343 research outputs found
Genetic evaluation and selection in multibreed populations
No full textCrossbreeding is used widely in animal production, thus theory and methods for genetic evaluation and selection are required for multibreed populations. This study developed theory for modelling genotypic means and covariances which are required to obtain genetic evaluation by best linear unbiased prediction (BLUP) for multibreed populations. Theory and methods for genetic evaluation and selection by BLUP using the multibreed covariance theory were presented, and the effects of using this covariance theory for genetic evaluation and selection were studied. With additive inheritance, the covariance between crossbred relatives can be computed using the formula for a purebred population, provided that the variance of crossbred individuals are computed correctly. The additive variance for a crossbred individual is a function of additive variances for the pure breeds, the covariance between parents, and segregation variances. The segregation variance is the genetic variance derived from the differences in allelic frequencies between pure breeds. An efficient algorithm to compute the inverse of the additive covariance matrix was also given. With dominance inheritance, the covariance between relatives in a multibreed population is a linear function of identity coefficients, coefficients of breed origin, and 25 dispersion parameters. A recursive procedure was given to compute the necessary identity coefficients. Genetic evaluations were obtained by BLUP via Henderson's mixed model equations. Constructing these equations requires the inverse of the multibreed covariance matrix. However, an efficient method to invert this covariance matrix has not yet been developed. Thus, alternative mixed model equations were presented for obtaining genetic evaluations efficiently in two-breed and three-breed terminal crossbreeding systems. Numerical examples were used to illustrate the multibreed evaluation procedures. Multibreed covariance theory under dominance inheritance was validated by comparing a covariance matrix estimated from simulated data with the theoretical covariance matrix. Selection index theory and computer simulation were used to study the advantage of using multibreed covariance theory for genetic evaluation and selection. A significant advantage was observed when differences in allelic frequencies between the pure breeds were large and the degree of dominance was greater than or equal to one. Results from this study suggested that use of multibreed covariance theory for genetic evaluation and selection may be most useful for low heritability traits such as fertility traits.Made available in DSpace on 2011-05-07T13:35:40Z (GMT). No. of bitstreams: 2
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Previous issue date: 1994Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:56:43Z
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Methods for checking the goodness of fit of alternative nonlinear mixed models with an application in fertility traits of beef cows
No full textTwo different methods for comparing alternative mixed models describing the continuous variable underlying all-none response traits were proposed. The first may be viewed as an extension of the analysis of deviance using posterior density functions associated with alternative models, rather than likelihood functions. The major problem with the statistic generated here (referred hereafter as STAT) resides in calculating the integration constants exactly. To avoid this problem a simpler statistic (referred hereafter as STAT1), based on the joint density of the data and the unknowns, was also proposed. The second method relies on the Bayesian concept of posterior odds ratios. Several alternatives for the specification of prior distributions and the hyperparameters were suggested, and an asymptotic normal approximation of the joint posterior density was presented. Finally, guidelines for the prediction of future observations were also proposed following Bayesian procedures. Three different traits: conception to A.I sires (CAI), fertility I (FI), and conception to pasture sires (CP1) were analyzed as an illustration. These traits represented different aspects of cow fertility measured as a successful conception, i.e., calving a viable calf, to an A.I. sire or to a service sire. Using STAT and STAT1 as criteria, the simplest model explaining CAI may include as fixed factors: days postpartum, hormonal treatment, and the interaction pasture program x age of cow. With respect to FI, influential fixed factors were: type of service sire, days postpartum, age of cow, hormonal treatment, breed of service sire, and the interaction breed of service sire x type of service sire. Fixed factors affecting CP1 were: type of pasture sire, pasture program, the two-way interaction breed of pasture sire x type of pasture sire, and the three-way interaction pasture program x breed of pasture sire x breed of cow. In general, inferences with respect to the models containing interactions were not possible when STAT was used as a criterion because negative values were obtained for this statistic. However, when feasible, it seemed easier to reject null hypotheses when STAT was used.Made available in DSpace on 2011-05-07T13:36:45Z (GMT). No. of bitstreams: 2
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Previous issue date: 1991Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding ([email protected]) on 2011-05-07T14:56:57Z
Item is restricted indefinitely.Restriction data tranferred 2014-07-01T11:26:40-05:00
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Group with Access UIUC Users [automated]
Release Date: none
Reason: ETDs are only available to UIUC Users without author permissionETDs are only available to UIUC Users without author permissionU of I Onl
Genetic evaluation and selection in multibreed populations
No full textCrossbreeding is used widely in animal production, thus theory and methods for genetic evaluation and selection are required for multibreed populations. This study developed theory for modelling genotypic means and covariances which are required to obtain genetic evaluation by best linear unbiased prediction (BLUP) for multibreed populations. Theory and methods for genetic evaluation and selection by BLUP using the multibreed covariance theory were presented, and the effects of using this covariance theory for genetic evaluation and selection were studied. With additive inheritance, the covariance between crossbred relatives can be computed using the formula for a purebred population, provided that the variance of crossbred individuals are computed correctly. The additive variance for a crossbred individual is a function of additive variances for the pure breeds, the covariance between parents, and segregation variances. The segregation variance is the genetic variance derived from the differences in allelic frequencies between pure breeds. An efficient algorithm to compute the inverse of the additive covariance matrix was also given. With dominance inheritance, the covariance between relatives in a multibreed population is a linear function of identity coefficients, coefficients of breed origin, and 25 dispersion parameters. A recursive procedure was given to compute the necessary identity coefficients. Genetic evaluations were obtained by BLUP via Henderson's mixed model equations. Constructing these equations requires the inverse of the multibreed covariance matrix. However, an efficient method to invert this covariance matrix has not yet been developed. Thus, alternative mixed model equations were presented for obtaining genetic evaluations efficiently in two-breed and three-breed terminal crossbreeding systems. Numerical examples were used to illustrate the multibreed evaluation procedures. Multibreed covariance theory under dominance inheritance was validated by comparing a covariance matrix estimated from simulated data with the theoretical covariance matrix. Selection index theory and computer simulation were used to study the advantage of using multibreed covariance theory for genetic evaluation and selection. A significant advantage was observed when differences in allelic frequencies between the pure breeds were large and the degree of dominance was greater than or equal to one. Results from this study suggested that use of multibreed covariance theory for genetic evaluation and selection may be most useful for low heritability traits such as fertility traits.U of I OnlyETDs are only available to UIUC Users without author permissio
Supplemental Material for Gianola and Fernando, 2020
No full textSupplementary Figures (tiff and pdf formats) and legends for GENETICS/2019/30248
Genetic Evaluation and Parameter Estimation Using Marker and Trait Information
No full textGenetic evaluation by BLUP using marker and trait information requires knowledge of genetic parameters, such as the recombination rate ( r) between a marker locus and a marked QTL. Maximum likelihood methods are widely used to estimate genetic parameters. This thesis presents a new approximation to the likelihood for a pedigree with loops, based on cutting all loops and extending the pedigree at the cuts. An optimum strategy to cut loops and an iterative extension technique are presented. The likelihood for a pedigree with loops is then approximated by the conditional likelihood for the entire cut-extended pedigree given the extended part. The approximation is efficient for large pedigrees with complex loops in terms of computing speed and memory requirements.Made available in DSpace on 2015-09-25T21:08:38Z (GMT). No. of bitstreams: 2
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Previous issue date: 1998Embargo set by: Seth Robbins for item 84931
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Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDsRestricted to the U of I community idenfinitely during batch ingest of legacy ETDsU of I Only102 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998
Estimation of genetic covariances between antibody response and bacterial burden
No full textFour unrelated males from a commercial broiler breeder male line were each mated to fifteen females of the same line. The offspring from four consecutive hatches received various treatments and were evaluated for resistance to Salmonella enteritidis. The standard errors for the genetic covariances indicated that the amount of data were not adequate to obtain reliable estimates of the genetic covariances. Thus, we have used likelihood theory to determine the amount of data required to obtain reliable estimates of variance and covariance components from a two-trait analysis. where both traits were not be measured on the same bird
Genetic Evaluation and Parameter Estimation Using Marker and Trait Information
No full text102 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1998.Genetic evaluation by BLUP using marker and trait information requires knowledge of genetic parameters, such as the recombination rate ( r) between a marker locus and a marked QTL. Maximum likelihood methods are widely used to estimate genetic parameters. This thesis presents a new approximation to the likelihood for a pedigree with loops, based on cutting all loops and extending the pedigree at the cuts. An optimum strategy to cut loops and an iterative extension technique are presented. The likelihood for a pedigree with loops is then approximated by the conditional likelihood for the entire cut-extended pedigree given the extended part. The approximation is efficient for large pedigrees with complex loops in terms of computing speed and memory requirements.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
A gene frequency model for QTL mapping using Bayesian inference
No full textAbstract Background Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR). Results To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM. Conclusions In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.</p
Contributions to improve the accuracy and computational efficiency of genomic prediction
No full textThe discovery of genome-wide high-density molecular markers (e.g., single-nucleotide
polymorphisms, SNPs) has revolutionized genetic analyses in human medicine, animal and
plant breeding. There are several active areas of research and development in whole-genome
analyses, including 1) collection or simulation of genomic data, 2) use of genomic data for
prediction or genome-wide association studies, and 3) validation of the performance of these analyses.
In this thesis, several statistical models and computational algorithms were proposed and investigated,
contributing to these three areas of research and development.
A contribution to the first area is a simulation strategy that drops down origins and positions of chromosomal segments rather than every allele state
to efficiently simulate sequence data and complex pedigree structures across multiple generations. A software tool called XSim, which incorporates the
efficient strategy, was developed with implementations in C++ and Julia. XSim allows the genome of founders to be characterized by real genome sequence
data and complex pedigree structures among descendants.
Several methods contributing to the use of genomic data for prediction and genome-wide association studies (GWAS) were proposed and investigated. Two
methods were proposed to improve the computational efficiency of Bayesian multiple-regression analyses. First, we showed how Gibbs samplers without the
use of the Metropolis-Hastings (MH) algorithm can be used for the BayesB method, where the prior for each marker
effect follows a mixture distribution with a point mass at zero with probability pi and a univariate-t distribution with probability
1-pi. We showed that by introducing a indicator variable in BayesB, indicating whether the marker effect for a locus is zero or non-zero, the marker effect
and locus-specific variance can be sampled using Gibbs. We considered three different versions
of the Gibbs sampler to sample each marker effect, locus-specific variance and
its indicator variable. Computational efficiencies defined as the number of effective samples per second of computing time
were compared with simulated data. Among the Gibbs samplers that were considered, the most efficient sampler is about 2.1 times as efficient as the MH
algorithm proposed by Meuwissen et al. and 1.7 times as efficient as that proposed
by Habier et al. Second, we proposed a strategy to parallelize
Gibbs sampling for each marker within each step of the
MCMC chain. This parallelization is accomplished by using an orthogonal data augmentation
strategy, where the marker covariate matrix is augmented by adding p new rows, where p is the number of markers,
such that its columns are orthogonal. The use of this strategy is expected to increase the speed of
Gibbs sampling with lower memory requirements. The parallel Gibbs sampling
approach using an augmented marker covariate matrix was shown for BayesC methods, where the prior for each marker
effect follows a mixture distribution with a point mass at zero and a univariate normal distribution. The full conditional distributions that are
needed for BayesC with orthogonal data augmentation (BayesC-ODA) were derived and the convergence
of BayesC-ODA was studied. In analyses of the simulated data, BayesC-ODA provided virtually
identical predictions of breeding values as BayesC when the chain length was about 20,000 to 80,000,
which is similar to the commonly used chain length of 50,000.
Two methods were proposed or investigated to improve prediction accuracy of Bayesian multiple-
regression analyses. First, we proposed a flexible variable selection model for multiple-trait analyses
with BayesCpi or BayesB priors. This model was compared to single-trait methods and a previously proposed
multi-trait model using real and simulated data. Flexible variable selection showed an advantage when data were from two simulated traits, where a locus had an effect only on one of the traits. Second, we
compared alternative approaches to single-trait genomic prediction using genotyped and non-genotyped Hanwoo
beef cattle. In those data analyses, the single-step methods, which take advantage of all pedigree,
phenotypic and genomic information simultaneously, gave similar or higher prediction accuracies compared to
methods using only genotyped or non-genotyped individuals. Alternative priors allowed single-step Bayesian regression methods (SSBR) to outperform
single-step genomic best linear unbiased prediction (SSGBLUP) in some cases.
One method contributing to the validation of the performance of whole-genome analyses was proposed. In leave-one-out cross validation (LOOCV), one individual is omitted for training with validation on the omitted individual.
Efficient LOOCV strategies were proposed for genomic best linear unbiased prediction (GBLUP) in scenarios when n>p or n
n is the number of observations and p is the number of markers. These strategies were compared
to naive application of LOOCV with simulated data. In these data analyses, efficient LOOCV, requiring little
more effort than a single analysis, was much faster than the naive LOOCV.</p
An algorithm to sample genotypes in complex pedigrees
No full textUntil recently, genetic analyses were based on polygenic models. In these analyses the effects of individual genes were not studied. With the advances that have been made in molecular genetics, it has become possible to study the effects of individual genes using segregation and linkage analyses, by either likelihood or Bayesian methods. These analyses require that several generations of individuals in the population have genetic information at the marker and trait loci. Depending on the cost and benefits of genotyping, it is common that only some individuals are genotyped. Thus, a large fraction of the population would usually have no genetic information available. When genetic data at the trait and marker loci are incomplete, genotypes must be sampled. Markov chain Monte Carlo (MCMC) methods, such as Scalar-Gibbs, have been used to sample these genotypes. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This thesis proposes a method to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling, and is called the ESIP sampler. The ESIP sampler is evaluated by computing genotype probabilities for a monogenic trait in a small hypothetical pedigree and in a large real pedigree. Further, results obtained by ESIP sampler are compared with other methods in the literature that sample genotypes at marker loci with more than two alleles. Of the methods that are guaranteed to be irreducible, ESIP was the most efficient. Finally, the ESIP sampler is used for mapping quantitative trait loci in a simulated pedigree using the Bayasian approach.</p
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