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Incorporating genomic annotation into single-step genomic prediction with imputed whole-genome sequence data

Journal of Integrative Agriculture 2022年第4期21卷 1126-1136页

作者： TENG Jin-yan YE Shao-pan GAO Ning CHEN Zi-tao DIAO Shu-qi LI Xiu-jin YUAN Xiao-long ZHANG Hao LI Jia-qi ZHANG Xi-quan ZHANG Zhe Guangdong Laboratory of Lingnan Modern Agriculture/Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding College of Animal ScienceSouth China Agricultural UniversityGuangzhou 510642P.R.China State Key Laboratory of Biocontrol School of Life SciencesSun Yat-sen UniversityGuangzhou 510006P.R.China Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding College of Animal Sciences and TechnologyZhongkai University of Agriculture and EngineeringGuangzhou 510225P.R.China

Single-step genomic best linear unbiased prediction(ss GBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence(WGS) data at the population level, more attention is being paid to the usage of WGS data in ss GBLUP. The predictive ability of ss GBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ss GBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms(SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ss GBLUP, haplotype-based ss GHBLUP, and four extended ss GBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation(GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ss GBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ss GBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ss GBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family(SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ss GBLUP, and valida

关键词： genomic selection prior information sequencing data genotype imputation haplotype

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Bayesian model predicts the aboveground biomass of Caragana microphylla in sandy lands better than OLS regression models

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Journal of Plant Ecology 2020年第6期13卷 732-737页

作者： Yi Tang Arshad Ali Li-Huan Feng School of Life Science Liaoning UniversityShenyang 110036LiaoningChina Department of Forest Resources Management College of ForestryNanjing Forestry UniversityNanjing 210037JiangsuChina Co-Innovation Center for Sustainable Forestry in Southern China Nanjing Forestry UniversityNanjing 210037JiangsuChina

Aims In forest ecosystems,different types of regression models have been frequently used for the estimation of aboveground biomass,where Ordinary Least Squares(OLS)regression models are the most common prediction ***,the relative performance of Bayesian and OLS models in predicting aboveground biomass of shrubs,especially multi-stem shrubs,has relatively been less studied in *** In this study,we developed the biomass prediction models for Caragana microphylla *** is a widely distributed multi-stems shrub,and contributes to the decrease of wind erosion and the fixation of sand dunes in the Horqin Sand Land,one of the largest sand lands in *** developed six types of formulations under the framework of the regression models,and then,selected the best model based on specific ***,we estimated the parameters of the best model with OLS and Bayesian methods with training and test data under different sample sizes with the bootstrap ***,we compared the performance of the OLS and Bayesian models in predicting the aboveground biomass of *** Findings The performance of the allometric equation(power=1)was best among six types of equations,even though all of those models were *** results showed that mean squared error of test data with non-informative prior Bayesian method and the informative prior Bayesian method was lower than with the OLS *** the tested predictors(*** height and basal diameter),we found that basal diameter was not a significant predictor either in OLS or Bayesian methods,indicating that suitable predictors and well-fitted models should be seriously *** study highlights that Bayesian methods,the bootstrap method and the type of allometric equation could help to improve the model accuracy in predicting shrub biomass in sandy lands.

关键词： bootstrap Caragana microphylla Horqin Sandy Land mean squared error prior information

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Assessment Method of Heavy NC Machine Reliability Based on Bayes Theory

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Transactions of Tianjin University 2016年第2期22卷 105-109页

作者：张雷王太勇胡占齐 Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education Tianjin University Tianjin NC System Engineering Center Tianjin University College of Mechanical Engineering Yanshan University

It is difficult to collect the prior information for small-sample machinery products when their reliability is assessed by using Bayes method. In this study, an improved Bayes method with gradient reliability(GR) results as prior information was proposed to solve the problem. A certain type of heavy NC boring and milling machine was considered as the research subject, and its reliability model was established on the basis of its functional and structural characteristics and working principle. According to the stress-intensity interference theory and the reliability model theory, the GR results of the host machine and its key components were obtained. Then the GR results were deemed as prior information to estimate the probabilistic reliability(PR) of the spindle box, the column and the host machine in the present method. The comparative studies demonstrated that the improved Bayes method was applicable in the reliability assessment of heavy NC machine tools.

关键词： heavy NC machine reliability assessment Bayes method prior information

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Prestack seismic stochastic inversion based on statistical characteristic parameters

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Applied Geophysics 2021年第1期18卷 63-74,129页

作者： Wang Bao-Li Lin Ying Zhang Guang-Zhi Yin Xing-Yao School of Geosciences China University of Petroleum(East China)Qingdao 266580China Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and TechnologyQingdao Shandong 266701China.

In the conventional stochastic inversion method,the spatial structure information of underground strata is usually characterized by ***,effectively characterizing the heterogeneity of complex strata is *** this paper,multiple parameters are used to fully explore the underground formation information in the known seismic reflection and well log *** spatial structure characteristics of complex underground reservoirs are described more comprehensively using multiple statistical characteristic *** propose a prestack seismic stochastic inversion method based on prior information on statistical characteristic *** to the random medium theory,this method obtains several statistical characteristic parameters from known seismic and logging data,constructs a prior information model that meets the spatial structure characteristics of the underground strata,and integrates multiparameter constraints into the likelihood function to construct the objective *** very fast quantum annealing algorithm is used to optimize and update the objective function to obtain the fi nal inversion *** model test shows that compared with the traditional prior information model construction method,the prior information model based on multiple parameters in this paper contains more detailed stratigraphic information,which can better describe complex underground reservoirs.A real data analysis shows that the stochastic inversion method proposed in this paper can effectively predict the geophysical characteristics of complex underground reservoirs and has a high resolution.

关键词： prior information random medium theory statistical characteristic parameters stochastic inversion very fast quantum annealing

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