A SNP panel and online tool for checking genotype concordance through comparing QR codes

Screening a new set of microhaplotypes in exonic regions for sample identity testing and paternity testing during whole exome sequencing analysis

Whole exome sequencing (WES) is widely used in clinical diagnosis. Before obtaining an accurate diagnosis, it is essential to conduct sample identity testing and paternity testing on trio samples. Currently, there is a lack of optimal genetic markers for these purposes, with limited literature available in this area. Microhaplotypes (MHs) are promising genetic markers due to their high polymorphism, low mutation rate, short amplified fragments, absence of stutter and amplification bias. These characteristics make them suitable for sample tracking and paternity testing during WES analysis. In this study, we screened out a set of polymorphic MHs in exonic regions for the above purposes. The results showed that the power of discrimination (PD) and probability of exclusion (PE) of this set of markers ranged from 0.2682 to 0.8878 and 0.0178 to 0.4583, respectively. Both the cumulative power of discrimination (CPD) and cumulative probability of exclusion (CPE) exceeded 0.999999, indicating the great value of these markers in paternity testing and individual identification in the study population. However, these markers had the effective number of alleles (Ae) values ranging from 1.1784 to 3.8727 (average 2.1805) and informativeness (In) values ranging from 0.0151 to 0.2209 (average 0.0766), showing limited value in DNA mixture analysis and biogeographical ancestry inference.

Development of a coding SNP panel for tracking the origin of whole-exome sequencing samples

Whole-exome sequencing (WES) is widely used to diagnose complex genetic diseases and rare conditions. The implementation of a robust and effective quality control system for sample identification and tracking throughout the WES process is essential. We established a multiplex panel that included 22 coding single-nucleotide polymorphism (cSNP) loci. The personal identification and paternity identification abilities of the panel were evaluated, and a preliminary validation of the practical feasibility of the panel was conducted in a clinical WES case. These results indicate that the cSNP panel could be a useful tool for sample tracking in WES.

Performance Evaluation of Three DNA Sample Tracking Tools in a Whole Exome Sequencing Workflow

INTRODUCTION

Next-generation sequencing applications are becoming indispensable for clinical diagnostics. These experiments require numerous wet- and dry-laboratory steps, each one increasing the probability of a sample swap or contamination. Therefore, identity confirmation at the end of the process is recommended to ensure the right data are used for each patient.

METHODS

We tested three commercially available, single nucleotide polymorphism (SNP)-based sample tracking kits in a diagnostic workflow to evaluate their ease of use and performance. The coverage uniformity, on-target specificity, sample identification, and genotyping performance were determined to assess the reliability and cost effectiveness of each kit.

RESULTS AND DISCUSSION

Hands-on time and manual steps are almost identical for the kits from pxlence and Nimagen. The Swift kit has an extra purification step, making it the longest and most demanding protocol. Furthermore, the Swift kit failed to correctly genotype 26 of the 46 samples. The Nimagen kit identified all but one sample and the pxlence kit unambiguously identified all samples, making it the most reliable and robust kit of this evaluation. The Nimagen kit showed poor on-target mapping rates, resulting in deeper sequencing needs and higher sequencing costs compared with the other two kits.

CONCLUSION

Our conclusion is that the Human Sample ID kit from pxlence is the most cost effective of the three tested tools for DNA sample tracking and identification.

PAGEANT: personal access to genome and analysis of natural traits

GWASs have identified numerous genetic variants associated with a wide variety of diseases, yet despite the wide availability of genetic testing the insights that would enhance the interpretability of these results are not widely available to members of the public. As a proof of concept and demonstration of technological feasibility, we developed PAGEANT (Personal Access to Genome & Analysis of Natural Traits), usable through Graphical User Interface or command line-based version, aiming to serve as a protocol and prototype that guides the overarching design of genetic reporting tools. PAGEANT is structured across five core modules, summarized by five Qs: (i) quality assurance of the genetic data; (ii) qualitative assessment of genetic characteristics; (iii) quantitative assessment of health risk susceptibility based on polygenic risk scores and population reference; (iv) query of third-party variant databases (e.g. ClinVAR and PharmGKB) and (v) quick Response code of genetic variants of interest. Literature review was conducted to compare PAGEANT with academic and industry tools. For 2504 genomes made publicly available through the 1000 Genomes Project, we derived their genomic characteristics for a suite of qualitative and quantitative traits. One exemplary trait is susceptibility to COVID-19, based on the most up-to-date scientific findings reported.

Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores

MOTIVATION

Identifying sample mix-ups in biobanks is essential to allow the repurposing of genetic data for clinical pharmacogenetics. Pharmacogenetic advice based on the genetic information of another individual is potentially harmful. Existing methods for identifying mix-ups are limited to datasets in which additional omics data (e.g. gene expression) is available. Cohorts lacking such data can only use sex, which can reveal only half of the mix-ups. Here, we describe Idéfix, a method for the identification of accidental sample mix-ups in biobanks using polygenic scores.

RESULTS

In the Lifelines population-based biobank, we calculated polygenic scores (PGSs) for 25 traits for 32 786 participants. We then applied Idéfix to compare the actual phenotypes to PGSs, and to use the relative discordance that is expected for mix-ups, compared to correct samples. In a simulation, using induced mix-ups, Idéfix reaches an AUC of 0.90 using 25 polygenic scores and sex. This is a substantial improvement over using only sex, which has an AUC of 0.75. Subsequent simulations present Idéfix's potential in varying datasets with more powerful PGSs. This suggests its performance will likely improve when more highly powered GWASs for commonly measured traits will become available. Idéfix can be used to identify a set of high-quality participants for whom it is very unlikely that they reflect sample mix-ups, and for these participants we can use genetic data for clinical purposes, such as pharmacogenetic profiles. For instance, in Lifelines, we can select 34.4% of participants, reducing the sample mix-up rate from 0.15% to 0.01%.

AVAILABILITYAND IMPLEMENTATION

Idéfix is freely available at https://github.com/molgenis/systemsgenetics/wiki/Idefix. The individual-level data that support the findings were obtained from the Lifelines biobank under project application number ov16_0365. Data is made available upon reasonable request submitted to the LifeLines Research office (research@lifelines.nl, https://www.lifelines.nl/researcher/how-to-apply/apply-here).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Preliminary Study of the Association between SOX17 Gene Variants and Intracranial Aneurysms Using Exome Sequencing

OBJECTIVE

Conflicting results regarding SOX17 genes and the risk of intracranial aneurysms (IA) exist in the Korean population, although significant positive correlations were noted in genome-wide association studies in European and Japanese populations. Therefore, we aimed to investigate an association between SOX17 gene variants and IA using exome sequencing data.

METHODS

This study included 26 age-gender matched IA patients and 26 control subjects. The SOX17 gene variants identified from whole-exome sequencing data were examined. Genetic associations to estimate odds ratio (OR) and 95% confidence interval (CI) were performed using the software EPACTS.

RESULTS

The mean age of the IA and control groups were 51.0±9.3 years and 49.4±14.3 years, respectively (p=0.623). Seven variants of SOX17, including six single nucleotide polymorphisms and one insertion and deletion, were observed. Among these variants, rs12544958 (A>G) showed the most association with IA, but the association was not statistically significant (OR, 1.97; 95% CI, 0.81-4.74; p=0.125). Minor allele frequencies of the IA patients and controls were 0.788 and 0.653, respectively. None of the remaining variants were significantly associated with IA formation.

CONCLUSION

No significant association between SOX17 gene variants and IA were noted in the Korean population. A large-scale exome sequencing study is necessary to investigate any Korean-specific genetic susceptibility to IA.