Published genomewide association (GWA) studies typically analyze and report single-nucleotide polymorphisms (SNPs)
and their neighboring genes with the strongest evidence of association (the “most-significant SNPs/genes” approach),
while paying little attention to the rest. Borrowing ideas from microarray data analysis, we demonstrate that pathwaybased
approaches, which jointly consider multiple contributing factors in the same pathway, might complement the
most-significant SNPs/genes approach and provide additional insights into interpretation of GWA data on complex
diseases.
作 者
相关链接
DOI URL
浙公网安备 33010202000686号 ( 浙ICP备09035230号-1 )
