A graphical model approach to pedigree construction using constrained optimisation

拥有大量无关个体遗传数据的人口生物库在检测影响公共卫生问题疾病的常见遗传变异方面取得了巨大成功。现在人们的注意力正转向寻找更罕见的变异，并研究基因-基因和基因-环境相互作用的影响。理想情况下，需要相关的个人，但家庭研究不再是常规收集。事实上，大多数大型人口研究，特别是从特定地理区域收集的研究，将包含（未申报的）亲属集。从现有的生物库数据中识别亲属将是非常有益的，无论是在进一步使用这些研究来寻找罕见的变异，还是在调整统计分析以考虑相关性方面。虽然如果目的仅仅是找到可能共享罕见变异的个体，那么粗略或一般的相关性度量可能就足够了，但如果能够有效地获得对真实关系或谱系的良好估计，则会更好：它将使更好的调整方法成为可能，并有助于搜索在不同家族中分离的具有许多变异的基因，而不是整个人群中的单一变异。我们的建议是开发有效的方法，从大人口研究的遗传数据重建谱系。我们将使用计算机科学中开发的快速组合优化算法。这些都是一般的图搜索算法，但是，因为系谱是一种特殊的图，遗传数据以非常特殊的方式相关，我们将调整算法来搜索有效的结构。通过施加约束来执行适应。该项目的主要挑战之一是制定有效工作的约束条件并纳入相关生物学。一般算法假设系谱中的所有个体都在研究中并且具有完整的遗传数据。这不适用于本申请，因为通常需要未观察到的个体提供研究中亲属之间的缺失联系。我们的算法将搜索所有可能的家系与失踪的个人。最后，我们将通过贝叶斯框架纳入额外的非遗传信息，以告知搜索某些关系是确定的或达到一定程度的置信度。我们所有的方法都将使用模拟数据开发，但将使用来自雅芳父母和儿童纵向研究（ALSPAC）的真实的数据进行测试。快速而有效的谱系重建将允许更充分地利用现有的人口队列研究进行遗传研究。

Population biobanks with genetic data on large numbers of unrelated individuals have been enormously successful in detecting common genetic variants affecting diseases of public health concern. Attention is now shifting towards finding rarer variants and to investigating gene-gene and gene-environment interaction effects. Ideally, related individuals are required for this, but family studies are no longer routinely collected. In reality most large population studies, especially those collected from a particular geographical region, will contain sets of (undeclared) relatives. Identification of relatives from existing biobank data would be highly beneficial, both in furthering the use of these studies to search for rare variants and in adjusting statistical analyses to take account of relatedness. Although a crude or general measure of relatedness might be enough if the aim is solely to find individuals who might share rare variants, having a good estimate of the true relationship, or pedigree, would be much better if this could be obtained efficiently: it would enable better adjustment methods and facilitate the search for genes with many variants segregating in different families rather than a single variant across the population. Our proposal is to develop efficient methods for reconstructing pedigrees from genetic data in large population studies. We will use fast combinatorial optimisation algorithms developed in computer science. These are general graph-searching algorithms but, because a pedigree is a special kind of graph and genetic data are correlated in very particular ways, we will adapt the algorithms to search for valid structures. Adaptation is performed by imposing constraints. One of the main challenges in the project is to formulate constraints that work efficiently and incorporate the relevant biology. The general algorithms assume that all individuals in the pedigree are in the study and have complete genetic data. This does not hold for this application as unobserved individuals will typically be required to provide the missing links connecting the relatives in the study. Our algorithms will search over all possible pedigrees with missing individuals. Finally, we will incorporate additional non-genetic information via a Bayesian framework to inform the search that some relationships are known with certainty or up to some degree of confidence, for example. All our methods will be developed using simulated data but will be tested using real data from the Avon Longitudinal Study of Parents and Children (ALSPAC). Fast and efficient pedigree reconstruction would permit much fuller use of existing population cohort studies for genetic research.