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 57 articles )

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  1. Grinberg, Nastasiya F.; Orhobor, Oghenejokpeme I.; King, Ross D.: An evaluation of machine-learning for predicting phenotype: studies in yeast, Rice, and wheat (2020)
  2. 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)
  3. Najafi, Amir; Motahari, Seyed Abolfazl; Rabiee, Hamid R.: Reliable clustering of Bernoulli mixture models (2020)
  4. Brzyski, Damian; Gossmann, Alexej; Su, Weijie; Bogdan, Małgorzata: Group SLOPE -- adaptive selection of groups of predictors (2019)
  5. Crawford, Lorin; Flaxman, Seth R.; Runcie, Daniel E.; West, Mike: Variable prioritization in nonlinear black box methods: a genetic association case study (2019)
  6. Liu, Zhonghua; Lin, Xihong: A geometric perspective on the power of principal component association tests in multiple phenotype studies (2019)
  7. 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)
  8. Castro, Bruno M.; Lemes, Renan B.; Cesar, Jonatas; Hünemeier, Tábita; Leonardi, Florencia: A model selection approach for multiple sequence segmentation and dimensionality reduction (2018)
  9. Liu, Zhonghua; Lin, Xihong: Multiple phenotype association tests using summary statistics in genome-wide association studies (2018)
  10. Stephen Turner: qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots (2018) not zbMATH
  11. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  12. Wu, Baolin; Pankow, James S.: Fast and accurate genome-wide association test of multiple quantitative traits (2018)
  13. Zhao, Huaqing; Mitra, Nandita; Kanetsky, Peter A.; Nathanson, Katherine L.; Rebbeck, Timothy R.: A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS) (2018)
  14. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  15. Zhao, Sihai Dave: Integrative genetic risk prediction using non-parametric empirical Bayes classification (2017)
  16. Anderson, Eric C.; Ng, Thomas C.: Bayesian pedigree inference with small numbers of single nucleotide polymorphisms via a factor-graph representation (2016)
  17. Briollais, Laurent; Dobra, Adrian; Liu, Jinnan; Friedlander, Matt; Ozcelik, Hilmi; Massam, Hélène: A Bayesian graphical model for genome-wide association studies (GWAS) (2016)
  18. Gazal, Steven; Génin, Emmanuelle; Leutenegger, Anne-Louise: Relationship inference from the genetic data on parents or offspring: a comparative study (2016)
  19. 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)
  20. Jakaitiene, Audrone; Sangiovanni, Mara; Guarracino, Mario R.; Pardalos, Panos M.: Multidimensional scaling for genomic data (2016)

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