LATEST INSIGHTS FROM MULTI-ANCESTRY FINE-MAPPING IN PSYCHIATRIC DISORDERS

Genome-wide association studies (GWAS) identify associated variants but do not tell the whole story. The vast majority of target genes of these associations remain unknown. Without them it is impossible to fully reap the benefits of genetic research, such as better understanding of disease mechanisms and discovery of novel drug targets. Translating genome-wide significant (GWS) loci into causal genes and mechanisms is particularly challenging for psychiatric disorders (PD) due to linkage disequilibrium (LD) between risk variants, incomplete understanding of the non-coding regulatory mechanisms in the brain, and the highly polygenic architecture of most PDs. Therefore, a systematic analysis that applies fine mapping to jointly identify and validate a credible set of causal variants and genes for PDs using relevant tissues and cell types is critical. However, most fine-mapping studies have focused on individuals of genetically determined European (EUR) ancestries; with the functional impact of most identified causal variants, especially the non-coding variants, remaining unclear. A crucial progression is inclusion of diverse populations in PD GWAS and utilize the characteristics of diverse ancestral groups the to empower identification of target genes and mechanisms.

In this symposium, we will demonstrate how we can integrate data from diverse ancestries and cutting-edge statistical genetics techniques to improve the fine-mapping resolution and prioritize high confidence causal variants and genes for PDs. Specifically, we will showcase innovations and insights from four different perspectives: (i) results from the first large-scale fine-mapping and gene prioritization study of major depressive disorder (MDD) in an ancestrally diverse sample, including individuals of African, East Asian and South Asian ancestry, and Hispanic/Latin American samples (Prof Karoline Kuchenbaecker), (ii) integration of a suite of fine-mapping methods and novel single nuclei gene expression data to unravel the genetic etiology of complex disorders such as bipolar disorder (BD) (Dr Maria Koromina), (iii) latest insights from multi-ancestry fine-mapping in schizophrenia (SCZ) using advanced statistical genetics techniques such as implementation of the Polygenic Priority Score (PoPS) and machine-learning models (Dr Karl Heilborn), (iv) leveraging a new method for polygenic risk scoring which incorporates functional genomics annotations (SBayesRC) to improve fine-mapping of complex traits including PDs (Dr Jian Zeng). Finally, Prof. Naomi Wray will summarize the state of the field with regards to multi-ancestry and multi-omics aided fine-mapping and provide perspectives on future research and the crucial next steps to translate results to clinical prediction, treatment, and prevention.