Enhancing polygenic scores for cardiometabolic traits through tissue- and cell-type-specific functional annotations.

IF 3.3 Q2 GENETICS & HEREDITY
Kristjan Norland, Daniel J Schaid, Iftikhar J Kullo
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Abstract

Functional genomic annotations can improve polygenic scores (PGS) within and between genetic ancestry groups. While general annotations are commonly used in PGS development, tissue- and cell-type-specific annotations derived from open chromatin and gene expression experiments may further enhance PGS for cardiometabolic traits. We developed PGS for 14 cardiometabolic traits in the UK Biobank using SBayesRC. We integrated GWAS summary statistics from FinnGen and GLGC with three annotation sources: (1) Baseline-LD model version 2.2 (general annotations), (2) cell-type-specific snATAC-seq peaks, and (3) tissue-specific eQTLs/sQTLs. We created PGS using two EUR LD reference panels (1.2 million [1.2M] HapMap3 variants and 7M imputed variants). Tissue- and cell-type-specific annotations showed stronger heritability enrichment than Baseline-LD annotations on average, particularly coronary snATAC-seq peaks and fine-mapped eQTLs. Without annotations, HapMap3 and 7M variant PGS performed similarly. However, with all annotations, 7M variant PGS outperformed HapMap3 variant PGS (8% average increase in relative performance in EUR). Compared to using no annotations, modeling Baseline-LD annotations improved performance by 5% for HapMap3 and 11% for 7M variant PGS, while modeling all annotations yielded improvements of 5% and 13%, respectively. Although annotations provided greater relative improvement for cross-ancestry prediction, they did not decrease the disparity in PGS performance between genetic ancestry groups. In conclusion, functional annotations improved PGS for cardiometabolic traits. Despite strong heritability enrichment, tissue- and cell-type-specific snATAC-seq and eQTL annotations provided marginal performance gains beyond general genomic annotations.

通过组织和细胞类型特异性功能注释增强心脏代谢性状的多基因评分。
功能基因组注释可以提高遗传祖先群体内部和之间的多基因评分(PGS)。虽然一般注释通常用于PGS的发育,但来自开放染色质和基因表达实验的组织和细胞类型特异性注释可能进一步增强PGS对心脏代谢性状的影响。我们使用SBayesRC为英国生物银行的14个心脏代谢特征开发了PGS。我们将FinnGen和GLGC的GWAS汇总统计数据与三个注释来源进行了整合:i)基线- ld模型v2.2(一般注释),ii)细胞类型特异性的snATAC-seq峰,以及iii)组织特异性的eQTLs/sQTLs。我们使用两个EUR LD参考面板(120万个HapMap3变体和7M个输入变体)创建了PGS。组织和细胞类型特异性注释比基线ld注释平均表现出更强的遗传力富集,特别是冠状动脉snATAC-seq峰和精细定位的eqtl。在没有注释的情况下,HapMap3和7M变体PGS的表现相似。然而,在所有注释中,7M变体PGS优于HapMap3变体PGS(在EUR中相对性能平均提高8%)。与不使用注释相比,为HapMap3和7M变体PGS建模基线- ld注释分别提高了5%和11%的性能,而为所有注释建模分别提高了5%和13%。尽管注释对跨祖先预测提供了更大的相对改进,但它们并没有减少遗传祖先群体之间PGS性能的差异。功能注释改进了心脏代谢特征的PGS。尽管有很强的遗传力富集,但与一般基因组注释相比,组织和细胞类型特异性的snATAC-seq和eQTL注释提供了边际性能增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
HGG Advances
HGG Advances Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
4.30
自引率
4.50%
发文量
69
审稿时长
14 weeks
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