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Improved functional mapping with GSA-MiXeR implicates biologically specific gene-sets and estimates enrichment magnitude

Frei, O.; Hindley, G.; Shadrin, A. A.; van der Meer, D.; Akdeniz, B. C.; Cheng, W.; O'Connell, K. S.; Bahrami, S.; Parker, N.; Smeland, O. B.; Holland, D.; Schizophrenia Working Group of the Psychiatric Genomics Consortium, ; de Leeuw, C.; Posthuma, D.; Andreassen, O. A.; Dale, A. M.

2022-12-13 psychiatry and clinical psychology
10.1101/2022.12.08.22283159 medRxiv
Show abstract

While genome-wide association studies (GWAS) are increasingly successful in discovering genomic loci associated with complex human traits and disorders, the biological interpretation of these findings remains challenging. We developed the GSA-MiXeR analytical tool for gene-set analysis (GSA), which fits a model for gene-set heritability enrichments for complex traits, accounting for linkage disequilibrium across variants, and allowing the quantification of partitioned heritability and fold enrichment for small gene-sets. We validate the method using extensive simulations and sensitivity analyses. When applied to height and schizophrenia, GSA-MiXeR implicates gene-sets with greater biological specificity compared to standard GSA approaches, including insulin-like growth factor for height, as well as calcium channel function, GABAergic and dopaminergic signaling for schizophrenia. Such biologically relevant gene-sets, often with less than ten genes, are more likely to provide new insights into the pathobiology of complex diseases and highlight potential drug targets.

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