MYH1) association with skeletal muscle development in economically important Jeju Native Pig (JNP) and Berkshire breeds. Herein, we present an in silico analysis, with a focus on (a) in silico approaches to predict the functional effect of non-synonymous SNP (nsSNP) in MYH1 on growth, and (b) molecular docking and dynamic simulation of MYH1 to predict the effects of those nsSNP on protein-protein association.This study was performed to identify the non- synonymous polymorphisms in the myosin heavy chain 1 gene  in JNP and 1 nsSNP (H973G) in Berkshire pigs. Moreover, protein-protein interactions were studied to investigate the effect of MYH1 mutations on association with hub proteins, and MYH1 was found to be closely associated with the protein myosin light chain, phosphorylatable, fast skeletal muscle MYLPF. The results of molecular docking studies on MYH1 (native and 4 mutants) and MYLFP demonstrated that the native complex showed higher electrostatic energy , van der Walls energy , and interaction energy  than the mutant complexes. Furthermore, the molecular dynamic simulation revealed that the native complex yielded a higher root-mean-square deviation (0.2–0.55 nm) and lower root-mean-square fluctuation (approximately 0.08–0.3 nm) as compared to the mutant complexes.The NextGENe (V 2.3.4.) tool was used to identify the variants in in silico analysis of polymorphic MYH1 and will serve as a valuable resource for further targeted molecular diagnosis and population-based studies conducted for improving the growth performance of JNP.The results suggest that the variants at L884T, K972C, N981G, and Q1285C in MYH1 in JNP might represent a cause for the poor growth performance for this breed. This study is a pioneering in-depth The online version of this article (doi:10.1186/s12863-016-0341-1) contains supplementary material, which is available to authorized users. Sus scrofa was domesticated over 9,000 years ago and has become one of the most important farm animals [ animals , 2. The  animals , 4. Ther animals .The functional capacity of skeletal muscle depends on both the quality and the quantity of muscle proteins. Different muscle proteins are synthesized at dissimilar rates  and are MYH) isoforms have been studied. These isoforms are mainly expressed in skeletal muscle during different developmental stages, including the embryonic period, and therefore play a role in the development of skeletal muscle [MYH1, which encodes isoform 1 of MYH family, is substantially involved in the metabolism and development of skeletal muscle [MYH1 gene (Chr. 12:57965087…57984759) encodes with 1,939 residues. MYH1 is critically important for fast and slow skeletal muscle development, thus it can impact on the overall development [Myosin is the most abundant protein expressed in striated muscle cells: myosin makes up ∼ 25 % of the total protein pool and is the main contractile protein that converts chemical energy into mechanical energy through ATP hydrolysis , 12. In l muscle . For insl muscle , 15. Theelopment .Porcine a key source of meat are widely consumed in several countries. During the last decade, pork meat quality has been targeted in large breeding programs, and has therefore been the focus of a substantial amount of research , 17. In MYH1 in JNP and Berkshire breeds. Moreover, MYH1 was one of 8 myofibrillar genes previously reported to be differentially expressed during prenatal development of skeletal muscle in Berkshire and JNP breeds [The identification of genes that determine body composition is of major interest in studies aimed at improving livestock production. To date, various RNA-seq approaches have been employed for investigating differential gene expression or for comparing the transcriptomes of distinct muscles in the pig using next-generation sequencing (NGS)-based methods, and has generated new insights into the molecular basis of myogenesis , 22. ComP breeds .MYH1 in JNP and Berkshire pigs using various online bioinformatics algorithms.The use of NGS methods serves as a powerful approach for generating massive amounts of genome-wide sequence-variation data, and therefore bioinformatics tools are being developed to provide computational predictions on the functional effects of sequence variations , 23. TheMYH1 gene of two breeds. Further, quantitative trait loci (QTL) are biologically meaningful loci at which gene expression is modified by genotype. Accordingly, we utilize the QTL map in SSC12 (Sus scrofa chromosome 12) from recently published studies as a means to assess the integrity of sequencing. Currently, a network approach is increasingly used in biological and genetic studies to gain insight into the connections between proteins that collaboratively generate complex traits [MYH1affect the interaction with hub proteins. The steps we present herein ultimately provide a straightforward approach that allows for more accurate identification of specific variations in the JNP and Berkshire breeds responsible for quantitative traits that can be confidently used as selection markers for improving growth performance.We conducted the detection approach based on utilizing the RNA-seq gene expression to identify the mutational variants in x traits , 25. Thex traits , 26. TheS. scrofa genome using TopHat (v2.0.2). The significant difference (P < 0.01) expression of genes identified by the general linear model.Transcriptome data were acquired from muscle tissue samples of JNP and Berkshire using Ilumina HiSeq2000. The generated data consisted of highly reliable RNA-seq reads on an average 90.8 % (muscle) reads passed the quality control. These sequences were mapped successfully to the MYH1 were investigation. The GO analysis related to the biological process of the different identified gene was examined  and muscle organ development (GO: 0007517) categorized under the biological process  obtained for MYH1 pathways are shown in Additional file P = 0.005) was for the pathway “Translocation of GLUT4 to the Plasma Membrane.”The detailed of function ontology, revelation of common processes and the pathways potentially associated with MYH1 were determined by normalizing the levels of the 5 pigs from each JNP and Berkshire breeds against the transcript levels of an endogenous reference gene, GAPDH. The 95 % confidence intervals clearly indicate that the mRNA expression of MYH1 is significantly higher in the Berkshire longissimus dorsi muscle samples than in JNP pig  higher level in Berkshire against the counterpart.Both RT-PCR and real-time qRT-PCR were performed to clarify the qualitative and quantitative expressions of the gene under study. Specifically, the relative mRNA expression levels of pig Fig.  and b. Tpig Fig. . MYH1 pr2)/‘best-fit’ line are crosses close to as many data points as possible. The analysis of relative expression showed that copy number ration (CNR) in Berkshire was found as 8.75 log2 whereas 8.97 log2 in JNP   in JNP and 1 variant (H973G) in Berkshire pigs are likely to exert deleterious functional effects on MYH1. In addition, we performed I-Mutant3 analysis for MYH1 in order to add another layer of refinement to the nsSNP characterization. The stability effects were evaluated by subtracting the unfolded Gibbs free-energy value of the native protein from that of the mutated protein. The I-Mutant 3.0 DDG simulation yielded the highest score (−1.92) for L884T in JNP.The subPSEC score estimates the likelihood of a functional effect arising from a single amino acid substitution using HMM-based statistical modeling and evolutionary relationships. Four amino acid variants  were found to be deleterious in JNP, with the subPSEC scores ranging from −3 to −5, whereas in Berkshire pigs, all subPSEC scores were < −3 and were therefore classified as tolerant.P = 0.1223), loss of methylation (P = 0.1405), gain of phosphorylation (P = 0.2086), gain of glycosylation (P = 0.2696), and loss of solvent accessibility (P = 0.3103).MutPred was used to analyze the top-5 functional disruptions caused by the missense mutations. Four amino acid substitutions in JNP—L884T, K972C, N981G, and Q1285C—led mainly to the loss of stability, ubiquitination, phosphorylation, and gain of glycosylation. However, the effect of the mutation in Berkshire was predicted negligible by MutPred  in JNP and H973G in Berkshire pigs for subsequent HOPE analysis.http://www.ebi.ac.uk/pdbe/pisa/) database contain protein assemblies that are highly likely to be biologically relevant. According to the PISA database, the original residue at the L884T mutation site is involved in multimeric contacts. The mutation introduces a residue at this position that is smaller than the original residue and might not be adequately large for making multimeric contacts. The mutated residue K972C introduces a residue that is less hydrophilic than the original, and that might affect hydrogen-bond formation. The 3D structure of N981G reveals that the wild-type residue is located at α-helix; after mutation this α-helix formation is affected. The conservation scores suggest that these mutations are probably damaging to the protein. In the case of Q1285C, the mutant residue is smaller than the wild-type residue, and this could potentially lead to a loss of external interactions. Moreover, the difference in the hydrophobicity of the wild-type and mutant residues could cause a loss of hydrophobic interactions with other molecules on the surface of the protein.We have selected 4 missense mutations in JNP: L884T, K972C, N981G, and Q1285C for additional study. The original wild-type residue and newly introduced mutant residues often differ in their properties. The PDBePISA  (The wild-type and mutant amino acid (H973G) of MYH1 protein of Berkshire differ in size. The mutant residue is smaller than the wild-type one, which leads to empty space in the core of the protein. In addition, Glycine is extremely flexible and can disrupt the structural rigidity of the protein required at this position. The mutated residue is located on the surface of a domain whose function is still unknown. Nevertheless, the residue at this position could still be in contact with other molecules or domains and such interactions might be affected due to the mutations.The STRING tool was used to predict the pattern of association  of MYH1 protein with the partner proteins MYLPF, MYL6, ACTG1, RHOA, CGN, TNNI3, TNNC2, MYL1, TNNI2, and TTN. Based on the confidence scores of the MYH1 protein interactions, MYLPF  was chosen for molecular docking analysis performed with L884T, K972C, N981G, and Q1285C variants of MYH1 -1I84 (Chain S) information, at a resolution of 24.86 Å  as reference, at are solution of 35.00 Å  were lower (Table We studied the molecular docking between MYH1 (native and mutant) and MYLPF in order to identify the variation in the overall interaction energy of the complexes. In the native complex, the electrostatic, van der Waals, and interaction energies were observed to be −466.5, −87.3, and −835.7 Kcal molMolecular dynamic simulations were performed using 25-ns trajectories. The changes in protein stability were calculated using backbone RMSDs for the native and mutant complexes of MYH1-MYLPF . The longissimus dorsi muscle samples were collected from 8 months full growth adult pigs, considered as the economical tread time period in South Korea when the farmer sold out the livestock for slaughter. The numbers for the experiment animals were decided based on the minimum average relatedness and according to method described by Erb [MYH1 and what the effects of sequence polymorphisms are, little is known in the JNP pig about its relative mRNA expression when comparing tissues with the western breed. This knowledge is essential in researching differences in MYH1 regulation in view of selection for improved meat quality.Present study included a total numbers of 10 animals  which provide new insights into the genetic basis of meat quality trait in pigs [Sus scrofa, our analysis demonstrated that the MYH1 in 12 chromosome use of aggregation study for meat quality and body growth. The early finding was also reported that QTL on Sus scrofa chromosome 12 influencing the growth related traits [The QTL analysis and expression profiling in different porcine resource population are aiming to identify genes controlling meat quality and carcass traits. Porcine  in pigs . To subsd traits . In factS score observed at 12.17.1285 with other three haplotypes. However, a larger population study is still needed to precisely estimate the effects and gene actions of these chromosomal regions on growth trials improvement program.The pioneer studies showed the selection based on minimum average relatedness, contributes to high percentage of polymorphic markers and are also indicative signature for homozygosity for each breed , 32. TheMYH1 in JNP and Berkshire breeds. Such missense variants are considered to be potentially critical for the function and structure of a protein, and thus they might provide markers for the selection of specific phenotypic traits [Previously, defects in the circadian expression of muscle genes were reported to cause large disruptions in the normal expression of genes crucial for adult muscle structure and metabolism , 34. Thec traits .MYH1 from JNP and Berkshire pigs using NextGENe. This software allows previously un-annotated genes to be discovered, and it also provides highly accurate alignment with known transcripts and an automated calculation of a confidence score for each variant identified [MYH1, we identified 10 SNPs that resulted deleterious amino acid substitutions for MYH1 protein in JNP and 3 such SNPs in Berkshire pigs, as compared with the reference sequence. Thus, the use of NextGENe might serve as a reliable and cost-effective approach for highly accurate genome-wide identification of DNA sequence variations.This study is the first attempt to screen residue variants in entified . Previouentified . While sMYH1 using sequence- and structure-based algorithms, such as SIFT, PROVEAN, PolyPhen-2, I-Mutant3, and PANTHER. In our functional SNP analysis, SIFT and PROVEAN predicted that all the identified variants were deleterious. Concordant results of PolyPhen-2 and PANTHER predicted that 50 % of all identified variants were highly deleterious. Structure-based prediction performed using I-Mutant3.0 also showed that all identified amino acid substitutions were highly destabilizing. The results obtained using the in silico algorithm tools indicated that the L884T, K972C, N981G, and Q1285C variants were deleterious in JNP and H973G was deleterious in Berkshire pigs. Therefore, these nsSNP might be functionally involved in phenotypic traits in these pigs.Research demonstrated on the allelic variation of single genes that can be potential importance for genetic improvement of pork quality. In this study, we screened for functional coding-region genetic variants in In silico analysis were performed using the HOPE tool indicated that misfolding and effects on molecular interactions of MYH1 can affect the protein’s structure and function. The mutated residues with the substitutions of L884T, K972C, N981G, and Q1285C possess specific sizes, charges, and hydrophobicity values, and the substitutions involve highly conserved residues in the wild-type protein. The mutated residues are located on the surface of a domain with as-yet unknown function, but the changes in charge are likely to disturb the ionic interactions of the protein; furthermore, interactions with other molecules or domains are also possibly affected by these mutations. For instance, changes in hydrophobicity caused by the mutations might affect multimerization.MYH1 that alter gene expression can lead to disruption of muscle-specific biological and patho-physiological processes that control muscle growth and metabolism. The leucine at position 884 has been reported to be replaced with threonine in JNP. Leucine is a ketogenic amino acid that can be converted to acetyl-CoA and acetoacetate in muscle tissue; these intermediates can be used to synthesize fatty acids. Leucine supplementation has been documented to lead to an improvement in muscle color and intramuscular fat content in longissimus muscle [By contrast, mutations in s muscle . Increass muscle , which is muscle .The K972C mutation can also affect usual functions of MYH1 in JNP. During growth, lysine is critical for protein synthesis, muscle deposition, and carcass quality. A diet featuring a diminished content of lysine resulted in a substantial reduction in growth performance . In thisBiochemical studies have revealed that glutamine, glutamate, proline, aspartate, asparagine, ornithine, citrulline, and arginine serve key regulatory functions in the immune response in pigs, and are inter convertible through complex inter-organ metabolism , 44. It MYH1 in the JNP breed can markedly influence skeletal muscle development. Interestingly, the food industry has been criticized in particular for the decline in the quality of pork obtained from lean breeds. The properties of lean meat are reported to be greatly influenced by the meat’s chemical composition [MYH1 might contribute not only to the deficient growth performance but also the meat quality of the JNP breed.Dietary supplementation with glutamine in early-weaned piglets prevents intestinal atrophy, and it also improves growth performance and meat quality in pigs. Investigation of the effects of glutamine on growth performance in piglets showed that this amino acid is extremely effective at improving growth performance . Glutamiposition , 50, 51.MYLPF is a positional and functional candidate gene for meat production in domestic animals [GO analysis is used to characterize protein function and to elucidate trends in protein datasets . Our pre animals . Phospho animals . Strong Elucidating the modulation of protein-protein associations is challenging, but is receiving increasing research attention . Wang anMYH1 in JNP and Berkshire breeds indicate a role of this gene in muscle growth, meat quality and marbling, thus MYH1can serve as the candidate gene in relation to livestock production and/or meat quality of swine. Further, we have planned to carry out populations study for performance test to confirm the associations within and between the breeds and the degree of correlation that will guide us to include the identified nsSNP for breed improvement program.In summary, the results of our GO annotation, nsSNP analysis, QTL mapping and mRNA expression analysis of MYH1in JNP and Berkshire breeds using sequence- and structure-based homology algorithms. In silico annotation of certain nsSNP could explain the functional effects of these mutations. Furthermore, pathway-based analysis of protein-protein interactions highlighted the importance of the interaction between MYH1 and MYLPF in skeletal muscle development. The results of this study suggest that the variants L884T, K972C, N981G, and Q1285C in MYH1 in JNP might represent a cause for the poor growth performance of this breed. Thus, these MYH1 variants might be useful as selection markers for improving growth performance in the JNP breed.This study represents the first comprehensive investigation that has identified functional nsSNP in n = 5 from each breed; average individual body weight, 84.76 ± 3.5 kg) were reared under the same environmental and nutritional conditions. All the pigs were provided ad libitum access to commercial feed  and water. Pigs were housed in concrete-floored pens that contained a nipple-bowl drinker and a feeder. Animals were sacrificed through entailed exsanguination following electric stunning, with all possible effort being devoted to minimize suffering.The study was conducted under strict accordance with the recommendations in the guide for the care and use of animals of the Animal Bioethics Committee (permit number: 2013–0009) of Jeju National University, Jeju-Si, Jeju-Do, Republic of Korea. The animals were handled accordance with proper animal welfare guidelines . Pure-brlongissimus dorsi muscle samples were collection from the total number of 10 animals (n = 5 from each of JNP and Berkshire breeds) and immediately submerged in RNAlater (Sigma-Aldrich) followed by the frozen in liquid nitrogen. Total RNA from the muscle samples were extracted using TRIzol . The RNA integrity has been verified onto a 0.8 % agarose gel and evaluating the 28S and 18S ribosomal RNA bands. The purity and RNA concentration was measured with a BioPhotometer (Eppendorf). Concentrations for muscle samples ranged between 94–138 ng/μl . Approximately, 1 μg of RNA from each sample converted to cDNA in the subsequent 20 μl reaction using Superscript III reverse transcriptase with oligo dT. The second strand of cDNA was according to the manufacturer’s recommended protocol . The cDNA was diluted with nuclease free water and stored at −20 °C and further used for RNA-seq libraries prepared.The S. scrofa 10.2) from the Ensembl database with default settings using Bowtie2.The TruSeq DNA Sample Prep. Kit  was used for library construction following the manufacturer’s guidelines. RNA sequencing has been performed on the Illumina HiSeq 2000 platform. The fastQC software was used to perform a quality check on raw sequence data. Paired-end sequence reads were mapped to the pig reference genome  database. SNP information for MYH1 was retrieved from NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp); Ensembl genome browser 72: Sus scrofa (http://asia.ensembl.org/Sus_scrofa/Info/Index) and compiled from experimental data. Figure in silico methods associated with structural and functional assessment of porcine MYH1 in our study.The mapping of sequenced transcriptome fragments on to the reference sequence is particularly important for identification of polymorphisms . Hence, MYH1 function in S. scrofa. Functional classification views of MYH1were obtained using the PANTHER classification system (http://www.pantherdb.org/). Furthermore, the pathways of MYH1 were analyzed using the Kyoto Encyclopedia of Gene and Genomics (KEGG) tool (http://www.genome.jp/kegg/pathway.html). The Biograph tool (http://www.biograph.be) and ToppGene suite (https://toppgene.cchmc.org//) were used for in-deep graphical representation of biological processes and gene pathway analysis, respectively.GO analysis was used to study the molecular, biological, and cellular processes associated with MYH1 and GAPDH sequences in order to design primers.The reverse transcription PCR (RT-PCR) was carried out through using 2.5 μl of cDNA. Each reaction included a positive porcine genomic DNA control, a negative control and a no-template control. The primers were designed using the online program Primer-3 Table . The NCBMYH1 was verified by quantitative real-time PCR (RT-qPCR) on the 10 animals from two breeds previously selected. The experiment setup and the descriptions for RT-qPCR analysis were explained according to MIQE guideline in this study [GAPDH control transcript. Samples that did not include reverse transcriptase were included as negative controls to monitor DNA contamination, and four blank samples were added as qPCR negative controls.Differential expression of is study . TranscrFurther, the western blotting assay was performed for expression profiling of MYH1 in JNP and Berkshire. For blotting assay, we used sixty micrograms of protein extract are diluted with 1:1with 2X loading buffer as described in our previous study  and loadMYH1 region whose expression significantly correlated to quantitative traits in commercial pig breeding schemes. QTLs analysis is based on the linkage between markers and QTL. The publicly available Animal QTL database for pig  is used for compare, confirm, and locate the most plausible location for the gene of interest associated with quantitative traits. Taking into account previously observations we highlight QTL located on the SSC12 (57978043….57981608) region on the reliability of QTL map using JavaScript-based genome browser or JBrowse.QTL analysis was conducted to map http://sift.jcvi.org/www/SIFT_seq_submit2.html) tool used homologous sequences. We submitted the amino acid sequence of porcine MYH1 as query obtained from the NCBI database. The output SIFT scores were classified as damaging when they ranged from 0.00 to 0.05; scores between 0.051 and 0.10 were considered potentially damaging; and 1.00 was considered neutral. Here, when scores were below the threshold fixed at 0.05, the relevant amino acid substitutions were predicted to be deleterious. Further, we used online tool PROVEAN  to predict the possible impact of a substituted amino acid on protein structure and function. A protein variant was considered deleterious if the final score was below the threshold score of −2.5; scores above this threshold were considered neutral.The missense variants obtained from NextGENe application were proposed for further verification. The open-source tools were employed for sequence- and structure-based approaches to predicate the non-synonymous SNP. The deleterious score of the substituted amino acids that alter protein function(s) was predicated by SIFT  was used for predicting the possible impact of amino acid substitutions on protein structure and function by using straightforward physical and evolutionary comparative considerations [PolyPhen-2  to accurately and robustly analyze the effect of mutations on protein functions. In PANTHER, multiple sequence alignments and hidden Markov model (HMM)-based statistical modeling are used for the evolutionary analysis of a given amino acid at a particular position in the protein of interest. The method can generate position-specific evolutionary conservation scores (PSECs), and the substitution PSEC (subPSEC) score is calculated from alignments to HMMs in the PANTHER database in order to separate neutral from deleterious SNPs. If a protein’s subPSEC value is 0, the substitution is interpreted as being functionally neutral; values that are ≤ −3 are considered deleterious.We used PANTHER (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) is support vector machine-based tools used for analyze protein stability. As the input query, we used the FASTA sequence of porcine MYH1protein obtained from NCBI and the amino acid substitutions and their respective positions in JNP and Berkshire breeds. This method can be used to predict whether a mutation’s effect on a protein is largely destabilizing , largely stabilizing , or weak . This value is calculated after subtracting the value of the unfolding Gibbs free energy of the wild-type protein from the corresponding value of the mutant protein.I-Mutant 3.0 (http://mutpred.mutdb.org/) was used for classifying amino acid substitutions as being disease-associated or neutral. MutPred is based on SIFT and a gain or loss of 14 different structural and functional properties [P-values” in MutPred are classified as actionable hypothesis (P < 0.05), confident hypothesis (P < 0.05), and very confident hypothesis (P < 0.01).The web-based tool MutPred (operties . We usedhttp://www.cmbi.ru.nl/hope/) is a next-generation web-based tool for automated mutant analysis. HOPE collects information from databases such as the 3D structure of proteins and protein sequences from the UniProt database that can be used to process these data and to predict the effects of a mutation on the 3D structure and function of a protein. The protein sequence of MYH1 and 4 most deleterious residual variants in JNP were used as the input in HOPE. The output report was focused on the effect of the mutations on protein structure.Mutations located in the protein-coding regions of a genome can be affect on it structure and functions. Knowledge of the effect of these mutations on the three-dimensional (3D) structure of a protein provides mechanistic insights into the protein’s functions and also enhances our understanding of substantial changes in the protein and can aid in the design of further experiments , 63. HOPhttp://string-db.org/) database was used to assemble, evaluate, and disseminate information regarding the structural, functional, and evolutional properties of proteins. STRING integrates and ranks these associations under a common framework. These data are weighted and integrated and then a confidence score is calculated for all protein interactions. The results of various computational predictions can be inspected from different designated views. Here, the closest associated protein was selected for a docking study.All protein-encoding genes can be grouped and organized in genome through protein-protein associations . A graphS. scrofa is not yet available in Protein Data Bank (PDB); therefore, we used MODELER 9.10 and modeled the structure of MYH1 by using the sequence available in the NCBI database. Sequences homologous to the target structure were retrieved by performing Psi-BLAST against the PDB database. Template sequences exhibiting the highest similarity to the target sequence were identified and considered for modeling studies. The obtained model was further subjected to structural validation. PyMOL was used for visualizing the model, calculating electrostatic potentials, and generating images. Moreover, we modeled the 3D homologous structures of the proteins that are closely associated with S. scrofa MYH1 by using MODELER 9.10. The SAVES v4  was used to evaluate the internal consistency and reliability of the homologous models. The Psi/Phi Ramachandran plot was generated using PROCHECK.The protein structure of MYH1 was modeled to compare the structural stability of the native and mutant proteins. The protein structure of MYH1 from S. scrofa MYLPF protein. Docking simulation was performed by using the AutoDock tool. Docking was optimized by using the Lamarckian genetic algorithm (LGA) for 70,000 rotations, and the population size was set to 150. The obtained confirmations were then summarized, collected, and extracted using AutoDock tools.The modeled structures of native and mutant MYH1 proteins were analyzed for molecular docking with the structure obtained for the http://www.gromacs.org/). Charges were neutralized by adding Cl− ions. Temperature is frequently equilibrated under constant number, volume, and temperature (NVT) ensemble. Considering our computational power, 25 ns of equilibration with position restraints of the temperature on the protein was set at 300 K. Desired pressures were supplied after the thermal equilibration in order to acquire the proper density. The trajectory analysis performed using molecular dynamic simulations allowed us to understand the stability of the system. We applied periodic boundary conditions to generate the final trajectory files, which were analyzed in order to obtain the root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF).The PDB files obtained from the docking study were used as an input for molecular dynamic simulation performed. Protein complexes were solvated in a cubic water box. The topology and coordinate files were generated using GROMACS server . After removing outliers (Grubb’s test) by dividing the covariance of the data sets with the product of their standard deviations the Pearson’s correlation coefficient (r) was calculated.Haplotype blocks and linkage disequilibrium (LD) plots have been constructed using Haploview version 4.2 with the default algorithm . To correct for technical inter-run variation among triplicate reactions of the same sample measured in different runs, the data were calibrated by calculating the average cycle threshold (Ct) value over all the samples in each run [The relative quantitative (RQ) of each run , 66. Afthttp://biopopdb.snu.ac.kr/PIG_DEG/). The software for RNA-seq analysis and mapping used in this study includes NextGENe V2.0 (http://www.softgenetics.com/NextGENe.html), TopHat (https://ccb.jhu.edu/software/tophat/index.shtml) [http://www.ncbi.nlm.nih.gov/snp/) [Sus scrofa (http://asia.ensembl.org/Sus_scrofa/Info/Index) [http://www.pantherdb.org/) [http://www.genome.jp/kegg/pathway.html) [http://www.biograph.be) [https://toppgene.cchmc.org///) [http://primer3.ut.ee/) [http://www.animalgenome.org/cgi-bin/QTLdb/SS/index [http://sift.jcvi.org/www/SIFT_seq_submit2.html) [http://genetics.bwh.harvard.edu/pph2/) [http://pantherdb.org/tools/csnpScoreForm.jsp) [http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) [http://mutpred.mutdb.org/) [http://www.cmbi.ru.nl/hope/) [http://string-db.org/. The 3D homologous models and the validation were performed using MODELER  [http://services.mbi.ucla.edu/SAVES. Molecular docking and dynamic simulation were performed using AutoDock tool at http://autodock.scripps.edu/resources/adt and GROMACS server at http://www.gromacs.org. Haplotype blocks and linkage disequilibrium plots have been constructed using Haploview version 4.2 (www.broad.mit.edu/mpg/haploview) [The checklist of "ARRIVE Guidelines  for Animal Research: Reporting In Vivo Experiments" for the present study was provided in supplementary data  . SNP infov/snp/)  and Enseo/Index) . The Gendb.org/) , Kyoto Eay.html) , Biograpraph.be)  and Topp.org///) . The pri.ut.ee/) . The QTLSS/index . The toot2.html) , PolyPheu/pph2/) , PANTHERorm.jsp) , I-Mutan3.0.cgi) , MutPreddb.org/)  and HOPEl/hope/) . Proteindeller/)  and SAVEploview) .


Present in 25.4% of the genomes, these deletion mutants presumably require functional complementation to complete their infection cycle. They might thus have a large impact on the fitness of the baculovirus population. Altogether, we found a wide breadth of genomic variation in the baculovirus population, suggesting it has high adaptive potential.Viruses rely on widespread genetic variation and large population size for adaptation. Large DNA virus populations are thought to harbor little variation though natural populations may be polymorphic. To measure the genetic variation present in a dsDNA virus population, we deep sequenced a natural strain of the baculovirus Autographa californica multiple nucleopolyhedrovirus. With 124,221X average genome coverage of our 133,926 bp long consensus, we could detect low frequency mutations (0.025%). K-means clustering was used to classify the mutations in four categories according to their frequency in the population. We found 60 high frequency non-synonymous mutations under balancing selection distributed in all functional classes. These mutants could alter viral adaptation dynamics, either through competitive or synergistic processes. Lastly, we developed a technique for the delimitation of large deletions in next generation sequencing data. We found that large deletions occur along the entire viral genome, with hotspots located in homologous repeat regions ( Evolution relies on variation . Most geHaploid and asexual diploid organisms are thought to incur a cost linked with clonality, which could hinder their adaptive response to changing environments ,5. HowevFollowing primary infection by few infectious particles, virus populations increase rapidly but usually incur high mutation rates, which can bring deleterious mutations to individual genomes, but also beneficial mutations allowing the virus to evade from host immune response. Theory predicts virus populations, as a cloud of diverse mutational variants, should occupy larger sequence space in dynamic environments . There aPanolis flammea caterpillar collected in the wild was indeed found to contain 24 baculovirus genotypic variants based solely on restriction fragment length polymorphism [Baculoviruses are large dsDNA viruses disseminated in the form of occlusion bodies OBs,  harborinmorphism . Deletermorphism  and may Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is the best-characterized baculovirus [Alphabaculovirus [i.e., OBs harboring numerous virions themselves containing around three rod-shaped nucleocapsids . Seven genomic variants have been identified by restriction analyses [ulovirus  that hasulovirus . It dispocapsids . AcMNPV e family . AcMNPV e family ,30 and tanalyses ,31,32,33analyses . We usedAutographa californica) by Crumb’s methods [in vivo amplification of an archival sample of the original AcMNPV isolate through a one-cycle infection of 500 highly susceptible cabbage looper (Trichoplusia ni) caterpillars using the diet plug method [3 diet plug. Viral amplification conditions, i.e., highly susceptible hosts caterpillar species and high viral dose [10 OBs were treated as described in [11 genomes).The baculovirus AcMNPV was first isolated in 1964 from a single field caterpillar of the alfalfa looper  to a 100-to-800 bp size range using the E210 Covaris instrument . Fragments were end-repaired, then 3’-adenylated, and Illumina adapters were added by using NEBNext Sample Reagent Set . Ligation products were purified by Ampure XP  and DNA fragments (>200 bp) were PCR-amplified using Illumina adapter-specific primers and Platinum® Pfx DNA polymerase . Amplified library fragments were size selected on 1.5% agarose gel at 260 bp. After library profile analysis by Agilent 2100 Bioanalyzer  and qPCR quantification , each library was sequenced using 151 bp-length read chemistry in a paired-end flow cell on the HiSeq™ 2000 sequencing system .A paired-end library was constructed by sonicating 2 µg of purified viral dsDNA (1.47 × 108 paired-end reads (Genbank accession number SRS533250). On the one hand, de novo assembly using the Newbler 2.8 program [hrs), which could not be resolved with the de novo assembly strategy. We manually compared with Geneious 8 the large contigs from the de novo assembly to the sequence from the mapping, to order the contigs, complete the misassembled sequence and finally generate the WP10 consensus genome (accession number KM609482). ORFs were predicted with Geneious and corrected by comparison with the C6 annotation and BLAST [Two approaches were conducted to assemble the produced 1.71 × 10 program  was carr program . The secnd BLAST . The WP1nd BLAST .8 paired-end reads, with at least 100 consecutive bases of Phred quality score above 30 , were re-mapped on the AcMNPV-WP10 consensus sequence using the bowtie 2 software [−3 for all the possible changes, or >2.5 × 10−4 per nucleotide) as threshold to the genuine detection of variation. Geneious (versions 6.1.7 and 8.0.5) was used to visually validate variations. The repartitions of the SNPs in different frequency groups was assessed by k-means clustering [versus neutral processes, Tajima’s D statistics [To detect polymorphism within the WP10 genome population, 1.50 × 10software . Single software . We prevsoftware . Howeverustering  with theustering . K-meansustering , combiniatistics  have beeWe developed a new approach to detect the boundaries of large deletions based on the analysis of the distance between pairs of Illumina paired-end reads. The theoretical size of the Illumina insert library is 260 nucleotides; the 151 bp paired-end reads are therefore expected to overlap by about 42 nucleotides . When mahr5 and hr1 in the population has been verified by Sanger sequencing. First a 25 µL amplification reaction was performed from 1 ng purified WP10 DNA by using 0.5 pmol µL−1 of each primer , 3 mM MgCl2, 0.2 mM dNTP and 0.75 unit Diamond Taq polymerase (Eurogentec) under a 30-cycles PCR program . Then the PCR product was purified using the NucleoSpin® Gel and PCR Clean-up kit  and sequenced on ABI PRISM 3100-Avant system using the BigDye Terminator kit according to manufacturer’s instructions .The presence of a large deletion between in vivo infection under permissive conditions. The sequencing effort amounted to 1.71 × 108 Illumina paired-end reads. Following de novo assembly, we obtained a WP10 consensus sequence of 133,926 bp with an overall A + T content of 59.3%. In total, 1.50 × 108 paired-end reads could be mapped on this genome, resulting in an extremely deep and uniform mean coverage of 124,221X . As around 1.47 × 1010 genomes of ~134 kb were nebulized .We sequenced a genome population of the baculovirus AcMNPV-WP10 (WP10). This WP10 isolate was obtained by amplifying an archival sample of the original AcMNPV Vail isolate  through  10−4 pg ), and raAc20/Ac21, Ac58/Ac59, Ac106/Ac107, Ac112/Ac113) (Ac106/107) is supported by a recent transcriptomics study [Ac20/21 = arif-1, Ac58/59 = ChaB-like, Ac112/113) might be alternatively spliced ORFs. WP10 consensus genome analysis also reveals some ORFs longer than reported for the C6 clone: Ac17 (da18), Ac52, Ac131 (pp34), Ac143 (odv-e18) and Ac145. However, Ac86 (pnk/pnl) is 221 bp shorter. These variations are due to single nucleotide polymorphisms (SNPs) and short indels (insertion/deletion), changing the positions of stop codons for Ac17, Ac52 and Ac131 and the position of the first methionine for Ac143 and Ac145. These observed variations are all compatible with the TSS found by transcriptomics [We annotated 151 ORFs . Our con2/Ac113) , as prevcs study , showingcs study . This suiptomics .8 paired-end reads with quality scores >30 on the WP10 consensus sequence. The mean sequence coverage for these analyses was 124,221X allowing at each position the significant detection of mutations present at a frequency higher than 2.5 × 10−4, above the sequencing error rate for any nucleotide. The accuracy and quality of the dataset was previously attested by PCR validation of the extremely rare insertion of transposable elements detected in single reads [−4 on our 133,926 bp long sequence, found in reads positioned in both orientations of genome and supported by both reads of a pair. When looking at the variation below 2.5 × 10−4 in frequency, we found all the possible mutations at each position, but as these SNP calls could be confused with sequencing error they were discarded. This suggests AcMNPV presents a high adaptive potential, as mutations that could bring a large fitness benefit in a different host are potentially already present in the population. The frequency of such mutation would determine how readily the viral genome population is functionally pre-adapted.To study in depth the genetic variation present within the WP10 genome population, we mapped 1.50 × 10le reads . We dete−3 mutations per nucleotide in the population. These SNPs are in extremely low frequency but are genuine as they are covered by more than 0.025% reads with 99.9% base call accuracy. Cluster 2 grouped over 12% of mutations with a mean frequency of 9.3 × 10−2. Cluster 3 corresponds to 5% of the mutations with an average frequency of 0.19. Cluster 4 grouped together only 118 SNPs (3.6% of the variations) but with the highest frequency of 0.35 within our AcMNPV genome population  that can be identified from the consensus sequence of our viral genome population, we performed a k-means clustering analysis on the SNPs. We obtained four clusters corresponding to groups of SNPs with similar frequencies , Table 2i.e., OB) contains over ten virions, each enclosing around three nucleocapsids , thus each OB contains around 30 genomes 133,926 bp long , by multiplying each SNP frequency by the number of bases, we found that each genome carries around 94 SNPs of any clusters and that each OB carries around 2815 mutations. However, even if cluster 1 mutations are in a relatively high number in the population when compared to the other clusters, their frequency at each site is the lowest (2.7 × 10−3). There is therefore little chance for each SNP from cluster 1 to be carried forward to the next generation. Thus, they likely reappear from mutations at each generation. Therefore, we propose that their mean frequency, 2.7 × 10−3 mutations per nucleotide, per infection cycle, might represent the mutation frequency of the population, that is closer to the RNA viruses mutation rate  than to the DNA viruses’ (1.8 × 10−8 mut/nt/rep) [in vivo infection cycle and not per genomic replication and it is thus difficult to compare to previous mutation rates studies due to differences in scale and variation between hosts. We cannot apply the fluctuation test of Luria and Delbruck [Considering an infectious baculovirus particle (/nt/rep) . We haveDelbruck  because Delbruck , which iiap-2 (distance = 15 nt), p15 (1 nt), Ac91 (16 nt), Ac109 (14 nt), p94 (11 nt) and Ac7 (3 nt). Of 209 SNPs found in coding regions, only 105 are non-synonymous, 60 of which involve a change in the amino-acid polarity [The variations found in clusters 4 and 3 have the second and third highest frequencies in the genome population (0.35 and 0.19). From these 282 SNPs, 73 are found in non-coding regions. Although none were found in the TSS regions , these mpolarity . They arlef-8, lef-9 and lef-4). Similarly, lef-9 was linked with different transmission phenotypes in Spodoptera exigua NPV [pif-1, pif-2, pif-3 and vp91) encoding essential components of the per os infectivity complex, which binds to insect midgut cells and is therefore involved in primary infection [pif-1 over-expression is detrimental to viral population fitness and leads to increased frequency of genotypes lacking pif-1 expressing capabilities to regulate the amount of PIF-1 protein available in the cell [Interestingly, we found SNPs changing amino-acid polarity in a number of essential core genes and core functions. We detected mutations in several genes involved in transcription, including three subunits of the RNA polymerase (igua NPV . Altogetnfection . We mighthe cell . Such cogp64 (Ac128), which is the envelope fusion protein, is oligomerized during transport to the plasma membrane [As cells are commonly multiply infected during a baculovirus infection ,24,57, bmembrane . When olmembrane . GeneticChitinase (Ac126)  . This concy SNPs ; one of i.e., 60 non-synonymous SNPs of the clusters 3 and 4 among 3243 SNPs. This is in large contrast with the extent of variation present in the population. When considering the SNPs from cluster 1, which represent the vast majority (78%) of all SNPs, 52% are non-synonymous but are in extremely low frequency in the population (0.12%), and thus appear functionally negligible.Like a magnet for finding a needle in a haystack, the k-mean clustering allowed us to point out the most significant protein variants in our viral population, t-test, p < 2.2 × 10−6), they therefore evolve under balancing selection. This supports our interpretation that these highly frequent SNPs are involved in the adaptive process of our viral population.To further assess under which process our baculovirus population evolved, we ran a homemade script estimating Tajima’s D for every positions of the genome. The estimated mean Tajima’s D was −0.55, which represents a population evolving close to the mutation drift equilibrium . Deleteri.e., the hrs 1, 2, 3, 4b and 5.As genomes harboring large deletions have been shown to bring synergistic fitness effects to some baculovirus populations , we endehrs in AcMNPV. These regions contain repeated 70 bp units with an imperfect 30 bp palindrome in their center [hrs are highly recombinogenic and highly variable in size, we investigated the possibility that our observations could be artifactual by excluding all pairs of reads less distant than the largest hr size (669 bp for hr2). By mapping the 2.81% more distant pairs of reads, we excluded deletions shorter than 669 bp and thus removed those deleting the hrs only. As we still have spikes in the same regions as when mapping the 5% most distant pairs of reads  [There are eight r center . As hrs of reads , we thusd Ac140) ,24. As whrs , which have been proposed to serve as replication origins [hrs is involved in the formation of these deletions remains open. For these deletion mutants to replicate though, they would need to have conserved a replication origin. This is a prerequisite for their maintenance and selection within the viral population. Alternatively, the deleted genome we found could result from badly resolved replication and random encapsidation within OBs. In AcMNPV, the adaptiveness of these deletion mutants has yet to be investigated. Deletion between hrs means genes are deleted as loci related sets, regardless of their function or whether they are essential. Each deletion mutant therefore needs to be complemented to complete its infection cycle. This would appear as a burden to the population, unless these mutants, by replicating more rapidly, could somehow enhance the replication of the whole virus population. For example, they might accelerate the production of proteins from the early release of RNA matrix.Interestingly, most large deletions seemed anchored on  origins , and the origins . Baculov origins . The rep origins  potentiaThe real extent of genetic variation in populations remains barely known although it is expected to greatly impact adaptation to the environment. To test this, we studied the genetic diversity of a generalist dsDNA virus population. Based on our ultra-deep sequencing data, we showed that variability is widely present in our AcMNPV genome population, both in the form of SNPs and of large deletions. Defective interfering (D.I.) particles, produced during viral replication are well known in RNA and DNA viruses ,67. SincThe evolutionary arms race  occurs a


We constructed triplicate pools of 70 slow-clearing (resistant) and 70 fast-clearing (sensitive) infections collected from the Thai–Myanmar border and sequenced these to high (∼150-fold) read depth. Allele frequency estimates from pools showed almost perfect correlation (Lin’s concordance = 0.98) with allele frequencies at 93 single nucleotide polymorphisms measured directly from individual infections, giving us confidence in the accuracy of this approach. By mapping genome-wide divergence (FST) between pools of drug-resistant and drug-sensitive parasites, we identified two large (>150 kb) regions (on chrs. 13 and 14) and 17 smaller candidate genome regions. To identify individual genes within these genome regions, we resequenced an additional 38 parasite genomes (16 slow and 22 fast-clearing) and performed rare variant association tests. These confirmed kelch as a major molecular marker for ART resistance (P = 6.03 × 10−6). This two-tier approach is powerful because pooled sequencing rapidly narrows down genome regions of interest, while targeted rare variant association testing within these regions can pinpoint the genetic basis of resistance. We show that our approach is robust to recurrent mutation and the generation of soft selective sweeps, which are predicted to be common in pathogen populations with large effective population sizes, and may confound more traditional gene mapping approaches.We explored the potential of pooled sequencing to swiftly and economically identify selective sweeps due to emerging artemisinin (ART) resistance in a South-East Asian malaria parasite population. ART resistance is defined by slow parasite clearance from the blood of ART-treated patients and mutations in the  This has led to a great interest in using pooled sequencing to map regions of genomic divergence between populations . Pooled This study was designed to evaluate the effectiveness and accuracy of pooled sequencing for malaria parasites obtained directly from infections. We show that a two-tier approach, which uses pooled sequencing followed by resequencing of a small number of parasite haplotypes and application of rare variant association tests, allows robust identification of a known causal gene. We also use coalescent simulation to examine thresholds for detection of selected genome regions, and highlight several other candidate genome regions.A). We classified these as slow (CR >6.5 h) or fast (CR <4.5 h) clearing, and genotyped the parasites from each infection using a custom 96- single nucleotide polymorphism (SNP) GoldenGate platform , and we opted to retain all single clones in the pools to maximize sample size. We selected the 70 fastest and 70 slowest clearing individuals (A), and pooled equimolar concentrations of DNA from each. These six pools were sequenced in multiplex to approximately 150-fold coverage on a single lane of an Illumina HiSeq 2500. We generated allele frequency estimates from aligned read depth data, limiting our analysis to 23,292 high quality (Q30) SNPs with greater than 70-fold coverage across all pools after removing SNPs in large antigenic gene families. The 23,292 SNPs included in our analysis had a median per pool read depth of 154 (range: 70–250).To validate the use of pooled sequencing in identifying the determinants of antimalarial drug resistance, we measured parasite CR in 291 ART-treated patients presenting to clinics on the Thai–Burmese border in the 2011–2012 malaria season A. We claplatform . Thirty-ividuals A, and poB and C). This was marginally lower than expected from a binomial sampling of alleles based on their allele frequency alone . We then measured the reproducibility of allele frequency estimation within pools across all 23,292 SNPs by performing pairwise comparisons between each replicate . This was substantially higher than concordance of allele frequency estimation between pools . Finally, by estimating pairwise FST between technical replicates from within each pool, we were able to estimate the expected false positive rate due to technical artifacts. We detected 362/23,292 (1.55%) and 32/23,292 (1.47%) of SNPs with an FST>0.1 within the fast and slow clearing pools, respectively. By calculating FST in 25 kb sliding windows with a 5 kb offset, we reduced the proportion of significant loci to 12/4,600 (0.26%) and 6/4,600 (0.13%) within fast and slow pools, respectively. This was further improved by excluding windows with high variability in estimating FST  after which no FST signals reached 0.1 within either fast or slow pools . As we expect selective sweeps to drive allele frequencies of multiple linked SNPs simultaneously, calculating FST in windows should not affect our ability to detect strong, recent selective sweeps. We show below that this threshold (FST>0.1) is appropriate using coalescent simulations, and that multiple SNPs within a 25 kb window are likely to be present at high FST where selection is acting.We explored how well our deep sequence data captured population allele frequencies. Using the genotypes of the 93 GoldenGate SNPs, we calculated the allele frequency of each SNP. We compared these to the mean allele frequency across replicate pools for the same sites and obtained highly concordant measures  windows with an FST>0.1 (A). These windows were nonrandomly distributed across the genome, with 92 windows falling within 100 kb of another highly diverged window . After collapsing windows within 100 kb together, there were 19 multiwindow regions . When ranking these by size (B), the largest genome region  contains the recently identified ART resistance gene PF3D7_1343700  used parameters estimated from independent studies of malaria parasite populations. In settings such as the Thai–Burmese border the majority of malaria cases are treated with drugs, resulting in a strong selective pressure on the parasite population (s) on the order of 0.1 or higher . This suggested a low, but not inconsequential false positive rate at this threshold. Notably, in these neutral simulations only a single marker in each run had an FST >0.1, these would be unlikely to produce significant hits in a windowed strategy and is in contrast to simulations with selection where greater than 20% of SNPs reached this threshold in plausible simulations . In our pooled sequencing experiments a FST threshold of 0.1 captures variants in the top 2.5% of values genome wide, providing further evidence that this threshold is reasonable.To estimate whether our pooled sequencing approach is well powered to detect sweeps driven by strong selection in large parasite populations, and to establish appropriate empirical thresholds for identifying genome regions of interest, we performed coalescent simulations using msms . These spulation . Consequr higher . Similarr higher , and thehis drug . We simu= 10,000 ) where aeNµL = 0, 0.1, 1, and 10). For simulations of soft sweeps (4eNµL ≥0.1) we conditioned on the presence of ≥2 beneficial mutations, discarding simulations which did not reach this threshold. The actual number of beneficial mutations in each simulation is shown in figure 3B, with up to 30 beneficial mutations present in the most extreme simulations. At very soft sweeps (4eNµL = 10), 97.9% of our simulations contained markers with FST >0.1 between resistance classes (A) with 100% of simulations containing high FST markers at lower, more plausible values of 4eNµL. The broad window of values for 4eNµL that we used for these simulations encompasses those plausible for Plasmodium falciparum. Obtaining a precise estimate of this compound parameter will be dependent on obtaining accurate per generation mutation rates and understanding the breadth of mutations which can result in the slow clearance phenotype.Previously described drug resistance alleles in malaria parasites frequently have multiple origins , 2008. I classes A with 10supplementary table S1, Supplementary Material online). The total number of genes (493) is too large for direct functional characterization, and further prioritization is needed. We performed direct whole-genome sequencing of an independent set of 38 parasite genomes  collected between 2008 and 2010 which were not present in either of the pools, and tested SNPs for association with CR. Traditional genome-wide association studies are based on simple population genetic models, whereby a region harbors a single causal mutation. We know these models are unlikely to hold in the case of kelch for which 17 causal mutations were recently described (pfmdr1  and 16 fast clearing [CR <2.5 h]) collected in 2008–2010 were sequenced directly. Library preparations were performed identically for pooled and nonpooled samples.Two micrograms of DNA was sheared using a Covaris S-series sonicator . Sheared DNA was end-repaired, A-tailed and multiplex-indexed adaptors ligated using NEBnext library preparation kits for Illumina (New England Biolabs). Following  we replaP. falciparum genome reference strain 3D7 v9.2  using BWA v0.6.1 (http://picard.sourceforge.net/). The Genome Analysis Toolkit v2.3-9 . For nonpooled samples we additionally recalled SNPs using the GATK HaplotypeCaller from v2.8-1 of the Genome Analysis Toolkit after restricting calling to genome regions with significant FST hits.One hundred one base pairs paired-end reads from.fastq files were mapped against the A v0.6.1 . The rest v2.3-9  was usedhttp://cran.r-project.org/web/packages/epiR/index.html). This was used in preference to more commonly used correlation coefficients as this measures the fit to a line with intercept 0, slope 1 . We performed random sampling of allele frequencies to asses if our correlations were as expected given random sampling of alleles from a pool using the approaches described in FST was estimated using PoPoolation2  were performed using msms  to the 3D7 genome. In sites where it was not possible to identify the ancestral allele due to missing or mixed sequencing data we used the reference strain allele. As the reference strain, 3D7, was isolated prior to widespread ART use, and is likely of African origin, drug resistance mutations are likely to be nonreference. Malaria parasites are haploid, though there are frequently mixed base calls in next-generation sequence data, arising from either technical bias or where two distinct parasite genotypes are present in a sample. Although we restricted our sequencing to single clone isolates a number of mixed base calls were observed. At these sites we adopted a dominant model where the presence of a nonreference SNP at a heterozygous site was treated as a homozygous nonreference SNP. As the power of rare variant tests is compromised by retaining neutral alleles in the test we removed all SNPs where the derived allele was not present in the slow-clearing parasites or had a derived allele frequency greater than 50% in the fast-clearing parasites.Rare variant association tests were performed in SKAT  using thhttp://www.ncbi.nlm.nih.gov/sra/) under project numbers PRJNA260844 and PRJNA260845.Raw sequence data have been submitted to the NCBI Sequence Read Archive .