Biomedical subjects

Chris Wallace

Publications and source records attributed to Chris Wallace.

3 recordsLinked to original sources

PubMed2025

Exploiting pleiotropy to enhance variant discovery with functional false discovery rates.

The cost of recruiting participants for genome-wide association studies (GWASs) can limit sample sizes and hinder the discovery of genetic variants. Here we introduce the surrogate functional false discovery rate (sfFDR) framework that integrates summary statistics of related traits to increase power. The sfFDR framework provides estimates of FDR quantities such as the functional local FDR and q value, and uses these estimates to derive a functional P value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses. Compared with a standard analysis, sfFDR substantially increased power (equivalent to a 52% increase in sample size) in a study of obesity-related traits from the UK Biobank and discovered eight additional lead SNPs near genes linked to immune-related responses in a rare disease GWAS of eosinophilic granulomatosis with polyangiitis. Collectively, these results highlight the utility of exploiting related traits in both small and large studies.

Humans↗

PubMed2025

MDA5 variants trade antiviral activity for protection from autoimmune disease.

Loss-of-function variants in MDA5, a key sensor of double-stranded RNA from viruses and retroelements, have been associated with protection from type 1 diabetes (T1D) in genome-wide association studies (GWAS). MDA5 loss-of-function variants have also been reported to increase the risk of inflammatory bowel disease (IBD). Whether these associations are linked or extend to other diseases remains unclear. Here, fine-mapping analysis of four large GWAS datasets shows that T1D-protective loss-of-function MDA5 variants also protect against psoriasis and hypothyroidism, while increasing the risk of IBD. The degree of autoimmune protection and IBD risk were linearly proportional. The magnitudes of the odds ratios for autoimmune protection and IBD risk were larger for rare MDA5 variants than for common variants, which were differentially expressed in different geographic populations. Our analysis suggests MDA5 genetic variants offer a direct fitness trade-off between viral clearance and autoimmune tissue damage.

Interferon-Induced Helicase, IFIH1↗

PubMed2025

Penalised regression improves imputation of cell-type specific expression using RNA-seq data from mixed cell populations compared to domain-specific methods.

Gene expression studies often use bulk RNA sequencing of mixed cell populations because single cell or sorted cell sequencing may be prohibitively expensive. However, mixed cell studies may miss expression patterns that are restricted to specific cell populations. Computational deconvolution can be used to estimate cell fractions from bulk expression data and infer average cell-type expression in a set of samples (e.g., cases or controls), but imputing sample-level cell-type expression is required for more detailed analyses, such as relating expression to quantitative traits, and is less commonly addressed. Here, we assessed the accuracy of imputing sample-level cell-type expression using a real dataset where mixed peripheral blood mononuclear cells (PBMC) and sorted (CD4, CD8, CD14, CD19) RNA sequencing data were generated from the same subjects (N=158), and pseudobulk datasets synthesised from eQTLgen single cell RNA-seq data. We compared three domain-specific methods, CIBERSORTx, bMIND and debCAM/swCAM, and two cross-domain machine learning methods, multiple response LASSO and ridge, that had not been used for this task before. We also assessed the methods according to their ability to recover differential gene expression (DGE) results. LASSO/ridge showed higher sensitivity but lower specificity for recovering DGE signals seen in observed data compared to deconvolution methods, although LASSO/ridge had higher area under curves than deconvolution methods. Machine learning methods have the potential to outperform domain-specific methods when suitable training data are available.

Humans↗