Decoding the genetics of cognitive functions

该项目的总体目标是通过三种不同的方法，确定人类认知能力背后的遗传因素。1) AIM 1将确定与不同认知领域相关的遗传因素。该项目将采用统计遗传方法，并由Paracchini监督。它将为沿着以下两条不同路线进行下游调查提供起点。2) AIM 2将使用比较基因组学方法来研究形成AIM 1中确定的区域的进化力量。凡尔纳斯将监督这个部分。3) AIM 3将使用转录组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。每个导师都有一个特定的目标，并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并监督进展。研究计划目标1 Paracchini是GenLang联盟的活跃成员，该联盟目前聚集了来自世界各地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读写能力（报告第一次GWAS的手稿即将提交），但参与者已经接受了多种认知测量的评估。基因组数据已经生成并处理用于GWAS分析。该项目将从GWAS的数学能力和记忆能力开始，这是不同年龄组的核心。分析将确定相关的遗传标记，并将解决以下问题。是否存在影响特定认知领域的遗传因素，或者大多数关联在多种测量中具有普遍性（或多效性）？这些问题将通过分析个体标记-性状关联以及遗传相关测试和多基因风险评分（PRS）分析来回答（参见参考文献1 Paracchini提供的这些方法的最新示例）。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，我们最近表明，在成人中诊断出的精神疾病的遗传关联也与儿童阅读能力的测量有关。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。通过类似的方法，我们将评估这种模式是特定于语言的，还是可以通过其他认知测量来检测。越来越多的证据，包括我们的语言GWAS，表明与复杂性状相关的遗传变异倾向于在调控序列中定位。这些区域包括超级增强子（即过度活跃的调节域），它可能是神经发育过程和人类进化的关键驱动因素。我们将测试序列跨越超级增强子是否与认知能力相关。（参见参考文献2中Vernes关于基因组进化特征识别的例子）。AIM 3 GWAS与控制基因表达调控的变异相关。最近，一系列高质量的功能基因组数据集已经发布，为跨人体组织和单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认知能力相关的标记联系起来。这些分析将揭示遗传变异的功能影响，并具有识别共调节基因网络的潜力。我们还将实施新的方法，允许将基因表达数据纳入GWAS分析，例如基于转录组范围汇总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