MOTIVATION: Over the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Motivated by the potential higher order associations, several studies investigated variance eQTLs. RESULTS: In this paper, we develop a Quantile Rank-score based test (QRank), which provides an easy way to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRank to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRank complements the existing methods, and identifies new eQTLs with heterogeneous effects across different quantile levels. Notably, we show that the eQTLs identified by QRank but missed by linear regression are associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog, and are also more likely to be tissue specific than eQTLs identified by linear regression. AVAILABILITY AND IMPLEMENTATION: An R package is available on R CRAN at https://cran.r-project.org/web/packages/QRank . CONTACT: xs2148@cumc.columbia.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Over the past decade, there has been a remarkable improvement in our understanding of the role of genetic variation in complex human diseases, especially via genome-wide association studies. However, the underlying molecular mechanisms are still poorly characterized, impending the development of therapeutic interventions. Identifying genetic variants that influence the expression level of a gene, i.e. expression quantitative trait loci (eQTLs), can help us understand how genetic variants influence traits at the molecular level. While most eQTL studies focus on identifying mean effects on gene expression using linear regression, evidence suggests that genetic variation can impact the entire distribution of the expression level. Motivated by the potential higher order associations, several studies investigated variance eQTLs. RESULTS: In this paper, we develop a Quantile Rank-score based test (QRank), which provides an easy way to identify eQTLs that are associated with the conditional quantile functions of gene expression. We have applied the proposed QRank to the Genotype-Tissue Expression project, an international tissue bank for studying the relationship between genetic variation and gene expression in human tissues, and found that the proposed QRank complements the existing methods, and identifies new eQTLs with heterogeneous effects across different quantile levels. Notably, we show that the eQTLs identified by QRank but missed by linear regression are associated with greater enrichment in genome-wide significant SNPs from the GWAS catalog, and are also more likely to be tissue specific than eQTLs identified by linear regression. AVAILABILITY AND IMPLEMENTATION: An R package is available on R CRAN at https://cran.r-project.org/web/packages/QRank . CONTACT: xs2148@cumc.columbia.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Jason M Torres; Eric R Gamazon; Esteban J Parra; Jennifer E Below; Adan Valladares-Salgado; Niels Wacher; Miguel Cruz; Craig L Hanis; Nancy J Cox Journal: Am J Hum Genet Date: 2014-10-30 Impact factor: 11.025
Authors: Jennifer Harrow; Adam Frankish; Jose M Gonzalez; Electra Tapanari; Mark Diekhans; Felix Kokocinski; Bronwen L Aken; Daniel Barrell; Amonida Zadissa; Stephen Searle; If Barnes; Alexandra Bignell; Veronika Boychenko; Toby Hunt; Mike Kay; Gaurab Mukherjee; Jeena Rajan; Gloria Despacio-Reyes; Gary Saunders; Charles Steward; Rachel Harte; Michael Lin; Cédric Howald; Andrea Tanzer; Thomas Derrien; Jacqueline Chrast; Nathalie Walters; Suganthi Balasubramanian; Baikang Pei; Michael Tress; Jose Manuel Rodriguez; Iakes Ezkurdia; Jeltje van Baren; Michael Brent; David Haussler; Manolis Kellis; Alfonso Valencia; Alexandre Reymond; Mark Gerstein; Roderic Guigó; Tim J Hubbard Journal: Genome Res Date: 2012-09 Impact factor: 9.043
Authors: Andrew Anand Brown; Alfonso Buil; Ana Viñuela; Tuuli Lappalainen; Hou-Feng Zheng; J Brent Richards; Kerrin S Small; Timothy D Spector; Emmanouil T Dermitzakis; Richard Durbin Journal: Elife Date: 2014-04-25 Impact factor: 8.140
Authors: Wodan Ling; Ni Zhao; Anna M Plantinga; Lenore J Launer; Anthony A Fodor; Katie A Meyer; Michael C Wu Journal: Microbiome Date: 2021-09-02 Impact factor: 14.650