Incorrect Genotype Research Articles

Molecular tool for efficient breeding of DOMINANT Greenshell laying hens and significant refinement of phenotypic selection focused on eggshell color

In recent decades, interest in non-traditionally colored eggs has increased. For breeders, this market interest means breeding lines of laying hens that lay eggs of varied colors, such as the blue-green eggshells (Dominant Greenshell) in this study. This study presents the results of genotyping the polymorphism of the O locus responsible for shell coloration and photometric measurement of eggshell color based on the CIELAb system, which was carried out on the unique Czech breeding population Dominant Greenshell. The aim was to use a combination of phenotyping using the CIELab System method and genotyping of the O locus using the end-point PCR approach with the main focus on the accuracy of distinguishing shell color genotypes, streamlining the selection of dominant homozygotes in the O locus, optimizing this technology for the most efficient and cost-effective selection procedure in practical hen breeding. The optometric method was able to reliably distinguish only dominant and recessive phenotypes and eliminate from the population only undesirable recessive homozygotes with a white colored shell. The parameter a* (redness/greenness) from the CIELab color space turned out to be absolutely key for distinguishing dominant and recessive phenotypes. Using the CART methodology, a classification tree built on discriminating optometric characteristics a-blunt was obtained, however, for the group of desirable O/O homozygotes, the selection approach would result in incorrect genotyping of 31% of individuals. Therefore, a combined approach based on rapid and simple elimination of recessive homozygotes using phenotyping (CIELab photometric measurement) and molecular identification of the EAV-HP insertion in the SLCO1B3 gene in dominant phenotypes, regardless of color intensity affected by laying time/order, and allowing reliable elimination, has proven to be the most effective method to distinguish heterozygotes from the breeding population. The combination of optometric and molecular selection methods then leads to more efficient selection, reduction of overall selection costs. This process led to the stabilization of the breeding population within one generation and the achievement of a pure homozygous line with regard to eggshell color.

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

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

Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance.

Genotype imputation is widely used to enrich genetic datasets. The operation relies on panels of known reference haplotypes, typically with whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individuals who require missing genotype imputation. However, it is broadly accepted that such an imputation panel will have an enhanced performance with the inclusion of diversity (haplotypes from many different populations). We investigate this observation by examining, in fine detail, exactly which reference haplotypes are contributing at different regions of the genome. This is achieved using a novel method of inserting synthetic genetic variation into the reference panel in order to track the performance of leading imputation algorithms. We show that while diversity may globally improve imputation accuracy, there can be occasions where incorrect genotypes are imputed following the inclusion of more diverse haplotypes in the reference panel. We, however, demonstrate a technique for retaining and benefitting from the diversity in the reference panel whilst avoiding the occasional adverse effects on imputation accuracy. What is more, our results more clearly elucidate the role of diversity in a reference panel than has been shown in previous studies.

Robust, flexible, and scalable tests for Hardy-Weinberg equilibrium across diverse ancestries.

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing–based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing different weights on incorrect and correct SNP class in calculating the loss metric and (iii)–(v) the naïve algorithm modified by random re-sampling (with replacement) of the incorrect SNPs to match 30%/60%/100% of the number of correct SNPs. The training data set was composed of data from three bulls and consisted of 2,227,995 correct (97.94%) and 46,920 incorrect SNPs, while the validation data set consisted of data from one bull with 749,506 correct (98.05%) and 14,908 incorrect SNPs. The results showed that for a rare event classification problem, like incorrect SNP detection in NGS data, the most parsimonious naïve model and a model with the weighting of SNP classes provided the best results for the classification of the validation data set. Both classified 19% of truly incorrect SNPs as incorrect and 99% of truly correct SNPs as correct and resulted in the F1 score of 0.21 — the highest among the compared algorithms. We conclude the basic models were less adapted to the specificity of a training data set and thus resulted in better classification of the independent, validation data set, than the other tested models.

Accurate calling of homeoallelic genotypes of iSelect markers using inbred structured populations

Genetic map construction is a foundational step in the analysis of structured experimental populations. For markers that hybridize to several genetically similar locations, or where several alleles are present (such as in multiparental populations), current methods often discard the marker or incorrectly call the genotypes. These errors result in information loss, or incorrect genotypes that can corrupt map construction. We present a new approach for simultaneously performing genetic map construction and marker calling. Our new approach allows the calling of a larger number of markers, a larger number of unique alleles per marker and the correct use of markers which hybridize to multiple genetically similar locations. We demonstrate our new approach using simulations, a biparental wheat population and an eight-parent population of spring bread wheat. Applying our method to the eight-parent population increased the number of mapped markers by 71%. We show that the new genetic map allows the investigation of synteny in ways that were not previously possible in that dataset. The method described in this article has been incorporated into R package mpMap2. It is available from CRAN and also from https://github.com/rohan-shah/mpMap2. Supplementary data are available at Bioinformatics online.

Comparison of direct sequencing of the NS5B region with the Versant HCV genotype 2.0 assay for genotyping of viral isolates in Mexico

Hepatitis C virus (HCV) infection affects an estimated 71 million people worldwide. HCV is classified into eight genotypes and >70 subtypes. Determination of HCV genotype is important for selection of type and duration of antiviral therapy, and genotype is also a predictor of treatment response. The most commonly used HCV genotyping method in clinical laboratories is a hybridization-based line probe assay (LiPA; Versant HCV Genotype 2.0). However, these methods have a lack of specificity in genotype identification and subtype assignment. Here, we compared the performance of Versant HCV Genotype 2.0 with the gold standard direct sequencing of the NS5B region, in 97 samples from Mexican patients. We found a genotypic concordance of 63.9% between these methods. While 68 samples (70%) were classified into HCV genotype 1 (GT1) by NS5B sequencing, it was not true for 17 samples (17.5%), which were not match HCV subtype by LiPA. Furthermore, nine of the 33 samples classified by NS5B sequencing as GT1a were not identified by LiPA. Use of direct sequencing could improve selection of the optimal therapy, avoid possible failures of therapy and avoid high costs resulting from incorrect genotyping tests in settings without broad access to pangenotypic regimens.

Comparison of Activated Protein C Resistance With Factor V Leiden Testing by Molecular Assay

Abstract Factor V Leiden (FVL) is a variant form of coagulation factor V that is the most common inherited risk factor for venous thromboembolism in people of European ancestry. FVL is associated with the missense mutation, p.R506Q, which encodes a FV protein resistant to cleavage by activated protein C (APC). Laboratory testing for FVL includes both phenotypic assays that assess APC resistance (APCR) and molecular assays that evaluate FVL genotype (FVLM) directly. Although APCR results are typically highly concordant with FVL genotype, discrepancies are known to occur and may result in inference of an incorrect FV genotype if only APCR testing is used. The objective of this study was to compare the results of these two testing methodologies and to identify potential explanations for discrepancies in results. Data were obtained by searching the electronic medical record of a large academic hospital for patients who underwent both APCR and FVLM testing from 2013 to 2018. APCR was evaluated using the ratio between two dilute Russell Viper Venom Time (DRVVT) tests, one preincubated with a protein C activator derived from A contortrix contortrix venom and the other with vehicle; the validated normal APCR ratio is ≥1.7. FVLM was performed by invader analysis utilizing fluorescence resonance energy transfer (FRET). In total, 424 patients underwent testing with both assays. Of 21 patients who had APCR assay clot times that exceeded the measurement range, all were FVLM negative, and 15 were anticoagulated during APCR testing. Of the 403 patients with reportable results for both tests, 385 (95.5%) patients had normal APCR and were FVLM negative. Of the 18 (4.5%) patients with discrepant results, 15 (3.7%) had an abnormally low APCR but were FVLM negative, and 3 (0.8%) had a normal APCR but were FVLM heterozygous. Among the 15 FVLM-negative patients with abnormal APCR <1.7, 11 (73.3%) were on warfarin with/out enoxaparin and had low protein S activity and/or low protein C activity. Of the 3 FVLM heterozygous patients with normal APCR, 1 was on apixaban. Our results demonstrated a high concordance between APCR phenotype and FVLM genotype. The concordance of APCR and FVLM may be limited in patients with low protein S or low protein C activity and those on a range of anticoagulant therapy.

An evaluation of sequencing coverage and genotyping strategies to assess neutral and adaptive diversity.

Whole genome sequences (WGS) greatly increase our ability to precisely infer population genetic parameters, demographic processes, and selection signatures. However, WGS may still be not affordable for a representative number of individuals/populations. In this context, our goal was to assess the efficiency of several SNP genotyping strategies by testing their ability to accurately estimate parameters describing neutral diversity and to detect signatures of selection. We analysed 110 WGS at 12× coverage for four different species, i.e., sheep, goats and their wild counterparts. From these data we generated 946 data sets corresponding to random panels of 1K to 5M variants, commercial SNP chips and exome capture, for sample sizes of five to 48 individuals. We also extracted low-coverage genome resequencing of 1×, 2× and 5× by randomly subsampling reads from the 12× resequencing data. Globally, 5K to 10K random variants were enough for an accurate estimation of genome diversity. Conversely, commercial panels and exome capture displayed strong ascertainment biases. Besides the characterization of neutral diversity, the detection of the signature of selection and the accurate estimation of linkage disequilibrium (LD) required high-density panels of at least 1M variants. Finally, genotype likelihoods increased the quality of variant calling from low coverage resequencing but proportions of incorrect genotypes remained substantial, especially for heterozygote sites. Whole genome resequencing coverage of at least 5× appeared to be necessary for accurate assessment of genomic variations. These results have implications for studies seeking to deploy low-density SNP collections or genome scans across genetically diverse populations/species showing similar genetic characteristics and patterns of LD decay for a wide variety of purposes.

Cleaning Genotype Data from Diversity Outbred Mice.

Data cleaning is an important first step in most statistical analyses, including efforts to map the genetic loci that contribute to variation in quantitative traits. Here we illustrate approaches to quality control and cleaning of array-based genotyping data for multiparent populations (experimental crosses derived from more than two founder strains), using MegaMUGA array data from a set of 291 Diversity Outbred (DO) mice. Our approach employs data visualizations that can reveal problems at the level of individual mice or with individual SNP markers. We find that the proportion of missing genotypes for each mouse is an effective indicator of sample quality. We use microarray probe intensities for SNPs on the X and Y chromosomes to confirm the sex of each mouse, and we use the proportion of matching SNP genotypes between pairs of mice to detect sample duplicates. We use a hidden Markov model (HMM) reconstruction of the founder haplotype mosaic across each mouse genome to estimate the number of crossovers and to identify potential genotyping errors. To evaluate marker quality, we find that missing data and genotyping error rates are the most effective diagnostics. We also examine the SNP genotype frequencies with markers grouped according to their minor allele frequency in the founder strains. For markers with high apparent error rates, a scatterplot of the allele-specific probe intensities can reveal the underlying cause of incorrect genotype calls. The decision to include or exclude low-quality samples can have a significant impact on the mapping results for a given study. We find that the impact of low-quality markers on a given study is often minimal, but reporting problematic markers can improve the utility of the genotyping array across many studies.

Unprepared and Misinformed Parents of Children with Sickle Cell Disease: Time to Rethink Awareness Campaigns.

Worldwide, sickle cell disease (SCD) poses a significant public health concern. It causes recurrent morbidity, and premature death is a distinct possibility, especially in Nigeria, which bears half the world’s burden of SCD patients. Nigeria has yet to establish a newborn screening program; consequently, most affected children are diagnosed between one and three years of age when a health problem arises. Parents are unprepared to identify SCD and seek comprehensive management early enough for the best outcome. Awareness of carrier status and knowledge of SCD would ensure informed reproductive choices.Questionnaires were employed to conduct a prospective, cross-sectional study of the occurrence of missed carrier status of one biological parent of children enrolled at the pediatric sickle cell disease clinic of a teaching hospital. The institution’s ethics committee approved the study.Of 133 respondents, 53% of the parents were unaware of being sickle cell carriers and did not expect to have children with SCD. Among families in which one or both parents proactively sought to know their genotype, 35% of all parents received incorrect genotyping results regarding the SCD risk of their offspring. The majority of incorrectly assigned hemoglobin AA results occurred in private laboratories. More than 60% of the respondents reported gaining awareness regarding SCD through antenatal care (51%) and schools (17%), as opposed to public awareness campaigns (8%).Many parents in our study population were unprepared for their children’s SCD diagnosis. Awareness campaigns need to be revisited as a major potential modality to increase outreach about SCD. Standardization and improved quality control of laboratory testing involving screening of the sickle cell trait could reduce the frequency of wrong genotype assignment.

Precision Medicine for Sickle Cell Disease through Whole Genome Sequencing

▪Although sickle cell disease (SCD) is a monogenic disorder, the severity and specific organ dysfunction and failure are strongly influenced by genetic modifiers. Rapid identification of all modifiers in patients and well-phenotyped cohorts will better define the impact of relevant variants on clinical status, inform disease biology, and identify new therapeutic strategies. We created the Sickle Genome Project (SGP), a whole genome sequencing (WGS) strategy, to define genomic variation and modifiers of SCD. We performed WGS on 871 African American SCD patients from St. Jude Children's Research Hospital who participated in the Sickle Cell Clinical Research and Intervention Program (SCCRIP, Hankins et al. Pediatr Blood Cancer, 2018) and Texas Children's Hospital Hematology Center (TCHC). We developed robust pipelines for accurate detection of single nucleotide polymorphisms (SNPs), identification of structural variants and data retrieval/sharing via the St. Jude Cloud platform (to be described elsewhere). Notable findings include:1) Confirmed associations of common genetic modifiers with SCD phenotypes, including levels of fetal hemoglobin (BCL11A, HBS1L-MYB, HBB), bilirubin (UGT1A1), and microalbuminuria (APOL1). Additional associations approaching genome-wide significance require further investigation, including replication in independent samples.2) Improved determination of the SCD modifier α-thalassemia. The most common α-thalassemia mutations in SCD are 3.7 kb or 4.2 kb deletions (-α3.7 and -α4.2 alleles), which arose from recombination between homologous HBA1 and HBA2 genes and are difficult to map using standard WGS reads. Three independent crossover events are described for -α3.7 and one for -α4.2 in SCD cohorts. We developed a novel approach to identify α-globin gene deletions by local de novo assembly of WGS data and coverage depth analysis. We identified 5 -α3.7 alleles (frequencies 0.77-32.12%) and 7 -α4.2 alleles (frequencies 0.19-5.77%). Collectively, the frequency of all -α alleles was 57%, reflecting at least 12 distinct recombination events, greatly exceeding previously published counts. These findings better define the evolution of α-globin genes to allow improved understanding of their regulation and influence on SCD.3) Characterization of β0-thalassemia alleles. Mutations in the extended β-globin locus influence SCD phenotypes. Five SGP patients had large β-globin (HBB) deletions associated with elevated fetal hemoglobin, which ameliorates symptoms of SCD. Twenty-three patients had HbSβ0-thalassemia, which reduces the severity of some SCD phenotypes. Overall, 48.6% (18/37) of patients clinically designated as HbSβ0 -thalassemia had no identified β-thalassemia mutation. Moreover, 4/680 patients (0.6%) designated HbSS were identified to be β0-thalassemia heterozygotes. The MCV, RBC and %HbA2 distributions overlapped substantially in correct vs. incorrect genotype assignments. Improved discrimination of HbSβ0 vs HbSS genotypes by WGS will better define associated phenotype differences to impact clinical care.4) Determination of a genetic variant linked to vaso-occlusive crisis (VOC). Previously, a single GWAS study linked rs3115229, located 63.7 kb 5′ upstream of the KIAA1109 gene, with VOC at borderline significance (P = 5.63 × 10−8) (Chaturvedi et al, Blood 130, 2017). Using WGS data for 327 SGP participants (HbSS or HbSβ0-thalassemia) enrolled in the SCCRIPP study, we found strong association (p = 7 x 10-5) between the onset of VOC and a 4-SNP diplotype within an adjacent LD block of the KIAA1109-TENR-IL2-IL21 region (chr4: 122.8Mb - 123.8Mb) which has been previously associated with numerous inflammatory disorders. We validated this association using imputed genome-wide array data in an independent group of SCD patients (Sleep and Asthma Cohort, n= 181 patients, p = 0.05) (Cohen et al, Ann Am Thorac Soc, 2016). This works provides confirmation that the region surrounding KIAA1109 is associated with pain crisis in SCD.Our studies provide new information on the genomic architecture of SCD patients and delineate a consolidated approach for future applications of precision medicine. [Display omitted] DisclosuresHankins:Novartis: Research Funding; Global Blood Therapeutics: Research Funding; NCQA: Consultancy; bluebird bio: Consultancy. Estepp:Global Blood Therapeutics: Consultancy, Research Funding; ASH Scholar: Research Funding; NHLBI: Research Funding; Daiichi Sankyo: Consultancy.

Prevention of sickle cell disease: observations on females with the sickle cell trait from the Manchester project, Jamaica.

Screening for haemoglobin genotype was offered to senior school students in Manchester parish in south central Jamaica to test whether this knowledge would influence choice of partner and reduce births with sickle cell disease. Over six academic years, 15,539 students, aged mostly 15-19years, were screened with voluntary compliance rising from 56 to 92% over this period. All subjects were given permanent genotype cards and carriers of abnormal genes were offered counselling which explained the reproductive options but avoided recommendations. Prior to screening, all had been offered illustrated lectures on the genetics and clinical features of sickle cell disease. The current study, confined to females with the sickle cell trait, interviewed 763/845 (90.3%) subjects seeking to assess retention of this knowledge and their response to subsequent boyfriends. Of those interviewed, 42 subjects were excluded (38 emigrated, one died, three received incorrect genotype cards) leaving 721 with complete information. Knowledge of genotype was retained in 95%, the outcome of future offspring correctly recalled in 91%, and haemoglobin genotype cards were still possessed by 89%. A current 'boyfriend' was acknowledged in 403 (56%) of whom the partner's genotype was known in 88 (74 determined by the project laboratory; 14 by other laboratories) and unknown in 315 (78%). Offers of free blood tests to all these partners were accepted by only 14 (4%). Seventeen (2.4%) were married but the husbands genotype was known in only five (four AA, one AS) of these. Most subjects retain knowledge of their genotype and of its significance for having affected children but the reluctance of partners to be tested was a major obstacle.

Nobody Is Perfect: Comparison of the Accuracy of PCR-RFLP and KASP™ Method for Genotyping. ADH1B and FTO Polymorphisms as Examples

DNA genotyping is among the most common analyses currently performed in scientific research. Two high-throughput genotyping techniques are widely used – the “classic” PCR-RFLP and probe-based methods such as TaqMan® PCR assay or KASP™ genotyping. The probe-based techniques are claimed to be more accurate than PCR-RFLP; however, the evidence for this claim is sparse. We have directly compared results of genotyping of two SNPs (rs1229984 and rs17817449) obtained by the PCR-RFLP and KASP™ in 1,502 adult Caucasians. The results were identical in 97.3 % and 95.9 % cases, respectively. Discrepancies (either different results or result obtained with one but not with the other method) were addressed by confirmatory analysis using direct sequencing. The sequencing revealed that both methods can give incorrect results, but the frequency of incorrect genotyping of rs1229984 and rs17817449 was very low for both methods – 0.1 % and 0.5 %, respectively, for PCR-RFLP and 0.1 % and 0.3 %, respectively, for KASP™. These results confirm that the KASP™ technique is slightly more accurate, but it achieves slightly lower call rates than PCR-RFLP. When carefully set up, both PCR-RFLP and KASP™ could have accuracy of 99.5 % or higher.

Association of Arg462Gln Polymorphism in RNASEL Gene With the Risk of Prostate CancerAmong Iranian Population

In the July issue of “Gene, Cell and Tissue”, R462Q Mutation in Prostate Cancer Specimens 1 investigated the effect of Arg462Gln polymorphism in sporadic prostate cancer patients among Iranian population. The study samples comprised of formalin fixed paraffin embedded specimen obtained from 51 histopathologically confirmed specimens from patients with prostate cancer and 70 from control individuals with non-cancerous prostate (prostatitis, hyperplasia). RNASEL Arg462Gln or G1385A polymorphism analysis performed using ARMS (Amplification Refractory Mutation System) PCR with allele specific primers were used to detect single-base substitution. This was followed by the association analysis between RNASEL genotype and prostate cancer. The authors could not find any significant association between the polymorphism and risk of disease. Few years back a worldwide epidemiological survey on prostate cancer revealed that it is the third leading causes of death in developed countries such as United States and emphasized that it is becoming a growing health concern in the developing nations as well 2. Like any other complex neoplastic disorder both genetic and non genetic factors interact in its development and progression 3. Age, ethnic background and family history are the three main epidemiological risk factors for this disease out of which family history is the strongest contributor. Based on family history, prostate cancer can be classified into sporadic, familial and hereditary. Hereditary predispositions are quite rare and contribute to less than 5% of the cases while familial cases account for 10-20% of cases. The rest are sporadic cases with typically only one person in their family who were diagnosed with the disease 4. Numerous studies have been conducted to identify the prostate cancer susceptibility genes using linkage analysis. Smith et al. identified the first susceptibility locus, designated as HPC1, which mapped to the long arm of chromosome 1 (1q24-25) (5), few years later, Carpten et al. reported RNASEL gene as one of the candidate for HPC1 (6). RNASEL is an endonuclease involving in apoptotic and antiviral activities of interferons. Along with truncating mutations in this gene, several missense variants were reported by Rokman et al. by screening the entire coding sequence of RNASEL using Single-strand conformation polymorphism (SSCP) (7). The Arg462Gln variant has a significant role in hereditary prostate cancer as the variant has three times less enzymatic activity compared to the wild genotype (8). They also reported that heterozygous individuals had double the risk of prostate cancer compared to the noncarrier cases with hereditary prostate cancer, but at the same time various studies did not find any association between sporadic prostate cancer and this variation in Caucasian and African American population (8, 9). The statistical analysis has been limited to unadjusted risk calculation. The risk calculation after adjustment to the age and clinical parameters would be a better approach. The control DNA was taken from hyperplastic prostate tissue so this tissue may not represent as a proper control sample. It is better to use DNA samples obtained from healthy individuals as the healthy control. Moreover the sample size is very small for any association study thus; a larger sample size is certainly warranted in future. Sometimes, ARMS PCR may produce incorrect genotypes, especially in homozygotes, so, it is better to recheck the genotypes by alternate sequencing methods. The authors did not find any association between Arg462Gln polymorphism and the risk of sporadic prostate cancer. Despite the limitations, this study is important as it may be first of its kind among Iranian population.

The molecular characterization of a depurinated trial DNA sample can be a model to understand the reliability of the results in forensic genetics

The role of DNA damage in PCR processivity/fidelity is a relevant topic in molecular investigation of aged/forensic samples. In order to reproduce one of the most common lesions occurring in postmortem tissues, a new protocol based on aqueous hydrolysis of the DNA was developed in vitro. Twenty-five forensic laboratories were then provided with 3.0μg of a trial sample (TS) exhibiting, in mean, the loss of 1 base of 20, and a molecular weight below 300 bp. Each participating laboratory could freely choose any combination of methods, leading to the quantification and to the definition of the STR profile of the TS, through the documentation of each step of the analytical approaches selected. The results of the TS quantification by qPCR showed significant differences in the amount of DNA recorded by the participating laboratories using different commercial kits. These data show that only DNA quantification "relative" to the used kit (probe) is possible, being the "absolute" amount of DNA inversely related to the length of the target region (r(2) = 0.891). In addition, our results indicate that the absence of a shared stable and certified reference quantitative standard is also likely involved. STR profiling was carried out selecting five different commercial kits and amplifying the TS for a total number of 212 multiplex PCRs, thus representing an interesting overview of the different analytical protocols used by the participating laboratories. Nine laboratories decided to characterize the TS using a single kit, with a number of amplifications varying from 2 to 12, obtaining only partial STR profiles. Most of the participants determined partial or full profiles using a combination of two or more kits, and a number of amplifications varying from 2 to 27. The performance of each laboratory was described in terms of number of correctly characterized loci, dropped-out markers, unreliable genotypes, and incorrect results. The incidence of unreliable and incorrect genotypes was found to be higher for participants carrying out a limited number of amplifications, insufficient to define the correct genotypes from damaged DNA samples such as the TS. Finally, from a dataset containing about 4500 amplicons, the frequency of PCR artifacts (allele dropout, allele drop-in, and allelic imbalance) was calculated for each kit showing that the new chemistry of the kits is not able to overcome the concern of template-related factors. The results of this collaborative exercise emphasize the advantages of using a standardized degraded DNA sample in the definition of which analytical parameters are critical for the outcome of the STR profiles.

Possible incorrect genotyping of heterozygous factor V Leiden and Prothrombin 20210 gene mutations by the GeneXpert assay

BackgroundThe GeneXpert analyzer is a hands-off system for the detection of Factor V Leiden and of Prothrombin G20210A (GPRO) gene thrombophilic mutations. Although the system is efficient and easy to use, we report the rare possibility of incorrect genotyping. Methods1648 samples were evaluated using the GeneXpert HemosIL Factor II and Factor V assay: 1319 were freshly analyzed while 329 were frozen, thawed and diluted with saline prior to analysis to avoid clogging of the instrument syringe. ResultsTwo samples, both heterozygous, one for the factor V Leiden and the other for the GPRO gene, were incorrectly genotyped as homozygous for the relative mutation. Inspection of the Ct values and amplification curves and genotyping with PCR revealed the correct genotype as heterozygous for factor V Leiden and GPRO mutation. DiscussionThe GeneXpert HemosIL Factor II and Factor V assay is an automated, fast genotyping assay requiring almost no sample manipulation, advantageous characteristics if compared with other PCR-based methods. However, an inattentive use of it can generate incorrect diagnosis. A careful handling of the sample, in particular correct dilution of frozen/thawed samples before analysis, and the inspection of the amplification curves and Ct values are required to avoid artifacts.

Collaborative EDNAP exercise on the IrisPlex system for DNA-based prediction of human eye colour.

The IrisPlex system is a DNA-based test system for the prediction of human eye colour from biological samples and consists of a single forensically validated multiplex genotyping assay together with a statistical prediction model that is based on genotypes and phenotypes from thousands of individuals. IrisPlex predicts blue and brown human eye colour with, on average, >94% precision accuracy using six of the currently most eye colour informative single nucleotide polymorphisms (HERC2 rs12913832, OCA2 rs1800407, SLC24A4 rs12896399, SLC45A2 (MATP) rs16891982, TYR rs1393350, and IRF4 rs12203592) according to a previous study, while the accuracy in predicting non-blue and non-brown eye colours is considerably lower. In an effort to vigorously assess the IrisPlex system at the international level, testing was performed by 21 laboratories in the context of a collaborative exercise divided into three tasks and organised by the European DNA Profiling (EDNAP) Group of the International Society of Forensic Genetics (ISFG). Task 1 involved the assessment of 10 blood and saliva samples provided on FTA cards by the organising laboratory together with eye colour phenotypes; 99.4% of the genotypes were correctly reported and 99% of the eye colour phenotypes were correctly predicted. Task 2 involved the assessment of 5 DNA samples extracted by the host laboratory from simulated casework samples, artificially degraded, and provided to the participants in varying DNA concentrations. For this task, 98.7% of the genotypes were correctly determined and 96.2% of eye colour phenotypes were correctly inferred. For Tasks 1 and 2 together, 99.2% (1875) of the 1890 genotypes were correctly generated and of the 15 (0.8%) incorrect genotype calls, only 2 (0.1%) resulted in incorrect eye colour phenotypes. The voluntary Task 3 involved participants choosing their own test subjects for IrisPlex genotyping and eye colour phenotype inference, while eye photographs were provided to the organising laboratory and judged; 96% of the eye colour phenotypes were inferred correctly across 100 samples and 19 laboratories. The high success rates in genotyping and eye colour phenotyping clearly demonstrate the reproducibility and the robustness of the IrisPlex assay as well as the accuracy of the IrisPlex model to predict blue and brown eye colour from DNA. Additionally, this study demonstrates the ease with which the IrisPlex system is implementable and applicable across forensic laboratories around the world with varying pre-existing experiences.

Failed triple therapy in a treatment-experienced patient with genotype 6 hepatitis C infection

The first published report of the use of triple therapy in a patient with hepatitis C virus (HCV) genotype 6 infection-a treatment that was prescribed due to incorrect HCV genotyping and which ultimately failed-is presented. A 70-year-old male U.S. resident of Vietnamese descent requested treatment for chronic HCV infection acquired decades earlier. He reported experiencing hepatitis C treatment failures twice before-13 years prior (interferon alfa monotherapy for six months) and 7 years prior (standard dual therapy with pegylated interferon alfa-2b and ribavirin for nine months). Initial viral genotyping indicated infection with HCV genotypes 1a and 6c (a form of mixed HCV disease amenable to triple therapy), and treatment with pegylated interferon alfa-2a, ribavirin, and boceprevir was initiated. By week 8 of triple therapy, the patient's viral load had decreased from 15,700,000 (7.20 log) to 462,882 (5.67 log) IU/mL, but the viral load subsequently rebounded to baseline levels, and treatment was discontinued at week 16. When repeat HCV genotyping was performed, it was discovered that initial genotyping was incorrect and that the man's infection involved not mixed genotypes but only genotype 6; he was not an appropriate candidate for triple therapy. The case emphasizes the need for clinicians to be cognizant of potential HCV genotyping errors, particularly with regard to patients of Southeast Asian descent. Three courses of interferon-based treatment, including triple therapy with boceprevir, failed to produce a sustained therapeutic response in a 70-year-old ethnic Vietnamese man with genotype 6 HCV infection.

Quantum Method for Fluorescence Background Removal in DNA Melting Analysis

Fluorescent high-resolution DNA melting analysis is a robust method of genotyping and mutation scanning. However, removing background fluorescence is important for accurate classification and to correctly display helicity. Linear baseline extrapolation, commonly used with absorbance, often fails at low temperatures when fluorescence is used. A new quantum method of background removal based on the inherent decrease of fluorescence with temperature is described. Absorbance and fluorescence melting curves were compared using synthetic targets including hairpins, unlabeled probes, and a 50 bp duplex. In addition, the quantum method was compared to a previously described exponential method for analysis of genotyping data produced after polymerase chain reaction (PCR), including those from small amplicons, unlabeled probes, and snapback primers. The quantum method best matched absorbance data and predicted helicity, with the exponential method displaying low-temperature bulges and domain artifacts that can lead to incorrect genotyping. When two melting domains were widely separated, quantum analysis produced a flat baseline between domains, while exponential analysis was temperature-dependent. Both methods have little effect on the melting temperature (Tm) although some differences were significant (hairpin Tm values increased 0.7 °C by the quantum method and decreased 1.5 °C by exponential method, p = 0.01). However, peak heights on derivative plots were strongly algorithm-dependent, with exponential analysis enhancing low-temperature peaks while dampening high-temperature peaks. Quantum-analyzed fluorescence curves were a better match to absorbance data in terms of shape, area, and peak height compared to other methods, indicating that DNA helicity is best approximated by the quantum method.