PvGAP: Development of a globally-applicable, highly-multiplexed microhaplotype amplicon panel forPlasmodium vivax

PvGAP: Development of a globally-applicable, highly-multiplexed microhaplotype amplicon panel for Plasmodium vivax | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search PvGAP: Development of a globally-applicable, highly-multiplexed microhaplotype amplicon panel for Plasmodium vivax View ORCID Profile Alfred Hubbard , View ORCID Profile Edwin Solares , Lauren Bradley , View ORCID Profile Brook Jeang , View ORCID Profile Delenasaw Yewhalaw , View ORCID Profile Daniel Janies , View ORCID Profile Eugenia Lo , View ORCID Profile Guiyun Yan , View ORCID Profile Elizabeth Hemming-Schroeder doi: https://doi.org/10.1101/2025.04.30.25326751 Alfred Hubbard 1 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, Maryland, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alfred Hubbard Edwin Solares 2 Department of Computer Science and Engineering , UC San Diego, San Diego, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edwin Solares Lauren Bradley 3 Department of Population Health & Disease Prevention, UC Irvine Wen School of Population and Public Health , Irvine, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Brook Jeang 3 Department of Population Health & Disease Prevention, UC Irvine Wen School of Population and Public Health , Irvine, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brook Jeang Delenasaw Yewhalaw 4 School of Medical Laboratory Sciences, Jimma University , Jimma, Ethiopia 5 Tropical and Infectious Diseases Research Center, Jimma University , Jimma, Ethiopia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Delenasaw Yewhalaw Daniel Janies 6 Department of Bioinformatics and Genomics, University of North Carolina at Charlotte , Charlotte, North Carolina, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel Janies Eugenia Lo 7 Department of Microbiology & Immunology, Drexel University , Philadelphia, Pennsylvania, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eugenia Lo Guiyun Yan 3 Department of Population Health & Disease Prevention, UC Irvine Wen School of Population and Public Health , Irvine, California, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guiyun Yan Elizabeth Hemming-Schroeder 8 College of Veterinary Medicine and Biomedical Sciences, Colorado State University , Fort Collins, Colorado, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elizabeth Hemming-Schroeder For correspondence: elizabeth.hemming-schroeder{at}colostate.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Plasmodium vivax malaria research has yet to fully benefit from the advances in genomic surveillance that have revolutionized P. falciparum epidemiology. Closing this gap is critical because genomic tools are necessary to monitor the spread of drug resistance, classify infections as local or imported, and distinguish reinfection, recrudescence, and relapse. To achieve these objectives, microhaplotype marker panels that allow powerful genotyping of polyclonal infections are needed. Methods We designed a Globally-applicable Amplicon Panel for P. vivax (PvGAP), selecting targets based on both genetic diversity and genetic distance from each other to maximize discriminatory capability between geographic regions. We evaluated this panel with field samples from Ethiopia and in silico using whole genomes from the MalariaGEN Pv4 database. Results PvGAP has 80 high diversity targets suitable for population genomics and eight targets of specific epidemiological interest, such as putative markers of drug resistance. We demonstrate PvGAP achieves robust amplification with field data and that it provides competitive accuracy for relatedness inference in three disparate geographic regions. Conclusions PvGAP joins existing P. vivax panels as a cost effective and practical option for genomic epidemiology of this neglected disease. It will support drug resistance surveillance, discrimination of local and imported cases, and it may aid in separating reinfection, recrudescence, and relapse in therapeutic efficacy studies, all critical needs of National Malaria Control Programs. Background Plasmodium vivax remains understudied compared to P. falciparum , despite the fact that it can cause severe and life-threatening malaria ( Anstey et al., 2012 ) and is endemic in tropical regions throughout the world, including most of Africa ( Twohig et al., 2019 ). Improving our understanding of P. vivax epidemiology is a key step to planning effective antimalarial interventions and ultimately eliminating malaria. Population genomics is one powerful approach for enhancing our collective understanding of malaria epidemiology. Genomic data from malaria parasites can be used to assess transmission between geographic locations and can support modeling of relapse, recrudescence, and reinfection ( Neafsey et al., 2021 ). However, the application of population genomics to malaria epidemiology is limited by technical constraints and costs. Classical biallelic SNP assays have low sensitivity to detect multiple parasite strains and parasite diversity within a host ( Koepfli & Mueller, 2017 ). On the other hand, whole genome sequencing (WGS) data provides comprehensive coverage, but is costly to produce ( Tessema et al., 2022 ) and store ( Neafsey et al., 2021 ). A cost-effective alternative with high sensitivity to detect minority clones is targeted deep sequencing of genetically-diverse amplicons ( Koepfli & Mueller, 2017 ; Tessema et al., 2022 ). This approach is particularly effective when used to assess multiallelic microhaplotypes, meaning targets that contain two or more SNPs. Microhaplotype markers have been shown to provide higher power for relatedness inference than biallelic SNPs, particularly in the case of polyclonal infections ( Tessema et al., 2022 ). The benefits of microhaplotype panels for understanding genetic relatedness and diversity have already been demonstrated in P. falciparum ( LaVerriere et al., 2022 ; Tessema et al., 2022 ). Several microhaplotype panels have been or soon will be published for P. vivax . Particularly noteworthy are the Kleinecke et al. (2025) and PvGTSeq ( Manrique-Valverde et al., 2025 ) panels, which both contain putative markers of drug resistance and numerous high diversity targets (93 for Kleinecke et al. and 213 for PvGTSeq). A handful of other panels have been developed with smaller sets of microhaplotypes ( Kattenberg et al., 2022 , 2024 ; Popkin-Hall et al., 2024 ; Rosado et al., 2025 ). Given that biallelic markers, which comprise most of the Kattenberg et al. (2022) , Kattenberg et al. (2024) , and Popkin-Hall et al. (2024) panels, are fundamentally limited in the presence of polyclonal infections ( Tessema et al., 2022 ) and the small size of the Rosado et al. (2025) panel (11 targets), these panels are likely to have less power for relatedness estimation than the PvGTSeq and Kleinecke et al. panels. They may retain sufficient power in certain settings and for other applications such as estimating the number of unique strains in an infection (i.e., the complexity of infection). This paper describes the design and validation of a Plasmodium vivax Globally-applicable Amplicon Panel (PvGAP) that is similar in scope to the Kleinecke et al. (2025) and PvGTSeq ( Manrique-Valverde et al., 2025 ) panels. PvGAP consists of 80 high-diversity microhaplotype markers, seven putative drug resistance markers, and a section of pvdbp ( P. vivax Duffy binding protein). The associated library preparation protocol is relatively low cost and uses non-proprietary reagents. Therefore, PvGAP is a powerful and flexible addition to the P. vivax population genomics toolkit that should suit a variety of epidemiological applications. Methods Panel design Whole genome sequence data from 198 P. vivax isolates from eight countries were downloaded from NCBI ( Table 1 ). Reads were aligned to the PvP01 reference genome ( Auburn et al., 2016 ) and variants were called using standard bioinformatic pipelines. Candidate loci were identified using a sliding-window method adapted from Tessema et al. (2022) . Genomic windows were filtered to remove repetitive regions, indels, and loci lacking within-country polymorphism. Remaining windows were evaluated based on within-country nucleotide diversity (π) and population differentiation (FST) among countries. Windows with high diversity and/or strong genetic structuring were prioritized across chromosomes, yielding 278 candidate targets distributed across the genome. Detailed bioinformatic methods, filtering criteria, and marker selection procedures are provided in Supplemental Text 1. View this table: View inline View popup Table 1: P. vivax genomes used in panel design These targets were submitted to GTseek LLC (Twin Falls, ID, USA) for multiplex primer design optimized to minimize primer crosstalk ( Campbell et al., 2015 ). After initial sequencing tests, poorly performing targets were removed, resulting in a final set of 80 loci. Eight additional loci targeting pvdbp and putative drug resistance markers were added to the panel ( Table 2 ), resulting in a final panel of 88 loci (Supplemental Data 1). View this table: View inline View popup Download powerpoint Table 2: P. vivax genes of particular epidemiological interest Panel evaluation with field samples Two sets of field samples were used to evaluate sequencing performance of the panel. First, ten high-parasitemia samples (six dried blood spot (DBS) and four whole blood samples) were processed using multiple laboratory workflows to assess how different DNA extraction methods (Chelex/saponin versus spin column kit) and enrichment strategies (selective whole genome amplification or SWGA versus targeted pre-amplification) affected sequencing outcomes. A detailed description of these procedures is provided in Supplemental Text 2. Second, to assess how parasite density influences sequencing output, serial dilutions were generated from five high-parasitemia DBS samples to mimic realistic low-parasitemia infections. The procedures and results of this analysis are described in the main text below. Sample collection The samples used for the serial dilutions were gathered as part of a sub-Saharan Africa International Center for Excellence for Malaria Research (ICEMR) project ( Githure et al., 2022 ; Yan et al., 2022 ). They were collected as DBS through both passive case detection and community cross-sectional surveys from two sites in Southwestern Ethiopia. Details on sample collection may be found in Getachew et al. (2023) . Library preparation and sequencing DNA was extracted from DBS using the Monarch Spin gDNA extraction kit (New England Biolabs). Briefly, a 6 mm DBS punch was lysed and genomic DNA purified following the manufacturer’s protocol with minor modifications, with DNA eluted in 35 µL of TE buffer. Parasite DNA concentration was quantified by qPCR using primers and probes described by Lee et al. (2015) and a standard curve generated from serial dilutions of the P. vivax Sal I Sau3AI gDNA library (MRA-40, BEI Resources). Additional details of DNA extraction and quantification are provided in Supplemental Text 1. Following quantification, samples were diluted to target parasite DNA concentrations of 1000, 500, 250, 100, 50, and 10 copies/µl. To generate serial dilutions that more closely mimic natural infections with low parasitemia (i.e., similar ratios of human to parasite gDNA), gDNA was extracted from uninfected human DBS following the same purification protocol and used as the diluent. Because a gDNA library was used as the quantification standard due to limited availability of parasite material, copy number estimates cannot be directly translated to parasitemia. However, assuming an approximate extraction efficiency of 30% and an input volume of 10 µL of whole blood per 6 mm DBS punch, a measured concentration of 10 parasite copies/µL of eluate would correspond to approximately 11.7 parasites/µL of whole blood. This estimate should be interpreted as an approximation given variability in DBS blood volume and extraction recovery. The library preparation protocol is described in detail in Supplemental Text 3. In brief, it consists of SWGA, adapted from Oyola et al. (2016) and Cowell et al. (2017) , to increase the density of parasite DNA; an adapted version of the GT-seq PCR protocol to affix primers (see Campbell et al. (2015) for the original technique and LaVerriere et al. (2022) for a previous application to P. falciparum ); and application of Nate’s Plates kits (GTseek LLC) to affix dual indexing tags and normalize sequence quantity across samples. Various QC assays are performed after each step and on the final libraries, as described in Supplemental Text 3. Sequencing was performed on an Illumina MiSeq, using the Reagent Kit v2 500 cycles kit in paired-end mode with a 10% PhiX spike in. Samples were amplified and sequenced in triplicate. Bioinformatics analysis The malaria amplicon pipeline published alongside LaVerriere et al. (2022) was used to extract and filter microhaplotypes, using primer and reference files appropriate for our P. vivax panel (Supplemental Data 2). This pipeline uses the Divisive Amplicon Denoising Algorithm (DADA2; Callahan et al., 2016 ) as the core method for identifying and filtering amplicons. The pipeline then applies a handful of postprocessing steps to remove likely sequencing artifacts and chimeras. Amplicons with fewer than five reads or a within-sample allele frequency (WSAF) below 1% were filtered out. Lin’s concordance correlation coefficient was computed among replicate pairs with the DescTools R package ( Signorell, 2025 ). Panel evaluation with paneljudge and MalariaGEN data Data preparation To assess the utility of the panel for population genetics and genomic epidemiology, whole genome sequences from the MalariaGEN Pv4 project ( MalariaGEN et al., 2022 ) were downloaded and virtual microhaplotypes were extracted. Variants that failed the QC process of the MalariaGEN authors (FILTER=“PASS” in the VCF) were excluded. Samples with an F WS below 0.95 were removed, as these are considered to be polyclonal ( Auburn et al., 2012 ). Finally, samples from longitudinal studies and returning travelers were removed. From the remaining samples, three populations were selected based on sample size and representation of distinct regions: Cambodia and Vietnam in 2015 and 2016 (n=72); Brazil, Colombia, and Peru in 2013 and 2014 (n=43); and Ethiopia in 2013 (n=49). The remaining variants were filtered to the genomic regions represented in PvGAP using bcftools view ( Danecek et al., 2021 ). Haplotype sequences were obtained for each sample and locus with bcftools consensus ( Danecek et al., 2021 ), using the PvP01 reference genome ( Auburn et al., 2016 ). To enable comparison with other amplicon panels, the diversity targets of the Kleinecke et al. (2025) and PvGTSeq ( Manrique-Valverde et al., 2025 ) panels were extracted with the same methods. Other P. vivax microhaplotype panels were excluded from this analysis because they either lack diverse microhaplotype targets ( Kattenberg et al., 2022 , 2024 ; Popkin-Hall et al., 2024 ) or are considerably smaller than the three panels included in the evaluation ( Rosado et al., 2025 ). paneljudge analysis For each population, the allele frequencies of all panel targets were calculated with the Dcifer R package ( Gerlovina et al., 2022 ). Cardinality (the number of unique alleles at a locus) and effective cardinality (the number of unique alleles adjusted for their frequency; see Taylor et al., 2019 ) were calculated from these frequencies with the paneljudge R package ( https://github.com/aimeertaylor/paneljudge ; v0.0.0.9). The paneljudge package was also used to simulate pairwise relatedness estimates for each panel under various true values of r : 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.5, 0.75, and 0.99. These simulations were performed with 100 sample pairs drawn from each population, 100 bootstrap replicates, and a switch-rate parameter value of 5. Multiplexing capacity To estimate multiplexing capacity, we used locus-level read counts from the sequencing dataset generated in this study. Total on-target reads per sample and locus-specific read proportions were calculated, and relative sample efficiencies were derived to capture uneven sample representation in multiplexed libraries. We then used empirically parameterized Monte Carlo resampling to model expected locus coverage across Illumina sequencing kits with different read outputs. For each kit and candidate multiplex level, we assumed that 30% of reads would be lost (10% due to PhiX and 20% due to off-target amplification). We repeated the resampling procedure 100 times. Samples were considered to pass if at least 75% of loci achieved ≥10 reads, and recommended multiplexing levels were defined as the largest sample number for which ≥80% of samples were predicted to meet this criterion. Additional details are provided in Supplemental Text 5. Results Panel characteristics Windows selected during panel design were chosen based on high nucleotide diversity and/or high F ST . Consistent with these criteria, all windows included in the final marker set exhibited high mean nucleotide diversity (median = 0.7552; IQR = 0.5555-0.9803; Figure 1A ) and/or high mean F ST (median = 0.4555, IQR = 0.31-0.5964; Figure 1B ) in the genome set used for panel design. Download figure Open in new tab Figure 1: Mean nucleotide diversity (A) and mean F ST (B) for the genome windows selected for inclusion in the final panel. Panel evaluation with field samples Detailed results for the samples used for laboratory workflow optimization are described in Supplemental Text 2. In brief, both extraction methods performed similarly, and differences between enrichment strategies were modest. SWGA yielded a slightly higher proportion of on-target reads, whereas targeted pre-amplification resulted in slightly more even coverage across markers. SWGA and the Chelex/saponin method were selected for the final protocol. In the serial dilution experiments, mean read counts across replicates (Supplemental Figure 1) indicate that overall amplification was satisfactory (median read depth per marker of 82; IQR = 22-391). A few loci exhibited lower amplification efficiency. Notably, these loci performed well in prior experiments (Supplemental Text 2), suggesting that this reduced performance is unlikely to represent intrinsic issues with the primers. Despite the reduced sequencing output at these loci, the proportion of targets with a minimum of 10 reads remained above 75% among all dilutions ( Figure 2 ), suggesting the SWGA protocol enables reliable amplification in low parasite copy number samples. Moreover, off-target reads were low, with a median of 79.75% on-target reads per sample (IQR = 71.68-80.85%). Download figure Open in new tab Figure 2: Scatterplot showing the relationship between parasite copy number and the percentage of loci with 10 or more reads (indicating successful amplification) for each replicate of the serial dilutions. The x -axis is log 10 scaled. The blue line is a simple linear regression, with shaded areas showing 95% confidence intervals. The horizontal red line indicates 75% successful amplification, a common threshold for resequencing a sample. Detection of minority alleles across replicates was inconsistent, though not strongly affected by parasite copy number (Supplemental Figure 2). Concordance among replicate pairs ranged widely, from nearly 0 to 1 when computed only on alleles present in both replicates of the pair (Supplemental Figure 3). Notably, concordance varied substantially among samples, possibly reflecting the presence of minor parasite strains at low densities or variation introduced during dilution preparation. When alleles only found in one replicate were included, concordance ranged from 0 to 0.6. Panel evaluation with paneljudge and MalariaGEN data Comparison of the PvGAP panel with the panels from Kleinecke et al. and PvGTseq revealed that cardinalities (i.e., the number of unique alleles at each target) followed a similar pattern, with most markers showing a cardinality below 10 in most populations ( Figure 3 ). Larger panels tended to have more high cardinality markers, though; PvGTSeq contains several markers with a cardinality above 20. In all panels, target effective cardinalities were fairly close to their theoretical maximum, the cardinality, suggesting the targets have sufficient allelic evenness to be informative for relatedness analyses ( Figure 3 ). Download figure Open in new tab Figure 3: Cardinality (the number of unique alleles) versus effective cardinality (the number of unique alleles adjusted for allele frequency) for each marker (horizontal ticks indicate markers), based on the MalariaGEN data. The diagonal line shows y=x , which is the theoretical maximum of effective cardinality. The facets show the markers separately for each panel and population. The paneljudge simulations indicate all three panels have substantial power for relatedness estimation. The RMSEs of were 0.0625 to 0.125 for data-generating r values between 0.15 and 0.75 and lower for data-generating r values near zero or one ( Figure 4 ). Similarly, the 95% confidence interval widths of these estimates tend to be wider (0.2-0.6) for intermediate data-generating r values and narrower near zero and one (Supplemental Figure 4). As expected from the number of diversity markers, PvGTSeq consistently yielded the lowest RMSE, followed by the Kleinecke et al. panel, followed by PvGAP. The differences are larger for intermediate data-generating r values and negligible for a data-generating r value of 0.99, indicating all panels are equally equipped to identify clonal pairs. PvGTSeq and PvGAP exhibit little difference in performance between geographic regions; the Kleinecke et al. panel exhibits slightly better performance in Southeast Asia than Ethiopia or Latin America. Notably, in all instances, for all panels, the 95% confidence interval widths are substantially less than one. Download figure Open in new tab Figure 4: The RMSE of for each data-generating r value, as simulated by paneljudge. Facets distinguish results for different populations. Line and point colors indicate different marker panels. Multiplexing capacity and sequencing cost Based on sample-to-sample unevenness, on-target read percentages, and locus-specific read counts observed in our dataset, we simulated multiplexing performance across Illumina sequencing kits to estimate feasible multiplexing levels and associated costs. For the Miseq v2 platform, the recommended multiplexing level is 136 samples per run, corresponding to an estimated sequencing cost of 13.24 USD per sample and a combined library preparation and sequencing cost of 28.24 USD per sample. Additional details and results for other sequencing kits are described in Supplemental Text 5. Discussion PvGAP represents a cost-effective approach to targeted sequencing of P. vivax microhaplotypes for genomic epidemiology applications in varied geographic locations. The panel consists of 88 loci, including 80 selected for population genetic and relationship inference and eight within genes of epidemiological interest (e.g., potential drug resistance/tolerance markers). Our assay consistently yields reliable sequencing depth and breadth of coverage, even in the case of samples with low parasite copy numbers (10 parasites/μL), and does not rely on proprietary reagents. In silico evaluation with MalariaGEN data and paneljudge demonstrates that PvGAP contains sufficient marker diversity for relatedness estimation in a variety of geographic regions, albeit with lower accuracy than larger panels. PvGAP displayed strong sequencing performance in terms of proportion of successfully amplified loci (all serial dilutions achieved >75% loci with ≥10× coverage) and percentage of on-target reads (median 79.75%). The former metric compares favorably to similar assays (Kleinecke et al. and PvGTseq). Multiplexing levels and on-target read percentages were not consistently reported by Kleinecke et al. (2025) and Manrique-Valverde et al. (2025) , limiting direct comparison of sequencing efficiency and scalability of assays, particularly for DBS-derived samples. Regarding reproducibility across technical replicates, major allele detection was highly reproducible, but concordance of WSAFs varied among samples. This result may stem from stochastic detection of low-frequency minority clones, but this cannot be confirmed as these are field samples with unknown true WSAFs. Future benchmarking with synthetic mixtures could clarify interpretation of WSAF estimates and the need for further methodological refinements, if any. In terms of relatedness estimation, the paneljudge analysis showed good performance of PvGAP, albeit slightly worse than similar, larger panels. All three panels evaluated (PvGAP, PvGTSeq, and Kleinecke et al) perform well when the true relatedness is high or low and somewhat worse when the true relatedness is intermediate (between 0.15 and 0.75). Although PvGTSeq consistently yielded the lowest RMSE, and PvGAP the highest RMSE (ranging from 0.028 to 0.125 depending on relatedness level and region), the importance of reducing RMSE should be evaluated in light of study objectives, budgetary considerations, and the biological limits inherent to malaria transmission inference. At present, the degree of precision required to reliably distinguish specific transmission events remains incompletely defined and likely varies by epidemiological setting and sampling design. Further empirical and simulation-based work is needed to determine the added value of sequencing larger, more costly panels that enable more accurate relatedness estimates. Regarding cost, PvGAP is an affordable and flexible option. Library preparation for PvGAP is approximately 13 USD per sample (excluding primer costs; see below), compared to roughly 10-20 USD (converted from AUD) for the Kleinecke et al. (2025) assay, depending on whether the PCR reaction volume is halved. Once sequencing is included, we estimate a combined cost of approximately 28 USD per sample for PvGAP in a Miseq v2 run. Manrique-Valverde et al. (2025) report an estimated cost of approximately 39 USD per sample for library preparation and sequencing for PvGTSeq. Primer synthesis constitutes an upfront cost that scales with the number of targets. We estimate each locus requires approximately 24 USD in primer synthesis. Assuming the same cost for PvGTSeq, initial investment increases by several thousand USD. Finally, like the PvGTseq panel, the PvGAP primers have been designed to minimize cross-talk during multiplexing and library preparation by relying on standard amplicon-based methods rather than vendor-specific capture chemistries. In contrast, the Kleinecke assay relies on IDT’s proprietary rhAmpSeq methodology ( Kleinecke et al., 2025 ), which may influence cost structure, supply-chain flexibility, and implementation considerations. PvGAP is a moderately-sized panel for P. vivax epidemiology that provides a practical balance of feasibility and resolution for relatedness inference. In comparison to similar, previously-published panels, PvGAP offers competitive cost, slightly worse accuracy for relatedness estimation due to fewer markers, and a flexible laboratory protocol that can be adapted to a variety of settings. By substantially expanding the array of available panels, PvGAP helps ensure that the appropriate tool will be available for every application of genomic epidemiology to vivax malaria. Author Contributions AH, EHS, and GY conceived of the study. DY and GY oversaw sample collection. EHS and ES designed the amplicon panel. LB, BJ, and EHS performed the laboratory work. AH performed data analyses and drafted the manuscript. DJ, EL, and EHS contributed to the interpretation of results. All authors provided critical feedback and helped shape the research, analysis, and manuscript. Funding Sources This work was supported by NIH grants U19AI129326, F32AI147460, R21AI190606, and R01AI162947 and the Lucille P. and Edward C. Giles Dissertation-Year Fellowship. Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The code necessary to generate all figures from the raw data is available on GitHub ( https://github.com/a-hubbard/vivax_microhap ) and Zenodo (will upload and include link once accepted). The raw sequence reads from the serial dilution experiment are available on SRA under Accession # PRJNA1435528. The references for the genomes used in panel design are described in Table 1 , and the genomes used in panel evaluation were obtained from https://www.malariagen.net/resource/30/ . Acknowledgements We thank the study participants and the field team of the International Centers of Excellence for Malaria Research (ICEMR) program for their involvement in this study. Footnotes Two major changes have been made to the manuscript: 1) the previous field data has been replaced with a serial dilution experiment to explore amplification in low parasite copy number samples; and 2) the MalariaGEN analysis has been reworked to use the paneljudge R package to assess accuracy for genetic relatedness estimation. Minor changes to organization and writing have also been made. References ↵ Anstey , N. M. , Douglas , N. M. , Poespoprodjo , J. R. , & Price , R. N. ( 2012 ). Plasmodium vivax: Clinical spectrum, risk factors and pathogenesis . In Advances in Parasitology (Vol. 80 , pp. 151 – 201 ). OpenUrl Auburn , S. , Benavente , E. D. , Miotto , O. , Pearson , R. D. , Amato , R. , Grigg , M. J. , Barber , B. E. , William , T. , Handayuni , I. , Marfurt , J. , Trimarsanto , H. , Noviyanti , R. , Sriprawat , K. , Nosten , F. , Campino , S. , Clark , T. G. , Anstey , N. M. , Kwiatkowski , D. P. , & Price , R. N. ( 2018 ). Genomic analysis of a pre-elimination Malaysian Plasmodium vivax population reveals selective pressures and changing transmission dynamics . Nature Communications , 9 ( 1 ), 2585 . doi: 10.1038/s41467-018-04965-4 OpenUrl CrossRef PubMed ↵ Auburn , S. , Böhme , U. , Steinbiss , S. , Trimarsanto , H. , Hostetler , J. , Sanders , M. , Gao , Q. , Nosten , F. , Newbold , C. I. , Berriman , M. , Price , R. N. , & Otto , T. D. ( 2016 ). A new Plasmodium vivax reference sequence with improved assembly of the subtelomeres reveals an abundance of pir genes . Wellcome Open Research , 1 , 4 . doi: 10.12688/wellcomeopenres.9876.1 OpenUrl CrossRef PubMed ↵ Auburn , S. , Campino , S. , Miotto , O. , Djimde , A. A. , Zongo , I. , Manske , M. , Maslen , G. , Mangano , V. , Alcock , D. , MacInnis , B. , Rockett , K. A. , Clark , T. G. , Doumbo , O. K. , Ouédraogo , J. B. , & Kwiatkowski , D. P. ( 2012 ). Characterization of within-host Plasmodium falciparum diversity using next-generation sequence data . PloS One , 7 ( 2 ), e32891 . doi: 10.1371/journal.pone.0032891 OpenUrl CrossRef PubMed Auburn , S. , Getachew , S. , Pearson , R. D. , Amato , R. , Miotto , O. , Trimarsanto , H. , Zhu , S. J. , Rumaseb , A. , Marfurt , J. , Noviyanti , R. , Grigg , M. J. , Barber , B. , William , T. , Goncalves , S. M. , Drury , E. , Sriprawat , K. , Anstey , N. M. , Nosten , F. , Petros , B. , … Price , R. N. ( 2019 ). Genomic Analysis of Plasmodium vivax in Southern Ethiopia Reveals Selective Pressures in Multiple Parasite Mechanisms . The Journal of Infectious Diseases , 220 ( 11 ), 1738 – 1749 . doi: 10.1093/infdis/jiz016 OpenUrl CrossRef PubMed ↵ Callahan , B. J. , McMurdie , P. J. , Rosen , M. J. , Han , A. W. , Johnson , A. J. A. , & Holmes , S. P. ( 2016 ). DADA2: High-resolution sample inference from Illumina amplicon data . Nature Methods , 13 ( 7 ), 581 – 583 . doi: 10.1038/nmeth.3869 OpenUrl CrossRef PubMed ↵ Campbell , N. R. , Harmon , S. A. , & Narum , S. R. ( 2015 ). Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing . Molecular Ecology Resources , 15 ( 4 ), 855 – 867 . doi: 10.1111/1755-0998.12357 OpenUrl CrossRef PubMed Chen , S.-B. , Wang , Y. , Kassegne , K. , Xu , B. , Shen , H.-M. , & Chen , J.-H. ( 2017 ). Whole-genome sequencing of a Plasmodium vivax clinical isolate exhibits geographical characteristics and high genetic variation in China-Myanmar border area . BMC Genomics , 18 ( 1 ), 131 . doi: 10.1186/s12864-017-3523-y OpenUrl CrossRef PubMed ↵ Cowell , A. N. , Loy , D. E. , Sundararaman , S. A. , Valdivia , H. , Fisch , K. , Lescano , A. G. , Baldeviano , G. C. , Durand , S. , Gerbasi , V. , Sutherland , C. J. , Nolder , D. , Vinetz , J. M. , Hahn , B. H. , & Winzeler , E. A. ( 2017 ). Selective Whole-Genome Amplification Is a Robust Method That Enables Scalable Whole-Genome Sequencing of Plasmodium vivax from Unprocessed Clinical Samples . mBio , 8 ( 1 ), e02257 – 16 . doi: 10.1128/mBio.02257-16 OpenUrl CrossRef PubMed Cowell , A. N. , Valdivia , H. O. , Bishop , D. K. , & Winzeler , E. A. ( 2018 ). Exploration of Plasmodium vivax transmission dynamics and recurrent infections in the Peruvian Amazon using whole genome sequencing . Genome Medicine , 10 ( 1 ), 52 . doi: 10.1186/s13073-018-0563-0 OpenUrl CrossRef PubMed ↵ Danecek , P. , Bonfield , J. K. , Liddle , J. , Marshall , J. , Ohan , V. , Pollard , M. O. , Whitwham , A. , Keane , T. , McCarthy , S. A. , Davies , R. M. , & Li , H. ( 2021 ). Twelve years of SAMtools and BCFtools . GigaScience , 10 ( 2 ), giab008 . doi: 10.1093/gigascience/giab008 OpenUrl CrossRef PubMed ↵ Gerlovina , I. , Gerlovin , B. , Rodríguez-Barraquer , I. , & Greenhouse , B. ( 2022 ). Dcifer: An IBD-based method to calculate genetic distance between polyclonal infections . Genetics , 222 ( 2 ). doi: 10.1093/genetics/iyac126 OpenUrl CrossRef ↵ Getachew , H. , Demissew , A. , Abossie , A. , Habtamu , K. , Wang , X. , Zhong , D. , Zhou , G. , Lee , M.-C. , Hemming-Schroeder , E. , Bradley , L. , Degefa , T. , Hawaria , D. , Tsegaye , A. , W. Kazura , J. , Koepfli , C. , Yan , G. , & Yewhalaw , D. ( 2023 ). Asymptomatic and submicroscopic malaria infections in sugar cane and rice development areas of Ethiopia . Malaria Journal , 22 ( 1 ), 341 . doi: 10.1186/s12936-023-04762-5 OpenUrl CrossRef PubMed ↵ Githure , J. I. , Yewhalaw , D. , Atieli , H. , Hemming-Schroeder , E. , Lee , M.-C. , Wang , X. , Zhou , G. , Zhong , D. , King , C. L. , Dent , A. , Mukabana , W. R. , Degefa , T. , Hsu , K. , Githeko , A. K. , Okomo , G. , Dayo , L. , Tushune , K. , Omondi , C. O. , Taffese , H. S. , … Yan , G. ( 2022 ). Enhancing Malaria Research, Surveillance, and Control in Endemic Areas of Kenya and Ethiopia . The American Journal of Tropical Medicine and Hygiene , 107 ( 4_Suppl ), 14 – 20 . doi: 10.4269/ajtmh.21-1303 OpenUrl CrossRef ↵ Kattenberg , J. H. , Cabrera-Sosa , L. , Figueroa-Ildefonso , E. , Mutsaers , M. , Monsieurs , P. , Guetens , P. , Infante , B. , Delgado-Ratto , C. , Gamboa , D. , & Rosanas-Urgell , A. ( 2024 ). Plasmodium vivax genomic surveillance in the Peruvian Amazon with Pv AmpliSeq assay . PLoS Neglected Tropical Diseases , 18 ( 7 ), e0011879 . doi: 10.1371/journal.pntd.0011879 OpenUrl CrossRef PubMed ↵ Kattenberg , J. H. , Nguyen , H. V. , Nguyen , H. L. , Sauve , E. , Nguyen , N. T. H. , Chopo-Pizarro , A. , Trimarsanto , H. , Monsieurs , P. , Guetens , P. , Nguyen , X. X. , Esbroeck , M. V. , Auburn , S. , Nguyen , B. T. H. , & Rosanas-Urgell , A. ( 2022 ). Novel highly-multiplexed AmpliSeq targeted assay for Plasmodium vivax genetic surveillance use cases at multiple geographical scales . Frontiers in Cellular and Infection Microbiology , 12 , 953187 . doi: 10.3389/fcimb.2022.953187 OpenUrl CrossRef PubMed ↵ Kleinecke , M. , Sutanto , E. , Rumaseb , A. , Hoon , K. S. , Trimarsanto , H. , Osborne , A. , Manrique , P. , Peters , T. , Hawkes , D. , Benavente , E. D. , Whitton , G. , Siegel , S. V. , Pearson , R. D. , Amato , R. , Rai , A. , Nhien , N. T. T. , Nguyen , H. C. , Assefa , A. , Degaga , T. S. , … Auburn , S. ( 2025 ). Microhaplotype deep sequencing assays to capture Plasmodium vivax infection lineages . Nature Communications , 16 ( 1 ), 7192 . doi: 10.1038/s41467-025-62357-x OpenUrl CrossRef PubMed ↵ Koepfli , C. , & Mueller , I. ( 2017 ). Malaria Epidemiology at the Clone Level . Trends in Parasitology , 33 ( 12 ), 974 – 985 . doi: 10.1016/j.pt.2017.08.013 OpenUrl CrossRef PubMed ↵ LaVerriere , E. , Schwabl , P. , Carrasquilla , M. , Taylor , A. R. , Johnson , Z. M. , Shieh , M. , Panchal , R. , Straub , T. J. , Kuzma , R. , Watson , S. , Buckee , C. O. , Andrade , C. M. , Portugal , S. , Crompton , P. D. , Traore , B. , Rayner , J. C. , Corredor , V. , James , K. , Cox , H. , … Neafsey , D. E. ( 2022 ). Design and implementation of multiplexed amplicon sequencing panels to serve genomic epidemiology of infectious disease: A malaria case study . Molecular Ecology Resources , 22 ( 6 ), 2285 – 2303 . doi: 10.1111/1755-0998.13622 OpenUrl CrossRef PubMed ↵ Lee , P. C. , Chong , E. T. J. , Anderios , F. , AL Lim , Y. , Chew , C. H. , & Chua , K. H. ( 2015 ). Molecular detection of human Plasmodium species in Sabah using PlasmoNex TM multiplex PCR and hydrolysis probes real-time PCR . Malaria Journal , 14 ( 1 ), 28 . doi: 10.1186/s12936-015-0542-5 OpenUrl CrossRef PubMed Lo , E. , Hostetler , J. B. , Yewhalaw , D. , Pearson , R. D. , Hamid , M. M. A. , Gunalan , K. , Kepple , D. , Ford , A. , Janies , D. A. , Rayner , J. C. , Miller , L. H. , & Yan , G. ( 2019 ). Frequent expansion of Plasmodium vivax Duffy Binding Protein in Ethiopia and its epidemiological significance . PLOS Neglected Tropical Diseases , 13 ( 9 ), e0007222 . doi: 10.1371/journal.pntd.0007222 OpenUrl CrossRef ↵ MalariaGEN , Adam , I. , Alam , M. S. , Alemu , S. , Amaratunga , C. , Amato , R. , Andrianaranjaka , V. , Anstey , N. M. , Aseffa , A. , Ashley , E. , Assefa , A. , Auburn , S. , Barber , B. E. , Barry , A. , Batista Pereira , D. , Cao , J. , Chau , N. H. , Chotivanich , K. , Chu , C. , … Yilma , D. ( 2022 ). An open dataset of Plasmodium vivax genome variation in 1,895 worldwide samples . Wellcome Open Research , 7 , 136 . doi: 10.12688/wellcomeopenres.17795.1 OpenUrl CrossRef PubMed ↵ Manrique-Valverde , P. C. , Hasund , C. M. , Kelley , K. A. , Amaya-Romero , J.-E. , Arévalo-Herrera , M. , Brosula , R. , Calzada , J. , Chenet , S. M. , Corredor , V. , Early , A. M. , Fontecha , G. , Forero-Peña , D. A. , Herrera , S. , Lana , J. T. , Laws , M. , Niles-Robin , R. , Obaldia , N. , Santamaria , A. M. , Schwabl , P. , … Neafsey , D. E. ( 2025 ). PvGTSeq and PvCRiSP: Two amplicon-based targeted sequencing panels for Plasmodium vivax (p. 2025.11.04.25339464 ). medRxiv . doi: 10.1101/2025.11.04.25339464 OpenUrl Abstract / FREE Full Text Menard , D. , Chan , E. R. , Benedet , C. , Ratsimbasoa , A. , Kim , S. , Chim , P. , Do , C. , Witkowski , B. , Durand , R. , Thellier , M. , Severini , C. , Legrand , E. , Musset , L. , Nour , B. Y. M. , Mercereau-Puijalon , O. , Serre , D. , & Zimmerman , P. A. ( 2013 ). Whole Genome Sequencing of Field Isolates Reveals a Common Duplication of the Duffy Binding Protein Gene in Malagasy Plasmodium vivax Strains . PLOS Neglected Tropical Diseases , 7 ( 11 ), e2489 . doi: 10.1371/journal.pntd.0002489 OpenUrl CrossRef PubMed ↵ Neafsey , D. E. , Taylor , A. R. , & MacInnis , B. L. ( 2021 ). Advances and opportunities in malaria population genomics . Nature Reviews. Genetics , ( 22 ), 502 – 517 . doi: 10.1038/s41576-021-00349-5 OpenUrl CrossRef PubMed ↵ Oyola , S. O. , Ariani , C. V. , Hamilton , W. L. , Kekre , M. , Amenga-Etego , L. N. , Ghansah , A. , Rutledge , G. G. , Redmond , S. , Manske , M. , Jyothi , D. , Jacob , C. G. , Otto , T. D. , Rockett , K. , Newbold , C. I. , Berriman , M. , & Kwiatkowski , D. P. ( 2016 ). Whole genome sequencing of Plasmodium falciparum from dried blood spots using selective whole genome amplification . Malaria Journal , 15 ( 1 ), 597 . doi: 10.1186/s12936-016-1641-7 OpenUrl CrossRef PubMed Parobek , C. M. , Lin , J. T. , Saunders , D. L. , Barnett , E. J. , Lon , C. , Lanteri , C. A. , Balasubramanian , S. , Brazeau , N. , DeConti , D. K. , Garba , D. L. , Meshnick , S. R. , Spring , M. D. , Chuor , C. M. , Bailey , J. A. , & Juliano , J. J. ( 2016 ). Selective sweep suggests transcriptional regulation may underlie Plasmodium vivax resilience to malaria control measures in Cambodia . Proceedings of the National Academy of Sciences of the United States of America , 113 ( 50 ), E8096 – E8105 . doi: 10.1073/pnas.1608828113 OpenUrl Abstract / FREE Full Text ↵ Popkin-Hall , Z. R. , Niaré , K. , Crudale , R. , Simkin , A. , Fola , A. A. , Sanchez , J. F. , Pannebaker , D. L. , Giesbrecht , D. J. , Kim , I. E. , Aydemir , Ö. , Bailey , J. A. , Valdivia , H. O. , & Juliano , J. J. ( 2024 ). High-throughput genotyping of Plasmodium vivax in the Peruvian Amazon via molecular inversion probes . Nature Communications , 15 ( 1 ), 10219 . doi: 10.1038/s41467-024-54731-y OpenUrl CrossRef PubMed ↵ Rosado , J. , Han , J. , Obadia , T. , Munro , J. , Traore , Z. , Schoffer , K. , Brewster , J. , Bourke , C. , Vinetz , J. M. , White , M. , Bahlo , M. , Gamboa , D. , Mueller , I. , & Ruybal-Pesántez , S. ( 2025 ). Understanding Plasmodium vivax recurrent infections using an amplicon deep sequencing assay, PvAmpSeq, identity-by-descent and model-based classification (p. 2025.05.26.25327775 ). medRxiv . doi: 10.1101/2025.05.26.25327775 OpenUrl Abstract / FREE Full Text Shen , H.-M. , Chen , S.-B. , Wang , Y. , Xu , B. , Abe , E. M. , & Chen , J.-H. ( 2017 ). Genome-wide scans for the identification of Plasmodium vivax genes under positive selection . Malaria Journal , 16 ( 1 ), 238 . doi: 10.1186/s12936-017-1882-0 OpenUrl CrossRef PubMed ↵ Signorell , A. ( 2025 ). DescTools: Tools for Descriptive Statistics (Version 0.99.59) [Computer software]. https://github.com/AndriSignorell/DescTools/ ↵ Taylor , A. R. , Jacob , P. E. , Neafsey , D. E. , & Buckee , C. O. ( 2019 ). Estimating Relatedness Between Malaria Parasites . Genetics , 212 ( 4 ), 1337 – 1351 . doi: 10.1534/genetics.119.302120 OpenUrl Abstract / FREE Full Text ↵ Tessema , S. K. , Hathaway , N. J. , Teyssier , N. B. , Murphy , M. , Chen , A. , Aydemir , O. , Duarte , E. M. , Simone , W. , Colborn , J. , Saute , F. , Crawford , E. , Aide , P. , Bailey , J. A. , & Greenhouse , B. ( 2022 ). Sensitive, Highly Multiplexed Sequencing of Microhaplotypes From the Plasmodium falciparum Heterozygome . The Journal of Infectious Diseases , 225 ( 7 ), 1227 – 1237 . doi: 10.1093/infdis/jiaa527 OpenUrl CrossRef ↵ Twohig , K. A. , Pfeffer , D. A. , Baird , J. K. , Price , R. N. , Zimmerman , P. A. , Hay , S. I. , Gething , P. W. , Battle , K. E. , & Howes , R. E. ( 2019 ). Growing evidence of Plasmodium vivax across malaria-endemic Africa . PLoS Neglected Tropical Diseases , 13 ( 1 ), e0007140 . doi: 10.1371/journal.pntd.0007140 OpenUrl CrossRef PubMed Winter , D. J. , Pacheco , M. A. , Vallejo , A. F. , Schwartz , R. S. , Arevalo-Herrera , M. , Herrera , S. , Cartwright , R. A. , & Escalante , A. A. ( 2015 ). Whole Genome Sequencing of Field Isolates Reveals Extensive Genetic Diversity in Plasmodium vivax from Colombia . PLOS Neglected Tropical Diseases , 9 ( 12 ), e0004252 . doi: 10.1371/journal.pntd.0004252 OpenUrl CrossRef PubMed ↵ Yan , G. , Lee , M.-C. , Zhou , G. , Jiang , A.-L. , Degefa , T. , Zhong , D. , Wang , X. , Hemming-Schroeder , E. , Mukabana , W. R. , Dent , A. E. , King , C. L. , Hsu , K. , Beeson , J. , Githure , J. I. , Atieli , H. , Githeko , A. K. , Yewhalaw , D. , & Kazura , J. W. ( 2022 ). Impact of Environmental Modifications on the Ecology, Epidemiology, and Pathogenesis of Plasmodium falciparum and Plasmodium vivax Malaria in East Africa . The American Journal of Tropical Medicine and Hygiene , 107 ( 4_Suppl ), 5 – 13 . doi: 10.4269/ajtmh.21-1254 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted March 15, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following PvGAP: Development of a globally-applicable, highly-multiplexed microhaplotype amplicon panel for Plasmodium vivax Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. 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