A sampling algorithm for segregation analysis

Tier, Bruce; Henshall, John

doi:10.1186/1297-9686-33-6-587

Cited by 5 publications

(4 citation statements)

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“…For example, the FPM part of the TR* models explained a significant fraction of the genetic variance in the S50 and S50s simulation scenarios, although no major genes were simulated, a result that was also reported by Tier & Henshall (2001). In these cases, one should base inferences on the full posterior density of the genetic variance (or heritability) of the FPM instead of only the posterior mean.…”

Section: (Iii) Mixed Inheritance Modelsmentioning

confidence: 88%

“…In the computation of probabilities of gene descent in pedigrees, these meiosis indicators have also been called inheritance vectors (Lander & Green, 1987), descent graphs (Sobel & Lange, 1996) and segregation indicators (Thompson, 1994). Implementation of descent graphs to sample genotypes jointly for all individuals has been explored by Tier & Henshall (2001) and Du & Hoeschele (2000) ; Du and Hoeschele reported that the joint sampling procedure did not improve the parameter estimation compared with simpler implementation of descent graphs.…”

Section: Introductionmentioning

confidence: 99%

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On flexible finite polygenic models for multiple-trait evaluation

Bink

2002

Genet. Res.

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SummaryFinite polygenic models (FPM) might be an alternative to the infinitesimal model (TIM) for the genetic evaluation of pedigreed multiple-generation populations for multiple quantitative traits. I present a general flexible Bayesian method that includes the number of genes in the FPM as an additional random variable. Markov-chain Monte Carlo techniques such as Gibbs sampling and the reversible jump sampler are used for implementation. Sampling of genotypes of all genes in the FPM is done via the use of segregation indicators. A broad range of FPM models, some combined with TIM, are empirically tested for the estimation of variance components and the number of genes in the FPM. Four simulation scenarios were studied, including genetic models with 5 or 50 additive independent diallelic genes affecting the traits, and random selection or selection on one of the traits was performed. The results in this study were based on ten replicates per simulation scenario. In the case of random selection, uniform priors on additive gene effects led to posterior mean estimates of genetic variance that were positively correlated with the number of genes fitted in the FPM. In the case of trait selection, assuming normal priors on gene effects also led to genetic variance estimates for the selected trait that were negatively correlated with the number of genes in the FPM. This negative correlation was not observed for the unselected trait. Treating the number of genes in the FPM as random revealed a positive correlation between prior and posterior mean estimates of this number, but the prior hardly affected the posterior estimates of genetic variance. Posterior inferences about the number of genes should be considered to be indicative where trait selection seems to improve the power of distinguishing between TIM and FPM. Based on the results of this study, I suggest not replacing TIM by the FPM, but combining TIM and FPM with the number of genes treated as random, to facilitate a highly flexible and thereby robust method for variance component estimation in pedigreed populations. Further study is required to explore the full potential of these models under different genetic model assumptions.

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Section: (Iii) Mixed Inheritance Modelsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

On flexible finite polygenic models for multiple-trait evaluation

Bink

2002

Genet. Res.

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“…Computationally, a descent graph can be stored and manipulated most efficiently using bit representation, with one 32-bit integer word encoding the meiosis indicators of 16 individuals, as suggested by Tier and Henshall (2001). For single (Tier and Henshall, 2001) or multiple unlinked loci (Du and Hoeschele, 2000a) with no genotype information available, genotype sampling can be performed by proposing small changes to the founder alleles and/or the descent graph in each cycle, and accepting or rejecting these in an MH step. Changes to the founder alleles are proposed by resampling a few alleles according to allele frequencies, and changes to the descent graph are proposed by swapping a few inheritance states.…”

Section: )mentioning

confidence: 99%

Mapping Quantitative Trait Loci in Outbred Pedigrees

Höschele¹

2007

Handbook of Statistical Genetics

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In this chapter, we present statistical methods for mapping quantitative-trait loci (QTLs) in outbred or complex pedigrees. Such pedigrees exist primarily in livestock populations, also in human populations, and occasionally in experimental animal or plant populations. The main focus of this chapter is on linkage mapping, but methods for linkage disequilibrium (LD) and combined linkage/LD mapping are also discussed. We describe least-squares and maximum likelihood (ML) methods for estimating QTL effects, and variance-components analysis by approximate (residual) ML for estimating QTL variance contributions. We describe Bayesian QTL mapping, its prior distributions and other distributional assumptions, its implementation via Markov chain Monte Carlo (MCMC) algorithms, its inferences, and contrast it with frequentist methodology. Genotype sampling algorithms using genotypic peeling, allelic peeling or descent graphs are described. Genotype samplers are a critical component of MCMC algorithms implementing ML and Bayesian analyses for complex pedigrees. Lastly, fine-mapping methods including chromosome dissection and LD mapping using current and historical recombinations, respectively, are outlined, and recently developed methods for joint LD and linkage mapping of disease genes and QTL are discussed. INTRODUCTIONIn this chapter, the focus is on statistical methods for mapping of quantitative-trait loci (QTLs) in populations which have not been formed recently by line crossing, and which have pedigree information available over multiple generations. Methods suitable for QTL mapping in (inbred) line crosses are described in Chapter 18. Pedigrees are used for QTL mapping in livestock and human populations, where the development and crossing of inbred lines is not feasible. Therefore, the methods discussed here have applications primarily in mapping genes for quantitative traits of economic importance in livestock and complex disease risk factors in humans. Examples include milk production in dairy 623 Handbook of Statistical Genetics, Third Edition . E dited by D . J. Balding, M . Bishop and C. Cannings.

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“…(1993) describe a non‐iterative, recursive method to compute the likelihood for a pedigree without loops. ‘The Gene Detective’ ( Tier and Henshall 2001) is a software based on MCMC (Markov Chain Monte Carlo) methods that sample the whole pedigree jointly. Maximum likelihood methods combined with iterative peeling, in which information from progressively more distant relatives is used with each further iterate, is the basis for the method of segregation analysis used in this study ( Kerr and Kinghorn 1996).…”

Section: Introductionmentioning

confidence: 99%

Cyclic genotyping strategies. II: True and perceived utilities under incorrect allele frequency assumptions

Macrossan

Kinghorn

2003

J Animal Breeding Genetics

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Summary This paper investigates the potential problems associated with assuming incorrect population frequencies for segregation analysis when animals are genotyped one by one in a cyclic fashion with segregation analysis carried out at each cycle. The base population allele frequencies of 0.1 and 0.5 studied, with the incorrect frequencies assumed for segregation analysis of 0.5 and 0.1 respectively, were investigated on the basis of their covering the range of possible frequencies and the most erroneous assumption of frequencies. An index modelled using linear regression (LR) was employed to choose the next animal to be genotyped in each cycle, based on segregation analysis at the incorrect frequency. The resultant utility was evaluated both at the correct frequency giving actual utility and at the incorrect frequency, the perceived utility. The results are compared with those in which all segregation analysis was carried out at the correct base population allele frequency. The assumption of incorrect population frequency for segregation analysis leads to a decline in the predictive performances of both indices in terms of the true utility of prediction. When utility is also computed at the incorrect frequency to give perceived utility, the assumption of incorrect population frequency of q = 0.5 for segregation analysis of populations simulated at q = 0.1 leads to a perceived decline in the predictive performance of the LR index. However, the assumption of incorrect population frequency of q = 0.1 for segregation analysis of populations simulated at q = 0.5 leads to a perceived increased utility. This latter paradox is explained in terms of genotype probabilities with reference to the method of construction of the genotype probability index.

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A sampling algorithm for segregation analysis

Cited by 5 publications

References 20 publications

On flexible finite polygenic models for multiple-trait evaluation

On flexible finite polygenic models for multiple-trait evaluation

Mapping Quantitative Trait Loci in Outbred Pedigrees

Cyclic genotyping strategies. II: True and perceived utilities under incorrect allele frequency assumptions

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