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) Human Genome Browser17 (see Table of Materials).
<ol>
	<li>In the Ensembl homepage, select the species (i.e. human) in the dropdown menu, and enter the gene of interest acronym in the field (i.e. KCNQ1). Click &#34;Go&#34;</li>
	<li>Select the link corresponding to the gene of interest (i.e. &#34;KCNQ1 (Human Gene)&#34;</li>
	<li>Select the link corresponding to the transcript of interest ID of interest from the &#34;Transcript table&#34; (i.e. TranscriptID ENST00000155840.10, NM_000218 [RNA transcript], NP_000209 [protein product of RNA transcript]). 
	NOTE: Review of the pertinent literature is needed to ensure the correct transcript consensus sequence is selected.</li>
	<li>Note the transcript-specific NM and NP identification numbers for future reference found in the &#34;RefSeq&#34; column of the &#34;Transcript Table&#34;.</li>
	<li>Select the link associated with the NP ID number to open a new webpage from the NCBI Protein database.</li>
	<li>Scroll down to the &#34;Origin&#34; section to obtain the protein (primary) sequence for the gene transcript of interest.</li>
	<li>Scroll up to the &#34;Features&#34; section to obtain a list of the protein features (functional domains, binding domains, post-translational modification sites). 
	NOTE: This information can also be obtained via the NCBI Protein database or from primary sources in the literature. This will be further discussed in step 5.</li>
</ol>
2. Create the Experimental Genetic Variant Database (the &#34;Signal&#34;)
NOTE: Here, we demonstrate how to create a database of disease-associated variants in the gene of interest with the frequency of the disease-associated variants among individuals with the disease of interest. This database can take many forms and represents the &#34;signal&#34; (phenotype-positive genetic variation) which will be normalized against the control variant database. This can include 1) disease-associated variants for comparison against VUSs&#160;to identify novel functional domains of the protein and/or 2) VUSs, including incidentally identified VUSs, to compare against disease-associated variants to determine likelihood of pathogenicity. Disease-associated variants in KCNQ1 will be presented for illustration; however, the method is the same for analysis of incidentally-identified VUSs or any other set of experimental variants.
<ol>
	<li>Identify cohort(s) of unrelated index cases/probands with the disease of interest for which the gene of interest was comprehensively genotyped for all probands (i.e. a study identifies 24 unrelated probands hosting variants in KCNQ1 out of 200 individuals with LQTS who were subjected to KCNQ1 genetic interrogation). 
	NOTE: These cohorts can be identified from the literature, from experimental genetic analysis, or a combination of both.
	<ol>
		<li>Exclude studies which are not cohort-based (i.e. a case report describing a single mutation-positive individual), do not provide the total number of individuals genotyped for the gene of interest, or do not comprehensively genetically analyze the gene (i.e. a &#34;targeted&#34; genetic screening of only KCNQ1 exons 2-4) These preclude calculation of the frequency of a variant.</li>
		<li>Include individuals who are unrelated probands and exclude related individuals as this can over-estimate variant frequencies (i.e. a study identifies 4 unrelated individuals with KCNQ1 mutations in a cohort of 20 patients with LQTS. One of these probands is part of a family with 5 other mutation-positive kindred. Exclude all family members and include only the 4 unrelated probands).</li>
	</ol>
	</li>
	<li>Compile all experimental genetic variants found in identified cohort(s)
	<ol>
		<li>Assign nomenclature that contains the wild-type amino acid, amino acid position, and variant amino acid (i.e. alanine at amino acid number 212 changed to valine, Ala212Val or A212V). One such type of nomenclature is demonstrated in Figure 1.</li>
		<li>Confirm that variant nomenclature of all experimental genetic variants is based on the same reference gene transcript as noted in step 1.4. If experimental genetic variants are not annotated on the same reference gene transcript, then reannotate variant position to a reference transcript using transcript alignment (see step 1.2)</li>
	</ol>
	</li>
	<li>Exclude variants that are not applicable depending on the question being explored.
	<ol>
		<li>Exclude variants localizing to non-coding regions of the genome or variants which do not alter the protein sequence such as synonymous, intronic variants, 5&#39; or 3&#39; untranslated region [UTR], and intergenic region variants (i.e. a reported pathologic variant in KCNQ1 which localizes to the 5&#39; UTR of the coding region would be excluded as it is not predicted to alter the protein sequence).</li>
		<li>Exclude variants that do not meet inclusion criteria for the study. For disease-associated variants, this includes variants that are no longer deemed pathologic.
		<ol>
			<li>Confirm that each variant is currently considered pathogenic, likely pathogenic, or at least not benign, by cross-referencing variants with the ClinVar database (see Table of Materials).</li>
			<li>Enter the gene and variant of interest into ClinVar search field (i.e. KCNQ1-Y111C), select &#34;Search&#34;</li>
			<li>Identify the variant of interest under the &#34;Variation/Location&#34; column.</li>
			<li>Note the consensus interpretation of pathogenicity under &#34;Clinical Significance&#34; column (i.e. KCNQ1-Y111C is interpreted as &#34;pathogenic&#34;).</li>
			<li>Include variants which are &#34;likely pathogenic&#34; or &#34;pathogenic.&#34;</li>
			<li>Include variants with designations of &#34;conflicting interpretations of pathogenicity,&#34; &#34;uncertain significance,&#34; or when no record is available (&#34;not provided&#34;) if warranted by the study.</li>
			<li>Exclude variants designated as &#34;likely benign&#34; (i.e. KCNQ1-A62T).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Calculate the minor allele frequency (MAF) of each experimental variant position.
	<ol>
		<li>Calculate how any alleles were positive for each respective variant (i.e. if a KCNQ1-Y111C heterozygous mutation is found in 2 unrelated individuals, the number of variant-positive alleles is 2).</li>
		<li>Calculate the total number of alleles sequenced within the cohort
		<ol>
			<li>Note the total number of individuals sequenced in each cohort study (step 2.1)</li>
			<li>Multiply the total number of individuals by 2 to determine the total number of alleles. 
			NOTE: This presumes diploid genomes whereby each individual hosts 2 of each allele.</li>
		</ol>
		</li>
		<li>Calculate the total number of variant-positive individuals for each amino acid position (alleles in step 2.4.1/alleles in step 2.4.2). For example, if 2 unrelated individuals each host heterozygous KCNQ1-Y111C mutations in cohorts of 100 and 200 LQTS-afflicted individuals, respectively, then the frequency of experimental variants at amino acid position 111 is 2 variants/((100+200 individuals)*2 alleles/individual) (i.e. combined MAF 0.0033).</li>
		<li>Calculate this value for each variant as the respective MAF of each experimental variant. For additional details see step 4.2.</li>
	</ol>
	</li>
</ol>
3. Create the Control Genetic Variant Database (the &#34;Noise&#34;)
NOTE: Here, we demonstrate how to create a database of control variants in the gene of interest with an associated frequency in a control population. This database represents the &#34;noise&#34; (phenotype-negative, population-based genetic variation) which is the background against which the experimental variant database will be normalized. This is referred to as &#34;control&#34; variation.
<ol>
	<li>Identify a cohort(s) of healthy, unrelated probands or utilize large population-based studies to identify rare variants among a given population. 
	NOTE: Sources for this database are diverse and include: 1) healthy individuals and/or otherwise phenotype-negative individuals subjected to Sanger sequencing, or publicly held databases of population-based individuals for which the disease in question is rare in frequency such as 2) 1000 Genome Project (N = 1,094 subjects)18, 3) National Heart, Lung, and Blood Institute GO Exome Sequencing Project (ESP, N = 5,379 subjects)19, 4) Exome Aggregation Consortium (ExAC, N = 60,706 subjects)13, and/or 5) Genome Aggregation Database (GnomAD, N = 138,632 individuals)13 (see Table of Materials). The GnomAD database will be utilized as an illustrative example.
	<ol>
		<li>Enter the gene of interest in the search box on the GnomAD homepage (i.e. KCNQ1).</li>
		<li>Confirm that the browser selected the correct gene and transcript of interest (step 1.4).</li>
		<li>Confirm that there is appropriate coverage of sequencing of the locus by reviewing &#34;mean coverage&#34; and &#34;coverage plot.&#34;</li>
		<li>Select for coding sequence genetic variation by selecting &#34;Missense + LoF.&#34;</li>
		<li>Select &#34;Export table to CSV,&#34; which will generate a TextEdit file named &#34;Unknown.&#34;</li>
		<li>Relabel the file and include a new extension &#34;*.csv&#34; (i.e. &#34;KCNQ1 Control Variation.csv&#34;).</li>
		<li>Open the file using an appropriate software program for analysis of *.csv files (see Table of Materials).</li>
	</ol>
	</li>
	<li>Identify the protein changing genetic variation in the column labeled &#34;Protein Consequence.&#34;</li>
	<li>Apply same exclusion criteria to these control genetic variants as the experimental genetic variants (step 2.3.1).</li>
	<li>Identify the MAF of each control variant.
	<ol>
		<li>Locate the &#34;Allele Count&#34; column, which denotes the number of alleles found to harbor the variant.</li>
		<li>Locate the &#34;Allele Number&#34; column, which denotes the total number of alleles sequenced at this given amino acid position. 
		NOTE: The total number of alleles sequenced will vary depending on coverage at that location. Areas of high coverage will approach 2*total number of individuals within GnomAD (i.e. for 138,632 individuals, complete coverage encompasses 277,264 total alleles genotyped).&#160; Conversely, areas of lower coverage will have a reduced&#160;total allele number</li>
		<li>Locate the variant MAF which is pre-calculated in the &#34;Allele Frequency&#34; column and represents &#34;Allele Count&#34; divided by &#34;Allele Number.&#34; 
		NOTE: Human genomes have two of each allele (i.e. 1 subject found to have a heterozygous variant in 10 people has a MAF of 1/20)</li>
		<li>Note the MAF for each variant as the respective MAF of each control variant. 
		NOTE: Variant specific MAF for each racial/ethnic group comprising GnomAD can be seen in the columns to the right of &#34;Allele Frequency.&#34;</li>
	</ol>
	</li>
	<li>Apply a MAF threshold for rare variants above which control variants are excluded as &#34;common.&#34;
	<ol>
		<li>Set the MAF threshold to the maximal value at which all truly disease-associated variants (see step 2) also observed in the control database are included below the threshold&#160; (i.e., among all disease-associated KCNQ1 variants also found in GnomAD the highest common variant MAF is 0.009, then all GnomAD variants above a threshold of 0.01 should be excluded).</li>
	</ol>
	</li>
	<li>Ensure that the experimental variant nomenclature is identical to control (see step 2.2).</li>
	<li>Save the file. In some cases, this may require changing the file type/extension.</li>
</ol>
4. Amino Acid Level Signal-to-noise Calculation and Mapping
<ol>
	<li>Calculate a MAF for each amino acid position with a control variant (see Figure 1 containing example KCNQ1 GnomAD variants).
	<ol>
		<li>In a graphing-capable spreadsheet, create a column of the positions of all experimental variants.</li>
		<li>Remove variant text to leave only the variant position. 
		NOTE: Various functions/formulas can be utilized to automatically delete these text elements within cells&#160;(Figure 1, column C; see Table of Materials).</li>
		<li>Sort the variants in ascending value to identify which positions have more than 1 variant associated with it (Figure 1, column E; i.e. amino acid position 10 is listed twice in column E which denotes 2 unique variants at the position).</li>
		<li>Combine the MAF for each variant associated with a given position by taking the sum of all MAFs for a given position (Figure 1, column G and H).</li>
	</ol>
	</li>
	<li>Calculate a MAF for each amino acid position with an experimental variant (see Figure 2 containing mock KCNQ1 pathologic variants).
	<ol>
		<li>In a similar fashion to 4.1.1, create a column of amino acid positions which have experimental variants (Figure 2, column B).</li>
		<li>For each variant position, calculate the MAF of all variants associated with that position from step 2.4 (Figure 2, column C-G).</li>
	</ol>
	</li>
	<li>Create a rolling average of MAF for both experimental and control variants.
	<ol>
		<li>Expand the columns created in 4.1 and 4.2 to include cells for amino acid positions that have no variant as a MAF = 0. (Figure 3).
		<ol>
			<li>Create a column containing all amino acid positions in the gene of interest (i.e. 1 to 676 for KCNQ1, Figure 3, column C and I).</li>
			<li>Add a MAF of 0 for all positions that do not have variants for both control and experimental data sets. 
			NOTE: This can be done automatically by utilizing the &#34;VLOOKUP&#34; function in a commonly utilized software program (Figure 3, column D and J, see Table of Materials).</li>
		</ol>
		</li>
		<li>Create a rolling average for each experimental and control prevalence column. 
		NOTE: This allows for inference of adjacent position pathogenicity and can be modified, or even excluded, to fit the needs of the study.&#160;
		<ol>
			<li>Create a column representing a rolling average of the MAF for both the for both control and experimental data sets (Figure 3, column E and K).</li>
			<li>In the rolling average column, place the average of the respective MAF for the 5 variant positions N-terminal and 5 variant positions C-terminal to the given position. 
			NOTE: This creates a rolling average of +/- 5. For positions with less than 5 amino acid residues preceding, or following, a rolling average location (i.e. the N- or C-terminus), the rolling average will only take into account those residues that are present (i.e. the rolling average at amino acid position 3 will be an average of the MAF at amino acid positions 1 though 8, calculated as the sum&#160;of these MAFs divided by 8).</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Calculate the minimum control frequency by dividing the lowest rolling MAF by 2.
	<ol>
		<li>Change any cell with a control MAF of 0 to the minimum frequency to avoid dividing by 0 when calculating a signal-to-noise ratio.</li>
	</ol>
	</li>
	<li>Calculate the amino acid level signal-to-noise ratio (Figure 4).
	<ol>
		<li>Divide each amino acid position experimental rolling average by the respective control rolling average.</li>
		<li>Graph this ratio (Y-axis) vs. amino acid position (X-axis).</li>
	</ol>
	</li>
</ol>
5. Protein Domain Topology Overlay
<ol>
	<li>Identify the consensus amino acid locations of functional domains/features, or areas of post-translational modification, of the protein of interest (step 1.7). 
	NOTE: A number of resources can be utilized to identify these domains. These resources, as well as resources for identifying putative domains in novel proteins, have been well reviewed in the literature20. This protocol will describe the protein database available through NCBI, which is widely utilized and robust (see Table of Materials).</li>
	<li>Identify amino acid positions associated with protein domains/features.
	<ol>
		<li>Open the NCBI webpage.</li>
		<li>Enter the NP of the protein of interest into the search field.</li>
		<li>Identify known protein domains and features are catalogues under &#34;Features.&#34;</li>
		<li>Identify and note the domain name/type and amino acid positions.</li>
		<li>Select the link corresponding to the feature to visualize the region on the protein of interest primary sequence.</li>
	</ol>
	</li>
	<li>Create a column containing the boundaries of the domains/features.
	<ol>
		<li>Create a column next to the signal:noise column so that the amino acid position column can be referenced (Figure 5A, column C).</li>
		<li>Identify the cells corresponding at the N-terminal or C-terminal aspect of each domain/feature and place a 1 in each cell (i.e. if the N-terminal domain of the S1 transmembrane domain of KCNQ1 is amino acid position 122, and the C-terminal domain is position 142, then a 1 is placed in the row for amino acid position 122 and 142).</li>
		<li>For overlapping domains/features, display multiple domains by changing the 1 to other values (i.e. 1.5, 2, 2.5); this can assist in distinguishing domains.</li>
	</ol>
	</li>
	<li>Create a graph with these boundaries as a Y-axis and amino acid position on the X-axis (Figure 5B).</li>
	<li>Overlay this graph with the signal-to-noise graph created in step 4.4.</li>
	<li>Identify correlations between known protein domains/features and the signal-to-noise analysis.</li>
</ol>
6. Variant Position Overlay
<ol>
	<li>Map individual variant positions for overlay of graphs produced in steps 4.4 and 5.4.
	<ol>
		<li>Create a column next to the domain/feature column such that rows in the column will correspond to amino acid positions (Figure 5A, column D).</li>
		<li>Place a 1 in each cell in the added row corresponding to a position containing a respective variant.</li>
		<li>Create a graph with this column as a Y-axis and amino acid position on the X-axis (Figure 5C).</li>
	</ol>
	</li>
	<li>Overlay this graph with the signal-to-noise graph created in step 4.4 and domain graph created in step 5.4.</li>
</ol>

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P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interpreting+Incidentally+Identified+Variants+in+Genes+Associated+With+Catecholaminergic+Polymorphic+Ventricular+Tachycardia+in+a+Large+Cohort+of+Clinical+Whole-Exome+Genetic+Test+Referrals.">Interpreting Incidentally Identified Variants in Genes Associated With Catecholaminergic Polymorphic Ventricular Tachycardia in a Large Cohort of Clinical Whole-Exome Genetic Test Referrals.</a> Circulation Arrhythmia and Electrophysiology. 10 (4), (2017).</li><li>Whiffin, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+high-resolution+variant+frequencies+to+empower+clinical+genome+interpretation.">Using high-resolution variant frequencies to empower clinical genome interpretation.</a> Genetics in Medicine. 19 (10), 1151-1158 (2017).</li><li>Walsh, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reassessment+of+Mendelian+gene+pathogenicity+using+7,855+cardiomyopathy+cases+and+60,706+reference+samples.">Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples.</a> Genetics in Medicine. 19 (2), 192-203 (2017).</li><li>Buske, O. J., Manickaraj, A., Mital, S., Ray, P. N., Brudno, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+deleterious+synonymous+variants+in+human+genomes.">Identification of deleterious synonymous variants in human genomes.</a> Bioinformatics. 31 (5), 799 (2015).</li><li>Wen, P., Xiao, P., Xia, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=dbDSM:+a+manually+curated+database+for+deleterious+synonymous+mutations.">dbDSM: a manually curated database for deleterious synonymous mutations.</a> Bioinformatics. 32 (12), 1914-1916 (2016).</li><li>Bagnall, R. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+Genome+Sequencing+Improves+Outcomes+of+Genetic+Testing+in+Patients+With+Hypertrophic+Cardiomyopathy.">Whole Genome Sequencing Improves Outcomes of Genetic Testing in Patients With Hypertrophic Cardiomyopathy.</a> Journal of the American College of Cardiology. 72 (4), 419-429 (2018).</li><li>Giudicessi, J. R., Roden, D. M., Wilde, A. A. M., Ackerman, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classification+and+Reporting+of+Potentially+Proarrhythmic+Common+Genetic+Variation+in+Long+QT+Syndrome+Genetic+Testing.">Classification and Reporting of Potentially Proarrhythmic Common Genetic Variation in Long QT Syndrome Genetic Testing.</a> Circulation. 137 (6), 619-630 (2018).</li><li>Sundaram, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+the+clinical+impact+of+human+mutation+with+deep+neural+networks.">Predicting the clinical impact of human mutation with deep neural networks.</a> Nature Genetics. 50, 1161-1170 (2018).</li><li>Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., Kitai, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+Intelligence+in+Precision+Cardiovascular+Medicine.">Artificial Intelligence in Precision Cardiovascular Medicine.</a> Journal of the American College of Cardiology. 69 (21), 2657-2664 (2017).</li></ol>

The authors have nothing to disclose.

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">
	<colgroup>
		<col />
		<col />
		<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. ...

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10.9K Views.  Baylor College of Medicine. 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 "hotspots" within a protein sequence (signal) that rises above the frequency of rare variants found in a population (noise).

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

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 nucleotides apart result in either the inclusion (S) or exclusion (&#916;S) of a single residue, Serine-701. Further downstream, splicing at a pair of alternative acceptor splice sites result in transcripts encoding either the 55 terminal residues of the transactivation domain (&#945;) or a truncated transactivation domain with 7 unique residues (&#946;). As outlined in this manuscript, measuring the proportions of STAT3's four spliced transcripts (S&#945;, S&#946;, &#916;S&#945; and &#916;S&#946;) was possible using absolute qPCR (quantitative polymerase chain reaction). The protocol therefore distinguishes and measures highly similar splice variants. Absolute qPCR makes use of calibrator plasmids and thus specificity of detection is not compromised for the sake of efficiency. The protocol necessitates primer validation and optimization of cycling parameters. A combination of absolute qPCR and efficiency-dependent relative qPCR of total STAT3 transcripts allowed a description of the fluctuations of STAT3 splice variants' levels in eosinophils treated with cytokines. The protocol also provided evidence of a co-splicing interdependence between the two STAT3 splicing events. The strategy based on a combination of the two qPCR techniques should be readily adaptable to investigation of co-splicing at other tandem splicing sites.

Tandem splicing events occur at sites less than 12 nucleotides apart. Quantifying ratios of such splice variants is feasible using an absolute quantitative PCR approach. This manuscript describes how splice variants of the gene STAT3, in which two splicing events results in Serine-701 inclusion/exclusion and &#945;/&#946; C-termini, can be quantified.

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. Therefore, DNA-protein interactions regulate multiple physiological, pathophysiological, and biological functions, such as cell differentiation, cell proliferation, cell cycle control, chromosome stability, epigenetic gene regulation, and cell transformation. In eukaryotic cells, the DNA interacts with histone and nonhistone proteins and is condensed into chromatin. Several technical tools can be used to analyze DNA-protein interactions, such as the Electrophoresis (gel) Mobility Shift Assay (EMSA) and DNase I footprinting. However, these techniques analyze the protein-DNA interaction in vitro, not within the cellular context. Chromatin immunoprecipitation (ChIP) is a technique that captures proteins at their specific DNA binding sites, thereby allowing for the identification of DNA-protein interactions within their chromatin context. It is done by fixation&#160;of the DNA-protein interaction, followed by&#160;immunoprecipitation of the protein of interest. Subsequently, the genomic site that the protein was bound to is characterized. Here, we describe and discuss ChIP and demonstrate its analytical value for the identification of the Transforming Growth Factor-&#946; (TGF-&#946;)-induced binding of the transcription factor SMAD2 to SMAD Binding Elements (SBE) within the promoter region of the tyrosine-protein kinase Kit (c-KIT) receptor ligand Stem Cell Factor (SCF).

DNA-protein interactions are essential for multiple biological processes. During the evaluation of cellular functions, the analysis of DNA-protein interactions is indispensable for understanding gene regulation. Chromatin immunoprecipitation (ChIP) is a powerful tool to analyze such interactions in vivo.

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

In research models of liver cancer, regeneration, inflammation, and fibrosis, flexible systems for in vivo gene expression and silencing are highly useful. Hydrodynamic tail vein injection of transposon-based constructs is an efficient method for genetic manipulation of hepatocytes in adult mice. In addition to constitutive transgene expression, this system can be used for more advanced applications, such as shRNA-mediated gene knock-down, implication of the CRISPR/Cas9 system to induce gene mutations, or inducible systems. Here, the combination of constitutive CreER expression together with inducible expression of a transgene or miR-shRNA of choice is presented as an example of this technique. We cover the multi-step procedure starting from the preparation of sleeping beauty-transposon constructs, to the injection and treatment of mice, and the preparation of liver tissue for analysis by immunostaining. The system presented is a reliable and efficient approach to achieve complex genetic manipulations in hepatocytes. It is specifically useful in combination with Cre/loxP-based mouse strains and can be applied to a variety of models in the research of liver disease.

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

Hydrodynamic tail vein injection of transposon-based integration vectors enables stable transfection of murine hepatocytes in vivo. Here, we present a practical protocol for transfection systems that enables the long-term constitutive expression of a single transgene or combined constitutive and doxycycline-inducible expression of a transgene or miR-shRNA in the liver.

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 being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Here we present the proteogenomic tool PoGo and protocols for fast, quantitative, post-translational modification and variant enabled mapping of peptides identified through mass spectrometry onto reference genomes. This tool is of use to integrate and visualize proteogenomic and personal proteomic studies interfacing with orthogonal genomics data.

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 as initial massive cell death and low therapeutic effects, impaired their broad clinical translation. Genetic engineering of stem cells prior to transplantation emerged as a promising method to optimize therapeutic stem cell effects. However, safe and efficient gene delivery systems are still lacking. Therefore, the development of suitable methods may provide an approach to resolve current challenges in stem cell-based therapies.
The present protocol describes the extraction and characterization of human dental follicle stem cells (hDFSCs) as well as their non-viral genetic modification. The postnatal dental follicle unveiled as a promising and easily accessible source for harvesting adult multipotent stem cells possessing high proliferation potential. The described isolation procedure presents a simple and reliable method to harvest hDFSCs from impacted wisdom teeth. Also this protocol comprises methods to define stem cell characteristics of isolated cells. For genetic engineering of hDFSCs, an optimized cationic lipid-based transfection strategy is presented enabling highly efficient microRNA introduction without causing cytotoxic effects. MicroRNAs are suitable candidates for transient cell manipulation, as these small translational regulators control the fate and behavior of stem cells without the hazard of stable genome integration. Thus, this protocol represents a safe and efficient procedure for engineering of hDFSCs that may become important for optimizing their therapeutic efficacy.

This protocol describes the transient genetic engineering of dental stem cells extracted from the human dental follicle. The applied non-viral modification strategy may become a basis for the improvement of therapeutic stem cell products.

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

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 viruses they vector. Despite numerous studies of the biology of these species in different environments, a key life history parameter, offspring sex ratios, has received little attention, yet is important for predicting population dynamics. The primary sex ratio (sex ratio at oviposition) of Bemisia tabaci has never been reported but can be found by determining the egg fertilization rate of this haplodiploid insect. The technique involves the dechorionation of eggs with bleach, a series of fixation steps, and the application of the general DNA fluorescent stain, DAPI (4&#8242;,6-diamidino-2-phenylindole, a DNA-binding fluorescent dye), to bind to female and male pronuclei. Here, we present the technique, and an example of its application, to test whether an endosymbiotic bacterium, Rickettsia sp. nr. bellii, influenced the primary sex ratio of B. tabaci. This method may assist in population studies of whiteflies, or in determining if sex allocation exists with certain environmental stimuli.

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

We present a simple cytogenetic technique using 4&#8242;,6-diamidino-2-phenylindole (DAPI) to determine the fertilization rate and primary sex ratio of the haplodiploid invasive pest Bemisia tabaci.

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 in human embryos. Preimplantation genetic testing for aneuploidy (PGT-A) is a genetic test that significantly improves reproductive outcomes by detecting chromosomal abnormalities of embryos. Next-generation sequencing (NGS) provides a high-throughput and cost-effective approach for genetic analysis and has shown clinical applicability in PGT-A. Here, we present a rapid and low-cost semiconductor sequencing-based NGS method for screening of aneuploidy in embryos. The first step of the workflow is whole genome amplification (WGA) of the biopsied embryo specimen, followed by construction of sequencing library, and subsequent sequencing on the semiconductor sequencing system. Generally, for a PGT-A application, 24 samples can be loaded and sequenced on each chip generating 60&#8722;80 million reads at an average read length of 150 base pairs. The method provides a refined protocol for performing template amplification and enrichment of sequencing library, making the PGT-A detection reproducible, high-throughput, cost-efficient, and timesaving. The running time of this semiconductor sequencer is only 2&#8722;4 hours, shortening the turnaround time from receiving samples to issuing reports into 5 days. All these advantages make this assay an ideal method to detect chromosomal aneuploidies from embryos and thus, facilitate its wide application in PGT-A.

Here, we introduce a semiconductor sequencing method for preimplantation genetic testing for aneuploidy (PGT-A) with the advantages of short turnaround time, low cost, and high throughput.

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

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 strength of linkage and association mapping methods, which trace the relationship between DNA sequence variants and phenotype across recombinant progeny from matings between individuals of a species. These approaches, although powerful, are not well suited to trait differences between reproductively isolated species. Here we describe a new method for genome-wide dissection of natural trait variation that can be readily applied to incompatible species. Our strategy, RH-seq, is a genome-wide implementation of the reciprocal hemizygote test. We harnessed it to identify the genes responsible for the striking high temperature growth of the yeast Saccharomyces cerevisiae relative to its sister species S. paradoxus. RH-seq utilizes transposon mutagenesis to create a pool of reciprocal hemizygotes, which are then tracked through a high-temperature competition via high-throughput sequencing. Our RH-seq workflow as laid out here provides a rigorous, unbiased way to dissect ancient, complex traits in the budding yeast clade, with the caveat that resource-intensive deep sequencing is needed to ensure genomic coverage for genetic mapping. As sequencing costs drop, this approach holds great promise for future use across eukaryotes.

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

Reciprocal hemizygosity via sequencing (RH-seq) is a powerful new method to map the genetic basis of a trait difference between species. Pools of hemizygotes are generated by transposon mutagenesis and their fitness is tracked through competitive growth using high-throughout sequencing. Analysis of the resulting data pinpoints genes underlying the trait.

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

Ralstonia solanacearum is a devastating soil borne vascular pathogen that can infect a large range of plant species, causing an important threat to agriculture. However, the Ralstonia model is considerably underexplored in comparison to other models involving bacterial plant pathogens, such as Pseudomonas syringae in Arabidopsis. Research targeted to understanding the interaction between Ralstonia and crop plants is essential to develop sustainable solutions to fight against bacterial wilt disease but is currently hindered by the lack of straightforward experimental assays to characterize the different components of the interaction in native host plants. In this scenario, we have developed a method to perform genetic analysis of Ralstonia infection of tomato, a natural host of Ralstonia. This method is based on Agrobacterium rhizogenes-mediated transformation of tomato roots, followed by Ralstonia soil-drenching inoculation of the resulting plants, containing transformed roots expressing the construct of interest. The versatility of the root transformation assay allows performing either gene overexpression or gene silencing mediated by RNAi. As a proof of concept, we used this method to show that RNAi-mediated silencing of SlCESA6 in tomato roots conferred resistance to Ralstonia. Here, we describe this method in detail, enabling genetic approaches to understand bacterial wilt disease in a relatively short time and with small requirements of equipment and plant growth space.

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

Here, we present a versatile method for tomato root transformation followed by inoculation with Ralstonia solanacearum to perform straightforward genetic analysis for the study 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 ...

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Intercellular communication mediated by direct interactions between membrane-embedded cell surface receptors is crucial for the normal development and functioning of multicellular organisms. Detecting these interactions remains technically challenging, however. This manuscript describes a systematic genome-scale CRISPR/Cas9 knockout genetic screening approach that reveals cellular pathways required for specific cell surface recognition events. This assay utilizes recombinant proteins produced in a mammalian protein expression system as avid binding probes to identify interaction partners in a cell-based genetic screen. This method can be used to identify the genes necessary for cell surface interactions detected by recombinant binding probes corresponding to the ectodomains of membrane-embedded receptors. Importantly, given the genome-scale nature of this approach, it also has the advantage of not only identifying the direct receptor but also the cellular components that are required for the presentation of the receptor at the cell surface, thereby providing valuable insights into the biology of the receptor.

This manuscript describes a genome-scale cell-based screening approach to identify extracellular receptor-ligand interactions.

Intercellular communication mediated by direct interactions between membrane-embedded cell surface receptors is crucial for the normal development and ...