Models with indirect genetic effects depending on group sizes: A simulation study assessing the precision of the estimates of the dilution parameter
Heidaritabar, Marzieh ; Bijma, Piter ; Janss, Luc ; Bortoluzzi, Chiara ; Nielsen, Hanne M. ; Madsen, Per ; Ask, Birgitte ; Christensen, Ole F. - \ 2019
Genetics, Selection, Evolution 51 (2019)1. - ISSN 0999-193X
Background: In settings with social interactions, the phenotype of an individual is affected by the direct genetic effect (DGE) of the individual itself and by indirect genetic effects (IGE) of its group mates. In the presence of IGE, heritable variance and response to selection depend on size of the interaction group (group size), which can be modelled via a 'dilution' parameter (d) that measures the magnitude of IGE as a function of group size. However, little is known about the estimability of d and the precision of its estimate. Our aim was to investigate how precisely d can be estimated and what determines this precision. Methods: We simulated data with different group sizes and estimated d using a mixed model that included IGE and d. Schemes included various average group sizes (4, 6, and 8), variation in group size (coefficient of variation (CV) ranging from 0.125 to 1.010), and three values of d (0, 0.5, and 1). A design in which individuals were randomly allocated to groups was used for all schemes and a design with two families per group was used for some schemes. Parameters were estimated using restricted maximum likelihood (REML). Bias and precision of estimates were used to assess their statistical quality. Results: The dilution parameter of IGE can be estimated for simulated data with variation in group size. For all schemes, the length of confidence intervals ranged from 0.114 to 0.927 for d, from 0.149 to 0.198 for variance of DGE, from 0.011 to 0.086 for variance of IGE, and from 0.310 to 0.557 for genetic correlation between DGE and IGE. To estimate d, schemes with groups composed of two families performed slightly better than schemes with randomly composed groups. Conclusions: Dilution of IGE was estimable, and in general its estimation was more precise when CV of group size was larger. All estimated parameters were unbiased. Estimation of dilution of IGE allows the contribution of direct and indirect variance components to heritable variance to be quantified in relation to group size and, thus, it could improve prediction of the expected response to selection in environments with group sizes that differ from the average size.
Models with indirect genetic effects depeding on group sizes - A simulation study assessing the precision of the estimates of the dilution
Heidaritabar, M. ; Bijma, P. ; Janss, Luc G. ; Bortoluzzi, C. ; Nielsen, H.M. ; Ask, B. ; Christensen, Ole Fredslund - \ 2018
In: Proceedings of the World Congress on Genetics Applied to Livestock Production. - WCGALP
With social interactions, the phenotype of an individual is influenced by the direct genetic effect (DGEs) of the individual, as well as the indirect genetic effects (IGEs) of its group mates. With IGEs, the heritable variance and response to selection depend on the group size. The change of IGE with group size can be modelled via a 'dilution' parameter ( d ), which reflects the magnitude of IGE as a function of group size. Very little is known of the estimability of d and the precision of its estimate. The relevance of d estimation is due to its impact on the dynamics of response to selection and heritable variation. We simulated data with varying group sizes and estimated d using IGE models including d parameter. Schemes investigated differed with respect to average group size (4, 6 or 8) and variability of group size (coefficient of variation= CV , ranging from 0.125 to 1.010) obtained based on either 2 or 3 group sizes within a scheme. A design where individuals were randomly allocated to groups was used to estimate d . Results showed that it was possible to estimate d in data with varying group sizes. All estimates were unbiased. With larger CV of group sizes, d could be estimated more precisely. Estimation of the relationship between the magnitude of IGEs and group size would allow for proper interpretation of direct and indirect variance components that contributes to heritable variation in relation to group size.
Models with indirect genetic e!ects depending on group sizes - simulation study
Heidaritabar, M. ; Bijma, P. ; Janss, Luc G. ; Bortoluzzi, C. ; Nielsen, H.M. ; Ask, B. ; Christensen, Ole Fredslund - \ 2017
In: Book of Abstracts of the 69th Annual Meeting of the European Federation of Animal Science. - Wageningen Academic Publishers (Book of abstracts 23) - ISBN 9789086863129 - p. 207 - 207.
Modeling heterogeneous (co)variances from adjacent-SNP groups improves genomic prediction for milk protein composition traits
Gebreyesus, Grum ; Lund, Mogens S. ; Buitenhuis, Bart ; Bovenhuis, Henk ; Poulsen, Nina A. ; Janss, Luc G. - \ 2017
Genetics, Selection, Evolution 49 (2017)1. - ISSN 0999-193X
Background: Accurate genomic prediction requires a large reference population, which is problematic for traits that are expensive to measure. Traits related to milk protein composition are not routinely recorded due to costly procedures and are considered to be controlled by a few quantitative trait loci of large effect. The amount of variation explained may vary between regions leading to heterogeneous (co)variance patterns across the genome. Genomic prediction models that can efficiently take such heterogeneity of (co)variances into account can result in improved prediction reliability. In this study, we developed and implemented novel univariate and bivariate Bayesian prediction models, based on estimates of heterogeneous (co)variances for genome segments (BayesAS). Available data consisted of milk protein composition traits measured on cows and de-regressed proofs of total protein yield derived for bulls. Single-nucleotide polymorphisms (SNPs), from 50K SNP arrays, were grouped into non-overlapping genome segments. A segment was defined as one SNP, or a group of 50, 100, or 200 adjacent SNPs, or one chromosome, or the whole genome. Traditional univariate and bivariate genomic best linear unbiased prediction (GBLUP) models were also run for comparison. Reliabilities were calculated through a resampling strategy and using deterministic formula. Results: BayesAS models improved prediction reliability for most of the traits compared to GBLUP models and this gain depended on segment size and genetic architecture of the traits. The gain in prediction reliability was especially marked for the protein composition traits β-CN, κ-CN and β-LG, for which prediction reliabilities were improved by 49 percentage points on average using the MT-BayesAS model with a 100-SNP segment size compared to the bivariate GBLUP. Prediction reliabilities were highest with the BayesAS model that uses a 100-SNP segment size. The bivariate versions of our BayesAS models resulted in extra gains of up to 6% in prediction reliability compared to the univariate versions. Conclusions: Substantial improvement in prediction reliability was possible for most of the traits related to milk protein composition using our novel BayesAS models. Grouping adjacent SNPs into segments provided enhanced information to estimate parameters and allowing the segments to have different (co)variances helped disentangle heterogeneous (co)variances across the genome.
Modeling heterogeneous co-variances for genomic regions in prediction for milk protein compositions
Gebreyesus, G. ; Lund, M.S. ; Janss, L. ; Bovenhuis, H. ; Buitenhuis, A.J. - \ 2016
In: Book of Abstracts of the 67st Annual Meeting of the European Federation of Animal Science. - Wageningen Academic Publishers (Book of abstracts 22) - ISBN 9789086862849 - p. 290 - 290.
Short communication: Multi-trait estimation of genetic parameters for milk protein composition in the Danish Holstein
Gebreyesus, G. ; Lund, M.S. ; Janss, L. ; Poulsen, N.A. ; Larsen, L.B. ; Bovenhuis, H. ; Buitenhuis, A.J. - \ 2016
Journal of Dairy Science 99 (2016)4. - ISSN 0022-0302 - p. 2863 - 2866.
Genetic parameter - Genomic relationship - Milk protein - Multi-trait model
Genetic parameters were estimated for the major milk proteins using bivariate and multi-trait models based on genomic relationships between animals. The analyses included, apart from total protein percentage, αS1-casein (CN), αS2-CN, β-CN, κ-CN, α-lactalbumin, and β-lactoglobulin, as well as the posttranslational sub-forms of glycosylated κ-CN and αS1-CN-8P (phosphorylated). Standard errors of the estimates were used to compare the models. In total, 650 Danish Holstein cows across 4 parities and days in milk ranging from 9 to 481 d were selected from 21 herds. The multi-trait model generally resulted in lower standard errors of heritability estimates, suggesting that genetic parameters can be estimated with high accuracy using multi-trait analyses with genomic relationships for scarcely recorded traits. The heritability estimates from the multi-trait model ranged from low (0.05 for β-CN) to high (0.78 for κ-CN). Genetic correlations between the milk proteins and the total milk protein percentage were generally low, suggesting the possibility to alter protein composition through selective breeding with little effect on total milk protein percentage.
Genomic prediction of growth in pigs based on a model including additive and dominance effects
Lopes, M.S. ; Bastiaansen, J.W.M. ; Janss, L. ; Knol, E.F. ; Bovenhuis, H. - \ 2016
Journal of Animal Breeding and Genetics (2016). - ISSN 0931-2668 - p. 180 - 186.
Phenotype prediction - SNP - Variance component
Independent of whether prediction is based on pedigree or genomic information, the focus of animal breeders has been on additive genetic effects or 'breeding values'. However, when predicting phenotypes rather than breeding values of an animal, models that account for both additive and dominance effects might be more accurate. Our aim with this study was to compare the accuracy of predicting phenotypes using a model that accounts for only additive effects (MA) and a model that accounts for both additive and dominance effects simultaneously (MAD). Lifetime daily gain (DG) was evaluated in three pig populations (1424 Pietrain, 2023 Landrace, and 2157 Large White). Animals were genotyped using the Illumina SNP60K Beadchip and assigned to either a training data set to estimate the genetic parameters and SNP effects, or to a validation data set to assess the prediction accuracy. Models MA and MAD applied random regression on SNP genotypes and were implemented in the program Bayz. The additive heritability of DG across the three populations and the two models was very similar at approximately 0.26. The proportion of phenotypic variance explained by dominance effects ranged from 0.04 (Large White) to 0.11 (Pietrain), indicating that importance of dominance might be breed-specific. Prediction accuracies were higher when predicting phenotypes using total genetic values (sum of breeding values and dominance deviations) from the MAD model compared to using breeding values from both MA and MAD models. The highest increase in accuracy (from 0.195 to 0.222) was observed in the Pietrain, and the lowest in Large White (from 0.354 to 0.359). Predicting phenotypes using total genetic values instead of breeding values in purebred data improved prediction accuracy and reduced the bias of genomic predictions. Additional benefit of the method is expected when applied to predict crossbred phenotypes, where dominance levels are expected to be higher.
Estimation of indirect genetic effects in group-housed mink (Neovison vison) should account for systematic interactions either due to kin or sex
Alemu, S.W. ; Berg, P. ; Janss, L. ; Bijma, P. - \ 2016
Journal of Animal Breeding and Genetics 133 (2016)1. - ISSN 0931-2668 - p. 43 - 50.
Social interactions among individuals are abundant, both in wild and in domestic populations. With social interactions, the genes of an individual may affect the trait values of other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. Most IGE models assume that individuals interact equally to all group mates irrespective of relatedness. Kin selection theory, however, predicts that an individual will interact differently with family members versus non-family members. Here, we investigate kin- and sex-specific non-genetic social interactions in group-housed mink. Furthermore, we investigated whether systematic non-genetic interactions between kin or individuals of the same sex influence the estimates of genetic parameters. As a second objective, we clarify the relationship between estimates of the traditional IGE model and a family-based IGE model proposed in a previous study. Our results indicate that male siblings in mink show different non-genetic interactions than female siblings in mink and that this may impact the estimation of genetic parameters. Moreover, we have shown how estimates from a family-based IGE model can be translated to the ordinary direct–indirect model and vice versa. We find no evidence for genetic differences in interactions among related versus unrelated mink.
Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population
Sell-Kubiak, E. ; Duijvesteijn, N. ; Lopes, M.S. ; Janss, L.L.G. ; Knol, E.F. ; Bijma, P. ; Mulder, H.A. - \ 2015
BMC Genomics 16 (2015)1. - ISSN 1471-2164
Double Hierarchical GLM - GWAS - Pigs - Residual variance - Total number born
Background: In many traits, not only individual trait levels are under genetic control, but also the variation around that level. In other words, genotypes do not only differ in mean, but also in (residual) variation around the genotypic mean. New statistical methods facilitate gaining knowledge on the genetic architecture of complex traits such as phenotypic variability. Here we study litter size (total number born) and its variation in a Large White pig population using a Double Hierarchical Generalized Linear model, and perform a genome-wide association study using a Bayesian method. Results: In total, 10 significant single nucleotide polymorphisms (SNPs) were detected for total number born (TNB) and 9 SNPs for variability of TNB (varTNB). Those SNPs explained 0.83 % of genetic variance in TNB and 1.44 % in varTNB. The most significant SNP for TNB was detected on Sus scrofa chromosome (SSC) 11. A possible candidate gene for TNB is ENOX1, which is involved in cell growth and survival. On SSC7, two possible candidate genes for varTNB are located. The first gene is coding a swine heat shock protein 90 (HSPCB = Hsp90), which is a well-studied gene stabilizing morphological traits in Drosophila and Arabidopsis. The second gene is VEGFA, which is activated in angiogenesis and vasculogenesis in the fetus. Furthermore, the genetic correlation between additive genetic effects on TNB and on its variation was 0.49. This indicates that the current selection to increase TNB will also increase the varTNB. Conclusions: To the best of our knowledge, this is the first study reporting SNPs associated with variation of a trait in pigs. Detected genomic regions associated with varTNB can be used in genomic selection to decrease varTNB, which is highly desirable to avoid very small or very large litters in pigs. However, the percentage of variance explained by those regions was small. The SNPs detected in this study can be used as indication for regions in the Sus scrofa genome involved in maintaining low variability of litter size, but further studies are needed to identify the causative loci.
Estimation of Additive, Dominance and Imprinting Genetic Variance Using Genomic Data
Soares Lopes, M. ; Bastiaansen, J.W.M. ; Janss, L. ; Knol, E.F. ; Bovenhuis, H. - \ 2015
G3 : Genes Genomes Genetics 5 (2015)12. - ISSN 2160-1836 - p. 2629 - 2637.
Traditionally, exploration of genetic variance in humans, plants, and livestock species has mostly been limited to the use of additive effects estimated using pedigree data. However, with the development of dense panels of SNPs (Single Nucleotide Polymorphisms), the exploration of genetic variation of complex traits is moving from quantifying the resemblance between family members to the dissection of genetic variation at individual loci. With SNPs we were able to quantify the contribution of additive, dominance, and imprinting variance to the total genetic variance using a SNP regression method. The method was validated in simulated data and applied to three traits (number of teats, backfat and life-time daily gain) in three purebred pig populations. In simulated data, the estimates of additive, dominance, and imprinting variance were very close to the simulated values. In real data, dominance effects account for a substantial proportion of the total genetic variance (up to 44%) for these traits in these populations. The contribution of imprinting to the total phenotypic variance of the evaluated traits was relatively small (1-3%). Our results indicate a strong relationship between additive variance explained per chromosome and chromosome length, which has been previously described for other traits in other species. We also show that a similar linear relationship exists for dominance and imprinting variance. These novel results improve our understanding of the genetic architecture of the evaluated traits and shows promise to apply the SNP regression method to other traits and species, including human diseases.
Indirect genetic effects for group-housed animals
Alemu, S.W. - \ 2015
Wageningen University. Promotor(en): Johan van Arendonk, co-promotor(en): L.G. Janss; Piter Bijma; P. Berg. - Wageningen : Wageningen University - ISBN 9788793176713 - 228
nerts - pluimvee - groepshuisvesting - genetische effecten - sociaal gedrag - agressief gedrag - interacties - heritability - veredelingsprogramma's - statistische analyse - genetische parameters - selectief fokken - mink - poultry - group housing - genetic effects - social behaviour - aggressive behaviour - interactions - heritability - breeding programmes - statistical analysis - genetic parameters - selective breeding
Alemu, SW(2015) Indirect Genetic effects for Group-housed Animals. Joint PhD thesis between Aarhus University, Denmark and Wageningen University, the Netherlands.
Social interactions among individuals are common both in plants and animals. With social interactions, the trait value of an individual may be influenced by the genes of its interacting partners, a phenomenon known as indirect genetic effects (IGE). An IGE is heritable effect of an individual on trait values of another individual. A large body of literature has shown that social interactions can create addition heritable variation in both plants and animals, for both behavioural and production traits.
When IGE are estimated it is usually assumed that an individual interacts equally with all its group mates, irrespective of genetic relatedness. This assumption may not be true in mixed groups of kin and non-kin, where an individual may interact systematically different with kin and non-kin. Current IGE models ignore such systematically different interactions between kin and non-kin. Thus, the main aim of this thesis was to develop and apply statistical methods to estimate IGE when interactions differ between kin and non-kin.
Social interactions are important in mink that are kept in groups for the production of fur. Group housing of mink increases aggression behaviours, which is reflected by an increase in the number of bite marks on the pelts, and reduces the welfare of the animals. We estimated the genetic parameter for bite mark traits in group-housed mink, to investigate the prospects for genetic improvement of bite mark traits. We found that there are good prospects to produce mink that have a low level of biting. Finally, we further concluded that genetic parameter estimation for bite mark score should take into account systematic interactions due to sex or kin.
In this thesis we also investigated genomic selection for socially affected traits, considering survival time in two lines of brown egg layers showing cannibalistic behaviour. Despite the limited reference population of ~234 progeny tested sires, the accuracy of estimated breeding values (EBV) was ~35% higher for genomic selection compared with the parent average-EBV. We found that the response to genomic selection per year for line B1 was substantially higher than for the traditional breeding scheme, whereas for line BD response was slightly higher than for the traditional breeding scheme. In conclusion, genetic selection with IGE combined with marker information can substantially reduce detrimental social behaviours such as cannibalism in layers and biting in group-housed mink.
Using SNP Markers to Estimate Additive, Dominance and Imprinting Genetic Variance
Lopes, M.S. ; Bastiaansen, J.W.M. ; Janss, L.L.G. ; Bovenhuis, H. ; Knol, E.F. - \ 2014
The contributions of additive, dominance and imprinting effects to the variance of number of teats (NT) were evaluated in two purebred pig populations using SNP markers. Three different random regression models were evaluated, accounting for the mean and: 1) additive effects (MA), 2) additive and dominance effects (MAD) and 3) additive, dominance and imprinting effects (MADI). Additive heritability estimates were 0.30, 0.28 and 0.27-0.28 in both lines using MA, MAD and MADI, respectively. Dominance heritability ranged from 0.06 to 0.08 using MAD and MADI. Imprinting heritability ranged from 0.01 to 0.02. Dominance effects make an important contribution to the genetic variation of NT in the two lines evaluated. Imprinting effects appeared less important for NT than additive and dominance effects. The SNP random regression model presented and evaluated in this study is a feasible approach to estimate additive, dominance and imprinting variance.
|Genetic and non-genetic indirect effects for bite mark traits in group housed mink.
Alemu, S.W. ; Berg, P. ; Janss, L.L.G. ; Möller, S. ; Bijma, P. - \ 2014
Indirect genetic effects and kin recognition: Estimating IGEs when interactions differ between kin and strangers
Alemu, S.W. ; Berg, P. ; Janss, L.L.G. ; Bijma, P. - \ 2014
Heredity 112 (2014). - ISSN 0018-067X - p. 197 - 206.
multilevel selection - sibling recognition - biological groups - forest tree - group-size - evolution - parameters - individuals - populations - competition
Social interactions among individuals are widespread, both in natural and domestic populations. As a result, trait values of individuals may be affected by genes in other individuals, a phenomenon known as indirect genetic effects (IGEs). IGEs can be estimated using linear mixed models. The traditional IGE model assumes that an individual interacts equally with all its partners, whether kin or strangers. There is abundant evidence, however, that individuals behave differently towards kin as compared with strangers, which agrees with predictions from kin-selection theory. With a mix of kin and strangers, therefore, IGEs estimated from a traditional model may be incorrect, and selection based on those estimates will be suboptimal. Here we investigate whether genetic parameters for IGEs are statistically identifiable in group-structured populations when IGEs differ between kin and strangers, and develop models to estimate such parameters. First, we extend the definition of total breeding value and total heritable variance to cases where IGEs depend on relatedness. Next, we show that the full set of genetic parameters is not identifiable when IGEs differ between kin and strangers. Subsequently, we present a reduced model that yields estimates of the total heritable effects on kin, on non-kin and on all social partners of an individual, as well as the total heritable variance for response to selection. Finally we discuss the consequences of analysing data in which IGEs depend on relatedness using a traditional IGE model, and investigate group structures that may allow estimation of the full set of genetic parameters when IGEs depend on kin.
Indirect genetic effects contribute substantially to heritable variation in aggression-related traits in group-housed mink (Neovison vison)
Alemu, S.W. ; Bijma, P. ; Moller, S. ; Janss, L. ; Berg, P. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X - 11 p.
group selection - variance-components - biological groups - mustela-vison - multilevel selection - social interactions - competition - parameters - model - populations
Background Since the recommendations on group housing of mink (Neovison vison) were adopted by the Council of Europe in 1999, it has become common in mink production in Europe. Group housing is advantageous from a production perspective, but can lead to aggression between animals and thus raises a welfare issue. Bite marks on the animals are an indicator of this aggressive behaviour and thus selection against frequency of bite marks should reduce aggression and improve animal welfare. Bite marks on one individual reflect the aggression of its group members, which means that the number of bite marks carried by one individual depends on the behaviour of other individuals and that it may have a genetic basis. Thus, for a successful breeding strategy it could be crucial to consider both direct (DGE) and indirect (IGE) genetic effects on this trait. However, to date no study has investigated the genetic basis of bite marks in mink. Result and discussion A model that included DGE and IGE fitted the data significantly better than a model with DGE only, and IGE contributed a substantial proportion of the heritable variation available for response to selection. In the model with IGE, the total heritable variation expressed as the proportion of phenotypic variance (T2) was six times greater than classical heritability (h2). For instance, for total bite marks, T2 was equal to 0.61, while h2 was equal to 0.10. The genetic correlation between direct and indirect effects ranged from 0.55 for neck bite marks to 0.99 for tail bite marks. This positive correlation suggests that mink have a tendency to fight in a reciprocal way (giving and receiving bites) and thus, a genotype that confers a tendency to bite other individuals can also cause its bearer to receive more bites. Conclusion Both direct and indirect genetic effects contribute to variation in number of bite marks in group-housed mink. Thus, a genetic selection design that includes both direct genetic and indirect genetic effects could reduce the frequency of bite marks and probably aggression behaviour in group-housed mink.
Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context
Bouwman, A.C. ; Valente, B.D. ; Janss, L.L.G. ; Bovenhuis, H. ; Rosa, G.J. - \ 2014
Genetics, Selection, Evolution 46 (2014). - ISSN 0999-193X
somatic-cell score - yield - traits - cattle - gene - associations - parameters - cows
BACKGROUND: Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select a network supported by data as the structure of a SEM. RESULTS: The IC algorithm adapted to mixed models settings was applied to study 14 correlated bovine milk fatty acids, resulting in an undirected network. The undirected pathway from C4:0 to C12:0 resembled the de novo synthesis pathway of short and medium chain saturated fatty acids. By using prior knowledge, directions were assigned to that part of the network and the resulting structure was used to fit a SEM that led to structural coefficients ranging from 0.85 to 1.05. The deviance information criterion indicated that the SEM was more plausible than the multi-trait model. CONCLUSIONS: The IC algorithm output pointed towards causal relations between the studied traits. This changed the focus from marginal associations between traits to direct relationships, thus towards relationships that may result in changes when external interventions are applied. The causal structure can give more insight into underlying mechanisms and the SEM can predict conditional changes due to such interventions.
Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait
Schurink, A. ; Janss, L.L.G. ; Heuven, H.C.M. - \ 2012
BMC Proceedings 6 (2012)suppl. 2. - ISSN 1753-6561 - 4 p.
Background Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. Results Estimated heritability of the simulated quantitative trait was 0.30 (SD = 0.02). Mean posterior probability of SNP modelled having a large effect ( pˆi) was 0.0066 (95%HPDR: 0.0014-0.0132). Mean posterior probability of variance of second distribution was 0.409 (95%HPDR: 0.286-0.589). The genome-wide association analysis resulted in 14 significant and 43 putative SNP, comprising 7 significant QTL on chromosome 1, 2 and 3 and putative QTL on all chromosomes. Assigning single or multiple QTL to significant SNP was not obvious, especially for SNP in the same region that were more or less in LD. Correlation between the simulated and estimated breeding values of 1,000 offspring without phenotypes was 0.91. Conclusions Bayesian Variable Selection using thousands of SNP was successfully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Simulated QTL with Mendelian inheritance were accurately identified, while imprinted and epistatic QTL were only putatively detected. The correlation between simulated and estimated breeding values of offspring without phenotypes was high.
Bayesian Variable Selection to identify QTL affecting a simulated quantitative trait
Schurink, A. ; Janss, L.L.G. ; Heuven, H.C.M. - \ 2011
- p. 20 - 20.
Bayesian - QTL - quantitative trait
Background. Recent developments in genetic technology and methodology enable accurate detection of QTL and estimation of breeding values, even in individuals without phenotypes. The QTL-MAS workshop offers the opportunity to test different methods to perform a genome-wide association study on
simulated data with a QTL structure that is unknown beforehand. The simulated data contained 3,220 individuals: 20 sires and 200 dams with 3,000 offspring. All individuals were genotyped, though only 2,000 offspring were phenotyped for a quantitative trait. QTL affecting the simulated quantitative trait were identified and breeding values of individuals without phenotypes were
estimated using Bayesian Variable Selection, a multi-locus SNP model in association studies. The probability of a SNP being modelled in the second distribution (pi) was estimated.
Results. Estimated heritability of the simulated quantitative trait was 0.31. Mean posterior probability of SNP modelled in the second distribution, i.e. SNP with large effect, was 0.0075 (95% highest posterior density region: 0.0014-0.0139). The genome-wide association analysis resulted in 18 significant SNP, comprising 6 QTL on chromosome 1, 2 and 3. In total 23 putative SNP, comprising 8 putative QTL were detected.
Conclusions. Bayesian Variable Selection using thousands of SNP was success-fully applied to genome-wide association analysis of a simulated dataset with unknown QTL structure. Strong and putative QTL were detected and breeding values were estimated for individuals with and without phenotypes.
A Bayesian approach to detect QTL affecting a simulated binary and quatitative trait
Bouwman, A.C. ; Janss, L.L.G. ; Heuven, H.C.M. - \ 2011
BMC Proceedings 5 (2011)Suppl. 3. - ISSN 1753-6561 - 6 p.
Background - We analyzed simulated data from the 14th QTL-MAS workshop using a Bayesian approach implemented in the program iBay. The data contained individuals genotypes for 10,031 SNPs and phenotyped for a quantitative and a binary trait. Results - For the quantitative trait we mapped 8 out of 30 additive QTL, 1 out of 3 imprinted QTL and both epistatic pairs of QTL successfully. For the binary trait we mapped 11 out of 22 additive QTL successfully. Four out of 22 pleiotropic QTL were detected as such. Conclusions - The Bayesian variable selection method showed to be a successful method for genome-wide association. This method was reasonably fast using dense marker maps
Bayesian multi-QTL mapping for growth curve parameters
Heuven, H.C.M. ; Janss, L.L.G. - \ 2010
BMC Proceedings 31 (2010)4 (S1). - ISSN 1753-6561 - p. 12 - 12.
Background Identification of QTL affecting a phenotype which is measured multiple times on the same experimental unit is not a trivial task because the repeated measures are not independent and in most cases show a trend in time. A complicating factor is that in most cases the mean increases non-linear with time as well as the variance. A two- step approach was used to analyze a simulated data set containing 1000 individuals with 5 measurements each. First the measurements were summarized in latent variables and subsequently a genome wide analysis was performed of these latent variables to identify segregating QTL using a Bayesian algorithm. Results For each individual a logistic growth curve was fitted and three latent variables: asymptote (ASYM), inflection point (XMID) and scaling factor (SCAL) were estimated per individual. Applying an 'animal' model showed heritabilities of approximately 48% for ASYM and SCAL while the heritability for XMID was approximately 24%. The genome wide scan revealed four QTLs affecting ASYM, one QTL affecting XMID and four QTLs affecting SCAL. The size of the QTL differed. QTL with a larger effect could be more precisely located compared to QTL with small effect. The locations of the QTLs for separate parameters were very close in some cases and probably caused the genetic correlation observed between ASYM and XMID and SCAL respectively. None of the QTL appeared on chromosome five. Conclusions Repeated observations on individuals were affected by at least nine QTLs. For most QTL a precise location could be determined. The QTL for the inflection point (XMID) was difficult to pinpoint and might actually exist of two closely linked QTL on chromosome one.