DNA Language Model and Interpretable Graph Neural Network Identify Genes and Pathways Involved in Rare Diseases (2024)

Abstract: Identification of causal genes and pathways is a critical step for understanding the genetic underpinnings of rare diseases. We propose novel approaches to gene prioritization and pathway identification using DNA language model, graph neural networks, and genetic algorithm. Using HyenaDNA, a long-range genomic foundation model, we generated dynamic gene embeddings that reflect changes caused by deleterious variants. These gene embeddings were then utilized to identify candidate genes and pathways. We validated our method on a cohort of rare disease patients with partially known genetic diagnosis, demonstrating the re-identification of known causal genes and pathways and the detection of novel candidates. These findings have implications for the prevention and treatment of rare diseases by enabling targeted identification of new drug targets and therapeutic pathways.

## introduction the landscape of genomics research has undergone a profound transformation with the advent of high-throughput sequencing technologies @xcite . the generation of a vast amount of genomics data offers unprecedented insights into human genetic diversity @xcite . however, this wealth of data brings significant challenges in terms of data analysis and interpretation. a main challenge in deciphering the underlying mechanisms of diseases is establishing a link between genotype and phenotype @xcite . this task becomes even harder in the context of rare diseases, where the scarcity of data reduces statistical power (seaby and ennis, 2020). traditional methods for finding diseaseassociated genes/pathways have predominantly relied on statistical approaches, such as correlating specific genetic variants with disease occurrence @xcite . these approaches show decent performance if the cohort size is large, which is often a big obstacle in rare disease studies. moreover, these methods usually utilize basic variant statistics (such as number of variant carriers), and might not take into account the gene-specific impact of variants on the gene sequence @xcite . another family of computational approaches for gene/pathway prioritization rely on the concept of guilt-by-association, where genes/pathways are considered potentially relevant based on their similarity to known disease genes @xcite . these methods work well in scenarios where some underlying genetic factors of the phenotype are well-studied, which is not the case for many diseases @xcite . moreover, these methods might introduce bias since they look for similar genes, thereby missing novel disease-causing genes @xcite . recent years have seen a remarkable rise in the performance of language models, particularly in the field of natural language processing (nlp) @xcite . these models 'learn' language by processing vast amounts of text data, enabling them to perform a wide range of downstream tasks such as translation, summarization, and question-answering with unprecedented accuracy and fluency @xcite . parallel to this development, the concept of language models has been applied to genomics, giving rise to dna language models (dna-lms) @xcite @xcite . genomic sequences, much like textual data, comprise long chains of information, in this case nucleotides instead of words. dna-lms apply the principles of nlp to interpret and analyze these sequences, translating the 'language' of dna into meaningful biological insights. by learning from extensive genomic data, these models can provide new perspectives on downstream biological processes @xcite . hyenadna @xcite is a longrange genomic foundation model pre-trained on the human reference genome at single nucleotide resolution. it can process long-range dna sequences and represent them as embeddings in a high-dimensional space. for any genomic region such as a gene, hyenadna generates embeddings that capture the inherent information of the dna sequence. these embeddings dynamically change in response to genetic variants, offering insights into how genetic alterations impact biological processes. we hypothesize that variants with strong deleterious effects have a detectable impact on gene embeddings. we designed complementary methods to identify genes and pathways that contain such deleterious variants and could therefore play a causal role in the pathogenesis of rare diseases. for gene prioritization, we propose two approaches (case-vscontrol and case-only) to quantitatively rank candidate genes (figure 1a ). for pathway identification, we propose a method that combines dna-lm, interpretable graph neural networks (gnn) @xcite and genetic algorithm @xcite (figure 1b ). we validate our methods on a cohort of rare disease patients with partially known genetic diagnosis, demonstrating the re-identification of known causal genes and the detection of novel candidates. ## discussion in this study we aim to harness the potential of dna foundation models to translate the intricate 'language' of dna into meaningful and actionable information. we propose a framework to utilize dna-lms for gene prioritization and pathway identification in rare disease studies. based on the hypothesis that variants with strong deleterious effects alter the gene embeddings significantly in the embedding space, we demonstrate that it is possible to prioritize disease-associated genes/pathways in a cohort of 120 children requiring intensive care support because of a severe illness caused by a respiratory virus. for gene prioritization, we propose two approaches to analyze the gene embeddings (figure 1a ): case-vs-control and case-only. the caseonly approach is particularly promising for rare disease research, where finding a well-matched control group is often challenging. the ability of the method to differentiate between mutant and non-mutant gene embeddings within the same patient cohort is a novel and practical solution to this long-standing issue. by applying the gene prioritization workflow, we successfully re-identified ifih1 -which encodes an rig-i-like receptor involved in the sensing of viral rna (rehwinkel and gack, 2020) -as the top candidate gene in our patient cohort. for pathway identification, we propose an integrative method, combining dna-lm with interpretable gnn and genetic algorithm (figure 1b ). this approach takes into account various information such as ppi, number of variant carriers, and context-specific impact of variants on gene sequences. by applying this method, we were able to identify potentially relevant genes (ifih1, oas1, oas3, mx1, ifnar1, il10rb, znfx1, nlrc5, trim40, and abce1) that can explain the disease pathogenesis. all the identified genes are coding for molecules that play an important role in antiviral defense. ifih1 encodes mda5, which is a cytoplasmic viral rna sensor that recognizes single-or doublestrand rna to launch a type 1 interferon response @xcite . oas1 and oas3 encode enzymes that activate host rnase l to degrade viral rna @xcite . abce1 encodes a protein that is involved in the regulation of oas/rnase l pathway @xcite . mx1 encodes a guanosine-triphosphatemetabolizing protein that antagonizes the replication process of viruses @xcite . ifnar1 and il10rb encode cytokine receptors that mediate the antiviral immunity @xcite . znfx1 encodes a protein that binds to viral rna and interacts with mitochondrial antiviral signaling (mavs) protein, promoting the expression of interferon-stimulated genes @xcite . nlrc5 and trim40 encode regulators of antiviral signaling pathways @xcite . deficiencies in some of these genes have been previously studied and shown to impair immunity against spe-cific human viruses @xcite @xcite @xcite @xcite . in this study we focused on dna-lms, although protein language models (plms) such as esm-1b @xcite have demonstrated state-ofthe-art performance in scoring missense variants. the reason we used a dna-lm instead of plm is that dna-lms can model various variant types (e.g., splicing, stop-gained, etc.) while plms focus only on missense variants. moreover, by using dna-lms, our method can be extended to other variant types such as those mapping to introns, branchpoint motives, or untranslated regions (utrs). while our method shows promise, there are inherent challenges and limitations. our proposed workflow identifies genes with significant changes in their embeddings, yet a careful analysis is required to quantify the minimum embedding distortion to be detectable by the model. moreover, the interpretation of gene embeddings requires careful consideration, since not all genetic variations captured in the embeddings might be clinically relevant. the potential for integrating dna-lms with other techniques, such as multi-omics, could further enhance our understanding of genetic diseases. this has significant implications for the identification of disease-causing genes/pathways, potentially leading to more targeted and effective treatments in personalized medicine. the demonstration that dna-lms can accurately identify genes and pathways involved in rare diseases paves the way for further research and application of artificial intelligence in various genomics research domains.