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Genomic-assisted prediction of genetic value with semiparametric procedures.

ABSTRACT: 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.

SUBMITTER: Gianola D

PROVIDER: S-EPMC1526664 | biostudies-other | 2006 Jul

REPOSITORIES: biostudies-other

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Genomic-assisted prediction of genetic value with semiparametric procedures.

Gianola Daniel D Fernando Rohan L RL Stella Alessandra A

Genetics 20060428 3

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 as ...[more]

PMID: 16648593

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Project description:A combined matrix that exploits genealogy together with marker-based information could improve the selection of elite individuals in breeding programs. We present genetic parameters for adaptive and growth traits in beef cattle by exploring linear combinations of pedigree-based (A) and marker-based (G) relationship matrices. We use a data set with 2,111 Brahman (BB) and 2,550 Tropical Composite (TC) cattle with genotypes for 729,068 SNP, and phenotypes for five traits. A weighted relationship matrix (WRM) combining G and A was constructed as WRM = ?G + (1 - ?)A. The weight (?) was explored at values from 0.0 to 1.0, at 0.1 intervals. Additionally, four alternative G matrices, in the WRM, were evaluated according to the selection of SNP used to generate them: 1) Gw: all autosomal SNP with minor allele frequency (MAF) > 1%; 2) Gg: autosomal SNP with MAF > 1% and mapped inside to gene coding regions; 3) Gp: autosomal SNP with MAF > 1% and previously reported to have significant pleiotropic effect in these two populations; and 4) Gc: autosomal SNP with MAF > 1% and with significant correlated effects previously reported in both BB and TC populations. In addition, two A matrices were evaluated: 1) A: all relationships between animals were considered after tracing back known ancestors; and 2) Ad: a distorted A matrix where a random 1% of the off-diagonal nonzero values were set to zero to simulate relationship errors. Five independent Ad matrices were explored each with a different random 1% of relationships masked. Criteria for comparing the resulting WRM included estimates of heritability (h2) and cross-validation accuracy (ACC) of genomic estimated breeding values. The choice of WRM had a greater impact on h2 than on ACC estimates. The 1% errors introduced in pedigree relationships generated large distortion in genetic parameters and ACC estimates. However, employing a ? > 0.7 was an efficient mechanism to compensate for the errors in A. Additionally, although significant (P-value < 0.0001), we found no consistent relationship between the type of SNP used to compute G and h2 or ACC estimates. We devised the optimal value of ? for maximum h2 and ACC at ? = 0.7 suggesting a 70% and 30% weighting to genomic and genealogical information, respectively, as an optimal strategy to compensate for pedigree errors, to improve genetic parameters estimates and lead to more accurate selection decisions.

Project description:BackgroundPresently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel.ResultsUsing genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracies were 0.208 ± 0.020 (0.181 ± 0.022) for the startle response and 0.272 ± 0.017 (0.307 ± 0.015) for starvation resistance in the female (male) lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243 ± 0.020 and 0.220 ± 0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction. Compared with the GBLUP using complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the starvation resistance and 27.40% and 35.36% for startle response in the female and male lines, respectively.ConclusionsOverall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.

Project description:BackgroundFor marker effect models and genomic animal models, computational requirements increase with the number of loci and the number of genotyped individuals, respectively. In the latter case, the inverse genomic relationship matrix (GRM) is typically needed, which is computationally demanding to compute for large datasets. Thus, there is a great need for dimensionality-reduction methods that can analyze massive genomic data. For this purpose, we developed reduced-dimension singular value decomposition (SVD) based models for genomic prediction.MethodsFast SVD is performed by analyzing different chromosomes/genome segments in parallel and/or by restricting SVD to a limited core of genotyped individuals, producing chromosome- or segment-specific principal components (PC). Given a limited effective population size, nearly all the genetic variation can be effectively captured by a limited number of PC. Genomic prediction can then be performed either by PC ridge regression (PCRR) or by genomic animal models using an inverse GRM computed from the chosen PC (PCIG). In the latter case, computation of the inverse GRM will be feasible for any number of genotyped individuals and can be readily produced row- or element-wise.ResultsUsing simulated data, we show that PCRR and PCIG models, using chromosome-wise SVD of a core sample of individuals, are appropriate for genomic prediction in a larger population, and results in virtually identical predicted breeding values as the original full-dimension genomic model (r = 1.000). Compared with other algorithms (e.g. algorithm for proven and young animals, APY), the (chromosome-wise SVD-based) PCRR and PCIG models were more robust to size of the core sample, giving nearly identical results even down to 500 core individuals. The method was also successfully tested on a large multi-breed dataset.ConclusionsSVD can be used for dimensionality reduction of large genomic datasets. After SVD, genomic prediction using dense genomic data and many genotyped individuals can be done in a computationally efficient manner. Using this method, the resulting genomic estimated breeding values were virtually identical to those computed from a full-dimension genomic model.

Project description:BACKGROUND:This study aimed at estimating genetic parameters of sex-influenced production traits, evaluating the impact of genotype-by-sex interaction, and identifying the selection criteria that could be included in multiple-trait genetic evaluation to increase the rate of genetic improvement in both sexes. To achieve this goal, we used 10 male and 10 female phenotypes, which were measured in a population of 2111 Australian Brahman cattle genotyped at high-density. RESULTS:Heritability estimates ranged from very low (0.03?±?0.03 for cows' days to calving at first calving opportunity, DC1), to moderate (0.33?±?0.08 for cows' adult body weight, AWTc), and to high (0.95?±?0.07 for cows' hip height, HHc). Genetic correlation (rg) estimates between male and female homologous traits were favorable and ranged from moderate to high values, which indicate that selection for any of the traits in one sex would lead to a correlated response with the equivalent phenotype in the other sex. However, the estimated direct response was greater than the indirect response. Moreover, Pearson correlations between estimated breeding values obtained from each sex separately and from female and male homologous traits combined into a single trait in univariate analysis ranged from 0.74 to 0.99, which indicate that small ranking variation might appear if male and female traits are included as single or separate phenotypes. Genetic correlations between male growth and female reproductive traits were not significant, ranging from -?0.07?±?0.13 to 0.45?±?0.65. However, selection to improve HHc and AWTc in cows may reduce the percentage of normal sperm at 24 months of age (PNS24), possibly due to correlated effects in the same traits in males, which are related to late maturing animals. CONCLUSIONS:Hip height in cows and PNS24, as well as blood insulin-like growth factor 1 (IGF1) concentration in bulls at 6 months of age are efficient selection criteria to improve male growth and female reproductive traits, simultaneously. In the presence of genotype-by-sex interactions, selection for traits in each sex results in high rates of genetic improvement, however, for the identification of animals with the highest breeding value, data for males and females may be considered a single trait.

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Genomic-assisted prediction of genetic value with semiparametric procedures.

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Genomic-assisted prediction of genetic value with semiparametric procedures.

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