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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

Flowchart for the analysis of the genome scan data.
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Figure 1: Flowchart for the analysis of the genome scan data.

Mentions: Based on GTA, BGTA is a backward greedy search algorithm that removes markers that lead to information gain until no further gain is possible (see the flowchart in Figure 1). BGTA screening returns a small "optimal" cluster of markers with the peak GTD score. Herein, a subset is deemed BGTA-irreducible if no marker can be removed without lowering the GTD score. For a large number of markers, such a backward screening is not informative initially due to sparseness issues in high dimensions. Thus, BGTA has been implemented to screen a large number of random marker subsets [5]. In this paper, GTD scores of retained local optimal clusters are recorded, which measure the information content of each retained local optimal cluster. Local optimal clusters of SNPs with GTD score higher than a selection threshold are selected as important.


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)

Flowchart for the analysis of the genome scan data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Flowchart for the analysis of the genome scan data.
Mentions: Based on GTA, BGTA is a backward greedy search algorithm that removes markers that lead to information gain until no further gain is possible (see the flowchart in Figure 1). BGTA screening returns a small "optimal" cluster of markers with the peak GTD score. Herein, a subset is deemed BGTA-irreducible if no marker can be removed without lowering the GTD score. For a large number of markers, such a backward screening is not informative initially due to sparseness issues in high dimensions. Thus, BGTA has been implemented to screen a large number of random marker subsets [5]. In this paper, GTD scores of retained local optimal clusters are recorded, which measure the information content of each retained local optimal cluster. Local optimal clusters of SNPs with GTD score higher than a selection threshold are selected as important.

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