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Rizvi, A. A.

Publications and source records attributed to Rizvi, A. A..

2 recordsLinked to original sources

Genome-wide association analyses identify variants in IRF4 associated with Acute Myeloid Leukemia and Myelodysplastic Syndrome susceptibility

The role of common genetic variation in susceptibility to acute myeloid leukemia (AML), and myelodysplastic syndrome (MDS), a group of rare clonal hematologic disorders characterized by dysplastic hematopoiesis and high mortality, remains unclear. We performed AML and MDS genome-wide association studies (GWAS) in the DISCOVeRY-BMT cohorts (2309 cases and 2814 controls). Association analysis based on subsets (ASSET) was used to conduct a summary statistics SNP-based analysis of MDS and AML subtypes. For each AML and MDS case and control we used PrediXcan to estimate the component of gene expression determined by their genetic profile and correlate this imputed gene expression level with risk of developing disease in a transcriptome-wide association study (TWAS). ASSET identified an increased risk for de novo AML and MDS (OR=1.38, 95% CI, 1.26-1.51, Pmeta=2.8x10-12) in patients carrying the T allele at rs12203592 in Interferon Regulatory Factor 4 (IRF4), a transcription factor which regulates myeloid and lymphoid hematopoietic differentiation. Our TWAS analyses showed increased IRF4 gene expression is associated with increased risk of de novo AML and MDS (OR=3.90, 95% CI, 2.36-6.44, Pmeta =1.0x10-7). The identification of IRF4 by both GWAS and TWAS contributes valuable insight on the role of genetic variation in AML and MDS susceptibility.

genomics↗

gwasurvivr: an R package for genome wide survival analysis

SummaryTo 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.\n\nAvailability and implementationgwasurvivr, including source code, documentation, and vignette are available at: http://bioconductor.org/packages/gwasurvivr\n\nContactAbbas Rizvi, rizvi.33@osu.edu; Lara E Sucheston-Campbell, suchestoncampbell.1@osu.edu\n\nSupplementary information: Supplementary data are available at https://github.com/suchestoncampbelllab/gwasurvivr_manuscript

genomics↗