Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies
Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies
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DOI：
10.1093/bioinformatics/btw720
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发表时间：
2017-03-15
期刊：
影响因子：
5.8
通讯作者：
Tse, David
中科院分区：
文献类型：
--
作者：
Bhaskar, Anand;Javanmard, Adel;Tse, David
动机：人类种群的遗传变异受到地理祖先的影响，这是由于历史上交配和迁移模式的空间地方性。传统上，遗传数据集的空间种群结构分析要么使用无模型算法，如主成分分析（PCA）和多维尺度，要么使用明确的等位基因频率进化的空间概率模型。我们开发了一个通用的概率模型和一个相关的推理算法，统一了基于模型和数据驱动的方法来可视化和推断人口结构。我们的空间推理算法也可以有效地应用于全基因组关联研究（GWAS）中的群体分层问题，当群体祖先与基因型和性状同时相关时，隐藏的群体结构可能会产生虚构的关联。结果：我们的地理祖先定位（GAP）算法将样本之间的局部遗传距离与其空间距离联系起来，可以用于视觉识别种群结构，并在二维连续体上准确推断个体的空间起源。在不同人群的模拟数据集和几个真实数据集上，GAP在重建空间祖先坐标方面的误差明显低于PCA。我们还开发了一种关联测试，使用由GAP推断的祖先坐标来准确地解释GWAS中祖先诱导的相关性。基于对芬兰北部队列中测量的10种代谢特征的数据集的模拟和分析，我们发现我们的方法比目前的方法具有更高的功效。可用性和实现：我们的软件可在https://github.com/anand-bhaskar/ gap获得。
Motivation: Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait.Results: Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches.Availability and Implementation: Our software is available at https://github.com/anand-bhaskar/ gap.