Eigenstrat

The EIGENSOFT package combines functionality from our population genetics methods (Patterson et al. 2006) and our EIGENSTRAT stratification correction method (Price et al. 2006). The EIGENSTRAT method uses principal components analysis to explicitly model ancestry differences between cases and controls along continuous axes of variation; the resulting correction is specific to a candidate marker’s variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. The EIGENSOFT package has a built-in plotting script and supports multiple file formats and quantitative phenotypes.


References in zbMATH (referenced in 56 articles )

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  1. Gerard, David; Stephens, Matthew: Unifying and generalizing methods for removing unwanted variation based on negative controls (2021)
  2. Jiang, Yingda; Chiu, Chi-Yang; Yan, Qi; Chen, Wei; Gorin, Michael B.; Conley, Yvette P.; Lakhal-Chaieb, M’Hamed Lajmi; Cook, Richard J.; Amos, Christopher I.; Wilson, Alexander F.; Bailey-Wilson, Joan E.; McMahon, Francis J.; Vazquez, Ana I.; Yuan, Ao; Zhong, Xiaogang; Xiong, Momiao; Weeks, Daniel E.; Fan, Ruzong: Gene-based association testing of dichotomous traits with generalized functional linear mixed models using extended pedigrees: applications to age-related macular degeneration (2021)
  3. Zheng, Zemin; Lv, Jinchi; Lin, Wei: Nonsparse learning with latent variables (2021)
  4. Broc, Camilo; Calvo, Borja; Liquet, Benoit: Penalized partial least square applied to structured data (2020)
  5. Galbraith, John W.; Zinde-Walsh, Victoria: Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects (2020)
  6. Liu, Zhonghua; Barnett, Ian; Lin, Xihong: A comparison of principal component methods between multiple phenotype regression and multiple SNP regression in genetic association studies (2020)
  7. Marbac, Matthieu; Sedki, Mohammed; Patin, Tienne: Variable selection for mixed data clustering: application in human population genomics (2020)
  8. Najafi, Amir; Motahari, Seyed Abolfazl; Rabiee, Hamid R.: Reliable clustering of Bernoulli mixture models (2020)
  9. Sportisse, Aude; Boyer, Claire; Josse, Julie: Imputation and low-rank estimation with missing not at random data (2020)
  10. Sun, Ryan; Lin, Xihong: Genetic variant set-based tests using the generalized Berk-Jones statistic with application to a genome-wide association study of breast cancer (2020)
  11. Artigue, Heidi; Smith, Gary: The principal problem with principal components regression (2019)
  12. Barber, Rina Foygel; Candès, Emmanuel J.: A knockoff filter for high-dimensional selective inference (2019)
  13. Berk, Lauren; Bertsimas, Dimitris: Certifiably optimal sparse principal component analysis (2019)
  14. Dey, Rounak; Lee, Seunggeun: Asymptotic properties of principal component analysis and shrinkage-bias adjustment under the generalized spiked population model (2019)
  15. Liu, Yaowu; Xie, Jun: Accurate and efficient (P)-value calculation via Gaussian approximation: a novel Monte-Carlo method (2019)
  16. Ogburn, Elizabeth L.; Shpitser, Ilya; Tchetgen Tchetgen, Eric J.: Comment on: “Blessings of multiple causes” (2019)
  17. Wang, Yixin; Blei, David M.: The blessings of multiple causes (2019)
  18. Liu, Yaowu; Xie, Jun: Powerful test based on conditional effects for genome-wide screening (2018)
  19. Tsakiris, Manolis C.; Vidal, René: Dual principal component pursuit (2018)
  20. Zhang, Teng; Yang, Yi: Robust PCA by manifold optimization (2018)

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