Biology subjects

Horta, D.

Publications and source records attributed to Horta, D..

4 recordsLinked to original sources

bioRxiv2021

CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq

Single cell RNA sequencing (scRNA-seq) enables characterizing the cellular heterogeneity in human tissues. Technological advances have enabled the first population-scale scRNA-seq studies in hundreds of individuals, allowing to assay genetic effects with single-cell resolution. However, existing strategies to perform genetic analyses using scRNA-seq remain based on principles established for bulk RNA-seq. In particular, current methods depend on a priori definitions of discrete cell types, and hence cannot assess allelic effects across subtle cell types and cell states. To address this, we propose Cell Regulatory Map (CellRegMap), a statistical framework to test for and quantify genetic effects on gene expression in individual cells. CellRegMap provides a principled approach to identify and characterize heterogeneity in allelic effects across cellular contexts of different granularity, including cell subtypes and continuous cell transitions. We validate CellRegMap using simulated data and apply it to two recent studies of differentiating iPSCs, where we uncover a previously underappreciated heterogeneity of genetic effects across cellular contexts. Finally, we identify fine-grained genetic regulation in neuronal subtypes for eQTL that are colocalized with human disease variants.

bioinformatics↗

bioRxiv2019

Systematic assessment of regulatory effects of human disease variants in pluripotent cells

Identifying regulatory genetic effects in pluripotent cells provides important insights into disease variants with potentially transient or developmental origins. Combining existing and newly-generated data, we characterized 1,367 iPSC lines from 948 unique donors, collectively analyzed within the \"Integrated iPSC QTL\" (i2QTL) Consortium. The sample size of our study allowed us to derive the most comprehensive map of quantitative trait loci (QTL) in pluripotent human cells to date. We mapped the effects of nearby common genetic variants on five expression phenotypes, identifying cis-QTL at gene-, exon-level and transcript-, splicing-, alternative polyadenylation-ratio (APA) for a total of 18,556 genes. For gene-level, we further quantified the effects of rare and singleton variants, and the effect of distal variants that act in trans (trans-eQTL), which we replicated in independent samples. Our data are a valuable community resource, uncovering novel regulatory effects that have not previously been described in differentiated cells and tissues. Building on this regulatory map, we functionally explore GWAS signals for over 4,336 trait loci, finding evidence for colocalization with common and rare iPSC QTL for traits such as height and BMI, and diseases, such as cancer and coronary artery disease.

genomics↗

bioRxiv2018

A linear mixed model approach to study multivariate gene-environment interactions

Different environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

genetics↗

bioRxiv2016

Joint genetic analysis using variant sets reveals polygenic gene-context interactions

Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.\n\nAuthor summaryGenetic effects on phenotypes can depend on external contexts, including environment. Statistical tests for identifying such interactions are important to understand how individual genetic variants may act in different contexts. Interaction effects can either be studied using measurements of a given phenotype in different contexts, under the same genetic backgrounds, or by stratifying a population into subgroups. Here, we derive a method based on linear mixed models that can be applied to both of these designs. iSet enables testing for interactions between context and sets of variants, and accounts for polygenic effects. We validate our model using simulations, before applying it to the genetic analysis of gene expression studies and genome-wide association studies of human blood lipid levels. We find that modeling interactions with variant sets offers increased power, thereby uncovering interactions that cannot be detected by alternative methods.

genetics↗