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. 2015 Sep 3;16(1):673. doi: 10.1186/s12864-015-1874-9

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© Tang et al. 2015

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — Decision tree to find the tagSNP and gene for the GWAS results based on linkage disequilibrium values. The tagSNP is at the terminal branch of the tree, SNP1 is the reference SNP, SNP2 Block is a block of SNPs linked to SNP1 (R² > 0.8), and SNP2 is a found SNP within the SNP2 Block based on the decisions made by the algorithm. The values of the association effect and significance (p) were obtained from the GWAS. Once a tagSNP was identified, it was assumed that the gene(s) causing the association was within 1 Kb