Major

Bioinformatics

Anticipated Graduation Year

2021

Access Type

Open Access

Abstract

The central dogma of biology describes how an individual's genome contributes to their unique phenotypes: genes encoded in DNA get transcribed into mRNA molecules, which get translated into proteins. Nevertheless, the path from genotype to phenotype is not straightforward, and unraveling the specific molecular mechanisms by which a genotype results in a phenotype is particularly challenging. Multi-omics approaches seek to combat this issue by integrating a variety of omics data into genetic analyses. The combination of genome-wide association studies, which calculate associations between SNPs and traits, transcriptome-wide association studies, which calculate associations between transcripts and traits, and expression quantitative trait loci, which identify SNPs associated with varying expression levels, can help further elucidate causative variants with respect to a phenotype. This project aims at bringing another omics relationship into the fold - associations between transcripts and protein levels by performing TWAS for proteins using protein level data from individuals in the Trans-Omics for Precision Medicine cohort and predicted expression levels from the PrediXcan software, calculated with Genotype-Tissue Expression Project expression models.

Faculty Mentors & Instructors

Dr. Heather Wheeler, Loyola University Chicago Department of Biology

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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Determining the Genetic Component of Protein Levels Using TWAS

The central dogma of biology describes how an individual's genome contributes to their unique phenotypes: genes encoded in DNA get transcribed into mRNA molecules, which get translated into proteins. Nevertheless, the path from genotype to phenotype is not straightforward, and unraveling the specific molecular mechanisms by which a genotype results in a phenotype is particularly challenging. Multi-omics approaches seek to combat this issue by integrating a variety of omics data into genetic analyses. The combination of genome-wide association studies, which calculate associations between SNPs and traits, transcriptome-wide association studies, which calculate associations between transcripts and traits, and expression quantitative trait loci, which identify SNPs associated with varying expression levels, can help further elucidate causative variants with respect to a phenotype. This project aims at bringing another omics relationship into the fold - associations between transcripts and protein levels by performing TWAS for proteins using protein level data from individuals in the Trans-Omics for Precision Medicine cohort and predicted expression levels from the PrediXcan software, calculated with Genotype-Tissue Expression Project expression models.