Name: determining the likelihood of variant pathogenicity using amino acid-level signal-to-noise analysis of genetic variation
Uploaded: 2019-01-16T14:00:00
Description: Watch this Scientific Journal Video about Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation at JoVE.com

APL is supported by the National Institutes of Health K08-HL136839.

1. Identify the Gene and Specific Splice Isoform of Interest
NOTE: Here, we demonstrate the use of Ensembl15 to identify the consensus sequence for the gene of interest which is associated with the pathogenesis of the disease of interest (i.e. KCNQ1 mutations are associated with LQTS). Alternatives to Ensembl include RefSeq via the National Center for Biotechnology Information (NCBI)16 and the University of California, Santa Cruz (UCSC) Hu...

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High-throughput genetic testing has advanced dramatically in its application and availability over the past decade. However, in many diseases with well-established genetic underpinnings, such as cardiomyopathies, expanded testing has failed to improve diagnostic yield21. Further, there is significant uncertainty regarding the diagnostic utility of many identified variants. This is partially due to a growing number of incidentally identified rare variants discovered on WES and WGS, which can lead t...

The rapid improvement in genetic sequencing platforms has revolutionized the accessibility and role of genetics in medicine. Once confined to a single gene, or a handful of genes, the reduction in cost and increase in speed of next generation genetic sequencing has led routine sequencing of the entirety of the genome&#39;s coding sequence (whole exome sequencing, WES) and the entire genome (whole genome sequencing, WGS) in the clinical setting. WES and WGS have&#160;been used frequently in the setting of critically ill neonates and children with concern for genetic syndrome where it is a proven diagnostic tool that can change clinical management1,2. 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, or unexpected positive results, of unknown diagnostic significance (VUS). While some of these variants are disregarded and not reported, variants localizing to genes associated with potentially fatal or highly morbid diseases are often reported. Current guidelines recommend reporting of incidental variants found in specific genes which may be of medical benefit to the patient, including genes associated with the development of sudden cardiac death-predisposing diseases such as cardiomyopathies and channelopathies3. Although this recommendation was designed to capture individuals at risk of a SCD-predisposing disease, the sensitivity of variant detection far exceeds specificity. This is reflected in a growing number of VUSs and incidentally identified variants with unclear diagnostic utility that far exceed the frequency of the respective diseases in a given population4. One such disease, long QT syndrome (LQTS), is a canonical cardiac channelopathy caused by mutations localizing to genes which encode cardiac ion channels, or channel interacting proteins, resulting in delayed cardiac repolarization5. This delayed repolarization, seen by a prolonged QT interval on resting electrocardiogram, results in an electrical predisposition to potentially fatal ventricular arrhythmias such as torsades de pointes. While a number of genes have been linked to the development of this disease, mutations in KCNQ1-encoded IKs potassium channel (KCNQ1, Kv7.1) is the cause of LQTS type 1 and is utilized as an example below6. Illustrating the complexity in variant interpretation, the presence of rare variants in LQTS-associated genes, so called &#34;background genetic variation&#34; has been previously described7,8.
In addition to large compendium-style databases of known pathogenic variants, several strategies exist for predicting the effect different variants will produce. Some are based on algorithms, such as SIFT and Polyphen 2, which can filter large numbers of novel non-synonymous variants to predict deleteriousness9,10. Despite broad use of these tools, low specificity limits their applicability when it comes to &#34;calling&#34; clinical VUSs11. &#34;Signal-to-noise&#34; analysis is a tool which identifies the likelihood of a variant being associated with disease based on the frequency of known pathologic variation at the loci in question normalized against rare genetic variation from a population. Variants localizing to genetic loci where there is a high prevalence of disease-associated mutations compared to population-based variation, a high signal-to-noise, are more likely to be disease-associated themselves. Further, rare variants found incidentally localizing to a gene with a high frequency of rare population variants compared to disease-associated frequency, a low signal-to-noise, may be less likely to be disease-associated. The diagnostic utility of signal-to-noise analysis has been illustrated in the latest guidelines for genetic testing for cardiomyopathies and channelopathies; however, it has only been employed at the whole gene level or domain-specific level12. Recently, given increased availability of both pathologic variants (disease databases, cohort studies in the literature) and population-based control variants (Exome Aggregation Consortium, ExAC and the Genome Aggregation Database, GnomAD13), this has been applied to the individual amino acid positions within the primary sequence of a protein. Amino acid-level signal-to-noise analysis has proven useful in categorizing incidentally identified variants in genes associated with LQTS as likely &#34;background&#34; genetic variation rather than disease-associated. Among the three major genes associated with LQTS, including KCNQ1, these incidentally identified variants lacked a&#160;significant signal-to-noise ratios, suggesting that the frequency of these variants at individual amino acid positions reflect rare population variation rather than disease-associated mutations. Furthermore, when protein-specific domain topology was overlaid against areas of high signal-to-noise, pathologic mutation &#34;hotspots&#34; localized to key functional domains of the proteins14. This methodology holds promise in determining 1) the likelihood a variant is disease- or population-associated and 2) identifying novel critical functional domains of a protein associated with human disease.

<table border="0" cellpadding="0" cellspacing="0" style="width:1277px;" width="1277">
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		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:461px;">1000 Genome Project</td>
			<td style="width:109px;">N/A</td>
			<td style="width:359px;">www.internationalgenome.org</td>
			<td style="width:348px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">ClinVar</td>
			<td>N/A</td>
			<td>www.ncbi.nlm.nih.gov/clinvar</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ensembl Genome Browser</td>
			<td>N/A</td>
			<td>uswest.ensembl.org/index.html</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:461px;">Excel</td>
			<td style="width:109px;">Microsoft</td>
			<td style="width:359px;">office.microsoft.com/excel/</td>
			<td>Used for all example formulas and functions</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Exome Aggregation Consortium&#160;</td>
			<td>N/A</td>
			<td>www.exac.broadinstitute.org</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Genome Aggregation Database&#160;</td>
			<td>N/A</td>
			<td>www.gnomad.broadinstitute.org</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">National Center for Biotechnology Information Domain and Structure Database</td>
			<td>N/A</td>
			<td>www.ncbi.nlm.nih.gov/guide/domains-structures/</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">National Center for Biotechnology Information Gene Database</td>
			<td>N/A</td>
			<td>www.ncbi.nlm.nih.gov/gene/</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">National Center for Biotechnology Information Protein Database</td>
			<td>N/A</td>
			<td>www.ncbi.nlm.nih.gov/protein/</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">National Heart, Lung, and Blood Institute GO Exome Sequencing Project</td>
			<td>N/A</td>
			<td>www.evs.gs.washington.edu/EVS/</td>
			<td></td>
		</tr>
		<tr height="34">
			<td height="34" style="height:34px;width:461px;">SnapGene</td>
			<td style="width:109px;">GSL Biotech LCC</td>
			<td style="width:359px;">www.snapgene.com</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">University of California, Santa Cruz Human Genome Browser</td>
			<td>N/A</td>
			<td>www.genome.ucsc.edu</td>
			<td></td>
		</tr>
	</tbody>
</table>

determining the likelihood of variant pathogenicity using amino acid-level signal-to-noise analysis of genetic variation

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 &#34;background&#34; variation). This method can identify amino acid residue location &#34;hotspots&#34; of high pathologic signal, which can be used to refine the diagnostic weight of VUSs such as those identified by next generation genetic testing.

A representative result for amino acid-level signal to noise analysis for KCNQ1 is depicted in Figure 6. In this example, rare variants identified in the GnomAD cohort (control cohort), incidentally-identified WES variants (experimental cohort #1), and LQTS case-associated variants deemed likely disease-associated (experimental cohort #2) are depicted. Further, the signal-to-noise analysis comparing the WES and LQTS cohort variant frequency normalized against...

Watch this Scientific Journal Video about Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation at JoVE.com

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

Amino acid-level signal-to-noise analysis determines the prevalence of genetic variation at a given amino acid position normalized to background genetic variation of a given population. This allows for identification of variant &#34;hotspots&#34; within a protein sequence (signal) that rises above the frequency of rare variants found in a population (noise).

Advancements in the cost and speed of next generation genetic sequencing have generated an explosion of clinical whole exome and whole genome testing. ...

Amino acid-level signal-to-noise analysis, provides a measure of the likelihood that a genetic variant is associated with a disease state, or is part of natural genetic variation within the population. This technique leverages two large genetic resources, disease associated mutations, available in the literature, or in the public domain with population based exsome and genome studies, that identify rare genetic variance. To identify a specific gene and splice isoform of interest, open the Ensembl homepage and, select the species from the dropdown menu.Enter the acronym for the gene of interest, and click go. Select the links corresponding to the gene of interest and the transcriptive interest, and the ID of interest from the Transcript table. Note the transcript specific RNA transcript and protein product of RNA transcript identification numbers in the reference sequence column of the transcript table for future reference.Select the link associated with the protein product of RNA transcript ID number to open a new webpage from the National Center for Biotechnology Information, or NCBI Protein Database, and scroll down to the Origin section to obtain the primary protein sequence for the gene transcript of interest. Then scroll up to the Features section to obtain a list of the protein features. To calculate a minor allele frequency for each amino acid position with a control variant, open a graphing capable spreadsheet and create a column of the positions of all of the experimental variants.Remove the variant texts to leave only the variant positions and sort the variants in ascending value to identify which positions have more than one associated variant. Obtain the sum of all of the minor allele frequencies for a given position, by combining the minor allele frequency for each variant associated with a given position, and calculate a minor allele frequency for each amino acid position with an experimental variant. Next, create a column of amino acid positions that have experimental variants, and calculate the minor allele frequency of all of the variants associated with that position for all of the variant positions.To create a rolling average of the minor allele frequencies for both the experimental and control variants, create a column containing all of the amino acid positions in the gene of interest, and add a minor allele frequency of zero for all of the positions that do not have variants for both the control and experimental data sets. To create a rolling average for each experimental and control prevalence column, create a column representing a rolling average of the minor allele frequency for both the control and experimental data sets, and in the rolling average column place the average of the respective minor allele frequency for the five variant positions N terminal and C terminal to each position. To calculate the cohort minimum frequency, divide the lowest minor allele identified by two and enter this value in any cell with the control minor allele frequency of zero.This will avoid dividing by zero, when calculating the signal-to-noise ratio. To calculate the amino acid-level signal-to-noise ratio, divide each amino acid position experimental rolling average by the respective control rolling average, and graph this ratio versus the amino acid position. To identify the consensus amino acid locations of functional domains and features, or areas of post translational modification of the protein of interest, identify the amino acid positions associated with the protein domains and features and open the NCBI webpage.Enter the protein product of the RNA transcript of the protein of interest into the search field, and identify the known protein domains and features, under Features. Identify and note the domain name and type and the amino acid positions and select the link corresponding to the feature to visualize the region on the protein of interest primary sequence. Create a column next to the signal-to-noise column, so that the amino acid position column can be referenced, and identify the cells corresponding at the N or C terminal aspect of each domain and feature.Then, place a one in each cell, create a graph with these boundaries on the y-axis and the amino acid position on the x-axis and overlay this graph with the signal-to-noise graph. To map individual variant positions for overlay of the signal-to-noise ratio and protein domain topology graphs, create a column next to the domain feature column such that rows in the column, correspond to the amino acid positions, and place a one in each cell in the added row, corresponding to a position containing a respective variant. Then, create a graph with this column as the y-axis and the amino acid positions on the x-axis, and overlay this graph with the signal-to-noise and protein domain topology graphs.Here, a representative result for an amino acid-level signal-to-noise analysis for the potassium voltage gated channel subfamily Q member one gene is depicted. Rare variance identified in the control cohort, and the experimental incidentally identified whole exsome sequencing, and long QT syndrome case associated variants deemed likely to be disease associated are shown. The signal-to-noise analyses comparing the whole exsome sequencing, and the long QT syndrome cohort variant frequency, normalized against the control cohort variant frequency, are also represented.In this experiment, the long QT syndrome associated variants demonstrated high signal-to-noise ratios in domains corresponding with the channel pour, the selectivity filter, and the potassium voltage gated channel subfamily E member one binding domain. In comparison, incidentally identified variants in the whole exsome sequencing cohort, did not clearly demonstrate specific regions of high signal-to-noise elevation, suggesting that these variants reflect the background genetic variation. This methodology could be applied to gauge the diagnostic weight of variants of unknown significance that arise during clinical genetic testing.

Research

JoVE Journal

Genetics

Merging Absolute and Relative Quantitative PCR Data to Quantify STAT3 Splice Variant Transcripts

Human signal transducer and activator of transcription 3 (STAT3) is one of many genes containing a tandem splicing site. Alternative donor splice sites 3 ...

Analysis of the c-KIT Ligand Promoter Using Chromatin Immunoprecipitation

Multiple cellular processes, including DNA replication and repair, DNA recombination, and gene expression, require interactions between proteins and DNA. ...

Constitutive and Inducible Systems for Genetic In Vivo Modification of Mouse Hepatocytes Using Hydrodynamic Tail Vein Injection

Constitutive and Inducible Systems for Genetic In Vivo Modification of Mouse Hepatocytes Using Hydrodynamic Tail Vein Injection

In research models of liver cancer, regeneration, inflammation, and fibrosis, flexible systems for in vivo gene expression and silencing are highly ...

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly ...

Isolation, Characterization and MicroRNA-based Genetic Modification of Human Dental Follicle Stem Cells

To date, several stem cell types at different developmental stages are in the focus for the treatment of degenerative diseases. Yet, certain aspects, such ...

Determining the Egg Fertilization Rate of Bemisia tabaci Using a Cytogenetic Technique

Determining the Egg Fertilization Rate of Bemisia tabaci Using a Cytogenetic Technique

A few species of sap-sucking whiteflies are some of the most damaging terrestrial pests worldwide because of the crop damage they inflict and plant ...

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Chromosomal aneuploidy, one of the main causes leading to embryonic development arrest, implantation failure, or pregnancy loss, has been well documented ...

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Target Induced Local Lesions In Genomes (TILLING) is a strategy of reverse genetics for the high-throughput screening of induced mutations. However, the ...

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

A central goal of modern genetics is to understand how and why organisms in the wild differ in phenotype. To date, the field has advanced largely on the ...

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Tomato Root Transformation Followed by Inoculation with Ralstonia Solanacearum for Straightforward Genetic Analysis of Bacterial Wilt Disease

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to ...