Biology subjects

Brechtmann, F.

Publications and source records attributed to Brechtmann, F..

3 recordsLinked to original sources

bioRxiv2024

Functional gene embeddings improve rare variant polygenic risk scores

Rare variant association testing is a powerful strategy for identifying effector genes underlying common traits. However, its effectiveness is limited by the scarcity of high-impact rare allele carriers, posing challenges for sensitivity and robustness. Here, we introduce FuncRVP, a rare variant association framework addressing this issue by leveraging functional information across genes. FuncRVP models the effects of rare variants as a weighted sum of gene impairment scores, with weights regularized through a prior based on functional gene embeddings. Modeling 41 quantitative traits from unrelated UK Biobank participants showed that FuncRVP consistently outperformed linear regressions on significantly associated genes and did so more effectively for traits with higher burden heritability. The framework demonstrated versatility, yielding consistent improvements across diverse gene embeddings. Moreover, FuncRVP generated more robust gene effect estimates and yielded more gene discoveries, especially among genetically constrained genes. These findings demonstrate the value of integrating functional information in rare variant association studies and showcase FuncRVP as a promising tool for enhancing phenotype prediction and gene discovery.

genetics↗

bioRxiv2023

Integration of variant annotations using deep set networks boosts rare variant association genetics

Rare genetic variants can strongly predispose to disease, yet accounting for rare variants in genetic analyses is statistically challenging. While rich variant annotations hold the promise to enable well-powered rare variant association tests, methods integrating variant annotations in a data-driven manner are lacking. Here, we propose DeepRVAT, a model based on set neural networks that learns burden scores from rare variants, annotations, and phenotypes. In contrast to existing methods, DeepRVAT yields a single, trait-agnostic, nonlinear gene impairment score, enabling both risk prediction and gene discovery in a unified framework. On 34 quantitative and 26 binary traits, using whole-exome-sequencing data from UK Biobank, we find that DeepRVAT offers substantial increases in gene discoveries and improved replication rates in held-out data. Moreover, we demonstrate that the integrative DeepRVAT gene impairment score greatly improves detection of individuals at high genetic risk. Finally, we show that pre-trained DeepRVAT scores generalize across traits, opening up the possibility to conduct highly computationally efficient rare variant tests.

bioinformatics↗

bioRxiv2018

OUTRIDER: A statistical method for detecting aberrantly expressed genes in RNA sequencing data

RNA sequencing (RNA-seq) is gaining popularity as a complementary assay to genome sequencing for precisely identifying the molecular causes of rare disorders. A powerful approach is to identify aberrant gene expression levels as potential pathogenic events. However, existing methods for detecting aberrant read counts in RNA-seq data either lack assessments of statistical significance, so that establishing cutoffs is arbitrary, or rely on subjective manual corrections for confounders. Here, we describe OUTRIDER (OUTlier in RNA-seq fInDER), an algorithm developed to address these issues. The algorithm uses an autoencoder to model read count expectations according to the co-variation among genes resulting from technical, environmental, or common genetic variations. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. The model is automatically fitted to achieve the best correction of artificially corrupted data. Precision-recall analyses using simulated outlier read counts demonstrated the importance of combining correction for co-variation and significance-based thresholds. OUTRIDER is open source and includes functions for filtering out genes not expressed in a data set, for identifying outlier samples with too many aberrantly expressed genes, and for the P-value-based detection of aberrant gene expression, with false discovery rate adjustment. Overall, OUTRIDER provides a computationally fast and scalable end-to-end solution for identifying aberrantly expressed genes, suitable for use by rare disease diagnostic platforms.

bioinformatics↗