Accuracy of Imputation of Microsatellite Markers from a 50K SNP Chip in Spanish Assaf Sheep

Transitioning from traditional to new genotyping technologies requires the development of bridging methodologies to avoid extra genotyping costs. This study aims to identify the optimum number of single nucleotide polymorphisms (SNPs) necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed. The accuracy of microsatellite marker imputation was assessed with three metrics: genotype concordance (C), genotype dosage (length r2), and allelic dosage (allelic r2), for all imputation scenarios tested (0.5–10 Mb microsatellite flanking SNP windows). The imputation accuracy for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r2), and 0.75 (allelic r2), indicating strong genotype concordance. The window with 2 Mb length provides the best accuracy for the imputation procedure and the design of an affordable low-density SNP chip for parentage testing. We additionally evaluated imputation performance under two null models, naive (imputing the most common allele) and random (imputing by randomly selecting the allele), which in comparison showed weak genotype concordances (0.41 and 0.15, respectively). Therefore, we describe a precise methodology in the present article to impute multiallelic microsatellite genotypes from a low-density SNP chip in sheep and solve the problem of parentage verification when different genotyping platforms have been used across generations.

Accuracy of Imputation of Microsatellite Markers from an SNP50K Chip in Spanish Assaf Sheep

Background: Traditional and new genotyping technologies must be combined by applying bridge methodologies that avoid double genotyping costs. This study aims to identify and evaluate a reliable approach to precisely impute microsatellite markers from SNP-chip panels to perform parental verifications in sheep. Moreover, we will assess the optimum number of SNPs necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed.Results: A total of 4,423 animals belonging to the Spanish Assaf sheep breed were genotyped for 19 microsatellites and an ovine custom 49,897 SNP array. The accuracy of microsatellite marker imputation, performed with BEAGLE v5.1 software, was assessed with three metrics, namely, genotype concordance (C), genotype dosage (length r2), and allelic dosage (allelic r2), for all imputation scenarios tested (0.5-10 Mb microsatellite flanking SNP windows). The accuracy of our imputation results for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r2), and 0.75 (allelic r2). Considering that the objective of the study was to assess a SNP window length that provides the best accuracy for the microsatellite imputation procedure to design an affordable low-density SNP chip for parentage testing, we considered 2 Mb to be the best SNP haplotype length for further analyses (SNPs/window =74.05, C= 0.970; length r2= 0.952, allelic r2=0.899). We additionally evaluated imputation performance under two null models, naive and random, which showed weak genotype concordance averages in comparison with imputed microsatellites (0.41 and 0.15, respectively).Conclusions: We presented for the first time a precise methodology in dairy sheep to impute multiallelic microsatellite genotypes from biallelic SNP markers. The use of a 2 Mb SNP flanking window for each microsatellite has been shown to achieve high accuracy in the imputation procedure while providing a low-density SNP chip that could be cost-effective. The results from this study will undoubtedly have a significant impact on sheep breeders overcoming the problem of parentage verification when different genotyping platforms have been used across generations.

Analysis of microRNA-34a expression profile and rs2666433 variant in colorectal cancer: a pilot study

MicroRNAs (miRNAs) are implicated in every stage of carcinogenesis and play an essential role as genetic biomarkers of cancer. We aimed to evaluate microRNA-34a gene (MIR34A) expression in colorectal cancer (CRC) tissues compared with non-cancer one and to preliminarily explore the association of one related variant to CRC risk. A total of 116 paraffin-embedded colon specimens were enrolled. MiR-34a was quantified by qPCR, and rs2666433 (A/G) genotyping was performed by TaqMan Real-Time PCR. Also, the somatic mutation burden was assessed. MIR34A expression in the CRC specimens was significantly upregulated (median = 21.50, IQR: 7.0–209.2; P = 0.001) relative to the non-cancer tissues. Allele (A) was highly prevalent in CRC tissues represented 0.56 (P < 0.001). AA/AG genotype carriers were 5.7 and 2.8 more likely to develop cancer than GG carriers. Tumor-normal tissue paired analysis revealed genotype concordance in 33 out of 58 tissue samples. Approximately 43% of the specimens showed a tendency for G to A shift. Additionally, a higher frequency of somatic mutation (92%) was observed in adenocarcinoma (P = 0.006). MIR34A expression and gene variant did not show associations with the clinicopathological data. However, G > A somatic mutation carriers had more prolonged DFS and OS. Bioinformatics analysis revealed miR-34a could target 30 genes that are implied in all steps of CRC tumorigenesis. In conclusion, this study confirms MIR34A upregulation in CRC tissues, and its rs2666433 (A/G) variant showed association with CRC and a high somatic mutation rate in cancer tissues. MiR-34a could provide a novel targeted therapy after validation in large-scale studies.

Clinical validation of full genotyping CLART® HPV4S assay on SurePath and ThinPrep collected screening samples according to the International Guidelines for Human Papillomavirus Test Requirements for Cervical Screening

Background To ensure the highest quality of human papillomavirus (HPV) testing in primary cervical cancer screening, novel HPV assays must be evaluated in accordance with the international guidelines. Furthermore, HPV assay with genotyping capabilities are becoming increasingly important in triage of HPV positive women in primary HPV screening. Here we evaluate a full genotyping HPV assay intended for primary screening.Methods The CLART® HPV4S (CLART4S) assay is a newly developed full-genotyping assay detecting 14 oncogenic (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68) and two non-oncogenic HPV genotypes (6, 11). It was evaluated using SurePath and ThinPrep screening samples collected from the Danish and Swedish cervical cancer screening programs, respectively. For calculation of sensitivity, 81 SurePath and 80 ThinPrep samples with confirmed ≥CIN2 were assessed. For clinical specificity analysis, 1,184 SurePath and 1,169 ThinPrep samples from women with <CIN2 histology were assessed. Sensitivity and specificity of the CLART4S assay was compared to an established reference test; the MGP-PCR (Modified General Primers GP5+/6+ with genotyping using Luminex). Inter and intra laboratory reproducibility of the assay was assessed using 540 SurePath and 520 ThinPrep samples, respectively. The genotype concordance between CLART4S and MGP-PCR was also assessed.Results In SurePath samples, the sensitivity of CLART4S was 0.90 (MGP-PCR =0.93) and the specificity was 0.91 (MGP-PCR=0.91); In ThinPrep samples the sensitivity of CLART4S was 0.98 (MGP-PCR=1.00) and specificity was 0.94 (MGP-PCR =0.87). The CLART4S was shown to be non-inferior to that of MGP-PCR for both sensitivity (p=0.002; p=0.01) and specificity (p=0.01; p=0.00) in SurePath and ThinPrep samples, respectively. Intra-laboratory reproducibility and inter-laboratory agreement was met for both media types. The individual genotype concordance between CLART4S and MGP-PCR was good agreement for almost all 14 HPV genotypes in both media types.Conclusions The CLART4S assay was proved non-inferior to the comparator assay MGP-PCR for both sensitivity and specificity using SurePath and ThinPrep cervical cancer screening samples from the Danish and Swedish screening programs, respectively. This is the first study to demonstrate clinical validation of a full-genotyping HPV assay conducted in parallel on both SurePath and ThinPrep collected samples.

Clinical validation of full genotyping CLART® HPV4S assay on SurePath and ThinPrep collected screening samples according to the International Guidelines for Human Papillomavirus Test Requirements for Cervical Screening

BackgroundTo ensure the highest quality of human papillomavirus (HPV) testing in primary cervical cancer screening, novel HPV assays must be evaluated in accordance with the international guidelines. Furthermore, HPV assay with genotyping capabilities are becoming increasingly important in triage of HPV positive women in primary HPV screening. Here we evaluate a full genotyping HPV assay intended for primary screening.MethodsThe CLART® HPV4S (CLART4S) assay is a newly developed full-genotyping assay detecting 14 oncogenic (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, 68) and two non-oncogenic HPV genotypes (6, 11). It was evaluated using SurePath and ThinPrep screening samples collected from the Danish and Swedish cervical cancer screening programs, respectively. For calculation of sensitivity, 81 SurePath and 80 ThinPrep samples with confirmed ≥CIN2 were assessed. For clinical specificity analysis, 1,184 SurePath and 1,169 ThinPrep samples from women with <CIN2 histology were assessed. Sensitivity and specificity of the CLART4S assay was compared to an established reference test; the MGP-PCR (Modified General Primers GP5+/6+ with genotyping using Luminex). Inter and intra laboratory reproducibility of the assay was assessed using 540 SurePath and 520 ThinPrep samples, respectively. The genotype concordance between CLART4S and MGP-PCR was also assessed.ResultsIn SurePath samples, the sensitivity of CLART4S was 0.90 (MGP-PCR =0.93) and the specificity was 0.91 (MGP-PCR=0.91); In ThinPrep samples the sensitivity of CLART4S was 0.98 (MGP-PCR=1.00) and specificity was 0.94 (MGP-PCR =0.87). The CLART4S was shown to be non-inferior to that of MGP-PCR for both sensitivity (p=0.002; p=0.01) and specificity (p=0.01; p=0.00) in SurePath and ThinPrep samples, respectively. Intra-laboratory reproducibility and inter-laboratory agreement was met for both media types. The individual genotype concordance between CLART4S and MGP-PCR was good agreement for almost all 14 HPV genotypes in both media types.ConclusionsThe CLART4S assay was proved non-inferior to the comparator assay MGP-PCR for both sensitivity and specificity using SurePath and ThinPrep cervical cancer screening samples from the Danish and Swedish screening programs, respectively. This is the first study to demonstrate clinical validation of a full-genotyping HPV assay conducted in parallel on both SurePath and ThinPrep collected samples.

Phenotypic and genotypic characterization of clinical Staphylococcus aureus isolates from Kenya

Background The increase and spread of virulent-outbreak associated, methicillin and vancomycin resistant (MRSA/VRSA) Staphylococcus aureus require a better understanding of the resistance and virulence patterns of circulating and emerging strains globally. This study sought to establish the resistance phenotype, and strains of 32 non-duplicate clinical MRSA and MSSA S. aureus isolates from four Kenyan hospitals, identify their resistance and virulence genes and determine the genetic relationships of MRSA with global strains. Methods Antimicrobial susceptibility profiles were determined on a Vitek 2, genomic DNA sequenced on an Illumina Miseq and isolates typed in-silico. Resistance and virulence genes were identified using ARIBA and phylogenies generated using RAxML. Results The MRSA isolates were 100% susceptible to vancomycin, teicoplanin, linezolid, and tigecycline. Nine distinct CC, 12 ST and 15 spa types including the novel t17826 and STs (4705, 4707) were identified with CC8 and CC152 predominating. MRSA isolates distributed across 3 CCs; CC5-ST39 (1), CC8 – ST241 (4), a novel CC8-ST4705 (1), ST8 (1) and CC152 (1). There was > 90% phenotype-genotype concordance with key resistance genes identified only among MRSA isolates: gyrA, rpoB, and parC mutations, mecA, ant (4′)-lb, aph (3′)-IIIa, ermA, sat-4, fusA, mphC and msrA. Kenyan MRSA isolates were genetically diverse and most closely related to Tanzanian and UK isolates. There was a significant correlation between map, hlgA, selk, selq and cap8d virulence genes and severe infections. Conclusion The findings showed a heterogeneous S. aureus population with novel strain types. Though limited by the low number of isolates, this study begins to fill gaps and expand our knowledge of S. aureus epidemiology while uncovering interesting patterns of distribution of strain types which should be further explored. Although last-line treatments are still effective, the potential for outbreaks of both virulent and resistant strains remain, requiring sustained surveillance of S. aureus populations.

BAMixChecker: an automated checkup tool for matched sample pairs in NGS cohort

Summary Mislabeling in the process of next generation sequencing is a frequent problem that can cause an entire genomic analysis to fail, and a regular cohort-level checkup is needed to ensure that it has not occurred. We developed a new, automated tool (BAMixChecker) that accurately detects sample mismatches from a given BAM file cohort with minimal user intervention. BAMixChecker uses a flexible, data-specific set of single-nucleotide polymorphisms and detects orphan (unpaired) and swapped (mispaired) samples based on genotype-concordance score and entropy-based file name analysis. BAMixChecker shows ∼100% accuracy in real WES, RNA-Seq and targeted sequencing data cohorts, even for small panels (<50 genes). BAMixChecker provides an HTML-style report that graphically outlines the sample matching status in tables and heatmaps, with which users can quickly inspect any mismatch events. Availability and implementation BAMixChecker is available at https://github.com/heinc1010/BAMixChecker Supplementary information Supplementary data are available at Bioinformatics online.

Evaluation of genotype quality parameters for SowPro90, a new genotyping array for swine1

Understanding early predictors of sow fertility has the potential to improve genomic predictions. A custom SNP array (SowPro90 produced by Affymetrix) was developed to include genetic variants overlapping quantitative trait loci for age at puberty, one of the earliest indicators of sow fertility, as well as variants related to innate and adaptive immunity. The polymorphisms included in the custom genotyping array were identified using multiple genomic approaches including deep genomic and transcriptomic sequencing and genome-wide associations. Animals from research and commercial populations (n = 2,586) were genotyped for 103,476 SNPs included in SowPro90. To assess the quality of data generated, genotype concordance was evaluated between the SowPro90 and Porcine SNP60 BeadArray using a subset of common SNP (n = 44,708) and animals (n = 277). The mean genotype concordance rate per SNP was 98.4%. Differences in distribution of data quality were observed between the platforms indicating the need for platform specific thresholds for quality parameters. The optimal thresholds for SowPro90 (≥97% SNP and ≥93% sample call rate) were obtained by analyzing the data quality distribution and genotype concordance per SNP across platforms. At ≥97% SNP call rate, there were 42,151 SNPs (94.3%) retained with a mean genotype concordance of 98.6% across platforms. Similarly, ≥94% SNPs and ≥85% sample call rates were established as thresholds for Porcine SNP60 BeadArray. At ≥94% SNPs call rate, there were 41,043 SNPs (91.8%) retained with a mean genotype concordance of 98.6% across platforms. Final evaluation of SowPro90 array content (n = 103,476) at ≥97% SNPs and ≥93% sample call rates allowed retention of 89,040 SNPs (86%) for downstream analysis. The findings and strategy for quality control could be helpful in identifying consistent, high-quality genotypes for genomic evaluations, especially when integrating genotype data from different platforms.

Quality control of large genome datasets using genome fingerprints

The 1000 Genomes Project is a foundational resource to modern human biomedicine, serving as a standard reference for human genetic variation. Recently, new versions of the 1000 Genomes Project dataset were released, expressed relative to the current version of the human reference sequence (GRCh38) and partially validated by benchmarking against reference truth sets from the Genome In A Bottle Consortium. We used our ultrafast genome comparison method (genome fingerprinting) to evaluate four versions of the 1000 Genomes Project datasets. These comparisons revealed several discrepancies in dataset membership, multiple cryptic relationships, overall changes in biallelic SNV counts, and more significant changes in SNV counts, heterozygosity and genotype concordance affecting a subset of the individuals. Based on these observations, we recommend performing global dataset comparisons, using genome fingerprints and other metrics, to supplement ‘best practice’ benchmarking relative to predefined truth sets.