@article {82, title = {Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis.}, journal = {Genet Med}, volume = {22}, year = {2020}, month = {2020 07}, pages = {1191-1200}, abstract = {

PURPOSE: The increasing use of electronic health records (EHRs) and biobanks offers unique opportunities to study Mendelian diseases. We described a novel approach to summarize clinical manifestations from patient EHRs into phenotypic evidence for cystic fibrosis (CF) with potential to alert unrecognized patients of the disease.

METHODS: We estimated genetically predicted expression (GReX) of cystic fibrosis transmembrane conductance regulator (CFTR) and tested for association with clinical diagnoses in the Vanderbilt University biobank (N = 9142 persons of European descent with 71 cases of CF). The top associated EHR phenotypes were assessed in combination as a phenotype risk score (PheRS) for discriminating CF case status in an additional 2.8 million patients from Vanderbilt University Medical Center (VUMC) and 125,305 adult patients including 25,314 CF cases from MarketScan, an independent external cohort.

RESULTS: GReX of CFTR was associated with EHR phenotypes consistent with CF. PheRS constructed using the EHR phenotypes and weights discovered by the genetic associations improved discriminative power for CF over the initially proposed PheRS in both VUMC and MarketScan.

CONCLUSION: Our study demonstrates the power of EHRs for clinical description of CF and the benefits of using a genetics-informed weighing scheme in construction of a phenotype risk score. This research may find broad applications for phenomic studies of Mendelian disease genes.

}, keywords = {Adult, Cystic Fibrosis, Cystic Fibrosis Transmembrane Conductance Regulator, Electronic Health Records, Humans, Mutation, Phenotype}, issn = {1530-0366}, doi = {10.1038/s41436-020-0786-5}, author = {Zhong, Xue and Yin, Zhijun and Jia, Gengjie and Zhou, Dan and Wei, Qiang and Faucon, Annika and Evans, Patrick and Gamazon, Eric R and Li, Bingshan and Tao, Ran and Rzhetsky, Andrey and Bastarache, Lisa and Cox, Nancy J} } @article {76, title = {Phenome-based approach identifies RIC1-linked Mendelian syndrome through zebrafish models, biobank associations and clinical studies.}, journal = {Nat Med}, volume = {26}, year = {2020}, month = {2020 01}, pages = {98-109}, abstract = {

Discovery of genotype-phenotype relationships remains a major challenge in clinical medicine. Here, we combined three sources of phenotypic data to uncover a new mechanism for rare and common diseases resulting from collagen secretion deficits. Using a zebrafish genetic screen, we identified the ric1 gene as being essential for skeletal biology. Using a gene-based phenome-wide association study (PheWAS) in the EHR-linked BioVU biobank, we show that reduced genetically determined expression of RIC1 is associated with musculoskeletal and dental conditions. Whole-exome sequencing identified individuals homozygous-by-descent for a rare variant in RIC1 and, through a guided clinical re-evaluation, it was discovered that they share signs with the BioVU-associated phenome. We named this new Mendelian syndrome CATIFA (cleft lip, cataract, tooth abnormality, intellectual disability, facial dysmorphism, attention-deficit hyperactivity disorder) and revealed further disease mechanisms. This gene-based, PheWAS-guided approach can accelerate the discovery of clinically relevant disease phenome and associated biological mechanisms.

}, keywords = {Abnormalities, Multiple, Animals, Behavior, Animal, Biological Specimen Banks, Chondrocytes, Disease Models, Animal, Extracellular Matrix, Fibroblasts, Guanine Nucleotide Exchange Factors, Humans, Models, Biological, Musculoskeletal System, Osteogenesis, Phenomics, Phenotype, Procollagen, Protein Transport, Secretory Pathway, Syndrome, Zebrafish, Zebrafish Proteins}, issn = {1546-170X}, doi = {10.1038/s41591-019-0705-y}, author = {Unlu, Gokhan and Qi, Xinzi and Gamazon, Eric R and Melville, David B and Patel, Nisha and Rushing, Amy R and Hashem, Mais and Al-Faifi, Abdullah and Chen, Rui and Li, Bingshan and Cox, Nancy J and Alkuraya, Fowzan S and Knapik, Ela W} } @article {110, title = {A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.}, journal = {Cancer Res}, volume = {80}, year = {2020}, month = {2020 10 15}, pages = {4346-4354}, abstract = {

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR <= 0.05, including seven previously reported genes (, and ) and six novel genes not yet reported for pancreatic cancer risk [6q27: OR (95\% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: OR (95\% CI), 0.78 (0.70-0.88); 14q24.3: OR (95\% CI), 1.35 (1.17-1.56); 17q12: OR (95\% CI), 6.49 (2.96-14.27); 17q21.1: OR (95\% CI), 1.94 (1.45-2.58); and 20p13: OR (95\% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (, and ) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.

}, keywords = {Age Factors, Case-Control Studies, European Continental Ancestry Group, Female, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Models, Genetic, Pancreatic Neoplasms, Polymorphism, Single Nucleotide}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-20-1353}, author = {Liu, Duo and Zhou, Dan and Sun, Yanfa and Zhu, Jingjing and Ghoneim, Dalia and Wu, Chong and Yao, Qizhi and Gamazon, Eric R and Cox, Nancy J and Wu, Lang} } @article {111, title = {A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.}, journal = {Nat Genet}, volume = {52}, year = {2020}, month = {2020 11}, pages = {1239-1246}, abstract = {

Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.

}, keywords = {Animals, Gene Expression Profiling, Genetic Association Studies, Humans, Lipoproteins, LDL, Mendelian Randomization Analysis, Mice, Models, Genetic, Multifactorial Inheritance, Predictive Value of Tests}, issn = {1546-1718}, doi = {10.1038/s41588-020-0706-2}, author = {Zhou, Dan and Jiang, Yi and Zhong, Xue and Cox, Nancy J and Liu, Chunyu and Gamazon, Eric R} } @article {56, title = {GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish.}, journal = {Am J Hum Genet}, volume = {104}, year = {2019}, month = {2019 Mar 07}, pages = {503-519}, abstract = {

Although the use of model systems for studying the mechanism of mutations that have a large effect is common, we highlight here the ways that zebrafish-model-system studies of a gene, GRIK5, that contributes to the polygenic liability to develop eye diseases have helped to illuminate a mechanism that implicates vascular biology in eye disease. A gene-expression prediction derived from a reference transcriptome panel applied to BioVU, a large electronic health record (EHR)-linked biobank at Vanderbilt University Medical Center, implicated reduced GRIK5 expression in diverse eye diseases. We tested the function of GRIK5 by depletion of its ortholog in zebrafish, and we observed reduced blood vessel numbers and integrity in the eye and increased vascular permeability. Analyses of EHRs in >2.6 million Vanderbilt subjects revealed significant comorbidity of eye and vascular diseases (relative risks 2-15); this comorbidity was confirmed in 150 million individuals from a large insurance claims dataset. Subsequent studies in >60,000 genotyped BioVU participants confirmed the association of reduced genetically predicted expression of GRIK5 with comorbid vascular and eye diseases. Our studies pioneer an approach that allows a rapid iteration of the discovery of gene-phenotype relationships to the primary genetic mechanism contributing to the pathophysiology of human disease. Our findings also add dimension to the understanding of the biology driven by glutamate receptors such as GRIK5 (also referred to as GLUK5 in protein form) and to mechanisms contributing to human eye diseases.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2019.01.017}, author = {Unlu, Gokhan and Gamazon, Eric R and Qi, Xinzi and Levic, Daniel S and Bastarache, Lisa and Denny, Joshua C and Roden, Dan M and Mayzus, Ilya and Breyer, Max and Zhong, Xue and Konkashbaev, Anuar I and Rzhetsky, Andrey and Knapik, Ela W and Cox, Nancy J} } @article {37, title = {An ancestry-based approach for detecting interactions.}, journal = {Genet Epidemiol}, volume = {42}, year = {2018}, month = {2018 Feb}, pages = {49-63}, abstract = {

BACKGROUND: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

RESULTS: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5{\texttimes}10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8{\texttimes}10-6).

CONCLUSION: We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

}, keywords = {Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People}, issn = {1098-2272}, doi = {10.1002/gepi.22087}, author = {Park, Danny S and Eskin, Itamar and Kang, Eun Yong and Gamazon, Eric R and Eng, Celeste and Gignoux, Christopher R and Galanter, Joshua M and Burchard, Esteban and Ye, Chun J and Aschard, Hugues and Eskin, Eleazar and Halperin, Eran and Zaitlen, Noah} }