Simianer, Henner

[["dc.bibliographiccitation.artnumber","e0141216"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Berger, Swetlana"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","de los Campos, Gustavo"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Preisinger, Rudolf"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T09:50:00Z"],["dc.date.available","2018-11-07T09:50:00Z"],["dc.date.issued","2015"],["dc.description.abstract","The understanding of non-random association between loci, termed linkage disequilibrium (LD), plays a central role in genomic research. Since causal mutations are generally not included in genomic marker data, LD between those and available markers is essential for capturing the effects of causal loci on localizing genes responsible for traits. Thus, the interpretation of association studies requires a detailed knowledge of LD patterns. It is well known that most LD measures depend on minor allele frequencies (MAF) of the considered loci and the magnitude of LD is influenced by the physical distances between loci. In the present study, a procedure to compare the LD structure between genomic regions comprising several markers each is suggested. The approach accounts for different scaling factors, namely the distribution of MAF, the distribution of pair-wise differences in MAF, and the physical extent of compared regions, reflected by the distribution of pair-wise physical distances. In the first step, genomic regions are matched based on similarity in these scaling factors. In the second step, chromosome-and genome-wide significance tests for differences in medians of LD measures in each pair are performed. The proposed framework was applied to test the hypothesis that the average LD is different in genic and non-genic regions. This was tested with a genome-wide approach with data sets for humans (Homo sapiens), a highly selected chicken line (Gallus gallus domesticus) and the model plant Arabidopsis thaliana. In all three data sets we found a significantly higher level of LD in genic regions compared to non-genic regions. About 31% more LD was detected genome-wide in genic compared to non-genic regions in Arabidopsis thaliana, followed by 13.6% in human and 6% chicken. Chromosome-wide comparison discovered significant differences on all 5 chromosomes in Arabidopsis thaliana and on one third of the human and of the chicken chromosomes."],["dc.description.sponsorship","Open-Access Publikationsfonds 2015"],["dc.identifier.doi","10.1371/journal.pone.0141216"],["dc.identifier.isi","000363920800029"],["dc.identifier.pmid","26517830"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12561"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35618"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A Scale-Corrected Comparison of Linkage Disequilibrium Levels between Genic and Non-Genic Regions"],["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","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","3"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Bioinformatics"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Martini, Johannes W."],["dc.contributor.author","Gao, Ning"],["dc.contributor.author","Cardoso, Diercles F."],["dc.contributor.author","Wimmer, Valentin"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Cantet, Rodolfo J."],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2019-07-09T11:42:59Z"],["dc.date.available","2019-07-09T11:42:59Z"],["dc.date.issued","2017"],["dc.description.abstract","Abstract Background Epistasis marker effect models incorporating products of marker values as predictor variables in a linear regression approach (extended GBLUP, EGBLUP) have been assessed as potentially beneficial for genomic prediction, but their performance depends on marker coding. Although this fact has been recognized in literature, the nature of the problem has not been thoroughly investigated so far. Results We illustrate how the choice of marker coding implicitly specifies the model of how effects of certain allele combinations at different loci contribute to the phenotype, and investigate coding-dependent properties of EGBLUP. Moreover, we discuss an alternative categorical epistasis model (CE) eliminating undesired properties of EGBLUP and show that the CE model can improve predictive ability. Finally, we demonstrate that the coding-dependent performance of EGBLUP offers the possibility to incorporate prior experimental information into the prediction method by adapting the coding to already available phenotypic records on other traits. Conclusion Based on our results, for EGBLUP, a symmetric coding {−1,1} or {−1,0,1} should be preferred, whereas a standardization using allele frequencies should be avoided. Moreover, CE can be a valuable alternative since it does not possess the undesired theoretical properties of EGBLUP. However, which model performs best will depend on characteristics of the data and available prior information. Data from previous experiments can for instance be incorporated into the marker coding of EGBLUP."],["dc.identifier.doi","10.1186/s12859-016-1439-1"],["dc.identifier.pmid","28049412"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14066"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58801"],["dc.language.iso","en"],["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","Genomic prediction with epistasis models: on the marker-coding-dependent performance of the extended GBLUP and properties of the categorical epistasis model (CE)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","19"],["dc.bibliographiccitation.journal","Frontiers in Genetics"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Pimentel, Eduardo da Cruz Gouveia"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","König, Sven"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2019-07-09T11:54:12Z"],["dc.date.available","2019-07-09T11:54:12Z"],["dc.date.issued","2011-05-02"],["dc.description.abstract","The objective of this study was to estimate the contribution of each autosome to genetic variation of milk yield, fat, and protein percentage and somatic cell score in Holstein cattle. Data on 2294 Holstein bulls genotyped for 39,557 autosomal markers were used. Three approaches were applied to estimate the proportion of genetic variance attributed to each chromosome. In two of them, marker-derived kinship coefficients were computed, using either marker genotypes observed on the whole genome or on subsets relative to each chromosome. Variance components were then estimated using residual maximum likelihood in method 1 or a regression-based approach in method 2. In method 3, genetic variances associated to each marker were estimated in a linear multiple regression approach, and then were summed up chromosome-wise. Generally, all chromosomes contributed to genetic variation. For most of the chromosomes, the amount of variance attributed to a chromosome was found to be proportional to its physical length. Nevertheless, for traits influenced by genes with very large effects a larger proportion of the genetic variance is expected to be associated with the chromosomes where these genes are. This is illustrated with the DGAT1 gene on BTA14 which is known to have a large effect on fat percentage in milk. The proportion of genetic variance for fat percentage associated with chromosome 14 was two to sevenfold (depending on the method) larger than would be predicted from chromosome size alone. Based on method 3 an approach is suggested to estimate the effective number of genes underlying the inheritance of the studied traits, yielding numbers between N ≈ 400 (for fat percentage) to N ≈ 900 (for milk yield). It is argued that these numbers are conservative lower bound estimates, but are in line with recent findings suggesting a highly polygenic background of production traits in dairy cattle."],["dc.format.extent","11"],["dc.identifier.doi","10.3389/fgene.2011.00019"],["dc.identifier.fs","583526"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8596"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60588"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1664-8021"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Genome Partitioning of Genetic Variation for Milk Production and Composition Traits in Holstein Cattle"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e81046"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Gredler, Birgit"],["dc.contributor.author","Seefried, Franz Reinhold"],["dc.contributor.author","Bapst, Beat"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T09:16:39Z"],["dc.date.available","2018-11-07T09:16:39Z"],["dc.date.issued","2013"],["dc.description.abstract","Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments (Me). The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of similar to 698 Holstein Friesian bulls genotyped with 50 K SNPs and 19332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to,600 K SNPs were available. Different k-fold (k = 2-10, 15, 20) cross-validation scenarios (50 replicates, random assignment) were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010), augmented by a weighting factor (w) based on the assumption that the maximum achievable accuracy is w < 1. The proportion of genetic variance captured by the complete SNP sets (w(2)) was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with,209000 SNPs in the Brown Swiss population studied."],["dc.description.sponsorship","Open-Acces-Publikationsfonds 2013"],["dc.identifier.doi","10.1371/journal.pone.0081046"],["dc.identifier.fs","603043"],["dc.identifier.isi","000328566100029"],["dc.identifier.pmid","24339895"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9531"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/27978"],["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","A Function Accounting for Training Set Size and Marker Density to Model the Average Accuracy of Genomic Prediction"],["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","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.artnumber","e94509"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Gholami, Mahmood"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Gaerke, Christian"],["dc.contributor.author","Preisinger, Rudolf"],["dc.contributor.author","Weigend, Annett"],["dc.contributor.author","Weigend, Steffen"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T09:41:12Z"],["dc.date.available","2018-11-07T09:41:12Z"],["dc.date.issued","2014"],["dc.description.abstract","Identifying signatures of selection can provide valuable insight about the genes or genomic regions that are or have been under selective pressure, which can lead to a better understanding of genotype-phenotype relationships. A common strategy for selection signature detection is to compare samples from several populations and search for genomic regions with outstanding genetic differentiation. Wright's fixation index, F-ST, is a useful index for evaluation of genetic differentiation between populations. The aim of this study was to detect selective signatures between different chicken groups based on SNP-wise F-ST calculation. A total of 96 individuals of three commercial layer breeds and 14 non-commercial fancy breeds were genotyped with three different 600K SNP-chips. After filtering a total of 1 million SNPs were available for F-ST calculation. Averages of F-ST values were calculated for overlapping windows. Comparisons of these were then conducted between commercial egg layers and non-commercial fancy breeds, as well as between white egg layers and brown egg layers. Comparing non-commercial and commercial breeds resulted in the detection of 630 selective signatures, while 656 selective signatures were detected in the comparison between the commercial egg-layer breeds. Annotation of selection signature regions revealed various genes corresponding to productions traits, for which layer breeds were selected. Among them were NCOA1, SREBF2 and RALGAPA1 associated with reproductive traits, broodiness and egg production. Furthermore, several of the detected genes were associated with growth and carcass traits, including POMC, PRKAB2, SPP1, IGF2, CAPN1, TGFb2 and IGFBP2. Our approach demonstrates that including different populations with a specific breeding history can provide a unique opportunity for a better understanding of farm animal selection."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.1371/journal.pone.0094509"],["dc.identifier.fs","607225"],["dc.identifier.isi","000336863900049"],["dc.identifier.pmid","24739889"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10097"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33680"],["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","Population Genomic Analyses Based on 1 Million SNPs in Commercial Egg Layers"],["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.artnumber","8"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Genetics Selection Evolution"],["dc.bibliographiccitation.volume","49"],["dc.contributor.author","Ni, Guiyan"],["dc.contributor.author","Cavero, David"],["dc.contributor.author","Fangmann, Anna"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2019-07-09T11:43:05Z"],["dc.date.available","2019-07-09T11:43:05Z"],["dc.date.issued","2017"],["dc.description.abstract","Abstract Background With the availability of next-generation sequencing technologies, genomic prediction based on whole-genome sequencing (WGS) data is now feasible in animal breeding schemes and was expected to lead to higher predictive ability, since such data may contain all genomic variants including causal mutations. Our objective was to compare prediction ability with high-density (HD) array data and WGS data in a commercial brown layer line with genomic best linear unbiased prediction (GBLUP) models using various approaches to weight single nucleotide polymorphisms (SNPs). Methods A total of 892 chickens from a commercial brown layer line were genotyped with 336 K segregating SNPs (array data) that included 157 K genic SNPs (i.e. SNPs in or around a gene). For these individuals, genome-wide sequence information was imputed based on data from re-sequencing runs of 25 individuals, leading to 5.2 million (M) imputed SNPs (WGS data), including 2.6 M genic SNPs. De-regressed proofs (DRP) for eggshell strength, feed intake and laying rate were used as quasi-phenotypic data in genomic prediction analyses. Four weighting factors for building a trait-specific genomic relationship matrix were investigated: identical weights, −(log10 P) from genome-wide association study results, squares of SNP effects from random regression BLUP, and variable selection based weights (known as BLUP"],["dc.identifier.doi","10.1186/s12711-016-0277-y"],["dc.identifier.pmid","28093063"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14160"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58821"],["dc.language.iso","en"],["dc.publisher","BioMed Central"],["dc.rights.access","openAccess"],["dc.rights.holder","The Author(s)"],["dc.title","Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0117468"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Bergfelder-Drueing, Sarah"],["dc.contributor.author","Grosse-Brinkhaus, Christine"],["dc.contributor.author","Lind, Bianca"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Schellander, Karl"],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Tholen, Ernst"],["dc.date.accessioned","2018-11-07T09:59:32Z"],["dc.date.available","2018-11-07T09:59:32Z"],["dc.date.issued","2015"],["dc.description.abstract","The number of piglets born alive (NBA) per litter is one of the most important traits in pig breeding due to its influence on production efficiency. It is difficult to improve NBA because the heritability of the trait is low and it is governed by a high number of loci with low to moderate effects. To clarify the biological and genetic background of NBA, genome-wide association studies (GWAS) were performed using 4,012 Large White and Landrace pigs from herdbook and commercial breeding companies in Germany (3), Austria (1) and Switzerland (1). The animals were genotyped with the Illumina PorcineSNP60 BeadChip. Because of population stratifications within and between breeds, clusters were formed using the genetic distances between the populations. Five clusters for each breed were formed and analysed by GWAS approaches. In total, 17 different significant markers affecting NBA were found in regions with known effects on female reproduction. No overlapping significant chromosome areas or QTL between Large White and Landrace breed were detected."],["dc.identifier.doi","10.1371/journal.pone.0117468"],["dc.identifier.isi","000351284600013"],["dc.identifier.pmid","25781935"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11767"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37611"],["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","A Genome-Wide Association Study in Large White and Landrace Pig Populations for Number Piglets Born Alive"],["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"]]