背景:多人群的疟疾风险分析至关重要,并且非常重要,同时克服了局限性。然而,通过全基因组关联研究从疟疾感染患者获得的遗传变异数据的多样性和积累的指数增长为探索遗传标记(风险因素)之间的显着差异开辟了前所未有的机会,特别是在人群对疟疾风险的抗性或易感性方面。因此,这项研究建议使用统计检验来分析大规模的遗传变异数据,包括来自三大洲11个人口的20,854个样本:非洲,大洋洲,和亚洲。
方法:尽管自1950年代以来,统计检验已被用于进行病例对照研究,以将风险因素与特定疾病联系起来,面临的几个挑战,包括数据的选择(序数与非序数)和测试(参数与非参数)。本研究克服了这些挑战,采用Mann-WhitneyU检验分析大规模遗传变异数据;探索群体之间标记的统计意义;并进一步鉴定高度分化的标记。
结果:这项研究的结果表明,在所有病例组和大多数对照组中,种群之间的遗传标记存在显着差异(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,大多数遗传标记存在显著差异(p<0.01),p值不同。此外,观察到几种遗传标记在所有种群中都有非常显著的差异(p<0.001),而其他人则存在于某些特定人群之间。此外,几种遗传标记在种群之间没有显着差异。
结论:这些发现进一步支持遗传标记在人群中对疟疾抗性或易感性的贡献不同。因此显示出疟疾感染可能性的差异。此外,这项研究证明了Mann-WhitneyU检验在分析大规模遗传变异数据中的遗传标记时的稳健性,从而表明在其他复杂疾病中探索遗传标记的替代方法。这些发现为遗传标记分析带来了巨大的希望,本研究中强调的管道可以完全复制以分析新数据。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.