GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning (2407.16940v2)

Abstract: Genetic variants (GVs) are defined as differences in the DNA sequences among individuals and play a crucial role in diagnosing and treating genetic diseases. The rapid decrease in next generation sequencing cost has led to an exponential increase in patient-level GV data. This growth poses a challenge for clinicians who must efficiently prioritize patient-specific GVs and integrate them with existing genomic databases to inform patient management. To addressing the interpretation of GVs, genomic foundation models (GFMs) have emerged. However, these models lack standardized performance assessments, leading to considerable variability in model evaluations. This poses the question: How effectively do deep learning methods classify unknown GVs and align them with clinically-verified GVs? We argue that representation learning, which transforms raw data into meaningful feature spaces, is an effective approach for addressing both indexing and classification challenges. We introduce a large-scale Genetic Variant dataset, named GV-Rep, featuring variable-length contexts and detailed annotations, designed for deep learning models to learn GV representations across various traits, diseases, tissue types, and experimental contexts. Our contributions are three-fold: (i) Construction of a comprehensive dataset with 7 million records, each labeled with characteristics of the corresponding variants, alongside additional data from 17,548 gene knockout tests across 1,107 cell types, 1,808 variant combinations, and 156 unique clinically verified GVs from real-world patients. (ii) Analysis of the structure and properties of the dataset. (iii) Experimentation of the dataset with pre-trained GFMs. The results show a significant gap between GFMs current capabilities and accurate GV representation. We hope this dataset will help advance genomic deep learning to bridge this gap.

• The paper presents GV-Rep as a comprehensive dataset curated from seven major sources to improve genetic variant representation.
• It details rigorous statistical analyses across chromosomes, diseases, and functional effects to highlight diverse genomic characteristics.
• Experimental evaluations show GFMs achieving over 65% AUROC in pathogenicity, yet underperforming in complex, cell-specific regulatory tasks.

Insights on "GV-Rep: A Large-Scale Dataset for Genetic Variant Representation Learning"

The paper introduces GV-Rep, a comprehensive large-scale genetic variant (GV) dataset designed to enhance representation learning within genomic deep learning models. This dataset addresses current challenges faced in the interpretation and classification of genetic variants, which are imperative for advancing genomic medicine.

Dataset Overview and Contributions

GV-Rep aggregates over 7.5 million GV records, including clinician-verified data, serving as an extensive resource for training and evaluating genomic foundation models (GFMs). The data compilation leverages inputs from seven major genomic databases, each offering distinct focuses such as ClinVar's pathogenicity annotations or Cell Passport's tissue-specific contexts. This diverse integration ensures that GV-Rep includes a broad spectrum of genetic and epigenetic interactions.

The authors outline three primary contributions:

1. Comprehensive Dataset Construction: This involves meticulous curation of data from various public sources combined with a clinician-verified set, aiming to enhance both the scale and applicability of the dataset for clinical usage.
2. Dataset Structure and Properties Analysis: Detailed evaluations of the dataset's statistical distributions across chromosomes, diseases, and functional effects provide insight into its diverse characteristics and utility.
3. Experimental Evaluation with GFMs: GV-Rep has been employed to fine-tune several GFMs, revealing notable gaps in current models' abilities to accurately represent GV complexity and variability across contexts.

Strong Numerical Results and Claims

GV-Rep's experiments with GFMs underscore significant gaps in existing models' capabilities. Notably, while models achieved over 65% AUROC in typical pathogenicity classifications, their performance in more complex tasks such as cell-specific gene expression regulation was marginally better than chance—highlighting the challenges associated with contextual and multifactorial genomic interpretation.

Implications and Future Directions

The introduction of GV-Rep has practical implications for both clinical genomics and artificial intelligence in medicine. By providing a resource that captures diverse genetic contexts, this dataset is poised to advance the development of more nuanced models that can better mimic and predict genomic intricacies.

The paper speculates on the future trajectory of AI-centered genomic analysis, emphasizing the need for incorporating sensitive genetic attributes to evaluate model fairness across demographics. This direction could address issues arising from underrepresented groups in genomic data.

Furthermore, integrating epigenetic data and expanding genomic data across species could refine AI models, offering deeper evolutionary insights and fostering translational research.

Conclusion

GV-Rep stands as a pivotal resource in the landscape of genomic variant analysis, addressing deficiencies in current representations employed by GFMs. While it sets the groundwork for advancements in genomic medicine through deep learning, the paper astutely identifies ongoing challenges and potential pathways for future exploration. As the field evolves, datasets such as GV-Rep are crucial in propelling refined AI methodologies, fostering more accurate and inclusive genomic interpretations.