Genome sequence data are no longer scarce. The UK Biobank alone comprises 200,000 individual genomes, with more on the way, leading the field of human genetics toward sequencing entire populations. Within the next decades, other model organisms will follow suit, especially domesticated species such as crops and livestock. Having sequences from most individuals in a population will present new challenges for using these data to improve health and agriculture in the pursuit of a sustainable future. Existing population genetic methods are designed to model hundreds of randomly sampled sequences but are not optimized for extracting the information contained in the larger and richer data sets that are beginning to emerge, with thousands of closely related individuals. Here we develop a new method called trio-based inference of dominance and selection (TIDES) that uses data from tens of thousands of family trios to make inferences about natural selection acting in a single generation. TIDES further improves on the state of the art by making no assumptions regarding demography, linkage, or dominance. We discuss how our method paves the way for studying natural selection from new angles.

中文翻译：

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推断正在进行的选择的模式和强度

基因组序列数据不再稀缺。仅英国生物样本库就包含 200,000 个个体基因组，还有更多的基因组正在开发中，引领人类遗传学领域朝着对整个人群进行测序的方向发展。在未来几十年内，其他模式生物将效仿，尤其是农作物和牲畜等驯化物种。拥有人口中大多数个体的序列将对使用这些数据改善健康和农业以追求可持续的未来提出新的挑战。现有的群体遗传方法旨在对数百个随机采样序列进行建模，但并未针对提取包含在开始出现的更大、更丰富的数据集中的信息进行优化，这些数据集包含数千个密切相关的个体。在这里，我们开发了一种称为基于三重奏的优势和选择推理 (TIDES) 的新方法，该方法使用来自数万个家庭三重奏的数据来推断单代的自然选择作用。TIDES 通过不对人口统计学、联系或支配做出任何假设，进一步改进了现有技术。我们讨论了我们的方法如何为从新角度研究自然选择铺平道路。