AbstractConnecting the findings from genetic association studies to trait biology poses a significant challenge. Our group previously proposed the omnigenic model to bridge this gap. In this model, the gene-regulatory network is expected to connect many genes to core programs for the trait. However, we have yet to develop a method for identifying regulatory architecture from association data. In this study, we derive gene regulatory effects on programs from Perturb-seq. Additionally, we estimate gene associations with the trait from loss-of-function burden tests. By comparing these two datasets, with an emphasis on the directions of association, we aim to find a consistent gene-to-program-to-trait (G2P2T) map. Using blood traits as a model, we found empirical evidence that genetic association is shaped by gene regulation, and we identified a trait-specific G2P2T map. This map aids in interpreting “how” genes are associated with traits. We propose that Perturb-seq in trait-relevant cell types, coupled with accurate estimation of gene-level effect sizes for the trait, is crucial for bridging the gap between genetics and biology.はじめにゲノムワイド関連解析に代表される大規模ゲノム研究は，これまでさまざまな形質や疾患に関わる遺伝的多型を多数同定してきた．その結果，例えば身長には 12,000 以上の遺伝的多型が関わることが明らかになった 1)．これは，ほぼすべての発現遺伝子が何らかの形で身長に影響を与えていることを示唆する．遺伝学的研究の目的の一つは，因果関係が明確な遺伝的多型と形質の関連解析から開始することで，さまざまな形質や疾患の発症原因を特定することである．しかし，これ程多くの遺伝子が発症に関わる場合，その知見を生物学的に解釈するのは単純ではない．東京大学大学院医学系研究科免疫疾患機能ゲノム学講座・特任助教太田 峰人― 277 ―ゲノム編集技術を駆使した免疫疾患遺伝的リスク解釈モデルの構築Combining genome-editing technology and genetic association studies for interpreting genetic risksin immune diseases
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