Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes.
Localizing Post-Admixture Adaptive Variants with Object Detection on Ancestry-Painted Chromosomes.
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DOI：
10.1093/molbev/msad074
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发表时间：
2023-04-04
影响因子：
10.7
通讯作者：
Goldberg, Amy
中科院分区：
文献类型：
--
作者：
Hamid, Iman;Korunes, Katharine L.;Schrider, Daniel R.;Goldberg, Amy
在混合或杂交群体建立期间，先前分化的群体之间的基因流动具有将适应性等位基因引入新群体的潜力。如果适应性等位基因在一个来源群体中是常见的，但在另一个来源群体中不是，那么随着适应性等位基因在混合群体中的频率增加，来自包含适应性等位基因的来源的遗传祖先也会在附近增加。因此，遗传祖先的模式已被用于识别人类和其他动物的混合后正选择，包括免疫，代谢和动物着色的例子。一种常见的方法是识别基因组中具有本地祖先的区域，与基因组其余部分的分布相比，独立考虑每个位点。然而，我们缺乏在各种人口统计情景下预期祖先分布的理论模型，导致潜在的假阳性和假阴性。此外，远亲之间的祖先模式往往不是独立的。因此，目前的方法倾向于推断包含许多基因的广泛基因组区域为选择下，限制了生物学解释。相反，我们开发了一种深度学习对象检测方法，应用于从本地祖先绘制的基因组生成的图像。这种方法保留了来自周围基因组背景的信息，并避免了用户定义汇总统计的潜在陷阱。我们发现，该方法是强大的各种人口统计错误指定使用模拟数据。应用于人类基因型数据从卡波佛得角，我们本地化一个已知的自适应基因座到一个单一的狭窄区域相比，使用其他两个祖先为基础的方法获得的多个或长窗口。
Gene flow between previously differentiated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry “outliers” compared with the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from Cabo Verde, we localize a known adaptive locus to a single narrow region compared with multiple or long windows obtained using two other ancestry-based methods.
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影响因子：
7.7
作者：
Hamid I;Korunes KL;Beleza S;Goldberg A
通讯作者：
Goldberg A
影响因子：
9.2
作者：
Aguillon, Stepfanie M.;Dodge, Tristram O.;Preising, Gabriel A.;Schumer, Molly
通讯作者：
Schumer, Molly
影响因子：
2.2
作者：
Isshiki M;Naka I;Kimura R;Nishida N;Furusawa T;Natsuhara K;Yamauchi T;Nakazawa M;Ishida T;Inaoka T;Matsumura Y;Ohtsuka R;Ohashi J
通讯作者：
Ohashi J
影响因子：
7.7
作者：
Busby, George B. J.;Band, Gavin;Spencer, Chris C. A.
通讯作者：
Spencer, Chris C. A.
影响因子：
10.7
作者：
Adrion, Jeffrey R.;Galloway, Jared G.;Kern, Andrew D.
通讯作者：
Kern, Andrew D.