Yi-Ju Li, PhD

Faculty Member, Duke Molecular Physiology Institute

Position

Associate Professor, Department of Biostatistics and Bioinformatics
Associate Professor, Department of Medicine
Associate Director, Biostatistics Core, Duke Translational Medicine Institute Duke University Medical Center

Contact

Carmichael Building

919 684 0604

yiju.li@duke.edu

Summary

Yi-Ju Li, PhD, is an Associate Professor in the Department of Biostatistics and Bioinformatics and Duke Molecular Physiology Institute (DMPI). She also serves as an Associate Director of the Biostatistics Core in the Duke Translational Medicine Institute (DTMI) and the group leader of the Biostatistics and Clinical Outcome group in the Department of Anesthesiology. Dr. Li is a statistical geneticist with research interest in statistical method development and its applications to genomic data derived from human disease genetic studies. Dr. Li has built her research expertise through the development of various family-based association methods for censored data, quantitative traits, and X-linked genes as well as her role as a key investigator in several collaborative genetic projects such as genetic studies of neurodegenerative diseases (Alzheimer's and Parkinson's disease), and eye disorders (myopia and Fuchs dystrophy). 

PhD, Statistics, NC State University, Raleigh, NC

As a statistical geneticist, Dr. Li’s research interest is primarily in statistical method development and its application for genetic research of human complex diseases. She has worked on several collaborative projects such as genetic studies of Alzheimer disease, Parkinson disease, Myopia, Fuchs Dystrophy, and several of postoperative outcomes of patients underwent non-emergent coronary artery bypass grafting with cardiopulmonary bypass.  In addition, Dr. Li and her collaborators have also developed a series of family-based association methods for testing quantitative traits, censored trait, and for detecting X-linked genes. Dr. Li’s group continues working on cutting edge statistical methods that can be applied to current genetic and genomic data.

Applied genetic studies of complex diseases:

  1. Alzheimer and Parkinson diseases (AD and PD): The primary focus is to investigate the overlapping genetic effects of AD and PD. Dr. Li and collaborators have investigated genetic modifiers for age-at-onset (AAO) of AD and PD. They completed the first genome wide linkage scan for AAO in AD and PD (1) and identified the first AAO gene, glutathione S-Transferase Omega 1 and 2 (GSTO1) for AD and PD (2,3).
  2. Myopia Genetics: Dr. Li collaborated with Dr. Terri Young at Duke Eye Center and Dr. Seang-Mei Saw at National University of Singapore for genetic studies of myopia. This collaboration has covered whole genome linkage scan, genome wide association studies (GWAS), the next generation sequencing. A number of publications on myopia genetics have been published including the largest genome wide linkage studies for high myopia (4) and refractive errors (5), and the identification of the first candidate gene, CTNND2 gene, for high myopia in Singaporean Chinese (6) based on a GWAS performed on Singapore Cohort study Of the Risk factors for Myopia (SCORM). Furthermore, Dr. Li’s group used both simulated data and SCORM GWAS data to evaluate seven copy number variant (CNV) detection methods, and concluded that QuantiSNP outperformed other methods and PennCNV detects less but more accurate CNVs (7).
  3. Fuchs Endothelium Corneal Dystrophy (FECD): FECD, first described by Ernst Fuchs in 1910, is a common hereditary disease of the corneal endothelium that leads to vision loss. The decrease of the corneal endothelium cells leads to the formation of excrescences in the central part of Descemet membrane (DM), termed guttae, and eventually progresses to corneal edema. Most patients begin showing symptoms after the age of 50.  Dr Li is collaborating with Dr Simon Gregory and Dr. Gordon Klintworth to study the genetic basis of the disease. The Duke FECD team has accumulated an FECD collection of over 1400 individuals and is considered one of the largest such collections in the country. Our FECD research covers a wide spectrum of genetic studies including genome wide linkage scan, sequencing of known candidate genes in our study cohort, genetic association studies, mitochondrial haplogroup association study, gene-environmental interaction, etc. To date, our FECD genetic team has completed the genome wide linkage scan for FECD (8,9), the replication study of TCF4 (9), and sequencing of several known candidate genes in African-Americans (10). Furthermore, in collaboration with Dr. Sudha Iyengar at Case-Western Reserve University, we have formed a FECD Genetics Consortium to conduct the largest scale GWAS for FECD using over 4500 samples. The detail of this study can be found in NCBI dbGaP  
  4. Genetic studies of postoperative outcomes after non-emergent coronary artery bypass graft (CABG) with cardiopulmonary bypass:  Dr. Li currently directs the Biostatistics and Clinical Outcomes group in the Department of Anesthesiology at Duke. Dr. Li is also a member of Neurologic Outcome Research Group (NORG) of The Duke Heart Center and is associated with several clinical and genetic studies derived from NORG (11,12). Specifically, Dr. Li’s group is in charge of several GWAS studies to investigate various postoperative outcomes after CABG, including but not limited to renal dysfunction, postoperative onset atrial fibrillation (AFib), cognitive decline, etc.  

Methodology development:

  1. Family-based association methods for detecting X-linked gene: Majority of family-based association methods were developed for testing autosomal markers. Dr. Li collaborated with Drs. Eden Martin and Richard Morris to develop a series of family-based association methods for binary and quantitative traits. This joint work led to two papers for qualitative traits (13,14) and one paper for quantitative traits (15).  
  2. Family-based association methods for quantitative traits with and without censoring for common variants: This research was motivated by the study of AAO in AD and PD. In collaboration with Drs. Eden Martin and Andrew Allen, a family-based association method, GATOR, for testing a quantitative trait or trait with censoring like AAO was developed (16,17). Additionally, in collaboration with Dr. Eden Martin, Dr. Li’s group developed an EMK method which is an extension of the family-based association method for quantitative trait proposed by Monks and Kaplan (2000) to general pedigree with the capacity to  test both allelic and genotypic association (18).
  3. Family-based association methods for testing rare variants: With the available rare variant data from next generation sequencing, Dr. Li is funded by the Methodology Development Grant from the Department of Biostatistics and Bioinformatics (B&B) to develop family-based association methods for testing rare variants for quantitative traits and censored traits. This project is in collaboration with Dr. Andrew Allen in the B&B Department.

Software developed by Dr. Li's group (available on the Software Downloads page):

  • CNVAnalyst
  • EMK
  • GATOR
  • X-LRT
  • XQTL 

Postdocs / Fellows

Wenjing Qi, PhD

Other Research Team Members

Igor Akushevich, PhD