School of Information Technology, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.
Jeffrey Cheah School of Medicine & Health Sciences, Monash University Malaysia, Subang Jaya, Selangor, Malaysia.
Malar J. 2022 Mar 9;21(1):79. doi: 10.1186/s12936-022-04104-x.
The malaria risk analysis of multiple populations is crucial and of great importance whilst compressing limitations. However, the exponential growth in diversity and accumulation of genetic variation data obtained from malaria-infected patients through Genome-Wide Association Studies opens up unprecedented opportunities to explore the significant differences between genetic markers (risk factors), particularly in the resistance or susceptibility of populations to malaria risk. Thus, this study proposes using statistical tests to analyse large-scale genetic variation data, comprising 20,854 samples from 11 populations within three continents: Africa, Oceania, and Asia.
Even though statistical tests have been utilized to conduct case-control studies since the 1950s to link risk factors to a particular disease, several challenges faced, including the choice of data (ordinal vs. non-ordinal) and test (parametric vs. non-parametric). This study overcomes these challenges by adopting the Mann-Whitney U test to analyse large-scale genetic variation data; to explore the statistical significance of markers between populations; and to further identify the highly differentiated markers.
The findings of this study revealed a significant difference in the genetic markers between populations (p < 0.01) in all the case groups and most control groups. However, for the highly differentiated genetic markers, a significant difference (p < 0.01) was present for most genetic markers with varying p-values between the populations in the case and control groups. Moreover, several genetic markers were observed to have very significant differences (p < 0.001) across all populations, while others exist between certain specific populations. Also, several genetic markers have no significant differences between populations.
These findings further support that the genetic markers contribute differently between populations towards malaria resistance or susceptibility, thus showing differences in the likelihood of malaria infection. In addition, this study demonstrated the robustness of the Mann-Whitney U test in analysing genetic markers in large-scale genetic variation data, thereby indicating an alternative method to explore genetic markers in other complex diseases. The findings hold great promise for genetic markers analysis, and the pipeline emphasized in this study can fully be reproduced to analyse new data.
在压缩限制的情况下，对多个群体的疟疾风险进行分析至关重要。然而，通过全基因组关联研究从疟疾感染患者中获得的遗传变异数据的多样性和积累呈指数级增长，为探索遗传标记（风险因素）之间的显著差异提供了前所未有的机会，特别是在群体对疟疾风险的抵抗力或易感性方面。因此，本研究提出使用统计检验来分析大规模遗传变异数据，该数据包含来自三大洲（非洲、大洋洲和亚洲）的 11 个群体的 20854 个样本。
尽管自 20 世纪 50 年代以来，统计检验已被用于病例对照研究，以将风险因素与特定疾病联系起来，但面临着一些挑战，包括数据（有序与非有序）和检验（参数与非参数）的选择。本研究通过采用 Mann-Whitney U 检验来分析大规模遗传变异数据，克服了这些挑战；探索群体之间标记的统计学意义；并进一步确定高度分化的标记。
本研究的结果表明，在所有病例组和大多数对照组中，群体之间的遗传标记存在显著差异（p<0.01）。然而，对于高度分化的遗传标记，在病例组和对照组中，大多数遗传标记的 p 值存在显著差异（p<0.01）。此外，观察到多个遗传标记在所有群体中存在非常显著的差异（p<0.001），而其他标记则存在于某些特定群体之间。此外，还有一些遗传标记在群体之间没有显著差异。
这些发现进一步表明，遗传标记在不同群体中对疟疾的抵抗力或易感性有不同的贡献，从而显示出疟疾感染可能性的差异。此外，本研究还证明了 Mann-Whitney U 检验在分析大规模遗传变异数据中的遗传标记的稳健性，从而为探索其他复杂疾病中的遗传标记提供了一种替代方法。该研究结果为遗传标记分析提供了很大的帮助，本研究强调的管道可以完全复制，以分析新的数据。
