title: Pathway analysis with next-generation sequencing data.
abstract: Although pathway analysis methods have been developed and successfully applied to association studies of common variants, the statistical methods for pathway-based association analysis of rare variants have not been well developed. Many investigators observed highly inflated false-positive rates and low power in pathway-based tests of association of rare variants. The inflated false-positive rates and low true-positive rates of the current methods are mainly due to their lack of ability to account for gametic phase disequilibrium. To overcome these serious limitations, we develop a novel statistic that is based on the smoothed functional principal component analysis (SFPCA) for pathway association tests with next-generation sequencing data. The developed statistic has the ability to capture position-level variant information and account for gametic phase disequilibrium. By intensive simulations, we demonstrate that the SFPCA-based statistic for testing pathway association with either rare or common or both rare and common variants has the correct type 1 error rates. Also the power of the SFPCA-based statistic and 22 additional existing statistics are evaluated. We found that the SFPCA-based statistic has a much higher power than other existing statistics in all the scenarios considered. To further evaluate its performance, the SFPCA-based statistic is applied to pathway analysis of exome sequencing data in the early-onset myocardial infarction (EOMI) project. We identify three pathways significantly associated with EOMI after the Bonferroni correction. In addition, our preliminary results show that the SFPCA-based statistic has much smaller P-values to identify pathway association than other existing methods.
authors.affiliation: Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
authors.forename: Jinying
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Human Genetics Center, Division of Biostatistics, University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Boerwinkle
authors.affiliation: Human Genetics Center, Division of Biostatistics, University of Texas Health Science Center at Houston, Houston, TX, USA.
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
mesh_terms: D001741:Black or African American / Q000235:genetics; D016022:Case-Control Studies; D003198:Computer Simulation; D030541:Databases, Genetic; D059472:Exome; D005787:Gene Frequency; D056726:Genetic Association Studies; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000235:genetics*; D020641:Polymorphism, Single Nucleotide; D025341:Principal Component Analysis; D017422:Sequence Analysis, DNA; D015398:Signal Transduction; D016212:Transforming Growth Factor beta / Q000235:genetics / Q000378:metabolism; D044465:White People / Q000235:genetics
chemical_list: D016212:Transforming Growth Factor beta