Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome—not just genome-wide-significant loci—to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant–trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

精细映射旨在识别影响复杂性状的因果变异。我们提出了 PolyFun，这是一个计算可扩展的框架，通过利用整个基因组的功能注释来提高精细定位的准确性——而不仅仅是全基因组的重要位点——来指定精细定位方法（如 SuSiE 或 FINEMAP）的先验概率。在模拟中，PolyFun + SuSiE 和 PolyFun + FINEMAP 得到了很好的校准，并且识别出的后因果概率 > 0.95 的变体比在非功能知情的对应物中识别出的变体多 20%。在对 49 个英国生物库性状的分析中（平均n = 318,000)，PolyFun + SuSiE 确定了 3,025 个精细映射的变异-性状对，后验因果概率 >0.95，与 SuSiE 相比提高了 >32%。我们使用来自 PolyFun + SuSiE 的后验平均每个 SNP 遗传力来进行多基因定位，构建最小的常见 SNP 集，从而解释 50% 的常见 SNP 遗传力；这些套装的大小从 28（头发颜色）到 3,400（身高）到 200 万（儿童数量）不等。总之，PolyFun 优先考虑功能跟进的变体，并提供对复杂特征架构的见解。