Single Nucleotide Polymorphism Barcodes Research Articles

Plasmodium vivax populations in the western Greater Mekong Subregion evaluated using a genetic barcode.

An improved understanding of the Plasmodium vivax populations in the Great Mekong Subregion (GMS) is needed to monitor the progress of malaria elimination. This study aimed to use a P. vivax single nucleotide polymorphism (SNP) barcode to evaluate the population dynamics and explore the gene flow among P. vivax parasite populations in the western GMS (China, Myanmar and Thailand). A total of 315 P. vivax patient samples collected in 2011 and 2018 from four regions of the western GMS were genotyped for 42 SNPs using the high-throughput MassARRAY SNP genotyping technology. Population genetic analysis was conducted to estimate the genetic diversity, effective population size, and population structure among the P. vivax populations. Overall, 291 samples were successfully genotyped at 39 SNPs. A significant difference was observed in the proportion of polyclonal infections among the five P. vivax populations (P = 0.0012, Pearson Chi-square test, χ2 = 18.1), with western Myanmar having the highest proportion (96.2%, 50/52) in 2018. Likewise, the average complexity of infection was also highest in western Myanmar (1.31) and lowest in northeast Myanmar (1.01) in 2018. The older samples from western China in 2011 had the highest pairwise nucleotide diversity (π, 0.388 ± 0.046), expected heterozygosity (He, 0.363 ± 0.02), and the largest effective population size. In comparison, in the neighboring northeast Myanmar, the more recent samples in 2018 showed the lowest values (π, 0.224 ± 0.036; He, 0.220 ± 0.026). Furthermore, the 2018 northeast Myanmar parasites showed high and moderate genetic differentiation from other populations with FST values of 0.162-0.252, whereas genetic differentiation among other populations was relatively low (FST ≤ 0.059). Principal component analysis, phylogeny, and STRUCTURE analysis showed that the P. vivax population in northeast Myanmar in 2018 substantially diverged from other populations. Although the 42 SNP barcode is a valuable tool for tracking parasite origins of worldwide parasite populations, a more extended barcode with additional SNPs is needed to distinguish the more related parasite populations in the western GMS.

Performance of SNP barcodes to determine genetic diversity and population structure of Plasmodium falciparum in Africa.

Panels of informative biallelic single nucleotide polymorphisms (SNPs) have been proposed to be an economical method to fast-track the population genetic analysis of Plasmodium falciparum in malaria-endemic areas. Whilst used successfully in low-transmission areas where infections are monoclonal and highly related, we present the first study to evaluate the performance of these 24- and 96-SNP molecular barcodes in African countries, characterised by moderate-to-high transmission, where multiclonal infections are prevalent. For SNP barcodes it is generally recommended that the SNPs chosen i) are biallelic, ii) have a minor allele frequency greater than 0.10, and iii) are independently segregating, to minimise bias in the analysis of genetic diversity and population structure. Further, to be standardised and used in many population genetic studies, these barcodes should maintain characteristics i) to iii) across various iv) geographies and v) time points. Using haplotypes generated from the MalariaGEN P. falciparum Community Project version six database, we investigated the ability of these two barcodes to fulfil these criteria in moderate-to-high transmission African populations in 25 sites across 10 countries. Predominantly clinical infections were analysed, with 52.3% found to be multiclonal, generating high proportions of mixed-allele calls (MACs) per isolate thereby impeding haplotype construction. Of the 24- and 96-SNPs, loci were removed if they were not biallelic and had low minor allele frequencies in all study populations, resulting in 20- and 75-SNP barcodes respectively for downstream population genetics analysis. Both SNP barcodes had low expected heterozygosity estimates in these African settings and consequently biased analyses of similarity. Both minor and major allele frequencies were temporally unstable. These SNP barcodes were also shown to identify weak genetic differentiation across large geographic distances based on Mantel Test and DAPC. These results demonstrate that these SNP barcodes are vulnerable to ascertainment bias and as such cannot be used as a standardised approach for malaria surveillance in moderate-to-high transmission areas in Africa, where the greatest genomic diversity of P. falciparum exists at local, regional and country levels.

SNP barcodes provide higher resolution than microsatellite markers to measure Plasmodium vivax population genetics

BackgroundGenomic surveillance of malaria parasite populations has the potential to inform control strategies and to monitor the impact of interventions. Barcodes comprising large numbers of single nucleotide polymorphism (SNP) markers are accurate and efficient genotyping tools, however may need to be tailored to specific malaria transmission settings, since ‘universal’ barcodes can lack resolution at the local scale. A SNP barcode was developed that captures the diversity and structure of Plasmodium vivax populations of Papua New Guinea (PNG) for research and surveillance.MethodsUsing 20 high-quality P. vivax genome sequences from PNG, a total of 178 evenly spaced neutral SNPs were selected for development of an amplicon sequencing assay combining a series of multiplex PCRs and sequencing on the Illumina MiSeq platform. For initial testing, 20 SNPs were amplified in a small number of mono- and polyclonal P. vivax infections. The full barcode was then validated by genotyping and population genetic analyses of 94 P. vivax isolates collected between 2012 and 2014 from four distinct catchment areas on the highly endemic north coast of PNG. Diversity and population structure determined from the SNP barcode data was then benchmarked against that of ten microsatellite markers used in previous population genetics studies.ResultsFrom a total of 28,934,460 reads generated from the MiSeq Illumina run, 87% mapped to the PvSalI reference genome with deep coverage (median = 563, range 56–7586) per locus across genotyped samples. Of 178 SNPs assayed, 146 produced high-quality genotypes (minimum coverage = 56X) in more than 85% of P. vivax isolates. No amplification bias was introduced due to either polyclonal infection or whole genome amplification (WGA) of samples before genotyping. Compared to the microsatellite panels, the SNP barcode revealed greater variability in genetic diversity between populations and geographical population structure. The SNP barcode also enabled assignment of genotypes according to their geographic origins with a significant association between genetic distance and geographic distance at the sub-provincial level.ConclusionsHigh-throughput SNP barcoding can be used to map variation of malaria transmission dynamics at sub-national resolution. The low cost per sample and genotyping strategy makes the transfer of this technology to field settings highly feasible.

Use of a Plasmodium vivax genetic barcode for genomic surveillance and parasite tracking in Sri Lanka

BackgroundSri Lanka was certified as a malaria-free nation in 2016; however, imported malaria cases continue to be reported. Evidence-based information on the genetic structure/diversity of the parasite populations is useful to understand the population history, assess the trends in transmission patterns, as well as to predict threatening phenotypes that may be introduced and spread in parasite populations disrupting elimination programmes. This study used a previously developed Plasmodium vivax single nucleotide polymorphism (SNP) barcode to evaluate the population dynamics of P. vivax parasite isolates from Sri Lanka and to assess the ability of the SNP barcode for tracking the parasites to its origin.MethodsA total of 51 P. vivax samples collected during 2005–2011, mainly from three provinces of the country, were genotyped for 40 previously identified P. vivax SNPs using a high-resolution melting (HRM), single-nucleotide barcode method. Minor allele frequencies, linkage disequilibrium, pair-wise FST values, and complexity of infection (COI) were evaluated to determine the genetic diversity. Structure analysis was carried out using STRUCTURE software (Version 2.3.4) and SNP barcode was used to identify the genetic diversity of the local parasite populations collected from different years. Principal component analysis (PCA) was used to determine the clustering according to global geographic regions.ResultsThe proportion of multi-clone infections was significantly higher in isolates collected during an infection outbreak in year 2007. The minor allele frequencies of the SNPs changed dramatically from year to year. Significant linkage was observed in sample sub-sets from years 2005 and 2007. The majority of the isolates from 2007 consisted of at least two genetically distinct parasite strains. The overall percentage of multi-clone infections for the entire parasite sample was 39.21%. Analysis using STRUCTURE software (Version 2.3.4) revealed the high genetic diversity of the sample sub-set from year 2007. In-silico analysis of these data with those available from other global geographical regions using PCA showed distinct clustering of parasite isolates according to geography, demonstrating the usefulness of the barcode in determining an isolate to be indigenous.ConclusionsPlasmodium vivax parasite isolates collected during a disease outbreak in year 2007 were more genetically diverse compared to those collected from other years. In-silico analysis using the 40 SNP barcode is a useful tool to track the origin of an isolate of uncertain origin, especially to differentiate indigenous from imported cases. However, an extended barcode with more SNPs may be needed to distinguish highly clonal populations within the country.

Identification of High-Order Single-Nucleotide Polymorphism Barcodes in Breast Cancer Using a Hybrid Taguchi-Genetic Algorithm: Case-Control Study.

BackgroundBreast cancer has a major disease burden in the female population, and it is a highly genome-associated human disease. However, in genetic studies of complex diseases, modern geneticists face challenges in detecting interactions among loci.ObjectiveThis study aimed to investigate whether variations of single-nucleotide polymorphisms (SNPs) are associated with histopathological tumor characteristics in breast cancer patients.MethodsA hybrid Taguchi-genetic algorithm (HTGA) was proposed to identify the high-order SNP barcodes in a breast cancer case-control study. A Taguchi method was used to enhance a genetic algorithm (GA) for identifying high-order SNP barcodes. The Taguchi method was integrated into the GA after the crossover operations in order to optimize the generated offspring systematically for enhancing the GA search ability.ResultsThe proposed HTGA effectively converged to a promising region within the problem space and provided excellent SNP barcode identification. Regression analysis was used to validate the association between breast cancer and the identified high-order SNP barcodes. The maximum OR was less than 1 (range 0.870-0.755) for two- to seven-order SNP barcodes.ConclusionsWe systematically evaluated the interaction effects of 26 SNPs within growth factor–related genes for breast carcinogenesis pathways. The HTGA could successfully identify relevant high-order SNP barcodes by evaluating the differences between cases and controls. The validation results showed that the HTGA can provide better fitness values as compared with other methods for the identification of high-order SNP barcodes using breast cancer case-control data sets.

SNP barcoding based on decision tree algorithm: A new tool for identification of mosquito species with special reference to Anopheles

Molecular taxonomy based identification of species in the form of DNA barcodes are extensively used in evolutionary systematics. Almost all the DNA barcodes contain detailed information of the barcoding gene along with uninformative sequences of a particular species. Therefore, a technique is highly essential to remove or to reduce the number of uninformative sequences and ought to create species-specific barcodes for differentiation. The actual variation in genetic sequences, called single nucleotide polymorphism (SNP) genotyping, can be utilized to develop a new tool for rapid, reliable, and high-throughput assay to distinguish the known species. SNPs act as important hereditary markers for uncovering the evolutionary history and normal genetic polymorphisms. Keeping in mind, we propose a decision tree-based barcoding (DTB) algorithm for generating SNP barcodes from the DNA barcoding sequence of several evolutionarily related species to accurately identify a single species. To address this issue, we analyzed mitochondrial COI gene sequences of 64 species of Anopheles mosquitoes. After alignment and truncating, 32 SNPs were discovered in COI gene sequences of Anopheles mosquitoes and then computed to set up the decision rule for constructing the decision tree. The decision tree based barcoding algorithm generates 126 nodes and 32 loci for discriminating 64 Anopheles mosquito species. Finally, we concluded that the DTB method is useful and effective for generating sequence tags for Anopheles mosquito species identification.

Individual field-collected glassy-winged sharpshooter vectors harbor sequences from two Xylella fastidiosa subspecies

During 2016 and 2017, glassy-winged sharpshooters (GWSS; Homalodisca vitripennis) collected from vineyards with a known history of Pierce’s disease or nearby citrus orchards in Kern County, California were assessed for presence of Xylella fastidiosa by quantitative PCR using total DNA samples extracted from GWSS heads. Conventional PCR products for three X. fastidiosa housekeeping genes (petC, leuA, and holC) were cloned from a subset of X. fastidiosa-positive GWSS DNA samples. Cloned sequences were assigned to X. fastidiosa subspecies based on Single Nucleotide Polymorphism (SNP) signature barcodes (7–12 polymorphic sites per amplicon that differentiate reference genomes of subspecies fastidiosa and multiplex). A total of 2005 clones were sequenced: 1499 (74.8%) were genotyped as subspecies fastidiosa and 491 (24.5%) as subspecies multiplex. Among cloned subspecies fastidiosa sequences, 95.4% had SNP barcodes identical to the corresponding gene present in subspecies fastidiosa strains cultured from Pierce’s disease-affected vines in the same region sampled in 2016 and 2017. Presence of SNP barcodes representing both subspecies in one or more cloned genes was commonly observed within individual GWSS. This observation indicates that populations of X. fastidiosa in some insects consists of two subspecies or of two genotypes of one subspecies in which a proportion of the population was derived from a lineage with a history of horizontal gene transfer and homologous recombination. These conclusions suggest that: 1) individual GWSS may visit multiple host species (inoculum sources); 2) sequential pathogen acquisition events can lead to co-colonization of GWSS; 3) competitive exclusion of X. fastidiosa in the foregut is weak or not operating; and 4) the vector foregut is a potential arena for exchange of genetic material among sympatric X. fastidiosa subspecies.

Comparison of msp genotyping and a 24 SNP molecular assay for differentiating Plasmodium falciparum recrudescence from reinfection

BackgroundCurrent World Health Organization guidelines for conducting anti-malarial drug efficacy clinical trials recommend genotyping Plasmodium falciparum genes msp1 and msp2 to distinguish recrudescence from reinfection. A more recently developed potential alternative to this method is a molecular genotyping assay based on a panel of 24 single nucleotide polymorphism (SNP) markers.MethodsPerformance parameters of these two genotyping methods were compared using data from two recently completed drug efficacy trials. Blood samples from two anti-malarial therapeutic trials were analysed by both msp genotyping and the 24 SNP assay. Additionally, to conserve time and resources, the statistical program R was used to select the most informative SNPs for a set of unrelated Malawian samples to develop a truncated SNP-based assay for the region surrounding Blantyre, Malawi. The ability of this truncated assay to distinguish reinfection from recrudescence when compared to the full 24 SNP assay was then analysed using data from the therapeutic trials.ResultsA total of 360 samples were analysed; 66 for concordance of msp and SNP barcoding methodologies, and 294 for assessing the most informative of the 24 SNP markers. SNP genotyping performed comparably to msp genotyping, with only one case of disagreement among the 50 interpretable results, where the SNP assay identified the sample as reinfection and the msp typing as recrudescence. Furthermore, SNP typing was more robust; only 6% of samples were uninterpretable by SNP typing, compared to 19.7% when msp genotyping was used. For discriminating reinfection from recrudescence, a truncated 6 SNP assay was found to perform at 95.1% the accuracy of the full 24 SNP bar code.ConclusionsThe use of SNP analysis has similar sensitivity to the standard msp genotyping in determining recrudescence from reinfection. Although more expensive, SNP typing is faster and less work intensive. Limiting the assay to those SNPs most informative in the geographical region of interest may further decrease the workload and the cost, making this technique a feasible and affordable alternative in drug efficacy trials.

Characterization of Plasmodium falciparum structure in Nigeria with malaria SNPs barcode

BackgroundPlasmodium falciparum malaria remains a major health challenge in Nigeria despite the global decline of its incidence and mortality rates. Although significant progress has been made in preventing the transmission of P. falciparum and controlling the spread of the infection, there is much to be done in the area of proper monitoring, surveillance of the parasite, investigating the population dynamics and drug resistance profiling of the parasite as these are important to its eventual eradication. Polymorphic loci of msp1, msp2 and/or glurp genes or microsatellites have been traditionally used to characterize P. falciparum population structure in various parts of Nigeria. The lack of standardization in the interpretation of results, as well as the inability of these methods to distinguish closely related parasites, remains a limitation of these techniques. Conversely, the recently developed 24 single nucleotide polymorphism (SNP)-based molecular barcode assay has the possibility of differentiating between closely related parasites and offer additional information in determining the population diversity of P. falciparum within and between parasite populations. This study is therefore aimed at defining the population diversity of P. falciparum in and between two localities in Nigeria using the SNPs barcode technique.MethodsThe 24-SNP high-resolution melt (HRM) barcode assay and msp2 genotyping was used to investigate both intra and inter population diversity of the parasite population in two urban cities of Nigeria.ResultsBased on SNP barcode analysis, polygenomic malaria infections were observed in 17.9% and 13.5% of population from Enugu and Ibadan, respectively, while msp2 analyses showed 21% and 19.4% polygenomic infections in Enugu and Ibadan, respectively. Low levels of genetic diversity (π) of 0.328 and 0.318 were observed in Enugu and Ibadan parasite populations, respectively, while the FST value of 0.02 (p = 0.055) was obtained when the genetic divergence of both populations was considered.ConclusionsThe 24-SNP barcode assay was effective in analysing P. falciparum population diversity. This study also showed that P. falciparum populations in Enugu and Ibadan had a degree of intra-population diversity, but very low divergence between the population. A low degree of polygenomic infections were also observed in the two parasite populations unlike previous years. This maybe as a result of the effect of artemisinin-based combination therapy (ACT), long-lasting insecticide-treated nets (LLITNs) and intermittent preventive treatments in the study populations.

Decision Tree Algorithm–Generated Single-Nucleotide Polymorphism Barcodes of rbcL Genes for 38 Brassicaceae Species Tagging

DNA barcode sequences are accumulating in large data sets. A barcode is generally a sequence larger than 1000 base pairs and generates a computational burden. Although the DNA barcode was originally envisioned as straightforward species tags, the identification usage of barcode sequences is rarely emphasized currently. Single-nucleotide polymorphism (SNP) association studies provide us an idea that the SNPs may be the ideal target of feature selection to discriminate between different species. We hypothesize that SNP-based barcodes may be more effective than the full length of DNA barcode sequences for species discrimination. To address this issue, we tested a ribulose diphosphate carboxylase (rbcL) SNP barcoding (RSB) strategy using a decision tree algorithm. After alignment and trimming, 31 SNPs were discovered in the rbcL sequences from 38 Brassicaceae plant species. In the decision tree construction, these SNPs were computed to set up the decision rule to assign the sequences into 2 groups level by level. After algorithm processing, 37 nodes and 31 loci were required for discriminating 38 species. Finally, the sequence tags consisting of 31 rbcL SNP barcodes were identified for discriminating 38 Brassicaceae species based on the decision tree–selected SNP pattern using RSB method. Taken together, this study provides the rational that the SNP aspect of DNA barcode for rbcL gene is a useful and effective sequence for tagging 38 Brassicaceae species.

Single nucleotide polymorphism barcoding of cytochrome c oxidase I sequences for discriminating 17 species of Columbidae by decision tree algorithm.

DNA barcodes are widely used in taxonomy, systematics, species identification, food safety, and forensic science. Most of the conventional DNA barcode sequences contain the whole information of a given barcoding gene. Most of the sequence information does not vary and is uninformative for a given group of taxa within a monophylum. We suggest here a method that reduces the amount of noninformative nucleotides in a given barcoding sequence of a major taxon, like the prokaryotes, or eukaryotic animals, plants, or fungi. The actual differences in genetic sequences, called single nucleotide polymorphism (SNP) genotyping, provide a tool for developing a rapid, reliable, and high‐throughput assay for the discrimination between known species. Here, we investigated SNPs as robust markers of genetic variation for identifying different pigeon species based on available cytochrome c oxidase I (COI) data. We propose here a decision tree‐based SNP barcoding (DTSB) algorithm where SNP patterns are selected from the DNA barcoding sequence of several evolutionarily related species in order to identify a single species with pigeons as an example. This approach can make use of any established barcoding system. We here firstly used as an example the mitochondrial gene COI information of 17 pigeon species (Columbidae, Aves) using DTSB after sequence trimming and alignment. SNPs were chosen which followed the rule of decision tree and species‐specific SNP barcodes. The shortest barcode of about 11 bp was then generated for discriminating 17 pigeon species using the DTSB method. This method provides a sequence alignment and tree decision approach to parsimoniously assign a unique and shortest SNP barcode for any known species of a chosen monophyletic taxon where a barcoding sequence is available.

A comparative analysis of chaotic particle swarm optimizations for detecting single nucleotide polymorphism barcodes.

Evolutionary algorithms could overcome the computational limitations for the statistical evaluation of large datasets for high-order single nucleotide polymorphism (SNP) barcodes. Previous studies have proposed several chaotic particle swarm optimization (CPSO) methods to detect SNP barcodes for disease analysis (e.g., for breast cancer and chronic diseases). This work evaluated additional chaotic maps combined with the particle swarm optimization (PSO) method to detect SNP barcodes using a high-dimensional dataset. Nine chaotic maps were used to improve PSO method results and compared the searching ability amongst all CPSO methods. The XOR and ZZ disease models were used to compare all chaotic maps combined with PSO method. Efficacy evaluations of CPSO methods were based on statistical values from the chi-square test (χ2). The results showed that chaotic maps could improve the searching ability of PSO method when population are trapped in the local optimum. The minor allele frequency (MAF) indicated that, amongst all CPSO methods, the numbers of SNPs, sample size, and the highest χ2 value in all datasets were found in the Sinai chaotic map combined with PSO method. We used the simple linear regression results of the gbest values in all generations to compare the all methods. Sinai chaotic map combined with PSO method provided the highest β values (β≥0.32 in XOR disease model and β≥0.04 in ZZ disease model) and the significant p-value (p-value<0.001 in both the XOR and ZZ disease models). The Sinai chaotic map was found to effectively enhance the fitness values (χ2) of PSO method, indicating that the Sinai chaotic map combined with PSO method is more effective at detecting potential SNP barcodes in both the XOR and ZZ disease models.

Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility

ObjectivesPositively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data. MethodsWe propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a “top combination strategy” to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data. ResultsWe simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility. ConclusionIDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes.

Plasmodium falciparum K76T pfcrt Gene Mutations and Parasite Population Structure, Haiti, 2006-2009.

Hispaniola is the only Caribbean island to which Plasmodium falciparum malaria remains endemic. Resistance to the antimalarial drug chloroquine has rarely been reported in Haiti, which is located on Hispaniola, but the K76T pfcrt (P. falciparum chloroquine resistance transporter) gene mutation that confers chloroquine resistance has been detected intermittently. We analyzed 901 patient samples collected during 2006-2009 and found 2 samples showed possible mixed parasite infections of genetically chloroquine-resistant and -sensitive parasites. Direct sequencing of the pfcrt resistance locus and single-nucleotide polymorphism barcoding did not definitively identify a resistant population, suggesting that sustained propagation of chloroquine-resistant parasites was not occurring in Haiti during the study period. Comparison of parasites from Haiti with those from Colombia, Panama, and Venezuela reveals a geographically distinct population with highly related parasites. Our findings indicate low genetic diversity in the parasite population and low levels of chloroquine resistance in Haiti, raising the possibility that reported cases may be of exogenous origin.

Discovering SNP Interactions Associated with Breast Cancer Using Evolutionary Algorithms

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. In this paper, two evolutionary methods were compared to detect associations of single nucleotide polymorphisms (SNPs): a genetic algorithm and Gauss particle swarm optimization. Genetic algorithm was developed with partial matched crossover operator and two different strategies for initialization: regular initialization and top-5 strategy initialization. In both methods for different SNP barcodes (SNP combinations with their corresponding genotypes) the difference between case and control data is computed systematically. The algorithms look for the best combination which is the barcode with maximum difference between the two groups. Analysis results support that the genetic algorithm with top-5 strategy for initialization provides higher frequency difference values than the Gauss particle swarm optimization. It is also proved that a genetic algorithm reduces a computational cost for obtaining higher frequency difference between the case and control group.

Development of a single nucleotide polymorphism barcode to genotype Plasmodium vivax infections.

Plasmodium vivax, one of the five species of Plasmodium parasites that cause human malaria, is responsible for 25–40% of malaria cases worldwide. Malaria global elimination efforts will benefit from accurate and effective genotyping tools that will provide insight into the population genetics and diversity of this parasite. The recent sequencing of P. vivax isolates from South America, Africa, and Asia presents a new opportunity by uncovering thousands of novel single nucleotide polymorphisms (SNPs). Genotyping a selection of these SNPs provides a robust, low-cost method of identifying parasite infections through their unique genetic signature or barcode. Based on our experience in generating a SNP barcode for P. falciparum using High Resolution Melting (HRM), we have developed a similar tool for P. vivax. We selected globally polymorphic SNPs from available P. vivax genome sequence data that were located in putatively selectively neutral sites (i.e., intergenic, intronic, or 4-fold degenerate coding). From these candidate SNPs we defined a barcode consisting of 42 SNPs. We analyzed the performance of the 42-SNP barcode on 87 P. vivax clinical samples from parasite populations in South America (Brazil, French Guiana), Africa (Ethiopia) and Asia (Sri Lanka). We found that the P. vivax barcode is robust, as it requires only a small quantity of DNA (limit of detection 0.3 ng/μl) to yield reproducible genotype calls, and detects polymorphic genotypes with high sensitivity. The markers are informative across all clinical samples evaluated (average minor allele frequency > 0.1). Population genetic and statistical analyses show the barcode captures high degrees of population diversity and differentiates geographically distinct populations. Our 42-SNP barcode provides a robust, informative, and standardized genetic marker set that accurately identifies a genomic signature for P. vivax infections.

Sinusoidal Map Based Particle Swarm Optimization Detect the SNP Barcode in Breast Cancer to Disease Susceptibility

Single nucleotide polymorphisms (SNPs) are the most common type of DNA sequence variation in the human genome and are widely used to investigate the association analysis of diseases. SNP barcode is a combination of SNPs with genotypes (AA, Aa, and aa for an SNP) to find the difference between case data set and control data set for analyzing the disease association amongst SNPs. Currently, the computational time of statistical method becomes the weak to analyze the big data to find the significant SNP barcode. Here, we applied a sinusoidal particle swarm optimization (SPSO) algorithm facilitate the statistical methods to analyze the associated SNPs. We systematically evaluated the synergistic effect of 26 SNPs from eight epigenetic modifier-related genes in breast cancer. The 2- to 5-order SNP barcodes were found to determine the risk effects in breast cancer. We found that five of eight genes (BAT8, DNMT3A, EHMT1, DNMT3A, and BAT8) were statistically significant to breast cancer and play the important role in the SNP barcode. In addition, we compared the search ability between PSO and SPSO in the 2- to 5-order SNP barcodes. The results indicated that SPSO can find the better SNP barcode than PSO. In conclusion, SPSO is a precise algorithm for finding a significant model of SNP barcode.

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm.

BackgroundFacial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear.MethodsThis study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups).ResultsThe analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance.ConclusionsThis study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.

Genetic algorithm-generated SNP barcodes of the mitochondrial D-loop for chronic dialysis susceptibility

Background and aims: Single nucleotide polymorphism (SNP) interaction analysis can simultaneously evaluate the complex SNP interactions present in complex diseases. However, it is less commonly applied to evaluate the predisposition of chronic dialysis and its computational analysis remains challenging. In this study, we aimed to improve the analysis of SNP–SNP interactions within the mitochondrial D-loop in chronic dialysis. Material & method: The SNP–SNP interactions between 77 reported SNPs within the mitochondrial D-loop in chronic dialysis study were evaluated in terms of SNP barcodes (different SNP combinations with their corresponding genotypes). We propose a genetic algorithm (GA) to generate SNP barcodes. The χ2 values were then calculated by the occurrences of the specific SNP barcodes and their non-specific combinations between cases and controls. Results: Each SNP barcode (2- to 7-SNP) with the highest value in the χ2 test was regarded as the best SNP barcode (11.304 to 23.310; p < 0.001). The best GA-generated SNP barcodes (2- to 7-SNP) were significantly associated with chronic dialysis (odds ratio [OR] = 1.998 to 3.139; p < 0.001). The order of influence for SNPs was the same as the order of their OR values for chronic dialysis in terms of 2- to 7-SNP barcodes. Conclusion: Taken together, we propose an effective algorithm to address the SNP–SNP interactions and demonstrated that many non-significant SNPs within the mitochondrial D-loop may play a role in jointed effects to chronic dialysis susceptibility.

Particle swarm optimization algorithm for analyzing SNP–SNP interaction of renin-angiotensin system genes against hypertension

Most non-significant individual single nucleotide polymorphisms (SNPs) were undiscovered in hypertension association studies. Their possible SNP-SNP interactions were usually ignored and leaded to missing heritability. In present study, we proposed a particle swarm optimization (PSO) algorithm to analyze the SNP-SNP interaction associated with hypertension. Genotype dataset of eight SNPs of renin-angiotensin system genes for 130 non-hypertension and 313 hypertension subjects were included. Without SNP-SNP interaction, most individual SNPs were non-significant difference between the hypertension and non-hypertension groups. For SNP-SNP interaction, PSO can select the SNP combinations involving different SNP numbers, namely the best SNP barcodes, to show the maximum frequency difference between non-hypertension and hypertension groups. After computation, the best PSO-generated SNP barcodes were dominant in non-hypertension in terms of the occurrences of frequency differences between non-hypertension and hypertension groups. The OR values of the best SNP barcodes involving 2-8 SNPs were 0.705-0.334, suggesting that these SNP barcodes were protective against hypertension. In conclusion, this study demonstrated that non-significant SNPs may generate the joint effect in association study. Our proposed PSO algorithm is effective to identify the best protective SNP barcodes against hypertension.