Biology subjects

Laub, D.

Publications and source records attributed to Laub, D..

5 recordsLinked to original sources

bioRxiv2026

Evaluating Aggregated Gene Level eQTL Scores

Genetic feature engineering, used in methods such as transcriptome-wide association study, supports gene-trait association testing by aggregating single variants into gene-level features predictive of expression. To evaluate how different model architectures, LD filtering thresholds, and variant prioritization methods affect expression prediction quality, we trained over 3 million models and evaluated their performance in independent cohorts. Using the best performing models to impute expression and immunotherapy response as an example trait, we found a significant association with the reactive oxygen species pathway (p=0.032). Our model training workflow will support genetic feature engineering towards improved complex trait modeling.

bioinformatics↗

bioRxiv2026

Genetic background shapes AI-predicted variant effects

Predicting the consequences of genetic variants remains a major goal in biomedicine. Conventional approaches typically assess single-nucleotide variants in the context of a single reference genome, without accounting for genetic diversity that can modulate variant effects. Here we introduce the personalized variant effect predictor (pVEP) framework, which quantifies how genetic background across thousands of human genomes from globally diverse populations shapes computational predictions of clinical variant effects. Across deep learning models spanning protein structure, splicing, and noncoding regulation, pVEP reveals that many clinical variants exhibit heterogeneous predicted effects across haplotypes, with the same variant predicted to be pathogenic in some genetic backgrounds and benign in others. We find support for underlying molecular mechanisms, including shifts in predicted protein contacts and changes in splice-site recognition. Overall, personalized genomic context emerges as a systematically underappreciated variable in variant annotation and clinical interpretation, with particular implications for genetically diverse populations.

genomics↗

bioRxiv2025

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProjector, an AI foundation model that transforms tumor mutation profiles into a compact representation of cancer subtype, with broad implications for diagnosis and therapy. MutationProjector is pre-trained by integrating genomic alterations from >30,000 tumors with extensive molecular knowledge, yielding a model that accurately reconstructs held-out genetic profiles (demonstrating strong generalization) and determines subtype representations from altered molecular pathways (enabling model interpretability). We evaluate MutationProjector in independent tasks related to prediction of immunotherapy response, prediction of chemotherapy response, and classification of metastasis, recording leading performance in all areas. Each task identifies key biomarkers of interest, including KMT2A and KRAS-STK11 alterations which govern immunotherapy response.

systems biology↗

bioRxiv2025

GenVarLoader: An accelerated dataloader for applying deep learning to personalized genomics

Deep learning sequence models trained on personalized genomics can improve variant effect prediction, however, applications of these models are limited by computational requirements for storing and reading large datasets. We address this with GenVarLoader, which stores personalized genomic data in new memory-mapped formats with optimal data locality to achieve [~]1,000x faster throughput and [~]2,000x better compression compared to existing alternatives.

genomics↗

bioRxiv2021

Autoimmune Alleles at the Major Histocompatibility Locus Modify Melanoma Susceptibility

Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T-cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo and psoriasis predisposing MHC-I alleles conferred a melanoma protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (N = 451) and an independent validation cohort (N = 586), MHC-I autoimmune allele carrier status was significantly associated with a later age of melanoma diagnosis. Furthermore, MHC-I autoimmune allele carriers were significantly associated with decreased risk of developing melanoma in the Million Veterans Program cohort (OR = 0.962, p = 0.024). Existing melanoma polygenic risk scores (PRS) did not predict autoimmune allele carrier status, suggesting these alleles provide new risk-relevant information. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles (population frequency > 1%). However, autoimmune alleles showed higher affinity relative to common alleles for particular windows of melanocyte conserved antigens suggesting a potential relationship between antigen processing, binding, and cell-surface presentation. Overall, this study presents evidence that MHC-I autoimmune risk alleles modulate melanoma risk unaccounted for by current PRS.

cancer biology↗