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. 2021 Apr 3;21:64. doi: 10.1186/s12874-021-01237-6

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 6 — Performance comparison of the different model types based on odds ratios. The performance is quantified by the distance (rooted mean squared error) between log odds ratios computed from generated samples and validation data. Each model is evaluated on the same 30 different data sets. Each of the 30 data sets contains genetic variation data from 50 SNPs from randomly selected genetic locations. As shown in Fig. 4, the original data sets with 500 samples (chromosomes) are equally split into two, five and 20 sites, respectively. Results are shown for the combined generated data sets collected from the sites