---
_id: '2944'
abstract:
- lang: eng
text: 'We propose a two-step procedure for estimating multiple migration rates in
an approximate Bayesian computation (ABC) framework, accounting for global nuisance
parameters. The approach is not limited to migration, but generally of interest
for inference problems with multiple parameters and a modular structure (e.g.
independent sets of demes or loci). We condition on a known, but complex demographic
model of a spatially subdivided population, motivated by the reintroduction of
Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters
ancestral mutation rate and male mating skew have been estimated for the whole
population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step,
we estimate in this study the migration rates independently for clusters of demes
putatively connected by migration. For large clusters (many migration rates),
ABC faces the problem of too many summary statistics. We therefore assess by simulation
if estimation per pair of demes is a valid alternative. We find that the trade-off
between reduced dimensionality for the pairwise estimation on the one hand and
lower accuracy due to the assumption of pairwise independence on the other depends
on the number of migration rates to be inferred: the accuracy of the pairwise
approach increases with the number of parameters, relative to the joint estimation
approach. To distinguish between low and zero migration, we perform ABC-type model
comparison between a model with migration and one without. Applying the approach
to microsatellite data from Alpine ibex, we find no evidence for substantial gene
flow via migration, except for one pair of demes in one direction.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: This study has made use of the computational resources provided by
IST Austria and the Edinburgh Compute and Data Facility (ECDF; http://www.ecdf.ed.ac.uk).
The ECDF is partially supported by the eDIKT initiative (http://www.edikt.org.uk).
S.A. acknowledges financial support by IST Austria, the Janggen-Pöhn Foundation,
St. Gallen, the Roche Research Foundation, Basel, the University of Edinburgh in
the form of a Torrance Studentship, and the Austrian Science Fund (FWF P21305-N13).
author:
- first_name: Simon
full_name: Aeschbacher, Simon
id: 2D35326E-F248-11E8-B48F-1D18A9856A87
last_name: Aeschbacher
- first_name: Andreas
full_name: Futschik, Andreas
last_name: Futschik
- first_name: Mark
full_name: Beaumont, Mark
last_name: Beaumont
citation:
ama: 'Aeschbacher S, Futschik A, Beaumont M. Approximate Bayesian computation for
modular inference problems with many parameters: the example of migration rates.
. Molecular Ecology. 2013;22(4):987-1002. doi:10.1111/mec.12165'
apa: 'Aeschbacher, S., Futschik, A., & Beaumont, M. (2013). Approximate Bayesian
computation for modular inference problems with many parameters: the example of
migration rates. . Molecular Ecology. Wiley-Blackwell. https://doi.org/10.1111/mec.12165'
chicago: 'Aeschbacher, Simon, Andreas Futschik, and Mark Beaumont. “Approximate
Bayesian Computation for Modular Inference Problems with Many Parameters: The
Example of Migration Rates. .” Molecular Ecology. Wiley-Blackwell, 2013.
https://doi.org/10.1111/mec.12165.'
ieee: 'S. Aeschbacher, A. Futschik, and M. Beaumont, “Approximate Bayesian computation
for modular inference problems with many parameters: the example of migration
rates. ,” Molecular Ecology, vol. 22, no. 4. Wiley-Blackwell, pp. 987–1002,
2013.'
ista: 'Aeschbacher S, Futschik A, Beaumont M. 2013. Approximate Bayesian computation
for modular inference problems with many parameters: the example of migration
rates. . Molecular Ecology. 22(4), 987–1002.'
mla: 'Aeschbacher, Simon, et al. “Approximate Bayesian Computation for Modular Inference
Problems with Many Parameters: The Example of Migration Rates. .” Molecular
Ecology, vol. 22, no. 4, Wiley-Blackwell, 2013, pp. 987–1002, doi:10.1111/mec.12165.'
short: S. Aeschbacher, A. Futschik, M. Beaumont, Molecular Ecology 22 (2013) 987–1002.
date_created: 2018-12-11T12:00:28Z
date_published: 2013-02-01T00:00:00Z
date_updated: 2023-02-23T14:07:19Z
day: '01'
department:
- _id: NiBa
doi: 10.1111/mec.12165
intvolume: ' 22'
issue: '4'
language:
- iso: eng
month: '02'
oa_version: None
page: 987 - 1002
publication: Molecular Ecology
publication_status: published
publisher: Wiley-Blackwell
publist_id: '3788'
quality_controlled: '1'
related_material:
record:
- id: '9758'
relation: research_data
status: public
scopus_import: 1
status: public
title: 'Approximate Bayesian computation for modular inference problems with many
parameters: the example of migration rates. '
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 22
year: '2013'
...
---
_id: '2962'
abstract:
- lang: eng
text: The choice of summary statistics is a crucial step in approximate Bayesian
computation (ABC). Since statistics are often not sufficient, this choice involves
a trade-off between loss of information and reduction of dimensionality. The latter
may increase the efficiency of ABC. Here, we propose an approach for choosing
summary statistics based on boosting, a technique from the machine learning literature.
We consider different types of boosting and compare them to partial least squares
regression as an alternative. To mitigate the lack of sufficiency, we also propose
an approach for choosing summary statistics locally, in the putative neighborhood
of the true parameter value. We study a demographic model motivated by the re-introduction
of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are
the mean and standard deviation across microsatellites of the scaled ancestral
mutation rate (θanc = 4 Ne u), and the proportion of males obtaining access to
matings per breeding season (ω). By simulation, we assess the properties of the
posterior distribution obtained with the various methods. According to our criteria,
ABC with summary statistics chosen locally via boosting with the L2-loss performs
best. Applying that method to the ibex data, we estimate θanc ≈ 1.288, and find
that most of the variation across loci of the ancestral mutation rate u is between
7.7×10−4 and 3.5×10−3 per locus per generation. The proportion of males with access
to matings is estimated to ω ≈ 0.21, which is in good agreement with recent independent
estimates.
acknowledged_ssus:
- _id: ScienComp
author:
- first_name: Simon
full_name: Aeschbacher, Simon
id: 2D35326E-F248-11E8-B48F-1D18A9856A87
last_name: Aeschbacher
- first_name: Mark
full_name: Beaumont, Mark
last_name: Beaumont
- first_name: Andreas
full_name: Futschik, Andreas
last_name: Futschik
citation:
ama: Aeschbacher S, Beaumont M, Futschik A. A novel approach for choosing summary
statistics in approximate Bayesian computation. Genetics. 2012;192(3):1027-1047.
doi:10.1534/genetics.112.143164
apa: Aeschbacher, S., Beaumont, M., & Futschik, A. (2012). A novel approach
for choosing summary statistics in approximate Bayesian computation. Genetics.
Genetics Society of America. https://doi.org/10.1534/genetics.112.143164
chicago: Aeschbacher, Simon, Mark Beaumont, and Andreas Futschik. “A Novel Approach
for Choosing Summary Statistics in Approximate Bayesian Computation.” Genetics.
Genetics Society of America, 2012. https://doi.org/10.1534/genetics.112.143164.
ieee: S. Aeschbacher, M. Beaumont, and A. Futschik, “A novel approach for choosing
summary statistics in approximate Bayesian computation,” Genetics, vol.
192, no. 3. Genetics Society of America, pp. 1027–1047, 2012.
ista: Aeschbacher S, Beaumont M, Futschik A. 2012. A novel approach for choosing
summary statistics in approximate Bayesian computation. Genetics. 192(3), 1027–1047.
mla: Aeschbacher, Simon, et al. “A Novel Approach for Choosing Summary Statistics
in Approximate Bayesian Computation.” Genetics, vol. 192, no. 3, Genetics
Society of America, 2012, pp. 1027–47, doi:10.1534/genetics.112.143164.
short: S. Aeschbacher, M. Beaumont, A. Futschik, Genetics 192 (2012) 1027–1047.
date_created: 2018-12-11T12:00:34Z
date_published: 2012-11-01T00:00:00Z
date_updated: 2021-01-12T07:40:05Z
day: '01'
department:
- _id: NiBa
doi: 10.1534/genetics.112.143164
external_id:
pmid:
- '22960215'
intvolume: ' 192'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522150/
month: '11'
oa: 1
oa_version: Submitted Version
page: 1027 - 1047
pmid: 1
publication: Genetics
publication_status: published
publisher: Genetics Society of America
publist_id: '3763'
quality_controlled: '1'
scopus_import: 1
status: public
title: A novel approach for choosing summary statistics in approximate Bayesian computation
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 192
year: '2012'
...
---
_id: '9758'
abstract:
- lang: eng
text: 'We propose a two-step procedure for estimating multiple migration rates in
an approximate Bayesian computation (ABC) framework, accounting for global nuisance
parameters. The approach is not limited to migration, but generally of interest
for inference problems with multiple parameters and a modular structure (e.g.
independent sets of demes or loci). We condition on a known, but complex demographic
model of a spatially subdivided population, motivated by the reintroduction of
Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters
ancestral mutation rate and male mating skew have been estimated for the whole
population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step,
we estimate in this study the migration rates independently for clusters of demes
putatively connected by migration. For large clusters (many migration rates),
ABC faces the problem of too many summary statistics. We therefore assess by simulation
if estimation per pair of demes is a valid alternative. We find that the trade-off
between reduced dimensionality for the pairwise estimation on the one hand and
lower accuracy due to the assumption of pairwise independence on the other depends
on the number of migration rates to be inferred: the accuracy of the pairwise
approach increases with the number of parameters, relative to the joint estimation
approach. To distinguish between low and zero migration, we perform ABC-type model
comparison between a model with migration and one without. Applying the approach
to microsatellite data from Alpine ibex, we find no evidence for substantial gene
flow via migration, except for one pair of demes in one direction.'
article_processing_charge: No
author:
- first_name: Simon
full_name: Aeschbacher, Simon
id: 2D35326E-F248-11E8-B48F-1D18A9856A87
last_name: Aeschbacher
- first_name: Andreas
full_name: Futschik, Andreas
last_name: Futschik
- first_name: Mark
full_name: Beaumont, Mark
last_name: Beaumont
citation:
ama: 'Aeschbacher S, Futschik A, Beaumont M. Data from: Approximate Bayesian computation
for modular inference problems with many parameters: the example of migration
rates. 2012. doi:10.5061/dryad.274b1'
apa: 'Aeschbacher, S., Futschik, A., & Beaumont, M. (2012). Data from: Approximate
Bayesian computation for modular inference problems with many parameters: the
example of migration rates. Dryad. https://doi.org/10.5061/dryad.274b1'
chicago: 'Aeschbacher, Simon, Andreas Futschik, and Mark Beaumont. “Data from: Approximate
Bayesian Computation for Modular Inference Problems with Many Parameters: The
Example of Migration Rates.” Dryad, 2012. https://doi.org/10.5061/dryad.274b1.'
ieee: 'S. Aeschbacher, A. Futschik, and M. Beaumont, “Data from: Approximate Bayesian
computation for modular inference problems with many parameters: the example of
migration rates.” Dryad, 2012.'
ista: 'Aeschbacher S, Futschik A, Beaumont M. 2012. Data from: Approximate Bayesian
computation for modular inference problems with many parameters: the example of
migration rates, Dryad, 10.5061/dryad.274b1.'
mla: 'Aeschbacher, Simon, et al. Data from: Approximate Bayesian Computation
for Modular Inference Problems with Many Parameters: The Example of Migration
Rates. Dryad, 2012, doi:10.5061/dryad.274b1.'
short: S. Aeschbacher, A. Futschik, M. Beaumont, (2012).
date_created: 2021-07-30T12:36:39Z
date_published: 2012-11-14T00:00:00Z
date_updated: 2023-02-23T11:05:19Z
day: '14'
department:
- _id: NiBa
doi: 10.5061/dryad.274b1
main_file_link:
- open_access: '1'
url: https://doi.org/10.5061/dryad.274b1
month: '11'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
record:
- id: '2944'
relation: used_in_publication
status: public
status: public
title: 'Data from: Approximate Bayesian computation for modular inference problems
with many parameters: the example of migration rates'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2012'
...