correlation coefficients are seen in the range of 0.73–0.87 . While the values are modest for KWS with AFR and ASN, they are high for KWS with AMR and EUR. The 1000 Genome AMR is made of three populations namely CLM: Colombian in Medellin, Colombia ; PUR: Puerto Rican in Puerto Rico ; and MXL: Mexican Ancestry in Los Angeles, California . Examination of the data generated by the 1000 Genomes Project Admixture working group on the proportion of source population ancestry  for each individual in each of the three populations of AMR reveals a high European ancestry – the mean value for European ancestry in AMR turns out to be 59.8%red SNPs .We identify 2,485 markers (from the UE data set) that show significant differences in allele frequencies when compared with populations from all the four continents . Out of The most prevalent genetic blood diseases in Kuwait are Thalassemia and Sickle cell anemia The repertoire of variants (SNPs/Indels) increases with the number of samples sequenced. In our study, the number of variants increases from 45,770 (for one exome) to 134,396 (after sequencing 15 exomes). The number of exomic variants we identify in 15 samples is in agreement with that observed in 7 exomes from the population of Qatar (another State from the Peninsula) Both the WGS samples (KWS1 and KWS2) are observed to have J1e [J-P58] Y-chromosome haplogroup which is seen in the Arabian Peninsula. The overall estimated time of expansion of J1e haplogroup is around 10,000 years and the ancestors of J1e haplogroups are observed in the Caucasus and eastern Anatolian populations (i) T2c1d1 (2), T2c (1) - Haplogroup T is predominantly of Eurasian lineage. T2b to T2e is found in high frequency in Saudi Arabia; (ii) U6 (1), U4a1 (1) – U haplogroup is supposed to have originated 55,000 years back in Eurasia. U6 is seen in 10% of North African population; U4 is seen widely distributed in Europe; (iii) R0a1a (1), R0a2c (2), R0a1 (1) – R0 haplogroup is seen frequently in the Arabian belt. Haplogroup R is a common macro-haplogroup seen in West Eurasia; (iv) H20 (1), H6b (1) - Haplogroup H is supposed to have originated in Southwest Asia ; H2 and H6 seems to be common in Eastern Europe and the Caucasus; (v) J1b3b (1), J1b2 (1), J2b2 (1) - Haplogroup J  is associated with people who migrated to Europe and developed farming and herding during the Neolithic Era ; and (vi) N1b1a (1) - is found in Middle East, Egypt, Caucasus and Europe.Furthermore, the mitochondrial haplogroups  are determined as J1b2 for KWS1 and H6B for KWS2. Both the haplogroups are observed in Arabian Peninsula, but have different origins. The mitochondrial J haplogroup originated in Near East or Caucasus  around 45,000 years from present, whereas the H haplogroup originated in West Asia around 20,000–25,000 years to present and was carried by early human migration to Europe In an effort to examine the consistency of the observed mitochondrial haplogroups from the 15 participants with those observed in native Saudi Arabian population, we considered the data from Abu-Amero et al. −16) with those derived using genotype data from a larger cohort of 43 individuals. Furthermore, we identify 39,762 variations consisting of 27,060 deletions, 4192 duplications, 1044 insertions, 1137 inversions, 3411 tandem duplicates, 1216 intra-chromosomal translocations, and 1702 inter-chromosomal translocations. The presented genome characterization  than with Africans (at 0.73) or Asians (at 0.76). Furthermore, the tribe exhibits a higher degree of intergenome similarlty with ‘Europeans’ than with Africans or Asians, assessed by examining shared genome-wide SNP positions between the genomes; the observation persists even when only those SNP positions that are associated with OMIM disease genes are considered. We further confirm the observation of Moore et al. We identify a set of 2,485  SNPs showing significant differences in allele frequencies with populations from other continents; these variants form Kuwait S (Saudi Arabian tribe) specific variants which can be used for future genetic studies. Two of the variants are particularly interesting:1) A nonsynoymous deleterious variant (rs2108622 (19:g.15990431C>T), that lies in cytochrome P450 4F2 (CYP4F2 [MIM:*604426]) gene, is associated with Warfarin drug response [MIM:#122700] and altered Vitamin K (VK1) metabolism. The risk allele is shown to be associated with higher Warfarin dosage requirement to elicit anticoagulation response. The risk allele T for this marker is seen in high frequency in KWS samples compared to other continent populations. An earlier study in Kuwait has reported a poor quality of anticoagulation with Warfarin 2) A 3′ UTR variant (rs6151429 [22:g.51063477T>C)]), associated with Metachromatic Leukodystrophy (MLD) [MIM:#250100], lie in ARSA [MIM:*607574] gene. The risk allele C for this marker is seen in high frequency in KWS samples, compared to other continent populations. MLD is an autosomal recessive leukodystrophy, characterized by a buildup of sulfatide fat in cells, especially in cells of the nervous system. The accumulation causes progressive destruction of the myelin sheath, leading to characteristic symptoms that include hypotonia, irritability, gait disturbances, mental deterioration, convulsions, paralysis, spastic tetrapearesis, difficulties in feeding and swallowing, abnormal eye movements, atrophy of the optic nerve, posture abnormalities, ataxia, dementia, and coma. It is estimated that MLD occurs at the rate of one in 40,000 individuals worldwide. However, some populations that include certain Arab groups in the Occupied Territories and a group of Jews who migrated from Southern Arabia (Habbanites) have shown to harbor much higher frequencies of the disease  states that the total population of Kuwait is 3.65 million, of which 35% are natives and the remaining expatriates. Per cent distribution of the 1.09 million Kuwaiti natives onto the three subgroups  is not known. It is to be borne in mind that the current study reports genome variants from just one of these three subgroups and not from the entire Kuwaiti native population. It is expected that the presented repertoire of genome variants will enlarge as we embark upon, in our future works, sampling and sequencing subjects from the other two subgroups in Kuwait.The distribution of total number of SNPs or indels identified with every new exome sequenced, follows a power series  seen in Saudi Arabia. This observation validates phylogenetic consistency and provides genetic evidence to demonstrate the similarity of the Kuwait S subgroup to Saudi Arabian natives. Here, it is worth mentioning that 81% of the surnames in Kuwait S subgroup are of Saudi Arabian tribe origin From the perspective of migration outflux into Europe: Recent reports, based on analysis of ancient European genomes, suggest that one of the three groups to which the present-day Europeans trace their ancestry is Middle Eastern farmers, who are thought to be descendents of humans who inhabited 100,000–120,000-year-old settlements in Israel and the Arabian Peninsula From the perspective of colonization history: In 3rd century, the ancient Greeks colonized the Bay of Kuwait under Alexander the Great; remains of Greek colonization excavated in Failaka (the island of Kuwait) include a large Hellenistic fort and two Greek temples Listed below are findings (observed in this study) that indicate a certain extent of ancestry affinity between Europeans and the 15 participants from the KWS subgroup: (i) extent of European ancestry admixture in the KWS subgroup is seen at a mean value of 10.55% see ; (ii) soIn conclusion, international efforts such as the HapMap and the 1000 Genomes projects, have considered genomes from continents that include Africa, Asia, Europe and North America, but the Arabian genome remains unexplored. Until now, genomic data of populations from Arabian Peninsula are poorly represented in databases, despite the implication for the study of the early human migrations out of Africa. The region is also a hot spot for the application of medical genetics The study was approved by the Scientific Advisory Board and the Ethics Advisory Committee at Dasman Diabetes Institute, Kuwait. Written informed consents for the study were obtained from participants before blood samples were collected.A group of 15 participants, belonging to genetically clustered Kuwait S group  was used to extract DNA as per manufacturer's protocols. DNA was quantified, with a requirement that the A260/A280 ratio is in the range of 1.8–2.1, using both the Quant-iT PicoGreen dsDNA Assay Kit  and the Epoch Microplate Spectrophotometer. Frozen DNA stocks were diluted to a working solution of 50 ng/µl as recommended by Illumina .Prior to library preparations, DNA was qualified by agarose gel analysis. DNA samples were sheared using Covaris E220 instrument  which delivers controlled and highly focused non-linear acoustic shock waves into each sample  allowing generation of uniformly sized fragments averaging at 400 base pairs in length. Sheared DNA was subsequently used to prepare sequencing libraries; the recommended protocols by the manufacturers  for TruSeq DNA sample preparation and cBot Paired End (PE) cluster generation kits were adopted. Precise library concentration and base pair size were validated using Qubit 2.0 Fluorometer  and the Bioanalyzer , respectively, to obtain adequate clustering density on the flow cells. Libraries were then normalized, for size and for optimal loading concentration on the Illumina sequencing flow cells, and for cluster generation using the cBOT. Libraries were loaded on separate lanes on the flow cells for cluster generation on the cBOT, at a final concentration of 12 pmol. Flow cells were loaded on the HiSeq 2000 for paired-end sequencing  using the TruSeq SBS 200 cycles chemistry.Exome sequencing libraries were prepared using Illumina's TruSeq DNA Sample Preparation Kit. DNA was fragmented (200–300 bp) using Covaris E220 instrument (as mentioned earlier), and the sheared fragments were ligated to the sequencing adaptor oligonucleotides . The adaptor-ligated fragments were amplified by PCR cycling and then enriched using the Illumina TruSeq Exome Enrichment Kit  which covers 1.22% of human genomic regions corresponding to the CDS (coding sequence) exons. The TruSeq kit adopts an in-solution sequence capture method for isolating exomic regions of interest in the human genome using hybrid selection. Subsequently the ligated fragments were captured by streptavidin-coated magnetic beads and were quantified. Libraries were then clustered on the flow cell at a density of 15 pmol using the cBot as before, and finally were sequenced by the Hiseq2000 using the paired-end reads protocol of 2×100 cycles.http://picard.sourceforge.net) to sort and index the input BAM files. Multiple files from a sample from different HiSeq runs were merged using MergeSamFiles functionality of Picard; the detailed alignment statistics for the 15 samples are presented in We used CASAVA (Consensus Assessment of Sequence And VAriation) v1.8.2  for demultiplexing and Bcl conversion. Sequenced paired-end reads in FASTQ format were aligned to human reference genome hg19 (UCSC) We used HugeSeq We used both SAMtools and GATK for variant calling. For the workflow with SAMtools, we used the SAMtools mpileup module in conjunction with Bcftools to generate variant calls. The vcfutils.pl script of SAMtools was used with the D option set to 4000 denoting maximum read depth to call a SNP. For variant calling with GATK, we used UnifiedGenotyper followed by VariantFiltration. In UnifiedGenotyper, we set dcov  to 1000, stand_call_conf  to 30, stand_emit_conf  to 10, glm  to BOTH . We also specified A  as value for the parameters of AlleleBalance, BaseCounts and VariantType. We split variants into separate files for SNPs and indels. VariantFiltration was used to filter variant calls based on certain specific criteria. For SNPs, we set clusterSize to 8, QD<2.0, MQ<40, FS>60, HaplotypeScore >13, MQRankSum <−12.5, and ReadsPosRankSum <−8. For indels, we used the following parameters: clusterSize to 8, QD<2.0, FS>200, and ReadsPosRankSum <−20.To reduce the likelihood of false discoveries due to the choice of the variant caller, we only utilized the consensus set of variants identified by both SAMtools and GATK workflows in all subsequent analysis. We used VCFtools v0.1.10 The validity of the SNP calls was confirmed by utilizing the genotype data from the same samples derived using the Illumina HumanOmniExpress BeadChip . The concordance rate of the SNP calls between the deep sequencing experiments and the genome-wide genotyping is >99.8% for whole genomes and >99.20% for exomes . The obsThe discordance in SNP calls is seen in a small number of cases . The disagreements in the SNP calls are seen more often with heterozygous SNPs than with homozygous SNPs in both exomes and whole genomes. As is the practice We used the SNP & Variation Suite v7.7 (SVS) We used four different algorithms implemented in HugeSeq pipeline, to detect structural variations from paired-end reads data. BreakDancer version 1.1 We compared the allele frequencies of deleterious SNPs (predicted using SIFT score) that are seen in both the 15 exomes data set and genome-wide genotype data set from a larger number of samples. The allele frequencies were independently calculated in each of these data sets using SVS package; regression analysis was performed using the R package We consider a total of 20 genomes see , coverinWe adopt the methods used by Moore et al. ijD =  (Ns - [Ns ∩_ NL])/Ns, where ijD is the total intergenome distance, Ns is the total number of variants in the genome (i or j) having the fewest number of variants; NL is the total number of variants in the genome (i or j) having the greatest number of variants; and Ns ∩ NL denotes the number of variants in the intersection of the two genomes.The distance metric is calculated as below: The distance was calculated for every  pair in the data set of 22 genomes  on a chromosome-by-chromosome (considering only the 22 autosomes) basis. The resulting 22 distance matrices were then used to produce a neighbor-joining tree (using PHYLIP The tree depicting OMIM phylogenetic comparison is constructed using the same method (as above) adopted to construct the neighbor-joining tree using shared genome variants, but restricting the shared variant locations between genomes to only those locations where one of the genomes contain an OMIM allele. The tree was bootstrapped 50 times, and labels on nodes are the resulting bootstraps.ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20110521/) The frequencies of the identified SNPs were compared with allele frequencies seen in 4 continent populations  from 1000 Genomes Project  sequence of 553 natives Saudi Arabia was generated using data provided by Abu-Amero et al. http://www.isogg.org/tree/) [Date of access: 7 July 2013]).The Y-chromosome variants were used to call haplogroups using AMY-tree software Genome browser is an effective means to share genome data to biomedical community; we have set up JBrowse (version 1.8.1), a graphical interface to enable access to the two reported whole genome sequences for the Saudi Arabian tribe subgroup of Kuwaiti population. JBrowse is an open-source project for genome browser ftp://dgr.dasmaninstitute.org). The data can be visualized using genome browser with other annotations tracks from UCSC at http://dgr.dasmaninstitute.org/DGR/gb.html. Proper functionality of the web server requires Firefox version 6 (or later versions) or Internet Explorer version 10 (or later versions). Comparison of allele frequencies between the KWS data and continental populations from 1000 Genomes Project is also available at the above ftp site.The exomic SNPs and indels (known and novel) identified from the UE data set of the 15 samples are presented as supporting information online Click here for additional data file.Figure S2Size distribution of biallelic indels from the UE and UW data sets.(TIF)Click here for additional data file.Figure S3Distribution of the observed (known and novel) exomic SNPs (from the 15 samples) as per minor allele frequencies.(TIF)Click here for additional data file.Figure S4Alignment of reads denoting the HBB gene fragment containing the SNP corresponding to the Hemoglobin Riyadh variant.(TIF)Click here for additional data file.Table S1Phenotype details on the 15 the samples sequenced.(DOCX)Click here for additional data file.Table S2Classification of triallelic SNP variants from the UE and UW data set.(PDF)Click here for additional data file.Table S3List of SNPs associated (GWAS-linked) with diseases and risk factors.(PDF)Click here for additional data file.Table S4Structural variations as seen in the two whole genome sequences of Saudi Arabian tribe ancestry.(PDF)Click here for additional data file.Table S5List of the 164 deleterious variants that show significant differences in allele frequencies between KWS samples and continental populations.(PDF)Click here for additional data file.Table S6Alignment statistics and genome coverage for the KWS1 and KWS2 (whole genome) samples. For calculating the percentage of genome covered, the length of human genome is considered as 3,000,000,000 bps (for WGS) and 62,000,000 (for exomes).(GIF)Click here for additional data file.Table S7Concordance rates for SNP calls between deep sequencing experiments and genome-wide genotyping.(PDF)Click here for additional data file.Table S8Details about genome sequences used to compare diversity of KWS genomes.(PDF)Click here for additional data file.Dataset S1Data set of all identified exomic SNPs (known and novel) from the UE data set of the 15 samples. The data is presented in Variant Call Format (VCF).(GZ)Click here for additional data file.Dataset S2Data set of all identified exomic indels (known and novel) from the UE data set of the 15 samples. The data is presented in Variant Call Format (VCF).(GZ)Click here for additional data file.Dataset S3Data set of identified novel non-exomic SNPs from the UW data set of the two whole genomes. The data is presented in Variant Call Format (VCF).(GZ)Click here for additional data file.Dataset S4Data set of all identified non-exomic indels (known and novel) from the UW data set of the two whole genomes. The data is presented in Variant Call Format (VCF).(GZ)Click here for additional data file.


Accurate identification of crop cultivars is crucial in assessing the impact of crop improvement research outputs. Two commonly used identification approaches, elicitation of variety names from farmer interviews and morphological plant descriptors, have inherent uncertainty levels. Genotyping-by-sequencing (GBS) was used in a case study as an alternative method to track released varieties in farmers’ fields, using cassava, a clonally propagated root crop widely grown in the tropics, and often disseminated through extension services and informal seed systems. A total of 917 accessions collected from 495 farming households across Ghana were genotyped at 56,489 SNP loci along with a “reference library” of 64 accessions of released varieties and popular landraces.ADMIXTURE analysis revealed that the optimum number of major varieties was 11 and matched the hierarchical clustering results. The majority of the accessions (69 %) belonged purely to one of the 11 groups, while the remaining accessions showed two or more ancestries. Further analysis using subsets of SNP markers reproduced results obtained from the full-set of markers, suggesting that GBS can be done at higher DNA multiplexing, thereby reducing the costs of variety fingerprinting. A large proportion of discrepancy between genetically unique cultivars as identified by markers and variety names as elicited from farmers were observed. Clustering results from ADMIXTURE analysis was validated using the assumption-free Discriminant Analysis of Principal Components (DAPC) method.Accurate cultivar identification and ancestry estimation was accomplished through two complementary clustering methods: (i) distance-based hierarchical clustering; and (ii) model-based maximum likelihood admixture analysis. Subsequently, 30 % of the identified accessions from farmers’ fields were matched to specific released varieties represented in the reference library. We show that genome-wide SNP markers from increasingly affordable GBS methods coupled with complementary cluster analysis is a powerful tool for fine-scale population structure analysis and variety identification. Moreover, the ancestry estimation provides a framework for quantifying the contribution of exotic germplasm or older improved varieties to the genetic background of contemporary improved cultivars.The online version of this article (doi:10.1186/s12863-015-0273-1) contains supplementary material, which is available to authorized users. Agricultural productivity in developing countries is affected by limited access to improved varieties, in addition to biotic, abiotic constraints and sub-optimal agronomic practices , 2. SuccTraditionally, estimation of improved variety adoption in socio-economic impact studies relies mostly on: expert opinion of breeders, extension services and other experts; elicited responses from farmers in farmer-level surveys; and morphological descriptors. However, such methods have several inherent uncertainty levels. For example, variety naming systems in the absence of formal seed systems can be quite temporally and spatially variable leading to inconsistencies in the names of a particular variety. Also, environmental conditions and different stages of plant development influence morphological descriptors , 6. FinaThese challenges can be overcome by using molecular markers which are not only unaffected by the environmental factors and crop developmental stages but are also ubiquitous throughout plant genomes. Genome-wide markers, like single nucleotide polymorphisms (SNP), not only facilitate germplasm classification using genetic distance estimates but can also be used to quantify the relative proportion of ancestries derived from various founder genotypes of currently grown cultivars . Such inIn the past, simple sequence repeats and anonymous markers such as amplified fragment length polymorphisms and randomly amplified DNA polymorphisms have been used in DNA-based fingerprinting applications . HoweverManihot esculenta Crantz) as a case study. Cassava is a highly heterozygous, clonally propagated species that originates from Latin America [Here, we report the use of GBS markers for cultivar identification with the objective of tracking released varieties in farmers’ fields, using cassava ( America . Its sta America . Africa  America . Its abi America . Despite America . Dissemin = 16) and key landraces (n = 48) maintained by the Council for Science and Industrial Research-Crops Research Institute (CSIR-CRI) of Ghana were genotyped alongside the accessions from farmers’ fields. It should be noted that many of the released varieties in Ghana are landraces with superior agronomic traits  and culinary qualities (root friability after boiling). These landraces were officially released as varieties following multi-year and -locational testing .This study was conducted in three regions of Ghana covering the largest cassava producing area accounting for 61 % of cassava production in the country in 2010 , 19. Thehttp://www.maizegenetics.net/#!tassel/c17q9). Resulting hapmap files (SNPs) were filtered with minor allele frequency (MAF) of 0.001 and coverage of 10x. SNPs were further processed by removing those with MAF of less than 0.01 and loci with more than 40 % missing data. The remaining missing SNP data-points were then imputed using GLMNET [DNA was isolated from 1045 genotypes, representing 917 accessions collected from farmer’s fields and a library of 64 clones genotyped in duplicate  pairwise distance-based hierarchical clustering; (ii) model-based maximum likelihood estimation of individual ancestries from multi-locus SNP genotype datasets using DMIXTURE ; and  matrix was calculated from 56,489 SNP markers in PLINK . A Ward’R . The criR  was takeR .ADMIXTURE analysis using the same set of 56,489 SNP markers was used to identify ancestries of the sampled cassava accessions. The number of sub-populations, K, was varied from 2 to 18 . The most appropriate K value was selected after considering (i) 10-fold cross-validations whereby the best K exhibits low cross-validation error compared to other K values [In the second approach, K values  and (ii)ADMIXTURE–based ancestry estimation was carried out using decreasing subsets of SNP markers. These were selected based on Weir and Cockerham [ST, a measure of differences in allele frequencies among the subpopulations detected by ADMIXTURE. For comparative purposes, equivalent numbers of markers were randomly selected, each twenty times with replacement. The objective here was to see how much we can reduce the number of markers while still obtaining cluster assignment results that is close to that obtained from the full set of markers. We used the ‘supervised’ ADMIXTURE method assuming K = 11 [To develop smaller sets of ancestry informative markers (AIMs) for follow-up studies using lower density genotyping, further ockerham  FST, a mg K = 11 . AccuracADMIXTURE assumes linkage equilibrium among loci and Hardy-Weinberg equilibrium within ancestral populations [ADMIXTURE and the hierarchical clustering algorithms, we carried out Discriminant Analysis of Principal Components (DAPC), an assumption-free multivariate clustering method [ADMIXTURE was obtained from DAPC. The results of DAPC analysis was then compared with those achieved from ADMIXTURE.The model-based clustering approach implemented in ulations . Howeverg method  using thg method  in a twog method  of PCA-tg method  was carrField surveys found that farmers cultivated between one and five different varieties of cassava in their fields, but majority of them (>80 %) grow only one or two varieties Fig. . A largeAn average genetic distance between repeat genotyping of the 64 accessions in the “reference library” was below 0.05  from 2 to 18, the most appropriate number was found to be K = 11, which produced the lowest 10-fold cross-validation error compared to other K values  were discernible with the exception of group IX that had small admixture from groups II and III  that share ancestry between two or more of the identified varieties were also detected. Of these, about 157 had at least 50 % of their ancestry coming from one of the eleven sub-populations, while 120 accessions have multiple ancestries , followed by less common varieties of “Afisiafi”, “Tek Bankye”, “Bankye Broni” and “Doku Duade”, which occurred in 17, 12, 21 and 4 households, respectively. Although “Nkabom” is a released variety in the CSIR-CRI library, it was found to correspond to a superior landrace from Nigeria (TMEB3), one of the first clones discovered to harbor dominant resistance to cassava mosaic disease [Following clustering of accessions into groups of genetically identical clones, actual variety identities were determined by matching each accession to the samples in the CSIR-CRI library. The library contained a total of 64 accessions but based on genetic similarity, these were collapsed into 34 unique cultivars  obtained with subsets of SNP markers, selected according to increasing FST , the corresponding equivalent numbers of randomly sampled SNPs, and the full set of 56,489 SNPs are presented in Fig. ST thresholds were the complete set of SNPs, 43007, 37900, 30962, 24560, 14426, 5359, 2755, 1392, 570 and 324, respectively. We found that the randomly drawn SNP panels slightly but consistently outperformed the SNP panels selected according to FST (average R2 = 0.97 for FST and R2 = 0.99 for the random SNPs). In addition, each of the 20 independently drawn samples for each subset produced very similar results, as indicated by the small standard deviation. A very high correlation with the full SNP data was obtained using SNPs with FST below 0.6 (5359 SNP) and both random and FST-based subsets performed similarly. Using 2755 SNPs (FST > 0.70) resulted in (0.05 units) lower correlations. The accuracy of 324 SNPs with FST > 0.95 was substantially lower (R2 = 0.90), even much less than that obtained from a similar number but randomly drawn SNPs (R2 = 0.96). Our results suggest there is loss of information in predicting admixture when going for markers with larger FST while random samples of SNPs give higher accuracies, though the actual differences are small. New SNPs-based variety identification studies for cassava would therefore require at least 300 informative SNP markers in order to have sufficient power to not only identify varieties but also estimate ancestries of these accessions.Accuracy of ancestry estimates , DAPC clustering recapitulated the groupings uncovered by both the distance-based hierarchical clustering topology as well as ancestry estimates achieved by ADMIXTURE. Comparison of the cluster membership results from the DAPC and ADMIXTURE analyses are summarized in Additional file ADMIXTURE, which was able to assign admixed individuals to multiple clusters. Indeed, whereas a total of 277 genotypes (or 339 when including the CSIR-CRI library accessions) did not meet the 90 % threshold for belonging to single cluster in the ADMIXTURE, only 15 genotypes did not to not exceed the same threshold in the DAPC analysis. Of the accessions belonging purely to clusters I to XI (i.e. ancestry > 90 %) from ADMIXTURE analysis, we found 100 % agreement with their corresponding DAPC clusters, except for cluster VI whose members were assigned to two DAPC groups (9 and 14) in roughly equal proportions .The SNP data sets supporting the result of this article are available at


With rising public concern for animal welfare, many major food chains and restaurants are changing their policies, strictly buying their eggs from non-cage producers. However, with the additional space in these cage-free systems to perform natural behaviours and movements comes the risk of injury. We evaluated the ability to maintain balance in adult laying hens with health problems  using a series of environmental challenges and compared such abilities with those of healthy birds (n = 5). Environmental challenges consisted of visual and spatial constraints, created using a head mask, perch obstacles, and static and swaying perch states. We hypothesized that perch movement, environmental challenges, and diminished physical health would negatively impact perching performance demonstrated as balance  and would require more exaggerated correctional movements. We measured perching stability whereby each bird underwent eight 30-second trials on a static and swaying perch: with and without disrupted vision (head mask), with and without space limitations (obstacles) and combinations thereof. Video recordings (600 Hz) and a three-axis accelerometer/gyroscope (100 Hz) were used to measure the number of jumps/falls, latencies to leave the perch, as well as magnitude and direction of both linear and rotational balance-correcting movements. Laying hens with and without physical health problems, in both challenged and unchallenged environments, managed to perch and remain off the ground. We attribute this capacity to our training of the birds. Environmental challenges and physical state had an effect on the use of accelerations and rotations to stabilize themselves on a perch. Birds with physical health problems performed a higher frequency of rotational corrections to keep the body centered over the perch, whereas, for both health categories, environmental challenges required more intense and variable movement corrections. Collectively, these results provide novel empirical support for the effectiveness of training, and highlight that overcrowding, visual constraints, and poor physical health all reduce perching performance. Gallus gallus domesticus) used worldwide for egg production . Birds with more porous wings performed more rotational body movements . Birds with higher wing loading performed fewer rotational body movements .There were significant main effects of perch state , environmental challenge , and axis of rotation  versus the static perch (127 ± 18.3 degrees s-1) and in the non-environmental challenged (598.7 ± 20 degrees s-1) versus the challenged (540.9 ± 20 degrees s-1) condition . The greatest intensities were exhibited about the cranio-caudal, x-axis (585. ± 12 degrees s-1) compared with the mediolateral y (561.8 ± 11 degrees s-1) and dorsoventral axes . Healthy birds did not differ significantly from birds with physical health problems in the highest rotation rates performed to maintain balance.Maximum intensity of angular velocity was varied significantly according to perch state  and more about the craniocaudal (x) axis (8.67 ± 0.3 degrees s-1) axis than the mediolateral (y) 8.28 ± 0.3 degrees s-1 and dorsoventral (z) 7.75 ± 0.27 degrees s-1 axes . There was a significant perch state and axis interaction in angular standard deviation . This was primarily due to higher variations in rotational movements about the craniocaudal (x) axis on the swaying perch. There was no significant effect of health state or challenge on variation in angular velocity. Angular velocity results are summarized in For a given average angular velocity, the variance about the mean (angular standard deviation) exhibited differences among treatments. Birds exhibited less variation on the static perch than on the swaying perch  and axis  on the number of peaks in linear acceleration , which could be attributed to a higher intensity of linear dorsoventral movements on the swaying perch , whereas on the static perch, linear movements were most intense in the mediolateral axis . On the swaying perch, the number of peaks in linear acceleration were similar across all three axes  whereas on the static perch, there was a higher number of peaks in the mediolateral axis .There were significant main effects of perch state  and axis  on maximum linear acceleration with the highest corrective acceleration (7.2 ± 0.1 m s-2) occurring on the swaying perch in comparison to the static perch (1 ± 0.1 m s-2) and decreasing corrective accelerations about the dorsoventral (z) axis (4.3 ± 0.1 m s-2), versus the mediolateral and craniocaudal axes (each 4 ± 0.1 m s-2). There was a significant maximum linear acceleration interaction between axis and perch state , which could be attributed to the highest maximum corrective accelerations of movements occurring along the craniocaudal (x) axis on the swaying perch (7.12 ± 0.13 m s-2), whereas on the static perch, maximum intensity of linear acceleration movements were lowest along the craniocaudal axis (x = 0.80 ± 0.091 m s-2).There was a significant effect of environmental challenge  on maximum linear acceleration (-2) versus the challenged (4 ± 0.15 m s-2) condition .The maximum linear acceleration movements were not significantly different between healthy birds and birds with health problems.There were significant main effects of perch state  and axis  on standard deviations of acceleration, with higher deviations occurring on the swaying perch (1.1 ± 0.04 m s-2) than on the static perch (0.3 ± 0.02 m s-2) and increasing variations about the craniocaudal (x) axis (0.66 ± 0.02 m s-2), the mediolateral (y) axis (0.678 ± 0.02 m s-2) and the dorsoventral (z) axis (0.68 ± 0.1 m s-2) respectively. There was a significant interaction between axis and perch state on standard deviation of acceleration , which could be attributed to higher variations along the dorsoventral axis on the swaying perch (x = 1.09 ± 0.04 m s-2), whereas on the static perch, standard deviation was highest along the mediolateral axis (y = 0.27 ± 0.02 m s-2). Linear acceleration varied less for birds with higher body weights .There were significant main effects of perch state , in which the variability of linear motion corrections along all three axes was the same in the masked challenge (0.71 ± 0.04 m s-2) whereas in all other test situations, variations in linear acceleration were different along the three axes . Linear acceleration results are summarized in There was a significant interaction between axis and environmental challenge , masked birds on the swaying perch would be expected to shift their reliance to the vestibular system for balance. The vestibular system in humans is essential for bipedal balance, while the vestibular system in birds is essential for flight. However, birds have a specialized organ involved in bipedal balance located within the lumbosacral vertebrae , 34, 63.Birds did not intentionally jump from perches when their vision was disrupted by the head mask. In birds, vision is needed to estimate the timing and distance needed for a successful takeoff and landing . Based oBirds in environmentally challenging conditions (i.e. space limitations due to obstacles and disruption of vision via a head mask) reached the lowest maximum intensity for both linear and rotational movements overall , and hadAdditionally, the presence of obstacles may limit the bird’s ability to perform proper wing flaps by disrupting the path of the wing. In humans, inhibiting arm movements as motor strategies for balance by fixing the arms to the side of the body when vision is disrupted increases instability . On the In the non-challenged situation (when vision and no obstacles were provided), laying hens with physical impairments of the integument (foot pad and wing feather damage) left the aerial perch  earlier than healthy birds . ResearcWhile perching, plantar pressure/loading on the foot pad is an unavoidable component of the tendon locking mechanism leading to contraction of the flexor muscles acting on the toes . This coIn wild birds, poor feather condition occurs immediately preceding and during moulting, which is a sensitive period where some species cannot be aerial without experiencing difficulties . The actFocusing on posture adjustments for balancing, we quantified the direction and intensity of a bird’s movement on the perch, with the gyroscope measuring a higher number of rotational movement corrections in birds with physical impairments than in healthy birds . RotatioIn humans, when a surface (e.g. the ground) shifts forward, the center of mass is displaced backwards in the opposite direction and the individual corrects this shift by re-adjusting the center of mass in the same direction (forward) as the surface shifts . As withLastly, birds with higher body weights had a lower number of falls from the perch and had less variable linear body movements. In birds, the majority of trunk weight lies in the pectoralis major (breast), the largest avian muscle which constitutes about 15 percent of body mass , 78. In Laying hens with and without physical health problems, in both challenged and unchallenged environments, successfully perched and remained off the ground. Environmental challenges and physical state had an effect on the ability to maintain balance, where laying hens used a combination of accelerations and rotations to stabilize themselves on a perch. In comparison to healthy birds, birds with physical health problems performed a higher number of rotational corrections to keep the body centered over the perch, thus indicating a more complex effort is required for them to maintain their balance. Less intense and less variable correction movements were performed under conditions with limited space and visual deprivation. This might suggest that balancing strategies are compromised in crowded housing environments and/or dim lighting conditions, putting birds at higher risk of falls and thereby injury. Injuries from falls are not only a welfare concern for the birds, but may also decrease egg production and quality due to pain and stress , which cS1 Dataset(ZIP)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 FigLeast square means ± SE are represented .(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S1 Table(TIF)Click here for additional data file.S2 Table(TIF)Click here for additional data file.S1 Video(WMV)Click here for additional data file.


PIGN, KIAA1468, TNFRSF11A, ZCCHC2, and PHLPP1 genes. Although additional studies will be needed to validate the importance of these regions in horses and the relevance of these regions in other species, the knowledge gained from the current study has the potential to be a step forward in unraveling the complex nature of hypersensitive reactions.While susceptibility to hypersensitive reactions is a common problem amongst humans and animals alike, the population structure of certain animal species and breeds provides a more advantageous route to better understanding the biology underpinning these conditions. The current study uses Exmoor ponies, a highly inbred breed of horse known to frequently suffer from insect bite hypersensitivity, to identify genomic regions associated with a type I and type IV hypersensitive reaction. A total of 110 cases and 170 controls were genotyped on the 670K Axiom Equine Genotyping Array. Quality control resulted in 452,457 SNPs and 268 individuals being tested for association. Genome-wide association analyses were performed using the GenABEL package in R and resulted in the identification of two regions of interest on Chromosome 8. The first region contained the most significant SNP identified, which was located in an intron of the DCC netrin 1 receptor gene. The second region identified contained multiple top SNPs and encompassed the  Often referred to as allergic eczema or allergic dermatitis, inflammation of the skin resulting from a hypersensitive reaction that is atypical within a population occurs not only in humans, but in a wide range of species from domestic dogs to hippopotamuses –3. AlthoTo date, most research investigating the genetic contribution to the manifestation of allergic dermatitis has taken place in humans ,5,8,9. HCulicoides and characterized by pruritic dermatosis, IBH severely reduces the welfare of affected horses . A description of how IBH severity scores were assigned is shown in Deoxyribonucleic acid was prepared from the hair roots using a standard hair-preparation procedure. Briefly, 186 μL Chelex 100 Resin  and 14 μL of proteinase K  were added to the sample. The mix was incubated at 56°C for 2 h and the proteinase K was inactivated for 10 min at 95°C. For DNA preparation from blood samples, 200 μL of blood was used and isolated on the Qiasymphony instrument using the Qiasymphony DSP DNA mini kit . Samples for two horses failed to meet the DNA quality requirements for genotyping and were replaced . Descrip-10; second QC FDR<0.2 in IBH controls only) [Prior to quality control (QC) the SNP data set consisted of 280 individuals genotyped using the 670K Axiom Equine Genotyping Array. Average relatedness of the individuals genotyped was 0.23. Iterative QC was performed with the GenABEL package in R to remove poorly genotyped and noisy data using the following thresholds: minor allele frequency (MAF) (<0.5%), missing genotypes per single nucleotide polymorphism (SNP) (>10%), missing SNPs per sample (>10%), and Hardy-Weinberg equilibrium (HWE) (first QC p<1els only) .Genome-wide association (GWA) analyses were performed using the GenABEL package in R (R Development Core Team 2011). An autosomal genomic kinship matrix was computed and standard K-means clustering was performed. To determine the number of clusters (subpopulations), K-means clustering with K = {1,2,…,10} were completed. For each iteration the sum of within-cluster sums of squares (∑WCSS) was calculated and plotted vs. K. The number of clusters corresponding with the first inflection point (K = 3) was then chosen to define the subpopulations . No outlP<0.05. Regions of interest were defined as any 1MB region that contained multiple SNPs below the suggested genome-wide significance threshold (P < 1x10-5) for two or more of the GWA approaches performed on the dataset.GWA analyses of IBH status classified as cases and controls were performed using both a structured association approach (“qtscore” function in GenABEL) and a principal component approach (“egscore” function). GWA analyses of IBH severity scores were performed using both a mixed model-structured association approach (“mmscore” function) and an additional principal component approach. Preliminary analyses did not indicate a significant effect of gender on IBH thus it was not included as a co-variant in the final analyses. Information pertaining to the environment of the horse was also excluded from all analyses as questionnaire responses regarding environment descriptions were inconsistent across respondents. After 100,000 permutations, associations with individual SNPs were considered genome-wide significant for p-values below 0.05. Due to computational limitations, only 5,000 permutations were performed for the mixed model-structured association analysis. However, the genome-wide significance threshold remained at Punadjusted) in 3 out of the 4 analyses and bordering on genome-wide significance (Pgenome-wide) in two (DCC) . The second region of interest  consisted of 4 SNPs and contained 5 genes: PIGN, KIAA1468, TNFRSF11A, ZCCHC2, and PHLPP1  were apparent with a single SNP, AX-104130346, located at 71,065,803 bp resulting in the lowest p-value  in two . This SNd PHLPP1 ; Table 5Much like autoimmune diseases, incidences of type I hypersensitive reactions have drastically increased over the past few decades . DespiteDCC gene, a gene whose corresponding protein has been associated with apoptosis and functions as a tumor suppressor [Of particular interest is the region containing the SNP that resulted in the lowest p-value in 3 out of the 4 analyses performed . The SNPppressor . In humappressor . This isppressor . Cancersppressor . It is pTNFRSF11A stands out as a potential candidate gene. Not only is TNFRSF11A the closest gene to SNP AX-104330407, but screening for TNFRSF11A has already been suggested as a potential diagnostic test for autoinflammatory disorders in humans [In addition to the genomic region described above we found a region of interest with 4 SNPs on ECA8 that encompassed a genomic region approximately 503kb in length. Within this region was SNP AX-104330407, a SNP that resulted in the lowest p-value in one of the GWA analyses and was in the top 10 results of another. While the region consists of five genes, n humans . Given tn humans .Although previous studies exploring IBH have shown fluctuating levels of significance for regions on Chromosomes 3, 9, 11, 20, and 27, no genes on ECA8 have previously been identified as important for susceptibility to IBH ,13,19. DLack of correspondence with previous GWA studies for the regions on ECA8 may also have resulted from use of the 50K and 70K genotyping arrays in earlier studies ,13,19. PAs such, it is important to put the results of the current study in context. Taking the position that if a genomic region or SNP was truly important in the expression of IBH, it would remain as one of the top regions or SNPs regardless of the methodology chosen, the current study reports the results of 4 different approaches . While aWhile the results of the current study will still require validation in a large independent data set, they potentially provide further insight into the genetic etiology of IBH in horses. By capitalizing on the newly available high density equine genotyping array and a highly inbreed breed of horse with a well-documented susceptibility to hypersensitive reactions, the current study identifies at least one candidate gene that has previously not even been suggested as important in the manifestation of IBH. The supplementary knowledge gained from this study together with other IBH studies in horses will conceivably bring researchers closer to fully understanding the biology underlying type I and type IV hypersensitive reactions in not only horses, but other species as well.