Wesley Wong, Lea Wang, Stephen S Schaffner, Xue Li, Ian Cheeseman, Timothy J C Anderson, Ashley Vaughan, Michael Ferdig, Sarah K Volkman, Daniel L Hartl, Dyann F Wirth
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MalKinID was calibrated to the genomic data from three laboratory-based genetic crosses (yielding 440 parent-child [PC] and 9060 full-sibling [FS] comparisons). MalKinID identified lab generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for PC and FS and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multi-sample comparisons. 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引用次数: 0
摘要
病原体基因组学是追踪传染病传播的有力工具。在疟疾中,通过后代鉴定(IBD)可用于评估寄生虫之间的遗传亲缘关系,并已被用于研究传播和输入。从理论上讲,IBD 可用来区分谱系关系,以重建传播历史,或为定量性状-病灶实验识别寄生虫。MalKinID (疟疾亲缘关系识别器)是一种新的分类模型,旨在根据全基因组的 IBD 比例和 IBD 片段分布来识别疟疾寄生虫之间的系谱关系。MalKinID 根据三个实验室基因杂交的基因组数据进行了校准(产生了 440 个亲子 [PC] 和 9060 个全同胞 [FS] 比较)。MalKinID 识别实验室产生的 F1 后代的灵敏度大于 80%,并显示 NF54 和 NHP4026 杂交的第二代后代中有 0.39(95% CI 0.28,0.49)个是 F1 后代,0.56(0.45,0.67)个是 F1 与亲本 NF54 株系的回交后代。在模拟的外交进口中,MalKinID 能高精度、高灵敏度地重建系谱历史,F1 评分超过 0.84。然而,当导入涉及近亲繁殖时,如在连续共输过程中,MalKinID 的精确度和灵敏度下降,PC 和 FS 的 F1 分数(精确度和灵敏度的调和平均值)分别为 0.76(0.56,0.92)和 0.23(0.0,0.4),FS 和 PC 的 F1 分数(精确度和灵敏度的调和平均值)分别为 0.50(0.50,0.10)和 0.50(0.10,0.10)。
MalKinID: A Classification Model for Identifying Malaria Parasite Genealogical Relationships Using Identity-by-Descent.
Pathogen genomics is a powerful tool for tracking infectious disease transmission. In malaria, identity-by-descent (IBD) is used to assess the genetic relatedness between parasites and has been used to study transmission and importation. In theory, IBD can be used to distinguish genealogical relationships to reconstruct transmission history or identify parasites for quantitative-trait-locus experiments. MalKinID (Malaria Kinship Identifier) is a new classification model designed to identify genealogical relationships among malaria parasites based on genome-wide IBD proportions and IBD segment distributions. MalKinID was calibrated to the genomic data from three laboratory-based genetic crosses (yielding 440 parent-child [PC] and 9060 full-sibling [FS] comparisons). MalKinID identified lab generated F1 progeny with >80% sensitivity and showed that 0.39 (95% CI 0.28, 0.49) of the second-generation progeny of a NF54 and NHP4026 cross were F1s and 0.56 (0.45, 0.67) were backcrosses of an F1 with the parental NF54 strain. In simulated outcrossed importations, MalKinID reconstructs genealogy history with high precision and sensitivity, with F1-scores exceeding 0.84. However, when importation involves inbreeding, such as during serial co-transmission, the precision and sensitivity of MalKinID declined, with F1-scores (the harmonic mean of precision and sensitivity) of 0.76 (0.56, 0.92) and 0.23 (0.0, 0.4) for PC and FS and <0.05 for second-degree and third-degree relatives. Disentangling inbred relationships required adapting MalKinID to perform multi-sample comparisons. Genealogical inference is most powered when 1) outcrossing is the norm or 2) multi-sample comparisons based on a predefined pedigree are used. MalKinID lays the foundations for using IBD to track parasite transmission history and for separating progeny for quantitative-trait-locus experiments.
期刊介绍:
GENETICS is published by the Genetics Society of America, a scholarly society that seeks to deepen our understanding of the living world by advancing our understanding of genetics. Since 1916, GENETICS has published high-quality, original research presenting novel findings bearing on genetics and genomics. The journal publishes empirical studies of organisms ranging from microbes to humans, as well as theoretical work.
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