Single source and multiple donor (mixed) samples of human mitochondrial DNA were analyzed and compared using the MinION and the MiSeq platforms. A generalized variant detection strategy was employed to provide a cursory framework for evaluating the reliability and accuracy of mitochondrial sequences produced by the MinION. The feasibility of long-read phasing was investigated to establish its efficacy in quantitatively distinguishing and deconvolving individuals in a mixture. Finally, a proof-of-concept was demonstrated by integrating both platforms in a hybrid assembly that leverages solely mixture data to accurately reconstruct full mitochondrial genomes. The most recent instrument and chemistry to perform single-molecule sequencing is the MinION™  which combines a customized protein nanopore, a sequencing flow cell, and accompanying electronics into a palm-sized device [de novo mutation detection in heterogeneous or mosaic mitochondrial populations.High-throughput, or massively parallel, sequencing has been a boon to many fields interested in omics, ranging from basic research to precision medicine to even the forensic sciences. Some technologies now offer the capability of single-molecule sequencing, generating reads averaging several thousand bases in length , 2. The d device , 4. Ashtd device  showed td device , whole gd device  of bacted device –9. This d device –13. Inted device –17, but The mitochondrial genome is an ideal molecule to study because its population genetic variance is well-defined; it lacks recombination, and is inherited maternally. Its haploid state, compact size , and concentration of variation in the control region have made the mitochondrial genome an informative target for numerous applications , 18–22. Even with its relatively high error rate, it is possible that the MinION system could assign the variant states correctly to contributors  of a mixed sample without relying on lower error rate short read MPS generated data from non-degraded samples. In the study herein, well-defined single source mitochondrial genome samples of the U2e1a1 haplogroup were mixed and sequenced blindly to determine the efficacy of the MinION system to accurately characterize the individual contributors of the artificially mixed sample. An unbiased approach was taken to evaluate single nucleotide polymorphisms (SNPs) identified by the MiSeq and MinION sequencers. Using a naïve approach, the variant allele frequency (VAF) (defined as the fraction of reads representing a variant in a heterogeneous (or heteroplasmic) sample) of the MiSeq platform was used to establish a conservative truth set with the intent to limit the number of false positives. Since alignment strategies and chemistries differ between the MiSeq and MinION technologies, it was deemed better to apply a global VAF threshold at the outset of SNP discovery and not to apply strict quality filters to the MiSeq generated data when comparing results. This approach ensured a platform independent, agnostic evaluation where local realignment and filtering of alignment artifacts present in loci of known variation length heteroplasmy compounded by homopolymeric repeats , 43. PutThe three single source samples  and one mixture (1:1 concentration of 005 and 047) were sequenced on the Illumina MiSeq and Oxford Nanopore Technologies MinION. The samples 005 and 047 were chosen for the mixture because they share the same haplogroup and can only be distinguished by private SNPs with genomic distances greater than typical short-read sequencing workflows. Alignment coverage for the mitochondrial genome in each of the four experiments and the average across the three single source samples are shown in SNP concordance between the platforms was measured by performing a broad assessment of VAF in each of the three single source samples. The SNPs discovered in the variant calling using the MiSeq data were used as the ground truth. F1-Scores, the harmonic mean of precision and recall, were plotted across the range of VAFs for each individual . The higA strong overall concordance was observed between the two platforms with F1-scores of 0.982  0.946 , and 0.957  for the three single source samples 004, 005, and 047, respectively. The site-specific agreement per SNP and coverage can be seen in must be lower than single source. In theory, a SNP should at most contribute half of the reads at any given locus and would be exactly half of that of a single source experiment. A private SNP should be represented by half of the reads and a shared SNP would be represented by all of the reads. Therefore, it should be no surprise that a shared SNP is far more likely to be detected alongside the obligate decrease in the VAF threshold. The F1-scores across all VAFs are similar until reaching 0.45 for the MiSeq to MiSeq comparison  was analyzed using both the MiSeq and MinION platforms. The combined single source 0.90 VAF MiSeq truth sets for 005 and 047 were used to explore a spectrum of possible detection VAFs in the MiSeq mixture . The MiSmparison . The 0.2a priori knowledge of data from the phased MinION and single source MiSeq reads  was extracted from whole-blood, previously described by King et al. . All samThe whole mitochondrial genome of each sample was enriched by generating two overlapping amplicons, ~8.3 kb and ~8.6 kb, by long-PCR amplification using the TaKaRa LA PCR Kit , following the protocol previously described in King et al. . The priAmplified product of samples 004, 005 and 047 were normalized to 0.2 ng/μL, and the latter two samples were mixed 1:1. Samples 005 and 047 were selected for the mixture study because they share the same haplogroup assignment (U2e1a1) . LibraryAmplified product of samples 004, 005 and 047 were purified using the Qiagen QIAquick PCR Purification Kit , per manufacturer’s instructions. Samples were analyzed using the MinION R7.3 chemistry (FLO-MAP003). Library preparation was carried out using the manufacturer’s instructions for amplicon sequencing using 1 μg of total input DNA (0.5 μg each of two amplicons from single contributors or 0.25 μg each for each amplicon from two-person mixtures). Flow cells were run for approximately 24 hours in total including “topping up” once 16 hours into the run. Basecalling was performed in Metrichor using versions 1.69 (sample 004) and 1.99 .Poretools  version M x N comparisons were made where the sets M (MiSeq) and N (MinION) both contain 19 SNP subsets comprised of m0.05, m0.10, …, m0.95 and n0.05, n0.10, …, n0.95 ranging the VAF (-F Freebayes option) from 0.05 to 0.95 in increments of 0.05 for each platform. Each SNP subset was filtered in the following way: variants were normalized and both biallelic block substitutions and multi-allelic variant calls were decomposed into individual calls using vt [For single source samples, variants were called on the mitochondria aligned data with Freebayes  version using vt  version using vt .M (MiSeq) call sets comprised of m0.15, m0.17, …, m0.53 subsets with varying allele from 0.17 to 0.53 in increments of 0.02 were made. A set N with a single member n0.90 was made using the combined truth sets from the 0.90 VAF single source calls sets from 005 and 047. The call set n0.90 contained 31 shared SNPs and 12 private SNPs between 005 and 047 . The MiSeq mixture VAFs from 0.23 to 0.29 were all equivalent call sets, where np 4736 is a false negative private for sample 047; however, a previously false negative shared SNP at 16,183 is detected in the mixture (M (MinION) call sets comprised of m0.15, m0.17, …, m0.53 subsets with VAFs from 0.17 to 0.53 in increments of 0.02 were made to compare against the set N with single member n0.25 of the MiSeq Mixture call set. All calculations for recall, precision, and F1-scores were made using the below definitions, and concordance was plotted using Circos [Mixture samples were analyzed in a similar manner. The only difference in approach was using a ploidy of 2 when running Freebayes and how the truth sets were generated. A set of  mixture  and 38. g Circos  version SNPs were assessed as True Positives, False Negatives, and False Positives at each site using the various MiSeq SNP sets as the ground truth for these comparisons.MinION reads covering the two full-length amplicons were extracted by separating bam records that intersected the bed interval  of 1000 to 8000 and an interval of 10,000 to 15,000. These reads were required to exceed 8000 bases to capture only the reads from the fully extended amplicon. Phasing was performed with SAMtools  phase veMiSeq read pairs were extracted and assigned from the MiSeq bam file using a combination of BEDTools and JVarkit git commit 865252a , 54 at tS1 TableThe complete set of SNP and INDEL calls presented here are from the previous publication . Sample (DOCX)Click here for additional data file.S1 FigA). The SNP at np 16,183 is excluded from the truth set due to poor alignment and length heteroplasmy of repetitive Cs in the locus. B) The repetitive sequence proceeding np 310 contains an insertion of a C that causes a false positive SNP call when the VAF is adjusted too low.Two 15bp windows are shown for both MiSeq and MinION alignments for sample 005 using IGV. The VAF used in the coverage track is set to 0.60 to display relative proportions of reads that are below the MiSeq threshold. (TIFF)Click here for additional data file.S2 Fig10 scale. The inner ring contains the per SNP concordance using a VAF of 0.90 for the MiSeq and 0.65 VAF for the MinION. The text color denotes the categorization of each SNP using a VAF combination providing the highest concordance: green text indicates that the SNP was a true positive, black text is for false negatives, and red is for false positives in the MinION call sets. An asterisk (*) means the SNP was not observed with the MiSeq VAF and not used for calculating recall, precision, and the F1-Score. Black arrows indicate the locations and orientations of the primers used for amplification.The coverage depth per base is shown for the MiSeq (orange) and MinION (blue) shown on the outer ring using a log(TIF)Click here for additional data file.S3 Fig10 scale. The inner ring contains the per SNP concordance using a VAF of 0.90 for the MiSeq and 0.65 VAF for the MinION. The text color denotes the categorization of each SNP using a VAF combination providing the highest concordance: green text indicates that the SNP was a true positive, black text is for false negatives, and red is for false positives in the MinION call sets. An asterisk (*) means the SNP was not observed at with the MiSeq VAF and not used for calculating recall, precision, and the F1-Score. Black arrows indicate the locations and orientations of the primers used for amplification.The coverage depth per base is shown for the MiSeq (orange) and MinION (blue) shown on the outer ring using a log(TIF)Click here for additional data file.


Genome-wide association study is especially challenging in detecting high-order disease-causing models due to model diversity, possible low or even no marginal effect of the model, and extraordinary search and computations. In this paper, we propose a niche harmony search algorithm where joint entropy is utilized as a heuristic factor to guide the search for low or no marginal effect model, and two computationally lightweight scores are selected to evaluate and adapt to diverse of disease models. In order to obtain all possible suspected pathogenic models, niche technique merges with HS, which serves as a taboo region to avoid HS trapping into local search. From the resultant set of candidate SNP-combinations, we use G-test statistic for testing true positives. Experiments were performed on twenty typical simulation datasets in which 12 models are with marginal effect and eight ones are with no marginal effect. Our results indicate that the proposed algorithm has very high detection power for searching suspected disease models in the first stage and it is superior to some typical existing approaches in both detection power and CPU runtime for all these datasets. Application to age-related macular degeneration (AMD) demonstrates our method is promising in detecting high-order disease-causing models. Detecting such model from a dataset with hundreds of thousands of SNPs is facing following two challenges6.With the rapid development of high-throughput genotyping technology, single-nucleotide polymorphism (SNP) data increases explosively, which establishes favorable conditions to detect cause of disease for researchers. Though genome wide association study (GWAS) has successfully identified many single SNP genetic variants associated with disease status or phenotypic traitsk-way SNP-combinations for a dataset with n SNP markers equals k-way SNP combinations at whole-genome scale when k > 3, even with high-performance computers available at present. The second challenge arises from the diverse nature of SNP interaction models, such as additive effect model, non-additive effect model and statistical epistasis model. Furthermore, some spurious multi-loci combination models may also be associated with phenotype due to statistics with high degree of freedom, the huge number of hypothesis tested and limited sample sizes8 which all could result in a high false discovery rate (FDR).The first challenge is the enormous computation burden imposed by the combination explosion of genotype. The number of candidate 22 have been proposed for improving the detecting speed. SNPHarvester algorithm9 uses stochastic strategy to generate multiple paths for identifying k-way SNP interaction models. BEAM23 introduces a Bayesian partition model and employs a Markov chain Monte Carlo sampling strategy to discover the model with maximum posterior probability. In Boost5, Boolean operation is adopted to examine all pairwise SNP interaction using exhaustive search. Sangseob Leem et al.11 introduces a fast algorithm for detecting high order epistatic interactions by performing clustering with k-means algorithm on all SNPs, in which the candidates of k-way are selected from the k clusters, reducing the number of combinations. Collins RL et al. use multifactor dimensionality reduction (MDR) to detect three-locus epistatic interaction12, the ReliefF algorithm is used first to select a small candidate set for reducing computation burden. Dynamic clustering and cloud computing10 are also employed to detect high-order genome-wide epistatic interaction in which forty virtual machines are constructed for speeding up the detection of multi-locus epistasis. Jonathan et al. present a multipoint method for studying the genome-wide association by imputation of genotypes13, which is a model-based imputation method for inferring genotypes at observed or unobserved SNPs.For the first challenge, several multi-loci detection algorithmsThe main problem of these algorithms is their huge computational cost and preference to some types of disease models, e.g., to the models with obvious marginal effect.et al. propose Cuckoo search (CS) algorithm14 to explore multi-loci epitasis. In the CS, by dividing SNP sites into M groups according to correlation among SNPs, only k-way (k < = M) SNP combinations are selected out of the M groups. Ant colony optimization (ACO) is adopted in AntEpiSeeker15 and MACOED17, where the former employs chi-square test(χ2) score to evaluate association between SNP combinations and phenotype, while the latter adopts Bayesian based K2-score and logistic regression based AIC in screening SNP combinations in the first stage, and χ2 is adopted to test the significance difference between control and case in the second stage. Shang J. et al. use particle swarm optimization (PSO) to discover SNP-SNP interactions18, which uses opposition-based learning, dynamic inertia weight and postprocedure to enhance the search ability of the PSO for finding SNP-SNP interactions. Although swarm intelligent search have the ability to speed up detection process, for high-order disease models unfortunately, they are easy to trap into local search. Table Recently swarm based intelligent optimization algorithm attracts much attentions in reducing computational burden due to its power of effectively resolving NP-hard problems in polynomial time. M Aflakparast 27 have been proposed. None of the methods are universally better but with either low statistical power or preference to some types of disease models.As to the second challenge which relates to diversity of disease models, logistic regression, linear regression, LD- and haplotype-based method, and Bayesian network scoringTo reduce computation burden and adapt to diversity of various types of disease models, we propose a Niche Harmony Search Algorithm to detect high-order SNP combinations (NHSA-DHSC) associated with complex diseases. It follows two stages: screening and testing. In screening stage, a new niche technique is merged into harmony search algorithm for exploring all suspected disease-causing SNP combinations. To quickly find as more types of disease-causing models as possible, we employ three computationally lightweight and functionally complementary evaluation functions, Bayesian network based K2-score, Gini-score and Joint entropy, for calculating the association between SNP combinations and disease status. The suspected SNP combination models gained from the screening stage are stored into a candidate set (CS). In testing stage, a modified G-test method is used to test the authenticity of the candidate SNP combination models in CS.Our experiments indicate that the proposed NHSA-DHSC is superior in detection power, running speed and identification ability for diverse disease models compared to current intelligent algorithms.k-way disease-causing models from all k-way SNP combinations where the k-way disease-causing model denotes a k-SNPs combination that has joint effect on the disease status (k is the number of SNPs). It is responsible for significantly increasing the risks of complex diseases29.Figure k-way SNP combinations, in which only a few models are disease-causing ones. Niche technology is used to obtain as many types of pathogenic models as possible from a large number of SNP combinations. To search for them efficiently, harmony search (HS) algorithm is used and three harmony memories HM1, HM2 and HM3 are employed to store candidate solutions: HM1 stores candidate SNP combinations screened by Bayesian network based K2-score, and HM2 stores the ones by Gini-score. The two scores are complementary in that the K2-score is superior in identifying models with low marginal effects and gini-score is, compared to K2-score, more capable of identifying high-order models when their genetic heritability (H2) is very low30. Joint entropy is adopted to evaluate the harmonies in HM3. Unlike K2-score and Gini-score, joint entropy is a heuristic factor for guiding the HS algorithm to quickly explore the high-order disease models with very low or even no marginal effects (DNME). We propose joint entropy as a heuristic factor since we found via our data experiments that the factor is really powerful in identifying some high-order disease models with even no marginal effect, for which the K2-score and Gini-score are powerless.As shown in Fig. k-way SNP-combinations randomly from all k-way SNP-combinations.Initialize HM1, HM2 and HM3 by selecting new repeatedly using HS rules to update the HM1, HM2 and HM3 according to the natural evolutional theory of “Survival of the fittest”  in Methods section).Generate new solution HUntil when HM1, HM2 and HM3 cannot be further updated, niche algorithm is automatically triggered to identify new niche  in Methods section). Meanwhile, some best solutions are chosen from HM1, HM2 and HM3 separately to substitute some worse solutions in elite sets Es1, Es2 and Es3.Reinitialize the HM1, HM2, and HM3 randomly, where the solutions from identified niche regions cannot be visited and evaluated for avoiding repeated search in the niche regions.When terminal condition is satisfied, solutions in Es1, Es2 and Es3 are merged and stored in candidate set (CS).Our screening process is as follows.31 to further verify the authenticity of the candidate models in CS.(Testing Stage) Some spurious disease models may be included in the CS in the first stage. In the second stage, we adopt modified G-test methodk-way SNP combination models having strong association with phenotype can be extracted one by one. The detailed process of the NHSA-DHSC algorithm is introduced in Methods section and the related techniques are presented in supplementary info file.In this study, the niche technique is mainly to discover some SNP-combinations with strong marginal effect and make the HS find all suspected disease-causing models, where the marginal effects do not only come from single SNP markers, but also may be synergistic effects of multi-SNP makers. In the search process of HS, the position and size of each niche region are recorded into a taboo table for forcing the HS algorithm to search new solutions in unexplored regions. In this way, all possible 23, SNPHarvester9, BOOST5, CSE14 and MACOED17).20 simulated disease models, which contain twelve 2-locus Disease models with Marginal Effects (DME) and eight high-order Disease models with No Marginal Effect (DNME), are employed to investigate the performance of the NHSA-DHSC algorithm. The simulation datasets for the 20 disease models, real Age-related macular degeneration (AMD) data, and seven evaluation metrics are introduced in Methods section in detail. We compare the experimental results of NHSA-DHSC with those gained from five state-of-the-art algorithms  As can be observed from Fig. st power of it obviously outperforms the CSE. Fig.  As shown in Fig.  SNP combinations associated with the AMD. The corresponding results are respectively listed in sheet S-2~S-4 of Supplementary Dataset File NHSA-DHSC algorithm are employed to detect -way SNP combination network is created using software Cytoscape 3.332 (http://www.cytoscape.org/). There are 571 nodes and 565 edges in the network. The edge of the network denotes a 2-way SNP-combination with p-values from G-test (case and control) less than 1e-8. The Node in the network represents a SNP locus which has joint effect with adjacent nodes on phenotype. Figure In Fig. CFH, BBS9, NA, INPP4B, ABL1 and ANKS1B). In Fig. Figure rs380390’,‘rs10272438’,‘rs1329428’,‘rs1363688’,‘rs1394608’) from AMD data. Then the proposed NHSA-DHSC is used to detect high-order SNP-combinations associated with AMD from the remaining SNP loci. The results are listed in sheet 2 of Supplementary Dataset File In order to further investigate the unknown disease models from AMD dataset, we first remove five widely reported SNPs . Figure Figure rs380390, rs1329428 and rs10272438. Degrees of the three SNP nodes are respectively equal to 421, 32 and 34, where the rs380390 and rs1329428 (both in an intron of the CFH gene) have been widely believed to be associated with the AMD38, the rs10272438 (in the BBS9 gene) also has been reported in . In addition, the rs1363688 (degree = 12) and rs7104698 (degree = 9), which are not in known gene regions, have been separately reported in refs rs3775652  is equal to 3.24 × 10−6 that is larger than the threshold p-value 1.0 × 10−8. However the rs10272438 and rs3775652 are connected with all other SNP nodes. As a consequence, we speculate that rs380390 and rs1329428 may be the disease-causing variation locus and both they were independently associated with Age-related macular degeneration (AMD), but there is very low synergetic effect to AMD among them. The two SNPs may be the potential driver variation locus to AMD and the CFH is the potential driver gene on the basis of single nucleotide variations.As shown in Fig. CFH, BBS9, NA, INPP4B, ABL1 and ANKS1B) have the most adjacency edges. There are 177 SNPs mapped to the CFH gene, 16 SNPs mapped to the BBS9 gene, 17 SNPs mapped to the INPP4B gene, 5 SNPs mapped to the ABL1 gene and 5 SNPs mapped to the ANKS1B gene. The NA (375 SNPs are mapped to NA) denotes non-gene coding region. In these genes, the gene CFH has been widely believed to be associated with age-related macular degeneration (AMD) disease44. Although the other five genes have not been reported to be related to the AMD, they are all associated with other complex disease. For example, it has reported that the gene BBS9 is associated with consanguineous Pakistani family with Bardet Biedl syndrome45, INPP4B is an oncogenic regulator in human colon cancer40 and is upregulated and functions as an oncogenic driver through SGK3 in a subset of melanomas46. Other four important genes  are also associated with somatic mutations in cancers.In the gene network of Fig. rs3775652’, ‘rs7104698’, ‘rs10511467’, ‘rs10512413’, ‘rs1930022’) having more adjacent nodes, where the SNP ‘rs3775652’ is in gene INPP4B, and SNP rs10512413 is in gene ABL1, other three SNPs are in non-gene coding region (NA). We can see evidently from Fig. INNP4B is related with many genes. Therefore, it can be speculated the SNPs ‘rs3775652’ and gene INPP4B should also be important effect on AMD.As shown in Fig. 3-way SNP-combinations with p-values from G-test less than 1e-11 and top eight 4-way SNP-combinations with p-values from G-test less than 1e-12, respectively.Tables 3-way SNP-combinations all contain the SNP locus that have strong marginal effect to AMD, such as rs380390, rs10272438 and rs1363688.We can see from Table rs2157998, rs1334722, rs1740752, rs7104698), ,  that don’t contain the known SNP locus associating with AMD and each SNP locus has low marginal effect. We can see from the last column that the SVM prediction accuracies of these SNP combinations are all larger than 75%, it may be worth to study for biologist.In Table In addition, we can notice that the all p-values from chi-square equal zero in Tables For high-order disease-causing models in which some individual SNP locus have strong marginal effects, existing intelligent optimization algorithms are easily trapped into local search, resulting in repeated search in a small region (part of SNPs), leading to the loss of the optimal solution. To tackle the problem, our method adopts niche technique to dynamically identify the SNP locus with marginal effect and then uses taboo table to store the identified SNP locus, which can effectively avoid repeated search in a local region and find all possible disease-causing models in a short time.30.For diverse disease-causing models, to our best knowledge, many existing algorithms usually employ single scoring method  to identify disease models, which usually results in preference to some specific types of disease model and failure to identify other types of disease models. To address the problem, NHSA-DHSC employs two lightweight identification methods (Bayesian network based K2-score and Gini-score), which has been turned out to be complementary each other in literatureFor some high-order disease-causing models with very low or even no marginal effect, existing intelligent optimization algorithms  and heuristic algorithms (BEAM) always are powerless, which is because no suitable heuristic factor can be found by traditional scoring methods for detecting the disease models. After a large amount of experiments, we find that joint entropy can provide some heuristic clues for guiding the HS to search the high-order disease models. Therefore, in this work, we employ three lightweight and complementary evaluation methods to identify the disease-causing models, where the joint entropy is utilized as heuristic factor to explore the disease models with very low or even no marginal effects.The experimental results on simulation DME datasets demonstrate that the proposed NHSA-DHSC algorithm has very high detection power in the first stage and very low FDR values. Compared with two outstanding intelligent search algorithms MACOED and CSE, our method has very obvious advantages on runtime, power, MEs and FDR.For eight high-order DNME models, our method can quickly find the disease-causing models except for DNME-7, which demonstrates that our method has ability to detect some of high-order disease models.rs380390’, ‘rs1329428’, ‘rs10272438’, ‘rs1363688’) associated with AMD and also found some new SNP markers (e.g. ‘rs3775652’) that are associated with AMD, we notice that two SNPs  widely believed to be associated with AMD may be different driver factors to AMD, they have not strong synergistic effect to AMD. In the analysis of gene network, many SNP-combinations are mapped into genes CFH, BBS9, ABL1, ANKS1B, IMMP2L, INPP4B, SGCD and non-coding regions, where CFH has believed to be associated with AMD, the other genes also have associated with some complex diseases. Therefore, we can speculate that the SNP  and gene CFH are associated directly with AMD; others may be indirectly associated with AMD. In other words, SNP  and gene CFH may be driver loci to AMD, others are passengers.In the real AMD experiment, the NHSA-DHSC successfully found some widely reported SNP locus , Y be the phenotype variable with values of {y1, y2,…, yJ}; we represent the homozygous major allele, heterozygous allele and homozygous minor allele as 0, 1 and 2, respectively. For a k-way combination model, I denotes the number of genotype combinations (there are 3k genotype combinations for a k-way SNP variables), J is the number of phenotype states Y . ni is the number of samples in the dataset with SNP loci taking the value of ith genotype combination, nij represents the number of samples that the ith genotype combination actually associated with phenotype yj.Let a set of SNP variables {Definition (high-order association). Let S = {S1, S2,…SK} be a set with k SNP loci. ϕ is a score function for evaluating the association between S and phenotype Y. The k-way SNP combination S is said to be synergistically associated with phenotype Y if and only if k-way disease-causing combination model can be expressed asth SNP site, The optimization problem for finding a 53. It mimics the process of improvising a musical harmony when a music orchestra is aiming at composing the most harmonious melody . HS algorithm does not dependent on substantial gradient information and an initial search point and it can solve both continuous and discrete combination optimization problems, efficiently. However, it is still not good enough to solve complex multimodal optimization problems if our demand for multiple candidate solutions is more prominent. Recently, some strategies are adopted to find multiple solutions in the study of intelligent optimization algorithm. For example, in refs Harmony search (HS) algorithm is a swarm intelligent optimization algorithmk decision variables. Some good harmonies form a harmony memory (HM) which later would be used for creating better harmonies. The harmony memory size (HMS) is defined as the number of harmonies in HM.For an optimization task, each harmony corresponds to be a vector consisting of k-way SNP combination, and the goal of optimization is to find some best harmonies (k-way model) which are associating with disease status Y.In this study, each harmony of HM denotes a k-way genetic variations as possible, we propose a niche HS algorithm (named NHSA-DHSC) for detecting k-way SNP combinations associated with phenotype, in which niche strategy55 are merged into HS algorithm for enhancing global exploration power of HS.To explore as many suspected The flowchart of NHSA-DHSC for the first screening phase is shown in Fig. Figure In the search process of NHSA-DHSC, the niche identification algorithm is automatically triggered to identify a niche region when the harmony memories HM1, HM2 and HM3 cannot be updated during several iterations. Within a niche region, the radius of the niche is recorded for preventing generating new solution in the niche, which can effectively avoid the search algorithm trapping into a local region. In the supplementary info file, the niche technique is introduced in detail.Three lightweight evaluation methods, Bayesian network based K2-score, Gini score and joint entropy, are adopted for improving detection power and speed of the HS algorithm, where the former two are for the adaptability of the diversity of disease models, and the latter is for the disease models with low marginal effect.30, some models that cannot be identified correctly by K2-score can be correctly identified by Gini-score and vice versa. Furthermore, as can be seen in equations  is the estimated probability that the ith genotype combination actually associated with phenotype Pi = ni/n) is the percentage of ith genotype combination in sample set.By for calculating the association of SNPs with the phenotype, Bayesian network based K2-score and Gini index (see supplementary info file) are first consideration. The two scoring methods are complementary for identifying diverse disease modelsquations –2, both k-way disease-causing model is just as if an isolated point in a very large search space, none of heuristic factors (clues) can guide HS algorithm to find the disease-causing model quickly.In general, intelligent search algorithm (e.g. HS) can obtain the global optimization solution with a very small number of evaluating to the trail solutions. It is based on some heuristic factors available to speed up the search process. Similarly, for the detection of disease causes, the heuristic factors are also very important, such as marginal effects of individual SNP to phenotype, which can guide the HS to search the disease-causing loci quickly. Nevertheless, sometimes individual SNP site contained in a high-order disease-causing model has no marginal effect on phenotype, and available evaluation methods, such as Bayesian network, logistic regression, mutual information and many more, cannot distinguish whether a k-way SNP-combination contains portion of disease-causing SNP locus. In this scenario, a k-way (k > 2) SNP-combination including part of disease-causing SNP markers often has smaller joint entropy than that including no SNPs in the causative combination model, which is because the genotype of disease-causing SNP-combinations often has smaller divergence than that of no disease-causing SNP-combinations. In other words, joint entropy has the capability of differentiating SNP combinations containing part of causative SNPs from the SNP combinations containing no causative SNPs. However, for the disease data with very low marginal effect, existing scoring methods usually have no ability to distinguish the SNP combination including some of risk factors from other SNP combination models, such as the Bayesian network based K2-score, logistic regression and Gini score.To address the problem, we did a great deal of experiments and found a 17, the two compared scoring methods are very effective for detecting disease models with marginal effect, we can see from the results that the joint entropy is more effective for detecting high-order disease-causing models with very low marginal effect than Bayesian network based K2-score and logistic regression.In supplementary info file, we compare joint entropy with logistic regression based AIC-score and Bayesian network based K2-scorek-way SNP-combination ni is the number of samples in the dataset taking the ith genotype combination, n is the total number of samples in the dataset.For a k-way SNP-combination, and the computation cost of the three scoring functions is also non-additivity, which means that we need not to repeatedly calculate the number of sample taking i-th genotype combination three times.It can be seen obviously from equations , 2) and and2) anX and phenotype Y are considered simultaneously. In this work, the main role of joint entropy is rather than an identification method of disease-causing model, it is utilized as heuristic factor to guide the harmony search algorithm to rapidly locate the disease-causing SNP markers with no or very low marginal effect.It’s important to note that the equation  consider31. Compared to chi-squared test, the G-test will lead to the same test effect for samples of a rational size. However, for some cell case it is always better than the chi-squared test56. And for testing goodness-of-fit, G-test statistical method is more efficient than Pearson χ2 test method58.G-test is a maximum likelihood statistical significance testk-way SNP combination model, the formula for calculating G value is as followOij and Eij are respectively the observed numbers and expected number of genotypes when phenotype takes the state yj and genotypes take ithk-combination. The ln denotes natural logarithm function. We can get the observed number Oij from dataset by using simple counting statistics method. The expected number Eij of genotype frequency could be obtained according to Hardy-Weinberg principle59.For 2-way SNP combination has nine 2-way genotypes as Table For high-order SNP combination, what often happens is that the number of some genotype combinations equals zero or very small, for example, a As shown in Table To enhance the statistical precision of G-test, we do a minor modification for calculating G-test value as follows,d (d = (I − 1)(J − 1) is modified correspondingly, as follows:The degree of freedom 17 have both marginal effects and interaction effects, which contain four multiplicative models (DME-1~DME-4), four threshold models (DME 5- DME 6) and four concrete models (DME 7- DME 12).The 12 DME models2 = 0.005, MAF = 0.05, 0.1, 0.2 and 0.5) are multiplicative models with two disease locus, in which the disease prevalence given the frequency of genotype combination increases multiplicatively with the incremental presence of the disease. The genetic heritability (H2) of DME 1- DME 4 are all equal to 0.005, minor allele frequencies (MAF) of them equal 0.05, 0.1, 0.2 and 0.5, respectively. It is very difficult to identify the disease locus from the four DME models due to very low genetic heritability.DME-1~DME-4  are the threshold models in which the prevalence of genotype frequency does not increase until the number of disease alleles pass the threshold. The four DME models have strong marginal effect and interaction effect, in which a SNP marker with strong marginal effect would form many false disease models with other SNP markers that are not truly associated with the phenotype state.DME-5~DME-8  are the concrete model that has low marginal effect and strong interaction effect. Characteristics of these twelve DME models are compared in Figs. 60 (https://sourceforge.net/projects/gametes/).DME-9~DME-12  constraints. The data sets of DNME models are downloaded from http://discovery.dartmouth.edu/model_free_data/, which contain 8 DNME data models Runtime: The time it takes for finding a disease-causing model from beginning search to the end.#S is the number of having found the disease-causing model from all #T dataset .Power = #S/#T. Power is a measure of the capability for detecting the disease-causing models from all dataset, where the k-way SNP combination models have been evaluated currently is defined as mean evaluation times (MEs) and the elapsed time from start to end is denoted as computation time (Runtime). The search algorithm would be terminated if the number of SNP combinations that are evaluated using evaluation functions is larger than maximum allowable number of times.MEs denotes the mean number of SNP-combinations that need be calculated the association with phenotype using scoring methods before the disease-causing model is found. In the experiment, if the known disease-causing models have been found, the searching algorithm would be terminated automatically ahead of meeting termination condition, the number that True positive rate: Specificity: Accuracy: False discovery rate: In simulation experiments, we adopt seven indices  to evaluate the performance of algorithms. The seven indices are defined as follows,The TPR, SPC and ACC in this study are employed to measure the statistical precision of the hypothesis testing method for having found disease-models in the screening stage. The TP is equal to the number of disease-models that have passed the threshold  of the testing method, FN is the number of disease-models failed to pass the threshold of the testing method. FP is the number of non-disease-models passed the threshold, TN equals the number of non-disease-models failed to pass the threshold.The parameters of NHSA-DHSC are set as follows:The sizes of HM1, HM2, HM3, Es1, Es2 and Es3 are all equal to 50 for dataset with 100SNPs and 100 for dataset with more than 100SNP sites, the maximal size of candidate set (CS) is 10. HMCR = 0.9 and PAR = 0.35. In the second stage, the threshold of p-value equals 0.05/N . In order to prevent from the preference of location, we randomly embed the locations of disease-causing SNPs into the simulation data.For CSE, the fraction of eggs discarded each generation is set to 0.25, maximum number of steps to take in a levy flight is set to 1, the number of groups is 10, and the number of nests is set to100.The parameters of MACOED are set as: the number of ants is 500 for dataset with 1000SNPs, and 50 for dataset with 100SNPs.k (for k-way models).For SNPHarvester, the maximal and minimal order of interactions is equal to The parameters setting for Boost and Beam are set to the default value of original papers.max) Tmax = 4500 for dataset with 100 SNP sites, Tmax =60000 for 1000 SNPs.To make a fair comparison, for three intelligent search algorithms NHSA-DHSC, CSE and MACOED, we set the same terminal condition  Core(TM) i3–3470 CPU@3.2 GHz, 8 GB memory, and all the program codes were written in MATLAB R2014b.The sizes of HM1, HM2, and HM3 are set to 500.The sizes of Es1, Es2, and Es3 are all equal to 2000.HMCR = 0.9 and PAR = 0.35.max = k×3E+6. (k is the number of SNP sites of high-order SNP combinations)TThe experimental environment is the same as that of simulation dataset.63 and making sense of the rare variants at GWAS are worth to study in the future.It has been widely acknowledged that multiple SNP loci may be an important contributor to pathogenic factors of complex disease, however, at present there is still no an effective approach in detecting multi-loci disease-causing models at GWAS due to enormous computation burden. Therefore, detecting high-order disease models has many rooms to explore using high-performance and cloud computing. In addition, with the rapid development of new gene sequencing technique, detecting the epistatic interactions in non-coding genomic regionsSupplementary infoDataset 1Dataset 2source codes of NSHA-DHSC


Zea mays L.) poses a significant risk to human health as it is ingested via the food chain. A genome-wide association study (GWAS) was conducted in a population of 269 maize accessions with 43,737 single nucleotide polymorphisms (SNPs) to identify candidate genes and favorable alleles for controlling Cd accumulation in maize.Accumulation of cadmium (Cd) in maize  and SYN36598  on chromosome 2 was associated with leaf Cd under three soil conditions varying in Cd content in 2015 and 2016. The significant SNP (SYN25051) at position 161,275,547 could explained 27.1% of the phenotype variation. Through QTL mapping using the IBMSyn10 double haploid (DH) population, we validated the existence of a major QTL identified by GWAS; Our findings provide new insights into the genetic control of Cd accumulation and could aid rapid development of maize genotypes with low-Cd accumulation by manipulation of the favorable alleles.The online version of this article (doi: 10.1186/s12864-017-4395-x) contains supplementary material, which is available to authorized users. Zea mays L.) is not only a global crop but also a primary food source for hundreds of millions of people in developing countries. In Asia, up to 50% of the ingested Cd comes from crops and their products, including maize [Cadmium (Cd) is a heavy metal that is highly toxic to all organisms and is one of the major environmental pollutants. One primary concern is its transfer from crop plants to the human diet. It is concluded that crop plants contribute more than 70% of cadmium intake in humans . Excessing maize . BecauseOsIRT1 controls Cd absorption and overexpression of OsIRT1 increases Cd accumulation [OsHMA3 has been identified as a regulator for xylem Cd transport in rice by mediating vacuolar sequestration of Cd in root cells [OsLCT1in leaf blades plays a role in translocation of Cd from enlarged vascular bundles to diffuse vascular bundles, which connects to the panicle [OsHMA2 is involved in the preferential distribution of Zn and Cd at the roots and nodes [The pathway of Cd transport has been elucidated in rice by genetic and genomic approaches . There amulation . Secondlmulation , 7. OsHMot cells . Third, ot cells . In rice panicle . OsHMA2 nd nodes . All resArabidopsis thaliana, none of homologous genes that have been involved in the natural genetic variation of Cd accumulation in rice and A. thaliana were cloned in maize. Traditionally, quantitative trait loci (QTLs) have been identified using linkage mapping populations such as recombinant inbred lines (RILs) and double haploid lines (DHs). However, such mapping populations are often generated from a cross between two parental lines. As a result, only a limited amount of natural allelic diversity can be captured in the population, leading to the identified QTLs spanning relatively large genomic regions and making identification of causal genes more difficult. Genome-wide association study (GWAS) utilizes the natural populations for QTL detection though marker-trait association [Cd accumulation is a complex quantitative trait, controlled by multiple genes. Although the mechanisms of Cd accumulation are widespread and comprehensive in rice and ociation , 13. Intociation . To dateP < 0.01) in Cd concentration in leaves, while significant genotype by environment (Y × G) interactions were observed (P < 0.01)  and middle-Cd (MSLCd) conditions at the seeding stage and under high-Cd condition at the maturing stage (HLCd) in 2015 and 2016, respectively. Analysis of variance (ANOVA) indicated that diverse accessions differed significantly   varied from 2.79 to 64.3 mg·kg−1, with an average of 22.3 mg·kg−1. The results showed that the Cd accumulation of leaves in the natural population had a large variation with variation coefficient (CV) over 48%. In addition, the mean value of leaf Cd concentration in the tropical group was significantly lower than that in the temperate group (P < 0.001) in different environments  for Cd accumulation across all measured environments ranged from 0.62 (HLCd) to 0.82 (LSLCd), indicating high repeatability over testing environments as well as roles of genetic factors in determining the trait. Overall, the maize plants exhibited significant genetic variations in Cd concentration when grown at different Cd levels.The mean HLCd value for individual accessions ranged from 6.76 to 128.4 mg·kgThe population structure was calculated using 5200 SNPs. Because the log likelihood of data (LnP(D)) from the STRUCTURE output continuously increased with K value, it was not capable of identifying groups. Accordingly, the ∆K value was calculated for each K, suggesting that the 269 genotypes could be assigned into three groups Fig. . The acc2 = 0.1, equilibrium was reached within 200–250 kb on chromosome 1, 250–300 kb on chromosome 2, and 300–600 kb on the rest of the chromosomes. The mean LD decay was 350–400 kb across all chromosomes.The extent of LD was estimated with all 43,737 SNPs using TASSEL 5.0. A rapid decline in LD was observed with increasing physical distance on all chromosomes Fig. , but the10(P) values from the simple model and Q model departed far from the expected distribution, leading to a high level of false-positive signals. The model controlling K and Q + K had similar effects on reducing the false positives, except that LSLCd16 in the Q + K model had lower power than the K model   Fig. .Fig. 4Ma2 values. SYN25051 (A/G), the most highly significant SNP with -log10(P) = 13.7 at position 161,275,547 on Chr2, explained 27.1% of the phenotype variation. In addition, 5 SNPs located on chromosome 5 and 6 SNPs located on chromosome 7 were associated with HLCd16 with R2 values ranging from 12.3 to 17.3%. Nine SNPs on chromosome 1were significantly associated with HLCd16.Forty SNPs in a single region on chromosome 2 were highly associated with leaf Cd concentration Fig. . Meanwhi−1) was significantly lower than that in Mo17 (64.1 mg·kg−1). Leaf Cd concentration ranged from 9.5 to 114.2 mg·kg−1 in the mapping population, but the phenotypic distribution exhibited a transgressive segregation  Fig. . In the GRMZM2G455491 and GRMZM2G175576, simultaneously encoding a cadmium/zinc-transporting ATPase, were located at 64.18 kb and 42.64 kb upstream of the PZE-102118224 and SYN395 loci, respectively. On average, the individuals carrying the major frequency alleles (G/G + G/G) of two SNPs  had 27.8 mg lower leaf Cd content than those with minor frequency alleles (A/A + A/A) . The individuals carrying the major frequency allele (G/G) had 29.3 mg Cd content higher than those with the minor frequency allele (A/A)  at these loci was 28.1 mg higher than those with major frequency alleles (A/A + C/C)  Fig. . A putat/A) Fig. . A ZIP tNPs Fig. . In the /C) Fig. .Table 3SGRMZM2G124103 and GRMZM2G047727 showed a relatively high level of expression in all developmental stages compared to the other candidate genes. GRMZM2G175576 in roots had a relatively high level of expression compared to other tissues. On the contrary, GRMZM2G455491 and GRMZM2G386138 exhibited a constitutively low level of expression in different tissues. The expression of 6 candidate genes were examined in leaves, stems and roots of Cd-stressed B73 seedlings by quantitative real-time PCR analyses. As illustrated in Fig. GRMZM2G175576 gene was observed in response to Cd stress, especially in the stems, exhibiting about a 9.5-fold increase in transcript abundance compared to the control. The relative expression levels of GRMZM2G085939 also significantly increased in the stems and leaves in response to Cd stress. In contrast to the other candidate genes, expression levels of GRMZM2G124103 and GRMZM2G171370 significantly decreased in roots and stems under Cd stress. The expression of GRMZM2G047727 and GRMZM2G085153 had minor response to Cd exposure. These results suggested that 4 candidate genes possibly played diverse roles in maize development and Cd-stress response and tolerance. Furthermore, possible sub-cellular locations of the candidate genes were predicted using ProtComp 9.0. This prediction suggested that GRMZM2G175576, GRMZM2G124103 and GRMZM2G171370 might be localized in plasma membrane, and vacuolar and nucleus organelles, respectively , an expression heatmap was constructed for these candidate genes in different tissues from 8 developmental stages Fig. . The resP = 8 × 10−13) that in the tropical group under high-Cd condition , sharing 77% amino acid sequence identity with the AVMA10. V-ATPase, a type of H pump, could maintain or adjust the concentration balance of cations on both sides of a membrane, including Na+, Ca2+, and Cd2+ [LbVHA-c1 may confer stress tolerance through tolerance peroxidase and superoxide dismutase activities, protecting membranes from damage and decreasing lipid peroxidation under salt stress [GRMZM2G085939, located downstream of PZE-102120786, encodes a calmodulin binding heat shock protein. Heat shock proteins are expressed in organisms at temperatures above the optimum growth temperatures. HSP17 and HSP70 have been involved in root responses of peruvian tomato (Lycopersicon peruvianum) to Cu and Cd [GRMZM2G085939 significantly increased in roots, stems, and leaves in response to Cd stress, suggesting that GRMZM2G085939 might be involved in protection and repair of the cellular proteins.Excess toxic metal may activate defense mechanisms against these toxic metals . Among aand Cd2+ . In a prt stress . These pu and Cd . We founGRMZM2G175576, GRMZM2G124103, and GRMZM2G085939,underlying the major QTL were proposed, including a gene (GRMZM2G175576) homologous to rice genes  that functioned similarly in phenotypic response. Future work will include cloning the genes and illustrating the molecular mechanisms for controlling Cd accumulation in maize plants. Meanwhile, the identified QTL region could be utilized for the development of Cd resistant plants.In briefly, temperate varieties of maize had twice the Cd concentration as tropical varieties. One region identified by GWAS was co-localized with quantitative trait loci (QTL) and linkage mapping. Three candidate genes, Two hundred and sixty nine maize accessions for the GWAS were selected from the Southwest China breeding program , includio08’, E 103o14’) during the months of October, 2015 and April, 2016. Eight seeds of each of 269 accessions were initially sown in plastic pots (22 cm diameter and 28 cm deep) containing 14 kg clayey soil with low Cd and pH of 6.3, and three seedlings were kept after germination. Plants were grown in the greenhouse for 12 d prior to Cd treatment. Cd treatments were applied as Cd solutions with CdCl2·2.5H2O at concentrations of 0  and 0.1 mmol·kg−1 . The average air temperatures in the greenhouse were 29 °C/21 °C (day/night). Irrigation was controlled manually during the experiment to avoid loss of mineral elements and Cd caused by excessive water. After 15 d of growth, the third and fourth leaves were harvested for measuring Cd concentration. The greenhouse experiment was a randomized complete block design with two replications.Two experiments were conducted for GWAS in maize. The seeding experiment was conducted in pots in a green house at Sichuan Agricultural University . Each 3 m row contained 14 plants 0.80 m apart. The maintenance of plants in the field followed routine practices of maize during the experiment. After seeds matured, five consecutive plants were chosen from the middle of each row, and the middle leaf below the tassel was harvested for measuring Cd concentration.A field trial for GWAS was conducted in the summers of 2015 and 2016 in Deyang city, Sichuan province of China . Initially, 269 accessions were grown in a greenhouse for 2 weeks under environmental conditions described above. To ensure and obtain uniform plant growth, seedlings were then visually selected and transplanted to the field for phenotypic trait evaluation. Plants were grown in the contaminated soil with 32.5 mg·kgFor the QTL mapping study, plants of IBMsyn10 DH population were initially grown in a greenhouse for 2 weeks and transplanted to the field. Phenotypic traits were collected from the matured plants of IBMsyn10 DH population in the summers of 2015 and 2016 in Deyang city, Sichuan province of China . Plant growing conditions and the procedures of plant sampling were the same as described previously. The field experiment followed a complete randomized plot design with two replications for both GWAS and QTL mapping for both years.3) and 33% hydrogen-peroxide (H2O2) on a heating block at 90 °C for 1 h and 180 °C for 5 h. The digested solutions were filtered after dilution with deionized water. Subsequently, the Cd concentration in the solutions was measured by using the inductively coupled plasma-atomic emission spectrometry (ICP-MS) .All samples were washed thoroughly with tap water to remove soil and dirt, and then were washed with deionized water three times. The leaves were dried and ground until 95%of the sample could pass through a 1 mm screen. The powdered sample (0.5 g) was digested with 20 mL of 67% concentrated nitric acid (HNOh2) of Cd concentration in leaves were performed using SPSS statistics 21.0. Broad-sense (h) was estimated as h2 = σg2/(σg2 + σge2 + σe2), where σg2, σe2, σge2 represent TypeIIISS (sums of squares) for genotype (G), environment (E), and interaction variance of G and E, respectively [Analysis of variance (ANOVA) and heritability  of 269 maize individuals was estimated from a randomly selected set of 5200 high-quality SNPs using STRUCTURE 2.3.4 software with the “admixture model” . The par2) in TASSEL 5.0 [Linkage disequilibrium (LD) between pairs of SNPs was estimated by using squared allele frequency correlations between two loci  and Mixed Linear Model (MLM) procedures  to contrGenotypic data previously obtained for the IBMsyn10 DH population was used for this QTL mapping . The linhttps://www.maizegdb.org/) [2·2.5H2O (200 μmol·L−1) for 0 h (control), 12, 24, and 48 h. Total RNA was extracted using TRIZOL reagent  and RNase-free DNase  from the roots, stems and leaves. cDNA was synthesized using 1 mg total RNA from each sample by PrimeScript RT Reagent Kit With gDNA Eraser . The primer sequences are shown in Additional file GADPH was used for normalization. Three replicates were used to calculate expression levels of candidate genes by using the 2-ΔΔCT method [http://linux1.softberry.com/berry.phtml) which compared homologous proteins of known localization and pentamer distributions in the LocDB and PotLocDB databases.We used the maize B73 reference genome   to identdb.org/) . To valiT method  for eachAdditional file 1: Table S1.The population structure analysis for accessions using 5200 SNPs by Structure 2.2.3. (XLS 53 kb)Additional file 2: Figure S1.Quantile-quantile (QQ) plots for leaf Cd concentration at seeding stage and maturing stage of maize. (PNG 104 kb)Additional file 3: Table S2.SNPs significantly associated with leaf Cd concentration in maize. (XLS 40 kb)Additional file 4: Figure S2.Manhattan plots of association analysis for leaf Cd concentration at seeding stage of maize in 2016. (PNG 64 kb)Additional file 5: Table S3.Haplotype analysis of polymorphic SNPs contained in the overlapped region (153.75–167.58 Mb) on chromosome 2. (XLS 84 kb)Additional file 6: Figure S3.The frequency distribution of leaf Cd concentration in maize IBMSyn10 double haploid (DH) population. (PNG 7 kb)Additional file 7: Table S4.Details of the candidate genes in the overlapped region and their putative function. (XLS 30 kb)Additional file 8: Table S5.The prediction analysis of putative protein sub-cellular localization. (XLS 28 kb)Additional file 9: Table S6.Pedigree information of 269 maize accessions used in this study. (XLS 45 kb)Additional file 10: Table S7.Primers of qRT-PCR assay used for quantifying expression levels of candidate genes within a QTL interval. (XLS 26 kb)


Theobroma cacao L.) is an important cash crop in tropical regions around the world and has a rich agronomic history in South America. As a key component in the cosmetic and confectionary industries, millions of people worldwide use products made from cacao, ranging from shampoo to chocolate. An Illumina Infinity II array was created using 13,530 SNPs identified within a small diversity panel of cacao. Of these SNPs, 12,643 derive from variation within annotated cacao genes. The genotypes of 3,072 trees were obtained, including two mapping populations from Ecuador. High-density linkage maps for these two populations were generated and compared to the cacao genome assembly. Phenotypic data from these populations were combined with the linkage maps to identify the QTLs for yield and disease resistance.Cacao ( Theobroma cacao L. (cacao) is a diploid understory tree crop with 10 chromosomes (2n = 2x = 20). Cacao has a long history of cultural significance for both the Aztec and Mayan civilizations, with some of the earliest archeological data suggesting that cacao was used as early as 1000 BC  originated  that combines the sequencing variants identified from two previous projects , and were submitted to Illumina for genotyping on the custom Infinium II BeadArray. The trees that were sampled came from the following populations / breeding trials: EET 95 × Silecia 1  mapping population , Scavina 12 × Unknown  mapping population , representatives of the clones from the Tecas , Ganaderia , and Malvinas field  clonal breeding trials, along with a sampling of their progenitors (66) from INIAP, Ecuador. Additional samples were collected from the Trinidad germplasm collection , the L12 progeny trial, with parents at CATIE , and selected clones from the SHRS, MCCS, and MRCC germplasm collections . The remaining 98 samples were internal control samples from the Matina 1-6, Criollo 13, or Pound 7 trees located in Miami.Data generated from all 3,072 samples were analyzed with GenomeStudio  using the Genotyping module  and SNP clustering was performed, as part of the Illumina Genotyping module, with the custom GenTrain clustering algorithm that incorporates several biological heuristics. The genotype data were filtered to remove the SNP markers that failed to generate data for any sample. Additionally, data points with low GenTrain score (<0.4) and GenCall score (<0.2) scores were removed. GenTrain score is a statistical score that evaluates clustering based on the shape of the clusters, their relative distance to each other, and mimics evaluations made by human experts. GenCall score is a quality metric ranging from 0 to 1. The lower the value the farther away from the center of the cluster a data point is; values below 0.15 are considered no calls. The data were exported as a whole, and subsets of the Stalin A and Stalin B mapping populations were generated.The Stalin A mapping population consisted of 731 F1 full-sib progeny from a cross between two parents, EET 95 and Silecia 1. The mother, EET-95, is a heterozygous (90.1%), high-yielding clone that is an F1 from an open-pollinated Nacional-type mother. It is often known for providing the fine flavor characteristics of Ecuadorian chocolate. The father, Silecia 1, is a homozygous (79.5%) cultivar from the upper region of the Amazon and has been shown to be tolerant to witches' broom Eskes, . This crPlink version 1.9 , number of sick pods (not including those with FP), number of wilted cherelles , and wet bean weight (grams) were observed at ~30-day intervals. Counts of the number of trunk cankers  and the incidence of witches' broom (WB) disease (M. perniciosa) were made annually, typically in July or August. Incidences of WB were counted for three classes of plant tissue: vegetative growth (Vegetativas), flower cushions (Coginete), and pods (Chirimoya). For the traits, broad-sense line-mean heritabilities were calculated with Genstat software . For each tree, the number of healthy pods, number of pods infected with Frosty Pod (FP) rot , along with those with low GenTrain (<0.4) and GenCall (<0.2) scores, were removed prior to analysis. Ultimately, data were obtained for 10,688 SNPs. The number of DNA samples submitted for genotyping on the Cacao15kSNP array totaled 3,072, including replicated controls. Only 34 DNA samples failed to provide any genotype information. In total, 32,470,144 data points were obtained  out of a possible 36,648,960 data points , representing 11% missing data.An examination of the allele calls generated in the unfiltered sequencing datasets that were used to select SNPs and the genotype data generated by the Cacao15kSNP array were performed. Six of the clones used in the diversity panel were placed on the Cacao15kSNP array as controls, and the genotype calls from the array are compared to the genotype calls obtained from sequencing during SNP discovery in Table Subsets of the data from the Cacao15kSNP array that represent markers segregating in either the Stalin A (EET 95 × Silecia 1) or Stalin B (Scavina 12 × unknown) mapping populations were generated. Only SNP markers that are heterozygous for at least one parent within these F1 populations were retained for mapping, while uninformative  markers were removed. SNP markers that segregate in at least one of the mapping populations genotyped on the Cacao6kSNP array were chosen for inclusion on the Cacao15kSNP array to serve as anchor markers  from the Wald test statistic is used to test significance  of 3.7 and 3.6 for Stalin A and Stalin B, respectively, and a percent explained greater than four. The −log10(p) values of the significant traits are plotted across each chromosome for Stalin A . The Stalin B population had significant tree by year interaction for % monilia infected pods (p = 0.00451) and lighter effect for fresh weight (p = 0.0429). All other traits in Table The phenotypic data collected from both the Stalin A and Stalin B populations were combined with their respective genetic maps to perform QTL mapping. The test for association is done in GenStat via mixed models with additive and dominance predictors. For each marker, the associated probability  appear to be caused by heterozygous calls from the Cacao15kSNP array that were identified as homozygotes by sequencing. In general, the more heterozygous a clone is, the larger the amount of non-concordance is found Table . AlthougDue in part to what is believed to be ancient domestication efforts, geographical isolation, and membership in the few genetic groups that display the ability to successfully self-pollinate, a comparison of the genotypes generated by representatives of the two sequenced cacao genomes (Criollo 13 and Matina 1-6) continue to show nearly 100% homozygosity. Matina 1-6 shows only 20 heterozygous SNPs, none of which are concordant between sequencing and the Cacao15kSNP array. Likewise, Criollo 13 has 42 heterozygous SNPs, only 5 of which are concordant Table  between The Stalin A map was built from nearly twice as many markers and individual genotypes as the Stalin B map and, as a result, is substantially smaller than the Stalin B map Table . LinkageA comparison of the Matina 1-6 genome assembly and the two Stalin maps was performed Figure . OverallDuring the selection of SNPs for this array, the decision was made to include markers from the genome assembly scaffolds that were not anchored onto the 10 chromosomes of the Matina 1-6 assembly  for Stalin B. Although the WB trait heritabilities in Stalin A are such that a QTL is not expected, the Stalin B heritabilities are higher enough that one might still expect to see QTLs. While the reasons for this are unknown, a few plausible explanations exist. In Stalin A, resistance was expected to come from Silecia 1. However, in a more recent diallele study of the General- and Specific Combining Ability of a small number of Ecuadorian clones, Silecia 1 did not have a General Combining Ability that was significantly different than zero. Unfortunately, the Specific Combining ability of EET 95 × Silecia 1 was not included in this study , respectively. The largest QTL identified in our analyses was in the Stalin A population for the number of pods with cherelle wilt, located on LG04, with a −log10p-value of 45 and a percent variance explained of 28.5. The number of total pods had a −log10p-value of 21.6 and a percent explained of 14.9. The peak markers for both of these traits were very close to each other, only 83.5 kbp apart, and the QTLs substantially overlapped with each other  we observed between these two traits . However, in Stalin B, a significant QTL for fresh weight  was identified on LG03. Additionally, various small effect QTLs were found for fresh weight per pod in both populations  both mapped to the same region related to yield in LG04, with similar effect sizes for each combination of parental alleles. In this case, it is not certain whether the region is capturing the incidence of cherelle wilt or if it is capturing the yield potential. It is possible that the two traits are truly controlled by two separate loci within the QTL, but the resolution of this analysis cannot distinguish them. To fully understand the cause of these traits, a much more detailed molecular analysis will be required.To identify the genotypic regions that are related to disease resistance and cherelle wilt, independent of the yield-related traits, we transformed these traits to remove this effect. Two of these transformations, the percent of monilia infected pods and the probability of monilia, mapped to LG09, a region that was not associated with yield in this study. However, the cherelle wilt counts and the cherelle wilt ratio (T. cacao with high marker density. This array was built upon previous SNP panels, and it incorporated thousands of additional markers, many of which came from genes within the previously identified QTL regions. Two of the most marker-dense cacao genetic maps were created for the Stalin A and Stalin B mapping populations, and markers from the previously unanchored Matina 1-6 scaffolds were placed within the 10 chromosomes. These mappings help improve the Matina1-6 reference genome assembly. As has been documented in other studies, QTL maps can be generated using high-density, SNP-based linkage maps, and our results provide guidance for making parental or clonal selections. In addition to the example of QTL mapping demonstrated here, having more marker data can be valuable for cacao breeding efforts by allowing for more robust genomic selection and genetic diversity studies, which are currently underway with the other samples genotyped on this array. The design and analysis of the Cacao15kSNP array provides the genotypic data necessary to advance cacao breeding efforts, and advances the goals of achieving an integrated selection platform in the years to come.Here we report the development and use of a SNP-based genotyping array that represents genomic variation in DL, processed samples, analyzed data and wrote the manuscript. CoS, processed samples, analyzed data and wrote the manuscript. GM, processed samples, analyzed data and wrote the manuscript. DR, processed samples, and analyzed data. CaS, processed samples, and analyzed data. FA, processed samples, and analyzed data. FF, analyzed data and wrote the manuscript. KM, processed samples, analyzed data. OC, processed samples, analyzed data. JM, conceived experiment, analyzed data and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Highly aggressive Africanized honeybees (AHB) invaded Puerto Rico (PR) in 1994, displacing gentle European honeybees (EHB) in many locations. Gentle AHB (gAHB), unknown anywhere else in the world, subsequently evolved on the island within a few generations. Here we sequence whole genomes from gAHB and EHB populations, as well as a North American AHB population, a likely source of the founder AHB on PR. We show that gAHB retains high levels of genetic diversity after evolution of gentle behaviour, despite selection on standing variation. We observe multiple genomic loci with significant signatures of selection. Rapid evolution during colonization of novel habitats can generate major changes to characteristics such as morphological or colouration traits, usually controlled by one or more major genetic loci. Here we describe a soft selective sweep, acting at multiple loci across the genome, that occurred during, and may have mediated, the rapid evolution of a behavioural trait. Africanized honey bees (AHB) are notoriously aggressive, but in Puerto Rico they have a ‘gentle’ phenotype. Here, Avalos et al. show that there has been a soft selective sweep at several loci in the Puerto Rican AHB population and suggest a role in the rapid evolution of gentle behaviour. Apis mellifera scutellata) in Brazil led to broad scale interbreeding with the local European-derived honeybees (EHB)1. This resulted in the infamously aggressive, admixed, invasive, New World hybrid population of Africanized honeybees (AHB). AHB achieved its current intercontinental distribution within 30 years, and is now commonly found in the Neotropic and southern ranges of the Nearctic3. Aggression of AHB towards humans is well documented4 and has caused several deaths and widespread public concern in many geographic locations previously dominated by EHB4. In 1994, highly aggressive, invasive AHB was first detected in the Caribbean Islands, in Puerto Rico5.In 1956, the escape of experimental colonies of an African subspecies of honeybee . Current levels of aggression in the Puerto Rico gAHB population resemble those observed in EHB. However, gAHB retains other traits typically associated with AHB, e.g., morphometric dimensions, Varroa parasite removal behaviours, and faster queen development6.A remarkable characteristic of the Puerto Rico invasion is that within ca. 12 honeybee generations 1994–2006, the highly aggressive founder AHBs had undergone a drastic reduction in aggression7 and its adaptive value dependent on environmental interactions, making it unlikely that a simple selective sweep can explain rapid evolution of this complex behavioural trait. Rapid phenotypic change on similar timescales to the evolution of gentleness in gAHB has been widely documented across a spectrum of organisms, yet the mechanisms mediating these changes differ from classical models of selection12. Mounting evidence suggests that admixture13 or selective sweeps at one or more loci14 likely mediate such rapid changes in phenotype. However, few examples are known of rapid evolution in behavioural traits, and without genomic analysis it is not clear how such a complex trait might undergo rapid change outside the context of selective breeding. Because they depend on the combined, possibly epistatic interactions of many gene products7, many of which may have multiple functions, behavioural phenotypes are likely to require more complex models than those where single loci confer clear selective advantages in isolation, and are ultimately swept to fixation when under sustained selection.Aggression is generally polygenicn = 30 from each of the three populations) at an average of ×20 coverage. This resulted in 2,808,570 SNP variants, which were characterized within and between populations. We took advantage of the honeybee’s haplodiploid sex determination system and sequenced only haploid  genomes, to eliminate ambiguity in the haplotype phase. We hypothesized that the genomic regions under selection unique to the evolution of gAHB would be those that differ from AHB, exhibit a more EHB-like allelic profile, and are under positive selection unique to the island population. To simplify our analysis, we investigated whether the decrease in aggression in gAHB was accompanied by altered frequency of the same alleles as those under selection during the evolution of EHB, which also originated from aggressive populations in Africa in the Pleistocene16 This assumption allowed us to develop a statistically powerful, triangulated analysis.To understand how this remarkable decrease in aggression evolved so rapidly, we sequenced whole genomes of gAHB from Puerto Rico, AHB from Mexico, and EHB from the U.S. 17 to identify highly localized signatures of greater genetic linkage within the gAHB population compared to the AHB or EHB populations, i.e., regions of selection in the genome  were used against a background set of all D. melanogaster reciprocal best hits identified in the honeybee genome (7073 genes) for a Gene Ontology (GO) analysis using the DAVID Gene Ontology bioinformatics database19. Several GO categories were significantly overrepresented in this set of 250 genes in regions of positive selection when compared to the universe of 7073 homologous D. melanogaster genes (Supplementary Table We used extended haplotype homozygosity (FST)20, which identified SNPs with extreme FST values in the gAHB vs. AHB, and EHB vs. AHB comparisons, but lower values in the gAHB vs. EHB comparison  and are thus killed by workers28. Recently it was shown that balancing selection at csd has been a key evolutionary driver in the invasion of Apis cerana in Australia25. This evidence suggests that the distribution of csd alleles in a population can be used to assess diversity in Apis populations in the context of biological invasions.We also employed an analysis of variation at the csd alleles was a driving component in the evolution of gAHB, and whether it could explain the high genetic diversity still present in the population. We used P(d) (Methods) to calculate a likelihood metric for the diploid males in the subsequent generation to the haploid gAHB drones that we sequenced. Of the three populations, gAHB had the highest likelihood of producing diploid drones. Almost twice the likelihood of a diploid male was predicted in gAHB than in the AHB population sampled, and more than three times the diploid male likelihood of the EHB population sampled. gAHB has a lower total number of csd alleles, supporting a bottleneck effect that has been maintained for ~10 years. In addition, the csd alleles in gAHB show very unequal frequencies. If balancing selection had been a driving factor in the evolution of gAHB , the expectation would be that multiple alleles at similar frequencies would be prevalent in the population. One allele is by far the most frequent in gAHB  and EHB-like (n = 34) clusters  samples were consistently assigned to the EHB cluster  measures. As linkage disequilibrium is rapidly lost in honey bee populations due to their extreme recombination rates, retention of linkage is a strong indicator of recent selective pressure. Finally, we observe haplotypes at loci under selection in gAHB that are rare or absent in the presumed AHB source population.Although we favor initial pressure from unsupervised human-driven selection as a primary driver in the evolution of gAHB, alternatives exist. One possibility is that the founding AHB colony happened to be biased towards haplotypes conferring gentle behaviour, which are now over-represented in gAHB as a result of drift in the founder population followed by a genetic bottleneck. It could be the case that, by chance, the founding AHB colony contained a relatively high proportion of alleles conducive to gentleness. All available reports37. According to this scenario, selection towards a more EHB-like resource acquisition strategy may have led to the current gAHB. This could only occur if either the same underlying traits and loci are involved in resource acquisition and aggression, if these loci are linked genetically, or by epistatic interactions. This speculative explanation would also be consistent with the observed soft selective sweep, retaining AHB haplotypes that likely confer aggressive traits in the population.Another factor that could be related to the evolution of gAHB is the constraint presented by resource cycles in tropical oceanic islands38, extremely high recombination rates23, and extensive outcrossing and thus gene flow between colonies39. Some newly emerged haplotypes may be the key to the separation of aggression at the colony level from other traits that was observed for the first time in gAHB. The future events in this selective process are uncertain. One possibility, consistent with classical evolutionary theory, is that the observed soft selective sweep would harden as the evolutionary process continued, fixing the less aggressive behaviour in the population. However, colony-level selection and haplodiploid outbreeding likely affect the population dynamics even of highly favorable alleles in honeybees in ways that may promote soft over hard selection over longer timescales. We suggest that these factors provide a selective advantage to the species as a whole by enabling multiple, sequential adaptive radiations. Such an advantage in invasive situations could explain the overall success of the haplodiploid system despite its very high biological cost25.Our findings have implications for understanding both rapid evolution and the genetics of biological invasions in colonial organisms. We show that haplotypes not found in our EHB or AHB samples rapidly became common in gAHB. These haplotypes were either present but rare in the founder population, or emerged after the invasion event. Rapid emergence of new haplotypes in honeybees is likely, due to their following genetic characteristics: high within-colony genetic diversityIt is also noteworthy that gentle honeybees that are genetically distinct from the extant EHB populations can evolve rapidly from a small founder population of AHB. This is an important result as EHB in particular are principal pollinators of many domesticated plant species, but they are under threat from multiple sources, endangering production of many important agricultural and horticultural crops. Genetically diverse gentle honeybees could help secure agricultural production by providing pollinators more resistant to threats such as parasites and diseases. The evolution of gAHB also provides a paradigm for the adaptation of social organisms to new environments. The ability of eusocial insects to generate and maintain diversity within a colony at loci mechanistically involved with complex traits such as behaviour may be a key to their evolutionary success.40. In total 30 EHB samples were collected from research apiaries maintained by the University of Illinois Bee Research Facility in Champaign County, Illinois. The remaining 30 AHB samples were collected from the research apiaries maintained by the Centro Nacional de Investigación Disciplinaria en Fisiología y Mejoramiento Animal, a member research division of the Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP) in Querétaro, Mexico. Individuals no younger than the white-eyed stage of pupal development were selected to assure unequivocal identification as a drone. Later in the season (October–November) when drone production slowed, we specifically targeted drones that had recently eclosed. All bee specimens were preserved in 95% ethanol upon collection and transported to research facilities at the University of Puerto Rico, Rio Piedras for DNA extraction and processing.A single male (drone) honey bee was collected at the pupal or recently eclosed stage from each of 90 unrelated colonies across three major geographic locations. Thirty gAHB samples were collected from apiaries across Puerto Rico and adjacent islands of Vieques and Culebra, which are known to be part of the breeding populationSamples were processed by separating the thorax from the rest of the body and split into two equal parts. One half of the thorax was stored as archival material, while the other half was washed with molecular grade water  to remove excess ethanol and placed in a micro-centrifuge tube in a container with ice. Sample DNA extraction was done using the QIAamp DNA Mini Kit  with minor modifications to the manufacturer’s protocol. Specifically, extraction method diverged from protocol at the end of the extraction, where two elution washes of 40 µl of buffer AE (from kit) were conducted rather than the one recommended wash. Resulting DNA quality and quantity was measured using three methods: agarose gel electrophoresis (1%), Nanodrop (NanoDrop ND-1000), and Qubit Fluorometer (per the manufacturer’s instructions).Following DNA extraction, samples were shipped to BGI facilities in Tai Po, Hong Kong, where 500 bp insert-size libraries were prepared for each sample per manufacturer’s directions and sequenced using the Illumina HiSeq 2000 platform. Extracted DNA was fragmented via Covaris sonicator, and quality assessment followed using Gel-Electrophotometry. Fragmented DNA was combined with End Repair Mix and incubated at 20° C for 30 min. We followed up this step with purification using the QIAquick PCR Purification Kit (QIAGEN), then added A-Tailing Mix and incubated at 37° C for 30 min. Adapters were added to this adenylated DNA mix using Adapter and Ligation Mix and an incubation reaction at 20° C for 15 min. Size selection was done on a 2% agarose gel to recover the target insert size (500 bp), followed by a gel purification step via QIAquick Gel Extraction kit (QIAGEN). Several rounds of PCR amplification with PCR Primer Cocktail and PCR Master Mix were performed to enrich the fragments. The PCR products were further selected by running another gel purification step to recover the target fragments. The final library was quantitated in two ways: (1) determining the average molecule length using the Agilent 2100 bioanalyzer instrument (Agilent DNA 1000 Reagents), and (2) quantifying the library by real-time quantitative PCR (qPCR) (TaqMan probes). Upon quantitation, high-quality libraries were paired-end sequenced using the Illumina HiSeq 2000 platform with 100 bp read length.41 were used to analyze the 90 haploid honeybee drone samples  then processed by clipping the remaining adapter sequences (TruSeq2-PE.fa:2:30:10:6:true options). Reads were trimmed at the leading and trailing ends if below the threshold quality score (28), and in addition a sliding window trimming approach was applied . Lastly, reads smaller than 30 bp post-trimming were excluded.Prior to input, raw read files were quality checked and trimmed with Trimmomatic version 0.3343 (version 0.7.10) using -M parameter. Aligned/properly mapped reads were de-duplicated with SAMBLASTER44 (version 0.1.22). De-duplicated samples were then realigned with GATK45 (version 3.4-0) RealignerTargetCreator followed by IndelRealigner (see Supplementary Note 45 (version 3.4-0) HaplotypeCaller (see Supplementary Note 45 (version 3.4-0) GenotypeGVCFs  with BWA MEM46. Removal was achieved by generating an index file for all “GroupUn” scaffolds using base packages in R47 and then applied via VCFtools version 0.1.1448  potential artifacts due to current reference assembly architecture and 2) reliable representation of SNPs across the data set. Unplaced Scaffolds were first excluded from our analysis, as they are regions prone to false SNP calls due to similarity or overlap with other scaffolds reliable potentia50. These regions were identified in the current assembly using NUCmer version 3.052, and regions with >80% identity were re-formatted as a **.bed file, then excluded  were also excluded from our data set as there are several highly similar NUMTs in the honeybee genome45 –VariantFiltration and –SelectVariants  was not applied, and different read depth (DP < 269.2685 || DP > 1484.405) and quality filters (QUAL < 15.776 || QUAL > 8072.961) were implemented to represent data-set specific quality analysis results. The compiled population data set, i.e., samples from this study and those from the reference study16, was used in a principal components analysis (PCA)  Fig. .53. The analysis was conducted using the adegenet package in R55. The approach first applies dimensional reduction via PCA using the glPca function to the SNP data set then conducts successive K-means clustering using the find.cluster function in the package. This successive process was performed with increasing number of clusters and derived a Bayesian information criterion as a goodness of fit measure for each cluster, this was used to select the optimal k 26 was applied to assess (1) the effective male population size across all three study populations , and (2) the likelihood of a founder effect in gAHB. The method used the GERBIL22 imputed SNP data set (described below) to extract all non-synonymous SNP variants within coding DNA sequence regions of the csd gene. Non-synonymy was determined via annotation with SnpEff version 4.256 using the Apis mellifera official gene set obtained from BeeBase. The combination of these non-synonymous coding SNPs thus represents allelic variants of the csd gene. A simple (ε = 0) median joining network was computed using the algorithm in PopArt58 to assess the distribution of alleles across gAHB, AHB, and EHB (detailed below). In addition, for each population, the likelihood of diploid males P(d) was derived using the formula:k is the number of csd alleles present in each one of the three populations , hi in the population, and K is the total number of csd alleles present across the three populations.An analysis of the complementary sex-determiner gene  was applied that calculated haploid FST, as described by Weir20. Briefly, the function incorporates as input a list of population membership, and a matrix where SNPs correspond to row entries and samples correspond to columns , then the function WC_Haplotype_Fst_Function.R was applied  reduces the level of entropy and thus increases the degree of association between variants. Implementation of Rsb was used to identify those SNPs that had low surrounding entropy in the gAHB population compared to the AHB or the EHB populations.Rsb used an imputed form of the SNP 0,1 data matrix created during haplotype block derivation using GERBIL (described below)22. This prior step allowed the rescue of missing SNP calls which would have otherwise been discarded during the calculation of the Rsb statistic. The imputed SNP matrix was re-formatted to a haplotype data format required by the rehh package in R47. This step resulted a mapping file, and 909 haplotype files where each of the 303 scaffolds with detected variation was represented by three files. Each one of the files contained samples from one of the populations . Data in the haplotype files followed the original matrix orientation with SNPs as rows and samples as columns, but alleles were coded using the nucleotide base pair rather than binary identification .Calculation of Rsb using the values in the iES_Tang_et_al_2007 entry. The iES_Tang_et_al_2007 values correspond to the integrated area under the haplotype homozygosity curve as the haplotype is extended outwards from each specific SNP in both directions17. The median deviation of the logarithmic ratio of iES between two populations for each SNP i (ln(Rsbi)’)17 yields ln(Rsbi), which is a normally distributed measure of fold change deviation between iES. A positive fold change indicates slower haplotype decay in the dividend population, which corresponds with greater linkage disequilibrium and is a signature of positive selection. Rsb is the exponential of ln(Rsb) bound between 0 and infinity, with values greater than 1 indicating positive selection. The rehh package in R outputs the fold change: ln(Rsb), which was exponentiated to yield Rsb  was implemented as described in ref. 21. Briefly, a fractional rank is first derived for each individual calculation , then converted to a Z-statistic. The resulting Z-score is averaged for each SNP and compared against a normal distribution of Z-scores to derive a p-value. The CSS p-value represents the degree of deviation from the distribution of Z-scores, which can then be used to detect significant deviations in signal strength or whose inverted form  can be applied as a granular scale of deviation from normal to detect outliers.To compare the FST, Rsb) then contrasted between the two metrics to identify overlapping outliers which are defined here as individuals with CSS scores > 99% of all SNP CSS scores in each metric .A CSS was derived for each individual metric  across the honeybee genome using marker data. Whole genome sequencing provides a greater degree of resolution, and makes it possible to directly assess LD signal across the honey bee genome61. The software GERBIL (version 1.1), a tool in the GEVALT software suite22, was used to capitalize on the greater resolution of genomic markers and aggregate SNPs into discrete haplotype blocks. As GERBIL is only able to use diploid input, the SNP data set was first converted to a homozygous diploid data set, then re-formatted to the format described in the GERBIL software manual. Briefly, individual tab-delimited files were produced for each of the scaffolds, with columns representing SNPs and every two consecutive rows corresponding to one sample. SNPs were represented in 0, 1 format or a “?” if missing. Re-formatted files were analyzed in an iterative fashion using default software parameters (sample code: gerbil.exe <input filename> <output filename>).It is widely known that honey bees possess extreme rates of recombination58 of the 72 haplotype blocks that overlapped with honeybee genes showing significant signatures of selection as described earlier. As haplotype blocks constitute regions where recombination was low in one or more individuals, component SNPs represent a grouping whose allelic pattern can provide insight into the degree of genetic variation. Networks were constructed using the median-joining network algorithm in Pop-Art version 1.757, with the simplest network drawn up for each haplotype (ε = 0)58.Distribution of haplotypes within and across the populations was assessed through median-joining networksFST calculation (WC_Haplotype_Fst_Function.R) along with detailed annotations is made available at our project repository .A full copy of the custom script R function used for https://github.com/HPCBio/Honeybee-VariantCalling. Haplotype networks can be readily re-constructed through upload (File > Open > **.nex) and execution of the median-joining network algorithm (Network > Median Joining Network) within the PopArt (version 1.7)58 software suite for any one of the files provided. An additional data set implemented in the population structure analysis is referenced in Wallberg et al.16 (doi:10.1038/ng.3077), and was obtained from the authors with permission. All other data are available from the corresponding authors upon request.All genomic sequencing data generated by this study is available via the NCBI Short Read Archive repository under the bioproject ID PRJNA381313. The NEXUS format files for of the haplotype blocks used for the haplotype network analysis is available at: Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2