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      High prevalence of deleterious mutations in concomitant nonsyndromic cleft and outflow tract heart defects

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          Abstract

          Our previous work demonstrating enrichment of outflow tract (OFT) congenital heart disease (CHD) in children with cleft lip and/or palate (CL/P) suggests derangements in common underlying developmental pathways. The current pilot study examines the underlying genetics of concomitant nonsyndromic CL/P and OFT CHD phenotype. Of 575 patients who underwent CL/P surgery at Children's Hospital Los Angeles, seven with OFT CHD, negative chromosomal microarray analysis, and no recognizable syndromic association were recruited with their parents (as available). Whole genome sequencing of blood samples paired with whole‐blood‐based RNA sequencing for probands was performed. A pathogenic or potentially pathogenic variant was identified in 6/7 (85.7%) probands. A total of seven candidate genes were mutated ( CHD7, SMARCA4, MED12, APOB, RNF213, SETX, and JAG1). Gene ontology analysis of variants predicted involvement in binding (100%), regulation of transcription (42.9%), and helicase activity (42.9%). Four patients (57.1%) expressed gene variants ( CHD7, SMARCA4, MED12, and RNF213) previously involved in the Wnt signaling pathway. Our pilot analysis of a small cohort of patients with combined CL/P and OFT CHD phenotype suggests a potentially significant prevalence of deleterious mutations. In our cohort, an overrepresentation of mutations in molecules associated with Wnt‐signaling was found. These variants may represent an expanded phenotypic heterogeneity within known monogenic disease genes or provide novel evidence of shared developmental pathways. The mechanistic implications of these mutations and subsequent developmental derangements resulting in the CL/P and OFT CHD phenotype require further analysis in a larger cohort of patients.

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          Most cited references60

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Transcript assembly and abundance estimation from RNA-Seq reveals thousands of new transcripts and switching among isoforms

            High-throughput mRNA sequencing (RNA-Seq) holds the promise of simultaneous transcript discovery and abundance estimation 1-3 . We introduce an algorithm for transcript assembly coupled with a statistical model for RNA-Seq experiments that produces estimates of abundances. Our algorithms are implemented in an open source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed more than 430 million paired 75bp RNA-Seq reads from a mouse myoblast cell line representing a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Analysis of transcript expression over the time series revealed complete switches in the dominant transcription start site (TSS) or splice-isoform in 330 genes, along with more subtle shifts in a further 1,304 genes. These dynamics suggest substantial regulatory flexibility and complexity in this well-studied model of muscle development.
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              A framework for variation discovery and genotyping using next-generation DNA sequencing data

              Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.
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                Author and article information

                Contributors
                rsubramanyan@surgery.usc.edu
                Journal
                Am J Med Genet A
                Am J Med Genet A
                10.1002/(ISSN)1552-4833
                AJMG
                American Journal of Medical Genetics. Part a
                John Wiley & Sons, Inc. (Hoboken, USA )
                1552-4825
                1552-4833
                06 April 2022
                July 2022
                : 188
                : 7 ( doiID: 10.1002/ajmg.a.v188.7 )
                : 2082-2095
                Affiliations
                [ 1 ] Division of Plastic and Reconstructive Surgery Keck School of Medicine of USC Los Angeles California USA
                [ 2 ] Operation Smile Inc Virginia Beach Virginia USA
                [ 3 ] Division of Cardiac Surgery, Department of Surgery Keck School of Medicine of USC Los Angeles California USA
                [ 4 ] Department of Translational Genomics Keck School of Medicine of USC Los Angeles California USA
                [ 5 ] Department of Pediatrics Cedars‐Sinai Medical Center Los Angeles California USA
                [ 6 ] Department of Pathology and Laboratory Medicine Children's Hospital Los Angeles Los Angeles California USA
                [ 7 ] Department of Pathology Keck School of Medicine of USC Los Angeles California USA
                [ 8 ] Division of Plastic and Maxillofacial Surgery Children's Hospital Los Angeles Los Angeles California USA
                [ 9 ] Department of Plastic Surgery Shriners Hospital for Children Los Angeles California USA
                [ 10 ] Heart Institute Children's Hospital Los Angeles Los Angeles California USA
                Author notes
                [*] [* ] Correspondence

                Subramanyan Ram Kumar, Division of Cardiac Surgery, Children's Hospital Los Angeles, 4650 Sunset Boulevard, Mailstop #66, Los Angeles, CA 90033, USA.

                Email: rsubramanyan@ 123456surgery.usc.edu

                Author information
                https://orcid.org/0000-0003-2731-837X
                https://orcid.org/0000-0003-1487-4203
                Article
                AJMGA62748
                10.1002/ajmg.a.62748
                9197864
                35385219
                cc028c6f-79d2-48c6-8b60-f4cefb44bf4d
                © 2022 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 26 February 2022
                : 29 August 2021
                : 18 March 2022
                Page count
                Figures: 2, Tables: 4, Pages: 14, Words: 10988
                Funding
                Funded by: National Center for Advancing Translational Sciences , doi 10.13039/100006108;
                Award ID: UL1TR000242
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                July 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.0 mode:remove_FC converted:07.10.2022

                Genetics
                cleft lip,cleft palate,congenital heart disease,genetic syndrome,whole genome sequencing
                Genetics
                cleft lip, cleft palate, congenital heart disease, genetic syndrome, whole genome sequencing

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