An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle

Abstract

Summary Imputation of moderate-density genotypes from low-density panels is of increasing interest in genomic selection, because it can dramatically reduce genotyping costs. Several imputation software packages have been developed, but they vary in imputation accuracy, and imputed genotypes may be inconsistent among methods. An AdaBoost-like approach is proposed to combine imputation results from several independent software packages, i.e. Beagle(v3.3), IMPUTE(v2.0), fastPHASE(v1.4), AlphaImpute, findhap(v2) and Fimpute(v2), with each package serving as a basic classifier in an ensemble-based system. The ensemble-based method computes weights sequentially for all classifiers, and combines results from component methods via weighted majority 'voting' to determine unknown genotypes. The data included 3078 registered Angus cattle, each genotyped with the Illumina BovineSNP50 BeadChip. SNP genotypes on three chromosomes (BTA1, BTA16 and BTA28) were used to compare imputation accuracy among methods, and the application involved the imputation of 50K genotypes covering 29 chromosomes based on a set of 5K genotypes. Beagle and Fimpute had the greatest accuracy among the six imputation packages, which ranged from 0·8677 to 0·9858. The proposed ensemble method was better than any of these packages, but the sequence of independent classifiers in the voting scheme affected imputation accuracy. The ensemble systems yielding the best imputation accuracies were those that had Beagle as first classifier, followed by one or two methods that utilized pedigree information. A salient feature of the proposed ensemble method is that it can solve imputation inconsistencies among different imputation methods, hence leading to a more reliable system for imputing genotypes relative to independent methods.

Criteria of GenCall score to edit marker data and methods to handle missing markers have an influence on accuracy of genomic predictions

“…Chen et al (2011) used three imputation methods (Beagle, DAGPHASE and findhap) in German Holstein data and the results showed that Beagle had the lowest error rate. Recently, in Angus cattle six imputation methods were compared (Beagle, IMPUTE, fastPHASE, AlphaImpute, findhap and Fimpute) and Beagle had the greatest imputation accuracy (Sun et al 2012).…”

Incorporação de informações genômicas no desenvolvimento de índices econômicos para a seleção de bovinos leiteiros

“…Dessa forma, modelos de análise de sobrevivência (DUCROCQ, 1993;VAN DOORMAAL;SHAEFFER;KENNEDY, 1984) devem ser considerados na análise genética de VP. Adicionalmente, o cálculo do seu valor econômico deve incorporar o custo de reposição da vaca e a taxa de interesse da vaca, similarmente ao realizado em Gay et al (2014).…”

Selección genómica en poblaciones reducidas de vacuno de leche.

“…Beagle as first classifier in the proposed ensemble (Sun et al, 2012). A different approach was proposed by Calus et al (2011) in this case using a multivariate mixed model framework.…”

“…Among them, Beagle (Browning and Browning, 2009) is reported as competitive when compared to other approaches (Johnston et al, 2011;Segelke et al, 2012;Sun et al, 2012).…”

“…Its competitive imputation accuracy and computational advantages when compared with other methods have been widely reported (Calus et al, 2011;Nothnagel et al, 2009;Segelke et al, 2012;Sun et al, 2012).…”