bioRxivGenomicsPreprint
Multi-scale systems genomics analysis predicts pathways, cell types and drug targets involved in normative human cognition variation
Shraddha PaiiD, Shirley HuiiD, Philipp WeberiD, Soumil Narayan, Owen WhitleyiD, Peipei LiiD, Viviane LabrieiD, Jan BaumbachiD, Gary D. BaderiD
Abstract
An open challenge in human genetics is to better understand the link between genotype variation and the various molecular, cellular, anatomical and physiological systems that it can affect. To address this challenge, we performed genotype-phenotype-systems analysis for accuracy in nine cognitive tasks from the Philadelphia Neurodevelopmental Cohort (3,319 individuals aged 8-21 years). We report a region of genome-wide significance within the 3 end of the FBLN1 gene (p=4.6x10-8), associated with nonverbal reasoning, a heritable form of complex reasoning ability. Integration of published brain-specific omic maps reveals that FBLN1 shows greatest expression in the fetal brain, is a marker of neural progenitor cells, is differentially expressed in schizophrenia and increases genetic risk for bipolar disorder. These findings suggest that nonverbal reasoning and FBLN1 variation warrant further investigation in studies of neurodevelopmental disorders and psychosis. Using genotype-pathway analysis, we identify pathways related to development and to autonomic nervous system dysfunction associated with working memory accuracy. Top-ranking pathway genes include those genetically associated with multiple diseases with working memory deficits, such as schizophrenia and Parkinsons disease, and that are also markers for specific brain cell types. Our findings identify novel molecular players involved in specific cognitive tasks and link variants to genes, pathways, cell types, diseases and drugs. This work advances the "molecules-to-behaviour" view of cognition, and provides a framework for using systems-level organization of data for other biomedical domains.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
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