Beta vulgaris L.) is an economically essential sugar crop worldwide. Its agronomic traits are highly diverse and phenotypically plastic, influencing taproot yield and quality. The National Beet Medium-term Gene Bank in China maintains more than 1700 beet germplasms with diverse countries of origin. However, it lacks detailed genetic background associated with morphological variability and diversity.Sugar beet (BVRB_5g097760); nudix hydrolase 15 (BVRB_8g182070) and TRANSPORT INHIBITOR RESPONSE 1 (BVRB_8g181550); transcription factor MYB77 (BVRB_2g023500); and ethylene-responsive transcription factor ERF014 (BVRB_1g000090) were predicted to be strongly associated with the taproot traits of root groove depth (RGD); root shape (RS); crown size (CS); and flesh colour (FC), respectively. For the aboveground traits, UDP-glycosyltransferase 79B6 (BVRB_9g223780) and NAC domain-containing protein 7 (BVRB_5g097990); F-box protein At1g10780 (BVRB_6g140760); phosphate transporter PHO1 (BVRB_3g048660); F-box protein CPR1 (BVRB_8g181140); and transcription factor MYB77 (BVRB_2g023500) and alcohol acyltransferase 9 (BVRB_2g023460) might be associated with the hypocotyl colour (HC); plant type (PT); petiole length (PL); cotyledon size (C); and fascicled leaf type (FLT) of sugar beet, respectively. AP-2 complex subunit mu (BVRB_5g106130), trihelix transcription factor ASIL2 (BVRB_2g041790) and late embryogenesis abundant protein 18 (BVRB_5g106150) might be involved in pollen quantity (PQ) variation. The candidate genes extensively participated in hormone response, nitrogen and phosphorus transportation, secondary metabolism, fertilization and embryo maturation.Here, a comprehensive genome-wide association study (GWAS) of 13 agronomic traits was conducted in a panel of 977 sugar beet accessions. Almost all phenotypic traits exhibited wide genetic diversity and high coefficient of variation (CV). A total of 170,750 high-quality single-nucleotide polymorphisms (SNPs) were obtained using the genotyping-by-sequencing (GBS). Neighbour-joining phylogenetic analysis, principal component analysis, population structure and kinship showed no obvious relationships among these genotypes based on subgroups or regional sources. GWAS was carried out using a mixed linear model, and 159 significant associations were detected for these traits. Within the 25 kb linkage disequilibrium decay of the associated markers, NRT1/PTR FAMILY 6.3 (The genetic basis of agronomical traits is complicated in heterozygous diploid sugar beet. The putative valuable genes found in this study will help further elucidate the molecular mechanism of each phenotypic trait for beet breeding.The online version contains supplementary material available at 10.1186/s12864-023-09522-y. Amaranthaceae family, sugar beet (Beta vulgaris L.) is native to the western and southern coasts of Europe and is mostly biennial, with the exception of some wild beets without swollen roots [Within the en roots . The tapen roots . In addien roots . Due to Beta species, including other cultivated beet crops and their wild relatives, is quite high [The agronomic characteristics of sugar beet, such as granularity, leaf bush type, root size, and root groove depth, are important for agricultural practices, and they are also of concern to growers and sugar manufacturers. Further genetic improvement of sugar beet will require an understanding of the genomic regions and genes that govern these specific traits. Sugar beet is a self-incompatible, typically outcrossing crop, and the total genetic diversity of sugar beet along with other ite high . In addiite high . Therefoite high  due to iRz2 in a crop wild relative (CWR) population of < 200 wild beets [B. vulgaris peptide transporter (PTR) gene, a carrier that influences root elongation [Understanding the genetic basis of phenotypic traits is a major challenge in crops, and sugar beet is no exception. Most heritable components of agronomic performance, which are highly correlated with morphological values, can be assayed with genetics- and genomics-based methods. By analysing phenotypic and genotyping data, the genetic architecture of important agronomic and physiological traits such as α-amino nitrogen or sugar content in sugar beet was uncovered by detecting main-effect quantitative trait loci (QTLs) , and prold beets . Twenty-ld beets . Two extongation . Using pongation . By weigongation .Next-generation sequencing (NGS) technology provides an effective way to obtain genetic information on sugar beet, and many genes associated with important agronomic traits can be exploited, which could then be used to further improve breeding. Li et al. obtained a high-quality, chromosome-level genome assembly for the pure line IMA1 . These gA total of 977 accessions were evaluated under field conditions, and the 13 descriptive traits related to sugar beet phenotypes were screened for assessment. These germplasms displayed all phenotypic variations of these 13 traits (Table The genetic diversity and coefficient of variation (CV) of these phenotypic traits were different Table . The invGBS yielded approximately 894.027 Gb in total for the 977 sugar beet accessions Table . The amoA total of 4,561,550 SNP loci were detected with GATK software. After filtering under the conditions of DP4, MISS0.2 and MAF0.05, 170,750 high-quality SNPs were finally obtained. Among these SNPs, 108,952 were mainly located in intergenic regions. There were 5131 SNPs located in the 1 kb region upstream of the genes. A total of 5882 nonsynonymous mutations were found in exons of the chromosome coding region Table . High-quTo determine the evolutionary relationships among the 977 sugar beet accessions, a maximum likelihood-based phylogenetic tree was constructed using the NJ method based on the GBS-derived SNP genotypes. As shown in Fig. Using the nucleotide polymorphisms, we performed principal component analysis (PCA) to quantify the population structure of these 977 panels Fig. c. The prADMIXTURE software was used to calculate the genetic components of each panel Fig. e and f. 10 (p)) ≥ 4.5 were detected on all nine chromosomes  plots, which showed that the expected value (red line) was roughly equal to the observed value (red dot) after controlling for Q and K Fig. . A totalmes Fig. . Among tr2 for all pairs of linked SNP loci was used to assess the extent of LD decay in this study. As expected, the r2 value declined with increasing physical distance between markers. The average r2 for the whole genome decreased to half (0.18) of its maximum value at a 25 kb distance, which resulted in the inclusion of fewer and more accurate candidate genes within an LD block  was found close to SNC_025819.2_1587696, and TRANSPORT INHIBITOR RESPONSE 1 (BVRB_8g181550) was present in the effective region of four different markers, and this gene was also associated with RS  was identified downstream of SNW_017567490.1_421148 and SNW_017567490.1_421085 on chromosome 8  for PQ were predicted to be AP-2 complex subunit mu, trihelix transcription factor ASIL2 and late embryogenesis abundant protein 18 present in specific regions on chromosomes 2 and 5  downstream of SNW_017567346.1_122974 was discovered on chromosome 2  was found in the linked region of SNW_017567326.1_144308 associated with FC  might be indirectly involved in the variation in PT via the mutation of SNC_025817.2_22180890 on chromosome 6  and alcohol acyltransferase 9 (BVRB_2g023460) on chromosome 2, with the former also detected for CS  [P values was closest to the distribution of expected P values  . Accordiues Fig. , indicatues Fig. . It mighues Fig. -S8.BVRB_5g097760) encoding protein NRT1/PTR FAMILY 6.3 related to the SNW_017567419.1_180782 marker. The NRT/PTR family has been reported to transport nitrate, and many members have been shown to be essential for the development of lateral and primary roots. In a study aiming to identify SNPs linked to the root elongation rate (RER) in sugar beet, the SNP10139 sequence was mapped to the peptide transporter (PTR) gene, a carrier that influences root elongation [MdNRT2.1 has a direct role in adventitious root formation and development [BVRB_8g182070) encoding nudix hydrolase 15 in mitochondria was also associated with RGD. The product of this gene has the capacity to hydrolyse NADPH, which is an essential cofactor required for cell growth and proliferation in the main organs (roots and leaves) [BVRB_8g181550) encoding the protein TRANSPORT INHIBITOR RESPONSE (TIR) 1 was found to be associated with both RGD and RS. It has been proven that Tir2 is required for temperature-dependent hypocotyl elongation. TIR2 expression in the proximal root meristem is auxin sensitive, and root curvature is associated with increased TIR2 expression in the lower epidermal cells and concomitant loss of expression on the upper side [BVRB_1g000090) was associated with FC. ERFs have been proven to play important roles in stress responses, plant hormones and fruit ripening, including pigment changes. Colour change in fruits due to lycopene accumulation mainly results from the regulation of LCYb, which is activated by CsERF061 in citrus colouration through carotenoid biosynthesis [BVRB_2g023500) exhibited a strong potential association due to its involvement in the auxin response. In MYB77-knockout Arabidopsis thaliana, the expression of this auxin-responsive gene was greatly attenuated, and the lateral root density in the MYB77 knockout was lower than that in the wild type under low concentrations of indole-3-acetic acid and under low-nutrient conditions [The taproot, which is developed from principal root and hypocotyl tissues , is the ongation . In addielopment . Another leaves) . In a st leaves) , 38. A pynthesis . For CS,nditions . Auxin pR locus contains a dominant allele that leads to a red hypocotyl [BvCYP76AD1, which represents the R locus, encodes a cytochrome P450 enzyme that is required for betalain biosynthesis [BVRB_9g223780) and NAC domain-containing protein 7 (BVRB_5g097990), might be associated with beet HC. The former was characterized to be involved in anthocyanin accumulation in Medicago truncatula [BVRB_6g140760) was identified. In M. truncatula, this type of protein was found to participate in the processes of indirect somatic embryogenesis and symbiotic nodulation and to be involved in cell division activation and cell cycle control. The transgenic lines exhibited variations in root and hypocotyl growth, leaf and silique development, ploidy level, and leaf parameters [BVRB_3g048660) encoding phosphate transporter PHO1 was observed close to the significantly correlated marker SNW_017567376.1_673904. Phosphorus can promote plant photosynthesis, promote root development, and make the stem stronger, which is beneficial to the early growth of seedlings. PHO1 is a Pi efflux transporter responsible for xylem loading of Pi in organs such as roots [BVRB_5g106130 encodes AP-2 complex subunit mu, which is a medium subunit of the heterotetramer AP2. The A. thaliana mutant ap2m displays multiple defects in pollen production and viability and in elongation of staminal filaments and pollen tubes, all of which are pivotal processes needed for fertilization [BVRB_2g041790 is annotated as the trihelix transcription factor ASIL2. There are two transitions involved in the induction of the embryo maturation programme midway through seed development and its repression during the vegetative phase of plant growth. The trihelix transcription factors of Arabidopsis ASIL1 and ASIL2 have been proposed to repress maturation both embryonically and postembryonically [Lilium longiflorum was abundant in cytoplasmic granules of the vegetative cell until pollen maturation, but after hydration, it appeared in the elongating pollen tube wall [BVRB_5g106150 was found to associate with PQ and encode late embryogenesis abundant protein 18, which might influence pollen maturation. Sugar beet has no apparent epicotyl, and the basal leaves are clustered, with long petioles. The FLT mainly presents as semicrawling, which is very conducive to the absorption of light and the improvement in sugar beet photosynthetic efficiency. The transcription factor MYB77 (BVRB_2g023500) and alcohol acyltransferase 9 (BVRB_2g023460) might be involved in basal leaf clustering because of their putative role in the auxin response or ester biosynthesis [BVRB_8g181140), close to two markers, SNW_017567490.1_421148 and SNW_017567490.1_421085, was associated with C. Its expression might influence the cell division rate during the early stages of leaf development, similar to the F-box protein AtFBX92 [For sugar beet, the agronomic traits of leaves, hypocotyls, and pollen and other plant morphological traits are also very important, and they are closely related to growth and development and resistance to biotic and abiotic stresses. Sugar beet usually displays the red or green HC phenotype. It has been reported that the ypocotyl . The genynthesis . Howeveruncatula , 47. Forrameters . PL mighas roots . As sugaonically , 52. Latonically . Pollen ube wall . In thisynthesis , 56. Theynthesis . In this AtFBX92 .Nevertheless, there were still some traits that either could not be associated with candidate genes (SR) or were associated with genes whose function seemed to have no putative relationship with the traits themselves (GV and PW). The materials that we selected had high diversity, rich variation, and many SNP variations. During sequence alignment, such variants may be filtered out due to the low mapping rate, resulting in the loss of SNPs. These findings may also be due to the large difference in the quantity of these traits, which leads to the filtering out of variation as noise during the screening process.Here, we associated phenotypes (13 descriptive agronomic traits) and genotypes using 170,750 GBS-derived SNPs after precise evaluation of the population structure and genetic diversity of 977 sugar beet germplasms. Through GWASs, several candidate genes linked with 159 significant SNPs were identified, and a number of interesting genes were inferred to be functional in the morphological variation of taproots and the growth and development of sugar beet. Future sugar beet breeding efforts must make use of the genetic and genomic resources available for efficient improvement.https://www.cgris.net/query/croplist.php). The experiment was carried out in the Hulan Experimental field  of Heilongjiang University in 2015–2018. The field design followed a randomized block arrangement, with two rows of blocks and three repetitions. Each row is 10 m long and planted with approximately 55 sugar beets. Three sugar beet germplasms with stable agronomic traits were used as annual experimental controls. Thirteen agronomic, growth and development components and quality-related traits were evaluated, including pollen quantity (PQ), plant type (PT), hypocotyl colour (HC), cotyledon size (C), petiole width (PW), petiole length (PL), fascicled leaf type (FLT), root shape (RS), crown size (CS), root groove depth (RGD), skin roughness (SR), flesh colour (FC) and growth vigour (GV). These descriptive traits were investigated and recorded under field growth conditions and were assigned and defined according to “the Descriptors and Date Standard for Beet (Beta vulgaris L.)” [The 977 studied sugar beet germplasms came from 21 countries Table  and are ris L.)” .2), medium , and large (≥ 126.5 mm2). In the flourishing vegetative growth stage of sugar beet, PW is described as the width of the thickest part of the petiole in the middle layer of the plant’s leaf cluster, and 0.8 and 1.3 cm are the critical values defining narrow, medium and broad; PL is the length from the base of the petiole to the base of the longest leaf, where short, medium and long is classified as < 20 cm, ≥ 20 and < 32 cm, and ≥ 32 cm, respectively; the erect, semicrawl, and crawl types (FLT) are described according the angles (70° and 30°) between most of the plant’s petioles and the ground. During the sugar beet harvest period, we observed the external shape of the taproot , the depth of the root grooves (RGD) on both sides of the taproot, the smoothness of the taproot surface (SR), and the colour of the flesh inside the taproot skin (SR); the size of the crown taproot  was defined as small (< 10%), medium  and large (≥ 20%). The seedling growth vigour (GV) was divided into five levels and defined as the strength and degree of vigour of seedling growth after emergence and before seedling setting.In each experimental plot, PQ is the amount of pollen in the stamens of sugar beets during flowering. “Little” is described as “anthers are light yellow, with few pollen scattered after the anthers open”; The “medium” amount of pollen shows relatively yellow anthers, after which crack, a considerable portion of the pollen is scattered; “Much” means that the anthers are very yellow, and after they crack, a large amount of mature pollen is scattered. PT is described as the phenotype of the main stems and lateral branches of > 70% of sugar beets during their blooming stage. HC is the colour of smooth parts below the cotyledons of > 90% of sugar beet seedlings, and in terms of the cotyledon area, the cotyledon size (C) is divided into small  using Burrows‒Wheeler Alignment (BWA) software .A total of 977 accessions of sugar beet were genotyped using a GBS approach. Fresh leaf tissue was harvested from the seedlings and stored at -80 °C. The purified and integrated genomic DNA was quantified and digested with restriction endonuclease. Each sample was amplified after adding a connector with a barcode and was used to construct the GBS library. Then, sequencing was performed using the Illumina HiSeq PE150 platform. The original image data obtained by high-throughput sequencing were converted into raw data through base calling. After strict filtering of sequencing data, such as reads containing the connector sequence, paired reads with an N content exceeding 10% or low-quality (≤ 5) reads exceeding 50% in single-end sequencing, high-quality clean data were obtained. Then, the effective high-quality sequencing data were compared to the sugar beet reference genome (SAMtools (1.9) was used to transform the format of the .sam file and build an index to generate a .bai file. Genome Analysis Toolkit  and ANNOVAR  were usewww.evolgenius.info/evolview) using the NJ method , and ggplot2 package was also used to produce Fig. Pairwise genetic distances among the 977 beet accessions were calculated. Phylogenetic clustering was performed and displayed by EvolView  software to determine the associations using the incorporated PCA and kinship results from population structure analysis . Population structure and kinship can effectively reduce false-positive results in mixed models.10  ≈ 6.53) was used as the standard cutoff in GWAS analysis. Due to its excessive conservatism and strictness, there were very few markers associated p-values in sugar beet genome that can meet this standard. Thus, we adjusted the threshold to 4.5 according to our and others’ experience [P value of these high-quality variation sites in the beet genome, Manhattan  and UniProt (https://www.uniprot.org/). The target trait related genes and chromosome data were inputted into the online analysis tool GENESCLOUD, and the chord graph drawing function (https://www.genescloud.cn/chart/ActiveChordPlot) was used to visualize the relationship between them  plots of 13 descriptive traits constructed using a mixed linear model (MLM). PQ, pollen quantity; PT, plant type; HC, hypocotyl colour; C, cotyledon size; PW, petiole width; PL, petiole length; FLT, fascicled leaf type; RS, root shape; CS, crown size; RGD, root groove depth; SR, skin roughness; F, flesh colour; GV, growth vigour. Fig. S2 Manhattan plot and LD heatmap of the candidate genes for RS. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S3 Manhattan plot and LD heatmap of the candidate genes for C. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S4 Manhattan plot and LD heatmap of the candidate genes for PQ. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S5 Manhattan plot and LD heatmap of the candidate genes for CS. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S6 Manhattan plot and LD heatmap of the candidate genes for FC. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S7 Manhattan plot and LD heatmap of the candidate genes for PT. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p. Fig. S8 Manhattan plot and LD heatmap of the candidate genes for PL. The orange vertical line indicates the position of the associated SNPs, and the orange horizontal line indicates -log10p.


Plasmodium falciparum as a potential marker of progress towards elimination. Here we assess three established methods (i) single nucleotide polymorphism (SNP) barcoding , (ii) microsatellite genotyping , and (iii) varcoding  to identify changes in parasite population genetics in response to a short-term indoor residual spraying (IRS) intervention. Typical of high seasonal transmission in Africa, multiclonal infections were found in 82.3%  and 57.8%  of asymptomatic individuals pre- and post-IRS, respectively, in Bongo District, Ghana. Since directly phasing multilocus haplotypes for population genetic analysis is not possible for biallelic SNPs and microsatellites, we chose ~200 low-complexity infections biased to single and double clone infections for analysis. Each genotyping method presented a different pattern of change in diversity and population structure as a consequence of variability in usable data and the relative polymorphism of the molecular markers . Varcoding and microsatellite genotyping showed the overall failure of the IRS intervention to significantly change the population structure from pre-IRS characteristics . The 24-SNP barcode provided limited information for analysis, largely due to the biallelic nature of SNPs leading to a high proportion of double-allele calls and a view of more isolate relatedness compared to microsatellites and varcoding. Relative performance, suitability, and cost-effectiveness of the methods relevant to sample size and local malaria elimination in high-transmission endemic areas are discussed.A major motivation for developing molecular methods for malaria surveillance is to measure the impact of control interventions on the population genetics of  This coOI) > 1) .P. falciparum population genetics in a range of transmission settings to define linkage disequilibrium  which encode for the major variant surface antigen of asexual blood stages known as P. falciparum erythrocyte membrane protein 1 (PfEMP1)  using sequences encoding the immunogenic Duffy-binding-like α domain (DBLα) of PfEMP1 variants, defined as DBLα types. Analysis of the relationship between DBLα types and exon 1 of var genes in Malawi and Ghana has shown that each DBLα type, especially upsB and upsC types , is predominantly associated with a single var gene, and therefore DBLα type diversity acts as a suitable surrogate for var diversity per host and in the population , which are largely non-overlapping (var repertoires (termed MOIvar) present in an isolate by simply counting the number of DBLα types in an isolate (repertoire size) and then dividing by the median number of DBLα types amplified per genome  due to the non-overlapping population structure of var repertoires allowing multiple P. falciparum clones to accumulate within a human host in high transmission (To characterize (PfEMP1) . Varcodipulation . Prior ppulation . Parasitrlapping  likely drlapping . This mar genome . The metr genome . Varcodismission . Using tsmission .P. falciparum locally and be easily deployed in endemic areas, especially in high transmission, in a cost-effective way. This requires us to identify genotyping techniques that provide information with few genetic markers to analyze relatively small sample sizes in regional laboratories. Here we assess the performance of three field applicable genotyping methods, i.e., (i) SNP barcoding , (ii) microsatellite genotyping , and (iii) varcoding under the conditions of high seasonal transmission in Ghana, one of the 11 highest burden countries for malaria globally  and after (October 2015) the implementation of three rounds of indoor residual spraying (IRS) using non-pyrethroid insecticides, managed under operational conditions  and median densities (520 parasites/μL to 320 parasites/μL) were also observed pre- to post-IRS . Although we note that varcoding did detect a subtle shift towards less similarity of var repertoires after IRS suggesting the method is sensitive to decreased transmission creating fewer recombinant genomes as a consequence of less outcrossing. The results of this study provide useful information for high-burden countries in Africa looking at strategies to deploy molecular surveillance to assess changes in parasite diversity and population structure in relatively small sample sizes from sentinel sites in local endemic areas.Each genotyping method presented a different pattern of change of population diversity and structure when sampling monoclonal or low-complexity infections, consistent with marker variability. The 24-SNP barcode was least informative, largely due to the biallelic nature of SNPs leading to a high proportion of double-allele calls (DACs), whereas microsatellites showed high haplotype diversity with ten markers but no measurable change in population structure after the IRS. For this sample size of 200 isolates, 22.1P. falciparum data utilized in this study was collected from participants with microscopically confirmed asymptomatic P. falciparum infections  at the end of the wet season  from two proximal catchment areas  in Bongo District, Ghana (hereinafter referred to collectively as “Bongo”)  and 2015 (41.6%) (The “Bongo”) . Using a“Bongo”) . Bongo, “Bongo”)  where P. (41.6%) . Detaile (41.6%) . The stu2.22.2.1varcoding, the sequences encoding the DBLα tags of P. falciparum var genes were amplified by PCR, pooled, and sequenced on the Illumina MiSeq platform (2x300bp paired-end configuration)   and post-IRS (N = 545) surveys  . This hi surveys . The DBL surveys . For a d2.2.2The 24-SNP molecular barcoding was undertaken for the isolates selected for this comparative analysis  using a 2.2.3Microsatellite genotyping and sequencing methods utilized for the isolates included in this analysis  have bee2.3varcoding  was calculated based on the number of DBLα types in an isolate due to the limited overlap between DBLα isolate repertoires as previously shown, especially in high transmission , isolates with 46 to 90 non-upsA DBLα types were estimated to be carrying two P. falciparum clones , isolates with 91 to 135 non-upsA DBLα types were estimated to be carrying three P. falciparum clones , and so on.Multiplicity of infection by smission . To calc to upsA . Based oarcoding . Using tvar could only be estimated for those isolates with DBLα sequence data, resulting in 742 (91.8%) and 510 (93.6%) isolates in the pre- and post-IRS surveys, respectively  and post-IRS (360 parasites/μL) surveys were of similar magnitudes and ~5-9 times higher compared to those isolates with no DBLα sequencing data pre-IRS (160 parasites/μL) and post-IRS (40 parasites/μL). Consistent with other high-transmission areas in SSA, we found that the majority of P. falciparum isolates were composed of multiclonal infections, i.e., 82.3% and 57.8% for the pre- and post-IRS surveys, respectively . At the extreme of the range, MOIvar detected a maximum of 18 and 12 P. falciparum coinfections per isolate for the pre- and post-IRS, respectively  and low quality DBLα sequencing data  were not included in the subset of low-complexity isolates selected pre-IRS  and post-IRS  and parasitemia post-IRS   .2.5h), the number of alleles (A), and expected heterozygosity (He) were calculated for the SNP and microsatellite markers using the R package poppr 2.9.3 (var (or DBLα type) expected heterozygosity (Hv) (var expected homozygosity (1 - Hv), and thus by calculating the pairwise type difference (PTD = 1 - PTS) statistic we can obtain var expected heterozygosity (Hv).To estimate genetic diversity, the number of unique multilocus haplotypes (pr 2.9.3 . For theity (Hv) . If each2.6AS) statistic, using only isolates with the “monoclonal infections” and complete multilocus infection haplotypes   and microsatellites . The PAS scores were calculated using PAS = nab/nl where nab is the number of alleles shared between the isolate haplotypes and nl is the number of loci examined . To measure similarity for the DBLα types we used the PTS statistic, calculated as PTS = ab/(na2n + b)n, where na and nb are the number of unique DBLα types in the repertoires of isolate a and isolate b, and nab are the number of DBLα types shared between isolate a and isolate b  and represent the proportion of sharing (or relatedness) between two isolates with scores ranging from 0  to 1 . Both the PAS and PTS statistics are measures of identity-by-state (IBS) used to assess similarly or relatedness between isolates and were not used to infer inheritance from a recent common ancestor ) , was assessed by comparing every isolate to every other isolate. To undertake the comparisons for the SNPs and microsatellites, we used the pairwise allele sharing (Png data) . These csolate b . The advt (IBD)) .2.7tidyverse 1.3.1  and 200 (100%) isolates in the pre- and post-IRS surveys, respectively. Using these successfully genotyped isolates, the SNP calls were then aggregated and all SNP loci with a call rate of at least 80% in both surveys were included . Based o3.1.2All details for the microsatellite genotyping and data cleaning for the 200 isolates selected pre- and post-IRS have been previously published . Briefly3.1.3varcoding we determined the number of unique DBLα types in each isolate. Since the underlying population structure of the var multigene family in Bongo pre-IRS was characterized by non-overlapping DBLα isolate repertoires  was unnecessary. As a result, all isolates successfully genotyped pre-IRS (N = 172) and post-IRS (N = 193) were included  was obtained from 172 (80.0%) and 193 (96.5%) isolates in the pre- and post-IRS surveys, respectively  . Using vertoires , we wereincluded .3.2A) being 1.9 and 2.0 in the pre- and post-IRS surveys, respectively (A) being similar in both surveys, despite the IRS intervention  matched the number of isolates (N) at each marker, except for the SNPs where two isolates shared the same infection haplotype (He (Hv for the DBLα types) remained relatively stable pre- to post-IRS across all three genetic markers . Thus, by using less diverse markers that require the selection of monoclonal infections (or those with the lowest complexity) we were underestimating parasite diversity in this high-transmission setting in SSA.For the pre-IRS survey, we found the number of haplotypes  resulting in 34 (21.7%) and 68 (34.0%) isolates for the pre- and post-IRS SNPs, and 81 (42.2%) and 84 (42.0%) isolates for the pre- and post-IRS microsatellites  or missing data because phasing is not necessary. Thus, all of the isolates successfully varcoded during the pre- (N = 172) and post-IRS (N = 193) surveys were included for this analysis    of the SNP panel and thus higher PAS scores may be observed when a greater number of low-heterozygosity loci are included (Using the SNP Pnotyped) . Althoug U test) . This reincluded . Since ~included , S5, thiincluded , D, S4.AS distributions using microsatellites showed that isolates were significantly less similar  post-IRS than pre-IRS  despite the median PAS scores being the same  and that there was a shift towards a lower median PTS value post-IRS   , making the same . Finallythe same . Using tsimilar) .AS (SNPs or microsatellites) and PTS scores, we observed that isolates that were identical or highly similar using their SNP barcodes, were found to be dissimilar (or unrelated) when assessed using their DBLα isolate repertoires  and post-IRS  . Thus, the reduced similarity reported using the sample of low-complexity infections in this analysis was confirmed with observations based on the larger dataset.Given that the N = 413)  without varcode all isolates in the larger dataset, additional analyses of population structure by age  were possible. Although multiclonal infections were observed across all age groups in Bongo, children (1–10 years) and adolescents (11-20 years) carried the largest proportion of these multiclonal infections pre- and post-IRS, as previously published . Finally, by examining the PTS distributions, we observed that DBLα isolate repertoires were significantly less similar in each age group pre- to post-IRS   . Again, 4P. falciparum population genetics in the highest burden countries of SSA where ~550 million people live at risk of infection  by the IRS intervention. We start our intervention with very high var repertoire diversity in the asymptomatic parasite population. We show IRS reduced transmission intensity by > 90% and so we expect to have greatly reduced outcrossing by both reducing the population of biting mosquitos and the lifespan of blood fed mosquitos thereby minimizing exposure of humans to new infections. As a consequence, we are largely looking at the decay of the parasite population in the human host (asymptomatic reservoir) with few new transmission events. As this parasite population at baseline pre-IRS has low var similarity, it maintains this feature or becomes less similar as the number of var repertoires to be compared declines post-IRS. Microsatellite genotyping shows the same pattern of low similarity for genome diversity per se, pre- and post-IRS. The extent of diversity post-IRS may also be contributed to via migration of diverse parasites from uncontrolled areas as our study site shares an immediate border with Burkina Faso. Some evidence for such migration came from our earlier microsatellite work where we observed significant spatiotemporal differentiation in Bongo. In this analysis we showed that not only were the catchment areas  genetically different post-IRS, but that the parasite population in Soe  was genetically differentiated pre- to post-IRS as measured using Jost’s D and GST  and transmission intensity  simultaneously . So, whaevalence . Thus, tAS/PTS for three established molecular assays, we get different pictures of both the extent of genetic diversity and similarity of the P. falciparum reservoir in Bongo pre- and post-IRS. Given the same initial sample size of ~200 isolates with low-complexity infections, the variable resolution of the methods relates to the relative polymorphism of the markers . Biallelic SNPs showed a less diverse parasite reservoir both pre- and post-IRS, underestimating the greater diversity of genomes seen with microsatellites and varcoding. The reduced diversity and higher genetic similarity observed using the SNP barcode was largely due to the biallelic nature of SNPs and the necessary removal of multiclonal infections, reducing sample size for analysis. Multiallelic microsatellites showed a parasite reservoir that was diverse and genetically dissimilar during both the pre- and post-IRS surveys. When considering measuring changes in neutral variation, it is clear that the more polymorphic microsatellite markers have greater resolution than the 20-SNP barcode. Genotyping a larger panel of SNP or microsatellite markers  to undertake relatedness estimates using IBD has been recommended  is required by other panels to improve performance when amplifying DNA from low-density infections from dried blood spots , it adds a considerable cost to the genotyping assays . AlthougMore recently with the development of newer genetic panels composed of a larger number of biallelic SNPs  or multiIn silico validation of these panels from countries with low or high parasite diversity have shown they are more accurate to assess genetic relatedness compared to the 24-SNP barcode. Nonetheless, simulated monoclonal infections were needed for this analysis using AMPLseq v1 , using amplicon sequencing, is that large number of isolates can be multiplexed into a single-pool, thus overall costs are mainly driven by the number of PCRs/clean-up, the number of samples indexed per sequencing run, and the next-generation sequencing (NGS) technology used ($20 to $40 USD per isolate)  . Howeverfections , withoutfections . Second,ar genes  and therar genes , varcodi updated . Thus, w regions .var genes and var repertoires diversify by recombination to create varcoding. Here we show that this method can be used in high-transmission settings to measure diversity and population structure even in multiclonal infections. This is achieved more easily than SNP barcoding and microsatellite genotyping as there is no need for pre-selection of isolates and phasing. Measuring PTS or IBS complements other previously published uses of the method to measure MOI (varcodes (In conclusion, we have exploited the fact that sure MOI  as well varcodes . These tSupplemental MaterialData Sheet 1Data Sheet 2


Candida auris isolates from 4 distinct clades and evaluated the performance of variant calling, single-nucleotide polymorphism (SNP) counts and phylogenetic inference results. Although these pipelines used different variant callers and filtering criteria, we found high overall agreement of SNPs from each pipeline. This concordance correlated with site quality, as SNPs discovered by a few pipelines tended to show lower mapping quality scores and depth of coverage than those recovered by all pipelines. We observed that the major differences between pipelines were due to variation in read trimming strategies, SNP calling methods and parameters, and downstream filtration criteria. We calculated specificity and sensitivity for each pipeline by aligning three isolates with chromosomal level assemblies and found that the GATK-based pipelines were well balanced between these metrics. Selection of trimming methods had a greater impact on SAMtools-based pipelines than those using GATK. Phylogenetic trees inferred by each pipeline showed high consistency at the clade level, but there was more variability between isolates from a single outbreak, with pipelines that used more stringent cutoffs having lower resolution. This project generated two truth datasets useful for routine benchmarking of C. auris variant calling, a consensus VCF of genotypes discovered by 10 or more pipelines across these 35 diverse isolates and variants for 2 samples identified from whole-genome alignments. This study provides a foundation for evaluating SNP calling pipelines and developing best practices for future fungal genomic studies.Genomic analyses are widely applied to epidemiological, population genetic and experimental studies of pathogenic fungi. A wide range of methods are employed to carry out these analyses, typically without including controls that gauge the accuracy of variant prediction. The importance of tracking outbreaks at a global scale has raised the urgency of establishing high-accuracy pipelines that generate consistent results between research groups. To evaluate currently employed methods for whole-genome variant detection and elaborate best practices for fungal pathogens, we compared how 14 independent variant calling pipelines performed across 35  Submitted data for each participating group , the standardized versions of these files used for comparison, and the variants for CA05 and CA06 using whole-genome alignment with B8441 are available in FigShare and are linked to the GitHub repository containing all code used to process and compare these data  [Illumina WGS for 35 https://www.isham.org/working-groups/genomic-epidemiology-fungal-infections). All that agreed to participate received an email with instructions to participate and to download the dataset of Illumina raw sequences for 35 isolates. Many of the samples contained more than one FASTQ read file per isolate, which most groups merged by sample before processing. All groups were requested to use the same reference genome for isolate B8441 (https://www.ncbi.nlm.nih.gov/nuccore/PEKT00000000.2/) [An invitation to participate in the study was sent to all members of the ISHAM Working Group on Genomic Epidemiology of Fungal Pathogens (0000.2/)  and to phttps://github.com/BU-ISCIII/WGS-Outbreaker) was used for SNP identification. Read quality was assessed with FastQC v0.11.8 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and reads were trimmed with Trimmomatic v0.32 with parameters ILLUMINACLIP:/opt/Trimmomatic-0.33/adapters/TruSeq3-PE.fa:2 : 30 : 10 TRAILING:10 SLIDINGWINDOW:4 : 15 MINLEN:70 [https://github.com/CFSAN-Biostatistics/snp-pipeline) with filtering criteria: minBaseQual: 0 , minConsFreq: 0.6 , minConsStrdDpth: 0 (disabled strand bias) and minConsStrdBias: 0 (disabled strand bias in the consensus step determination). A phylogenetic tree was constructed using maximum likelihood using RAxML v8.2.9 (inference parameters: ‘-m GTRCAT -V -w’ with 100 bootstrap duplicates).The pipeline WGS-Outbreaker  were then used to filter, sort and convert SAM files. Variants were called with GenomeAnalysisTK v3.6 HaplotypeCaller with ploidy=1, stand_call_conf=30.0 and stand_emit_conf=10.0 options [https://github.com/maufrais/Scripts/blob/master/SNPs/variant_filtarion.py): individual variants were removed if the depth of coverage (DP) <10, the genotype quality (GQ) <80 and the homozygous allele balance (ABHom) <0.9. A dataset of 228 820 sites was created where filtered-out variants were replaced by a gap. The phylogenetic tree was generated using Phylip v3.67 Maximum Parsimony dnapars tool [This pipeline is an adaptation of the one used to analyse the diploid genome of albicans . It was cutadapt  with ‘-qcutadapt . SAMtoolcutadapt  and Pica options . SNPs wears tool , where thttp://broadinstitute.github.io/picard /). The library origin was conserved for those samples that have more than one set of paired-end reads. Then, samples were processed using a pipeline implemented in the workflow description language (WDL) to run on a local compute cluster via Cromwell (https://github.com/broadinstitute/cromwell). In this pipeline, reads were aligned to the C. auris assembly of isolate B8441 (PEKT00000000.2) using BWA-MEM v0.7.12 [https://github.com/broadinstitute/broad-fungalgroup/blob/master/scripts/SNPs/filterGatkGenotypes.py), and the number of these flagged sites for each sample was reviewed manually for any outliers with extreme numbers of filtered sites in any category (none were observed).FASTQ files were converted to unaligned BAMs using Picard v1.782  in at least one isolate were output in a FASTA file using a custom script . The number of pairwise differences between isolates was calculated from the FASTA alignment using mega v7 to create the matrix file [Alignment metrics were calculated on each sample BAM file using Picard v1.782. For phylogenetic analysis, sites with an unambiguous SNP  [https://github.com/broadinstitute/fungal-wdl/gatk3). The matrix construction and phylogenetic analysis were carried out as described for dataset 03.The GATK pipeline described above in dataset03 was reimplemented via Workflow Description Language (WDL) and deployed in the cloud-native platform Terra, (l-gatk3) . The indhttps://github.com/BU-ISCIII/WGS-Outbreaker) was used for SNP identification. Read quality was assessed with FastQC v0.11.8 and reads were trimmed with Trimmomatic v0.32 with parameters ILLUMINACLIP:/opt/Trimmomatic-0.33/adapters/TruSeq3-PE.fa:2 : 30 : 10 TRAILING:10 SLIDINGWINDOW:4 : 15 MINLEN:70 [The pipeline WGS-Outbreaker  and the whole genome for 35 samples was generated by incorporating variants found in the corresponding samples by using bcftools consensus.The 35 samples were quality checked and filtered using FastQC v0.11.9 and Trimmomatic v0.35 . The Illmega v7 and the most parsimonious tree was reported. Pairwise SNP numbers between isolates were calculated from the multiple sequence alignment.The reference genome and draft genomes were generated for 35 isolates and concatenated in a single file for multiple sequence alignment using MAFFT v7.419  in the dhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc), and then trimmed using Trimmomatic v0.36 [mega X v10.0.5 for Windows 64 platform with Tree-Bisection-Reconnection selected for MP search method, 10 as the number of initial trees, 1 as MP search level and 10 maximum trees to retain. To generate pairwise SNP numbers between samples, we used our own Python script , simply calculating the total number of different nucleotides between the sequences in the final FASTA alignment file. The analysis was also performed without the trimming step. This step slightly influenced the results, as the number of SNPs differed insignificantly (0.02 %).Sequencing reads were quality controlled using FastQC v0.11.8 (ic v0.36  with theic v0.36 . Sequencic v0.36 . The obtic v0.36 . The seqblast approach (https://github.com/davideyre/bug-flow/blob/master/bin/genRefMask.py). To pass quality filtering, SNPs identified were required to have a quality >30, to be homozygous under the diploid model, demonstrate >75 % of bases in agreement with the consensus call and have a minimum depth of 5, with at least one read in each direction. Quality filters were determined based on previous repeat sequencing of the same bacterial isolates, targeting a false SNP rate of less than 1 per 100 Mb of reference genome called, i.e. prioritizing specificity in settings where WGS is used to exclude the possibility of transmission. Phylogenetic trees were constructed by merging input FASTA files and setting all invariant sites with missing data to match the reference for computational efficiency. An initial maximum-likelihood tree was constructed using PhyML v20 120 412 using the ‘BEST’ topology search option and a GTR substitution model.Reads were aligned using with Stampy v1.0.23  . Varianthttp://prinseq.sourceforge.net/) using the parameters -trim_left 15 -trim_qual_left 20 -trim_qual_right 20 min_len 100 min_qual_mean 25 -derep 14 and then processed using NASP pipeline [mega v7 [The reads were trimmed using Prinseq v0.20.3 .Reads were aligned with BWA mem v0.7.4, converted to BAM with SAMtools v0.1.18 view and sorted with SAMtools sort, and paired read alignment reads were selected with SAMtools view with parameter -f 0 x2 . Pilon vhttps://github.com/jessieuehling/GWAS_scripts) were used to parse the results into a SNP table (MergeSNPs.py), and modify that SNP table into a FASTA file (SNP_fasta.py) for phylogenetic analyses.In this pipeline, raw reads were analysed without trimming or analyses of base call quality. Paired reads were aligned to the reference genome using BWA aln and BWA-sampe (BWA v0.7.15-r1140) under default parameters and the intermediate files were converted to BAM using SAMtools view (SAMtools v1.3.1) with -bS options. The resulting BAM files were sorted and indexed using SAMtools sort and index commands with default parameters. Next, VCF files were generated using SAMtools mpileup with -uf options. Then SNPs were called using bcftools call (bcftools v1.3.1) with the --ploidy 1 and -c options, and viewed using the bcftools view command. The resulting files were piped to SAMtools vcfutils.pl script with the maximum read depth filtered using the varFilter -D 200 option. A series of custom Python scripts  were handled separately. Reads were trimmed with Trimmomatic: trimmomatic-0.32.jar PE -phred33 LEADING:3 TRAILING:3 SLIDINGWINDOW:4 : 15 MINLEN:50 . AnnotatC. auris inferred using a curated core gene library comprising 4250 genes as of September 2022. Single-copy orthologues were identified with OrthoMCL v1.458  using one representative annotated genome assembly for each clade using . Assemblies were uploaded to the website via the upload page https://pathogen.watch/upload) and queried against the core library of representative genes with blastn v2.2.30. Pairwise distances between assemblies were scored by comparing all variant positions from all pairs of core-gene sets, removing loci that show an unusually large  number of variant sites, counting SNPs (generating a downloadable pairwise difference matrix) and normalizing by the relative proportion of the core present in each assembly (generating a downloadable pairwise score matrix). The pairwise score matrix was then used to infer a midpoint-rooted neighbour-joining tree, as described in more detail in the documentation . The Pathogenwatch collection of this dataset can be explored here: https://pathogen.watch/collection/573n97bw1rn4-c-auris-cdc-pilot-core-4250. Assemblies were also queried with blastn v2.2.30 for the presence of single point mutations known (as of September 2022) to confer resistance to fluconazole, 5-flucytosine, anidulafungin, caspofungin and micafungin.Pathogenwatch is a web application that supports SNP-based neighbour-joining trees for https://github.com/broadinstitute/isham_wgs). Matrix files were compared using the R notebook code provided as a supplementary file (Data S1). All subsequent analyses were performed on the minimized VCFs. Summary statistics, such as allele frequency distributions, missingness distributions and site number were calculated by bcftools v1.8 [Of the 14 datasets (e.g. results obtained from 14 pipelines), a subset of 12 (datasets 1–12) provided valid VCF files and were thus used for downstream comparison. To compare these VCF files, we standardized the sample order, sample names and contig names. Sample names were standardized to CA01-CA35 and were ordered lexicologically. Contig names were standardized to scaffold00001-scaffold00015. We removed sites that were not SNPs and not flagged as PASS, with heterozygous genotypes and non-canonical characters in ALT or REF fields  from the VCFs. We also removed unused ALT alleles and standardized diploid genotypes to haploid (changed genotypes of 1/1 to 1). For ease of analysis, minimal versions of the VCFs were also produced by removing all INFO fields and non-GT FORMAT fields. VCF files were cleaned and compared using code available as a supplementary IPython notebook , quality by depth (QD) and total depth (DP).https://github.com/broadinstitute/funpipe), a package for fungal genomic pipeline development. To summarize SNP sites and genotype calls identified by most of the pipelines, we defined a consensus genotype as a genotype that was called in 10 or more call sets (out of the 12); we included sites missed by up to 2 pipelines based on the high overall frequency of sites supported by 10 or 11 pipelines. We produced a VCF including all consensus genotypes, hereafter referred to as the consensus VCF.Sample level concordance was evaluated across datasets for each sample. We implemented a function vcf.pairwise_concord to calculate the pairwise concordances of all samples. The implementation was benchmarked against the GATK GenotypeConcordance tool and was included in the Python 3 package funpipe , and onThe truth SNP sets were used to evaluate the quality of each call set. The GATK GenotypeConcordance tool was used to access the sensitivity and specificity of each SNP calling method. Since not all pipelines called reference alleles, we considered all ‘unavailable’ categories reference calls. Sensitivity was defined as false negative/, while specificity was defined as 1−false positive/. The harmonic mean of specificity and sensitivity (F1 score) was calculated as 2×specificity×sensitivity/(specificity+sensitivity).C. auris isolates (CA01-CA35) was provided to all participant research groups to call variants using their preferred pipeline. Twenty-one isolates were sequenced with Illumina HiSeq2500, and 14 isolates were sequenced with Illumina MiSeq v2, which generatied paired 250 base reads on both platforms (Table S1). These samples were selected for having at least 40× sequencing coverage and representing each of the 4 major C. auris clades [Pair-ended WGS data from 35 lates CA0-CA35 wass clades ) and B11s clades ), with cs clades  for variA total of 14 SNP calling pipelines from 11 groups were evaluated. Groups employed different sequence quality control methods, variant callers and filtering criteria. Eight pipelines used quality trimming of reads prior to alignment. All pipelines used BWA for read alignment, with the exception of pipeline 9, which used the Stampy aligner. For variant calling, seven pipelines used GATK , four used SAMtools (three versions) and the other three used either Pilon, CFSAN or an assembly-based method . Among tEach group provided the methods for their pipeline and an output dataset, including a VCF file of their variant calls, a matrix file of the number of pairwise SNP differences between samples, a whole-genome FASTA alignment and a phylogenetic tree inferred from the SNP calls. Insertions and deletions were reported in eight of the datasets, but they were not further evaluated due to the scope of this study. For ease of description, we used the order and abbreviation of the variant calling method to identify each dataset (sd=7126)  to 230 151 (pipeline 11), with an average of 221 942 SNPs (sd=7126) . Of the sd=7126) , which a haploid . The totC. auris but low variation within clades [As there is high variability between the clades of n clades , we nextTo evaluate the specificity and sensitivity of each pipeline, we generated an orthogonal call set for two samples, CA05 (B11221) and CA06 (B11245), by aligning genome assemblies of these isolates to the B8441 reference used for variant calling. These genomes for B11221 and B11245 were previously assembled to the chromosomal level using long-read sequences  . In totaWe assessed the impact of different pipelines and filtering criteria using this measurement of sensitivity and specificity for each pipeline. Overall, GATK methods (pipelines 2 to 8) balanced achieving high sensitivity and specificity relative to the other variant calling methods . Many ofTo compare SNP call sets across pipelines, we evaluated the sites discovered in datasets generated by all pipelines (common SNPs) or only one of the pipelines (private SNPs). A total of 75 % of all SNPs were found in all datasets and 87 % of sites were common across 10 or more datasets . Most prWe then evaluated which pipelines missed the SNPs found by nearly all other pipelines, which likely represent high-confidence sites. We tabulated which pipelines missed a call that was found in 11, 10 and 9 other datasets . For theTo better understand the features of private SNPs, we evaluated the quality of those SNPs using dataset 7 and dataset 12, as they included quality annotations. We examined the distribution of the map quality score (MQ), quality by depth (QD) and depth (DP) for each SNP sharing category. While SNPs that were identified by all pipelines showed high quality and depth, we found that as the number of pipelines that identified each particular SNP decreased, the quality metrics of this SNP also decreased, with more sites showing lower MQ, QD and DP values . This confirmed that most private SNPs were of lower quality and represented borderline SNP calls.After comparing SNP site discovery across pipelines, we examined SNPs at the isolate (sample) level. For each isolate, we calculated the number of the 12 pipelines that discovered each SNP and aggregated these counts for all isolates in a given clade . By examTo examine the effect of different variant calling methods on inferring the genetic relationship of closely related isolates, we included three pairs of control samples that came from the different DNA extractions of the same isolate (CA25 and CA26), same patient (CA28 and CA29) or same outbreak (CA08 and CA27). We expected that SNPs predicted from the same isolate should be identical, and those from the same patient or same outbreak should show roughly 3–7 SNP differences on average . Except C. auris. In azole-resistant isolates, known point mutations  in the gene encoding the lanosterol-14α-demethylase enzyme targeted by the triazoles (ERG11) were correctly identified by all pipelines. In susceptible isolates lacking these variants (i.e. wild-type genotype as the reference genome), a reference allele was reported by the subset of pipelines that called the reference alleles, whereas pipelines that did not include reference calling did not report a genotype at these positions.As WGS analyses are increasingly used for identification of drug resistance mutations, we evaluated whether all pipelines (including Pathogenwatch) were able to discover point mutations associated with azole resistance in sd=18 471), 8105 fewer than the average number of SNPs discovered by each pipeline from VCF files. Pipelines 4, 7,10, 11 and 12 excluded more than 9 % of variants sites (30 210 on average) in the FASTA alignment relative to the total SNPs discovered. This suggests that some pipelines removed a large proportion of the SNPs during conversion of the VCF to a multi-FASTA alignment.We also evaluated the FASTA alignment of whole-genome variants generated from VCF files to identify differences that could be attributed to the SNP calling protocol or additional processing steps used to create alignment files. FASTA alignments were submitted for 13 pipelines (1 to 13). Half of the alignments included a reference genome call. Only pipeline 7 included invariant sites, gaps and ambiguous nucleotides. For consistency, these positions were stripped from the alignment to only include positions that vary in at least one isolate. The average length of the FASTA alignment in all 13 datasets was 213 837 bases  reported high rates of SNP differences between the three pairs of control samples . InspectPhylogenetic trees generated using maximum parsimony were submitted for 10 datasets. Half of them included a reference genome call and half did not report the reference. To compare the phylogenetic trees, the reference genome taxa were pruned for all trees to have 35 sample taxa. Consensus trees were generated using Consense (Phylip) with a 50 % support threshold. The overall topology of four major clusters is conserved in all trees . Some saC. auris isolates and performed a systematic comparison of SNP calls and phylogenetic trees from each group. Accurate detection of variants in the genomes of microbial pathogens is an important prerequisite for understanding transmission patterns during outbreaks and studying the molecular evolution of fungi. Due to an increasing incidence of drug resistance among fungi and outbreaks of fungal diseases, and in parallel the expanding volume of available WGS data, there is an urgent need for consistent data analysis pipelines across different research groups. To this end, we assembled a consortium of research groups working on fungal pathogens from 11 research institutes in 6 countries, across 4 continents, to evaluate the similarities of individually developed SNP calling pipelines. We generated a set of WGS data from 35 C. auris genome. We did not evaluate the effects of lower depths of coverage on pipeline performances, as this question was outside of the scope of our investigation, however lower sequencing targets may be suitable depending on the application. Many studies aim for 30–50× depth of coverage for WGS studies focused on SNP genotyping, but higher depths may be needed for genotyping organisms with more complex genomes or studies aiming to detect low-frequency variants [All isolates in our study were sequenced with at least 40× depth of coverage to ensure that pipelines had minimal areas of low coverage across the variants . HoweverERG11 linked to azole resistance. However, there were still large differences across call datasets in the predicted SNPs that could impact on the analysis of isolate relationships, including inferring transmission chains based on SNPs. We observed that differences in read trimming, SNP calling methods and downstream filtration steps contributed to the variation between the datasets generated by different pipelines.Overall, more than 80 % of the SNP sites were discovered by more than nine pipelines, and all pipelines were able to accurately reconstruct the phylogenetic relationships among isolates and accurately predict substitutions in Although variant calling methods using short-read sequencing data have matured during the last decade, the community has not yet reached a consensus on the best practices for SNP discovery in microbial genomes. Despite using different methods, most pipelines in this study were able to correctly identify most sites in the regions with good mapping qualities that were well represented in the sequence data and were not located in the repetitive regions of the genome. However, the agreement between datasets from different pipelines declined for sites located within regions with low mapping quality and depth of coverage, as these sites were removed by various read trimming and downstream filtering steps that varied between the pipelines. In selecting such parameters, users need to consider how their pipeline balances sensitivity and specificity, and this choice depends in part on the intended downstream analyses. In this study, several GATK-based pipelines performed well in both these categories, resulting in a high composite F1 score. While many GATK-based pipelines employed similar variant calling and filtration strategies, selected based on the GATK best practice recommendations , there hC. auris. However, six pipelines used the diploid mode and did not filter sites with heterozygous calls from the submitted VCF of SNP calls, which resulted in a substantial number of the false-positive calls within these datasets . While in theory, trimming should increase the quality of SNPs by removing low-quality positions, this step often removes sites found in difficult-to-sequence regions of the genome, decreasing the sensitivity of SNP detection; low-quality regions can be handled by SNP callers that allow for soft clipped bases that are not part of an alignment. The trimming method seemed to have a notable impact on the performance of a SAMtools-based pipeline (pipeline 10 vs 11), as switching from Trimmomatic to Prinseq greatly lowered the sensitivity. However, the performance of the GATK-based methods  was not clearly affected by the use of Trimmomatic for read trimming. Additional features in GATK, such as joint genotyping, which is reported to help increase the precision in removing false positives and to increase the sensitivity in detecting low-frequency variants, may contribute to greater flexibility in handling read alignments.C. auris, demonstrating that the clade assignment was able to tolerate some SNP disagreements. However, the relationships among isolates within each clade were affected by differences in the SNP calls, which affected the inferred phylogeny and conclusions about the relationship of isolates collected from the four major clades or from outbreaks. While excessive filtering would result in an inference of too close relationships, weak filtering could introduce false-positive SNPs and overestimates of divergence.All groups were able to reproduce the expected relationships among the four clades of Although this study was not directly set up to evaluate the effect of different reference genome assemblies on SNP calls, the selection of reference genome can impact on the level of SNPs identified. While most studies have used a clade I reference genome, relying on alignments to a single reference may miss clade-specific or highly divergent regions present in other isolates. As complete genome assemblies have been generated for each clade , studiesC. auris, using assemblies of two isolates from other clades in addition to the reference genome used for SNP identification. A richer collection of assemblies would increase the power for estimations of the sensitivity and specificity of each pipeline. An independent cross-validation set, such as Sanger sequencing or genotyping, would also be valuable to confirm selected sites. Some variant calling methods can utilize a curated truth set, such as the Variant Quality Score Recalibration function of GATK or DeepVariant, a deep learning based variant caller [C. auris, which includes well-characterized genomic reads from isolates from outbreaks from clade I [C. auris and compare the results to those described here.To evaluate the SNP calls, we developed an initial truth set for t caller ; howevert caller . Generat clade I . As an ahttps://github.com/CDCgov/mycosnp-nf) [C. auris infections. Another option for easier composite data analysis is establishing a set of easily comparable pipelines, such as those implemented on Terra. For example, in the human genomics community, a set of functional equivalent pipelines were established producing comparable results, which helped establish a community consensus [C. auris [ERG11 gene linked to resistance.Global outbreaks have called for synergistic efforts in computational analysis of population genomics and genomic epidemiology in fungi, similar to parallel efforts in bacteria or viruses. Similar approaches have been started to be implemented in microbial pathogens to measure the performance of different pipelines . As mostosnp-nf) , a GATK-onsensus . In addiC. auris . An asseC. auris, most other fungi have more complex genomes, with most having larger genome sizes and higher repetitive content; diploid and heterozygous genomes also add challenges for variant calling. Yet, the relative simplicity of C. auris genome, the abundance of genomic data for this pathogen, and the urgent public health need for tools for monitoring the transmission of this fungus make it a good model system for evaluating performances of different variant calling methods. A similar approach can be applied for other more complex fungal genomes.In designing benchmarking projects for other fungi, we recommend several considerations. First, having high-quality reference genomes is fundamental for any variant calling project. Second, selecting a dataset that is representative of the species population diversity, and also contains some controls such as the same sample or the same sample as the reference genome, as we included here. Third, there needs to be some idea of ground truth, or the true set of SNP variants, which can be built by combining high-quality data sources and evaluating it using control samples. We acknowledge that compared to The availability of cloud-based computational biology platforms and container technologies made it easy to share pipelines with the required computing environment, so that different groups could have easy access to the same methods to generate reproducible results. In this study, we included an example of converting an on-premises pipeline to a cloud-based pipeline (i.e. the Terra platform). Web tools that are accessible to users of all bioinformatics skills levels and focused on public health delivery  provide an easy-to-use alternative for tree building and prediction of antimicrobial resistance mutations. Whether they are on the cloud or on-premises, pipelines need to be clearly documented so that others can reproduce them and compare results. We envision that consensus in SNP discovery would greatly benefit our microbial genomics community in future research.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


In addition, we found 49 SNPs that were correlated with RL, RBN, RN, PDW, PH, PFW, RFD, and NRN using genome-wide efficient mixed-model association (GEMMA), with a significant level of p < 1×10–6. Additionally, the final results for eight traits associated with 193 significant SNPs by using multi-locus random-SNP-effect mixed linear model (mrMLM) model and 272 significant SNPs associated with 11 traits by using IIIVmrMLM. Within the linkage intervals of significantly associated SNP, we identified eight known related genes to NUE in rice, namely, OsAMT2;3, OsGS1, OsNR2, OsNPF7.4, OsPTR9, OsNRT1.1B, OsNRT2.3, and OsNRT2.2. According to the linkage disequilibrium (LD) decay value of this population, there were 75 candidate genes within the 150-kb regions upstream and downstream of the most significantly associated SNP . These candidate genes included 22 transposon genes, 25 expressed genes, and 28 putative functional genes. The expression levels of these candidate genes were measured by real-time quantitative PCR (RT-qPCR), and the expression levels of LOC_Os05g51700 and LOC_Os05g51710 in C347 were significantly lower than that in C117; the expression levels of LOC_Os05g51740, LOC_Os05g51780, LOC_Os05g51960, LOC_Os05g51970, and LOC_Os10g33210 were significantly higher in C347 than C117. Among them, LOC_Os10g33210 encodes a peptide transporter, and LOC_Os05g51690 encodes a CCT domain protein and responds to NUE in rice. This study identified new loci related to NUE in rice, providing new genetic resources for the molecular breeding of rice landraces with high NUE.Nitrogen is essential for crop production. It is a critical macronutrient for plant growth and development. However, excessive application of nitrogen fertilizer is not only a waste of resources but also pollutes the environment. An effective approach to solving this problem is to breed rice varieties with high nitrogen use efficiency (NUE). In this study, we performed a genome-wide association study (GWAS) on 419 rice landraces using 208,993 single nucleotide polymorphisms (SNPs). With the mixed linear model (MLM) in the Tassel software, we identified 834 SNPs associated with root surface area (RSA), root length (RL), root branch number (RBN), root number (RN), plant dry weight (PDW), plant height (PH), root volume (RL), plant fresh weight (PFW), root fractal dimension (RFD), number of root nodes (NRN), and average root diameter (ARD), with a significant level of  Nitrogen is one of the macronutrients necessary for rice growth and development, and its contribution to crop yield can reach 40%-50% . HoweverqNUE6  has been identified as controlling NUE due to its inherent complexity . Based oqNUE6 , qRDWN6XqRDWN6XB , and qRDd qRDW-6  on rice  and NUE . Increas and NUE . It has tabolism . The intsTCP19-H . Furtherases NUE . The aboOsNRT1 is the first identified low-affinity nitrate transporter in rice, which regulates nitrate uptake in roots , and NO2- in the cytoplasm is converted to NH4+ by nitrite reductase (NiR). Following this, NH4+ participates in the glutamate cycle, which comprises glutamine synthetase/glutamate synthase (GS/GOGAT). GS catalyzes the binding of NH4+ and glutamate to produce glutamine. GOGAT then utilizes glutamine and 2-ketoglutarate (2-OG), an intermediate of the tricarboxylic acid cycle, to generate two glutamate molecules   .p < 2.39×10–7), and using GEMMA, we identified 49 significantly associated SNPs (p < 1×10–6). Finally, RT-qPCR was used to validate the genes involved in NUE, providing additional genetic resources for the cultivation of new rice landraces with high nitrogen efficiency.To identify new genes related to NUE, this study used genome-wide association analysis to identify the loci that affected 11 traits of 419 rice landraces, including RSA, RL, and RBN. By using the software MLM model, we detected 834 significantly associated SNPs . The nutrient solution was a thousand-fold dilution of Yoshida culture solution B .http://rice.uga.edu/). GATK and SAM toolkits were used to analyze SNP calling. A total of 208,993 SNPs were obtained based on a minor allele frequency (MAF) > 0.05 and a deletion rate < 0.5  model, where Q was the population structure and K was the kinship coefficient. The SNPs with p < 1×10–6 were considered to have significant correlations. Manhattan and Q-Q plots were also generated in the R environment.At the same time, GEMMA, another commonly used software for GWAS, was used to conduct association analysis on 208,993 SNPs genotypes and seedling phenotype data. The SNPs with http://rice.uga.edu/). The candidate regions were selected based on LD decay of 419 rice landraces, with 150-kb intervals upstream and downstream of the SNPs showing a significant correlation.The Nipponbare was used as the reference sequences , plus 0.4 μl forward/reverse primers, 2 μl product cDNA, and 7.2 μl Nuclease-Free Water. Actin was used as the internal reference gene . All priThe RT-PCR reaction was carried out on the BIO-RAD T100 Thermal Cycler PCR instrument , and the RT-qPCR reaction was carried out on the BIO-RAD CFX 96 Touch fluorescence quantitative PCR imaging system .^ΔΔCt method  into 1-10 groups. As a result, the 419 rice landraces were divided into five populations (2) was taken. The result showed that the LD decline distance of the total population on a genome-wide level was 150 kb  under two different nitrogen concentrations . LOC_Os05g51780 encodes a zinc finger protein, which belongs to a family of zinc finger transcription factors that can transmit signals. Some studies have found that zinc finger transcription factors are involved in nitrogen assimilation; under a condition with sufficient water, zinc finger transcription factors could up-regulate target genes OsNR1.2 and OsPrx24, which can improve nitrogen assimilation and promote the opening of stomata. Under osmotic stress, zinc finger transcription factors could down-regulate the expression of OsNR1.2 and OsPrx24, leading to inhibited nitrogen assimilation and stomatal closure, which enhances the drought tolerance of rice. LOC_Os05g51780 was also associated with RSA, RFD, PDW, PFW, and RV. In LOC_Os05g51690 and LOC_Os05g51830, although there was no significant difference in the expression of these two genes in C117 and C347, they were associated with RSA, RFD, PDW, PFW, and RV. LOC_Os05g51690 is involved in the response to the lack of macronutrients, and it affects rice growth. Through alternative splicing, LOC_Os05g51690 produces two transcripts with the same 5’ end, NRRa and NRRb, which encode 308aa and 223aa proteins, respectively. NRRa has one more CCT domain at the C-terminus than NRRb. NRRa and NRRb can regulate the structure of rice roots to improve macronutrient absorption; they also play a negative regulatory role in root growth and regulate the heading timing of rice (LOC_Os05g51830 is located in the nucleus and expressed throughout the life cycle of rice. It can regulate rice seed germination under abiotic stress conditions. The overexpression of LOC_Os05g51830 reduced the responses to abscisic acid (ABA), salt stress, and osmotic stress during seed germination and delayed seed germination  can be found below: XY acquired the funding and participated in supervision; DL participated in supervision; ZL and YQ conducted the field trials and data collection and data analysis; ZL and XX carried out data visualization; BN and ZZ were in charge of the investigation; ZL, XY, and DL wrote, reviewed, and edited the draft. All authors contributed to the article and approved the submitted version.


Heterobasidion parviporum, we have investigated the genetic basis of phenotypic variation among laboratory-designed heterokaryons carrying the same pair of haploid nuclei, but a different mitochondrial genome. Radial growth rate data of thirteen unrelated homokaryotic parents and of their heterokaryotic offspring were combined with SNP data extracted from parental genome sequences to identify nuclear and mitochondrial loci involved in mitonuclear interactions. Two nuclear loci encoding mitochondrial proteins appeared as best candidates to engage in a genetic interaction affecting radial growth rate with a non-conserved mitochondrial open reading frame of unknown function and not reported apart from the Russulales order of Basidiomycete fungi. We believe our approach could be useful to investigate several important traits of fungal biology where mitonuclear interactions play a role, including virulence of fungal pathogens.The mitochondrial and nuclear genomes of Eukaryotes are inherited separately and consequently follow distinct evolutionary paths. Nevertheless, the encoding of many mitochondrial proteins by the nuclear genome shows the high level of integration they have reached, which makes mitonuclear genetic interactions all the more conceivable. For each species, natural selection has fostered the evolution of coadapted alleles in both genomes, but a population-wise divergence of such alleles could lead to important phenotypic variation, and, ultimately, to speciation. In this study in the Basidiomycete  Mitochondria are functionally and genetically dynamic organelles essential to the eukaryotic cell  in host wood , and a non-conserved mitochondrial locus of unknown function and whose distribution is limited to the Russulales order of Agaricomycetes. We believe that our results on mitonuclear genetics of growth rate in H. parviporum could pave the way to similar investigations of mitochondrial DNA-dependent variation of important traits e.g., virulence in fungal pathogens.The present study is an investigation of the genetic basis of mitonuclear interactions in H. parviporum homokaryotic isolates out of thirty initially crossed in our previous study  were estimated by the method of the least squares, using published data of growth measurements of parental homokaryons and their heterokaryotic offspring . Positions of mitochondrial SNP loci were searched in H. irregulare annotated mitochondrial genome (accession NC_024555.1) using BLASTN software (https://blast.ncbi.nlm.nih.gov/Blast.cgi) and published mitochondrial ORF predictions  at the origin of a non-synonymous mutation in isolates Ho9 and Ho32. In brief, the procedure relies on standalone nucleotide blast searches with H. parviporum genomic fragments surrounding each SNP as queries against a custom database including 13,405 open reading frames (ORF) of the related species H. irregulare  published as nc-ORF5 in Himmelstrand et al. . Proteins similar to actual nc-ORF5 translation product were searched using BLASTP software (https://blast.ncbi.nlm.nih.gov/Blast.cgi).Transcripts of the 936 bp-long hypothetical ORF , a large group with high maximal growth rate and low phenotypic variance (107 isolates), and a medium group with high maximal growth rate, but a high phenotypic variance that seems intrinsic to their parental combinations (36 isolates).r2 = 0.55714). In the heterokaryons however, a high QCD is neither explained by a low median growth rate (r2 = 0.26644), or by having one or two parents with a high QCD (r2 = 0.0155). This analysis ruled out the possibility that high level of data dispersion could have experimental cause and rather points at specific heterokaryotic combinations to explain this phenomenon.Dispersion of growth rate data was analyzed both in the heterokaryons and their homokaryotic parents to detect those with high level of data dispersion, and, if so, to identify a plausible cause for it. Quartile coefficients of dispersion (QCD) of growth rate data were calculated at different levels of subdivision . Their distributions show that differences of growth rate between assays (isolate level of data subdivision) contribute the most to data dispersion: while 0.76% of the dishes data and of 7.1% of the assays data have a QCD larger than 0.125, data of 38 heterokaryons (26 %) have a QCD larger than this value. A QCD larger than 0.125 was subsequently considered the hallmark of high level of data dispersion throughout this study. Data dispersion between assays in the parental homokaryons is weakly correlated to a lower median growth rate _2 = 3,25 mm/day; median32(9)_2 = 7,125 mm/day; effect size r = 0.8589; P < 0.001; two tailed]. This data subset was used to show that 9(32) has indeed the slowest growth rate of all heterokaryons formed with Ho9 as acceptor parent, pointing out the fact that its combination with Ho32 is unique in this sample , , and , for three reasons: (1) differences of phenotype of their heterokaryons are statistically grounded; (2) the complete offspring resulting from reciprocal crossings of their three parents is available for a grouped analysis; (3) based on results of previous SNP call and filtering, mitochondrial haplotypes of parents Ho9 and Ho18 were supposed to be identical, which was expected to facilitate genotyping. Using data of the assay with the highest median of each heterokaryon and each parent of these three pairs, a series of 36 statistical pairwise tests was carried out, in order to assess differences of growth rate  grows significantly slower than any other heterokaryon made with Ho9 as acceptor parent  and 18(9) is significant too. Other filtering of the SNPs called from nuclear and mitochondrial genome sequences of all homokaryotic isolates were set in order to identify markers linked to these two loci.Phenotypic variance between the six isolates of pairs , , , and their three parents are best explained by the intervention of different alleles at a minimum of four nuclear loci, and mne3 . Specifir parent  shows thnc-ORF5 in the mitochondrial genome sequence of H. irregulare . In H. irregulare, nc-ORF5 is predicted to encode a protein of unknown function and made of 311 amino acid residues, annotated in the mitochondrial proteome of H. irregulare under NCBI accession YP009048485.1. Transcript and coding sequence of this locus were found to be shorter than predicted in the 5' end . Interestingly, a second indel identified after mitochondrial SNP filtering is localized 44 bp downstream of the first SNP, in the promoter/5' UTR sequence of the same ORF . Ho9 and Ho18 have the same allele of this indel. Overall, three different alleles of nc-ORF5 promoter/5' UTR exist in H. parviporum homokaryons used in this study, depending on the allelic combination of these two indels: Ho18 and Ho32 have a first allele mt2-1, Ho9, Ho11, Ho20, Ho26, Ho27, Ho30, and Ho32 have a second allele mt2-2, and Ho2, Ho5, and Ho24 have a third one mt2-3 .- for mne2, a partially negative interaction between mitochondrial allele nuc2-1 for sure  with the following mutation events, all in exon 5  with a single mutation due to a nucleotide transition C to G in exon 2 . One SNP was found 60 bp downstream of the stop codon of the gene encoding the hypothetical calcium uniporter already mentioned (H. irregulare accession number: XM_009552795.1). One SNP was found 113 bp upstream of the start codon of a gene encoding a beta subunit a of putative F1F0-type ATPase (H. irregulare accession number: XM_009544597.1). One SNP was found 11 bp upstream of the start codon of a gene encoding another mitochondrial carrier protein (H. irregulare accession number: XM_009545544.1).Of the 793 SNPs not localized into an exon of a Our phenotypic data show that two major competing trends are at work in heterokaryons, with opposite effects on growth rate. The first trend is “heterokaryon vigor”  after carrying out different growth rate assays over time. These heterokaryons can nevertheless grow as fast as the others. Their genetic potential for this specific trait is therefore not different from the rest of the heterokaryons. The fact that their phenotype does vary more between assays is due to other factors, unidentified so far, although probably linked to the heterokaryotic state, during which parental haploid nuclei coexist in the same cytoplasm, but do not fuse Raper, . The hetH. parviporum  and a genomic locus (nuc2) involved in a genetic interaction in trans across the two genomes. Different combination of alleles at these two loci contribute to phenotypic variation in the offspring of the crossings with these three isolates.Analysis of heterokaryons growth rate data showed that higher values reflect their genetic potential for this trait better, which made possible the identification of pairs of heterokaryons issued from the same parents, but whose phenotypes are different. It confirms our previous detection of mitochondrial genetic effects in the same cohort of heterokaryons of nuc2 were searched using phenotypic information of heterokaryons from all other crossings involving parent Ho9 and SNPs markers called from the genome sequences of all homokaryotic isolates , but also carrying mutations that might affect their expression level are considered the best possible candidates at this stage. Both are involved in molecular trafficking to the mitochondrion . Although the gene encoding a calcium uniporter cannot be ruled out at this stage, the other one encoding a mitochondrial carrier protein is a more likely candidate for nuc2 for several reasons: Mutations in this gene result from multiple transversions (3) and one indel in the same exon, whereas the mutation in the gene encoding the calcium uniporter results from a single transition; A multiple protein alignment with orthologs from other species shows that substitution R to K in the sequence of the carrier is in a conserved motif, and this substitution is present in Ho9 and Ho32 only; Amino-acid substitutions/deletion observed in the carrier are more likely to lead to a change of balance between structure and function.Candidates for genomic locus nuc2, but a non-conserved gene instead, with orthologs in the Russulales order only. The ortholog of this gene in the mitochondrial genome of H. irregulare has been previously identified as nc-ORF5  and by excluding proteins from the Russulales retrieved exclusively a hypothetical protein from soilborne gammaproteobacteria , and could be used for that purpose. Another option would be to shuffle mitochondria into various homokaryotic nuclear backgrounds by reisolating homokaryotic conidia from heterokaryons cultures  studying the expression of all mitochondrial and nuclear candidate genes to confirm or infirm hypotheses made about them, (2) providing molecular genetics evidence of mitonuclear interaction by carrying complementation experiments of homokaryon Ho9 with wild-type copies of candidate nuclear loci for Heterobasidion spp. as possibly involved in virulence variation related to mitochondrial background  can be found in the article/P-HC and ÅO: conceived and designed the experiments, performed the experiments, analyzed the data, and revised the paper. P-HC: exploratory data analysis, statistics, and bioinformatics and wrote the paper. ÅO: additional growth rate assays. All authors contributed to the article and approved the submitted version.This work was supported by the Carl Tryggers Foundation for Scientific Research.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.