Researchers develop decision tree to help clinicians predict genetic diagnoses in neurodevelopmental cases

New study identifies motor delay and hypotonia as key predictors of genetic diagnoses, offering a practical tool to guide clinicians in neurodevelopmental care.

Study: Clinical factors associated with genetic diagnosis in suspected neurogenetic disorders in a tertiary care clinic. Image Credit: Gorodenkoff/Shutterstock.comStudy: Clinical factors associated with genetic diagnosis in suspected neurogenetic disorders in a tertiary care clinic. Image Credit: Gorodenkoff/Shutterstock.com

In a recent study published in Genetics in Medicine, a group of researchers identified clinical factors associated with genetic diagnoses in Neurodevelopmental disorder (NDD) (affects brain development, causing cognitive or motor delays) patients and developed a decision tool to guide genetic testing decisions.

Background 

NDDs, such as global developmental delay, autism spectrum disorder (ASD) (impacts social interaction and communication with repetitive behaviors), and intellectual disability, have significant genetic heritability.

Advances in genetic testing, including chromosomal microarray (CMA) and exome sequencing (ES), have improved diagnostic rates. With CMA, genetic variants are identified in 10% to 20% of cases, and with ES, over 40%.

Studies show that individuals with genetic diagnoses often have more medical comorbidities. Further research is required to validate decision tools and identify additional phenotypic factors to improve genetic diagnosis accuracy in different clinical populations.

About the study 

In the present study, 110 patients, along with their legal guardians, provided informed consent for prospective clinical data collection. Additionally, charts of 206 patients were retrospectively reviewed under an Institutional Review Board (IRB)- approved waiver.

Data from all patients were manually extracted from the University of California, Los Angeles (UCLA) electronic health record and coded into an encrypted database. Seventy patients were excluded due to incomplete genetic testing or lack of insurance authorization.

Patients were included if they had a known or suspected neurogenetic disorder and had completed at least one genetic test, such as CMA, fragile X testing, mitochondrial Deoxyribonucleic Acid (DNA) testing, single-gene sequencing, or ES.

The study created three cohorts based on genetic test results: pathogenic or likely pathogenic (P/LP), negative, and an ES-negative subset. Clinical data, including age at milestones, history of motor and language delays, congenital heart disease, and other factors, were extracted and coded for analysis.

Statistical analyses began with χ2 tests and two-sample Wilcoxon tests to explore associations between clinical variables and genetic diagnosis. Subsequently, logistic regression, classification, and regression tree (CART) analysis were used to identify clinical characteristics most strongly associated with a genetic diagnosis.

Study results 

The study cohort consisted of 246 patients referred to the UCLA Center for Autism Research and Treatment in Neurogenetics (CARING) Clinic, a multidisciplinary academic health system clinic, between January 1, 2014, and January 1, 2019.

Referrals came from physicians within and outside the academic system, patient advocacy groups, research studies, and self-referrals from families.

All patients included in the study had completed at least one genetic test, with results available in their electronic health record (EHR). Of the 246 patients, 153 had undergone genetic testing before referral to the clinic, 47 received some testing both before and after referral, and 46 had all testing done after referral.

Among the 246 patients, 152 (61.8%) were found to have a P/LP variant, while 94 (38%) were found to have no variants, benign variants, or variants of uncertain significance (VUS). The P/LP cohort consisted of 62 different genetic diagnoses, with 12 recurring diagnoses shared by two or more patients, while 50 diagnoses were unique to individual patients.

The negative cohort underwent a variety of tests, with the most common being CMA performed on 76 patients. A subset of 33 patients in the negative cohort also underwent ES and served as a more stringent negative control group.

Patients with a P/LP variant were more likely to be female (47% vs. 20% and 12% in the negative and ES-negative cohorts, respectively). The average age of presentation across the study group was 9.4 years, with no significant difference in age across the three cohorts.

There were no substantial differences in racial or ethnic distribution among the groups, with White patients being the predominant group.

Logistic regression analysis identified several patient characteristics associated with a higher likelihood of having a P/LP variant. Patients with a history of motor delay had significantly increased odds of having a P/LP variant compared to those without motor delay.

Other factors associated with increased odds included congenital heart disease, hypotonia, and early intervention. For every one-month delay in walking, the likelihood of a P/LP variant increased by 5% to 11%. A history of language delay was also associated with P/LP variants when compared with the ES-negative cohort.

Using CART analysis, the study found that motor delay was the primary predictor of a genetic diagnosis, correctly classifying 75% of patients with a P/LP variant. Other factors, such as age of walking, hypotonia, and age at initial evaluation, further improved the classification accuracy.

Conclusions 

To summarize, this study confirmed that motor delay, female sex, and hypotonia were strongly associated with an increased likelihood of having a genetic diagnosis. Each one-month delay in walking increased the likelihood of a pathogenic or likely pathogenic variant by 5% to 11%.

Congenital heart disease was also linked to genetic diagnoses, but epilepsy was not, possibly due to sample characteristics. Lastly, the CART analysis showed motor delay and hypotonia as useful screening factors for genetic testing.

Journal reference:
Vijay Kumar Malesu

Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

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