{"abstract":[{"text":"This thesis is concerned with the inference of current population structure based on geo-referenced genetic data. The underlying idea is that population structure affects its spatial genetic structure. Therefore, genotype information can be utilized to estimate important demographic parameters such as migration rates. These indirect estimates of population structure have become very attractive, as genotype data is now widely available. However, there also has been much concern about these approaches. Importantly, genetic structure can be influenced by many complex patterns, which often cannot be disentangled. Moreover, many methods merely fit heuristic patterns of genetic structure, and do not build upon population genetics theory. Here, I describe two novel inference methods that address these shortcomings. In Chapter 2, I introduce an inference scheme based on a new type of signal, identity by descent (IBD) blocks. Recently, it has become feasible to detect such long blocks of genome shared between pairs of samples. These blocks are direct traces of recent coalescence events. As such, they contain ample signal for inferring recent demography. I examine sharing of IBD blocks in two-dimensional populations with local migration. Using a diffusion approximation, I derive formulas for an isolation by distance pattern of long IBD blocks and show that sharing of long IBD blocks approaches rapid exponential decay for growing sample distance. I describe an inference scheme based on these results. It can robustly estimate the dispersal rate and population density, which is demonstrated on simulated data. I also show an application to estimate mean migration and the rate of recent population growth within Eastern Europe. Chapter 3 is about a novel method to estimate barriers to gene flow in a two dimensional population. This inference scheme utilizes geographically localized allele frequency fluctuations - a classical isolation by distance signal. The strength of these local fluctuations increases on average next to a barrier, and there is less correlation across it. I again use a framework of diffusion of ancestral lineages to model this effect, and provide an efficient numerical implementation to fit the results to geo-referenced biallelic SNP data. This inference scheme is able to robustly estimate strong barriers to gene flow, as tests on simulated data confirm.","lang":"eng"}],"file_date_updated":"2020-07-14T12:45:23Z","supervisor":[{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","last_name":"Barton","orcid":"0000-0002-8548-5240","first_name":"Nicholas H"}],"date_created":"2018-12-11T11:45:10Z","degree_awarded":"PhD","publication_status":"published","language":[{"iso":"eng"}],"oa":1,"_id":"200","file":[{"access_level":"open_access","checksum":"8cc534d2b528ae017acf80874cce48c9","date_created":"2018-12-12T10:14:55Z","content_type":"application/pdf","date_updated":"2020-07-14T12:45:23Z","file_id":"5111","file_name":"IST-2018-963-v1+1_thesis.pdf","creator":"system","relation":"main_file","file_size":5792935},{"relation":"source_file","file_size":113365,"checksum":"6af18d7e5a7e2728ceda2f41ee24f628","access_level":"closed","date_created":"2019-04-05T09:30:12Z","date_updated":"2020-07-14T12:45:23Z","content_type":"application/zip","file_id":"6224","file_name":"2018_thesis_ringbauer_source.zip","creator":"dernst"}],"day":"21","publisher":"Institute of Science and Technology Austria","department":[{"_id":"NiBa"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_processing_charge":"No","oa_version":"Published Version","alternative_title":["ISTA Thesis"],"publist_id":"7713","date_updated":"2023-09-20T12:00:56Z","status":"public","license":"https://creativecommons.org/licenses/by-nc/4.0/","author":[{"id":"417FCFF4-F248-11E8-B48F-1D18A9856A87","full_name":"Ringbauer, Harald","last_name":"Ringbauer","orcid":"0000-0002-4884-9682","first_name":"Harald"}],"ddc":["576"],"page":"146","doi":"10.15479/AT:ISTA:th_963","type":"dissertation","has_accepted_license":"1","tmp":{"short":"CC BY-NC (4.0)","name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","image":"/images/cc_by_nc.png","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode"},"date_published":"2018-02-21T00:00:00Z","citation":{"chicago":"Ringbauer, Harald. “Inferring Recent Demography from Spatial Genetic Structure.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:th_963\">https://doi.org/10.15479/AT:ISTA:th_963</a>.","ista":"Ringbauer H. 2018. Inferring recent demography from spatial genetic structure. Institute of Science and Technology Austria.","apa":"Ringbauer, H. (2018). <i>Inferring recent demography from spatial genetic structure</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:th_963\">https://doi.org/10.15479/AT:ISTA:th_963</a>","mla":"Ringbauer, Harald. <i>Inferring Recent Demography from Spatial Genetic Structure</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th_963\">10.15479/AT:ISTA:th_963</a>.","short":"H. Ringbauer, Inferring Recent Demography from Spatial Genetic Structure, Institute of Science and Technology Austria, 2018.","ama":"Ringbauer H. Inferring recent demography from spatial genetic structure. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th_963\">10.15479/AT:ISTA:th_963</a>","ieee":"H. Ringbauer, “Inferring recent demography from spatial genetic structure,” Institute of Science and Technology Austria, 2018."},"related_material":{"record":[{"id":"563","status":"public","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","status":"public","id":"1074"}]},"title":"Inferring recent demography from spatial genetic structure","publication_identifier":{"issn":["2663-337X"]},"pubrep_id":"963","month":"02","year":"2018"}