PLINK is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview, there is some support for the subsequent visualization, annotation and storage of results.

References in zbMATH (referenced in 38 articles )

Showing results 1 to 20 of 38.
Sorted by year (citations)

1 2 next

  1. Castro, Bruno M.; Lemes, Renan B.; Cesar, Jonatas; Hünemeier, Tábita; Leonardi, Florencia: A model selection approach for multiple sequence segmentation and dimensionality reduction (2018)
  2. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  3. Zhao, Sihai Dave: Integrative genetic risk prediction using non-parametric empirical Bayes classification (2017)
  4. Anderson, Eric C.; Ng, Thomas C.: Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation (2016)
  5. Briollais, Laurent; Dobra, Adrian; Liu, Jinnan; Friedlander, Matt; Ozcelik, Hilmi; Massam, Hélène: A Bayesian graphical model for genome-wide association studies (GWAS) (2016)
  6. Gazal, Steven; Génin, Emmanuelle; Leutenegger, Anne-Louise: Relationship inference from the genetic data on parents or offspring: a comparative study (2016)
  7. Hung, Hung; Lin, Yu-Ting; Chen, Penweng; Wang, Chen-Chien; Huang, Su-Yun; Tzeng, Jung-Ying: Detection of gene-gene interactions using multistage sparse and low-rank regression (2016)
  8. Jakaitiene, Audrone; Sangiovanni, Mara; Guarracino, Mario R.; Pardalos, Panos M.: Multidimensional scaling for genomic data (2016)
  9. Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi: Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation (2016)
  10. Stange, Jens; Dickhaus, Thorsten; Navarro, Arcadi; Schunk, Daniel: Multiplicity- and dependency-adjusted $p$-values for control of the family-wise error rate (2016)
  11. Thompson, Katherine L.; Linnen, Catherine R.; Kubatko, Laura: Tree-based quantitative trait mapping in the presence of external covariates (2016)
  12. Bae, Harold; Perls, Thomas; Steinberg, Martin; Sebastiani, Paola: Bayesian polynomial regression models to fit multiple genetic models for quantitative traits (2015)
  13. Jan Graffelman: Exploring Diallelic Genetic Markers: The HardyWeinberg Package (2015) not zbMATH
  14. Kozlitina, Julia; Schucany, William R.: A robust distribution-free test for genetic association studies of quantitative traits (2015)
  15. Carmi, Shai; Wilton, Peter R.; Wakeley, John; Pe’er, Itsik: A renewal theory approach to IBD sharing (2014)
  16. Gupta, Mayetri: An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices (2014)
  17. Bryc, Katarzyna; Bryc, Wlodek; Silverstein, Jack W.: Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations (2013)
  18. Crossett, Andrew; Lee, Ann B.; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn: Refining genetically inferred relationships using treelet covariance smoothing (2013)
  19. Liu, Jia-Rou; Kuo, Po-Hsiu; Hung, Hung: A robust rerank approach for feature selection and its application to pooling-based GWA studies (2013)
  20. Scutari, Marco; Mackay, Ian; Balding, David: Improving the efficiency of genomic selection (2013)

1 2 next