Despite the crucial role that heterosis has played in crop improvement, its genetic and molecular bases are still elusive. Several types of structured populations were used to discover the genetic architecture underlying complex phenotypes, and several QTL related to heterosis were detected. However, such analyses generally lacked the statistical power required for the detailed characterization of individual QTL. Currently, QTL introgression into near-isogenic materials is considered the most effective strategy to this end, despite such materials inevitably contain a variable, unknown and undesired proportion of non-isogenic genome.Zea mays L.) near-isogenic lines (NILs) suitable for the fine characterization of three major heterotic QTL previously detected. Here we describe the results of the detailed genomic characterization of these NILs that we undertook to establish their genotypic structure, to verify the presence of the expected genotypes within target QTL regions, and to determine the extent and location of residual non-isogenic genomic regions.An introgression program based on residual heterozygous lines allowed us to develop five pairs of maize (The SNP genotyping approach allowed us to determine the parent-of-origin allele for 14,937 polymorphic SNPs and to describe in detail the genotypic structure of all NILs. The correct introgression was confirmed for all target QTL in the respective NIL and several non-isogenic regions were detected genome-wide. Possible linkage drag effects associated to the specific introgressed regions were observed. The extent and position of other non-isogenic regions varied among NIL pairs, probably deriving from random segregating sections still present at the separation of lineages within pairs.The results of this work strongly suggest that the actual isogenicity and the genotypic architecture of near-isogenic materials should be monitored both during the introgression procedure and on the final materials as a paramount requisite for a successful mendelization of target QTL. The information here gathered on the genotypic structure of NILs will be integrated in future experimental programs aimed at the fine mapping and isolation of major heterotic QTL, a crucial step towards the understanding of the molecular bases of heterosis in maize. Despite the crucial role that heterosis has played in crop improvement over the years, its genetic and molecular bases are still elusive -3. Sever12:13 population derived from the single cross B73 × H99 [ In previous work undertaken to shed light on the genetic basis of heterosis in maize, we performed a QTL analysis on genetic materials derived from a RIL-F73 × H99 . The lev73 × H99  suitable73 × H99 . In part73 × H99 ,26. In t73 × H99 ,25, just73 × H99 , allowin73 × H99 , Genotyp73 × H99  or large73 × H99 . The latHere we describe the results of the detailed genotyping, obtained by using the Illumina MaizeSNP50 BeadChip , of five1 hybrid and for two reference B73 samples.Before proceeding to the genetic characterization of NILs, we assessed the reliability of the MaizeSNP50 BeadChip platform and determined its descriptive power on the yet untested B73 × H99 genetic system. To this aim, we performed thorough quality controls of SNPs and analyzed their genomic distribution based upon the genotypic calls obtained for the NILs’ parental inbred lines (B73 and H99), for their FIn order to assess the consistency of SNP genotype calls across replicate samples, we compared to each other the two B73 reference samples included as internal controls in the experiment (see Methods for details on the adopted filtering criteria for this specific task). They were identical for all the 47,937 considered SNPs and were subsequently treated as a single sample (indicated as “B73”). B73 sample was then compared to inbred line B73 from Scuola Superiore Sant’Anna  in order to determine the level of divergence between samples from different seed stocks. The full list of the B73 vs. B73-SSA compared SNP genotypes is available in Additional file 1 hybrid; 618 unmapped or mapped to the unknown chromosome; 114 heterozygous in either of the NIL parental lines ; 625 having inconsistent genotypes in the B73-SSA × H99 F1 hybrid; and, finally, 5 having inconsistent genotype in NILs samples.Before proceeding to the analysis of the genetic structure of NILs, a working subset of 42,771 good quality SNPs  was identified by applying stringent quality filtering criteria which also considered pedigree consistency (see Methods) to the full SNP dataset  resulted overall non-independent from the chromosome where the SNPs were mapped. Pairwise tests indicated that this was due to significant deviations from independence for all chromosomes except chromosomes 6 and 10. In particular, chromosomes 3, 4, 7 and 9 showed PM ratios higher than expected under the hypothesis of independence, whereas the opposite was true for chromosomes 1, 2, 5 and 8. All non-independent PM ratios , or both. For example, the significantly low PM ratio observed in chromosome 2 corresponds to a significant depletion of polymorphic SNPs only, whereas in chromosome 5 it is due to polymorphic and monomorphic SNPs being at the same time, respectively, less and more than expected by their uniform distribution.The comparison of genotype calls between B73-SSA and H99 showed that 14,937 SNPs, about 35% of the retained SNPs, are polymorphic between the two inbred lines, with proportions ranging from 31.1% in chromosome 5 to 38.5% in chromosome 7 , the largest annotated maize centromere by a factor of five [The genome-wide distribution of distances between adjacent polymorphic SNPs is shown in Figure  of five .Polymorphic SNPs were also found significantly non-uniformly distributed within each of the ten maize chromosomes arbitrarily divided in 10 Mbp bins Figure . OrthogoB73/B73 and in the H99/H99 genotype, respectively . Out of the 14,036 polymorphic SNPs successfully scored in all three samples of set 4.10_R55, genotype inheritance in NIL-BH with respect to the genotype observed in its two parental lines (NIL-BB and NIL-HH) could be unambiguously confirmed for 13,858 SNPs (98.7%). Among these, 705 of the 805 (87.6%) heterozygous SNPs overall detected in NIL-BH corresponded to contrasting homozygous genotypes in NIL-BB and NIL-HH. For the remaining 178 non-matching SNPs (1.3%), four different situations were observed: (1) a heterozygous genotype in NIL-BH when one of the parents was heterozygous ; (2) a heterozygous genotype in all three samples (4 SNPs); (3) a homozygous genotype in NIL-BH and contrasting homozygous genotypes in parental NILs (3 SNPs); (4) a homozygous genotype in NIL-BH when one of the parents was heterozygous . Cases (1) and (2) could be accounted for by the co-dominant nature of SNP markers , whereas cases (3) and (4) are not expected by segregation. However, the fact that the 3 SNPs of case (3) delimit a region between 157.3 and 176.2 Mbp on chromosome 4 where other 86 adjacent SNPs show consistent genotypes , suggests that in this case the observed genotypic inconsistency might be actually due to SNP calling inaccuracy in the NIL-BH sample. Besides invoking possible SNP call errors in any of the three samples, a reasonable explanation for the 75 SNPs of case (4) could reside in a “sampling effect”, that is, only NIL-BH individuals homozygous at these SNPs were actually genotyped, having been selected by effect of chance among individuals in fact segregating in a 1:1 proportion.n = 3) to 22.0% in chromosome 5 (n = 5). The largest proportions of non-isogenic regions within QTL specific chromosomes (excluding the QTL region) were observed in both NIL pairs for QTL 3.05, with 63.7% and 46.9% in NIL R40 and R8, respectively. Non-isogenicity for the QTL chromosome were very similar to each other for the two NIL pairs for QTL 4.10, 15.9% and 17.1% in pair R40 and R55 respectively, and much lower than that of the two pairs for QTL 3.05. Finally, the observed residual non-isogenicity in chromosome 10 for the NIL pair 10.03_R63 was 6.0%.A summary of the number, chromosomal distribution and size of all detected non-isogenic regions is reported in Table H99/H99, in the two contrasting NILs. Non-isogenicity around QTL 4.10 also extended beyond the QTL boundaries in both NIL pairs, also suggesting the presence of linkage drag, although of a more limited extent. In this case, non-isogenic regions extended by about 39.3 Mbp (39.0 Mbp upstream and 0.3 Mbp downstream) in NIL pair R40 and by about 6.9 Mbp (4.9 Mbp upstream and 2.0 Mbp downstream) in NIL pair R55. Albeit non contiguous, the non-isogenic region in pair 4.10_R55 might be considered to extended upstream for further 20.8 Mbp (up to position 216.1 Mbp) for a total of about 25.7 Mbp upstream, thus overlapping to a larger extent that of the other QTL 4.10 NIL pair, similarly to what observed above for the QTL 3.05 NIL pairs. Finally, the non-isogenic regions flanking QTL 10.03 region extended about 9.3 Mbp (0.2 Mbp upstream and 9.1 Mbp downstream).The chromosome positions of all non-isogenic regions detected for each NIL pair, along with the corresponding SNP genotypes of the individual NILs can be observed in Figure Additional unlinked non-isogenic regions between contrasting NILs were also detected in all pairs and mapped genome-wide. Non-isogenic blocks were generally found as stretches of SNPs of coherent genotypes, whereas recombination within non-isogenic blocks was observed in a few cases only (e.g. blocks on chromosomes 3 and 6 in NIL pair 3.05_R40). Chromosomal regions having a heterozygous genotype in one of the compared NILs only constitute for the most part full independent blocks, being only in fewer instances one of the two boundaries of otherwise larger homozygous non-isogenic blocks. The only exception to this latter situation was observed in NIL pair 3.05_R40 for the non-isogenic block mapped at interval 130.5-135.7 Mbp on chromosome 10 (20 SNPs), which consists in fact of three sub-blocks, the middle one of which being heterozygous in NIL-BB only. Only 2–3 isolated SNPs per NIL pair are heterozygous in both NILs. Finally, positions of unlinked non-isogenic blocks across all NIL pairs appear to be largely unrelated. A more extended overlapping of non-isogenic blocks could be observed in NIL pairs 3.05_R40 and 4.10_R40, clearly a manifestation of their relatedness since they were developed in the same genetic RIL 40 background.In this study we undertook the analysis of the detailed genotypic structure of near-isogenic materials specifically produced for the introgression of three heterotic QTL in maize that we detected and then further characterized ,25,26. T1 hybrid and its parental lines. In our case, this procedure had the additional purpose, due to the lack of replicate samples, to reduce the chances of spurious polymorphism detection and consequent inaccurate genotype calls in NILs samples. It must be considered that the quality of the genotyping data was assessed by Ganal and coworkers with respect to an average failure rate based on a large sample set [First of all we compared the genotypic structure of B73-SSA, the inbred line from which all NILs were derived, with that of the reference B73 accession, for which both replicate SNP scoring produced identical results, confirming the technical reproducibility of genotype calls. The few differences detected between B73-SSA and B73 were in line with the level of inconsistency already observed with duplicate samples from different seed sources . Overallmple set , rather The number of polymorphic SNPs detected between B73-SSA and H99 inbred lines by the Illumina MaizeSNP50 chip was adequate for the purposes of the present study, which was to describe the detailed genetic structure and genotype inheritance patterns in bi-parental materials from them derived. However, no absolute considerations upon the polymorphism level hereby detected between these two lines could be made, nor any comparisons with those previously detected among others. In fact, despite being designed upon a large maize germplasm panel, many of the SNP markers present on the Illumina MaizeSNP50 chip were selected upon data available from inbred lines B73 and Mo17. An anomalous high number of polymorphic SNPs (ca. 52%) with respect to previous knowledge about genetic diversity in maize was observed between these two lines when analyzed by this SNP platform in the original study on a large maize diversity panel . This suOur analysis showed that the distribution of SNPs in the genome is in general not uniform. Considering in particular the chromosomes bearing the introgressed QTL, chromosomes 3 and 4 showed a high polymorphic vs. monomorphic ratio, in both cases due to a lower than expected number of non-informative monomorphic SNPs. Chromosome 4 has also a significant low number of SNPs when compared to other chromosomes. However, SNP density varies sensibly within each chromosome, showing a marked tendency for an over-representation of polymorphic SNPs in telomeric regions which has been already observed for IBM and LHRF populations . This asper se a refinement of QTL 3.05 mapping position.The distances between adjacent polymorphic SNPs between B73 and H99 present on the chip, being for the vast majority shorter than 1 Mbp, allowed us to draw detailed maps of the genetic structure of this unique NIL material, to our knowledge the only available introgression material for heterotic QTL in maize. First of all, we obtained an accurate definition of the allelic structure at QTL regions, where the successful introgression of coherent chromosome blocks of the expected genotypes within the flanking markers used for marker-assisted selection (MAS) was confirmed in all contrasting NILs. Therefore, polymorphic SNPs mapping within the QTL introgression regions represent high-density markers that can be used for the fine mapping of the underlying heterotic QTL through the scoring of QTL-specific segregating populations derived from the cross of contrasting lines within each of the NIL pairs. Recombination events and regions of non-isogenicity were also pinpointed genome-wide for all NIL pairs to an unprecedented level of detail, also establishing an invaluable asset towards the characterization and the isolation of the introgressed QTL. In particular, the homogeneous introgression of the same QTL in NIL pairs having different, and known, recombinant structures will allow us to undertake a fine mapping approach of QTL 3.05 and QTL 4.10 in distinct, yet comparable, near-isogenic segregating populations. Such an approach might allow, in turn, the detection of epistatic effects and the isolation of disturbing factors, thus increasing the chances of both characterizing and fine mapping these QTL. In the case of NIL pairs 3.05_R8 and 3.05_R40, the target QTL region is interrupted by two and one isogenic regions, respectively. However, experimental evidences show that the QTL effect is still present in both NIL pairs ,26, suggIn all NILs SNPs were found organized along chromosomes in blocks of different length bearing concordant genotypes, consistent with the presence of coherent bi-parental chromosomal recombination blocks, as expected given the adopted introgression design , and fur1-S1 individuals from which the progenitors of the contrasting NILs in each pair were selected, although other random effects that possibly occurred throughout the inbreeding and selection scheme adopted for the QTL introgression cannot be excluded. The remarkable genotypic similarity observed between NIL pairs having a common ancestor  clearly shows that sizes and distribution of recombination blocks reflect the genetic structure of the specific RIL genotype originating each NIL pair. However, despite the fact that NILs were produced through the same breeding scheme, different level of non-isogenicity were observed in different pairs. This can be ascribed to the effective residual heterozygosity of the single progenitor F4:5 sister plants employed for the independent introgression procedures that led to the production of each NIL pair [The non-isogenic regions detected in other parts of the genome are randomly distributed and, from the comparison between NIL pairs introgressing the same QTL in distinct backgrounds, do not appear to be related to the QTL introgression procedure. They rather seem to largely reflect the presence of different residual heterozygous regions peculiar to the single BCNIL pair . The aveThe adopted SNP genotyping strategy allowed us to draw detailed maps of the genetic structure of unique NIL materials introgressing agronomically relevant heterotic QTL, to establish the successful introgression of the expected genotypes in target QTL regions in all NILs pairs and to detect residual regions of non-isogenicity genome-wide. Effects of linkage drag were found to be related to some extent to the particular region of introgression, whereas other residual non-isogenic regions appeared not to be related to the marker-assisted introgression procedure. The results of this work clearly show that the level of isogenicity and the genetic architecture of near-isogenic materials cannot, and must not, be taken for granted on the sole assumption of theoretical expectations. On the contrary, a detailed molecular characterization of NIL materials, possibly during the introgression process and surely on the final products, is a paramount pre-requisite for a successful mendelization and, eventually, isolation of target QTL.The extensive characterization of the NIL materials here presented constitutes an invaluable asset towards the characterization and the isolation of the introgressed heterotic QTL. In fact, the integration of the high-density SNP markers identified within the QTL introgression regions and of the genomic information gathered on the architecture of these NILs will greatly improve the scope of future experimental programs aimed at the fine mapping and isolation of major heterotic QTL, a crucial step towards the understanding of the molecular bases of heterosis in maize.1-S2:5 lines derived by single-seed descent from BC1-S2 lines previously developed [B73/B73 and in the H99/H99 genotype, respectively, while being isogenic anywhere else in the genome. Each pair of NILs was independently obtained starting from different RIL-F4:5 individuals selected because heterozygous at the target QTL (RHL-F4:5) and then crossed twice to the corresponding RIL-F11:13 as recurrent parents, according to the scheme reported in Figure 11:13 backgrounds no. 8 and 40) and QTL4.10 , whereas only one pair was produced for QTL10.03 .Five pairs of maize NILs were analyzed, consisting of BCeveloped . The two1 hybrid were included in this study as reference samples for parent-of-origin allele assignment and pedigree consistency check on the genotype calls. As a further control for pedigree consistency in NIL pairs, the NIL hybrid obtained by crossing the contrasting NILs within pair 4.10_R55  was also included in the present study. Seed stocks of the two parental inbred lines were the same originally used for the development of all the genetic materials derived from the B73 × H99 RIL-F11:13 population [Parental inbred lines B73 and H99, belonging respectively to Iowa Stiff Stalk Synthetic (BSSS) population (Reid Yellow Dent heterotic group) and to Illinois Synthetic 60C population (Lancaster Sure Crop heterotic group) , plus thpulation ,37 emplopulation . In part1 hybrid; (3) unknown mapping position or mapping to the unknown chromosome; (4) heterozygous calls in B73-SSA, H99 or both; (5) inconsistent genotype in the B73 × H99 F1 hybrid ; (6) inconsistent genotype in NILs .For each sample, DNA was extracted from 100 mg of young leaf tissues pooled from six seedlings by GenElute™ Plant Genomic DNA Miniprep Kit  as per the manufacturer’s protocol . Genomic DNA was checked for quality by electrophoresis on 1% agarose gel and quantified by absorbance using the Nanodrop™ 2000 spectrophotometer . A total of 14 genomic DNA samples  were sent at R.T. to TraitGenetics GmbH  to be processed on the MaizeSNP50 BeadChip . Two independent genomic DNA stocks of the reference B73 inbred line were included by TraitGenetics GmbH as genotype reference controls in the experiment. Allele calling was performed by TraitGenetics GmbH through full automatic processing of raw data using the latest version of the cluster file officially released for this platform  and SNP genotype calls were provided as tables in MS Excel format. No inferences on possible insertion/deletion (INDEL) polymorphisms  were made upon failed SNP calls, since the employed genotyping technology is not adequate to discriminate such occurrences from experimental failures. SNP mapping positions refer to their chromosome coordinates on the reference B73 maize genome assembly (RefGen_v1) as provided by TraitGenetics GmbH as MS Excel along with genotype calls. When comparing B73-SSA vs. the two reference B73 samples, all SNPs with unknown mapping position (or mapping to the unknown chromosome) and SNPs with failed calls either in B73-SSA or in both reference B73 samples were discarded. For SNPs with failed calls only in one of the reference B73 samples, the genotypes were assigned on the base of the remaining reference call. Before proceeding to the analysis of NIL data, SNPs that matched any of the following criteria  were removed from the dataset : (1) failed calls in all samples; (2) failed calls in one or more samples among B73-SSA, H99 or B73 × H99 Fdf). Polymorphic SNPs were also tested for uniform distribution within single chromosomes after assigning them to 10 Mbp bins according to their chromosome position. In this case, the expected number of SNPs for each bin was calculated on a per chromosome basis, applying Benjamini-Hochberg FDR method for multiple test correction to significance levels [df’s available from the above described general tests were decomposed in order to perform (df – 1) orthogonal tests aimed at identifying the classes  contributing with significant deviations to the rejection of the null hypothesis in the respective general tests.All mapped, good-quality, monomorphic and polymorphic classes of SNPs were individually tested for uniform distribution among chromosomes (with respect to chromosome length in bp) by goodness-of-fit chi-square tests with the appropriate degrees of freedom . SNPs inheritance patterns in NILs were then obtained by plotting the genotypes of polymorphic SNPs against their chromosome positions. The direct comparison of the genotypes of polymorphic SNPs between NILs within each pair allowed us to identify non-isogenic regions both within and outside the target QTL introgressed segments. Plots representing both the genotype and the isogenicity status of all polymorphic SNPs against their chromosome positions were obtained in MS Excel for each NIL pair in order to visualize at once both the genotypic structure of each NIL and all the detected non-isogenic regions between contrasting NILs within each pair.B73-SSA: B73 Scuola Superiore Sant’Anna; CGH: Comparative Genomic Hybridization; CNV: Copy Number Variation; df: Degrees of freedom; FDR: False Discovery Rate; GBS: Genotype by Sequencing; IL: Introgression Lines; MAS: Marker-assisted selection; NIL: Near-Isogenic Lines; QTL: Quantitative Trait Loci; RIL: Recombinant Inbred Lines; SNP: Single Nucleotide Polymorphism; SSR: Simple Sequence Repeats.The Authors declare that they have no competing interests.GP prepared the molecular samples, analyzed the data and drafted the manuscript. GP, EF, PL and MEP produced the NIL materials, conceived and designed the study. HHA and EF participated in analyzing the data. GP, HHA, EF, PL and MEP interpreted the data and critically revised the manuscript. All authors read and approved the final manuscript.GP current address: CeRSA - Plant Genomics Group, Parco Tecnologico Padano, Via Einstein - Loc. Cascina Codazza, 26900 Lodi, Italy.Comparison of SNP genotypes in B73-SSA vs. B73 reference samples. This table reports the genotypes of B73-SSA and of the two reference B73 samples at all SNPs available on the MaizeSNP50 chip.Click here for fileList of all SNPs with quality and polymorphism status descriptors. This table reports the genotypes of all experimental samples analyzed at all SNPs available on the MaizeSNP50 chip. The last two columns report, respectively, the quality status of all SNPs and the monomorphic/polymorphic status of good quality SNPs.Click here for fileHeat maps of SNP genomic distribution. Genomic heat maps (2 Mbp bins) of the number of all mapped good-quality SNPs and of the proportion of polymorphic SNPs (A3 page size).Click here for fileStatistics on polymorphic SNP genotyping scores in NILs. These tables report the chromosomal distribution of polymorphic SNPs genotypes in each NIL pair. The bottom table  reports the average distribution of genotypes across all NIL samples (excluding NIL 4.10_R55-BH).Click here for file


Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping.http://dna.engr.uconn.edu/software/GeneSeq/.In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Recent advances in massively parallel sequencing have dramatically increased throughput compared to the classic Sanger technology, with several commercially available platforms including 454, Illumina, ABI SOLiD, and Helicos delivering billions of bases per day. This has enabled sequencing of several individual genomes -8, usheralleles at autosomal loci, and most medical applications of personal genomics require accurate identification of both variants, the combination of which is referred to as genotype. Of particular interest are loci that are heterozygous, i.e., loci for which the two chromosomes carry different alleles. However, identifying heterozygous loci from low-coverage whole-genome sequencing data poses a significant challenge. Sequencing data is obtained using the so called “shotgun” approach, whereby millions of short DNA fragments called reads are generated from randomly selected locations on the two chromosomes. If, for example, there are only two reads generated from a heterozygous locus, there is a 50% chance that one allele would be missed. To compensate for sequencing errors, existing methods for detecting heterozygous loci have even higher minimum allele coverage requirements, e.g., in [In this paper we focus on one of the most fundamental genomic analyses, namely determining the genotypes at known loci of genome variation such as single nucleotide polymorphisms (SNPs). Diploid organisms including humans inherit two  variants or e.g., in ,8, callilinkage disequilibrium (LD).A simple way to improve genotype calling accuracy is to increase sequencing depth, as the probability of “missing” an allele decreases with the number of reads. After taking into account the effect of sequencing errors it has been estimated that, in the absence of additional information, achieving 99% sensitivity at detecting heterozygous SNPs would require an average sequencing depth of over 21× . Our maihttp://dna.engr.uconn.edu/software/GeneSeq/. We also present experimental results on publicly available 454, Illumina, and ABI SOLiD whole-genome sequencing datasets showing that integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods. For example, at 6× average mapped read coverage, our algorithm calls heterozygous SNP genotypes with about 96% accuracy, and accuracy can be further increased to 98-99% by leaving uncalled a small percentage of SNP genotypes with low posterior probabilities. This accuracy is comparable to that achieved by microarray-based genotyping platforms. Coupled with continued decreases in sequencing costs, the reduced sequencing depth required when using LD information renders low-coverage sequencing as a potentially more cost-effective alternative to microarrays for the next generation of genome wide association studies (GWAS). For example, the ABI SOLiD 4hq is expected to deliver 300Gb of sequencing data per run, or the equivalent of 16 individual genomes at 6× coverage, with a cost of only $600 per genome [LD patterns over millions of common SNPs have been mapped for several populations as part of the Hapmap project . The strr genome . Undoubtr genome .In this section we begin by describing a simplified statistical model that assumes independence between loci, then extend it to include dependences between alleles at different SNPs due to LD. We next formalize the multilocus genotype calling problem in the context of the extended model and show that computing the most likely multilocus genotype is computationally hard. Finally, we present a posterior decoding heuristic which independently selects the most likely genotype at each locus conditional on the entire set of reads.X) to denote random variables and lowercase italic letters  to denote generic values taken by them. Vectors of random variables and generic values are denoted by boldface uppercase , respectively boldface lowercase letters . When there is no ambiguity on the underlying probabilistic event we use P(x) to denote P(X = x), with similar shorthands used for joint and conditional probabilities of multiple events. For simplicity we consider only bialelic SNPs on autosomes. For every SNP locus, we denote the two possible alleles by 0 and 1, and the three genotypes by 0,1, and 2, with 0 and 2 denoting the homozygous 0 and homozygous 1 genotypes, and 1 denoting the heterozygous genotype.We use uppercase italic letters  the allele observed in the read at this locus. Since our focus is on genotyping SNPs represented in a reference panel, we further assume that panel SNPs at which the individual under study has novel allele variants (observed in [r we have that r(i) ∈ {0,1}. The probability that allele r(i) is affected by a sequencing error is denoted by rεi)(. In our experiments we set rεi)( = 10r-qi) denotes the Phred quality score of r(i) [Let erved in  at only erved in . Based oiG be a random variable denoting the unknown SNP genotype at locus i, and let ri = {ir,1, … ,ii,cr} be the arbitrarily ordered set of shotgun reads covering locus i, where ic is the coverage at this locus. Since for a homozygous genotype the allele of origin for a read is the same regardless of which chromosome is sampled, we get:Let andr covering a heterozygous SNP locus i allele r(i) can be observed either as the result of sampling r from the chromosome bearing allele r(i) and correctly sequencing it, or as the result of sampling the other chromosome followed by a sequencing error. Hence:For a read iĝ = argmax ig∈{0,1,2}P(ig|ri) for every SNP locus i, where the posterior probabilities P(ig|ri) are obtained from (1)-(3) by applying Bayes’ formula:A natural approach to single-locus SNP genotyping is to call a genotype of P(iG = g) denotes the population frequency of genotype g, estimated from the reference panel. If read mapping uncertainty is available in the form of probabilities m(r) that read r is mapped at the correct position, such information can be accounted for in genotype calling by replacing the above conditional probabilities with genotype weights obtained from (1)-(3) by rising the terms corresponding to read r to power m(r). Although the resulting weights can no longer be interpreted as conditional genotype probabilities, they naturally allow interpreting the presence of a read r with mapping confidence m(r) < 1 as the equivalent of observing an m(r) fraction of an identical read mapped with confidence 1.and hierarchical factorial HMM (HF-HMM). Indeed, we use a distributed state  to explial HMMs  to captuM and M′ , P(if+1|if), and P(ih|if)) using the classical Baum-Welch algorithm [At the core of the model are two left-to-right HMMs us works ,17,28-30lgorithm  based onWe define P(ri|ig) are given by equations (1)-(3), and in the following we will assume that probabilities P(ri|ig) are precomputed in O(m) time, where This implies that Given:Trained HMM models M, M′ and set of shotgun readsr = Find:Multilocus genotypeg* ∈ {0,1,2}n with maximum posterior probability, i.e.,g* = argmaxgP        (6)Maximum Multilocus Genotype Probability Problem (MMGPP) denote the optimization version of MGP that requires finding maxgP.In this section we show that MGP is NP-hard. Let Theorem 1. For any ∊ > 0, MMGPP cannot be approximated withinunless P=NP, and it cannot be approximated withinunless ZPP=NP. Furthermore, this holds even if M′ = M.Proof. Lyngsø et al. [M0 can be reduced in approximation preserving manner to MMGPP with M′ = M. The haplotype models M and M′ are obtained from M0 as follows  ≠ 0 only for multilocus genotypes with g1 = 1 and ig ∈ {0,1} for i = 2, … , n.Note that g,Furthermore, for such a genotype g can only be observed when the maternal haplotype is 0n and the paternal haplotype is g or vice-versa, and each of these configurations have a probability of P/4. The innaproximability result follows from [P is constant fraction of P.The last equality comes from the fact that ows from  since, bSince an algorithm similar to the forward algorithm for HMMs can be used to compute in polynomial time the marginal probability of a given genotype, Theorem 1 implies the following:Corollary 2.MGP is NP-Hard.i the genotype iĝ with maximum posterior probability given the read data r. Note that, unlike the single SNP genotype calling method, where we condition only on the set ri of reads overlapping locus i, in the posterior decoding algorithm we take into account the entire set of reads:We next present an MGP heuristic similar to the posterior decoding algorithm for HMMs. Specifically, the algorithm selects for each SNP locus Posterior decoding algorithmStep 1. For each i = 1, … , in, ĝ ← argmaxigP(ig|r)Step 2. Returnĝ = O(m + nK3) implementation of the posterior decoding algorithm. Since P(ig|r) ∝ P, for implementing the maximization in Step 1 it suffices to compute marginal probabilities P for every i = 1, … , n and ig ∈ {0,1,2}. For each SNP locus i and each pair of founders forward probability be backward probability be P can be written asBelow we detail an where P can be computed in O(nK2) once the forward and backward probabilities Thus all probabilities The forward probabilities can be computed using the recurrence:i = 2, … , n, wherefor every fi, and so its repeated computation can be avoided by replacing (9) with:The inner sum in equation (9) is independent of A similar optimization can be applied when computing the backward probabilities, resulting in the following recurrence:O(nK3) by using recurrences (8), (11), and (12), respectively (13), (14), and (15), resulting in an overall runtime of O(m + nK3), where m is the number of reads, n is the number of SNPs, and K is a user selected parameter denoting the number of founders in the HMM models of haplotype diversity in the parental populations (we used K = 7 in our experiments).Forward and backward probabilities can thus be computed in We evaluated the HMM-based posterior decoding algorithm on shotgun sequencing datasets generated using three different sequencing technologies, as follows:1. Watson 454: A set of 74.4 million reads downloaded from the NCBI SRA database (submission number: SRA000065). The reads, with an average length of ~265 bp, were generated using the Roche 454 FLX platform as part of James Watson’s personal genome project. This is a subset of the 106.5 million 454 reads analyzed in . Unless 2. NA18507 Illumina: A set of 525 million paired-end reads downloaded from the NCBI SRA database (submission number: SRA000271). These 36bp reads, which were generated using the Illumina Genome Analyzer from a Hapmap Yoruban individual identified as NA18507, are a subset of the dataset analyzed in [3. NA18507 SOLiD: A set of 900 million single ABI SOLiD reads generated from Hapmap individual NA18507 was kindly provided by the authors of . Reads vWe mapped 454 reads on build 36.3 of the reference human genome using the NUCMER tool of the MUMmer package  with defm mapped reads we created four subsets of sizes m/16, m/8, m/4 and m/2 by picking reads at random. For each subset we called genotypes using the HMM-based posterior decoding algorithm, the binomial test of [To evaluate the effects of read coverage on genotype calling for each dataset of  test of  and MAQ  test of , two wid test of . Unfortu test of ,39, whicFig. Fig. N = 242 CEU haplotypes available in Hapmap3. Similar to experiments with varying read coverage, we generated subsets of approximately N/16, N/8, N/4, and N/2 randomly selected reference haplotypes, and compared the accuracy achieved by running the HMM posterior algorithm using these subsets to that obtained using all N reference haplotypes. Fig. To assess the effect of the size of the reference panel on genotyping accuracy, we conducted additional experiments on the Watson 454 reads using Since our algorithm computes a posterior probability for each SNP genotype, further increases in calling accuracy can be obtained at the expense of leaving uncalled a small percentage of SNP genotypes with low posterior probability. Such “no-calls” are commonly used in microarray-based genotyping for SNPs for which hybridization signals are ambiguous. Fig. In this paper we introduced a statistical model for multi-locus genotyping that integrates shotgun sequencing data with LD information extracted from a reference panel. Although finding the multi-locus genotype with maximum posterior probability under the integrated model is NP-Hard, experimental results suggest that a simple posterior decoding algorithm produces highly accurate genotype calls even from low-coverage sequencing data. Compared to current LD-oblivious genotype calling methods, our method allows researchers to achieve a desired accuracy target with reduced sequencing costs. For example, genotype calling accuracy achieved at 5-6× average coverage by a previously proposed binomial test is matched by the HMM-based posterior decoding algorithm using less than 1/4 of the reads. While a full comparison of sequencing and microarray based genotyping in the context of GWAS is beyond the scope of this paper, experimental results on three publicly available datasets generated using the 454, Illumina, and ABI SOLiD sequencing platforms suggest that at a mapped coverage depth of 5-6× our algorithm achieves an accuracy that is comparable to that of microarray platforms. Concordance rates reported for microarrays often exceed 99.9% , and arIn ongoing work we are exploring efficient algorithms for LD-based haplotype reconstruction from paired shotgun sequencing reads. We also plan to empirically compare our method with similar tools developed as part of the 1000 genomes project ,39.IIM and YW conceived the study. JD, JK, SD, and YH implemented the methods and conducted the experiments. JD and IIM drafted the manuscript. All authors participated in the development of the methods, data analysis, and manuscript revision. All authors have read and approved the final manuscript.The authors declare that they have no competing interests.


ESR1. There have been conflicting reports about the association of this locus with breast cancer in Europeans, and a GWAS in Europeans identified a different SNP, tagged here by rs12662670. We examined the associations of both SNPs in up to 61,689 cases and 58,822 controls from forty-four studies collaborating in the Breast Cancer Association Consortium, of which four studies were of Asian and 39 of European descent. Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (CI). Case-only analyses were used to compare SNP effects in Estrogen Receptor positive (ER+) versus negative (ER−) tumours. Models including both SNPs were fitted to investigate whether the SNP effects were independent. Both SNPs are significantly associated with breast cancer risk in both ethnic groups. Per-allele ORs are higher in Asian than in European studies . SNP rs2046210 is associated with a significantly greater risk of ER− than ER+ tumours in Europeans . In these Asian studies, by contrast, there is no clear evidence of a differential association by tumour receptor status. Each SNP is associated with risk after adjustment for the other SNP. These results suggest the presence of two variants at 6q25.1 each independently associated with breast cancer risk in Asians and in Europeans. Of these two, the one tagged by rs2046210 is associated with a greater risk of ER− tumours.The 6q25.1 locus was first identified via a genome-wide association study (GWAS) in Chinese women and marked by single nucleotide polymorphism (SNP) rs2046210, approximately 180 Kb upstream of  SNP rs2046210 did not show a clear association in GWAS carried out in women of European ancestry, and replication studies indicated its effect, if any, was weaker in Europeans  for 40–60 cycles.2 = 0.89 in the European samples used in Turnbull et al. 2 = 0.72 and r2 = 0.66 in HapMap2 JPT and CHB samples, respectively).SNP rs12662670 was chosen as the most easily assayable surrogate for the best European GWAS hit, rs3757318, for which no working Taqman™ assay could be designed. These two SNPs are correlated at rThe primer and probe sequences used were:SNP rs2046210For Forward primerTGCCTCAACTGTCTTGTGAATCTTTReverse primerCTACTGTAGAATCATTTTCCTCACACATACAACAGTCACATACGCATCTAG allele probeVIC CAGTCACATACACATCTAA allele probe FAM SNP rs12662670For Forward primerCTAACGAAGGCAGAGCAAAAAGAAAReverse primerCACACATGCATGACACGTAAATCTTATTAAATTCTTGTAAGTTTCCT allele probeVIC AATTCTTGTCAGTTTCCG allele probe FAM Four studies  used the Sequenom iPLEX MassARRAY™ system  with oligonucleotide design performed using MassARRAY Assay Design software (version 3.1).Tables S2a and S2b for information on the respective phases for SNPs rs2046210 and rs12662670). All studies followed standard quality control guidelines (for details see http://www.srl.cam.ac.uk/consortia/bcac/about/about.html). Data were excluded for any sample that failed genotyping for >20% of the SNPs typed in a given phase of genotyping. All study data were excluded for any SNP with overall call rate <95% or duplicate concordance <94% (based on at least 2% of samples in each study being genotyped in duplicate) or departure of genotype distribution from Hardy-Weinberg equilibrium in controls (p<0.005). In addition, all genotyping centres assayed an identical plate of 80 control DNA samples  and had to achieve call rates and duplicate concordance >98% in order for their data to be included. Data for both SNPs from one study (NBCS) were excluded from further analyses after quality control rules were applied. Quality control data for the individual studies are shown in Tables S2a and S2b. Thus, for SNP rs2046210 forty out of forty-one assayed studies , and for SNP rs12662670 thirty-three out of thirty-four assayed studies  were included in the statistical analysis.SNPs were genotyped in three different BCAC genotyping phases along with other SNPs of interest to the consortium  group of interest.2. Tests were two-sided.The primary analysis estimated ORs for the main effect of the SNP, adjusted for the studies that provided data for the respective analysis . ORs adjusted for both study and age were essentially identical and we did not therefore present the age-adjusted analyses. Per allele ORs were estimated under the assumption of a log-additive mode of inheritance, i.e. the SNP was coded according to the number of minor alleles 0, 1 or 2. Additionally, ORs by genotype were calculated, i.e. two indicator variables indicating the presence of the heterozygous genotype and the genotype homozygous for the minor allele, respectively, were entered the model. The primary p-values were derived by means of a Wald-Test assuming a log-additive mode of inheritance (one degree of freedom). Following Laird and Mosteller, heterogeneity of per allele ORs between studies was assessed by the p-value derived from the Q statistic Genetic main effects by ER status were estimated using case-control logistic regression and restricting the case sample to ER+ or ER− cases, respectively. To test for significant differences between main effects of rs2046210 or rs12662670 in ER+ versus ER− cases, logistic regression analyses were conducted in cases only. In these case-only analyses, the binary ER status was the outcome/dependent variable and the respective SNP and the indicator variables representing the studies were the independent variables.Variation in OR by age was evaluated by testing for an interaction between age-group  and SNP, separately for each subgroup defined by ethnicity and ER status. Thus, the multiplicative SNP by age-group interaction term entered the model in addition to the main effect terms for SNP, age-group and study.To investigate whether the association with breast cancer risk could be explained by one SNP or whether both SNPs had independent effects on disease risk, we fitted logistic regression models which included both SNPs, in addition to indicator variables for the studies, as independent variables in the model. Analyses were carried out separately for Europeans and Asians and for ER− versus ER+ cases and controls. Additionally, haplotype analyses were performed using logistic regression models that included the estimated two-marker haplotypes (coded according to a log-additive model) except for the reference haplotype  and the indicator variables for study. Haplotypes were estimated using the expectation-maximization algorithm.All analyses, were performed using R version 2.11.0 Table S1. In addition to the originally discovered SNP rs2046210, SNP s12662670 was genotyped as a surrogate for the best tag from Turnbull et al. (rs3757318) Tables S3a and S3b. The associations of each SNP are presented in −14] than in Europeans  and this difference is statistically significant [pheterogeneity  = 1.4×10−7]. SNP rs12662670 shows a similar pattern, with a higher OR associated with the minor G allele in Asian studies  than in Europeans  and again this difference is statistically significant [pheterogeneity  = 0.002]. In each case there is no evidence for departure from a log-additive model (a co-dominant mode of inheritance).Key characteristics for each participating study are shown in −8 for rs2046210, p = 2.9×10−3 for rs12662670;  = 0.028 for rs2046210, p = 0.012 for rs12662670). In each ethnicity the estimated ORs for each SNP, after adjustment for the other SNP, are of similar magnitudes: For rs2046210 in Europeans OR (A/G) = 1.08 (95% CI 1.05–1.11) and for rs12662670 in Europeans OR (G/T) = 1.07 (95% CI 1.02–1.12). For rs2046210 in Asians OR (A/G) = 1.17 (95% CI 1.02–1.36) and for rs12662670 in Asians OR (G/T) = 1.21 (95% CI 1.04–1.40). Similar effect estimates are also obtained for haplotypes carrying one minor allele though estimates do not reach statistical significance for the very rare haplotype carrying the major (G) allele of rs2046210 along with the minor (G) allele of rs12662670 . For each Asian study, the number of in-situ cases is less than 100 and so effect estimates are inaccurate but do not differ from those for invasive cancer (data not shown).The OR estimates for 4, −17 vs. OR (ER+) = 1.07 (95% CI 1.04–1.1), p = 1.3×10−7, pheterogeneity  = 5.1×10−6. This difference remains significant after adjustment for rs12662670. A similar, although non-significant, difference is observed in European women for SNP rs12662670 (The associations of these two SNPs with tumour sub-types defined by ER status (ER+ and ER−) were also investigated and are presented in 12662670 Table 3.Table S5). In Asian studies the data are too sparse to give meaningful results. In the combined ethnicities and the European studies alone, the magnitudes of the observed associations are greater in younger women.We further investigated whether the magnitudes of these SNP associations on tumour sub-types differed by age at diagnosis/interview (see Table S1), which could have led to an overestimation of the ORs relative to those expected in a population-based case-control study. However, exclusion of these studies does not materially affect the estimated ORs for either SNP (see Table S6).Fourteen of the European studies had been designed to over-sample cases with a family history of breast cancer  = 1.27 (95% CI 1.22–1.34), p = 2.2×10−24; pheterogeneity  = 0.04]. A stronger association of SNP rs2046210 with ER− tumours is also consistent with the report from the Consortium of Modifiers of BRCA1/2 (CIMBA) Table S5). By contrast, the CIMBA consortium reported that SNP rs9397435  shows evidence of modification of risk in both BRCA1 and BRCA2 mutation carriers (who mainly develop ER− and ER+ tumours respectively) We also find evidence that SNP rs2046210 is more strongly associated with ER− than ER+ disease in both European and Asian women. In the present study this differential association with receptor status is statistically significant in European studies (and remains after adjustment for rs12662670) but is not quite significant in Asians which may be due to a lack of power attributable to the comparatively small number of Asian individuals involved in our study Table 3.2 = 0.12 in Europeans and r2 = 0.56 in Asians) and all four possible combinations (haplotypes) of these two SNPs clearly exist.Previous fine-scale mapping publications on this locus 2>0.9) with rs2046210 and those with rs12662670. If there were a single causal variant, directly responsible for the associations seen with both SNPs, it would need to be correlated with both SNPs. Such a variant has not been yet identified (e.g. by the 1000 Genomes Project). It would presumably be relatively rare. An alternative, and we think, more plausible, explanation for the pattern of associations may be the existence of two independent causative variants, one correlated with rs2046210 and another correlated with rs12662670. If this is the case, the former variant may be more strongly associated with ER− breast cancer than the latter. The reason why both SNPs confer higher relative risks in Asians than in Europeans is unclear. Within the BCAC studies, ER− tumours are relatively more prevalent among Asian (36%) compared to European cases (23%), but this is not sufficient to explain the higher ORs in Asians, since the effects persist after stratification by ER status. It remains possible that the higher relative risks are due to differential patterns of linkage disequilibrium if the, as yet, unidentified causal variants are not strongly correlated with the SNPs identified to date. These questions may be resolved by comprehensive re-sequencing of this locus and fine scale mapping to identify the causal variant (or variants) responsible for the observed breast cancer risks. One aim of the iCOGS Project Examination of linkage disequilibrium plots of the regions surrounding these two SNPs in Europeans Figure 7ESR1 gene, whose transcription start site is located approximately 180 Kb downstream of SNP rs2046210. ESR1 encodes ERα and has long been implicated in breast carcinogenesis. However, it is possible that the proximity of this SNP to ESR1 may be providing a false lead – both SNPs (rs2046210 and rs12662670) lie in the flanking region of C6orf97 and there are numerous other genes in close physical proximity .(DOC)Click here for additional data file.Table S2Characteristics of the study populations genotyped for rs2046210 (a) and rs12662670 (b).(DOC)Click here for additional data file.Table S3Genotype frequencies of SNP rs2046210 (a) and rs12662670 (b) in the different studies.(DOC)Click here for additional data file.Table S4Association of rs2046210 and rs12662670 with in-situ/invasive breast cancer.(DOC)Click here for additional data file.Table S5Association of rs2046210 and rs12662670 with risk of ER−*/ER+**breast cancer.(DOC)Click here for additional data file.Table S6Association of rs2046210 and rs12662670 with breast cancer.(DOC)Click here for additional data file.


Solanum chacoense (chc 80-1), S. commersonii, S. demissum, S. sparsipilum, S. spegazzinii, S. stoloniferum] and cultivated S. tuberosum Group Phureja (phu DH) potato species with contrasting levels of SGAs. Amplicons were sequenced for five candidate genes: 3-hydroxy-3-methylglutaryl coenzyme A reductase 1 and 2  and 2.3-squalene epoxidase (SQE) of primary metabolism, and solanidine galactosyltransferase (SGT1), and glucosyltransferase (SGT2) of secondary metabolism. SNPs (n = 337) producing 354 variations were detected within 3.7 kb of sequenced DNA. More polymorphisms were found in introns than exons and in genes of secondary compared to primary metabolism. Although no significant deviation from neutrality was found, dN/dS ratios < 1 and negative values of Tajima’s D test suggested purifying selection and genetic hitchhiking in the gene fragments. In addition, patterns of dN/dS ratios across the SGA pathway suggested constraint by natural selection. Comparison of nucleotide diversity estimates and dN/dS ratios showed stronger selective constraints for genes of primary rather than secondary metabolism. SNPs (n = 24) with an exclusive genotype for either phu DH (low SGA) or chc 80-1 (high SGA) were identified for HMG2, SQE, SGT1 and SGT2. The SolCAP 8303 Illumina Potato SNP chip genotyping revealed eight informative SNPs on six pseudochromosomes, with homozygous and heterozygous genotypes that discriminated high, intermediate and low levels of SGA accumulation. These results can be used to evaluate SGA accumulation in segregating or association mapping populations.Natural variation in five candidate genes of the steroidal glycoalkaloid (SGA) metabolic pathway and whole-genome single nucleotide polymorphism (SNP) genotyping were studied in six wild [ Group Tuberosum) and wild relative species, the most commonly accumulated are the triose glycosides of solanidine, α-chaconine and α-solanine  are secondary metabolites mainly produced in solanaceous species. These compounds function in potato species as a defense against pathogens and insects  catalyzes the formation of mevalonic acid. ompounds . In potaynthesis . CycloarSGT1), solanidine glucosyltransferase (SGT2), and rhamnosyltransferase (SGT3)  ,b. SGT1 HMG1, HMG2, SQE, SGT1, and SGT2) in six wild potato species with different levels of SGAs  compared with the cultivated S. tuberosum Group Phureja, to identify patterns of variation and their possible association with the synthesis and accumulation of SGAs. We assessed levels of genetic variation and single-nucleotide polymorphisms (SNPs) in SGA candidate genes to determine their possible role in governing accumulation of SGAs. A whole-genome SNP genotyping analysis was performed to identify new genomic regions putatively associated with the accumulation of these compounds in potato species.Genetic studies of wild and cultivated potato species have concluded that multiple genetic factors interact for the production of SGAs  and S. tuberosum Group Phureja DH OT-B × N-B P5/AR2 (phu DH) were used. chc 80-1, and the production of leptines associated with resistance to Colorado potato beetle (Leptinotarsa decemlineata Say). By contrast, phu DH with low levels of SGAs is a dihaploid derived from an intermonoploid somatic hybrid  were selected for five wild potato species from the NRSP-6 collection . These ic hybrid . Instead3 solution overnight, and then sown in cell packs with Fafard super fine germination mix . A single seedling per accession was transferred to D-40 Deepots  with Premier Horticulture Pro-mix BX 15 days after sowing. Simultaneously, after 1 wk of acclimation to growth in soil, in vitro grown plants of chc 80-1 and phu DH clones were transferred to D-40 Deepots. The plants were grown under controlled environment  set to 60% relative humidity, 14-hr photoperiod, 250 µmol/m2/s light intensity and day/night temperatures of 20/16°. Plants were fertilized with MiracleGro All Purpose  at the rate of 1 g/L every 15 d. Three biological replications were established per individual plant from each accession. At 15-d intervals serial apical cuttings were taken to generate the three temporal replications planted into D-40 Deepots from 1 month-old established plants. At 55 days after cutting, leaves 4−6 from the shoot apex were harvested for SGA extraction. Young leaves were collected for DNA extraction from the starter plants.Five seeds per accession were treated with 1000 mg/L GAHMG1 the sequence from the draft genome of DM 1-3 516 S. tuberosum Group Phureja potato was also used , SGT1 (PGSC0003DMG400011749), and SGT2 (PGSC0003DMG400017508), the fragment was located exclusively in a single exonic region, whereas the PCR products for HMG2 (PGSC0003DMG400003461) and SQE (PGSC0003DMG400004923) captured some intronic sequence. The amplified region of HMG1 coded for a 292 amino acid (aa) sequence with a catalytic and tetramerization interface domains. For HMG2 the fragment covered 134 aa with catalytic, NADP(H) binding, substrate binding, and tetramerization residues. The 134-aa segment of SQE spanned more than one domain of its gene family. A region of 277 aa with a glycosyltransferase multidomain was amplified for SGT1, and a segment encoding 189 aa with active site conserved domains and thymidine diphosphate-binding site residues was isolated for SGT2.Genomic DNA was extracted from leaf tissue of 12 accessions by a modified cetyltrimethylammonium bromide protocol . Homologlso used . A DNA sEx Taq polymerase buffer, 0.2 mM of each dNTP, 0.24 µM of each primer, and 0.2 units of high-fidelity TaKaRa Ex Taq DNA polymerase  and 100 ng of genomic DNA template. Standard cycling conditions were 5 min initial denaturation at 95° followed by 30 cycles of 0.5 min at 94°, 0.5 min annealing at the appropriate Tm, and a 2-min extension time  were aligned to identify consensus sequences. Sequencing errors were corrected by visual inspection of peak quality in chromatograms. The set of consensus sequences per accession was aligned to identify alleles. A minimum alignment of at least three identical consensus sequences defined an allelic sequence from the pool of sequences. In some cases, additional sequence errors were detected when consensus sequences following the same pattern were aligned. These were corrected, and the amended sequences were used for identification of allelic sequences. Sequence alignments and editing were done using the different applications included in the DNASTAR Lasergene 9 core suite software for sequence analysis and assembly and Sequence Scanner Software v1.0 from Applied Biosystems.PCR products were gel-purified with QIAquick Gel Extraction Kit  and cloned into pJET1.2/blunt vector  using JM109 competent cells . At least five colonies from each cloning reaction were sequenced with the use of both forward and reverse primers . Forward and reverse sequences  software version 5.0  and MoleS. tuberosum and genomic sequences from the sequenced potato genome S. tuberosum Group Phureja DM 1-3 516 R44 were added to the analysis of each gene. Nucleotide diversity for the entire population per gene was calculated using Tajima-Nei’s π estimator . Significant SNPs were used for cluster analysis to identify informative SNPs (SNPs putatively associated with SGA accumulation). The physical position in the potato genome of informative SNPs was obtained from A whole-genome SNP genotyping using the SolCAP 8303 Illumina Infinium potato SNP chip was performed on our 12 accessions alongside the 4 HCO3), 5 mL of 50 mM NH4 HCO3:MeOH (1:1 v/v), and 7.5 mL of water. The final SGA extract was eluted with 1.2 mL of elution buffer .Leaf tissue harvested from each of three biological replications of the 12 accessions was freeze-dried for 72 hr in a lyophilizer , then ground and SGAs extracted using a modified procedure . An init202nm was used to quantify the different SGAs detected and separated in a frame of retention time between 5 and 15 minu. α-Solanine and α-chaconine standard solutions were used as reference peaks. Separation of the plant extracts produced peaks associated with SGA compounds specific of each wild species. The different peaks detected in each sample were added to calculate the total amount of SGAs per sample. A unique quantification unit of mAU of SGAs per mg of dry weight leaf tissue (mAU/mg DW) was used for further analysis.A high-performance liquid chromatography  on a C-18 reverse-phase column  procedure was used to separate the SGAs, and a photodiode array detector to quantified them. Elution of SGAs was attained by using a binary gradient system consisting of Solvent A  and Solvent B  at a flow rate of 0.3 mL/min at 25° column temperature. The gradient elution was: 0−0.5 min, 0% B; 0.5−8.5 min, 0–30% B; 8.5−12 min, 30–100% B; 12−16 min, 100% B; 16−16.5 min, 100–0% B; and 16.5−21 min, 0% B. Eluent was monitored at 202 nm. Purified standards solutions of α-solanine and α-chaconine (Sigma-Aldrich) were used to generate calibration curves. Peak absorbance area at AA completely randomized design was used for plants in the growth chamber and for SGA analysis. The amount of total SGAs was estimated in leaf tissue collected from each biological replication per accession. Analyses of variance were conducted for the total SGA response variable using as source either accessions or allelic states of polymorphic SNPs within candidate genes and the SNP array. The average of repetitions was used for statistical analysis of SNPs. SGA data were transformed with logarithm for all analyses. All statistical analyses were done using JMP 9 .SGT2 aa sequence of S. tuberosum (GenBank accession ABB29874) was used to model the structural effects of nonsynonymous and indel polymorphisms of chc 80-1 and phu DH allelic fragments. The amplified fragment spans 189 aa from positions 197 to 385 in the 482 aa sequence of SGT2. The original 189 aa fragment of S. tuberosum occurring in the position of our amplicon was substituted by either chc 80-1 or phu DH fragments and used for protein modeling. Both aa sequences were submitted to the Interactive Threading Assembly Refinement Algorithm I-TASSER server http://zhanglab.ccmb.med.umich.edu/I-TASSER/ (The -TASSER/ . The outvs. transversions was greater for HMG1 (3.8) and similar for all other genes (2.0−2.4). Genes involved in primary metabolism, HMG1, HMG2 and SQE, had fewer SNPs in exon regions, 0.03, 0.05, and 0.05 SNPs/bp respectively, than those involved in secondary metabolism, SGT1 and SGT2, with 0.13 and 0.10 SNPs/bp, respectively. However, HMG2 and SQE fragments spanning regions with one and two introns that had the greatest SNP rates, 0.26 and 0.13 SNPs/bp, for each gene. Indels with 1−3 codon deletions were found in HMG1, SGT1, and SGT2, and various sized indels were identified in introns of HMG2 and SQE. Codons with 2-3 SNP sites and several variants in each were detected in SGT1 and SGT2; such variable codons would be expected to yield proteins with highly variable amino acids in the position.Sequences of genomic DNA fragments corresponding to conserved coding regions of five candidate genes were obtained by PCR amplification, cloning, sequencing, and sequence analysis. One or two allelic sequences per gene were detected per individual since the accessions derived from diploid heterozygous species . In somer2 = 0.96, P < 0.003*; n = 5, r2 = 0.85, P < 0.026; and n = 5, r2 = 0.77, P < 0.049*), with smaller ratios  in the genes of primary metabolism  than in those (0.42 and 0.39) of secondary metabolism (SGT1 and SGT2) respectively. The nucleotide diversity estimations followed the same pattern for exon regions, and increased greatly for HMG2 and SQE when introns were taken into account for total estimations. Positions of the candidate genes in the metabolic pathway , for regression analysis, were assigned based on the distance (in the number of reactions) between these genes. At least seven reactions (1 unit) could occur between HMG1/HMG2 and SQE in the putative pathway of sterol biosynthesis proposed by SQE to SGT1/SGT1 using as reference the putative metabolic pathway of cholesterol suggested by Codon and nucleotide diversity were estimated for the pool of sequences of each gene . In the P = 0.0023 for nonparametric test and P < 0.0001 in normal ANOVA). For the most part, the seedlings selected randomly from paired accessions within each species expected to be low or high for SGAs based on the data available from NRSP-6 differed dramatically from each other with regard to total SGAs  of the high selections and the highest three  of the low selections , which separated phu DH, which did not produce SGAs from the other individuals. The different alleles of HMG2_snp_202 would be expected to code for either serine, a polar amino acid, or alanine, a nonpolar amino acid. HMG2_snp_128 and 199 separated the highest SGA producer, chc 80-1, from the other samples, with codons specifying either arginine/lysine (both polar) or alanine/serine, with different polarity. One SNP (SQE_snp_220) of 22 that were polymorphic in SQE was potentially associated with SGA accumulation (P < 0.001*). This SNP is nonsynonymous, specifying a change from lysine to glutamine, both polar amino acids, and separated phu DH from the other samples. SGT1 had 106 polymorphic SNPs and for 11 the SGA accumulation levels showed significant differences among SNP genotypes with P < 0.006. Seven were nonsynonymous, specifying amino acids with similar polarity, and four were synonymous. The 11 SNPs  were mainly heterozygous in phu DH and homozygous for all other individuals, with the exception of two that were also heterozygous in 1-2 other individuals. Three SNPs  that were heterozygous for the greatest SGA producer, chc 80-1, separated it from all other individuals. SNP SGT1_snp_210 was detected by ANOVA, whereas the other two were found during the cluster analysis. All of them were nonsynonymous, specifying amino acids changes of similar polarity, lysine by asparagine, aspartic acid by glutamic acid and different polarity, and glycine by arginine (SGT1_snp_256). In SGT2 there were 53 polymorphic SNPs, and seven were putatively associated with SGA accumulation. We observed only two homozygous genotypes for six of these SNPs in our germplasm panel, whereas there were four possible different homozygotes for SGT2_snp_264. Four of these SNPs were nonsynonymous and three synonymous; two of the nonsynonymous changed from polar to non-polar and the other two kept same the polarity. Four SNPs  with an exclusive genotype for phu DH separated it from other accessions. These SNPs were detected by ANOVA, two were synonymous and two nonsynonymous specifying changes of glutamine by histidine and serine by leucine. Four SNPs  exhibited a specific genotype for chc 80-1. Two produced amino acid changes in chc 80-1 from glutamic acid to tryptophan and valine to isoleucine, and two were synonymous. A comparison of protein models of SGT2 using either the chc or phu amplicons to specify aa from positions 197-385 and the S. tuberosum aa profile to complete the 482 aa protein yielded models with different secondary structure . In this analysis, a monoploid line derived from chc 80 1−4 was used instead of chc 80−1 itself, and the ANOVA was used to identify the most likely SNPs associated with SGA accumulation. The stringency was increased to identify significant SNPs that clustered at least two accessions with low or high SGA levels, with mean difference between SNP clusters equal to or greater than 5 logarithm units  at 63.6 Mb mainly clustered in two SNP genotypes (AA and AG) six accessions with the greatest accumulation of SGAs. Five SNPs were found on pseudochromosome 2, of which solcap_snp_c2_30160 at 19.1 Mb grouped in two SNP genotypes (CC and TC) eight accessions with the greatest levels of accumulation, with a gradual decrease from the four with CC to the four with TC. Pseudochromosomes 3, 4, and 5 with two, four and one SNPs did not group the accessions into any logical arrangement. Of five SNPs on pseudochromosome 6, solcap_snp_5775 at 45.3 Mb was informative in grouping eight accessions with the greatest levels of SGA accumulation in homozygous (CC) and heterozygous (TC) genotypes similar to the distribution by solcap_snp_c2_30160 on pseudochromosome 2. Solcap_snp_c2_18573 at 53.2 Mb of five SNPs on pseudochromosome 7 again grouped the 8 accessions with the greatest levels of SGAs under similar patterns. Solcap_snp_c1_1512 one of the two SNPs on pseudochromosome 9, at 28.8 Mb clustered nine accessions; the five with the greatest levels of SGAs had the CC allele whereas the four with lower levels had the TC genotype. The SNP on pseudochromosome 10 did not have any defined cluster associated with high or low SGA accessions. Finally, three informative SNPs were identified out of five on pseudochromosome 11  between 4.5 and 8.9 Mb. These SNPs have at least three accessions with the greatest SGA level, three in intermediate levels and two with the lowest levels grouped in different SNP genotypes. The SNP on pseudochromosome 12 was not informative. The group of eight informative SNPs defined putative haplotypes for low, intermediate and high SGA accumulation  with low levels of SGA were located in high and intermediate SGA cluster.A whole genome SNP chip analysis was done on the twelve potato accessions to identify genomic regions putatively associated with SGA accumulation (hm units . Then thhm units . Clusterhm units . Two accmulation . CompariA levels . The neiHMG1 and SGT1) indicated an excess of rare nucleotide polymorphisms with low frequency compared with the expectation under neutral theory which could be explained by effect of genetic hitchhiking selection  that provide precursors for various groups of end products , but also with greater protein variation of modifying enzymes in the secondary metabolism, such as SGT1 and SGT2, that direct the synthesis of unusual SGAs since the glycoside chain structure adds different toxicity properties to the final compound  as well as within the genus am genes . Similaram genes . Signifiam genes . In factHMG2 and SQE compared with their exons, contrasting effects of selection within a gene could explain differential levels of nucleotide diversity on introns. High levels of sequence conservation are expected if regulatory elements are present in the introns  and the greatest accumulation of SGAs in chc 80-1. The whole genome analysis detected eight informative SNPs on six pseudochromosomes  with homozygous and heterozygous genotypes that discriminated high, intermediate, and low levels of SGA accumulation. Hierarchical cluster analysis showed that the selected informative SNPs did not follow the phylogenetic pattern found when total polymorphic SNPs were used. In general, three haplotypes could describe each accumulation level. These haplotypes cluster most of the accessions except for cmm 7 and sto 61 that did not have the allelic structure found for low level of SGAs. Sequence and analysis of polymorphisms in candidate genes is a strategy to find variation associated with phenotypes of interest. Then informative SNPs should be tested in a segregating or association mapping population to elucidate the genetic control of allelic variation in the trait of interest. 2 population of phu DH × chc 80-1 (unpublished data). Allelic sequences of chc 80-1 for HMG2 and SGT2 were significantly associated with greater levels of SGA accumulation. In addition to the informative SNPs detected within the candidate gene HMG2 for our germplasm panel, the SNP chip analysis detected one locus nearby this gene at 19.1 Mb on pseudochromosome 2. This chromosome has been associated with a QTL for synthesis of SGAs  absence of SGAs in HMG2, SQE, SGT1, and SGT2 we identified amino acids specific to phu DH and chc 80-1. In HMG2 the amino acid changes were near a conserved tetramerization sequence. Alignment of multiple HMG homologs sequences showed that there were some variable base-pairs within an otherwise highly conserved region. Further studies will determine if regulatory sequences in the intron or nonsequenced gene and its contiguous regions could influence phenotype more than the amino acid changes studied here. The SNP of SQE was within the squalene monooxygenase conserved domain where multiple polar amino acids were found for homologous sequences. For SGT1, the nine amino acid changes located within the multidomain region of the glycosyltransferase family could play a role in the catalytic activity of this protein. However, in the F2 population analysis there was no association of this gene and SGA synthesis or accumulation. Both phu DH and chc 80-1 had one of two sequences where amino acid changes were concentrated, so the second allelic sequences could also neutralize part of amino acid changes effects. For SGT2 the seven amino acid changes including a deletion found within the active site of this protein could influence its activity or specificity. In fact, differences in the secondary structure of the S. tuberosum SGT2 aa sequence were found when the original segment was substituted by chc 80-1 and phu DH allelic fragments (Supporting Information, File S1 and Table S1.Association of traits with allelic variation of SNPs in candidate genes could also lead to identification of sequence polymorphisms directly involved in the phenotypic variation or functional markers . The fraragments . Overall