Literature DB >> 11290733

Prediction of total genetic value using genome-wide dense marker maps.

T H Meuwissen1, B J Hayes, M E Goddard.   

Abstract

Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

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Year:  2001        PMID: 11290733      PMCID: PMC1461589     

Source DB:  PubMed          Journal:  Genetics        ISSN: 0016-6731            Impact factor:   4.562


  9 in total

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Authors:  M K Halushka; J B Fan; K Bentley; L Hsie; N Shen; A Weder; R Cooper; R Lipshutz; A Chakravarti
Journal:  Nat Genet       Date:  1999-07       Impact factor: 38.330

2.  How to count ... human genes.

Authors:  S A Aparicio
Journal:  Nat Genet       Date:  2000-06       Impact factor: 38.330

3.  Extensive genome-wide linkage disequilibrium in cattle.

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Journal:  Genome Res       Date:  2000-02       Impact factor: 9.043

4.  Efficiency of marker-assisted selection in the improvement of quantitative traits.

Authors:  R Lande; R Thompson
Journal:  Genetics       Date:  1990-03       Impact factor: 4.562

Review 5.  Strategies to utilize marker-quantitative trait loci associations.

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Journal:  J Dairy Sci       Date:  1998-09       Impact factor: 4.034

6.  Linkage disequilibrium and homozygosity of chromosome segments in finite populations.

Authors:  J A Sved
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Journal:  Lancet       Date:  1978-10-28       Impact factor: 79.321

8.  Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing.

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Journal:  Genetics       Date:  1995-02       Impact factor: 4.562

9.  Linkage disequilibrium mapping in isolated founder populations: diastrophic dysplasia in Finland.

Authors:  J Hästbacka; A de la Chapelle; I Kaitila; P Sistonen; A Weaver; E Lander
Journal:  Nat Genet       Date:  1992-11       Impact factor: 38.330

  9 in total
  1915 in total

1.  On marker-assisted prediction of genetic value: beyond the ridge.

Authors:  Daniel Gianola; Miguel Perez-Enciso; Miguel A Toro
Journal:  Genetics       Date:  2003-01       Impact factor: 4.562

2.  Genetic Basis of Maize Resistance to Multiple Insect Pests: Integrated Genome-Wide Comparative Mapping and Candidate Gene Prioritization.

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Journal:  Genes (Basel)       Date:  2020-06-24       Impact factor: 4.096

3.  Genetic variances of SNP loci for milk yield in dairy cattle.

Authors:  Petr Pešek; Josef Přibyl; Luboš Vostrý
Journal:  J Appl Genet       Date:  2014-11-16       Impact factor: 3.240

4.  Evaluation of genome-wide selection efficiency in maize nested association mapping populations.

Authors:  Zhigang Guo; Dominic M Tucker; Jianwei Lu; Venkata Kishore; Gilles Gay
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5.  Bias correction for estimated QTL effects using the penalized maximum likelihood method.

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Journal:  Heredity (Edinb)       Date:  2011-09-21       Impact factor: 3.821

6.  Stochastic search variable selection for identifying multiple quantitative trait loci.

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Journal:  Genetics       Date:  2003-07       Impact factor: 4.562

7.  Bayesian association-based fine mapping in small chromosomal segments.

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Journal:  Genetics       Date:  2004-09-15       Impact factor: 4.562

8.  Toward a theory of marker-assisted gene pyramiding.

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9.  Accuracy of genomic selection in European maize elite breeding populations.

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10.  Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data.

Authors:  A Ferragina; G de los Campos; A I Vazquez; A Cecchinato; G Bittante
Journal:  J Dairy Sci       Date:  2015-09-18       Impact factor: 4.034

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