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. 2019 May 17;9:7523. doi: 10.1038/s41598-019-44046-0

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Power for GAMuT, KMR, and linear regression is plotted as a function of causal SNP, for LRFN5. Top plot assumes the causal SNP is associated with 18 of the 21 BDI questions. Middle plot assumes 12 of 21 questions are associated with causal SNP. Bottom plot assumes only 6 of 21 questions are associated with the causal SNP. Results are shown applying GAMuT using a projection matrix and a linear kernel for modeling phenotypic similarity. GAMuT and KMR analyses used a weighted linear genotype kernel, with weights based on sample MAF. Sample size is 2,500. Power is calculated assuming a significance (α) level of 0.001.