A Novel Markov Chain Monte Carlo Approach for Constructing Accurate Meiotic Maps

George, Andrew W.

doi:10.1534/genetics.105.042705

Cited by 6 publications

(5 citation statements)

References 44 publications

(42 reference statements)

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“…Consequently, several authors have attempted to reduce map inaccuracy by estimating linkage maps from large amounts of pedigree data [Broman et al, 1998;George, 2005;Kong et al, 2002Kong et al, , 2004Matise et al, 2003]. This approach requires either the costly ascertainment of large samples or the pooling of existing samples, which may be complicated by the lack of availability of the original genotype data or the difficulty in sharing such data.…”

Section: Introductionmentioning

confidence: 99%

Improving estimates of genetic maps: a meta‐analysis‐based approach

Stewart¹

2007

Genetic Epidemiology

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Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps.

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Section: Introductionmentioning

confidence: 99%

Improving estimates of genetic maps: a meta‐analysis‐based approach

Stewart¹

2007

Genetic Epidemiology

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“…Let u represent the matrix of true recombination fractions between each pair of markers. Element u jk gives the true recombination fraction between markers j and k. Instead of specifying a map via a matrix of recombination fractions, a common alternative is to specify the order of the markers and the recombination fractions between the adjacent markers (George 2005). We let d = (d 1 , .…”

Section: Preliminariesmentioning

confidence: 99%

Characterizing Uncertainty in High-Density Maps from Multiparental Populations

et al. 2014

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Multiparental populations are of considerable interest in high-density genetic mapping due to their increased levels of polymorphism and recombination relative to biparental populations. However, errors in map construction can have significant impact on QTL discovery in later stages of analysis, and few methods have been developed to quantify the uncertainty attached to the reported order of markers or intermarker distances. Current methods are computationally intensive or limited to assessing uncertainty only for order or distance, but not both simultaneously. We derive the asymptotic joint distribution of maximum composite likelihood estimators for intermarker distances. This approach allows us to construct hypothesis tests and confidence intervals for simultaneously assessing marker-order instability and distance uncertainty. We investigate the effects of marker density, population size, and founder distribution patterns on map confidence in multiparental populations through simulations. Using these data, we provide guidelines on sample sizes necessary to map markers at sub-centimorgan densities with high certainty. We apply these approaches to data from a bread wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population genotyped using the Illumina 9K SNP chip to assess regions of uncertainty and validate them against the recently released pseudomolecule for the wheat chromosome 3B.

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“…A further study of the reliability of maps based on the multipoint likelihood, e.g. in a Bayesian framework (George 2005), could be useful. The results of using the algorithm described in this paper could be a starting point for such a study.…”

Section: (B)mentioning

confidence: 99%

Ordering dominant markers in F2 populations

Jansen

2008

Euphytica

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Ordering dominant markers in F 2 populations is considered a difficult problem. The difficulties arise from the fact that recombinations cannot be observed directly from the data. In general, the multi-point maximum likelihood would be the appropriate criterion for ordering markers. This criterion takes into account all available information present in marker data. However, calculation of multi-point maximum likelihoods is very timedemanding, especially if the number of markers is large. In this paper, ordering markers by minimising the number of recombinations between adjacent markers is used as a simple alternative to multi-point maximum likelihood. Contrary to multi-point maximum likelihood, this method does not involve any probability assumptions about the occurrence of recombination events. Simulated data indicate that the minimum number of recombinations between adjacent markers is approximately a linear function of the map length obtained by multi-point maximum likelihood. As a consequence, it will lead to more or less the same optimum marker orders. Optimisation of marker orders with regard to the number of recombinations between adjacent markers is carried out by a modified form of simulated annealing. The reliability of the resulting marker orders is studied by generating marker orders that are plausible with the data using a Metropolis algorithm.

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A Novel Markov Chain Monte Carlo Approach for Constructing Accurate Meiotic Maps

Cited by 6 publications

References 44 publications

Improving estimates of genetic maps: a meta‐analysis‐based approach

Improving estimates of genetic maps: a meta‐analysis‐based approach

Characterizing Uncertainty in High-Density Maps from Multiparental Populations

Ordering dominant markers in F2 populations

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