http://www.gsejournal.org The latest research articles published by Genetics Selection Evolution 2013-05-01T00:00:00Z Background: When introgression of undesired exogenous genetic material occurs in a population intended to remain pure, actions are necessary to recover the original background. It has been shown that genome-wide information can replace pedigree information for different objectives and is a valuable tool in the fields of genetic conservation and breeding. In this simulation study, molecular information provided by 50 000 SNP was used to minimise the molecular coancestry between individuals of an admixed population and the foreign individuals that originally introgressed a native population in order to remove the exogenous DNA. Results: This management method, which detects the ‘purest’ individuals to be used as parents for the next generation, allowed recovery of the native genetic background to a great extent in all simulated scenarios. However, it also caused an increase in inbreeding larger than expected because of the lower number of individuals selected as parents and the higher coancestry between them. In scenarios involving several introgression events the method was more efficient than in those involving a single introgression event because part of the genetic information was mixed with the native genetic material for a shorter period. Conclusions: Genome-wide information can be used to identify the purest individuals via the minimisation of molecular coancestry between individuals of the admixed and exogenous populations. Removal of the undesired genetic material is more efficient with a molecular-based approach than with a pedigree-based approach. http://www.gsejournal.org/content/45/1/13 Carmen Amador Jesús Fernández Theo Meuwissen Genetics Selection Evolution 2013, null:13 2013-05-01T00:00:00Z doi:10.1186/1297-9686-45-13 /content/figures/1297-9686-45-13-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 13 2013-05-01T00:00:00Z XML Background: The most common application of imputation is to infer genotypes of a high-density panel of markers on animals that are genotyped for a low-density panel. However, the increase in accuracy of genomic predictions resulting from an increase in the number of markers tends to reach a plateau beyond a certain density. Another application of imputation is to increase the size of the training set with un-genotyped animals. This strategy can be particularly successful when a set of closely related individuals are genotyped. Methods: Imputation on completely un-genotyped dams was performed using known genotypes from the sire of each dam, one offspring and the offspring’s sire. Two methods were applied based on either allele or haplotype frequencies to infer genotypes at ambiguous loci. Results of these methods and of two available software packages were compared. Quality of imputation under different population structures was assessed. The impact of using imputed dams to enlarge training sets on the accuracy of genomic predictions was evaluated for different populations, heritabilities and sizes of training sets. Results: Imputation accuracy ranged from 0.52 to 0.93 depending on the population structure and the method used. The method that used allele frequencies performed better than the method based on haplotype frequencies. Accuracy of imputation was higher for populations with higher levels of linkage disequilibrium and with larger proportions of markers with more extreme allele frequencies. Inclusion of imputed dams in the training set increased the accuracy of genomic predictions. Gains in accuracy ranged from close to zero to 37.14%, depending on the simulated scenario. Generally, the larger the accuracy already obtained with the genotyped training set, the lower the increase in accuracy achieved by adding imputed dams. Conclusions: Whenever a reference population resembling the family configuration considered here is available, imputation can be used to achieve an extra increase in accuracy of genomic predictions by enlarging the training set with completely un-genotyped dams. This strategy was shown to be particularly useful for populations with lower levels of linkage disequilibrium, for genomic selection on traits with low heritability, and for species or breeds for which the size of the reference population is limited. http://www.gsejournal.org/content/45/1/12 Eduardo Pimentel Monika Wensch-Dorendorf Sven König Hermann Swalve Genetics Selection Evolution 2013, null:12 2013-04-26T00:00:00Z doi:10.1186/1297-9686-45-12 /content/figures/1297-9686-45-12-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 12 2013-04-26T00:00:00Z XML Background: Genomic selection is an appealing method to select purebreds for crossbred performance. In the caseof crossbred records, single nucleotide polymorphism (SNP) effects can be estimated using an additivemodel or a breed-specific allele model. In most studies, additive gene action is assumed. However,dominance is the likely genetic basis of heterosis. Advantages of incorporating dominance in genomicselection were investigated in a two-way crossbreeding program for a trait with different magnitudesof dominance. Training was carried out only once in the simulation. Results: When the dominance variance and heterosis were large and overdominance was present, a dominancemodel including both additive and dominance SNP effects gave substantially greater cumulative re-sponse to selection than the additive model. Extra response was the result of an increase in heterosisbut at a cost of reduced purebred performance. When the dominance variance and heterosis were re-alistic but with overdominance, the advantage of the dominance model decreased but was still signif-icant. When overdominance was absent, the dominance model was slightly favored over the additivemodel, but the difference in response between the models increased as the number of quantitative traitloci increased. This reveals the importance of exploiting dominance even in the absence of overdom-inance. When there was no dominance, response to selection for the dominance model was as highas for the additive model, indicating robustness of the dominance model. The breed-specific allelemodel was inferior to the dominance model in all cases and to the additive model except when thedominance variance and heterosis were large and with overdominance. However, the advantage of thedominance model over the breed-specific allele model may decrease as differences in linkage disequi-librium between the breeds increase. Retraining is expected to reduce the advantage of the dominancemodel over the alternatives, because in general, the advantage becomes important only after five orsix generations post-training. Conclusion: Under dominance and without retraining, genomic selection based on the dominance model is superiorto the additive model and the breed-specific allele model to maximize crossbred performance throughpurebred selection. http://www.gsejournal.org/content/45/1/11 Jian Zeng Ali Toosi Rohan Fernando Jack Dekkers Dorian Garrick Genetics Selection Evolution 2013, null:11 2013-04-26T00:00:00Z doi:10.1186/1297-9686-45-11 /content/figures/1297-9686-45-11-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 11 2013-04-26T00:00:00Z PDF AlphaImpute is a flexible and accurate genotype imputation tool that was originally designed for the imputation of genotypes on autosomal chromosomes. In some species, sex chromosomes comprise a large portion of the genome. For example, chromosome Z represents approximately 8% of the chicken genome and therefore is likely to be important in determining genetic variation in a population. When breeding programs make selection decisions based on genomic information, chromosomes that are not represented on the genotyping platform will not be subject to selection. Therefore imputation algorithms should be able to impute genotypes for all chromosomes. The objective of this research was to extend AlphaImpute so that it could impute genotypes on sex chromosomes. The accuracy of imputation was assessed using different genotyping strategies in a real commercial chicken population. The correlation between true and imputed genotypes was high in all the scenarios and was 0.96 for the most favourable scenario. Overall, the accuracy of imputation of the sex chromosome was slightly lower than that of autosomes for all scenarios considered. http://www.gsejournal.org/content/45/1/10 John Hickey Andreas Kranis Genetics Selection Evolution 2013, null:10 2013-04-25T00:00:00Z doi:10.1186/1297-9686-45-10 /content/figures/1297-9686-45-10-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 10 2013-04-25T00:00:00Z XML Background: GC content varies greatly between different genomic regions in many eukaryotes. In order to determine whether this organization named isochore organization influences gene expression patterns, the relationship between GC content and gene expression has been investigated in man and mouse. However, to date, this question is still a matter for debate. Among the avian species, chicken (Gallus gallus) is the best studied representative with a complete genome sequence. The distinctive features and organization of its sequence make it a good model to explore important issues in genome structure and evolution. Methods: Only nuclear genes with complete information on protein-coding sequence with no evidence of multiple-splicing forms were included in this study. Chicken protein coding sequences, complete mRNA sequences (or full length cDNA sequences), and 5′ untranslated region sequences (5′ UTR) were downloaded from Ensembl and chicken expression data originated from a previous work. Three indices i.e. expression level, expression breadth and maximum expression level were used to measure the expression pattern of a given gene. CpG islands were identified using hgTables of the UCSC Genome Browser. Correlation analysis between variables was performed by SAS Proprietary Software Release 8.1. Results: In chicken, the GC content of 5′ UTR is significantly and positively correlated with expression level, expression breadth, and maximum expression level, whereas that of coding sequences and introns and at the third coding position are negatively correlated with expression level and expression breadth, and not correlated with maximum expression level. These significant trends are independent of recombination rate, chromosome size and gene density. Furthermore, multiple linear regression analysis indicated that GC content in genes could explain approximately 10% of the variation in gene expression. Conclusions: GC content is significantly associated with gene expression pattern and could be one of the important regulation factors in the chicken genome. http://www.gsejournal.org/content/45/1/9 You Rao Xue Chai Zhang Wang Qing Nie Xi Zhang Genetics Selection Evolution 2013, null:9 2013-04-04T00:00:00Z doi:10.1186/1297-9686-45-9 /content/figures/1297-9686-45-9-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 9 2013-04-04T00:00:00Z XML Background: Outbreaks of infectious pancreatic necrosis (IPN) in Atlantic salmon can result in reduced growth rates in a fraction of the surviving fish (runts). Genetic and environmental variation also affects growth rates within different categories of healthy animals and runts, which complicates identification of runts. Mixture models are commonly used to identify the underlying structures in such data, and the aim of this study was to develop Bayesian mixture models for the genetic analysis of health status (runt/healthy) of surviving fish from an IPN outbreak. Methods: Five statistical models were tested on data consisting of 10 972 fish that died and 3959 survivors with recorded growth data. The most complex models (4 and 5) were multivariate normal-binary mixture models including growth, sexual maturity and field survival traits. Growth rate and liability of sexual maturation were treated as two-component normal mixtures, assuming phenotypes originated from two potentially overlapping distributions, (runt/normal). Runt status was an unobserved binary trait. These models were compared to mixture models with fewer traits (Models 2 and 3) and a classical linear animal model for growth (Model 1). Results: Assuming growth as a mixture trait improved the predictive ability of the statistical model considerably (Model 2 vs. 1). The final models (4 and 5) yielded the following results: estimated (underlying) heritabilities were moderate for growth in healthy fish (0.32 ± 0.04 and 0.35 ± 0.05), runt status (0.39 ± 0.07 and 0.36 ± 0.08) and sexual maturation (0.33 ± 0.05), and high for field survival (0.47 ± 0.03 and 0.48 ± 0.03). Growth in healthy animals, runt status and survival showed consistent favourable genetic associations. Sexual maturation showed an unfavourable non-significant genetic correlation with runt status, but favourable genetic correlations with other traits. The estimated fraction of healthy fish was 81-85%. The estimated breeding values for runt status and (normal) growth were consistent for the most complex models (4 and 5), but showed imperfect correlations with estimated breeding values from the simpler models. Conclusions: Modelling growth in IPN survivors as a mixture trait improved the predictive ability of the model compared with a classical linear model. The results indicated considerable genetic variation in health status among survivors. Mixture modelling may be useful for the genetic analysis of diseases detected mainly through indicator traits. http://www.gsejournal.org/content/45/1/8 Marie Lillehammer Jørgen Ødegård Per Madsen Bjarne Gjerde Terje Refstie Morten Rye Genetics Selection Evolution 2013, null:8 2013-03-25T00:00:00Z doi:10.1186/1297-9686-45-8 /content/figures/1297-9686-45-8-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 8 2013-03-25T00:00:00Z XML Background: In China, the reference population of genotyped Holstein cattle is relatively small with to date, 80 bulls and 2091 cows genotyped with the Illumina 54 K chip. Including genotyped Holstein cattle from other countries in the reference population could improve the accuracy of genomic prediction of the Chinese Holstein population. This study investigated the consistency of linkage disequilibrium between adjacent markers between the Chinese and Nordic Holstein populations, and compared the reliability of genomic predictions based on the Chinese reference population only or the combined Chinese and Nordic reference populations. Methods: Genomic estimated breeding values of Chinese Holstein cattle were predicted using a single-trait GBLUP model based on the Chinese reference dataset, and using a two-trait GBLUP model based on a joint reference dataset that included both the Chinese and Nordic Holstein data. Results: The extent of linkage disequilibrium was similar in the Chinese and Nordic Holstein populations and the consistency of linkage disequilibrium between the two populations was very high, with a correlation of 0.97. Genomic prediction using the joint versus the Chinese reference dataset increased reliabilities of genomic predictions of Chinese Holstein bulls in the test data from 0.22, 0.15 and 0.11 to 0.51, 0.47 and 0.36 for milk yield, fat yield and protein yield, respectively. Using five-fold cross-validation, reliabilities of genomic predictions of Chinese cows increased from 0.15, 0.12 and 0.15 to 0.26, 0.17 and 0.20 for milk yield, fat yield and protein yield, respectively. Conclusions: The linkage disequilibrium between the two populations was very consistent and using the combined Nordic and Chinese reference dataset substantially increased reliabilities of genomic predictions for Chinese Holstein cattle. http://www.gsejournal.org/content/45/1/7 Lei Zhou Xiangdong Ding Qin Zhang Yachun Wang Mogens Lund Guosheng Su Genetics Selection Evolution 2013, null:7 2013-03-21T00:00:00Z doi:10.1186/1297-9686-45-7 /content/figures/1297-9686-45-7-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 7 2013-03-21T00:00:00Z XML Background: One method to improve durably animal welfare is to select, as reproducers, animals with the highest ability to resist or tolerate infection. To do so, it is necessary to distinguish direct and indirect mechanisms of resistance and tolerance because selection on these traits is believed to have different epidemiological and evolutionary consequences. Methods: We propose structural equation models with latent variables (1) to quantify the latent risk of infection and to identify, among the many potential mediators of infection, the few ones that influence it significantly and (2) to estimate direct and indirect levels of tolerance of animals infected naturally with pathogens. We applied the method to two surveys of bovine mastitis in the Walloon region of Belgium, in which we recorded herd management practices, mastitis frequency, and results of bacteriological analyses of milk samples.Results and discussionStructural equation models suggested that, among more than 35 surveyed herd characteristics, only nine (age, addition of urea in the rations, treatment of subclinical mastitis, presence of dirty liner, cows with hyperkeratotic teats, machine stripping, pre- and post-milking teat disinfection, and housing of milking cows in cubicles) were directly and significantly related to a latent measure of bovine mastitis, and that treatment of subclinical mastitis was involved in the pathway between post-milking teat disinfection and latent mastitis. These models also allowed the separation of direct and indirect effects of bacterial infection on milk productivity. Results suggested that infected cows were tolerant but not resistant to mastitis pathogens. Conclusions: We revealed the advantages of structural equation models, compared to classical models, for dissecting measurements of resistance and tolerance to infectious diseases, here bovine mastitis. Using our method, we identified nine major risk factors that were directly associated with an increased risk of mastitis and suggested that cows were tolerant but not resistant to mastitis. Selection should aim at improved resistance to infection by mastitis pathogens, although further investigations are needed due to the limitations of the data used in this study. http://www.gsejournal.org/content/45/1/6 Johann Detilleux Léonard Theron Jean-Noël Duprez Edouard Reding Marie-France Humblet Viviane Planchon Camille Delfosse Carlo Bertozzi Jacques Mainil Christian Hanzen Genetics Selection Evolution 2013, null:6 2013-03-06T00:00:00Z doi:10.1186/1297-9686-45-6 /content/figures/1297-9686-45-6-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 6 2013-03-06T00:00:00Z XML Background: Genomic prediction uses two sources of information: linkage disequilibrium between markers and quantitative trait loci, and additive genetic relationships between individuals. One way to increase the accuracy of genomic prediction is to capture more linkage disequilibrium by regression on haplotypes instead of regression on individual markers. The aim of this study was to investigate the accuracy of genomic prediction using haplotypes based on local genealogy information. Methods: A total of 4429 Danish Holstein bulls were genotyped with the 50K SNP chip. Haplotypes were constructed using local genealogical trees. Effects of haplotype covariates were estimated with two types of prediction models: (1) assuming that effects had the same distribution for all haplotype covariates, i.e. the GBLUP method and (2) assuming that a large proportion (π) of the haplotype covariates had zero effect, i.e. a Bayesian mixture method. Results: About 7.5 times more covariate effects were estimated when fitting haplotypes based on local genealogical trees compared to fitting individuals markers. Genealogy-based haplotype clustering slightly increased the accuracy of genomic prediction and, in some cases, decreased the bias of prediction. With the Bayesian method, accuracy of prediction was less sensitive to parameter π when fitting haplotypes compared to fitting markers. Conclusions: Use of haplotypes based on genealogy can slightly increase the accuracy of genomic prediction. Improved methods to cluster the haplotypes constructed from local genealogy could lead to additional gains in accuracy. http://www.gsejournal.org/content/45/1/5 Vahid Edriss Rohan Fernando Guosheng Su Mogens Lund Bernt Guldbrandtsen Genetics Selection Evolution 2013, null:5 2013-03-06T00:00:00Z doi:10.1186/1297-9686-45-5 /content/figures/1297-9686-45-5-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 5 2013-03-06T00:00:00Z XML Since the publication of our article (Rizzi et al, Genet Sel Evo, 2012, 44:21), it has come to our attention that our text inadvertentlyincludes some statements taken from the introductory section of the following paper byPaabo et al. without citation:Paabo S, Poinar H, Serre D, Jaenicke-Despres V, Hebler J, Rohland N, Kuch M, Krause J,Vigilant L, Hofreiter M: Genetic analyses from ancient DNA. Annu Rev Genet 2004, 38:645-679.We apologise for this oversight and any inconvenience this may have caused.References1. Rizzi E, Lari M, Gigli E, De Bellis G, Caramelli D: Ancient DNA studies: newperspectives on old samples. Genet Sel Evol 2012, 44:21.2. Paabo S, Poinar H, Serre D, Jaenicke-Despres V, Hebler J, Rohland N, Kuch M, Krause J,Vigilant L, Hofreiter M: Genetic analyses from ancient DNA. Annu Rev Genet 2004,38:645-679. http://www.gsejournal.org/content/45/1/4 Ermanno Rizzi Martina Lari Elena Gigli Gianluca De Bellis David Caramelli Genetics Selection Evolution 2013, null:4 2013-02-18T00:00:00Z doi:10.1186/1297-9686-45-4 /content/figures/1297-9686-45-4-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 4 2013-02-18T00:00:00Z XML