Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24986826
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Eur+J+Hum+Genet
2015 ; 23
(4
): 507-15
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Pathway analysis with next-generation sequencing data
#MMPMID24986826
Zhao J
; Zhu Y
; Boerwinkle E
; Xiong M
Eur J Hum Genet
2015[Apr]; 23
(4
): 507-15
PMID24986826
show ga
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.
free
free
free
