GWAS Catalog

GWAS Catalog - The NHGRI-EBI Catalog of human genome-wide association studies. The Catalog was founded by the NHGRI in 2008, in response to the rapid increase in the number of published genome-wide association studies (GWAS). These studies provide an unprecedented opportunity to investigate the impact of common variants on complex disease; however identifying published GWAS can be challenging, and the vast wealth of data contained within these publications is effectively inaccessible to researchers without systematic cataloguing and summarization of the observed associations. The GWAS Catalog provides a consistent, searchable, visualisable and freely available database of SNP-trait associations, which can be easily integrated with other resources, and is accessed by scientists, clinicians and other users worldwide.

References in zbMATH (referenced in 15 articles )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Guan, Boxin; Zhao, Yuhai; Yin, Ying; Li, Yuan: A differential evolution based feature combination selection algorithm for high-dimensional data (2021)
  2. Huang, Jian; Jiao, Yuling; Liu, Jin; Yang, Can: REMI: regression with marginal information and its application in genomewide association studies (2021)
  3. Li, Jingyi Jessica; Chen, Yiling Elaine; Tong, Xin: A flexible model-free prediction-based framework for feature ranking (2021)
  4. Molstad, Aaron J.; Sun, Wei; Hsu, Li: A covariance-enhanced approach to multitissue joint eQTL mapping with application to transcriptome-wide association studies (2021)
  5. Meng, Jingbo; Zhu, Wensheng; Li, Canhui; Jon, Kyongson: A novel association test for rare variants based on algebraic statistics (2020)
  6. Vanderweele, Tyler J.; Mathur, Maya B.; Chen, Ying: Outcome-wide longitudinal designs for causal inference: a new template for empirical studies (2020)
  7. Wu, Chong; Xu, Gongjun; Shen, Xiaotong; Pan, Wei: A regularization-based adaptive test for high-dimensional GLMs (2020)
  8. Liu, Zhonghua; Lin, Xihong: A geometric perspective on the power of principal component association tests in multiple phenotype studies (2019)
  9. Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.: A simple, consistent estimator of SNP heritability from genome-wide association studies (2019)
  10. Yu, Xinghao; Xiao, Lishun; Zeng, Ping; Huang, Shuiping: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information (2019)
  11. Deutsch, J. M.: Computational mechanisms in genetic regulation by RNA (2018)
  12. Mukhopadhyay, Subhadeep: Decentralized nonparametric multiple testing (2018)
  13. Ali, Fadhaa; Zhang, Jian: Mixture model-based association analysis with case-control data in genome wide association studies (2017)
  14. Yang, Jincai; Gu, Huichao; Jiang, Xingpeng; Huang, Qingyang; Hu, Xiaohua; Shen, Xianjun: Identifying the risky SNP of osteoporosis with ID3-PEP decision tree algorithm (2017)
  15. Astle, William; Balding, David J.: Population structure and cryptic relatedness in genetic association studies (2009)