Plasmodium falciparum apical membrane antigen 1 (ama1), and multidrug resistance gene 1 (mdr1), were used to characterise the complexity of infection (COI) and drug-resistance genotypes, respectively.Antimalarial therapeutic efficacy studies are routinely conducted in malaria-endemic countries to assess the effectiveness of antimalarial treatment strategies. Targeted amplicon sequencing (AmpSeq) uniquely identifies and quantifies genetically distinct parasites within an infection. In this study, AmpSeq ofP. falciparum-positive samples were obtained from a triple artemisinin combination therapy clinical trial conducted in 30 children under 13 years of age between 2018 and 2019 in Kilifi, Kenya. Nine of the 30 participants presented with recurrent parasitemia from day 26 (624h) onwards. Theama1 andmdr1 genes were amplified and sequenced, whilemsp1,msp2and glurp data were obtained from the original clinical study.ama1 andmsp1,msp2 and glurp; overall,ama1 detected more microhaplotypes. Based on ama1, a stable number of microhaplotypes were detected throughout treatment until day 3. Additionally, a recrudescent infection was identified with anama1 microhaplotype initially observed at 30h and later in an unscheduled follow-up visit. Using the relative frequencies ofama1 microhaplotypes and parasitemia, we identified a fast (<1h) and slow (>5h) clearing microhaplotype. As expected, only twomdr1 microhaplotypes (NF and NY) were identified based on the combination of amino acid polymorphisms at codons 86 and 184. The COI was comparable betweenThis study highlights AmpSeq as a tool for highly-resolution tracking of parasite microhaplotypes throughout treatment and can detect variation in microhaplotype clearance estimates. AmpSeq can also identify slow-clearing microhaplotypes, a potential early sign of selection during treatment. Consequently, AmpSeq has the capability of improving the discriminatory power to distinguish recrudescences from reinfections accurately. P. falciparum malaria (Artemisinin-based combination therapies (ACTs) have led to high cure rates formalaria . msp1),msp2 and glutamate-rich protein (glurp)  and the inability to detect low-density parasite clones  is a highly polymorphic merozoite surface antigen (mdr1) modulate parasite susceptibility to ACT partner drugs such as amodiaquine, lumefantrine, piperaquine and mefloquine  across several African studies, possibly due to ACT selection pressure  are being evaluated (glurp) . The ampglurp) . Howeverantigen  and servama1) combined in a single deep sequencing run with a drug resistance marker (mdr1) to identify and track changes in the complexity of infections (COI), i.e., the number ofama1 genotypes per infection as well as themdr1 wild-type and mutant genotypes throughout treatment.In this study, we examined samples from a TACT efficacy study in Kilifi  described inP. falciparum isolates, 3D7 and Dd2 (BEI Resources), and from 200μl of frozen patient blood samples using the QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer’s instructions. The DNA from 3D7 and Dd2 were mixed in the following ratios to generate artificial mixtures of sequencing controls: 0.5:0.5, 0.75:0.25, 0.85:0.15, 0.95:0.05 and 1:0 to determine the lowest limit of a microhaplotype detection. The sequencing controls allowed for the detection of two ama1 (3D7 and Dd2) and three mdr1 (one from 3D7 and two from Dd2) microhaplotypes. Dd2 contained two mdr1 gene copies generated due to adaptation to in vitro culture. Amplicons spanningama1  andmdr1  were generated in duplicate from each control and sample, using primers designed in this study (Table S1) as follows: 1µl of template DNA , 0.2µl of Q5® High-Fidelity DNA Polymerase , 1µl (10mM) forward primers tagged with Roche® molecular identifiers , and reverse primers, 0.4µl of 10mM dNTPs, 4µl of 5X Q5 reaction buffer, and 12.4µl of nuclease-free water. For bothama1 andmdr1, the cycling conditions were: initial denaturation (98°C - 30 sec), followed by 30 cycles of denaturation (98°C - 10 sec), annealing (60°C - 30 sec), extension (72°C - 30 sec), and final extension (72°C - 2 min). PCR products were visualised on 1% agarose gels stained with RedSafe™ Nucleic Acid Staining Solution (iNtRON Biotechnology DR). Amplicon failures were repeated with 1.5µl of template DNA.Henceforth, we use the term “microhaplotype” to refer to the set of amino acid polymorphisms found on a single DNA amplicon. DNA was extracted from culture-adapted laboratoryama1 andmdr1 amplicon libraries were mixed to generate the final pool for paired-end sequencing (2x300bp chemistry) using MiSeq Reagent Kit v3 (Illumina).PCR amplicons were purified using the Zymo ZR-96 DNA Clean & Concentrator-5 Kit (Zymo Research) and quantified using Quant-iT™ dsDNA Assay Kit, High Sensitivity (Invitrogen). Both procedures were done following the manufacturer’s instructions. Subsequently, the PCR amplicons were normalised to equal amounts of 1ng each using EB Buffer (Qiagen) and mixed to create amplicon pools of non-overlapping 26 MIDs. The KAPA Dual-Indexed Adapter Kit and the KAPA Hyper Prep Kit (Roche) were used for library preparation, and the Agilent High Sensitivity D1000 ScreenTape System (5067-5584) confirmed adapter ligation. Eventually, theama1 microhaplotypes (varying at the nucleotide level) in each sample, while codons 86 and 184 defined themdr1 microhaplotypes.ama1 expected heterozygosity (eH) was calculated using the formula below, where n is the sample size andip is the relative frequency of the ith microhaplotype in the population  at each time point by the frequency of each microhaplotype . Additionally, the 3D7 and Dd2ama1 microhaplotypes were detected consistently across all mixtures and in the expected proportions. This provided evidence that the assay could detect mixed infections in clinical samples, but only when the minor microhaplotype was at a relative frequency of ≥5% .The expected microhaplotypes were successfully detected from the sequencing controls . Samples were grouped into three categories based on parasitemia: high , moderate  and low (<100 parasites/μl). Many samples collected between 0h–12h had high parasitemia, those collected between 18h–30h had moderate parasitemia, while those collected after 30h were primarily low parasitemia . The median read depth was 11,147 reads  forama1 and 11,548 reads  formdr1, consistent with decreasing parasitemia, post-treatment samples had lower sequencing success.From the 30 individuals sampled, 11 were in both the ART+PQ and ART+PQ+MF drug arms, respectively, while 8 were in the AL drug arm. There was no difference in the baseline median parasitaemia between the drug arms . From thama1 microhaplotypes  at 0h, 0.5h – 72h (during treatment) and post-treatment (after 72h) was 2.44 [2.13-1.75], 2.37 [2.27-2.48] and 2.0 [1.28-2.7], respectively. Overall, 33ama1 microhaplotypes were detected from the 330 successfully sequenced samples (Table S2), and only 10 of these microhaplotypes were detected at frequencies >5%.Throughout treatment, the mean COI  and number oflotypes  were relama1 microhaplotype was seen throughout. Only one exception, individual PID42 had data post-72h and a change in theama1 microhaplotype was detected before and after 72h , theama1 microhaplotypes detected maintained relatively stable frequencies up to <24h, with most changes occurring after 24h. In five individuals , there were sporadic detections of rareama1 microhaplotypes . All the remaining seven individuals with post-treatment data cleared their infections. However, the recurrent sample (672h) for PID65 contained and at 0h . All parmsp1/msp2/glurp data  during treatment (0h-72h), with the same trend post-treatment (>72h):msp1 COI = 2.2,msp2 (COI = 2),ama1 (COI = 1.8) andglurp (COI = 1.2), and no significant difference was observed (p=0.2).Based on microscopy data, all the nine individuals recurrences  were categorised as new infections based onama1 microhaplotype (based on the number of reads to quantify each microhaplotype) per infection demonstrated a similar clearance rate across most microhaplotypes. PID16 was excluded from any subsequent analysis since data was available for 6 hours only , PID59 and PID65. PID59 also contained the slowest clearingama1 microhaplotype (V1) at 7h. There was no significant difference in the mean clearance half-lives when the microhaplotypes were grouped as major and minor (<5%) microhaplotypes .Parasite clearance half-lives among all 30 study participants were below 5h except for PID59, with a parasite clearance half-life of 5.7h . Nonethers only . Particimdr1 infections (either NF or NY) throughout treatment , there were sporadic detections of raremdr1 microhaplotypes  were detected. Of the 30 individuals with baseline data, 22 had mixed infections with both mdr1 microhaplotypes, while 8 had monoclonaleatment . Of the lotypes .mdr1 infections and with post-treatment (>72h) sequence data, three of these individuals switched from a predominance of the NF microhaplotype to the NY microhaplotype during follow-up either during recurrence infection (unscheduled visit) or on day 42 (1008h). Only PID18 maintained the same dominant microhaplotype (NY) throughout the treatment and follow-up period on day 42 (1008h) (p = 0.52).In the remaining 8/22 individuals with mixed(1008h) . There wama1 microhaplotypes decreased over time, one microhaplotype continued to increase in frequency. This patient did not experience a recurrence and had no microscopically detectable parasites by 72h. Therefore, the increase in oneama1 microhaplotype may have originated from the nucleic material of dead parasites. Most of these distinct changes inama1 microhaplotype frequencies occurred post-treatment from day 7 onwards, when reinfections are likely as the levels of drugs in the body continue to decrease. Importantly, our analysis of rare microhaplotypes was limited to a 5% cut-off based on sequencing controls to increase our confidence in calling mixed infections.Children in this moderate-high malaria transmission setting in Kilifi maintained a stable homogeneous microhaplotype population throughout treatment until day 3 (72h). This is not surprising since febrile infections tend to occur with low COI even with high parasite densities . This is consistent with previous findings . Coupled with the high sensitivity of amplicon sequencing to capture minor variants, this assay provides a higher resolution to better distinguish reinfections from recrudescent infections in moderate to high transmission settings where polyclonal infections are common.Improved accuracy in distinguishing infections post-treatment is essential when considering the WHO recommendation of abandoning a drug if failure rates are >10% , determiThe assessment of genotypes throughout treatment follows the trajectory of infection, identifies the number of infecting microhaplotypes and rare variants per individual. This allows for the early detection of emerging resistant variants by identifying slow-clearing variants. Furthermore, examining drug resistance mutation frequencies during treatment can identify distinct shifts in occurrences likely to indicate directional selection of a rapidly rising variant . The AmpWhile this study serves as a proof of concept, the sample size employed was not aimed at establishing the clear superiority of AmpSeq over msp1/msp2/glurp genotyping for tasks such as PCR correction and determining infection complexity. Moreover, the adoption of AmpSeq in low and middle-income countries (LMICs) is challenged by its current cost and the requisite molecular biology and bioinformatics expertise needed for effective implementation. Nevertheless, the constantly declining costs of next-generation sequencing, coupled with emerging technologies like the Oxford Nanopore platform that offer reduced buy-in and maintenance expenses, hint at a potentially more accessible AmpSeq in the future.Regarding its performance, our study illustrates that AmpSeq yields results comparable to those obtained from msp1/msp2/glurp genotyping. However, AmpSeq boasts additional benefits over msp1/mps2/glurp genotyping. It demonstrates scalability advantages and heightened resolution based on sequence identity, surpassing the limitations of msp1/msp2/glurp genotyping, such as the reliance on labour-intensive gel electrophoresis-based band size analysis or fragment analysis that can be subject to interpretation errors and the inability to detect low-density parasite clones. An additional AmpSeq's strength lies in its potential for multiplexing, enabling the simultaneous identification of drug resistance markers alongside genotyping. This multiplexing capability enhances the assay's versatility and could offer valuable insights into the parasite's drug resistance profiles.In light of these considerations, while the current prerequisites of expertise and cost might impede the immediate integration of AmpSeq in LMICs, the evolving landscape of next-generation sequencing technologies and cost reductions inspire optimism regarding AmpSeq's future accessibility. Therefore, this study recognises the prospective value and adaptability of AmpSeq for tasks like PCR correction and determining infection complexity, even though broader adoption may necessitate ongoing advancements in affordability and the availability of expertise. ama1 and mdr1 microhaplotypes in blood samples collected at regular intervals from patients treated with three different regimens. This study shows the potential of AmpSeq as a tool for tracking parasite microhaplotypes throughout treatment. However, I think that there is some clarification needed regarding the methods and results. I have some comments for the authors to address:In this study, the researchers investigated the potential of AmpSeq for monitoring the within-host diversity of parasites before and after ACT treatment. They examined the presence or absence ofMethods:1. DNA preparation and PCR from sequencing controls and clinical samples The authors noted that they could detect the minority allele in a mixture of 95%:5% and regarded any reads less than 5% of the total as noise. However, I could not find any information regarding the parasite densities of the artificial control mixtures. I assume the different ratios were all at the same total parasite density? Reliable detection of a 5% minority clone at e.g., 1,000 parasites/uL  does not guarantee detection of a 5% minority clone at 100 or 10 parasites/uL. It seems like the authors have chosen to include all samples with a “blood sampling limit” of ≥10 parasites/uL, do you have any control that can confirm the detection of 5% minority clones at this parasite density? If you look at your control mixtures, what are the number of reads for those? In control set 4, Rep_4, marker mdr1, did you obtain fewer reads for this sample and thus missed the YY microhaplotype (maybe it was only below the 5% threshold)?2. DNA preparation and PCR from sequencing controls and clinical samplesama1  andmdr1  were generated in duplicate from each control and sample,…”. However, Figure S1 indicates 6 replicates for each control set. Under sequence data analysis you again mention: “Microhaplotypes were discarded if they did not occur in the two PCR replicates and if their combined relative frequency was <5%.” Please specify. Regarding the sequencing controls, you mention the following: “Amplicons spanning3. Sequence data analysis Was there a minimum number of reads needed to call a microhaplotype, e.g., 10 reads? The lower the total reads for a sample, the more I expect that whether or not certain microhaplotypes were detected was a matter of chance, especially for low-abundant minority clones. As an example, replicate 1 has 1,000 total reads and two clones, one with 960 reads and the second with 40 reads. You would discard the minor clone because of the >5% threshold. Replicate 2 has 500 total reads and also two clones. One with 460 reads and the second with 40 reads. You would consider this a polyclonal infection because the minor clone is above 5%. Could you give a brief explanation if only the 5% threshold was considered to call a microhaplotype or if the number of reads was also considered?Results:4. AmpSeq in artificial mixtures of sequencing controls The authors state: “This provided evidence that the assay could detect mixed infections in clinical samples, but only when the minor microhaplotype was at a relative frequency of ≥5%.” This is very much density-dependent. What was the parasite density of the controls? Please add the density of the controls, as evidence is only there for the parasite densities used (which was?). With decreasing parasitemia, the likelihood of detecting minority clones at 5% also declines (becomes a chance event at one point).ama1 microhaplotypes were detected consistently across all mixtures and in the expected proportions.” Looking at control set 2, the minority clone seems to be around 25%, and not at the expected 15%. Similarly, for control set 3, the minority clone is around 15% whereas it would be expected at around 15%. Please check. Figure S1. Control set 2 and 3 are either mixed up, or the observed proportions do not really match the expected proportions. In the results section the authors state “…the 3D7 and Dd2 Figure S1. In the legend, the authors state “Based on our control mixtures, the minimum variant frequency we were able to detect was 0.5%.”. I think this should be 5% and not 0.5%.5. AmpSeq of pre-and post-treatment samplesn, is indicated in the last row of each panel”. I do not see anyn in either of the panels. Figure S2. The legend states “Successfully sequenced samples are shown in green, those that failed PCR in red, and those that failed sequencing are in yellow.” I do not see any green or yellow color in the heatmap, I see blue, red, and black. Please correct. Similarly, the authors state “For each time-point, the number of samples successfully sequenced,6. Pfama1 genetic diversity during and after treatment Figure 1. Could the authors maybe provide the parasite densities for each sample at each time point instead of only stating high and low? This could explain some of the rare haplotypes popping up potentially. Where rare haplotypes occurred, did the authors see if the same rare haplotype was detected at other time points but possibly below the 5% threshold (check if reads were present)? Was there a difference in the number of reads between the monoclonal samples and polyclonal samples? Maybe it would be good if some of those sporadic microhaplotypes were present but below either the 5% BIC or the 10 parasites/μl blood sampling limit .ama1 microhaplotype leading to 33 different microhaplotypes at the AA level? What about SNPs that are synonymous? In the Methods section under “Sequence data analysis” you state the following: “COI was defined as the number of distinctama1 microhaplotypes (varyingat the nucleotide level) in each sample”. Table S2. All 33 ama1 variants reported seem to be nonsynonymous. Is it true that the authors only observed nonsynonymous mutations in the Except for PID60, all these sporadic microhaplotypes were detected above the sampling limit of 10 parasites/μl (Table S3). So why is this sporadic microhaplotype shown for PID60 if it fell below the 10 parasites/μl? In the methods section you specify this threshold, thus should be removed. Were there other sporadic microhaplotypes that fell below the 10 parasites/ul threshold but were excluded?7. Parasite clearance estimates “Participant PID32 had one ama1 microhaplotype, V9, that was cleared quite rapidly at <1h…”. Looking at Figure 1B, I do not see any microhaplotype that was cleared <1h. I only see a microhaplotype (light blue) that persists throughout and a sporadic microhaplotype at 8h). Am I missing something here? Without any parasite densities for the samples/time points, it’s impossible to tell based on the proportions of Figure 1 (as we do not see any drop in parasitemia if only the proportions of each microhaplotype are shown). Again, consider adding this information somewhere.8. AmpSeq compared to msp1/msp2/glurp genotyping of recurrent infectionsmsp1/msp2/glurp since you provide this metric forama1 and mention in the discussion “theama1 locus genotyped in this study is a highly discriminatory marker”. Might be helpful to also calculate expected heterozygosity for9. Pfmdr1 genetic diversity pre-and post-treatmentama1 but polyclonal based onmdr1. I would expect the other way around, where several different ama1 microhaplotypes likely share the same mdr1 microhaplotype. For most of them, the mdr1 minor microhaplotype is present at very low proportions. PID42 is somewhat concordant (when the 2nd clone takes over at 642h) as you detect the second clone but miss the first clone (possibly just below the 5% threshold?). Any explanation for this? Maybe take a closer look at the blood sampling limit or the 5% BIC?ama1 generally seemed to have worked better . Interestingly, some of the COI based on ama1 and mdr1 are not concordant. PID06, PID07, PID18, PID42, PID47, and PID53 are all monoclonal based onIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Molecular diagnostics and genomic epidemiology of malaria.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Plasmodium falciparum infection, PCR correction and drug resistance following administration of triple combination ACTs. There are a few minor comments for the authors to address:Parasite clearance: The authors have shown half-life graphs of parasite clearance estimates, adding a kaplan-meier curve of parasite clearance by microscopy and PCR (if data is available) will support parasite genotyping at the 72hr time point post-treatment, with evidence of PCR detectable parasites at that time point.msp1/msp2/glurp genotyping for PCR correction and determining complexity of infection. The authors may want to comment further on the cost-effectiveness and availability of expertise (e.g bioinformatics) for AmpSeq in LMICs.Though the manuscript is a proof-of-concept study with added advantage of simultaneous drug resistance genotyping, it has not sufficiently shown superiority of AmpSeq overmdr1) using read counts obtained from the AmpSeq data.The authors used artificial mixtures of two parasite strains to generate sequencing controls with artificial microhaplotypes similar to natural mixed infections. The authors should comment if they were able to determine copy number variation  as a molecular marker to track within-host parasite diversity during and after the ACT treatments. They constructed ama1 haplotypes based on the deep sequence data and evaluated the presence/absence of haplotypes in the blood samples frequently obtained from treated patients using three different regimens. Despite that sample numbers were not so large, this study showed important findings.Since dead-parasites’ DNA can persist in the human blood after treatment, the evaluate amount  of haplotypes may not have directly reflected the real situation. This may become a potential confounding factor. Could you comment on this?Could you discuss the potential artifact of parasite DNA from persistent gametocytes after treatment?In PID27, one clone overtook the other two clones and became dominant 6 hour after treatment. This clone has been regarded as slow clearing haplotype in the manuscript. What did the authors think the cause of slow clearing? I am interested in the discussion about this observation. I think there is a possibility that DNA from dead-sequestered parasites may have affected the result. If a particular clone tends to sequester and that DNA of such clone releases into blood stream after treatment, it will be expected that relative frequency of such parasite increases after treatment. I feel that, from your data, these may be unlikely to occur in the studied subject except from PID 27.Minor haplotypes<5% were excluded from the analysis in this study. This cut-off was set based on “the lowest sequencing control’s mixture (95% 3D7 vs 5% Dd2)”. This is a bit unclear for me because next to this control mixture was 0% vs 100%. If control mixture of 99% 3D7 and 1% Dd2 shows almost same ratio in the TADS, will the authors take this cut-off?In the abstract, they described that “This study highlights TADS as a sensitive tool for tracking parasite haplotypes throughout treatment ~”. However, it might be difficult to say “sensitive tool” without detection limit of this deep-sequencing method. Furthermore, I am very interested in whether TADS is relatively useful compared to traditional methods in terms of sensitivity. However, I could not find the data that compared the parasite detection ability between TADS and molecular methods  recommended by WHO. Would it be possible to show further data? Major and Rwanda . These can be included in the Introduction.Page 4; “Nonetheless, artemisinin (ART) resistance emerged and spread in Southeast (SE) Asia, evidenced as delayed parasite clearance following ACT treatment.” Emergence of artemisinin resistance has been already evidenced in our study in Northern Uganda Page 4; “The DNA from 3D7 and Dd2, were mixed as follows to come up with sequencing controls: 100%:100%, 75%:25%, 85%:15%, 95%:5% and 100%:0% to determine the lowest limit of haplo-type detection.”  “Does “100%:100%” mean “50%:50%”?Page 4; How much blood volume did you use for deep sequencing?Page 5; The word “haplotype” is defined as “The paired consensus reads for each sample were trimmed and clustered to estimate the frequency of clusters (henceforth referred to as “haplotypes”)”. However, “haplotype” was also used when it implies parasite clone, e.g. “TADS can also identify slow clearing haplotypes, a potential early sign of selection during treatment.” I understand that strikingly they are not clones, but haplotypes used here are different from the aforementioned definition.Page 5; “There was successful detection of the two expected ama1 haplotypes from the 3D7 and Dd2 laboratory isolates, as well as three mdr1 haplotypes, one from 3D7 and the 2 copies of Dd2”. I think some readers might feel curious about two haplotypes in the laboratory clone (Dd2). Could add some explanation?Page 6; “There was only one exception as individual PID42 had data post-72h and there was a change in the ama1 haplotype detected before and after 72h.” How did you interpret this case?Figure S1; There are inconsistency of color between the figure and legend. It seems to be successfully sequenced samples in blue and failed sequencing in black. Furthermore, I could not find the number of samples successfully sequenced in the last row in each panel. MinorIs the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?Not applicableAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Malaria drug resistanceI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. MajorWe respond to this under point 2. 1. Since dead-parasites’ DNA can persist in the human blood after treatment, the evaluate amount  of haplotypes may not have directly reflected the real situation. This may become a potential confounding factor. Could you comment on this? It’s possible that some of the signals detected originated from gametocytes, dead or dormant parasites and we have included this as one of the study’s limitations in the discussion:“Microhaplotypes detected post-treatment might have originated from circulating gametocytes, dormant or dead parasites. However, all participants were gametocyte negative throughout treatment except PID38 who had five gametocytes/μl at 72h but was negative thereafter, so it is unlikely that gametocytaemia biased our findings. As for genetic material originating from dormant or dead parasites, parasite mRNA can also be detected up to two weeks after successful treatment in microscopy-negative individuals . However, additional work is needed to determine whether these originate from viable infections” 3. In PID27, one clone overtook the other two clones and became dominant 6 hour after treatment. This clone has been regarded as slow clearing haplotype in the manuscript. What did the authors think the cause of slow clearing? I am interested in the discussion about this observation. I think there is a possibility that DNA from dead-sequestered parasites may have affected the result. If a particular clone tends to sequester and that DNA of such clone releases into blood stream after treatment, it will be expected that relative frequency of such parasite increases after treatment. I feel that, from your data, these may be unlikely to occur in the studied subject except from PID 27.We have included a sentence in the discussion to highlight this individual and we hypothesize that the increase in one ama1 haplotype may have come from DNA of dead parasites:“Notably, PID27 had a COI of three at 0h, and while the relative frequencies of two ama1 microhaplotypes decreased over time, one microhaplotype continued to increase in frequency. This patient did not experience a recurrence and had no microscopically detectable parasites by 72h. Therefore, the increase in one ama1 microhaplotype may have originated from the nucleic material of dead parasites.”  2. Could you discuss the potential artifact of parasite DNA from persistent gametocytes after treatment?The control mixture 0% vs 100% was basically a clonal mixture of 3D7 and this helped to evaluate whether we would detect false positives since we expect to find only one microhaplotype. Conversely, the control’s mixture 95% 3D7 vs 5% Dd2 was multiclonal to determine the ability of the assay to pick up multiclonal infections as well as multiclonal infections with minor variants. To respond to the last question, yes, if we included an artificial mixture of 3D7 and Dd2 with lower than 5% frequency and detected haplotypes, we would have taken that cut-off. In the abstract, they described that “This study highlights TADS as a sensitive tool for tracking parasite haplotypes throughout treatment ~”. However, it might be difficult to say “sensitive tool” without detection limit of this deep-sequencing method. Furthermore, I am very interested in whether TADS is relatively useful compared to traditional methods in terms of sensitivity. However, I could not find the data that compared the parasite detection ability between TADS and molecular methods  recommended by WHO. Would it be possible to show further data? 4. Minor haplotypes <5% were excluded from the analysis in this study. This cut-off was set based on “the lowest sequencing control’s mixture (95% 3D7 vs 5% Dd2)”. This is a bit unclear for me because next to this control mixture was 0% vs 100%. If control mixture of 99% 3D7 and 1% Dd2 shows almost same ratio in the TADS, will the authors take this cut-off?Unfortunately, we do not have this data and we acknowledge this limitation in the discussion:“…Finally, future studies should include artificial sequencing controls of decreasing parasitaemia from, e.g., dilutions ranging from 10,000 to 1 parasite/μl to reliably determine the limit of detection, especially in post-treatment with low parasitemia...”   Minor1 and Rwanda .2 These can be included in the Introduction.We have included this in the introduction:“…Two recent studies have identified early signs of ART resistance in Rwanda  and Uganda  and this looming threat of widespread ACT resistance would be catastrophic in sub-Saharan Africa, where the burden of malaria is the most significant (WHO 2021a)…”  Page 4; “Nonetheless, artemisinin (ART) resistance emerged and spread in Southeast (SE) Asia, evidenced as delayed parasite clearance following ACT treatment.” Emergence of artemisinin resistance has been already evidenced in our study in Northern Uganda To make it easier for the reader to understand the mixtures, we have switched from percentages to ratios of 0.5:0.5, 0.75:0.25, 0.85:0.15, 0.95:0.05 and 1:0.    Page 4; “The DNA from 3D7 and Dd2, were mixed as follows to come up with sequencing controls: 100%:100%, 75%:25%, 85%:15%, 95%:5% and 100%:0% to determine the lowest limit of haplotype detection.”  “Does “100%:100%” mean “50%:50%”?We have included the volume in the methods:200μl of frozen patient blood samples using the QIAamp DNA Blood Mini Kit (Qiagen) according to the manufacturer’s instructions…” “…DNA was extracted from P. falciparum laboratory reference clones, 3D7 and Dd2 (BEI Resources), and from Page 4; How much blood volume did you use for deep sequencing? We acknowledge how confusing this was. Hence, we have clarified the use of “haplotype” by settling on the term “microhaplotype” to mean the set of amino acid polymorphisms found on a single sequence.   Page 5; “There was successful detection of the two expected ama1 haplotypes from the 3D7 and Dd2 laboratory isolates, as well as three mdr1 haplotypes, one from 3D7 and the 2 copies of Dd2”. I think some readers might feel curious about two haplotypes in the laboratory clone (Dd2). Could add some explanation?We have included the following statement in the methods:“…The sequencing controls allowed for the detection of two ama1 (3D7 and Dd2) and three mdr1 (one from 3D7 and two from Dd2) microhaplotypes. Dd2 contained two mdr1 gene copies generated due to adaptation to in vitro culture…” Page 5; The word “haplotype” is defined as “The paired consensus reads for each sample were trimmed and clustered to estimate the frequency of clusters (henceforth referred to as “haplotypes”)”. However, “haplotype” was also used when it implies parasite clone, e.g. “TADS can also identify slow clearing haplotypes, a potential early sign of selection during treatment.” I understand that strikingly they are not clones, but haplotypes used here are different from the aforementioned definition.To present the data on changes of ama1 microhaplotypes throughout treatment, we grouped participants into those with monoclonal infections and those with multiclonal infections. PID42 had a monoclonal infection since like all individuals in this group, only one ama1 haplotype was observed per timepoint. However, unlike all monoclonal infections where the same clone at baseline was seen throughout treatment, PID42 had a change in clones at the time of recurrence.   Figure S2; There are inconsistencies of color between the figure and legend. It seems to be successfully sequenced samples in blue and failed sequencing in black. Furthermore, I could not find the number of samples successfully sequenced in the last row in each panel.Thank you for pointing out the inconsistencies in colours. We have updated the figure legend for match the colours depicted in the figure. The last row indicating the number of successfully sequenced samples was also included in Figure. S2.   Page 6; “There was only one exception as individual PID42 had data post-72h and there was a change in the ama1 haplotype detected before and after 72h.” How did you interpret this case? We recently published a paper investigated the use of deep sequenced amplicons in malaria drug clinical trials, in which we used extensive computer simulations of parasite dynamics post-treatment to identify how best to incorporate and interpret deep sequenced amplicon data: Jones, S., et al. (2021). The aim was to identify best practice for their application and to identify traps and errors likely to arise in in their initial applications. That paper was focused on molecular correction, but a prerequisite for this is good quality genotyping data and we spent a lot of time considering this. The authors of the current paper appear unaware of this work which is unfortunate as they appear to have fallen into some of the pitfalls we anticipate, in particular (i) the necessity of estimating a blood sampling limit (ie. the minimum number or density of parasites in a patient that is likely to be detected by the sequencing) and (ii) ability of deep sequencing to detect genetic material in circulating gametocytes. These are discussed below.A unique genetic variant at the amplified locus. This terminology has slipped into the bio-informatics jargon but in genetics is known as an “allele”.The genotype along a whole contiguous stretch of chromosome which may contain several distant allelesThe entire malaria clonal genotype (because it is haploid in the human stages.) So in their Abstract they use “haplotype” to mean two different things. In the first part it means allele ”. Whereas later it means the whole malaria genotype e.g. “we identified a fast (<1h) and slow (>5h) clearing haplotype.” and “clone” would be much better here than “haplotype”. One of our recommendations was to be very careful using the word “haplotype” as it can mean at least three distinct things in the current context  This is not just being pedantic: using the same term to mean different thing is confusing even for authors and it is important to establish some sort of unambiguous, consensus terminology early in the development of field. I’ll enumerate comment for ease of cross reference. (1) Please be accurate and consistent with use of the term “haplotype” and consider revising the manuscript accordingly.we used the abbreviation “AmpSeq”, the current authors used “TADS” to mean the same thing. It would be nice to establish a consistent terminology noting the latest WHO recommendation also uses “AmpSeq” i.e.https://www.who.int/publications/i/item/9789240038363 (2) Note that in our publication If “TADS” is already in the literature I would request (but not demand) they consider changing TADS to Ampseq. Like the use of “haplotype” it is not essential but if a field starts off by using different anacronyms for the same thing it tends to persist which is in nobody’s long-term interest. If “TADS” is not already in use in the literature it would be hard to justify  using a new terms when one already exist.Methods.) and termed it the Bioinformatics cut-off (BIC), and identified it as a vital piece of information to be included in publications, so it is nice to see it clearly defined here. However, on page 8 it states “our analysis of rare haplotypes was limited to a 5% cut-off based on sequencing controls” so some clarification is needed. Is it 5% of total reads or 5% of controls? If the latter, this could be explained much more clearly. (3) I have sat through seeming interminable technical discussions on amplicon sequencing, most of which went over my head. I do know enough to realise it is not as straightforward as most people think so suggest an expert on genotyping also reviews this manuscript. The authors noted that they could detect the minority allele in mixture of 95%:5% and regarded any reads less than 5% of total as noise. We previously found this threshold to be extremely important  post treatment; see comment (10) below. They have total parasitaemia and relative frequencies of ama1 alleles in each patient so they can back-calculate likely number/density of each clone and hence figure out whether it is in danger of falling below the sampling limit. The calculations tend to be estimates but they need to do this before interpreting their figure 1 i.e. they need to be confident that all the ama1 signals in the patient are being detected and there is no danger that, at later timepoints,  one ama1 signal becoming so low (because the clone(s) containing it has fallen to very low density)  that it is no longer detected in the assay despite its continued persistence in the patient (4) It looks like the authors extracted their DNA from frozen blood samples. One of things we previously identified as important was the “blood sampling limit”ama1 haplotypes ” i.e. because parasitaemias of the clone containing that ama1 allele were around the sampling limit so that whether or not they were detected was a matter of chance. The second plausible reason is that it fluctuated around the 5% BIC so sporadically exceed the BIC. Discussion around these points would be good and it also emphasises why it is important to establish the sampling limit before interpreting the results The blood sampling limit is also one plausible explanation for their observation (page 6) that” In five individuals , there were sporadic detections of rarenot frequencies (this has caused immense confusion in the past). The problem can be illustrated by a simple example: assume there are only two samples: sample #1 has alleles A,B and C, and sample #2 has A, B, and D. Then according to their statement the “frequencies” of A,B, C and D will be 100%, 100%, 50% and 50% respectively which is obviously wrong as in this context the allele frequencies should sum to 100%. I assume the statement is written incorrectly and that they counted total number of clones in the two samples i.e. 3+3=6 then calculated the frequencies of A,B, C and D as 2/6. 2/6, 1/6 and 1/6 respectively i.e. ensuring frequencies sum to 100%. They then need, for reasons given below,  to calculate expected heterozygosity (He) to quantify genetic variability ie.the probably that two randomly selected clones have different alleles (5) I am worried about these statements “The frequency of each haplotype in the population was calculated using the total number of samples that contained the haplotype over the total number of samples genotyped.” This will calculate prevalence I also worry about their statement on page 10 when discussing table S2 i.e. “The frequencies were calculated by dividing the number of reads of each haplotype by the total number of reads obtained .” These “frequencies” are likely biased towards alleles in clones in high-parasitaemia patients. So I think the calculation of frequency suggested above is better, especially because it can be used to calculate He. (6) I assume the genotyping at msp1/msp2/glurp was the standard WHO-recommended method of gel electrophoresis. As above, the sensitivity  used in their assay should be stated. Labs typically regard genotyping peaks less than 25% or 30% of the largest peak as noise but there is no consistency in this  but this needs to be stated.Interpretation  The problem is that current drugs clear the asexual form of falciparum but not the gametocytes which continue to circulate and the increased sensitivity of deep sequencing allows detection of alleles in the gametocytes. Suppose a clone initially has 1% gametocytes then after treatment their proportion increases (roughly) 2 fold with every half-life of the asexual forms i.e.  gametocytes increase (approx.) from 1% to 2% to 4% to 8% etc after 1,2,3,4, etc half-lives. The observed clearance half-life will therefore gradually decrease as the drug-insensitive gametocytes make up an increasing proportion of the clone. Whether the gametocytes will be observable in the initial test slide depends on the microscopy and on the clone size. So it’s a two-stage calculation Firstly, the extent to which any putative gametocytes will gradually reduce observed clearance rates post-treatment. Secondly, the likely impact of gametocyte signals will also depend on the blood sampling limit (i.e. whether gametocytes are of sufficient density to de detectable) which I have suggested the authors calculate  (7) My main concern is that the authors seem to take no account of the possible impact of detecting alleles in gametocytes. We identified  this as a key consideration as illustrated in Figure 2 of our main text and extensively discussed in our Supplementary Material Part 3: “Simulating the potential impact of gametocytes on the accuracy of molecular correction”. (8) First paragraph of section “Pfama1 genetic diversity during and after treatment”. The slight decrease in COI is presumably a consequence of clones dropping below the blood sampling limit (see above), or below the 5% BIC,  and being lost. Given that monoclonal infections  cannot lose diversity, it would be better to exclude these infections from the calculations to get a better idea of the drop in COIama1 haplotype V9.”. Looking it Figure 1, it appears that PID32 was largely monoclonal except for a single detection of another ama1 allele at time 8hours which may well be spurious. More explanation is needed. (9) I am extremely curious about the assertion made in the caption to figure 2 i.e. “PID32 had the fastest clearing (0.8h)Immediate and sustained  difference in clearance rates between clones in the same patient. PID27 therefore looks the most interesting to me as is suggested significant differences in clearance between the two clones immediately post-treatment . Figure 2B also shows that the two clearance rates in this patient are clearly distinguishable on the Y axis unlike the other patients . Note  this could be due to differences in drug sensitivity or, less likely, to differential impact of immunity acting on the two clone.An impact of gametocytes. All clones would retain roughly the same proportions immediately post-treatment, but one eventually rises. The one that increases in proportion could arise as gametocytes become a significant proportion of its biomass (see point 7 above) which are not cleared by the drug . PID08 seems to show this pattern.Impact of a sampling limit. All clones maintain same proportions until the lower one falls below the sampling limit and disappears, hence the 100% has to be retained by increased proportions on those remaining above the sampling limit. This would be an artefact. Interesting, I can’t see evidence of this of Figure 1 so I’m guessing sampling limit was not a big issue  So once background calculation have been made for blood sampling limit and impact of gametocytes there are (as far as I can see) three plausible patterns post treatment. So these are three paradigms. There may be variation caused by factors such as sequestration,  but given that the authors chose to present and discuss the profiles, I strongly suggest they improve their discussion to include the putative/likely impact of gametocytes and sampling limits.ama1 is a highly discriminatory marker with a larger number of variants with a prevalence of <5%, providing a higher resolution to better distinguish new from recrudescent infections in moderate to high transmissions settings where polyclonal infections are common.” This is probably incorrect and definitely incomplete. Msp1, msp2 and glurp are all highly variable, possible more so than ama1: they would need to report He values for ama1 to substantiate this assertion of greater genetic diversity. The most important point about deep sequencing vs msp1/msp2/glurp is sequencing has much better ability to detect minor clones… they need to cite our previous work  here as we explicitly discussed the impact of improved sensitivity in  detail. (11) Page 9 they state “Hence, Recommendations.et al), but there was a team of us who spent a lot of time and effort anticipating likely problems (and how to mitigate them) and this is highly relevant to the work presented here.  I would recommend major revisions to the data interpretation and discussion. The underlying approach and data seem robust to me. The manuscript is, in my opinion, methodologically and genetically naive in parts. I realize it will appear arrogant to insist they go and read our previous  investigations of this (i.e. JonesMinor comments. (a) Page 4. The discussion of limitations of current WHO-recommended method is a bit dated and it would be useful to cite our two recent quantitative investigations i.e.. Felger, I., et al. (2020), and Jones, S., et al. (2019).                 https://www.who.int/publications/i/item/9789240038363. Also note that WHO recently updated their genotyping recommendations to acknowledge the future importance of amplicon sequencing (b) I see no point in parts (A) of figures 1 and 3…. If an infection is monoclonal the one allele will inevitable be present at 100%  i.e. the plots tell us nothing. (c) Page 8. The statement “The clearance rates were similar within and between individuals irrespective of clonality, suggesting that the three antimalarial treatments were equally effective in clearing haplotypes and parasitaemia.” Is a bit naive in my opinion. Differences in artemisinin sensitivity putatively result in differences in clearance rate post-treatment. However there is no suggestion this occurs for the partner drugs which mainly kill parasites when sequestered:  resistance to the partner drug will likely first show as a reduced parasite reduction ratio not as reduced clearance.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?NoAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Malaria drug treatment and resistance.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We have clarified the use of “haplotype” by settling on the term “microhaplotype” to mean the set of amino acid polymorphisms found on a single DNA amplicon. The term microhaplotype is now a preferred term in similar work.Reference: Tessema, S. K., et. al. (2020). Sensitive, Highly Multiplexed Sequencing of Microhaplotypes From the Plasmodium falciparum Heterozygome. The Journal of Infectious Diseases, 1–11.LaVerriere, E., et. al. (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.Taylor, A. R., Jacob, P. E., Neafsey, D. E., & Buckee, C. O. (2019). Estimating Relatedness Between Malaria Parasites. Genetics, 212(4), 1337–1351.WHO: Informal consultation on methodology to distinguish reinfection from recrudescence in high malaria transmission areas. https://www.who.int/publications/i/item/97892400383631. Please be accurate and consistent with use of the term “haplotype” and consider revising the manuscript accordingly.1 we used the abbreviation “AmpSeq”, the current authors used “TADS” to mean the same thing. It would be nice to establish a consistent terminology noting the latest WHO recommendation also uses “AmpSeq” i.e. 2. Note that in our publicationhttps://www.who.int/publications/i/item/9789240038363Throughout the manuscript, we have switched to using AmpSeq. If “TADS” is already in the literature I would request (but not demand) they consider changing TADS to Ampseq. Like the use of “haplotype” it is not essential but if a field starts off by using different anacronyms for the same thing it tends to persist which is in nobody’s long-term interest. If “TADS” is not already in use in the literature it would be hard to justify using a new terms when one already exist.Methods.1) and termed it the Bioinformatics cut-off (BIC), and identified it as a vital piece of information to be included in publications, so it is nice to see it clearly defined here. However, on page 8 it states “our analysis of rare haplotypes was limited to a 5% cut-off based on sequencing controls” so some clarification is needed. Is it 5% of total reads or 5% of controls? If the latter, this could be explained much more clearly.We have clarified that this 5% is based on the proportion of the minor clone in the artificial mixture of the two laboratory clones, that is 95% 3D7 and 5% Dd2. 2.  I have sat through seeming interminable technical discussions on amplicon sequencing, most of which went over my head. I do know enough to realise it is not as straightforward as most people think so suggest an expert on genotyping also reviews this manuscript. The authors noted that they could detect the minority allele in mixture of 95%:5% and regarded any reads less than 5% of total as noise. We previously found this threshold to be extremely important  post-treatment; see comment (10) below.They have total parasitaemia and relative frequencies of ama1 alleles in each patient so they can back-calculate likely number/density of each clone and hence figure out whether it is in danger of falling below the sampling limit. The calculations tend to be estimates but they need to do this before interpreting their figure 1 i.e. they need to be confident that all the ama1 signals in the patient are being detected and there isno danger that, at later time points,  one ama1 signal becoming so low (because the clone(s) containing it has fallen to very low density)  that it is no longer detected in the assay despite its continued persistence in the patient. 4. It looks like the authors extracted their DNA from frozen blood samples. One of the things we previously identified as important was the “blood sampling limit”ama1 haplotypes ” i.e. because parasitaemias of the clone containing that ama1 allele were around the sampling limit so that whether or not they were detected was a matter of chance. The second plausible reason is that it fluctuated around the 5% BIC so sporadically exceed the BIC. Discussion around these points would be good and it also emphasises why it is important to establish the sampling limit before interpreting the results.We have responded to this in comment 10, below. 5.  I am worried about these statements “The frequency of each haplotype in the population was calculated using the total number of samples that contained the haplotype over the total number of samples genotyped.” This will calculate prevalence not frequencies (this has caused immense confusion in the past). The problem can be illustrated by a simple example: assume there are only two samples: sample #1 has alleles A,B and C, and sample #2 has A, B, and D. Then according to their statement the “frequencies” of A, B, C and D will be 100%, 100%, 50% and 50% respectively which is obviously wrong as in this context the allele frequencies should sum to 100%. I assume the statement is written incorrectly and that they counted total number of clones in the two samples i.e. 3+3=6 then calculated the frequencies of A,B, C and D as 2/6. 2/6, 1/6 and 1/6 respectively i.e. ensuring frequencies sum to 100%. They then need, for reasons given below,  to calculate expected heterozygosity (He) to quantify genetic variability ie. the probably that two randomly selected clones have different alleles.We agree with the suggestion. However, we have dropped the statement “The frequency of each haplotype in the population was calculated using the total number of samples that contained the haplotype over the total number of samples genotyped.” This population frequency statistic was not used anywhere in the paper and was erroneously included. I also worry about their statement on page 10 when discussing table S2 i.e. “The frequencies were calculated by dividing the number of reads of each haplotype by the total number of reads obtained .” These “frequencies” are likely biased towards alleles in clones in high-parasitaemia patients. So, I think the calculation of frequency suggested above is better, especially because it can be used to calculate He. The blood sampling limit is also one plausible explanation for their observation (page 6) that” In five individuals , there were sporadic detections of rare total number of reads obtained per sample”.This has been edited to “The frequencies were calculated by dividing the number of reads of each microhaplotype by theTherefore, we did not use just total reads but total reads per sample. Sequencing libraries from all samples were normalised to equimolar concentrations before pooling and sequencing to avoid having more input DNA from higher parasitaemia samples. Additionally, one of our cut-offs for identifying minor variants required that they were identified in other samples so this reduces bias due to high parasitaemia samples.  msp1/msp2/glurp was the standard WHO-recommended method of gel electrophoresis. As above, the sensitivity  used in their assay should be stated. Labs typically regard genotyping peaks less than 25% or 30% of the largest peak as noise but there is no consistency in this  but this needs to be stated.We have included the following statement in the methods section:“msp1/msp2/glurp genotyping was performed according to the WHO-recommended method of gel electrophoresis (WHO 2008). These data were obtained from the original study, and the following was ensured during the analysis: Each PCR product had to have well-defined and easy to visualise, bands had to be bright and sharp to be of sufficient quality for scoring, PCRs were repeated if bands appeared in the negative control, the interpretation of results did not include products with less than 100 bp and did not account for faint bands or bands that formed smile-shaped patterns on the gel ” 6.  I assume the genotyping atInterpretation1  The problem is that current drugs clear the asexual form of falciparum but not the gametocytes which continue to circulate and the increased sensitivity of deep sequencing allows detection of alleles in the gametocytes. Suppose a clone initially has 1% gametocytes then after treatment their proportion increases (roughly) 2 fold with every half-life of the asexual forms i.e.  gametocytes increase (approx.) from 1% to 2% to 4% to 8% etc after 1,2,3,4, etc half-lives. The observed clearance half-life will therefore gradually decrease as the drug-insensitive gametocytes make up an increasing proportion of the clone. Whether the gametocytes will be observable in the initial test slide depends on the microscopy and on the clone size. So it’s a two-stage calculation Firstly, the extent to which any putative gametocytes will gradually reduce observed clearance rates post-treatment. Secondly, the likely impact of gametocyte signals will also depend on the blood sampling limit (i.e. whether gametocytes are of sufficient density to de detectable) which I have suggested the authors calculate  7. My main concern is that the authors seem to take no account of the possible impact of detecting alleles in gametocytes. We identified  this as a key consideration as illustrated in Figure 2 of our main text and extensively discussed in our Supplementary Material Part 3: “Simulating the potential impact of gametocytes on the accuracy of molecular correction”.This has been included as one of our limitations. None of the 30 individuals was gametocyte on day 0 and they remained negative up to day 42. One participant, however, was gametocyte positive on day 3 but negative thereafter so it’s unlikely that gametocytaemia biased our findings.   8. First paragraph of section “Pfama1 genetic diversity during and after treatment”. The slight decrease in COI is presumably a consequence of clones dropping below the blood sampling limit (see above), or below the 5% BIC,  and being lost. Given that monoclonal infections cannot lose diversity, it would be better to exclude these infections from the calculations to get a better idea of the drop in COI.After excluding monoclonal infections, we still see a decrease in COI from before treatment, at 0h the mean COI was 2.44, 0.5h to 72h the mean COI was 2.37 and 2 thereafter. These new values have been included in the text. 9. I am extremely curious about the assertion made in the caption to figure 2 i.e. “PID32 had the fastest clearing (0.8h) ama1 haplotype V9.”. Looking at Figure 1, it appears that PID32 was largely monoclonal except for a single detection of another ama1 allele at time 8hours which may well be spurious. More explanation is needed.  Regarding the single ama1 microhaplotype detected at 8h, we have shown in extended data table S3 that it was detected above the blood sampling limit. Still, we have no reason to believe that PID32’s infection being clonal would have biased our estimate of determining parasite clearance.  10. So once background calculation have been made for blood sampling limit and impact of gametocytes there are (as far as I can see) three plausible patterns post treatment.  We did not see a difference in the sensitivity of the drugs; hence we hypothesize that acquired immunity may have had a larger role to play in the difference in clearing times for PID27.Immediate and sustained  difference in clearance rates between clones in the same patient. PID27 therefore looks the most interesting to me as is suggested significant differences in clearance between the two clones immediately post-treatment . Figure 2B also shows that the two clearance rates in this patient are clearly distinguishable on the Y axis unlike the other patients . Note  this could be due to differences in drug sensitivity or, less likely, to differential impact of immunity acting on the two clone.     10. An impact of gametocytes. All clones would retain roughly the same proportions immediately post-treatment, but one eventually rises. The one that increases in proportion could arise as gametocytes become a significant proportion of its biomass (see point 7 above) which are not cleared by the drug . PID08 seems to show this pattern.As mentioned above, all participants in the current study were gametocyte negative throughout treatment, thus it’s likely that gametocytaemia biased our findings.We set our arbitrary blood sampling limit to 10 parasites/μl based on the lower limit used in the PMID:34252299. In multiclonal infections, some microhaplotypes started to fall below this limit, however, for the seven individuals with microhaplotypes that were detected sporadically, only one (PID60) had a microhaplotype below this limit.We have included this comment in the discussion, and we recommended future studies to include artificial sequencing-controls with decreasing parasitaemia, e.g. 10,000 down to 1 parasite/μl to determine their limit of detection. Impact of a sampling limit. All clones maintain same proportions until the lower one falls below the sampling limit and disappears, hence the 100% has to be retained by increased proportions on those remaining above the sampling limit. This would be an artefact. Interesting, I can’t see evidence of this of Figure 1 so I’m guessing sampling limit was not a big issue .We had mentioned earlier that the sporadic detections of rare microhaplotypes could have originated from changes in parasite densities, sequestered parasites, and lingering nucleic material from dead parasites.We have also included in our limitations that these rare microhaplotypes could have originated from gametocytes too. However, this was unlikely in this study since all participants were gametocyte negative throughout treatment. 11. Page 9 they state “Hence, ama1 is a highly discriminatory marker with a larger number of variants with a prevalence of <5%, providing a higher resolution to better distinguish new from recrudescent infections in moderate to high transmissions settings where polyclonal infections are common.” This is probably incorrect and definitely incomplete. Msp1, msp2 and glurp are all highly variable, possible more so than ama1: they would need to report He values for ama1 to substantiate this assertion of greater genetic diversity. The most important point about deep sequencing vs msp1/msp2/glurp is sequencing has much better ability to detect minor clones… they need to cite our previous work  here as we explicitly discussed the impact of improved sensitivity in detail.e we obtained for the ama1 locus we genotyped. Additionally, we have added that amplicon sequencing is more sensitive than msp1/msp2/glurp genotyping because it can better capture minority variants. The section now reads as follows:This section has been amended to include the high H“hence, the ama1 locus genotyped in this study is a highly discriminatory marker with a larger number of variants with a prevalence of <5% and a high expected heterozygosity value (0.96). Coupled with the high sensitivity of amplicon sequencing to capture minor variants, this assay provides a higher resolution to better distinguish reinfections from recrudescent infections in moderate to high transmissions settings where polyclonal infections are common.” So these are three paradigms. There may be variation caused by factors such as sequestration,  but given that the authors chose to present and discuss the profiles, I strongly suggest they improve their discussion to include the putative/likely impact of gametocytes and sampling limits.


SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.Migraine—a painful, throbbing headache disorder—is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models—MASHR, elastic net, and SMultiXcan—to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (The online version contains supplementary material available at 10.1007/s00439-023-02568-8. Migraine is a complex neurological trait affecting 14% of the population worldwide  single nucleotide polymorphisms (SNPs) associated with migraine . Standard TWAS is usually performed in all available Genotype-Tissue Expression project (GTEx) . Details of quality control and imputation are provided in the original publication  SNPs were identified, of which 170 were LD-independent (r2 < 0.1) index SNPs. The 170 index SNPs mapped to 123 independent genomic risk loci of which 86 were novel. Details of quality control and imputation are provided in the original publication .The majority of GWAS risk SNPs are non-coding and are thus expected to impact the expression of the gene by altering its regulation .The GTEx v8 eQTL data is aligned to the Genome Reference Consortium Human Build 38  and thus contains variants that were not tested in older GWAS that utilised variants compiled from earlier genome builds. Therefore, the migraine GWAS summary statistics were harmonised and lifted over from build 37 to build 38. Then imputation of summary statistics for missing variants was performed for the migraine GWAS before the differential expression imputation. The gene prediction model SNP weights for each tissue were available in the form of SQLite weight files on predictdb.org . The second threshold (Bonferroni-matSpD) utilised a Bonferroni-adjusted threshold, which adjusted for the number of gene-tissue pairs tested after taking into account the substantial covariance in expression across genes within each tissue . The second threshold was developed because multiple-test adjustment using the total raw number of genes is expected to be too stringent and not reflect true biology . The effective number of independent genes analysed for differential expression in each tissue was estimated using matrix spectral decomposition (matSpD) , which adjusted for the total number of gene-tissue pairs tested  Nyholt . The matThe above-mentioned multiple testing adjustments were used on two sets of results. One set analysed all 49 GTEx v8 tissues, and another set analysed only the top 5 most likely pathogenically-relevant tissues for migraine.λi is an eigenvalue of the correlation matrix, and (4) joint effects for each gene are estimated using single tissue results from step 1 and the correlation matrix from step 3 to give a joint imputed differential expression for each gene. SMultiXcan provides results for a combined differential expression p value for a single gene, best tissue , worst tissue , and the mean and standard deviation of z-scores for all tissues in which the specific gene is differentially expressed  was used to impute differential gene expression associated with migraine risk using cross-tissue models. SMultiXcan tests the joint effects of gene expression variation across tissue in four steps: (1) gene expression is imputed within single tissues using single tissue elastic net prediction models using models trained on 49 GTEx tissues, (2) a correlation matrix is generated for imputed gene expression and principal components of the predicted expression data matrix are used as explanatory variables, (3) components of smallest variation are discarded from the correlation matrix generated from step 2 to avoid numerical issues caused by collinearity. To select the number of components, the threshold of SMultiXcan was applied to all 49 GTEx tissues and on the top 5 most likely pathogenically-relevant tissues for migraine.Results from previous analyses by ourselves  genes by calculating posterior probabilities (PP) to distinguish between pleiotropy  and LD. For each gene that was differentially expressed and that passed multiple testing for each threshold, eQTL summary statistics were extracted for cis-SNPs from GTEx v8 and tested for co-localisation with migraine-risk SNPs. Given the default prior probabilities of a SNP’s association with expression (P1 = 10−4), trait (P2 = 10−4) and both (P12 = 10−5), COLOC produces posterior probabilities for five hypotheses—H0: no association with either trait; H1: association with trait 1, not with trait 2; H2: association with trait 2, not with trait 1; H3: association with trait 1 and trait 2, two independent SNPs; and H4: association with trait 1 and trait 2, one shared SNP. A large posterior probability for H3 (PP3) means that there are two independent SNPs associated with each trait at the same locus. A large posterior probability for H4 (PP4) means there is a single SNP associated with both traits at a given locus. Therefore, the higher the PP4 (PP4 > 0.5), the higher the confidence for co-localisation (pleiotropy), where the same SNP that affects migraine, affects the expression of the gene. Co-localisation may also be inferred via a low PP3 (PP3 < 0.5).COLOC  and more than one differentially expressed gene were subjected to conditional analyses to determine the marginal effect of each GWAS index SNP on gene expression. At these genomic loci, conditional association analysis was performed separately for each index SNP using GCTA-COJO  utilising 1000 Genome Project European reference genotype data. Given the list of SNPs, GEC calculates the effective number of independent SNPs that were later used in binomial tests.To test whether the putative novel migraine risk genes identified by TWAS were more likely to be located at GWS risk loci in a recent, more powerful migraine GWAS we performed a novel gene-based analysis. First, we performed gene-based GATES  and the effective number of independent genes (MTBe). As described in the methods section, SMultiXcan imputes gene expression considering the correlation of eQTLs across multiple tissues and generates a single p value. Given differences in gene–gene co-expression correlation across tissues, it is not feasible to estimate the effective number of independent genes via the matSpD approach, therefore multiple test correction for the SMultiXcan results used a Bonferroni adjustment for the total raw number of genes tested.We compared 10 approaches that used different prediction models , different significance thresholds (Bonferroni or Bonferroni-matSpD), and different sets of tissues . Table –6 . Similarly, the total number of genes tested across the top 5 GIDEE tissues was 12,988, resulting in a Bonferroni-adjusted significance threshold was 3.84 × 10–6. As shown in Table The total number of genes tested across all 49 GTEx tissues by SMultiXcan was 21,646, therefore the Bonferroni-adjusted significance threshold was 2.30 × 10r and MTBe for elastic net models were 8.87 × 10–7 (0.05/56360) and 1.72 × 10–6 (0.05/29071), respectively. Similarly, the MTBr and MTBe for MASHR models were 2.95 × 10–7 (0.05/169302) and 5.63 × 10–7 (0.05/88842), respectively. The total number of genes tested across the 13 GTEx brain tissues by SMultiXcan was 14,218, therefore the Bonferroni-adjusted significance threshold was 3.52 × 10–6 .Although not part of our primary analyses, we also performed TWAS analyses restricted to the 13 GTEx brain tissues. The MTBMetaXcan outputs a z-score that quantifies the association of predicted differential gene expression with the trait. The positive or negative sign of the z-score indicates overexpression or underexpression of the gene’s association with migraine, respectively. A two-sided p value is calculated from the z-score. For each approach mentioned in Tables For each TWAS approach, the genes crossing the respective multiple test burden that were present within ± 500 kb of a GWS independent index SNP in the Gormley et al. Table RP11-326G21.1, NEURL3, GOT1, RBM20, GPR26, CDIP1, FGF11, ATP5SL, TMEM91, TGFB1) at 8 independent loci identified in the brain subgroup analyses were not identified in the primary analyses (Supplementary Table 2c). Given these 10 genes were not as significant as the genes implicated by the all 49 tissue analyses, and the 13 GTEx brain tissues were not previously found to be enriched for regulatory signals at migraine GWAS loci, we consider these ten genes to be less robust than the genes associated in our primary TWAS analyses. Therefore, we only present results in the main text from subsequent analyses for the top 5 and all 49 GTEx tissues.Compared to using the top 5 tissues and all 49 GTEx tissues, TWAS using the 13 GTEx brain tissues identified considerably fewer differentially expressed genes across the genome and in the vicinity of migraine index SNPs  had a common set of genes implicated by all approaches. Among these 12 loci, three loci have secondary association signals . It is important to note that NA in Table A total of 12 loci , that had differentially expressed and co-localised genes  identified by elastic net models but not by MASHR models. Table SUGCT implicated via elastic net were also implicated via MASHR models. GJA1 and ZCCHC14 did not reach the relevant significance level threshold of 1.28 × 10–6 for MASHR models using matSpD in the top 5 tissues (Table BTBD7 and ITPK1) were not significant for the MASHR models. This probably reflects differences in the predictor SNPs used in MASHR models compared to the elastic net models . Similarly, SLC24A3 at rs4814864 was not significant using the MASHR model.Table and high PP4 values). Two genes KCNK5 and LVYE1 had PP4 > 0.5.Five loci were implicated with MASHR prediction models but not with elastic net models Table . Most ofCARF, ICAIL, NBEAL1, FAM117B, and WDR12) at the rs138556413 locus, ICAIL and NBEAL1 have the strongest TWAS p value with migraine as compared to other genes. Therefore, ICAIL and NBEAL1 are the most probable causal genes at rs138556413. REST is prioritised at locus rs7684253. However, REST was not identified in the top 5 migraine-relevant tissues. HPSE2 is the most likely causal gene at rs12260159. RP11-732A21.2 is the most likely causal gene at rs10895275. RNF213 and HTRA1 are hypothesised as causal genes at rs17857135 and rs2223089, respectively. Interestingly, near the rs10786156 index SNP (GWAS P = 2.0 × 10–14), there is a secondary index SNP rs75473620 (GWAS P = 5.8 × 10–9), and the different TWAS models prioritised different genes at this locus. Elastic net models prioritised NOC3L, while MASHR and SMultiXcan prioritised PLCE1. NOC3L is differentially expressed in the brain nucleus accumbens basal ganglia with a p value of 4.27 × 10–8. Whereas PLCE1 is differentially expressed in the brain cerebellar hemisphere with a p value of 5.33 × 10–9. Therefore, PLCE1 is hypothesised to be the most likely causal gene at this locus. A summary of the number of loci characterised by each of the 10 approaches  that did not have a uniform set of genes across all approaches. For these loci, we suggest that the gene(s) most significantly associated  with migraine is most likely the causal gene. For example, of the five genes , beta (0.087) and p value (2.83 × 10–27) was used. The conditioning window was set to 2 Mb around the index SNP. The TWAS pipeline was re-run including harmonisation, summary statistics imputation, and differential expression imputation (using MASHR models) using the conditioned GWAS summary statistics. Table There were three genomic loci  having independent migraine index SNPs and more than one gene differentially expressed. r2 and is colour-coded according to the strength of correlation. The European 1000 Genomes Project data was used as a reference panel to calculate LD. LocusZoom plots also display local recombination hotspots as well. The recombination rate is displayed on the right side of the y axis.Figures TSPAN2 and NGF, as when conditioned on rs2078371, the differential expression p values for these genes drop from 1.95 × 10–13 to 0.674 and 9.35 × 10–8 to 0.839, respectively . In contrast, when conditioned on rs7544256, the TWAS results for TSPAN2 and NGF remained significant. Similarly, the conditional analyses showed that rs11172113 is driving differential expression for LRP1 and STAT6. Conditioning on rs11172113 resulted in decreased TWAS significance in all tissues. Whereas both genes LRP1 and STAT6 remained significant after conditioning on rs11172055. The same pattern of results was observed for rs2971603 (rs67338227) and rs4839827. The primary lead SNP rs2971603 (rs67338227) was driving the differential expression for FHL5 and UFL1.Conditional analysis results show that rs2078371 is driving the differential expression results for both In an attempt to further characterise these migraine risk loci, we attempted to identify genes with imputed differential expression driven by the secondary index SNPs  by comparing the ‘normal’ TWAS p value and conditioned TWAS p value for all genes present at these loci. Supplementary Table 4 and Supplementary Table 5 show the list of top genes present ± 500 kb of each index SNP having the smallest p value when conditioned on the secondary SNP, along with normal TWAS p value. However, no genes at these loci were identified to have differential expression driven by the secondary SNP. Thus, indicating that the primary lead SNPs are driving differential expression of both genes at these loci.Across all the approaches, a total of 62 putative novel migraine risk genes were identified. The total number of genes from each threshold Tables , 2 not wTable Next, we determined how many of the putative novel migraine risk genes identified from TWAS analysis of the Gormley et al.  GWAS datIn terms of loci, a total of 24 loci having differentially expressed genes with at least one of the TWAS approaches, were identified using Gormley et al.  migrainePmax = 0.000923), and then identified all the genes across the genome that had an equivalent level of significance with a topSNP p value (5 × 10–8 < topSNP P < Pmax). We then compared the topSNP p values from Gormley et al.  of the 62 putative novel migraine risk genes mapped to GWS migraine risk loci in Hautakangas et al (P = 2.38 × 10–20).Afterwards, we examined the 62 putative novel risk genes identified by TWAS and assigned a topSNP , which is 658,818 bp upstream from the CYP2C9 transcription start site .The majority of the differentially expressed genes identified by our TWAS analysis of the Gormley et al.  migraines et al. , but it s et al. , CYP2C9 p = 0.05/total number of gene-tissue pairs).Importantly, all the genes verified by Hautakangas et al.  were impr2 > 0.1) with Hautakangas et al. (TNFRSF14 (r2 = 0.0003), HIST2H2BF (r2 = 0.0004), FCGR1A (r2 = 0.0004), and YJEFN3 (r2 = 0.0087) were not in LD with Hautakangas et al.  in Gormley et al  approaches. Prior to TWAS approaches, a ‘single eQTL’ approach was used, which tested each GWAS SNP for association with a SNP associated with an eQTL (eSNP). However, this approach has two major drawbacks. Firstly, the multiple testing burden is increased due to testing many eSNPs and secondly, a single eSNP may not completely capture the genetic variation influencing the gene’s expression. The TWAS approach overcomes this hurdle by additively modelling multiple SNPs in the cis-region of the gene, defined as 500 kb upstream of the gene’s transcription start site and 500 kb downstream of the gene’s transcription end site, thus reducing the multiple testing burden and capturing more genetic variation by combining information from multiple SNPs to impute (predict) gene expression. TWAS consists of two steps (i) generation of weights/prediction models for cis-SNPs on gene expression using independent genotype and expression data from healthy individuals, (ii) imputing gene expression values using a reference panel for individual genotype data or GWAS summary statistics and then testing for the association of imputed gene expression values with the trait of interest.Therefore, the accuracy of gene expression imputation depends upon the quality of prediction models. The early versions of prediction models were elastic net models based upon version 6 and version 7 of GTEx aligned to build 37. The latest models are based on version 8 of GTEx aligned to build 38 and include two classes of prediction models: elastic net and MASHR. The equation used to impute the z-score is the same in both model classes; however, there are differences. Elastic net uses all the cis-SNPs present in the region and generates an additive model to compute SNP weights for gene expression prediction. Elastic net models have a quality check metric that measures the correlation between the predicted transcriptome and assayed expression data (in the reference data such as GTEx). The gene prediction models that have a significant correlation (FDR < 0.05) are made publicly available. This quality check metric is available only for elastic net models. Whereas elastic net uses all cis-SNPs, MASHR only uses fine-mapped SNPs from the DAP-G algorithm to calculate SNP weights for gene expression prediction. MASHR models tend to have a low number of SNPs (mostly 1 or 2 SNPs) incorporated into the prediction model to impute gene expression. All MASHR prediction models are made available for users and there are no model quality checks like the elastic net models.Both elastic net and MASHR models were used for single-tissue analyses. These analyses showed that the elastic net models were able to identify a greater number of significant differentially expressed genes compared to the MASHR models on already established migraine GWS loci.GWS loci were characterised based on genes having differential expression and evidence of co-localisation between GWAS risk SNPs and eQTL SNPs. The threshold used for the co-localised signal was PP4 > 0.5 as recommended by the authors. However, an interpretation of these posterior probabilities requires a bit of caution. For example, a gene having low PP4 does not necessarily mean that there is no evidence of co-localisation provided PP3 is low and PP0, PP1 and PP2 are high. This can result from low power in the datasets  with a migraine index SNP. Some of the genes  were associated with other plausible pathogenic pathways. For example, SLC24A3 and KCNK5 are involved in ion channel activity, and RNF213 is involved in metal ion homoeostasis. It is interesting given all three genes implicated in a rare monogenic form of migraine—familial hemiplegic migraine (FHM)—are involved in ion transport  in Hautakangas et al. (p = 2.38 × 10–20).TWAS analyses of the Gormley et al  data alss et al. . Therefos et al.   that was not found by the top 5 tissue analysis. The MASHR top 5 tissue analysis identified six loci  that were not identified via the all 49 tissue analysis. Four loci  were common between the MASHR all 49 tissue and top 5 tissue analyses.The elastic net model produced a similar pattern of results. Analysis of the top 5 tissues identified a greater number of putative novel genomic risk loci compared to the analysis of all 49 tissues—identifying 11 and seven loci, respectively. The elastic net top 5 tissue analysis identified seven loci  that were not identified via the all 49 tissue analysis. All 49 tissue analysis identified three loci  that were not found by the top 5 tissue analysis. Four loci  were common between the elastic net top 5 and all 49 tissue analyses.The SMultiXcan analysis of all 49 tissues identified a greater number of putative novel genomic risk loci compared to the analysis of the top 5 tissues—identifying 14 and nine loci, respectively. Two loci (rs1499963 and rs28756401) were specific to the top 5 tissues analysis. Seven loci  were exclusive to all 49 tissues analysis. Seven loci  were identified via both the SMultiXcan top 5 and all 49 tissue analyses.RP11-353N4.6, EIF2D, LRP4, UBALDI, SUGP1, and TM6SF2) are at loci with multiple differentially expressed genes, some of which were implicated by MetaXcan and/or FOCUS analysis by Hautakangas et al. (ABHD16A (near rs74434374), HCG20 (near rs9468830), and HOXD8 (near rs72923449) identified by our TWAS pipeline analysis that were not implicated by MetaXcan or FOCUS analysis by Hautakangas et al. . Thus, performing TWAS analyses on the latest more powerful migraine GWAS would be expected to identify additional novel migraine risk loci.TWAS approaches can be viewed as methods directed to improve the discovery power of the GWAS without increasing sample size by incorporating functional information. Some authors suggest that the secondary usage of these methods is to increase the statistical power of GWAS to identify novel loci , rs6478241 (ASTN2) and rs2651899 (PRDM16) previously identified in European samples , rs11172113 (LRP1), rs2651899 (PRDM16) Supplementary file2 (XLSX 21 KB)Supplementary file3 (XLSX 43 KB)Supplementary file4 (XLSX 24 KB)Supplementary file5 (XLSX 60 KB)Supplementary file6 (DOCX 44 KB)Below is the link to the electronic supplementary material.