Clinical and Translational Informatics Solutions for Pediatric Genomic Medicine

When the Human Genome Project was completed in 2003, Francis Collins predicted that a variety of maladies including Alzheimer’s, Parkinson’s, diabetes and cancer would all be cured by virtue of our detailed knowledge of the human genome. While this has not happened, the genetic basis for a growing number of rare, monogenic diseases has been discovered, powered in part by the rapidly falling cost of sequencing. Despite this, it can still take several years for children with rare genetic diseases to obtain a definitive diagnosis and longer still for this diagnosis to influence care.

The Chaudhari Lab is interested in developing, implementing and evaluating strategies to improve the integration of timely genetic diagnosis into clinical care, emphasizing that pediatric genomic medicine is a vital component of a learning health system. Its team uses techniques from a variety of disciplines including data science, natural language processing, machine learning, user centered design, psychometrics and statistical process control to integrate genomic medicine into clinical care and study the impact of this paradigm shift in clinical care.

Areas of Research

 
Rapid Genome Sequencing

The Chaudhari Lab conducts rapid genome sequencing (with or without transcriptomics and metagenomics) on samples from patients identified by clinical geneticists as likely to benefit from obtaining a molecular diagnosis in days rather than weeks (typically patients in intensive care units or those being considered for transplantation).

In addition to assessing test performance characteristics and tracking outcomes, opportunities exist for ancillary studies seeking follow-up with clinicians and/or patients and families as well as gene discovery in selected cases.

Computational Phenotyping

While the cost and time required to conduct whole genome sequencing has fallen rapidly, phenotyping remains a largely manual exercise.

The Chaudhari Lab develops, evaluates and implements tools and approaches to automated phenotyping with a focus on performance in scenarios relevant to the practice of pediatric rare disease genomic medicine.

Reanalysis

Only 30% of patients referred for whole exome or genome sequencing for suspected rare monogenic disease will obtain a diagnosis at the time of initial assessment with these technologies. Recent research suggests, however, that another 10% can be subsequently diagnosed on reanalysis.

Current practices support reanalysis primarily at the request of a clinician. This research focuses on developing, implementing and evaluating informatics approaches to prioritizing selected cases for laboratory-initiated reanalysis.

Somali Genome Initiative

The current practice of pediatric genomic medicine for rare diseases is focused on resequencing with short read technologies because this approach is very cost effective. Underpinning this approach is the use of population allele frequency data and the rare disease-rare variant hypothesis to rapidly exclude large number of variants as not being plausible causes of rare disease.

While it has long been known that genomic diversity is significantly higher in populations of African ancestry as compared to populations of European or other non-African ancestry, individuals of African ancestry remain underrepresented in the databases of human genetic variation used. The functional consequence is that when individuals of African ancestry participate in pediatric genomic medicine, they have significantly more variants for the lab to review (slowing interpretation) and more variants of uncertain significance are reported increasing the complexity of post-test genetic counseling, decreasing patient and family satisfaction and making it harder to get other families from similar communities to participate in pediatric genomic medicine research and/or clinical care.

Central Ohio has one of the world’s largest Somali diaspora communities. This research seeks to find culturally competent ways to engage this community in pediatric genomic medicine research and ultimately to sequence enough Somali individuals to accurately assess the allele frequencies of genetic variants in this underserved population.

Measuring Genomic Knowledge

While pediatric genomic medicine was historically delivered in medical genetics clinics, making this approach to care more accessible will require non-geneticists to become more engaged with specific aspects of this approach. Some basic knowledge of clinical genomics will be vital for almost all members of the health care team.

This research seeks to develop a validated instrument which measures genomic medicine knowledge amongst members of pediatric healthcare teams.

Meet Our Team

Bimal Chaudhari

Bimal P. Chaudhari, MD, MPH
Principal Investigator

Bimal P. Chaudhari, MD, MPH, is a principal investigator in the Steve and Cindy Rasmussen Institute for Genomic Medicine, a member of the Sections of Neonatology, Genetics and Genomic Medicine and a founding member of the Radiogenomics Research Affinity Group at Nationwide Children’s Hospital. Dr. Chaudhari is also an assistant professor of Pediatrics at The Ohio State University College of Medicine.

Clinically, Dr. Chaudhari is interested in the evaluation and management of structural birth defects and suspected Mendelian disease during fetal medicine consultation as well as postnatally in neonatal intensive care units. His academic work focuses on methods to improve the utility of genetic testing outside of the medical genetics clinic. He does this through a combination of outreach, education and research. 

Dr. Chaudhari’s research focuses on a learning health systems approach to applications of genomic medicine in acute and critical care pediatric populations. This work involves transdisciplinary collaboration to design, implement and evaluate interventions which address the challenge of making genomic medicine salient to non-geneticist health care providers. To that end, he is principal investigator on multiple protocols related to the delivery of rapid genome sequencing in acute and critical care settings. Overlapping fields and techniques used include data visualization, machine learning, natural language processing, ontologies, clustering and similarity measures, human computer interaction, computer supported collaborative work, user centered design and clinical decision support. By both generating foundational knowledge necessary to design such interventions and implementing and evaluating them in the real world, Dr. Chaudhari’s work seeks to promote safety, improve outcomes and increase value in medically complex neonatal and pediatric populations.

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Austin Antoniou, PhD
Postdoctoral Associate

Austin was born in Phoenix and lived in Arizona until earning his B.S. in Mathematics at The University of Arizona in 2014. He moved to Columbus to continue studying math at The Ohio State University, where he worked as a graduate teaching associate and researcher until completing his Ph.D. in 2020. In the Fall of 2020, he started as a postdoctoral scientist at IGM.

His current research revolves around developing mathematical and computational insight into patient phenotype data. In particular, he is concerned with how to best measure similarity between terms in ontologies and how to use similarity measures to improve the gene ranking algorithms. These questions have the underlying goal of accelerating diagnoses for patients who undergo genome or exome sequencing.

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Brandon Stone, MD
Resident Physician

Brandon Stone, MD is a resident in the combined Pediatrics/Medical Genetics Residency at Nationwide Children’s Hospital and The Ohio State Univeristy. He hails from Michigan where he completed his undergraduate (Michigan State University) and medical (Wayne State University) degrees. His work involves identification of individuals with undiagnosed or mild genetic disorders which may be overlooked in current clinical practice.

Affiliate Lab Members

Stephanie Lauden, MD
Attending Physician

Marco Leung, PhD
Clinical Lab Director