Biology subjects

Massarat, A. R.

Publications and source records attributed to Massarat, A. R..

3 recordsLinked to original sources

bioRxiv2026

Haplotype-based fine-mapping of variant associations for complex traits

Genome-wide association studies (GWAS) have identified tens of thousands of associations between genomic regions and complex traits in humans, but identifying the causal variants underlying these associations is challenging. Statistical fine-mapping techniques attempt to quantify the probability of causality of individual variants while accounting for the correlation induced by linkage disequilibrium (LD) between nearby variants. Existing fine-mapping solutions are limited to considering additive and independent contributions of individual variants, and also assume the causal variant is included in the analysis. Here, we introduce a novel fine-mapping method, happler, which models the effects of variant haplotypes (sets of alleles of two or more variants in cis) in addition to individual variants. By identifying haplotype proxies for causal variants, happler can infer additional effects including those driven by combinations of variants in cis and ungenotyped causal variants, such as complex structural variants, which are in some cases better tagged by one or more haplotypes than by individual SNPs. Simulation experiments show that whereas happler can correctly identify causal haplotype effects, traditional methods based on individual variants can produce inconclusive results in such cases. We applied happler to identify 32 haplotypes associated with gene expression using 359 individuals from the Geuvadis dataset. Of these, five are confidently fine-mapped, with stronger posterior inclusion probabilities than any individual variant, in an external cohort from the Genotype Tissue Expression (GTEx) project. Finally, we used the AoU dataset to demonstrate the utility of haplotypes for tagging rare causal variants that are absent from the input dataset.

bioinformatics↗

bioRxiv2025

Estimating the cis-heritability of gene expression using single cell expression profiles controls false positive rate of eGene detection

For gene expression traits, cis-genetic heritability can quantify the strength of genetic regulation in particular cell types, elucidating the cell-type-specificity of disease variants and genes. To estimate gene expression heritability, standard models require a single gene expression value per individual, forcing data from single cell RNA-sequencing (scRNA-seq) experiments to be "pseudobulked". Here, we show that applying standard heritability models to pseudobulk data overestimates gene expression heritability and produces inflated false positive rates for detecting cis-heritable genes. Therefore, we introduce a new method called scGeneHE (single cell Gene expression Heritability Estimation), a Poisson mixed-effects model that quantifies the cis-genetic component of gene expression using individual cellular profiles. In simulations, scGeneHE has a consistently well-calibrated false positive rate for eGene detection and unbiasedly estimates cis-heritability at many parameter settings. We applied scGeneHE to scRNA-seq data from 969 individuals, 11 immune cell types, and 822,552 cells from the OneK1K cohort to infer cell-type-specificity of genetic regulation at risk genes for immune-mediated diseases and trace the fluctuation of cis-heritability across cellular populations of varying resolution. In summary, we developed a new statistical method that resolves the analytical challenge of estimating gene expression cis-heritability from native scRNA-seq data.

genetics↗

bioRxiv2021

Discovering single nucleotide variants and indels from bulk and single-cell ATAC-seq

Genetic variants and de novo mutations in regulatory regions of the genome are typically discovered by whole-genome sequencing (WGS), however WGS is expensive and most WGS reads come from non-regulatory regions. The Assay for Transposase-Accessible Chromatin (ATAC-seq) generates reads from regulatory sequences and could potentially be used as a low-cost capture method for regulatory variant discovery, but its use for this purpose has not been systematically evaluated. Here we apply seven variant callers to bulk and single-cell ATAC-seq data and evaluate their ability to identify single nucleotide variants (SNVs) and insertions/deletions (indels). In addition, we develop an ensemble classifier, VarCA, which combines features from individual variant callers to predict variants. The Genome Analysis Toolkit (GATK) is the best-performing individual caller with precision/recall on a bulk ATAC test dataset of 0.92/0.97 for SNVs and 0.87/0.82 for indels. On bulk ATAC-seq reads, VarCA achieves superior performance with precision/recall of 0.99/0.95 for SNVs and 0.93/0.80 for indels. On single-cell ATAC-seq reads, VarCA attains precision/recall of 0.98/0.94 for SNVs and 0.82/0.82 for indels. In summary, ATAC-seq reads can be used to accurately discover non-coding regulatory variants in the absence of whole-genome sequencing data and our ensemble method, VarCA, has the best overall performance.

bioinformatics↗