biostudies-literatureGrilz-Seger GÖsterreichische ForschungsförderungsgesellschaftBundesministerium für Nachhaltigkeit und Tourismus (AT)Bundesministerium für Nachhaltigkeit und Tourismus174https://www.ebi.ac.uk/biostudies/studies/S-EPMC6402180biostudies-literatureUnknown20(1)<h4>Background</h4>The sample ascertainment bias due to complex population structures remains a major challenge in genome-wide investigations of complex traits. In this study we derived the high-resolution population structure and levels of autozygosity of 377 Lipizzan horses originating from five different European stud farms utilizing the SNP genotype information of the high density 700 k Affymetrix Axiom™ Equine genotyping array. Scanning the genome for overlapping runs of homozygosity (ROH) shared by more than 50% of horses, we identified homozygous regions (ROH islands) in order to investigate the gene content of those candidate regions by gene ontology and enrichment analyses.<h4>Results</h4>The high-resolution population network approach revealed well-defined substructures accordingBMC genomicsHigh-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse.PMC6402180101332843464Neuditschko MDobretsberger MBrem GDruml TGrilz-Seger GHorna MfalseHigh-resolution population structure and runs of homozygosity reveal the genetic architecture of complex traits in the Lipizzan horse.<h4>Background</h4>The sample ascertainment bias due to complex population structures remains a major challenge in genome-wide investigations of complex traits. In this study we derived the high-resolution population structure and levels of autozygosity of 377 Lipizzan horses originating from five different European stud farms utilizing the SNP genotype information of the high density 700 k Affymetrix Axiom™ Equine genotyping array. Scanning the genome for overlapping runs of homozygosity (ROH) shared by more than 50% of horses, we identified homozygous regions (ROH islands) in order to investigate the gene content of those candidate regions by gene ontology and enrichment analyses.<h4>Results</h4>The high-resolution population network approach revealed well-defined substructures according2019-01-01T00:00:00Z2019 Mar2026-05-03T01:19:18.182Z2025-05-29T19:54:19.661ZS-EPMC64021803083695910.1186/s12864-019-5564-x