Full Genome Analysis to Guide Precision Medicine
David Martin, MD
Senior Scientist, Children's Hospital Oakland Research Institute
Genes are packages of DNA and compose the blueprint for the human body, from its one-cell stage on through the lifespan. A child inherits a set of genes from both parents, which undergo a process of combining at conception to form a whole genome. Occasionally, a gene will be defective, caused by a mutation in the DNA sequence. The mutated form of the gene may either be passed down from a parent, whose gene was also mutated, or take a novel form, such as from a mistake in the process of copying the DNA. Some mutations cause the gene to not work properly, which occasionally leads to a lifelong disability, such as in mobility or learning. Genetic disorders commonly present during infancy and childhood, when the development of the human body is rapidly laying the foundation for the rest of a lifetime.
The extreme cases of pediatric disorders (referred to as "extreme phenotypes" in the scientific literature) are the subject of this study. The medical resources required in diagnosing a child with a rare or extreme disorder are usually extensive and do not always lead to a resolution. Although some cases fall into recognizable syndromes with a known genetic basis, a great many do not.
This project aimed to provide resolution for dozens of patients and their families, while also developing a pipeline that will continue into what the research team considers, "a program of discovery, integrated with other centers, that will eventually resolve the genetic basis of most or all extreme [disorders]." Through these efforts, it will eventually be possible to know how many disorders actually have a genetic basis.
Full Genome Analysis
Most current clinical methods of genetic testing are only sensitive to mutations that are common and extensively studied. Access to an individual's complete genome would allow scanning for mutations across all genes simultaneously for any disorder or condition that has a known genetic link. Sequencing the full genome of infants with extreme symptoms for whom other diagnostic methods have failed has shown that it can guide clinical decision-making in a high proportion of cases.
An example of this scenario featured a child born with small and immature muscles and other conditions that did not lead clinicians to any specific disorder. Over several years and many failed diagnostic tests, including focused gene sequencing, no diagnosis was achieved. The research team was involved in ultimately sequencing the full genome, which successfully identified a genetic basis for the child’s condition.
Full Genome Analysis (FGA) is a method of genome sequencing that was recently developed by a member of the research team to serve extreme cases like that described above. It differs from other sequencing methods in its comprehensiveness: whereas other methods (including whole exome sequencing and microarray techniques) only sequence minor sections of the genome that are already considered important based on current understanding, FGA takes stock of all genetic material without skipping any sections. The adoption of this method into routine clinical practice would require development of standards and a more complete catalog linking genetic variations with clinical outcomes.
Reading the human genome to identify genetic causes of disorders is an extremely recent ability. Today, several methods are available to sequence and analyze a person’s genetic background, but they address the technological challenge differently. Most genetic tests available today narrowly focus on the most common causes of genetic disorders, like particular mutations. For disorders that are rare or have an unknown cause, a complete view of the genome is necessary.
In response to this need, the research team developed a method called Full Genome Analysis (FGA) that reads the genome in a more comprehensive manner than all other available techniques. The method serves two main purposes: 1) diagnosing clinical cases whose underlying mechanisms remain unidentified using other methods and 2) providing a tool to systematically discover genetic mutations previously unknown to cause disease.
This project brought together doctors and scientists to study whether FGA would be useful in the clinic. The multidisciplinary and multi-institute research team applied FGA to 45 pediatric cases of extreme undiagnosed disorders suspected to be genetic in nature. Most the children were from traditionally underserved backgrounds. The importance of involving communities of color cannot be understated, as current genetic references do not yet reflect California’s demographic spread.
After sequencing the genomes of the affected children, their parents, and in some cases, their siblings, the new method was able to identify the likely cause of disorder for 40% of cases (18 total). In response to this success, the research team developed a standardized pipeline for the acquisition and delivery of FGA data for clinical decision-making.
The long-term goal of this work is to contribute genomic data to a catalog of all genetic mutations and variations that can cause human disease. By doing so, it would allow any clinician in the world the opportunity to diagnose even the rarest genetic disorders. Conducted with prevention in mind, FGA would allow clinicians to assess disease risk and potentially take therapeutic action before symptoms become extreme and biological damage has occurred.
Research Team and Collaborators
Children’s Hospital Oakland Research Institute (CHORI)
- David Martin, MD
- Dario Boffelli, PhD
UC San Francisco
- Puy-Yan Kwok, MD, PhD
- Ophir Klein, MD, PhD
- Anne Slavotinek, MD, PhD
- Joseph Shieh, MD, PhD
- Bryce Mendelsohn, MD, PhD
- Renata Gallagher, MD
- Jessica Tenney, MD
- Daniah Beleford, MD
- Hazel Perry, MD
UCSF Benioff Children’s Hospital Oakland
- Art D’ Harlinque, MD
- Steven Brenner, PhD
- Andrew Sharo
- Jingqi Chen, PhD
- Brandon Hunter, MS, MBA
L to R, Top Row: [CHORI] David Martin, MD; Dario Boffelli, PhD; [CHO] Art D'Harlingue, MD; [UCB] Steven E. Brenner, PhD; Middle Row: [UCSF] Ophir Klein, MD, PhD; Pui-Yan Kwok, MD, PhD; Bryce Mendelsohn, MD, PhD; Neil Risch, PhD; Bottom Row: Joseph Shieh, MD, PhD; Anne Slavotinek, MD, PhD; Hazel Perry, MS; [Illumina] Ryan Taft, PhD