Biology subjects

Fletez-Brant, K.

Publications and source records attributed to Fletez-Brant, K..

6 recordsLinked to original sources

bioRxiv2026

GEMS (GWAS Effect Mechanisms via Sequence-to-Function): Unveiling cell type-specific mechanisms for GWAS variants

Genome-wide association studies (GWAS) have linked thousands of loci to common diseases, yet for non-coding variants, which constitute the vast majority of the signal, the cell types and states through which individual risk variants act remain largely unknown. Here, we develop GEMS (GWAS Effect Mechanisms via Sequence-to-Function), a framework that trains supervised classifiers on multimodal variant effect predictions (ATAC-seq and RNA-seq) from the sequence-to-function model Borzoi and applies SHAP-based attribution to associate individual GWAS variants with the cell types most relevant to their disease association. Applied to asthma, LDL cholesterol, multiple sclerosis, and type 2 diabetes, GEMS prioritizes known disease-relevant cell types concordant with stratified LD-score regression. At the variant level, GEMS associates a median of 58.7% of fine-mapped variants with at least one cell type via ATAC-seq and 65.9% via RNA-seq, extending cellular annotation beyond the chromatin accessibility peaks in the single-cell atlas used for model training. ATAC-seq variant-cell-type associations are significantly enriched in cis-regulatory elements from independent experimental studies not used in model training, and enrichment persists after excluding variants overlapping training atlas peaks. For multiple sclerosis, hierarchical biclustering of variant--cell-type associations partitions risk variants into immune and CNS modules driven by distinct transcription factor families, recapitulating the immune-mediated and neurodegenerative components of the disease and demonstrating that integrative analysis of sequence-to-function predictions can stratify phenotype-associated variants into mechanistically distinct, cell-type-specific regulatory programs.

genetics↗

bioRxiv2026

gRely: Relyability for genome trained sequence-to-expression models

Sequence-to-function (S2F) models predict molecular phenotypes from DNA sequence and are increasingly applied to variant effect prediction (VEP), where the goal is to quantify how genetic variants alter gene expression. However, S2F model predictions are not uniformly reliable: accuracy varies substantially across variants, genes, and tissues, and current practice relies on crude magnitude thresholding to enrich for trustworthy predictions, which discards the majority of variants where S2F models could still provide signal. We developed gRely, a meta-modeling framework that estimates the probability that a given Borzoi VEP correctly predicts eQTL direction, using 1,121 features derived from the target variant, gene, and model outputs. On held-out tissues, gRely achieves a mean average precision of 0.885 (random baseline 0.744). Critically, within the low-magnitude regime where thresholding fails entirely, gRely identifies a high-confidence subset with 76% accuracy compared to a 58% baseline, recovering reliable predictions that magnitude filtering would discard. Interpretation via SHAP reveals that in this low-magnitude regime, gene expression level and cross-replicate signal concentration replace VEP magnitude as the primary discriminators of reliability. gRely is the first framework to provide per-prediction confidence scores for S2F model VEPs, and generalizes across architectures, producing consistent improvements on AlphaGenome predictions. By making reliability quantifiable, gRely enables principled filtering rather than blanket thresholding, and marks a step toward trustworthy deployment of S2F models in genomic research and clinical applications.

genomics↗

bioRxiv2026

Agentic systems are adept at solving well-scoped, verifiable problems in computational biology

We introduce CompBioBench, a benchmark of 100 diverse tasks for evaluating agentic systems in computational biology. Unlike mathematics and programming, which more readily admit systematic verification, biological data are inherently noisy and open to interpretation. To enable objective evaluation without reducing tasks to prescriptive checklists, we propose a new benchmark construction strategy based on synthetic/augmented data and metadata scrambling/scrubbing of real datasets to create challenging problems that have a single ground-truth answer and require multi-step reasoning, tool use, bespoke code, and interaction with real-world external resources. The benchmark spans genomics, transcriptomics, epigenomics, single-cell analysis, human genetics, and machine learning workflows. Questions are curated by domain experts to cover a broad range of skills with varying difficulty. We evaluate leading general-purpose agentic systems starting from a bare-minimum environment, requiring them to fetch data and tools as needed to solve each problem. We find strong end-to-end performance, with Codex CLI (GPT 5.4) reaching 83% accuracy, Gemini CLI (3.1 Pro) reaching 82%, Claude Code (Opus 4.6) reaching 81%, and Claude Code (Opus 4.7) reaching 78%. On the hardest questions, Claude Code (Opus 4.6) reaches 69%, Codex CLI (GPT 5.4) reaches 59%, and Gemini CLI (3.1 Pro) reaches 49%. CompBioBench provides a practical testbed for measuring the progress of agentic systems in computational biology and for guiding future benchmark design. Data and a public leaderboard are available at https://huggingface.co/collections/Genentech/compbiobench-v1.

bioinformatics↗

bioRxiv2026

Systematic assessment of machine learning-based variant annotation methods for rare variant association testing

Machine learning-based annotation methods are increasingly used to assess the pathogenicity of genetic variants, but their performance at prioritizing variants for gene-level association testing remains poorly characterized. Here, we systematically benchmark five annotation methods -- CADD v1.6, CADD v1.7, AlphaMissense, ESM-1b, and GPN-MSA -- using four primary gene-based tests and six annotation-level aggregation tests across 14 quantitative traits measured in up to 350,377 UK Biobank participants. Using a novel framework based on Wasserstein dis-tances, we quantify how annotation choice affects test calibration and power. Tests using CADD annotations achieve the highest signal separation, while tests using AlphaMissense annotations exhibit systematically lower calibration. All combinations of methods produced significant re-sults that were enriched (1.8-5.8-fold) for loss-of-function intolerant genes, though tests using GPN-MSA annotations displayed the highest such enrichment. Replication across symmetric phenotypes and loss-of-function burden tests was generally similar across methods. Our anal-ysis provides practical guidance for annotation method selection in rare variant studies and establishes a distributional framework for calibration assessment.

bioinformatics↗

bioRxiv2019

Common DNA sequence variation influences 3-dimensional conformation of the human genome

The 3-dimensional (3D) conformation of chromatin inside the nucleus is integral to a variety of nuclear processes including transcriptional regulation, DNA replication, and DNA damage repair. Aberrations in 3D chromatin conformation have been implicated in developmental abnormalities and cancer. Despite the importance of 3D chromatin conformation to cellular function and human health, little is known about how 3D chromatin conformation varies in the human population, or whether DNA sequence variation between individuals influences 3D chromatin conformation. To address these questions, we performed Hi-C on Lymphoblastoid Cell Lines (LCLs) from 20 individuals. We identified thousands of regions across the genome where 3D chromatin conformation varies between individuals and found that this conformational variation is often accompanied by variation in gene expression, histone modifications, and transcription factor (TF) binding. Moreover, we found that DNA sequence variation influences several features of 3D chromatin conformation including loop strength, contact insulation, contact directionality and density of local cis contacts. We mapped hundreds of Quantitative Trait Loci (QTLs) associated with 3D chromatin features and found evidence that some of these same variants are associated at modest levels with other molecular phenotypes as well as complex disease risk. Our results demonstrate that common DNA sequence variants can influence 3D chromatin conformation, pointing to a more pervasive role for 3D chromatin conformation in human phenotypic variation than previously recognized.

genomics↗

bioRxiv2017

Removing unwanted variation between samples in Hi-C experiments

Hi-C data is commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation changes across the contact map. We present BNBC, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a QTL analysis as well as differential enrichment across cell types.

genomics↗