Phenotypic and Genotypic Methods for the Identification and Quantification of Cyanobacteria in Lake Water.

Early monitoring of Microcystis, a cyanobacterium that produces microcystin, is paramount in order to confirm the presence of Microcystis spp. Both phenotypic and genotypic methods have been used. The phenotypic methods provide the presence of the microcystis but do not confirm its species type and toxin produced. Additionally, phenotypic methods cannot differentiate toxigenic from non-toxigenic Microcystis. Therefore, the current protocol also describes genetic methods based on PCR to detect toxigenic Microcystis spp. based on microcystin synthetase E (mcy E) gene and 16-23S RNA genes for species-specific identification, which can effectively comprehend distinct lineages and discrimination of potential complexity of microcystin populations. The presence of these microcystin toxins in blood, in most cases, indicates contamination of drinking water by cyanobacteria. The methods presented herein are used to identify microcystin toxins in drinking water and blood.

Effect of genotyping errors on linkage map construction based on repeated chip analysis of two recombinant inbred line populations in wheat (Triticum aestivum L.).

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Rapid and multi-target genotyping of Helicobacter pylori with digital microfluidics.

Helicobacter pylori (H. pylori) infection correlates closely with gastric diseases such as gastritis, ulcers, and cancer, influencing more than half of the world's population. Establishing a rapid, precise, and automated platform for H. pylori diagnosis is an urgent clinical need and would significantly benefit therapeutic intervention. Recombinase polymerase amplification (RPA)-CRISPR recently emerged as a promising molecular diagnostic assay due to its rapid detection capability, high specificity, and mild reaction conditions. In this work, we adapted the RPA-CRISPR assay on a digital microfluidics (DMF) system for automated H. pylori detection and genotyping. The system can achieve multi-target parallel detection of H. pylori nucleotide conservative genes (ureB) and virulence genes (cagA and vacA) across different samples within 30 min, exhibiting a detection limit of 10 copies/rxn and no false positives. We further conducted tests on 80 clinical saliva samples and compared the results with those derived from real-time quantitative polymerase chain reaction, demonstrating 100% diagnostic sensitivity and specificity for the RPA-CRISPR/DMF method. By automating the assay process on a single chip, the DMF system can significantly reduce the usage of reagents and samples, minimize the cross-contamination effect, and shorten the reaction time, with the additional benefit of losing the chance of experiment failure/inconsistency due to manual operations. The DMF system together with the RPA-CRISPR assay can be used for early detection and genotyping of H. pylori with high sensitivity and specificity, and has the potential to become a universal molecular diagnostic platform.

A wild rice CSSL population facilitated identification of salt tolerance genes and rice germplasm innovation.

Salt stress is one of the major factors that limits rice production. Therefore, identification of salt-tolerant alleles from wild rice is important for rice breeding. In this study, we constructed a set of chromosome segment substitution lines (CSSLs) using wild rice as the donor parent and cultivated rice Nipponbare (Nip) as the recurrent parent. Salt tolerance germinability (STG) was evaluated, and its association with genotypes was determined using this CSSL population. We identified 17 QTLs related to STG. By integrating the transcriptome and genome data, four candidate genes were identified, including the previously reported AGO2 and WRKY53. Compared with Nip, wild rice AGO2 has a structure variation in its promoter region and the expression levels were upregulated under salt treatments; wild rice WRKY53 also has natural variation in its promoter region, and the expression levels were downregulated under salt treatments. Wild rice AGO2 and WRKY53 alleles have combined effects for improving salt tolerance at the germination stage. One CSSL line, CSSL118 that harbors these two alleles was selected. Compared with the background parent Nip, CSSL118 showed comprehensive salt tolerance and higher yield, with improved transcript levels of reactive oxygen species scavenging genes. Our results provided promising genes and germplasm resources for future rice salt tolerance breeding.

A comprehensive benchmark of graph-based genetic variant genotyping algorithms on plant genomes for creating an accurate ensemble pipeline.

BACKGROUND: Although sequencing technologies have boosted the measurement of the genomic diversity of plant crops, it remains challenging to accurately genotype millions of genetic variants, especially structural variations, with only short reads. In recent years, many graph-based variation genotyping methods have been developed to address this issue and tested for human genomes. However, their performance in plant genomes remains largely elusive. Furthermore, pipelines integrating the advantages of current genotyping methods might be required, considering the different complexity of plant genomes. RESULTS: Here we comprehensively evaluate eight such genotypers in different scenarios in terms of variant type and size, sequencing parameters, genomic context, and complexity, as well as graph size, using both simulated and real data sets from representative plant genomes. Our evaluation reveals that there are still great challenges to applying existing methods to plants, such as excessive repeats and variants or high resource consumption. Therefore, we propose a pipeline called Ensemble Variant Genotyper (EVG) that can achieve better genotyping performance in almost all experimental scenarios and comparably higher genotyping recall and precision even using 5× reads. Furthermore, we demonstrate that EVG is more robust with an increasing number of graphed genomes, especially for insertions and deletions. CONCLUSIONS: Our study will provide new insights into the development and application of graph-based genotyping algorithms. We conclude that EVG provides an accurate, unbiased, and cost-effective way for genotyping both small and large variations and will be potentially used in population-scale genotyping for large, repetitive, and heterozygous plant genomes.

DPYD genotyping and 5-fluoropyrimidine toxicity: An overview of systematic reviews protocol / Genotipado del gen DPYD y toxicidad de 5-fluoropirimidinas: Protocolo de una revisión de revisiones sistemáticas

Introduction: The increased risk of severe and life-threatening toxicity in patients with dihydropyridine dehydrogenase (DPD) deficiency, under treatment with fluoropyrimidines, has been widely studied. An up-to-date overview of systematic reviews summarizing existing literature can add value by highlighting most relevant information and supports decision-making regarding treatment in DPD deficient patients. The main objective of this overview of systematic reviews is to identify published systematic reviews on the association between germline variations in the DPYD gene and fluoropyrimidine toxicity.Methods and analysis: This protocol was developed following the Preferred Reported Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) checklist, and the overview of systematic reviews will be reported in accordance with the PRISMA statement. PubMed, Embase, Scopus, and the Cochrane Library will be searched from inception to 2023. Systematic reviews irrespective of study designs that analyze the association between germline variations in the DPYD and fluoropyrimidine toxicity will be considered. Methodological quality will be assessed using AMSTAR2 checklist (Measurement Tool to Assess Systematic Reviews 2). Two independent investigators will perform the study selection, quality assessment, and data collection. Discrepancies will be solved by a third investigator.(AU)

Introducción: El incremento del riesgo de toxicidad grave y potencialmente mortal en pacientes con deficiencia de dihidropiridina deshidrogenasa (DPD) en tratamiento con fluoropirimidinas ha sido ampliamente estudiado. Una revisión actualizada de las revisiones sistemáticas publicadas, que agrupe la literatura existente, puede añadir valor al resaltar la información más relevante y respaldar la toma de decisiones con respecto al tratamiento en pacientes con deficiencia de DPD. El objetivo principal de esta revisión de revisiones sistemáticas es identificar revisiones sistemáticas publicadas sobre la asociación entre variaciones en el linaje germinal del gen DPYD y la toxicidad de las fluoropirimidinas. Métodos y análisis: Este protocolo se ha desarrollado siguiendo la lista de verificación de los Protocolos para Revisiones Sistemáticas y Metaanálisis Preferidos (PRISMA-P), y la revisión de las revisiones sistemáticas se comunicará de acuerdo con la declaración PRISMA. Se realizará una búsqueda en PubMed, Embase, Scopus y la Biblioteca Cochrane desde su inicio hasta 2023. Se considerarán aquellas revisiones sistemáticas, independientemente de los diseños de estudio, que analicen la asociación entre variaciones en el linaje germinal del gen DPYD y la toxicidad de las fluoropirimidinas. La calidad metodológica se evaluará utilizando la lista de verificación AMSTAR2 (Herramienta de Medición para Evaluar Revisiones Sistemáticas 2). Dos investigadores independientes realizarán la selección de estudios, la evaluación de la calidad y la recopilación de datos. Las discrepancias se resolverán mediante un tercer investigador.(AU)

Genotipado del gen DPYD y toxicidad de 5-fluoropirimidinas: Protocolo de una revisión de revisiones sistemáticas / DPYD genotyping and 5-fluoropyrimidine toxicity: An overview of systematic reviews protocol

Introduction: The increased risk of severe and life-threatening toxicity in patients with dihydropyridine dehydrogenase (DPD) deficiency, under treatment with fluoropyrimidines, has been widely studied. An up-to-date overview of systematic reviews summarizing existing literature can add value by highlighting most relevant information and supports decision-making regarding treatment in DPD deficient patients. The main objective of this overview of systematic reviews is to identify published systematic reviews on the association between germline variations in the DPYD gene and fluoropyrimidine toxicity.Methods and analysis: This protocol was developed following the Preferred Reported Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) checklist, and the overview of systematic reviews will be reported in accordance with the PRISMA statement. PubMed, Embase, Scopus, and the Cochrane Library will be searched from inception to 2023. Systematic reviews irrespective of study designs that analyze the association between germline variations in the DPYD and fluoropyrimidine toxicity will be considered. Methodological quality will be assessed using AMSTAR2 checklist (Measurement Tool to Assess Systematic Reviews 2). Two independent investigators will perform the study selection, quality assessment, and data collection. Discrepancies will be solved by a third investigator.(AU)

Introducción: El incremento del riesgo de toxicidad grave y potencialmente mortal en pacientes con deficiencia de dihidropiridina deshidrogenasa (DPD) en tratamiento con fluoropirimidinas ha sido ampliamente estudiado. Una revisión actualizada de las revisiones sistemáticas publicadas, que agrupe la literatura existente, puede añadir valor al resaltar la información más relevante y respaldar la toma de decisiones con respecto al tratamiento en pacientes con deficiencia de DPD. El objetivo principal de esta revisión de revisiones sistemáticas es identificar revisiones sistemáticas publicadas sobre la asociación entre variaciones en el linaje germinal del gen DPYD y la toxicidad de las fluoropirimidinas. Métodos y análisis: Este protocolo se ha desarrollado siguiendo la lista de verificación de los Protocolos para Revisiones Sistemáticas y Metaanálisis Preferidos (PRISMA-P), y la revisión de las revisiones sistemáticas se comunicará de acuerdo con la declaración PRISMA. Se realizará una búsqueda en PubMed, Embase, Scopus y la Biblioteca Cochrane desde su inicio hasta 2023. Se considerarán aquellas revisiones sistemáticas, independientemente de los diseños de estudio, que analicen la asociación entre variaciones en el linaje germinal del gen DPYD y la toxicidad de las fluoropirimidinas. La calidad metodológica se evaluará utilizando la lista de verificación AMSTAR2 (Herramienta de Medición para Evaluar Revisiones Sistemáticas 2). Dos investigadores independientes realizarán la selección de estudios, la evaluación de la calidad y la recopilación de datos. Las discrepancias se resolverán mediante un tercer investigador.(AU)

Construction of relatedness matrices in autopolyploid populations using low-depth high-throughput sequencing data.

KEY MESSAGE: An improved estimator of genomic relatedness using low-depth high-throughput sequencing data for autopolyploids is developed. Its outputs strongly correlate with SNP array-based estimates and are available in the package GUSrelate. High-throughput sequencing (HTS) methods have reduced sequencing costs and resources compared to array-based tools, facilitating the investigation of many non-model polyploid species. One important quantity that can be computed from HTS data is the genetic relatedness between all individuals in a population. However, HTS data are often messy, with multiple sources of errors (i.e. sequencing errors or missing parental alleles) which, if not accounted for, can lead to bias in genomic relatedness estimates. We derive a new estimator for constructing a genomic relationship matrix (GRM) from HTS data for autopolyploid species that accounts for errors associated with low sequencing depths, implemented in the R package GUSrelate. Simulations revealed that GUSrelate performed similarly to existing GRM methods at high depth but reduced bias in self-relatedness estimates when the sequencing depth was low. Using a panel consisting of 351 tetraploid potato genotypes, we found that GUSrelate produced GRMs from genotyping-by-sequencing (GBS) data that were highly correlated with a GRM computed from SNP array data, and less biased than existing methods when benchmarking against the array-based GRM estimates. GUSrelate provides researchers with a tool to reliably construct GRMs from low-depth HTS data.

Template-specific optimization of NGS genotyping pipelines reveals allele-specific variation in MHC gene expression.

Using high-throughput sequencing for precise genotyping of multi-locus gene families, such as the major histocompatibility complex (MHC), remains challenging, due to the complexity of the data and difficulties in distinguishing genuine from erroneous variants. Several dedicated genotyping pipelines for data from high-throughput sequencing, such as next-generation sequencing (NGS), have been developed to tackle the ensuing risk of artificially inflated diversity. Here, we thoroughly assess three such multi-locus genotyping pipelines for NGS data, the DOC method, AmpliSAS and ACACIA, using MHC class IIß data sets of three-spined stickleback gDNA, cDNA and "artificial" plasmid samples with known allelic diversity. We show that genotyping of gDNA and plasmid samples at optimal pipeline parameters was highly accurate and reproducible across methods. However, for cDNA data, the gDNA-optimal parameter configuration yielded decreased overall genotyping precision and consistency between pipelines. Further adjustments of key clustering parameters were required tο account for higher error rates and larger variation in sequencing depth per allele, highlighting the importance of template-specific pipeline optimization for reliable genotyping of multi-locus gene families. Through accurate paired gDNA-cDNA typing and MHC-II haplotype inference, we show that MHC-II allele-specific expression levels correlate negatively with allele number across haplotypes. Lastly, sibship-assisted cDNA-typing of MHC-I revealed novel variants linked in haplotype blocks, and a higher-than-previously-reported individual MHC-I allelic diversity. In conclusion, we provide novel genotyping protocols for the three-spined stickleback MHC-I and -II genes, and evaluate the performance of popular NGS-genotyping pipelines. We also show that fine-tuned genotyping of paired gDNA-cDNA samples facilitates amplification bias-corrected MHC allele expression analysis.

Seq2Sat and SatAnalyzer toolkit: Towards comprehensive microsatellite genotyping from sequencing data.

Accurate and efficient microsatellite loci genotyping is an essential process in population genetics that is also used in various demographic analyses. Protocols for next-generation sequencing of microsatellite loci enable high-throughput and cross-compatible allele scoring, common issues that are not addressed by conventional capillary-based approaches. To improve this process, we have developed an all-in-one software, called Seq2Sat (sequence to microsatellite), in C++ to support automated microsatellite genotyping. It directly takes raw reads of microsatellite amplicons and conducts read quality control before inferring genotypes based on depth-of-read, read ratio, sequence composition and length. We have also developed a module for sex identification based on sex chromosome-specific locus amplicons. To allow for greater user access and complement autoscoring, we developed SatAnalyzer (microsatellite analyzer), a user-friendly web-based platform that conducts reads-to-report analyses by calling Seq2Sat for genotype autoscoring and produces interactive genotype graphs for manual editing. SatAnalyzer also allows users to troubleshoot multiplex optimization by analysing read quality and distribution across loci and samples in support of high-quality library preparation. To evaluate its performance, we benchmarked our toolkit Seq2Sat/SatAnalyzer against a conventional capillary gel method and existing microsatellite genotyping software, MEGASAT, using two datasets. Results showed that SatAnalyzer can achieve >99.70% genotyping accuracy and Seq2Sat is ~5 times faster than MEGASAT despite many more informative tables and figures being generated. Seq2Sat and SatAnalyzer are freely available on github (https://github.com/ecogenomicscanada/Seq2Sat) and dockerhub (https://hub.docker.com/r/rocpengliu/satanalyzer).

Genotyping of SNPs in bread wheat at reduced cost from pooled experiments and imputation.

KEY MESSAGE: Pooling and imputation are computational methods that can be combined for achieving cost-effective and accurate high-density genotyping of both common and rare variants, as demonstrated in a MAGIC wheat population. The plant breeding industry has shown growing interest in using the genotype data of relevant markers for performing selection of new competitive varieties. The selection usually benefits from large amounts of marker data, and it is therefore crucial to dispose of data collection methods that are both cost-effective and reliable. Computational methods such as genotype imputation have been proposed earlier in several plant science studies for addressing the cost challenge. Genotype imputation methods have though been used more frequently and investigated more extensively in human genetics research. The various algorithms that exist have shown lower accuracy at inferring the genotype of genetic variants occurring at low frequency, while these rare variants can have great significance and impact in the genetic studies that underlie selection. In contrast, pooling is a technique that can efficiently identify low-frequency items in a population, and it has been successfully used for detecting the samples that carry rare variants in a population. In this study, we propose to combine pooling and imputation and demonstrate this by simulating a hypothetical microarray for genotyping a population of recombinant inbred lines in a cost-effective and accurate manner, even for rare variants. We show that with an adequate imputation model, it is feasible to accurately predict the individual genotypes at lower cost than sample-wise genotyping and time-effectively. Moreover, we provide code resources for reproducing the results presented in this study in the form of a containerized workflow.

[Establishment of a genotyping method for the junior blood group and identification of a rare blood type with partial DVI.3 and Jr(a-)].

OBJECTIVE: To develop a genotyping method for the Junior blood type and report on a rare blood type with Jr(a-). METHODS: Healthy O-type RhD+ volunteer donors of the Shenzhen Blood Center from January to May 2021 (n = 1 568) and a pedigree with difficult cross-matching (n = 3) were selected as the study subjects. Serological methods were used for proband's blood type identification, unexpected antibody identification, and antibody titer determination. Polymerase chain reaction-sequence specific primer (PCR-SSP) method was used for typing the proband's RhD gene. ABCG2 gene coding region sequencing and a PCR-SSP genotyping method were established for determining the genotypes of the proband and his family members and screening of Jra antigen-negative rare blood type among the 1 568 blood donors. RESULTS: The proband's ABO and RhD blood types were respectively determined as B and partial D (RHDDVI.3/RHD01N.01), Junior blood type Jra antigen was negative, and plasma had contained anti-D and anti-Jra. Sequencing of the ABCG2 gene revealed that the proband's genotype was ABGG201N.01/ABGG201N.01 [homozygous c.376C>T (p.Gln126X) variants], which is the most common Jr(a-) blood type allele in the Asian population. Screening of the voluntary blood donors has detected no Jr(a-) rare blood type. Statistical analysis of the heterozygotes suggested that the allelic frequency for ABCG2*01N.01 (c.376T) was 0.45%, and the frequency of Jr(a-) rare blood type with this molecular background was about 0.2‰. CONCLUSION: A very rare case of partial DVI.3 type and Jr(a-) rare blood type has been identified. And a method for identifying the Junior blood type through sequencing the coding regions of the ABCG2 gene and PCR-SSP has been established.

Recovering Misidentified Samples Through Genetic Discordance Clustering.

The many logistical and technical challenges associated with sample and data handling in largescale genotyping studies can increase the risk of sample misidentification, which may compromise subsequent analyses. However, the standard quality assurance methods typical for large genotyping arrays can often be further utilized to identify and recover problematic samples. This article emphasizes the importance of identifying and correcting underlying sample misidentification rather than simply excluding known discrepancies, which may potentially include undetected issues. Lastly, we provide a screening protocol to complement standard quality assessments as a guideline for identifying mismatched samples and a tool for assessing the most common causes of sample misidentification. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC.

Systematic evaluation of the Precision ID GlobalFiler™ NGS STR panel v2 using single-source samples of various quantity and quality and mixed DNA samples.

Massively parallel sequencing (MPS) techniques were developed approximately 15 years ago. Meanwhile, several MPS kits for forensic identification, phenotypic information, ancestry, and mitochondrial DNA analysis have been developed and their use has been established. Sequencing short tandem repeats (STRs) has certain advantages over the currently used length-based genotyping methods, which are based on PCR amplification followed by capillary electrophoresis (CE). MPS is more discriminative and includes the possibility of testing high numbers of targets (> 100), different types of markers [STRs and single nucleotide polymorphisms (SNPs)], as well as the use of smaller amplicons (< 300 bp). This study evaluated in 24 experimental runs the Precision ID GlobalFiler™ NGS STR panel v2 from ThermoFisher, which targets 31 autosomal STRs, amelogenin, and three Y-markers (one STR, SRY, and Yindel). Single-source samples were used in 18 experimental runs, for systematic evaluation. These included assessing library preparation benchmark conditions, limited DNA input, as well as testing repeatability, number of samples per run, and degraded DNA samples. Full profiles were consistently obtained from as little as 50 pg DNA input. Using the optional recovery PCR method improved outcomes for samples with low DNA input. Full profiles were also obtained from severely degraded DNA samples with degradation indices (DI) of > 60. In addition, six experimental runs were performed testing various two-person mixtures with mixture ratios ranging from 1:20 to 20:1. Major and minor contributors were distinguishable by their read counts (coverage), because less DNA input yielded lower read counts, analogous to the traditional CE technology, where less DNA produces lower peak heights. Mixture ratios of approximately 1:1 were indistinguishable, while a greater imbalance, i.e., higher mixture ratios, made the mixture more distinguishable between major and minor contributors. Based on this information, the highest success rate of correctly deconvoluted four-allelic loci was from mixtures with 1:3 ratios. At higher mixture ratios, the drop-out rate of the minor contributor increased, reducing the number of four-allelic loci.

Diversity and distribution of Aphanomyces astaci in a European hotspot of ornamental crayfish introductions.

Ornamental trade has become an important introduction pathway of non-native aquatic species worldwide. Correspondingly, there has been an alarming increase in the number of established crayfish of aquarium origin in Europe over the previous decade. The oomycete Aphanomyces astaci, the pathogen causing crayfish plague responsible for serious declines of European crayfish populations, is dispersed with introduced North American crayfish. The role of ornamental taxa in introducing and spreading different genotypes of this pathogen in open waters remains unclear. We investigated the distribution, prevalence, and diversity of A. astaci in Budapest, Hungary, which became a hotspot of aquarium crayfish introductions. Their establishment in this area was facilitated by locally abundant thermal waters. We screened for A. astaci in six host taxa from 18 sites sampled between 2018 and 2021: five cambarids (Cambarellus patzcuarensis, Faxonius limosus, Procambarus alleni, P. clarkii, P. virginalis) and one native astacid (Pontastacus leptodactylus). The pathogen was confirmed at five sampled sites in four host taxa: P. virginalis, P. clarkii, F. limosus, and for the first time in European open waters also in P. alleni. Genotyping was successful only in individuals from two different brooks where multiple host species coexisted but revealed unexpected patterns. Mitochondrial B-haplogroup of A. astaci, previously usually reported from Pacifastacus leniusculus or infected European species, was detected in P. virginalis at both sites, and in both F. limosus and P. virginalis sampled from a thermally stable tributary of Barát brook in 2018. In contrast, A-haplogroup of A. astaci was detected in coexisting F. limosus, P. virginalis and P. clarkii sampled in the same watercourse just a few hundred meters downstream in 2020. Additional genotyping methods indicated that a previously unknown A. astaci strain was associated with the latter haplogroup. One P. virginalis individual from 2020 was apparently co-infected by strains representing both mitochondrial haplogroups. The results indicated multiple sources of A. astaci in Budapest, likely directly associated with the introduction of ornamental species, interspecific transmission of this pathogen among ornamental hosts, and potential for a quick spatial or temporal turnover of dominant A. astaci strains at a certain locality. This highlights that in regions with high richness of potential A. astaci hosts, host taxon/pathogen genotype combinations become unpredictable, which might prevent reliable genotyping of pathogen sources in local crayfish mass mortalities.

Developing DPYD Genotyping Method for Personalized Fluoropyrimidines Therapy.

BACKGROUND: Fluoropyrimidine drugs are widely used in chemotherapy to treat solid tumors. However, severe toxicity has been reported in 10% to 40% of patients. The DPYD gene encodes the rate-limiting enzyme dihydropyrimidine dehydrogenase responsible for fluoropyrimidine catabolism. The DPYD variants resulting in decreased or no enzyme activity are associated with increased risk of fluoropyrimidine toxicity. This study aims to develop a pharmacogenetic test for screening DPYD variants to guide fluoropyrimidine therapy. METHODS: A multiplex allele-specific polymerase chain reaction (AS-PCR) assay, followed by capillary electrophoresis, was developed to detect 5 common DPYD variants (c.557A > G, c.1129-5923C > G, c.1679T > G, c.1905 + 1G > A, and c.2846A > T). Deidentified population samples were used for screening positive controls and optimizing assay conditions. Proficiency testing samples with known genotypes were analyzed for test validation. All variants detected were confirmed by Sanger sequencing. RESULTS: From the deidentified population samples, 5 samples were heterozygous for c.557A > G, 2 samples were heterozygous for c.1129-5923C > G (HapB3), and 1 sample was heterozygous for c.2846A > T. The 20 proficiency samples matched with their assigned genotypes, including 13 wild-type samples, 3 samples heterozygous for c.1679T > G, 2 samples heterozygous for c.1905 + 1G > A, and 2 samples heterozygous for c.2846A > T. One of the 3 patient samples was heterozygous for c.1129-5923C > G (HapB3). All the variants detected by the multiplex AS-PCR assay were concordant with Sanger sequencing results. CONCLUSIONS: A robust multiplex AS-PCR assay was developed to rapidly detect 5 variants in the DPYD gene. It can be used for screening DPYD variants to identify patients with increased risk of toxicity when prescribed fluoropyrimidine therapy.

Clinical performance of the novel full-genotyping OncoPredict HPV Quantitative Typing assay using the VALGENT framework.

Clinical validation of human papillomavirus (HPV) assays according to international criteria is prerequisite for their implementation in cervical cancer screening. OncoPredict HPV Quantitative Typing (QT) assay (Hiantis Srl, Milan, Italy) is a novel full-genotyping multiplex real-time PCR quantitative assay targeting E6/E7 genes, allowing individual viral load determination of 12 high-risk (HR) HPV types. Quality controls for sample adequacy, efficiency of nucleic acid extraction and PCR inhibition are included in the assay. Clinical performance of OncoPredict HPV QT test was assessed as part of the "Validation of HPV Genotyping Tests" (VALGENT-2) framework, consisting of 1300 cervical liquid-based cytology (LBC) samples of women aged between 20 and 60 years who had originally attended for routine cervical screening in Scotland. The clinical accuracy of the OncoPredict HPV QT (index test) for the detection of CIN2+ was assessed relative to the GP5+/6+ Enzyme ImmunoAssay (GP5+/6+ EIA) (comparator test), using noninferiority criteria. Intra- and interlaboratory reproducibility of the assay was assessed on a subpopulation, comprising 526 samples. The relative sensitivity and specificity for OncoPredict HPV QT vs GP5+/6+-PCR-EIA were 1.01 (95% CI: 0.99-1.03) and 1.03 (95% CI: 1.0-1.06) respectively. The P-values for noninferiority were ≤0.001. The intra- and inter-laboratory reproducibility demonstrated a high concordance (>98.7%) with kappas for individual types ranging from 0.66 to 1.00. OncoPredict HPV QT fulfills the international validation criteria for the use of HPV tests in cervical cancer screening.

GenAPoPop 1.0: A user-friendly software to analyse genetic diversity and structure from partially clonal and selfed autopolyploid organisms.

Autopolyploidy is quite common in most clades of eukaryotes. The emergence of sequence-based genotyping methods with individual and marker tags now enables confident allele dosage, overcoming the main obstacle to the democratization of the population genetic approaches when studying ecology and evolution of autopolyploid populations and species. Reproductive modes, including clonality, selfing and allogamy, have deep consequences on the ecology and evolution of population and species. Analysing genetic diversity and its dynamics over generations is one efficient way to infer the relative importance of clonality, selfing and allogamy in populations. GenAPoPop is a user-friendly solution to compute the specific corpus of population genetic indices, including indices about genotypic diversity, needed to analyse partially clonal, selfed and allogamous polysomic populations genotyped with confident allele dosage. It also easily provides the posterior probabilities of quantitative reproductive modes in autopolyploid populations genotyped at two-time steps and a graphical representation of the minimum spanning trees of the genetic distances between polyploid individuals, facilitating the interpretation of the genetic coancestry between individuals in hierarchically structured populations. GenAPoPop complements the previously existing solutions, including SPAGEDI and POLYGENE, to use genotypings to study the ecology and evolution of autopolyploid populations. It was specially developed with a simple graphical interface and workflow, and comes with a simulator to facilitate practical courses and teaching of population genetics for autopolyploid populations.

MCPtaggR: R package for accurate genotype calling in reduced representation sequencing data by eliminating error-prone markers based on genome comparison.

Reduced representation sequencing (RRS) offers cost-effective, high-throughput genotyping platforms such as genotyping-by-sequencing (GBS). RRS reads are typically mapped onto a reference genome. However, mapping reads harbouring mismatches against the reference can potentially result in mismapping and biased mapping, leading to the detection of error-prone markers that provide incorrect genotype information. We established a genotype-calling pipeline named mappable collinear polymorphic tag genotyping (MCPtagg) to achieve accurate genotyping by eliminating error-prone markers. MCPtagg was designed for the RRS-based genotyping of a population derived from a biparental cross. The MCPtagg pipeline filters out error-prone markers prior to genotype calling based on marker collinearity information obtained by comparing the genome sequences of the parents of a population to be genotyped. A performance evaluation on real GBS data from a rice F2 population confirmed its effectiveness. Furthermore, our performance test using a genome assembly that was obtained by genome sequence polishing on an available genome assembly suggests that our pipeline performs well with converted genomes, rather than necessitating de novo assembly. This demonstrates its flexibility and scalability. The R package, MCPtaggR, was developed to provide functions for the pipeline and is available at https://github.com/tomoyukif/MCPtaggR.

A rapid and reference-free imputation method for low-cost genotyping platforms.

Most current genotype imputation methods are reference-based, which posed several challenges to users, such as high computational costs and reference panel inaccessibility. Thus, deep learning models are expected to create reference-free imputation methods performing with higher accuracy and shortening the running time. We proposed a imputation method using recurrent neural networks integrating with an additional discriminator network, namely GRUD. This method was applied to datasets from genotyping chips and Low-Pass Whole Genome Sequencing (LP-WGS) with the reference panels from The 1000 Genomes Project (1KGP) phase 3, the dataset of 4810 Singaporeans (SG10K), and The 1000 Vietnamese Genome Project (VN1K). Our model performed more accurately than other existing methods on multiple datasets, especially with common variants with large minor allele frequency, and shrank running time and memory usage. In summary, these results indicated that GRUD can be implemented in genomic analyses to improve the accuracy and running-time of genotype imputation.