The coalescent process in genetics : mapping complex diseases and other applications

聚结过程是一种用来模拟种群谱系的随机过程，在过去的十五年中受到了广泛的关注，主要是由于在人类遗传学研究中的大量应用。我们引入了一种方法，该方法使用结合重组的凝聚过程（Larribe等人，2002年，Larribe， 2004年），以估计影响疾病的基因突变的位置，即为了进行遗传精细作图。在遗传标记的“窗口”上使用复合条件似然策略（Larribe和Lessard， 2008），我们提高了估计的稳定性，并大大增加了该方法可用的遗传标记的数量。然后，我们通过设计从表型和基因型中估计单倍型和因果等位基因的方法，在复杂遗传疾病的背景下进一步发展了该方法（Larribe等人，2015）；此外，该方法现在能够考虑允许不完全外显率和表型的遗传模型。遗传学界的普遍做法仍然是基于成千上万的关联统计，每对遗传标记进行分析，从而忽略了个体之间的依赖性和标记之间的空间遗传依赖性。因此，在我们的建议中，我们继续通过解决上面提到的两个依赖问题，为这个问题提供一个精细的解决方案。与许多其他方法相比，我们的方法不是基于测试关联，而是基于似然的遗传作图方法；因此，它包含了丰富的遗传模型。我们建议通过允许更复杂的遗传模型来扩展我们的研究计划，展示基因之间的相互作用，并考虑多种罕见变异；我们还希望能够处理数量性状，并在基因组规模上实施该方法。最后，为了分析今天在分子生物学中产生的大量数据，我们建议使用我们在复合可能性方面的专业知识结合创新方法来构建谱系。由于许多遗传疾病对遗传学家来说仍然是一个谜，因此这种先进的作图方法的发展对未来的医学研究应该是重要的。

The coalescent process, a stochastic process used to simulate population genealogies, has attracted much attention during then past fifteen years, mostly due to the numerous applications in human genetics research. We have introduced a method that uses the coalescent process with recombination (Larribe et al., 2002, Larribe, 2004), in order to estimate the location of a genetic mutation influencing a disease, i.e. in order to do genetic fine mapping. Using a composite-conditional-likelihood strategy (Larribe and Lessard, 2008) on "windows" of genetic markers, we have improved the stability of the estimates and greatly increased the number of genetic markers usable by the method. Then, we have further developed the approach in the context of complex genetic diseases, by devising methods for estimating haplotypes and causal alleles from phenotypes and genotypes (Larribe et al., 2015); moreover, the method is now able to take into account genetic models allowing incomplete penetrance and phenocopy. It is still common practice in the genetics world to base the analysis on thousands of association statistics, each for a pair of genetic markers, and thus to ignore the dependence between individuals and the spatial genetic dependency between markers. Therefore, in our proposal we continue the difficult task of offering a refined solution to this problem by addressing both dependency issues mentioned above. In contrast with many other approaches, our method is not based on testing association, but is a genetic mapping methodology based on the likelihood; as such, it incorporates rich genetic models. We propose to extend our research program by allowing more complex genetic models, exhibiting interaction between genes and by considering multiple rare variants; we also want to be able to treat quantitative traits and to implement the method at the genome scale. Finally, in order to analyze the vast amount of data generated today in molecular biology, we propose to use our expertise in composite likelihood combined with innovative methods to build genealogies. As a great number of genetic diseases are still a mystery to geneticists, the impact of the development of such advanced mapping methods should be important for future medical research.