Poster Presentation 40th Annual Lorne Genome Conference 2019

Diagnostic yield from WES, WGS and RNA testing among 213 neuromuscular families: known versus novel disease genes, coding versus non-coding variants. (#271)

Leigh B Waddell 1 2 , Monkol Lek 3 , Emily C Oates 4 , Roula Ghaoui 5 , Gina L O'Grady 6 , Sarah A Sandaradura 1 7 , Beryl B Cummings 8 9 10 , Elise Valkanas 8 9 10 , Richard Roxburgh 11 , Samantha Bryen 1 2 , Adam Bournazos 1 2 , Frances Evesson 1 2 12 , Jamie L Marshall 8 9 10 , Katherine Chao 8 9 10 , Kristi J Jones 1 2 7 , Lyndal Douglas 7 , Miriam Rodrigues 11 , Mark Davis 13 , Nigel G Laing 13 14 , Kathryn N North 15 16 , Nigel F Clarke 1 2 , Daniel G MacArthur 8 9 10 17 , Sandra T Cooper 1 2 12
  1. Kids Neuroscience Centre, Kids Research, The Children's Hospital at Westmead, Westmead, NSW, Australia
  2. Discipline of Child and Adolescent Health, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW, Australia
  3. Lek Lab, Yale University, New Haven, CT, USA
  4. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Randwick, NSW, Australia
  5. Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia
  6. Starship Children's Health, Auckland District Health Board, Auckland, New Zealand
  7. Clinical Genetics, The Children's Hospital at Westmead, Westmead, NSW, Australia
  8. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
  9. Medical and Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA, USA
  10. Center for Mendelian Genomics, Broad Institute of Harvard & MIT, Cambridge, MA, USA
  11. Department of Neurology, Auckland District Health Board, Auckland, New Zealand
  12. Functional Neuromics, Children's Medical Research Institute, Westmead, NSW, Australia
  13. Department of Diagnostic Genomics, PathWest Laboratory Medicine, Perth, WA, Australia
  14. Centre for Medical Research, University of Western Australia, Harry Perkins Institute of Medical Research, Perth, W, Australia
  15. Murdoch Children's Research Institute, Parkville, VIC, Australia
  16. The Royal Children’s Hospital, Melbourne, VIC, Australia
  17. Harvard Medical School, Harvard University, Cambridge, MA, USA

Aim: A review of diagnostic outcomes from complementary massively parallel sequencing techniques in a large cohort of 213 families with neuromuscular disorders (NMD).

Methods: 85 trios and 128 individuals were subject to whole exome sequencing (WES) at the Broad Institute of Harvard and MIT or NMD panel screening at PathWest.  5/213 underwent additional whole genome sequencing (WGS; Broad Institute); 13/213 underwent additional muscle RNA sequencing (RNAseq; Broad Institute); 18/213 underwent both WGS and muscle RNAseq (Broad Institute). 15/213 had additional muscle mRNA laboratory investigations (Kids Neuroscience Centre).  

Results: To date, a genetic diagnosis has been identified in 129/213 (61%) families. Six novel disease genes have been identified, with an average time of 31 months from identification of the gene, to E-publication in a peer reviewed journal.  Seven novel or expanded phenotypes for known NMD genes were also determined. One third (42/129) of diagnosed families possessed at least one splicing variant.

WES alone provided a diagnosis for 92/129 (71%) diagnosed families. Additional sequencing and functional genomics pipelines (WGS, RNAseq, mRNA studies) were required for provision of a genetic diagnosis for 37/129 (29 %) diagnosed families. 29/37 (78%) families not diagnosed via initial WES screening, were shown subsequently to possess (at least one) splice-altering variant in known disease genes.

Discussion: Our results from a large cohort of families with NMD propose a step-wise approach to genomic analyses: initial screening via a targeted panel, triaging to WES, then WGS/RNA studies.  Many cases solved by RNAseq and mRNA studies required additional insight from intronic variation provided by WGS. Functional genomics investigation to reach publication was lengthy (average 2.5 years), expensive and highly interdisciplinary.

Our collective data confirm that variants in common disease genes occur commonly – with ‘tricky variants’ in known disease genes (16%) more common than novel gene discovery (5%).