Simianer, Henner

[["dc.bibliographiccitation.artnumber","340"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","22"],["dc.contributor.author","Geibel, Johannes"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Pook, Torsten"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2021-11-25T10:56:52Z"],["dc.date.accessioned","2022-08-16T12:40:55Z"],["dc.date.available","2021-11-25T10:56:52Z"],["dc.date.available","2022-08-16T12:40:55Z"],["dc.date.issued","2021-05-12"],["dc.date.updated","2022-07-29T12:07:01Z"],["dc.description.abstract","Abstract\r\n \r\n Background\r\n Population genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays. The resulting bias in the estimation of allele frequency spectra and population genetics parameters like heterozygosity and genetic distances relative to whole genome sequencing (WGS) data is known as SNP ascertainment bias. Full correction for this bias requires detailed knowledge of the array design process, which is often not available in practice. This study suggests an alternative approach to mitigate ascertainment bias of a large set of genotyped individuals by using information of a small set of sequenced individuals via imputation without the need for prior knowledge on the array design.\r\n \r\n \r\n Results\r\n The strategy was first tested by simulating additional ascertainment bias with a set of 1566 chickens from 74 populations that were genotyped for the positions of the Affymetrix Axiom™ 580 k Genome-Wide Chicken Array. Imputation accuracy was shown to be consistently higher for populations used for SNP discovery during the simulated array design process. Reference sets of at least one individual per population in the study set led to a strong correction of ascertainment bias for estimates of expected and observed heterozygosity, Wright’s Fixation Index and Nei’s Standard Genetic Distance. In contrast, unbalanced reference sets (overrepresentation of populations compared to the study set) introduced a new bias towards the reference populations. Finally, the array genotypes were imputed to WGS by utilization of reference sets of 74 individuals (one per population) to 98 individuals (additional commercial chickens) and compared with a mixture of individually and pooled sequenced populations. The imputation reduced the slope between heterozygosity estimates of array data and WGS data from 1.94 to 1.26 when using the smaller balanced reference panel and to 1.44 when using the larger but unbalanced reference panel. This generally supported the results from simulation but was less favorable, advocating for a larger reference panel when imputing to WGS.\r\n \r\n \r\n Conclusions\r\n The results highlight the potential of using imputation for mitigation of SNP ascertainment bias but also underline the need for unbiased reference sets."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","BMC Genomics. 2021 May 12;22(1):340"],["dc.identifier.doi","10.1186/s12864-021-07663-6"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/93514"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112739"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","1471-2164"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.subject","SNP ascertainment bias"],["dc.subject","Imputation"],["dc.subject","Chickens"],["dc.subject","Population genetics"],["dc.title","How imputation can mitigate SNP ascertainment Bias"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","193"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Geibel, Johannes"],["dc.contributor.author","Praefke, Nora P."],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Reimer, Christian"],["dc.date.accessioned","2022-04-01T10:02:42Z"],["dc.date.accessioned","2022-08-18T12:34:12Z"],["dc.date.available","2022-04-01T10:02:42Z"],["dc.date.available","2022-08-18T12:34:12Z"],["dc.date.issued","2022-03-09"],["dc.date.updated","2022-07-29T12:07:04Z"],["dc.description.abstract","Background\r\nStructural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens.\r\n\r\nResults\r\nThe final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP – SNP and DUP – SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays.\r\n\r\nConclusions\r\nThe results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.citation","BMC Genomics. 2022 Mar 09;23(1):193"],["dc.identifier.doi","10.1186/s12864-022-08418-7"],["dc.identifier.pii","8418"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105985"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112927"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1471-2164"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Chickens"],["dc.subject","Single nucleotide polymorphisms"],["dc.subject","Structural variants"],["dc.subject","Linkage disequilibrium"],["dc.title","Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]