[{"related_material":{"record":[{"relation":"research_data","status":"public","id":"9758"}]},"language":[{"iso":"eng"}],"date_updated":"2025-09-29T13:25:36Z","status":"public","external_id":{"isi":["000314220900008"]},"publication":"Molecular Ecology","department":[{"_id":"NiBa"}],"isi":1,"article_processing_charge":"No","year":"2013","type":"journal_article","corr_author":"1","month":"02","publisher":"Wiley-Blackwell","day":"01","issue":"4","publist_id":"3788","abstract":[{"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.","lang":"eng"}],"acknowledged_ssus":[{"_id":"ScienComp"}],"title":"Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. ","oa_version":"None","publication_status":"published","page":"987 - 1002","quality_controlled":"1","intvolume":"        22","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).","volume":22,"_id":"2944","scopus_import":"1","author":[{"full_name":"Aeschbacher, Simon","id":"2D35326E-F248-11E8-B48F-1D18A9856A87","first_name":"Simon","last_name":"Aeschbacher"},{"last_name":"Futschik","first_name":"Andreas","full_name":"Futschik, Andreas"},{"first_name":"Mark","last_name":"Beaumont","full_name":"Beaumont, Mark"}],"doi":"10.1111/mec.12165","date_created":"2018-12-11T12:00:28Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_published":"2013-02-01T00:00:00Z","citation":{"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.","ieee":"S. Aeschbacher, A. Futschik, and M. Beaumont, “Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. ,” <i>Molecular Ecology</i>, vol. 22, no. 4. Wiley-Blackwell, pp. 987–1002, 2013.","ama":"Aeschbacher S, Futschik A, Beaumont M. Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. . <i>Molecular Ecology</i>. 2013;22(4):987-1002. doi:<a href=\"https://doi.org/10.1111/mec.12165\">10.1111/mec.12165</a>","chicago":"Aeschbacher, Simon, Andreas Futschik, and Mark Beaumont. “Approximate Bayesian Computation for Modular Inference Problems with Many Parameters: The Example of Migration Rates. .” <i>Molecular Ecology</i>. Wiley-Blackwell, 2013. <a href=\"https://doi.org/10.1111/mec.12165\">https://doi.org/10.1111/mec.12165</a>.","mla":"Aeschbacher, Simon, et al. “Approximate Bayesian Computation for Modular Inference Problems with Many Parameters: The Example of Migration Rates. .” <i>Molecular Ecology</i>, vol. 22, no. 4, Wiley-Blackwell, 2013, pp. 987–1002, doi:<a href=\"https://doi.org/10.1111/mec.12165\">10.1111/mec.12165</a>.","short":"S. Aeschbacher, A. Futschik, M. Beaumont, Molecular Ecology 22 (2013) 987–1002.","apa":"Aeschbacher, S., Futschik, A., &#38; Beaumont, M. (2013). Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. . <i>Molecular Ecology</i>. Wiley-Blackwell. <a href=\"https://doi.org/10.1111/mec.12165\">https://doi.org/10.1111/mec.12165</a>"}},{"external_id":{"pmid":["22960215"],"isi":["000310793900018"]},"status":"public","article_processing_charge":"No","publication":"Genetics","department":[{"_id":"NiBa"}],"isi":1,"language":[{"iso":"eng"}],"date_updated":"2025-09-30T08:06:06Z","publisher":"Genetics Society of America","corr_author":"1","month":"11","issue":"3","day":"01","publist_id":"3763","type":"journal_article","year":"2012","page":"1027 - 1047","main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3522150/"}],"quality_controlled":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"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."}],"pmid":1,"publication_status":"published","oa_version":"Submitted Version","title":"A novel approach for choosing summary statistics in approximate Bayesian computation","date_created":"2018-12-11T12:00:34Z","doi":"10.1534/genetics.112.143164","author":[{"last_name":"Aeschbacher","first_name":"Simon","id":"2D35326E-F248-11E8-B48F-1D18A9856A87","full_name":"Aeschbacher, Simon"},{"full_name":"Beaumont, Mark","last_name":"Beaumont","first_name":"Mark"},{"full_name":"Futschik, Andreas","last_name":"Futschik","first_name":"Andreas"}],"_id":"2962","scopus_import":"1","oa":1,"date_published":"2012-11-01T00:00:00Z","citation":{"apa":"Aeschbacher, S., Beaumont, M., &#38; Futschik, A. (2012). A novel approach for choosing summary statistics in approximate Bayesian computation. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.112.143164\">https://doi.org/10.1534/genetics.112.143164</a>","ama":"Aeschbacher S, Beaumont M, Futschik A. A novel approach for choosing summary statistics in approximate Bayesian computation. <i>Genetics</i>. 2012;192(3):1027-1047. doi:<a href=\"https://doi.org/10.1534/genetics.112.143164\">10.1534/genetics.112.143164</a>","mla":"Aeschbacher, Simon, et al. “A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation.” <i>Genetics</i>, vol. 192, no. 3, Genetics Society of America, 2012, pp. 1027–47, doi:<a href=\"https://doi.org/10.1534/genetics.112.143164\">10.1534/genetics.112.143164</a>.","short":"S. Aeschbacher, M. Beaumont, A. Futschik, Genetics 192 (2012) 1027–1047.","chicago":"Aeschbacher, Simon, Mark Beaumont, and Andreas Futschik. “A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation.” <i>Genetics</i>. Genetics Society of America, 2012. <a href=\"https://doi.org/10.1534/genetics.112.143164\">https://doi.org/10.1534/genetics.112.143164</a>.","ieee":"S. Aeschbacher, M. Beaumont, and A. Futschik, “A novel approach for choosing summary statistics in approximate Bayesian computation,” <i>Genetics</i>, 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."},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","intvolume":"       192","volume":192},{"year":"2012","type":"research_data_reference","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","day":"14","citation":{"apa":"Aeschbacher, S., Futschik, A., &#38; Beaumont, M. (2012). Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates. Dryad. <a href=\"https://doi.org/10.5061/dryad.274b1\">https://doi.org/10.5061/dryad.274b1</a>","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:<a href=\"https://doi.org/10.5061/dryad.274b1\">10.5061/dryad.274b1</a>","mla":"Aeschbacher, Simon, et al. <i>Data from: Approximate Bayesian Computation for Modular Inference Problems with Many Parameters: The Example of Migration Rates</i>. Dryad, 2012, doi:<a href=\"https://doi.org/10.5061/dryad.274b1\">10.5061/dryad.274b1</a>.","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. <a href=\"https://doi.org/10.5061/dryad.274b1\">https://doi.org/10.5061/dryad.274b1</a>.","short":"S. Aeschbacher, A. Futschik, M. Beaumont, (2012).","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, <a href=\"https://doi.org/10.5061/dryad.274b1\">10.5061/dryad.274b1</a>."},"oa":1,"date_published":"2012-11-14T00:00:00Z","month":"11","_id":"9758","publisher":"Dryad","date_created":"2021-07-30T12:36:39Z","doi":"10.5061/dryad.274b1","author":[{"first_name":"Simon","last_name":"Aeschbacher","full_name":"Aeschbacher, Simon","id":"2D35326E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Futschik, Andreas","first_name":"Andreas","last_name":"Futschik"},{"last_name":"Beaumont","first_name":"Mark","full_name":"Beaumont, Mark"}],"oa_version":"Published Version","title":"Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates","date_updated":"2025-09-29T13:25:35Z","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."}],"related_material":{"record":[{"id":"2944","status":"public","relation":"used_in_publication"}]},"department":[{"_id":"NiBa"}],"article_processing_charge":"No","main_file_link":[{"url":"https://doi.org/10.5061/dryad.274b1","open_access":"1"}],"status":"public"}]