今年的国际自闭症研究学会（Insar）Annual Meeting is being held between 1st and 4th May. The meeting will be opened with a keynote address, ‘从头编码和非编码区域的差异：我们可以从有关病因途径的数据中学到什么凯瑟琳·罗德教授。

罗德教授是卡内基·梅隆大学（Carnegie Mellon University）的统计与计算生物学教授的副教务长。除了获得2013年Janet L. Norwood奖，以及1997年的Copps Snedecor和总统奖外，她还发表了迄今为止的275篇论文。

Thanks for taking the time to speak to us. Your research is predominantly based around statistical genetics. Would you be able to explain where your main fields of expertise lie, and how statistical genetics can be applied autism research?

KR:作为统计学家，我自然认为统计推理可以应用于几乎任何定量问题。关于自闭症研究有许多开放定量问题。我的专业领域是统计建模，高维推断和机器学习。

虽然可以找到用于许多问题的现成工具，但我认为仔细建模是获得几乎所有统计问题的最佳解决方案的第一步。我认为，如果解决方案满足三个约束，则该解决方案是最佳的：1）模型近似生物过程的关键特征；2）它提供了有关正在调查的问题的有力推断；3）该方法对不可避免的违反建模假设是可靠的。

大多数遗传问题都涉及大量变量：例如，基因组中数百万个核苷酸多态性（SNP）和数十万个基因。解决这些问题需要高维推断的工具；其中最熟悉的是主要组件分析，但是其他更灵活的工具可以提供改进的推论。机器学习工具是遗传研究中出现的高维问题的理想选择。

最后，良好的统计工作总是与对正在研究的科学问题的良好理解。我的大部分工作都是与Dr. Bernie Devlin，，，，University of Pittsburgh School of Medicine. Bernie is expert in genetics and statistics and provides a bridge between the methods and the application.

您能在Insar的2019年年度会议上告诉我们有关主题演讲的主题吗？

KR:从头variants have provided a powerful pathway to discover risk factors and genes involved in autism spectrum disorder (ASD). Most recently, the Autism Sequencing Consortium posted a manuscript in which we identified 102 risk genes, some new and others with stronger evidence than previously documented. It is exciting to have access to this substantial list of risk genes, but on their own, a list of genes does not provide much insight into the etiology of ASD. Thus, we explore several avenues to understand how, where and when these genes and mutations are likely to exert the most impact.

单细胞和大量RNA序列数据提供了有关哪些细胞类型受ASD风险基因突变影响最大的见解。共表达网络鉴定了共表达的风险基因的簇，这暗示了未成熟的神经元在风险中，并将从神经祖细胞到神经元的过渡联系起来是ASD中非典型神经发育的一个潜在起源。

Alternatively, evaluation of从头破坏蛋白质相互作用的突变提供了在蛋白质水平上的见解，揭示了哪些基因 - 基因相互作用已被破坏，并鉴定了被破坏的相互作用网络中的集线器基因。我们面临的一个重大挑战是如何协调从转录获得的见解与我们从蛋白质结构域中的发现获得的见解。确实，尽管我们经常通过共表达研究基因相互作用，但基因产物通常相互作用是在蛋白质水平上。然而，蛋白质数据在人体的特定发育期或区域不可用，这限制了我们在了解时空动力学方面的实用性。在主题演讲中，我将尝试合并转录和蛋白质相互作用网络的见解，以便对ASD的病因有更深入的了解。

我还将简要介绍一个令人兴奋的新领域，即全基因组测序结果如何表明从头以进化和功能特征为特征的启动子区域中的突变有助于ASD。

What are you most looking forward to seeing at INSAR this year?

KR:My work is focused on discovering risk genes, as well as their mechanisms of action, so I’m always most interested in what can be done in practice with these discoveries. Sessions that caught my eye are “Novel Therapeutic Approaches (gene, protein or RNA targeted therapies)” and “Drug Discovery and Development in ASD”.

You’ve published three articles inMolecular Autismin the past. Can you give us some background on one of these, and tell us why the findings are of importance to the field?

KR:Let me tell you about the 2012 Klei et al.article，，，，because it relates to the work I’ll present in the INSAR panel session: ‘Molecular Genetics: Genetic and Genomic Discovery in Autism: From SNPs, to Exomes and Genomes’.

In that paper we obtained the best estimate of the heritability of autism attributable to common variants at that point in time. Previous estimates had underestimated this heritability because the studies used family-based samples instead of unrelated controls in their estimates. We later built on this finding in the Gaugler et al (2014)article，，，，where we showed that most of the heritability of ASD is explained by common variants of small effect.

In the INSAR panel session I’ll take this line of research further still to show that while rare variation confers risk, both rare从头and inherited variation acts within the context of a common-variant genetic load, and this load accounts for the largest portion of ASD liability. Even among individuals who carry rare highly damaging variants, there is sound evidence for substantial common risk variation, a result that is contrary to some popular beliefs.

如果您喜欢Roeder教授的主题演讲和问答，可以通过遵循博客下面的链接来阅读她的分子自闭症文章。