Dataset Information

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.

ABSTRACT: Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An approach to assist in determining the likelihood of pathogenicity is signal-to-noise analysis at the protein sequence level. This protocol describes a method for amino acid-level signal-to-noise analysis that leverages variant frequency at each amino acid position of the protein with known protein topology to identify areas of the primary sequence with elevated likelihood of pathologic variation (relative to population "background" variation). This method can identify amino acid residue location "hotspots" of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

SUBMITTER: Jones EG

PROVIDER: S-EPMC6698361 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Publications

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.

Jones Edward G EG Landstrom Andrew P AP

Journal of visualized experiments : JoVE 20190116 143

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. While this has led to increased identification of likely pathogenic mutations associated with genetic syndromes, it has also dramatically increased the number of incidentally found genetic variants of unknown significance (VUS). Determining the clinical significance of these variants is a major challenge for both scientists and clinicians. An appr ...[more]

PMID: 30735170

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Project description:Parallel imaging using phased array coils facilitates accelerated magnetic resonance imaging (MRI) and spectroscopy (MRS). Parallel data reconstruction requires the combination of data from individual coil elements, but limited combination algorithms currently exist for higher-order phased arrays and MRS data. Here, we present a systematic framework for identifying coil proximity-related signal inhomogeneities and noise levels in phased array coils that may affect sensitivity of parallel MRS. Single-voxel MRS was acquired in nine voxel positions in a brain spectroscopy phantom on a 3T whole-body MR scanner using commercially available 64-, 32-, and 20-channel phased array coils. Spectra produced by individual coil elements were combined using both a signal-to-noise ratio (SNR) threshold and based on the position of individual coil elements. SNR and metabolite Cramer-Rao lower bounds (CRLBs) from the final combined spectra were used as metrics to compare combination strategies and the effects of the phased array geometry and individual coil proximity. Comparisons were performed using one-way repeated measures ANOVA and post-hoc Tukey's range test (p<0.05). The 32-channel phased array coil produced the highest overall SNR compared to the 64-channel (p=0.0009) or 20-channel coils (p=0.003). Low SNR spectra from individual coil elements in the 64-channel coil can reduce the overall SNR when simply combining spectra from all elements. SNR varied significantly as a function of voxel position (F=58.3, p<0.0001) and SNR threshold for all phased arrays (p<0.05 for 64-, 32-, and 20-channel coils). Metabolite CRLBs were dependent on the combination strategy. We demonstrate the importance of the sampling voxel position and coil proximity on overall SNR in parallel MRS data acquisition, with significant SNR improvements after selectively filtering individual spectra based on pre-determined SNR thresholds which must be optimized for each phased array coil element and volume of interest.

Dataset Information

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.

Publications

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation.

Similar Datasets

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