Dynamics of genomic change during evolutionary rescue in the seed beetle<i>Callosobruchus maculatus</i>

Rêgo, Alexandre; Messina, Frank J.; Gompert, Zachariah

doi:10.1101/364158

Cited by 15 publications

(32 citation statements)

References 95 publications

(138 reference statements)

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“…These insects infest stores of grain legumes (Tuda et al, 2014). Previous analyses of generation-scale allele frequency change from this experiment showed strong selection caused rapid adaptation and substantial evolutionary change at many genetic loci (Rêgo et al, 2019). Genome-wide association mapping in a back-cross (BC) mapping population derived from this line documented modest heritability for female weight at eclosion, with 17% of the trait variance explained by genetic markers (Rêgo et al, 2020).…”

Section: Analysis Of Simulated Datamentioning

confidence: 65%

“…Large-scale spatio-temporal population genomic data sets are still uncommon, especially for natural populations. However, advances in the analysis of ancient or museum DNA and the expanding field of evolve-and-resequence experiments may help overcome this limitation (e.g., Bi et al, 2013; Barghi et al, 2017;Sproul & Maddison, 2017;Cridland et al, 2018;Rêgo et al, 2019). Moreover, few systems currently combine such sampling with detailed studies of trait genetics and the environment.…”

Section: Resultsmentioning

confidence: 99%

“…Additional details regarding these data and experiments are provided by Rêgo et al (2019) and Rêgo et al (2020). SNP-genotype associations for the mapping population were estimated with the Bayesian sparse linear mixed model approach implemented in gemma (version 0.98) (Zhou et al, 2013).…”

Section: Analysis Of Simulated Datamentioning

confidence: 99%

“…These methods can detect selection when it occurs in a consistent manner over time, but were not designed to identify temporally fluctuating selection. Methods (e.g., Illingworth & Mustonen, 2011;Mathieson & McVean, 2013;Feder et al, 2014;Gompert, 2016;Buffalo & Coop, 2019;Kelly & Hughes, 2019), and associated data sets (e.g., Bi et al, 2013;Reich, 2018;Rêgo et al, 2019;Bi et al, 2019), with temporal sampling in natural or experimental populations are better suited for detecting fluctuating selection and are becoming more common. Mueller et al (1985) proposed one of the first explicit tests for fluctuating selection, which was based on the proposition that genetic loci evolving in response to the same (unknown) environmental variations should exhibit correlated patterns of allele frequency change over time.…”

Section: Introductionmentioning

confidence: 99%

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A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Gompert

2020

Preprint

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1Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, 2 direction and intensity of selection can limit net evolutionary change over longer time periods. 3 Fluctuating selection could affect molecular diversity levels and the evolution of plasticity 4 and ecological specialization. Nonetheless, this phenomenon remains understudied, in part 5 because of analytical limitations and the general difficulty of detecting selection that does not 6 occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate 7 Bayesian computation (ABC) method to detect and quantify fluctuating selection on poly-8 genic traits from population-genomic time-series data. I propose a model for environment-9 dependent phenotypic selection. The evolutionary genetic consequences of selection are then 10 modeled based on a genotype-phenotype map. Using simulations, I show that the proposed 11 method generates accurate and precise estimates of selection when the generative model for 12 the data is similar to the model assumed by the method. Performance of the method when 13 applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Cal-14 losobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. 15Despite some limitations, these results suggest the proposed method provides a powerful 16 approach to connect causes of (variable) selection to traits and genome-wide patterns of 17 evolution. Documentation and open source computer software (fsabc) implementing this 18 method are available from GitHub (https://github.com/zgompert/fsabc.git).19 20 mate Bayesian computation, computational statistics, Callosobruchus macula-21 tus 22 selection on a trait is intense (Walsh & Lynch, 2018). A general approach to overcome this 76 limitation was suggested by Berg & Coop (2014) (also see Josephs et al., 2019). Specifically, 77 polygenic adaptation can be inferred by incorporating genotype-phenotype associations from 78 genome-wide association studies (GWAS) in population-genomic tests for selection. This 79 makes it possible to accumulate evidence of selection across trait-associated loci, and thus 80 detect selection even when none of the individual loci experience strong selection. Thus far, 81 this analytical framework has mostly been applied to static population-genomic data sets, 82 and not to detect fluctuating selection from temporal data. 83 Herein, I fill this analytical gap by presenting an approximate Bayesian computation 84 (ABC) method to detect and quantify fluctuating selection on polygenic traits from time-85 series data. With this method, phenotypic selection is modeled as an explicit function of the 86 state of the environment (similar to Gompert, 2016). The population-genomic consequences 87 of selection are then modeled based on estimated genotype-phenotype associations (similar 88 to Berg & Coop, 2014). This allows inferences to be informed by patterns of change across 89 multiple genetic loci, populations, ...

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Section: Analysis Of Simulated Datamentioning

confidence: 65%

Section: Resultsmentioning

confidence: 99%

Section: Analysis Of Simulated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Gompert

2020

Preprint

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“…In particular, rapid adaptation in C. maculatus by placement in environments containing hosts of marginal quality has been well documented (Fox, Stillwell, Amarillo-S, Czesak, & Messina, 2004;Fox, Wagner, Cline, Thomas, & Messina, 2009;Fox, Zitomer, Deas, & Messina, 2017;Messina, 2004;Messina & Gompert, 2017). Furthermore, evolutionary rescue in C. macualtus in response to populations being experimentally shifted to a marginal quality host, Lens culinaris, was determined to have been caused by strong selection on a small number of loci (Rêgo, Messina, & Gompert, 2018).…”

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confidence: 99%

Influence of host use adaptation on the dispersal propensity of Callosobruchus maculatus

Bergeron

Clary

Mercader

2019

J Applied Entomology

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Local adaptation can lead to significant differences in host use that may influence population growth and spread. Here, we test the potential for adaptation of one behavioural component (host acceptance) to lead to cross‐adaptation for a separate behavioural component (dispersal propensity) using the cowpea seed beetle, Callosobruchus maculatus. C. maculatus originating from the same source population were subjected to selection for host use by rearing them for over 40 generations on either the preferred host of the ancestral population, Vigna radiata, or a marginal host for the ancestral population, Cicer arietinum. Host acceptance was then assayed using four choice and no‐choice oviposition assays including a low‐quality host, Lens culinaris, a marginal host, C. arietinum, and two high‐quality hosts, V. radiata and V. unguiculata. Dispersal was assayed in interconnected arenas containing one of three different hosts: V. radiata, V. unguiculata or C. arietinum. As expected, differences in host acceptance were present, in this case consisting of greater acceptance of the lower quality hosts in the C. arietinum population, but no significant differences in host preference hierarchy. Dispersal propensity in the C. arietinum population was significantly lower than in the V. radiata population, despite the absence of any difference in selection pressures for dispersal. Furthermore, significant differences in dispersal propensity in arenas containing different hosts were present in the V. radiata population, but not in the C. arietinum population. Results highlight the need to consider local adaptation when developing management recommendations, even for behaviours for which selection pressures are not directly apparent.

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Direct and indirect effects of male genital elaboration in female seed beetles

et al. 2021

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Our understanding of coevolution between male genitalia and female traits remains incomplete. This is perhaps especially true for genital traits that cause internal injuries in females, such as the spiny genitalia of seed beetles where males with relatively long spines enjoy a high relative fertilization success. We report on a new set of experiments, based on extant selection lines, aimed at assessing the effects of long male spines on females in Callosobruchus maculatus . We first draw on an earlier study using microscale laser surgery, and demonstrate that genital spines have a direct negative (sexually antagonistic) effect on female fecundity. We then ask whether artificial selection for long versus short spines resulted in direct or indirect effects on female lifetime offspring production. Reference females mating with males from long-spine lines had higher offspring production, presumably due to an elevated allocation in males to those ejaculate components that are beneficial to females. Remarkably, selection for long male genital spines also resulted in an evolutionary increase in female offspring production as a correlated response. Our findings thus suggest that female traits that affect their response to male spines are both under direct selection to minimize harm but are also under indirect selection (a good genes effect), consistent with the evolution of mating and fertilization biases being affected by several simultaneous processes.

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Dynamics of genomic change during evolutionary rescue in the seed beetleCallosobruchus maculatus

Cited by 15 publications

References 95 publications

A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Influence of host use adaptation on the dispersal propensity of Callosobruchus maculatus

Direct and indirect effects of male genital elaboration in female seed beetles

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