Maximizing genetic gain over multiple generations with quantitative trait locus selection and control of inbreeding1

Villanueva, Beatriz; Dekkers, Jack C. M.; Woolliams, John; Settar, Petek

doi:10.2527/2004.8251305x

Cited by 18 publications

(19 citation statements)

References 18 publications

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“…In the long term, this may lead to suboptimal use of available genetic variation (Villanueva et al, 2004). To sequentially select different regions, the effects of the SNPs need to change, which can happen when the model is retrained and effects are re-estimated.…”

Section: Genetic Variation Under Selection In Layer Chicken M Heidarimentioning

confidence: 99%

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

et al. 2014

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Genomic selection (GS) is a DNA-based method of selecting for quantitative traits in animal and plant breeding, and offers a potentially superior alternative to traditional breeding methods that rely on pedigree and phenotype information. Using a 60 K SNP chip with markers spaced throughout the entire chicken genome, we compared the impact of GS and traditional BLUP (best linear unbiased prediction) selection methods applied side-by-side in three different lines of egg-laying chickens. Differences were demonstrated between methods, both at the level and genomic distribution of allele frequency changes. In all three lines, the average allele frequency changes were larger with GS, 0.056 0.064 and 0.066, compared with BLUP, 0.044, 0.045 and 0.036 for lines B1, B2 and W1, respectively. With BLUP, 35 selected regions (empirical Po0.05) were identified across the three lines. With GS, 70 selected regions were identified. Empirical thresholds for local allele frequency changes were determined from gene dropping, and differed considerably between GS (0.167-0.198) and BLUP (0.105-0.126). Between lines, the genomic regions with large changes in allele frequencies showed limited overlap. Our results show that GS applies selection pressure much more locally than BLUP, resulting in larger allele frequency changes. With these results, novel insights into the nature of selection on quantitative traits have been gained and important questions regarding the long-term impact of GS are raised. The rapid changes to a part of the genetic architecture, while another part may not be selected, at least in the short term, require careful consideration, especially when selection occurs before phenotypes are observed.

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Section: Genetic Variation Under Selection In Layer Chicken M Heidarimentioning

confidence: 99%

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

et al. 2014

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“…Some previous simulation experiments (Gibson 1994;Spelman and Garrick 1997;Vukasinovic et al 1998;Villanueva et al 2004) studied a mixed inheritance mode, i.e., one where the total genetic value of an individual is due to a single major gene (QTL) and a polygenic effect. Selection was conducted by combining the QTL effect (assumed known without error) and the estimated polygenic effect.…”

Section: Introductionmentioning

confidence: 99%

Long-term impacts of genome-enabled selection

et al. 2011

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The objective was to evaluate the effects of directional selection based on estimated genomic breeding values (GEBVs) for a quantitative trait. Selection affects GEBV prediction accuracy as well as genetic architecture via changes in allelic frequencies and linkage disequilibrium (LD), and the resulting changes are different from those in the absence of selection. How marker density affects long-term GEBV accuracy and selection response needs to be understood as well. Simulations were used to characterize the impact of selection based on GEBVs over generations. Single-nucleotide polymorphism (SNP) marker effects were estimated with the Bayesian Lasso method in the base generation, and these estimates were used to calculate the GEBVs in subsequent generations. GEBV accuracy decreased over generations of selection, and it was lower than under random selection, where a decay took place as well. In the long term, selection response tended to reach a plateau, but, at higher marker density, both the magnitude and duration of the response were larger. Selection changed quantitative trait loci (QTL) allele frequencies and generated new but unfavorable LD for prediction. Family effects had a considerable contribution to GEBV accuracy in early generations of selection.

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“…In addition, insight into the genetic bases of growth can be used to make better selection decisions. Molecular genetics have been used to identify several genes and markers associated with quantitative traits including genetic variation explaining phenotypic differences in growth [1], [2].…”

Section: Introductionmentioning

confidence: 99%

RNA-Seq Identifies SNP Markers for Growth Traits in Rainbow Trout

et al. 2012

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Fast growth is an important and highly desired trait, which affects the profitability of food animal production, with feed costs accounting for the largest proportion of production costs. Traditional phenotype-based selection is typically used to select for growth traits; however, genetic improvement is slow over generations. Single nucleotide polymorphisms (SNPs) explain 90% of the genetic differences between individuals; therefore, they are most suitable for genetic evaluation and strategies that employ molecular genetics for selective breeding. SNPs found within or near a coding sequence are of particular interest because they are more likely to alter the biological function of a protein. We aimed to use SNPs to identify markers and genes associated with genetic variation in growth. RNA-Seq whole-transcriptome analysis of pooled cDNA samples from a population of rainbow trout selected for improved growth versus unselected genetic cohorts (10 fish from 1 full-sib family each) identified SNP markers associated with growth-rate. The allelic imbalances (the ratio between the allele frequencies of the fast growing sample and that of the slow growing sample) were considered at scores >5.0 as an amplification and <0.2 as loss of heterozygosity. A subset of SNPs (n = 54) were validated and evaluated for association with growth traits in 778 individuals of a three-generation parent/offspring panel representing 40 families. Twenty-two SNP markers and one mitochondrial haplotype were significantly associated with growth traits. Polymorphism of 48 of the markers was confirmed in other commercially important aquaculture stocks. Many markers were clustered into genes of metabolic energy production pathways and are suitable candidates for genetic selection. The study demonstrates that RNA-Seq at low sequence coverage of divergent populations is a fast and effective means of identifying SNPs, with allelic imbalances between phenotypes. This technique is suitable for marker development in non-model species lacking complete and well-annotated genome reference sequences.

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Maximizing genetic gain over multiple generations with quantitative trait locus selection and control of inbreeding1

Cited by 18 publications

References 18 publications

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Systematic differences in the response of genetic variation to pedigree and genome-based selection methods

Long-term impacts of genome-enabled selection

RNA-Seq Identifies SNP Markers for Growth Traits in Rainbow Trout

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