Allele Frequency Estimates Research Articles

MOIRE: a software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data.

Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data is challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. We present Multiplicity Of Infection and allele frequency REcovery (MOIRE), a Bayesian approach to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. MOIRE accommodates both polyallelic and SNP data, making it applicable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Extensive simulations and real-world data from a malaria study in Namibia demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to seven with moderate-sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings when allele frequencies or genotyping panels differ. Compared to existing software, MOIRE enables more comprehensive insights into within-host diversity and population structure. MOIRE is available as an R package at https://eppicenter.github.io/moire/.

MOIRE: A software package for the estimation of allele frequencies and effective multiplicity of infection from polyallelic data.

Malaria parasite genetic data can provide insight into parasite phenotypes, evolution, and transmission. However, estimating key parameters such as allele frequencies, multiplicity of infection (MOI), and within-host relatedness from genetic data has been challenging, particularly in the presence of multiple related coinfecting strains. Existing methods often rely on single nucleotide polymorphism (SNP) data and do not account for within-host relatedness. In this study, we introduce a Bayesian approach called MOIRE (Multiplicity Of Infection and allele frequency REcovery), designed to estimate allele frequencies, MOI, and within-host relatedness from genetic data subject to experimental error. Importantly, MOIRE is flexible in accommodating both polyallelic and SNP data, making it adaptable to diverse genotyping panels. We also introduce a novel metric, the effective MOI (eMOI), which integrates MOI and within-host relatedness, providing a robust and interpretable measure of genetic diversity. Using extensive simulations and real-world data from a malaria study in Namibia, we demonstrate the superior performance of MOIRE over naive estimation methods, accurately estimating MOI up to 7 with moderate sized panels of diverse loci (e.g. microhaplotypes). MOIRE also revealed substantial heterogeneity in population mean MOI and mean relatedness across health districts in Namibia, suggesting detectable differences in transmission dynamics. Notably, eMOI emerges as a portable metric of within-host diversity, facilitating meaningful comparisons across settings, even when allele frequencies or genotyping panels are different. MOIRE represents an important addition to the analysis toolkit for malaria population dynamics. Compared to existing software, MOIRE enhances the accuracy of parameter estimation and enables more comprehensive insights into within-host diversity and population structure. Additionally, MOIRE's adaptability to diverse data sources and potential for future improvements make it a valuable asset for research on malaria and other organisms, such as other eukaryotic pathogens. MOIRE is available as an R package at https://eppicenter.github.io/moire/.

HLA alleles, haplotypes frequencies, and their association with hematological disorders: a report from 1550 families whose patients underwent allogeneic bone marrow transplantation in Egypt.

HLA alleles are representative of ethnicities and may play important roles in predisposition to hematological disorders. We analyzed DNA samples for HLA-A, -B, -C, -DRB1, and -DQB1 loci, from 1550 patients and 4450 potential related donors by PCR-SSO (Polymerase chain reaction sequence-specific oligonucleotides) and estimated allele frequencies in donors and patients from 1550 families who underwent bone marrow transplantation (BMT) in Egypt. We also studied the association between HLA allele frequencies and incidence of acute myeloid leukemia, acute lymphoblastic leukemia, and severe aplastic anemia. The most frequently observed HLA class I alleles were HLA- A*01:01 (16.9%), A*02:01 (16.1%), B*41:01 (8.7%), B*49:01 (7.3%), C*06:02 (25.1%), and C*07:01 (25.1%), and the most frequently observed class II alleles were HLA-DRB1*11:01 (11.8%), DRB1*03:01 (11.6%), DQB1*03:01 (27.5%), and DQB1*05:01 (18.9%). The most frequently observed haplotypes were A*33:01~B*14:02 ~ DRB1*01:02 (2.35%) and A*01:01~B*52:01~DRB1*15:01 (2.11%). HLA-DRB1*07:01 was associated with higher AML odds (OR, 1.26; 95% CI, 1.02-1.55; p = 0.030). Only HLA-B38 antigen showed a trend towards increased odds of ALL (OR, 1.52; 95% CI, 1.00-2.30; p = 0.049) HLA-A*02:01, -B*14:02, and -DRB1*15:01 were associated with higher odds of SAA (A*02:01: OR, 1.35; 95% CI, 1.07-1.70; p = 0.010; B*14:02: OR, 1.43; 95% CI, 1.06-1.93; p = 0.020; DRB1*15:01: OR, 1.32; 95% CI, 1.07-1.64; p = 0.011). This study provides estimates of HLA allele and haplotype frequencies and their association with hematological disorders in an Egyptian population.

Estimation of allelic, genotypic and Phenotypic frequencies for ABO and Rh blood groups among blood donors at the Central Blood Bank in Benghazi, Libya.

Estimation of allelic, genotypic and Phenotypic frequencies for ABO and Rh blood groups among blood donors at the Central Blood Bank in Benghazi, Libya.

A DNA profiling system for conservation management of Kamchatka brown bear (Ursus arctos piscator); population data and system performance from 16 autosomal STRs

Despite the high density of brown bears (Ursus arctos piscator) on the Kamchatka peninsula their genetic variation has not been studied by STR analysis. Our aim was, therefore, to provide population data from the Kamchatka brown bear population applying a validated DNA profiling system. Twelve dinucleotide STRs commonly used in Western-European (WE) populations and four additional ones (G10C, G10J, G10O, G10X), were included. Template input ≥ 0.2 ng was successfully amplified. Measurements of precision, stutter and heterozygous balance showed that markers could be reliably genotyped applying the thresholds used for genotyping WE brown bears. However, locus G10X revealed an ancient allele-specific polymorphism that led to suboptimal amplification of all 174 bp alleles (Kamchatka and WE). Allele frequency estimates and forensic genetic parameters were obtained from 115 individuals successfully identified by genotyping 434 hair samples. All markers met the Hardy-Weinberg and linkage equilibrium expectations, and the power of discrimination ranged from 0.667 to 0.962. The total average probability of identity from the 15 STRs was 1.4 ×10−14 (FST = 0.05) while the total average probability of sibling identity was 6.0 ×10−6. Relationship tests revealed several parent-cub and full sibling pairs demonstrating that the marker set would be valuable for the study of family structures. The population data is the first of its kind from the Kamchatka brown bear population. Population pairwise FST`s revealed moderate genetic differentiation that mirrored the geographic distances to WE populations. The DNA profiling system, providing individual-specific profiles from non-invasive samples, will be useful for future monitoring and conservation purposes

Assisting the analysis of insertions and deletions using regional allele frequencies.

Accurate estimation of population allele frequency (AF) is crucial for gene discovery and genetic diagnostics. However, determining AF for frameshift-inducing small insertions and deletions (indels) faces challenges due to discrepancies in mapping and variant calling methods. Here, we propose an innovative approach to assess indel AF. We developed CRAFTS-indels (Calculating Regional Allele Frequency Targeting Small indels), an algorithm that combines AF of distinct indels within a given region and provides "regional AF" (rAF). We tested and validated CRAFTS-indels using three independent datasets: gnomAD v2 (n=125,748 samples), an internal dataset (IGM; n=39,367), and the UK BioBank (UKBB; n=469,835). By comparing rAF against standard AF, we identified rare indels with rAF exceeding standard AF (sAF≤10-4 and rAF>10-4) as "rAF-hi" indels. Notably, a high percentage of rare indels were "rAF-hi", with a higher proportion in gnomAD v2 (11-20%) and IGM (11-22%) compared to the UKBB (5-9% depending on the CRAFTS-indels' parameters). Analysis of the overlap of regions based on their rAF with low complexity regions and with ClinVar classification supported the pertinence of rAF. Using the internal dataset, we illustrated the utility of CRAFTS-indel in the analysis of de novo variants and the potential negative impact of rAF-hi indels in gene discovery. In summary, annotation of indels with cohort specific rAF can be used to handle some of the limitations of current annotation pipelines and facilitate detection of novel gene disease associations. CRAFTS-indels offers a user-friendly approach to providing rAF annotation. It can be integrated into public databases such as gnomAD, UKBB and used by ClinVar to revise indel classifications.

Phenomic landscape and pharmacogenomic implications for HLA region in a Taiwan Han Chinese population

BackgroundThe human leukocyte antigen (HLA) genes, exhibiting significant genetic diversity, are associated with susceptibility to various clinical diseases and diverse in drug responses. High costs of HLA sequencing and the population-specific architecture of this genetic region necessitate the establishment of a population-specific HLA imputation reference panel. Moreover, there is a lack of understanding about the genetic and phenotypic landscape of HLA variations within the Taiwanese population.MethodsWe created models for a Taiwanese-specific HLA imputation reference panel. These models were trained with the array genotype data and HLA sequencing data from 845 Taiwanese subjects. HLA imputation was applied for 59,448 Taiwanese subjects to characterize the HLA allele and haplotype frequencies. Additionally, a phenome-wide association study (PheWAS) was conducted to identify the phenotypes associated with HLA variations. The association of the biallelic HLA variants with the binary and quantitative traits were evaluated with additive logistic and linear regression models, respectively. Furthermore, an omnibus test with likelihood-ratio test was applied for each HLA amino acid position in the multiallelic HLA amino acid polymorphisms to compare the difference between a fitted model and a null model following a χ2 distribution of n-1 degree of freedom at a position with n residues. Finally, we estimated the prevalence of adverse drug reactions (ADR)-related HLA alleles in the Taiwanese population.ResultsIn this study, the reference panel models displayed remarkable accuracy, with averages of 99.3%, 98.9%, and 99.1% for 2-, 4-, 6-digit alleles of the eight classical HLA genes, respectively. For PheWAS, a total of 18,136 significant associations with HLA variants across 26 phenotypes are identified (p < 5×10-8), highlighting the pleiotropy feature of the HLA region. Among the independent signals, 15 are novel, including the association of HLA-B pos 138 variation with ankylosing spondylitis (AS), and rs9266290 and rs9266292 with allergy. Through an analysis spanning the entire HLA region, we identified clusters of phenotype correlations. Finally, the carriers of pharmacogenomic related HLA alleles, including HLA-C*01:02 (35.86%), HLA-B*58:01 (20.9%), and HLA-B*15:02 (8.38%), were characterized in the Taiwanese general population.ConclusionsWe successfully delivered the HLA imputation for 59,448 Taiwanese subjects and characterized the genetic and phenotypic landscapes of the HLA variations. In addition, we quantified the estimated prevalence of the ADR-related HLA alleles in the Taiwanese population. The developed HLA imputation reference panel could be used for estimation of population HLA allele frequencies, which can facilitate further studies in the role of HLA variants in a wider range of phenotypes in the population.

Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability.

Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.

Kernel density estimation of allele frequency including undetected alleles.

Whereas undetected species contribute to estimation of species diversity, undetected alleles have not been used to estimated genetic diversity. Although random sampling guarantees unbiased estimation of allele frequency and genetic diversity measures, using undetected alleles may provide biased but more precise estimators useful for conservation. We newly devised kernel density estimation (KDE) for allele frequency including undetected alleles and tested it in estimation of allele frequency and nucleotide diversity using population generated by coalescent simulation as well as well as real population data. Contrary to expectations, nucleotide diversity estimated by KDE had worse bias and accuracy. Allele frequency estimated by KDE was also worse except when the sample size was small. These might be due to finity of population and/or the curse of dimensionality. In conclusion, KDE of allele frequency does not contribute to genetic diversity estimation.

Sampling strategies for genotyping common bean (Phaseolus vulgaris L.) Genebank accessions with DArTseq: a comparison of single plants, multiple plants, and DNA pools.

Genotyping large-scale gene bank collections requires an appropriate sampling strategy to represent the diversity within and between accessions. A panel of 44 common bean (Phaseolus vulgaris L.) landraces from the Alliance Bioversity and The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) gene bank was genotyped with DArTseq using three sampling strategies: a single plant per accession, 25 individual plants per accession jointly analyzed after genotyping (in silico-pool), and by pooling tissue from 25 individual plants per accession (seq-pool). Sampling strategies were compared to assess the technical aspects of the samples, the marker information content, and the genetic composition of the panel. The seq-pool strategy resulted in more consistent DNA libraries for quality and call rate, although with fewer polymorphic markers (6,142 single-nucleotide polymorphisms) than the in silico-pool (14,074) or the single plant sets (6,555). Estimates of allele frequencies by seq-pool and in silico-pool genotyping were consistent, but the results suggest that the difference between pools depends on population heterogeneity. Principal coordinate analysis, hierarchical clustering, and the estimation of admixture coefficients derived from a single plant, in silico-pool, and seq-pool successfully identified the well-known structure of Andean and Mesoamerican gene pools of P. vulgaris across all datasets. In conclusion, seq-pool proved to be a viable approach for characterizing common bean germplasm compared to genotyping individual plants separately by balancing genotyping effort and costs. This study provides insights and serves as a valuable guide for gene bank researchers embarking on genotyping initiatives to characterize their collections. It aids curators in effectively managing the collections and facilitates marker-trait association studies, enabling the identification of candidate markers for key traits.

Liquid biopsies and those three little words: finding the perfect match for the MTB.

Monitoring ctDNA by liquid biopsies seems to represent the perfect match for precision oncology and its cornerstone clinical framework: the molecular tumour board (MTB). Detecting and scrutinising the success of targeted therapies or tracking and, for that matter, addressing the therapy with the evolutive nature of a tumour are some of the main advancements one considers to be important for the MTB. One challenge is correlating the estimated allele frequency of each identified genetic alteration determined by analysing the ctDNA sequencing results and matching these with the range of suitable drugs, which may limit the simultaneous treatment of all tumour variations. This limitation arises because a new biopsy would typically be required to evaluate the response to treatment. As a result, evaluating the success of MTB recommendations relies on traditional staging methods, highlighting an existing diagnostic gap. Thus, optimising liquid biopsy technology could enhance the efficacy of MTB treatment recommendations and ensuing tailored therapies. Herein, we discuss the prospect of ctDNA analyses in the molecular tumour board.

Morpho-genetic assessment and dissecting the genetic architecture for Cercospora leaf spot (CLS) resistance in mung bean [Vigna radiata (L.) Wilczek

Cercospora leaf spot (CLS) has a substantial negative impact on mung bean production across the world. It is crucial to identify and incorporate alleles that might be helpful in combating CLS in order to maintain mung bean health, and productivity. Therefore, 90 mung bean genotypes, including four check varieties, and 66 polymorphic SSR markers were included in the present study during pre-rabi 2018 and pre-rabi 2019. Morpho-genetic analysis revealed a higher value of GCV for yield, cluster and pod numbers per plant, and plant height. All traits had excellent genetic advance (GA), with the exception of the number of seeds per pod, which was medium. PCA was used to separate up to 85.656 % of the variation in the characteristics between the two primary PCs. Molecular dissection revealed, the markers CEDG118 and CEDG074 to yield more genetic diversity, followed by CEDG073, CEDG030 and CEDG147. Estimation of allele frequencies revealed some alleles of the markers found linked to CLS resistance, i.e., CEDG001 (111 bp and 135 bp), CEDG006 (135 bp), CEDG153 (117 bp), CEDG210 (193 bp), CEDG117 (112 bp), CEDG020 (143 bp), CEDG067 (93 bp), CEDG146 (111 bp), and CEDG071 (231 bp and 267 bp). The highest private allelic frequency was employed by the markers CEDG153 and CEDG146. The dendrogram divided the panel population into two major clusters. However, two distinct genetic structure were found within the population. According to these findings, the mung bean germplasm panel used in the present study may have the ability to improve the local gene pool and aid in the development of CLS resistance breeding strategies using genomic tools and techniques.

Environmental DNA reveals the genetic diversity and population structure of an invasive species in the Laurentian Great Lakes

Environmental DNA (eDNA) has been established as a noninvasive and efficient approach to sample genetic material from aquatic environments. Although most commonly used to determine species presence and measure biodiversity, eDNA approaches also hold great potential to obtain population-level genetic information from water samples. In this study, we sequenced a panel of multiallelic microsatellite markers from filtered water and fish tissue samples to uncover patterns of intraspecific diversity in the freshwater Round Goby (Neogobius melanostomus) across their invaded range in the Laurentian Great Lakes region. Although we found that the concentration of nuclear eDNA is lower than mitochondrial eDNA, we nonetheless detected over two-thirds of all nuclear alleles identified from genotyped tissues in our eDNA samples, with the greatest recovery of common alleles in the population. Estimates of allele frequencies and genetic variability within and between populations were detected from eDNA in patterns that were consistent with individual tissue-based estimates of genetic diversity and differentiation. The strongest genetic differentiation in both eDNA and tissues exists in an isolation by distance pattern. Our study demonstrates the potential for eDNA-based approaches to characterize key population parameters required to effectively monitor, manage, or sustain aquatic species.

Degree of leukochimerism with calf age in Angus-crossbred twin sets.

Twinning in cattle is infrequent and usually undesired. It can result in an increased occurrence of abortion and dystocia, reduced calf survival and a high likelihood of freemartinism in mixed-sex twins. Twin gestations are also commonly associated with the formation of placental vascular anastomoses (PVA) between twins. Through PVA they share blood, hormones (leading to freemartinism in mixed sex twins) and hematopoietic stem cells, which are the progenitors of white blood cells. The sharing of stem cells between twins can result in leukochimeric twin sets. These are twins that have white blood cells derived from both self and the co-twin owing to the fetal migration of hematopoietic stem cells from the extraembryonic mesoderm of the yolk sac to final sites like bone marrow and thymus. This study examined the degree to which this leukochimerism changes with age. DNA was extracted from hair bulbs containing mesenchymal dermal papilla to determine the individual's true genotype and blood samples were obtained at six time points from 1 week to 8 months of age to assess leukochimerism. Samples were genotyped using a medium density SNP chip, and quantitative estimates of allele frequency were determined using SNPs for which members of a twin set had alternative homozygous genotypes. The results indicate statistically significant changes in the proportion of self and co-twin with age and suggest that by 2-4 months of age the genotypic mix in white blood cells represents the hematopoetic stem cell population resident in the individual (i.e. permanently found in thymus and bone marrow).

PoolHelper: An R package to help in designing Pool‐Seq studies

Abstract Next‐generation sequencing of pooled samples (Pool‐seq) is an important tool in population genomics and molecular ecology. In Pool‐seq, the relative number of reads with an allele reflects the allele frequencies in the sample. However, unequal individual contributions to the pool and sequencing errors can lead to inaccurate allele frequency estimates, influencing downstream analysis. When designing Pool‐seq studies, researchers need to decide the pool size (number of individuals) and average depth of coverage (sequencing effort). An efficient sampling design should maximise the accuracy of allele frequency estimates while minimising the sequencing effort. We describe a novel tool to simulate single nucleotide polymorphism (SNP) data using coalescent theory and account for sources of uncertainty in Pool‐seq. We introduce an R package, poolHelper, enabling users to simulate Pool‐seq data under different combinations of average depth of coverage and pool size, accounting for unequal individual contributions and sequencing errors, modelled by adjustable parameters. The mean absolute error is computed by comparing the sample allele frequencies obtained based on individual genotypes with the frequency estimates obtained with Pool‐seq. poolHelper enables users to simulate multiple combinations of pooling errors, average depth of coverage, pool sizes and number of pools to assess how they influence the error of sample allele frequencies and expected heterozygosity. Using simulations under a single population model, we illustrate that increasing the depth of coverage does not necessarily lead to more accurate estimates, reinforcing that finding the best Pool‐seq study design is not straightforward. Moreover, we show that simulations can be used to identify different combinations of parameters with similarly low mean absolute errors. This can help users to define an effective sampling design by using those combinations of parameters that minimise the sequencing effort. The poolHelper package provides tools for performing simulations with different combinations of parameters (e.g. pool size, depth of coverage, unequal individual contribution) before sampling and generating data, allowing users to define sampling schemes based on simulations. This allows researchers to focus on the best sampling scheme to answer their research questions. poolHelper is comprehensively documented with examples to guide effective use.

Comprehensive and realistic simulation of tumour genomic sequencing data.

Accurate identification of somatic mutations and allele frequencies in cancer has critical research and clinical applications. Several computational tools have been developed for this purpose but, in the absence of comprehensive 'ground truth' data, assessing the accuracy of these methods is challenging. We created a computational framework to simulate tumour and matched normal sequencing data for which the source of all loci that contain non-reference bases is known, based on a phased, personalized genome. Unlike existing methods, we account for sampling errors inherent in the sequencing process. Using this framework, we assess accuracy and biases in inferred mutations and their frequencies in an established somatic mutation calling pipeline. We demonstrate bias in existing methods of mutant allele frequency estimation and show, for the first time, the observed mutation frequency spectrum corresponding to a theoretical model of tumour evolution. We highlight the impact of quality filters on detection sensitivity of clinically actionable variants and provide definitive assessment of false positive and false negative mutation calls. Our simulation framework provides an improved means to assess the accuracy of somatic mutation calling pipelines and a detailed picture of the effects of technical parameters and experimental factors on somatic mutation calling in cancer samples.

Alternative allele frequency recallibration after NGS data analysis.

3077 Background: With the spread of NGS in routine clinical practice data analysis methods actively evolve to increase and refine information yield. Not much attention is given to variant allele fraction (VAF) estimation though it impacts clinical insignificance of subclonal somatic variants and identification of variant origin. Methods: Real-world sequencing data of 2379 samples (including 781 tumor-only and 1598 blood-only sequencing datasets) obtained via amplicon-based sequencing was used for the retrospective analysis of variant allele frequency estimation of identified genetic variants employing different methods including ITVC, SiNVICT, Mutect2 and SGA. Original method (AODvAF) utilizing alignment-free algorithm based on wild-type and alternative genome subsequence abundance estimation was developed and compared with the original output from NGS data analysis software. Anticipated VAF estimation was based on analyte type and database annotation. Results: Across 3447 identified potentially germline variations (PGV) VAF estimations provided by ITVC, SiNVICT and SGA were highly correlated (Pearson correlation coefficient [PCC] 1.00), while Mutect2 and AODvAF demonstrated discordant results (Mutect2/AODvAF vs ITVC PCC 0.87/0.82). VAF miscalculation defined by absolute difference between estimated and anticipated VAF of 10% and more was noted for 25%, 26%, 22%, 15% and 7% for ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Across 308 pathogenic BRCA1, BRCA2, PALB2 mutations identified in blood-only sequencing 25, 15, 16, 10 and 5 were characterized by VAF of 30% and lower based on ITVC, SiNVICT, SGA, Mutect2 and AODvAF estimations respectively (54, 49, 33, 51 and 9 for VAF 40% and lower), which may lead to misinterpretation of these variants as post-zygotic. Automatic in-silico annotation of variant origin (germline vs somatic) based on ISOWN was correct for 89%, 91%, 94%, 93% and 98% of these variants. Variant origin for 55 pathogenic variants of genes associated with hereditary cancer syndromes identified in tumor-only sequencing was verified by Sanger sequencing. Cohen's kappa coefficient for variant origin discrimination (Sanger vs in-silico prediction) was 0.91, 0.90, 0.91, 0.94 and 0.97 based on VAF provided by ITVC, SiNVICT, SGA, Mutect2 and AODvAF respectively. Conclusions: Accurate VAF calculation allows precise variant origin identification and impacts interpretation of variants. Developed method allows correction of VAF after NGS data core analysis.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

TrEMOLO: accurate transposable element allele frequency estimation using long-read sequencing data combining assembly and mapping-based approaches

Transposable Element MOnitoring with LOng-reads (TrEMOLO) is a new software that combines assembly- and mapping-based approaches to robustly detect genetic elements called transposable elements (TEs). Using high- or low-quality genome assemblies, TrEMOLO can detect most TE insertions and deletions and estimate their allele frequency in populations. Benchmarking with simulated data revealed that TrEMOLO outperforms other state-of-the-art computational tools. TE detection and frequency estimation by TrEMOLO were validated using simulated and experimental datasets. Therefore, TrEMOLO is a comprehensive and suitable tool to accurately study TE dynamics. TrEMOLO is available under GNU GPL3.0 at https://github.com/DrosophilaGenomeEvolution/TrEMOLO.

An NBS-LRR protein in the Rpp1 locus negates the dominance of Rpp1-mediated resistance against Phakopsora pachyrhizi in soybean.

The soybean Rpp1 locus confers resistance to Phakopsora pachyrhizi, causal agent of rust, and resistance is usually dominant over susceptibility. However, dominance of Rpp1-mediated resistance is lost when a resistant genotype (Rpp1 or Rpp1b) is crossed with susceptible line TMG06_0011, and the mechanism of this dominant susceptibility (DS) is unknown. Sequencing the Rpp1 region reveals that the TMG06_0011 Rpp1 locus has a single nucleotide-binding site leucine-rich repeat (NBS-LRR) gene (DS-R), whereas resistant PI 594760B (Rpp1b) is similar to PI 200492 (Rpp1) and has three NBS-LRR resistance gene candidates. Evidence that DS-R is the cause of DS was reflected in virus-induced gene silencing of DS-R in Rpp1b/DS-R or Rpp1/DS-R heterozygous plants with resistance partially restored. In heterozygous Rpp1b/DS-R plants, expression of Rpp1b candidate genes was not significantly altered, indicating no effect of DS-R on transcription. Physical interaction of the DS-R protein with candidate Rpp1b resistance proteins was supported by yeast two-hybrid studies and in silico modeling. Thus, we conclude that suppression of resistance most likely does not occur at the transcript level, but instead probably at the protein level, possibly with Rpp1 function inhibited by binding to the DS-R protein. The DS-R gene was found in other soybean lines, with an estimated allele frequency of 6% in a diverse population, and also found in wild soybean (Glycine soja). The identification of a dominant susceptible NBS-LRR gene provides insight into the behavior of NBS-LRR proteins and serves as a reminder to breeders that the dominance of an R gene can be influenced by a susceptibility allele.