{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/123263"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/123263","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"1,000 ancient genomes uncover 10,000 years of natural selection in Europe","abstract":"Ancient DNA has revolutionized our understanding of human history. However, its potential for revealing how cultural evolution to adapt new lifestyles may have driven biological adaptation has not been met. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, allowing us to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods, and to identify 25 loci with evidence of rapid changes in allele frequency during these periods. All 10 candidate loci in the Historical period were detected in previous scans, with many associated with vitamin D metabolism. Strikingly, 17 out of 19 of the candidates from the Neolithic and Bronze Age periods were not previously detected, highlighting the unique power of ancient DNA to reveal selective events not uncoverable in modern samples due to subsequent drift or admixture. In the Neolithic period, we found evidence for human pathogen co-evolution: we detected selection at a locus that confers resistance to Salmonella infection at a time when ancient Salmonella have been shown to adapt to human hosts. Neolithic period candidates are also related to body weight and insulin secretion, suggesting adaptation to a primarily starch-based diet; in contrast, major shifts of allele frequencies in the Bronze Age tended to occur more in variants affecting pigmentation. We also tested for selection on complex traits, leveraging strategies to address confounders that have impeded such analyses in recent years. In the Neolithic period, we detected selection favoring alleles that today decrease body weight, perhaps reflecting genomic adaptation to changes in food availability. In the Historical Period, we detected selection favoring variants that today increase risk for autoimmune disease and cardiovascular disease, plausibly due to a selective pressure for more active inflammatory response in facing the increased exposure to infectious disease. These changes resulted in higher risk for diseases that cause pervasive morbidity in today’s aging populations","abstract_html":"Ancient DNA has revolutionized our understanding of human history. However, its potential for revealing how cultural evolution to adapt new lifestyles may have driven biological adaptation has not been met. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, allowing us to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods, and to identify 25 loci with evidence of rapid changes in allele frequency during these periods. All 10 candidate loci in the Historical period were detected in previous scans, with many associated with vitamin D metabolism. Strikingly, 17 out of 19 of the candidates from the Neolithic and Bronze Age periods were not previously detected, highlighting the unique power of ancient DNA to reveal selective events not uncoverable in modern samples due to subsequent drift or admixture. In the Neolithic period, we found evidence for human pathogen co-evolution: we detected selection at a locus that confers resistance to Salmonella infection at a time when ancient Salmonella have been shown to adapt to human hosts. Neolithic period candidates are also related to body weight and insulin secretion, suggesting adaptation to a primarily starch-based diet; in contrast, major shifts of allele frequencies in the Bronze Age tended to occur more in variants affecting pigmentation. We also tested for selection on complex traits, leveraging strategies to address confounders that have impeded such analyses in recent years. In the Neolithic period, we detected selection favoring alleles that today decrease body weight, perhaps reflecting genomic adaptation to changes in food availability. In the Historical Period, we detected selection favoring variants that today increase risk for autoimmune disease and cardiovascular disease, plausibly due to a selective pressure for more active inflammatory response in facing the increased exposure to infectious disease. These changes resulted in higher risk for diseases that cause pervasive morbidity in today’s aging populations","abstract_has_math":false,"creators":["Le, Megan Khiet"],"institution":"The University of Texas at Austin","degree_name":"Master of Science in Computational Science, Engineering, and Mathematics","degree_level":"Masters","degree_discipline":"Computational Science, Engineering, and Mathematics","degree_department":null,"school":null,"contributors":[],"advisors":["Press, William H.","Narasimhan, Vagheesh M."],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-04-17","date_published":"2023-04-17","updated_at":"2026-07-24T05:01:04Z","subjects":["Ancient DNA","Evolutionary genetics","Population genetics"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.26153/tsw/50061"],"render_values":[{"text":"https://doi.org/10.26153/tsw/50061","href":"https://doi.org/10.26153/tsw/50061","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/2152/123263","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Press, William H.","Narasimhan, Vagheesh M."]},{"key":"dc:creator","label":"Author","values":["Le, Megan Khiet"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-12-20T01:06:13Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-20T01:06:13Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-04-17"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Science, Engineering, and Mathematics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computational Science, Engineering, and Mathematics"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Texas at Austin"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ancient DNA","Evolutionary genetics","Population genetics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/2152/123263","https://doi.org/10.26153/tsw/50061"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Ancient DNA has revolutionized our understanding of human history. However, its potential for revealing how cultural evolution to adapt new lifestyles may have driven biological adaptation has not been met. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, allowing us to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods, and to identify 25 loci with evidence of rapid changes in allele frequency during these periods. All 10 candidate loci in the Historical period were detected in previous scans, with many associated with vitamin D metabolism. Strikingly, 17 out of 19 of the candidates from the Neolithic and Bronze Age periods were not previously detected, highlighting the unique power of ancient DNA to reveal selective events not uncoverable in modern samples due to subsequent drift or admixture. In the Neolithic period, we found evidence for human pathogen co-evolution: we detected selection at a locus that confers resistance to Salmonella infection at a time when ancient Salmonella have been shown to adapt to human hosts. Neolithic period candidates are also related to body weight and insulin secretion, suggesting adaptation to a primarily starch-based diet; in contrast, major shifts of allele frequencies in the Bronze Age tended to occur more in variants affecting pigmentation. We also tested for selection on complex traits, leveraging strategies to address confounders that have impeded such analyses in recent years. In the Neolithic period, we detected selection favoring alleles that today decrease body weight, perhaps reflecting genomic adaptation to changes in food availability. In the Historical Period, we detected selection favoring variants that today increase risk for autoimmune disease and cardiovascular disease, plausibly due to a selective pressure for more active inflammatory response in facing the increased exposure to infectious disease. These changes resulted in higher risk for diseases that cause pervasive morbidity in today’s aging populations"]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["1,000 ancient genomes uncover 10,000 years of natural selection in Europe"]}]}],"canonical_facts":{"dc:contributor.advisor":["Press, William H.","Narasimhan, Vagheesh M."],"dc:creator":["Le, Megan Khiet"],"dc:date.accessioned":["2023-12-20T01:06:13Z"],"dc:date.available":["2023-12-20T01:06:13Z"],"dc:date.issued":["2023-04-17"],"dc:description.abstract":["Ancient DNA has revolutionized our understanding of human history. However, its potential for revealing how cultural evolution to adapt new lifestyles may have driven biological adaptation has not been met. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, allowing us to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods, and to identify 25 loci with evidence of rapid changes in allele frequency during these periods. All 10 candidate loci in the Historical period were detected in previous scans, with many associated with vitamin D metabolism. Strikingly, 17 out of 19 of the candidates from the Neolithic and Bronze Age periods were not previously detected, highlighting the unique power of ancient DNA to reveal selective events not uncoverable in modern samples due to subsequent drift or admixture. In the Neolithic period, we found evidence for human pathogen co-evolution: we detected selection at a locus that confers resistance to Salmonella infection at a time when ancient Salmonella have been shown to adapt to human hosts. Neolithic period candidates are also related to body weight and insulin secretion, suggesting adaptation to a primarily starch-based diet; in contrast, major shifts of allele frequencies in the Bronze Age tended to occur more in variants affecting pigmentation. We also tested for selection on complex traits, leveraging strategies to address confounders that have impeded such analyses in recent years. In the Neolithic period, we detected selection favoring alleles that today decrease body weight, perhaps reflecting genomic adaptation to changes in food availability. In the Historical Period, we detected selection favoring variants that today increase risk for autoimmune disease and cardiovascular disease, plausibly due to a selective pressure for more active inflammatory response in facing the increased exposure to infectious disease. These changes resulted in higher risk for diseases that cause pervasive morbidity in today’s aging populations"],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2152/123263","https://doi.org/10.26153/tsw/50061"],"dc:language.iso":["en"],"dc:subject":["Ancient DNA","Evolutionary genetics","Population genetics"],"dc:title":["1,000 ancient genomes uncover 10,000 years of natural selection in Europe"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computational Science, Engineering, and Mathematics"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science in Computational Science, Engineering, and Mathematics"],"thesis:institution_name":["The University of Texas at Austin"]},"updated_at":"2026-07-24T05:01:04Z"}