Assessment of cues potentially mediating host selection of Leptoglossus occidentalis on Pinus contorta

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Evaluation of <Emphasis Type="Italic">in silico</Emphasis> algorithms for use with ACMG/AMP clinical variant interpretation guidelines

Background

The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.

Results

Using 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017).

Conclusions

Our analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

     

In aged primary T cells,mitochondrial stress contributes to telomere attrition measured by a novel imaging flow cytometry assay

The decline of the immune system with age known as immune senescence contributes to inefficient pathogen clearance and is a key risk factor for many aged‐related diseases. However, reversing or halting immune aging requires more knowledge about the cell biology of senescence in immune cells. Telomere shortening, low autophagy and mitochondrial dysfunction have been shown to underpin cell senescence. While autophagy has been found to control mitochondrial damage, no link has been made to telomere attrition. In contrast, mitochondrial stress can contribute to telomere attrition and vice versa. Whereas this link has been investigated in fibroblasts or cell lines, it is unclear whether this link exists in primary cells such as human lymphocytes and whether autophagy contributes to it. As traditional methods for measuring telomere length are low throughput or unsuitable for the analysis of cell subtypes within a mixed population of primary cells, we have developed a novel sensitive flow‐FISH assay using the imaging flow cytometer. Using this assay, we show a correlation between age and increased mitochondrial reactive oxygen species in CD8⁺ T‐cell subsets, but not with autophagy. Telomere shortening within the CD8⁺ subset could be prevented in vitro by treatment with a ROS scavenger. Our novel assay is a sensitive assay to measure relative telomere length in primary cells and has revealed ROS as a contributing factor to the decline in telomere length.     

Evaluating the influence of quality control decisions and software algorithms on SNP calling for the affymetrix 6.0 SNP array platform

Objective: Our goal was to evaluate the influence of quality control (QC) decisions using two genotype calling algorithms, CRLMM and Birdseed, designed for the Affymetrix SNP Array 6.0. Methods: Various QC options were tried using the two algorithms and comparisons were made on subject and call rate and on association results using two data sets. Results: For Birdseed, we recommend using the contrast QC instead of QC call rate for sample QC. For CRLMM, we recommend using the signal-to-noise rate ≥4 for sample QC and a posterior probability of 90% for genotype accuracy. For both algorithms, we recommend calling the genotype separately for each plate, and dropping SNPs with a lower call rate (<95%) before evaluating samples with lower call rates. To investigate whether the genotype calls from the two algorithms impacted the genome-wide association results, we performed association analysis using data from the GENOA cohort; we observed that the number of significant SNPs were similar using either CRLMM or Birdseed. Conclusions: Using our suggested workflow both algorithms performed similarly; however, fewer samples were removed and CRLMM took half the time to run our 854 study samples (4.2 h) compared to Birdseed (8.4 h).