Inference and Applications of Genetic Relatedness in Human Populations

任何一个人类个体群体都是通过一个不可观察的家谱关系网络紧密联系在一起的，这个网络一直延伸到过去。偶尔，成对的个体会有一个共同的祖先，他们只生活在几十代或几百代以前，共同继承了他们基因组的大部分，这是“血统相同”（IBD）。IBD区域的准确检测在许多基因组分析中具有重要意义；IBD片段可以识别遗传亲缘关系，勾勒出致病突变的存在，并揭示所分析群体的精细人口统计学特征。然而，在大型数据集中检测IBD片段存在许多计算挑战。扩展到包含数十万个样本的数据集的算法（Gusev et al.）。基因组研究2009，Naseri等。Biorxiv 2017)，采用无模型字符串匹配方法，有利于计算速度而不是IBD检测精度。另一方面，精确的基于模型的算法（Browning和Browning， Genetics, 2013）无法扩展到大型数据集。我们最近开发了一种新的准确高效的IBD检测算法。我们利用了高效模式匹配算法的最新发展（例如Durbin， Bioinformatics 2014），并结合了一种新的概率方法来准确验证一个地区是否存在IBD共享（Palamara等）。自然遗传学，2018)。已知IBD共享为罕见致病突变的关联提供了一种有效的替代途径（Gusev等）。AJHG 2011)，这在目前可用的常见snp数据集（例如UK Biobank）中无法观察到。因此，我们将使用我们的新算法来检测疾病表型与人类基因组中罕见或低频因果变异之间的新关联。我们还将探索和开发新的基于深度学习的技术，这些技术以前几乎从未在群体遗传学中使用过。我们提出的方法将有助于阐明低频基因组变异在常见疾病遗传结构中的作用，并为检测具有大表型效应的罕见突变提供新的工具。该项目属于MRC“健康长寿-分子数据集与疾病”研究领域。该项目旨在通过创建新的工具来挖掘大型基因组数据集中的致病变异证据，包括mrc资助项目（例如英国生物银行数据集）产生的数据集，从而利用遗传学来了解疾病的易感性。开发用于分析该领域大型数据集的新计算工具是MRC的明确目标：https://www.mrc.ac.uk/research/strategy/aim-1/theme-2/objective-5/

Any group of human individuals is tightly connected through an unobservable network of genealogical relationships, which extends deep into the past. Occasionally, pairs of individuals will share a common ancestor that lived only tens or hundreds of generations ago, co-inheriting large portions of their genomes that are "identical-by-descent" (IBD). Accurate detection of IBD regions is of great interest in a number of genomic analyses; IBD segments can identify genetic relatives, outline the presence of disease-causing mutations, and reveal fine-scale demographic properties of the analyzed groups.Detection of IBD segments in large data sets, however, presents a number of computational challenges. Algorithms that scale to data sets comprising hundreds of thousands of samples (Gusev et al. Genome Research 2009, Naseri et al. Biorxiv 2017), resort to model-free string-matching approaches that favor computational speed over IBD detection accuracy. Accurate model-based algorithms (Browning and Browning, Genetics, 2013), on the other hand, cannot scale to large data sets. We recently developed a new accurate and efficient algorithm for IBD detection. We exploited recent developments in efficient pattern matching algorithms (e.g. Durbin, Bioinformatics 2014), in combination with a new probabilistic approach to accurately verify the presence of IBD sharing in a region (Palamara et al. Nature Genetics, 2018). IBD sharing is known to provide an effective alternative route to performing association for rare disease-causing mutations (Gusev et al. AJHG 2011), which cannot be observed in currently available data sets of common SNPs (e.g. UK Biobank).We will thus use our novel algorithm to detect new associations between disease phenotypes and rare or low-frequency causal variation in the human genome. We will also explore and develop new deep learning based techniques which have almost never been employed in population genetics before. The methods we propose to develop will help elucidate the role of low-frequency genomic variation in the genetic architecture of common disease and provide new tools for detecting rare mutations with large phenotypic effects.The project falls within the MRC "Living a long & healthy life - Molecular datasets & disease" research area. This project aims to use genetics to understand predisposition to disease, by creating new tools to mine evidence of disease-causing variation in large genomic data sets, including data sets resulting from MRC-funded projects (e.g. the UK Biobank data set). The development of new computational tools for analysis of large data sets in this domain is an explicit goal of the MRC: https://www.mrc.ac.uk/research/strategy/aim-1/theme-2/objective-5/