Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.
Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.
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DOI：
10.1371/journal.pgen.1008973
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发表时间：
2021-04
期刊：
影响因子：
4.5
通讯作者：
Kraft P
中科院分区：
文献类型：
--
作者：
Feng H;Mancuso N;Gusev A;Majumdar A;Major M;Pasaniuc B;Kraft P
全转录组关联研究（TWAS）测试性状与遗传预测基因表达水平之间的关联。TWAS的作用部分取决于基因表达的遗传预测因子与因果相关的基因表达值之间的相关性。因此，当用于训练遗传预测因子的表达数量性状位点（eQTL）数据样本量小或无法获得因果相关组织的数据时，TWAS能力可能较低。在这里，我们建议通过使用稀疏典型相关分析（sCCA）整合TWAS中的多个组织来解决这些问题。我们发现sCCA-TWAS与单组织TWAS结合使用聚集体柯西关联测试（ACAT）优于传统的单组织TWAS。在经验驱动的模拟中，sCCA+ACAT方法在检测与表型相关的基因时产生了最高的功率，即使没有直接测量因果组织中的表达，同时在基因表达和表型之间没有关联时控制I型误差。例如，当基因表达解释了2%的结果变异性，GWAS样本量为20,000时，sCCA特征的ACAT联合测试与单组织、单组织结合广义Berk-Jones （GBJ）方法、单组织结合S-MultiXcan方法、maximum或使用主成分分析（PCA）方法总结跨组织表达模式之间的平均功效差异分别为5%、8%、5%和38%。功率的增加可能是由于sCCA的跨组织特征更容易被检测到遗传。当应用于10个复杂性状的公开统计数据时，sCCA+ACAT测试能够增加可测试基因的数量，并平均鉴定出单性状TWAS遗漏的400个额外的基因-性状关联。我们的研究结果表明，使用sCCA聚合多个组织的eQTL数据可以提高TWAS的敏感性，同时控制假阳性率。全转录组关联研究（Transcriptome-wide association studies， TWAS）通过利用基因预测的转录物表达水平与结果之间的关系，可以提高遗传关联研究的统计能力。我们提出了一个新的TWAS管道，整合了多个组织表达水平的遗传调控数据。我们使用稀疏典型相关分析生成跨组织表达特征，然后使用聚合柯西关联检验将跨组织和单组织特征的表达-结果关联的证据结合起来。我们表明，这种方法比传统的单组织TWAS方法具有更高的功率。将这些方法应用于10个复杂性状的公开可用的汇总统计，还可以确定单组织方法所遗漏的关联。
Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expression quantitative trait locus (eQTL) data used to train the genetic predictors have small sample sizes, or when data from causally relevant tissues are not available. Here, we propose to address these issues by integrating multiple tissues in the TWAS using sparse canonical correlation analysis (sCCA). We show that sCCA-TWAS combined with single-tissue TWAS using an aggregate Cauchy association test (ACAT) outperforms traditional single-tissue TWAS. In empirically motivated simulations, the sCCA+ACAT approach yielded the highest power to detect a gene associated with phenotype, even when expression in the causal tissue was not directly measured, while controlling the Type I error when there is no association between gene expression and phenotype. For example, when gene expression explains 2% of the variability in outcome, and the GWAS sample size is 20,000, the average power difference between the ACAT combined test of sCCA features and single-tissue, versus single-tissue combined with Generalized Berk-Jones (GBJ) method, single-tissue combined with S-MultiXcan, UTMOST, or summarizing cross-tissue expression patterns using Principal Component Analysis (PCA) approaches was 5%, 8%, 5% and 38%, respectively. The gain in power is likely due to sCCA cross-tissue features being more likely to be detectably heritable. When applied to publicly available summary statistics from 10 complex traits, the sCCA+ACAT test was able to increase the number of testable genes and identify on average an additional 400 additional gene-trait associations that single-trait TWAS missed. Our results suggest that aggregating eQTL data across multiple tissues using sCCA can improve the sensitivity of TWAS while controlling for the false positive rate. Transcriptome-wide association studies (TWAS) can improve the statistical power of genetic association studies by leveraging the relationship between genetically predicted transcript expression levels and an outcome. We propose a new TWAS pipeline that integrates data on the genetic regulation of expression levels across multiple tissues. We generate cross-tissue expression features using sparse canonical correlation analysis and then combine evidence for expression-outcome association across cross- and single-tissue features using the aggregate Cauchy association test. We show that this approach has substantially higher power than traditional single-tissue TWAS methods. Application of these methods to publicly available summary statistics for ten complex traits also identifies associations missed by single-tissue methods.
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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：
30.8
作者：
Finucane HK;Reshef YA;Anttila V;Slowikowski K;Gusev A;Byrnes A;Gazal S;Loh PR;Lareau C;Shoresh N;Genovese G;Saunders A;Macosko E;Pollack S;Brainstorm Consortium;Perry JRB;Buenrostro JD;Bernstein BE;Raychaudhuri S;McCarroll S;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
30.8
作者：
Nelson, Christopher P.;Goel, Anuj;Deloukas, Panos
通讯作者：
Deloukas, Panos
影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1126/science.1262110
发表时间：
2015-05-08
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
GTEx Consortium
通讯作者：
GTEx Consortium