Decoding the genetics of cognitive functions

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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