title: Comparison of three boosting methods in parent-offspring trios for genotype imputation using simulation study.
abstract: BACKGROUND
  Genotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker.
  
  
  METHODS
  In this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel.
  
  
  RESULTS
  Comparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB.
  
  
  CONCLUSIONS
  The conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.
authors.affiliation: Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Mikhchi
authors.affiliation: Department of Animal Science, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Mahmood
authors.initials: M
authors.lastname: Honarvar
authors.affiliation: Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Nasser
authors.initials: N
authors.lastname: Emam Jomeh Kashan
authors.affiliation: Department of Animal Science, Ferdowsi University of Mashhad, Mashhad, Iran.
authors.forename: Saeed
authors.initials: S
authors.lastname: Zerehdaran
authors.affiliation: Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Aminafshar
keywords: Boosting methods; Computation time; Imputation accuracy; Trios