世界トップレベル研究拠点（WPI）慶應義塾大学ヒト生物学-微生物叢-量子計算研究センター(Bio2Q)は以下のとおり公開セミナーを開催いたします。

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概要 (Abstract)

Over 90% of disease-associated variants from genome-wide association studies (GWAS) lie in non-coding regions, where defining causal variants, their effector genes, and cellular context remains a central challenge. To address this, we statistically fine-map disease loci and apply SCENT (Single-Cell ENhancer-Target gene mapping), a nonparametric method that links enhancers to target genes from single-cell multiome (RNA+ATAC) data. Applying this framework to our multi-ancestry rheumatoid arthritis GWAS, we found that fine-mapped causal variants are strongly enriched in T-cell-type-specific regulatory elements and nominated effector genes at non-coding loci that could become therapeutic targets. We are further extending SCENT to one of the most advanced embryoid model to reach early developmental cell states that are otherwise inaccessible in humans, building a cis-regulatory atlas relevant to complex developmental disorders.

Together, this integration of statistical genetics, single-cell genomics, and human-specific models provides a powerful framework to decode the functional impact of non-coding disease risk variants.