PT  - JOURNAL ARTICLE
AU  - Rebekah A. Oomen
AU  - Anna Kuparinen
AU  - Jeffrey A. Hutchings
TI  - Consequences of single-locus and tightly linked genomic architectures for evolutionary responses to environmental change
AID  - 10.1101/2020.01.31.928770
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.31.928770
4099  - http://biorxiv.org/content/early/2020/02/04/2020.01.31.928770.short
4100  - http://biorxiv.org/content/early/2020/02/04/2020.01.31.928770.full
AB  - Genetic and genomic architectures of traits under selection are key factors influencing evolutionary responses. Yet, knowledge of their impacts has been limited by a widespread assumption that most traits are controlled by unlinked polygenic architectures. Recent advances in genome sequencing and eco-evolutionary modelling are unlocking the potential for integrating genomic information into predictions of population responses to environmental change. Using eco-genetic simulations, we demonstrate that hypothetical single-locus control of a life history trait produces highly variable and unpredictable harvesting-induced evolution relative to the classically applied multi-locus model. Single-locus control of complex traits is thought to be uncommon, yet blocks of linked genes, such as those associated with some types of structural genomic variation, have emerged as taxonomically widespread phenomena. Inheritance of linked architectures resembles that of single loci, thus enabling single-locus-like modeling of polygenic adaptation. Yet, the number of loci, their effect sizes, and the degree of linkage among them all occur along a continuum. We review how linked architectures are often associated, directly or indirectly, with traits expected to be under selection from anthropogenic stressors and are likely to play a large role in adaptation to environmental disturbance. We suggest using single-locus models to explore evolutionary extremes and uncertainties when the trait architecture is unknown, refining parameters as genomic information becomes available, and explicitly incorporating linkage among loci when possible. We discuss some challenges involved in modelling the evolutionary dynamics of linked genomic architectures and implementing such knowledge in the conservation and management of natural populations.