A simple greedy algorithm for reconstructing pedigrees

Cowell, Robert G.

doi:10.1016/j.tpb.2012.11.002

Cited by 16 publications

(15 citation statements)

References 21 publications

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“…Likelihood-based approaches to pedigree reconstruction tend to be heuristic and typically deliver a high likelihood, but not necessarily optimal, solution [3,14,45,53]. Approaches that find an overall guaranteed maximum likelihood pedigree are computationally intensive and require either an exhaustive or complete search.…”

Section: Introductionmentioning

confidence: 99%

On the use of dense SNP marker data for the identification of distant relative pairs

Sun

Jobling

Taliun

et al. 2016

Theoretical Population Biology

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There has been recent interest in the exploitation of readily available dense genome scan marker data for the identification of relatives. However, there are conflicting findings on how informative these data are in practical situations and, in particular, sets of thinned markers are often used with no concrete justification for the chosen spacing. We explore the potential usefulness of dense single nucleotide polymorphism (SNP) arrays for this application with a focus on inferring distant relative pairs. We distinguish between relationship estimation, as defined by a pedigree connecting the two individuals of interest, and estimation of general relatedness as would be provided by a kinship coefficient or a coefficient of relatedness. Since our primary interest is in the former case, we adopt a pedigree likelihood approach. We consider the effect of additional SNPs and data on an additional typed relative, together with choice of that relative, on relationship inference. We also consider the effect of linkage disequilibrium. When overall relatedness, rather than the specific relationship, would suffice, * Theoretical Population Biology. In Press. http://dx.doi.org/10.1016/j.tpb.2015.10 † Corresponding author. E-mail address: Nuala.Sheehan@leicester.ac.uk 1 we propose an approximate approach that is easy to implement and appears to compete well with a popular moment-based estimator and a recent maximum likelihood approach based on chromosomal sharing. We conclude that denser marker data are more informative for distant relatives. However, linkage disequilibrium cannot be ignored and will be the main limiting factor for applications to real data.

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Section: Introductionmentioning

confidence: 99%

On the use of dense SNP marker data for the identification of distant relative pairs

Sun

Jobling

Taliun

et al. 2016

Theoretical Population Biology

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“…It would seem in this case that there is an advantage in being able to obtain a truly optimal solution rather than a high likelihood one. Unlike previous work for smaller pedigrees (Cussens et al, 2013;Cowell, 2013), the true pedigree is not found quickly with 15 microsatellite markers and does not typically feature in the top k pedigrees. This is because in this case the true pedigree will generally not have maximum likelihood.…”

Section: Discussionmentioning

confidence: 61%

“…Unlike other heuristic likelihood-based approaches (Almudevar, 2003;Riester et al, 2009;Cowell, 2013), the method returns a solution that is guaranteed to have maximal likelihood. Using simulated data, it outperforms other competing approaches for the relatively straightforward situation that is typically considered whereby all individuals have complete data at unlinked markers, founder genotypes are in Hardy-Weinberg equilibrium and transmission of genes from parents to offspring is Mendelian.…”

Section: Introductionmentioning

confidence: 99%

Improved maximum likelihood reconstruction of complex multi-generational pedigrees

Sheehan¹,

Bartlett²,

Cussens³

2014

Theoretical Population Biology

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The reconstruction of pedigrees from genetic marker data is relevant to a wide range of applications. Likelihood-based approaches aim to find the pedigree structure that gives the highest probability to the observed data. Existing methods either entail an exhaustive search, and are hence restricted to small numbers of individuals, or they take a more heuristic approach and deliver a solution that will probably have high likelihood but is not guaranteed to be optimal. By encoding the pedigree learning problem as an integer linear program we can exploit efficient optimisation algorithms to construct pedigrees guaranteed to have maximal likelihood for the standard situation where we have complete marker data at unlinked loci and segregation of genes from parents to offspring is Mendelian. Previous work demonstrated efficient reconstruction of pedigrees of up to about 100 individuals. The modified method that we present here is not so restricted: we demonstrate its applicability with simulated data on a real human pedigree structure of over 1600 individuals. It also compares well with a very competitive approximate approach in terms of solving time and accuracy. In addition to identifying a maximum likelihood pedigree, we can obtain any number of pedigrees in decreasing order of likelihood. This is useful for assessing the uncertainty of a maximum likelihood solution and permits model averaging over high likelihood pedigrees when this would be appropriate. More importantly, when the solution is not unique, as will often be the case for large pedigrees, it enables investigation into the properties of maximum likelihood pedigree estimates * Corresponding author: Department of Health Sciences, University of Leicester, 2nd Floor Adrian Building, University Road, Leicester LE1 7RH, UK Email address: nas11@le.ac.uk (Nuala A Sheehan)Preprint submitted to Theoretical Population Biology July 21, 2014 which has not been possible up to now. Crucially, we also have a means of assessing the behaviour of other approximate approaches which all aim to find a maximum likelihood solution. Our approach hence allows us to properly address the question of whether a reasonably high likelihood solution that is easy to obtain is practically as useful as a guaranteed maximum likelihood solution. The efficiency of our method on such large problems bodes well for extensions beyond the standard setting where some pedigree members may be latent, genotypes may be measured with error and markers may be linked.

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“…To address this problem, many methods have been developed to estimate pedigrees from genetic data. Existing methods fall broadly into two categories: those that estimate pairwise relationships only (Thompson 1975;McPeek and Sun 2000;Smith et al 2001;Sun et al 2001;Milligan 2003;Sun and Dimitromanolakis 2014) and those that aim to reconstruct the entire pedigree (Thomas and Hill 2000;Almudevar 2003;Wang 2004;Hadfield et al 2006;Gasbarra et al 2007;Cowell 2009;Riester et al 2009;Wang and Santure 2009;Kirkpatrick et al 2011;Almudevar and Anderson 2012;Wang 2012;Cowell 2013;He et al 2013;Cussens et al 2013;Staples et al 2014;Anderson and Ng 2016;Staples et al 2016;Ko and Nielsen 2017;Ramstetter et al 2018). Although pairwise methods are computationally fast, estimated pairwise relationships do not necessarily translate to the correct pedigree, as piecing together pairwise relationships may not produce a valid pedigree.…”

Section: Introductionmentioning

confidence: 99%

Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

Nielsen

2018

Preprint

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Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size (N e ), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and N e from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of a composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and N e with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

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A simple greedy algorithm for reconstructing pedigrees

Cited by 16 publications

References 21 publications

On the use of dense SNP marker data for the identification of distant relative pairs

On the use of dense SNP marker data for the identification of distant relative pairs

Improved maximum likelihood reconstruction of complex multi-generational pedigrees

Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

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