Trypanosoma cruzi, the agent of Chagas disease, has been controversial and debated for many years. Field studies have identified both predominantly clonal and sexually recombining natural populations. Two of six natural T. cruzi lineages (TcV and TcVI) show hybrid mosaicism, using analysis of single-gene locus markers. The formation of hybrid strains in vitro has been achieved and this provides a framework to study the mechanisms and adaptive significance of genetic exchange. Using whole genome sequencing of a set of experimental hybrids strains, we have confirmed that hybrid formation initially results in tetraploid parasites. The hybrid progeny showed novel mutations that were not attributable to either (diploid) parent showing an increase in amino acid changes. In long-term culture, up to 800 generations, there was a variable but gradual erosion of progeny genomes towards triploidy, yet retention of elevated copy number was observed at several core housekeeping loci. Our findings indicate hybrid formation by fusion of diploid T. cruzi, followed by sporadic genome erosion, but with substantial potential for adaptive evolution, as has been described as a genetic feature of other organisms, such as some fungi.Protozoa and fungi are known to have extraordinarily diverse mechanisms of genetic exchange. However, the presence and epidemiological relevance of genetic exchange in  Trypanosoma cruzi is a kinetoplastid protozoan and the etiologic agent of Chagas disease, one of the neglected and highest impact parasitic infections in the Americas   . Genotyplineages . In addiescribed . Geneticescribed . The hybin fungi . While cin fungi , the conDrosophila sp.)  and three hybrid clones that were generated from a P1 × P2 cross    although12, 2C1) . In addiT. cruzi genome at the microevolutionary scale, we selected two TcI strains  and three hybrid clones that were generated from a P1 × P2 cross   . The two12, 2C1) . At the 12, 2C1) . At the 12, 2C1) , which e12, 2C1) , these dT. cruzi hybrids were subject to erosion over an extended period of clonal replication. To dissect the underlying mechanisms, we applied a comparative genomics approach based on sequence data for parental and hybrid samples from the beginning and end of the microevolution experiment.Next, we looked for evidence of instability at specific loci in the hybrid genomes using PCR-based multilocus microsatellite genotyping, which had previously provided some evidence for non-Mendelian inheritance in the hybrids from the original P1 × P2 cross . SpecifiT. cruzi genomes. The haploid estimated genome size of P1 differed from P2 by approximately 2 Mbp, and both parental strains genomes were found to be slightly larger than the 44 Mbp reference TcI-Sylvio X10/1 strain  comprises up to 20–30% of the total icircles . This raicircles  and the To evaluate the impact of hybridization on genetic diversity, we compared variant densities in the high-quality mapping regions of the genomes of all hybrid and parental strains. The average SNP density in these regions was higher in all hybrids than in parental strains: 3.48 SNP/kb for 1C2-0, 3.29 SNP/kb for 1D12-0, and 3.49 SNP/kb for 2C1-0 , partialTo investigate the impact of these mutations on the variable regions of the genome, we compared the ratio of surface protein-coding genes per non-surface protein-coding genes. This ratio is approximately 4-fold higher in SNPs common to all hybrids and in SNPs exclusive to each hybrid clone than in those inherited from the parental strains . In addiT. cruzi (T. cruzi strains (Chromosome copy number variation (CCNV) was determined using the combination of read depth coverage (RDC) and allele balance (AB) methods . An incrT. cruzi , which wT. cruzi . AneuploT. cruzi . The RDCT. cruzi . The RDCT. cruzi . HoweverT. cruzi , which m strains .RDC analysis does not allow differentiation between different levels of euploidy (or near-euploidy), therefore whole genome ploidy was estimated by analysing the proportion of the alleles in heterozygous SNP positions of all single-copy genes . In bothIn order to address chromosome specificities and confirm the somy estimates, the AB analysis was performed independently for each chromosome. The proportion of each allele with heterozygous SNPs per chromosome position was plotted for 46 TcI chromosomes –5, thus CCNV and whole genome ploidy were evaluated in three replicate clones for each parental and hybrid strains after 800 generations in continuous in vitro culture. The trisomies in chromosomes 2 and 21 of P2  were no T. cruzi, resulting in distinct genome patterns after hybridization events occur.To investigate the patterns of genome erosion and CCNV in the hybrids, we performed a CNV analysis throughout the whole genome of parental and hybrid clones after extended culture growth. Despite the stability of DNA content in parental clones during our experiment , similarThe effect of extended culture growth on genetic diversity was evaluated in all parental and hybrid evolved clones. Regarding the parental strains, all P1 replicates showed a similar SNP density in the retained high-quality regions after culture growth , while all P2 replicates showed an increase in SNP density after culture growth  . The P2 To better understand how these genomes evolved during culture growth, the SNP density was also evaluated in each chromosome separately. The SNP density pattern per chromosome remained similar for all samples in first generation (t=0) and after culture growth (t=800). Some chromosomes  display higher SNP density than the others across all samples . P2 evolT. cruzi genetic cross have clearly demonstrated that, while the parental strains were diploid, all initial hybrid clones were essentially tetraploid. We showed that genomic variants exclusive to both parental strains were present in all hybrid clones, indicating that the tetraploid profile observed is obtained by the fusion of the two disomic parental genomes, although more complex pathways cannot be ruled out in the absence of direct observational evidence. In addition, we showed that genome erosion seems to be gradual, with sequential losses of chromosome copies rather than coordinated jumps between ploidy levels, though with some variation in erosion rates between different evolved hybrid lines. As previously proposed  in distinct eukaryotes . Our resher DTUs . As descc effect . Since mc effect . From ouT. cruzi genome  were estimated to be respectively 70 and 95 generations removed from the hybridization events. Parasites were passaged into fresh media every 2 weeks at a seeding density of 2 × 105/ml, equating typically to 1% of the stationary phase cultures.For routine culture, escribed . Parasit6cells/ml, and incubated for 45 min with 10 μg/ml propidium iodide and 10 μg/ml RNAse A at 37°C. Fluorescence was detected using a FACSCalibur flow cytometer for a minimum of 10,000 events. FlowJo software  was used to plot histograms and identify G1-0 and G2-M peaks. Mean G1-0 values were taken to infer relative DNA content. An internal control T. cruzi strain, Esm cl3, was included in each run. Relative DNA content values were calculated as ratios compared to the internal standard. For experimental hybrids, the ratios relative to each parent (P1 or P2) were also determined using the mean standard:parent ratios derived from 12 independent experiments.DNA content was determined as previously described . In brieGenotyping was done for four microsatellite loci: MCLF10, 10,101(TA), 7,093(TC), and 10,101(TC) as previously described . BrieflyTotal genomic DNA was isolated directly from long-term parasite cultures using the Gentra Puregene Tissue Kit (Qiagen) according to the manufacturers’ instructions. A total of 40 ng of genomic DNA was used as a template to prepare the sequencing libraries with the Rubicon Kit and the Illumina TruPlex kit. For the two parent strains, a 180 and 350 bp insert sizes paired end libraries were produced, plus an additional 8 kb insert size mate paired library. For the hybrid offspring, a single paired end library was produced with an insert size of 350 bp. All libraries were sequenced using the Illumina HiSeq 2500 platform. The library complexity of each parent was analysed using the 17mer distribution of the Illumina libraries using Jellyfish .https://software.broadinstitute.org/allpaths-lg/blog/) assembler with K=96, using the TcI Sylvio X10/1 reference strain (https://www.repeatmasker.org/), to the Companion annotation pipeline  using the HaplotypeCaller algorithm with a minimum quality value of 30 and a minimum depth of coverage of 10. To avoid the influence of low mapping quality in repeated regions, a strict mapping quality filter was applied and any SNP in regions with mapping quality below 50 was removed using BCFtools  and SNPs exclusive to each strain were identified using both VCFtools and BEDtools (VCFtools package  was usedBEDtools . The funBEDtools .https://orthovenn2.bioinfotoolkits.net/home). For chromosomes lacking single-copy genes, CCNV was estimated based on the ratio between the mean RDC of each chromosome position and the mean coverage of all genome positions. If the ratio between the median chromosome coverage and the median genome coverage was approximately one, the chromosome had the same copy as the genome overall, while fluctuations in this ratio (lower than 0.75 or higher than 1.25) were putative aneuploidies.CCNV and ploidy estimation were performed using a combination of RDC and AB methods as previously described . Brieflyhttps://ggplot2.tidyverse.org), pheatmap (https://CRAN.R-project.org/package=pheatmap), and vcfR  and surface molecules genes were assigned from the available TcI Sylvio X10/1 annotation (here).Genome erosion patterns were identified by CNV estimations using Control-FREEC package . Gene Onnotation   feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Decision letter after peer review:Trypanosoma cruzi reveals hybridization and clonal mechanisms driving rapid diversification of genome sequence and structure" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Jos Van der Meer as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Julius Lukes (Reviewer #1); Fernán Agüero (Reviewer #2).Thank you for submitting your article "Experimental microevolution of The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. The authors should revise their methods for SNP calling. What GATK variant caller was used? UnifiedGenotyper or HaplotypeCaller? The latter is recommended. It is also unclear which filtering criteria were used to reduce the likelihood of false-positive SNPs. Simply setting the mapping quality to a minimum of 50 is not sufficient. We recommend to have a look at the methods of Schwabl et al. 2019 (https://doi.org/10.1038/s41467-019-11771-z) where rigorous analyses were done to remove false-positive SNPs. In particular, it would be helpful to mask the reference genome based on the mappability of its own reads. This is also important for later sections in the manuscript where the authors claim increased diversity in repetitive surface molecule genes.Editor: Also, it is not clear whether Indels were analysed at all, they appear once in the Methods and not in the rest of the manuscript.2. Figure 1e, Table 1 and corresponding main text – We are puzzled regarding the number of SNPs that are unique to each hybrid clone and that are common to all hybrids.First, it seems to us that the authors have done SNP calling in all strains independently, which may result in genotypes being missed because of low local coverages. Joint genotyping would mitigate such biases. We would recommend to do a joint genotyping across all parental and hybrid strains, and then only retain those SNP positions for which there is no missing data.Second, in the main text on page 7, it is argued that these SNPs would have occurred in culture during the approximately 50 generations of growth BEFORE hybrid formation. But in the last sentence of that paragraph it is stated that these SNPs appeared after the hybridisation event. How can the authors tell whether these SNPs occurred during culture before hybridization, or whether they happened just after hybridization? The setup of the experiment should be more clearly explained: how many generations happened before hybridization, how many after, when exactly was sequencing done during this process? This is also important for later sections that claim that hybridization resulted in a burst of novel mutations.It seems like the authors are discovering the impact of tandem repeat regions (copy number variations) on SNP discovery. Most of page 10 and Figure 3 merely describes the fact that you will have more false-positive SNPs within the repeat regions. I'm not sure why this is of interest here. Such SNP's should not have been included in the first place.3. Why was the mapping quality in surface molecules inferior to the rest? That begs for explanation. "Surface molecule genes", frequently used here, sounds somewhat weird to this reviewer. Couldn't it be rather "Surface protein-coding genes" or the like?4. I understand the implications of trying to map short reads to repetitive regions. For example, PAINTS (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912377/) removes noisy genomic regions (large gene families) before attempting to do ploidy estimations. This seems intuitive, and this is also explained in this Ms, in light of this maybe authors can save some space by omitting text and figures that are used to discuss and show these difficulties? Maybe there is not much to gain by showing that estimating ploidies in chromosomes with low numbers of housekeeping genes is difficult. Just state that and put the data into Supplementary file?5. Based on the results presented, I don't agree that there is gradual erosion overall across all clones.1D12 hybrid seems to have gone through an initial loss of DNA content but then its DNA content seems to have remained stable . In addition, it's genome-wide somy profile remained stable at tetrasomy . Also, there also seems to be an increase in DNA content after 600 generations. Finally, microsatellite alleles are only consistently lost in hybrid 2C1, at three different loci. No loss was observed in hybrid 1D12 and only one allele was lost in hybrid 1C2. This also raises the question what has caused the loss of DNA content in hybrid 1D12. Based on microsats and somy, there is no clear evidence for genomic erosion. So, what part of the genome is lost in the beginning of the experiment? The authors could do a comparative genomic analyses of the parental genomes and the hybrid genomes, to figure out what portions of the genomes are commonly/uniquely lost in the hybrids.6. One of the conclusions from the study of accumulation of mutations, is that surface molecules may mutate faster. The reasoning for this if I read and understood correctly, is that there were many "mutations that appeared in each parental clone after culture growth". Do authors mean to say that these mutations appeared after the genetic cross experiment in Gaunt et al. 2003? Can the authors clarify how these parental clones were kept and passaged since then? How many passages did they undergo before being sequenced? The same clarification is warranted for hybrids. When the authors state that "while the parental strains were diploid, all initial hybrid clones were essentially tetraploid" , the readership would like to know if these essential tetraploid states were observed just after the hybridization (genetic exchange) event (e.g. clones were kept in liquid nitrogen since then and only thawed for brief culture before sequencing) or if these states had some passages in culture after the genetic exchange event (how many?).7. Data and code availability: The mentioned accession number was not found in the BioProject database. Please make code available via GitHub or other repository. Essential revisions:1. The authors should revise their methods for SNP calling. What GATK variant caller was used? UnifiedGenotyper or HaplotypeCaller? The latter is recommended. It is also unclear which filtering criteria were used to reduce the likelihood of false-positive SNPs. Simply setting the mapping quality to a minimum of 50 is not sufficient. We recommend to have a look at the methods of Schwabl et al. 2019 (https://doi.org/10.1038/s41467-019-11771-z) where rigorous analyses were done to remove false-positive SNPs. In particular, it would be helpful to mask the reference genome based on the mappability of its own reads. This is also important for later sections in the manuscript where the authors claim increased diversity in repetitive surface molecule genes.Editor: Also, it is not clear whether Indels were analysed at all, they appear once in the Methods and not in the rest of the manuscript.We thank the reviewers for highlighting these issues. These are important points for this complex genome and we agree that they should be clear. HaplotypeCaller was used, and more information on the SNP calling was added in the revised manuscript. We are familiar with the suggested methods, since the corresponding author co-authored the Schwabl et al. (2019) paper. We have now used the masking used in that paper to compare the two strategies. Only a small number of SNPs are lost when the masking is added, and the distribution is unchanged. Thus, the conclusions are not changed. In addition, we found that our strategy removed a larger proportion of the genome from the analysis than the masking strategy. As the strains used in the hybrid strategy differ significantly from the reference in many repetitive regions, it is likely that the strategy using only the parent/hybrid reads gives more accurate results. The strict mapping, combined with visualization of the mapping quality and comparing with the repeat content, has enabled us to more reliably remove unreliable SNPs in these extremely repetitive regions. As we produced the reference genome , we have performed extensive mapping of the reference reads to the sequence to identify repetitive regions. Indels were not found to be informative.2. Figure 1e, Table 1 and corresponding main text – We are puzzled regarding the number of SNPs that are unique to each hybrid clone and that are common to all hybrids.First, it seems to us that the authors have done SNP calling in all strains independently, which may result in genotypes being missed because of low local coverages. Joint genotyping would mitigate such biases. We would recommend to do a joint genotyping across all parental and hybrid strains, and then only retain those SNP positions for which there is no missing data.As explained below, these results are not caused by the SNP calling. As the coverage is high, joint genotyping would add significantly to the analysis. Moreover, the independent clone/parental strain analysis would not cause biased SNP detection, since a detailed analysis has compared the same regions of each sequenced genome, being consistent among the raw reads despite the sequencing coverage.Second, in the main text on page 7, it is argued that these SNPs would have occurred in culture during the approximately 50 generations of growth BEFORE hybrid formation. But in the last sentence of that paragraph it is stated that these SNPs appeared after the hybridisation event. How can the authors tell whether these SNPs occurred during culture before hybridization, or whether they happened just after hybridization? The setup of the experiment should be more clearly explained: how many generations happened before hybridization, how many after, when exactly was sequencing done during this process? This is also important for later sections that claim that hybridization resulted in a burst of novel mutations.We agree that more detail on sample origins and hybrids formation is required, though exact passage numbers from the original crossing experiment (performed in 1998) were not recorded, so these are best estimates from a revised assessment of all the available information (not included in the main text).P1 and P2 clones were transfected with the drug resistance genes on episomal constructs, drug selected and polyclonal populations were grown out (~15 generations). Cloning generated the P1-hyg and P2-neo lines (~25 generations). Stationary phase cultures containing metacyclic trypomastigotes were used for the mixed Vero infections (crosses) as described in Gaunt et al., 2003 (~10 generations to point of mixing). The P1 and P2 samples that were sequenced in this study, for t=0, were grown from the pre-transfection parasites, adding another ~20 generations of culture. Thus, the t=0 parent sequences are removed by ~70 generations from the experimental cross.Parasites were recovered from mixed Vero cell infections between 7 and 28 days post-inoculation (~15 generations as amastigotes), then selected (on both drugs) and a polyclonal population was grown out (~15 generations). Cloning generated the original 6 hybrid lines (~25 generations), which were cryopreserved. When work resumed on analysing the hybrids’ DNA contents  the t=0 DNA extraction occurred after ~40 generations of epimastigote culture. Thus, the t=0 hybrid sequences are removed by ~95 generations from the experimental cross. So, at the beginning of the microevolution experiment presented in this study, the parents and hybrids were already separated by approximately 165 generations. Figure 1 and the methods section has now been modified to reflect these details.In hindsight, it would have been better to have less divergence between the time of DNA sampling and the original cross experiment, but such a long-term microevolution experiment was not planned in advance and the idea developed over time. However, it would never have been possible to avoid some distance between the parent samples, the hybrid clone samples and the actual hybrisation event because of the culture bottlenecks inherent to the process of cell infections, drug selection and cloning, as well as growing out sufficient numbers of parasites to yield enough DNA for the sequencing technologies available at the time.In light of the above-mentioned aspects, we consider that SNPs that are common to all hybrids were all almost certainly generated from before hybridisation, falling into two main categories: SNPs that were detected in the three t=0 hybrid samples but not detected in the parent samples, and SNPs occuring in the parent parasite lines during culture before the cross was set up. As above, DNA for this study was derived from parent samples approximately 70 generations separating them from the cross.T. brucei and Lesishmania.We might rule out less likely origins for SNPs common to all hybrid such as homoplasies, that would be extremely rare across three independent hybrid lines, or those occuring during growth of a hypothetical common progenitor hybrid before the cloning procedure was done. This latter seems unlikely because hybrid ID12 inherited P2 kDNA maxicircles while 1C2 and 2C1 inherited P1 kDNA maxicircles. Thus, a minimum of two independent hybridisation events is more parsimonious than a mixed maxicircle common progenitor. It is noteworthy to mention that maxicircle inheritance is uniparental in The hybrid clone-specific SNPs are assumed to all be downstream of the hybridisation events, though it is conceivable that a very small fraction might have been undetectable in all samples but one for technical reasons. These are the SNPs being referred to at the end of this section where we state “this ratio was approximately 3.3-fold higher in the SNPs that appeared after the hybridization event”parent parasite growth before the cross was set up“ and added a sentence to explain about the kDNA. Figure 1 was changed to show the time separating the DNA samples from the hybridisation event. We also clarify at the end of section that SNPs that appeared after hybridisation equates to hybrid clone-specific SNPs.We thus changed on page 7 “50 generations of growth before hybrid formation” to “70 generations of It seems like the authors are discovering the impact of tandem repeat regions (copy number variations) on SNP discovery. Most of page 10 and Figure 3 merely describes the fact that you will have more false-positive SNPs within the repeat regions. I'm not sure why this is of interest here. Such SNP's should not have been included in the first place.T. cruzi genome is unusual, even for kinetoplastid parasites. It is effectively divided into two parts, core and repetitive, and the two behave very differently in most respects, both regarding sequence analysis and biology. We have therefore chosen to include this information in order to illustrate the features of the genome for readers are less familiar with this organism and its genome, specially concerning the sequence variability within the multicopy gene families of a clonal organism that would allow detection of distinct SNPs among distinct members of a given gene family. We feel that this will be helpful, as T. cruzi is radically different from other trypanosomatids and it is suited for the readership of this more broad journal. We have rephrased the text to reflect to clarify this.As the reviewers are already aware, the 3. Why was the mapping quality in surface molecules inferior to the rest? That begs for explanation. "Surface molecule genes", frequently used here, sounds somewhat weird to this reviewer. Couldn't it be rather "Surface protein-coding genes" or the like?The difference in mapping quality thus reflects the extreme nature of the sequence composition of the surface-molecule coding gene-containing regions of the genome. Even though rigorous mapping parameters were used, results such as these are expected due to the large and variable number of gene copies and other repeats that cannot reliably be used for mapping. We have changed the terminology as suggested.4. I understand the implications of trying to map short reads to repetitive regions. For example, PAINTS (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912377/) removes noisy genomic regions (large gene families) before attempting to do ploidy estimations. This seems intuitive, and this is also explained in this Ms, in light of this maybe authors can save some space by omitting text and figures that are used to discuss and show these difficulties? Maybe there is not much to gain by showing that estimating ploidies in chromosomes with low numbers of housekeeping genes is difficult. Just state that and put the data into Supplementary file?T. cruzi genome for readers are less familiar with this organism and its genome. We consider that this will be useful and helpful, as comprehension of distinct T. cruzi DTU genome structures have been impaired by a number of variables such as the number of multicopy gene families and their contents.As mentioned above, we included and opted for maintaining such information in order to illustrate the unique features of the 5. Based on the results presented, I don't agree that there is gradual erosion overall across all clones.1D12 hybrid seems to have gone through an initial loss of DNA content but then its DNA content seems to have remained stable . In addition, it's genome-wide somy profile remained stable at tetrasomy . Also, there also seems to be an increase in DNA content after 600 generations. Finally, microsatellite alleles are only consistently lost in hybrid 2C1, at three different loci. No loss was observed in hybrid 1D12 and only one allele was lost in hybrid 1C2. This also raises the question what has caused the loss of DNA content in hybrid 1D12. Based on microsats and somy, there is no clear evidence for genomic erosion. So, what part of the genome is lost in the beginning of the experiment? The authors could do a comparative genomic analyses of the parental genomes and the hybrid genomes, to figure out what portions of the genomes are commonly/uniquely lost in the hybrids.We agree that the three hybrid lines have distinct genome erosion patterns not fully captured by our use of the term “gradual”. Revision of figure 1C to account for kDNA and infer 3n1k and 4n1k levels has allowed us to better visualise these trends. This shows there was in fact little DNA loss between the hybridisation (diploid fusion) events and t=0 for all three lines. While 2C1 shows the most consistent rate of genome erosion, with all three 800 generation evolved clones approximating a 3C genome size, the 1C2 shows more of a “stop-start” pattern, where most of the erosion occurs between 0-200 and 400-600 generations, reaching a 3C genome size, but two of the 800 generation clones then gain ~10%. The 1D12 clone, as the reviewer correctly observes, shows an initial 9% loss between t=0 and t=200 but was then quite stable between 3C and 4C.In terms of the microsatellite data, we only typed four loci so there is very limited value in using them to interpret the total DNA erosion patterns. The purpose of such typing was just to help guide decisions on the analytical approach, i.e., whether to continue culturing for more years, whether to pursue a whole genome sequencing approach and for which samples. We reasoned that once we saw any evidence of microsattellite allele dropout then sequencing was likely to be justified.Comparative genomic analyses of the parental genomes and the hybrid genomes is described on section “Patterns of genome erosion after in vitro microevolution” (Pg. 13) and we have now expanded the text of the Results section relating to Figure 1C and 1D to cover these aspects of the data and provided more nuance. Also, Gene Ontology analysis of contracted genes in hybrid strains after culture growth is presented in Supplementary Table 9.6. One of the conclusions from the study of accumulation of mutations, is that surface molecules may mutate faster. The reasoning for this if I read and understood correctly, is that there were many "mutations that appeared in each parental clone after culture growth". Do authors mean to say that these mutations appeared after the genetic cross experiment in Gaunt et al. 2003? Can the authors clarify how these parental clones were kept and passaged since then? How many passages did they undergo before being sequenced? The same clarification is warranted for hybrids. When the authors state that "while the parental strains were diploid, all initial hybrid clones were essentially tetraploid" , the readership would like to know if these essential tetraploid states were observed just after the hybridization (genetic exchange) event (e.g. clones were kept in liquid nitrogen since then and only thawed for brief culture before sequencing) or if these states had some passages in culture after the genetic exchange event (how many?).Please refer to the answer to point #2 above about the origin and timing of the DNA samples. We have added text to clarify this issue.7. Data and code availability: The mentioned accession number was not found in the BioProject database. Please make code available via GitHub or other repository.The sequence data was submitted to the NCBI BioProject database under accession number PRJNA748998 and will be publicly available upon publication as now stated on the MS. URL: https://dataview.ncbi.nlm.nih.gov/object/PRJNA748998?reviewer=ejap1p42k4u6baufrhtpgls5e2.The scripts, mostly used for plotting the figures in “R” are relatively standard and we do not think they warrant inclusion.


Plasmodium falciparum infections are crucial in malaria population genomics. Here we optimized a falciparum variant calling pipeline based on GATK version 4 (GATK4) and applied it to 6,626 public Illumina WGS samples.Accurate variant calls from whole genome sequencing (WGS) of We optimized parameters that control the heterozygosity, local assembly region size, ploidy, mapping and base quality in both GATK HaplotypeCaller and GenotypeGVCFs leveraging control WGS and accurate PacBio assemblies of 10 laboratory strains. From these controls we generated a high-quality training dataset to recalibrate the raw variant data.On current high-quality samples , we show improved sensitivity (86.6 ± 1.7% for SNPs and 82.2 ± 5.9% for indels) compared to the default GATK4 pipeline  and previous variant calling with GATK version 3 . The sensitivity of our pipeline on simulated mixed infection samples (80.8 ± 6.1% for SNPs and 78.3 ± 5.1% for indels) was again improved relative to default GATK4 . Precision was high and comparable across all pipelines on each type of data tested. We further show that using the combination of high-quality SNPs and indels increases the resolution of local population population structure detection in sub-Saharan Africa. We finally demonstrate that increasing ploidy improves the detection of drug resistance mutations and estimation of complexity of infection.falciparum variant calling which should help improve genomic studies of malaria.Overall, we provide an optimized GATK4 pipeline and resource for  Plasmodium falciparum. Control efforts are challenged by the lack of highly effective vaccines, drug and insecticide resistance, failure of rapid diagnostic tests due to hrp2 and hrp3 deletions, and the impact of the COVID-19 pandemic  mutations as the molecular markers of artemisinin resistance as well as providing a detailed description of the parasite population structure within and across continents (Whole genome sequencing (WGS) represents a key tool for genomic epidemiology and surveillance of parasite populations that circulate in malaria endemic regions. Significant advances have been made in malaria research since the first WGS data of e genome . Today, e genome . Studiesntinents .P. falciparum is AT-rich with large regions of low complexity and duplicated sequence, such as the subtelomeric regions which contain highly-related gene families. This presents a general challenge for mapping reads accurately to the reference genome. The P. falciparum genome is extremely repetitive in many (coding and non-coding) regions, with high frequencies of AT-rich tandem repeats that create challenges for accurate calling of variants  at unknown relative abundances , insertions-deletions (indels) and structural variations. First, the genome of variants . Indels undances .P. falciparum is entirely haploid in the human host during its lifecycle, the performance of the variant calling is affected by the high COI of clinical samples collected from patients in areas of high malaria transmission. The number of clones can sometimes reach double digits, and given their numbers and relative frequency are variable and unobserved, standard variant calling pipelines based on ploidy do not properly apply. Moreover, given the proportion of an individual clone within an infection can be extremely low, the detection of its presence and variants it may contain can be computationally challenging.Despite the fact that P. falciparum. One of the most important features of GATK is the variant recalibration step which consists of using a training dataset to score and filter out low quality variants that are more likely false than true from the raw variant data in variant call format files (VCFs). However, developing a Mendelian error-free (or error-depleted) training dataset based on sequence reads generated in the wet laboratory is currently a challenge in malaria. Additionally, GATK was designed for diploid organisms, where there are equal proportions of each chromosome present in the sample; in natural malaria infections with COI > 1 it is more difficult to distinguish the few copies of actual variants in minor clones from sequencing errors We hypothesized that this issue could be partially overcome by partitioning the ploidy until each expected variant present in the sample gets enough probability to be called.The genomic analysis toolkit (GATK)  is a welP. falciparum WGS data analysis. We generate a custom variant training dataset in silico based on accurate PacBio assemblies of laboratory strains as templates to filter out low quality variants. We test and adjust prior variant rates and multiple parameters that control the read and mapping qualities at various stages of the pipeline. We also make a reference true callset with select known laboratory strains to evaluate the performance of our pipeline. Finally, we apply the pipeline to available public WGS data, including Malariagen’s Pf6 dataset, demonstrating improvements in population genomic analyses. Since indels have been challenging to properly call and rarely incorporated into these analyses, our study focuses on them and demonstrates their utility in increasing the resolution of population structures when combined with SNPs.Here we leverage the flexibility of GATK4 to optimize SNP and indel calling for improved P. falciparum, we sought to create an improved training “truth set” for the pipeline. To filter raw VCFs with a high quality truth callset, which is difficult to obtain using wet lab methods, we decided to generate a robust in silico training variant dataset from the PacBio assemblies . We first ran the variant calling pipeline with default settings of GATK4’s HaplotypeCaller and GenotypeGVFs tools on high-quality public Illumina read data  of 10 laboratory strains  for which there exist accurate PacBio assemblies , we found greatly improved sensitivity . With GATK3 recalibrated with the cross training dataset, the sensitivity for both SNPs and indels was the lowest. As next step in GATK4 optimization, we altered multiple parameters of HaplotypeCaller and GenotypeGVFs of pipeline 1 to make pipeline 2, including adjusting expected SNP and indel rates (--heterozygosity and -indel-heterozygosity) and parameters that control base quality , mapping (-mbq and -DF) and local assembly size (--min-assembly-region-size), aiming to further improve the sensitivity and reduce false calls. While default values of pipeline 1 are fairly robust, sensitivity increased significantly to 86.6 ± 1.7% for SNPs and 82.2 ± 5.9% for indels when modified parameters were used  for SNPs and 73.1 ± 5.1% for indels . When weWe finally tested the GATK4 pipelines on simulated mixed infection samples after combining high-quality reads of IT + KH01 at different proportions  to make 100X coverage. Sensitivity of our optimized pipelines was higher than that of the default GATK4 pipeline trained with the cross dataset for both SNPs and indels  . InteresTo understand how sequencing quality affects the results of our pipelines, we added shorter (old) Illumina read samples (n = 7) from the Pf6 dataset and analyzed any potential effect of the read coverage, insert size, read length and read quality on variant calling performance . In spite of the great variation in the read coverage (median between 35 and 180X), neither the specificity nor the precision was correlated with this parameter . InteresSince the performance of indel calling was markedly improved with GATK4, we examined if the inclusion of indels improved the resolution of global and local population structure using Malariagen’s Pf6 samples. After restricting our analysis to the core genome and filtering out low quality variants; our fully optimized pipeline detected more variants in total compared to GATK3. The majority (55%) of indel positions overlapped with each other and with SNP sites and formed complex multiallelic markers . We used high quality SNPs and indels from the core regions of chromosome 1, excluding hypervariable and subtelomeric regions, after pruning them for linkage disequilibrium and selecting high-quality samples with less than 20% missing genotypes . We calculated the pairwise variance-standardized genetic relationship matrix and performed a t-distributed stochastic neighbor embedding (tSNE) analysis using SNPs and indels, individually and in combination. We observed a similar general population structure between sub-Saharan Africa, South-East Asia, South America and Papua New Guinea similarly across all three variant sets as previously reported , includiGiven the decreased performances of both GATK4 and GATK3 in mixed infections in diploid mode in general, we tested our pipelines at ploidy 6 to improve detection of low abundance within-sample variants (due to minor low abundance parasites). Since most downstream analytic tools cannot fully process hexaploid VCFs, such as decomposing complex indels into smallest fragments prior to comparing two callsets, we limited our evaluation to SNPs. The total number of additional true positive SNPs detected by the hexaploid mode relative to the diploid one on chromosome 13 was 449, 359, 112, 102, 95 and 78 for 15%:95%, 10%:90%, 15%:85%, 20%:80%, 25%:75% and 50%:50% of IT:KH01 mixed infections, respectively . For the 50%:50% mixed infection sample, the hexaploid mode did not substantially improve variant detection, confirming our hypothesis whereby the diploid mode works more accurately when there are equal proportions of individual strains in the sample. These findings suggest that the polyploid modes would be more suitable for monitoring molecular markers of malaria drug resistance or detection of low abundance strains.k13 resistant mutations that have been reported by the World Health Organization  in field isolates from sub-Saharan Africa, South-Asia Asia and South America. We computed COI based on common SNPs on chromosome 13 called using our optimized pipeline with ploidy of 6 and 2 and the GATK3 at diploid mode. Variants with minor allele frequencies > 1% and missing genotypes > 10% were selected for the COI calculation using the REAL McCOIL package . OverallP. falciparum WGS and develop a new robust training dataset for accurate removal of low-quality variants from raw VCFs, provides an optimized variant calling pipeline that outperforms existing similar resources  threshold and increased local assembly size for haplotypecaller to better capture indels. Here we made 2kb overlapping artificial reads with 100X coverage but these key parameters can be more flexibly modified based on the goal of the variant calling project, which is hard to obtain with high precision with wet laboratory methods .P. falciparum as previously demonstrated  we tested, the performance of variant calls was relatively lower in mixed than single infection samples. This general issue was partially resolved by increasing the ploidy in the HaplotypeCaller package which also allowed us to detect true artemisinin-resistant SNPs on P. falciparum WGS studies and both our diploid and polyploid methods can be borrowed to analyze the genomic data of other complex microorganisms in which calling accurate variants is elusive.In conclusion, we provide an optimized variant calling pipeline based on GATK4 that produces high quality SNPs and indel data from monogenomic and mixed infection samples. We used the output of the pipeline in downstream population genetic analyses using publicly available WGS data to demonstrate the value of incorporating indels in such studies alongside SNPs. This pipeline should contribute to improving the quality of Reference callset (“gold standard”) generationfalciparum 3D7 reference genome (Version 3) with minimap2 version 2.17. Mapped single chromosome-long reads were used to retain all mismatches as variants using optimized bcftools (version 1.13) mpileup and bcftools call commands. Variant calling was validated by visualizing VCFs and BAMs together in IGV (version 2.4.17) to confirm the concordance between variants and mismatches. Subtelomeric and internal hypervariable regions were removed as these regions undergo frequent exchange between chromosomes such that orthology is not maintained.We aligned publicly available accurate PacBio assemblies of 10 laboratory strains 7G8, Dd2, GA01, GB4, GN01, HB3, IT, KH01, KH02 and SN01  in fastain silico training dataset generation from PacBio assembliesNew Biopython scripts were used to make 2kb fragments of each genome after every 20 nucleotide positions and fragments were saved as fasta files. This strategy produces overlapping 2kb reads with 100X coverage from which fastq files were created by setting the quality of bases to highest quality (“~” in ASCII format) using bbmap. These reads were mapped onto the 3D7 reference genome using minimap2 (version 2.17). BAM files were sorted and duplicate reads marked using tools Picard SortSam and GATK MarkDuplicatesSpark prior to variant calling, respectively. The HaplotypeCaller package of GATK version 4.1.6.0 was used to call potential variants and create genome VCFs (gVCFs). The 10 gVCFs were combined using GenomicsDBImport (GATK4’s tool) before running a joint genotyping with GenotypeGVCFs (GATK4’s tool) within the core genome. The final VCF was compared to the reference callset for each laboratory strain using RTG Tools to verify the quality of the training SNPs and indels. This tool was employed for all subsequent VCF comparisons, in which our accurate reference callsets were used as baselines along with the following arguments -decompose and - -squash-ploidy that normalize complex variants into smaller constituents and ignore zygosity differences between VCFs that are compared, respectively.We harnessed the same 10 PacBio assemblies as templates to computationally generate a positive training dataset for GATK. https://github.com/Karaniare/Optimized_GATK4_pipeline). Illumina reads of laboratory stains and Pf6 samples were downloaded from SRA using sratoolkit (version 2.8.2–1). Trimmed paired reads were competitively aligned to the P. falciparum 3D7 and human (hg38) reference genomes with bwa (version 0.7.15). Reads mapping specifically onto the 3D7 genome were selected before cleaning the bam files using CleanSam. Clean BAM files were sorted and processed for duplicate marking using SortSam and MarkDuplicatesSpark, respectively. Mixed infection samples were simulated after combining 95%, 90%, 85%, 80%, 75% and 50% mapped reads of the KH01 strain with 5%, 10%, 15%, 20%, 25% and 50% mapped reads of IT strain to make final BAMs with 100X coverage. To optimize HaplotypeCaller (GATK4) for initial variant calling from P. falciparum WGS data with higher sensitivity, we adjusted multiple parameters as follows: -heterozygosity (prior SNP rate) 0.0029, -indel-heterozygosity (prior indel rate) 0.0017, -min-assembly-region-size 100, min-base-quality-score  5 and -base-quality-score-threshold 12. Heterozygosity values were chosen based on SNP and indel rates calculated from the reference callsets of the 10 laboratory strains. Base quality score filtering thresholds were made less stringent to allow the algorithm to process higher amounts of reads. The mapping quality filter  was also disabled for the same reason. Optimized HaplotypeCaller was run on each sample independently to initially detect potential variants that are stored in gVCFs. For joint genotyping option, gVCFs were first combined into a genomic database using GenomicsDBImport. Per-chromosome genotyping of the gVCFs was performed using the GenotypeGVCFs command in which -stand-call-conf  was set to 30 to remove the majority of false positive variants that passed HaplotypeCaller’s filters. We enabled the -genomicsdb-use-vcf-codec argument in the joint genotyping script to allow for more space in info fields when annotation sizes exceed 32-bit while processing the genomic database. Since the genotyping step is computationally costly, we ran GenotypeGVCFs in parallel slurm jobs on each chromosome partitioned into 200kb segments. We used GATK4’s GatherVcfs to merge segments into single VCFs. When indels and SNPs are called at the same positions, they are recorded as multi-allelic and incorrectly treated as indels by GATK’s VCF filtering engine. To accurately filter low-quality  SNPs and indels independently, bcftools norm commands were used to split multi-allelic positions before applying GATK4’s VariantRecalibrator and AppyVQSR. The VariantRecalibrator analysis was performed based on QD, DP, FS, SOR and MQ using either the public cross data  or our in silico positive training dataset made with the 10 laboratory strains. The -lod-score-cutoff (VQSLOD cutoff) in AppyVQSR was set to 0 for SNP and −2.0 for indel (due to the mapping issue of the low complexity repeat regions) to annotate low quality variants. Low quality variants (VQSLOD < 0 for SNP and VQSLOD < −2.0) were tagged with the annotation “LOW_VQSLOD” and can be used to remove them from the VCFs. We used bcftools norm to merge variants with the same positions into multi-allelic loci as multi-position VCFs are hard to handle in downstream analyses. The final VCFs were functionally annotated with SnpEff (version 5.0d). Bcftools view and bcftools query were used for all VCF subsetting and data extraction and the outputs were plotted with R Studio.The application of an optimized pipeline to Illumina sequencing is illustrated in supplementary Figure S5. Code for the pipeline is available  in comparison to 2 (diploid mode). We analyzed validated k13 mutations associated with artemisinin resistance. The annotated VCF was subset at k13 gene locus on chromosome 13  using bcftools view and amino acid changes and positions were extracted with SnpEff’s SnpSift.jar package. In order to identify samples that carry these mutations, we used GATK4’s VariantsToTable to extract the GT information along with REF and POS into a table. We applied the same approach to analyze k13 mutations in MalariaGEN’s Pf6 GATK3 VCF which was downloaded from the publicly available repository ftp://ngs.sanger.ac.uk/production/malaria/pfcommunityproject/Pf6/Pf_6_vcf/. For mutated samples that were not found in the GATK3 VCF, BAMs were visualized in IGV to confirm the mutations were present across multiple reads.We ran our optimized pipeline on Pf6 data with -bcftools view to extract SNPs with minor allele frequencies > 1% from chromosome 13 in VCFs which included 6,626 samples from West Africa, East Africa, Central Africa, Central West Africa, South-East Africa, South-East Asia West, South-East Asia East, South America, South Asia and Papua_New_Guinea. We used bcftools +fill-tags to add fractions of missing genotype annotations to the VCFs and bcftools view to select SNPs with less than 10% missingness rates. We used custom scripts based on vcftools to create genotype tables from diploid and hexaploid VCFs in which reference homozygous, alternate homozygous and heterozygous calls were coded as 0, 2 and 1. We finally used the genotype tables to compute discrete COIs with the REAL McCOIL package . We tested SNPs and indels of chromosome 1 individually and in combination to assess the impact of both types of variants combined on the population structure. Multiallelic sites in SNP or indel-specific VCF were split and optimized


More than 40 QTLs associated with 14 stress-related, quality and agro-morphological traits were identified. A catalogue of favourable SNP markers for MAS and a list of candidate genes are provided.Manihot esculenta) is one of the most important starchy root crops in the tropics due to its adaptation to marginal environments. Genetic progress in this clonally propagated crop can be accelerated through the discovery of markers and candidate genes that could be used in cassava breeding programs. We carried out a genome-wide association study (GWAS) using a panel of 5130 clones developed at the International Institute of Tropical Agriculture—Nigeria. The population was genotyped at more than 100,000 SNP markers via genotyping-by-sequencing (GBS). Genomic regions underlying genetic variation for 14 traits classified broadly into four categories: biotic stress (cassava mosaic disease and cassava green mite severity); quality (dry matter content and carotenoid content) and plant agronomy (harvest index and plant type) were investigated. We also included several agro-morphological traits related to leaves, stems and roots with high heritability. In total, 41 significant associations were uncovered. While some of the identified loci matched with those previously reported, we present additional association signals for the traits. We provide a catalogue of favourable alleles at the most significant SNP for each trait-locus combination and candidate genes occurring within the GWAS hits. These resources provide a foundation for the development of markers that could be used in cassava breeding programs and candidate genes for functional validation.Cassava (The online version of this article (10.1007/s11103-020-01038-3) contains supplementary material, which is available to authorized users. Manihot esculenta Crantz) is not only the most widely consumed starchy-root staple but also an emerging multi-purpose and industrial crop in Africa, Asia, and Latin America  and genomic selection (GS) can be used to accelerate genetic improvement particularly by reducing generational interval and increasing selection intensity  during the dry season causes chlorosis and restricted growth resulting in a significant negative impact on root yield. The main type of resistance to CGM is attributed to apical leaf pubescence but may also include other mechanisms  Cassava Breeding Program, we examined the genetic architecture of 14 continuous and categorical traits, including defense against biotic stresses, agro-morphological and quality-related traits Table  and has s Shukla .For quality traits, we considered total carotenoid content and dry matter content variation. Biofortification breeding to increase provitamin A carotenoids in storage roots is an important goal in cassava improvement programs around the world  and storage roots (periderm and cortex colour). Colour of cassava leaf petioles as well as apical leaves ranges from light green to purple due to anthocyanin pigmentation. Anthocyanins play various roles in plants, including protection against ultraviolet light, overcoming different abiotic and biotic stresses and other physiological processes such as leaf senescence . The trials were planted across four different locations in Nigeria; Ubiaja , Ibadan , Mokwa (9°21′N 5°00′E) and Ikenne  from 2013 to 2016. This population consisted of 715 elite lines from the genetic gain (GG) population, 2,323 full-sib progenies derived from 88 elite GG progenitors (Cycle1—C1), and 2,092 full-sib progenies derived from top 89 cycle one progenitors (Cycle2—C2). The mean family size for C1 clones was 15, ranging from 1 to 77 clones while that of C2 was 7.6 ranging from 1 to 20 clones. The GG population is a collection of the “Tropical Manihot Selection (TMS)” cultivars developed in the past five decades by IITA  for the 14 traits related to biotic stress (cassava mosaic disease and cassava green mite severity); storage root quality (dry matter content and carotenoid content); plant agronomy (harvest index and plant type); and several agro-morphological traits related to leaves, stems and roots  distributed across two locations (Ibadan and Ubiaja) and three seasons . In 2012–2013 season, the trial was planted as a preliminary yield trial (PYT) in Ubiaja. A subset of the GG population consisting of 88 clones, mainly parents that were used to generate C1, were evaluated in replicated multi-environment PYTs across 11 environments (three to four locations for three seasons). The C1 population was split into three sets and each evaluated as a CET in Ibadan, Ikenne and Mokwa in 2013–2014 season. In the second season (2014–2015), selections from C1 were planted in the same locations still as CET but using larger plots of 20 plants. In 2015–2016 season, selections from C1 were advanced to several multi-environment advanced yield trials (AYT) planted in one to three locations each. Similar to C1, the C2 population was also split into several subsets and planted as CETs across four locations in 2014–2015 season. Selections from these trials were established as PYT in 2015–2016 season. We also run several multi-generational field trials involving clones from C0 and C1 in CET and AYT, and C0, C1, C2 in a PYT planted across two locations. The CET plots were composed of a single row with five plants per plot, in an augmented block design, with two checks as a control. PYT plots consisted of two rows with ten plants per plot, in a randomised complete block design with two replications and five checks as a control. AYT plots contained four rows with 20 plants per plot, in a randomised complete block design with three reps and five checks as a control. Planting was performed from June to July (during the rainy season) and harvested around the same time the following year. Spacings between rows and plants were 1 and 0.8 m in all trials, respectively, except in CETs where we used 0.5 m within rows. All field trial management was performed, whenever necessary, following the technical recommendations and standard agricultural practices for cassava  of more than 0.4 were retained for subsequent analyses after imputation.Genomic DNA was extracted from freeze-dried leaf samples following a modified Dellaporta CTAB method  using the following mixed linear model (MLM) with the lme4  approach implemented in GCTA software .To assess the genetic architecture of the studied traits, we fit a linear regression model using peak SNPs at the identified loci as independent variables against the traits’ BLUPS as the response variables. The relative allele substitution effects at each marker were assessed using pairwise https://phytozome.jgi.doe.gov/pz/portal.html) .Candidate loci were explored using a combination of GWAS p values, local linkage disequilibrium . The raw BLUPs values are presented in Supplementary Table S2. The heritability and variance component estimates associated with the studied traits are shown in Table The relative magnitude of phenotypic correlation pairs ranged from 0.27 between total carotenoid variation by colour chart (TC-chart) and root cortex colour to  − 0.65 for mature leaf greenness and petiole colour Fig. . We dete2 value as an estimate of LD decay using a window of 1 Mbp, followed by fitting a non-linear regression curve of LD versus distance. The whole-genome LD decay peaked at r2 of 0.349 and dropped to an r2 of 0.212 at a distance of 10 kb  was conducted to visualise the extent and degree of population structure present within the panel. While the first two principal components (PC) explained 7.4% of the total phenotypic variation, PC3 to PC10 together explained about 14% of the total variation . The favourable allele at this marker (T) was found to occur at high frequency in the population (Freq > 0.66). The two additional loci on chromosome 14 are tagged by markers S14_4626854  and S14_9004550 . The SNP at the CMD2 locus had a larger effect  compared to the two new loci on chromosome 14 .The genetic basis of CMD resistance has been studied extensively using bi-parental linkage mapping . The second locus was tagged by marker S6_20589894 . Additional loci on chromosomes 12 , 15 , and 16  were detected from the MLMe analysis. Along with the new locus on chromosome 6, these regions have not been previously reported to be associated with dry matter content variation. They are suitable for further genetic studies including identification of underlying candidate genes.GWAS for variation in dry matter content following the MLMi analysis revealed two major loci, of which one was previously reported . The second locus occurred on chromosome 12 with SNP S12_6055806 showing the strongest association with the trait . Analysis of the same trait using the MLMe approach uncovered several other regions scattered across chromosomes 3, 4, 6, 8, 9, 14, 15, 16, and 18.The MLMi-based GWAS for harvest index, the ratio of fresh root weight to total plant weight, uncovered two genomic regions that are significantly associated with the trait. The first peak is in chromosome 2, tagged by SNP S2_2809137  and S2_6566608 , respectively. A single genomic region on chromosome 3 (4.54 Mbp) was found to be linked to periderm colour. The most significant SNP at this locus (S3_4545411) had a p value of 1.4 × 10−123.GWAS for outer cortex colour uncovered two association signals located on chromosomes 1 (3.05 Mbp region) and 2 (6.56 Mbp region) and tagged by SNPs S1_3047840  and 25.30 Mbp region . Three loci were found to be associated with stem colour variation, one in chromosome 2 and two on chromosome 8. The most significant locus was around 13.6 Mbp region of chromosome 8 and is tagged by marker S8_13604799 . The other two loci were tagged by SNPs S2_13928566  and S8_22630799 .Two association signals were detected for plant type from MLMe while no marker passed the Bonferroni threshold in the MLMi analysis. The detected loci jointly occurred on chromosome 1 at 2.19 Mbp region . Notably, the same SNP was found to underlie the variation in mature leaf greenness. It is therefore not surprising that these traits are negatively correlated in our population. SNP effect analysis showed that while allele “T” at S1_23452638 had a positive effect on petiole colour, the same allele showed a negative effect on leaf greenness. Regression of the marker on the traits for leaf colour and petiole colour returned an R2 0.57 and 0.62, respectively.Genetic architecture of leaf morphology traits showed that one to three major loci control them, indicating simple genetic architecture. We found a single genomic region around 23.45 Mbp of chromosome 1 to be associated with leaf petiole colour and is tagged by SNP S1_23452638  followed by the markers on chromosome 8  and 3 . Multiple regression returned an R2 of 0.31 for this trait suggesting either a more complex architecture or imprecise scoring of the variation in the trait. The GWAS result for leaf shape uncovered two major loci on chromosome 15 occurring around 10.27 Mbp and 20.57 Mbp regions. The first peak tagged by SNP S15_10273255 was highly significant (3.7 × 10−174) while the second peak was tagged by S15_20573383 . Fitting linear model with the two top SNPs for this trait returned R2 of 0.40.Variation in the colour apical leaves was found to be associated with three loci occurring on chromosomes 2, 3, and 8. The most significant marker was S2_6086714 . Most of the morphology and colour related traits for leaves and roots had between 1 and 3 peaks of association except apical leaf pubescence with six loci. Peaks associated with variation in total carotenoid content and resistance to CMD had a large effect . The major loci controlling these traits had known candidate genes reported previously. Still, the new loci identified in these as well as the other traits are attractive candidates for follow-up studies.To assess the genetic architecture of the studied traits, we fit a linear model using peak SNPs at the identified loci Table  as indepT-A-C, respectively. Two hundred fifty-one genotypes were fixed for the favourable alleles in the population. Their average CMD severity BLUP was the lowest among all haplotype combinations . We also note dominance allele effect at the first SNP which is linked to the CMD2 locus, which agrees with previous results from biparental QTL studies  at each major-effect locus were chosen. Allelic substitution effects for these markers are shown in Supplementary Figure S3. Selected traits include resistance to CMD and CGM, increased dry matter and carotenoid content. For CMD resistance, the haplotypes at the top SNPs S12_7926132 , S14_4626854 (A/G) and S14_9004550 (T/C) are T-C-C-G, respectively. Alleles associated with increased pubescence particularly for loci that co-locates with CGM resistance of chromosomes 8 (S8_6409580 (C/G)) and 12 (S12_5524524 (C/T)) are C-T.The CGM resistance-linked haplotype for SNPs S1_28672656 (A/T), S8_6409580 (C/G), S12_5524524 (C/T) and S17_23749968 (A/G) are T-G-G, respectively. In contrast, that for chromosome 5 is T. The favourable haplotype for the two loci on chromosome 8 is A-T while for chromosomes 15 and 16 SNPs it is T-T, respectively. No individuals in the population were fixed for the favourable alleles across all the SNPs.Several SNP loci from chromosome 1 , 5 (S5_3387558), 8 , 15 (S15_7659426), and 16 (S16_484011) were associated with increased carotenoid content. The favourable haplotype for chromosome 1 SNPs are A. We note that this marker also co-located with the major locus for carotenoid content and allele A has a non-favourable effect in carotenoid content. The favourable haplotypes at the other loci on chromosomes 6 (S6_20589894 G/A), 12 , 15 (S15_1012346 C/T) and 16 (S16_25663808 T/C) are G-C-T-C. A total of 61 genotypes fixed for the favourable alleles at the SNPs on chromosomes 1, 6 and 12. Their average dry matter BLUP was the highest among all haplotype combinations . Finally, pairwise comparisons of genotypic classes at each locus were found to be significant  across all loci except for SNP S16_484011 for carotenoid content and S16_25663808 for dry matter content .The favourable allele at the most significant dry matter locus on chromosome 1  is The most significant GWAS peaks were further investigated for the presence of potential candidate genes using local linkage disequilibrium, coupled to gene annotation. We matched the highly significant SNP markers identified in the MLMe analysis with gene annotation in the regions up and downstream derived from phytozome online database. Seventeen candidate genes that colocalised with the identified putative genomic loci on height chromosomes were retrieved from the cassava genome available on phytozome v.12.1 and are presented in Table Several of these identified genes were further selected and highlighted based on their significance within a given biological pathway. Additionally, we provide regional Manhattan plots for each locus-trait combination in Supplementary Figure S4. These plots include candidate genes within 100 kb of the top SNP marker  with some adjustments based on the extent of local linkage disequilibrium with the candidate SNP.Three genes associated with total carotenoid content were identified on chromosomal regions 1, 5 and 15. Of these, Manes.01G124200 (Phytoene synthase) occurred within the previously reported genomic region  and Manes.01G123800 (sucrose synthase) previously reported  is located 24 Kbp away Manes.02G035900 a homologue of BFRUCT4 (vacuolar invertase) a key enzyme in sucrose hydrolysis and involved in the export of reduced carbon sink organs such as roots . Furthermore, the population was derived from two successive cycles of recurrent selection using elite parents with good breeding values for yield, dry matter content and resistance against CMD . GWAS analysis using PCA and the kinship matrix gave very similar results to the analysis that considered kinship alone in controlling for spurious associations. For this reason, as well as for computational efficiency, we used the MLM model for the full analysis.Our data replicated the previously identified associations for CMD and CGM resistance traits, dry matter and carotenoid content. Also, they showed more robust evidence of a major gene effect in this larger sample. In addition to the confirmed loci, we uncovered additional genomic regions with significant associations that were previously not reported. For example, two additional regions on chromosome 14 were found to contribute to increased resistance to CMD virus and present useful targets for further genetic analysis. Genotypes carrying favourable resistance alleles at these as well as the CMD2 major effect locus on chromosome 12 exhibit high levels of resistance against CMD and rarely show symptoms. However, despite the concerted global efforts to identify the causal gene underlying the CMD2 locus in cassava, there has not been any breakthrough in cloning and functionally identifying the actual gene. We hope that these resources will guide the cassava community in narrowing down on the candidate genes to carry out the functional analysis.Although our primary interest was in traits that are under active breeding selection, we also measured several morphological traits that we knew to be heritable. Among the morphological traits presented here, to our best knowledge, only leaf shape and apical leaf pubescence were previously reported  Supplementary material 2 (XLSX 736.9 kb)Below is the link to the electronic supplementary material.


Interestingly, SNP93_ALDH7A1 was not significant (p = 0.459) in Miyazaki cattle, and SNP37_ICAM1 showed a weakly significant association (p = 0.043) in Hyogo cattle. Thus, each population would likely have different QTGs and QTNs for beef marbling in the QTL region. In the Hyogo population, it was not possible to determine the accurate range of the linkage disequilibrium (LD) block in LD block analysis because of a strong LD structure throughout the assessed region. In Miyazaki cattle, however, an LD block containing SNP37_ICAM1 had a range of 15.8–16.1 Mbp, suggesting that QTNs would be located within this region. The functions of 19 genes in the LD block were investigated. ICAM1 is known to play an important role in adipocyte differentiation; given this function and the effect of amino acid substitution, SNP37_ICAM1 was identified as a promising candidate QTN for beef marbling. Further research on the effect of SNP37_ICAM1 on adipocyte differentiation is expected to provide insights into the mechanism underlying beef marbling formation.In our previous study, we used genome resequencing to detect all candidate polymorphisms within a quantitative trait loci (QTL) region for beef marbling reported previously at 10–30 Mbp on bovine chromosome 7, and we selected 6044 polymorphisms as candidate quantitative trait nucleotides (QTNs). In the present study, we aimed to identify quantitative trait genes (QTGs) and QTNs in this QTL region by verifying the effect of SNPs on beef marbling in two Japanese Black cattle populations using a Dynamic Array integrated fluidic circuit. In total, 96 selected SNPs were genotyped in 441 and 529 animals in Hyogo and Miyazaki cattle populations, respectively. The most significant  Japanese Black is the main breed of beef cattle in Japan, and beef marbling is abundant in the meat of this breed . Beef maSLC27A6, was identified as a putative candidate polymorphism, and the effect of this SNP on beef marbling was verified. Indeed, SLC27A6 K81M had a more significant p-value (p = 0.0009) than the top SNP (p = 0.0049), suggesting that it was a promising candidate QTN for beef marbling. However, additional verification of the remaining candidate polymorphisms is required to identify all candidate QTNs and QTGs. Furthermore, verification of the effects of these SNPs using an additional population will also help validate the QTNs.In a previous study, we investigated the quantitative trait loci (QTLs) for beef marbling by performing a genome-wide association study (GWAS) in Japanese Black cattle in Hyogo prefecture, and we identified a candidate region (10–30 Mbp) in bovine chromosome 7 (BTA7)  that wasTherefore, in the current study, we used a Dynamic Array integrated fluidic circuit (IFC) to verify the effects of the remaining candidate polymorphisms in two cattle populations from Hyogo and Miyazaki prefectures, with the aim of identifying QTGs and QTNs in the aforementioned QTL region.All experiments in the current study were conducted according to the Kobe University Animal Experimentation Regulations. We used two Japanese Black cattle populations bred in Hyogo and Miyazaki prefectures. The Hyogo population comprised 441 cattle (352 steers and 89 heifers), which were produced from 7 sires and slaughtered at 31.69 ± 1.24 months of age on average. The Miyazaki population comprised 529 cattle (477 steers and 52 heifers), which were produced from 6 sires and slaughtered at 29.10 ± 1.62 months of age on average. The average BMS scores in the Hyogo and Miyazaki populations were 5.66 ± 1.76 and 6.08 ± 1.96, respectively. Genomic DNA was extracted from 50-mg longissimus cervicis muscle samples using a standard phenol–chloroform method. An ethical review and approval for use of these samples were not required because the samples were collected from cattle in slaughterhouses that were slaughtered for sale on the market.In total, 96 SNPs were selected from 6044 candidate SNPs based on gene functions and annotations for each polymorphism . InformaGenotyping was performed using a Fluidigm Biomark HD system . First, preamplification was performed using specific target amplification and locus-specific primers according to the manufacturer’s instructions. Second, sample mixes for 96 individuals and assay mixes for 96 SNPs were prepared for genotyping using the 96.96 IFC. The sample and assay mixes contained the preamplification products for each individual and allele-specific primer 1 and 2 for each SNP, respectively. The composition of the sample and assay mixes followed that described in the manufacturer’s protocol. Each 5-μL sample mix and 4-μL assay mix was separately inletted into the IFC. Amplification was performed using the following thermocycling protocol: 70 °C for 30 min; 25 °C for 10 min; 95 °C for 5 min; four cycles of 95 °C for 15 s, 64 °C to 61 °C (decreasing by 1 °C per cycle) for 45 s, and 72 °C for 15 s; and 34 cycles of 95 °C for 15 s, 60 °C for 45 s, and 72 °C for 15 s. Fluorescence data collected from the Fluidigm Biomark HD system were analyzed using Fluidigm SNP Genotyping Analysis software to classify the samples into three genotypes based on the fluorescence intensities of FAM and HEX.ALDH7A1 and SNP37_ICAM1 as fixed effects in the Hyogo and Miyazaki populations, respectively. Differences between least-squares means for genotypes within a gene were assessed using Tukey–Kramer’s honestly significant difference test. LD blocks were constructed using HAPLOVIEW 4.0 [ANOVA was used to assess the effects of all genotyped SNPs on BMS. The analytical model for the Hyogo population included the effect of sire, sex, slaughter year, slaughter month, genotype, and linear and quadratic covariates for the age at slaughter. A similar analytical model was applied to the Miyazaki population. However, the effects of slaughter year and month were removed because they were not statistically significant in the Miyazaki population. We also performed ANOVA in considering the genotypes of SNP93_VIEW 4.0 .Using the Fluidigm Biomark HD system, the 96 SNPs selected as candidate polymorphisms were genotyped in 441 and 529 animals in the Hyogo and Miyazaki populations, respectively. Information on the 96 SNPs and genotyping results for each population are shown in p-values and a p-value plot are provided in p < 0.05), and the most significant p-value was detected in an SNP in a splice region of ALDH7A1 . In Miyazaki cattle, seven SNPs from seven genes were statistically significant (p < 0.05), and the most significant p-value was detected in a missense SNP in the intercellular adhesion molecule-1 (ICAM1) .The effects of 67 and 65 SNPs on the BMS in the Hyogo and Miyazaki populations, respectively, were assessed using ANOVA  in the Hyogo population but not significant (p = 0.4593) in the Miyazaki population. Comparing the least-squares means between genotypes, significant differences were detected among AA, AC, and CC in Hyogo cattle, suggesting that animals with the A allele have a higher BMS score than those with the C allele. In the Miyazaki population, no significant differences were observed among genotypes.ICAM1 were 0.31 and 0.38 in the Hyogo and Miyazaki populations, respectively. This SNP was most significant (p = 3.33 × 10−4) in the Miyazaki population and showed a weakly significant association (p = 0.043) in the Hyogo population. Comparing the least-squares means among genotypes, a significant difference was detected between AA and AG in the Miyazaki population, suggesting that animals with the G allele have higher BMS scores than those with the A allele. In Hyogo population, no significant differences were observed among genotypes.The minor allele frequencies of SNP37_SLC27A6, was verified as a candidate polymorphism for beef marbling. However, in the current study, 78 SNPs were verified simultaneously, and 4 polymorphisms, namely, an SNP in the 3′-untranslated region (UTR) of LYL (p = 6.83 × 10−5), an SNP in the 3′-UTR of NACC1 (p = 6.83 × 10−5), a missense polymorphism of ALDH7A1 (p = 3.75 × 10−5), and a splice region of ALDH7A1 , were more strongly associated with the BMS than p.Lys81Met of SLC27A6. These results demonstrate that the IFC is a powerful tool in verifying a large number of candidate SNPs.We investigated the effects of 96 candidate polymorphisms on the BMS in 441 Japanese Black cattle in Hyogo. Of the 78 SNPs that were successfully genotyped, 48 were significantly associated with the BMS. In our previous study, one SNP, the p.Lys81Met polymorphism of ALDH7A1 were considered as fixed effects, the significance of almost all SNPs in the assessed region decreased, indicating that there was a single QTL in the region and that a QTN would be in LD with SNP93_ALDH7A1. Further analysis of the LD block region containing SNP93_ALDH7A1 will likely lead to identification of QTGs and QTNs. However, LD block analysis indicated that it was not possible to determine the range of the LD block in the Hyogo population because a strong LD relationship was found throughout the candidate region (10–30 Mbp). For example, a moderate LD relationship (r2 = 0.37) was observed between SNP37_ICAM1 (16.05 Mbp) and SNP93_ALDH7A1 (28.59 Mbp), even though they are >10-Mbp apart. Therefore, it was necessary to condense the candidate area by conducting similar verification analysis using another Japanese Black population and comparing the LD block results. Accordingly, we also investigated the effects of these SNPs in the candidate area on Miyazaki cattle.When the genotypes of SNP93_ICAM1, a missense polymorphism (p.Ala332Thr) of ICAM1, was the most significant SNP. In contrast, the most significant SNP in Hyogo cattle, SNP93_ALDH7A1, was not significant in Miyazaki cattle. Moreover, SNP37_ICAM1 was only weakly significant in the Hyogo population compared with the strong association of other SNPs, including SNP93_ALDH7A1, suggesting that SNP37_ICAM1 was unlikely to be in strong LD with QTNs in Hyogo cattle. Overall, these results suggest that each population likely has different QTLs for beef marbling in the assessed regions. In previous studies, the assessed region has been reported as a QTL for BMS-related traits; therefore, it is considered to be derived from the same QTN. However, our results revealed that there were at least two different QTNs depending on the cattle population. The effects of QTLs on the BMS are expected to be clarified by identifying the responsible QTN in each population.In the Miyazaki population, 76 SNPs were successfully genotyped, and SNP37_ALDH7A1, is located near the end of the candidate region (28.6 Mbp); therefore, the peak significance of the SNPs is unclear. In further research, it will be important to condense the candidate region by further expanding the target region, especially downstream of the 30-Mbp region, and increasing the number of markers used for verification. In the Miyazaki population, the LD relationship among SNPs in the assessed region was weaker than that in the Hyogo cattle, and an LD block containing SNP37_ICAM1 had a range of 15.8–16.1 Mbp, suggesting that QTNs would be located within the region. Among 19 genes that were located in the region  family contains critical transcription factors that function with adipogenic genes [C/EBPβ is expressed in preadipocytes to induce adipocyte differentiation [C/EBPβ to enhance its transcriptional activity in relation to gene expression was required for cell differentiation into adipocytes. This functional relationship implies that MAPKs, especially ERK and p38, are important factors for adipogenesis.The CCAAT enhancer binding protein , suggesting that ICAM1 is involved in cell differentiation [ICAM1 is highly expressed in mouse and human preadipocytes in vivo [ICAM1 expression was increased only at the preadipocyte stage during the differentiation of MSCs into adipocytes, with low expression observed in interstitial progenitor cells and mature adipocytes. On the basis of these previous findings, ICAM1 likely enhances adipogenesis via activation of ERK and p38 in preadipocytes, resulting in an increase in mature adipocytes.Xu et al. found that overexpression of ntiation . Merrick in vivo . The latICAM1 likely plays an important role in adipose differentiation and may affect the expansion of adipose tissue. SNP37_ICAM1, which had the most significant association with the BMS of the candidate SNPs, causes an amino acid substitution from alanine to threonine; therefore, SNP37_ICAM1 could act as a responsible polymorphism in beef marbling by altering the formation and function of the ICAM1 protein. Additional research on the effects of this missense polymorphism on the ICAM1 protein and determination of how it affects adipocyte differentiation will help elucidate the mechanism underlying beef marbling formation.In summary, In two Japanese Black cattle populations, we examined a number of candidate SNPs for a previously reported QTL to identify QTGs and QTNs. Our results suggested that each population likely has a different QTL, even though they are the same breed. Furthermore, in one population, we identified a promising QTG and QTN for beef marbling. Further detailed analysis is needed to identify the QTG and QTN for the QTL in the other population; nevertheless, our findings improve our understanding of the QTLs for beef marbling in beef cattle breeds.


Oryz asativa L.) accessions were used to perform a genome-wide association study (GWAS) on three root-related traits in rice, including main root length (MRL), average root length (ARL), and total root number (TRN). As a result, 13 quantitative trait loci (QTLs)  significantly associated with the three traits were identified, among which three  were overlapped with OsGNOM1, OsARF12 and qRL8.1, respectively, and ten were novel QTLs. Moreover, we also detected epistatic interactions affecting root-related traits and identified 19 related genetic interactions. These results lay a foundation for cloning the corresponding genes for rice root structure, as well as provide important genomic resources for breeding high yield rice varieties.Root architecture is a determinant factor of drought resistance in rice and plays essential roles in the absorption of water and nutrients for the survival of rice plants. Dissection of the genetic basis for root structure can help to improve stress-resistance and grain yield in rice breeding. In this study, a total of 391 rice ( Oryz asativa L.) is one of the most important crops, providing staple food for more than half of world population . Ea. Ea50]. The seeds of all 391 rice accessions were dried in a 42 °C oven for 7 days to break dormancy. Twenty mature and full seeds were selected, washed with distilled water, and germinated in a 32 °C incubator for 64 h. Eight seeds of each variety germinated evenly were placed in the germination box, and the germination test was carried out at 30 °C in the light incubator. On the 10th day of germination, the root length and total root number (TRN) of each material were measured . The lenhttps://snp-seek.irri.org/index.zul, accessed on 15 January 2021) [https://www.opensourceagenda.com/projects/vcf2dis, accessed on 20 February 2021) based on filtered files, and the phylogenetic tree was constructed using iTol . GCTA [https://speciationgenomics.github.io/ADMIXTURE/, accessed on 18 May 2021) software, and the genetic structure of the whole population was predicted. In order to construct the kinship matrix required for the mixed linear model as the covariance matrix, this study used the software Tassel (version 5.0) [The resequencing data of 391 rice accessions have been published in NCBI, and the variation site information can be obtained by SNP-Seek database (ry 2021) ,53. We ury 2021) , MAF (0.1). GCTA  (versionion 5.0)  to analyXian rice population, and Geng rice population. MRL, TRN, and ARL of the whole population, Xian, and Geng rice were analyzed by GWAS using R software package rMVP [p value of dominance was 1.0 × 10−5 to control the genetic false positive error rate of the population. The Manhattan and quantile graphs of GWAS results were created using ‘rMVP’ package of R. The LD attenuation of different populations, different chromosomes, and different genome regions in the same chromosome was different. Hence, for a certain site, the resolution is determined by the local LD attenuation level. The average LD decay distance of accession resources was analyzed by software PopLDdecay (version 3.4), and the screening standard was the physical distance when R2 was reduced to half of the maximum. The LD decay distance of the whole population, Xian population, and Geng population was 350 kb, 125 kb and 346 kb, respectively  was used to search candidate genes in the 700 kb genomic region of selected important SNPs. Among all candidate genes, four types of genes, including expression protein, hypothetical protein, retrotransposon, and transposon, were excluded. ANNOVAR software was used to further select the polymorphism significantly related to GWAS trait variation and predicted to induce amino acid exchange or change splicing connection as candidate genes [In order to identify candidate genes related to MRL, TRL, and ARL, the rice genome annotation project  > 3.0) of MRL, TRN, and ARL under GLM for epistasis interaction analysis. Epistasis method of 3 V multi-locus random SNP effect mixed linear model (3VmrMLM) [We selected 10,801, 4227, and 6056 SNPs from GWAS (−Log3VmrMLM)  was usedXian (n = 213), Geng (n = 147), Aus (n = 21), and admix population (n = 10)  .Xian rice was 15.14 cm and 9.71 cm, respectively. There was no significant difference in mean MRL between Xian rice and Geng rice  ≥ 5), respectively  were identified for the Geng panel and ten QTLs  were identified for the Xian panel  and one TRN-related QTL (TRN11.1) were detected in the Xian rice population, and no QTL co-located with ARL was detected  and mixed linear model (MLM) were used to conduct GWAS on three root traits . Considering the attenuation distance of linkage disequilibrium (LD) in rice, adjacent SNPs with spans less than 700 kb were defined as single QTLs, and the SNP with the lowest ectively . The GLMectively . In genemosome 8 a,b. QTLsmosome 9 c,d. Threwith TRN e,f. One an panel . Five MRdetected . In Gengpulation . There wc traits c–e. In oqMRL1.1 qMRL1.2 qMRL3.1 qMRL3.2 [qMRL3.3 qMRL4.1 [qMRL7.1, and qMRL8.1 [qMRL1.1, qMRL1.2, qMRL3.1, qMRL3.3, and qMRL7.1). Further annotation with the ANNOVAR software screened the genes that can predict the induction of amino acid exchange or change the splicing point. In qMRL1.1, qMRL1.2, qMRL3.1, qMRL3.3, and qMRL7.1, there were respectively two, three, one, fifteen, and ten genes causing non-synonymous mutations  associautations . Intereste genes . The QTLte genes a–c. Multte genes g,f. HapAmutation e. These  or HapB . In the ectively . Multipland HapB . These rXian rice, and HapB was mainly distributed in Geng rice (Xian rice carrying HapD was significantly lower than that of Geng rice carrying HapB (In the two loci (qARL1.1 and qARL9.1) significantly associated with ARL, the candidate region in chromosome 9 was mapped from 6862 to 7642 kb a–c. Thiseng rice d. The ARing HapB e. Based Geng rice. The difference between HapC and HapA was in 35 non-synonymous mutations , and screened eight (qTRN9.1), one (qTRN9.2), and 17 (qTRN11.1) non-synonymous mutation genes in 350 Kb upstream and downstream of the lead SNP in the three QTLs, respectively . Accordiutations a,d. Multutations e. These 10 (p) > 3) results of GLM. As a result, 19 significant loci were detected in MRL (n = 9), ARL (n = 6) and TRN (n = 4), among which 13 were associated with GWAS results whereas six were not   . When onRice root is a very important and complex system controlling rice yield and adaptation to a complex environment . HoweverGWAS is a genetic survey of the entire genome to detect variations associated with traits in natural populations and is a powerful method for profiling complex traits. Its efficiency is largely determined by marker density, population size, and statistical methods ,39. Prevn = 8), ARL (n = 2), TRN (n = 3)) ) . In refevel QTLs . qMRL1.1 and qARL1.1) of LOC_Os01g52250 in Xian rice  associated with rice ARL. Recent studies have reported that OsHKT4 mutant plants are highly sensitive to salt stress. Compared with the wild type, the mutant exhibited retarded growth and reduced fresh weight and stem length. OsMYBc can regulate the expression of OsHKT4 and affect the salt tolerance of rice, thereby affecting its development [OsMYBc regulates salt tolerance of rice by controlling ARL. These results provide a new theoretical basis for research on the regulation mechanism of rice root length in the future. It has been reported that the slender rod gene SLR1 encodes a DELLA protein, which is a negative regulator of GA signaling [OsMPK15 associated with TRN in our candidate genes. LOC_Os11g17080 (OsMPK15) is a cloned gene on chromosome 11 and annotated as a mitogen-activated protein kinase. Knockout of OsMPK15 can lead to the constitutive expression of disease persistence-related (PR) genes, increase the accumulation of reactive oxygen species triggered by chitin, enhance the accumulation of SA and JA, and significantly promote the resistance of rice [OsMPK15 also regulates rice TRN by controlling SA and JA. Our results provide a reliable resource for exploring genes related to rice root length traits. In Oryza sativa, there are ancient and definitive differences between the two major subspecies, Xian and Geng, but the breeding history suggests a finer genetic architecture. In this study, there were significant differences in genetic diversity between 213 Xian and 147 Geng rice accessions. A total of 5 QTLs were detected in Xian and the total subpopulation, whereas no QTLs were detected in Geng. During the domestication process, most homozygous individuals of rice were composed of homozygous individuals, and infiltration from other populations was limited, resulting in genome diversity within and among rice populations.Rice root length and root growth angle are also involved in the resistance ability of rice to drought , which aian rice  and the ian rice , and the pathway . JA is a pathway . It has  in rice . OsRTH1 elopment ,72. Therignaling . It can  of rice . We specp = 1.0 × 10−3. We detected a total of 19 pairs of genes with significant epistasis, among which six were not detected in the two models of GWAS (Rice root is a complex system affected by genetic factors, environment, and population stratification. In GWAS, SNPs are usually detected to understand the association between the whole genome and the traits of interest. Unit point analysis is usually used to estimate the effect of single SNPs. However, it can only identify SNPs with relatively strong effects, while missing some important SNPs with minor effects ,76. We u of GWAS . These gIn general, we obtained a set of loci significantly associated with rice root traits by GWAS and epistasis analysis of 391 rice accessions. The candidate genes were further screened by haplotype block structure analysis of SNPs that are significantly associated with agronomic traits and functional annotation of each gene. As expected, although a large linkage disequilibrium contains many SNPs in candidate regions, our results demonstrate that the number of candidate genes can be significantly reduced by combining haplotype block structure with gene function annotation. Further epistatic analysis improved the accuracy of genome prediction. Generally, our results should be helpful for future gene functional analysis and provide valuable information for rice gene cloning research.Our identified genetic regions associated with root development traits and the newly identified loci may contribute to marker-assisted QTL pyramid / gene introgression breeding programs in the future to stabilize and improve rice yield. It also provides a rich source of information on the natural genetic variation in the evolution, domestication, and breeding of indica and japonica rice associated with seed root length and other adaptive traits. Future research can be focused on verifying the role of these candidate genes and their functional variations. Genetic transformation and DNA insertion mutation screening will be used to determine whether these genes affect rice root structure.