Gillian M Blue1, Edwin P Kirk2, Eleni Giannoulatou3, Sally L Dunwoodie4, Joshua W K Ho3, Desiree C K Hilton5, Susan M White6, Gary F Sholler1, Richard P Harvey4, David S Winlaw7. 1. Kids Heart Research, The Children's Hospital at Westmead, Sydney, Australia; The Heart Centre for Children, The Children's Hospital at Westmead, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia. 2. Department of Medical Genetics, Sydney Children's Hospital, Sydney, Australia; School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, Australia. 3. Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, Australia; University of New South Wales, Sydney, Australia. 4. Victor Chang Cardiac Research Institute, Darlinghurst, Sydney, Australia; St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia; School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia. 5. Kids Heart Research, The Children's Hospital at Westmead, Sydney, Australia; The Heart Centre for Children, The Children's Hospital at Westmead, Sydney, Australia. 6. Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Australia. 7. Kids Heart Research, The Children's Hospital at Westmead, Sydney, Australia; The Heart Centre for Children, The Children's Hospital at Westmead, Sydney, Australia; Sydney Medical School, University of Sydney, Sydney, Australia. Electronic address: david.winlaw@health.nsw.gov.au.
Abstract
BACKGROUND: Many genes have been implicated in the development of congenital heart disease (CHD). Next-generation sequencing offers opportunities for genetic testing but is often complicated by logistic and interpretative hurdles. OBJECTIVES: This study sought to apply next-generation sequencing technology to CHD families with multiple affected members using a purpose-designed gene panel to assess diagnostic potential for future clinical applications. METHODS: We designed a targeted next-generation sequencing gene panel for 57 genes previously implicated in CHD. Probands were screened in 16 families with strong CHD histories and in 15 control subjects. Variants affecting protein-coding regions were classified in silico using prediction programs and filtered according to predicted mode of inheritance, minor allele frequencies, and presence in databases such as dbSNP (Single Nucleotide Polymorphism Database) and ESP (Exome Sequencing Project). Disease segregation studies were conducted in variants identified in CHD cases predicted to be deleterious and with minor allele frequencies <0.1%. RESULTS: Thirteen potential disease-causing variants were identified in 9 families. Of these, 5 variants segregated with disease phenotype, revealing a likely molecular diagnosis in 31% of this cohort. Significant increases in the number of "indels, nonsense, and splice" variants, as well as variants classified as "probably damaging" were identified in CHD cases but not in control subjects. Also, there was a significant increase in the total number of "rare" and "low" frequency variants (minor allele frequencies <0.05) in the CHD cases. CONCLUSIONS: When multiple relatives are affected by CHD, a gene panel-based approach may identify its cause in up to 31% of families. Identifying causal variants has implications for clinical care and future family planning.
BACKGROUND: Many genes have been implicated in the development of congenital heart disease (CHD). Next-generation sequencing offers opportunities for genetic testing but is often complicated by logistic and interpretative hurdles. OBJECTIVES: This study sought to apply next-generation sequencing technology to CHD families with multiple affected members using a purpose-designed gene panel to assess diagnostic potential for future clinical applications. METHODS: We designed a targeted next-generation sequencing gene panel for 57 genes previously implicated in CHD. Probands were screened in 16 families with strong CHD histories and in 15 control subjects. Variants affecting protein-coding regions were classified in silico using prediction programs and filtered according to predicted mode of inheritance, minor allele frequencies, and presence in databases such as dbSNP (Single Nucleotide Polymorphism Database) and ESP (Exome Sequencing Project). Disease segregation studies were conducted in variants identified in CHD cases predicted to be deleterious and with minor allele frequencies <0.1%. RESULTS: Thirteen potential disease-causing variants were identified in 9 families. Of these, 5 variants segregated with disease phenotype, revealing a likely molecular diagnosis in 31% of this cohort. Significant increases in the number of "indels, nonsense, and splice" variants, as well as variants classified as "probably damaging" were identified in CHD cases but not in control subjects. Also, there was a significant increase in the total number of "rare" and "low" frequency variants (minor allele frequencies <0.05) in the CHD cases. CONCLUSIONS: When multiple relatives are affected by CHD, a gene panel-based approach may identify its cause in up to 31% of families. Identifying causal variants has implications for clinical care and future family planning.
Authors: Emmi Helle; Aldo Córdova-Palomera; Tiina Ojala; Priyanka Saha; Praneetha Potiny; Stefan Gustafsson; Erik Ingelsson; Michael Bamshad; Deborah Nickerson; Jessica X Chong; Euan Ashley; James R Priest Journal: Genet Epidemiol Date: 2018-12-04 Impact factor: 2.135
Authors: Stephanie LaHaye; Don Corsmeier; Madhumita Basu; Jessica L Bowman; Sara Fitzgerald-Butt; Gloria Zender; Kevin Bosse; Kim L McBride; Peter White; Vidu Garg Journal: Circ Cardiovasc Genet Date: 2016-07-14
Authors: Oliver James Dillon; Sebastian Lunke; Zornitza Stark; Alison Yeung; Natalie Thorne; Clara Gaff; Susan M White; Tiong Yang Tan Journal: Eur J Hum Genet Date: 2018-02-16 Impact factor: 4.246