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>

<ol>
	<li>Yang, Y., 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=Clinical+whole-exome+sequencing+for+the+diagnosis+of+mendelian+disorders.">Clinical whole-exome sequencing for the diagnosis of mendelian disorders.</a> New England Journal of Medicine. 369 (16), 1502-1511 (2013).</li><li>Meng, 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=Use+of+Exome+Sequencing+for+Infants+in+Intensive+Care+Units:+Ascertainment+of+Severe+Single-Gene+Disorders+and+Effect+on+Medical+Management.">Use of Exome Sequencing for Infants in Intensive Care Units: Ascertainment of Severe Single-Gene Disorders and Effect on Medical Management.</a> Journal of the American Medical Association Pediatrics. 171 (12), 173438 (2017).</li><li>Kalia, S. S., 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=Recommendations+for+reporting+of+secondary+findings+in+clinical+exome+and+genome+sequencing,+2016+update+(ACMG+SF+v2.0):+a+policy+statement+of+the+American+College+of+Medical+Genetics+and+Genomics.">Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics.</a> Genetics in Medicine. 19 (2), 249-255 (2017).</li><li>Landstrom, A. P., 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=The+Achilles'+heel+of+cardiovascular+genetic+testing:+distinguishing+pathogenic+mutations+from+background+genetic+noise.">The Achilles' heel of cardiovascular genetic testing: distinguishing pathogenic mutations from background genetic noise.</a> Clinical Pharmacology and Therapeutics. 90 (4), 496-499 (2011).</li><li>Landstrom, A. P., Tester, D. J., Ackerman, M. J., Lawless, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+genetic+testing+for+sudden+death+predisposing+heart+conditions+in+athletes.">Role of genetic testing for sudden death predisposing heart conditions in athletes.</a> Sports Cardiology Essentials. , (2011).</li><li>Wang, Q., 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=Positional+cloning+of+a+novel+potassium+channel+gene:+KVLQT1+mutations+cause+cardiac+arrhythmias.">Positional cloning of a novel potassium channel gene: KVLQT1 mutations cause cardiac arrhythmias.</a> Nature Genetics. 12 (1), 17-23 (1996).</li><li>Kapa, S., 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=Genetic+testing+for+long-QT+syndrome:+distinguishing+pathogenic+mutations+from+benign+variants.">Genetic testing for long-QT syndrome: distinguishing pathogenic mutations from benign variants.</a> Circulation. 120 (18), 1752-1760 (2009).</li><li>Ackerman, M. J., 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=Ethnic+differences+in+cardiac+potassium+channel+variants:+implications+for+genetic+susceptibility+to+sudden+cardiac+death+and+genetic+testing+for+congenital+long+QT+syndrome.">Ethnic differences in cardiac potassium channel variants: implications for genetic susceptibility to sudden cardiac death and genetic testing for congenital long QT syndrome.</a> Mayo Clinic Proceedings. 78 (12), 1479-1487 (2003).</li><li>Kumar, P., Henikoff, S., Ng, P. C. <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+effects+of+coding+non-synonymous+variants+on+protein+function+using+the+SIFT+algorithm.">Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm.</a> Nature Protocols. 4 (7), 1073-1081 (2009).</li><li>Adzhubei, I., Jordan, D. M., Sunyaev, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+functional+effect+of+human+missense+mutations+using+PolyPhen-2.">Predicting functional effect of human missense mutations using PolyPhen-2.</a> Current Protocols in Human Genetics. , (2013).</li><li>Flanagan, S. E., Patch, A. M., Ellard, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+SIFT+and+PolyPhen+to+predict+loss-of-function+and+gain-of-function+mutations.">Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations.</a> Genetic Testing and Molecular Biomarkers. 14 (4), 533-537 (2010).</li><li>Ackerman, M. J., 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=HRS/EHRA+expert+consensus+statement+on+the+state+of+genetic+testing+for+the+channelopathies+and+cardiomyopathies+this+document+was+developed+as+a+partnership+between+the+Heart+Rhythm+Society+(HRS)+and+the+European+Heart+Rhythm+Association+(EHRA).">HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA).</a> Heart Rhythm. 8 (8), 1308-1339 (2011).</li><li>Lek, M., 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=Analysis+of+protein-coding+genetic+variation+in+60,706+humans.">Analysis of protein-coding genetic variation in 60,706 humans.</a> Nature. 536 (7616), 285-291 (2016).</li><li>Landstrom, A. 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=Amino+acid-level+signal-to-noise+analysis+of+incidentally+identified+variants+in+genes+associated+with+long+QT+syndrome+during+pediatric+whole+exome+sequencing+reflects+background+genetic+noise.">Amino acid-level signal-to-noise analysis of incidentally identified variants in genes associated with long QT syndrome during pediatric whole exome sequencing reflects background genetic noise.</a> Heart Rhythm. 15 (7), 1042-1050 (2018).</li><li>Hubbard, T., 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=Ensembl+2005.">Ensembl 2005.</a> Nucleic Acids Research. 33, 447-453 (2005).</li><li>O'Leary, N. A., 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=Reference+sequence+(RefSeq)+database+at+NCBI:+current+status,+taxonomic+expansion,+and+functional+annotation.">Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation.</a> Nucleic Acids Research. 44, 733-745 (2016).</li><li>Kent, W. J., 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=The+human+genome+browser+at+UCSC.">The human genome browser at UCSC.</a> Genome Research. 12 (6), 996-1006 (2002).</li><li>The 100 Genome Projects Consortium. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+integrated+map+of+genetic+variation+from+1,092+human+genomes.">An integrated map of genetic variation from 1,092 human genomes.</a> Nature. 491 (7422), 56-65 (2012).</li><li>Fu, W., 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=Analysis+of+6,515+exomes+reveals+the+recent+origin+of+most+human+protein-coding+variants.">Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants.</a> Nature. 493 (7331), 216-220 (2013).</li><li>Mulder, N. J., Apweiler, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tools+and+resources+for+identifying+protein+families,+domains+and+motifs.">Tools and resources for identifying protein families, domains and motifs.</a> Genome Biology. 3 (1), (2002).</li><li>Cirino, A. 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=A+Comparison+of+Whole+Genome+Sequencing+to+Multigene+Panel+Testing+in+Hypertrophic+Cardiomyopathy+Patients.">A Comparison of Whole Genome Sequencing to Multigene Panel Testing in Hypertrophic Cardiomyopathy Patients.</a> Circulation Cardiovascular Genetics. 10 (5), (2017).</li><li>Landstrom, A. 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><tbody><tr><td>1000 Genome Project</td><td>N/A</td><td>www.internationalgenome.org</td><td></td></tr><tr><td>ClinVar</td><td>N/A</td><td>www.ncbi.nlm.nih.gov/clinvar</td><td></td></tr><tr><td>Ensembl Genome Browser</td><td>N/A</td><td>uswest.ensembl.org/index.html</td><td></td></tr><tr><td>Excel</td><td>Microsoft</td><td>office.microsoft.com/excel/</td><td>Used for all example formulas and functions</td></tr><tr><td>Exome Aggregation Consortium&amp;nbsp;</td><td>N/A</td><td>www.exac.broadinstitute.org</td><td></td></tr><tr><td>Genome Aggregation Database&amp;nbsp;</td><td>N/A</td><td>www.gnomad.broadinstitute.org</td><td></td></tr><tr><td>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><td>National Center for Biotechnology Information Gene Database</td><td>N/A</td><td>www.ncbi.nlm.nih.gov/gene/</td><td></td></tr><tr><td>National Center for Biotechnology Information Protein Database</td><td>N/A</td><td>www.ncbi.nlm.nih.gov/protein/</td><td></td></tr><tr><td>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><td>SnapGene</td><td>GSL Biotech LCC</td><td>www.snapgene.com</td><td></td></tr><tr><td>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. ...

determining-likelihood-variant-pathogenicity-using-amino-acid-level

Research

JoVE Journal

Genetics

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

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

RNA viruses use RNA dependent RNA polymerases to replicate their genomes. The intrinsically high error rate of these enzymes is a large contributor to the generation of extreme population diversity that facilitates virus adaptation and evolution. Increasing evidence shows that the intrinsic error rates, and the resulting mutation frequencies, of RNA viruses can be modulated by subtle amino acid changes to the viral polymerase. Although biochemical assays exist for some viral RNA polymerases that permit quantitative measure of incorporation fidelity, here we describe a simple method of measuring mutation frequencies of RNA viruses that has proven to be as accurate as biochemical approaches in identifying fidelity altering mutations. The approach uses conventional virological and sequencing techniques that can be performed in most biology laboratories. Based on our experience with a number of different viruses, we have identified the key steps that must be optimized to increase the likelihood of isolating fidelity variants and generating data of statistical significance. The isolation and characterization of fidelity altering mutations can provide new insights into polymerase structure and function1-3. Furthermore, these fidelity variants can be useful tools in characterizing mechanisms of virus adaptation and evolution4-7.

The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.

RNA viruses use RNA dependent RNA polymerases to replicate their genomes. The intrinsically high error rate of these enzymes is a large contributor to the ...

Immunology and Infection

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The technique is highly efficient with millions of sequencing reads being produced in a short time span and at relatively low cost. Specifically, targeted NGS is able to focus investigations to genomic regions of particular interest based on the disease of study. Not only does this further reduce costs and increase the speed of the process, but it lessens the computational burden that often accompanies NGS. Although targeted NGS is restricted to certain regions of the genome, preventing identification of potential novel loci of interest, it can be an excellent technique when faced with a phenotypically and genetically heterogeneous disease, for which there are previously known genetic associations. Because of the complex nature of the sequencing technique, it is important to closely adhere to protocols and methodologies in order to achieve sequencing reads of high coverage and quality. Further, once sequencing reads are obtained, a sophisticated bioinformatics workflow is utilized to accurately map reads to a reference genome, to call variants, and to ensure the variants pass quality metrics. Variants must also be annotated and curated based on their clinical significance, which can be standardized by applying the American College of Medical Genetics and Genomics Pathogenicity Guidelines. The methods presented herein will display the steps involved in generating and analyzing NGS data from a targeted sequencing panel, using the ONDRISeq neurodegenerative disease panel as a model, to identify variants that may be of clinical significance.

Targeted next-generation sequencing is a time- and cost-efficient approach that is becoming increasingly popular in both disease research and clinical diagnostics. The protocol described here presents the complex workflow required for sequencing and the bioinformatics process used to identify genetic variants that contribute to disease.

Next-generation sequencing (NGS) is quickly revolutionizing how research into the genetic determinants of constitutional disease is performed. The ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is pathogenic, one must query many databases to determine whether the gene of interest is linked to a genetic disease, whether the specific variant has been reported before, and what functional data is available in model organism databases that may provide clues about the gene&#8217;s function in human. MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) is a one-stop data collection tool for human genes and variants and their orthologous genes in seven model organisms including in mouse, rat, zebrafish, fruit fly, nematode worm, fission yeast, and budding yeast. In this Protocol, we provide an overview of what MARRVEL can be used for and discuss how different datasets can be used to assess whether a variant of unknown significance (VUS) in a known disease-causing gene or a variant in a gene of uncertain significance (GUS) may be pathogenic. This protocol will guide a user through searching multiple human databases simultaneously starting with a human gene with or without a variant of interest. We also discuss how to utilize data from OMIM, ExAC/gnomAD, ClinVar, Geno2MP, DGV and DECHIPHER. Moreover, we illustrate how to interpret a list of ortholog candidate genes, expression patterns, and GO terms in model organisms associated with each human gene. Furthermore, we discuss the value protein structural domain annotations provided and explain how to use the multiple species protein alignment feature to assess whether a variant of interest affects an evolutionarily conserved domain or amino acid. Finally, we will discuss three different use-cases of this website. MARRVEL is an easily accessible open access website designed for both clinical and basic researchers and serves as a starting point to design experiments for functional studies.

Here, we present a protocol to access and analyze many human and model organism databases efficiently. This protocol demonstrates the use of MARRVEL to analyze candidate disease-causing variants identified from next-generation sequencing efforts.

Through whole-exome/genome sequencing, human geneticists identify rare variants that segregate with disease phenotypes. To assess if a specific variant is ...

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human genetics research. Although a number of in silico algorithms have been developed to predict the pathogenicity of variants identified in these datasets, functional studies are critical to determining how specific genomic variants affect protein function, especially for missense variants. In the Undiagnosed Diseases Network (UDN) and other rare disease research consortia, model organisms (MO) including Drosophila, C. elegans, zebrafish, and mice are actively used to assess the function of putative human disease-causing variants. This protocol describes a method for the functional assessment of rare human variants used in the Model Organisms Screening Center Drosophila Core of the UDN. The workflow begins with gathering human and MO information from multiple public databases, using the MARRVEL web resource to assess whether the variant is likely to contribute to a patient&#39;s condition as well as design effective experiments based on available knowledge and resources. Next, genetic tools (e.g., T2A-GAL4 and UAS-human cDNA lines) are generated to assess the functions of variants of interest in Drosophila. Upon development of these reagents, two-pronged functional assays based on rescue and overexpression experiments can be performed to assess variant function. In the rescue branch, the endogenous fly genes are &#34;humanized&#34;&#160;by replacing the orthologous Drosophila gene with reference or variant human transgenes. In the overexpression branch, the reference and variant human proteins are exogenously driven in a variety of tissues. In both cases, any scorable phenotype (e.g., lethality, eye morphology, electrophysiology) can be used as a read-out, irrespective of the disease of interest. Differences observed between reference and variant alleles suggest a variant-specific effect, and thus likely pathogenicity. This protocol allows rapid, in vivo assessments of putative human disease-causing variants of genes with known and unknown functions.

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

The goal of this protocol is to outline the design and performance of in vivo experiments in Drosophila melanogaster to assess the functional consequences of rare gene variants associated with human diseases.

Advances in sequencing technology have made whole-genome and whole-exome datasets more accessible for both clinical diagnosis and cutting-edge human ...

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Cancer Research

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

The number of significant genetic associations with common complex traits is constantly increasing. However, most of these associations have not been understood at molecular level. One of the mechanisms mediating the effect of DNA variants on phenotypes is gene expression, which has been shown to be particularly relevant for complex traits1.
This method tests in a cellular context the effect of specific DNA sequences on gene expression. The principle is to measure the relative abundance of transcripts arising from the two alleles of a gene, analysing cells which carry one copy of the DNA sequences associated with disease (the risk variants)2,3. Therefore, the cells used for this method should meet two fundamental genotypic requirements: they have to be heterozygous both for DNA risk variants and for DNA markers, typically coding polymorphisms, which can distinguish transcripts based on their chromosomal origin (Figure 1). DNA risk variants and DNA markers do not need to have the same allele frequency but the phase (haplotypic) relationship of the genetic markers needs to be understood. It is also important to choose cell types which express the gene of interest. This protocol refers specifically to the procedure adopted to extract nucleic acids from fibroblasts but the method is equally applicable to other cells types including primary cells.
DNA and RNA are extracted from the selected cell lines and cDNA is generated. DNA and cDNA are analysed with a primer extension assay, designed to target the coding DNA markers4. The primer extension assay is carried out using the MassARRAY (Sequenom)5 platform according to the manufacturer's specifications. Primer extension products are then analysed by matrix-assisted laser desorption/ionization time of-flight mass spectrometry (MALDI-TOF/MS). Because the selected markers are heterozygous they will generate two peaks on the MS profiles. The area of each peak is proportional to the transcript abundance and can be measured with a function of the MassARRAY Typer software to generate an allelic ratio (allele 1: allele 2) calculation. The allelic ratio obtained for cDNA is normalized using that measured from genomic DNA, where the allelic ratio is expected to be 1:1 to correct for technical artifacts. Markers with a normalised allelic ratio significantly different to 1 indicate that the amount of transcript generated from the two chromosomes in the same cell is different, suggesting that the DNA variants associated with the phenotype have an effect on gene expression. Experimental controls should be used to confirm the results.

Genetic associations often remain unexplained at a functional level. This method aims to assess the effect of phenotype-associated genetic markers on gene expression by analyzing cells heterozygous for transcribed SNPs. The technology allows accurate measurement by MALDI-TOF mass spectrometry to quantify allele-specific primer extension products.

The number of significant genetic associations with common complex traits is constantly increasing. However, most of these associations have not been ...

Biology

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the subcellular distribution of a specific tagged fluorescent marker in the secretory pathway. Arabidopsis is a powerful biological model for genetic studies because of its genome size, generation time, and conservation of molecular mechanisms among kingdoms. The array genotyping as an approach to map the mutation in alternative to the traditional method based on molecular markers is advantageous because it is relatively faster and may allow the mapping of several mutants in a really short time frame. This method allows the identification of proteins that can influence the integrity of any organelle in plants. Here, as an example, we propose a screen to map genes important for the integrity of the endoplasmic reticulum (ER). Our approach, however, can be easily extended to other plant cell organelles (for example see1,2), and thus represents an important step toward understanding the molecular basis governing other subcellular structures.

A fundamental quest in cell biology is to define the mechanisms that underlie the identity of the organelles that make eukaryotic cells. Here we propose a method to identify the genes responsible for the morphological and functional integrity of plant organelles using fluorescence microscopy and next-generation sequencing tools.

This protocol describes a fluorescence microscope-based screening of Arabidopsis seedlings and describes how to map recessive mutations that alter the ...

Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus (AAV) Capsid Variants

Gene delivery vectors derived from Adeno-associated virus (AAV) are one of the most promising tools for the treatment of genetic diseases, evidenced by encouraging clinical data and the approval of several AAV gene therapies. Two major reasons for the success of AAV vectors are (i) the prior isolation of various naturally occurring viral serotypes with distinct properties, and (ii) the subsequent establishment of powerful technologies for their molecular engineering and repurposing in high throughput. Further boosting the potential of these techniques are recently implemented strategies for barcoding selected AAV capsids on the DNA and RNA level, permitting their comprehensive and parallel in vivo stratification in all major organs and cell types in a single animal. Here, we present a basic pipeline encompassing this set of complementary avenues, using AAV peptide display to represent the diverse arsenal of available capsid engineering technologies. Accordingly, we first describe the pivotal steps for the generation of an AAV peptide display library for the in vivo selection of candidates with desired properties, followed by a demonstration of how to barcode the most interesting capsid variants for secondary in vivo screening. Next, we exemplify the methodology for the creation of libraries for next-generation sequencing (NGS), including barcode amplification and adaptor ligation, before concluding with an overview of the most critical steps during NGS data analysis. As the protocols reported here are versatile and adaptable, researchers can easily harness them to enrich the optimal AAV capsid variants in their favorite disease model and for gene therapy applications.

Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus AAV Capsid Variants

AAV peptide display library generation and subsequent validation through the barcoding of candidates with novel properties for the creation of next-generation AAVs.

Gene delivery vectors derived from Adeno-associated virus (AAV) are one of the most promising tools for the treatment of genetic diseases, evidenced by ...

Bioengineering

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

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

Summary

Explore More Videos

Chapters in this video

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Isolation of Next-Generation Gene Therapy Vectors through Engineering, Barcoding, and Screening of Adeno-Associated Virus (AAV) Capsid Variants

In Vivo Modeling of the Morbid Human Genome using Danio rerio