2018 SRF Summer Scholar Profile: Guanlan Dong

Posted by Greg Chin on October 03, 2018 | SRF Education
 

2018 SRF Summer Scholar Guanlan Dong

Guanlan Dong

 

SRF Summer Scholar
Stanford University

 

My name is Guanlan Dong and I’m double majoring in math and computer science at Washington University in St. Louis. At WashU, I work in a cancer biology lab where we develop computational tools and use sequencing technology to analyze tumor data. This summer, I have been working in Dr. Michael Snyder’s lab at Stanford University. The Snyder lab focuses on inventing and applying omics technologies to the understanding of fundamental biology and human variation. Under the guidance of my mentor Dr. Lihua Jiang, I’m trying to correlate SNPs in the genome and isoform expression in the transcriptome to our proteome data. We hope to build a website that integrates the multi-omics analyses and make it easily accessible to the scientific community.

With recent advancements in sequencing technology, the entire human profiles of DNA, RNA, and protein become available on an increasingly large scale. The study of “-omics” takes these genomics, transcriptomics, and proteomics profiles to explore the underlying network and their connection to complex traits and diseases. The Genotype-Tissue Expression (GTEx) Consortium aims to link genetic variations to gene expression across a variety of human tissues. As part of the GTEx effort, my project focuses on associating single nucleotide polymorphisms (SNPs) in human genomes to protein expression in different tissues, as well as analyzing tissue-specific expression of different forms of protein encoded by the same gene. In the end, I will integrate all the data and analyses by building a publicly accessible website. Using samples with normal health conditions, we aim to establish an expression baseline that will significantly benefit future studies on complex traits and diseases. In other words, by comparing patients’ expression profiles in different “-omics” with our healthy baseline, one could identify causal genes and pathological mechanisms of target diseases. I will use senile cardiac amyloidosis (SCA) as an example to further demonstrate its application potential. SCA is a prevalent aging disease in senior populations that is often misdiagnosed. Therefore, many biomarkers have been discovered to help with early diagnosis and risk assessment. I will evaluate plasma biomarkers of SCA by analyzing their expression in heart tissues. In addition, by looking at the correlation between RNA and protein expression for each biomarker in plasma samples, it may be possible to substitute protein assays with RNA sequencing which is more sensitive and thus could be used for earlier detection. In the future, if “-omics” profiles of SCA patients are obtained, we can also identify genetic biomarkers by comparing their genomic profiles with corresponding protein expression.