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 63 articles )

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  1. Kerdoncuff, Elise; Lambert, Amaury; Achaz, Guillaume: Testing for population decline using maximal linkage disequilibrium blocks (2020)
  2. Kong, Dehan; An, Baiguo; Zhang, Jingwen; Zhu, Hongtu: L2RM: low-rank linear regression models for high-dimensional matrix responses (2020)
  3. Liu, Yaowu; Xie, Jun: Cauchy combination test: a powerful test with analytic (p)-value calculation under arbitrary dependency structures (2020)
  4. 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)
  5. Mathieu Emily, Nicolas Sounac, Florian Kroell, Magalie Houée-Bigot: Gene-Based Methods to Detect Gene-Gene Interaction in R: The GeneGeneInteR Package (2020) not zbMATH
  6. Najafi, Amir; Motahari, Seyed Abolfazl; Rabiee, Hamid R.: Reliable clustering of Bernoulli mixture models (2020)
  7. Nie, Yunlong; Opoku, Eugene; Yasmin, Laila; Song, Yin; Wang, Jie; Wu, Sidi; Scarapicchia, Vanessa; Gawryluk, Jodie; Wang, Liangliang; Cao, Jiguo; Nathoo, Farouk S.: Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics (2020)
  8. Orhobor, Oghenejokpeme I.; Alexandrov, Nickolai N.; King, Ross D.: Predicting Rice phenotypes with meta and multi-target learning (2020)
  9. Zhao, Qingyuan; Wang, Jingshu; Hemani, Gibran; Bowden, Jack; Small, Dylan S.: Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2020)
  10. Brzyski, Damian; Gossmann, Alexej; Su, Weijie; Bogdan, Małgorzata: Group SLOPE -- adaptive selection of groups of predictors (2019)
  11. Crawford, Lorin; Flaxman, Seth R.; Runcie, Daniel E.; West, Mike: Variable prioritization in nonlinear black box methods: a genetic association case study (2019)
  12. Liu, Zhonghua; Lin, Xihong: A geometric perspective on the power of principal component association tests in multiple phenotype studies (2019)
  13. 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)
  14. Castro, Bruno M.; Lemes, Renan B.; Cesar, Jonatas; Hünemeier, Tábita; Leonardi, Florencia: A model selection approach for multiple sequence segmentation and dimensionality reduction (2018)
  15. Liu, Zhonghua; Lin, Xihong: Multiple phenotype association tests using summary statistics in genome-wide association studies (2018)
  16. Stephen Turner: qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots (2018) not zbMATH
  17. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  18. Wu, Baolin; Pankow, James S.: Fast and accurate genome-wide association test of multiple quantitative traits (2018)
  19. 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)
  20. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)

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