http://www.gsejournal.org The latest research articles published by Genetics Selection Evolution 2012-05-08T00:00:00Z Background: In classical pedigree-based analysis, additive genetic variance is estimated from between-family variation, which requires the existence of larger phenotyped and pedigreed populations involving numerous families (parents). However, estimation is often complicated by confounding of genetic and environmental family effects, with the latter typically occurring among full-sibs. For this reason, genetic variance is often inferred based on covariance among more distant relatives, which reduces the power of the analysis. This simulation study shows that genome-wide identity-by-descent sharing among close relatives can be used to quantify additive genetic variance solely from within-family variation using data on extremely small family samples. Methods: Identity-by-descent relationships among full-sibs were simulated assuming a genome size similar to that of humans (effective number of loci ~80). Genetic variance was estimated from phenotypic data assuming that genomic identity-by-descent relationships could be accurately re-created using information from genome-wide markers. The results were compared with standard pedigree-based genetic analysis. Results: For a polygenic trait and a given number of phenotypes, the most accurate estimates of genetic variance were based on data from a single large full-sib family only. Compared with classical pedigree-based analysis, the proposed method is more robust to selection among parents and for confounding of environmental and genetic effects. Furthermore, in some cases, satisfactory results can be achieved even with less ideal data structures, i.e., for selectively genotyped data and for traits for which the genetic variance is largely under the control of a few major genes. Conclusions: Estimation of genetic variance using genomic identity-by-descent relationships is especially useful for studies aiming at estimating additive genetic variance of highly fecund species, using data from small populations with limited pedigree information and/or few available parents, i.e., parents originating from non-pedigreed or even wild populations. http://www.gsejournal.org/content/44/1/16 Jorgen Odegard Theo Meuwissen Genetics Selection Evolution 2012, null:16 2012-05-08T00:00:00Z doi:10.1186/1297-9686-44-16 /content/figures/1297-9686-44-16-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 16 2012-05-08T00:00:00Z PDF Background: In fish, the most studied production traits in terms of heritability are body weight or growth,stress or disease resistance, while heritability of cortisol levels, widely used as a measure ofresponse to stress, is less studied. In this study, we have estimated heritabilities of two growthtraits (body weight and length) and of cortisol response to confinement stress in the Europeansea bass.FindingsThe F1 progeny analysed (n = 922) belonged to a small effective breeding population withcontributions from an unbalanced family structure of just 10 males and two females.Heritability values ranged from 0.54 (+/-0.21) for body weight to 0.65 (+/-0.22) for standardbody length and were low for cortisol response i.e. 0.08 (+/-0.06). Genetic correlations werepositive (0.94) between standard body length and body weight and negative between cortisoland body weight and between cortisol and standard body length (0.60 and 0.55,respectively). Conclusion: This study confirms that in European sea bass, heritability of growth-related traits is high andthat selection on such traits has potential. However, heritability of cortisol response to stressis low in European sea bass and since it is known to vary greatly among species, furtherstudies are necessary to understand the reasons for these differences. http://www.gsejournal.org/content/44/1/15 Filip Volckaert Bart Hellemans Costas Batargias Bruno Louro Cécile Massault Jeroen Van Houdt Chris Haley Dirk-Jan de Koning Adelino Canario Genetics Selection Evolution 2012, null:15 2012-04-20T00:00:00Z doi:10.1186/1297-9686-44-15 /content/figures/1297-9686-44-15-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 15 2012-04-20T00:00:00Z PDF Background: Over the past years, reports have indicated that honey bee populations are declining and thatinfestation by an ecto-parasitic mite (Varroa destructor) is one of the main causes. Selectivebreeding of resistant bees can help to prevent losses due to the parasite, but it requires that arobust breeding program and genetic evaluation are implemented. Genomic selection hasemerged as an important tool in animal breeding programs and simulation studies haveshown that it yields more accurate breeding value estimates, higher genetic gain and low gainand low rates of inbreeding. Since genomic selection relies on marker data, simulationsconducted on a genomic dataset are a pre-requisite before selection can be implemented.Although genomic datasets have been simulated in other species undergoing geneticevaluation, simulation of a genomic dataset specific to the honey bee is required since thisspecies has a distinct genetic and reproductive biology. Our software program was aimed atconstructing a base population by simulating a random mating honey bee population. Aforward-time population simulation approach was applied since it allows modeling of geneticcharacteristics and reproductive behavior specific to the honey bee. Results: Our software program yielded a genomic dataset for a base population in linkagedisequilibrium. In addition, information was obtained on (1) the position of markers on eachchromosome, (2) allele frequency, (3) chi2 statistics for Hardy-Weinberg equilibrium, (4) asorted list of markers with a minor allele frequency less than or equal to the input value, (5)average r2 values of linkage disequilibrium between all simulated marker loci pair for allgenerations and (6) average r2 value of linkage disequilibrium in the last generation forselected markers with the highest minor allele frequency. Conclusion: We developed a software program that takes into account the genetic and reproductivebiology specific to the honey bee and that can be used to constitute a genomic datasetcompatible with the simulation studies necessary to optimize breeding programs.The source code together with an instruction file is freely accessible athttp://msproteomics.org/Research/Misc/honeybeepopulationsimulator.html http://www.gsejournal.org/content/44/1/14 Pooja Gupta Tim Conrad Andreas Spötter Norbert Reinsch Kaspar Bienefeld Genetics Selection Evolution 2012, null:14 2012-04-20T00:00:00Z doi:10.1186/1297-9686-44-14 /content/figures/1297-9686-44-14-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 14 2012-04-20T00:00:00Z PDF Background: Independent studies have shown that several single nucleotide polymorphisms (SNP) in the human FTO (fat mass and obesity associated) gene are associated with obesity. SNP have also been identified in the pig FTO gene, among which some are associated with selected fat-deposition traits in F2 crosses and commercial populations. In this study, using both commercial pig populations and an experimental Meishan x Pietrain F2 population, we have investigated the association between one FTO SNP and several growth and carcass traits. Association analyses were performed with the FTO polymorphism either alone or in combination with polymorphisms in flanking loci. Methods: SNP (FM244720:g.400C>G) in exon 3 of porcine FTO was genotyped by PCR-RFLP and tested for associations with some growth, carcass and fat-related traits. Proportions of genetic variance of four pig chromosome 6 genes (FTO, RYR1, LIPE and TGFB1) on selected traits were evaluated using single- and multi-locus models. Results: Linkage analysis placed FTO on the p arm of pig chromosome 6, approximately 22 cM from RYR1. In the commercial populations, allele C of the FTO SNP was significantly associated with back fat depth and allele G with muscling traits. In the Meishan x Pietrain F2 pigs, heterozygotes with allele C from the Pietrain sows and allele G from the Meishan boar were more significantly associated with fat-related traits compared to homozygotes with allele G from the Pietrain and allele G from the Meishan breed. In single- and multi-locus models, genes RYR1, TGFB1 and FTO showed high associations. The contribution in genetic variance from the polymorphism in the FTO gene was highest for back fat depth, meat area on the musculus longissimus lumborum et thoracis tissues and metabolite glucose-6-phosphate dehydrogenase. Conclusions: Our results show that in pig, FTO influences back fat depth in the commercial populations, while in the Meishan x Pietrain F2 pigs with a CG genotype, heterosis occurs for several fat-related traits. http://www.gsejournal.org/content/44/1/13 Vera Dvorakova Heinz Bartenschlager Antonin Stratil Pavel Horak Roman Stupka Jaroslav Citek Michal Sprysl Anna Hrdlicova Hermann Geldermann Genetics Selection Evolution 2012, null:13 2012-04-17T00:00:00Z doi:10.1186/1297-9686-44-13 /content/figures/1297-9686-44-13-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 13 2012-04-17T00:00:00Z PDF Background: Studies to detect associations between DNA markers and traits of interest in humans and livestock benefit from increasing the number of individuals genotyped. Performing association studies on pooled DNA samples can provide greater power for a given cost. For quantitative traits, the effect of an SNP is measured in the units of the trait and here we propose and demonstrate a method to estimate SNP effects on quantitative traits from pooled DNA data. Methods: To obtain estimates of SNP effects from pooled DNA samples, we used logistic regression of estimated allele frequencies in pools on phenotype. The method was tested on a simulated dataset, and a beef cattle dataset using a model that included principal components from a genomic correlation matrix derived from the allele frequencies estimated from the pooled samples. The performance of the obtained estimates was evaluated by comparison with estimates obtained using regression of phenotype on genotype from individual samples of DNA. Results: For the simulated data, the estimates of SNP effects from pooled DNA are similar but asymptotically different to those from individual DNA data. Error in estimating allele frequencies had a large effect on the accuracy of estimated SNP effects. For the beef cattle dataset, the principal components of the genomic correlation matrix from pooled DNA were consistent with known breed groups, and could be used to account for population stratification. Correctly modeling the contemporary group structure was essential to achieve estimates similar to those from individual DNA data, and pooling DNA from individuals within groups was superior to pooling DNA across groups. For a fixed number of assays, pooled DNA samples produced results that were more correlated with results from individual genotyping data than were results from one random individual assayed from each pool. Conclusions: Use of logistic regression of allele frequency on phenotype makes it possible to estimate SNP effects on quantitative traits from pooled DNA samples. With pooled DNA samples, genotyping costs are reduced, and in cases where trait records are abundant this approach is promising to obtain SNP associations for marker-assisted selection. http://www.gsejournal.org/content/44/1/12 John Henshall Rachel Hawken Sonja Dominik William Barendse Genetics Selection Evolution 2012, null:12 2012-04-17T00:00:00Z doi:10.1186/1297-9686-44-12 /content/figures/1297-9686-44-12-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 12 2012-04-17T00:00:00Z XML Background: BackgroundThe risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures. Methods: We studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise. Results: Results showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source. Conclusions: It was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation. http://www.gsejournal.org/content/44/1/11 Kahsay Nirea Anna Sonesson John Woolliams Theo Meuwissen Genetics Selection Evolution 2012, null:11 2012-04-11T00:00:00Z doi:10.1186/1297-9686-44-11 /content/figures/1297-9686-44-11-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 11 2012-04-11T00:00:00Z PDF Background: Many methods for the genetic analysis of mastitis use a cross-sectional approach, which omits information on, e.g., repeated mastitis cases during lactation, somatic cell count fluctuations, and recovery process. Acknowledging the dynamic behavior of mastitis during lactation and taking into account that there is more than one binary response variable to consider, can enhance the genetic evaluation of mastitis. Methods: Genetic evaluation of mastitis was carried out by modeling the dynamic nature of somatic cell count (SCC) within the lactation. The SCC patterns were captured by modeling transition probabilities between assumed states of mastitis and non-mastitis. A widely dispersed SCC pattern generates high transition probabilities between states and vice versa. This method can model transitions to and from states of infection simultaneously, i.e. both the mastitis liability and the recovery process are considered. A multilevel discrete time survival model was applied to estimate breeding values on simulated data with different dataset sizes, mastitis frequencies, and genetic correlations. Results: Correlations between estimated and simulated breeding values showed that the estimated accuracies for mastitis liability were similar to those from previously tested methods that used data of confirmed mastitis cases, while our results were based on SCC as an indicator of mastitis. In addition, unlike the other methods, our method also generates breeding values for the recovery process. Conclusions: The developed method provides an effective tool for the genetic evaluation of mastitis when considering the whole disease course and will contribute to improving the genetic evaluation of udder health. http://www.gsejournal.org/content/44/1/10 Jessica Franzen Daniel Thorburn Jorge Urioste Erling Strandberg Genetics Selection Evolution 2012, null:10 2012-04-04T00:00:00Z doi:10.1186/1297-9686-44-10 /content/figures/1297-9686-44-10-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 10 2012-04-04T00:00:00Z PDF Background: Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods: An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results: Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions: The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations. http://www.gsejournal.org/content/44/1/9 John Hickey Brian Kinghorn Bruce Tier Julius van der Werf Matthew Cleveland Genetics Selection Evolution 2012, null:9 2012-03-30T00:00:00Z doi:10.1186/1297-9686-44-9 /content/figures/1297-9686-44-9-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 9 2012-03-30T00:00:00Z PDF Background: A single-step blending approach allows genomic prediction using information of genotyped and non-genotyped animals simultaneously. However, the combined relationship matrix in a single-step method may need to be adjusted because marker-based and pedigree-based relationship matrices may not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare single-step blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16 traits in the Nordic Holstein population. Methods: The data consisted of de-regressed proofs (DRP) for 5 214 genotyped and 9 374 non-genotyped bulls. The bulls were divided into a training and a validation population by birth date, October 1, 2001. Five approaches for genomic prediction were used: 1) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step blending method. In the adjusted GBLUP and single-step methods, the genomic relationship matrix was adjusted for the difference of scale between the genomic and the pedigree relationship matrices. A set of weights on the pedigree relationship matrix (ranging from 0.05 to 0.40) was used to build the combined relationship matrix in the single-step blending method and the GBLUP method with a polygenetic effect. Results: Averaged over the 16 traits, reliabilities of genomic breeding values predicted using the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3% higher than reliabilities from the simple GBLUP method (without a polygenic effect). The adjusted single-step blending and original single-step blending methods (relative weight of 0.20) had average reliabilities that were 2.1% and 1.8% higher than the simple GBLUP method, respectively. In addition, the GBLUP method with a polygenic effect led to less bias of genomic predictions than the simple GBLUP method, and both single-step blending methods yielded less bias of predictions than all GBLUP methods. Conclusions: The single-step blending method is an appealing approach for practical genomic prediction in dairy cattle. Genomic prediction from the single-step blending method can be improved by adjusting the scale of the genomic relationship matrix. http://www.gsejournal.org/content/44/1/8 Hongding Gao Ole Christensen Per Madsen Ulrik Nielsen Yuan Zhang Mogens Lund Guosheng Su Genetics Selection Evolution 2012, null:8 2012-03-28T00:00:00Z doi:10.1186/1297-9686-44-8 /content/figures/1297-9686-44-8-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 8 2012-03-28T00:00:00Z PDF Background: A newly recognized type of genetic variation, Copy Number Variation (CNV), is detected in mammalian genomes, e.g. the cattle genome. This form of variation can potentially cause phenotypic variation. Our objective was to determine whether dense SNP (single nucleotide polymorphisms) panels can capture the genetic variation due to a simple bi-allelic CNV, with the prospect of including the effect of such structural variations into genomic predictions. Methods: A deletion type CNV on bovine chromosome 6 was predicted from its neighboring SNP with a multiple regression model. Our dataset consisted of CNV genotypes of 1,682 cows, along with 100 surrounding SNP genotypes. A prediction model was fitted considering 10 to 100 surrounding SNP and the accuracy obtained directly from the model was confirmed by cross-validation.Results and conclusionsThe accuracy of prediction increased with an increasing number of SNP in the model and the predicted accuracies were similar to those obtained by cross-validation. A substantial increase in accuracy was observed when the number of SNP increased from 10 to 50 but thereafter the increase was smaller, reaching the highest accuracy (0.94) with 100 surrounding SNP. Thus, we conclude that the genotype of a deletion type CNV and its putative QTL effect can be predicted with a maximum accuracy of 0.94 from surrounding SNP. This high prediction accuracy suggests that genetic variation due to simple deletion CNV is well captured by dense SNP panels. Since genomic selection relies on the availability of a dense marker panel with markers in close linkage disequilibrium to the QTL in order to predict their genetic values, we also discuss opportunities for genomic selection to predict the effects of CNV by dense SNP panels, when CNV cause variation in quantitative traits. http://www.gsejournal.org/content/44/1/7 Naveen Kadri Patrick Koks Theo Meuwissen Genetics Selection Evolution 2012, null:7 2012-03-23T00:00:00Z doi:10.1186/1297-9686-44-7 /content/figures/1297-9686-44-7-toc.gif Genetics Selection Evolution 1297-9686 ${item.volume} 7 2012-03-23T00:00:00Z PDF