Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population

Bink, M.C.A.M.; Anderson, Amy D.; Weg, Eric van de; Thompson, E. A.

doi:10.1007/s00122-008-0824-1

Cited by 33 publications

(45 citation statements)

References 35 publications

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“…The significance of this problem in human association studies is currently a subject of considerable debate (Marchini et al, 2004;Devlin et al, 2004;Hinds et al, 2004;Helgason et al, 2005;Clayton et al, 2005;Voight and Pritchard, 2005;Setakis et al, 2006;Zhao et al, 2007). If no pedigree/ancestry information is available, there are different approaches to estimate the unobserved structure of population or of the pedigree using neutral molecular markers (Pritchard et al, 2000;Blouin, 2003;Excoffier and Heckel, 2006;Weir et al, 2006;Gasbarra et al, 2007;Bink et al, 2008). In many cases, however, exact information specifying the interrelations between individuals may be available.…”

Section: Introductionmentioning

confidence: 99%

Correcting for relatedness in Bayesian models for genomic data association analysis

Pikkuhookana

Sillanpää

2009

Heredity

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For small pedigrees, the issue of correcting for known or estimated relatedness structure in population-based Bayesian multilocus association analysis is considered. Two such relatedness corrections: [1] a random term arising from the infinite polygenic model and [2] a fixed covariate following the class D model of Bonney, are compared with the case of no correction using both simulated and real marker and geneexpression data from lymphoblastoid cell lines from four CEPH families. This comparison is performed with clinical quantitative trait locus (cQTL) models-multilocus association models where marker data and expression levels of gene transcripts as well as possible genotype Â expression interaction terms are jointly used to explain quantitative trait variation. We found out that regardless of having a correction term in the model, the cQTL-models fit a few extra smalleffect components (similar to finite polygenic models) which itself serves as a relatedness correction. For small data and small heritability one may use the covariate model, which clearly outperforms the infinite polygenic model in small data examples.

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Section: Introductionmentioning

confidence: 99%

Correcting for relatedness in Bayesian models for genomic data association analysis

Pikkuhookana

Sillanpää

2009

Heredity

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“…Bink et al (2008) In this article, we focus on the case where neutral genotypic data are available for a set of subpopulations, and the problem is to infer the matrix of coancestry coefficients among these local populations. We model the demographic histories of the subpopulations by an admixture of evolutionary independent lineages, thus extending the F model in a way that relaxes the structural assumption noted above.…”

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confidence: 99%

“…N the fields of animal and plant breeding, coancestry coefficients are often used as measures of relatedness between individuals (Bink et al 2008). For example, in a noninbred population the coancestry between full-sibs or between a parent and an offspring is 1 4 , and the coancestry between half-sibs is 1 8 (Lynch and Walsh 1998).…”

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confidence: 99%

“…Bink et al (2008) survey seven such methods, concluding that the surveyed estimators have poor statistical properties, except in the special case that the allele frequencies are known for a hypothetical reference population. Furthermore, as Fernandez and Toro (2006) point out, many of these estimators have undesired mathematical properties; e.g., they may yield logically incompatible estimates for different pairs of individuals.…”

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confidence: 99%

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Estimating Population-Level Coancestry Coefficients by an Admixture F Model

Karhunen

Ovaskainen

2012

Genetics

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In this article, we develop an admixture F model (AFM) for the estimation of population-level coancestry coefficients from neutral molecular markers. In contrast to the previously published F model, the AFM enables disentangling small population size and lack of migration as causes of genetic differentiation behind a given level of F ST . We develop a Bayesian estimation scheme for fitting the AFM to multiallelic data acquired from a number of local populations. We demonstrate the performance of the AFM, using simulated data sets and real data on ninespine sticklebacks (Pungitius pungitius) and common shrews (Sorex araneus). The results show that the parameterization of the AFM conveys more information about the evolutionary history than a simple summary parameter such as F ST . The methods are implemented in the R package RAFM. IN the fields of animal and plant breeding, coancestry coefficients are often used as measures of relatedness between individuals (Bink et al. 2008). For example, in a noninbred population the coancestry between full-sibs or between a parent and an offspring is 1 4 , and the coancestry between half-sibs is 1 8 (Lynch and Walsh 1998). Coancestry is the same as probability of identity by descent (IBD) at the limit of a low mutation rate and given a noninbred ancestral population. Two genes are said to be identical by descent if and only if they have not mutated since the most recent common ancestor.Individual-level coancestry coefficients (or probabilities of IBD) are useful in gene mapping, because they tell how much the genomes of two individuals are expected to resemble each other; i.e., they summarize the expected level of genetic similarity. In analogy, population-level coancestry coefficients can be used as measures of relatedness between local populations, and they can be combined with phenotypic data to detect signals of selection in quantitative traits, as opposed to those caused by random drift (Merilä and Crnokrak 2001;Mckay and Latta 2002;Ovaskainen et al. 2011).Coancestry coefficients can be calculated directly, if pedigree information is available, but their estimation for natural populations is often challenging. One approach for doing so is to use the link between coancestry coefficients and coalescence times (Rousset 2004). Coalescence time distributions can be solved, at least numerically, for a population that is in a stationary state, assuming that the demographic parameters are known (Bahlo and Griffiths 2001). However, in the context of evolutionary ecology of natural populations, this is rarely the case, as there is often limited direct information on demographic history, and it can be unrealistic to assume any kind of stationarity. Instead, a common approach is to infer the demographic history using neutral molecular markers genotyped from the present generation. One statistical framework for estimating coancestry coefficients in this way is given by the F model (Falush et al. 2003;Gaggiotti and Foll 2010). However, this approach suffers from the structural...

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“…Evaluation of relatedness estimators within real and simulated data in both plants and animals (e.g., see Van de Casteele et al 2001;Milligan 2003;Oliehoek et al 2006;Rodríguez-Ramilo et al 2007;Bink et al 2008) has generally focused on bias and sampling error of estimated genetic variances or relatedness values. Relatively little attention has been paid to their efficiency for estimation of breeding values.…”

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confidence: 99%

Estimating Breeding Values With Molecular Relatedness and Reconstructed Pedigrees in Natural Mating Populations of Common Sole, Solea Solea

Blonk

Komen

Kamstra³

et al. 2010

Genetics

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Captive populations where natural mating in groups is used to obtain offspring typically yield unbalanced population structures with highly skewed parental contributions and unknown pedigrees. Consequently, for genetic parameter estimation, relationships need to be reconstructed or estimated using DNA marker data. With missing parents and natural mating groups, commonly used pedigree reconstruction methods are not accurate and lead to loss of data. Relatedness estimators, however, infer relationships between all animals sampled. In this study, we compared a pedigree relatedness method and a relatedness estimator (''molecular relatedness'') method using accuracy of estimated breeding values. A commercial data set of common sole, Solea solea, with 51 parents and 1953 offspring (''full data set'') was used. Due to missing parents, for 1338 offspring, a pedigree could be reconstructed with 10 microsatellite markers (''reduced data set''). Cross-validation of both methods using the reduced data set showed an accuracy of estimated breeding values of 0.54 with pedigree reconstruction and 0.55 with molecular relatedness. Accuracy of estimated breeding values increased to 0.60 when applying molecular relatedness to the full data set. Our results indicate that pedigree reconstruction and molecular relatedness predict breeding values equally well in a population with skewed contributions to families. This is probably due to the presence of few large full-sib families. However, unlike methods with pedigree reconstruction, molecular relatedness methods ensure availability of all genotyped selection candidates, which results in higher accuracy of breeding value estimation.

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Comparison of marker-based pairwise relatedness estimators on a pedigreed plant population

Cited by 33 publications

References 35 publications

Correcting for relatedness in Bayesian models for genomic data association analysis

Correcting for relatedness in Bayesian models for genomic data association analysis

Estimating Population-Level Coancestry Coefficients by an Admixture F Model

Estimating Breeding Values With Molecular Relatedness and Reconstructed Pedigrees in Natural Mating Populations of Common Sole, Solea Solea

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