Genome-wide association studies allow detection of non-genotyped disease-causing variants through testing of nearby genotyped SNPs. This approach may fail when there are no genotyped SNPs in strong LD with the causal variant. Several genotyped SNPs in weak LD with the causal variant may, however, considered together, provide equivalent information. This observation motivates popular but computationally intensive approaches based on imputation or haplotyping. Here we present a new method and accompanying software designed for this scenario. Our approach proceeds by selecting, for each genotyped “anchor” SNP, a nearby genotyped “partner” SNP, chosen via a specific algorithm we have developed. These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test. In simulations, our method captures much of the signal captured by imputation, while taking a fraction of the time and disc space, and generating a smaller number of false-positives. We apply our method to a case/control study of severe malaria genotyped using the Affymetrix 500K array. Previous analysis showed that fine-scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome-wide significance levels. Our method also increases the signal of association from  Over the last 5–10 years, genome-wide association studies (GWAS) have proved a popular and highly successful approach for identifying genomic regions (loci) that harbor single-nucleotide polymorphisms (SNPs) associated with complex diseases [Visscher et al., imputation [Browning and Browning, haplotyping [Allen and Satten, combinations of genotyped SNPs rather than by any individual genotyped SNP. However, these approaches tend to be highly computer-intensive and may require extensive postanalysis quality control to produce reliable results [Browning and Browning, The utility of a GWAS is premised on the idea that a nongenotyped disease-causing variant can be detected through testing a nearby genotyped SNP that is in strong LD with the causal variant. For populations whose genetic variation is not well tagged, this premise may fail if the disease-causing variant is neither present, nor highly correlated with a variant present, on the genotyping array used. Use of more sophisticated analysis approaches based on anchor SNP, a nearby genotyped partner SNP (chosen from a window of SNPs surrounding the anchor SNP). The partner SNP is selected on the basis of a specific algorithm we have developed, which uses the correlation between the two SNPs to construct a score in which higher scoring potential partner SNPs are expected to be more “useful.” See Methods for details of how we define “useful.” These two SNPs are then used as predictors in a linear or logistic regression analysis to generate a final artificial-imputation (AI) significance test for the anchor SNP. The procedure is repeated for every genotyped anchor SNP across the genome.Here we describe a new GWAS method and software implementation, SnipSnip, that is specifically designed to improve the signal of association at poorly tagged causal variants and to increase power over standard single-SNP analysis in situations where there are a number of SNPs in low LD with the causal variant. The method proceeds by selecting, for each genotyped p be the probability an individual is diseased, then association between disease status and genotypes at the anchor and partner SNPs can be modelled by a logistic regression model:x1 and x2 are the number of minor alleles at the anchor and partner SNPs respectively  and β0, β1, and β2 are regression coefficients (to be estimated). For a set of cases and controls the likelihood function is given by:i and j index cases and controls (with n and m the number of cases and controls), respectively. This model is compared with the logistic regression model for the partner SNP only:Two different logistic regression models are used in the construction of the AI test. Let 2 test statistic on one degree of freedom:P-value gives the significance of the anchor SNP conditional on the partner SNP. That is, we test whether the additional information from the anchor SNP improves the signal of association (with disease status) over that provided by the partner SNP alone. If the local LD pattern is such that the anchor and partner SNPs provide different information concerning a causal variant, then this situation (of improved significance) may be expected to occur.The AI test uses a likelihood ratio test to compare models We also considered an alternative test that evaluated the significance of adding an anchor-partner SNP interaction term to model y, x1, x2, and ε are the quantitative trait value, number of minor alleles at the anchor and partner SNPs, and error term, respectively. The error terms are assumed to be normally distributed. The test statistic is given by a n is the number of subjects), which compares the residual sum of squares from the fitted linear models.A similar AI test can be constructed for quantitative trait phenotypes instead of case/control status. In this case, the two following linear regression models are compared:w corresponds to considering The partner SNP is chosen from a window of potential partner SNPs surrounding the anchor SNP on the genome. The SNP window is defined by a fixed number of SNPs to the left and right of the anchor SNP . A SNP window of size r2 correlation coefficient  to map the Pearson's  Ziegler ) betweenuseful test result. We define useful in this context as the ability to detect a causal variant when single-SNP GWAS methods fail. In particular, we are interested in obtaining improved performance for our method over standard single-SNP testing in regions of low LD and poorly tagged causal variants. Intuitively, we would expect that that a partner SNP that has “too high” a correlation with the anchor SNP would not be that useful for the AI test, as the anchor SNP is unlikely to provide much extra information for association testing compared to that provided by the partner alone. Equally, a partner SNP that has “too low” a correlation with the anchor SNP will not be that useful, as it does not add any information compared to that provided by the anchor alone, and so the AI test will not be very different from the anchor's single-SNP test. Therefore, our goal is to pick a partner whose correlation with the anchor SNP is not “too high” and not “too low.” Naïvely, we may think it would be a good idea simply to choose the SNP that gives the most significant AI test result as the partner SNP. However, this approach would inflate significance levels across all anchor SNPs, resulting in an invalid test. Instead we use a simulation procedure (see below) to precompute the metric that is used to select the partner SNP in such a way that higher scores are assigned to correlation patterns that tend to lead to higher significance for the AI test than for single-SNP testing, when there is a causal variant in the vicinity of the two SNPs.Our goal here is to choose the partner SNP that is most likely to provide a r2 can be calculated on the basis of the unphased genotype data alone, see Wellek and Ziegler [We note that other measures could be used instead of, or in addition to, the correlation between the two SNPs, when choosing the appropriate partner SNP for each anchor SNP. A detailed investigation of possible alternative measures would be an interesting topic for future work. For example, one could make use of alternative LD measures such as  Ziegler ). Investalternative to single-SNP testing, rather we propose it as a useful additional test that can, in some circumstances, detect a complementary signal. We are therefore not overly concerned about situations where single-SNP testing already works well, we simply want to ensure that our correlation metric performs well in situations where there is, in fact, a complementary signal to be found.It is not obvious which anchor-partner SNP correlations are most useful. In order to determine this, we used an empirical approach to sample from the entire sample space of possible anchor-disease-partner SNP haplotype configurations. Our reasoning was that the performance of both single-SNP tests and our proposed AI test will be largely determined by the population haplotype frequencies at these three SNPs. By sampling from this haplotype space, we could investigate which haplotype configurations result in situations where the AI test has improved power over single-SNP testing, and what resulting correlations between the anchor and partner SNP are induced in these situations. We note that, in practice, the utility of the AI test (in comparison to single-SNP testing) will be lowest when the disease SNP is already well tagged by the anchor SNP. If the disease and anchor SNP are highly correlated , then we would expect single-SNP tests to perform well and no improvement to be seen from use of more complicated methods such as imputation, AI or haplotype testing. This does not concern us as we are not proposing AI as an We thus performed one million computer simulations to calculate, for different haplotype configurations (resulting in different anchor-partner SNP correlations) the resulting AI and single-SNP logistic regression tests. In each simulation replicate, a random haplotype frequency partition was chosen to model the haplotype frequencies at three SNPs . The eight haplotype frequencies were randomly simulated as follows:The eight haplotype frequencies were initially undefined over the unit interval.A random haplotype was picked from the remaining undefined haplotype frequencies .S is the sum of the previously defined haplotype frequencies.A frequency for the haplotype was picked from a uniform distribution scaled from 0 to Steps 2 and 3 were repeated until all the haplotype frequencies were defined, with the last haplotype frequency taking the value remaining within the unit interval.Once the eight haplotype frequencies had been assigned, genotype data for 1,000 cases and 1,000 controls were simulated using these haplotype frequencies and assuming multiplicative penetrance values of 0.01, 0.015 and 0.0225 for genotypes containing 0, 1, and 2 causal alleles, respectively. From each simulation replicate the following results were recorded: (i) the anchor-partner SNP correlation using all case/control data; (ii) the AI test statistic; and (iii) the single-SNP logistic regression test statistic at the anchor SNP.To determine the usefulness of a given correlation, the differences between the test statistics were calculated by subtracting the single-SNP logistic regression test statistic from the AI test statistic. A threshold was set to select the top 1% of these differences. The correlations were then divided into bins of size 0.05. For each correlation bin the probability of the test statistic differences being in the top 1% was calculated using all simulated anchor-partner SNP correlations falling in that bin. That is, given a correlation in a certain correlation bin, we estimated the probability that the corresponding test statistic difference is in the top 1%. A map was then constructed to convert the anchor-partner SNP correlations to a score by using the midpoints of the bins and the calculated probabilities to fit a continuous curve, with additional points specified at  and . A polynomial of degree 5 was fitted to the points . The resultant curve and calculated points are shown in supplementary The same procedure was repeated using different sets of multiplicative penetrances and for different numbers of cases and controls, with no discernable difference between the resultant curves. Decreasing the top percentage test statistic difference threshold used to construct the curve  resulted in a shift of the curve peak toward 0.25 . However, lower correlations are also more likely to give a nonsignificant AI test result. There is a trade-off between: (i) a low chance of finding a partner SNP with a possibly very significant AI test result; and (ii) a higher chance of finding a suitable partner SNP but with a possibly lower AI test result. We deemed the top 1% threshold to be a sensible choice in practice.We also constructed similar correlation maps under the assumption of dominant and recessive penetrances at the causal SNP. The resultant curves are shown in supplementary We note that the algorithm used to choose the partner SNP is statistically independent of the subsequent AI test carried out using these two SNPs. . Thus, we expect the type I error rate of the overall procedure to be comparable to that of a standard single-SNP GWAS in which we analyse each genotyped SNP individually; we do not expect the type I error rate to be inflated on account of the search procedure used to choose the partner SNPs. Simulations (see Results) suggest that this expectation is indeed correct.http://www.staff.ncl.ac.uk/richard.howey/snipsnip/. Execution times are favorable, even when single-SNP analyses are also performed. For example, for 2,118 subjects and 17,515 SNPs, analysis using a 10-SNP window without inclusion of covariates takes around 17 sec in SnipSnip  respectively. Imputation was carried out using the program IMPUTE2 [Howie et al., We used computer simulations to evaluate the performance of our proposed AI test and to compare it to other approaches. The different scenarios considered corresponded to “causal” SNPs located in different regions on chromosome 11. Specifically, the causal SNPs were: Scenario 1: rs240686 at Build 36 base pair (BP) position 63989461; Scenario 2: rs10501081 at Build 36 BP position 27192637; Scenario 3: rs9804546 at Build 36 BP position 7088763. In each scenario the genotype relative risks were assumed to be 1.5 and 2.25 for possession of one and two risk alleles at the causal SNP, respectively. .P-value of 10−8, 10−6, or 10−4, respectively. Any detected SNP lying within 110 kb of the causal SNP was thus considered as a true-positive. To evaluate type I error, data were simulated under the null hypothesis by assuming genotype relative risks of 1.0 and 1.0 for possession of one and two risk alleles, respectively, with  detections of any SNPs lying within 110 kb of the “null” causal SNP calculated at nominal P-value thresholds 10−4, −3.Detection power was defined as the proportion of replicates in which we detected a SNP (or the middle SNP of a 5-SNP haplotype) lying within 110 kb of the causal SNP, with “detection” corresponding to the SNP or haplotype achieving a nominal The optimal SNP window size for AI is clearly dependent on the data, and so cannot be known in advance. Different SNP window sizes could be tried but this would raise a multiple testing issue. If the SNP window size is too large, then there is a greater chance of picking a poorly performing partner SNP. This is because, if the anchor and partner SNP are far apart, they are less likely to be both correlated with a causal variant but may still achieve a high correlation score. Conversely, if the SNP window is too small, then we do not allow a sufficient choice of partner SNPs as to pick the optimal one. A compromise has to be made between these extremes. We suggest using a SNP window size of 10 SNPs (and probably no more than 20 SNPs), which seems to provide a reasonable compromise in practice.P-value of 10−4, −3, respectively, for single-SNP logistic regression, haplotype analysis, and the AI test we expect the overall probability of type I error to correspond to a times the nominal P-value, where a is the number of tested anchor SNPs (or haplotypes) within the 110 kb detection region , assuming independence of the a tests. These expected proportions of type I errors are shown as dashed lines. The observed probabilities of type I error for AI, single-SNP logistic regression and haplotype analysis lie, as expected, mostly below these lines because the tested anchor SNPs (or haplotypes) are not completely independent, and so use of a Bonferroni correction to account for multiple testing of a anchor SNPs is overly conservative. The type I error probabilities thus achieve their desired levels and do not appear to be overly sensitive to the choice of SNP window size.number  of type I errors. We attribute this phenomenon to the larger multiple testing burden that is incurred when carrying out imputation, on account of larger number of SNPs that are imputed (and therefore tested). To illustrate this phenomenon, we present in Interestingly, P-value) threshold by one unit would achieve approximately the right level. Taking this “rule-of-thumb,” we should therefore logically compare the powers to detect P-values of 10−8, 10−6, or 10−4 using AI with the powers to detect P-values of 10−9, 10−7, or 10−5 using imputation, if we are to be comparing “like with like.” Applying this idea to our current results, we find this has the effect of reducing the detection power of imputation  in these scenarios to a value similar to that of AI , while maintaining a comparable number of type I errors (data not shown).The logical consequence of this observation is that the threshold for declaring “genome-wide significance” when carrying out an imputation-based GWAS should be higher than when carrying out a standard (genotyped SNPs only) GWAS, if one wishes to maintain the same number of false-positives  as is incurred in a standard GWAS. The precise threshold to be used will depend on how many SNPs have been successfully imputed (and are thus tested) and the degree of LD between them; visually, in this example, it appears that increasing the required To our knowledge, it has not generally been appreciated that use of imputation may require the use of a more stringent threshold for declaring genome-wide significance, at least when using standard frequentist tests of association. Most studies using genome-wide imputation  have usSupplementary haplotype through testing a SNP that marks this underlying causal haplotype. This is a potentially attractive property of imputation that has not, to our knowledge, been previously demonstrated. However, it does raise an important issue regarding the interpretation of imputation results. Given a set of imputation results such as those seen in the bottom plots of As an additional check on the type I error rate for AI, we constructed quantile-quantile (Q-Q) plots and calculated genomic control inflation factors [Devlin and Roeder, correlation metric constructed for a dominant/recessive causal SNP (see Methods) when the causal SNP really is dominant/recessive tends to perform slightly better than using one constructed for a multiplicative causal SNP. However, the difference in power is not large and the multiplicative metric still has higher power in some instances. For AI we therefore recommend using the multiplicative correlation metric unless there is a strong prior belief in a dominant or recessive causal variant.The effect of using a dominant or recessive analysis model and/or a dominant or recessive correlation metric, when the causal SNP was simulated to operate under a dominant or recessive model, was also investigated for Scenarios 1-3. Supplementary www.MalariaGEN.net). It is expected that many SNPs may be poorly tagged as this array was originally developed for European rather than African populations. The original dataset consisted of 1,059 cases and 1,496 controls typed at around 500,000 SNPs. These data were previously analyzed by Jallow et al. [As a demonstration of our method applied to real data, we used a case/control study of severe malaria from The Gambia, genotyped on the Affymetrix 500K array  using the smartpca routine of the EIGENSOFT package [Price et al., smartpca were then included as explanatory variables in the logistic regression models fitted by PLINK.  for the Gambian case/control samples at the known causal SNP, rs334. TableTableWe also analyzed an additional MalariaGEN Gambian case-parent trio dataset, consisting of 658 severe malaria cases and their parents genotyped on the Illumina 650Y array . Our method is designed for analysis of case/control rather than trio data, but trio data can be analyzed by converting it to cases and pseudocontrols  SNP in the case-parent trio study differed from that found in the case/control study, most likely due to the different genotyping arrays used in the two studies, which contain SNPs that show varying patterns of LD with the causal SNP, rs334.We carried out imputation (without prephasing) within the Gambian case/control samples, and within the cases and pseudocontrols derived from the Gambian case-parent trio samples, in the 4 Mb region around the known causal SNP (rs334) on chromosome 11. We used the program IMPUTE2 [Howie et al., We note that the signal at rs334 detected here using 1000 Genomes Project samples as a reference panel for imputation  is consiw et al.  when usiw et al.  on a parWe additionally carried out genome-wide imputation with prephasing [Howie et al., For the case/control dataset, the results shown in supplementary It took about 10 days to carry out genome-wide pre-phasing and imputation of these two datasets, which was enabled by running the analyses for each of the 22 chromosomes in parallel; without access to a compute cluster we anticipate this analysis would have taken several months. More importantly, prephasing and imputation  of these two datasets generated around 270 GB (for the case/control dataset) and 488 GB (for the case/pseudocontrol dataset) of output and intermediate files respectively, i.e., a total of 758 GB of storage was required in order to run this experiment. In comparison, analysis using AI in SnipSnip took between 1 second and 2.5 min per chromosome and generated a mere 105 MB of output files.We also carried out genome-wide haplotype analysis of the MalariaGEN data in UNPHASED, using a sliding window of 5-SNP haplotypes , we can gain a considerable boost in power compared to single-SNP analysis. Our application to the MalariaGEN data illustrates the utility of our approach in identifying, at genome-wide levels of significance, a region known to harbor a causal SNP that confers protection from malaria.higher power than imputation, but rather to generate a method that captured much of the signal that is captured by imputation, while taking a fraction of the time, computational effort and disc space. Both AI and imputation capitalize on the fact that, although there may be no genotyped SNPs in strong LD with the causal variant, there may be several genotyped SNPs in weak LD with the causal variant that, when considered together, provide equivalent information. However, capturing this signal through genome-wide imputation is computationally demanding, requires careful postimputation filtering and quality control to produce reliable results, and generally requires a relatively high level of expertise in scripting  as well as access to (and familiarity with using) high performance computing facilities such as a multinode Linux cluster, if the analysis is to be performed in a reasonable time. Setting up the required input files and pipelines for analysis  can be quite fiddly and, in our experience, even well-qualified and experienced researchers can make inadvertant mistakes when setting up these analyses, frequently resulting in an analysis, or part of an analysis, needing to be repeated. Finally, the output (and intermediate) files generated by this procedure are often unwieldy, taking up many GB of disc space. Clearly none of these concerns are major obstacles for the numerous well-funded and appropriately staffed research groups and consortia that routinely use imputation, but they do highlight the attraction of a convenient (and much faster) complementary approach such as the AI test proposed here. The question is not, perhaps, so much, whether AI improves over imputation, but whether imputation analysis improves significantly over AI, given its higher computational (including disc space) and manpower cost.One might expect that, in many cases, imputation analysis using an appropriately chosen reference panel could reproduce the signals identified by SnipSnip. Indeed, our goal in developing AI was not to generate a method that necessarily showed any On that note, standard imputation is obviously a much more attractive option if the goal is to generate common panels of SNPs (genotyped or imputed), in order to enable large-scale meta-analyses of studies that have been carried out using different genotyping arrays [Band et al., any available reference panel. The extent to which this will occur in practice is unknown, but it is not inconceivable that there may be situations where this occurs, particularly, for example, in studies of indigenous or isolated populations [Bourgain et al., A drawback of standard imputation is that it, in principal, requires access to an appropriate reference sample; results can be highly sensitive to the reference panel used  and not to the effect of any single untyped SNP. Our simulation results from Scenario 4 suggest that this type of signal may, in fact, be detectable through standard imputation, although this comes with a higher cost in terms of the probability of making a type I error. Although methods for genome-wide haplotype analysis exist [Allen and Satten, Our method has some similarities with previously proposed multimarker tests that either directly test sets of adjacent (or nearby) SNPs [Chapman et al., conditional on the inclusion of another in the regression model). Such conditional regression models are not new in the genetics literature; following a suggestion by Cordell and Clayton [already been detected through single-SNP testing; to our knowledge such strategies have not generally been used within sliding windows on a genome-wide scale, with a view to detecting new signals at additional genomic locations (over and above those locations detected through single-SNP testing), as proposed here.Our proposed method makes use of conditional regression models  that takes only a fraction of the time/computational complexity/disc space of these more complex methods, and may be used with standard GWAS data, without any requirement for additional reference samples, to identify genetic regions of interest. Software implementing our method is freely available from


Brassica napus breeding, traits related to commercial success are of highest importance for plant breeders. However, such traits can only be assessed in an advanced developmental stage. Molecular markers genetically linked to such traits have the potential to accelerate the breeding process of B. napus by marker-assisted selection. Therefore, the objectives of this study were to identify (i) genome regions associated with the examined agronomic and seed quality traits, (ii) the interrelationship of population structure and the detected associations, and (iii) candidate genes for the revealed associations. The diversity set used in this study consisted of 405 B. napus inbred lines which were genotyped using a 6K single nucleotide polymorphism (SNP) array and phenotyped for agronomic and seed quality traits in field trials. In a genome-wide association study, we detected a total of 112 associations between SNPs and the seed quality traits as well as 46 SNP-trait associations for the agronomic traits with a P < 1.28e-05 (Bonferroni correction of α = 0.05) for the inbreds of the spring and winter trial. For the seed quality traits, a single SNP-sulfur concentration in seeds (SUL) association explained up to 67.3% of the phenotypic variance, whereas for the agronomic traits, a single SNP-blossom color (BLC) association explained up to 30.2% of the phenotypic variance. In a basic local alignment search tool (BLAST) search within a distance of 2.5 Mbp around these SNP-trait associations, 62 hits of potential candidate genes with a BLAST-score of ≥100 and a sequence identity of ≥70% to A. thaliana or B. rapa could be found for the agronomic SNP-trait associations and 187 hits of potential candidate genes for the seed quality SNP-trait associations.In  Brassica napus breeding, traits related to commercial success are of highest importance  markers which were physically linked to candidate genes for glucosinolate biosynthesis in Arabidopsis thaliana, to be associated with variation of the seed glucosinolate content in B. napus. However, the results of Linkage disequilibrium (LD) analyses suggested that the number of such SSR-markers is at the lower end of what is required to have a high power to detect marker-phenotype associations for seed quality traits in rapeseed  in our large-size worldwide diversity set of 405 The objectives of our study were to identify (i) genome regions associated with the examined agronomic and seed quality traits, (ii) the interrelationship of population structure and the detected associations, and (iii) candidate genes for the revealed associations.A subset of 405 rapeseed inbreds from the diversity set examined by Bus et al.  was usedB. napus diversity set was evaluated in field experiments for several agronomic traits, and the harvested seeds were analyzed by near infrared reflectance spectroscopy (NIRS) to extract the seed quality parameters MOI, OIL, PRT, GSL, SUL, OLA, LIA, and ERA according to the standard protocol of VDLUFA and the parameters NDF, ADF, and ADL according to Wittkop et al. . As described in detail in Körber et al.  between the reference sequences of potential A. thaliana as well as B. rapa genes and the reference sequences of B. napus (v4.1)  search  of each genotype-trait combination, which were the basis for all further analyses, were calculated for the agronomic and seed quality traits from the winter trial using model (1) and the spring trial using model (2):yijm was the observation of the ith genotype in the mth block at the jth location, μ an intercept term, gi the genotypic effect of the ith genotype, lj the effect of the jth location, bjm the effect of the mth block at the jth location, eijm the residual, yijkm the observation of the ith genotype in the mth block of the kth replication at the jth location, gi*lj the interaction effect of the ith genotype and the jth location, rjk the effect of the kth replicate at the jth location, bjkm the effect of the mth block in the kth replicate of the jth location, and eijkm the residual.The adjusted entry means  was calculated, where linked loci were defined as loci located on the same chromosome, and plotted against the physical distance in megabase pairs. The overall decay of LD was evaluated by nonlinear regression of r2 according to Hill and Weir  estimates between all inbreds and the WR-MCLUST groups 1–2 were calculated according to Wright  = 2KσKij between inbreds i and j were calculated based on the above mentioned SSR markers according to:Sij was the proportion of marker loci with shared variants between inbreds i and j and T the average probability that a variant from one parent of inbred i and a variant from one parent of inbred j are alike in state, given that they are not identical by descent ] SNP-trait associations for 12 of the 15 examined seed quality traits for the 184 B. napus inbreds of the spring trial. The SNP-seed quality trait associations explained individually from 0.0 to 63.1% of the phenotypic variance. For the 12 seed quality traits, between 1 and 21 SNPs were identified to be significantly associated with a single seed quality trait. These associations explained, on average, in a simultaneous fit 38.9% of the phenotypic variance for a single seed quality trait with a range from 11.7 to 87.2% ] associated with a single seed quality trait. The identified loci explained individually from 0.0 to 67.3% of the phenotypic variance. The SNP-trait associations explained, on average, in a simultaneous fit 35.2% of the phenotypic variance for a single trait with a range of 9.8–76.7%  for their whole collection and within 1.2 cM for their 00İ winter types. The extent of LD in the collection of Delourme et al. (B. napus genome on average an around ten times more rapidly decayed mean LD for the A-genome (0.25–0.30 Mbp) than for the C-genome (2.00–2.50 Mbp). Due to the variation within the decay of LD between B. napus subgroups and even between chromosomes, potential candidate genes for SNP-trait associations were searched in our study 2.5 Mbp up- and downstream of each association.In our s et al.  estimatee et al.  varied de et al.  identifiB. napus which is on average one SNP every 0.7 cM expecting that the B. napus genome has a length of ~2500 cM , a total of 96.8% of the adjacent SNPs on the A genome and 83.0% of the adjacent SNPs on the C genome had a distance smaller than the average range of LD. These results indicate that the SNP marker density of our study is expected to provide a sufficient power to detect SNP-trait associations in the B. napus diversity set.In our study, 1577 SNPs mapped to the A genome, whereas 1889 SNPs mapped to the C genome of SNP-trait associations detected for the agronomic traits in the spring and winter trial explained in a simultaneous fit on average 18.1 and 21.9% of the phenotypic variance, respectively Tables . This isThe SNP-trait associations for the seed quality traits in the spring and winter trial explained in a simultaneous fit on average 38.9 and 35.2% of the phenotypic variance, respectively  which causes in case of a loss A. thaliana to flower extremely late could be identified  which is antagonistic with its homologous gene, TERMINAL FLOWER1 (TFL1) and promotes flowering together with the gene LFY could be mapped 2.3 Mbp upstream of BOF.A2.s.1 and 2.1 Mbp downstream of BOF.C2.s.1. Furthermore, the gene GNC which is a GATA transcription factor act upstream from the flowering time regulator SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1/AGL20) to directly repress SOC1 expression and thereby repress flowering  which promotes flowering by a positive-feedback loop with SOC1 at the shoot apex  protein so-called floral identity genes such as APETALA1 (AP1)  gene stably maintains FLOWERING LOCUS C (FLC) repression after a cold treatment , we identified one significant SNP-EOF association EOF.A5.s.1 which explained 13.9% of the phenotypic variance Figure . The latph3.3 for plant height which was detected in a DH population on chromosome A3 at 36.5 cM  gene  of A. thaliana which catalyzes the condensation step of the first three elongation cycles of the Glucosinolate biosynthesis pathway  as well as next to GSL.A9.w.4 the HIGH ALIPHATIC GLUCOSINOLATE 1 (HAG1) gene  which is a positive regulator of aliphatic methionine-derived glucosinolates  which are a group of sulfur-rich secondary metabolites, and the sulfur concentration (SUL), were tightly positive correlated between the trait pairs (Figures We detected 34 SNP-trait associations which co-localized between two or more different traits Figure . For theGSL-SUL we identified by a BLAST-search the candidate genes MAM1, MAM3/IMS2, SUR1, CYP79B2, and CYP79B3 (Tables GSL-SUL are in good accordance with the fact that glucosinolates may represent up to 30% of the total sulfur content of plant organs (Falk et al., GSL-SUL associations suggested pleiotropic effects or might be due to linkage between the underlying genes, because the extent of LD decays over distances of 677 kb in the B. napus diversity set in this study. However, to answer this question conclusively additional approaches like RNA-seq analysis or high resolution fine mapping in segregating populations will be necessary.For several co-localizing SNPs associated with the trait pair 3 Tables –S6. Thes3 Tables . These cNK, AB, and JL performed the statistical and bioinformatic analyses. IP provided the 6K array data. BW and RS carried out most of the field experiments. NK drafted the manuscript. BS designed and supervised the project. All authors read and approved the final manuscript.This research was funded by the Deutsche Forschungsgemeinschaft and the Max Planck Society.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the post-GWAS (Genome-Wide Association Scan) era, the interpretation of GWAS results is crucial to screen for highly relevant phenotype-genotype association pairs. Based on the single genotype-phenotype association test and a pathway enrichment analysis, we propose a Metabolite-pathway-based Phenome-Wide Association Scan (M-PheWAS) to analyze the key metabolite-SNP pairs in rice and determine the regulatory relationship by assessing similarities in the changes of enzymes and downstream products in a pathway. Two SNPs, sf0315305925 and sf0315308337, were selected using this approach, and their molecular function and regulatory relationship with Enzyme EC:5.5.1.6 and with flavonoids, a significant downstream regulatory metabolite product, were demonstrated. Moreover, a total of 105 crucial SNPs were screened using M-PheWAS, which may be important for metabolite associations. Since the publication of the sequencing data of the human genome  may help identify gene-disease associations. Denny et al.  proposedy et al.  applied y et al.  developeAlthough PheWAS is also subject to disadvantages, such as a lack of EMR data and population inconsistencies, which affect the ability to validate findings, PheWAS hopefully offers a method to explain GWAS data  to discover currently unknown relationships in vast amounts of GWAS data. PheWAS analyzes the association of phenotypes (metabolites) and genotypes and uses pathway-based approaches to identify metabolites and enzymes that are enriched in certain pathways. M-PheWAS outputs a smaller group of SNPs than previous GWAS approaches. The molecular mechanism relating genotypes to phenotypes is then interpreted based on systematic omics data. Finally, 273 SNP-target gene pairs referring to 105 unique SNPs were obtained from evidence based on 74 gene-EC pair and 52 unique enzyme matches identified by pathway enrichment.Oryza sativa, including 200 varieties from China, 132 lines from the International Rice Molecular Breeding Program, and 148 varieties from the US Department of Agriculture rice gene bank. These datasets primarily consisted of two subspecies of rice: indica and japonica .For screened SNPs, the target gene information was obtained from a search for expressed quantitative trait loci within 210 recombinant inbred lines, which detected 13,647 eQTLs for 10,725 e-traits .Furthermore, the eQTL data obtained by Wang et al.  were utiAs shown in the workflow diagram Figure , the M-PInitially, a Wilcoxon rank sum test (with Bonferroni adjustment) was used to study the genotype-phenotype associations for each genotype . This approach yielded a group of strongly associated metabolites for each SNP. For QC, we checked each SNP-metabolite relationship and deleted SNP-metabolite association pairs that contained the unknown metabolites based on a mass spectrum analysis.Each SNP that passed the single SNP-metabolite analysis and QC was analyzed in parallel using two methods. First, a pathway enrichment analysis was conducted to determine the related Upper Tier Metabolite Pathway (UTMP). Second, eQTL information was used to direct SNPs to target genes and enzymes. The identified UTMP pathway was then searched for targeted enzymes upstream of the target metabolite. The consistency of the enzyme level and downstream metabolite level was checked to verify the effect of the genotype due to SNP variations.A non-parametric test is suitable for identifying associations in metabolite expression data. An observation of the data structure did not indicate underlying consistent distributions in expression values. As shown in Figure p-value of 0.01245. Similarly, we predict that the proportion of flavonoid among unknown metabolites is also high.Second, the significance of proportion of flavonoids was analyzed. Because the proportion of flavonoids in the metabolites of the raw data was high, we used a binomial test to confirm that the metabolites of flavonoids identified by M-PheWAS were non-randomly distributed. The raw dataset contained 127 flavonoid compounds of a total of 840 metabolites. Furthermore, seven classes of metabolites are known for the SNP sf0315308337, including four flavonoids identified by M-PheWAS. We used a binomial test to assess the significance, which yielded a p-value of 0.01245. Thus, the flavonoid metabolite pathway was then considered the key UTMP pathway, .The SNP-metabolite association database from GWAS consists of 2947 SNPs and 840 metabolites  that matched the first three parts of an EC number were defined as a “general match.” Metabolites that clustered in the UTMP pathway clustered into an upper tier class of metabolites. If this class is the same as the enriched class of metabolites from the SNP-metabolite pair, a general match is obtained.The full results of the general match are shown in Table Exact matches were conducted to search for consistencies in metabolites in a parallel analysis. After implementing the M-PheWAS strategy, an exactly matching metabolite was identified for SNP sf0315305925 and sf0315308337. Specifically, SNP sf0315305925 corresponds to seven metabolites, i.e., Lupulin A, Tricin 4′-O- ether 5-O-hexoside, Naringenin (in KEGG osa00941), Apigenin 5-O-glucoside, Naringenin O-malonylhexoside, DL-alpha, epsilon-Diaminopimelic acid, and Tricin 4′-O-(B-guaiacylglyceryl) ether 5-O-hexosyl-O-hexoside, whereas SNP sf0315308337 is associated with seven other metabolites, including Lupulin A, Naringenin (in KEGG osa00941), Tricin 4′-O- ether 5-O-hexoside, DL-alpha epsilon-Diaminopimelic acid, di-C,C-pentosyl-luteolin, Sinapic acid, and a Tricin 4′-O- ether derivative. These 14 pairs all passed the single SNP-metabolite association scan. Moreover, these metabolites mostly originate from a UTMP flavonoid synthesis process . Furthermore, this SNP was mapped to the same 40 target genes Table . Among thttp://www.uniprot.org/uniprot/Q84T92).As an exact match, SNP sf0315305925 and sf0315308337 are important SNPs, and these two SNPs share the 40 same target genes. One of these genes encodes the enzyme EC:5.5.1.6, which has been described as “Experimental evidence at transcript level.” This description indicates that the existence of a protein has not been strictly proven but that expression data  indicate the existence of a transcript  and the Fundamental Research Funds for the Central Universities (Project No. 2013PY120).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Arabidopsis thaliana using quantitative trait locus (QTL) analysis.Perception and transduction of temperature changes result in altered growth enabling plants to adapt to increased ambient temperature. While PHYTOCHROME-INTERACTING FACTOR4 (PIF4) has been identified as a major ambient temperature signaling hub, its upstream regulation seems complex and is poorly understood. Here, we exploited natural variation for thermo-responsive growth in GIRAFFE2.1, a major QTL explaining ~18 % of the phenotypic variation for temperature-induced hypocotyl elongation in the Bay-0 x Sha recombinant inbred line population. Transgenic complementation demonstrated that allelic variation in the circadian clock regulator EARLY FLOWERING3 (ELF3) is underlying this QTL. The source of variation could be allocated to a single nucleotide polymorphism in the ELF3 coding region, resulting in differential expression of PIF4 and its target genes, likely causing the observed natural variation in thermo-responsive growth.We identified PIF4 expression during nightly growing periods seems to be affected by a coding sequence quantitative trait nucleotide that confers a selective advantage in certain environments. In addition, natural ELF3 alleles seem to differentially integrate temperature and photoperiod information to induce architectural changes. Thus, ELF3 emerges as an essential coordinator of growth and development in response to diverse environmental cues and implicates ELF3 as an important target of adaptation.In combination with other recent studies, this work establishes the role of ELF3 in the ambient temperature signaling network. Natural variation of ELF3-mediated gating of The online version of this article (doi:10.1186/s12870-015-0566-6) contains supplementary material, which is available to authorized users. This demonstrated that allelic variation in ELF3 affects TIHE in the Bay × Sha population.Consistent with a role of ELF3 in thermomorphogenesis, we found that . et al.  that conome Fig. , which eELF3 variants display several promoter polymorphisms  are significantly higher than in elf3-4 [ProBay-0:ELF3Bay-0] , at 28 °C and the absence of a QTL at 20 °C . Control and 28 °C seedlings were subsequently harvested 4 h after the shift. We found that loss of res Fig. . At 20 °els Fig. , demonstels Fig. . At 28 °-0] Fig. , again r-0] Fig. . ImportaSMALL AUXIN UPREGULATED RNA (SAUR) family or ARABIDOPSIS THALIANA HOMEOBOX PROTEIN2 (AtHB2) have previously been shown to be activated by PIF4 in a temperature-dependent manner [elf3-4 [ProBay-0:ELF3Bay(A362V)] were significantly higher than those in elf3-4 [ProBay-0:ELF3Bay-0]. This strongly suggested that the A362V SNP causes natural variation of temperature-induced PIF4 expression levels directly resulting in alterations of auxin-mediated cell elongation.We then connected this scenario to the level of PIF4 target genes responsible for cell elongation. Genes involved in cell elongation such as the t manner , 24, 25.ELF3 as a negative regulator of ambient temperature signaling. Physiological and gene expression data indicate that ELF3 protein might be involved in down-regulating transcript levels of the major ambient temperature signaling hub PIF4, and thereby affect thermo-responsive growth. Transgenic complementation assays furthermore demonstrated that a nonsynonymous SNP between the natural accessions Bay-0 and Sha significantly affects the ability of ELF3 to regulate temperature-induced PIF4 transcript levels, its target genes, and hypocotyl elongation.Our findings shown here illustrate the power of natural variation approaches and support ELF3 polymorphisms seem to contribute to hypocotyl elongation in response to different temperatures. Box et al. [ELF3 are likely responsible for TIHE differences in natural accessions. The authors presented convincing evidence that warmth relieves the gating of growth by ELF3 at night. Specifically, ELF3 gating of transcriptional targets responds rapidly to changes in temperature by temperature-dependent binding of ELF3 to target promoters including PIF4. Together with Box et al.’s [In general, different types of polymorphisms, such as nonsynonymous SNPs or expression level polymorphisms, can contribute to the expression of a particular trait . In linex et al.  recentlyx et al. , and eleet al.’s  non-tranELF3 QTLs in both studies were identified in different photoperiods. ELF3 polymorphisms causal for variation within the MAGIC population were identified under short-day conditions. In contrast, our study identified the ELF3 polymorphism under a long-day photoperiod and subsequent analysis of HIF lines showed long-day specificity , Sha (236AV), heterogeneous inbred families , Bay-0 × Sha population (33RV). Lines used for complementation assays have been described in Anwer et al. . Natural2O for 3 days at 4 °C before sowing. Seedlings were germinated and grown under sterile conditions and the indicated temperatures on Arabidopsis thaliana solution (ATS) medium [−2 s−1 white light and a long-day photoperiod (16/8) unless stated otherwise. Hypocotyl length was measured using the RootDetection software package (http://www.labutils.de/). Petiole length and leaf expansion were measured on 12 days-old seedlings using ImageJ. All growth assays including phenotyping of the Bay-0 × Sha population have been repeated at least three times with similar results of which one representative data set is shown.Seeds were surface-sterilized and kept in deionized H) medium . HypocotDescribed QTL mapping was applied using Haley-Knott Regression  at 1 cM −2 s−1), far-red , or blue light  was generated with an LED SNAP-LITE . Green light  was generated from a filtered fluorescent light bulb. Peak wavelength verification and light intensity measurements were done with a SPEC-UV/PAR spectroradiometer . Plant images were taken after plants were incubated in darkness or under light for the indicated time. Hypocotyl length was measured using ImageJ software.Seeds were surface-sterilized, stratified at 4 °C for 2 days, and dispersed on 0.8 % agar (w/v) Murashige and Skoog medium. Monochromatic red  were performed as previously described [Surface-sterilized seeds were placed on ATS medium and grown for 7 days under long-day photoperiod (16/8) and 100 μmol mR (www.r-project.org). To assess differences in the treatment response among different genotypes, we used the “genotype x treatment” interaction results of a two-way ANOVA analysis performed on the absolute values in R. Statistically significant differences were highlighted in box plots showing the relative length data.Statistical differences among absolute length or expression values were analyzed by one-way ANOVA using default parameters in


Lateolabrax maculatus) is a non-model species of ecological and commercial importance and widely distributed in northwestern Pacific. A total of 22 648 SNPs was discovered across the genome of L. maculatus by paired-end sequencing of restriction-site associated DNA (RAD-PE) for 30 individuals from two populations. The nucleotide diversity (π) for each population was 0.0028±0.0001 in Dandong and 0.0018±0.0001 in Beihai, respectively. Shallow but significant genetic differentiation was detected between the two populations analyzed by using both the whole data set  and the putatively neutral SNPs . However, the two populations were highly differentiated based on the putatively adaptive SNPs . Moreover, a total of 356 SNPs representing 298 unique loci were detected as outliers putatively under divergent selection by FST-based outlier tests as implemented in BAYESCAN and LOSITAN. Functional annotation of the contigs containing putatively adaptive SNPs yielded hits for 22 of 55 (40%) significant BLASTX matches. Candidate genes for local selection constituted a wide array of functions, including binding, catalytic and metabolic activities, etc. The analyses with the SNPs developed in the present study highlighted the importance of genome-wide genetic variation for inference of population structure and local adaptation in L. maculatus.Next-generation sequencing and the collection of genome-wide single-nucleotide polymorphisms (SNPs) allow identifying fine-scale population genetic structure and genomic regions under selection. The spotted sea bass ( Considering the ongoing worldwide depletion of most marine populations , accuratEntosphenus tridentatus [Salmo salar [Anguilla anguilla [Gasterosteus aculeatus [Oncorhynchus nerka [Oncorhynchus tshawytscha [Gadus morhua [Scophthalmus maximus [Genome-wide genetic variations can provide reliable estimates of population demographic parameters –9 and iddentatus ), Atlantmo salar ) and Euranguilla ). Moreovculeatus ), Sockeyus nerka ), Chinooawytscha ), Atlants morhua ) and tur maximus ).In recent years, advances in high-throughput reduced-representation genome sequencing (RRGS) technology have provided an unprecedented opportunity to conduct population genomic studies in both model and non-model organisms. Restriction-site associated DNA tag sequencing (RAD-seq) is a powerful RRGS protocol , 22. RADLateolabrax maculatus, belongs to the family Moronidae (Perciformes) [Lateolabrax maculatus is distinguished newly described species from the Japanese sea bass, L. japonicus and is characterized by many clear black dots on lateral body region [L. maculatus is a species of high commercial value and mainly found in moving water of inshore rocky reefs. Population decline of L. maculatus has been recorded due to overfishing and habitat deterioration resulting from anthropogenic activities [L. maculatus [L. maculatus may experience divergent selection in heterogenous environments. Furthermore, naturally spawned fry of L. maculatus were captured from coasts of China, Korea, and Taiwan and transported to different regions of China, Japan and Korea for cage cultivation in the past three decades [The spotted sea bass, iformes) , 29. Laty region . It is wy region , 32. L. tivities , 34. Altaculatus , fine-sc decades , 36. TheL. maculatus by paired-end sequencing of restriction-site associated DNA (RAD-PE) for 30 individuals collected from two populations across its distribution range in China. The SNPs were then used to evaluate the levels of genetic diversity and population divergence between the two populations. Outlier tests were also conducted to detect loci under putative selection. Finally, function annotation of the outlier loci was performed to determine whether the potentially adaptive loci localized to known genes or conserved genomic regions.In the present study, we generated a novel resource of genome-wide SNPs for Lateolabrax maculatus is not protected by Chinese law. No fishing license was required for collection of samples from all locations. It is a commercially harvested species in China. The fish were collected by trawling by local fishermen for commercial purposes and were already dead when collected. No of the authors was involved in the collection of the fish. Animal Ethics Committee approval was not needed because no handing of live animals was involved.The field studies did not involve any endangered or protected species. Ta = 26.5°C, Ta, average annual sea surface temperature) and the other from Dandong, Liaoning Province . Muscle tissue samples of a total of 30 individuals (16 from Beihai and 14 from Dandong) were collected and preserved in 96% ethanol for DNA extraction. Genomic DNA was extracted from ~100 mg muscle tissue using a standard phenol-chloroform extraction protocol [TM 2000 (Thermo Scientific) spectrophotometer and a Qubit®2.0 fluorometric quantitation. The optimal concentration was no less than 50 ng/μL, and the total DNA recovered was more than 2 μg.Samples were collected from two separate locations of heterogenous environments in May 2014: one from coast of Beihai, Guangxi Province . Then, Illumina P1 adapter containing individual-specific index (6 bp) was ligated to the digested products. The adapter-ligated DNA was sheared and separated by electrophoresis on a 2% agarose gel. Fragments in the 200–600 bp size range were collected using a MinElute Gel Extraction Kit . After treating double-stranded DNA ends with blunt-ending enzymes and adding 3’-adenine over-hangs, a modified Illumina P2 adapter was ligated. Finally, the libraries were enriched by high-fidelity PCR amplification (8–12 cycles). RADs for each individual were sequenced on an Illumina HiSeqTM 2500 sequencing platform at Novegene in Beijing, China. Due to the unavailability of existing genomic information for the diploid L. maculatus, one individual was deep sequenced (approximately 32× coverage) to assemble reliable contigs as a reference assembly for downstream alignment and SNP calling.RAD-PE libraries were prepared using the protocol outlined by Baird et al.  and Ette [et al. . GenomicEcoRI motif (^AATTC).RAD sequence reads obtained from the Illumina runs were sorted according to individual-specific index sequences. To avoid low-quality reads with artificial bias, raw reads were filtered using the following criteria: 1) removing reads with adapter contamination; 2) reads with ≥ 10% unidentified nucleotides were removed; 3) reads with > 50% bases having phred quality < 5 were removed; 4) putative duplication reads were removed to reduce the impact of PCR artifacts on allele frequency estimation; 5) reads were checked for presence of the partial For the reference individual, the remaining first reads with restriction enzyme recognition site after quality control were clustered into RAD cluster tags using cd-hit-est . A maximSAMtools package [FIS values between –0.3 and 0.3 and observed heterozygosity values < 0.5 were retained for subsequent analyses [Allowing one permissible alignment per pair read, quality-filtered reads of each individual were aligned to the assembled reference sequences using BWA (version 0.6.2) with default parameters  . Followi package . SNPs weanalyses ; (vi) onFST-based outlier tests were applied to identify loci that showed divergent patterns of differentiation compared to neutral expectations, and therefore have been potentially affected by selection. First, polymorphic loci were screened for outliers using the coalescent method of Beaumont & Nichols [FST of 0.055 and simulated FST of 0.052. Second, outlier SNPs were also detected by using the Bayesian simulation approach of Beaumont & Balding [Two  Nichols  as imple Nichols . LOSITAN Balding  as imple Balding . BAYESCAtools package [HO) and expected (HE) heterozygosity for each population. The loci with minimum depth of 4 were generated using ref_map.pl in Stacks version 1.32 [FST values between populations based on different datasets were calculated using ARLEQUIN version 3.5.1.3 [The VCF package  was usedion 1.32 . Then thion 1.32 . Furtherion 1.32 . NetViewion 1.32 . The VCFion 1.32 . FST val 3.5.1.3 , and sigGeneClass version 2.0 [Assignment power of four data sets was evaluated with leave-one-out tests in sion 2.0  to compaBlast2Go suite (http://www.blast2go.com/b2ghome) [Contigs containing the outlier SNPs were used as queries in nucleotide searches with BLASTX against the non-redundant protein database of bony fishes at the National Center for Biotechnology Information (NCBI) website . In case of multiple hits, the best match was selected for each outlier containing contig. Gene ontology (GO) functional annotation of the contigs with significant BLASTX hits were obtained using b2ghome) , which cEcoRI motif (AATTC), 19.50 million reads were finally retained, presenting a clean duplication rate of 11.36% and digestion ratio of 93.35%, respectively . Overall, the data showed a high phred quality , a stable GC content ranging from 38.67% to 41.7% and a high digestion rate from 76.62% to 98.25%. The Raw RAD-seq reads pairs have been deposited in the Sequence Read Archive database under Accession no. SRP072011.RAD-PE sequencing generated 24.29 million raw read pairs (6.07 G (gigabases) raw data) for the reference individual. After quality filtering, 23.57 million clean read pairs (5.89 G clean data) with the effective rate of 97.03% were retained. After removal of PCR duplicates and only keeping read pairs with the partial ectively . For theectively . After qN50 size of 509 bp and a GC content of 40.11%  with an f 40.11% . After tf 40.11% .FIS values between –0.3 and 0.3 and HO < 0.5 in both populations, 42 733 SNPs were finally retained  was 0.3030±0.0945 in Dandong and 0.2807±0.0422 in Beihai. The value of observed heterozygosity (HO) was 0.3093±0.1246 in Dandong and 0.2781±0.0535 in Beihai. The nucleotide diversity (π) was higher in Dandong (0.0028±0.0001) than in Beihai (0.0018±0.0001). FST values of each SNP varied widely across loci with average of 0.0357, ranging from −0.0535 to 1.For all SNPs, the value of expected heterozygosity  and neutral SNPs . As expected, FST estimation based on the outlier SNPs yielded a much larger value .To remove linkage disequilibrium, only one SNP was randomly chosen from each RAD tag for subsequent population genetic analyses, which produced a final data set of 22 648 SNPs. Admixture results based on all three different SNP data sets  showed that individuals from Dandong and Beihai were clearly separated from each other . BesidesAssignment accuracy was 100% by using both the complete outlier data set and the equal number of neutral data set. The accuracy based on 20 randomly chosen outlier SNPs,(≥ 93.8%) was higher than that based on 20 randomly chosen neutral SNPs (≥ 78.6%) .BLASTX analysis of the 298 contigs harboring outlier SNPs against various bony fish genomes resulted in significant hits to 40 fish species. BLASTX similarity results showed that 55 of the 298 contigs corresponded to known proteins in the UniProt database . Functional categorization of the annotated sequences involved in binding and recognition, catalytic and metabolic activities, etc . GO funcL. maculatus using RAD-PE method. To our knowledge, this was the first report about the generation of such a large panel of novel SNPs for L. maculatus. Furthermore, we highlighted the potential advantages of the genome-wide SNPs for inference of population divergence and candidate adaptive markers detection of L. maculatus.In present study, we developed a genome-wide SNP resource of L. japonicus, the limited number of available molecular markers has constrained population genetic studies of L. maculatus in the past 10 years. Only 37 polymorphic microsatellites were developed [L. maculatus was also available in GenBank [L. maculatus were based on a handful of microsatellite markers, mitochondrial sequence analysis, and random amplified polymorphic DNA (RAPD) markers, which obtained inconsistent results [As a newly described species from eveloped , 58. In  GenBank . Most pr results , 60, 61.L. maculatus, the contigs generated using paired-end RAD data provided sufficient flanking region around SNPs for design of high-throughput SNP genotyping arrays. This approach has been proved successful for SNP assay design simultaneous with SNP discovery in several studies [The transition/transversion ratio was 1.59, which suggested a small influence of sequencing error on calling SNP. Similar transition/transversion ratios have also been observed in the great tit (1.7:1 ) and the studies , 63, 64.et al. [L. maculatus generally showed higher nucleotide diversities than southern ones, with the lowest one found in Beihai. All these results was consistent with the hypotheses that the glacial refugium of L. maculatus was located in the basin of East China Sea and the genetic diversity is expected to be higher in the ancestral population than in the derived population. Our genome-wide SNP data set demonstrated high power in resolving population genetic structure of L. maculatus. Both the Structure and NetView P analyses with the whole SNP dataset revealed a clear separation of distinct genetic clusters corresponding to the two geographic populations. However, no genealogical clustering that corresponded to sampling localities was detected by using mtDNA control region sequences [L. maculatus.The nucleotide diversity was 0.0028 in Dandong and 0.0018 in Beihai. Similar level of variations was identified in the other marine species, such as European eel (π = 0.00529) and small yellow croaker (π = 0.00105) , 65. Theet al.  found thequences . Previouequences –68. The equences –15, whicL. maculatus. In the past three decades, naturally spawned fry of L. maculatus were captured from coasts of China, Korea, and Taiwan and distributed to different regions of China, Japan and Korea for cage cultivation [L. maculatus imported from China has been reported in various localities around western Japan, where the spotted sea bass is vigorously cultured [L. maculatus to population-of-origin in aquaculture using naturally spawned fry, which would facilitate the scientific management and sustainable exploitation of the genetic resource of natural populations of L. maculatus. Since the two populations analyzed in the present study were geographically distant and genetically differentiated, screening of further samples from geographically close localities will be required to assess the accuracy reported in this study. Non-neutral markers can be useful for individual and composition assignment [L. maculatus. Outlier loci have also been proved successful for individual and compositional assignment in various fishes. For example, Larson et al. [Oncorhynchus tshawytscha) can be used to create high-resolution panels for genetic monitoring and population assignment.In the present study, both the putative outlier loci and neutral loci were powerful in population assignment of tivation , 36. Esccultured . These nsignment . Indeed,n et al.  demonstrL. maculatus were located in unknown proteins and non-coding genomic regions influenced by selection through genetic hitchhiking [HERC1), which is involved in the ubiquitin mediated proteolysis. contig_1782285  is a gene involved in pathways for glycerolipid metabolism and glycerophospholipid metabolism. Contig_612117  is a key transcriptional coregulator in adipose tissue, which works with several different partner proteins to regulate the development of both white and brown adipocytes [LMF2) may be required for maturation and transport of active lipoprotein lipase through the secretary pathway. Contig_2602294  is an important regulator of the cellular antiviral response [ARNTL2), which is an essential component within the clock gene regulatory network. Contig_628717, contig_2583004, contig_432419, and contig_525464 were annotated as zinc finger protein gene (ZNF), which was reported to play broad-spectrum cellular functions in eukaryotic cells biology [L. maculatus populations were collected from the Yellow Sea and the South China Sea with highly heterogeneous environments. Indeed, variance in ecologically important life history traits such as growth rate, size at maturity and spawning season have been observed among populations of L. maculatus [L. maculatus re-colonized the extensive continental shelf of the China sea from glacial refugium in the East China Sea after the Last Glacial Maximum Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 File(7Z)Click here for additional data file.S2 File(VCF)Click here for additional data file.S3 File(VCF)Click here for additional data file.S4 File(VCF)Click here for additional data file.


Our aim was to compare the accuracy of family- or disease-specific targeted haplotyping and direct mutation-detection strategies with the accuracy of genome-wide mapping of the parental origin of each chromosome, or karyomapping, by single-nucleotide polymorphism genotyping of the parents, a close relative of known disease status, and the embryo cell(s) used for preimplantation genetic diagnosis of single-gene defects in a single cell or small numbers of cells biopsied from human embryos following in vitro fertilization.Genomic DNA and whole-genome amplification products from embryo samples, which were previously diagnosed by targeted haplotyping, were genotyped for single-nucleotide polymorphisms genome-wide detection and retrospectively analyzed blind by karyomapping.Single-nucleotide polymorphism genotyping and karyomapping were successful in 213/218 (97.7%) samples from 44 preimplantation genetic diagnosis cycles for 25 single-gene defects with various modes of inheritance distributed widely across the genome. Karyomapping was concordant with targeted haplotyping in 208 (97.7%) samples, and the five nonconcordant samples were all in consanguineous regions with limited or inconsistent haplotyping results.Genome-wide karyomapping is highly accurate and facilitates analysis of the inheritance of almost any single-gene defect, or any combination of loci, at the single-cell level, greatly expanding the range of conditions for which preimplantation genetic diagnosis can be offered clinically without the need for customized test development. This is because DNA amplification is required for mutation detection and in a low, but significant, proportion of single cells, one of the parental alleles randomly fails to amplify. This can make heterozygous loci appear homozygous. This process is known as allele dropout (ADO) and can result in diagnostic errors. Moreover, the extreme sensitivity of the amplification and detection methods used makes them susceptible to contamination. To avoid these potential causes of diagnostic errors, therefore, the current standard of practice is to combine targeted haplotyping of the gene locus by multiplex amplification of one or more closely linked or intragenic informative polymorphic markers with or without direct mutation detection.4 Because ADO occurs independently at different loci, the analysis of multiple markers and the mutation itself should allow the detection of any ADO, whereas the detection of unrelated repeat alleles indicates that the sample was contaminated.Preimplantation genetic diagnosis (PGD), following in vitro fertilization (IVF), preimplantation embryo biopsy, and genetic analysis of a single cell or small numbers of cells, is now clinically well established as an alternative to invasive methods of prenatal diagnosis for couples at risk of a range of single-gene defects (SGDs) and chromosome abnormalities.6 Nevertheless, both of these approaches are labor intensive, time consuming, and costly to develop and therefore limit testing to specialist laboratories.Developing family-specific tests requires the investigation of candidate markers, most often short tandem repeats (STRs), within the family, to ascertain which markers are informative , followed by optimization in single cells. A similar approach is preimplantation genetic haplotyping, in which a generic disease-specific test is developed with a sufficiently large panel of potentially informative markers such that at least two or more markers should be informative in a majority of couples.7 By genotyping the parents at several hundred thousand SNPs throughout the genome on a SNP microarray, a dense set of informative SNP markers are indentified for each of the four parental chromosomes. The phase of the alleles for each informative SNP locus along each chromosome and linkage of the risk alleles with the parental chromosomes are then established by reference to the genotype of the relative of known disease status. The parental origin of each chromosome, or the chromosome segment in recombinant chromosomes, in the embryo cell is then ascertained by comparison with the genotype of the reference. Furthermore, to eliminate errors caused by ADO at the single-cell level, the possible genotype calls at informative SNP loci in the embryo are subcategorized and phasing is based primarily on those that could not have arisen by ADO.As an alternative, a comprehensive linkage-based test has been proposed recently in which single-nucleotide polymorphism (SNP) genotyping of the parents, a close relative of known disease status, and the embryo cell(s), following whole-genome amplification, are used for genome-wide mapping, or karyomapping, of the parental origin of each chromosome in the embryo.7 Chromosome aneuploidy is a major cause of IVF failure, miscarriage, and, rarely, affected live births.8 Furthermore, several randomized controlled trials have shown significant improvement in rates of pregnancy and live births following testing for aneuploidy in patients undergoing IVF for infertility.11 The combination of accurate linkage-based diagnosis of SGDs and detection of chromosome aneuploidy may, therefore, improve implantation and rates of healthy live births following transfer of unaffected euploid embryos.The principal advantage of genome-wide karyomapping relative to targeted approaches is that it is applicable to any familial SGD, or any combination of loci, within the chromosome regions covered by informative SNP loci, without the need for development of patient- or disease-specific tests. Another main advantage is that analysis of SNP markers for each parental chromosome also allows for identification of a range of chromosome abnormalities at high resolution, including trisomies of meiotic origin (in which both haplotypes from one parent can be detected in restricted regions of the chromosome) and monosomies/partial deletions .Here, we report the outcome of a multicenter validation study in which parental, reference, and 218 embryo samples from 44 PGD cases were genotyped retrospectively for ~300,000 genome-wide SNP loci in a 24-h protocol compatible with the selection and transfer of unaffected embryos without the need for cryopreservation. Each sample was karyomapped, the disease status of the embryo was analyzed blind, and the results were compared for concordance with the original diagnosis based on targeted haplotyping with direct mutation detection.All of the IVF clinics providing samples from PGD cycles used standard IVF protocols for ovarian stimulation, egg collection, and fertilization in vitro by intracytoplasmic sperm microinjection to avoid possible contamination. The morning after the egg collection and fertilization by intracytoplasmic sperm microinjection, each oocyte was examined for the presence of two pronuclei indicating normal fertilization and then either cultured to the 6- to 10-cell stage on day 3 postfertilization for cleavage-stage biopsy and removal of a single blastomere or cultured to the blastocyst stage on day 5 or day 6 for trophectoderm (multiple-cell) biopsy. The embryo samples were then transferred to polymerase chain reaction (PCR) tubes and sent to one of the participating laboratories for targeted haplotyping and mutation detection. After 24–48 h following the diagnosis and transfer or cryopreservation of any unaffected embryos, an additional single-cell biopsy was performed on day 4 or a further trophectoderm biopsy was performed on day 6 on embryos no longer of therapeutic use, with patients' informed consent. In some cases, the embryos had been cryopreserved and were thawed and biopsied for retrospective analysis. Finally, in one center in which whole-genome amplification is used routinely before conventional analysis, aliquots of the archived DNA products were used.With minor variations among the participating laboratories, whole-genome amplification from single-blastomere or trophectoderm samples was performed by multiple displacement amplification (MDA) following cell lysis and neutralization with a short 2-h incubation. Typically, single blastomeres were washed three times in phosphate-buffered saline  containing 0.1% polyvinylpyrrolidone   and subsequently transferred to a PCR tube containing 2 µl phosphate-buffered saline. The last washing droplet served as a negative embryo control. Blastomere samples were then stored at −20 °C. The sample was brought to an end volume of 4 µl with phosphate-buffered saline, and MDA was performed according to the manufacturer's instructions with a short 2-h incubation .Multiplex fluorescent PCR and capillary electrophoresis were used for analysis of STR markers using protocols based on previously published methods. Direct mutation detection was achieved by either minisequencing or standard sequencing methods. Typically, this involved lysing the biopsied cells by incubation at 65 °C for 10 min in lysis buffer  and 200 mmol/l NaOH) before amplification. Multiplex PCR for the polymorphic markers and the mutation was then carried out using a multiplex PCR kit . The PCR reaction was performed in a final volume of 25–50 μl containing 1× multiplex PCR master mix in 20 mmol/l Tricine (Sigma) and primers for the specific condition . The primer concentrations of the individual protocol in the multiplexed PCRs varied between 0.06 and 1 μmol/l per primer pair, with one of the primers fluorescently labeled. All PCR reactions were performed with an initial activation step of 15-min denaturation at 95 °C. The denaturation–annealing–elongation cycles were conducted as described by the PCR protocol for the specific disorder. The PCR products were diluted 10–20 times, and the labeled amplicons were separated by capillary electrophoresis . Fragment lengths were analyzed using GeneMapper software (Applied Biosystems).For SNP genotyping, 400-ng samples of parental and reference genomic DNA or 8 μl of MDA product from the embryo samples were processed according to the standard protocol, modified to reduce the initial whole-genome amplification and hybridization steps so that genotyping on a 300K SNP bead array  could be completed in less than 24 h, over a period of 2 days. Bead array data were then imported directly into dedicated software for karyomapping . With genomic DNA samples, call rates of >98% were generally achieved. With MDA products from single blastomeres and multiple-cell trophectoderm samples, call rates were significantly lower, in the 75–95% range. However, karyomap analysis of these samples was highly consistent, indicating few genotyping errors (excluding ADO). Samples with call rates of <60% (for euploid samples), indicating failure of amplification, had a high incidence of erroneous heterozygous calls, which prevented reliable karyomap analysis. These samples were excluded from further analysis.7 Briefly, using a child of known disease status as a reference, for example, (i) the parental SNP genotypes were examined to identify all of the informative SNP loci in which one parent was homozygous (AA or BB) and the other heterozygous (AB), (ii) the allele present on only one of the four parental chromosomes was identified, and (iii) each SNP locus (excluding the Y chromosome) in the embryo was phased relative to the reference based on the presence or absence of this allele in the two samples. Finally, (iv) the inheritance of the unaffected or affected genes was ascertained by examining the parental haplotypes in the region of interest, including the gene locus, in comparison with the reference. The use of a grandparent or other relative of one of the parents as a reference allowed the SNP loci to be phased for that parent only and, consequently, karyomapping of that parent's chromosome in the embryo. In addition, for nonsibling references, only those loci in which the reference is homozygous are displayed. This removes any ambiguities from haplotypes present in the reference that were not inherited by the parents and also reduces the amount of available data by ~50% on average.The SNP genotype data from the parents, relative, and embryo samples were used for karyomap analysis as previously described.Table 1). Key SNPs are defined as sample genotypes at informative loci, the phases in which cannot have been altered by ADO, whereas non-key SNPs are those sample genotypes that are most likely to be accurate but will also include any erroneous phasing results caused by ADO. The haploblocks and the position of crossovers in the central haploblock bar were estimated by Hidden Markov modeling using all of the informative SNP data. These haploblocks are used as a guide for phasing—the diagnosis of an embryo is made considering all available SNP information, the call rate of the embryo, and its estimated level of ADO. Finally, “no calls” in the sample are represented by white dots running along the central axis of the haploblock bar.BlueFuse Multi (Illumina) displays the detailed karyomaps for each parental chromosome as two rows of informative SNPs (colored dots) mapped to their physical location  above and below a continuous haploblock bar, all of which are color coded to indicate the parental haplotype. Haplotypes produced by karyomapping are relative to the reference. Blue and orange represent the paternal and maternal haplotypes, respectively, inherited by the reference. Red and green are the paternal and maternal haplotypes, respectively, that were not inherited by the reference. The two rows of informative SNPs correspond to “key” (above) and “non-key” SNPs (below) . For the autosomal dominant conditions, a grandparent of known disease status (n = 21), or in one case, the brother of the “at-risk” parent was used as a reference. In the remaining cases , a child of known disease status was used. All of the corresponding gene loci had large numbers of potentially informative SNPs represented on the bead array in both the 5′ and the 3′ 2-Mb flanking regions (Supplementary Table S1 online). Furthermore, with the exception of myotonic dystrophy type 1, all of the genes also had intragenic SNPs represented (range: 2–78), and together with Duchenne muscular dystrophy, there were 320 intragenic SNPs across the dystrophin gene on the X chromosome.SNP genotyping and karyomapping were successful in 213/218 (97.7%) embryo samples from 44 PGD cycles for 25 different SGDs, with various modes of inheritance and an inherited chromosome microdeletion, distributed widely across the genome —including two trophectoderm biopsies and three single-cell biopsies—failed to be karyomapped because the SNP genotyping call rates were too low , presumably as a consequence of suboptimal or failed whole-genome amplification.Figure 1a. The SNP genotype call rates from the single blastomeres were high, and following karyomapping there was excellent coverage of informative, key, and non-key SNPs across chromosome 11 in each case (except in the pericentromeric regions). In addition, there is general agreement between the key and non-key SNPs, and the haploblock boundaries in successive haploblock regions, with only a few isolated, presumed genotyping errors in the key SNPs and a small proportion of ADOs in the non-key SNPs.Typical karyomaps for three embryos, based on SNP genotyping of single blastomeres following cleavage-stage biopsy and whole-genome amplification for PGD of β-thalassemia, are shown in Supplementary Figure S1 online).All six chromosomes in the three embryos were recombinant with multiple crossovers between the parental haplotypes. The positions of some of these crossovers, however, appear to be identical in all three embryos. This is an artifact of the initial assumption that the chromosomes in the reference, an existing child in this case, are nonrecombinant. Because this will often not be the case, wherever there is a crossover in the reference chromosome, the karyomapping algorithm will add a “common” crossover to all of the other embryos. By contrast, when a grandparent is used as reference to karyomap across two generations, only crossovers in the embryo are identified . Only two key SNPs are present in Embryo 1 in this region, which supports the opposite haplotype for the reference , and hence this was not resolved by the hidden Markov modeling at the single-cell level.HBB) gene locus are clear. However, in a few samples, presumed miscalled key SNPs in the region of the gene locus need to be taken into account. In the case of Embryo 3, there are two key SNP miscalls immediately 5′ to the gene locus and one non-key SNP . In this case, a crude estimate of the probability of a double recombination between the closest flanking key SNPs, consistent with the majority across this region of the chromosome (blue dots), spaced ~415 kb apart is 1.72 × 10−5. This is clearly much lower than the chance of random miscalls in this sample examining the haploblock as a whole. The median spacing of SNPs on the bead array is ~6 kb, and, on average, this results in a median spacing of informative SNPs of 26 kb. There is wide variation, as can be seen in these samples, but only a small minority of intervals exceeds 1–2 Mb (data not shown). In general, the position of recombination events between parental chromosomes is identified with high resolution. In three single-blastomere embryo samples , there was a recombination within the 2-Mb flanking region proximal to the gene in two PGD cases for Huntington disease. The gene locus was phased in all three samples, relying on the presence of informative SNPs proximal to the gene locus.In each embryo sample, although there is a common crossover distal to the gene locus in the paternal chromosome, the paternal and maternal haplotypes for the β-globin . Detailed examination of the STR marker and gene loci in this case showed that it was not possible to find informative STR markers distal to the gene, and the proximal STRs were only semi-informative. The original diagnosis was therefore mainly based on the results of the mutation analysis, which has clearly been affected by ADO in several samples. When the phase of the STR alleles was predicted blindly based on their position, taking into account all four recombinations in the parental chromosomes, there was 100% concordance at the STR level.In 37 PGD cycles, all of the embryos had been unambiguously diagnosed by targeted haplotyping and direct mutation analysis, and blinded karyomap analysis was concordant with the original diagnosis in 156/156 (100%) embryos. In one PGD cycle for Peutz–Jeghers syndrome, however, a recombination between the STR markers was detected along with other presumed ADOs , some or all of the STR markers were only semi-informative, and the results frequently deviated from the expected parental haplotypes. Hence, the original diagnosis was often based mainly on mutation detection. In these cases, karyomapping was concordant in 52/57 (91.2%) embryo samples. However, in five (8.8%) of the single-blastomere samples, the results were nonconcordant. To investigate these discrepancies, the expected STR and mutation site alleles were predicted in a blinded manner, based on the karyomap of each of the embryos. This showed that in two of these nonconcordant samples, there was only a single STR or marker allele that had led to the original diagnosis and that the karyomapping evidence from the many key or non-key SNPs in the region of interest strongly supported the contrary diagnosis . Furthermore, because the STR markers were in general only semi-informative, the strength of evidence in the other samples was similarly weak. The reasons for these nonconcordances therefore need further investigation. Overall, 208/213 (97.7%) embryo samples were completely concordant with the original analysis based on targeted haplotyping and mutation detection.In the remaining seven cycles, which were identified by karyomapping as having extensive regions of consanguinity  with different modes of inheritance, the use of siblings, grandparents , or, in one case, the brother of one of the parents as the reference, karyomapping of single- or multiple-embryo-cell samples was successful and, excluding the consanguineous cases, 100% concordant with the original diagnosis based on targeted haplotyping and direct mutation detection. Thus, the comprehensive methodology of genome-wide karyomapping is applicable to all of these SGDs and greatly expands the range of conditions for which PGD can be offered clinically without the need for customized patient- or disease-specific test development, avoiding delays in patient treatment and reducing costs. Furthermore, for late-onset autosomal dominant conditions, including Huntington disease and cancer predisposition syndromes, karyomapping with a grandparent of known status as a reference allows exclusion testing for the affected grandparental chromosome directly, without revealing the status of the at-risk parent.Comparison of SNP genotyping and karyomapping with the current standard of practice for identifying the inheritance of SGDs at the single-cell level by targeted haplotyping and direct mutation detection, in a blinded retrospective analysis of samples from a large series of PGD cases, has demonstrated that karyomapping is both highly accurate and versatile. Frequently, couples become aware that they are at risk of having a child with an inherited disease only following the birth of an affected child. In this situation, the existing child can be used as a reference to phase the informative SNPs and provide linkage to the affected chromosomes in the parents, allowing the embryo samples to be karyomapped. For dominant conditions, however, the existence of an affected grandparent alerts the couple to the risk and it is necessary to use a grandparent as reference. In a broad range of conditions distributed across the genome . Furthermore, a significant minority of embryos had complex karyotype-wide patterns of aneuploidy consistent with various types of abnormal fertilization. Further work is needed, however, to validate karyomapping for the detection of aneuploidy by comparing results with established techniques, such as array comparative genomic hybridization or next-generation sequencing.Another major advantage of karyomapping, relative to targeted haplotyping analysis, is the ability to detect a range of chromosome abnormalities and their parental origin, including meiotic trisomies, monosomies, and deletions.7 For example, PGD can be used in conditions such as β-thalassemia to select unaffected, human leukocyte antigen––matched embryos for transfer, so that cord blood stem cells can be collected at birth for transplantation to an existing affected child, and this strategy has been effective in curing these children.12 However, at the single-cell level, this requires the development of highly multiplexed amplification protocols with markers distributed across the human leukocyte antigen region and at the gene locus.4 With karyomapping, all that is necessary is to phase the relevant regions on the short arms of chromosomes 6 and 11 using the existing affected child as a reference. For carriers of balanced structural chromosome rearrangements, particularly reciprocal or Robertsonian translocations, there is a high risk of segregating unbalanced combinations of the normal and derivative chromosomes in meiosis, which can result in infertility, repeated miscarriage, or affected live births depending on the chromosomes involved and the size of the translocated segments. Karyomapping detects both segmental trisomies and deletions in unbalanced embryos (unpublished data). In addition, analysis of at least one unbalanced embryo allows the parental haplotypes proximal to the translocation break points to be determined and normal and balanced embryos to be distinguished, which is not possible by quantitative methods such as array comparative genomic hybridization.Other applications of genome-wide karyomapping include testing for the inheritance of multiple loci and structural chromosome imbalance.13The major limitation of karyomapping for diagnosing inheritance of SGDs is that it is an indirect linkage-based method only and does not include detection of the mutation(s). Thus, karyomapping of embryo samples, without additional testing for the mutation, relies completely on the strength of the evidence for phase within the family. It is not a substitute for careful analysis of the family history and mutation testing of relevant family members. This also means that karyomapping cannot be performed without a reference, which is not always available for every couple. Moreover, karyomapping is not immediately applicable to de novo mutations, most often encountered in autosomal dominant conditions. However, in these cases, it is current practice to establish phase using single sperm cells in affected males, or simply to use linked markers as a backup to direct mutation detection, and the same approaches could be used with karyomapping. Another limitation is that karyomapping cannot detect sequence-identical chromosome duplication that can result from malsegregation of chromosomes during the early cleavage divisions of the embryo. However, meiotic trisomies, which are inherited by fertilization with an aneuploid gamete, can be detected, and these are much more likely to affect pregnancy.Karyomapping relies on bead array technology and hence incurs higher consumable costs than PCR-based techniques. However, karyomapping allows for significant savings in labor because it does not require the complex, time-consuming workup required for customized tests using highly multiplexed STR analysis. On balance, it is expected that the per-sample cost for karyomapping should be comparable to or less than that for existing methods, depending on the complexity of the analysis.14 In principle, the advantage of whole-genome sequencing for karyomapping would be that analysis of SNPs, and possibly other markers, across the genome to improve diagnostic accuracy could be combined with mutation and copy-number analysis. However, whole-genome amplification from a single cell or small numbers of cells has been shown to introduce a spectrum of different variants at the sequence level, which will be a challenge to filter out and interpret.15 Furthermore, such comprehensive preimplantation embryo testing raises various ethical issues.16 The advantage of using a microarray platform to genotype a defined set of SNP loci for karyomapping is that there are no incidental findings at the sequence level. Nevertheless, because the SNP density is high, even relatively small partial chromosome deletions may be detected, some of which may have serious clinical consequences. The partial deletions detected in this study were relatively large, and further work will be needed to validate the limits of resolution and clinical significance of these chromosome abnormalities.Recently, targeted next-generation sequencing has been used for mutation detection in single cells as a proof-of-principle study for use in PGD.S.A.N., A.J.B., A.P.C.B., and A.H.H. (part time) are employed by Illumina. The other authors declare no conflict of interest.The karyomapping algorithm is covered by European and Japanese patents  licensed to Illumina.