Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations

MetaGS: an accurate method to impute and combine SNP effects across populations using summary statistics

Background Meta-analysis describes a category of statistical methods that aim at combining the results of multiple studies to increase statistical power by exploiting summary statistics. Different industries that use genomic prediction do not share their raw data due to logistic or privacy restrictions, which can limit the size of their reference populations and creates a need for a practical meta-analysis method. Results We developed a meta-analysis, named MetaGS, that duplicates the results of multi-trait best linear unbiased prediction (mBLUP) analysis without accessing raw data. MetaGS exploits the correlations among different populations to produce more accurate population-specific single nucleotide polymorphism (SNP) effects. The method improves SNP effect estimations for a given population depending on its relations to other populations. MetaGS was tested on milk, fat and protein yield data of Australian Holstein and Jersey cattle and it generated very similar genomic estimated breeding values to those produced using the mBLUP method for all traits in both breeds. One of the major difficulties when combining SNP effects across populations is the use of different variants for the populations, which limits the applications of meta-analysis in practice. We solved this issue by developing a method to impute missing summary statistics without using raw data. Our results showed that imputing summary statistics can be done with high accuracy (r > 0.9) even when more than 70% of the SNPs were missing with a minimal effect on prediction accuracy. Conclusions We demonstrated that MetaGS can replace the mBLUP model when raw data cannot be shared, which can lead to more flexible collaborations compared to the single-trait BLUP model

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use

Invited review: Reliability computation from the animal model era to the single-step genomic model era

The calculation of exact reliabilities involving the inversion of mixed model equations poses a heavy computational challenge when the system of equations is large. This has prompted the development of different approximation methods. We give an overview of the various methods and computational approaches in calculating reliability from the era before the animal model to the era of single-step genomic models. The different methods are discussed in terms of modeling, development, and applicability in large dairy cattle populations. The paper also describes the problems faced in reliability computation. Many details dispersed throughout the literature are presented in this paper. It is clear that a universal solution applicable to every model and input data may not be possible, but we point out several efficient and accurate algorithms developed recently for a variety of very large genomic evaluations

Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

<p>Abstract</p> <p>Background</p> <p>The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information.</p> <p>Methods</p> <p>Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values.</p> <p>Results</p> <p>As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population.</p> <p>Conclusions</p> <p>Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire's estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values.</p

Interbull validation test for genomic evaluations

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Data transformation for rank reduction in multi-trait MACE model for international bull comparison

Since many countries use multiple lactation random regression test day models in national evaluations for milk production traits, a random regression multiple across-country evaluation (MACE) model permitting a variable number of correlated traits per country should be used in international dairy evaluations. In order to reduce the number of within country traits for international comparison, three different MACE models were implemented based on German daughter yield deviation data and compared to the random regression MACE. The multiple lactation MACE model analysed daughter yield deviations on a lactation basis reducing the rank from nine random regression coefficients to three lactations. The lactation breeding values were very accurate for old bulls, but not for the youngest bulls with daughters with short lactations. The other two models applied principal component analysis as the dimension reduction technique: one based on eigenvalues of a genetic correlation matrix and the other on eigenvalues of a combined lactation matrix. The first one showed that German data can be transformed from nine traits to five eigenfunctions without losing much accuracy in any of the estimated random regression coefficients. The second one allowed performing rank reductions to three eigenfunctions without having the problem of young bulls with daughters with short lactations

Dissolved Gas Analysis in Transformer Oil Using Ni Catalyst Decorated PtSe₂ Monolayer: A DFT Study

In this paper, the first-principles theory is used to explore the adsorption behavior of Ni catalyst decorated PtSe2 (Ni-PtSe2) monolayer toward the dissolved gas in transformer oil, namely CO and C2H2. Some Ni atoms from the catalyst are trapped in the Se vacancy on the pure PtSe2 surface. The geometry configurations of Ni-PtSe2 monolayer before and after gas adsorption, the electronic property of Ni-PtSe2 monolayer upon gas adsorption, and the sensibility and recovery property of Ni-PtSe2 monolayer are explored in this theoretical work. Through the simulation, the Ead of CO and C2H2 gas adsorption systems are calculated as −1.583 eV and −1.319 eV, respectively, both identified as chemisorption and implying the stronger performance of the Ni-PtSe2 monolayer on CO molecule, which is further supported by the DOS and BS analysis. According to the formula, the sensitivity of Ni-PtSe2 monolayer towards CO and C2H2 detection can reach up to 96.74% and 99.91% at room temperature (298 K), respectively, which manifests the favorable sensing property of these gases as a chemical resistance-type sensor. Recovery behavior indicates that the Ni-PtSe2 monolayer is a satisfied gas scavenger upon the noxious gas dissolved in transformer oil, but its recovery time at room temperature is not satisfactory. To sum up, we monitor the status of the transformer to guarantee the stable operation of the power system through the Ni-PtSe2 monolayer upon the detection of CO and C2H2, which may realize related applications, and provide the basis and reference to cutting-edge research in the field of electricity in the future

Mo₂C-Based Microfluidic Gas Sensor Detects SF₆ Decomposition Components: A First-Principles Study

Mo2C is a two-dimensional material with high electrical conductivity, low power consumption, and catalytic effect, which has promising applications in the field of microfluidic gas detection. First principles were used to study the adsorption characteristics of Mo2C monolayer on four typical decomposition gases of SF6 (H2S, SO2, SOF2, and SO2F2), and to explore the feasibility of its application in the detection of SF6 decomposition components. The results showed that Mo2C chemisorbed all four gases, and the adsorption capacity was H2S 2 2 2F2. The adsorption mechanism of Mo2C as a microfluidic sensor was analyzed in combination with its charge-density difference and density of states. On the other hand, the different work-function change trends after adsorbing gases provide the possibility for Mo2C to selectively detect gases as a low-power field-effect transistor sensor. All content can be used as theoretical guidance in the realization of Mo2C as a gas-sensitive material for the detection of SF6 decomposition components