Sara Mostafavi

Assistant Professor

Relevant Degree Programs


Graduate Student Supervision

Master's Student Supervision (2010 - 2018)
Molecular interpretation of genome-wide association studies using multiomics analysis (2018)

Genome-wide association studies have found thousands of single-nucleotide polymorphismsassociated with various human traits. Recently, a powerful statisticalapproach called MetaXcan has been proposed for interpreting genome-wide associationsat the gene level. We extended MetaXcan to a multiomics application,using a brain cortex reference dataset that includes gene expression, DNA methylation,and histone acetylation data from approximately 400 individuals. Our approach,Multi-MetaXcan, consists of three steps. In the first step, we use regularizedregression to build models that predict gene expression and variation in epigenomicmodifications from single-nucleotide polymorphisms. We call these modelsgenotype-based imputation models. In the second step, we apply these models tomap genome-wide associations to gene-level and epigenomic-level associations.Finally, in the third step, our model summarizes all molecular-level associations atthe gene level by building epigenome-based imputation models that predict geneexpression levels from nearby epigenomic marks like CpG sites and transcriptionallyactive regions. In summary, Multi-MetaXcan identifies trait-associatedgenes whose expression levels are impacted by single-nucleotide polymorphismsand their influence on intermediate molecular traits such as DNA methylation andhistone acetylation. We applied Multi-MetaXcan to a major depressive disordergenome-wide association study. As the result, we discovered 12 genes, 25 transcriptionallyactive regions, and 163 CpG sites associated with major depressivedisorder corresponding to 74 genes in total. 26 of these genes fall within or closeto previously identified major depressive disorder-associated genomic regions. Importantly,the inclusion of epigenomic data resulted in an additional 62 genes thatwere not identified by gene expression imputation model alone.

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