Summary To address the limited software options for performing survival analyses with millions of SNPs, we developed gwasurvivr, an R/Bioconductor package with a simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or Sanger imputation servers), IMPUTE2 or PLINK files. To decrease the number of iterations needed for convergence when optimizing the parameter estimates in the Cox model, we modified the R package survival; covariates in the model are first fit without the SNP, and those parameter estimates are used as initial points. We benchmarked gwasurvivr with other software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools). gwasurvivr is significantly faster and shows better scalability as sample size, number of SNPs and number of covariates increases. Availability and implementation gwasurvivr, including source code, documentation and vignette are available at: http://bioconductor.org/packages/gwasurvivr. Supplementary information Supplementary data are available at Bioinformatics online.

The coronavirus disease 2019 (COVID-19) is an infectious disease that mainly affects the host respiratory system with ~ 80% asymptomatic or mild cases and ~ 5% severe cases. Recent genome-wide association studies (GWAS) have identified several genetic loci associated with the severe COVID-19 symptoms. Delineating the genetic variants and genes is important for better understanding its biological mechanisms. We implemented integrative approaches, including transcriptome-wide association studies (TWAS), colocalization analysis, and functional element prediction analysis, to interpret the genetic risks using two independent GWAS datasets in lung and immune cells. To understand the context-specific molecular alteration, we further performed deep learning-based single-cell transcriptomic analyses on a bronchoalveolar lavage fluid (BALF) dataset from moderate and severe COVID-19 patients. We discovered and replicated the genetically regulated expression of CXCR6 and CCR9 genes. These two genes have a protective effect on lung, and a risk effect on whole blood, respectively. The colocalization analysis of GWAS and cis -expression quantitative trait loci highlighted the regulatory effect on CXCR6 expression in lung and immune cells. In the lung-resident memory CD8 + T (T RM ) cells, we found a 2.24-fold decrease of cell proportion among CD8 + T cells and lower expression of CXCR6 in the severe patients than moderate patients. Pro-inflammatory transcriptional programs were highlighted in the T RM cellular trajectory from moderate to severe patients. CXCR6 from the 3p21.31 locus is associated with severe COVID-19. CXCR6 tends to have a lower expression in lung T RM cells of severe patients, which aligns with the protective effect of CXCR6 from TWAS analysis. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-021-02305-z.

Summary:Researchers are increasingly interested in evaluating time-to-event outcomes such as survival in the context of genetic variation. However, there are limited software options for performing survival analyses with millions of SNPs. To address this, we developed gwasurvivr, an R/Bioconductor package to conduct fast and efficient genome wide survival analyses. gwasurvivr accepts data in VCF (as outputted from Michigan or Sanger imputation servers) and IMPUTE2 format, and provides a simple interface to run large-scale analyses. We benchmarked gwasurvivr with other GWAS software capable of conducting genome wide survival analysis (genipe, SurvivalGWAS_SV, and GWASTools) and have demonstrated improved scalability that includes shorter run times for large sample sizes and larger number of SNPs.