Statistical Analysis of Spike Mutation Co-Occurrence in SARS-CoV-2 (Cramér’s V & Fisher's Test on 79K Genomes)

This dataset presents co-occurrence patterns among SARS-CoV-2 spike protein mutations, based on 79,176 genome accessions analyzed using statistical association measures. Key metrics include <b>Cramér’s V</b>, <b>Chi-Square Test</b>, and <b>Fisher’s Exact Test</b>, enabling identification of mutation pairs with strong linkage and potential biological relevance.High-scoring mutation pairs suggest functional or evolutionary relationships such as epistasis, immune-driven selection, or lineage-defining changes. These results are crucial for ongoing surveillance, vaccine refinement, and viral dynamics research.<b>Methods:</b><br>Mutation co-occurrence was evaluated using:<b>Cramér’s V</b>: for strength of association<b>Chi-Square Test</b>: for significance in large sample sizes<b>Fisher’s Exact Test</b>: for rare variants<b>Use Cases:</b><br>This dataset supports mutation tracking, epistasis studies, antigenic shift monitoring, and integration into genomic surveillance systems.<b>Attribution:</b><br>Mutation data and analysis pipeline powered by <b>GVAtlas.org</b>.<b>Contact:</b><br>[e-mail]

本数据集基于79176条基因组序列登录号，采用统计关联测度开展分析，揭示了严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）刺突蛋白突变之间的共现模式。核心分析指标包括<b>克莱姆V值（Cramér’s V）</b>、<b>卡方检验（Chi-Square Test）</b>与<b>费希尔精确检验（Fisher’s Exact Test）</b>，可用于识别存在强关联且具备潜在生物学意义的突变对。得分较高的突变对可提示其存在功能或进化层面的关联，例如上位性（epistasis）、免疫驱动选择或谱系标志性突变。上述研究结果对于当前的病毒监测、疫苗优化及病毒动力学相关研究均具有重要价值。<br><b>研究方法：</b><br>突变共现关系的评估采用如下方式：<br>• <b>克莱姆V值（Cramér’s V）</b>：用于衡量关联强度<br>• <b>卡方检验（Chi-Square Test）</b>：适用于大样本量下的显著性检验<br>• <b>费希尔精确检验（Fisher’s Exact Test）</b>：用于稀有变异的关联分析<br><b>应用场景：</b><br>本数据集可支持突变追踪、上位性研究、抗原转变（antigenic shift）监测，以及集成至基因组监测系统中。<br><b>数据归属：</b><br>本数据集的突变数据与分析流程由<b>GVAtlas.org</b>提供技术支持。<br><b>联系方式：</b><br>[e-mail]