SmartPhase: Accurate and fast phasing of heterozygous variant pairs for genetic diagnosis of rare diseases

被引:15
作者
Hager, Paul [1 ]
Mewes, Hans-Werner [2 ]
Rohlfs, Meino [3 ]
Klein, Christoph [3 ]
Jeske, Tim [1 ,3 ]
机构
[1] Helmholtz Zentrum Munchen GmbH, Inst Bioinformat & Syst Biol, Neuherberg, Germany
[2] Tech Univ Munich, Sch Life Sci, Freising Weihenstephan, Germany
[3] Ludwig Maximilians Univ Munchen, Univ Hosp, Dr von Hauner Childrens Hosp, Dept Pediat, Munich, Germany
关键词
GENOMICS;
D O I
10.1371/journal.pcbi.1007613
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
There is an increasing need to use genome and transcriptome sequencing to genetically diagnose patients suffering from suspected monogenic rare diseases. The proper detection of compound heterozygous variant combinations as disease-causing candidates is a challenge in diagnostic workflows as haplotype information is lost by currently used next-generation sequencing technologies. Consequently, computational tools are required to phase, or resolve the haplotype of, the high number of heterozygous variants in the exome or genome of each patient. Here we present SmartPhase, a phasing tool designed to efficiently reduce the set of potential compound heterozygous variant pairs in genetic diagnoses pipelines. The phasing algorithm of SmartPhase creates haplotypes using both parental genotype information and reads generated by DNA or RNA sequencing and is thus well suited to resolve the phase of rare variants. To inform the user about the reliability of a phasing prediction, it computes a confidence score which is essential to select error-free predictions. It incorporates existing haplotype information and applies logical rules to determine variants that can be excluded as causing a recessive, monogenic disease. SmartPhase can phase either all possible variant pairs in predefined genetic loci or preselected variant pairs of interest, thus keeping the focus on clinically relevant results. We compared SmartPhase to WhatsHap, one of the leading comparable phasing tools, using simulated data and a real clinical cohort of 921 patients. On both data sets, SmartPhase generated error-free predictions using our derived confidence score threshold. It outperformed WhatsHap with regard to the percentage of resolved pairs when parental genotype information is available. On the cohort data, SmartPhase enabled on average the exclusion of approximately 22% of the input variant pairs in each singleton patient and 44% in each trio patient. SmartPhase is implemented as an open-source Java tool and freely available at http://ibis.helmholtzmuenchen.de/smartphase/.
引用
收藏
页数:12
相关论文
共 14 条
[1]   The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update [J].
Afgan, Enis ;
Baker, Dannon ;
Batut, Berenice ;
van den Beek, Marius ;
Bouvier, Dave ;
Cech, Martin ;
Chilton, John ;
Clements, Dave ;
Coraor, Nate ;
Gruening, Bjoern A. ;
Guerler, Aysam ;
Hillman-Jackson, Jennifer ;
Hiltemann, Saskia ;
Jalili, Vahid ;
Rasche, Helena ;
Soranzo, Nicola ;
Goecks, Jeremy ;
Taylor, James ;
Nekrutenko, Anton ;
Blankenberg, Daniel .
NUCLEIC ACIDS RESEARCH, 2018, 46 (W1) :W537-W544
[2]   OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders [J].
Amberger, Joanna S. ;
Bocchini, Carol A. ;
Schiettecatte, Francois ;
Scott, Alan F. ;
Hamosh, Ada .
NUCLEIC ACIDS RESEARCH, 2015, 43 (D1) :D789-D798
[3]   Rare-disease genetics in the era of next-generation sequencing: discovery to translation [J].
Boycott, Kym M. ;
Vanstone, Megan R. ;
Bulman, Dennis E. ;
MacKenzie, Alex E. .
NATURE REVIEWS GENETICS, 2013, 14 (10) :681-691
[4]   Rare variant phasing and haplotypic expression from RNA sequencing with phASER [J].
Castel, Stephane E. ;
Mohammadi, Pejman ;
Chung, Wendy K. ;
Shen, Yufeng ;
Lappalainen, Tuuli .
NATURE COMMUNICATIONS, 2016, 7
[5]   Comparison of phasing strategies for whole human genomes [J].
Choi, Yongwook ;
Chan, Agnes P. ;
Kirkness, Ewen ;
Telenti, Amalio ;
Schork, Nicholas J. .
PLOS GENETICS, 2018, 14 (04)
[6]   Improving genetic diagnosis in Mendelian disease with transcriptome sequencing [J].
Cummings, Beryl B. ;
Marshall, Jamie L. ;
Tukiainen, Taru ;
Lek, Monkol ;
Donkervoort, Sandra ;
Foley, A. Reghan ;
Bolduc, Veronique ;
Waddell, Leigh B. ;
Sandaradura, Sarah A. ;
O'Grady, Gina L. ;
Estrella, Elicia ;
Reddy, Hemakumar M. ;
Zhao, Fengmei ;
Weisburd, Ben ;
Karczewski, Konrad J. ;
O'Donnell-Luria, Anne H. ;
Birnbaum, Daniel ;
Sarkozy, Anna ;
Hu, Ying ;
Gonorazky, Hernan ;
Claeys, Kristl ;
Joshi, Himanshu ;
Bournazos, Adam ;
Oates, Emily C. ;
Ghaoui, Roula ;
Davis, Mark R. ;
Laing, Nigel G. ;
Topf, Ana ;
Kang, Peter B. ;
Beggs, Alan H. ;
North, Kathryn N. ;
Straub, Volker ;
Dowling, James J. ;
Muntoni, Francesco ;
Clarke, Nigel F. ;
Cooper, Sandra T. ;
Bonnemann, Carsten G. ;
MacArthur, Daniel G. .
SCIENCE TRANSLATIONAL MEDICINE, 2017, 9 (386)
[7]   KNIME4NGS: a comprehensive toolbox for next generation sequencing analysis [J].
Hastreiter, Maximilian ;
Jeske, Tim ;
Hoser, Jonathan ;
Kluge, Michael ;
Ahomaa, Kaarin ;
Friedl, Marie-Sophie ;
Kopetzky, Sebastian J. ;
Quell, Jan-Dominik ;
Mewes, H. -Werner ;
Kueffner, Robert .
BIOINFORMATICS, 2017, 33 (10) :1565-1567
[8]  
Kaplanis J, 2018, EXOME WIDE ASSESSMEN
[9]   Genetic diagnosis of Mendelian disorders via RNA sequencing [J].
Kremer, Laura S. ;
Bader, Daniel M. ;
Mertes, Christian ;
Kopajtich, Robert ;
Pichler, Garwin ;
Iuso, Arcangela ;
Haack, Tobias B. ;
Graf, Elisabeth ;
Schwarzmayr, Thomas ;
Terrile, Caterina ;
Konarikova, Eliska ;
Repp, Birgit ;
Kastenmueller, Gabi ;
Adamski, Jerzy ;
Lichtner, Peter ;
Leonhardt, Christoph ;
Funalot, Benoit ;
Donati, Alice ;
Tiranti, Valeria ;
Lombes, Anne ;
Jardel, Claude ;
Glaeser, Dieter ;
Taylor, Robert W. ;
Ghezzi, Daniele ;
Mayr, Johannes A. ;
Roetig, Agnes ;
Freisinger, Peter ;
Distelmaier, Felix ;
Strom, Tim M. ;
Meitinger, Thomas ;
Gagneur, Julien ;
Prokisch, Holger .
NATURE COMMUNICATIONS, 2017, 8
[10]  
Martin M, 2016, WHATSHAP FAST ACCURA, DOI [10.1101/085050, DOI 10.1101/085050]