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- Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS. - To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care. - The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center. - The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets.

Background Illumina DNA methylation arrays are high-throughput platforms for cost-effective genome-wide profiling of individual CpGs. Experimental and technical factors introduce appreciable measurement variation, some of which can be mitigated by careful “preprocessing” of raw data. Methods Here we describe the ENmix preprocessing pipeline and compare it to a set of seven published alternative pipelines (ChAMP, Illumina, SWAN, Funnorm, Noob, wateRmelon, and RnBeads). We use two large sets of duplicate sample measurements with 450 K and EPIC arrays, along with mixtures of isogenic methylated and unmethylated cell line DNA to compare raw data and that preprocessed via different pipelines. Results Our evaluations show that the ENmix pipeline performs the best with significantly higher correlation and lower absolute difference between duplicate pairs, higher intraclass correlation coefficients (ICC) and smaller deviations from expected methylation level in mixture experiments. In addition to the pipeline function, ENmix software provides an integrated set of functions for reading in raw data files from mouse and human arrays, quality control, data preprocessing, visualization, detection of differentially methylated regions (DMRs), estimation of cell type proportions, and calculation of methylation age clocks. ENmix is computationally efficient, flexible and allows parallel computing. To facilitate further evaluations, we make all datasets and evaluation code publicly available. Conclusion Careful selection of robust data preprocessing methods is critical for DNA methylation array studies. ENmix outperformed other pipelines in our evaluations to minimize experimental variation and to improve data quality and study power.

KRAS and NRAS mutations are identified resistance mutations to anti-epidermal growth factor receptor monoclonal antibodies in patients with metastatic colorectal cancer. BRAF status is also routinely assessed for its poor prognosis value. In our institute, next-generation sequencing (NGS) is routinely used for gene-panel mutations detection including KRAS, NRAS and BRAF, but DNA quality is sometimes not sufficient for sequencing. In our routine practice, Idylla platform is used for the analysis of samples that don't reach sufficient quality criteria for NGS assay. In this study, data from mCRC samples analyzed from May 2017 to 2018 were retrospectively collected. All samples with a poor DNA quality for sequencing have been assessed using Idylla platform. First, KRAS Idylla assay cartridge has been used for the determination of KRAS mutational status. All KRAS wild-type samples have then been analyzed using NRAS-BRAF assay. Among 669 samples, 67 samples failed the DNA quality control and have been assessed on Idylla KRAS mutation test. Among 67 samples, 50 (75%) samples had a valid result with Idylla KRAS mutation test including 22 carrying a KRAS mutation. For 28 samples, NRAS and BRAF mutational statuses have been assessed using Idylla NRAS-BRAF mutation test. Among 28 samples, 27 (96%) had a valid result including 2 samples bearing a NRAS mutation and 3 samples bearing a BRAF mutation. Our study shows that an integrated workflow using NGS and Idylla platform allows the determination of KRAS, NRAS and BRAF mutational statuses of 651/669 (97.3%) samples and retrieve 49/67 (73.1%)samples that don't reach DNA quality requirements for NGS.

Background/objectivesMany personality traits correlate with BMI, but the existence and direction of causal links between them are unclear. If personality influences BMI, knowing this causal direction could inform weight management strategies. Knowing that BMI instead influences personality would contribute to a better understanding of the mechanisms of personality development and the possible psychological effects of weight change. We tested the existence and direction of causal links between BMI and personality.Subjects/methodsWe employed two genetically informed methods. In Mendelian randomization, allele scores were calculated to summarize genetic propensity for the personality traits neuroticism, worry, and depressive affect and used to predict BMI in an independent sample (N = 3 541). Similarly, an allele score for BMI was used to predict eating-specific and domain-general phenotypic personality scores (PPSs; aggregate scores of personality traits weighted by BMI). In a direction of causation (DoC) analysis, twin data from five countries (N = 5424) were used to assess the fit of four alternative models: PPSs influencing BMI, BMI influencing PPSs, reciprocal causation, and no causation.ResultsIn Mendelian randomization, the allele score for BMI predicted domain-general (β = 0.05; 95% CI: 0.02, 0.08; P = 0.003) and eating-specific PPS (β = 0.06; 95% CI: 0.03, 0.09; P < 0.001). The allele score for worry also predicted BMI (β = −0.05; 95% CI: −0.08, −0.02; P < 0.001), while those for neuroticism and depressive affect did not (P ≥ 0.459). In DoC, BMI similarly predicted domain-general (β = 0.21; 95% CI:, 0.18, 0.24; P < 0.001) and eating-specific personality traits (β = 0.19; 95% CI:, 0.16, 0.22; P < 0.001), suggesting causality from BMI to personality traits. In exploratory analyses, links between BMI and domain-general personality traits appeared reciprocal for higher-weight individuals (BMI > ~25).ConclusionsAlthough both genetic analyses suggested an influence of BMI on personality traits, it is not yet known if weight management interventions could influence personality. Personality traits may influence BMI in turn, but effects in this direction appeared weaker.

The Ion S5 (Thermo Fisher Scientific) and Miseq (Illumina) NGS systems are both widely used in the clinical laboratories conducting PGT-A. Each system employs discrepant library preparation steps, sequencing principles, and data processing algorithms. The automatic interpretation via Ion Reporter software (Thermo Fisher Scientific) and the manual interpretation via BlueFuse Multi software (Illumina) for chromosomal copy number variation (CNV) represent very different reporting approaches. Thus, it is intriguing to compare their ability of ploidy detection as PGT-A/NGS system. In the present study, four aneuploid cell lines were individually mixed with a diploid cell line at different aneuploid ratios of 0% (0:5), 10% (1:9), 20% (1:4), 40% (2:3), 50% (3:3), 60% (3:2), 80% (4:1) and 100% (5:0) to assess the sensitivity and specificity for whole chromosomal and segmental aneuploidy detection. The clinical biopsies of 107 blastocysts from 46 IVF/PGT-A cycles recruited between December 2019 and February 2020 were used to calculate the concordance. Initially, the pre-amplified products were divided into two aliquots for different library preparation procedures of each system. Applying the same calling criteria, automatic identification was achieved through the Ion Reporter, while well-trained technicians manually identified each sample through the BlueFuse Multi. The results displayed that both systems reliably distinguished chromosomal CNV of the mixtures with at least 10% aneuploidy from karyotypically normal samples ([Ion S5] whole-chromosomal duplication: 2.14 vs. 2.05, p value = 0.009, segmental deletion: 1.88 vs. 2.05, p value = 0.003; [Miseq] whole-chromosomal duplication: 2.12 vs. 2.03, p value = 0.047, segmental deletion: 1.82 vs. 2.03, p value = 0.002). The sensitivity and specificity were comparable between the Ion S5 and Miseq ([sensitivity] 93% vs. 90%, p  = 0.78; [specificity] 100% vs. 100%, p value = 1.0). In the 107 clinical biopsies, three displayed chaotic patterns (2.8%), which could not be interpreted for the ploidy. The ploidy concordance was 99.04% (103/104) per embryo and 99.47% (2265/2277) per chromosome pair. Since their ability of detection were proven to be similar, the automatic identification in Ion S5 system presents comparatively faster and more standardized performance.

Molecular genetic testing is recommended for diagnosis of inherited cardiac disease, to guide prognosis and treatment, but access is often limited by cost and availability. Recently introduced high-throughput bench-top DNA sequencing platforms have the potential to overcome these limitations. We evaluated two next-generation sequencing (NGS) platforms for molecular diagnostics. The protein-coding regions of six genes associated with inherited arrhythmia syndromes were amplified from 15 human samples using parallelised multiplex PCR (Access Array, Fluidigm), and sequenced on the MiSeq (Illumina) and Ion Torrent PGM (Life Technologies). Overall, 97.9% of the target was sequenced adequately for variant calling on the MiSeq, and 96.8% on the Ion Torrent PGM. Regions missed tended to be of high GC-content, and most were problematic for both platforms. Variant calling was assessed using 107 variants detected using Sanger sequencing: within adequately sequenced regions, variant calling on both platforms was highly accurate (Sensitivity: MiSeq 100%, PGM 99.1%. Positive predictive value: MiSeq 95.9%, PGM 95.5%). At the time of the study the Ion Torrent PGM had a lower capital cost and individual runs were cheaper and faster. The MiSeq had a higher capacity (requiring fewer runs), with reduced hands-on time and simpler laboratory workflows. Both provide significant cost and time savings over conventional methods, even allowing for adjunct Sanger sequencing to validate findings and sequence exons missed by NGS. MiSeq and Ion Torrent PGM both provide accurate variant detection as part of a PCR-based molecular diagnostic workflow, and provide alternative platforms for molecular diagnosis of inherited cardiac conditions. Though there were performance differences at this throughput, platforms differed primarily in terms of cost, scalability, protocol stability and ease of use. Compared with current molecular genetic diagnostic tests for inherited cardiac arrhythmias, these NGS approaches are faster, less expensive, and yet more comprehensive.

Eliciting and understanding patient and research participant preferences regarding return of secondary test results are key aspects of genomic medicine. A valid instrument should be easily understood without extensive pretest counseling while still faithfully eliciting patients’ preferences. We conducted focus groups with 110 adults to understand patient perspectives on secondary genomic findings and the role that preferences should play. We then developed and refined a draft instrument and used it to elicit preferences from parents participating in a genomic sequencing study in children with intellectual disabilities. Patients preferred filtering of secondary genomic results to avoid information overload and to avoid learning what the future holds, among other reasons. Patients preferred to make autonomous choices about which categories of results to receive and to have their choices applied automatically before results are returned to them and their clinicians. The Preferences Instrument for Genomic Secondary Results (PIGSR) is designed to be completed by patients or research participants without assistance and to guide bioinformatic analysis of genomic raw data. Most participants wanted to receive all secondary results, but a significant minority indicated other preferences. Our novel instrument—PIGSR—should be useful in a wide variety of clinical and research settings. Genet Med19 3, 337–344.

Massively parallel DNA sequencing of clinical samples holds great promise for the gene-based diagnosis of human inherited diseases because it allows rapid detection of putatively causative mutations at genome-wide level. Without additional evidence complementing their initial bioinformatics evaluation, however, the clinical relevance of such candidate genetic variants often remains unclear. In consequence, dedicated 'matching' services have been established in recent years that aim at the discovery of other, comparable case reports to facilitate individual diagnoses. However, legal concerns have been raised about the global sharing of genetic data, particularly in Europe where the recently enacted General Data Protection Regulation EU-2016/679 classifies genetic data as highly sensitive. Hence, unrestricted sharing of genetic data from clinical cases on platforms outside the national jurisdiction increasingly may be perceived as problematic. To allow collaborative data producers, particularly large consortia of diagnostic laboratories, to acknowledge these concerns while still practicing efficient case matching internally, novel tools are required. To this end, we developed VarWatch, an easy-to-deploy and highly scalable case matching software that provides users with comprehensive programmatic tools and a user-friendly interface to fulfil said purpose.