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.firstpage","695"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Genetics"],["dc.bibliographiccitation.lastpage","708"],["dc.bibliographiccitation.volume","188"],["dc.contributor.author","Ober, Ulrike"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Long, Nanye"],["dc.contributor.author","Porcu, Emilio"],["dc.contributor.author","Schlather, Martin"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T08:54:41Z"],["dc.date.available","2018-11-07T08:54:41Z"],["dc.date.issued","2011"],["dc.description.abstract","Genomic data provide a valuable source of information for modeling covariance structures, allowing a more accurate prediction of total genetic values (GVs). We apply the kriging concept, originally developed in the geostatistical context for predictions in the low-dimensional space, to the high-dimensional space spanned by genomic single nucleotide polymorphism (SNP) vectors and study its properties in different gene-action scenarios. Two different kriging methods [\"universal kriging\" (UK) and \"simple kriging\" (SK)] are presented. As a novelty, we suggest use of the family of Matern covariance functions to model the covariance structure of SNP vectors. A genomic best linear unbiased prediction (GBLUP) is applied as a reference method. The three approaches are compared in a whole-genome simulation study considering additive, additive-dominance, and epistatic gene-action models. Predictive performance is measured in terms of correlation between true and predicted GVs and average true GVs of the individuals ranked best by prediction. We show that UK outperforms GBLUP in the presence of dominance and epistatic effects. In a limiting case, it is shown that the genomic covariance structure proposed by VanRaden (2008) can be considered as a covariance function with corresponding quadratic variogram. We also prove theoretically that if a specific linear relationship exists between covariance matrices for two linear mixed models, the GVs resulting from BLUP are linked by a scaling factor. Finally, the relation of kriging to other models is discussed and further options for modeling the covariance structure, which might be more appropriate in the genomic context, are suggested."],["dc.identifier.doi","10.1534/genetics.111.128694"],["dc.identifier.isi","000292538900019"],["dc.identifier.pmid","21515573"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22728"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Genetics Soc Am"],["dc.relation.issn","0016-6731"],["dc.title","Predicting Genetic Values: A Kernel-Based Best Linear Unbiased Prediction With Genomic Data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","5954"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Journal of Dairy Science"],["dc.bibliographiccitation.lastpage","5964"],["dc.bibliographiccitation.volume","96"],["dc.contributor.author","Kramer, M."],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Bapst, Beat"],["dc.contributor.author","Bieber, A."],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T09:20:54Z"],["dc.date.available","2018-11-07T09:20:54Z"],["dc.date.issued","2013"],["dc.description.abstract","The aim of this study was to estimate genetic parameters and accuracies of breeding values for a set of functional, behavior, and conformation traits in Brown Swiss cattle. These traits were milking speed, udder depth, position of labia, rank order in herd, general temperament, aggressiveness, milking temperament, and days to first heat. Data of 1,799 phenotyped Brown Swiss cows from 40 Swiss dairy herds were analyzed taking the complete pedigree into account. Estimated heritabilities were within the ranges reported in literature, with results at the high end of the reported values for some traits (e.g., milking speed: 0.42 +/- 0.06, udder depth: 0.42 +/- 0.06), whereas other traits were of low heritability and heritability estimates were of low accuracy (e.g., milking temperament: 0.04 +/- 0.04, days to first heat: 0.02 +/- 0.04). For most behavior traits, we found relatively high heritabilities (general temperament: 0.38 +/- 0.07, aggressiveness: 0.12 +/- 0.08, and rank order in herd: 0.16 +/- 0.06). Position of labia, arguably an indicator trait for pathological urovagina, was genetically analyzed in this study for the first time, and a moderate heritability (0.28 +/- 0.06) was estimated."],["dc.description.sponsorship","European Commission [222623]"],["dc.identifier.doi","10.3168/jds.2012-6236"],["dc.identifier.isi","000323185600050"],["dc.identifier.pmid","23871377"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28984"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Inc"],["dc.relation.issn","0022-0302"],["dc.title","Estimation of genetic parameters for novel functional traits in Brown Swiss cattle"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","963"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Theoretical and Applied Genetics"],["dc.bibliographiccitation.lastpage","976"],["dc.bibliographiccitation.volume","129"],["dc.contributor.author","Martini, Johannes W. R."],["dc.contributor.author","Wimmer, Valentin"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Simianer, Henner"],["dc.date.accessioned","2018-11-07T10:15:21Z"],["dc.date.available","2018-11-07T10:15:21Z"],["dc.date.issued","2016"],["dc.description.abstract","Key message Models based on additive marker effects and on epistatic interactions can be translated into genomic relationship models. This equivalence allows to perform predictions based on complex gene interaction models and reduces computational effort significantly. In the theory of genome-assisted prediction, the equivalence of a linear model based on independent and identically normally distributed marker effects and a model based on multivariate Gaussian distributed breeding values with genomic relationship as covariance matrix is well known. In this work, we demonstrate equivalences of marker effect models incorporating epistatic interactions and corresponding mixed models based on relationship matrices and show how to exploit these equivalences computationally for genome-assisted prediction. In particular, we show how models with epistatic interactions of higher order (e.g., three-factor interactions) translate into linear models with certain covariance matrices and demonstrate how to construct epistatic relationship matrices for the linear mixed model, if we restrict the model to interactions defined a priori. We illustrate the practical relevance of our results with a publicly available data set on grain yield of wheat lines growing in four different environments. For this purpose, we select important interactions in one environment and use this knowledge on the network of interactions to increase predictive ability of grain yield under other environmental conditions. Our results provide a guide for building relationship matrices based on knowledge on the structure of trait-related gene networks."],["dc.description.sponsorship","KWS SAAT SE"],["dc.identifier.doi","10.1007/s00122-016-2675-5"],["dc.identifier.isi","000374478600008"],["dc.identifier.pmid","26883048"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/40794"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","1432-2242"],["dc.relation.issn","0040-5752"],["dc.title","Epistasis and covariance: how gene interaction translates into genomic relationship"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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.firstpage","339"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Theoretical and Applied Genetics"],["dc.bibliographiccitation.lastpage","350"],["dc.bibliographiccitation.volume","123"],["dc.contributor.author","Albrecht, Theresa"],["dc.contributor.author","Wimmer, Valentin"],["dc.contributor.author","Auinger, Hans-Juergen"],["dc.contributor.author","Erbe, Malena"],["dc.contributor.author","Knaak, Carsten"],["dc.contributor.author","Ouzunova, Milena"],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Schoen, Chris-Carolin"],["dc.date.accessioned","2018-11-07T08:54:53Z"],["dc.date.available","2018-11-07T08:54:53Z"],["dc.date.issued","2011"],["dc.description.abstract","This is the first large-scale experimental study on genome-based prediction of testcross values in an advanced cycle breeding population of maize. The study comprised testcross progenies of 1,380 doubled haploid lines of maize derived from 36 crosses and phenotyped for grain yield and grain dry matter content in seven locations. The lines were genotyped with 1,152 single nucleotide polymorphism markers. Pedigree data were available for three generations. We used best linear unbiased prediction and stratified cross-validation to evaluate the performance of prediction models differing in the modeling of relatedness between inbred lines and in the calculation of genome-based coefficients of similarity. The choice of similarity coefficient did not affect prediction accuracies. Models including genomic information yielded significantly higher prediction accuracies than the model based on pedigree information alone. Average prediction accuracies based on genomic data were high even for a complex trait like grain yield (0.72-0.74) when the cross-validation scheme allowed for a high degree of relatedness between the estimation and the test set. When predictions were performed across distantly related families, prediction accuracies decreased significantly (0.47-0.48). Prediction accuracies decreased with decreasing sample size but were still high when the population size was halved (0.67-0.69). The results from this study are encouraging with respect to genome-based prediction of the genetic value of untested lines in advanced cycle breeding populations and the implementation of genomic selection in the breeding process."],["dc.description.sponsorship","German Federal Ministry of Education and Research (BMBF) [FKZ: 0315528A]"],["dc.identifier.doi","10.1007/s00122-011-1587-7"],["dc.identifier.isi","000291600800012"],["dc.identifier.pmid","21505832"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22778"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Springer"],["dc.relation.issn","0040-5752"],["dc.title","Genome-based prediction of testcross values in maize"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","83"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Journal of Animal Breeding and Genetics"],["dc.bibliographiccitation.lastpage","84"],["dc.bibliographiccitation.volume","131"],["dc.contributor.author","Simianer, Henner"],["dc.contributor.author","Erbe, Malena"],["dc.date.accessioned","2018-11-07T09:42:07Z"],["dc.date.available","2018-11-07T09:42:07Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.1111/jbg.12072"],["dc.identifier.isi","000332780900001"],["dc.identifier.pmid","24628722"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33884"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.relation.issn","1439-0388"],["dc.relation.issn","0931-2668"],["dc.title","Genetics, genomics, breeding - why scale matters"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.subtype","letter_note"],["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"]]