CDKN2A locus, for example, there is strong evidence that not only multiple SNPs but also multiple genes are involved. Our results illustrate the variability in the biology underlying genome-wide susceptibility loci and make steps toward accounting for some of the “missing heritability.”At least 17 genomic regions are established as harboring melanoma susceptibility variants, in most instances with genome-wide levels of significance and replication in independent samples. Based on genome-wide single nucleotide polymorphism (SNP) data augmented by imputation to the 1,000 Genomes reference panel, we have fine mapped these regions in over 5,000 individuals with melanoma (mainly from the GenoMEL consortium) and over 7,000 ethnically matched controls. A penalized regression approach was used to discover those SNP markers that most parsimoniously explain the observed association in each genomic region. For the majority of the regions, the signal is best explained by a single SNP, which sometimes, as in the tyrosinase region, is a known functional variant. However in five regions the explanation is more complex. At the  In genome-wide association studies, researchers identify genetic variants that frequently associate with a particular disease, though the variants identified may not contribute to the molecular cause of the disease. This study took a closer look at 17 regions associated with melanoma, fine mapping the regions both in people with melanoma and in healthy controls. Though single SNPs account for the association in some regions, they found that in a few regions, several SNPs – and possibly multiple genes – contributed to the association signal. These findings illustrate the importance of not overlooking the interaction between multiple genetic markers when conducting such studies.Genome-wide association (GWA) studies have been extremely successful at identifying genomic regions associated with complex diseases and phenotypic traits.TERT, within which there are SNPs associated with a number of cancers including melanoma, reported multiple independent SNP associations with both telomere length and breast cancer risk.Although GWA studies involve the genotyping of hundreds of thousands of markers across the genome, a variant not available on the genotyping platform may be more strongly associated with outcome than the single most significant genotyped SNP. Coverage of the region may be greatly improved without extra genotyping by imputation of ungenotyped markers, allowing greater refinement of the association signal. The genetic information gained by imputation may help to identity potential causal variants that are in linkage disequilibrium (LD) with the associated genotyped markers and improve the selection of genes chosen for denser genotyping or sequencing. Furthermore, in some genomic regions multiple markers may better explain the association signal, and multiple variants may be independently associated with the trait. A recent study of the region around the telomerase reverse transcriptase gene MX2) span <100 kb, whereas associated SNPs in the 20q11.2-q12 region (near ASIP) cover more than 1 Mb. This variation may be due largely to differing patterns of LD around a single variant or could be indicative of a more complex arrangement of functional variants. We have previously shown in a melanoma case-control study that fine mapping of an association signal through imputation can help to implicate a gene (MC1R) with known functional relevance,Recent GWA studies of melanoma susceptibility, pigmentation-related phenotypes and nevi have led to the discovery and confirmation of a number of genomic regions associated with risk of melanoma.The aim of this study is to refine the association signal in each of the 17 genomic regions previously shown to be associated with melanoma risk using a large case-control dataset.Cases for the GenoMEL GWA study of melanoma were preferentially selected to have a family history of melanoma, multiple primary tumors or an onset before 40 years of age, mainly from centers across Europe. In all 2,744 cases and 1,834 controls from Phases 1 or 2 of the study were included in this analysis (see Supporting Information i) a call-rate of <97% ; (ii) evidence of non-European origin from principal components analysis; (iii) sex as ascertained by genotyping not matching reported sex; (iv) evidence of first degree relationship or identity with another sample; (v) recommendation to be excluded by the WTCCC (for WTCCC samples only). To ensure high quality imputation, very stringent QC measures were applied within each genotyped array; SNPs could therefore be excluded from just a subset of our entire sample. SNPs were excluded for any of the following reasons: (i) Hardy–Weinberg equilibrium p value <10−4 in controls; (ii) call-rate <97%; (iii) recommendation for exclusion by the WTCCC (for WTCCC samples only); (iv) minor allele frequency (MAF) < 0.03.GenoMEL Phase 1 samples were genotyped on the Illumina HumanHap300 BeadChip version 2 duo array and the Illumina HumanCNV370 array. GenoMEL Phase 2 samples were genotyped on Illumina Human610-Quad array. The additional UK cases were genotyped on the Illumina HumanOmniExpressExome BeadChip. The WTCCC samples were genotyped on the Illumina HumanHap 1.2 M array, but only SNPs that were also on either the HumanHap300 or Human610 array were retained for imputation. Samples and SNPs on the HumanOmniExpressExome array were subjected to the same stringent QC as the GenoMEL GWA datasets, previously described in detail.ASIP) centered on the reference SNP. Only those SNPs that were either (i) genotyped on all arrays (Type A); (ii) imputed with an INFO score ≥0.8 on all arrays (Type B); or (iii) imputed with an INFO score ≥0.5 (but not ≥0.8) on all arrays and with a MAF ≥ 0.03 (Type C) were retained for analysis.Imputation was conducted separately on each array using IMPUTEv2A total of 17 regions were analyzed, all of which have been reported to include a SNP associated with melanoma risk, in most instances with a genome-wide level of statistical significance and replication in independent samples  using logistic regression implemented in SNPTEST2,p value < 10−6 in the initial single SNP unadjusted analysis. Penalized logistic regression is an effective method for the simultaneous analysis of large numbers of correlated variables and was therefore used to jointly analyze all SNPs in each of these narrower intervals. The analysis was carried out using Hyperlasso,−4, with the shape parameter fixed at 0.05,2 between estimated SNP dosages).Each of the regions was narrowed down to the interval covering 500 kb on either side of any SNP with SLC45A2, TYR, ASIP, TERT, and CDKN2A) with different features (see Results) were also analyzed with alternative penalty functions (lasso and elastic net) using the glmnet functionr2 > 0.95) prior to the penalized regression analysis. The penalty for each term in these analyses is of the form λ (α|β| + (1 − α) βα = 1, and for the elastic net we used α = 0.5. The multiplier λ was chosen by cross-validation implemented in glmnet. As before, geographical region was included in each model and genotypes were standardized.For comparison, five regions (OCA2 to 55% in PARP1); most exclusions were on the basis of poor quality of imputation  were retained for analysis after post-imputation SNP QC , no SNP was associated at p < 10−5 in these data; these regions were not analyzed further after the initial single SNP analysis. In addition MC1R has been analyzed separately,For three regions (SLC45A2 and FTO), the most significantly associated SNP in our data was the same as the top SNP reported in the reference paper. For each of the remaining regions, a more significantly associated SNP was found ; for each of these regions a single-SNP model was selected in at least 89% of the iterations. For the remaining few iterations a 2-SNP model was selected. When a single-SNP model was selected, the SNP was almost always either the most significant SNP from the single SNP analysis  was selected slightly more frequently (16 times), and 8 other SNPs were selected in different iterations, all also in strong LD . For the PARP1 region only 6 out of 100 iterations selected a SNP actually located in the PARP1 gene; otherwise a SNP located near the 5′ end was selected. Across all iterations, 28 different SNPs were selected, but all were in very strong LD with the top SNP rs1858550 (r2 ≥ 0.97). Similarly, for the CASP8 region, only 17% of the iterations converging to a single-SNP model selected a SNP in CASP8, with the majority selecting a SNP in the neighboring gene AL2CR12. The top SNP rs2349073 was selected most frequently (13 iterations), but others were similarly frequent and all were in strong LD (r2 ≥ 0.94). Only one SNP was selected, in all 100 iterations, for the SLC45A2 region, this being the previously reported missense SNP rs16891982. This SNP was also selected using either the lasso or elastic net penalty. Similarly, the missense SNP rs1126809 was selected in 44% of the single-SNP models for the TYR region; in most other iterations an alternate SNP in LD with this was selected . Using the alternative penalty functions also resulted in selection of rs1126809. For the ATM region, in 82 of the 100 iterations a SNP in the ATM gene itself was selected; although 15 different SNPs were selected, all were in almost complete LD with the top SNP rs4753835 (r2 ≥ 0.98). Only three distinct SNPs were selected for the FTO region, all in the FTO gene; one was the top SNP rs12596638, and the other two were in almost perfect LD with this (r2 ≥ 0.99). In the MX2 region the top SNP rs443099, or one of five other SNPs within the MX2 gene in almost perfect LD (r2 ≥ 0.98), was selected in 81% of the iterations converging on a single-SNP model. For all but one of the remaining iterations, rs390789, a SNP in weaker LD (r2 = 0.86) and not in MX2, was selected.For the PLA2G6 region a single-SNP model was selected in 66 iterations (Supporting Information r2 ≥ 0.82). In other iterations 2-SNP models were selected involving SNPs both of which were reasonably strongly associated with rs3891103 . In most models, both SNPs were located in the PLA2G6 gene. The results are not as clear-cut as for the above single-SNP regions; a possible explanation is that a single causal SNP exists in the region that is not in very strong LD with any single genotyped or imputed SNP.For the ASIP gene , the other SNP being either rs6088372 also in RALY (green diamond) or rs74325991 (red diamond) which is not located in a gene. Using the alternative penalties, the 2-SNP model including rs6059655 and rs6088372 was selected.Hyperlasso gave a variety of models for the ne Table . Most ofTERT, CDKN2A and CCND1) that showed strong evidence of further association when adjusting for the top SNP  and the top single SNP rs2447853  in the neighboring CLPTM1L gene . The LD between rs2447853 and the 2 SNPs in TERT was r2 = 0.09, D′ = 0.11 for rs7705526 and r2 = 0.14, D′ = 0.18 for rs2736099. When regressing melanoma case/control status on these 3 SNPs, the signal at CLPTM1L becomes stronger than in the single-SNP analysis (p = 5.5 × 10−14). The 4-SNP model was equivalent to this 3-SNP model, but with an extra SNP, rs1801075, which lies between TERT and CLPTM1L, or a SNP in strong LD with this, which includes SNPs within CLPTM1L . This 4-SNP model was selected using the alternative penalty functions. The imputation quality was noticeably poor around the TERT gene; only 29% of variants in the gene  are included in the analysis, compared with 50% across all regions, and 64 of the 78 SNPs that are included are relatively poorly imputed (Type C) SNPs.Either 3- or 4-SNP models were selected to explain the signal in the on Table . When taCDKN2A region is explained in most iterations by a particular 3-SNP (31 iterations) or 4-SNP model (54 iterations) . There was almost no change in the OR and p value for this SNP when adding the other 2 SNPs in the 3-SNP model in a logistic regression analysis. The other 2 SNPs in the model also gave reasonably strong signals in the multiple logistic regression analysis. These were located in CDKN2B-AS1 or CDKN2A  and CDKN2A . The LD between rs869330 and the other 2 SNPs was r2 = 0.03 for both rs3088440 and rs3731204 (D′ < 0.07). The LD between rs3088440 and rs3731204 was r2 = 0.12, although D′ = 0.94: the minor allele for rs3088440 hardly ever occurs with the minor allele of rs3731204. The 4-SNP model was equivalent to the 3-SNP model with an additional SNP, rs1011970 or a proxy, in CDKN2A-AS1 . The remaining 15 iterations converged on 3-, 4- or 5-SNP models, which were similar . Using the lasso penalty, a 6-SNP model was selected, including the 4 SNPs in the above 4-SNP model, plus an additional 2 SNPs from among those highly correlated with the top SNP rs869330 .The signal in the s) Table . All theCCND1 region (65 iterations) was a 2-SNP model with neither SNP located in a gene . The models included the top SNP rs12422135 (p < 10−9) or a proxy, all of which were around 80 kb from CCND1 . The second SNP in the model was rs2290419 (p < 10−5) located distal to MYEOV . These SNPs are not in LD with one another . In 20 iterations a third SNP was also selected . In the remaining 15 iterations, alternative 3- or 4-SNP models were selected, all of which were equivalent to one of these two models plus one additional SNP.The most frequent model selected for the ne Table , Fig. 2dASIP, where the estimate doubles from 0.13 to 0.26%. For the 3 clearly more complex regions, the percentage of variance explained by the models in Table TERT, 71% for CDKN2A and 68% for CCND1).For each region the percentage of variance in melanoma risk explained by the reported SNP and the top SNP from this study is shown in Table p < 10−5 after conditioning on the most significant SNP, equivalent to a Bonferroni correction for 5,000 independent tests in the region, and these results are borne out by the Hyperlasso analysis. It is possible that even independent signals represent a haplotypic effect, although we saw little evidence of haplotypic effects from the SNPs in the multiple variant regions. It is becoming increasingly clear that multiple independent causal variants may contribute to disease susceptibility at a single locus.23We have refined the association signals for regions that have been previously associated with melanoma, using a pragmatic statistical approach that includes adjusted analyses and penalized logistic regression. We have shown that the complexity of the association signal within a specific genomic region ranges from those regions best explained by a single variant to those that can only be explained by 3 or 4 variants. The evidence for multiple independent signals is strong: in three regions there is a secondary signal reaching ARNT, PARP1, CASP8, SLC45A2, TYR, ATM, FTO and MX2. For two of the regions (SLC45A2 and TYR), the likely casual variants are known. The SNP rs16891682 in SLC45A2 was the only SNP selected by Hyperlasso and was also detected using other penalty functions; this SNP would likely be identified by any reasonable method. The SNP rs1126809 in TYR was selected using lasso/elastic net and about half of the time using the Hyperlasso method; the remaining iterations all converged on a single-SNP model where the SNP selected was in reasonably strong LD with rs1126809 (r2 ≥ 0.89). For the other single-variant regions identified, although no one SNP was selected much more frequently than the others, this was largely due to the inability of any statistical method to distinguish between almost perfectly correlated variables. The evidence from analysis of these regions suggests that either the most strongly associated SNP or one in very strong LD with it is the most likely explanation of the association signal; bioinformatic analysis of this relatively small set of SNPs can now be used to suggest the most promising candidates for functional investigation.We found strong evidence that a single SNP explains the association signal in 8 of the 13 regions analyzed here: TERT, CDKN2A and CCND1. Interestingly all three of these regions have previously been reported as harboring multiple risk variants for other diseases or traits. Independent associations have previously been reported in TERT for breast cancer and telomere length.Figure 2b, and rs2736108, which is only nominally associated with melanoma in our analysis (p = 0.008). Our reported second SNP in TERT, rs2736099 (orange diamond), is in only moderate LD with rs2736108 , although both are strongly associated with telomere length in univariate analysis ,CDKN2B-AS1 locus, Fig. 2c). The interval around rs10811656 has been studied using chromatin conformation capture in human vascular endothelial cellsCDKN2A/B locus and MTAP. This complex region is clearly of major significance in a number of diseases.We found 3 strong independent signals in the 9p21 region, the strongest being in CCND1 region in relation to oestrogen-receptor-positive breast cancer. Although different SNPs to ours were identified, their signals were between 69.32 and 69.38 Mb (Build 37), which is roughly the region spanned by two of the three signals in our model . Although this region is itself intergenic, on the basis of functional studies these authors conclude that CCND1 is the likely target gene for the variants identified.French et al.Here we have employed penalized regression with a NEG prior to fine map these loci. This choice of prior was motivated by its sharp peak at zero, which shrinks the regression coefficients strongly when they are close to zero, leading to sparse models. In a comparison of penalized logistic regression methods with different penalties, single locus analysis and stepwise regression, Ayers and CordellA major limitation of statistical fine mapping based on imputation is that about half of all possible variants  are dropped because they cannot be reliably imputed, at least with the density of genotyping used in our study and after strict QC. There is therefore a need to more densely genotype, or preferably sequence, parts of these regions to follow up these analyses. The analysis presented here helps to prioritize which of the associated loci require further investigation and, within these, to narrow down the regions to be sequenced.We found a substantial (70%) improvement in the proportion of variance in melanoma risk explained by multiple SNP models compared with single SNPs in selected regions, although overall the proportion of variance explained by all loci is only modestly increased. This has been explored in other traits,SLC45A2 and TYR), where coding variants explaining the signal have been previously identified, fine mapping must be followed up using bioinformatics and experimental approaches. Methods to identify and follow up non-coding functional variants have recently been reviewed,in silico tools and a range of molecular experimental techniques that can take the process forward to identify the causal mechanisms.Statistical fine mapping does not in itself identify the causal SNP(s) but it does take us closer to achieving this goal by narrowing down the number of SNPs to be considered for further investigation. In all but the very simplest regions (


SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions. There is an increasing interest in understanding the underlying genetic architecture of complex diseases, such as cancer, heart disease, diabetes, Crohn's disease, and many others, which represent the major part of current clinical diseases , 2. Resek=13C100,000k. The second challenge is the complexity of genetic architecture of a complex disease. Limited or even no prior knowledge available for a complex disease, such as the order and the effect magnitude of a SNP-SNP interaction, makes it difficult for the development of heuristic methods. The third evaluation measures that determine how well a SNP combination contributes to the phenotype are limited. Evaluation measures should be efficient in computational cost and insensitive to both SNP combination order and dependency type. Though several evaluation measures have been widely used in the detection of SNP-SNP interactions, developing new evaluation measures that can effectively and efficiently capture SNP-SNP interactions is still a direction.In general, the detection of SNP-SNP interactions is a great challenge . The fir needles-in-a-haystack problem and exhaustive algorithms lose their ability . Similarly, the individual experience of the pth particle, that is, the position of the pth particle with the highest fitness value until iteration t, can be denoted as pbestt(p) = , and the common knowledge of swarm, that is, the best position of all particles with the highest fitness value until iteration t, is denoted as gbestt = .Based on above numerical mapping, the position of the 1(p), Velocity1(p), pbest1(p), and gbest1 are randomly initialized in their respective domains.Before the first iteration, Position(2) Updating Dynamic Inertia Weight. Inertia weight is used to control the impact of the previous velocity of a particle on its current velocity. A large inertia weight facilitates the global exploration and thus enables the method to execute a search over various regions, while a small inertia weight facilitates the local exploitation, which searches a promising region  while vpkt+1, the position of SNPpkt can be updated by the following two equations:pkt+1 being a SNP index, an integer between 1 and M is randomly sampled if vpkt+1 and SNPpkt+1 help to increase the diversity of the search, the more possibility of jumping out local optima and getting into global optima.(5) Updating Particle Experiences and Common Knowledge of Swarm. Another strategy introduced to IOBLPSO is the OBL. The basic principle of OBL is the consideration of a solution and its corresponding opposite solution simultaneously to approximate the global optima [t(p), its corresponding opposite solution can be defined asl optima . In the t(p), Positiont′(p), and pbestt(p), the individual experience of the pth particle at iteration t + 1, that is, pbestt+1(p), is updated to the one among them with highest fitness value, which can be written asMI(Positiont(p); Y), MI(Positiont′(p); Y), and MI(pbestt(p); Y)). From this equation, it can be seen that the employed OBL strategy facilitates IOBLPSO not only expanding the search space and enhancing the global explorative ability, but also accelerating the convergence and avoiding premature convergence.By comparing fitness values of Positiont + 1, for example, gbestt+1, is updated or maintained as gbestt depends on fitness values of individual experiences of all particles at iteration t + 1 and can be defined asSimilarly, whether the common knowledge of the swarm at iteration (6) Deep Searching with a Postprocedure. A postprocedure is provided when completing the iteration process to carry out a deep search of SNP-SNP interactions in a highly suspected SNP sets. First, all SNPs are descending sorted according to their counters in countT, and the specified number of top SNPs  are selected into the highly suspected SNP sets. Second, IOBLPSO conducts an exhaustive search within the highly suspected SNP sets to determine whether fitness value of one or more SNP combinations is higher than that of gbestT. If indeed detected, gbestT is updated by the best one among them. gbestT is therefore the final result of IOBLPSO.K = 2) are exemplified for the study [Six commonly used models of SNP-SNP interactions with their orders being equal to 2 , and xi ∈ {0,1} is the detection tag; that is, if 2 ground-truth SNPs in data set i are detected with no false positives, xi = 1; otherwise, xi = 0. Though Power 1 seems not practical since false positives are inevitable for any statistical tests and fewer false positives result in larger false negatives, we still introduce it because it is advantageous in practical applications and might be of interest to biologists due to false positives implying wasted experimental effort to validate the results.Power 1 , 21, 31 yi is the number of ground-truth SNPs in the top 2 SNPs identified in data set i.Sometimes, allowing some small Type-I error rate is more reasonable; thus Power 2 , 7, 21 iComputational complexity is also considered. We measure running time in the same computational environment to assess realistic applicability of compared methods.To demonstrate the validity of IOBLPSO, its detection power is evaluated by comparison with several typical SNP-SNP interaction detection methods, that is, BOOST , AntEpiSP and the number of iterations T are respective set to 100 and 100; both acceleration factors C1 and C2 are set to 2 [W is set to 0.65. It is believed that performance of IOBLPSO mainly depends on parameters . Hence we further examine the influence of these parameters on detection power with , , , , and .In the study, parameters of each method are generally set as default. Only a few are changed according to suggestions in order to balance result accuracy and computational cost. For BOOST, interaction threshold is set to 10, that is, results of BOOST are the SNP-SNP interactions whose likelihood ratio test statistic values >10 with 4 degrees of freedom. For AntEpiSeeker, the numbers of ants and iterations is set to 500 and 10, respectively. For TEAM, permutation number is set to 100. For a fair comparison, parameter settings of PSO based methods are the same. Specifically, the number of particles set to 2 ; the ineDetection power of compared methods on simulation data sets is reported in P, T) settings is comparable and sometimes superior to that of compared methods, which might be the result of introducing three effective strategies into IOBLPSO: OBL expanding the search space and enhancing the global explorative ability, dynamic inertia weight guiding the particles to more promising regions, and postprocedure carrying out a deep search in highly suspected SNP sets; with the numbers of particles or iterations grow, detection power of both IOBLPSO and the PSO increase quickly, especially IOBLPSO; IOBLPSO identifies almost all ground-truth SNPs on all models with the parameter setting , even with  or ; IOBLPSO has perfect detection power on Model 1 and Model 2; that is to say, compared with other PSO based methods, IOBLPSO needs less particles and/or iterations to obtain higher detection power, implying that IOBLPSO can handle large scale data sets for GWAS and its scalability is better than others; for Model 1 and Model 2, Power 1 and Power 2 of IOBLPSO reach a prefect level, Power 1 and Power 2 of other methods have different values since these two models display not only interaction effects but also marginal effects, leading to compared methods sometimes only identifying several ground-truth SNPs, but not SNP-SNP interactions; similarly, for each method on Model 3~Model 6, Power 1 and Power 2 of each compared method are almost always equal because single ground-truth SNPs show no main effects; in terms of computational complexity, though IOBLPSO is not the fast one among all compared methods, it can finish the work at affordable time costs; more importantly, its time costs can be estimated and controlled by setting the numbers of particles and iterations freely under the premise of ensuring sufficient accuracy.It is seen that IOBLPSO outperforms compared methods on all cases regardless of models, the numbers of particles, and iterations. Specifically, no matter, according to Power 1 or Power 2, detection power of IOBLPSO on all models and  and the number of iterations T  , each running 5 times. The order of SNP-SNP interactions K is set to 2 since the small sample size of 146 individuals is insufficient for secure detection of any higher order SNP-SNP interactions. Both acceleration factors C1 and C2 are set to 2. Detected SNP-SNP interactions associated with AMD are listed in In the study, potential of IOBLPSO can also be verified by analyzing a real AMD data set , which c CFH gene in chromosome 1. There are biologically plausible mechanisms for the involvement of CFH in AMD and at least 100 mutations in CFH have been proven to increase the risk of AMD and other disorders. CFH is a regulator that activates the alternative pathway of the complement cascade, the mutations in which can lead to an imbalance in normal homeostasis of the complement system. This phenomenon is thought to account for substantial tissue damage in AMD [It has been widely accepted that rs380390 and rs1329428 are believed to be significantly associated with AMD . These te in AMD . In the  LOC644301 and KIAA1715. KIAA1715 is usually found in adult brain regions. Although no evidences were reported with this gene related to AMD, it may be a plausible candidate gene associated with AMD [ TFEC and TES in chromosome 7q31 [The SNP-SNP interaction , also reported by , 43, haswith AMD , 43. Anoome 7q31 . Therefoome 7q31 –50.P value, the interaction  is the most statistically significant one among all detected SNP-SNP interactions, which might be the reason of it being frequently detected. This fact implies that IOBLPSO is capable of capturing SNP-SNP interactions with statistically significant P values, though its fitness function is the mutual information. The SNP-SNP interaction  [It is interesting that the SNP-SNP interaction  was successfully detected 8 times by the IOBLPSO and by other methods , 21, 51,9328536) , 53, rs1Detection of SNP-SNP interactions is believed to be important in understanding underlying mechanism of complex diseases. In this study, we proposed an improved opposition-based learning particle swarm optimization, or IOBLPSO, to detect SNP-SNP interactions. To the best of our knowledge, IOBLPSO is the first PSO based method to detect SNP-SNP interactions among possible SNP combinations. Highlights of IOBLPSO are the introduction of three strategies: OBL, dynamic inertia weight, and a postprocedure. Among them, OBL is the core, which is presented in the stage of updating particle experiences and common knowledge of swarm, not only for enhancing the global explorative ability, but also for avoiding premature convergence. Dynamic inertia weight is computed before the stage of updating particle velocities to allow particles to cover a wider search space while the considered SNP is likely to be a random SNP and to converge on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is introduced as the final stage for carrying out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on lots of simulation data sets under the evaluation measures of detection power and computational complexity. Results demonstrate that IOBLPSO is promising for the detection of all simulation models of SNP-SNP interactions. IOBLPSO is also applied on a real AMD data set, results of which not only show the strength of IOBLPSO on real applications, but also capture important features of genetic architecture of AMD that have not been described previously. These features might provide new clues for biologists on the exploration of AMD associated genetic factors.IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions and has several merits. First, IOBLPSO is easy to be implemented, and its time costs can be estimated and controlled. Second, OBL and other two strategies help to improve the performance of IOBLPSO. Third, mutual information is effective in measuring SNP-SNP interactions. Fourth, compared with other methods, IOBLPSO needs less particles and/or iterations to obtain higher detection power, implying that IOBLPSO can handle large scale data sets for GWAS and its scalability is better than others. Though IOBLPSO is a beneficial exploration in the detection of SNP-SNP interactions, it still has several limitations; for example, multiple SNP-SNP interactions in a data set are not considered simultaneously; IOBLPSO is sensitive to those SNPs that display strong main effects. Furthermore, recent advancements in sequencing technology have enabled the sequencing of the whole-exome or even whole-genome of a cohort. The rare or de novo mutations resulting from these experiments should be considered. For example, Wu et al. recently proposed a bioinformatics method called SPRING for prioritizing candidate mutations . It is t


Cyprinus carpio) but, as yet, no high-throughput genotyping platform is available for this species. C. carpio is an important aquaculture species that accounts for nearly 14% of freshwater aquaculture production worldwide. We have developed an array for C. carpio with 250,000 SNPs and evaluated its performance using samples from various strains of C. carpio.A large number of single nucleotide polymorphisms (SNPs) have been identified in common carp  were found to be polymorphic sites. Genotyping accuracy was checked using genotyping data from a group of full-siblings and their parents, and over 99.8% of the qualified SNPs were found to be reliable. Analysis of the linkage disequilibrium on all samples and on three domestic C.carpio strains revealed that the latter had the longer haplotype blocks. We also evaluated our SNP array on 80 samples from eight species related to C. carpio, with from 53,526 to 71,984 polymorphic SNPs. An identity by state analysis divided all the samples into three clusters; most of the C. carpio strains formed the largest cluster.The SNPs used on the array were selected from two resources: the transcribed sequences from RNA-seq data of four strains of C. carpio. Our evaluation of this array indicates that it will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species.The Carp SNP array described here is the first high-throughput genotyping platform for  Cyprinus carpio) is naturally distributed across Europe and Asia. It was domesticated about 2,000 years ago, and is cultured in over 100 countries worldwide with over 3 million metric tons of global annual production [C. carpio producer, and there are abundant domesticated strains and populations in China, including Sonpu mirror carp, Hebao red carp, Xingguo red carp, Yellow River carp, and Oujiang color carp, as well as many hybrid strains, all of which are the basis and genetic resources for selective breeding using modern genetic tools.Common carp  of important traits, as well as for genome-assisted selection in breeding programs. Genome-scale SNP genotyping is most efficiently performed using SNP arrays or chips. Arrays of this type have been used widely in genetic studies in humans, as well as in important model organisms and agriculture species.A major gap in the The reductions in the cost of acquiring sequence data using next-generation sequencing technologies has led to the development of genotyping by sequencing (GBS) approaches, which use whole genome sequencing, reduced representative genome sequencing, or target-enriched DNA sequencing data to determine genotypes. The most popular GBS protocol is restriction-site-associated DNA (RAD) tag sequencing in which DNA fragments flanking particular restriction sites are targeted for sequencing, thereby allowing the discovery and genotyping of SNPs at these targeted locations . AlthougTherefore high-density SNP genotyping arrays remain the tools of choice for high-resolution genetics analysis. Many SNP arrays or chips have been developed for either Illumina or Affymetrix platforms, including the human 500 K array, the Genome-Wide Human SNP Array 5.0 and 6.0, the porcine 60 K SNP array , the bovC. carpio and other closely related Cyprinid species, and genotyping is performed intensively for diverse purposes, no SNP genotyping array is available for C. carpio.Although the importance of high-density SNP genotyping arrays has been recognized widely, as yet there are only a few such SNP genotyping arrays for aquaculture species. After the submission of this manuscript, an Affymetrix Axiom® myDesign Custom Array containing 132,033 Atlantic salmon SNPs was developed . MeanwhiC. carpio SNP array, the Carp SNP array, based on the Affymetrix Axiom platform. The Carp SNP array was validated with 1,072 samples from various C. carpio populations and strains. To assess its potential use in closely related Cyprinids, we also validated the array in 80 individuals from eight related species. A pilot study was conducted to demonstrate the accuracy and efficiency of the genome-scale genotyping and linkage disequilibrium (LD) decay was analyzed in all samples and in several domesticated strains. Identity by state (IBS) clustering of all samples was conducted, which demonstrated the reliability of the Carp SNP array.Here, we report the design and validation of the first high-density The pipeline and design parameters described below are summarized in Figure In previous studies, over 700,000 SNPs have been identified in transcript sequences and classified . All theC. carpio genome. When the dog SNP array was developed, more than 2.5 million potential SNPs were identified, with one SNP per 0.9 kb between breeds and one SNP per 1.5 kb within breeds. In other studies, 2.8 million SNPs were detected in chicken [C. carpio SNP array.SNP identification was performed separately within each strain. The criteria used for calling SNPs were as following: (1) mapping quality score ≥ 20; (2) relevant base quality score ≥ 20; (3) SNP quality score ≥ 20 and SNP position must be covered by at least 10 reads; and (4) minor allele count (MAC) ≥ 2 and minor allele frequency (MAF) ≥ 5%. A total of 8,058,251 SNPs were identified in Songpu carp, 11,412,638 SNPs in Yellow River carp, 8,688,799 SNPs in Heilongjiang River carp, 7,123,672 SNPs in Oujiang color carp, and 9,955,915 SNPs in Hebao carp  at the beginning or end of reads . Based oin-silico analysis to predict their reproducibility on the Axiom platform. The p -convert value, which is calculated using a random forest model, is designed to predict the probability that the SNP will convert on the array. The random forest model considers many factors, such as probe sequence, binding energies, unexpected non-specific binding and probability of hybridization to multiple genomic regions [The 378,815 selected SNPs were submitted to Affymetrix for  regions . P-conveC. carpio chromosomes were calculated, we found that the SNP densities ranged from 137 sites/Mb to 187 sites/Mb. Scaffolds that have not been assigned to one of the 50 chromosomes were joined to form a pseudo ‘P’ chromosome, which had a SNP density of 122 sites/Mb  8,204 non-synonymous SNPs and 5,219 SNPs in UTR regions with each SNP at least 100 bp from any adjacent SNP; (2) 133,603 SNPs in transcribed sequences that were at least 1.8 kb from any adjacent selected SNP; (3) 100,974 SNPs from the genome re-sequencing data that were shared between strains and separated by at least 10 kb from any adjacent selected SNP; and (4) 2,000 strain-specific SNPs that were at least 17 kb from any other SNP on the array  passed the manufacturing quality control and could be genotyped. With a stringent call rate threshold of 95%, there were 185,150 (74.06%) polymorphic sites, 4,202 (1.68%) sites with no minor homology genotype, 180 (0.07%) monomorphic sites, and 33,742 (13.50%) sites below the call rate threshold  SNPs were found to be polymorphic. With a moderate call rate threshold of 80%, there were 54,116 (21.65%) polymorphic sites, 6,748 (2.70%) sites with no minor homology genotype, 88 (0.04%) monomorphic sites, and 23,981 (9.59%) sites below the call rate threshold  formed the largest cluster, within which different strains were grouped together. The Oujiang color carp and Heilongjiang carp genotyping results were both from the first 96-well plate of this array, so a replicate experiment should be performed along with the next batch of samples. C. carassius, D. rerio and L. waleckii formed the second cluster, close to the largest cluster. The third cluster consisted of C. idella, M. piceus, H. molitrix, H. nobilis and M. amblycephala and showed distinct divergences from the other two clusters. The IBS clustering results are consistent with several phylogenetic analyses of Cyprinidae reported previously [Population structure analyses have commonly been conducted before GWAS analyses ,49 and ss Figure . All theeviously -52, indiC. carpio. After evaluation with large samples, nearly three fourths of the designed 250,000 SNPs proved to be polymorphic in C. carpio. Besides, the Carp SNP array was also evaluated in related species. LD was calculated and longer haplotype blocks were observed in domesticated strains. IBS was conducted and most of the samples were assigned to different clusters. This study indicates that the Carp SNP array will be valuable for farmed carp and for genetic and population biology studies in C. carpio and related species.We developed the Carp SNP array which is the first high-throughput genotyping platform for This study was approved by the Animal Care and Use Committee (ACUC) of the Centre for Applied Aquatic Genomics at the Chinese Academy of Fishery Sciences. All sampling procedures complied with the guidelines of ACUC on the care and use of animals for scientific purposes.C. carpio comprising 18 accessions  were collected. The five strains were Songpu carp from Heilongjiang Fishery Research Institute, Yellow River carp from Henan Academy of Fishery Sciences, Heilongjiang River carp from Fuyuan County in Heilongjiang Province, Hebao carp from Wuyuan County in Jiangxi Province, and Oujiang color carp from Longquan County in Zhejiang Province. Fin chips or blood samples were collected and DNA was extracted using a DNeasy Blood & Tissue Kit . The samples are listed in Table Five strains  of The paired-end reads from each accession were aligned to the reference genome using BWA  to genert bases was set and SNPs within the t bases were excluded. For SNPs that originated from the transcriptome data, t was set lower than 2 kb so that all the cSNPs were included in the final pool. For SNPs from the genome re-sequencing data, t was set over 10 kb because most of these SNPs were from non-coding regions.SNP selection was carried out in multiple steps using different criteria. All the filtration parameters were set to minimize the risk of false positive sites and to select SNPs that were relatively evenly distributed across the genome. All the original SNPs were classified to six different databases and selected in a certain order. First, non-synonymous SNPs and SNPs in UTR regions were selected; then other transcriptome SNPs were added; and finally, strain-shared and strain-specific SNPs were added to the pool of candidate SNPs. During the SNP selection steps, several custom-made scripts were used to qualify flanking sequences. To ensure an even distribution of SNPs over the genome, a custom-made algorithm (described below) was used. When a new SNP was introduced into the final pool, a threshold of C. carpio and 80 samples from carp-related species were collected. Genomic DNA was extracted from blood using a DNeasy 96 Blood & Tissue Kit (Qiagen). All the DNA samples were quantified by NanodropND-1000 spectrophotometer  and sent to GeneSeek  for genotyping. The genotype data were extracted and converted to Ped/Map format. PLINK software [2 values (Y axis) and the physical distances (X axis) for each pair of SNPs each kilo base-pair. IBS clustering was conducted with PLINK using the “--mds-plot 2”, “--cluster”, and “--genome” parameters, with a P-value threshold of 1E-3. The PLINK MDS file was extracted and a scatter plot was drawn using d$C1 (X axis) and d$C2 (Y axis) in the R software package .To evaluate the Carp SNP array, 1,072 samples from software  was usedsoftware . X-Y ploThe authors declare that they have no competing interests.C. carpio families of multiple strains, and collected samples for array analysis. YJ participated in manuscript preparation. QL, YZhu and YLiu worked on DNA extraction. PX conceived and supervised C. carpio SNP identification and the array project. XS supervised the C. carpio genome project. All authors read and approved the final manuscript.JX worked on sample collection, genome sequencing, SNP identification and evaluation, array design and manuscript preparation. ZZ worked on sample collection and SNP array evaluation. XZ, XZheng, YK, JL, YZ, JF, CL, JY, QL established


The success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built. Here, we explore the benefit of combining two unrelated populations into a single reference population.The datasets consisted of 1829 Brahman and 1973 Tropical Composite cattle with measurements on five phenotypes relevant to tropical adaptation and genotypes for 71,726 genome-wide single nucleotide polymorphisms (SNPs). The underlying genomic correlation for the same phenotype across the two breeds was explored on the basis of consistent linkage disequilibrium (LD) phase and marker effects in both breeds.The proportion of genetic variance explained by the entire set of SNPs ranged from 37.5 to 57.6 %. Estimated genomic correlations were drastically affected by the process used to select SNPs and went from near 0 to more than 0.80 for most traits when using the set of SNPs with significant effects and the same LD phase in the two breeds. We found that, by carefully selecting the subset of SNPs, the missing heritability can be largely recovered and accuracies in genomic predictions can be improved six-fold. However, the increases in accuracy might come at the expense of large biases.Our results offer hope for the effective implementation of genomic selection schemes in situations where the number of breeds is large, the sample size within any single breed is small and the breeding objective includes many phenotypes. To improve the accuracy of genomic selection, large reference populations are usually recommended for estimating genome-based predictions of additive genetic effects or breeding values –3. When To improve prediction accuracy through the analysis of combined data, there must be a positive genetic correlation for the phenotype of interest between the two breeds. In addition, since genomic prediction that uses genome-wide single nucleotide polymorphisms (SNPs) is almost entirely based on linkage disequilibrium (LD) between the SNP alleles and the causative allele(s) at the quantitative trait locus (QTL), combining data with inconsistent phase relationships between the SNP alleles and the causative alleles will serve to destroy evidence of associations. Here, inconsistent phase means that the same SNP shows LD to the QTL but a different allele at the SNP shows a positive relationship to the QTL while consistent phase means that the same allele at the SNP has a positive relationship to the QTL. Obviously, combining data from SNPs with consistent phases in different breeds will reinforce the association. However, recent attempts to weight a genomic relationship matrix (GRM) using either LD phase consistency between breeds or estimates of marker effects have failed to improve the accuracy of genomic predictions . It shouInstead of merely considering phase or allele effect consistency to improve the accuracy of genomic predictions, one could directly use genetic correlations between breeds for the same phenotype. When pedigree information allows common relatives of two populations to be traced, the genetic correlation for the same phenotype across the two populations can be estimated, e.g. , 16. TheBos indicus cattle. The same phenotype in the two breeds was analysed by fitting a bi-variate model that used both the numerator relationship matrix (NRM) based on pedigree and the GRM. The model allows for estimation of (1) the genomic correlation of a given trait across the two breeds; and (2) the “missing heritability” based on the fraction of genetic variance that is not captured by the markers. We showed that building a GRM by using a carefully selected set of SNPs, either with the same or different LD phases between SNP alleles and the QTL, and with SNP effects on the extremes of the distribution, resulted in partial recovery of the missing heritability. It also affected the estimates of genomic correlations, which in turn affected the accuracy of the genomic predictions.To determine whether it is feasible to use genomic correlations to improve genetic predictions, we used data on 1829 Brahman (BB) and 1973 Tropical Composite (TC) cows and bulls genotyped for 71,726 SNPs that were highly polymorphic in Animal Care and Use Committee approval was not obtained for this study because no new animals were handled in this experiment. The experiment was performed on trait records and DNA samples that had been collected previously. The resource population used in this study was established by the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) to understand the genetic links between adaptability and components of herd profitability in northern Australia .B. indicus cattle .Animals, phenotypes and genotypes used in this study were a subset of those used in Porto-Neto et al. . In brieWe chose five phenotypes of relevance to tropical adaptation: (1) NAVEL: penile sheath score expressed as the correlated trait navel score in females and scored from 1 (very pendulous) to 9 ; (2) COLOR: coat colour scored on a light (1) to dark (6) scale; (3) COAT: coat score subjectively scored from an extremely short and slick coat (1) to a very woolly coat (7), which was subsequently converted to a continuous 21 points scale); (4) COND: body condition score ; and (5) YWT: yearling weight (kg).Figure For each breed, a 10-variate analysis was performed, using the five traits separately for cows and bulls. The mixed-model that we selected had already been used on this population  and inclftp://tzv.fal.de/pub/vce6/) because it provides standard errors for parameter estimates and best linear unbiased estimates (BLUE) solutions to the fixed effects. These BLUE are used to compute adjusted phenotypes which will be needed in the computation of accuracy and bias of genomic estimated breeding values (GEBV).REML estimates for variance components, heritabilities and genetic correlations for these within-breed analyses were obtained using VCE 6.0.2  in columns with animals in rows, and i-th SNP in the across-breed dataset, and I is an identity matrix included to make G invertible by enlarging the diagonal elements.Random effects were assumed to follow a normal distribution with zero mean and a variance–covariance matrix as follows:p matrix , which wVanRaden , with thVanRaden  to make A and G, were built across both breeds (BB and TC), the off-diagonal elements in A relating BB with TC animals were zero as they were unlinked via pedigree. However, off-diagonal elements in G relating BB with TC animals were not necessarily zero since they could still be related through allele sharing at the genotyped SNPs. This non-zero relationship between animals across populations made it possible to estimate It is important to note that, although both relationship matrices, Variance components were estimated via REML using the Qxpak5 software program .G and was estimated as follows:Following Román-Ponce et al. , estimatIn addition to the bi-variate analyses, we performed genome-wide association analyses (GWAS) separately within each population of cows (BB and TC) and for each of the five traits using Qxpak5 software . The GWAThe SNP effect estimates for a given trait in the population of BB cows were compared with those for the same trait in the population of TC cows to select SNPs that had large effects in both populations and in either the same or opposite directions. For each trait, the 7173 (or 10 %) most significant SNPs and in the same direction in both breeds were selected. The resulting five lists (one for each trait) were merged into a single list namely “list-of-same”. Similarly, the five lists of the 10 % most significant SNPs with estimated effects in opposite directions in the two breeds were merged into a single list, namely “list-of-different”. Finally, all overlapping SNPs between “list-of-same” and “list-of-different” were removed.This selection approach resulted in two lists of non-overlapping SNPs . The percentage of SNPs that influenced one, two, three, four or all five traits was 75.2, 19.7, 4.2, 0.89 and 0.08 % for the 16,207 SNPs in “list-of-same”; and 76.9, 19.3, 3.4, 0.38 and 0.05 % for the 16,951 SNPs in “list-of-different”.GS i.e. the GRM built using the SNPs in “list-of-same”; and GD i.e. the GRM built using the SNPs in the “list-of-different”. The performance of GS and GD was explored in a new set of bi-variate mixed models, which also included the polygenic effect of the pedigree-based numerator relationship matrix A. The resulting estimates of variance components and the fraction of missing heritability were compared to those obtained with GW, the GRM built using the entire list of 71,726 SNPs.These two lists were used to construct: G was based on one of three possible options:GW = the GRM built with the whole set of 71,726 SNP genotypes;GS = the GRM built with the set of SNPs that had estimated effects in the same direction in the two breeds and across all five traits;GD = the GRM built with the set of SNPs that had estimated effects in opposite directions in the two breeds and across all five phenotypes.For the genomic prediction models, we merged data from both breeds and both sexes into a single dataset, and carried out REML analyses using a uni-variate mixed-effect model that used the random additive genetic animal effects based on the marker-based relationship matrix as follows:In the above-mentioned models, the phenotypes from bulls from the two breeds were always treated as missing values. The phenotypes of cows were treated as missing values separately for each breed depending on the validation population being tested (treated as missing). Therefore, for each phenotype, we explored two genomic prediction models: (1) one with BB cows as the only observed phenotype; and (2) one with TC cows as the only observed phenotype.Using cows of one breed to predict cows of the other breed.Using cows of one breed to predict bulls of the same breed.Using cows of one breed to predict bulls of the other breed.This scheme allowed us to investigate the accuracy of genomic predictions using three approaches:GS and GD, it establishes the upper bounds of the achievable accuracies. Prediction accuracy was estimated from the correlation between the genomic estimated breeding value (GEBV) and the observed phenotype of the validation animals adjusted for the fixed effects estimated from the within-breed 10-variate analyses . Prediction bias was assessed from the regression coefficient of adjusted phenotypes on GEBV.While the first approach does not represent an independent validation analysis because the cows from both breeds were used in the identification of SNPs for Table For within-breed analyses, and using the pedigree as the only information to relate animals, Table Table With the exception of COND for which the estimated genomic correlation was moderate and equal to 0.63, estimated genomic correlations for all other traits were near 0 but positive. In a study of five traits in dairy cattle, Zhou et al.  reportedGS; Table GD; Table SNPs that are not associated to a trait may add errors to a prediction so we repeated the previous analyses by restricting the SNPs used to construct genomic relationships to those that had consistent phases (defined above) in both breeds. Note that SNPs for all five traits were included in the pool of SNPs, although a SNP did not usually show significant effects for all five traits. The same set of parameter estimates was explored using the GRM built with those SNPs . Indeed, the accuracies using GW and GS were effectively identical, although only 23 % of the SNPs were used to calculate GS. Accuracies using GD were slightly lower for all traits, except for NAVEL in the TC sample, for which the accuracy was substantially lower. Since a major gene is known to affect NAVEL [GD might lack markers in a critical part of the genome so that all the local variance was not captured. In spite of the negative estimates for genomic correlations when using the “list-of-different” SNPs, the accuracies using GD were all positive when the predictions were made within-breed.However, selection of SNPs did not affect either the accuracy or the bias of genomic predictions when these predictions were made within-breed Table . The 16,ct NAVEL , the “liGS or GD, respectively. Averaged across all traits, using GS, led to a 79 % (from 0.13 to 0.24) increase in prediction accuracy for TC bulls based on BB cows, along with a decrease of 0.10 in bias (from 1.13 to 1.03). Similarly, using GS, led to an average increase in prediction accuracy for BB bulls based on TC cows of 84 % (from 0.10 to 0.18) along with an improvement of 0.12 in bias (from 0.49 to 0.61). However, the use of GD led to a major decrease in accuracy when selecting bulls of one breed based on cows of another breed as can be seen in the average negative accuracy of 0. Also, negative estimates of bias were obtained when using GD in across-breed predictions, either within-sex  shown the hidden relationship between breeds that is uncovered by using SNPs; (2) estimated the genetic correlation between breeds for five traits and the missing heritabilities; (3) found that the genetic correlations were generally low, thus, although we have uncovered the genetic relationships, combining data for the two breeds would not materially add to the improvement in genomic prediction; and (4) shown that the effects of genetic correlations in genomic prediction can be manipulated by the choice of SNPs and can result in both changes to accuracy and increases in bias in prediction across breeds although accuracy and bias of prediction within breeds appears unaffected.GS and GD was intrinsically simple, i.e. we selected the 10 % SNPs that were most associated with each trait and had the same direction of effects (GS) or opposite directions of effects (GD) and then avoided any SNP that fell in both categories. Other approaches for the selection of SNPs should be investigated. For instance, an approach that focuses on SNPs in coding regions or in DNA regions that are not coding for proteins but are functional could be explored. In this regard, Koufariotis et al. [Our results have some limitations but they do go beyond what others have found in the field. For example, the allele frequencies used in the computation of the GRM were estimated from the combined two-breed genotype data. Other choices such as breed-specific allele frequencies or the use of a common base population could be explored e.g., as in , 28, 29.s et al.  found ths et al.  reporteds et al.  showed ts et al.  first cos et al.  reportedOur work illustrates some of the challenges ahead to improve genomic prediction. First, in the construction of relationship matrices, the exclusion of phenotypically relevant variants and the inclusion of irrelevant variants both have deleterious effects . RecentlSome of the areas that will require more research concern highly divergent breeds or sex limited traits. For example, with higher values of the proportion of variation accounted for by the GRM and higher genetic correlations, genomic prediction accuracies improved substantially, especially when comparing cows to cows. It is natural to expect that phenotypes that are measured on one sex are likely to be more representative of animals of that sex, and thus, prediction accuracies for bulls using cows as reference animals are expected to be less accurate. Indeed, while none of the traits explored here were sex-limited, the estimated genetic correlations for some of the traits in the two sexes deviated from 1. Nevertheless, since cows were used as the calibration sample, it is possible that a component of the improved prediction for cow data is due to this fact. We suggest that this approach should be used with caution and tested on other datasets to determine its generality. In addition, when analysing the impact of genetic distance between populations on the accuracy of GEBV, Varona et al.  showed tFinally, the changes in bias and accuracy when the SNP lists were altered are interesting and instructive. We found that selecting SNPs associated with at least one trait that had the same direction of effect in the two breeds had a major impact on improving the amount of information contained in the GRM. This method substantially reduced both the amount of information explained by the pedigree and the missing heritability, which in our data, appeared to be highly correlated. Adjusting the GRM and NRM to a common base population using the approaches of Roman-Ponce et al.  did not We showed that the accuracy of across-breed genomic predictions improved when a subset of SNPs with consistent effects across populations is used to generate the predictions, instead of the randomly selected or equally spaced SNPs currently used for genomic selection. These results offer hope for the effective implementation of genomic prediction in situations where there are many breeds, the sample size is small within any single breed and there are many phenotypes in the breeding objective.


Single Nucleotide Polymorphism (SNP) genotyping analysis is very susceptible to SNPs chromosomal position errors. As it is known, SNPs mapping data are provided along the SNP arrays without any necessary information to assess in advance their accuracy. Moreover, these mapping data are related to a given build of a genome and need to be updated when a new build is available. As a consequence, researchers often plan to remap SNPs with the aim to obtain more up-to-date SNPs chromosomal positions. In this work, we present G-SNPM a GPU (Graphics Processing Unit) based tool to map SNPs on a genome.G-SNPM is a tool that maps a short sequence representative of a SNP against a reference DNA sequence in order to find the physical position of the SNP in that sequence. In G-SNPM each SNP is mapped on its related chromosome by means of an automatic three-stage pipeline. In the first stage, G-SNPM uses the GPU-based short-read mapping tool SOAP3-dp to parallel align on a reference chromosome its related sequences representative of a SNP. In the second stage G-SNPM uses another short-read mapping tool to remap the sequences unaligned or ambiguously aligned by SOAP3-dp . In the last stage, G-SNPM analyzes the alignments obtained by SOAP3-dp and SHRiMP2 to identify the absolute position of each SNP.To assess G-SNPM, we used it to remap the SNPs of some commercial chips. Experimental results shown that G-SNPM has been able to remap without ambiguity almost all SNPs. Based on modern GPUs, G-SNPM provides fast mappings without worsening the accuracy of the results. G-SNPM can be used to deal with specialized Genome Wide Association Studies (GWAS), as well as in annotation tasks that require to update the SNP mapping probes. GWAS have shown that genetic variants are often responsible of traits expressed in phenotypes. Genetic variants may be associated with the cause  or withSpecialized tools as LiftOver , Assemblaltcdfenvs has been used to investigate how probes found on Affymetrix microarrays were matching on more recent curated collections of human transcripts. Experiments showed that not all the probes matching a reference sequence were consistent with the grouping of probes by the manufacturer of the chips [Researchers use tools as BLAST  or BLAT he chips . HoweverIn this work, we present an improved version of G-SNPM (standing for GPU-SNP Mapping) , an accuhttp://www.interomics.eu/sp1-wp2.G-SNPM is available at the following address G-SNPM is a tool that maps a sequence representative of a SNP against a reference sequence in order to find the absolute position of the SNP in that sequence. For genotyping analysis a SNP is represented by a oligonucleotide probe for each possible allele. In turn, these probes can be synthetically described by a regular expression obtained by combining the flanking sequences of a SNP with a grouping construct that represents its possible alleles . Since public repositories as dbSNP provide short and fixed length flanking sequences, we assume that typically the probes used to design a SNP chip are represented by short sequences. Starting from this consideration, a short-read mapping tool could be successfully used to cope with the SNP mapping task.In the following of this section, we first introduce existing state-of-the-art short-read mapping tools. Then, we propose our strategy, devised to deal with SNP mapping problems. Successively, we discuss about the adopted alignment constraints. Finally, we briefly resume the minimal hardware and software equipment required to use G-SNPM.Several tools have been devised to perform short-read mappings. Without aiming to be exhaustive, let us cite some of the most popular solutions, as MAQ , RMAP 3,33, BowtIn general, the mentioned solutions exploit some heuristics to find a good compromise between accuracy and running time. Recently, GPU-based solutions have been proposed to cope with different bioinformatics problems -48. GPUsR that uses a single grouping construct to describe the possible alleles. However, short-read mapping tools are not designed to work with sequences described by a regular expression with specialized constructs. Then, two trivial approaches could be used to map a SNP with a short-read mapping tool. As for the former approach  analyzes each alignment to look for false positives, ii) removes them, and then iii) updates the edit distance of those alignments classified as true positives. To detect a unique SNP chromosomal position, a unique alignment must be considered valid. To this end, G-SNPM analyzes all valid alignments of each SNP sequence to detect the best hit and discard the others. Basically, the best hit might be detected by calculating the score alignment of each hit and selecting the best. However, G-SNPM analyzes a more complex score. In particular, it detects the best hit by analyzing the BWA-like MAPQ score provided with the last releases of SOAP3-dp that is intended to indicate confidence of read placement accuracy. This score assigns a Phred-like mapping quality score to each read based on match uniqueness, sequence identity, end-pairing, and inferred insert size.As previously explained, the adopted mapping strategy requires that G-SNPM analyzes the resulting alignments to filter out false positives. During the alignment, SOAP3-dp aligns each aIt is aimed at refining the mapping process. At this stage, G-SNPM tries to remap those SNPs (if any) that have not been mapped at the first stage of the pipeline; in other words, those SNPs for which SOAP3-dp has not been able to provide valid alignments for their representative sequence and/or those SNPs for which G-SNPM has not been able to find unambiguous mapping chromosomal positions . G-SNPM uses the Smith-Waterman based short-read mapping tool SHRiMP2 to perform this stage of the pipeline. As for the first stage, also in this stage G-SNPM adopts an identical policy to detect and discard false positives alignments that might be found by SHRiMP2, while exploiting the SHRiMP2 mapping quality score to detect the best alignment. At the end of this stage, G-SNPM reports those SNPs for which SHRiMP2 has been unable to find a unique valid alignment of their representative sequences or an unambiguous SNP chromosomal position.i) those SNPs that G-SNPM has been unable to map against a unique chromosomal sequence and ii) those SNPs unmapped by the chip vendor.G-SNPM analyzes unique valid alignments of each successful mapped SNP to calculate the absolute position of each SNP. An output file is generated, containing for each SNP, its name, the related chromosome, the original SNP position, and the mapped SNP position. Moreover, information about the alignment as the strand, and the CIGAR string are also provided. Then, the pipeline is re-executed to map against the overall genome In G-SNPM reference DNA sequences are accepted in standard FASTA format, whereas SNPs must be represented by using two files: a FASTA file with the representative reads of the SNPs, and another flat file with information about the SNP, in particular the original absolute SNP position and its alleles. Currently, automatic generation of these files is provided for SNP probes of the Illumina Chip. G-SNPM analyzes Illumina files to automatically generate the previously described files for each chromosome.G-SNPM defines different mapping constraints at the first and second stage of its pipeline, according to the different two mapping tools exploited.Typically, due the time required to find an alignment, short-read mapping tools allow to set some parameters to limit the maximum alignments allowed for read sequence. For instance, by default Bowtie allows only one alignment for read sequence. In general, this limitation might affect the quality of the final results, especially when no sensitive alignment parameters are imposed. Short-read mapping tools that exploit modern GPUs allow to easily by-pass the limitations of this constraint. By default, SOAP3-dp generates up to 1000 alignments for read. We deem that this is a good constraint and did not modified it in G-SNPM. However, users can easily modify it to decrease, increase, or avoid the upper limit to the alignments that may be found for each sequence.As already pointed out, SOAP3-dp is the evolution of SOAP3 that exploits dynamic programming to support indels in alignments. Depending on whether dynamic programming is enabled or not, SOAP3-dp will generate gapped or ungapped alignments. When dynamic programming is enabled, SOAP3-dp performs the alignment in two steps. In the first step it looks for ungapped alignments that meet a given constraint on the allowed number of mismatches. Up to 4 mismatches are allowed for this step. In the second step, it exploits dynamic programming to look for gapped alignments. By default, in the first step SOAP3-dp allows up to 2 mismatches to speed-up the overall alignment process. However, G-SNPM modifies this constraint to allow alignments with up 4 mismatches. Users can decreases this value in G-SNPM.N of available CPU-cores, and then runs SHRiMP2 on N-1 cores; a CPU-core is reserved to the operating system. However, it is possible to set manually how many CPU-cores must be assigned to SHRiMP2.SHRiMP2 is an accurate short-read mapping tool that has been designed to parallelize the alignment process on multi-core CPUs. By default SHRiMP2 uses only a CPU-core. Then, to speed-up the analysis performed at this stage, G-SNPM assigns all available CPU-cores to SHRiMP2. In particular, it automatically detects the number Depending on the number of available CPU cores, it might be useful to limit the maximum number of alignments for sequence, with the aim to reduce the overall mapping time. However, it should be noted that most SNPs are successfully mapped at the first stage of the pipeline. So, the activation of the second stage is sporadic and involves only some SNP sequences. We deemed useful not imposing any limitation on the number of alignments at this stage, to prevent any worsening of the overall accuracy of G-SNPM. At this stage, SHRiMP2 is enabled to allow ungapped alignments. Alignment score and penalties are those of default of SHRiMP2 . It is possible to change these values to meet user constraints.G-SNPM works on linux based systems with a custom installation of Python (release >=2.7.3) and equipped with a CUDA (Compute Unified Device Architecture) enabled GPU-card. We tested it on two families of NVIDIA GPU cards. In particular tests have been carried out on the NVIDIA FERMI architecture based GTX 480 card, and on the NVIDIA Kepler architecture based k10 and k20c cards. Currently, SOAP3-dp can be run on CUDA-3.2 and CUDA-4.2 releases, while no support for the CUDA 5.0 release has been provided yet. We suggest to scientists interested to use G-SNPM to install the CUDA-4.2 release.i) 1.2 millions of SNPs of the Illumina Chip HumanOmni 1S (version 1) aligned by the chip vendor on the build 37.1 of the human genome, ii) 370 thousands of SNPs of the Illumina Chip CNV370 (version 3) aligned on the build 36.1 of the human genome, and iii) 318 thousands of SNPs of the Illumina Chip HH300 (version 2) also aligned by the chip vendor on the build 36.1 of the human genome. Experiments have been mainly executed i) to highlight discrepancies in respect in map positions provided by the chip vendor, and ii) to assess the capability of G-SNPM to deal with the mapping problem. In the following of this section, we first briefly summarize both the hardware configuration and the short-read mapping tool releases exploited to carry out experiments. Then, we describe the way data have been prepared, so that a scientist can easily reproduce experiments. Finally, we present and discuss results.To assess G-SNPM, we used it in the task to remap about Experiments described hereinafter have been carried out on a 12 cores Intel Xeon CPU E5-2667 2.90GHz with 128 GB of RAM. An NVIDIA Kepler architecture based Tesla k20c card with 0.71 GHz clock rate and equipped with 4.8 GB of global memory has been exploited to execute SOAP3-dp. Moreover, we used the following software releases: SOAP3-dp rel. 2.3.116 and SHRiMP2 rel. 2.2.3..csv file version of the Manifest of the analyzed chips from the Illumina website. Then, we used our Illumina parser, which is distributed together with G-SNPM, to automatically generate the working files used by G-SNPM. Successively, we downloaded the builds 36.1, 37.1 and 37.3 of the human genome from the NCBI Reference Sequence Database [We downloaded the Database . Then, wWe used G-SNPM to perform two different experiments. As for the former, we used it to remap the SNPs of each chip against the same genome build previously used by the chip vendor. This experiment permits to put into evidence and to analyze possible discrepancies between the SNPs positions obtained with G-SNPM and those provided by the chip vendor. As for the second experiment, we first used G-SNPM to remap the SNPs against the newest build 37.3 of the human genome and then, we analyzed the reliability of the updated positions. Table i) the overall number of SNPs mapped using G-SNPM, ii) the number of those uniquely mapped, iii) the number of SNPs for which G-SNPM has been unable to find any alignment, and iv) the number of SNPs for which our tool found positions that differ from those provided by the chip vendor. As for the chip HumanOmni 1S, G-SNPM has been able to remap 4.460 of the 5.314 SNPs for which the chip vendor did not provide any mapping position. Most of these SNPs have been mapped at the first stage of G-SNPM. In particular, they have been mapped by SOAP3-dp looking for ungapped alignments and without exploit any heuristic. Only 35 of these SNPs have been mapped looking for gapped SNPs. In the last column of Table Table i) due to the refinement of the reference sequence, some SNPs are no longer present in the latest build or that ii) the refinement of the reference sequence required complex gapped alignments that G-SNPM is unable to find, due to the procedures adopted in the two stages of its pipeline. As in the previous experiment, almost all SNPs have been mapped at the first stage of G-SNPM, while looking for ungapped alignments.Table i) those obtained using a genome remapping tool, and with ii) those retrieved by dbSNP. As for the first comparison, we used the NCBI Genome Remapping Service because at the time of writing of the manuscript it is the only assembly-assembly converter tool able to project features from the build 36.1 to the build 37.3, whereas neither the NCBI Genome Remapping Service nor the UCSC LiftOver and Ensembl AssemblyConverter services are currently able to project features from the build 37.1 to the build 37.3. Therefore, this experiment has not been performed for the chip HumanOmni 1S. The NCBI Genome Remapping Service projects the coordinates of a chromosomal region between two different builds of a genome. In this case, we are interested to project against the build 37.3 the coordinates of those regions that contain the SNPs in the build 36.1. Assuming that the SNPs positions provided by the chip vendors are correct, we can identify these regions retrieving the sequences representative of the SNPs, their relative positions within these sequences, and their absolute positions within the chromosome sequence. This information is present in .csv files of the Manifest of the chips analyzed for this study. Table To analyze the reliability of our tool, we compared the SNPs positions on the build 37.3 obtained with G-SNPM with "D" option. In the table, results are summarized for both cases. It should be observed that the running time greatly increases when the "D" option is used. Only a small percentage of SNPs is further mapped against the overall genome sequence at the second stage of G-SNPM. The time for mapping the SNPs of chip HH300, version 2, do not change after activating this option "D", as all SNPs are in fact mapped at the first stage. Moreover, the table shows that G-SNPM aligns almost 1.2 million of SNPs of the HumanOmni 1S chip faster than the almost 370 thousands SNPs of the CNV370 chip, version 3, and the almost 318 thousands SNPs of the HH300 chip, version 2. Justification must be sought in the fact that in the HumanOmni 1S chip almost all SNPs are mapped at the first stage of G-SNPM. As for the others, G-SNPM required more time to try to map SNPs at the second stage. Table Table G-S NPM is a useful and powerful tool that can simplify the work of researchers that plan to remap the SNPs chromosomal positions before to perform any GWAS. Typically, researchers use sequence alignment tools as BLAST or BLAT to update the mapping position of a SNP to a genome or a transcriptome. However, no generalized and/or computationally efficient solutions have been proposed to address this problem. G-SNPM is the only general-purpose tool devised to deal with the mapping of SNPs. Being based on modern GPUs, it exploits the computational power of these hardware accelerators to guarantee a very fast mapping without compromising the accuracy. G-SNPM can be easily integrated in specialized pipelines and workflows devised to cope with specialized GWAS, as well as annotation tasks that requires to remap the SNP probes.BWT: Burrows-Wheeler Transform; CUDA: Compute Unified Device Architecture; GPU: Graphics Processing Unit; GWAS: Genome Wide Association Studies; NGS: Next-Generation Sequencing; SNP: Single Nucleotide Polymorphism.The authors declare that they have no competing interests.AM and AO conceived the tool. AM designed and implemented the tool, and drafted the manuscript. AM, AO and GA defined experiments. EM tested the tool during its development. AM, AO and EM analyzed experimental results. AM and GA revised the manuscript. LM coordinated the project, granted access to the computational facilities and maintained the bioinformatics resources. All authors read and approved the final manuscript.


The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools , we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications. The robustness and the reproducibility are the sine qua non of every data intended to be used for clinical applications. These factors have been the main issue hindering large scale applicability of array-based technologies for clinics. High throughput sequencing (HTS) offers alternative solutions to array based technologies with respect to genotyping, and HTS data are considered to be more robust and comprehensive. The performance of HTS platforms has been tested in various studies –3, but tHere we investigate the robustness of the Illumina HiSeq platform, currently the most widely used HTS technology in genome sequencing. In order to achieve this, we resequenced the genomes of two individuals from the Turkish Genome Project  twice. TOur main contribution in this paper is a detailed investigation of the types and causes of exclusive variants within the call sets that are expected to be substantially the same. In addition, we try to identify strategies to handle such discrepancies when there is a second WGS dataset generated from the genome of the same donor. With further technological advancements and the cost improvements, sequencing a sample many times can be expected to be prevalent, as storing the data may become more expensive than resequencing the same sample. Here the same donor sample is sequenced twice, to evaluate the outcome of this highly possible situation in the future. For such cases, when there are more than one WGS sequence of the same donor, we state our remarks on how to exploit all the data fruitfully. In Section 1, we describe the methods used in the study. In Section 2, we present the results of the study and show the shared and exclusive sets of different SNP groups. And finally, in Section 3, we provide our remarks on the results and conclude.Genomic DNA from two individuals were collected and purified in 2011, only once from the blood of two volunteers for a previously published study . The souSB1  and SB2 , and the data generated at TÜBİTAK for the same individuals as ST1 and ST2.The genomes of the two individuals were already sequenced using Illumina HiSeq2000 in 2011 at BGI Shenzhen . The samTo discover SNPs and short indels, we mapped the reads to the human reference genome (NCBI GRCh37) using the BWA aligner (version 0.6.2) [SB1, ST1, SB2 and ST2.After the initial alignment and the PCR-duplicate removal, we realigned the indel-containing reads to the reference genome using GATK Realigner tool. We then used the GATK UnifiedGenotyper tool to generate the SNP and indel call sets. We also used the GATK HaplotypeCaller as an alternative approach for variant calling. Next, we eliminated likely false positives using the GATK Variant Quality Score Recalibration (VQSR) tool with GATK resource bundle v2.5. Finally, we further filtered the call sets using the GATK VariantFiltration to remove low confidence calls (SnpCluster filter to remove SNPs if there are more than 3 SNPs in a 10 bp window). We applied the same variant calling pipeline to each of the four datasets separately: SBT1, and SBT2) generated at BGI and TÜBİTAK.As a second experiment, we tested whether pooling data from multiple sequencing runs for the same samples improve callset reproducibility. Our main question here was to understand if the slight differences in the coverage and depth of the datasets could be ameliorated by merging data for discovery, and if this would improve genotyping accuracy. For this purpose, we applied the SNP/indel detection pipeline to both samples by pooling two sequencing datasets . Primary run IDs relevant to this study are: SRR839600 for X to 49.2X. The reads sequenced at TÜBİTAK (ST1 and ST2) were 14bp longer than the reads sequenced at BGI (SB1 and SB2), and the GC contents were similar  were compared with each other to quantify and characterize any differences. The SNP and indel statistics are summarized in We generated SNP call sets for each sample and for pooled data sets (Methods). 4 SNP call sets: Separately generated call sets. Briefly, after potential false positive removal (Methods), we observed approximately 95% agreement between the pairs of SNP call sets generated from both genomes ( genomes . The ind genomes . The numhttp://genome.ucsc.edu), CNV call sets from the 1000 Genomes Project [To understand the causes of different calls from the same genomes, we investigated the underlying sequence content of the discrepancies of novel SNP and indel calls in detail. First, we downloaded the human reference genome annotations  from the UCSC genome browser ( Project . We then Project , and 0.3The significance of the discrepant SNP and indels in terms of predicted functionality was more closely investigated . We founThe number of shared and discrepant SNP and indel calls are shown on With the improvements in cost efficiency, speed, and analysis algorithms, HTS platforms are now being considered to be used routinely as part of health care. This assumption prompted a pilot project called ClinSeq  that aimIn an effort to answer this question, we analyzed the genomes of two individuals, each sequenced twice using the same technology, albeit at different locations. Since our aim was to investigate the maturity of sequencing platforms in this study, we used the same tools to characterize both single nucleotide and short indel variants. Under the assumption of 100% robustness, one would expect to characterize the same set of variants in both sequencing datasets from the same genomes, however, this is not what we found.We believe multiple factors contribute to this effect. First, since the library preparation is different, one may expect difference in GC% bias, as clearly seen in Sequencing machines, alignment and genomic variant discovery and genotyping algorithms change rapidly, and one must be careful when interpreting results. Here we demonstrated potential problems that may arise within HTS-based studies. Discrepancies between call sets generated from the same genomes may be complementary false positives and false negatives in each callset, in addition to common genotyping errors. Luckily, much of the differences were found within non-genic regions and common repeats, which are of less importance for most studies.