Bayesian hierarchical modelling for inferring genetic interactions in yeast.
Bayesian hierarchical modelling for inferring genetic interactions in yeast.
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DOI：
10.1111/rssc.12126
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发表时间：
2016-04
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影响因子：
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通讯作者：
Wilkinson DJ
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文献类型：
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作者：
Heydari J;Lawless C;Lydall DA;Wilkinson DJ
定量适合度分析（QFA）是一种用于测量微生物种群生长的高通量实验和计算方法。QFA筛选可用于比较查询基因中有突变和无突变的细胞群体的健康状况，以推断全基因组的遗传相互作用强度，检查数千种不同的基因型。我们引入了人口增长率和遗传相互作用的贝叶斯分层模型，这些模型比当前的方法更好地反映了QFA实验设计。我们的新方法同时对种群动态和遗传相互作用进行建模，从而避免了通过单变量适应度总结在模型之间传递信息。更紧密地匹配实验结构，贝叶斯分层方法更有效地使用数据，并在已发布的数据集中找到与酵母端粒相互作用的基因的新证据。
Quantitative fitness analysis (QFA) is a high throughput experimental and computational methodology for measuring the growth of microbial populations. QFA screens can be used to compare the health of cell populations with and without a mutation in a query gene to infer genetic interaction strengths genomewide, examining thousands of separate genotypes. We introduce Bayesian hierarchical models of population growth rates and genetic interactions that better reflect QFA experimental design than current approaches. Our new approach models population dynamics and genetic interaction simultaneously, thereby avoiding passing information between models via a univariate fitness summary. Matching experimental structure more closely, Bayesian hierarchical approaches use data more efficiently and find new evidence for genes which interact with yeast telomeres within a published data set.