Identification of copy number variants from exome sequence data

BMC Genomics

Robert Lyle¹  Olaug Kristin Rødningen⁴  Asbjørg Stray-Pedersen²  Barbro Stadheim⁴  Geir E Tjønnfjord³  Ying Sheng⁴  Tove Skodje⁴  Bjørn Evert Kristiansen⁴  Hanne Sørmo Sorte⁴  Pubudu Saneth Samarakoon⁴ 

[1] Department of Medical Genetics, University of Oslo, Oslo, Norway;Baylor-Hopkins Center for Mendelian Genomics of the Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA;Institute of Clinical Medicine, University of Oslo, Oslo, Norway;Department of Medical Genetics, Oslo University Hospital, Postboks 4956, Oslo 0424, Norway

关键词: Custom aCGH;    CNV prediction;    Exome;   

Others  :  1216289
DOI  :  10.1186/1471-2164-15-661

 received in 2014-02-17, accepted in 2014-07-01,  发布年份 2014

【 摘 要 】

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions

In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

【 授权许可】

   
2014 Samarakoon et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

【 参考文献 】

• [1]Bamshad MJ, Ng SB, Bigham AW, Tabor HK, Emond MJ, Nickerson DA, Shendure J: Exome sequencing as a tool for mendelian disease gene discovery. Nat Rev Genet 2011, 12:745-755.
• [2]DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 2011, 43:491-498.
• [3]Handsaker RE, Korn JM, Nemesh J, McCarroll SA: Discovery and genotyping of genome structural polymorphism by sequencing on a population scale. Nat Genet 2011, 43:269-276.
• [4]De Ligt J, Boone PM, Pfundt R, Vissers LELM, Richmond T, Geoghegan J, O’Moore K, de Leeuw N, Shaw C, Brunner HG, Lupski JR, Veltman JA, Hehir-Kwa JY: Detection of clinically relevant copy number variants with whole-exome sequencing. Hum Mutat 2013, 34:1439-1448.
• [5]Krumm N, Sudmant PH, Ko A, O’Roak BJ, Malig M, Coe BP, Quinlan AR, Nickerson DA, Eichler EE, Project NNES: Copy number variation detection and genotyping from exome sequence data. Genome Res 2012, 22:1525-1532.
• [6]Clarke L, Zheng-Bradley X, Smith R, Kulesha E, Xiao C, Toneva I, Vaughan B, Preuss D, Leinonen R, Shumway M, Sherry S, Flicek P: The 1000 genomes project: data management and community access. Nat Methods 2012, 9:459-462.
• [7]1000 genomes exome http://www.1000genomes.org/category/exome webcite
• [8]Coriell Institute for Medical Research http://www.coriell.org/ webcite
• [9]Novocraft: Novocraft. http://novocraft.com/main/index.php webcite
• [10]Picard http://picard.sourceforge.net/ webcite
• [11]Sathirapongsasuti JF, Lee H, Horst BA, Brunner G, Cochran AJ, Binder S, Quackenbush J, Nelson SF: Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics 2011, 27:2648-2654.
• [12]Li J, Lupat R, Amarasinghe KC, Thompson ER, Doyle MA, Ryland GL, Tothill RW, Halgamuge SK, Campbell IG, Gorringe KL: CONTRA: copy number analysis for targeted resequencing. Bioinformatics 2012, 28:1307-1313.
• [13]Love MI, Myšičková A, Sun R, Kalscheuer V, Vingron M, Haas SA, Planck M: Modeling read counts for CNV detection in exome sequencing data. Stat Appl Genet Mol Biol 2011., 10doi:10.2202/1544-6115.1732
• [14]Plagnol V, Curtis J, Epstein M, Mok K, Stebbings E, Grigoriadou S, Wood NW, Hambleton S, Burns SO, Thrasher A, Kumararatne D, Doffinger R, Nejentsev S: A robust model for read count data in exome sequencing experiments and implications for copy number variant calling. Bioinformatics 2012, 28:2747-2754.
• [15]Fromer M, Moran JL, Chambert K, Banks E, Bergen SE, Ruderfer DM, Handsaker RE, McCarroll SA, O’Donovan MC, Owen MJ, Kirov G, Sullivan PF, Hultman CM, Sklar P, Purcell SM: Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet 2012, 91:597-607.
• [16]Agilent eArray http://www.genomics.agilent.com/en/Custom-Design-Tools/eArray/?cid=AG-PT-122&tabId=AG-PR-1047 webcite
• [17]Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F, Aken BL, Barrell D, Zadissa A, Searle S, Barnes I, Bignell A, Boychenko V, Hunt T, Kay M, Mukherjee G, Rajan J, Despacio-Reyes G, Saunders G, Steward C, Harte R, Lin M, Howald C, Tanzer A, Derrien T, Chrast J, Walters N, Balasubramanian S, Pei B, Tress M, et al.: GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 2012, 22:1760-1774.
• [18]Venkatraman ES, Olshen AB: A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 2007, 23:657-663.
• [19]Coe BP, Ylstra B, Carvalho B, Meijer GA, Macaulay C, Lam WL: Resolving the resolution of array CGH. Genomics 2007, 89:647-653.
• [20]XHMM: eXome-Hidden Markov Model http://atgu.mgh.harvard.edu/xhmm/index.shtml webcite
• [21]Ameziane N, Errami A, Léveillé F, Fontaine C, De Vries Y, Van Spaendonk RML, De Winter JP, Pals G, Joenje H: Genetic subtyping of Fanconi anemia by comprehensive mutation screening. Hum Mutat 2008, 29:159-166.