Mapping of leaf rust resistance genes in common wheat 'Guinong08-6' / Mapa de genes de resistência à ferrugem da folha em trigo 'Guinong08-6'

Wheat leaf rust (Puccinia triticina Eriks.), a devastating disease of wheat in the world, causes severe yield losses and therefore the development of resistant cultivars is very important. Here, a Chinese wheat line (Guinong08-6) showed adult-plant resistance against mixed fungal isolates of leaf rust, which is common in Guiyang region. It was crossed with a susceptible wheat line (Guinong19) to develop F1, F2, and F3 hybrids. Combined SSR and STS markers were used to map leaf rust resistance genes in Guinong08-6, and the resistance phenotype of Guinong08-6 was co-regulated by two complementary dominant genes, named LrGn08-6A and LrGn08-6B. LrGn08-6A was mapped to chromosome 2AS with markers URIC-LN2 and Xgpw2204, which flanked the gene at distances of 1.8 centimorgan (cM) and 14.83 cM, respectively. LrGn08-6B was mapped to chromosome 4DL with markers Xgpw342 and Xbarc93, which both flanked the gene at a distance of 26.57 cM. Genetic and molecular marker analyses demonstrated that LrGn08-6A, which was inherited from Aegilops ventricosa may be the resistance gene Lr37, while LrGn08-6B may be a newly discovered leaf rust resistance gene.(AU)

A ferrugem da folha do trigo (Puccinia triticina Eriks.), importante doença do trigo em todo o mundo, causa graves perdas de rendimento e, portanto, o desenvolvimento de cultivares resistentes é muito importante. Nesta pesquisa, uma linhagem chinesa de trigo (Guinong08-6) mostrou resistência de plantas adultas a uma mistura de isolados do patógeno, na região de Guiyang, China. Essa linhagem foi cruzada com uma linhagem suscetível de trigo (Guinong19) para desenvolver híbridos F1, F2 e F3. Combinados de marcadores SSR e STS foram usados para mapear genes de resistência à ferrugem da folha em Guinong08-6, e o fenótipo de resistência de Guinong08-6 foi co-regulado por dois genes dominantes complementares, chamados LrGn08-6A e LrGn08-6B. LrGn08-6A foi mapeado para o cromossomo 2AS com marcadores URIC-LN2 e Xgpw2204, que flanquearam o gene em distâncias de 1,8 centimorgano (cM) e 14,83 cM, respectivamente. LrGn08-6B foi mapeado para o cromossomo 4DL com marcadores Xgpw342 e Xbarc93, e ambos flanquearam o gene a uma distância de 26,57 cM. As análises genéticas e moleculares de marcadores demonstraram que LrGn08-6A, que foi herdado de Aegilops ventricosa, pode ser o gene de resistência Lr37, enquanto LrGn08-6B pode ser um gene recentemente descoberto de resistência à ferrugem da folha do trigo.(AU)

Evaluation of a new additive-dominance genomic model and implications for quantitative genetics and genomic selection

The Fisher's infinitesimal model is traditionally used in quantitative genetics and genomic selection, and it attributes most genetic variance to additive variance. Recently, the dominance maximization model was proposed and it prioritizes the dominance variance based on alternative parameterizations. In this model, the additive effects at the locus level are introduced into the model after the dominance variance is maximized. In this study, the new parameterizations of additive and dominance effects on quantitative genetics and genomic selection were evaluated and compared with the parameterizations traditionally applied using the genomic best linear unbiased prediction method. As the parametric relative magnitude of the additive and dominance effects vary with allelic frequencies of populations, we considered different minor allele frequencies to compare the relative magnitudes. We also proposed and evaluated two indices that combine the additive and dominance variances estimated by both models. The dominance maximization model, along with the two indices, offers alternatives to improve the estimates of additive and dominance variances and their respective proportions and can be successfully used in genetic evaluation.

Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.

Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.(AU)