Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.
Quantifying portable genetic effects and improving cross-ancestry genetic prediction with GWAS summary statistics.
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DOI：
10.1038/s41467-023-36544-7
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发表时间：
2023-02-14
影响因子：
16.6
通讯作者：
Lu, Qiongshi
中科院分区：
文献类型：
--
作者：
Miao, Jiacheng;Guo, Hanmin;Song, Gefei;Zhao, Zijie;Hou, Lin;Lu, Qiongshi
根据欧洲人全基因组关联研究（GWAS）计算的多基因风险评分（PRS）在非欧洲人群中的预测准确性大大降低，限制了其临床实用性，并引起了对祖先人群健康差异的担忧。在这里，我们引入了一个名为X-Wing的统计框架，以提高祖先多样化人群的预测性能。X-Wing量化了群体之间复杂性状的局部遗传相关性，采用了一种依赖于注释的估计程序来放大群体之间的相关遗传效应，并将多个群体特定的PRS组合成一个统一的分数，仅以GWAS汇总统计量作为输入。通过广泛的基准测试，我们证明了X-Wing精确定位了便携式遗传效应，并大大提高了非欧洲人群的PRS性能，与基于GWAS汇总统计的最先进方法相比，预测R2的相对增益为14.1%-119.1%。总的来说，X-Wing解决了现有方法的关键局限性，并可能在跨人群多基因风险预测中具有广泛的应用。多基因风险评分用于改善常见疾病的风险预测，但通常会降低非欧洲血统个体的准确性。在这里，作者提出了一种方法，提高多基因风险评分的表现在祖先不同的人群。
Polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of Europeans are known to have substantially reduced predictive accuracy in non-European populations, limiting their clinical utility and raising concerns about health disparities across ancestral populations. Here, we introduce a statistical framework named X-Wing to improve predictive performance in ancestrally diverse populations. X-Wing quantifies local genetic correlations for complex traits between populations, employs an annotation-dependent estimation procedure to amplify correlated genetic effects between populations, and combines multiple population-specific PRS into a unified score with GWAS summary statistics alone as input. Through extensive benchmarking, we demonstrate that X-Wing pinpoints portable genetic effects and substantially improves PRS performance in non-European populations, showing 14.1%–119.1% relative gain in predictive R2 compared to state-of-the-art methods based on GWAS summary statistics. Overall, X-Wing addresses critical limitations in existing approaches and may have broad applications in cross-population polygenic risk prediction. Polygenic risk scores are used to improve risk prediction for common diseases but typically have reduced accuracy for individuals of non-European ancestry. Here, the authors present an approach that improves polygenic risk score performance in ancestrally diverse populations.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
影响因子：
37.8
作者：
Dehghan A;Dupuis J;Barbalic M;Bis JC;Eiriksdottir G;Lu C;Pellikka N;Wallaschofski H;Kettunen J;Henneman P;Baumert J;Strachan DP;Fuchsberger C;Vitart V;Wilson JF;Paré G;Naitza S;Rudock ME;Surakka I;de Geus EJ;Alizadeh BZ;Guralnik J;Shuldiner A;Tanaka T;Zee RY;Schnabel RB;Nambi V;Kavousi M;Ripatti S;Nauck M;Smith NL;Smith AV;Sundvall J;Scheet P;Liu Y;Ruokonen A;Rose LM;Larson MG;Hoogeveen RC;Freimer NB;Teumer A;Tracy RP;Launer LJ;Buring JE;Yamamoto JF;Folsom AR;Sijbrands EJ;Pankow J;Elliott P;Keaney JF;Sun W;Sarin AP;Fontes JD;Badola S;Astor BC;Hofman A;Pouta A;Werdan K;Greiser KH;Kuss O;Meyer zu Schwabedissen HE;Thiery J;Jamshidi Y;Nolte IM;Soranzo N;Spector TD;Völzke H;Parker AN;Aspelund T;Bates D;Young L;Tsui K;Siscovick DS;Guo X;Rotter JI;Uda M;Schlessinger D;Rudan I;Hicks AA;Penninx BW;Thorand B;Gieger C;Coresh J;Willemsen G;Harris TB;Uitterlinden AG;Järvelin MR;Rice K;Radke D;Salomaa V;Willems van Dijk K;Boerwinkle E;Vasan RS;Ferrucci L;Gibson QD;Bandinelli S;Snieder H;Boomsma DI;Xiao X;Campbell H;Hayward C;Pramstaller PP;van Duijn CM;Peltonen L;Psaty BM;Gudnason V;Ridker PM;Homuth G;Koenig W;Ballantyne CM;Witteman JC;Benjamin EJ;Perola M;Chasman DI
通讯作者：
Chasman DI
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
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
16.6
作者：
Duncan, L.;Shen, H.;Domingue, B.
通讯作者：
Domingue, B.