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Artificial selection response due to polygenic adaptation from a multilocus, multiallelic genetic architecture
Dalarna University, School of Technology and Business Studies, Statistics. SLU.ORCID iD: 0000-0002-1057-5401
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2017 (English)In: Molecular biology and evolution, ISSN 0737-4038, E-ISSN 1537-1719, Vol. 34, no 10, 2678-2689 p.Article in journal (Refereed) Published
Abstract [en]

The ability of a population to adapt to changes in their living conditions, whether in nature or captivity, often depends on polymorphisms in multiple genes across the genome. In-depth studies of such polygenic adaptations are difficult in natural populations, but can be approached using the resources provided by artificial selection experiments. Here, we dissect the genetic mechanisms involved in long-term selection responses of the Virginia chicken lines, populations that after 40 generations of divergent selection for 56-day body weight display a 9-fold difference in the selected trait. In the F15 generation of an intercross between the divergent lines, 20 loci explained >60% of the additive genetic variance for the selected trait. We focused particularly on fine-mapping seven major QTL that replicated in this population and found that only two fine-mapped to single, bi-allelic loci; the other five contained linked loci, multiple alleles or were epistatic. This detailed dissection of the polygenic adaptations in the Virginia lines provides a deeper understanding of the range of different genome-wide mechanisms that have been involved in these long-term selection responses. The results illustrate that the genetic architecture of a highly polygenic trait can involve a broad range of genetic mechanisms, and that this can be the case even in a small population bred from founders with limited genetic diversity.

Place, publisher, year, edition, pages
2017. Vol. 34, no 10, 2678-2689 p.
Keyword [en]
epistasis, genetic architecture, genetic variation, multiallelic, multilocus, polygenic adaptation
National Category
Biological Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-26369DOI: 10.1093/molbev/msx194ISI: 000411814800019PubMedID: 28957504OAI: oai:DiVA.org:du-26369DiVA: diva2:1146442
Available from: 2017-10-03 Created: 2017-10-03 Last updated: 2017-10-18Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf