Selected Publications

An Opportunity to Fill a Gap for Newborn Screening of Neurodevelopmental Disorders

Wendy K. Chung, Stephen M. Kanne, Zhanzhi Hu

Int. J. Neonatal Screen. 2024, 10(2), 33; https://doi.org/10.3390/ijns10020033

Screening newborns using genome sequencing is being explored due to its potential to expand the list of conditions that can be screened. Previously, we proposed the need for large-scale pilot studies to assess the feasibility of screening highly penetrant genetic neurodevelopmental disorders. Here, we discuss the initial experience from the GUARDIAN study and the systemic gaps in clinical services that were identified in the early stages of the pilot study.

Review: Child Psychiatry in the Era of Genomics: The Promise of Translational Genetics Research for the Clinic

Sarah E. Fitzpatrick, Irene Antony,   Erika L. Nurmi, MD,  ∙ Thomas V. Fernandez, MD1 ∙ Wendy K. Chung, MD, PhD2,3 ∙ Catherine A. Brownstein, ∙ Joseph Gonzalez-Heydrich,  Raquel E. Gur, Amanda R. Merner, Gabriel Lázaro-Muñoz,   Matthew W. State, Kevin M. Simon,  Ellen J. Hoffman. 

 JAACAP Open, In Press (August 8, 2024); DOI: 10.1016/j.jaacop.2024.06.002

There has been remarkable progress in recent years in understanding the genetic underpinnings of child psychiatric disorders. Concurrently, genetic testing is becoming increasingly available in the clinic. However, many clinicians report a lack of familiarity with genetics and how genetic testing might inform a clinical evaluation. This review aims to introduce clinicians to cutting-edge research in child psychiatric genetics and discuss the emerging role of genetic tests in clinical practice.

A Probabilistic Graphical Model for Estimating Selection Coefficients of Nonsynonymous Variants from Human Population Sequence Data

Yige Zhao, Tian Lan, Guojie Zhong, Jake Hagen , Hongbing Pan, Wendy K Chung , Yufeng Shen 

Nat Commun. 2025, 16(1),4670: https://doi.org/10.1038/s41467-025-59937-2 

Accurately predicting the effect of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Commonly used computational methods predict pathogenicity, which does not capture the quantitative impact on fitness in humans. We develop a method, MisFit, to estimate missense fitness effect using a graphical model. MisFit jointly models the effect at a molecular level ( dd ) and a population level (selection coefficient, ss ), assuming that in the same gene, missense variants with similar dd have similar ss . We train it by maximizing probability of observed allele counts in 236,017 individuals of European ancestry. We show that ss is informative in predicting allele frequency across ancestries and consistent with the fraction of de novo mutations in sites under strong selection. Further, ss outperforms previous methods in prioritizing de novo missense variants in individuals with neurodevelopmental disorders. In conclusion, MisFit accurately predicts ss and yields new insights from genomic data.

Reference-informed Prediction of Alternative Splicing and Splicing-altering Mutations from Sequences

Chencheng Xu, Suying Bao, Ye Wang, Wenxing Li, Hao Chen, Yufeng Shen, Tao Jiang, Chaolin Zhang 

Genome Res, 2024; 34(7):1052-1065;  https://doi.org/10.1101/gr.279044.124 

Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes, and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incorporating the known splice site usage of a reference gene sequence to improve its prediction on splicing-altering mutations. We benchmarked DeltaSplice and several other state-of-the-art methods on various prediction tasks, including evolutionary sequence divergence on lineage-specific splicing and splicing-altering mutations in human populations and neurodevelopmental disorders, and demonstrated that DeltaSplice outperformed consistently. DeltaSplice predicted ∼15% of splicing quantitative trait loci (sQTLs) in the human brain as causal splicing-altering variants. It also predicted splicing-altering de novo mutations outside the splice sites in a subset of patients affected by autism and other neurodevelopmental disorders (NDDs), including 19 genes with recurrent splicing-altering mutations. Integration of splicing-altering mutations with other types of de novo mutation burdens allowed the prediction of eight novel NDD-risk genes. Our work expanded the capacity of in silico splicing models with potential applications in genetic diagnosis and the development of splicing-based precision medicine.