A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics.
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DOI：
10.1371/journal.pgen.1009697
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发表时间：
2021-07
期刊：
影响因子：
4.5
通讯作者：
Zhao H
中科院分区：
文献类型：
--
作者：
Zhou G;Zhao H
复杂性状的遗传预测对疾病的预防、监测和治疗具有很大的前景。由于遗传结构在不同性状上的多样性、个体水平数据的训练和参数调整的有限访问以及对计算资源的需求，阻碍了准确风险预测模型的发展。为了克服大多数现有方法对底层遗传结构进行明确假设和需要单独验证数据集进行参数调优的局限性，我们开发了一种基于汇总统计的非参数方法，该方法不依赖于验证数据集来调优参数。在我们的实现中，我们改进了常用的似然假设，以处理汇总统计数据与外部参考面板之间的差异。我们还利用参考连杆不平衡矩阵的块结构来实现并行算法。通过对12个性状的仿真和应用表明，该方法对不同的遗传结构具有较强的适应性，具有统计鲁棒性和计算效率。我们的方法可以在https://github.com/eldronzhou/SDPR上找到。最近，人们对从遗传信息中预测个体表型很感兴趣，这对疾病的预防、监测和治疗有很大的希望。研究发现，不同复杂性状的遗传结构存在很大差异，包括涉及的遗传变异数量和遗传变异效应大小的分布。如何建立这种遗传贡献的模型是准确预测复杂性状的一个关键方面。到目前为止，大多数现有的方法都对遗传贡献的形状做出了具体的假设。如果这些假设不正确，预测的准确性可能会受到影响。在这里，我们提出了一种方法来学习遗传贡献的形状，而不做任何明确的假设。通过仿真和实际数据分析，发现该方法具有较好的鲁棒性。我们的方法实际上也更可行，因为它支持使用公共汇总统计，并且只消耗少量的计算资源。
Genetic prediction of complex traits has great promise for disease prevention, monitoring, and treatment. The development of accurate risk prediction models is hindered by the wide diversity of genetic architecture across different traits, limited access to individual level data for training and parameter tuning, and the demand for computational resources. To overcome the limitations of the most existing methods that make explicit assumptions on the underlying genetic architecture and need a separate validation data set for parameter tuning, we develop a summary statistics-based nonparametric method that does not rely on validation datasets to tune parameters. In our implementation, we refine the commonly used likelihood assumption to deal with the discrepancy between summary statistics and external reference panel. We also leverage the block structure of the reference linkage disequilibrium matrix for implementation of a parallel algorithm. Through simulations and applications to twelve traits, we show that our method is adaptive to different genetic architectures, statistically robust, and computationally efficient. Our method is available at https://github.com/eldronzhou/SDPR. Recently there has been much interest in predicting an individual’s phenotype from genetic information, which has great promise for disease prevention, monitoring, and treatment. It has been found that there is great variation in the genetic architecture underlying different complex traits, including the number of genetic variants involved and the distribution of the effect sizes of genetic variants. How to model such genetic contribution is a key aspect for accurate prediction of complex traits. So far, most existing methods make specific assumptions about the shape of the genetic contribution. If these assumptions are not correct, the prediction accuracy might be compromised. Here we propose a method that learns the shape of the genetic contribution without making any explicit assumptions. We found that our method achieved robust performance when compared with other recently developed methods through simulation and real data analysis. Our method is also practically more feasible, since it supports the use of public summary statistics and consumes only small amount of computational resources.
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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：
64.8
作者：
Michailidou K;Lindström S;Dennis J;Beesley J;Hui S;Kar S;Lemaçon A;Soucy P;Glubb D;Rostamianfar A;Bolla MK;Wang Q;Tyrer J;Dicks E;Lee A;Wang Z;Allen J;Keeman R;Eilber U;French JD;Qing Chen X;Fachal L;McCue K;McCart Reed AE;Ghoussaini M;Carroll JS;Jiang X;Finucane H;Adams M;Adank MA;Ahsan H;Aittomäki K;Anton-Culver H;Antonenkova NN;Arndt V;Aronson KJ;Arun B;Auer PL;Bacot F;Barrdahl M;Baynes C;Beckmann MW;Behrens S;Benitez J;Bermisheva M;Bernstein L;Blomqvist C;Bogdanova NV;Bojesen SE;Bonanni B;Børresen-Dale AL;Brand JS;Brauch H;Brennan P;Brenner H;Brinton L;Broberg P;Brock IW;Broeks A;Brooks-Wilson A;Brucker SY;Brüning T;Burwinkel B;Butterbach K;Cai Q;Cai H;Caldés T;Canzian F;Carracedo A;Carter BD;Castelao JE;Chan TL;David Cheng TY;Seng Chia K;Choi JY;Christiansen H;Clarke CL;NBCS Collaborators;Collée M;Conroy DM;Cordina-Duverger E;Cornelissen S;Cox DG;Cox A;Cross SS;Cunningham JM;Czene K;Daly MB;Devilee P;Doheny KF;Dörk T;Dos-Santos-Silva I;Dumont M;Durcan L;Dwek M;Eccles DM;Ekici AB;Eliassen AH;Ellberg C;Elvira M;Engel C;Eriksson M;Fasching PA;Figueroa J;Flesch-Janys D;Fletcher O;Flyger H;Fritschi L;Gaborieau V;Gabrielson M;Gago-Dominguez M;Gao YT;Gapstur SM;García-Sáenz JA;Gaudet MM;Georgoulias V;Giles GG;Glendon G;Goldberg MS;Goldgar DE;González-Neira A;Grenaker Alnæs GI;Grip M;Gronwald J;Grundy A;Guénel P;Haeberle L;Hahnen E;Haiman CA;Håkansson N;Hamann U;Hamel N;Hankinson S;Harrington P;Hart SN;Hartikainen JM;Hartman M;Hein A;Heyworth J;Hicks B;Hillemanns P;Ho DN;Hollestelle A;Hooning MJ;Hoover RN;Hopper JL;Hou MF;Hsiung CN;Huang G;Humphreys K;Ishiguro J;Ito H;Iwasaki M;Iwata H;Jakubowska A;Janni W;John EM;Johnson N;Jones K;Jones M;Jukkola-Vuorinen A;Kaaks R;Kabisch M;Kaczmarek K;Kang D;Kasuga Y;Kerin MJ;Khan S;Khusnutdinova E;Kiiski JI;Kim SW;Knight JA;Kosma VM;Kristensen VN;Krüger U;Kwong A;Lambrechts D;Le Marchand L;Lee E;Lee MH;Lee JW;Neng Lee C;Lejbkowicz F;Li J;Lilyquist J;Lindblom A;Lissowska J;Lo WY;Loibl S;Long J;Lophatananon A;Lubinski J;Luccarini C;Lux MP;Ma ESK;MacInnis RJ;Maishman T;Makalic E;Malone KE;Kostovska IM;Mannermaa A;Manoukian S;Manson JE;Margolin S;Mariapun S;Martinez ME;Matsuo K;Mavroudis D;McKay J;McLean C;Meijers-Heijboer H;Meindl A;Menéndez P;Menon U;Meyer J;Miao H;Miller N;Taib NAM;Muir K;Mulligan AM;Mulot C;Neuhausen SL;Nevanlinna H;Neven P;Nielsen SF;Noh DY;Nordestgaard BG;Norman A;Olopade OI;Olson JE;Olsson H;Olswold C;Orr N;Pankratz VS;Park SK;Park-Simon TW;Lloyd R;Perez JIA;Peterlongo P;Peto J;Phillips KA;Pinchev M;Plaseska-Karanfilska D;Prentice R;Presneau N;Prokofyeva D;Pugh E;Pylkäs K;Rack B;Radice P;Rahman N;Rennert G;Rennert HS;Rhenius V;Romero A;Romm J;Ruddy KJ;Rüdiger T;Rudolph A;Ruebner M;Rutgers EJT;Saloustros E;Sandler DP;Sangrajrang S;Sawyer EJ;Schmidt DF;Schmutzler RK;Schneeweiss A;Schoemaker MJ;Schumacher F;Schürmann P;Scott RJ;Scott C;Seal S;Seynaeve C;Shah M;Sharma P;Shen CY;Sheng G;Sherman ME;Shrubsole MJ;Shu XO;Smeets A;Sohn C;Southey MC;Spinelli JJ;Stegmaier C;Stewart-Brown S;Stone J;Stram DO;Surowy H;Swerdlow A;Tamimi R;Taylor JA;Tengström M;Teo SH;Beth Terry M;Tessier DC;Thanasitthichai S;Thöne K;Tollenaar RAEM;Tomlinson I;Tong L;Torres D;Truong T;Tseng CC;Tsugane S;Ulmer HU;Ursin G;Untch M;Vachon C;van Asperen CJ;Van Den Berg D;van den Ouweland AMW;van der Kolk L;van der Luijt RB;Vincent D;Vollenweider J;Waisfisz Q;Wang-Gohrke S;Weinberg CR;Wendt C;Whittemore AS;Wildiers H;Willett W;Winqvist R;Wolk A;Wu AH;Xia L;Yamaji T;Yang XR;Har Yip C;Yoo KY;Yu JC;Zheng W;Zheng Y;Zhu B;Ziogas A;Ziv E;ABCTB Investigators;ConFab/AOCS Investigators;Lakhani SR;Antoniou AC;Droit A;Andrulis IL;Amos CI;Couch FJ;Pharoah PDP;Chang-Claude J;Hall P;Hunter DJ;Milne RL;García-Closas M;Schmidt MK;Chanock SJ;Dunning AM;Edwards SL;Bader GD;Chenevix-Trench G;Simard J;Kraft P;Easton DF
通讯作者：
Easton DF
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
影响因子：
9.8
作者：
Chun, Sung;Imakaev, Maxim;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.
影响因子：
16.6
作者：
Duncan, L.;Shen, H.;Domingue, B.
通讯作者：
Domingue, B.