Dr. Suzanne Lewis is a Clinical Professor in the Department of Medical Genetics at University of British Columbia (UBC) and Senior Investigator at British Columbia Children’s Hospital Research Institute (BCCHR), Vancouver, Canada. She is also the Chair of the iTARGET Autism Project and Vice-Chair of Autism Canada Chief Medical Officer and VP Research of Pacific Autism Family Network. She and her team are focusing on earlier and more accurate diagnoses, management and treatment of Autism Spectrum Disorders (ASDs) using genetic, genomic and comprehensive phenotyping studies to explore the causes and development of ASDs. iTARGET (or Individualized Treatments for Autism Recovery using Genetic-Environment Targets) Autism is Canada’s first family-centered, genetics-based initiative that fully integrates the research, clinical and patient communities within British Columbia. It is an interdisciplinary project involving a core team of over 20 researchers and a network of over 80 investigators from 16 major centers across Canada, the US, Scandinavia, the UK and China. The collaborative nature of this project aims to capture expertise in a wide variety of relevant fields, ranging from gene variant discovery to translating lab work into clinical practice. Collectively, they bring together clinical, genetic, neuroscientific, microbiological and bioinformatics expertise in ASD.

ASDs are defined by significant challenges with communication, social reciprocity, and structured behavior. They are the most common childhood developmental disability with incidence rising at an alarming rate.  Currently, 1 in 68 individuals is diagnosed with ASD, indirectly impacting more than 1 in 20 people, including parents, siblings, and grandparents. The lifetime cost of education, healthcare, and social services averages $5-8 million per Canadian with ASD. Significant evidence has demonstrated that both genetic and environmental factors contributing to the cause and development of ASDs. De novo DNA copy number variants (CNVs) and single nucleotide variants (SNVs)/small insertions and deletions (Indels) are found in >10% of cases with ASDs; while familial recessive mutations can be found in 3% of cases. Thus far, more than 300-1000 ASD related genes have been identified; however, none of them can be found in >3% of cases with ASDs, suggesting a diverse genetic heterogeneity of the disorders.

To further explore other new candidate disease genes and potential genetic factors of ASDs, Dr. Suzanne Lewis and her team are conducting whole genome sequencing (WGS), deep ‘whole body’ phenotyping and extended ‘omic‘ studies in 500 ASD affected individuals and their parents. Of equal importance, the whole genome sequence data generated by this project will contribute to existing studies of genomic associations with ASDs through collaboration with the largest-scale genome study of autism, the MSSNG Study led by Dr. Stephen Scherer (www.mss.ng) and funded by Autism Speaks.

Analysis of the vast amount of WGS data, in conjunction with dense phenotype data, will be performed by Dr. Scherer for de novo and rare variant analysis, Dr. Steven Jones at the BC Genome Sciences Centre for de novo alignment, large CNV analysis, and Dr. Evica Rajcan-Separovic at BCCHR for individual trio-phenotype association analysis.  The team at BCCHR required a cost-effective and -efficient way to analyze the big data. After comparison with other sequencing analysis software, they selected VarSeq from Golden Helix to analyze the de novo and familial mutations, and CNVs of these trio data in the project. According to their previous experience in using SVS, another product offered by Golden Helix, they are very confident that they have not just purchased the software but also been able to get the prompt technical support from the company. The technical support team not only provide excellent customer service (such as free training, quick feedback for troubleshooting, developing customized add-on features) but also keep on improving and adding new features to the software with the latest version of other dataset resources. VarSeq provided them with a very powerful, user-friendly, cost-effective and fast-updated tool to manage their data more efficiently.