This work was supported by the Children's Discovery Institute grant MC-II-2006-1 (RDM and TED), the NIH Epigenetics Roadmap grant [1R01DA025744-01 and 3R01DA025744-02S1] (RDM and FLMV), U01AG023746 (SC), the Saigh Foundation (FLMV and TED), 1K08CA140720-01A1 and Alex's Lemonade Stand "A" Award support (TED). We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis. The Center is partially supported by NCI Cancer Center Support Grant #P30 CA91842 to the Siteman Cancer Center and by ICTS/CTSA Grant# UL1RR024992 from the NationalCenter for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. This publication is solely the responsibility of the authors and does not necessarily represent the official view of NCRR or NIH.

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	<li>Vallania, F. L. M., Druley, T. E., Ramos, E., Wang, J., Borecki, I., Province, M., Mitra, 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=High-throughput+discovery+of+rare+insertions+and+deletions+in+large+cohorts.">High-throughput discovery of rare insertions and deletions in large cohorts.</a> Genome Research. 20, 1391-1397 (2010).</li><li>Shendure, J., Mitra, R., Varma, C., Church, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+Sequencing+Technologies:+Methods+and+Goals.">Advanced Sequencing Technologies: Methods and Goals.</a> Nature Reviews of Genetics. 5, 335-344 (2004).</li><li>The 1000 Genomes Project Consortium. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+map+of+human+genome+variation+from+population-scale+sequencing.">A map of human genome variation from population-scale sequencing.</a> Nature. 467, 1061-1073 (2010).</li><li>Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+the+missing+heritability+of+complex+diseases.">Finding the missing heritability of complex diseases.</a> Nature. 461, 747-753 (2009).</li><li>Reich, D. E., Lander, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=On+the+allelic+spectrum+of+human+disease.">On the allelic spectrum of human disease.</a> Trends Genet. 17, 502-510 (2001).</li><li>Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., Hobbs, H. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+rare+alleles+contribute+to+low+plasma+levels+of+HDL+cholesterol.">Multiple rare alleles contribute to low plasma levels of HDL cholesterol.</a> Science. 305, 869-872 (2004).</li><li>Ji, W., Foo, J. N., O'Roak, B. J., Zhao, H., Larson, M. G., Simon, D. B., Newton-Cheh, C., State, M. W., Levy, D., Lifton, R. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rare+independent+mutations+in+renal+salt+handling+genes+contribute+to+blood+pressure+variation.">Rare independent mutations in renal salt handling genes contribute to blood pressure variation.</a> Nat. Genet. 40, 592-599 (2008).</li><li>Ahituv, N., Kavaslar, N., Schackwitz, W., Ustaszewska, A., Martin, J., Hebert, S., Doelle, H., Ersoy, B., Kryukov, G., Schmidt, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medical+sequencing+at+the+extremes+of+human+body+mass.">Medical sequencing at the extremes of human body mass.</a> Am. J. Hum. Genet. 80, 779-791 (2007).</li><li>Druley, T. E., Vallania, F. L., Wegner, D. J., Varley, K. E., Knowles, O. L., Bonds, J. A., Robison, S. W., Doniger, S. W., Hamvas, A., Cole, F. S., Fay, J. C., Mitra, 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=Quantification+of+rare+allelic+variants+from+pooled+genomic+DNA.">Quantification of rare allelic variants from pooled genomic DNA.</a> Nat. Methods. 6, 263-265 (2009).</li><li>Mitra, R. D., Butty, V., Shendure, J., Housman, D., Church, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+Genotyping+and+Haplotyping+with+Polymerase+Colonies.">Digital Genotyping and Haplotyping with Polymerase Colonies.</a> Proc. Natl. Acad. Sci. 100, 5926-5931 (2003).</li></ol>

No conflicts of interest declared.

There is increasing evidence that the incidence and therapeutic response of common, complex phenotypes and diseases such as obesity8, hypercholesterolemia4, hypertension7 and others may be moderated by personal profiles of rare variation. Identifying the genes and pathways where these variants aggregate in affected populations will have profound diagnostic and therapeutic implications, but analyzing affected individuals separately can be time and cost prohibitive. Population-based analysis offers a more efficient method for surveying genetic variation at multiple loci.
We present a novel pooled-DNA sequencing protocol paired with the SPLINTER software package designed to identify this type of genetic variation across populations. We demonstrate the accuracy of this method in identifying and quantifying minor alleles within a large pooled population of 947 individuals, including rare variants that were called de novo from the pooled sequencing and validated by individual pyrosequencing. Our strategy mainly differs from other protocols by the incorporation of a positive and a negative control within every experiment. This allows SPLINTER to achieve much higher accuracy and power compared to other approaches1. The optimal coverage of 25-fold per allele is fixed independently of the size of the pool, making the analysis of large pools feasible as this requirement only scales linearly with the pool size. Our approach is very flexible and can be applied to any phenotype of interest but also to samples that are naturally heterogeneous, such as mixed cell populations and tumor biopsies. Given the ever-increasing interest in pooled sequencing from large target regions such as the exome or genome, our library prep and SPLINTER analysis is compatible with custom-capture and whole-exome sequencing, but the alignment utility in the SPLINTER package was not designed for large references sequences. Therefore, we have successfully utilized the dynamic programming aligner, Novoalign, for genome-wide alignments followed by variant calling from the pooled sample (Ramos et al., submitted). Thus, our pooled sequencing strategy can scale successfully to larger pools with increasing amounts of target sequence.

<table border="1">
<tbody>
 <tr>
 <td>Reagent Name</td>
 <td>Company</td>
 <td>Catalogue Number</td>
 <td>Section</td>
 </tr>
 <tr>
 <td>PfuUltra High-Fidelity</td>
 <td>Agilent</td>
 <td>600384</td>
 <td>1.4</td>
 </tr>
 <tr>
 <td>Betaine</td>
 <td>SIGMA</td>
 <td>B2629</td>
 <td>1.4</td>
 </tr>
 <tr>
 <td>M13mp18 ssDNA vector</td>
 <td>NEB</td>
 <td>N4040S</td>
 <td>1.5</td>
 </tr>
 <tr>
 <td>pGEM-T Easy</td>
 <td>Promega</td>
 <td>A1360</td>
 <td>1.5</td>
 </tr>
 <tr>
 <td>T4 Polynucleotide Kinase</td>
 <td>NEB</td>
 <td>M0201S</td>
 <td>2.2</td>
 </tr>
 <tr>
 <td>T4 Ligase</td>
 <td>NEB</td>
 <td>M0202S</td>
 <td>2.2</td>
 </tr>
 <tr>
 <td>Polyethylene Glycol 8000 MW</td>
 <td>SIGMA</td>
 <td>P5413</td>
 <td>2.2</td>
 </tr>
 <tr>
 <td>Bioruptor sonicator</td>
 <td>Diagenode</td>
 <td>UCD-200-TS</td>
 <td>2.3</td>
 </tr>
 </tbody>
</table>

detection of rare genomic variants from pooled sequencing using splinter

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought3. In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease4,5. Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants6-8. However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today's platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators.
To address this need, we have developed a pooled sequencing approach1,9 and a novel software package1 for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (<a href="http://www.ibridgenetwork.org/wustl/splinter" target="_blank">http://www.ibridgenetwork.org/wustl/splinter</a>). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.

Watch this Scientific Journal Video about Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER at JoVE.com

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any ...

detection-rare-genomic-variants-from-pooled-sequencing-using

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JoVE Journal

Biology

15.0K Views.  Washington University School of Medicine. Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.