Biology subjects

Hayeck, T. J.

Publications and source records attributed to Hayeck, T. J..

6 recordsLinked to original sources

bioRxiv2026

Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions

Different regions of genes perform distinct functions and vary in their importance to human health. Evolutionary intolerance provides a powerful means of identifying regions where disruptive mutations are under strong purifying selection, informing genetic disease discovery and variant interpretation. However, estimating intolerance in small sub-genic regions from population variation alone is underpowered and unstable. We present PRIME, a Bayesian model that stabilizes estimates of regional missense intolerance by sharing information hierarchically across regions. Importantly, PRIME produces a full joint posterior across all genes, allowing complex inferential questions that are difficult or impossible to address with existing approaches to be answered. We utilize this to identify regions enriched for pathogenic and experimentally deleterious missense variants, improve prioritization of Mendelian disease genes by focusing on their most intolerant regions, and uncover conserved patterns of purifying selection across protein families. Integrating PRIME with existing computational variant predictors improves pathogenicity prediction, demonstrating that regional missense intolerance provides complementary information for clinical variant interpretation.

genomics↗

bioRxiv2026

Multi-omics Differential Inference for Functional Interpretation (MoDIFI): A Statistical Framework to Prioritize Cell Lines for Neurodevelopmental Variants

Noncoding variants contribute to neurodevelopmental disorders (NDDs), but their regulatory effects are often cell-type specific, making it difficult to choose an in vitro model for high-throughput assays such as massively parallel reporter assays. We asked: given a set of noncoding variants, which cell line and regulatory regions are most likely to reveal measurable allele-specific effects? We generated matched multiomics profiles across commonly used NDD in vitro models: human neuronal lines (i.e., IMR-32, SH-SY5Y, SK-N-SH), mouse neuronal lines (i.e., HT-22, Neuro-2a), and a non-neuronal line (i.e., HEK-293), using RNA-seq, ATAC-seq, and Hi-C under consistent conditions. To integrate these orthogonal data types, we developed MoDIFI (Multi-omics Differential Inference for Functional Interpretation), a Bayesian framework that quantifies cell-line-specific regulatory activity by computing posterior inclusion probabilities (PIPs) for differential gene-loop interactions. MoDIFI identifies regulatory regions supported by coordinated 3D contacts, accessibility, and transcriptional output, producing cell-line-resolved regulatory maps that highlight both shared synaptic programs and context-dependent mechanisms. These results provide a practical strategy for prioritizing the most informative cell lines and candidate regulatory elements for targeted functional testing of NDD-relevant noncoding variation.

genomics↗

bioRxiv2025

Functional Category-Specific Intolerance Reflects Genic Function and Clinical Relevance

A key problem in genetics is associating variants with disease phenotypes. In aid of this, much progress has been made in quantifying the functional impact of individual variants on the gene product it codes for. However, the intolerance of the sequence in which those variants are found to functional variation is also a key determinant of whether a deleterious variant is pathogenic or not. Previous approaches to estimating genic intolerance have combined functional variant types, i.e., missense, loss-of-function, etc., or restricted analyses to only one type, i.e., pLI, missense-Z etc. Here we take a different approach and jointly model patterns of intolerance across multiple functional variant types. We refer to this approach as CATMINT. We show that CATMINT is competitive with previous gene level intolerance metrics in predicting disease relevant genes, with CATMINT ranking among the top performing scores across differing types of genes. However, perhaps more exciting is that CATMINT enables variant category specific intolerance estimation, revealing distinct functional profiles across genes/gene families. Analysis of ClinVar data shows that CATMINT intolerance patterns in disease genes recapitulate patterns of pathogenic variants within those genes, supporting the utility of category-specific intolerance in clinical variant interpretation. Further, we use the statistical framework utilized by CATMINT to conduct power analyses, allowing us to classify genes according to the power those genes have to detect intolerance. This allows us, for example, to identify genes that are underpowered and undetected, but may nevertheless be highly intolerant. Together, these results define a framework for understanding how selective pressures shape gene-specific sensitivity to different classes of mutation, improving the resolution of variant interpretation and gene prioritization in clinical and functional genomics.

evolutionary biology↗

bioRxiv2021

Improved Characterization of Balancing Selection Genome Wide and a Detailed Look at HLA Genes

Balancing selection occurs when multiple alleles are kept at elevated frequencies in equilibrium due to opposing evolutionary pressures. A new statistical method was developed to test for selection using efficient Bayesian techniques. Selection signals in three different data sets, generated with variable sequencing technologies, were compared: clinical trios, HLA NGS typed samples, and whole-genome long-read samples. Genome-wide, selection was observed across multiple gene families whose biological functions favor diversification, revealing established targets as well as 45 novel genes under selection. Using high-resolution HLA typing and long-read sequencing data, for the characterization of the MHC, revealed strong selection in expected peptide-binding domains as well as previously understudied intronic and intergenic regions of the MHC. Surprisingly, SIRPA, demonstrated dramatic selection signal, second only to the MHC in most settings. In conclusion, employing novel statistical approaches and improved sequencing technologies is critical to properly analyze complex genomic regions.

genetics↗

bioRxiv2020

De Novo Mutation in an Enhancer of EBF3 in simplex autism

Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of de novo protein-coding variants within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2,671 families with autism, with a specific focus on de novo variation in enhancers with previously characterized in vivo activity. We identified three independent de novo mutations limited to individuals with autism in the enhancer hs737. These mutations result in similar phenotypic characteristics, affect enhancer activity in vitro, and preferentially occur in AAT motifs in the enhancer with predicted disruptions of transcription factor binding. We also find that hs737 is enriched for copy number variation in individuals with NDDs, is dosage sensitive in the human population, is brain-specific, and targets the NDD gene EBF3 that is genome-wide significant for protein coding de novo variants, demonstrating the importance of understanding all forms of variation in the genome. One Sentence SummaryWhole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

genomics↗

bioRxiv2018

Improved Pathogenic Variant Localization using a Hierarchical Model of Sub-regional Intolerance

Different parts of a gene can be of differential importance to development and health. This regional heterogeneity is also apparent in the distribution of disease mutations which often cluster in particular regions of disease genes. The ability to precisely estimate functionally important sub-regions of genes will be key in correctly deciphering relationships between genetic variation and disease. Previous methods have had some success using standing human variation to characterize this variability in importance by measuring sub-regional intolerance, i.e., the depletion in functional variation from expectation within a given region of a gene. However, the ability to precisely estimate local intolerance was restricted by the fact that only information within a given sub-region is used, leading to instability in local estimates, especially for small regions. We show that borrowing information across regions using a Bayesian hierarchical model, stabilizes estimates, leading to lower variability and improved predictive utility. Specifically, our approach more effectively identifies regions enriched for ClinVar pathogenic variants. We also identify significant correlations between sub-region intolerance and the distribution of pathogenic variation in disease genes, with AUCs for classifying de novo missense variants in Online Mendelian Inheritance in Man (OMIM) genes of up to 0.86 using exonic sub-regions and 0.91 using sub-regions defined by protein domains. This result immediately suggests that considering the intolerance of regions in which variants are found may improve diagnostic interpretation. We also illustrate the utility of integrating regional intolerance into gene-level disease association tests with a study of known disease genes for epileptic encephalopathy.

genetics↗