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无相局部祖先推断减轻了交换机错误对基于相位的方法的影响

Siddharth Avadhanam, Amy L Williams

bioRxiv : the preprint server for biology

|December 18, 2023

概括

使用无相方法比以相为基础的方法提高了局部祖先推断 (LAI) 的准确性,无论分阶段质量如何. 无相LAI也提供了较少偏差的估计混合时间在种群遗传学.

科学领域:

人口遗传学人口遗传学
医学遗传学医学遗传学
计算生物学计算生物学

背景情况:

当地祖先推断 (LAI) 对于基因分析至关重要,例如混合映射和人口历史研究.
LAI方法的准确性受到包括基因型分期质量和混合后的时间在内的因素的影响.
现有的LAI方法可以广泛分为基于阶段和无阶段的方法.

研究的目的:

在不同阶段的质量和人口情景下经验性地评估四种LAI方法的性能.
为了比较基于阶段的LAI方法与无阶段的LAI方法的准确性,使用模拟的非洲-欧洲祖先个体.
评估LAI准确度对下游遗传分析的影响,例如估计混合后的时间.

主要方法:

模拟的非洲和欧洲血统混合的个体被生成.
分析了四种不同的LAI方法,包括基于阶段和无阶段的方法.
将TRACTOR相位校正算法应用于基于相位的方法进行比较.

主要成果:

与基于阶段的方法相比,无阶段的LAI方法始终表现出更高的Pearson与基本真相的相关性 (2.6-4.6%更高),无论阶段化选项如何.
即使在应用相位校正 (TRACTOR) 之后,无相位的LAI也保持了更高的精度 (2.4-3.8%高于皮尔森相关性).
基于阶段的LAI导致自添加以来的时间估计上升偏差 (约10代),而无阶段的LAI显示最小偏差.

结论:

LAI的准确性在很大程度上取决于基因型分阶段的质量,特别是基于阶段的方法.
无阶段的LAI方法提供了更强大,更准确的替代方案,特别是当分阶段质量不理想或未知时.
选择LAI方法对下游人口遗传分析有重大影响,包括人口推断.

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