Integrating Pool-seq uncertainties into demographic inference
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Integrating Pool-seq uncertainties into demographic inference. / Carvalho, João; Morales, Hernán E.; Faria, Rui; Butlin, Roger K.; Sousa, Vítor C.
In: Molecular Ecology Resources, Vol. 23, No. 7, 2023, p. 1737-1755.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Integrating Pool-seq uncertainties into demographic inference
AU - Carvalho, João
AU - Morales, Hernán E.
AU - Faria, Rui
AU - Butlin, Roger K.
AU - Sousa, Vítor C.
N1 - Publisher Copyright: © 2023 John Wiley & Sons Ltd.
PY - 2023
Y1 - 2023
N2 - Next-generation sequencing of pooled samples (Pool-seq) is a popular method to assess genome-wide diversity patterns in natural and experimental populations. However, Pool-seq is associated with specific sources of noise, such as unequal individual contributions. Consequently, using Pool-seq for the reconstruction of evolutionary history has remained underexplored. Here we describe a novel Approximate Bayesian Computation (ABC) method to infer demographic history, explicitly modelling Pool-seq sources of error. By jointly modelling Pool-seq data, demographic history and the effects of selection due to barrier loci, we obtain estimates of demographic history parameters accounting for technical errors associated with Pool-seq. Our ABC approach is computationally efficient as it relies on simulating subsets of loci (rather than the whole-genome) and on using relative summary statistics and relative model parameters. Our simulation study results indicate Pool-seq data allows distinction between general scenarios of ecotype formation (single versus parallel origin) and to infer relevant demographic parameters (e.g. effective sizes and split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod Littorina saxatilis, sampled on a narrow geographical scale at two Swedish locations where two ecotypes (Wave and Crab) are found. Our model choice and parameter estimates show that ecotypes formed before colonization of the two locations (i.e. single origin) and are maintained despite gene flow. These results indicate that demographic modelling and inference can be successful based on pool-sequencing using ABC, contributing to the development of suitable null models that allow for a better understanding of the genetic basis of divergent adaptation.
AB - Next-generation sequencing of pooled samples (Pool-seq) is a popular method to assess genome-wide diversity patterns in natural and experimental populations. However, Pool-seq is associated with specific sources of noise, such as unequal individual contributions. Consequently, using Pool-seq for the reconstruction of evolutionary history has remained underexplored. Here we describe a novel Approximate Bayesian Computation (ABC) method to infer demographic history, explicitly modelling Pool-seq sources of error. By jointly modelling Pool-seq data, demographic history and the effects of selection due to barrier loci, we obtain estimates of demographic history parameters accounting for technical errors associated with Pool-seq. Our ABC approach is computationally efficient as it relies on simulating subsets of loci (rather than the whole-genome) and on using relative summary statistics and relative model parameters. Our simulation study results indicate Pool-seq data allows distinction between general scenarios of ecotype formation (single versus parallel origin) and to infer relevant demographic parameters (e.g. effective sizes and split times). We exemplify the application of our method to Pool-seq data from the rocky-shore gastropod Littorina saxatilis, sampled on a narrow geographical scale at two Swedish locations where two ecotypes (Wave and Crab) are found. Our model choice and parameter estimates show that ecotypes formed before colonization of the two locations (i.e. single origin) and are maintained despite gene flow. These results indicate that demographic modelling and inference can be successful based on pool-sequencing using ABC, contributing to the development of suitable null models that allow for a better understanding of the genetic basis of divergent adaptation.
KW - approximate Bayesian computation
KW - demographic inference
KW - ecotype formation
KW - Pool-seq
KW - R package
U2 - 10.1111/1755-0998.13834
DO - 10.1111/1755-0998.13834
M3 - Journal article
C2 - 37475177
AN - SCOPUS:85165293379
VL - 23
SP - 1737
EP - 1755
JO - Molecular Ecology
JF - Molecular Ecology
SN - 0962-1083
IS - 7
ER -
ID: 361691951