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","16"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genetics Selection Evolution"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Martini, Johannes W. R."],["dc.contributor.author","Schrauf, Matias F."],["dc.contributor.author","Garcia-Baccino, Carolina A."],["dc.contributor.author","Pimentel, Eduardo C. G."],["dc.contributor.author","Munilla, Sebastian"],["dc.contributor.author","Rogberg-Muñoz, Andres"],["dc.contributor.author","Cantet, Rodolfo J. C."],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Gao, Ning"],["dc.contributor.author","Wimmer, Valentin"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2019-07-09T11:45:22Z"],["dc.date.available","2019-07-09T11:45:22Z"],["dc.date.issued","2018-04-13"],["dc.description.abstract","Background The single-step covariance matrix H combines the pedigree-based relationship matrix A with the more accurate information on realized relatedness of genotyped individuals represented by the genomic relationship matrix G. In particular, to improve convergence behavior of iterative approaches and to reduce inflation, two weights τ and ω have been introduced in the definition of H−1, which blend the inverse of a part of A with the inverse of G. Since the definition of this blending is based on the equation describing H−1, its impact on the structure of H is not obvious. In a joint discussion, we considered the question of the shape of H for non-trivial τ and ω . Results Here, we present the general matrix H as a function of these parameters and discuss its structure and properties. Moreover, we screen for optimal values of τ and ω with respect to predictive ability, inflation and iterations up to convergence on a well investigated, publicly available wheat data set. Conclusion Our results may help the reader to develop a better understanding for the effects of changes of τ and ω on the covariance model. In particular, we give theoretical arguments that as a general tendency, inflation will be reduced by increasing τ or by decreasing ω."],["dc.identifier.doi","10.1186/s12711-018-0386-x"],["dc.identifier.pmid","29653506"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15168"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59215"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The effect of the H−1 scaling factors τ and ω on the structure of H in the single-step procedure"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0130497"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Gholami, Mahmood"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Preisinger, Rudolf"],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Servin, Bertrand"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T09:54:41Z"],["dc.date.available","2018-11-07T09:54:41Z"],["dc.date.issued","2015"],["dc.description.abstract","An increasing interest is being placed in the detection of genes, or genomic regions, that have been targeted by selection because identifying signatures of selection can lead to a better understanding of genotype-phenotype relationships. A common strategy for the detection of selection signatures is to compare samples from distinct populations and to search for genomic regions with outstanding genetic differentiation. The aim of this study was to detect selective signatures in layer chicken populations using a recently proposed approach, hapFLK, which exploits linkage disequilibrium information while accounting appropriately for the hierarchical structure of populations. We performed the analysis on 70 individuals from three commercial layer breeds (White Leghorn, White Rock and Rhode Island Red), genotyped for approximately 1 million SNPs. We found a total of 41 and 107 regions with outstanding differentiation or similarity using hapFLK and its single SNP counterpart FLK respectively. Annotation of selection signature regions revealed various genes and QTL corresponding to productions traits, for which layer breeds were selected. A number of the detected genes were associated with growth and carcass traits, including IGF-1R, AGRP and STAT5B. We also annotated an interesting gene associated with the dark brown feather color mutational phenotype in chickens (SOX10). We compared FST, FLK and hapFLK and demonstrated that exploiting linkage disequilibrium information and accounting for hierarchical population structure decreased the false detection rate."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0130497"],["dc.identifier.isi","000358158900010"],["dc.identifier.pmid","26151449"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11948"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/36589"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights.access","openAccess"],["dc.title","Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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"]]

[["dc.bibliographiccitation.artnumber","824"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Ni, Guiyan"],["dc.contributor.author","Strom, Tim-Mathias"],["dc.contributor.author","Pausch, Hubert"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Preisinger, Rudolf"],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Erbe, Malena"],["dc.date.accessioned","2018-11-07T09:50:03Z"],["dc.date.available","2018-11-07T09:50:03Z"],["dc.date.issued","2015"],["dc.description.abstract","Background: The technical progress in the last decade has made it possible to sequence millions of DNA reads in a relatively short time frame. Several variant callers based on different algorithms have emerged and have made it possible to extract single nucleotide polymorphisms (SNPs) out of the whole-genome sequence. Often, only a few individuals of a population are sequenced completely and imputation is used to obtain genotypes for all sequence-based SNP loci for other individuals, which have been genotyped for a subset of SNPs using a genotyping array. Methods: First, we compared the sets of variants detected with different variant callers, namely GATK, freebayes and SAMtools, and checked the quality of genotypes of the called variants in a set of 50 fully sequenced white and brown layers. Second, we assessed the imputation accuracy (measured as the correlation between imputed and true genotype per SNP and per individual, and genotype conflict between father-progeny pairs) when imputing from high density SNP array data to whole-genome sequence using data from around 1000 individuals from six different generations. Three different imputation programs (Minimac, FImpute and IMPUTE2) were checked in different validation scenarios. Results: There were 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6 % (81.6 %, 88.0 %) of SNPs detected by GATK (SAMtools, freebayes) in total. Genotype concordance (GC) defined as the proportion of individuals whose array-derived genotypes are the same as the sequence-derived genotypes over all non-missing SNPs on the array were 0.98 (GATK), 0.97 (freebayes) and 0.98 (SAMtools). Furthermore, the percentage of variants that had high values (>0.9) for another three measures (non-reference sensitivity, non-reference genotype concordance and precision) were 90 (88, 75) for GATK (SAMtools, freebayes). With all imputation programs, correlation between original and imputed genotypes was >0.95 on average with randomly masked 1000 SNPs from the SNP array and >0.85 for a leave-one-out cross-validation within sequenced individuals. Conclusions: Performance of all variant callers studied was very good in general, particularly for GATK and SAMtools. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, while it had lowest numbers in Mendelian conflicts in available father-progeny pairs. Correlations of real and imputed genotypes remained constantly high even if individuals to be imputed were several generations away from the sequenced individuals."],["dc.identifier.doi","10.1186/s12864-015-2059-2"],["dc.identifier.fs","614806"],["dc.identifier.isi","000363102100001"],["dc.identifier.pmid","26486989"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13207"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35633"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1471-2164"],["dc.rights.access","openAccess"],["dc.title","Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genetics Selection Evolution"],["dc.bibliographiccitation.volume","50"],["dc.contributor.author","Cardoso, Diercles F."],["dc.contributor.author","de Albuquerque, Lucia Galvão"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Qanbari, Saber"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","do Nascimento, André V."],["dc.contributor.author","Venturini, Guilherme C."],["dc.contributor.author","Scalez, Daiane C. Becker"],["dc.contributor.author","Baldi, Fernando"],["dc.contributor.author","de Camargo, Gregório M. Ferreira"],["dc.contributor.author","Mercadante, Maria E. Zerlotti"],["dc.contributor.author","do Santos Gonçalves Cyrillo, Joslaine N."],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Tonhati, Humberto"],["dc.date.accessioned","2020-12-10T18:38:50Z"],["dc.date.available","2020-12-10T18:38:50Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1186/s12711-018-0381-2"],["dc.identifier.eissn","1297-9686"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15486"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15216"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77449"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Genome-wide scan reveals population stratification and footprints of recent selection in Nelore cattle"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e0245178"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Geibel, Johannes"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Pook, Torsten"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2021-06-01T09:42:18Z"],["dc.date.available","2021-06-01T09:42:18Z"],["dc.date.issued","2021"],["dc.description.abstract","Single nucleotide polymorphisms (SNPs), genotyped with arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom ™ Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown. This was mainly caused by the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1371/journal.pone.0245178"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85209"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.relation.eissn","1932-6203"],["dc.relation.orgunit","Department für Nutztierwissenschaften"],["dc.rights","CC BY 4.0"],["dc.title","How array design creates 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","308"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Ha, Ngoc-Thuy"],["dc.contributor.author","Sharifi, Ahmad Reza"],["dc.contributor.author","Geibel, Johannes"],["dc.contributor.author","Mikkelsen, Lars Friis"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2020-12-10T18:38:51Z"],["dc.date.available","2020-12-10T18:38:51Z"],["dc.date.issued","2020"],["dc.description.abstract","Background Göttingen Minipigs (GMP) is the smallest commercially available minipig breed under a controlled breeding scheme and is globally bred in five isolated colonies. The genetic isolation harbors the risk of stratification which might compromise the identity of the breed and its usability as an animal model for biomedical and human disease. We conducted whole genome re-sequencing of two DNA-pools per colony to assess genomic differentiation within and between colonies. We added publicly available samples from 13 various pig breeds and discovered overall about 32 M loci, ~ 16 M. thereof variable in GMPs. Individual samples were virtually pooled breed-wise. FST between virtual and DNA pools, a phylogenetic tree, principal component analysis (PCA) and evaluation of functional SNP classes were conducted. An F-test was performed to reveal significantly differentiated allele frequencies between colonies. Variation within a colony was quantified as expected heterozygosity. Results Phylogeny and PCA showed that the GMP is easily discriminable from all other breads, but that there is also differentiation between the GMP colonies. Dependent on the contrast between GMP colonies, 4 to 8% of all loci had significantly different allele frequencies. Functional annotation revealed that functionally non-neutral loci are less prone to differentiation. Annotation of highly differentiated loci revealed a couple of deleterious mutations in genes with putative effects in the GMPs . Conclusion Differentiation and annotation results suggest that the underlying mechanisms are rather drift events than directed selection and limited to neutral genome regions. Animal exchange seems not yet necessary. The Relliehausen colony appears to be the genetically most unique GMP sub-population and could be a valuable resource if animal exchange is required to maintain uniformity of the GMP."],["dc.identifier.doi","10.1186/s12864-020-6590-4"],["dc.identifier.eissn","1471-2164"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17224"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77458"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Assessing breed integrity of Göttingen Minipigs"],["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","36"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genetics Selection Evolution"],["dc.bibliographiccitation.volume","53"],["dc.contributor.author","Malomane, Dorcus K."],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2021-06-01T09:42:14Z"],["dc.date.accessioned","2022-08-18T12:40:25Z"],["dc.date.available","2021-06-01T09:42:14Z"],["dc.date.available","2022-08-18T12:40:25Z"],["dc.date.issued","2021-04-14"],["dc.date.updated","2022-07-29T12:18:19Z"],["dc.description.abstract","Background Migration of a population from its founder population is expected to cause a reduction of its genetic diversity and facilitates differentiation between the population and its founder population, as predicted by the theory of genetic isolation by distance. Consistent with that theory, a model of expansion from a single founder predicts that patterns of genetic diversity in populations can be explained well by their geographic expansion from their founders, which is correlated with genetic differentiation. Methods To investigate this in chicken, we estimated the relationship between the genetic diversity of 160 domesticated chicken populations and their genetic distances to wild chicken populations. Results Our results show a strong inverse relationship, i.e. 88.6% of the variation in the overall genetic diversity of domesticated chicken populations was explained by their genetic distance to the wild populations. We also investigated whether the patterns of genetic diversity of different types of single nucleotide polymorphisms (SNPs) and genes are similar to that of the overall genome. Among the SNP classes, the non-synonymous SNPs deviated most from the overall genome. However, genetic distance to the wild chicken still explained more variation in domesticated chicken diversity across all SNP classes, which ranged from 83.0 to 89.3%. Conclusions Genetic distance between domesticated chicken populations and their wild relatives can predict the genetic diversity of the domesticated populations. On the one hand, genes with little genetic variation across populations, regardless of the genetic distance to the wild population, are associated with major functions such as brain development. Changes in such genes may be detrimental to the species. On the other hand, genetic diversity seems to change at a faster rate within genes that are associated with e.g. protein transport and protein and lipid metabolic processes. In general, such genes may be flexible to changes according to the populations’ needs. These results contribute to the knowledge of the evolutionary patterns of different functional genomic regions in the chicken."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.citation","Genetics Selection Evolution. 2021 Apr 14;53(1):36"],["dc.identifier.doi","10.1186/s12711-021-00628-z"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17763"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85187"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112981"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1297-9686"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Genetic diversity in global chicken breeds in relation to their genetic distances to wild populations"],["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","345"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Genomics"],["dc.bibliographiccitation.volume","20"],["dc.contributor.author","Malomane, Dorcus K."],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Reimer, Christian"],["dc.contributor.author","Schmitt, Armin O."],["dc.contributor.author","Weigend, Steffen"],["dc.date.accessioned","2019-07-09T11:51:19Z"],["dc.date.available","2019-07-09T11:51:19Z"],["dc.date.issued","2019"],["dc.description.abstract","Background Since domestication, chickens did not only disperse into the different parts of the world but they have also undergone significant genomic changes in this process. Many breeds, strains or lines have been formed and those represent the diversity of the species. However, other than the natural evolutionary forces, management practices (including those that threaten the persistence of genetic diversity) following domestication have shaped the genetic make-up of and diversity between today\\’s chicken breeds. As part of the SYNBREED project, samples from a wide variety of chicken populations have been collected across the globe and were genotyped with a high density SNP array. The panel consists of the wild type, commercial layers and broilers, indigenous village/local type and fancy chicken breeds. The SYNBREED chicken diversity panel (SCDP) is made available to serve as a public basis to study the genetic structure of chicken diversity. In the current study we analyzed the genetic diversity between and within the populations in the SCDP, which is important for making informed decisions for effective management of farm animal genetic resources. Results Many of the fancy breeds cover a wide spectrum and clustered with other breeds of similar supposed origin as shown by the phylogenetic tree and principal component analysis. However, the fancy breeds as well as the highly selected commercial layer lines have reduced genetic diversity within the population, with the average observed heterozygosity estimates lower than 0.205 across their breeds\\’ categories and the average proportion of polymorphic loci lower than 0.680. We show that there is still a lot of genetic diversity preserved within the wild and less selected African, South American and some local Asian and European breeds with the average observed heterozygosity greater than 0.225 and the average proportion of polymorphic loci larger than 0.720 within their breeds\\’ categories. Conclusions It is important that such highly diverse breeds are maintained for the sustainability and flexibility of future chicken breeding. This diversity panel provides opportunities for exploitation for further chicken molecular genetic studies. With the possibility to further expand, it constitutes a very useful community resource for chicken genetic diversity research."],["dc.format.extent","15"],["dc.identifier.doi","10.1186/s12864-019-5727-9"],["dc.identifier.pmid","31064348"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16104"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59926"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","The SYNBREED chicken diversity panel: a global resource to assess chicken diversity at high genomic resolution"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]