Genomewide predictions from maize single-cross data

Abstract

Maize (Zea mays L.) breeders evaluate many single-cross hybrids each year in multiple environments. Our objective was to determine the usefulness of genomewide predictions, based on marker effects from maize single-cross data, for identifying the best untested single crosses and the best inbreds within a biparental cross. We considered 479 experimental maize single crosses between 59 Iowa Stiff Stalk Synthetic (BSSS) inbreds and 44 non-BSSS inbreds. The single crosses were evaluated in multilocation experiments from 2001 to 2009 and the BSSS and non-BSSS inbreds had genotypic data for 669 single nucleotide polymorphism (SNP) markers. Single-cross performance was predicted by a previous best linear unbiased prediction (BLUP) approach that utilized marker-based relatedness and information on relatives, and from genomewide marker effects calculated by ridge-regression BLUP (RR-BLUP). With BLUP, the mean prediction accuracy (r(MG)) of single-cross performance was 0.87 for grain yield, 0.90 for grain moisture, 0.69 for stalk lodging, and 0.84 for root lodging. The BLUP and RR-BLUP models did not lead to r(MG) values that differed significantly. We then used the RR-BLUP model, developed from single-cross data, to predict the performance of testcrosses within 14 biparental populations. The r(MG) values within each testcross population were generally low and were often negative. These results were obtained despite the above-average level of linkage disequilibrium, i.e., r(2) between adjacent markers of 0.35 in the BSSS inbreds and 0.26 in the non-BSSS inbreds. Overall, our results suggested that genomewide marker effects estimated from maize single crosses are not advantageous (cofmpared with BLUP) for predicting single-cross performance and have erratic usefulness for predicting testcross performance within a biparental cross.

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…Massman et al (2013) reported that RCGS had a superior response for stover yield, as well as stover and grain yield indices that were 14-50% higher than those of MARS. Recently, in tropical maize, by employing RCGS on eight bi-parental populations evaluated under drought stress environments, Beyene et al (2015) reported an average gain of 0.086 ton ha −1 per cycle, and hybrids derived from cycle 3 produced 7.3% (0.176 ton ha −1 ) higher grain yield than those from cycle 0 and conventional pedigree breeding methods.…”

Identifying superior spring wheat genotypes through diallel approaches

“…It allows estimating the variance components by REML and the predicting the genetic values using the BLUP (Henderson, 1975;Resende, 2002;Massman et al, 2013). BLUP predictors and REML estimators allow inferences regarding the additive and non-additive genetic effects related to the GCA and SCA (Piepho et al, 2008;Möhring et al, 2011).…”

Seleção genômica para melhoramento vegetal com diferentes estruturas populacionais

“…Nakaya & Isobe (2012) argumentam que marcadores em LD significativo com os QTLs de interesse, em geral, não são mantidos em diferentes cruzamentos, o que dificulta o uso da GWS. Como exemplo, se efeitos de marcadores forem estimados a partir de híbridos simples de milho, os valores genético-genômicos estimados em populações heterogêneas não fornecem informações acuradas (Massman et al, 2013). Harris et al (2009), ao utilizar duas raças de bovinos como populações de referência, verificaram que o uso de uma raça como população de referência e de outra raça para validação resulta em baixa acurácia (-0,10 a 0,3).…”