Germplasm identification is essential for plant breeding and conservation. In this study, we developed a new method, DT-PICS, for efficient and cost-effective SNP selection in germplasm identification. The method, based on the decision tree concept, could efficiently select the most informative SNPs for germplasm identification by recursively partitioning the dataset based on their overall high PIC values, instead of considering individual SNP features. This method reduces redundancy in SNP selection and enhances the efficiency and automation of the selection process. DT-PICS demonstrated significant advantages in both the training and testing datasets and exhibited good performance on independent prediction, which validates its effectiveness. Thirteen simplified SNP sets were extracted from 749,636 SNPs in 1135 Arabidopsis varieties resequencing datasets, including a total of 769 DT-PICS SNPs, with an average of 59 SNPs per set. Each simplified SNP set could distinguish between the 1135 Arabidopsis varieties. Simulations demonstrated that using a combination of two simplified SNP sets for identification can effectively increase the fault tolerance in independent validation. In the testing dataset, two potentially mislabeled varieties (ICE169 and Star-8) were identified. For 68 same-named varieties, the identification process achieved 94.97% accuracy and only 30 shared markers on average; for 12 different-named varieties, the germplasm to be tested could be effectively distinguished from 1,134 other varieties while grouping extremely similar varieties (Col-0) together, reflecting their actual genetic relatedness. The results suggest that the DT-PICS provides an efficient and accurate approach to SNP selection in germplasm identification and management, offering strong support for future plant breeding and conservation efforts. Arabidopsis thaliana, as a model plant, has great value in modern genetics [genetics . Before genetics . Misidengenetics . Moreovegenetics . Using pgenetics . Unlike genetics . Moleculgenetics , Arabidogenetics , cucumbegenetics , soybeangenetics , and so Given the stability and effectiveness of germplasm identification methods, the International Union for the Protection of New Varieties of Plants (UPOV) has identified DNA molecular markers as SSR and SNP marker ,11. HoweUsing SNPs for the germplasm identification of Arabidopsis has been applied to some extent. The Arabidopsis germplasm identification tools AraGeno and SNPmatch, developed by Rahul Pisupati et al. and based on the 1001 Genomes Project, can be used directly online to identify germplasm to be tested by comparing its whole-genome SNPs with a reference database of 10.7 million SNPs. However, it is based on whole genome alignments. Additionally, the identification accuracy is highly dependent on the number of markers in the germplasm to be tested, which decreases as the number of markers decreases. It may fail to identify germplasm when the number of markers is less than 10,000 . RelativTo achieve a strong overall distinction, it is recommended that markers be complementary to each other. The decision tree method (DT) is one of the well-known methods for data classification. The most distinctive markers can be quickly selected by DT for recursive partitioning and the redundancy between markers can be reduced; thus, the core features can be quickly filtered out for classification . AlthougIn this study, we developed a DT-PICS (Decision Tree-PIC Selection) method for Arabidopsis germplasm identification. At first, for 1135 Arabidopsis varieties, the simplified SNP set with an overall high PIC value was screened out with DT-PICS. Then, multiple simplified SNP sets were combined to construct the fingerprint map to enhance the fault tolerance in independent identification. We then compiled all the code into R scripts for automatic analysis. This method has good portability and can also be used for the fingerprint construction of other plants.The distribution of PIC values for the 736, 195 cSNPs in Train_Data  shown in When combining multiple simplified SNP sets, a significant increase in distinguishing accuracy was observed according to SNP modification levels C, indicaWe performed simulation experiments by combining the 13 simplified SNP sets in all possible pairwise combinations for joint identification, resulting in 78 combinations, and the DT-PICS method showed the best performance in both the original and modified SNP datasets . Among tIn independent predictions, for 68 same-named varieties, the variety with the highest similarity was recorded as the identification result. The DT-PICS method showed the highest identification accuracy, with an average of 94.97%, and only 30 shared markers between training and test datasets on average . In contFurthermore, all 12 different-named varieties were able to be distinguished from each other using DT-PICS SNPs. However, when each of these varieties was individually added to the Train_Data, they could be distinguished from the other 1134 varieties, but were always classified as the same variety as col-0. Further investigation into their genetic similarity with Col-0 at the whole-genome level revealed an average similarity of 99.99%. It is suggested that these 12 different-named varieties could be regarded as derivatives of Col-0 rather than independent new varieties. The DT-PICS method can effectively distinguish between the germplasm to be tested and other varieties while grouping extremely similar varieties together, reflecting their actual genetic relatedness. In contrast, the HPS and RS methods may sometimes classify a single germplasm into multiple different varieties, which results in increased uncertainty in the classification process. These results further demonstrate the superiority of DT-PICS for germplasm identification.To demonstrate the flexibility of DT-PICS SNP selection, a gradient approach was used by randomly selecting a certain number of markers from all 769 DT-PICS SNPs to distinguish the 80 varieties in Test_Data. The distinguishing accuracy gradually increased with increases in the number of selected SNPs, reaching stability at 70 SNPs. Additionally, identification accuracy did not show any significant difference with same-size randomly selected DT-PIC SNPs, indicating that marker selection could be based on practical needs in practical applications, rather than being limited to specific marker sets B. These QR codes for 1135 Arabidopsis varieties were created. Each code contained the variety’s name and its DT-PICS SNP from two simplified SNP sets .Germplasm identification is crucial for managing genetic diversity in germplasm resources . Howeversum) that take into account both the overall performance of the SNP dataset and the unique characteristics of each marker. This approach allowed us to avoid selecting redundant SNPs with high PIC values that do not provide additional discriminatory power and to identify low-PIC-value SNPs that contain novel information and are valuable for germplasm identification. Additionally, the DT method could efficiently select the most informative SNPs for germplasm identification by recursively partitioning the dataset based on their overall high PIC values, which can reduce the cost and time required for genotyping large numbers of varieties [In this study, with the strategy of the DT method concept for “divide and conquer” and “greedy” based on PIC values, a DT-PICS germplasm identification method based on the SNPs of the whole genomes of 1135 Arabidopsis varieties was developed. In contrast to most existing marker selection methods that focus solely on individual SNP’s high PIC values ,21,22, oarieties ,18.Remarkably, when markers are selected using DT-PICS, the greedy algorithm may result in an uneven distribution of markers across sets, altering the population structure within the sample. This can increase the genetic distance between initially similar varieties and decrease it between more distinct varieties, rendering the set of markers less suitable for assessing variety similarity. Therefore, DT-PICS SNP is only applicable for germplasm identification but not suitable for comparing similarities between varieties or cluster analyses. Thus, DT-PICS SNP has a better distinguishing ability for similar varieties.Using the DT-PICS method, 13 simplified SNP sets containing a total of 769 DT-PICS SNPs were generated, with an average of 59 SNPs per set, which were capable of distinguishing all 1135 Arabidopsis germplasms. Our results show that the DT-PICS method performed well in both Train_Data and Test_Data, indicating its superior identification ability for both same-named and different-named germplasms compared to the HPS and RS marker selection methods. Additionally, we observed a significant increase in distinguishing accuracy when using a portion of DT-PICS SNPs instead of hPIC SNPs. The gradient experiment demonstrated that a randomly selected set of DT-PICS SNPs (approximately 70 SNPs) achieved high distinguishing accuracy, which further highlights the flexibility and practicality of the DT-PICS method. Furthermore, we identified two potentially mislabeled varieties (ICE169 and Star-8), suggesting the importance of further investigation to ensure the accurate identification of germplasms.Although the DT-PICS method has demonstrated its scientific validity and practicality, there are still some aspects that can be further improved. On the one hand, in Test_Data for the same-named varieties, the one with the highest similarity was selected as the identification result. However, for some cases where the same-named varieties’ similarity was not very high C, such ahttps://1001genomes.org, accessed on 4 April 2022) [There were two Arabidopsis SNP genotype datasets involved in this study: one training dataset and one test dataset . The trail 2022) ,24, inclil 2022) . Then, Sil 2022) , and 1,1http://www.ncbi.nlm.nih.gov/, accessed on 14 April 2022). Then, 749,636 cSNPs located in the gene’s coding region were extracted and used for following Arabidopsis fingerprint mapping modeling. SNPs that were genotypically identical in all samples were removed. The training dataset contained 1135 varieties with 736, 195 cSNPs, denoted as Train_Data .SNPs include coding-region SNPs (cSNPs), intergenic SNPs (iSNPs), and perigenic SNPs (pSNPs). The variation rate of cSNPs within exons is only 20% of that of other surrounding sequences. Although the number of them is relatively small, they are important in the study of biological breeding and genetic diseases. Therefore, in this study, we screened cSNPs located in the coding region of genes for further Arabidopsis germplasm identification. Arabidopsis gene annotation information was obtained from the genome database of NCBI GenBank , including 68 same-named and 12 different-named varieties as those in Train_Data [The test dataset contained 80 Arabidopsis varieties downloaded from the MPICao2010 of 1001 Genomes Project  of the jth split dataset (j = 1~l). The remaining SNPs were sorted with the values of The PIC values of the remaining SNPs were recalculated in each split dataset , and the PIC values of all SNPs in each split dataset were summed to obtain n. As shown in The above steps were repeated until the number of leaf nodes and the number of split datasets equaled the total number of samples Stage 2: Redundant SNP deletion. One SNP in the M set was randomly shielded at a time. If the remaining selected SNPs could still distinguish all varieties, this indicated that the shielded SNP was redundant and should be filtered out. Otherwise, the SNP remained in the M set. We repeated this step several times until all the markers in the M set had been checked. As shown in To evaluate the effectiveness of germplasm identification with the above simplified SNP sets, markers selected from three methods, DT-PICS, HPS (High PIC selection), and RS (Random Selection), were separately performed for germplasm identification in simulation analyses. The HPS method selected SNPs with PIC values > 0.45 (hPIC) in Train_data as high PIC SNPs; the RS method selected SNPs randomly from Train_Data for identification. Five experimental levels, with all DT SNPs, 2/3 DT SNPs + 1/3 hPIC SNPs, 1/3 DT SNPs + 2/3 hPIC SNPs, all hPIC SNPs, and all RS SNPs, were set up with five repeats at each level. The number of markers at each level equaled the average value of all simplified SNP sets selected by DT-PICS mentioned above. To compare the distinguishing accuracy of the three methods, we separately modified 5%, 10%, and 15% SNPs of the germplasm to simulate the inconsistency of SNP genotypes among different samples of the same germplasm that can be caused by mutations, deletions, differences in sequencing platforms, and other factors in practical applications.For some selected markers that may not have been detected in the tested germplasm during the SNP calling in different sequencing platforms, the accuracy of independent prediction was reduced due to insufficient core markers. As a result, multiple simplified SNP sets should be combined for independent prediction to improve the stability and fault tolerance of the DT-PICS. The combination of multiple simplified sets should contain a relatively small size of markers and can well distinguish germplasms from each other in practical applications. To save identification costs as much as possible, it is necessary to determine the optimal number of simplified SNP sets. Similar to the analysis method using a single simplified SNP set mentioned above, 5%, 10%, or 15% SNP genotypes of tested germplasm were randomly modified, then combined with several simplified SNP sets to determine the optimal combination for independent germplasm identification.Three methods  were used to identify 68 same-named and 12 different-named varieties in Test_Data, as in Train_Data. The prediction accuracy was evaluated based on the identification accuracy and the distinguishing accuracy mentioned above.www.barcode-generator.org (accessed on 13 March 2023) [QR codes were generated for the varieties used in this study using two combinations of simplified SNP sets that had shown the best performance in the training set. The codes were generated using an online tool available at ch 2023) . Each vaThis study presents a novel DT-PICS method for quickly and accurately identifying varieties, which offers several advantages over existing methods. The approach is flexible and practical, with no need for manual selection and a low number of markers. Moreover, an Rscript  was comp


To the Editor:Clinical practice guidelines (CPGs) are usually developed using the internationally recognized Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, which requires consideration of the certainty of the estimated treatment effects when making recommendations . RecentlEligible CQs were identified from the J-SSCG2020 data that satisfied the criteria outlined in Table Figure This is the first study to compare the traditional method and the net benefit approach for determining the CoE. Our findings suggest that the net benefit approach tends to provide higher or equivalent CoE ratings for CQs that use dichotomous outcomes to calculate net effect estimate. The results of this study added novel insights for guideline developers to create CPGs that implement the net benefit approach concept.These results are attributed to the net benefit approach's emphasis on critical outcomes for patients, such as death. By contrast, the traditional approach is susceptible to outcomes with lower CoEs, particularly when the point estimates for benefits or harms are inconsistent. The net benefit approach compensates for this weakness of the traditional method by considering the relative importance of each outcome.This study has some limitations. First, most outcomes of the CQs not meeting the OIS criteria and only one CPG was evaluated. Second, one CQ showed a lower CoE in the net benefit approach than in the traditional method, so the cause could not be well discussed because of the limited sample size. Likewise, we could not discuss the possibility that the net benefit approach would lead to overestimate CoE. Further studies are required to address these limitations and evaluate the usefulness of the net benefit approach more comprehensively. Nonetheless, the significance of this report lies in providing the first comparison between traditional method and the net benefit approach to determine CoE.We examined the disparities in CoE evaluation between the traditional and novel net benefit approach methods for 20 CQs from the J-SSCG2020. Our findings suggest that the CoE calculated using the net benefit approach tends to be equal to or higher than that calculated using the traditional method. Further rigorous investigations are necessary to identify the underlying reasons for this difference.Additional file 1: Figure S1. Steps to determining the certainty of the net effect estimate. Figure S2. Patterns of change in certainty of evidence (CoE) between the traditional method and the net benefit approach by clinical question. Table S1. The details of the included Clinical Questions.Additional file 2.  Utility tool for the net benefit approach.


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.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 malaria remains a persistent threat for sub-Saharan Africa, where approximately 95% of total malaria cases and 96% of all malaria deaths occur , which can frequently recombine due to the obligatory sexual (meiotic) phase of the life cycle in the mosquito . One method may not be the solution for all malaria endemic areas nor for comparative studies.In the microbiological world, ntensity . With frmosquito . Identifulations . Given tulations  for an e“Molecular barcodes” of single nucleotide polymorphisms (SNPs) have been proposed as a molecular surveillance tool and heralded as the new frontier of malaria surveillance, revisiting research in human, animal, and plant genetics almost 20 years ago . This haP. falciparum as a result of malaria interventions; detect geographic origins of infection, whether local or imported; distinguish parasite clones from one another, using neutral theory; as well as identify spatial differentiation between parasite populations. 24-SNP , South . 24-SNP , and als. 24-SNP , Ndirand. 24-SNP  and Mada. 24-SNP ). Other . 24-SNP  and with. 24-SNP  populatiThe immediate problem with the use of SNP barcodes on samples from moderate-to-high transmission settings is the high prevalence of multiclonal infections and whether haplotypes can be accurately constructed for population genetic analysis. This is known as phasing and is more challenging with biallelic SNPs , compareP. falciparum community Project version 6 (It is recommended that SNP barcodes i) are biallelic, ii) have a minor (least frequent) allele frequency greater than 0.10 and iii) independently segregating, so that genetic diversity and population structure analyses are not biased. Further, for these barcodes to be standardised as a one-size-fits-all panel, they iv) should work across a range of geographies and v) be temporally stable. SNP genotypes of isolates obtained from the MalariaGEN ersion 6  were usePlasmodium falciparum Community Project   , hereinaribed in . Standarribed in . As P. fN = 2,317 isolates)  used Whole Genome Sequencing library strategy, ii) passed the quality control (“QC pass”), and iii) sequencing was performed using the Illumina HiSeq 2000 paired-end sequencing platform . We usedsolates) . This thsolates) . These isolates) . SupplemTo be able to understand the genetic diversity and population structure of each parasite isolate and test whether small panels or “barcodes” provide enough information, we chose to analyse published 24- and 96-SNP barcodes  that werWe mined each parasite isolate genome for their genotype at the 24 genome-wide SNPs in the “molecular barcode” Taq-Man assay as described . Brieflywww.plasmodb.org) and were chosen if they were highly polymorphic for parasites from the Thai-Burma border, assayable, not in genes encoding surface proteins , transporters or telomeric genes that may be under strong selection, were distributed across all 14 chromosomes and were found to have MAFs between 0.10 and 0.50. No formal linkage or neutrality analysis was reported in regard to the generation of the SNP barcode. The 96-SNP panel was used to analyse genetic diversity and population structure of asymptomatic and clinical isolates from pregnant women and children younger than 5 years old at the Thai-Burma border  from 2001 to 2010  and 0.0036% (N = 10) of alleles in the filtered 24- and 96-SNP datasets, respectively.Published positions of the 24- and 96-SNP barcodes  were basPlasmoDB , respectPlasmoDB . Variantthods in ). Using thods in , we aligthods in . Allelesthods in . This exFWS) for each isolate from the Pf6 dataset (HW) relative to the allele frequency within the total parasite population (HS) using the read count for each locus in the Pf6 dataset. FWS is presented as a proportion that ranges from 0 to 1, where FWS values closer to 1 indicate high inbreeding rates  and lower FWS values indicate low inbreeding rates (more diverse/mixed genotypes) in the parasite population. An infection is said to predominantly contain a single genotype when FWS ≥ 0.95 . IncludN = 2,317) in analyses by constructing the “dominant” haplotype for each isolate that has a MAC. This artificially generates a monoclonal infection for all genotypes. A “dominant” allele was defined as an allele call with the highest number of supporting reads  per SNP locus using the ratio of AD . For loci where both alleles were supported equally , an allele was selected at random to complete the construction of haplotypes without MACs  attempts to include both mono- and multiclonal infections . The percentage of data loss for the latter method was calculated as the number of multiclonal infections divided by the total number of infections per study population.The second more “conservative” method removes all multiclonal infections, as defined by Performance of the 24- and 96-SNP molecular barcodes were analysed to estimate genetic diversity and population structure as described below.https://github.com/UniMelb-Day-Lab/SNP_MinorAlleleFreq). In short, MAFs for each locus were calculated by removing MACs from the numerator and denominator to reduce bias. This custom script generates a table describing in each row a locus with the number of isolates with data, the number of MACs, the major and minor alleles, and the minor allele frequency calculated. Because samples were haploid, Hardy-Weinberg Equilibrium was not applicable in this study.MAFs are central to analyses using SNP data and is therefore important to accurately estimate. Subsequent to our investigation of methods for handling multiclonal infections that found the conservative approach  as the mA MAF <0.10 indicated that a locus was not representative and that alleles were moving towards fixation in the population, while a MAF ≥0.10 indicated that the locus can discriminate between isolates in the population. As MAFs impact the inference of population structure , MAFs weThe standardised index of association , expected heterozygosity (He) and population genetics analyses, MACs were replaced with no value (“NA”), hereinafter known as the “cleaned monoclonal infections” dataset  via R package “poppr” v. 2.7.1  were used to calculate FST and pairwise FST, referred to as the “complete monoclonal infections” dataset (FST). Given that there were only three regions to generate a matrix, comparisons between regions could not be performed.Pairwise population genetic distances across each population scale were determined by Weir and Cockerham’s . 0.5-10 . FST ulations , and was dataset , which r dataset  with 999 dataset  and geneFST, providing an estimate of how much data was required to find population structure given genetic differentiation in the population  using the “complete monoclonal infections” dataset of the 20- and 75-SNP barcodes . DAPC ispulation . Pairwispulation .PAS) score for isolates within each study population for each barcode using the “complete monoclonal infections” haplotypes with no missing data. PAS is an identity-by-state (IBS) measure of genetic similarity that can be used across relatively few loci and was calculated as the number of alleles shared between two multilocus haplotypes (NAB) divided by the number of SNP loci (NL) (PAS = NAB/NL) (PAS score characterised variation in multilocus haplotypes from clones (PAS = 1.0) to genetically dissimilar (PAS ≤ 0.25)  . The P ≤ 0.25) . Larger- ≤ 0.25)  and therHe were compared within each study location over time using the cleaned monoclonal infections data. The function “Hs.test” in “adegenet” was used to test the difference in He between two time points (x and y) using the equation He(x) - He(y) using 999 Monte-Carlo test simulations , which may be in close proximity to these SNP loci. We obtained data on the drug resistance classification (sensitive/resistant/undetermined) and marker genotypes for each isolate from the Pf6 dataset (pfcrt on chromosome 7. A chi-squared test (χ2) was used for univariate analyses of discrete variables to compare proportions. PAS were compared between study locations over time using the “complete monoclonal infections” data. As such, isolates from Navrongo 2011 were removed as there were <25 complete infection haplotypes for analysis. A non-parametric Wilcoxon rank-sum test was used to compare the PAS between two time points in Base R v. 3.5.0  and Kinshasa, DRC (2012 and 2013) . MAFs anulations . Subsequ dataset . Resistav. 3.5.0 .p-value was <0.05.All statistical analysis were carried out in R  implemenThe availability of SNP genotypes in the Pf6 database allowed us to test the performance of the 24- and 96-SNP barcodes to examine population diversity and structure. There were 2,922 isolates sampled in Africa that met the selection criteria (see Methods). Of these, haplotypes were generated for 2,317 (79%) isolates from 25 study populations (study location by year) across 10 moderate-to-high transmission countries in Africa. Study population sample sizes varied from 26 isolates  to 235 isolates  . There wFWS, where values ≥0.95 indicated that infections predominantly contained a single genome. We showed that 52.3% of overall infections were found to be multiclonal and that these multiclonal infections dominated in most study populations , demonstrating that proportions of MACs in an isolate’s haplotype is a reliable predictor of within-host diversity for an isolate for cases where FWS is unavailable . For the “dominant allele” method, distributions of AD ratios were both positively skewed for both barcodes . The medFWS < 0.95). While this approach ensured a higher confidence in the constructed haplotypes, the result was a reduction in the total number of isolates from 2,317 to 1,105  observed in the 20-SNP and 75-SNP molecular barcodes, respectively, using the “cleaned monoclonal infections” dataset for all locations (He) was low when using both barcodes  , countries  nor study populations . This is best explained by the low minor allele frequencies for individual loci per barcode across the continent.There were 96.8% and 97.2% unique multilocus haplotypes (ocations . Of the ocations . Despite= 0.432)  and did p < 0.05, p < 0.05) were found in 63 pairs of loci across all populations; the most common pairs were Pf3D7_02_v3_842805 vs. Pf3D7_10_v3_1402510, and Pf3D7_07_v3_628392 vs. P3D7_10_v3_82375 that were observed in only 3/63 pairs (4.76%) each (p < 0.05) was found in 1,179 pairs of loci across all populations, with the most common pair, Pf3D7_06_v3_1184506 vs. Pf3D7_06_v3_1206498, found in only 12/1,179 pairs (1.02%), indicating weak evidence of physical linkage of two markers on chromosome 6 (The standardised index of association was used to assess multilocus linkage disequilibrium (LD), or non-random associations among SNP loci, using “complete monoclonal infection” haplotypes with no missing data. Overall, there was no evidence of linkage disequilibrium for both SNP barcodes (6%) each . For themosome 6 . OverallFST between regions, countries, and study populations using the complete monoclonal infections dataset  and 75-SNP (mean FST: 0.00339) barcodes  at the regional-level, between Guinea and Tanzania (20-SNP: FST = 0.0078) and Malawi and Cameroon (75-SNP: FST = 0.0080) at the country-level, and between Nzerekore 2011 (Guinea) and Mkuzi-Muheza 2013 (Tanzania) (20-SNP: FST = 0.0087) and Chikwawa 2011  and Buea 2013 (Cameroon) (75-SNP: FST = 0.0080) at the study population level  (PAS = 0) and very few (8.9%) sharing up to 50% of alleles (0.2 ≤ PAS < 0.5)  . Using t < 0.5) .He values were not significantly different over time  and Kinshasa, DRC (2012 and 2013). Firstly, we used the “cleaned monoclonal infections” dataset to investigate whether the genetic diversity was stable over time. The mean 6 allele . Over ti6 allele . Only thPAS scores between study locations over time using the “complete monoclonal infections” data. PAS scores were significantly different in Kinshasa over time (Wilcoxon: 20-SNP: p = 0.031 and 75-SNP: p < 0.001) and in Navrongo from 2010 to 2013 for the 20-SNP barcode (Wilcoxon: p = 0.032) but not the 75-SNP barcode (Wilcoxon: p = 0.078)  .P. falciparum infections. Both 24- and 96-SNP barcodes could recapitulate a signal of large-scale genetic differentiation by geographic distance as shown by Mantel Test and DAPC, consistent with minimal divergence of loci with high gene flow across the African continent (He) and similarity (PAS) were not reflective of highly outcrossing populations, likely because these small molecular barcodes were not strictly biallelic and had similar and low minor allele frequencies  and 674 (26.6%) respectively passed the quality control and filtering steps, resulting in an enormous loss of data and expense. These issues of cost-effectiveness are of relevance to public health where only approximately $1-10 per person per annum is spent on malaria control in endemic countries in Africa . A study of this magnitude is currently very expensive (approximately $86 USD per isolate)  , laborioisolate) ), due topfcrt  is driving variation at Pf3D7_07_v3_435497 in the 20-SNP barcode. This observation corresponds to the policy change to artemether-lumefantrine (AL) and artesunate-amodiaquine (ASAQ) in Ghana in 2007, where reports have indicated a higher use of AL (pfcrt K76). This provides an example of how important longitudinal investigations of molecular panels are to ensure population genetics theories are being upheld. Any temporal variation in allele frequencies related to outcrossing must complicate the calculation of priors for Bayesian inference.Longitudinal investigations using SNP barcodes must err on the side of caution. Given the recent changes in antimalarial drug policy and use , it is pse of AL  that selse of AL ; increasse of AL . In DRC,se of AL , coincidP. falciparum structure with only 12 markers , can distinguish highly diverse multiclonal infections, but cannot construct haplotypes (var DBLa typing (“varcoding”) to evaluate genetic diversity and population structure in a high-transmission setting in Ghana and found that while microsatellites provided greater resolution than SNPs, varcoding was superior in identifying finer-scale relatedness and population structuring.Alternatively, adaptive genes may present an innovative approach  consisteplotypes . A recenplotypes  comparedP. falciparum as well as spatial and temporal variation. Alternative markers such as STRs and microhaplotypes are possible solutions to study P. falciparum population structure using neutral theory.Molecular barcodes are a practical and low-cost solution to avoid relying on whole-genome sequencing for surveillance. However here we show the application of SNP barcodes encounters challenges in sub-Saharan Africa in moderate-to-high transmission settings due to the high number of multiclonal infections, frequent outcrossing, and large effective population size of


Plasmodium vivax infections constrains the surveillance of antimalarial efficacy and transmission of this neglected parasite. Recurrent infections within an individual may arise from activation of dormant liver stages (relapse), blood-stage treatment failure (recrudescence) or new inoculations (reinfection). Molecular inference of familial relatedness (identity-by-descent or IBD) based on whole genome sequence data, together with analysis of the intervals between parasitaemic episodes , can help resolve the probable origin of recurrences. Whole genome sequencing of predominantly low-density P. vivax infections is challenging, so an accurate and scalable genotyping method to determine the origins of recurrent parasitaemia would be of significant benefit. We have developed a P. vivax genome-wide informatics pipeline to select specific microhaplotype panels that can capture IBD within small, amplifiable segments of the genome. Using a global set of 615 P. vivax genomes, we derived a panel of 100 microhaplotypes, each comprising 3-10 high frequency SNPs within <200 bp sequence windows. This panel exhibits high diversity in regions of the Asia-Pacific, Latin America and the horn of Africa (median HE = 0.70-0.81) and it captured 89% (273/307) of the polyclonal infections detected with genome-wide datasets. Using data simulations, we demonstrate lower error in estimating pairwise IBD using microhaplotypes, relative to traditional biallelic SNP barcodes. Our panel exhibited high accuracy in predicting the country of origin (median Matthew’s correlation coefficient >0.9 in 90% countries tested) and it also captured local infection outbreak and bottlenecking events. The informatics pipeline is available open-source and yields microhaplotypes that can be readily transferred to high-throughput amplicon sequencing assays for surveillance in malaria-endemic regions.Challenges in understanding the origin of recurrent  Plasmodium vivax is a major public health threat affecting the poorest and most vulnerable populations in more than 49 endemic countries1. Over the past decade, enhanced malaria control efforts in areas outside of sub-Saharan Africa have achieved a marked decline in P. falciparum infections, but a relative rise in the proportion of P. vivax cases1. Several biological attributes of P. vivax render this species more resilient to interventions than P. falciparum2. P. vivax forms dormant liver stage parasites (hypnozoites) that can reactivate weeks to months after initial inoculation causing recurrent episodes of malaria (relapses). A single mosquito inoculation can cause multiple relapses, sustaining transmission over extended periods. Relapses are thought to cause over 60% of clinical cases3. Knowledge of the biology and epidemiology of P. vivax relapse, including the underlying reactivation mechanism(s) and host and parasite determinants, are vital for informing public health strategies to combat this parasite, but our understanding of these processes is limited4. A major obstacle to our increased understanding has been the difficulty of classifying recurrent P. vivax infections accurately. These recurrences can arise from reinfection (new mosquito inoculations), recrudescence (blood-stage treatment failures), or relapse (reactivation of dormant liver stages). Discriminating between these causes is challenging.The malaria parasite P. vivax. Accumulating reports suggest that chloroquine, the most widely used drug for treating the P. vivax blood stages, is failing in several endemic regions, but recrudescence (drug failure) can be confused with relapse, confounding efficacy studies5. Accurate diagnosis of the cause of recurrent infection is also essential to clinical studies of hypnozoiticidal (anti relapse) regimens (primaquine and tafenoquine) as this relies on distinguishing relapse from reinfection7. Reinfection dilutes observed differences in recurrence between hypnozoiticidal interventions and biases treatment effect estimates towards interventions which provide longer post-treatment prophylaxis8.Accurate methods to disentangle relapse from reinfection and recrudescence events are critical to improving our understanding of the therapeutic efficacy of current treatment regimens for P. falciparum infections deriving from reinfections  and recrudescences (which contain homologous parasites) using genotyping at a handful of polymorphic markers, represented a crucial advance for P. falciparum clinical research9. It allowed therapeutic efficacy studies to be conducted in endemic areas without the need for concomitant detailed epidemiological assessment. However, the situation is more complex for P. vivax malaria since relapsing parasites can be genetically homologous or heterologous to the initial infection11. However, recent genomic studies have revealed that a proportion of paired P. vivax isolates (from acute and recurrent infections) that would be classified as heterologous using traditional genotyping approaches share homology in large segments of the genome, inferring familial relatedness14. In mosquito hosts capturing blood meals with mixtures of parasite genomes, the obligate meiotic stage will generate meiotic recombinants, producing sporozoites that share parents. Every natural infection deriving from more than one sporozoite may comprise mixtures of meiotic recombinants. Pairs of infections with evidence of recent identity by descent (IBD) consistent with close relationships such as siblings or half-siblings (as much as ≥50% and ≥25% IBD respectively), are more likely to have derived from the same mosquito inoculation than from different inoculations and are, therefore, more likely to reflect relapse than reinfection events. IBD therefore has significant potential to enable more accurate classification of recurrent P. vivax infections. IBD-based measures would also allow finer resolution of the spatial connectivity between P. vivax populations than is possible using classical methods such as the fixation index or phylogenetic approaches15. With appropriate genetic data, IBD can be used to characterise major transmission networks (foci) for targeted intervention, or the risks of infection spread between communities and across borders.The ability to discriminate between recurrent P. vivax patient isolates often have low parasite densities which currently precludes whole genome sequencing at large scale, even when using selective whole genome amplification methods16. Restricting analyses to infections with high parasite densities is not ideal as these are atypical of P. vivax infection and hence may not be representative of the true diversity in patient infections. High-throughput genotyping offers a more operationally feasible approach that can be applied to low volume samples such as dried blood spots and would be more readily implemented in surveillance frameworks in malaria-endemic countries17. However, selecting parsimonious marker sets that can capture genome wide IBD effectively is challenging18. To date, genotyping methods for P. vivax have relied on either capillary sequencing of microsatellite markers or next generation sequencing (NGS) of Single Nucleotide Polymorphisms (SNPs)2. In a recent P. falciparum study, targeted NGS of short regions comprising multiple highly variable SNPs (microhaplotypes) provided a simpler, cheaper, and higher-throughput approach than microsatellite typing, with substantially higher resolution of individual clones than with single SNPs19.While genomic data provides the greatest information to estimate IBD, P. vivax genomes, we identified microhaplotype markers that could estimate IBD across pairs of infections in diverse parasite populations. The goal was to establish a universal P. vivax IBD barcode to improve the interpretation of therapeutic evaluations of hypnozoiticidal and schizonticidal antimalarial drugs, elucidate the biology and epidemiology of P. vivax relapses, and provide NMCPs and other agencies with actionable information on parasite transmission within and across national borders. In silico analyses confirmed that P. vivax microhaplotypes can provide much higher resolution to estimate relatedness consistent with close relationships compared to biallelic SNP barcodes that are currently used.Using 1,816 publicly available Fws ≥0.95)20. This sample set was used to select variants and identify potential microhaplotype candidate regions for targeted sequencing, with the aim of identifying 200 bp regions of the P. vivax genome with high diversity in isolates from all geographic regions. In addition to country classifications, the samples were assigned regional population classifications derived from the MalariaGEN Pv4 project and based on genetic clustering patterns20. Although most parasites were from the Asia-Pacific region, the sample set exhibited broad geographic representation, spanning 17 countries and with all regional populations represented by at least 30 isolates (range n=31-151)  .21 were then benchmarked for their ability to discriminate relatedness with IBD estimation among real and simulated infection pairs. Four SNPs within the Broad barcode had to be excluded from analyses because of high genotype failure rates , or multi-allelic status . This left 38 SNPs for evaluation; herein we refer to this panel as the 38-SNP Broad barcode.A total of 13,090 candidate high quality biallelic SNPs with global (pooled across regions) high minor allele frequency (MAF >0.1) and low genotype failure rate (missingness < 0.1) were identified in coding regions of the genome (excluding hypervariable regions) to minimise potential changes in primer-binding sites and missingness) . We thenP. vivax infections was first conducted using simulated data generated on pairs of infections across a range of relatedness (r) values using the paneljudge package22. All data were simulated under the model used to estimate relatedness. In each geographic region, the 95% confidence intervals (CIs) around the estimate r=1 , closely followed by r=0 (strangers), and highest when r=0.5 (siblings) indicating lowest accuracy in predicting sibling relationships. In all geographic regions, the CI intervals were shortest around the estimate r=0.5 had 95% CIs between 0.1 and 0.8. Whilst the Random-SNP microhaplotype panel followed similar trends to the High-diversity SNP panel in each geographic region, the Broad barcode displayed particularly large CI intervals relative to the microhaplotype panels in Maritime Southeast Asia and Oceania. The same trends in marker panel performance were observed using the root mean squared error (RMSE) of the estimates of r  reflecting potential recrudescence or relapse events, whilst 1 pair was distant relatives (0.05≤ IBD <0.25) and 2 pairs were strangers (IBD <0.05). The distant relatives and strangers reflect potential reinfection or relapse events. When the data were restricted to the 100-microhaplotype markers, the resulting hmmIBD-based estimates correlated highly with the genome-wide measures of IBD . One pair of infections (PJ0167-C and PJ0166-C) that were classified as strangers with genomic data (IBD = 0.048) were classified as distant relatives (IBD = 0.156) with the microhaplotypes, but the other 13 pairs had concordant recurrence classifications.In addition to the simulated data, the efficacy of the three panels in estimating IBD between infections was also evaluated using ‘real’ data from sequenced pairs of primary and recurrent  episode . Genome-Further evaluation of the highest performance panel, the High-diversity SNP microhaplotype panel, was conducted to assess its utility in capturing key population genetic features. The genetic diversity of the High-diversity SNP panel was assessed for isolates from each of the 7 geographic regions using measures of heterozygosity and effective cardinality. Heterozygosities across the microhaplotypes varied, but overall diversity was high in all geographic regions  . The lowFWS score. Using thresholds of COI = 1 and FWS ≥ 0.95 to define a monoclonal infection, there were no significant differences in the proportion of polyclonal infections defined by the microhaplotype and genome-wide data in any geographic region  measures and correlated with the genome-wide estimates derived from the c region . Furthermic data .20. Analysis of all the available P. vivax data demonstrated three major clusters with PC1 and PC2 representing South America, Africa and West Asia (group 1), East and West Southeast Asia (group 2), and Oceania and Maritime Southeast Asia (group 3) . In East Southeast Asia, cross-border networks were observed between Thailand and Vietnam.The capacity of the High-diversity SNP microhaplotype panel to capture spatial transmission patterns including differentiation of geographically distinct populations was also assessed, using Principal Coordinate Analysis (PCoA) and IBD-based networks. The microhaplotype-based PCoA trends were consistent with spatial trends observed with genome-wide datasetsgroup 3) . Clear d P<0.05, . In Mariplotypes . Furtherplotypes 20. The lP. vivax cases by determining an infection’s country of origin. Using the data from 21 countries with a minimum sample size of 4 from the available P. vivax global dataset, we determined the accuracy of the 494 SNPs within the microhaplotype panel in predicting country of origin using a recently developed Bi-Allele Likelihood (BALK) classifier28. The predictive accuracy of the 494 microhaplotype SNPs was compared to the 38-SNP Broad barcode and three recently identified P. vivax geographic barcodes selected specifically for determining country of origin 28. The median Matthew’s correlation coefficient  of the 494 microhaplotype SNPs were greater than or equal to the 38-SNP Broad barcode, GEO33 and GEO50 in 21 (100%) countries  in clinical trials. Our simulations showed that the microhaplotype panels yielded consistently higher accuracy in IBD estimation (across different IBD levels and in different populations) than the 42-SNP Broad barcode that is currently the most widely used SNP barcode for P. vivax. This is not surprising as the microhaplotypes have substantially greater genetic information content compared with the 38 evaluable Broad SNPs. We also demonstrated greater efficacy of IBD estimation when microhaplotypes were selected to meet specified diversity criteria (High-diversity SNP panel) relative to microhaplotypes with random SNP selections (any-SNP panel). In accordance with previous predictions18, our simulation-based results demonstrated error rates below 12% in the estimation of pairwise IBD in all populations tested using the high-diversity microhaplotype panel. The error rates were highest for the prediction of infections with 50% IBD. Further work is needed to understand how this might impact clinical predictions of individual treatment response or population-level drug efficacy both in the context of descriptive data analyses and mathematical models29. In descriptive analyses, data summaries  and a set of rules  are used to classify recurrences categorically. Rules-based classification is not the same as estimating the probability of relapse given the data under a statistical model . Nevertheless, improved data informativeness for IBD estimation likely translates into improved model-based probabilistic classification performance, at least in the case of the model of Taylor, because that model machinery includes an intermediary that evaluates the probability of the data given IBD partitions compatible with networks of sibling, clonal and stranger parasites29.A key requirement of the 19.It should be acknowledged that panel evaluation based on simulated data was done in a highly idealised manner that captures panel performance in its most favourable light. Data were simulated under the same model used to estimate relatedness, such that the model was perfectly specified in relation to the simulated data. Real data are generated by an ancestral process that the model does not capture, i.e., the model is miss-specified in relation to real data. The impact of miss-specified allele frequencies, miss-classified multiclonal infections, etc. was not evaluated. In addition, it should be acknowledged that genotyping errors and failures (missing data) were not included in our simulations. However, we anticipate that genotyping failures should be infrequent with NGS-based amplicon sequencing methods, where each locus is typically covered by hundreds of readsP. vivax isolates, collected from recurrent infections in clinical trials, confirmed the ability of the high diversity microhaplotype panel to estimate pairwise IBD. High correlations were observed between microhaplotype and genome wide IBD estimates in a set of 14 paired P. vivax isolates from the same patient before and after treatment. Using our assigned IBD thresholds, only one of 14 pairs of infections had mismatching classifications of ‘stranger’ versus ‘distant relative’, which are both likely to reflect either reinfections or relapses. However, the available genomic data did not comprise any infection pairs with IBD estimates around 50% , which our simulations predicted to be the most difficult relationships to determine accurately. The number of genomic pairs was limited owing to the difficulty in obtaining enough DNA and thus high-quality sequence data from recurrent infections, which typically exhibit low parasite densities. This is a major incentive for using targeted genotyping approaches. Nonetheless, we demonstrated high correlations between the microhaplotype and genome wide IBD estimates in the assessments of day 0 samples, where a range of IBD relationships were observed.Data from pairs of 12. The proportion of relapses amongst the recurrences in our and the Peruvian study is uncertain. In a therapeutic efficacy study undertaken in Cambodia, 20 patients were relocated to a malaria-free area excluding the possibility of reinfection; the authors were able to define confidently 5 recurrences as relapses and demonstrated that 4 (80%) of these were related to the initial infection30. As more comprehensive data become available, a clearer picture of the epidemiology of recurrent P. vivax infections will emerge. Even in areas where the prevalence of related recurrences is low, mathematical modelling approaches that combine genetic data with time-to-event data will help to resolve the probable cause of recurrence29.In some scenarios, genetic data alone will not be informative of the likely origin of a recurrence. A single mosquito inoculation may carry clonal parasites or related parasites generated through recombination of heterologous clones. If a human host carries hypnozoites from a single inoculation, the relapsing parasites will therefore either be homologous or related (meiotic siblings) to the incident infection. In situations where the relapses are homologous, they will be genetically indistinguishable from recrudescence. If a host carries hypnozoites from one or more previous mosquito inoculations, relapsing parasites are liable to be unrelated/heterologous to the incident infection, and thus genetically indistinguishable from reinfections. In our study, a high proportion of infection pairs (79%) had homologous genomes, although the majority (86%) of pairs derived from low seasonal transmission settings in Malaysian Borneo, southern Vietnam, Cambodia and western Thailand. A therapeutic efficacy study undertaken in Peru also identified a moderately high proportion of genetically homologous recurrences P. vivax transmission dynamics. Strategies to contain P. vivax effectively will be assisted by a more comprehensive understanding of the major routes of infection spread within and across borders. The SNP-based data from our microhaplotype panel displayed clear geographic trends and high accuracy in predicting the country of origin, suggesting utility in detecting and mapping imported P. vivax cases. Although the high-diversity microhaplotype panel was not intentionally selected for country prediction, we captured 494 high diversity bi-allelic SNPs with 100 microhaplotypes and these highly informative genetic data enabled high performance country prediction compared to recently described geographic marker panels  that were selected specifically to differentiate P. vivax populations by country28. The accuracy in pairwise IBD estimation using the microhaplotype data also demonstrates a unique potential for tracking infection spread at micro-epidemiological spatial resolution, to inform the dispersal of infections within and between communities. For example, the microhaplotype data from Malaysia effectively captured a previously described clonal expansion, as well as more subtle population structure reflecting different foci of infection. The spatial analyses conducted here used biallelic SNP data at the microhaplotypes; whilst the high density of SNPs (n=494) provided rich genetic information, even greater information content can be achieved using multiallelic microhaplotypes once new software to deal with these complex datasets becomes available.Another key requirement of the microhaplotype panel was the ability to capture spatial Some global regions are not well represented in the Pv4 genomic dataset that were used for marker selection, such as the Indian subcontinent, Central and South America. It is therefore unclear how well the microhaplotypes described here will capture IBD in these regions. However, the informatics pipeline that was established for the microhaplotype selection can be applied readily to update the panel as needed once additional whole genome data become available from new geographical regions. The pipeline can also be used to select country- or region-specific panels where needed.FWS measures when thresholds of COI=1 and FWS ≥0.95 were applied. Only a few of infections displayed differences in classification of polyclonality between the microhaplotype and genomic datasets. In interpreting these differences, it should be acknowledged that the 0.95 FWS threshold is only a guideline, and that population distributions of within-host diversity generally reflect a continuum, not discrete clusters of monoclonal and polyclonal infections.Information on within-host infection complexity is important to capture epidemiologically relevant transmission dynamics. We observed high concordance in the proportion of polyclonal infections captured by microhaplotype-based COI and genome-wide FWS demonstrated significantly larger values in the COI=1  vs COI>1  infection group in all geographic regions investigated, highlighting consistency between the two data sets. Further work is needed to determine how to phase microhaplotype profiles in highly complex infections where any number of clones may be present in varying proportions, but tools such as the Dcifer software provide an important step forward31. Indeed, phasing of polyclonal malaria infections is not a challenge unique to microhaplotypes.Within-host diversity at SNP barcodes also follows a continuum, but this is less marked owing to the lower genetic resolution. When monoclonal thresholds were only applied to the COI data, the P. vivax genome has an abundance of globally diverse microhaplotype regions that can effectively capture information on infection lineages and spatial connectivity, overcoming the previous requirement to generate genomic data in samples that are often notoriously difficult to sequence. With targeted, deep sequencing platforms, these markers have great potential to inform on the complex diversity within individual infections and associated insights on transmission. The establishment of targeted microhaplotype genotyping tools for P. vivax will transform the assessment of clinical surveys in this species, enhance knowledge of relapse biology, and greatly improve surveillance of infections.The P. vivax community study (Pv4.0) in the vivax and Malaria Genomic Epidemiology Networks 20, as well as previously published external studies34. Of the 1,895 samples described in Pv4.0 data note, the 1,816 samples reflect data for which we had access prior to the open release. All genomic datasets were generated using Illumina short-read sequencing platforms. Sequence alignment, SNP discovery and variant calling, population assignments, and Fws (within-sample F statistic) calculations for within-host allele infection complexity were undertaken using previously described P. vivax Genome Variation Project release 4.0 (Pv4.0). The FWS estimates the fixation of alleles within each infection relative to the diversity observed in the total population on a scale from 0 to 1 and were provided as part of the Pv4 dataset35. Using a threshold of FWS ≥0.95 as a proxy to identify a monoclonal infection, all polyclonal infections were excluded from subsequent analysis. High-quality samples were selected using a threshold of ≥50% core genome, notated by the “analysis-set” flag in the MalariaGEN Pv4 dataset. The patient metadata provided by the contributing VivaxGEN partner studies was used to identify independent isolates for marker selection, and recurrent isolates for downstream evaluation of selected marker sites. The MalariaGEN curated metadata was used to define 7 regional-level geographic assignments with the following categories: Africa (AF), South and Central America (SAM), West Southeast Asia (WSEA), East Southeast Asia (ESEA), Maritime Southeast Asia (MSEA), Oceania (OCE) and West Asia (WAS). The regional classifications were based on genomic clustering patterns in the Pv4.0 data notated as “analysis population” flag.The initial dataset comprised 1,816 samples from 17 countries derived from the 17. The maximum amplicon size for Illumina amplicon-based sequencing is 200 bp, which set the criteria for the maximum length of each microhaplotype. The target number of microhaplotypes was set to 100 based on a mathematical modelling study, which demonstrated that under idealised settings , IBD estimates with low root mean squared error (RMSE<0.1) between monoclonal malaria parasites with relatedness equal to 0.5 (relatedness with the highest RMSE) are obtainable using ~100 polyallelic markers18. Diminishing returns in RMSE reduction were observed above 100 polyallelic markers, highlighting this target number as a pragmatic balance of IBD accuracy against the economic cost of primers18. Panels of microhaplotypes , with minor allele frequency (MAF) <0.1 and genotype failure rates >0.1. Finally, candidate microhaplotype windows were identified by scanning the core genome (excluding hypervariable regions) in coding regions with 50 bp overlapping, 200 bp windows to identify amplicon-sized segments containing at least one candidate SNP, producing what we define as the heterozygome map of the accessible regions of the P. vivax genome  and the “High-diversity” panel had each of its markers chosen with the highest possible heterozygosity in a given genomic region  for each of the 7 geographic regions using measures of effective cardinality and heterozygosity computed with the R-based paneljudge package (https://github.com/aimeertaylor/paneljudge). Analyses were conducted on the high-quality monoclonal samples using one of the two alleles selected at random to reconstruct microhaplotypes at heterozygote genotype positions using the “haploidify” function in Scikit-allel. Measures of within-host diversity were also generated using THE REAL McCOIL package on the biallelic SNPs in the High-diversity microhaplotype panel and correlated against the genome-wide FWS estimates of infection complexity36.To evaluate the efficacy of the selected microhaplotype panel relative to the Broad sklearn.decomposition PCA method of scikit-learn package version 1.2.0 on a microhaplotype-based pairwise distance matrix calculated from the proportion of non-identical microphalotype alleles across all positions. Spatial clustering patterns were also assessed using IBD-based measures of differentiation implemented with the hmmIBD package as described below and plotted using the R-based igraph package (https://igraph.org).The potential of the biallelic SNPs within the microhaplotype panel in capturing spatial patterns of transmission was assessed using Principal Coordinate Analysis (PCoA) to illustrate the regional level geographic clustering patterns. PCoA analysis was performed on the same high-quality monoclonal samples (n=615) by conducting PCA measures using P. vivax isolates using SNP data  at biallelic SNPs in the High-diversity SNP microhaplotype panel, Broad P. vivax barcode, and three recently identified P. vivax geographic barcodes 28. Details of the BALK classifier can be found in the original paper28. Country prediction performance was assessed using data from 799 of the 922 high-quality samples in the available Pv4.0 genomic dataset. The 799 sample-set corresponds with the sample set used in the original paper describing the BALK classifier28. Briefly, the sample set was derived by filtering samples to include a single representative of samples with near-identical genomes (defined as pairs with genetic distance <0.001), subjecting to iterative data quality filtering to obtain the best representative number of samples and informative SNPs without any missing genotype by removing samples with higher missingness iteratively, and then removing samples that appeared to be imported based on genome-wide data clustering patterns. A total of 21 countries had ≥4 samples. The comparative predictive performance of the SNP panels was evaluated using a stratified 10-fold cross-validation with 500 repeats, reporting the Matthews Correlation Coefficient (MCC).A Bi-allele Likelihood (BALK) classifier was used to predict the country of origin of paneljudge package for their ability to estimate relatedness (IBD) using a range of simulations to compute the uncertainty (error rate) in estimations of relatedness (r) ranging from 0 (strangers) to 1  under varying allele frequency distributions in different geographic regions. Details of the equations, assumptions, and parameter options in the paneljudge package can be found on GitHub (https://github.com/aimeertaylor/paneljudge). Briefly, for each of a range of data-generating r values , data were simulated on a pair of haploid genotypes, generating 100 haploid genotype pairs. Estimates of 37) and the inter-marker distance in base pairs. The accuracy of a given marker panel in estimating r was measured by the CI width and RMSE, ranging from ~0 (near perfect informativeness) to 1 (uninformative). The CI width and RMSE of r never reach 0 as a genome of finite length may have many realised relatednesses compatible with a given probability of IBD. This means that there will always be some variance in realised relatedness around r. For comparative value, paneljudge simulations were run on a panel of 100 single SNP markers  and for a set of 38 assayable biallelic SNPs from the 42-SNP Broad barcode that has been widely used by the P. vivax surveillance community39. The 100-SNP biallelic panel was composed by selecting the highest diversity SNP in each microhaplotype.The 2 selected microhaplotype panels (Random microhaplotype panel and High-diversity microhaplotype panel), as well as the 42-SNP Broad barcode panel, were evaluated with P. vivax infections with high-quality genomic data (≥50% genome covered by at least 5 reads) which were monoclonal infections (FWS ≥0.95). IBD estimates between infection pairs were generated using the hmmIBD version 3.0 software40. As the version 3 software does not enable adjustments in the genotyping error rate for polyallelic markers  the microhaplotype data were run as biallelic SNP markers rather than polyallelic markers. Sample pairs were run in geographically defined batches using allele frequency estimates based on the regional estimates from the baseline samples  using default parameters. Genome-wide and microhaplotype-based recurrence classifications were assigned using the following infection pair IBD thresholds; clone (pairwise IBD ≥0.95), close relative (0.25≤ IBD <0.95), distant relative (0.05≤ IBD <0.25), stranger (IBD <0.05). The concordance between the microhaplotype and genome wide IBD estimates was evaluated by the correlation coefficient between the datasets, and the proportion of recurrence classification mismatches.In addition to simulation-based data, the efficacy of the selected microhaplotype panel in estimating IBD was assessed relative to ‘gold-standard’ genome-wide data in pairs of primary (day 0) and recurrent Supplement 1Supplement 2Supplement 3Supplement 4Supplement 5Supplement 67


Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition.Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle.Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction.Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software. While genomic information in livestock breeding and management has predominately been used for parentage verification and discovery as well as genomic evaluations, it also has other potential applications such as the prediction of breed composition . In the The ability to accurately predict the breed composition of an animal using genomic information has many potential uses. Firstly, genomic information can be used to verify that an animal is a purebred, thus preserving the integrity of the herd book. This may be of particular benefit for rare breeds where limited purebred breeding individuals exist or where no pedigree information is recorded and, therefore, it can help correct pedigree errors and estimate kinships when ancestry data are missing. Secondly, prediction of breed composition could assist in delivering consumer confidence in the authenticity of products from certain breeds which may command a higher market price . Furtherst and PCA have been used to select informative SNPs to discriminate between cattle breeds  which was developed to primarily increase the accuracy of genomic predictions whilest generating genotype information for mutations of interest . All aniormation .Expected breed composition was available on all animals based on their recorded ancestry; 98,883 genotyped animals from 13 breeds were expected (based on ancestry) to be purebred. Breeds included were Angus, Aubrac, Blonde d'Aquitaine, Belgian Blue, Charolais, Friesian, Hereford, Holstein, Limousin, Parthenaise, Saler, Shorthorn, and Simmental. Using the available genotypes, a principal component analysis (PCA) based on a genomic relationship matrix was calculated using the approaches described by K) from 11 to 14. K = 13 was the chosen number of breed clusters as it had the lowest cross-validation error; each of the 13 breeds separated into a distinct cluster. Individuals with a subsequent ancestry assignment of Admixture V1.3  was alsoWithin breed identity-by-state (IBS) clustering was performed on all purebred animals in Plink V1.9 , which iTo validate whether breed composition could be predicted using SNP data, a population of purebred and crossbred animals which had no direct relationship  to the purebred training population was generated. Where possible, 250 purebred-verified animals from each of the 13 breeds were included in the validation population.K = 13) was completed on all genotyped animals. The 500 purebred animals within each of the 13 breeds from the training population were fixed as purebred in a supervised Admixture analysis and the breed composition of all remaining admixed animals was predicted. Animals compromised of, at most 4 breeds were subsequently selected where each of the breeds represented had to belong to one of the 13 purebred populations included in the present study. In the two-way crosses, animals which had an Admixture breed composition prediction between 45%–55%:45%–55%, 20%–30%:70%–80% or 70%–80%:20%–30% were retained as a two-way validation population, consisting of 2,281 animals. Animals with an admixed breed composition comprised of In order to identify a known admixed population for validating SNP-BLUP and Admixture breed composition predictions, a supervised Admixture analysis  analysis as:The pairwise g breeds . The pais breeds . A phylosoftware  to visuaIISNP-BLUP using MIX99 software  was conducted in Admixture  were generated using seven different SNP selection strategies. The SNP selection population  was usedThe number of predefined SNP required per chromosome was randomly selected until each of the respective panel densities was obtained.“cluster” (V2.1.2 The partitioning-around-medoids (PAM) algorithm clusters SNPs on each chromosome together based on their proximity in genomic position, not taking LD into account. The algorithm was run for each chromosome separately with the number of clusters created per chromosome set to the number of predefined SNPs for that chromosome. The SNP located in the middle of each cluster was selected, as described by ” V2.1.2 .a) b) th highest ranking for the c) Highest ranking SNPs based on The fixation index (a) PCA ranking and block method: The SNP with the highest SNP weighting within each block was chosen.b) PCA ranking and PAM method: The SNP with the highest SNP weighting within each PAM cluster was selected.th highest ranking based on PCA SNP weightings were chosen per chromosome, irrespective of location on the chromosome, where n was the number of predefined number of SNPs for that chromosome.c) Highest ranking SNPs based on PCA: SNPs in the nSNP weightings were calculated using the “smartpca” algorithm in Eigensoft v7.2.1  applied a) SNP-BLUP variance and block method: The SNP with the largest standard deviation of SNP effects within each block was chosen.b) SNP-BLUP variance and PAM method: The SNP with the largest standard deviation of SNP effects within each PAM cluster was selected.th highest ranking based on the standard deviation of SNP effects were chosen per chromosome, irrespective of location on the chromosome, where n was the number of predefined number of SNPs for that chromosome.d) Highest ranking SNPs based on SNP-BLUP variance: SNPs in the nSNP-BLUP was used to estimate the SNP effects within the SNP selection population of each breed individually using all 49,213 SNPs. From this, the standard deviation (SD) of the BLUP model solutions per SNP were estimated within the SNP selection population of all 13 breeds and SNPs were ranked based on the SD of the SNP effect across all 13 breeds; SNPs with a larger standard deviation were given a higher ranking. Three alternative methods of picking SNPs based on using the SNP-BLUP SD were investigated.Random Forest is a machine-learning method  that emp. The regression model was run 13 times, once for each breed. Each SNP received a weighting, and SNPs which were the most informative for distinguishing between breed classes ranked highest. The highest ranking SNPs of a predefined number per chromosome were retained.Partial least square discriminant analysis (PLSDA) is another machine learning method based on the PLS approach . In the Breed composition predictions from SNP-BLUP using all 49,213 SNPs were considered the gold standard and used for comparing the prediction performance from each of the low-density panels. Animals in the purebred validation population were considered to be accurately assigned when their estimated breed proportion of a specific breed was predicted to be ≥0.90. The difference in the main breed proportion estimates for crossbred animals predicted using all the low-density panels and the gold standard 49,213 SNPs were compared. In addition, the three SNP selection methods with the smallest mean difference in breed composition predictions from the gold standard, were also used for breed composition prediction using Admixture . The AdmThe greatest genetic differentiation was observed between the Salers and both the Simmental and Shorthorns ( 0.039) . The str 0.039) . The PCAr breeds . The clor breeds .p > 0.05) from zero, suggesting that there is no systematic difference between the methods that would lead to over or underestimation of breed composition. Both SNP-BLUP and Admixture accurately assigned The mean difference in predicted breed composition between SNP-BLUP and Admixture using all 49,213 SNPs was 0.04 across both purebred and crossbreds, which was not different , how these SNPs were selected did not impact greatly the predictions.The only discrepancies observed between SNP-BLUP and Admixture breed composition predictions for purebreds using the full SNP dataset was for Blonde d'Aquitaine, Belgian Blue, and Parthenaise. Some animals in these breeds were misassigned to another closely related breed; this phenomenon may be due to the relatively smaller sample sizes of these breeds in the validation population  as well as their close genetic resemblance to other breeds. The largest discrepancy in prediction of breed composition detected between Admixture and SNP-BLUP predictions for crossbred animals was for the two-way Holstein-Friesian crosses ; becauseIn order to assess how mislabeled individuals in the training population affect breed composition predictions using SNP-BLUP and Admixture, 250 purebred Angus animals were substituted with 250 two-way cross Angus animals in the training population; they were all labelled as purebred Angus. All 250 purebred Angus animals in the validation population were predicted by Admixture to be ≥0.90 Angus. In contrast, when predictions were based on SNP-BLUP, only six of the 250 purebred Angus animals in the validation population were predicted to be ≥0.60 Angus. Therefore, SNP-BLUP appears to be more sensitive to mislabeled individuals in the training population, while Admixture was able to accurately estimate breed composition even with 250  mislabeled individuals present.SNP-BLUP required a minimum of 2,000 and 3,000 SNPs to accurately predict purebred and crossbred breed composition, respectively; the respective values for Admixture was 500 and 1,000 SNPs, corroborating results of Many factors impact the number of SNPs required for the accurate prediction of breed composition. These factors include, but are not limited to, the breeds included in the study , given tst SNP selection method for identifying informative SNPs which can be used for the prediction of breed composition in cattle has been extensively reported previously (st and PCA highest methods both chose SNPs located in close proximity and consequently in strong LD on each autosome, particularly when panel density was st method (st highest methods, these SNP selection strategies performed better at predicting breed composition than SNPs selected using the other SNP selection methods evaluated, all of which had weaker LD among SNPs. This suggests that informative SNPs for the prediction of breeds may be in LD and cluster together in close proximity along the genome, and the benefit of increasing SNP panel density was less with the PCA and Fst highest methods in comparison to the other methods that had weaker LD among SNPs. The success of the Feviously , as has eviously . Unlike eviously , a linka000 SNPs . This wa000 SNPs  and Fst t method  of rankiAlthough machine learning algorithms have been widely applied to cattle breeding for the prediction of a wide variety of traits such as lameness , longevist-based selection focused on the standardized variance in allele frequency among populations, while PCA-based selection focused on patterns in the data, identifying SNPs that had high loadings on the first three principal components, which captured the most significant patterns in the data. On the other hand, the PAM method only considered the genomic position of the SNP. st, PCA and Random Forest methods.The little overlap in SNPs selected across SNP selection approaches is likely due to the difference between the SNP selection methods used. Out of all SNP selection methods investigated, Random Forest was the only one that considered possible correlations among SNPs. FIt should be noted that ensuring purebred animals are recorded correctly and a careful selection of the most genetically diverse animals within breed to be included in the training population is crucial. As suggested by others , when prThe present study demonstrated that genomic information can be utilised in generating accurate predictions of breed composition which could potentially be useful for increasing the accuracy of genetic evaluations by being better able to fit breed covariates in an admixed population. st highest approach performed the best in terms of predicting purebred and crossbred breed composition, but only a marginal difference was observed between the performance of SNPs selected across all SNP selection methods when There was a strong similarity in predicted breed composition per animal between the SNP-BLUP and Admixture approaches investigated when panel density was ≥3,000 SNPs. This suggests that the prediction of breed composition could be readily integrated into the SNP-BLUP pipelines used for genomic evaluations thus replacing the use of a stand-alone software. Despite approximately 50,000 SNPs existing on most routinely-used genotyping panels, only small subsets of highly informative SNPs are required to accurately predict breed composition. This study provides a blueprint for the utilisation of the readily available next-generation sequencing technologies in the prediction of breed composition, by offering possible methods for how to identify the most informative SNPs and the optimum panel density. In general, SNPs selected using the F