SSRs, SNPs and DArTs comparison on estimation of relatedness and genetic parameters precision from a small half-sib sample population of Eucalyptus grandis

CAPPA, EP; JAROSLAV KLÁPTSTE; GARCÍA, MARTÍN; PAMELA V VILLALBA; MARCUCCI POLTRI, SN

MOLECULAR BREEDING

SPRINGER

Lugar: Berlin; Año: 2016 vol. 36 p. 97 - 97

1380-3743

Simple Sequence Repeats (SSR) are the most widely used molecular markers for relatedness inference due to their multi-allelic nature and high informativeness. However, there is a growing trend towards using high-throughput and inter-specific transferable Single Nucleotide Polymorphisms (SNP) and Diversity Array Technology (DArT) in forest genetics owing to their wide genome coverage. We compared the efficiency of 15 SSRs, 181 SNPs, and 2,816 DArTs to estimate the relatedness coefficients, and their effects on genetic parameters´ precision, in a relatively small data set of an open-pollinated progeny trial of Eucalyptus grandis (Hill ex Maiden) with limited relationship from the pedigree. Both simulations and real data of Eucalyptus grandis were used to study the statistical performance of three relatedness estimators based on co-dominant markers. Relatedness estimates in pairs of individuals belonging to the same family (related) were higher for DArTs than for SNPs and SSRs. DArTs performed better compared to SSRs and SNPs in estimated relatedness coefficients in pairs of individuals belonging to different families (unrelated), and showed higher ability to discriminate unrelated from related individuals. The likelihood-based estimator exhibited the lowest root mean squared error (RMSE); however, the differences in RMSE among the three estimators studied were small. For the growth traits, heritability estimates based on SNPs yielded, on average, smaller standard errors compared to those based on SSRs and DArTs. Estimated relatedness in the realized relationship matrix and heritabilities can be accurately inferred from co-dominant or sufficiently dense dominant markers in a relatively small E. grandis data set with shallow pedigree.