SAS Output

An Integrative Analyzer for Publicly Available COVID-19 Genome-wide Association Studies

September 23, 2024: 9:30 AM - 10:00 AM

Careers, Training & Education, Brookside A

Authors	Abstract
Zhongshan Cheng	COVID-19 has led to over 7 million deaths globally, with human genetic factors playing a significant role in the variability of disease severity. To identify genetic contributors to severe COVID-19, we developed the "COVID19_GWAS_Analyzer" using SAS OnDemand for Academics. This genome-wide association study (GWAS) analyzer facilitates comprehensive explorative analysis of large-scale, publicly available COVID-19 datasets, including GWAS summary statistics, expression quantitative trait loci (eQTL) data, and single-cell expression data. The package incorporates over 400 SAS macros designed for mining these datasets and other related large-scale COVID-19 resources. The primary objective is to support biologists and geneticists in identifying candidate genes for further investigation based on COVID-19 GWAS summary data. Moreover, the flexibility of combining SAS macros within the SAS OnDemand for Academics platform enables detailed exploration of additional GWAS datasets. The "COVID19_GWAS_Analyzer" is freely available on GitHub at https://github.com/chengzhongshan/COVID19_GWAS_Analyzer.

Zhongshan Cheng

COVID-19 has led to over 7 million deaths globally, with human genetic factors playing a significant role in the variability of disease severity. To identify genetic contributors to severe COVID-19, we developed the "COVID19_GWAS_Analyzer" using SAS OnDemand for Academics. This genome-wide association study (GWAS) analyzer facilitates comprehensive explorative analysis of large-scale, publicly available COVID-19 datasets, including GWAS summary statistics, expression quantitative trait loci (eQTL) data, and single-cell expression data. The package incorporates over 400 SAS macros designed for mining these datasets and other related large-scale COVID-19 resources. The primary objective is to support biologists and geneticists in identifying candidate genes for further investigation based on COVID-19 GWAS summary data. Moreover, the flexibility of combining SAS macros within the SAS OnDemand for Academics platform enables detailed exploration of additional GWAS datasets. The "COVID19_GWAS_Analyzer" is freely available on GitHub at https://github.com/chengzhongshan/COVID19_GWAS_Analyzer.

Paper