Three-step Bayesian factor analysis applied to QTL detection in crosses between outbred pig populations

AbstractMarker assisted selection (MAS) can be used to improve the efficiency of genetic selection of traits for which phenotypic measurements are expensive or cannot be obtained on selection candidates, such as carcass traits. Marker information required for MAS may be acquired through the identification of QTLs. Generally, univariate models are used for QTL detection, although multiple-trait models (MTM) may enhance QTL detection and breeding value estimation. In MTM, however, the number of parameters can be large and, if traits are highly correlated, such as carcass traits, estimates of (co)variance matrices may be close to singular. Because of this, dimension reduction techniques such as Factor Analysis (FA) may be useful. The aim of our project is to evaluate the use of FA for structuring (co)variance matrices in the context of Bayesian models for QTL detection in crosses between outbred populations. In our method, QTL effects are postulated at the level of common factors (CF) rather than the original traits, using a three-step approach. In a first step, a MTM is fitted to arrive at estimates of systematic effects and prediction of breeding values (procedure A) and only systematic effect (procedure B). These estimates/predictions are then used to generate an adjusted phenotype that is further analyzed with a Bayesian FA model. This step yields estimates of factor scores for each animal and CF. In the last step, the scores relative to each CF are analyzed independently using probabilities for the line of origin combination. To illustrate the methodology, data on 416 F2 pigs (Brazilian Piau X commercial) with ten traits (5 fat-related, 2 loin measurements, and 3 carcass classification systems) were analyzed. For each of the three resulting CFs, an independent QTL scan was performed on chromosome 7 considering three models: I) null (i.e., absence of QTL); II) additive effect QTL, and III) additive and dominance effect QTL. The posterior probability (PP) of each model was calculated from Bayes factor for each considered procedures (A and B). A Three-step Bayesian factor analysis allowed us to calculate the probability of QTLs that simultaneously affect a group of carcass traits for each position of SSC 7. The removal of systematic effects in the first step of the evaluation (procedure B) allowed that the factor analysis, which was performed in the second step, identify three distinct factors that explained 85% of the total traits variation. For the common factor that represented fat-related traits (bacon depth, midline lower backfat thickness, higher backfat thickness on the shoulder; midline backfat thickness after the last rib; midline backfat thickness on the last lumbar vertebrae) the third step of the analysis showed that the highest probability of an additive QTL effect at the 65cM position was 86%

Genomic selection for boar taint compounds and carcass traits in a commercial pig population

AbstractThis study aimed to compare two different Genome-Wide Selection (GWS) methods (Ridge Regression BLUP − RR-BLUP and Bayesian LASSO − BL) to predict the genomic estimated breeding values (GEBV) of four phenotypes, including two boar taint compounds, i.e., the concentrations of androstenone (andro) and skatole (ska), and two carcass traits, i.e., backfat thickness (fat) and loin depth (loin), which were measured in a commercial male pig line. Six hundred twenty-two boars were genotyped for 2,500 previously selected single nucleotide polymorphisms (SNPs). The accuracies of the GEBV using both methods were estimated based on Jack-knife cross-validation. The BL showed the best performance for the andro, ska and loin traits, which had accuracy values of 0.65, 0.58 and 0.33, respectively; for the fat trait, the RR-BLUP accuracy of 0.61 outperformed the BL accuracy of 0.56. Considering that BL was more accurate for the majority of the traits, this method is the most favoured for GWS under the conditions of this study. The most relevant SNPs for each trait were located in the chromosome regions that were previously indicated as QTL regions in other studies, i.e., SSC6 for andro and ska, SSC2 for fat, and SSC11, SSC15 and SSC17 for loin

Inclusão de animais oriundos da técnica de transferência de embriões, na estimação de parâmetros genéticos de bovinos da raça Simental

peer reviewedWeight records from Simmental cattle provided by the Associação Brasileira dos Criadores das Raças Simental e Simbrasil (ABCRSS) were used in genetic parameters estimation by with (MTM2) or without (MTM1) inclusion of animals from the embryo transfer technique (ET). The genetic parameters were estimated by restrict maximum likelihood (REML). The direct heritabilities (h2d) in MTM1 were 0.04, 0.11, 0.20, 0.27, 0.31, 0.42, and in MTM2 were 0.11, 0.11, 0.17, 0.21, 0.22 and 0.26, respectively, for ages at 100, 205, 365, 450, 550 and 730 days. The inclusion of animals from the technique of embryo transfer can result in potential accuracy gains in genetic evaluations and more genetic gains because of the reduction of the generation interva

Independent component regression applied to genomic selection for carcass traits in pigs

O objetivo deste trabalho foi avaliar a eficiência do método de regressão via componentes independentes (ICR) na estimação de valores genéticos genômicos e dos efeitos de marcadores SNP para características de carcaça de uma população F2 de suínos (Piau x linhagem comercial). Os métodos foram avaliados por meio da concordância entre os valores genéticos preditos e os fenótipos corrigidos, observados por validação cruzada, e também foram comparados com outros métodos geralmente utilizados para os mesmos propósitos, tais como RR‑BLUP, PCR e PLS. Os métodos ICR e PCR apresentam resultados similares, mas o método ICR apresenta maiores valores de acurácia.The objective of this work was to evaluate the efficiency of the independent component regression (ICR) method for the estimation of genomic values and of SNP marker effects for carcass traits in a F2 pig population (Piau x commercial line). The methods were evaluated by the agreement between the genetic predicted values and the corrected phenotypes observed by cross‑validation, and they were also compared with other methods generally used for the same purposes, such as RR‑BLUP, PCR, and PLS. The ICR and PCR methods show similar results, but ICR has the highest accuracy prediction values

Seleção tradicional e associada a marcadores moleculares na avaliação genética animal

The objective of this work was to compare selection based on breeding values predicted by classical best linear unbiased prediction (BLUP), BLUP associated with molecular markers (BLUPM) and individual selection (IS) using data simulated with the Genesys program. To obtain the genetic similarity matrix to be used in BLUPM, a hundred microsatellite markers (simple sequence repeats) were simulated using a similarity coefficient corresponding to the mean Euclidean distance between quantitative data. The different selection methods were compared using populations of an effective size of 66.66 and a mean of 30 repetitions, and mean phenotypic values were determined. Genetic gain obtained over 20 generations of selection was higher for BLUP than BLUPM, which in turn was superior to IS. Similar genetic gains were obtained for BLUPM and BLUP only when the gain for the first five generations was considered, and these gains were higher than those obtained with IS. Selected reproducers mating systems did not lead to differences in genetic gain for the BLUP-based methods.O objetivo deste trabalho foi comparar a seleção, utilizando valores genéticos preditos pelo BLUP clássico (BLUP), BLUP marcadores (BLUPM) e pela seleção individual (SI), usando simulação com o programa Genesys. Para obter a matriz de similaridade genética utilizada no BLUPM, foram simulados cem marcadores moleculares do tipo microssatélite (SSR – Simple Sequence Repeat), por meio de um coeficiente de similaridade correspondente à distância euclideana média para dados quantitativos. A fim de comparar os diferentes métodos, utilizaram-se populações com tamanho efetivo de 66,66 e média de 30 repetições, avaliando-se os valores fenotípicos médios. Os ganhos ao longo das 20 gerações de seleção foram maiores para o BLUP em relação ao BLUPM, e este foi superior à SI. Quanto ao ganho obtido nas cinco primeiras gerações, o BLUPM apresentou ganhos semelhantes ao BLUP e superiores à SI. Diferentes sistemas de acasalamento dos reprodutores selecionados não revelaram diferenças em ganho genético nos métodos baseados no BLUP

Traditional and associated selection with molecular markers in the genetic evaluation of animals

O objetivo deste trabalho foi comparar a seleção, utilizando valores genéticos preditos pelo BLUP clássico (BLUP), BLUP marcadores (BLUPM) e pela seleção individual (SI), usando simulação com o programa Genesys. Para obter a matriz de similaridade genética utilizada no BLUPM, foram simulados cem marcadores moleculares do tipo microssatélite (SSR – Simple Sequence Repeat), por meio de um coeficiente de similaridade correspondente à distância euclideana média para dados quantitativos. A fim de comparar os diferentes métodos, utilizaram-se populações com tamanho efetivo de 66,66 e média de 30 repetições, avaliando-se os valores fenotípicos médios. Os ganhos ao longo das 20 gerações de seleção foram maiores para o BLUP em relação ao BLUPM, e este foi superior à SI. Quanto ao ganho obtido nas cinco primeiras gerações, o BLUPM apresentou ganhos semelhantes ao BLUP e superiores à SI. Diferentes sistemas de acasalamento dos reprodutores selecionados não revelaram diferenças em ganho genético nos métodos baseados no BLUP.The objective of this work was to compare selection based on breeding values predicted by classical best linear unbiased prediction (BLUP), BLUP associated with molecular markers (BLUPM) and individual selection (IS) using data simulated with the Genesys program. To obtain the genetic similarity matrix to be used in BLUPM, a hundred microsatellite markers (simple sequence repeats) were simulated using a similarity coefficient corresponding to the mean Euclidean distance between quantitative data. The different selection methods were compared using populations of an effective size of 66.66 and a mean of 30 repetitions, and mean phenotypic values were determined. Genetic gain obtained over 20 generations of selection was higher for BLUP than BLUPM, which in turn was superior to IS. Similar genetic gains were obtained for BLUPM and BLUP only when the gain for the first five generations was considered, and these gains were higher than those obtained with IS. Selected reproducers mating systems did not lead to differences in genetic gain for the BLUP-based methods