Name: candidate gene testing in clinical cohort studies with multiplexed genotyping and mass spectrometry
Uploaded: 2018-06-21T12:30:00.000+00:00
Duration: 5 min 53 s
Description: Watch this Scientific Journal Video about Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry at JoVE.com

The authors have no acknowledgements.

The genetic material used herein was ethically approved for use by the Office for Children HREC (Human Research Ethics Committee) (CDF/07/492), the Department of Human Services HREC (10/07) and the Royal Children&#8217;s Hospital (RCH) HREC (27047).
1. Designing the Multiplexed Assay
<ol>
	<li>Prepare a SNP list for assay design.
	<ol>
		<li>Input the target region into the tagger function of Haploview (https://www.broadinstitute.org/haploview/downloads). Use the LD (correlation, r2) between SNPs spanning the target region to generate a list of SNPs, which provides full coverage of the desired region with the fewest number of required variants. Generate a list of SNPs such that all alleles to be captured are correlated above a user-defined threshold (e.g., r2&#160;&#8805;0.8).</li>
	</ol>
	</li>
	<li>Identification of suitable sequences for forward, reverse, and extension primers
	<ol>
		<li>Enter the generated list of SNPs into the assay design tool of choice. 
		Note: The following instructions apply to the assay design tool used to generate the representative results, as specified in the Table of Materials.</li>
		<li>Once the assay design tool has been launched, select New Genotyping Design. Enter a name for the assay design into the Design Name field.</li>
		<li>Provide the SNP list generated in step 1.1.1 by clicking on the button labeled Edit Text Input with the instructions rs or FASTA to the left of the button. 
		Note: SNPs can be entered as a text list of reference (rs) SNP IDs or uploaded in FASTA format, or the SNP Group file format used by the tool. To upload files in these formats, select the File Upload button. The upper limit of variants that can be designed into a single assay, termed &#8216;well,&#8217;&#160;is 40 SNPs. It is possible to specify SNPs that are a priority for the design before starting the run; these will be designed first. Control SNPs can be specified: for example, an assay to detect gender in case of sample mix ups or a control to determine that sufficient template was added if working with low or poor quality DNA. However, it should be noted that using control and priority SNPs can reduce the number of SNPs that can be designed into a single well.</li>
		<li>From the Preset menu, select the High Multiplexing iPLEX Preset option. From the Organism menu, select the organism from which the DNA to be genotyped derives. For the representative results presented here, this was Human. 
		Note: Mouse and bovine organisms are also compatible with the tool. One can also select Other if working with an alternative organism such as plant or microbe; however, the automated steps for finding proximal SNPs and optimal primer areas will not be available due to the absence of a reference genome.</li>
		<li>At the Database menu, select the latest build of the genome, or an alternative build if required, from the Chemistry menu. Select iPLEX and for the Multiplex Level field, enter the highest level of multiplexing: 40. Now, select Begin Run. &#8220;Step 1&#8221; of the tool will commence. 
		Note: During step 1, the tool retrieves and formats the SNP sequences. Here, errors might include formatting of the FASTA file in which case the FASTA file should be checked and reformatted as necessary. Another error is SNP rs numbers that have been merged with another rs number, in which case the rs number should be searched in a SNP database such as the NCBI&#8217;s dbSNP to identify the new SNP rs number. Sequences around the SNP are searched for any known proximal SNPs that could interfere with the primer design. Potential PCR primer sites are compared against the genome to avoid non-specific amplification due to multiple hits in the genome.</li>
		<li>Await completion of &#8220;Step 2&#8221; of the design tool in which proximal SNPs will be identified. 
		Note: If using an organism that is not supported by this step, annotate the sequences with proximal SNPs in IUPAC annotation if this information is available. There are rarely errors during this step; however, errors may occur if the wrong reference organism or genome was selected, or if cDNA sequences were entered rather than genomic DNA. To optimize step 2, under Advanced Settings, it is possible to filter out SNPs based on a population if working within a specific population. Rare SNPs can also be filtered out by excluding those with a frequency below 1%. Finally, SNPs that do not have a validated status or do not have population information can also be excluded if desired.</li>
		<li>Await completion of &#8220;Step 3&#8221; of the design tool that identifies optimal primer regions. If preferred primer locations have been identified, or if working with a different organism, manually input primer locations by annotating the input sequence (the SNP group files) with the preferred primer regions in uppercase and the rest of the sequence in lower case, select Preserve Case from the Advanced Settings menu for this step. 
		Note: Errors for Steps 2 &#38; 3 may include: (1) A proximal SNP blocking the design of a primer with the solution being to mask with a non-template base and order an oligo with a universal base like Isonine, or, design two primers, one with each of the alleles of the proximal SNP, or, design one primer with the common allele only. (2) Another common error is the failure to identify a unique site for the primer with the solution being to modify the amplicon length in order to identify a unique site. In the tool used for this paper, the default is 80 - 120 bp. This can be altered from 60 - 400 bp in the advanced settings dialogue under the Amplicon Length settings in &#8220;3. Identify Optimal Primer Areas&#8221; and also under the Amplicon tab of &#8220;4. Design Assays.&#8221;&#160;Ensure both are changed. However, it should be noted that increasing the length of the amplicon when using degraded DNA is not recommended.</li>
		<li>Await completion of the assay design step (&#8220;step 4&#8221; of the design tool), here the tool aims to provide optimal pass separation between the extension primers and the alleles of all the SNPs in the multiplexed designs. After this step is complete, export the Oligo Order file, which is formatted for easy ordering through an oligo ordering vendor of choice. 
		Note: Amplification primers (Forward (F) and Reverse (R)) are designed to produce an optimal amplicon size of 80 to 120 bp. The amplification primer mass must differ from that of the extension primer and its product to avoid conflicting results in the mass spectrum and for overall PCR performance. The extension primer should be 15 to 30 nucleotides long (4,500 - 9,000 Da), designed so that the 3&#8217; end will attach directly adjacent to the site of the polymorphism. All of these settings are automatically set in the tool used to generate the representative results.</li>
	</ol>
	</li>
	<li>Export the design from the chosen tool and order primers from an oligomer provider: 25 nmol of each of the PCR primers (forward and reverse), and 100 nmol of each of the extension primers. See Table 1 for the primers used to generate the representative results.</li>
</ol>
2. Genotyping (1 - 2 days)
<ol>
	<li>Prepare amplification primers.
	<ol>
		<li>If lyophilized primers were ordered, reconstitute with molecular grade water. Use a concentration of 100 &#956;M/&#956;L for forward and reverse primers and 500 &#956;M/&#956;L for extension.</li>
		<li>Pool forward &#38; reverse primers for each multiplexed assay into a stock primer mix, containing each primer at a concentration of 0.5 mM. 
		Note: For example, a 400 &#956;L primer pool for 400 reactions: 
		<img alt="Equation" src="/files/ftp_upload/57601/57601eq1v2.jpg" /> 
		2 &#956;L of each primer, at a concentration of 100 &#956;M/&#956;L, is required for the stock primer pool. For a 30-plex assay with 60 forward and reverse primers, a total of 120 &#956;L of primer would be required (the concentrated primer pool). 280 &#956;L molecular-grade water would then be added to make a final volume of 400 &#956;L of primer mix.</li>
	</ol>
	</li>
	<li>Amplification using Polymerase Chain Reaction (PCR)
	<ol>
		<li>Make a master mix for the PCR containing 100 nM of the primer mix detailed above, 500 &#956;M of dNTPs, 2 mM of MgCl2, and 0.5 U of a DNA polymerase enzyme. 1 U is required for greater than 26-plex assays. Refer to Table 2.</li>
		<li>Add 4 &#956;L of this mix to each well of the 384-well PCR plate, along with 1 &#956;L of genomic DNA at a concentration of 5 - 10 ng/&#956;L. 
		Note: Quality controls should also be included on the plate, such as a non-template control, and a positive control that has previously performed well with the assay (during a test run). If running multiple plates, multiple DNA samples from the other plate(s), e.g., in triplicate, should be run as technical controls for inter-plate variability.</li>
		<li>Centrifuge the plate at 200 x g for 1 min at room temperature with a plate cover in place to ensure all liquid is contained at the bottom of the plate wells.</li>
		<li>Run the PCR with the following thermocycler conditions: 4 min incubation at 94 &#176;C to activate the DNA polymerase; 45 cycles of the following (denaturation at 94 &#176;C for 20 s, annealing at 56 &#176;C for 30 s, and extension at 72 &#176;C for 1 min) and then a final extension at 72 &#176;C for 3 min. Refer to PCR program in Table 3.</li>
		<li>Perform agarose gel electrophoresis to ensure the DNA amplification is successful. Use 1 &#181;L of PCR product (with 3 &#181;L of loading buffer) on a 2% (w/v) agarose gel, made by mixing agarose powder and 0.5% Tris Borate EDTA (TBE) buffer, and run for 45 min at 100 V.</li>
	</ol>
	</li>
	<li>Shrimp Alkaline Phosphatase (SAP) Purification Step 
	Note: This step is used to remove unincorporated nucleotides. The SAP enzyme cleaves phosphate groups to prevent excess primer and unincorporated dNTPs from interfering in the upcoming extension reaction.
	<ol>
		<li>Make a master mix containing 1.7 units/&#956;L of Shrimp Alkaline Phosphatase (SAP) enzyme, 10x SAP buffer and make up to volume with molecular-grade water. Refer to Table 4.</li>
		<li>Add 2 &#956;L of the freshly made up SAP master mix to each well of the plate, already containing 5 &#956;L from the PCR reaction. Centrifuge briefly and return to thermocycler at 37 &#176;C for 40 min and then at 85 &#176;C for 5 min for enzyme inactivation. Refer to Table 5.</li>
	</ol>
	</li>
	<li>Primer Extension Reaction 
	Note: The concentration of the extension primers in the extension primer mix must be adjusted by size (Linear Primer Adjustment &#8211; LPA). This is because there is an inverse relationship in peak intensity (on the mass spectra) and product mass. Adjustment of the extension primer concentration corrects for this relationship to produce peaks of equal intensities. Adjusted primers used to generate the representative results are provided in Tables 6 &#38; 7. The volumes provided are to generate 1.5 mL of extension primer mix.
	<ol>
		<li>Calculate dilution factors for each primer using a gradient algorithm, available from Agena Biosciences (&#8216;Linear Primer Adjustment&#8217;), which adjusts for the mass and concentration of the primer, and the total number of primers in the multiplexed reaction (see Tables 6 &#38; 7). Enter the UEP mass for each primer into the UEP_MASS field of the Linear Primer Adjustment sheet. 
		Note: The mass adjusted concentration for the primer will be automatically calculated in cell with the header text &#8220;Target Reaction Concentration (&#181;M).&#8221;&#160;The volume of primer to be added to the primer pool will be output in the cell under the header text &#8220;Volume Stock Primer to be Added to Pool (&#181;L).&#8221;&#160;The volume of water to be added to the primer pool to make up the calculated concentrations is output to the cell next to the text &#8220;Volume of PCR Grade RNase free H2O to be added Primer Pool (&#181;L).&#8221;</li>
		<li>Take the volume of each primer as specified in the Linear Primer Adjustment file and make up a primer pool. Add the volume of water calculated by the algorithm to dilute the primer pool as determined in step 2.4.1. 
		Note: If the gradient algorithm method is unavailable, another method is to divide the primers into low mass and high mass and add double the concentration of the high mass primers relative to the low mass group. Three or four groups may be required for a high-plex assay (that is, more than 19 SNPs). However, this method will not yield as optimal call rates as the gradient algorithm method.</li>
		<li>Assemble the extension master mix. For the low plex assay (1 - 18 plex), add 0.2 &#956;L of buffer, 0.1 &#956;L of termination mix, 0.94 &#956;L of the LPA adjusted primer mix, and 0.0205 &#956;L of enzyme (prepared on ice). For the high plex assay (19 - 36 plex), add double concentration of termination mix and enzyme (termination mix 0.2 &#956;L and iPLEX enzyme 0.041 &#956;L). Refer to Table 8.</li>
		<li>Add 2 &#956;L of the extension reaction master mix to the plate from the previous reaction, now bringing the total volume to 9 &#956;L per well. Centrifuge briefly and place in the thermocycler: 94 &#176;C for a 30 s initial incubation, 40 cycles of 1 x 94 &#176;C for 5 s with 5 x (52 &#176;C for 5 s, followed by 80 &#176;C for 5 s), and then 72 &#176;C for 3 min. Refer to Table 9.</li>
	</ol>
	</li>
	<li>Resin purification. 
	Note: Excess salts can now be removed from the reaction with the use of resin.
	<ol>
		<li>Add 16 &#956;L of deionized water to the wells of the retrieved plate, bringing the volume in the plate up to 25 &#956;L.</li>
		<li>Apply resin, usually 6 mg of dimple resin plate (purchased separately), evenly to the whole plate. Leave the resin to dry in the resin dimple plate for 20 min at room temperature before applying to the reaction plate. After addition of the resin, rotate plate for 5 min at room temperature either by hand or with a suspension mixer at a slow speed (e.g., 7.5 rpm). 
		Note: Prior to loading the genotyping analyte onto the genotyping chip, the samples and the assay layout must be input into the software interface. This can be achieved using the Design Summary file from the assay design tool.</li>
	</ol>
	</li>
	<li>Measurement on a mass spectrometry platform.
	<ol>
		<li>Centrifuge the plate at 3,200 x g for 5 min to avoid application of resin to the mass spectra chip.</li>
		<li>Upload the plate layout of samples to the system&#8217;s software interface prior to the run. 
		Note: Step 2.6.3 should only be performed by trained personnel.</li>
		<li>Apply the genotyping analyte mixture from the plate to the genotyping chip with a dispensing system. Next, load the chip onto the MALDI-TOF system to generate the mass spectra from the analyte mixture, which can then be interpreted with the aid of the platform&#8217;s software interface. 
		Note: The system generates the mass spectra by directing short pulses of UV light at each &#8216;pad,&#8217;&#160;corresponding to a single well of the genotyping plate, of the mass spectra chip. This pulse results in desorption and ionization of the analyte. These ionized DNA molecules are then propelled to the top of the vacuum tube by a high voltage electrostatic field, separating out lighter ions, which travel faster and reach the detector first, from the heavier ions. The &#8220;time of flight&#8221; of these analytes are recorded by the system, from which the mass of each DNA fragment can be determined and thus the nucleotide base present at the SNP can be inferred.</li>
	</ol>
	</li>
</ol>
3. Genotyping Data
Note: Quality control and data interpretation are not the focus of this methodology paper. However, the following will briefly cover interpretation of the mass spectra.
<ol>
	<li>Manual inspection and quality control of mass spectra. 
	Note: The raw mass spectra are generated by the system and analyzed with the software as listed in the Table of Materials. A Gaussian mixture clustering method is applied to this output to make probabilistic genotyping calls.
	<ol>
		<li>Open the analysis software. Select Typer Analyzer. From the File menu, select Open Wells from File and select the xml file with the spectrum data generated in step 2.6.3. The chip name for the run should be visible, select it and a &#8220;traffic light&#8221; pane of the 384-well plate will be displayed with a list of the SNPs in the selected well. From here, select Call Cluster Plots to view a scatterplot of data points for each SNP. 
		Note: It is recommended that a &#8216;no call&#8217; result be applied for low intensity SNPs. In the software used for the representative results, a clustering magnitude cut-off of 5, which is higher than default, is suggested for stringent quality control. This setting can be adjusted in the Magnitude Cutoff field under Parameters in the Post Processing Clusters pane. Select Autocluster from the Tools tab to run a cluster analysis.</li>
		<li>Perform manual inspection of genotype calls by examining the Cartesian Plots in the Post Processing Pane. In the Assay pane, select the SNP of interest and click on the Post-Processing Clusters tab, a cluster plot with genotype clustering will be visible with sample IDs and corresponding genotypes for the SNP.</li>
		<li>Examine the genotype call clusters and assess how well each individual call clusters with the other calls for the SNP. The software provides some boundary lines on the plot for each genotype for guidance; see example of a genotype calls cluster plot from an IL13 SNP (Figure 1). If the call does not cluster tightly with other calls and sits clearly outside of a cluster, or if it has very low intensity, right click the datapoint and select Change Call to manually set it to No Call.</li>
		<li>To ensure high quality genotyping data is used for downstream analysis, exclude SNPs and individuals with call rates below a QC threshold, e.g. 90%. Once appropriate adjustments have been made to the call data, export the data for statistical applications. In the software used to generate the representative results (listed in the Table of Materials) this is achieved with the Plate Data selection from the view menu and selecting Save As.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Aronson, S. J., Rehm, H. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Building+the+foundation+for+genomics+in+precision+medicine.">Building the foundation for genomics in precision medicine.</a> Nature. 526 (7573), 336-342 (2015).</li><li>Ball, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Designing+a+GWAS:+power,+sample+size,+and+data+structure.">Designing a GWAS: power, sample size, and data structure.</a> Genome-Wide Association Studies and Genomic Prediction. , 37-98 (2013).</li><li>Kwon, J. M., Goate, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+candidate+gene+approach.">The candidate gene approach.</a> Alcohol research and health. 24 (3), 164-168 (2000).</li><li>Oeth, P., Mistro, G. D., Marnellos, G., Shi, T., van den Boom, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Qualitative+and+quantitative+genotyping+using+single+base+primer+extension+coupled+with+matrix-assisted+laser+desorption/ionization+time-of-flight+mass+spectrometry+(MassARRAY).">Qualitative and quantitative genotyping using single base primer extension coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MassARRAY).</a> Single Nucleotide Polymorphisms: Methods and Protocols. , 307-343 (2009).</li><li>Pusch, W., Kostrzewa, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+MALDI-TOF+mass+spectrometry+in+screening+and+diagnostic+research.">Application of MALDI-TOF mass spectrometry in screening and diagnostic research.</a> Current pharmaceutical design. 11 (20), 2577-2591 (2005).</li><li>Su, K. 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=Pretreatment+epidermal+growth+factor+receptor+(EGFR)+T790M+mutation+predicts+shorter+EGFR+tyrosine+kinase+inhibitor+response+duration+in+patients+with+non-small-cell+lung+cancer.">Pretreatment epidermal growth factor receptor (EGFR) T790M mutation predicts shorter EGFR tyrosine kinase inhibitor response duration in patients with non-small-cell lung cancer.</a> Journal of clinical oncology. 30 (4), 433-440 (2012).</li><li>Singhal, N., Kumar, M., Kanaujia, P. K., Virdi, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MALDI-TOF+mass+spectrometry:+an+emerging+technology+for+microbial+identification+and+diagnosis.">MALDI-TOF mass spectrometry: an emerging technology for microbial identification and diagnosis.</a> Frontiers in microbiology. 6, 791 (2015).</li><li>Cricca, 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=High-throughput+genotyping+of+high-risk+Human+Papillomavirus+by+MALDI-TOF+Mass+Spectrometry-based+method.">High-throughput genotyping of high-risk Human Papillomavirus by MALDI-TOF Mass Spectrometry-based method.</a> New Microbiologica. 38 (2), 211-223 (2015).</li><li>Ashley, 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+Variation+at+the+Th2+Immune+Gene+IL13+is+Associated+with+IgE-mediated+Paediatric+Food+Allergy.">Genetic Variation at the Th2 Immune Gene IL13 is Associated with IgE-mediated Paediatric Food Allergy.</a> Clinical &amp; Experimental Allergy. 47 (8), 1032-1037 (2017).</li><li>Bousman, C. 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=Effects+of+NRG1+and+DAOA+genetic+variation+on+transition+to+psychosis+in+individuals+at+ultra-high+risk+for+psychosis.">Effects of NRG1 and DAOA genetic variation on transition to psychosis in individuals at ultra-high risk for psychosis.</a> Translational psychiatry. 3 (4), e251 (2013).</li><li>Moffatt, M. F., 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+large-scale,+consortium-based+genomewide+association+study+of+asthma.">A large-scale, consortium-based genomewide association study of asthma.</a> New England Journal of Medicine. 363 (13), 1211-1221 (2010).</li><li>Granada, 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=A+genome-wide+association+study+of+plasma+total+IgE+concentrations+in+the+Framingham+Heart+Study.">A genome-wide association study of plasma total IgE concentrations in the Framingham Heart Study.</a> Journal of Allergy and Clinical Immunology. 129 (3), 840-845 (2012).</li><li>Oeth, 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=iPLEX+assay:+Increased+plexing+efficiency+and+flexibility+for+MassARRAY+system+through+single+base+primer+extension+with+mass-modified+terminators.">iPLEX assay: Increased plexing efficiency and flexibility for MassARRAY system through single base primer extension with mass-modified terminators.</a> Sequenom application note. 27, (2005).</li><li>Ellis, J. A., Ong, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+MassARRAY+System+for+Targeted+SNP+Genotyping.">The MassARRAY System for Targeted SNP Genotyping.</a> Genotyping: Methods and Protocols. , 77-94 (2017).</li><li>Johnson, A. 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=SNAP:+A+web-based+tool+for+identification+and+annotation+of+proxy+SNPs+using+HapMap.">SNAP: A web-based tool for identification and annotation of proxy SNPs using HapMap.</a> Bioinformatics. 24 (24), 2938-2939 (2008).</li></ol>

The authors have nothing to disclose.

Here, we demonstrate the method of multiplexed genotyping using mass spectrometry. The representative results were generated using PCR paired with MALDI-TOF mass spectrometry4 with assay chemistry listed in the Table of Materials13. With this platform, we generated a total of 11,295 genotypes on 1,255 individuals for 9 SNPs within 40 h in the lab.
We illustrate the usefulness of the technique in answering genetic hypotheses by generating and analyzing SNP data for the candidate gene IL13 in a discovery clinical cohort phenotyped for food allergy and with independent replication. The platform is relatively robust to lower DNA quality and requires minimal starting material, an input of only 10 ng. Critical steps in the protocol include the following: careful troubleshooting of the assay design process to ensure maximal SNP inclusion in the multiplexed assays, successful amplification of the target regions during the amplification PCR, successful single nucleotide extension during the extension reaction, and loading of the assay and sample plate layout into the analysis software in order for the software to accurately assign the mass spectra data.
As previously mentioned, the assay design tool is compatible with the use of mouse and bovine polymorphisms, in addition to human. For other organisms such as microbe or plant, &#34;other&#34; can be selected in the &#34;Organism&#34; menu. However, the automated steps for finding proximal SNPs and identifying optimal primer areas will not be available.
During the design process, if a proximal SNP is blocking the identification of an optimal primer area, one can either remove the SNP from the design and select another in high LD (e.g., r2&#160;= 1), or choose to design two primers, one with the reference allele of the SNP and one with the alternative allele, or designing one primer with the common allele, or masking the SNP with a universal base (e.g., Inosine). If there is an issue with identifying a unique site for the primer, one can change the amplicon length settings to find a unique site. The default setting is 80 - 120 bp but this can be modified from 60 - 400 bp. However, it should be noted that if working with degraded DNA (e.g., from FFPE tissue), it is not ideal to increase the amplicon length.
Errors during the assay design step of the tool might include high hairpin or primer dimer potential. Both settings can be relaxed to try an accommodate the designs. However, it is recommended that thresholds are not relaxed below 0.8; start with 0.9 to see if this setting is sufficient to rescue the SNPs and reduce to 0.8 only if necessary. In the advanced settings, it is possible to change the number of design iterations (up to 10) under the Multiplex tab of &#34;4. Design Assays&#34;, as well as the best iteration selection criteria to either Highest Average Multiplex or Fewest Rejects by Low Plex. Increasing the number of design iterations can be advantageous because the tool will take the first SNP in the order they were provided and will design an assay. Then it tests if the next SNP is compatible for the same multiplex assay. Thus, the order of the SNPs matter. With the multiple iterations option selected, the software will randomize the order of SNPs and try other iterations. This may increase the number of SNPs able to be designed into each multiplex or may decrease the number of SNP rejects. However, it will also come at a time cost, as the design tool will take much longer at this step.
Multiplexed genotyping is a quick and affordable approach for investigation of the loci of interest. The method is streamlined and allows the running of approximately 760 samples of up to 40-plex SNPs in 10 hours, permitting tens of thousands of genotypes to be generated in less than one day14. The mass spectra can be easily analyzed with tools provided by the platform.
Some restrictions to the platform include an estimated loss of 5 - 10% of SNPs that will fail the assay design process. This can sometimes be corrected by selection of an alternative tag or proxy SNP. The Broad Institute&#39;s SNP Annotation and Proxy Search (SNAP) database is one such tool that facilitates identification of proxy SNPs15. Another limitation of the system is that it is a targeted platform and therefore does not allow for extensive novel discovery, as achieved with genome-wide approaches. Furthermore, as the approach uses tag-SNP proxies to maximize coverage across genes, fine-mapping studies may subsequently be needed to determine the precise causal variant.
Multiplexed genotyping is still a useful platform for the interrogation of disease associated SNPs identified in GWA study approaches or hypothesis driven candidate and pathway gene exploration. Future applications for the platform are likely to be novel uses for diagnostics and screening in fields such as cancer and clinical virology. Overall multiplexed genotyping with mass spectrometry is a quick, cost-effective and reliable medium-throughput method for genotyping candidate loci.

In human complex disease, genetic variants contribute to disease susceptibility and quantifying these variants may be useful for understanding pathogenesis, identifying high risk patient groups, and treatment responders. Indeed, the promise of precision medicine is dependent upon utilizing genomic information to identify patient subgroups1. Unfortunately, within the complex disease biology space, where disease phenotypes are underpinned by substantial genetic heterogeneity, low penetrance, and variable expressivity, cohort size requirements for genome-wide approaches to identify novel candidates are often prohibitively large2. Alternatively, a targeted candidate gene approach begins with an a priori hypothesis about specific genes/pathways in disease etiology3. Pathway analysis tools are commonly used to investigate the pathophysiology of an identified target loci, generating numerous candidate pathways to be explored. We demonstrate here a multiplexed genotyping approach allowing for the investigation of tens to hundreds of SNPs with one assay, suited to human cohort studies4. This approach is relatively high through-put, permitting hundreds to thousands of DNA samples to be genotyped for novel discovery studies and investigation of specific pathways. The methods outlined here are useful for identifying risk alleles and their associations with clinical traits in a relatively rapid and inexpensive manner. This platform has been highly advantageous for screening and diagnostic purposes5,6, and more recently, for microbial infection7 and human papillomavirus8.
This protocol begins with selection of a set of genes for investigation, i.e., the target regions, typically determined through literature searching, or a priori hypotheses for involvement in the disease process; or perhaps selected for replication as the leading associations of a discovery genome-wide association (GWA) study. From the gene set, the researcher will select a refined list of tag SNPs. That is, the linkage disequilibrium (LD), or correlation, amongst variants in the region is used to identify a representative &#39;tag SNP&#39; for a group of SNPs in high LD, known as a haplotype. The high LD of the region means that the SNPs are often inherited together such that genotyping one SNP is sufficient to represent the variation at all SNPs in the haplotype. Alternatively, if following up on a definitive list of SNPs from many regions, e.g., replication for a GWA study, this process may be unnecessary. For multiplexed genotyping, an assay must then be designed around these targets such that the amplification primers are of differing mass to those of the extension primers and products to produce interpretable mass spectra. These parameters are easily implemented by a multiplexed genotyping assay design tool. The forward and reverse primers from this design will be used to target the markers of interest and amplify the sequence containing the SNP. The extension primers attach directly proximal to the SNPs and a single, mass-modified, &#39;terminator&#39; base that is complementary to the SNP is added. The terminator base prevents further extension of the DNA. The mass-modification of the base enables fragments differing by a single base to be detected by mass spectrometry. The plate containing the genotyping chemistry is then applied to a chip for measurement on a mass spectrometry platform. After applying appropriate quality controls to the raw genotyping calls detected by the system, the data can be exported and used for statistical analysis to test for association with disease phenotypes.

<table><tbody><tr><td>Genomic DNA&amp;nbsp;</td><td>-</td><td>-</td><td>1 &amp;mu;L at a concentration of 5-10 ng/&amp;mu;L</td></tr><tr><td>Primers: forward and reverse amplification and extension</td><td>IDT</td><td>-</td><td>see manuscript section 1.2.1 on design of primers</td></tr><tr><td>Deionized water&amp;nbsp;</td><td>E.g. Milli-Q water&amp;nbsp;</td><td>-</td><td>deionized with 18.2 M&amp;Omega;.cm resistivity</td></tr><tr><td>Genotyping reagent kit. iPLEX Gold Chemistry reagent set&amp;nbsp;</td><td>Agena Bioscience</td><td>#10148-2</td><td>includes all reagents for reactions in 2.2.1, 2.3.1 and 2.4.2 , chip and resin</td></tr><tr><td>PCR plates (384-well)</td><td>Abgene</td><td>#ABGAB-1384</td><td>For the MassARRAY system plates by Abgene are compatible</td></tr><tr><td>Micropipettes</td><td></td><td></td><td>single and 8-channel</td></tr><tr><td>Centrifuge&amp;nbsp;</td><td></td><td></td><td>compatible with 384-well plates</td></tr><tr><td>Thermocycler</td><td></td><td></td><td>compatible with PCR programs as detailed in 2.2.4, 2.3.2 and 2.4.3</td></tr><tr><td>Dimple resin plate&amp;nbsp;</td><td>Agena Bioscience</td><td></td><td>6mg, 384-well</td></tr><tr><td>Plate rotator&amp;nbsp;</td><td></td><td></td><td></td></tr><tr><td>MassARRAY Analyzer 4 System</td><td>Agena Biosciences</td><td></td><td>MALDI-TOF (matrix-assisted laser desorption/ionization &amp;ndash; time of flight) Mass Spectrometer.</td></tr><tr><td>RS1000 Nanodispenser</td><td>Agena Biosciences</td><td></td><td></td></tr><tr><td>Assay Design Suite</td><td>Agena Biosciences</td><td></td><td>Tool used to design the multiplex genotyping assays</td></tr><tr><td>Hot Start Taq</td><td></td><td></td><td>DNA polymerase enzyme&amp;nbsp;</td></tr><tr><td>Resin&amp;nbsp;</td><td>Agena Biosciences</td><td></td><td>Supplied with iPLEX kit</td></tr></tbody></table>

candidate gene testing in clinical cohort studies with multiplexed genotyping and mass spectrometry

Complex diseases are often underpinned by multiple common genetic variants that contribute to disease susceptibility. Here, we describe a cost-effective tag single nucleotide polymorphism (SNP) approach using a multiplexed genotyping assay with mass spectrometry, to investigate gene pathway associations in clinical cohorts. We investigate the food allergy candidate locus Interleukin13 (IL13) as an example. This method efficiently maximizes the coverage by taking advantage of shared linkage disequilibrium (LD) within a region. Selected LD SNPs are then designed into a multiplexed assay, enabling up to 40 different SNPs to be analyzed simultaneously, boosting cost-effectiveness. Polymerase chain reaction (PCR) is used to amplify the target loci, followed by single nucleotide extension, and the amplicons are then measured using matrix-assisted laser desorption/ionization-time of flight(MALDI-TOF) mass spectrometry. The raw output is analyzed with the genotype calling software, using stringent quality control definitions and cut-offs, and high probability genotypes are determined and output for data analysis.

Watch this Scientific Journal Video about Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry at JoVE.com

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Identification of genetic variants contributing to complex human disease allows us to identify novel mechanisms. Here, we demonstrate a multiplex genotyping approach to candidate genes or gene pathway analysis that maximizes the coverage at low cost and is amenable to cohort-based studies.

Complex diseases are often underpinned by multiple common genetic variants that contribute to disease susceptibility. Here, we describe a cost-effective ...

candidate-gene-testing-clinical-cohort-studies-with-multiplexed

Nanyang Technological University

Research

JoVE Journal

Genetics

10.1K Views.  Max-Delbrück Center for Molecular Medicine. Identificatie van genetische varianten bij te dragen tot complexe ziekten bij de mens laat ons toe om nieuwe mechanismen te identificeren. Hier, aantonen wij een multiplex genotypering aanpak aan kandidaat-genen of gene traject analyse die maximaliseert de dekking tegen lage kosten en is vatbaar voor cohort gebaseerde studies.

Video: Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, the de novo mutation rate in humans is relatively high, so new mutations are generated at a high frequency in the population. However, de novo mutations have not been reported in most common diseases. Mutations in genes leading to severe diseases where there is a strong negative selection against the phenotype, such as lethality in embryonic stages or reduced reproductive fitness, will not be transmitted to multiple family members, and therefore will not be detected by linkage gene mapping or association studies. The observation of very high concordance in monozygotic twins and very low concordance in dizygotic twins also strongly supports the hypothesis that a significant fraction of cases may result from new mutations. Such is the case for diseases such as autism and schizophrenia. Second, despite reduced reproductive fitness1 and extremely variable environmental factors, the incidence of some diseases is maintained worldwide at a relatively high and constant rate. This is the case for autism and schizophrenia, with an incidence of approximately 1% worldwide. Mutational load can be thought of as a balance between selection for or against a deleterious mutation and its production by de novo mutation. Lower rates of reproduction constitute a negative selection factor that should reduce the number of mutant alleles in the population, ultimately leading to decreased disease prevalence. These selective pressures tend to be of different intensity in different environments. Nonetheless, these severe mental disorders have been maintained at a constant relatively high prevalence in the worldwide population across a wide range of cultures and countries despite a strong negative selection against them2. This is not what one would predict in diseases with reduced reproductive fitness, unless there was a high new mutation rate. Finally, the effects of paternal age: there is a significantly increased risk of the disease with increasing paternal age, which could result from the age related increase in paternal de novo mutations. This is the case for autism and schizophrenia3. The male-to-female ratio of mutation rate is estimated at about 4&#x2013;6:1, presumably due to a higher number of germ-cell divisions with age in males. Therefore, one would predict that de novo mutations would more frequently come from males, particularly older males4. A high rate of new mutations may in part explain why genetic studies have so far failed to identify many genes predisposing to complexes diseases genes, such as autism and schizophrenia, and why diseases have been identified for a mere 3% of genes in the human genome. Identification for de novo mutations as a cause of a disease requires a targeted molecular approach, which includes studying parents and affected subjects. The process for determining if the genetic basis of a disease may result in part from de novo mutations and the molecular approach to establish this link will be illustrated, using autism and schizophrenia as examples.

Molecular genetic strategy for finding de novo mutations causing common disorders such as autism and schizophrenia.

There are several lines of evidence supporting the role of de novo mutations as a mechanism for common disorders, such as autism and schizophrenia. First, ...

Medicine

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.

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. ...

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular sub-classification and temporal monitoring of tumor response to therapy. Here, we describe our dPCR-based method for the detection of tumor somatic mutations in cell free DNA (cfDNA), readily available in&#160;patient biofluids. Although limited in the number of mutations that can be tested for in each assay, this method provides a high level of sensitivity and specificity. Monitoring of mutation abundance, as calculated by MAF, allows for the evaluation of tumor response to therapy, thereby providing a much-needed supplement to radiographic imaging.

Here, we present a protocol to detect tumor somatic mutations in circulating DNA present in patient biological fluids (biofluids). Our droplet digital polymerase chain reaction (dPCR)-based method enables quantification of the tumor mutation allelic frequency (MAF), facilitating a minimally invasive complement to diagnosis and temporal monitoring of tumor response.

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular ...

Cancer Research

Pre-Implantation Genetic Testing for Aneuploidy on a Semiconductor Based Next-Generation Sequencing Platform

Next-generation sequencing has gained increasing importance in the clinical application in the determination of genetic variants. In the pre-implantation genetic test, this technique has its unique advantages in scalability, throughput, and cost. For the pre-implantation genetic test for aneuploidy analysis, the semiconductor-based next-generation sequencing (NGS) system presented here provides a comprehensive approach to determine structural genetic variants at a minimum resolution of 8 Mb. From sample acquisition to the final report, the working process requires multiple steps with close adherence to protocols. Since various critical steps could determine the outcome of amplification, quality of the library, coverage of reads, and output of data, descriptive information with visual demonstration other than words could offer more detail to the operation and manipulation, which may have a great impact on the results of all critical steps. The methods presented herein will display the procedures involved in whole genome amplification (WGA) of biopsied Trophectoderm (TE) cells, genomic library construction, sequencer management, and finally, generating copy number variants' reports.

The protocol presents the overall in-lab procedures required in pre-implantation genetic testing for aneuploidy on a semiconductor-based next-generation sequencing platform. Here we present the detailed steps of whole genome amplification, DNA fragment selection, library construction, template preparation, and sequencing working flow with representative results.

Next-generation sequencing has gained increasing importance in the clinical application in the determination of genetic variants. In the pre-implantation ...

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 ...

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

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using bioinformatics pipelines (&#34;dry bench&#34;). Both elements are essential to produce accurate and reliable results, which are particularly critical for clinical laboratories. Targeted NGS technologies have increasingly found favor in oncology applications to help advance precision medicine objectives, yet the methods often involve disconnected and variable wet and dry bench workflows and uncoordinated reagent sets. In this report, we describe a method for sequencing challenging cancer specimens with a 21-gene panel as an example of a comprehensive targeted NGS system. The system integrates functional DNA quantification and qualification, single-tube multiplexed PCR enrichment, and library purification and normalization using analytically-verified, single-source reagents with a standalone bioinformatics suite. As a result, accurate variant calls from low-quality and low-quantity formalin-fixed, paraffin-embedded (FFPE) and fine-needle aspiration (FNA) tumor biopsies can be achieved. The method can routinely assess cancer-associated variants from an input of 400 amplifiable DNA copies, and is modular in design to accommodate new gene content. Two different types of analytically-defined controls provide quality assurance and help safeguard call accuracy with clinically-relevant samples. A flexible &#34;tag&#34; PCR step embeds platform-specific adaptors and index codes to allow sample barcoding and compatibility with common benchtop NGS instruments. Importantly, the protocol is streamlined and can produce 24 sequence-ready libraries in a single day. Finally, the approach links wet and dry bench processes by incorporating pre-analytical sample quality control results directly into the variant calling algorithms to improve mutation detection accuracy and differentiate false-negative and indeterminate calls. This targeted NGS method uses advances in both wetware and software to achieve high-depth, multiplexed sequencing and sensitive analysis of heterogeneous cancer samples for diagnostic applications.

An integrated system for targeted next-generation sequencing of oncology specimens is described. This cross-platform system is optimized for low-quality and low-quantity tumor biopsies, accommodates low DNA inputs, includes well-characterized multi-variant controls, and features a novel variant caller that is informed by quantitative pre-analytical quality control measures.

All next-generation sequencing (NGS) procedures include assays performed at the laboratory bench (&#34;wet bench&#34;) and data analyses conducted using ...

Infinium Assay for Large-scale SNP Genotyping Applications

Genotyping variants in the human genome has proven to be an efficient method to identify genetic associations with phenotypes. The distribution of variants within families or populations can facilitate identification of the genetic factors of disease. Illumina&#39;s panel of genotyping BeadChips allows investigators to genotype thousands or millions of single nucleotide polymorphisms (SNPs) or to analyze other genomic variants, such as copy number, across a large number of DNA samples. These SNPs can be spread throughout the genome or targeted in specific regions in order to maximize potential discovery. The Infinium assay has been optimized to yield high-quality, accurate results quickly. With proper setup, a single technician can process from a few hundred to over a thousand DNA samples per week, depending on the type of array. This assay guides users through every step, starting with genomic DNA and ending with the scanning of the array. Using propriety reagents, samples are amplified, fragmented, precipitated, resuspended, hybridized to the chip, extended by a single base, stained, and scanned on either an iScan or Hi Scan high-resolution optical imaging system. One overnight step is required to amplify the DNA. The DNA is denatured and isothermally amplified by whole-genome amplification; therefore, no PCR is required. Samples are hybridized to the arrays during a second overnight step. By the third day, the samples are ready to be scanned and analyzed. Amplified DNA may be stockpiled in large quantities, allowing bead arrays to be processed every day of the week, thereby maximizing throughput.

A protocol is described that uses Illumina&#39;s Infinium assays to perform large-scale genotyping. These assays can reliably genotype millions of SNPs across hundreds of individual DNA samples in three days. Once generated, these genotypes can be used to check for associations with a variety of different diseases or phenotypes.

Genotyping variants in the human genome has proven to be an efficient method to identify genetic associations with phenotypes. The distribution of ...

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Efforts to detect and investigate key oncogenic mutations have proven valuable to facilitate the appropriate treatment for cancer patients. The establishment of high-throughput, massively parallel "next-generation" sequencing has aided the discovery of many such mutations. To enhance the clinical and translational utility of this technology, platforms must be high-throughput, cost-effective, and compatible with formalin-fixed paraffin embedded (FFPE) tissue samples that may yield small amounts of degraded or damaged DNA. Here, we describe the preparation of barcoded and multiplexed DNA libraries followed by hybridization-based capture of targeted exons for the detection of cancer-associated mutations in fresh frozen and FFPE tumors by massively parallel sequencing. This method enables the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus conferring high sensitivity for detecting low frequency mutations.

We describe the preparation of barcoded DNA libraries and subsequent hybridization-based exon capture for detection of key cancer-associated mutations in clinical tumor specimens by massively parallel "next generation" sequencing. Targeted exon sequencing offers the benefits of high throughput, low cost, and deep sequence coverage, thus yielding high sensitivity for detecting low frequency mutations.

Efforts  to detect and investigate key oncogenic mutations have proven valuable to  facilitate the appropriate treatment for cancer patients. The ...

A Multi-detection Assay for Malaria Transmitting Mosquitoes

The Anopheles gambiae species complex includes the major malaria transmitting mosquitoes in Africa. Because these species are of such medical importance, several traits are typically characterized using molecular assays to aid in epidemiological studies. These traits include species identification, insecticide resistance, parasite infection status, and host preference. Since populations of the Anopheles gambiae complex are morphologically indistinguishable, a polymerase chain reaction (PCR) is traditionally used to identify species. Once the species is known, several downstream assays are routinely performed to elucidate further characteristics. For instance, mutations known as KDR in a para gene confer resistance against DDT and pyrethroid insecticides. Additionally, enzyme-linked immunosorbent assays (ELISAs) or Plasmodium parasite DNA detection PCR assays are used to detect parasites present in mosquito tissues. Lastly, a combination of PCR and restriction enzyme digests can be used to elucidate host preference (e.g., human vs. animal blood) by screening the mosquito bloodmeal for host-specific DNA. We have developed a multi-detection assay (MDA) that combines all of the aforementioned assays into a single multiplex reaction genotyping 33SNPs for 96 or 384 samples at a time. Because the MDA includes multiple markers for species, Plasmodium detection, and host blood identification, the likelihood of generating false positives or negatives is greatly reduced from previous assays that include only one marker per trait. This robust and simple assay can detect these key mosquito traits cost-effectively and in a fraction of the time of existing assays.

Malaria transmitting mosquitoes have a number of epidemiologically important characteristics that can only be detected using molecular techniques. Utilizing a MALDI-TOF based SNP genotyping platform, we developed an assay for simultaneously detecting multiple key traits (species, insecticide resistance, parasite infection and host choice) of malaria vectors.

The Anopheles gambiae species complex includes the major malaria transmitting mosquitoes in Africa. Because these species are of such medical importance, ...

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Samenvatting

Meer video's verkennen

Hoofdstukken in deze video