GCTA

GCTA: a tool for genome-wide complex trait analysis. For most human complex diseases and traits, SNPs identified by genome-wide association studies (GWAS) explain only a small fraction of the heritability. Here we report a user-friendly software tool called genome-wide complex trait analysis (GCTA), which was developed based on a method we recently developed to address the ”missing heritability” problem. GCTA estimates the variance explained by all the SNPs on a chromosome or on the whole genome for a complex trait rather than testing the association of any particular SNP to the trait. We introduce GCTA’s five main functions: data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation. We focus on the function of estimating the variance explained by all the SNPs on the X chromosome and testing the hypotheses of dosage compensation. The GCTA software is a versatile tool to estimate and partition complex trait variation with large GWAS data sets.


References in zbMATH (referenced in 16 articles )

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  1. Fan, Zhou; Johnstone, Iain M.: Eigenvalue distributions of variance components estimators in high-dimensional random effects models (2019)
  2. Gong, Gail; Wang, Wei; Hsieh, Chih-Lin; Van Den Berg, David J.; Haiman, Christopher; Oakley-Girvan, Ingrid; Whittemore, Alice S.: Data-adaptive multi-locus association testing in subjects with arbitrary genealogical relationships (2019)
  3. Jeng, X. Jessie; Zhang, Teng; Tzeng, Jung-Ying: Efficient signal inclusion with genomic applications (2019)
  4. Li, Weidong; Zhou, Qingniao; Gao, Yong; Jiang, Yonghua; Huang, Yuanjie; Mo, Zengnan; Zou, Yiming; Hu, Yanling: eQTL analysis from co-localization of 2739 GWAS loci detects associated genes across 14 human cancers (2019)
  5. Zhou, Quan; Guan, Yongtao: Fast model-fitting of Bayesian variable selection regression using the iterative complex factorization algorithm (2019)
  6. Bonnet, Anna: Heritability estimation in case-control studies (2018)
  7. Jing Zhao; Jian’an Luan; Peter Congdon: Bayesian Linear Mixed Models with Polygenic Effects (2018) not zbMATH
  8. Steinsaltz, David; Dahl, Andrew; Wachter, Kenneth W.: Statistical properties of simple random-effects models for genetic heritability (2018)
  9. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  10. Darnell, Gregory; Georgiev, Stoyan; Mukherjee, Sayan; Engelhardt, Barbara E.: Adaptive randomized dimension reduction on massive data (2017)
  11. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  12. Gazal, Steven; Génin, Emmanuelle; Leutenegger, Anne-Louise: Relationship inference from the genetic data on parents or offspring: a comparative study (2016)
  13. Bonnet, Anna; Gassiat, Elisabeth; Lévy-Leduc, Céline: Heritability estimation in high dimensional sparse linear mixed models (2015)
  14. Coram MA, Candille SI, Duan Q, Chan KHK, Li Y, Kooperberg C, Reiner AP, Tang H.: Leveraging Multi-ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach (2015) not zbMATH
  15. Crossett, Andrew; Lee, Ann B.; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn: Refining genetically inferred relationships using treelet covariance smoothing (2013)
  16. Hardin, Johanna; Garcia, Stephan Ramon; Golan, David: A method for generating realistic correlation matrices (2013)