title: Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.
abstract: Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation.
authors.affiliation: Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran. Electronic address: abbas_mikhchi@yahoo.com.
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 Emam Jomeh
authors.initials: NE
authors.lastname: Kashan
authors.affiliation: Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Aminafshar
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D005787:Gene Frequency / Q000235:genetics; D005838:Genotype; D060005:Genotyping Techniques; D006801:Humans; D000069550:Machine Learning; D008297:Male; D010290:Parents; D020641:Polymorphism, Single Nucleotide / Q000235:genetics
keywords: Computation time; Genotype imputation; Imputation accuracy; Machine learning methods