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Constructing gene association networks for rheumatoid arthritis using the backward genotype-trait association (BGTA) algorithm.

Ding Y, Cong L, Ionita-Laza I, Lo SH, Zheng T - BMC Proc (2007)

Bottom Line: For the candidate genes, we found strong signals for PTPN22 and SUMO4.Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15.Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, Columbia University, New York, New York 10027, USA. yding@stat.columbia.edu

ABSTRACT

Background: Rheumatoid arthritis (RA, MIM 180300) is a common and complex inflammatory disorder. The North American Rheumatoid Arthritis Consortium (NARAC) data, as part of the Genetic Analysis Workshop 15 data, consists of both genome scan and candidate gene studies on RA patients.

Results: We applied the backward genotype-trait association (BGTA) algorithm to capture marginal and gene x gene interaction effects of multiple susceptibility loci on RA disease status. A two-stage screening approach was used for the genome scan, whereas a comprehensive study of all possible subsets was conducted for the candidate genes. For the genome scan, we constructed an association network among 39 genetic loci that demonstrated strong signals, 19 of which have been reported in the RA literature. For the candidate genes, we found strong signals for PTPN22 and SUMO4. Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15. To control for false positives, we used permutation tests to constrain the family-wise type I error rate to 1%.

Conclusion: Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them. For the first time, we report possible interactions between single-nucleotide polymorphisms/genes, which may be useful for biological interpretation.

No MeSH data available.


Related in: MedlinePlus

Association network of candidate gene loci with significant signals.
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Figure 4: Association network of candidate gene loci with significant signals.

Mentions: For subsets identified with more than one SNP, we constructed a graphical network using the graph exploration system GUESS [10] (Figures 3 and 4). A direct edge indicates a two-SNP cluster. For clusters with more than two SNPs, a non-SNP node was created with all involved SNP connected to it.


Constructing gene association networks for rheumatoid arthritis using the backward genotype-trait association (BGTA) algorithm.

Ding Y, Cong L, Ionita-Laza I, Lo SH, Zheng T - BMC Proc (2007)

Association network of candidate gene loci with significant signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367461&req=5

Figure 4: Association network of candidate gene loci with significant signals.
Mentions: For subsets identified with more than one SNP, we constructed a graphical network using the graph exploration system GUESS [10] (Figures 3 and 4). A direct edge indicates a two-SNP cluster. For clusters with more than two SNPs, a non-SNP node was created with all involved SNP connected to it.

Bottom Line: For the candidate genes, we found strong signals for PTPN22 and SUMO4.Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15.Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, Columbia University, New York, New York 10027, USA. yding@stat.columbia.edu

ABSTRACT

Background: Rheumatoid arthritis (RA, MIM 180300) is a common and complex inflammatory disorder. The North American Rheumatoid Arthritis Consortium (NARAC) data, as part of the Genetic Analysis Workshop 15 data, consists of both genome scan and candidate gene studies on RA patients.

Results: We applied the backward genotype-trait association (BGTA) algorithm to capture marginal and gene x gene interaction effects of multiple susceptibility loci on RA disease status. A two-stage screening approach was used for the genome scan, whereas a comprehensive study of all possible subsets was conducted for the candidate genes. For the genome scan, we constructed an association network among 39 genetic loci that demonstrated strong signals, 19 of which have been reported in the RA literature. For the candidate genes, we found strong signals for PTPN22 and SUMO4. Based on significant association evidence, we built an association network among the loci of PTPN22, PADI4, DLG5, SLC22A4, SUMO4, and CARD15. To control for false positives, we used permutation tests to constrain the family-wise type I error rate to 1%.

Conclusion: Using the BGTA algorithm, we identified genetic loci and candidate genes that were associated with RA susceptibility and association networks among them. For the first time, we report possible interactions between single-nucleotide polymorphisms/genes, which may be useful for biological interpretation.

No MeSH data available.


Related in: MedlinePlus