The primary goal of human genetics is to understand the relationship between DNA sequence variation and disease. Although we can now identify variation across the genome, our ability to predict the functional impact of specific genetic variants remains limited. For example, de novo protein-truncating variants that introduce premature termination codons (PTCs) into messenger RNAs (mRNAs) are enriched in the genomes of individuals with neurodevelopmental conditions, including autism spectrum disorder (ASD). However, the mechanisms by which PTC variants lead to these conditions can vary depending on the specific variant and the protein it affects. Some PTC-bearing mRNAs are degraded, leading to loss of the encoded protein by nonsense-mediated decay (NMD), while others escape NMD and produce truncated or aberrant proteins. Therefore, understanding the molecular consequences of PTC variants, including their NMD outcome, is essential for predicting their impact on human phenotypes and developing targeted therapies.

Despite the critical impact of NMD, there remain challenges in NMD outcome prediction of PTC variants. One key knowledge gap is an improved understanding of the rules governing NMD target selection. According to the exon junction complex-dependent model, mammalian NMD degrades mutant mRNAs with PTCs located greater than or equal to 50–55 base pairs upstream of the last exon-exon junction. Although current predictive tools based on this model determine the NMD outcomes of nearly 50 percent of PTC variants in humans at a genome-wide level, the remaining PTC-variants are exceptions to that rule. This led Zeynep Coban Akdemir and her team to their first central hypothesis that enhanced prediction models can be built to determine whether a PTC-bearing mutant mRNA will or will not undergo NMD by elucidating other rules informative for NMD efficiency through integration of computational and experimental approaches, including fluorescence-based reporter assays and CRISPR/Cas9-based gene editing.

Another gap in knowledge is that although much research has been done on NMD-triggering alleles (potential loss-of-function alleles) underlying ASD, relatively less work has been done on NMD-escape alleles (potential dominant-negative or gain-of-function alleles). Therefore, the second central hypothesis is that a systematic survey and identification of genes with NMD-escape alleles will uncover novel genes associated with ASD and gain further insights into their disease mechanisms. Addressing these gaps in knowledge is expected to advance our understanding of the functional impact of genetic variants associated with ASD.

This project has the potential to make a significant impact on the field of ASD genetics by providing enhanced tools and annotation resources for predicting the NMD outcomes of PTC variants, including the identification of NMD-escape alleles. The systematic profiling and characterization of putative NMD-escape alleles could also lead to the discovery of novel ASD risk genes and an improved understanding of the mechanisms underlying ASD, which could ultimately lead to the development of new targeted therapies.