Improving polygenic prediction in ancestrally diverse populations.
Improving polygenic prediction in ancestrally diverse populations.
复制标题
改进祖先多样性群体中的多基因预测。
DOI：
10.1038/s41588-022-01054-7
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发表时间：
2022-05
期刊：
影响因子：
30.8
通讯作者：
Ge, Tian
中科院分区：
文献类型：
--
作者：
Ruan, Yunfeng;Lin, Yen-Feng;Feng, Yen-Chen Anne;Chen, Chia-Yen;Lam, Max;Guo, Zhenglin;He, Lin;Sawa, Akira;Martin, Alicia R.;Qin, Shengying;Huang, Hailiang;Ge, Tian
多基因风险评分（PRS）减弱了跨人群预测性能。由于现有的全基因组关联研究（GWAS）主要在欧洲血统的个体中进行，PRS的有限可转移性降低了其在非欧洲人群中的临床价值，并可能加剧医疗保健差异。最近在基因组研究中平衡祖先不平衡的努力扩大了非欧洲GWAS的规模，尽管其中大多数仍然动力不足。在这里，我们提出了一种新的PRS构建方法，PRS-CSx，它通过整合来自多个群体的GWAS汇总统计量来提高跨群体多基因预测。PRS-CSx通过共享的连续收缩先验耦合了群体间的遗传效应，通过在汇总统计量之间共享信息并利用发现样本间的连锁不平衡（LD）多样性，实现了更准确的效应量估计，同时继承了PRS-CS的计算效率和鲁棒性。我们表明，PRS-CSx优于替代方法的性状与广泛的遗传结构，跨群体遗传重叠和发现GWAS样本量的模拟，并提高了非欧洲人群的数量性状和精神分裂症风险的预测。
Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) were predominantly conducted in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most of them remain underpowered. Here we present a novel PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium (LD) diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations.
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影响因子：
2.1
作者：
Márquez-Luna C;Loh PR;South Asian Type 2 Diabetes (SAT2D) Consortium;SIGMA Type 2 Diabetes Consortium;Price AL
通讯作者：
Price AL
影响因子：
30.8
作者：
Atkinson EG;Maihofer AX;Kanai M;Martin AR;Karczewski KJ;Santoro ML;Ulirsch JC;Kamatani Y;Okada Y;Finucane HK;Koenen KC;Nievergelt CM;Daly MJ;Neale BM
通讯作者：
Neale BM
DOI：
10.1093/bioinformatics/btz633
发表时间：
2020-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Lam M;Awasthi S;Watson HJ;Goldstein J;Panagiotaropoulou G;Trubetskoy V;Karlsson R;Frei O;Fan CC;De Witte W;Mota NR;Mullins N;Brügger K;Lee SH;Wray NR;Skarabis N;Huang H;Neale B;Daly MJ;Mattheisen M;Walters R;Ripke S
通讯作者：
Ripke S
影响因子：
30.8
作者：
Bulik-Sullivan, Brendan K.;Loh, Po-Ru;Finucane, Hilary K.;Ripke, Stephan;Yang, Jian;Patterson, Nick;Daly, Mark J.;Price, Alkes L.;Neale, Benjamin M.
通讯作者：
Neale, Benjamin M.
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ