Functional models in genome-wide selection


Autoři: Ernandes Guedes Moura aff001;  Andrezza Kellen Alves Pamplona aff002;  Marcio Balestre aff003
Působiště autorů: Federal Institute of Maranhão - Campus São João dos Patos, São João dos Patos, Maranhão, Brasil aff001;  Federal Institute of the Triângulo Mineiro – Campus Uberaba, Uberaba, Minas Gerais, Brasil aff002;  Department of Statistics - Federal University of Lavras, Lavras, Minas Gerais, Brazil aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222699

Souhrn

The development of sequencing technologies has enabled the discovery of markers that are abundantly distributed over the whole genome. Knowledge about the marker locations in reference genomes provides further insights in the search for causal regions and the prediction of genomic values. The present study proposes a Bayesian functional approach for incorporating the marker locations into genomic analysis using stochastic methods to search causal regions and predict genotypic values. For this, three scenarios were analyzed: F2 population with 300 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 12,150 SNP markers that were distributed through ten linkage groups; F populations with 320 individuals and three different heritability levels (0.2, 0.5, and 0.8), along with 10,020 SNP markers that were distributed through ten linkage groups; and data related to Eucalyptus spp. to measure the model performance in a real LD setting, with 611 individuals whose phenotypes were simulated from QTLs distributed through a panel of 36,812 SNPs with known positions. The performance of the proposed method was compared with those of other genome selection models, namely, RR-BLUP, Bayes B and Bayesian Lasso. The Bayesian functional model presented higher or similar predictive ability when compared with those classical regressions methods in simulated and real scenarios on different LD structures. In general, the Bayesian functional model also achieved higher computational efficiency, using 12 SNPs per MCMC round. The model was efficient in the identification of causal regions and showed high flexibility of analysis, as it is easily adaptable to any genomic selection model.

Klíčová slova:

Genome analysis – Genomic libraries – Heredity – Molecular genetics – Quantitative trait loci – Structural genomics – Functional genomics – Genomic signal processing


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Článek vyšel v časopise

PLOS One


2019 Číslo 10