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Pathway analysis with next-generation sequencing data.

TitlePathway analysis with next-generation sequencing data.
Publication TypeJournal Article
Year of Publication2015
AuthorsZhao J, Zhu Y, Boerwinkle E
Secondary AuthorsXiong M
JournalEur J Hum Genet
Volume23
Issue4
Pagination507-15
Date Published2015 Apr
ISSN1476-5438
KeywordsAfrican 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.

DOI10.1038/ejhg.2014.121
Alternate JournalEur J Hum Genet
PubMed ID24986826
PubMed Central IDPMC4666565
Grant ListRC2 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