Genomic-assisted prediction of genetic value with semiparametric procedures.
نویسندگان
چکیده
Semiparametric procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are presented. The methods focus on the treatment of massive information provided by, e.g., single-nucleotide polymorphisms. It is argued that standard parametric methods for quantitative genetic analysis cannot handle the multiplicity of potential interactions arising in models with, e.g., hundreds of thousands of markers, and that most of the assumptions required for an orthogonal decomposition of variance are violated in artificial and natural populations. This makes nonparametric procedures attractive. Kernel regression and reproducing kernel Hilbert spaces regression procedures are embedded into standard mixed-effects linear models, retaining additive genetic effects under multivariate normality for operational reasons. Inferential procedures are presented, and some extensions are suggested. An example is presented, illustrating the potential of the methodology. Implementations can be carried out after modification of standard software developed by animal breeders for likelihood-based or Bayesian analysis.
منابع مشابه
Accuracy of Genomic Prediction under Different Genetic Architectures and Estimation Methods
The accuracy of genomic breeding value prediction was investigated in various levels of reference population size, trait heritability and the number of quantitative trait locus (QTL). Five Bayesian methods, including Bayesian Ridge regression, BayesA, BayesB, BayesC and Bayesian LASSO, were used to estimate the marker effects for each of 27 scenarios resulted from combining three levels for her...
متن کاملPredictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive
A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used ge...
متن کاملSemiparametric Bootstrap Prediction Intervals in time Series
One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...
متن کاملRobust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data
Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variable...
متن کاملComparison of Single and Multi-Step Bayesian Methods for Predicting Genomic Breeding Values in Genotyped and Non-Genotyped Animals- A Simulation Study
The purpose of this study was to compare the accuracy of genomic evaluation for Bayes A, Bayes B, Bayes C and Bayes L multi-step methods and SSBR-C and SSBR-A single-step methods in the different values of π for predicting genomic breeding values of the genotyped and non-genotyped animals. A genome with 40000 SNPs on the 20 chromosom was simulated with the same distance (100cM). The π valu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Genetics
دوره 173 3 شماره
صفحات -
تاریخ انتشار 2006