Comparison of single-trait and multiple-trait genomic prediction models

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Abstract

Background

In this study, a single-trait genomic model (STGM) is compared with a multiple-trait genomic model (MTGM) for genomic prediction using conventional estimated breeding values (EBVs) calculated using a conventional single-trait and multiple-trait linear mixed models as the response variables. Three scenarios with and without missing data were simulated; no missing data, 90% missing data in a trait with high heritability, and 90% missing data in a trait with low heritability. The simulated genome had a length of 500 cM with 5000 equally spaced single nucleotide polymorphism markers and 300 randomly distributed quantitative trait loci (QTL). The true breeding values of each trait were determined using 200 of the QTLs, and the remaining 100 QTLs were assumed to affect both the high (trait I with heritability of 0.3) and the low (trait II with heritability of 0.05) heritability traits. The genetic correlation between traits I and II was 0.5, and the residual correlation was zero.

Results

The results showed that when there were no missing records, MTGM and STGM gave the same reliability for the genomic predictions for trait I while, for trait II, MTGM performed better that STGM. When there were missing records for one of the two traits, MTGM performed much better than STGM. In general, the difference in reliability of genomic EBVs predicted using the EBV response variables estimated from either the multiple-trait or single-trait models was relatively small for the trait without missing data. However, for the trait with missing data, the EBV response variable obtained from the multiple-trait model gave a more reliable genomic prediction than the EBV response variable from the single-trait model.

Conclusions

These results indicate that MTGM performed better than STGM for the trait with low heritability and for the trait with a limited number of records. Even when the EBV response variable was obtained using the multiple-trait model, the genomic prediction using MTGM was more reliable than the prediction using the STGM.

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Most cited references 23

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Mojo Hand, a TALEN design tool for genome editing applications

Kevin Neff, David Argue, Alvin C H Ma … (2013)

Background Recent studies of transcription activator-like (TAL) effector domains fused to nucleases (TALENs) demonstrate enormous potential for genome editing. Effective design of TALENs requires a combination of selecting appropriate genetic features, finding pairs of binding sites based on a consensus sequence, and, in some cases, identifying endogenous restriction sites for downstream molecular genetic applications. Results We present the web-based program Mojo Hand for designing TAL and TALEN constructs for genome editing applications (http://www.talendesign.org). We describe the algorithm and its implementation. The features of Mojo Hand include (1) automatic download of genomic data from the National Center for Biotechnology Information, (2) analysis of any DNA sequence to reveal pairs of binding sites based on a user-defined template, (3) selection of restriction-enzyme recognition sites in the spacer between the TAL monomer binding sites including options for the selection of restriction enzyme suppliers, and (4) output files designed for subsequent TALEN construction using the Golden Gate assembly method. Conclusions Mojo Hand enables the rapid identification of TAL binding sites for use in TALEN design. The assembly of TALEN constructs, is also simplified by using the TAL-site prediction program in conjunction with a spreadsheet management aid of reagent concentrations and TALEN formulation. Mojo Hand enables scientists to more rapidly deploy TALENs for genome editing applications.

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Accurate prediction of genetic values for complex traits by whole-genome resequencing.

Theo Meuwissen, Mike Goddard (2010)

Whole-genome resequencing technology has improved rapidly during recent years and is expected to improve further such that the sequencing of an entire human genome sequence for $1000 is within reach. Our main aim here is to use whole-genome sequence data for the prediction of genetic values of individuals for complex traits and to explore the accuracy of such predictions. This is relevant for the fields of plant and animal breeding and, in human genetics, for the prediction of an individual's risk for complex diseases. Here, population history and genomic architectures were simulated under the Wright-Fisher population and infinite-sites mutation model, and prediction of genetic value was by the genomic selection approach, where a Bayesian nonlinear model was used to predict the effects of individual SNPs. The Bayesian model assumed a priori that only few SNPs are causative, i.e., have an effect different from zero. When using whole-genome sequence data, accuracies of prediction of genetic value were >40% increased relative to the use of dense approximately 30K SNP chips. At equal high density, the inclusion of the causative mutations yielded an extra increase of accuracy of 2.5-3.7%. Predictions of genetic value remained accurate even when the training and evaluation data were 10 generations apart. Best linear unbiased prediction (BLUP) of SNP effects does not take full advantage of the genome sequence data, and nonlinear predictions, such as the Bayesian method used here, are needed to achieve maximum accuracy. On the basis of theoretical work, the results could be extended to more realistic genome and population sizes.

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Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study.

Shengqiang Zhong, Jack C. M. Dekkers, Rohan Fernando … (2009)

We compared the accuracies of four genomic-selection prediction methods as affected by marker density, level of linkage disequilibrium (LD), quantitative trait locus (QTL) number, sample size, and level of replication in populations generated from multiple inbred lines. Marker data on 42 two-row spring barley inbred lines were used to simulate high and low LD populations from multiple inbred line crosses: the first included many small full-sib families and the second was derived from five generations of random mating. True breeding values (TBV) were simulated on the basis of 20 or 80 additive QTL. Methods used to derive genomic estimated breeding values (GEBV) were random regression best linear unbiased prediction (RR-BLUP), Bayes-B, a Bayesian shrinkage regression method, and BLUP from a mixed model analysis using a relationship matrix calculated from marker data. Using the best methods, accuracies of GEBV were comparable to accuracies from phenotype for predicting TBV without requiring the time and expense of field evaluation. We identified a trade-off between a method's ability to capture marker-QTL LD vs. marker-based relatedness of individuals. The Bayesian shrinkage regression method primarily captured LD, the BLUP methods captured relationships, while Bayes-B captured both. Under most of the study scenarios, mixed-model analysis using a marker-derived relationship matrix (BLUP) was more accurate than methods that directly estimated marker effects, suggesting that relationship information was more valuable than LD information. When markers were in strong LD with large-effect QTL, or when predictions were made on individuals several generations removed from the training data set, however, the ranking of method performance was reversed and BLUP had the lowest accuracy.

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Author and article information

Contributors

Gang Guo

Fuping Zhao

Yachun Wang

Yuan Zhang

Lixin Du

Guosheng Su

Journal

Journal ID (nlm-ta): BMC Genet

Journal ID (iso-abbrev): BMC Genet

Title: BMC Genetics

Publisher: BioMed Central

ISSN (Electronic): 1471-2156

Publication date Collection: 2014

Publication date (Electronic): 4 March 2014

Volume: 15

Page: 30

Affiliations

[1 ]National Center for Molecular Genetics and Breeding of Animal, Institute of Animal Sciences, Chinese academy of Agricultural Sciences, Beijing 100193, China

[2 ]Beijing Sanyuan Lvhe Dairy Cattle Breeding Center, Beijing 100076, China

[3 ]College of Animal Science and Technology, China Agricultural University, Beijing 100193, China

[4 ]Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, Aarhus University, Tjele DK-8830, Denmark

Article

Publisher ID: 1471-2156-15-30

DOI: 10.1186/1471-2156-15-30

PMC ID: 3975852

PubMed ID: 24593261

SO-VID: 64a2e78a-3048-4777-a4fd-31aa9b5d42d0

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Comparison of single-trait and multiple-trait genomic prediction models

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Abstract

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Genome Engineering using CRISPR

Most cited references 23

Mojo Hand, a TALEN design tool for genome editing applications

Accurate prediction of genetic values for complex traits by whole-genome resequencing.

Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study.

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