Accurate Genotyping Research Articles

Pharmacogenetic Practice of Anticancer Drugs: Multiple Approaches for an Accurate and Comprehensive Genotyping.

The application of pharmacogenetics in oncology is part of the routine clinical practice. In particular, genotyping of dihydropyrimidine dehydrogenase (DPYD) and UDP-glucuronosyltransferase (UGT1A1) is crucial to manage the treatment of patients taking fluoropyrimidines and irinotecan. The unique approach of our laboratory to the pharmacogenetic diagnostic service in oncology is to combine two real-time PCR methods, LightSNiP assay (TIB MOLBIOL), and more recently FRET (Fluorescent Resonance Energy Transfer) probes technology (Nuclear Laser Medicine), plus TaqMan assay (Thermo Fisher) for the confirmation of the presence of variant alleles on DNA from a second extraction. We found that both the FRET and LightSNiP assays, where detection occurs by melting curve analysis, offer an advantage over the competing TaqMan technology. Whereas unexpected genetic variants may be missed using a mutation-specific TaqMan assay, the information thus obtained can be useful to adjust the therapy in case of unexpected post-treatment toxicity. The combination of TaqMan and FRET assays helped us to achieve more accurate genotyping and a correct result for the patient. The added value of the DPYD FRET assay is the possibility of detecting, with the same amplification profile of the polymorphisms detailed in the guidelines, also the c.2194G>A (*6 rs1801160), cited in the recommendations as a variant to be investigated in case of severe toxicity. Regarding the UGT1A1 (TA)n promoter polymorphism (rs3064744), the distinctive and positive feature of the FRET assay is to allow clearly identifying all those potential variant alleles, including the (TA)5 and (TA)8 alleles, that are frequent in African Americans. Our clinical practice emphasizes the importance of not only rapid and easy-to-use assays, such as the new FRET ones, but also of accurate and comprehensive genotyping for good pharmacogenetic diagnostic activity.

SVJedi-graph: improving the genotyping of close and overlapping structural variants with long reads using a variation graph.

Structural variation (SV) is a class of genetic diversity whose importance is increasingly revealed by genome resequencing, especially with long-read technologies. One crucial problem when analyzing and comparing SVs in several individuals is their accurate genotyping, that is determining whether a described SV is present or absent in one sequenced individual, and if present, in how many copies. There are only a few methods dedicated to SV genotyping with long-read data, and all either suffer of a bias toward the reference allele by not representing equally all alleles, or have difficulties genotyping close or overlapping SVs due to a linear representation of the alleles. We present SVJedi-graph, a novel method for SV genotyping that relies on a variation graph to represent in a single data structure all alleles of a set of SVs. The long reads are mapped on the variation graph and the resulting alignments that cover allele-specific edges in the graph are used to estimate the most likely genotype for each SV. Running SVJedi-graph on simulated sets of close and overlapping deletions showed that this graph model prevents the bias toward the reference alleles and allows maintaining high genotyping accuracy whatever the SV proximity, contrary to other state of the art genotypers. On the human gold standard HG002 dataset, SVJedi-graph obtained the best performances, genotyping 99.5% of the high confidence SV callset with an accuracy of 95% in less than 30 min. SVJedi-graph is distributed under an AGPL license and available on GitHub at https://github.com/SandraLouise/SVJedi-graph and as a BioConda package.

BG05 Artificial intelligence prediction of splice site mutation consequences in junctional epidermolysis bullosa: signposts to severity

Abstract Over one-fifth of disease-causing mutations in junctional epidermolysis bullosa (JEB) are splice site variants. These affect pre-mRNA splicing through the activation of cryptic splice sites and/or exon skipping, resulting in altered mRNA and translated proteins. Prediction of the precise location of activated cryptic splice sites is vital, as these can potentially alter the reading frame of the resulting transcript. Out-of-frame transcripts contain premature termination complexes (PTCs), which generally result in nonfunctional protein and often correlate with severe JEB in conjunction with other PTC-introducing mutations. SpliceAI is a 32-layer convolutional neural network that examines 10 000 nucleotides of flanking sequence around a variant of interest to predict its effects on RNA splicing. It was used to predict splice site mutation consequences in five patients with JEB with LAMB3 mutations. Genotypes, predicted transcripts and JEB subtypes are summarized in Table 1. SpliceAI predicted PTCs in cases 1 and 2, where transcripts were either out of frame or novel PTCs were introduced following aberrant splicing. An in-frame transcript with no PTC was predicted for case 3, which may explain this individual’s mild phenotype. Reverse transcriptase polymerase chain reaction data were reviewed for cases 4 and 5, and two predicted transcripts were confirmed. Two additional in-frame transcripts not predicted by SpliceAI were found to be produced in low levels. It is likely that these in-frame transcripts generated partially functional protein, alleviating JEB severity to a degree. This study highlights the importance of examining RNA transcripts for accurate genotype–phenotype correlation. In silico tools such as SpliceAI show potential in elucidating mechanisms of severity. They may facilitate selection of cases for further laboratory investigation and can guide which regions of the gene or transcript to sequence for confirmatory analyses. Where RNA analyses were available, SpliceAI correctly predicted two of the resultant transcripts. Further validation of this tool is required with additional cases in this rare disease.Table 1Genotypes, predicted transcripts and JEB subtypes found with SpliceAICaseZygosityMutation (LAMB3)Exon skipping predictionCryptic splice site activation predictionPredicted outcome commentsJEB subtype1Homozygousc.2701+1G>AOOF exon 18 skipping20 nt excluded from exon 18Both outcomes OOFSevere2Heterozygousc.565-2A>G, c.2914C>T (p.R972X)OOF exon 7 skipping117 nt included from intron 6 (including TGA PTC at c.565-60)In-frame cryptic activation with novel PTC introducedSevereOOF exon skipping3Heterozygousc.1705C>T (p.R569X), c.943+2T>COOF exon 9 skipping27 nt included from intron 9In-frame cryptic activationIntermediateOOF exon skipping4Homozygousc.298+5G>COOF exon 4 skipping64 nt included from intron 4Both outcomes OOFIntermediate5Heterozygousc.3119G>A (p.W1040X), c.629-12T>APredicted to be unlikely10 nt included from intron 7OOF cryptic activationIntermediatent, nucleotides; OOF, out of frame.

O-302 Triploid conceptions are predominantly caused by female meiosis II errors and their risk increases with advancing maternal age

Abstract Study question What are the incidence and origin of ploidy abnormalities in embryos derived from 2 pronuclei (2PN) zygotes, and is their risk linked to maternal age? Summary answer While embryo haploidy is usually due to male meiosis errors, triploidy is mainly caused by female meiosis II errors, whose incidence increases with maternal age. What is known already Normal fertilization is denoted by the appearance of 2PN 16-18 hours after insemination. Deviation from 2PN is considered evidence of abnormal fertilization and ploidy anomalies. Nonetheless, even 2PN embryos can be diagnosed with ploidy abnormalities during preimplantation genetic testing (PGT). The incidence of 3PN has already been linked to advanced maternal age. However, no conclusive and well-powered studies have yet investigated the incidence, origin, and maternal age correlation of genetically-diagnosed ploidy abnormalities. A targeted NGS-based approach that simultaneously analyzes copy number and genotyping data provides a comprehensive ploidy status assessment and helps address numerous basic and clinical research questions. Study design, size, duration Retrospective study including 96,660 trophectoderm biopsies analyzed between 2020 and 2022 using a targetedNGS-based PGT platform. A total of 1,063 embryos carrying haploidy or triploidy were used to investigate maternal age correlation, and parental/meiotic origin of the anomaly. Of these, 57 embryos were from concomitant PGT-M (PGT for monogenic disorders) cycles where also parental DNA was available. These trios (embryo-parents) were used to independently estimate parental/meiotic contribution to the ploidy abnormality and genome-wide recombination events. Participants/materials, setting, methods Targeted-NGS’s accuracy in genotyping and copy number (CN) detection were previously validated on triploid cell lines and multifocal blastocyst biopsies with known ploidy status derived from abnormally fertilized oocytes. Parental and meiotic origins were estimated using two independent approaches. First, they were inferred using sex chromosomes CN from 1,063 embryos with altered ploidy status. Secondly, they were directly calculated using genotyping data from 57 trios (embryo and parents) from PGT-M cycles. Main results and the role of chance The prevalence of ploidy abnormalities in 2PN-derived embryos was 1.1% (n = 1,063/96,660), with triploids accounting for 83.0% (n = 882/1,063) and haploids for 17.0% (n = 181/1,063). Remarkably, the incidence of ploidy anomalies is positively correlated with maternal age (OR = 1.046 per year; p < 0.001). Triploidy showed strong correlation with age (OR = 1.059 per year; p < 0.001), while haploidy did not (OR = 0.96 per year; p = 0.1). Based on sex chromosomes CN analysis, the extra haploid set of triploid embryos was almost completely of maternal origin (94.6%; 95%CI:93.0-96.1), with male errors accounting for only 5.4% (95%CI:4.0-7.1). On the other hand, haploid embryos were the result of paternal errors in 97.8% of cases (95%CI:94.4-99.4), with the missing haploid set being of maternal origin in the remaining 2.2% (95%CI:0.6-5.6). In terms of triploidy’s meiotic origin, two-thirds of the errors occurred during MII (95%CI:63.4-69.8), while one-third occurred during MI (95%CI:30.2-36.5). An independent method using genotyping data of 57 trios confirmed the predominance of paternal error in haploidy (n = 12/14) and the exclusively maternal origin of all embryonic triploidies (n = 43/43). The extra haploid set resulted from an error during MI in 27.9% (n = 12/43) and during MII in 72.1% (n = 31/43) of cases. Interestingly, 16.3% of triploids (n = 7/43) showed no genome-wide recombination events. Limitations, reasons for caution Despite the uniquely large sample size, the inference based on sex chromosomes CN suffers the limitations of the modeling assumptions (independence between parental and meiotic errors), which require further validation. Due to the low prevalence of paternal errors, clinical correlation with male factor could not be investigated with sufficient power. Wider implications of the findings Thanks to the exceedingly high sample size, this is the first study to reveal an increased risk of triploid conception with advancing female age (76% higher at age 40 than at age 30), providing meaningful insights for patients counseling. Importantly, the meiotic origin of ploidy anomalies in embryos were unveiled. Trial registration number not applicable

Characterization of complex structural variation in the CYP2D6-CYP2D7-CYP2D8 gene loci using single-molecule long-read sequencing.

Complex regions in the human genome such as repeat motifs, pseudogenes and structural (SVs) and copy number variations (CNVs) present ongoing challenges to accurate genetic analysis, particularly for short-read Next-Generation-Sequencing (NGS) technologies. One such region is the highly polymorphic CYP2D loci, containing CYP2D6, a clinically relevant pharmacogene contributing to the metabolism of >20% of common drugs, and two highly similar pseudogenes, CYP2D7 and CYP2D8. Multiple complex SVs, including CYP2D6/CYP2D7-derived hybrid genes are known to occur in different configurations and frequencies across populations and are difficult to detect and characterize accurately. This can lead to incorrect enzyme activity assignment and impact drug dosing recommendations, often disproportionally affecting underrepresented populations. To improve CYP2D6 genotyping accuracy, we developed a PCR-free CRISPR-Cas9 based enrichment method for targeted long-read sequencing that fully characterizes the entire CYP2D6-CYP2D7-CYP2D8 loci. Clinically relevant sample types, including blood, saliva, and liver tissue were sequenced, generating high coverage sets of continuous single molecule reads spanning the entire targeted region of up to 52kb, regardless of SV present (n = 9). This allowed for fully phased dissection of the entire loci structure, including breakpoints, to accurately resolve complex CYP2D6 diplotypes with a single assay. Additionally, we identified three novel CYP2D6 suballeles, and fully characterized 17 CYP2D7 and 18 CYP2D8 unique haplotypes. This method for CYP2D6 genotyping has the potential to significantly improve accurate clinical phenotyping to inform drug therapy and can be adapted to overcome testing limitations of other clinically challenging genomic regions.

BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.

While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE). Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples. We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmixand https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

Imputation of ancient human genomes

Due to postmortem DNA degradation and microbial colonization, most ancient genomes have low depth of coverage, hindering genotype calling. Genotype imputation can improve genotyping accuracy for low-coverage genomes. However, it is unknown how accurate ancient DNA imputation is and whether imputation introduces bias to downstream analyses. Here we re-sequence an ancient trio (mother, father, son) and downsample and impute a total of 43 ancient genomes, including 42 high-coverage (above 10x) genomes. We assess imputation accuracy across ancestries, time, depth of coverage, and sequencing technology. We find that ancient and modern DNA imputation accuracies are comparable. When downsampled at 1x, 36 of the 42 genomes are imputed with low error rates (below 5%) while African genomes have higher error rates. We validate imputation and phasing results using the ancient trio data and an orthogonal approach based on Mendel’s rules of inheritance. We further compare the downstream analysis results between imputed and high-coverage genomes, notably principal component analysis, genetic clustering, and runs of homozygosity, observing similar results starting from 0.5x coverage, except for the African genomes. These results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method that can improve ancient DNA studies.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data

ObjectiveGenotype imputation is a commonly used technique that infers un-typed variants into a study’s genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there’s a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. MethodsWe aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. ResultsWe present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. ConclusionUsers can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. Statement of significanceMulti-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Comparison of multiple imputation and other methods for the analysis of imputed genotypes

BackgroundAnalysis of imputed genotypes is an important and routine component of genome-wide association studies and the increasing size of imputation reference panels has facilitated the ability to impute and test low-frequency variants for associations. In the context of genotype imputation, the true genotype is unknown and genotypes are inferred with uncertainty using statistical models. Here, we present a novel method for integrating imputation uncertainty into statistical association tests using a fully conditional multiple imputation (MI) approach which is implemented using the Substantive Model Compatible Fully Conditional Specification (SMCFCS). We compared the performance of this method to an unconditional MI and two additional approaches that have been shown to demonstrate excellent performance: regression with dosages and a mixture of regression models (MRM).ResultsOur simulations considered a range of allele frequencies and imputation qualities based on data from the UK Biobank. We found that the unconditional MI was computationally costly and overly conservative across a wide range of settings. Analyzing data with Dosage, MRM, or MI SMCFCS resulted in greater power, including for low frequency variants, compared to unconditional MI while effectively controlling type I error rates. MRM andl MI SMCFCS are both more computationally intensive then using Dosage.ConclusionsThe unconditional MI approach for association testing is overly conservative and we do not recommend its use in the context of imputed genotypes. Given its performance, speed, and ease of implementation, we recommend using Dosage for imputed genotypes with MAF ge 0.001 and Rsq ge 0.3.

Abstract IA006: Overcoming complex polyclonality for accurate clinical genotyping in metastatic prostate cancer

Abstract Background: De novo metastatic castration-sensitive prostate cancer (mCSPC) is highly aggressive, but the lack of routine tumor tissue in this setting hinders genomic stratification and jeopardizes precision oncology efforts. Accurate molecular profiling at diagnosis is important for future genomics-informed risk stratification strategies and biomarker-guided treatment. Currently, it is unclear the extent that intrapatient tumor heterogeneity impacts clinical cancer genotyping. Methods: We performed genomic profiling of 607 synchronous primary foci, metastatic lesions, and plasma cell-free DNA from a rare clinical trial cohort of 43 patients with de novo mCSPC who underwent radical prostatectomy at diagnosis. Surgery is not currently standard practice in this disease setting. All samples were subjected to targeted DNA sequencing using a bespoke prostate cancer-specific panel and a subset were also subjected to whole-exome sequencing. Results: Sequencing-derived tissue tumor fraction was highly heterogeneous between samples, and was below 40% across all foci in approximately 20% of patients, potentially precluding routine detection of key classes of clinically-relevant biomarkers. In samples with high tumor fraction, the genomic landscape of mCSPC closely resembled metastatic castration-resistant prostate cancer. In same-patient samples, intra-prostate heterogeneity in mutation, copy number, and whole-genome duplication was pervasive and affected tumor suppressor genes including PTEN, TP53, and RB1. Phylogenetic modeling demonstrated additional complexity in several patients driven by polyclonal metastatic seeding from the reservoir of primary populations. While the metastatic clones were often identified in the primary site, frequent discordance between select primary foci and synchronous metastases in clinically-relevant genes, plus highly variable per-sample tumor fraction, resulted in false genotyping of the dominant disease, when relying on a single tissue focus. However, in silico modeling demonstrated that analysis of multiple prostate diagnostic biopsy cores can rescue misassigned somatic genotypes. Conclusions: Our work reveals extensive polyclonality that undermines standard precision genotyping in de novo mCSPC, nominates practical strategies for improved biomarker profiling and genomics-informed risk stratification and offers deep biological insight into the relationship between primary and untreated metastases. Citation Format: Alexander W. Wyatt, Andrew J. Murtha, Evan Warner, Kim Van der Eecken, Edmond M. Kwan, Cameron Herberts, Joonatan Sipola, Sarah W.S. Ng, Xinyi E. Chen, Nicolette M. Fonseca, Elie Ritch, Elena Schönlau, Cecily Q. Bernales, Gráinne Donnellan, Kevin Beja, Amanda Wong, Sofie Verbeke, Nicolaas Lumen, Jo Van Dorpe, Bram De Laere, Matti Annala, Gillian Vandekerkhove, Piet Ost. Overcoming complex polyclonality for accurate clinical genotyping in metastatic prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr IA006.

Toward absolute abundance for conservation applications: Estimating the number of contributors via microhaplotype genotyping of mixed-DNA samples.

Molecular methods including metabarcoding and quantitative polymerase chain reaction have shown promise for estimating species abundance by quantifying the concentration of genetic material in field samples. However, the relationship between specimen abundance and detectable concentrations of genetic material is often variable in practice. DNA mixture analysis represents an alternative approach to quantify specimen abundance based on the presence of unique alleles in a sample. The DNA mixture approach provides novel opportunities to inform ecology and conservation by estimating the absolute abundance of target taxa through molecular methods; yet, the challenges associated with genotyping many highly variable markers in mixed-DNA samples have prevented its widespread use. To advance molecular approaches for abundance estimation, we explored the utility of microhaplotypes for DNA mixture analysis by applying a 125-marker panel to 1179 Chinook salmon (Oncorhynchus tshawytscha) smolts from the Sacramento-San Joaquin Delta, California, USA. We assessed the accuracy of DNA mixture analysis through a combination of mock mixtures containing DNA from up to 20 smolts and a trophic ecological application enumerating smolts in predator diets. Mock DNA mixtures of up to 10 smolts could reliably be resolved using microhaplotypes, and increasing the panel size would likely facilitate the identification of more individuals. However, while analysis of predator gastrointestinal tract contents indicated DNA mixture analysis could discern the presence of multiple prey items, poor and variable DNA quality prevented accurate genotyping and abundance estimation. Our results indicate that DNA mixture analysis can perform well with high-quality DNA, but methodological improvements in genotyping degraded DNA are necessary before this approach can be used on marginal-quality samples.

Restriction site-associated DNA sequencing technologies as an alternative to low-density SNP chips for genomic selection: a simulation study in layer chickens

BackgroundTo reduce the cost of genomic selection, a low-density (LD) single nucleotide polymorphism (SNP) chip can be used in combination with imputation for genotyping selection candidates instead of using a high-density (HD) SNP chip. Next-generation sequencing (NGS) techniques have been increasingly used in livestock species but remain expensive for routine use for genomic selection. An alternative and cost-efficient solution is to use restriction site-associated DNA sequencing (RADseq) techniques to sequence only a fraction of the genome using restriction enzymes. From this perspective, use of RADseq techniques followed by an imputation step on HD chip as alternatives to LD chips for genomic selection was studied in a pure layer line.ResultsGenome reduction and sequencing fragments were identified on reference genome using four restriction enzymes (EcoRI, TaqI, AvaII and PstI) and a double-digest RADseq (ddRADseq) method (TaqI-PstI). The SNPs contained in these fragments were detected from the 20X sequence data of the individuals in our population. Imputation accuracy on HD chip with these genotypes was assessed as the mean correlation between true and imputed genotypes. Several production traits were evaluated using single-step GBLUP methodology. The impact of imputation errors on the ranking of the selection candidates was assessed by comparing a genomic evaluation based on ancestry using true HD or imputed HD genotyping. The relative accuracy of genomic estimated breeding values (GEBVs) was investigated by considering the GEBVs estimated on offspring as a reference. With AvaII or PstI and ddRADseq with TaqI and PstI, more than 10 K SNPs were detected in common with the HD SNP chip, resulting in an imputation accuracy greater than 0.97. The impact of imputation errors on genomic evaluation of the breeders was reduced, with a Spearman correlation greater than 0.99. Finally, the relative accuracy of GEBVs was equivalent.ConclusionsRADseq approaches can be interesting alternatives to low-density SNP chips for genomic selection. With more than 10 K SNPs in common with the SNPs of the HD SNP chip, good imputation and genomic evaluation results can be obtained. However, with real data, heterogeneity between individuals with missing data must be considered.

The size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle

BackgroundLow-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available.MethodsSequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data.ResultsDeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed.ConclusionsVariant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.

Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data.

Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes. Therefore, although many tools have been developed to genotype HLA genes using common sequencing data, none of them work for KIR genes. Even specialized KIR genotypers could not resolve all the KIR genes. Here we describe T1K, a novel computational method for the efficient and accurate inference of KIR or HLA alleles from RNA-seq, whole-genome sequencing, or whole-exome sequencing data. T1K jointly considers alleles across all genotyped genes, so it can reliably identify present genes and distinguish homologous genes, including the challenging KIR2DL5A/KIR2DL5B genes. This model also benefits HLA genotyping, where T1K achieves high accuracy in benchmarks. Moreover, T1K can call novel single-nucleotide variants and process single-cell data. Applying T1K to tumor single-cell RNA-seq data, we found that KIR2DL4 expression was enriched in tumor-specific CD8+ T cells. T1K may open the opportunity for HLA and KIR genotyping across various sequencing applications.

Pangenomic genotyping with the marker array

We present a new method and software tool called rowbowt that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. rowbowt can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool rowbowt available at https://github.com/alshai/rowbowt.

CRISPR-Cas9-based repeat depletion for high-throughput genotyping of complex plant genomes.

High-throughput genotyping enables the large-scale analysis of genetic diversity in population genomics and genome-wide association studies that combine the genotypic and phenotypic characterization of large collections of accessions. Sequencing-based approaches for genotyping are progressively replacing traditional genotyping methods because of the lower ascertainment bias. However, genome-wide genotyping based on sequencing becomes expensive in species with large genomes and a high proportion of repetitive DNA. Here we describe the use of CRISPR-Cas9 technology to deplete repetitive elements in the 3.76-Gb genome of lentil (Lens culinaris), 84% consisting of repeats, thus concentrating the sequencing data on coding and regulatory regions (single-copy regions). We designed a custom set of 566,766 gRNAs targeting 2.9 Gbp of repeats and excluding repetitive regions overlapping annotated genes and putative regulatory elements based on ATAC-seq data. The novel depletion method removed ∼40% of reads mapping to repeats, increasing those mapping to single-copy regions by ∼2.6-fold. When analyzing 25 million fragments, this repeat-to-single-copy shift in the sequencing data increased the number of genotyped bases of ∼10-fold compared to nondepleted libraries. In the same condition, we were also able to identify ∼12-fold more genetic variants in the single-copy regions and increased the genotyping accuracy by rescuing thousands of heterozygous variants that otherwise would be missed because of low coverage. The method performed similarly regardless of the multiplexing level, type of library or genotypes, including different cultivars and a closely related species (L. orientalis). Our results showed that CRISPR-Cas9-driven repeat depletion focuses sequencing data on single-copy regions, thus improving high-density and genome-wide genotyping in large and repetitive genomes.

Development of a novel microarray data analysis tool without normalization for genotyping degraded forensic DNA

Since the arrest of the Golden State Killer in the US in April 2018, forensic geneticists have been increasingly interested in the investigative genetic genealogy (IGG) method. While this method has already been in practical use as a powerful tool for criminal investigation, we have yet to know well the limitations and potential risks. In this current study, we performed an evaluation study focusing on degraded DNA using the Affymetrix Genome-Wide Human SNP Array 6.0 platform (Thermo Fisher Scientific). We revealed one of the potential problems that occur during SNP genotype determination using a microarray-based platform. Our analysis results indicated that the SNP profiles derived from degraded DNA contained many false heterozygous SNPs. In addition, it was confirmed that the total amount of probe signal intensity on microarray chips derived from degraded DNA decreased significantly. Because the conventional analysis algorithm performs normalization during genotype determination, we concluded that noise signals could be genotype-called. To address this issue, we proposed a novel microarray data analysis method without normalization (nMAP). Although the nMAP algorithm resulted in a low call rate, it substantially improved genotyping accuracy. Finally, we confirmed the usefulness of the nMAP algorithm for kinship inferences. These findings and the nMAP algorithm will make a contribution to the advance of the IGG method.

Prediction of HLA genotypes from single-cell transcriptome data.

The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping.

On the causes, consequences, and avoidance of PCR duplicates: Towards a theory of library complexity.

Library preparation protocols for most sequencing technologies involve PCR amplification of the template DNA, which open the possibility that a given template DNA molecule is sequenced multiple times. Reads arising from this phenomenon, known as PCR duplicates, inflate the cost of sequencing and can jeopardize the reliability of affected experiments. Despite the pervasiveness of this artefact, our understanding of its causes and of its impact on downstream statistical analyses remains essentially empirical. Here, we develop a general quantitative model of amplification distortions in sequencing data sets, which we leverage to investigate the factors controlling the occurrence of PCR duplicates. We show that the PCR duplicate rate is determined primarily by the ratio between library complexity and sequencing depth, and that amplification noise (including in its dependence on the number of PCR cycles) only plays a secondary role for this artefact. We confirm our predictions using new and published RAD-seq libraries and provide a method to estimate library complexity and amplification noise in any data set containing PCR duplicates. We discuss how amplification-related artefacts impact downstream analyses, and in particular genotyping accuracy. The proposed framework unites the numerous observations made on PCR duplicates and will be useful to experimenters of all sequencing technologies where DNA availability is a concern.

Abstract 1045: RePhyNER: Overcoming limitations of access to matched germline for serial liquid biopsy applications

Abstract Background: Comparison of a blood or tissue sample with a matched germline control is a key step for accurate detection of somatic mutations. This is particularly important for tracking minimal residual disease (MRD), since alterations erroneously considered to be tumor derived may lead to false detection. Use of blood leukocytes as a germline control is critical not only for appropriate censoring of constitutional allelic variants, but also for addressing those associated with clonal hematopoiesis (CH). However, such matched germline is not always available, or may be suboptimal when contaminated with tumor cells. We hypothesized that accurate tumor genotyping might be feasible without a matched germline, using a dedicated algorithmic framework relying on clonal relationships of alleles of interest (i.e., phylons). Methods: Here, we introduce RePhyNER (Recursive Phylon Nomination, Enumeration, and Recovery), an algorithm for addressing this challenge in serial liquid biopsies. RePhyNER relies on the assumption that when comparing two specimens from the same individual (e.g., before and after therapy), the genomic variants of interests (e.g., tumor somatic mutations) behave differently in their allelic levels when compared to variants needing to be censored (e.g., germline constitutional alleles or CH). Using the change in mean allelic level of all variants in ≥2 samples, RePhyNER relies on a recursive approach to test each candidate somatic variant’s corresponding change gradient against this distribution using count-based statistics. Results: We used simulations to assess the importance of multiple parameters including fraction of contaminating variants or alleles and mean allelic fold-change. These simulations showed substantially superior performance of a recursive approach and revealed that for ≥2-fold changes in the mean VAF in a pair of samples, RePhyNER accurately removed >99% of contaminating alleles while preserving ~100% of true variants. We then applied RePhyNER to 108 plasma cell-free DNA (cfDNA) samples from 47 patients with classic Hodgkin Lymphoma (cHL) profiled by PhasED-Seq and compared MRD-detection performance with or without matched germline. RePhyNER substantially improved specificity by ~30% (68% vs 99%), and Precision by ~50% (46% vs 96%), while only modestly reducing sensitivity (100% vs 91%). We next applied PhasED-Seq to a cohort of cHL patients (n=65) without matched germline information. MRD positivity within the first 2 cycles of therapy was associated with inferior outcome when using RePhyNER (P<0.05 vs ns), correctly reclassifying 26% of detected patients to undetected. Conclusions: RePhyNER enables germline-free and accurate genotyping for MRD detection. Notably, RePhyNER obviates the need for additional germline profiling overcoming key limitations described above and avoids the additional associated costs of sequencing. Citation Format: Mohammad Shahrokh Esfahani, Stefan Alig, Emily Hamilton, Joseph Schroers-Martin, Brian Sworder, Jan Boegeholz, Mari Olsen, Chih Long Liu, David Kurtz, Maximilian Diehn, Ash Alizadeh. RePhyNER: Overcoming limitations of access to matched germline for serial liquid biopsy applications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1045.