PLINK

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 61 articles )

Showing results 21 to 40 of 61.
Sorted by year (citations)
  1. Briollais, Laurent; Dobra, Adrian; Liu, Jinnan; Friedlander, Matt; Ozcelik, Hilmi; Massam, Hélène: A Bayesian graphical model for genome-wide association studies (GWAS) (2016)
  2. Gazal, Steven; Génin, Emmanuelle; Leutenegger, Anne-Louise: Relationship inference from the genetic data on parents or offspring: a comparative study (2016)
  3. 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)
  4. Jakaitiene, Audrone; Sangiovanni, Mara; Guarracino, Mario R.; Pardalos, Panos M.: Multidimensional scaling for genomic data (2016)
  5. 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)
  6. Stange, Jens; Dickhaus, Thorsten; Navarro, Arcadi; Schunk, Daniel: Multiplicity- and dependency-adjusted (p)-values for control of the family-wise error rate (2016)
  7. Thompson, Katherine L.; Linnen, Catherine R.; Kubatko, Laura: Tree-based quantitative trait mapping in the presence of external covariates (2016)
  8. Bae, Harold; Perls, Thomas; Steinberg, Martin; Sebastiani, Paola: Bayesian polynomial regression models to fit multiple genetic models for quantitative traits (2015)
  9. 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
  10. Jan Graffelman: Exploring Diallelic Genetic Markers: The HardyWeinberg Package (2015) not zbMATH
  11. Kozlitina, Julia; Schucany, William R.: A robust distribution-free test for genetic association studies of quantitative traits (2015)
  12. Carmi, Shai; Wilton, Peter R.; Wakeley, John; Pe’er, Itsik: A renewal theory approach to IBD sharing (2014)
  13. Gupta, Mayetri: An evolutionary Monte Carlo algorithm for Bayesian block clustering of data matrices (2014)
  14. Bryc, Katarzyna; Bryc, Wlodek; Silverstein, Jack W.: Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete subpopulations (2013)
  15. Crossett, Andrew; Lee, Ann B.; Klei, Lambertus; Devlin, Bernie; Roeder, Kathryn: Refining genetically inferred relationships using treelet covariance smoothing (2013)
  16. Liu, Jia-Rou; Kuo, Po-Hsiu; Hung, Hung: A robust rerank approach for feature selection and its application to pooling-based GWA studies (2013)
  17. Scutari, Marco; Mackay, Ian; Balding, David: Improving the efficiency of genomic selection (2013)
  18. Yushi Liu, Toru Nyunoya, Shuguang Leng, Steven A Belinsky, Yohannes Tesfaigzi, Shannon Bruse: Softwares and methods for estimating genetic ancestry in human populations (2013) not zbMATH
  19. Zhao, Jing Hua; Luan, Jian’an: Mixed modeling with whole genome data (2012)
  20. Andrey A. Shabalin: Matrix eQTL: Ultra fast eQTL analysis via large matrix operations (2011) arXiv