Abstract
Genome-wide association studies often emphasize single-nucleotide polymorphisms with the smallest p-values with less attention given to single-nucleotide polymorphisms not ranked near the top. We suggest that gene pathways contain valuable information that can enable identification of additional associations. We used gene set information to identify disease-related pathways using three methods: gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA). Association tests were performed for common single-nucleotide polymorphisms and aggregated rare variants with traits Q1 and Q4. These pathway methods were evaluated by type I error, power, and the ranking of the VEGF pathway, the gene set used in the simulation model. GSEA and IPA had high power for detecting the VEGF pathway for trait Q1 (91.2% and 93%, respectively). These two methods were conservative with deflated type I errors (0.0083 and 0.0072, respectively). The VEGF pathway ranked 1 or 2 in 123 of 200 replicates using IPA and ranked among the top 5 in 114 of 200 replicates for GSEA. The empirical enrichment method had lower power and higher type I error. Thus pathway analysis approaches may be useful in identifying biological pathways that influence disease outcomes.
| Original language | English |
|---|---|
| Article number | S18 |
| Journal | BMC Proceedings |
| Volume | 5 |
| Issue number | SUPPL. 9 |
| DOIs | |
| State | Published - 2011 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Medicine(all)
Cite this
Pathway analysis following association study. / Ngwa, Julius S.; Manning, Alisa K.; Grimsby, Jonna L.; Lu, Chen; Zhuang, Wei V.; Destefano, Anita L.
In: BMC Proceedings, Vol. 5, No. SUPPL. 9, S18, 2011.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Pathway analysis following association study
AU - Ngwa, Julius S.
AU - Manning, Alisa K.
AU - Grimsby, Jonna L.
AU - Lu, Chen
AU - Zhuang, Wei V.
AU - Destefano, Anita L.
PY - 2011
Y1 - 2011
N2 - Genome-wide association studies often emphasize single-nucleotide polymorphisms with the smallest p-values with less attention given to single-nucleotide polymorphisms not ranked near the top. We suggest that gene pathways contain valuable information that can enable identification of additional associations. We used gene set information to identify disease-related pathways using three methods: gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA). Association tests were performed for common single-nucleotide polymorphisms and aggregated rare variants with traits Q1 and Q4. These pathway methods were evaluated by type I error, power, and the ranking of the VEGF pathway, the gene set used in the simulation model. GSEA and IPA had high power for detecting the VEGF pathway for trait Q1 (91.2% and 93%, respectively). These two methods were conservative with deflated type I errors (0.0083 and 0.0072, respectively). The VEGF pathway ranked 1 or 2 in 123 of 200 replicates using IPA and ranked among the top 5 in 114 of 200 replicates for GSEA. The empirical enrichment method had lower power and higher type I error. Thus pathway analysis approaches may be useful in identifying biological pathways that influence disease outcomes.
AB - Genome-wide association studies often emphasize single-nucleotide polymorphisms with the smallest p-values with less attention given to single-nucleotide polymorphisms not ranked near the top. We suggest that gene pathways contain valuable information that can enable identification of additional associations. We used gene set information to identify disease-related pathways using three methods: gene set enrichment analysis (GSEA), empirical enrichment p-values, and Ingenuity pathway analysis (IPA). Association tests were performed for common single-nucleotide polymorphisms and aggregated rare variants with traits Q1 and Q4. These pathway methods were evaluated by type I error, power, and the ranking of the VEGF pathway, the gene set used in the simulation model. GSEA and IPA had high power for detecting the VEGF pathway for trait Q1 (91.2% and 93%, respectively). These two methods were conservative with deflated type I errors (0.0083 and 0.0072, respectively). The VEGF pathway ranked 1 or 2 in 123 of 200 replicates using IPA and ranked among the top 5 in 114 of 200 replicates for GSEA. The empirical enrichment method had lower power and higher type I error. Thus pathway analysis approaches may be useful in identifying biological pathways that influence disease outcomes.
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U2 - 10.1186/1753-6561-5-S9-S18
DO - 10.1186/1753-6561-5-S9-S18
M3 - Article
C2 - 22373100
AN - SCOPUS:82455215047
VL - 5
JO - BMC Proceedings
JF - BMC Proceedings
SN - 1753-6561
IS - SUPPL. 9
M1 - S18
ER -