Additive genetic variance and covariance between relatives in synthetic wheat crosses with variable parental ploidy levels.

Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii-T. aestivum and T. turgidum-T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle.

BACKGROUND: Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness. RESULTS: The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. CONCLUSIONS: GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.

Estimativas de variância genética aditiva em populações selecionadas e não-selecionadas via simulação Monte Carlo utilizando o software R / Additive genetic variance estimates in selected and unselected populations via Monte Carlo simulation using the R software

Para disponibilizar um sistema de fornecimento de dados que objetivando-se subsidiar pesquisas de Melhoramento Genético Animal direcionadas à comparação de metodologias de avaliação genética, foi avaliado o comportamento da variância genética aditiva de populações selecionadas e não selecionadas, por seis gerações sucessivas, via simulação Monte Carlo. Por meio de um modelo genético aditivo, foram simuladas populações de 40 animais (20 machos e 20 fêmeas), sob seleção e acasalamento aleatório. Da geração zero até a quinta geração notou-se na população selecionada uma redução de 44,4 por cento na variância genética aditiva, devido a um aumento de 11,58 por cento no coeficiente de endogamia. Na população não selecionada a redução da variância genética aditiva foi menor (27,46 por cento) em relação à população selecionada, também devido a aumento de 10,26 por cento no coeficiente de endogamia.

The additive genetic variance in selected and unselected populations was evaluated in six successive generations via Monte Carlo simulation. The aim was to build a data system to help researches compare genetic evaluation methodologies in Animal Breeding. By means of an additive genetic model, populations of 40 individuals (20 males and 20 females) were simulated, under selected and random mating system. From the generation zero until the fifth generation, the selected population showed reduction of 44.4 percent in additive genetic variance due to an increase of 11.58 percent in inbreeding coefficient. In the unselected population the reduction in additive genetic variance was lower (27.46 percent) in relation to the selected population, due to the increasing of 10.26 percent in inbreeding coefficient.

Efeito da violação de pressuposições da metodologia de modelos mistos na avaliação genética animal / Effect of assumption violations of the mixed model methodology on the genetic evaluation

Estudos de simulação foram conduzidos para verificar o efeito da violação de pressuposições da metodologia de modelos mistos, variâncias genéticas conhecidas sem erro e distribuição normal dos erros aleatórios sobre os ganhos genéticos obtidos durante 10 gerações de seleção. Outros parâmetros, como valor fenotípico e acurácia, também foram avaliados. Inicialmente, foi simulado um genoma constituído de uma única característica quantitativa governada por 500 locos. 0 genoma foi utilizado na construção de uma população-base, na qual a característica quantitativa possuía herdabilidade inicial de 0,10. Para se obter uma estrutura de parentesco a partir das populações-base, foi gerada uma população inicial a partir da qual o processo de seleção teve início e os erros nos componentes de variâncias e as distribuições dos efeitos de ambiente foram introduzidos. Para pressuposição de que a variância genética era conhecida, utilizaram-se as intensidades de erro de 0 por cento, -10 por cento, -30 por cento, -50 por cento, 10 por cento, 30 por cento e 50 por cento, enquanto que para a pressuposição de que a distribuição dos erros aleatórios era normal, utilizaram-se as distribuições normal, exponencial, poisson e uniforme. A cada geração foram selecionados 20 machos e 100 fêmeas, acasalados ao acaso, cada macho acasalado com cinco fêmeas, produzindo cinco descendentes por acasalamento. Esse processo foi repetido 30 vezes para minimização dos efeitos da flutuação gênica. Para a primeira pressuposição, não foi verificado efeito das intensidades de erro, aplicadas ao componente de variância genética aditiva sobre o ganho genético durante as 10 gerações de seleção. 0 mesmo resultado foi verificado para a distribuição dos erros aleatórios, ou seja, não houve influência de diferentes distribuições nos ganhos genéticos verificados (AU)

Simulation studies were conducted to evaluate the effects of two assumption violations of the methodology of mixed models (the variances are known without errors and normal distribution of the random errors) on the genetic gains. Phenotypic values and accuracy during 10 generations of selection were also studied. Initially, a genome of only one quantitative trait governed by 500 loci was simulated. The genome was used to construct the base population in which the initial heritability of the quantitative trait was 0.10. To obtain a relationship structure from the base population, a initial population was generated. To this population a selection process and the errors in the components of variance and the distribution of the environment effects were introduced. To violate the assumption that the genetic variance is known without errors, the following errors intensities 0%, -10%, -30%, -50%, 10%, 30% and 50% were tested, and to violate the assumption that the distribution of the random errors is normal, the following distribution were considered: normal, exponential, poisson and uniform. In each generation 20 males and 100 females were selected and they were mated in the ratio of one male to five females and five offspring per each mating. To minimize random drift this process was reapeted 30 times. There was no evidence of the effect of both assumption violations on the genetic gain, the phenotypic value and accuracy over 10 generations of selection. (AU)