Title | Pathway analysis with next-generation sequencing data. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Zhao J, Zhu Y, Boerwinkle E |
Secondary Authors | Xiong M |
Journal | Eur J Hum Genet |
Volume | 23 |
Issue | 4 |
Pagination | 507-15 |
Date Published | 2015 Apr |
ISSN | 1476-5438 |
Keywords | African Americans, Case-Control Studies, Computer Simulation, Databases, Genetic, European Continental Ancestry Group, Exome, Gene Frequency, Genetic Association Studies, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Myocardial Infarction, Polymorphism, Single Nucleotide, Principal Component Analysis, Sequence Analysis, DNA, Signal Transduction, Transforming Growth Factor beta |
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. |
DOI | 10.1038/ejhg.2014.121 |
Alternate Journal | Eur J Hum Genet |
PubMed ID | 24986826 |
PubMed Central ID | PMC4666565 |
Grant List | RC2 HL102923 / HL / NHLBI NIH HHS / United States RC2 HL102926 / HL / NHLBI NIH HHS / United States RC2 HL-102926 / HL / NHLBI NIH HHS / United States 1R01AR057120-01 / AR / NIAMS NIH HHS / United States RC2 HL-102923 / HL / NHLBI NIH HHS / United States R01 HL106034 / HL / NHLBI NIH HHS / United States R01 GM104411 / GM / NIGMS NIH HHS / United States 1R01HL106034-01 / HL / NHLBI NIH HHS / United States R01 AR057120 / AR / NIAMS NIH HHS / United States RC2 HL-102924 / HL / NHLBI NIH HHS / United States RC2 HL102924 / HL / NHLBI NIH HHS / United States RC2 HL-102925 / HL / NHLBI NIH HHS / United States RC2 HL103010 / HL / NHLBI NIH HHS / United States RC2 HL-103010 / HL / NHLBI NIH HHS / United States RC2 HL102925 / HL / NHLBI NIH HHS / United States |