A study published in Nature reveals that both rare and common genetic variants influence single-cell immune gene expression, using data from 2,339 individuals across two Australian biomedical cohorts.
Methodology and Key Findings
The research team analyzed single-cell gene expression across 28 immune cell types, employing the SAIGE-QTL method to detect associations between genetic variants and gene expression. This approach combined single-variant and set-based tests, accounting for factors like sex, age, and mitochondrial gene counts. Common variants (MAF ≥ 1%) and rare variants (MAF < 1%) were evaluated separately, with rare variants analyzed using burden tests and the SKAT method.
Researchers identified eQTLs (expression quantitative trait loci) in cell types such as CD4TCM cells and CD16Mono, with peak numbers ranging from 43,014 to 269,371 per cell type. The study also classified eQTL patterns across cell types, noting scenarios where variants are identified with concordant effects or independent variants.
Variant quality control was performed using Allele-specific Variant Quality Score Recalibration (AS-VQSR) with GATK v.4.2.6.1, applied to all quality-control-pass, unrelated samples regardless of ancestry. Cell typing followed the single-cell eQTLGen consortium recommendations.
Rare-variant eQTLs were tested using SNPs and indels with MAF < 1% and missingness less than 15%, while common variants were analyzed with a cis window around genes.
Details Data Integration from TOB and BioHEART
The analysis drew from the Tasmanian Ophthalmic Biobank (TOB) and the BioHEART study, which included a total of 2,339 consenting individuals. Blood samples were collected under ethical protocols (2020/ETH02479 and 2019/ETH08376). The TOB cohort consisted of 983 individuals recruited between January and February 2018 by the Royal Hobart Hospital and Menzies Institute of Medical Research in Tasmania, Australia, consisting of 562 female and 421 male individuals.
This included UKBB GWAS summary statistics for 44 blood cell count and serum traits, supplemented with an extra 26 blood trait GWAS performed in ref. 58. Colocalizations with PPH4 > 0.8 were considered significant.
For a subset of gene–cell-type combinations the model failed to converge (0.27%) and these were excluded from the analysis, ensuring robustness through shuffling tests for our most abundant cell type, CD4TCM cells, which show no evidence of false positives for both common and rare-variant tests.
Implications for Disease Research
The number of peaks identified (q < 0.05) per cell type varied, with a median value reported.