Boltz, T. | Search bioRxiv

bioRxiv2021

Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-Seq limits sample size, sequencing depth, and therefore, discovery power. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-Seq of whole blood tissue across 1490 individuals at low-coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-Seq study of 570 individuals at high-coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-Seq data to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power. Our study suggests that lowering coverage while increasing the number of individuals is an effective approach to increase discovery power in RNA-Seq studies.

genomics↗

bioRxiv2020

Gene expression imputation provides insight into the genetic architecture of frontotemporal dementia

The etiology of genetically sporadic frontotemporal dementia is poorly understood. Although genome-wide association studies for frontotemporal dementia have identified a small number of candidate risk regions, most of the risk genes remain largely unknown. To identify candidate genes with predicted expression levels associated with frontotemporal dementia, we integrated genome-wide summary statistics with external reference gene expression data, using a transcriptome-wide association studies approach. FUSION software was used to leverage summary statistics on frontotemporal dementia (n=2,340 cases, n=7,252 controls) and clinical subtypes (behavioral variant frontotemporal dementia n=1,337 cases/2,754 controls; semantic dementia n=308 cases/616 controls; progressive non-fluent aphasia n=269 cases/538 controls, frontotemporal dementia with motor neuron disease n=200 cases/400 controls) from the International Frontotemporal Dementia Genomics Consortium with 53 expression quantitative loci tissue type panels (n=12,205 from five consortia). Significance was assessed using a 5% false discovery rate threshold. We identified 73 significant gene-tissue associations for frontotemporal dementia, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n=19 genes, 26%). Furthermore, the 17q21.31 inversion locus contained 23 significant associations, representing six unique genes whose predicted expression associated with frontotemporal dementia. Other top hits included SEC22B on chromosome 1, a gene involved in vesicle trafficking, TRGV5 on chromosome 17 and ZNF302 on chromosome 19. A single gene finding was observed for behavioral variant frontotemporal dementia (i.e., RAB38 on chromosome 11) with evidence from multiple tissue types. For the other clinical subtypes no significant associations were observed. We used transcriptome-wide association studies to prioritize candidate genes for frontotemporal dementia and identified a number of specific genes, including potential novel candidate genes (such as SEC22B) and previously reported risk regions (e.g., 17q.21.31). Most significant associations were observed in the dorsolateral prefrontal cortex, despite the modest sample size of the gene expression reference panel of this tissue type. This suggests that our findings are specific to frontotemporal dementia and are likely to be biologically relevant highlights of genes at different frontotemporal dementia risk loci that are contributing to the disease pathology.

genetics↗