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Della Coletta R, Fernandes SB, Monnahan PJ, Mikel MA, Bohn MO, Lipka AE, Hirsch CN. Importance of genetic architecture in marker selection decisions for genomic prediction. Theor Appl Genet 2023; 136:220. [PMID: 37819415 DOI: 10.1007/s00122-023-04469-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023]
Abstract
KEY MESSAGE We demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait. Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Samuel B Fernandes
- Department of Crop, Soil and Environmental Sciences at University of Arkansas, Fayetteville, AR, 72701, USA
| | - Patrick J Monnahan
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Mark A Mikel
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
- Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Martin O Bohn
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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Abstract
To date, genomic prediction has been conducted in about 20 aquaculture species, with a preference for intra-family genomic selection (GS). For every trait under GS, the increase in accuracy obtained by genomic estimated breeding values instead of classical pedigree-based estimation of breeding values is very important in aquaculture species ranging from 15% to 89% for growth traits, and from 0% to 567% for disease resistance. Although the implementation of GS in aquaculture is of little additional investment in breeding programs already implementing sib testing on pedigree, the deployment of GS remains sparse, but could be boosted by adaptation of cost-effective imputation from low-density panels. Moreover, GS could help to anticipate the effect of climate change by improving sustainability-related traits such as production yield (e.g., carcass or fillet yields), feed efficiency or disease resistance, and by improving resistance to environmental variation (tolerance to temperature or salinity variation). This chapter synthesized the literature in applications of GS in finfish, crustaceans and molluscs aquaculture in the present and future breeding programs.
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Affiliation(s)
- François Allal
- MARBEC, Université de Montpellier, CNRS, Ifremer, IRD, Palavas-les-Flots, France.
| | - Nguyen Hong Nguyen
- School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD, Australia
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Camerlink I, Ursinus WW, Bartels AC, Bijma P, Bolhuis JE. Indirect Genetic Effects for Growth in Pigs Affect Behaviour and Weight Around Weaning. Behav Genet 2018; 48:413-420. [PMID: 29922987 PMCID: PMC6097724 DOI: 10.1007/s10519-018-9911-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Accepted: 06/11/2018] [Indexed: 12/31/2022]
Abstract
Selection for indirect genetic effects (IGE), i.e. the genetic effect of an individual on a trait of another individual, is a promising avenue to increase trait values in plant and animal breeding. Studies in livestock suggest that selection for IGE for growth (IGEg) might increase animals' capacity to tolerate stress. We assessed the effect of a stressful phase (weaning) on the behaviour and performance of pigs (n = 480) divergently selected for high or low IGEg. High IGEg pigs were significantly slower to explore the feed and gained less weight than low IGEg pigs in the days after weaning. In line with previous findings, high IGEg animals may have prioritized the formation of social ranks.
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Affiliation(s)
- Irene Camerlink
- Adaptation Physiology Group, Wageningen University and Research, Wageningen, The Netherlands.
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands.
- Institute of Animal Husbandry and Animale Welfare, University for Veterinary Medicine Vienna, Veterinärplatz 1, 1210, Vienna, Austria.
| | - Winanda W Ursinus
- Adaptation Physiology Group, Wageningen University and Research, Wageningen, The Netherlands
- Animal Behaviour & Welfare, Wageningen Livestock Research, Wageningen University and Research, Wageningen, The Netherlands
| | - Andrea C Bartels
- Adaptation Physiology Group, Wageningen University and Research, Wageningen, The Netherlands
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - J Elizabeth Bolhuis
- Adaptation Physiology Group, Wageningen University and Research, Wageningen, The Netherlands
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