Rapid Automated Screening for SARS-CoV-2 B.1.617 Lineage Variants (Delta/Kappa) through a Versatile Toolset of qPCR-Based SNP Detection

Background: The recent emergence of distinct and highly successful SARS-CoV-2 lineages has substantial implications for individual patients and public health measures. While next-generation-sequencing is routinely performed for surveillance purposes, RT-qPCR can be used to rapidly rule-in or rule-out relevant variants, e.g., in outbreak scenarios. The objective of this study was to create an adaptable and comprehensive toolset for multiplexed Spike-gene SNP detection, which was applied to screen for SARS-CoV-2 B.1.617 lineage variants. Methods: We created a broad set of single nucleotide polymorphism (SNP)-assays including del-Y144/145, E484K, E484Q, P681H, P681R, L452R, and V1176F based on a highly specific multi-LNA (locked nucleic acid)-probe design to maximize mismatch discrimination. As proof-of-concept, a multiplex-test was compiled and validated (SCOV2-617VOC-UCT) including SNP-detection for L452R, P681R, E484K, and E484Q to provide rapid screening capabilities for the novel B.1.617 lineages. Results: For the multiplex-test (SCOV2-617VOC-UCT), the analytic lower limit of detection was determined as 182 IU/mL for L452R, 144 IU/mL for P681R, and 79 IU/mL for E484Q. A total of 233 clinical samples were tested with the assay, including various on-target and off-target sequences. All SNPs (179/179 positive) were correctly identified as determined by SARS-CoV-2 whole genome sequencing. Conclusion: The recurrence of SNP locations and flexibility of methodology presented in this study allows for rapid adaptation to current and future variants. Furthermore, the ability to multiplex various SNP-assays into screening panels improves speed and efficiency for variant testing. We show 100% concordance with whole genome sequencing for a B.1.617.2 screening assay on the cobas6800 high-throughput system.

A review of methods for detecting single-nucleotide polymorphisms in the Toll-like receptor gene family

The Toll-like receptors play an essential role in immunity through targeting the pathogen-associated molecular patterns. Nucleotide variations in TLR genes, especially single-nucleotide polymorphisms, have been shown to alter host immune susceptibility to several infections and diseases. Since TLR genes’ polymorphisms can be a promising biomarker, ongoing investigations aim to develop, optimize and validate SNP detection methods. This review discusses various TLR SNP detection methods, either used extensively or occasionally, but having a vast potential in high-throughput settings. Methods such as PCR-restriction fragment length polymorphism, TaqMan® assay, direct sequencing and matrix-assisted laser desorption ionization – time of flight mass spectroscopy MS are frequently used methods whereas Illumina GoldenGate® assay, reverse hybridization technology, PCR–confronting two-pair primers, KBiosciences KASPar® SNP assay, SNP stream®, PCR-fluorescence hybridization and SNaPshot® are powerful but sporadically used methods. We suggest that, for individual laboratories, the detection method of choice depends on a combination of factors such as throughput volume, reproducibility, feasibility and cost–effectiveness.

RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization and Cis-Regulation Analysis by Allele-Specific Expression in Livestock Species

In addition to their common usages to study gene expression, RNA-seq data accumulated over the last 10 years are a yet-unexploited resource of SNPs in numerous individuals from different populations. SNP detection by RNA-seq is particularly interesting for livestock species since whole genome sequencing is expensive and exome sequencing tools are unavailable. These SNPs detected in expressed regions can be used to characterize variants affecting protein functions, and to study cis-regulated genes by analyzing allele-specific expression (ASE) in the tissue of interest. However, gene expression can be highly variable, and filters for SNP detection using the popular GATK toolkit are not yet standardized, making SNP detection and genotype calling by RNA-seq a challenging endeavor. We compared SNP calling results using GATK suggested filters, on two chicken populations for which both RNA-seq and DNA-seq data were available for the same samples of the same tissue. We showed, in expressed regions, a RNA-seq precision of 91% (SNPs detected by RNA-seq and shared by DNA-seq) and we characterized the remaining 9% of SNPs. We then studied the genotype (GT) obtained by RNA-seq and the impact of two factors (GT call-rate and read number per GT) on the concordance of GT with DNA-seq; we proposed thresholds for them leading to a 95% concordance. Applying these thresholds to 767 multi-tissue RNA-seq of 382 birds of 11 chicken populations, we found 9.5 M SNPs in total, of which ∼550,000 SNPs per tissue and population with a reliable GT (call rate ≥ 50%) and among them, ∼340,000 with a MAF ≥ 10%. We showed that such RNA-seq data from one tissue can be used to (i) detect SNPs with a strong predicted impact on proteins, despite their scarcity in each population (16,307 SIFT deleterious missenses and 590 stop-gained), (ii) study, on a large scale, cis-regulations of gene expression, with ∼81% of protein-coding and 68% of long non-coding genes (TPM ≥ 1) that can be analyzed for ASE, and with ∼29% of them that were cis-regulated, and (iii) analyze population genetic using such SNPs located in expressed regions. This work shows that RNA-seq data can be used with good confidence to detect SNPs and associated GT within various populations and used them for different analyses as GTEx studies.

Recalibration of mapping quality scores in Illumina short-read alignments improves SNP detection results in low-coverage sequencing data

Background Low-coverage sequencing is a cost-effective way to obtain reads spanning an entire genome. However, read depth at each locus is low, making sequencing error difficult to separate from actual variation. Prior to variant calling, sequencer reads are aligned to a reference genome, with alignments stored in Sequence Alignment/Map (SAM) files. Each alignment has a mapping quality (MAPQ) score indicating the probability a read is incorrectly aligned. This study investigated the recalibration of probability estimates used to compute MAPQ scores for improving variant calling performance in single-sample, low-coverage settings. Materials and Methods Simulated tomato, hot pepper and rice genomes were implanted with known variants. From these, simulated paired-end reads were generated at low coverage and aligned to the original reference genomes. Features extracted from the SAM formatted alignment files for tomato were used to train machine learning models to detect incorrectly aligned reads and output estimates of the probability of misalignment for each read in all three data sets. MAPQ scores were then re-computed from these estimates. Next, the SAM files were updated with new MAPQ scores. Finally, Variant calling was performed on the original and recalibrated alignments and the results compared. Results Incorrectly aligned reads comprised only 0.16% of the reads in the training set. This severe class imbalance required special consideration for model training. The F1 score for detecting misaligned reads ranged from 0.76 to 0.82. The best performing model was used to compute new MAPQ scores. Single Nucleotide Polymorphism (SNP) detection was improved after mapping score recalibration. In rice, recall for called SNPs increased by 5.2%, while for tomato and pepper it increased by 3.1% and 1.5%, respectively. For all three data sets the precision of SNP calls ranged from 0.91 to 0.95, and was largely unchanged both before and after mapping score recalibration. Conclusion Recalibrating MAPQ scores delivers modest improvements in single-sample variant calling results. Some variant callers operate on multiple samples simultaneously. They exploit every sample’s reads to compensate for the low read-depth of individual samples. This improves polymorphism detection and genotype inference. It may be that small improvements in single-sample settings translate to larger gains in a multi-sample experiment. A study to investigate this is ongoing.

Development and comparison of RNA-Sequencing pipelines for more accurate SNP identification: Practical example of functional SNP detection associated with feed efficiency in Nellore beef cattle.

Optimization of an RNA-Sequencing (RNA-Seq) pipeline can maximize power and accuracy for identifying genetic variants, including SNPs, which may serve as genetic markers to select for feed efficiency, leading to economic benefits for beef production. This study determined an optimized pipeline for variant detection using a dataset with multiple samples and tissues. The RNA-Seq data (GEO Accession ID: PRJEB7696 and PRJEB15314) from muscle and liver tissue, respectively, from 12 Nellore beef steers selected from 585 steers with residual feed intake measures (RFI; n=6 low-RFI, n=6 high-RFI) were used. Three RNA-Seq pipelines were compared including multi-sample calling from i) non-merged samples; ii) merged samples by group for low-RFI and for high-RFI for each tissue, iii) merged samples by group and tissue for low- and high-RFI for both tissues. The RNA-Seq reads were aligned against the UMD3.1 bovine reference genome (release 94) assembly using STAR. Variants were called using BCFtools and variant effect prediction (VeP) and functional annotation (ToppGene) analyses were performed. Approaches were compared by comparing read depth, overlap of SNP detection results, and following SNP annotation for positional candidate genes. On average, total reads detected for Approach i) individual liver and muscle samples were 18,362,086.3 and 35,645,898.7, respectively. For Approach ii), total reads detected for each merged group of samples was 162,030,705, and for Approach iii) was 324,061,410, revealing the highest read depth. Additionally, Approach iii) encompassed the majority of localized positional genes detected by each approach, suggesting Approach iii) be applied to maximize detection power, read depth, and accuracy of SNP calling, therefore increasing confidence of variant detection and reducing false positive rate. Approach iii) was used to detect unique SNPs fixed within low- (12,145) and high-RFI (14,663) groups. Annotation of moderate to high functional impact SNPs revealed co-localized positional candidate genes for each RFI group (2,886 for low-RFI, 3,075 for high-RFI), which were significantly (P<0.05) associated with immune and metabolism pathways. The most optimized RNA-Seq pipeline allowed for more accurate identification of SNP, associated positional candidate genes, and associated metabolic pathways in muscle and liver tissues, providing insight on the genetic architecture of feed efficiency in beef cattle.

Eliminating primer dimers and improving SNP detection using self-avoiding molecular recognition systems

Despite its widespread value to molecular biology, the polymerase chain reaction (PCR) encounters modes that unproductively consume PCR resources and prevent clean signals, especially when high sensitivity, high SNP discrimination, and high multiplexing are sought. Here, we show how “self-avoiding molecular recognition systems” (SAMRS) manage such difficulties. SAMRS nucleobases pair with complementary nucleotides with strengths comparable to the A:T pair, but do not pair with other SAMRS nucleobases. This should allow primers holding SAMRS components to avoid primer–primer interactions, preventing primer dimers, allowing more sensitive SNP detection, and supporting higher levels of multiplex PCR. The experiments here examine the PCR performances of primers containing different numbers of SAMRS components placed strategically at different positions, and put these performances in the context of estimates of SAMRS:standard pairing strengths. The impact of these variables on primer dimer formation, the overall efficiency and sensitivity of SAMRS-based PCR, and the value of SAMRS primers when detecting single nucleotide polymorphisms (SNPs) are also evaluated. With appropriately chosen polymerases, SNP discrimination can be greater than the conventional allele-specific PCR, with the further benefit of avoiding primer dimer artifacts. General rules guiding the design of SAMRS-modified primers are offered to support medical research and clinical diagnostics products.

High-throughput ultra-sensitive discrimination of single nucleotide polymorphism via click chemical ligation

CuAAC-based ligation-assisted assays: the CuAAC chemical ligation reaction for SNP detection.