TY - JOUR
T1 - A powerful hybrid approach to select top single-nucleotide polymorphisms for genome-wide association study
AU - Wang, Jian
AU - Shete, Sanjay
N1 - Funding Information:
We appreciate the two anonymous reviewers for their insightful and constructive comments. This work was supported by National Institutes of Health [grant number 1R01CA131324].
PY - 2011/1/6
Y1 - 2011/1/6
N2 - Background: Genome-wide association (GWA) study has recently become a powerful approach for detecting genetic variants for common diseases without prior knowledge of the variant's location or function. Generally, in GWA studies, the most significant single-nucleotide polymorphisms (SNPs) associated with top-ranked p values are selected in stage one, with follow-up in stage two. The value of selecting SNPs based on statistically significant p values is obvious. However, when minor allele frequencies (MAFs) are relatively low, less-significant p values can still correspond to higher odds ratios (ORs), which might be more useful for prediction of disease status. Therefore, if SNPs are selected using an approach based only on significant p values, some important genetic variants might be missed. We proposed a hybrid approach for selecting candidate SNPs from the discovery stage of GWA study, based on both p values and ORs, and conducted a simulation study to demonstrate the performance of our approach.Results: The simulation results showed that our hybrid ranking approach was more powerful than the existing ranked p value approach for identifying relatively less-common SNPs. Meanwhile, the type I error probabilities of the hybrid approach is well-controlled at the end of the second stage of the two-stage GWA study.Conclusions: In GWA studies, SNPs should be considered for inclusion based not only on ranked p values but also on ranked ORs.
AB - Background: Genome-wide association (GWA) study has recently become a powerful approach for detecting genetic variants for common diseases without prior knowledge of the variant's location or function. Generally, in GWA studies, the most significant single-nucleotide polymorphisms (SNPs) associated with top-ranked p values are selected in stage one, with follow-up in stage two. The value of selecting SNPs based on statistically significant p values is obvious. However, when minor allele frequencies (MAFs) are relatively low, less-significant p values can still correspond to higher odds ratios (ORs), which might be more useful for prediction of disease status. Therefore, if SNPs are selected using an approach based only on significant p values, some important genetic variants might be missed. We proposed a hybrid approach for selecting candidate SNPs from the discovery stage of GWA study, based on both p values and ORs, and conducted a simulation study to demonstrate the performance of our approach.Results: The simulation results showed that our hybrid ranking approach was more powerful than the existing ranked p value approach for identifying relatively less-common SNPs. Meanwhile, the type I error probabilities of the hybrid approach is well-controlled at the end of the second stage of the two-stage GWA study.Conclusions: In GWA studies, SNPs should be considered for inclusion based not only on ranked p values but also on ranked ORs.
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U2 - 10.1186/1471-2156-12-3
DO - 10.1186/1471-2156-12-3
M3 - Article
C2 - 21211033
AN - SCOPUS:78650855850
SN - 1471-2156
VL - 12
JO - BMC genetics
JF - BMC genetics
M1 - 3
ER -