Mutations in PLK4, encoding a master regulator of centriole biogenesis, cause microcephaly, growth failure and retinopathy

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Abstract

Centrioles are essential for ciliogenesis. However, mutations in centriole biogenesis genes have been reported in primary microcephaly and Seckel syndrome, disorders without the hallmark clinical features of ciliopathies. Here we identify mutations in the master regulator of centriole duplication, the PLK4 kinase, and its substrate TUBGCP6 in patients with microcephalic primordial dwarfism and additional congenital anomalies including retinopathy, extending the human phenotype spectrum associated with centriole dysfunction. Furthermore, we establish that different levels of impaired PLK4 activity result in growth and cilia phenoptyes, providing a mechanism by which microcephaly disorders can occur with or without ciliopathic features.

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Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Kenneth Livak, Thomas D. Schmittgen (2001)

The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).

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Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Author and article information

Journal

Journal ID (nlm-journal-id): 9216904

Journal ID (pubmed-jr-id): 2419

Journal ID (nlm-ta): Nat Genet

Journal ID (iso-abbrev): Nat. Genet.

Title: Nature genetics

ISSN (Print): 1061-4036

ISSN (Electronic): 1546-1718

Publication date Nihms-submitted: 3 December 2015

Publication date (Electronic): 26 October 2014

Publication date (Print): December 2014

Publication date PMC-release: 11 December 2015

Volume: 46

Issue: 12

Pages: 1283-1292

Affiliations

[1 ]Medical Research Council (MRC) Human Genetics Unit, Institute of Genetics and Molecular Medicine (IGMM), University of Edinburgh, Edinburgh, UK.

[2 ]Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany.

[3 ]Institute of Biochemistry I, Medical Faculty, University of Cologne, Cologne, Germany.

[4 ]Health Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan.

[5 ]Center for Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.

[6 ]Division of Human Genetics, Innsbruck Medical University, Innsbruck, Austria.

[7 ]Institute for Clinical Genetics, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

[8 ]Cytogenetics Laboratory, South East of Scotland Genetics Service, Western General Hospital, Edinburgh, UK.

[9 ]Cell Cycle Control and Carcinogenesis, German Cancer Research Center (DKFZ), Heidelberg, Germany.

[10 ]Department of Genetics, INSERM U781, Université Paris Descartes, Sorbonne Paris Cité, Hopital Necker, Assistance Publique–Hôpitaux de Paris (AP-HP), Paris, France.

[11 ]CARGO and IGMA Hôpitaux Universitaires de Strasbourg, INSERM U1112, Université de Strasbourg, Strasbourg, France.

[12 ]Division of Clinical and Metabolic Genetics, Department of Paediatrics, The Hospital for Sick Children and University of Toronto, Toronto, Ontario, Canada.

[13 ]Unit of Human Developmental Genetics, Institut Pasteur, Paris, France.

[14 ]Najmabadi Pathology and Genetics Center, Tehran, Iran.

[15 ]University College London (UCL) Institute of Ophthalmology, London, UK.

[16 ]Moorfields Eye Hospital, London, UK.

[17 ]Southwest Thames Regional Genetics Service, St. George’s Hospital Medical School, London, UK.

[18 ]Casey Eye Institute, Oregon Health and Science University, Portland, Oregon, USA.

[19 ]Institute of Human Genetics, University of Cologne, Cologne, Germany.

[20 ]Wellcome Trust Sanger Institute, Cambridge, UK.

[21 ]Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany.

Author notes

Correspondence should be addressed to A.P.J. ( andrew.jackson@ 123456igmm.ed.ac.uk ) or P.N. ( nuernberg@ 123456uni-koeln.de )

Author contributions

P.N., H.T., J.A, M.S.H., A.B., K.M., M.E.H., J.E.M. and L.S.B. performed exome sequencing and analysis. Sequencing, genotyping, linkage analysis and other molecular genetic experiments were performed by L.S.B, C.A.M., J.E.M, M.R.T, I.A, M.S.H., G.N. Cell biology experiments were designed and performed by C.A.M., A.L., C.K, M.E.Ha, I.A., M.S.H., R.M, A.A.N and I.H. A.K. designed and performed the zebrafish experiments with help from A.L., C.A.M., J.D., P.H. Structural analysis, W.H. D.H., F.K, Z.A, S.T., V.C.D, H.D, L.D., A.Ka, R.M-L., A.T.M, A.S, C.S., R.W, S.M.B., ascertained patients, obtained samples and/or assisted with phenotypic analysis and clinical studies. C.A.M and A.P.J wrote the paper with help from P.N, A.K. and L.S.B. The study was planned and supervised by P.N. and APJ.

Article

Manuscript ID: EMS60554

DOI: 10.1038/ng.3122

PMC ID: 4676084

PubMed ID: 25344692

SO-VID: 0b9dd270-e4a5-4915-b227-a6c6c5f2127f

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Mutations in PLK4, encoding a master regulator of centriole biogenesis, cause microcephaly, growth failure and retinopathy

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 52

Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

Fast and accurate short read alignment with Burrows–Wheeler transform

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

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