Decoding the genetics of cognitive functions

该项目的总体目标是使用三种不同的方法识别人类认知能力的遗传因素。1)目标1将识别与不同认知域相关的遗传因素。该项目构成部分将采用统计遗传方法，并由Paracchini负责监督。它将为以下两条不同路线的下游研究提供起点。2)AIM 2将使用比较基因组学方法来调查形成AIM 1中确定的区域的进化力量。Vernes将监督这一部分。3)AIM 3将使用转录数据集在功能水平上解释AIM 1的结果。该项目的这一部分将使用生物信息学方法，并将由Lynch监督。每个主管都会针对特定的目标，在特定的领域对学生进行培训。整个团队将定期开会，讨论进展情况并监测进展情况。研究计划AIM 1 Paracchini是根朗联盟的活跃成员，该联盟目前已聚集了来自全球20个不同实验室的33,000名参与者。虽然该联盟的重点是语言和识字能力(即将提交第一次GWA报告的手稿)，但参与者已经接受了多种认知测量。基因组数据已经生成并进行了处理，以供GWAs分析。该项目将从GWAS的数学能力和记忆技能开始，这是不同人群的核心能力。该分析将确定相关的遗传标记，并将解决以下问题。是否有遗传因素影响特定的认知域，或者大多数关联在多个测量上具有普遍性(或多效性效应)？这些问题将通过分析个体标记-性状关联以及遗传相关性和多基因风险得分(PRS)分析来回答(参见Paracchini的参考文献1以了解这些方法的最新例子)。PRS分析将允许评估认知与一生中不同健康结果之间的关系。例如，我们最近发现，在成人中被诊断为精神疾病的基因与儿童阅读能力的测量也存在关联。目的2我们最近发现，与语言能力相关的遗传因素与人类谱系中进化非常迅速的区域重叠。更具体地说，它们位于尼安德特人枯竭的地区，可能是因为智人的关键功能。用类似的方法，我们将评估这种模式是否特定于语言，或者是否可以被检测到用于其他认知测量。越来越多的证据，包括我们的GWA语言，表明与复杂特征相关的遗传变异倾向于定位在调控序列中。这些区域包括超级增强子(即超级活性调节结构域)，它们可能是神经发育过程和人类进化的关键驱动因素。我们将测试跨越超级增强子的测序是否丰富了与认知能力的联系。(有关基因组中进化签名的识别示例，请参阅Vernes的参考文献2)。目的3丰富基因表达调控变异体与GWAs的相关性。最近公布了一系列高质量的功能基因组数据集，为人类组织和单个细胞的基因表达和表观遗传标记提供了参考数据。我们将把这些资源与与认知能力相关的标记联系起来。这些分析将揭示基因变异的功能效应，并有可能识别共同调控的基因网络。我们还将实施新的方法，允许将基因表达数据纳入GWAS分析，例如基于转录组摘要统计的孟德尔随机化(TWMR)，该方法已被证明是成功的

The overall aim of the project is to identify genetic factors underlying cognitive abilities inhumans, using three distinct approaches.1) AIM 1 will identify genetic factors associated with different cognitive domains. This project component will employ statistical genetic approaches and will be supervised by Paracchini. It will provide the starting point for downstream investigations along the two following separate routes.2) AIM 2 will use comparative genomic methods to investigate the evolutionary forces that shaped the regions identified in AIM 1. Vernes will supervise this component.3) AIM 3 will use transcriptomic datasets to interpret at functional level the results of AIM 1. This part of the project will use bioinformatics approaches and will be supervised by Lynch. Alt Each supervisor will lead on a specific aim and train the students in specific areas. The entire team will meet regularly to discuss progress and monitor progress. RESEARCH PLANS AIM 1 Paracchini is an active member of the GenLang Consortium which has now aggregated a total of N > 33,000 participants from 20 different laboratories around the world. While the focus of the consortium is on language and literacy abilities ( manuscript reporting the first GWAS is about to be submitted), the participants have been assessed with multiple cognitive measures. Genomic data have already been generated and processed for GWAS analysis. The project will start with GWAS for maths abilities and memory skills, which are core across the different cohorts. The analysis will identify associated genetic markers and will address the following questions. Are there genetic factors influencing specific cognitive domains or do most associations have generalised (or pleotropic effects) on multiple measures? These questions will be answered by analyzing individual marker-trait associations and with tests of genetic correlation and polygenic risk score (PRS) analysis (see Ref 1 by Paracchini for a recent example of these methods). PRS analysis will allowing assessing the relationship between cognition and different health outcomes throughout life. For example, we recently showed that genetic associations with psychiatric disorders diagnosed in adults are also associated with measures of reading abilities in children. AIM 2 We recently showed that genetic factors associated with language abilities overlap with regions that evolved very rapidly on the human lineage. More specifically, they are located in Neanderthal depleted regions possibly because of key functions in Homo Sapiens. With a similar approach, we will evaluate whether this pattern is specific to language or can be detected for other cognitive measures. Increasing evidence, including our language GWAS, suggest that genetic variants associated to complex traits tend to localise in regulatory sequences. These regions include super-enhancers (i.e. hyper-active regulator domains) which could be key drivers of neurodevelopment processes as well as human evolution. We will test whether associations with cognitive abilities are enriched for sequencing spanning super-enhancers. (See Ref 2 by Vernes for examples of identification of signatures of evolution in genomes). AIM 3 GWAS associations are enriched for variants controlling gene expression regulations. Very recently (= in the last month) a series of high quality functional genomic datasets have been released providing reference data for gene expression and epigenetic markers across human tissues and for single cells. We will link these resources to the markers associated with cognitive abilities. These analyses will reveal functional effects of genetic variants and with the potential of identifying networks of co-regulated genes. We will also implement new methods that allow incorporating gene expression data into GWAS analysis, e.g. the transcriptome-wide summary statistics-based Mendelian Randomization (TWMR), which has been shown to be successful for a numbe