My research interests primarily lie in two parts: 1) understanding genetic architecture of complex diseases and traits, and 2) clinical implementation of human genetics discoveries, for example, pharmacogenomics. I received my Ph.D. degree in Genomics and Computational Biology from University of Pennsylvania. My dissertation focused on identifying complex trait or disease-associated genes via genomic regulation-informed gene-based analyses. I am now a postdoctoral fellow in the Klein Lab (PharmGKB group). I am currently working on the Pharmacogenomics Clinical Annotation Tool (PharmCAT), a one-stop bioinformatics tool that analyzes pharmacogenomics variants from genotypic datasets and generates reports with genotype-based prescribing recommendations to supports clinical pharmacogenomics implementations and treatment decisions.
Bachelor of Science, Fudan University (2015)
Doctor of Philosophy, University of Pennsylvania (2020)
PhD, University of Pennsylvania, Genomics and Computational Biology (2020)
BS, Fudan University, Life Sciences (2015)
Teri Klein, Postdoctoral Faculty Sponsor
Pharmacogenomics Clinical Annotation Tool (PharmCAT)
Stanford, CA, USA
Teri Klein, (12/2/2020)
Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.
2021; 17 (4): e1009464
As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59*10-12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49*10-12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.
View details for DOI 10.1371/journal.pgen.1009464
View details for PubMedID 33901188
Genome-first approach to rare EYA4 variants and cardio-auditory phenotypes in adults.
While newborns and children with hearing loss are routinely offered genetic testing, adults are rarely clinically tested for a genetic etiology. One clinically actionable result from genetic testing in children is the discovery of variants in syndromic hearing loss genes. EYA4 is a known hearing loss gene which is also involved in important pathways in cardiac tissue. The pleiotropic effects of rare EYA4 variants are poorly understood and their prevalence in a large cohort has not been previously reported. We investigated cardio-auditory phenotypes in 11,451 individuals in a large biobank using a rare variant, genome-first approach to EYA4. We filtered 256 EYA4 variants carried by 6737 participants to 26 rare and predicted deleterious variants carried by 42 heterozygotes. We aggregated predicted deleterious EYA4 gene variants into a combined variable (i.e. "gene burden") and performed association studies across phenotypes compared to wildtype controls. We validated findings with replication in three independent cohorts and human tissue expression data. EYA4 gene burden was significantly associated with audiometric-proven HL (p=[Formula: see text], Mobitz Type II AV block (p=[Formula: see text]) and the syndromic presentation of HL and primary cardiomyopathy (p=0.0194). Analyses on audiogram, echocardiogram, and electrocardiogram data validated these associations. Prior reports have focused on identifying variants in families with severe or syndromic phenotypes. In contrast, we found, using a genotype-first approach, that gene burden in EYA4 is associated with more subtle cardio-auditory phenotypes in an adult medical biobank population, including cardiac conduction disorders which have not been previously reported. We show the value of using a focused approach to uncover human disease related to pleiotropic gene variants and suggest a role for genetic testing in adults presenting with hearing loss.
View details for DOI 10.1007/s00439-021-02263-6
View details for PubMedID 33745059
Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression
WORLD SCIENTIFIC PUBL CO PTE LTD. 2018: 448–59
Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.
View details for Web of Science ID 000461831500041
View details for PubMedID 29218904
View details for PubMedCentralID PMC5749400
A Genome-First Approach to Rare Variants in Dominant Postlingual Hearing Loss Genes in a Large Adult Population.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: To investigate the importance of rare variants in adult-onset hearing loss.STUDY DESIGN: Genomic association study.SETTING: Large biobank from tertiary care center.METHODS: We investigated rare variants (minor allele frequency <5%) in 42 autosomal dominant (DFNA) postlingual hearing loss (HL) genes in 16,657 unselected individuals in the Penn Medicine Biobank. We determined the prevalence of known pathogenic and predicted deleterious variants in subjects with audiometric-proven sensorineural hearing loss. We scanned across known postlingual DFNA HL genes to determine those most significantly contributing to the phenotype. We replicated findings in an independent cohort (UK Biobank).RESULTS: While rare individually, when considering the accumulation of variants in all postlingual DFNA genes, more than 90% of participants carried at least 1 rare variant. Rare variants predicted to be deleterious were enriched in adults with audiometric-proven hearing loss (pure-tone average >25 dB; P = .015). Patients with a rare predicted deleterious variant had an odds ratio of 1.27 for HL compared with genotypic controls (P = .029). Gene burden in DIABLO, PTPRQ, TJP2, and POU4F3 were independently associated with sensorineural hearing loss.CONCLUSION: Although prior reports have focused on common variants, we find that rare predicted deleterious variants in DFNA postlingual HL genes are enriched in patients with adult-onset HL in a large health care system population. We show the value of investigating rare variants to uncover hearing loss phenotypes related to implicated genes.
View details for DOI 10.1177/01945998211029544
View details for PubMedID 34281439
How Does the "Cookie-Bite" Audiogram Shape Perform in Discriminating Genetic Hearing Loss in Adults?
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
"Cookie-bite" or U-shaped audiograms-specifically, those showing midfrequency sensorineural hearing loss (HL)-are traditionally taught to be associated with genetic HL; however, their utility as a screening tool has not been reported. We aim to determine the performance of a cookie-bite audiogram shape in stratifying patients carrying putative loss-of-function variants in known HL genes from wild-type controls. We merged audiometric and exome sequencing data from adults enrolled in a large biobank at a tertiary care center. Of 321 patients, 50 carried a putative loss-of-function variant in an HL gene. The cookie-bite shape was present in 9 of those patients, resulting in low sensitivity (18%) and positive predictive value (15%) in stratifying genetic carrier status; 84% of patients with a cookie-bite audiogram did not carry a genetic variant. A cookie-bite audiogram should not be used to screen adults for possible genetic testing.
View details for DOI 10.1177/01945998211015181
View details for PubMedID 34058916
Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2019; 24: 296–307
Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s). Tissue-specific eQTL information grants TWAS the ability to perform association analysis on tissues whose gene expression profiles are otherwise hard to obtain, such as liver and heart. However, as eQTLs are tissue context-dependent, whether and how the tissue-specificity of eQTLs influences TWAS gene prioritization has not been fully investigated. In this study, we addressed this question by adopting two distinct TWAS methods, PrediXcan and UTMOST, which assume single tissue and integrative tissue effects of eQTLs, respectively. Thirty-eight baseline laboratory traits in 4,360 antiretroviral treatment-naïve individuals from the AIDS Clinical Trials Group (ACTG) studies comprised the input dataset for TWAS. We performed TWAS in a tissue-specific manner and obtained a total of 430 significant gene-trait associations (q-value < 0.05) across multiple tissues. Single tissue-based analysis by PrediXcan contributed 116 of the 430 associations including 64 unique gene-trait pairs in 28 tissues. Integrative tissue-based analysis by UTMOST found the other 314 significant associations that include 50 unique gene-trait pairs across all 44 tissues. Both analyses were able to replicate some associations identified in past variant-based genome-wide association studies (GWAS), such as high-density lipoprotein (HDL) and CETP (PrediXcan, q-value = 3.2e-16). Both analyses also identified novel associations. Moreover, single tissue-based and integrative tissuebased analysis shared 11 of 103 unique gene-trait pairs, for example, PSRC1-low-density lipoprotein (PrediXcan's lowest q-value = 8.5e-06; UTMOST's lowest q-value = 1.8e-05). This study suggests that single tissue-based analysis may have performed better at discovering gene-trait associations when combining results from all tissues. Integrative tissue-based analysis was better at prioritizing genes in multiple tissues and in trait-related tissue. Additional exploration is needed to confirm this conclusion. Finally, although single tissue-based and integrative tissue-based analysis shared significant novel discoveries, tissue context-dependency of eQTLs impacted TWAS gene prioritization. This study provides preliminary data to support continued work on tissue contextdependency of eQTL studies and TWAS.
View details for PubMedID 30864331
View details for PubMedCentralID PMC6417797
Collective feature selection to identify crucial epistatic variants
2018; 11: 5
Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
View details for DOI 10.1186/s13040-018-0168-6
View details for Web of Science ID 000430966900001
View details for PubMedID 29713383
View details for PubMedCentralID PMC5907720