IMPUTE

IMPUTE is a program for estimating (”imputing”) unobserved genotypes in SNP association studies. The program is designed to work seamlessly with the output of the genotype calling program CHIAMO and the population genetic simulator HAPGEN, and it produces output that can be analyzed using the program SNPTEST. There are currently three different versions of the IMPUTE software available for download: version 0.5 implements the methodology described in Marchini et al. (2007); version 1 is essentially the same as version 0.5, with a couple of added features; and version 2 implements a major extension that was introduced in Howie et al. (2009).


References in zbMATH (referenced in 9 articles )

Showing results 1 to 9 of 9.
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  1. Sacco, Chiara; Viroli, Cinzia; Falchi, Mario: A statistical test for detecting parent-of-origin effects when parental information is missing (2017)
  2. Yuan, Lin; Yuan, Chang-An; Huang, De-Shuang: FAACOSE: a fast adaptive ant colony optimization algorithm for detecting SNP epistasis (2017)
  3. Emily, Mathieu: AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies (2016)
  4. 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)
  5. Huang, Yen-Tsung; VanderWeele, Tyler J.; Lin, Xihong: Joint analysis of SNP and gene expression data in genetic association studies of complex diseases (2014)
  6. Sampson, Joshua N.; Wheeler, Bill; Li, Peng; Shi, Jianxin: Leveraging local identity-by-descent increases the power of case/control GWAS with related individuals (2014)
  7. Steinrücken, Matthias; Paul, Joshua S.; Song, Yun S.: A sequentially Markov conditional sampling distribution for structured populations with migration and recombination (2013)
  8. Wen, Xiaoquan; Stephens, Matthew: Using linear predictors to impute allele frequencies from summary or pooled genotype data (2010)
  9. Kooperberg, Charles; LeBlanc, Michael; Dai, James Y.; Rajapakse, Indika: Structures and assumptions: strategies to harness gene $\times$ gene and gene $\times$ environment interactions in GWAS (2009)