A graphical model approach to pedigree construction using constrained optimisation

拥有大量不相关个体遗传数据的人口生物库在检测影响公共卫生关注疾病的常见遗传变异方面取得了巨大成功。现在的注意力转移到寻找更罕见的变异和研究基因-基因和基因-环境相互作用的影响。理想情况下，这需要相关个体，但家庭研究不再常规收集。实际上，大多数大型人口研究，特别是从特定地理区域收集的研究，将包含一组（未申报的）亲属。从现有的生物库数据中识别亲属将是非常有益的，既可以进一步利用这些研究来寻找罕见的变异，也可以调整统计分析以考虑亲缘关系。如果仅仅是为了寻找可能共享罕见变异的个体，那么粗略或一般的亲缘关系测量可能就足够了，但如果能够有效地获得对真正关系或谱系的良好估计，将会更好：它将使更好的调整方法成为可能，并有助于寻找在不同家庭中分离的许多变异的基因，而不是在整个人群中寻找单一变异的基因。我们的建议是开发有效的方法来重建谱系从遗传数据在大群体研究。我们将使用计算机科学中开发的快速组合优化算法。这些是一般的图搜索算法，但是，由于谱系是一种特殊的图，并且遗传数据以非常特殊的方式关联，我们将调整算法来搜索有效的结构。适应是通过施加约束来实现的。该项目的主要挑战之一是制定有效工作的约束并结合相关生物学。一般算法假设谱系中的所有个体都在研究中，并且具有完整的遗传数据。这并不适用于本应用程序，因为未观察到的个体通常需要提供连接研究中亲属的缺失链接。我们的算法会搜索所有可能有失踪人员的血统。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具有一定程度的信心，例如。我们所有的方法都将使用模拟数据开发，但将使用雅芳父母和儿童纵向研究（ALSPAC）的真实数据进行测试。快速有效的系谱重建将允许更充分地利用现有的群体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.