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Covariance functions have been proposed to predict breeding values and genetic (co)variances as a function of phenotypic within herd-year averages (environmental parameters) to include genotype by environment interaction. The objective of this paper was to investigate the influence of definition of environmental parameters and non-random use of sires on expected breeding values and estimated genetic variances across environments. Breeding values were simulated as a linear function of simulated herd effects. The definition of environmental parameters hardly influenced the results. In situations with random use of sires, estimated genetic correlations between the trait expressed in different environments were 0.93, 0.93 and 0.97 while simulated at 0.89 and estimated genetic variances deviated up to 30% from the simulated values. Non random use of sires, poor genetic connectedness and small herd size had a large impact on the estimated covariance functions, expected breeding values and calculated environmental parameters. Estimated genetic correlations between a trait expressed in different environments were biased upwards and breeding values were more biased when genetic connectedness became poorer and herd composition more diverse. The best possible solution at this stage is to use environmental parameters combining large numbers of animals per herd, while losing some information on genotype by environment interaction in the data. 相似文献
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Zhang J Wang C Terroni PL Cagampang FR Hanson M Byrne CD 《American journal of physiology. Regulatory, integrative and comparative physiology》2005,288(1):R112-R118
Whether a high-unsaturated-fat, high-protein (HFP), and low-carbohydrate (CHO) diet during gestation has long-lasting beneficial effects on lipid metabolism in the offspring was investigated using a mouse model. Female mice were fed either a standard (CHO rich) chow diet or a CHO HFP diet, before and during gestation and lactation. All offspring were weaned onto the same chow until adulthood. Although liver cholesterol concentration and fasting plasma triglyceride (TG), cholesterol, and free fatty acid concentrations were not affected in either male or female HFP offspring, hepatic TG concentration was reduced by approximately 51% (P < 0.05) in the female adult offspring from dams on the HFP diet, compared with females from dams on the chow diet (a trend toward reduced TG concentration was also observed in the male). Furthermore, hepatic protein levels for CD36, carnitine palmitoyltransferase-1 (CPT-1), and peroxisomal proliferator activated receptor-alpha (PPAR-alpha) were increased by approximately 46% (P < 0.001), approximately 52% (P < 0.001), and approximately 14% (P = 0.035), respectively, in the female HFP offspring. Liver TG levels were negatively correlated with protein levels of CD 36 (r = -0.69, P = 0.007), CPT-1 (r = -0.55, P = 0.033), and PPAR-alpha (r = -0.57, P = 0.025) in these offspring. In conclusion, a maternal HFP diet during gestation and lactation reduces hepatic TG concentration in female offspring, which is linked with increased protein levels in fatty acid oxidation. 相似文献
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Rianne van Binsbergen Marco CAM Bink Mario PL Calus Fred A van Eeuwijk Ben J Hayes Ina Hulsegge Roel F Veerkamp 《遗传、选种与进化》2014,46(1):41
Background
The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.Methods
Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.Results
Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.Conclusions
Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability. 相似文献45.
Mario PL Calus 《遗传、选种与进化》2014,46(1):24
Background
Since both the number of SNPs (single nucleotide polymorphisms) used in genomic prediction and the number of individuals used in training datasets are rapidly increasing, there is an increasing need to improve the efficiency of genomic prediction models in terms of computing time and memory (RAM) required.Methods
In this paper, two alternative algorithms for genomic prediction are presented that replace the originally suggested residual updating algorithm, without affecting the estimates. The first alternative algorithm continues to use residual updating, but takes advantage of the characteristic that the predictor variables in the model (i.e. the SNP genotypes) take only three different values, and is therefore termed “improved residual updating”. The second alternative algorithm, here termed “right-hand-side updating” (RHS-updating), extends the idea of improved residual updating across multiple SNPs. The alternative algorithms can be implemented for a range of different genomic predictions models, including random regression BLUP (best linear unbiased prediction) and most Bayesian genomic prediction models. To test the required computing time and RAM, both alternative algorithms were implemented in a Bayesian stochastic search variable selection model.Results
Compared to the original algorithm, the improved residual updating algorithm reduced CPU time by 35.3 to 43.3%, without changing memory requirements. The RHS-updating algorithm reduced CPU time by 74.5 to 93.0% and memory requirements by 13.1 to 66.4% compared to the original algorithm.Conclusions
The presented RHS-updating algorithm provides an interesting alternative to reduce both computing time and memory requirements for a range of genomic prediction models. 相似文献46.
Christos Dadousis Roel F Veerkamp Bj?rg Heringstad Marcin Pszczola Mario PL Calus 《遗传、选种与进化》2014,46(1)
Background
Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction.Methods
The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK.Results
In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC.Conclusions
On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge.Electronic supplementary material
The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users. 相似文献47.
Background
Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely.Methods
SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors.Results
BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10−8 instead of 10−10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS.Conclusions
Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model.Electronic supplementary material
The online version of this article (doi:10.1186/s12711-014-0052-x) contains supplementary material, which is available to authorized users. 相似文献48.
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W H Stork J A Van Boxsel A F De Goeij AF+DEGOEIJ P L De Haseth PL+DEHASETH M Mandel 《Biophysical chemistry》1974,2(2):127-136
Binding of auramine O to poly-(methacrylic acid) (PMA) has been established over a large range of C0p/C0d values using spectroscopic methods (UV absorption and visible fluorescence emission spectra), equilibrium and sedimentation dialysis, potentiometric and viscosimetric titrations. All the results show qualitative agreement with those obtained previously with the system crystal violet-PMA although the binding seems to be less strong for auramine O than for crystal violet. From dialysis experiments binding isotherms were obtained at three different degrees of neutralization α'; at α' = 0.10 the results could be fitted to a Langmuir isotherm but at α' equal to 0.40 and 0.65 deviations with respect to such an isotherm occur. The results of potentiometric and viscosimetric titrations confirm that the conformational transition which the dye-free PMA exhibits upon ionization is affected by the dye binding. The region in which the conformational transion occurs is broadened and is less sharply defined in the presence of auramine O. 相似文献