Prediction of identity by descent probabilities from marker-haplotypes

Meuwissen, Theo H. E.; Goddard, Mike E.

doi:10.1186/1297-9686-33-6-605

Cited by 199 publications

(165 citation statements)

References 20 publications

Supporting

Mentioning

164

Contrasting

Order By: Relevance

“…This also explains why this estimator, with higher values, performed well in the scenario of equal allele frequencies, i.e., these frequencies deviated from both those in the founder population and those in the sampled population. In addition, when knowledge about recent population history and population substructures is available, other approaches may be considered (Gasbarra et al 2007;Meuwissen and Goddard 2001).…”

Section: Influence Of Inbreedingmentioning

confidence: 99%

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

et al. 2008

View full text Add to dashboard Cite

Several estimators have been proposed that use molecular marker data to infer the degree of relatedness for pairs of individuals. The objective of this study was to evaluate the performance of seven estimators when applied to marker data of a set of 33 key individuals from a large complex apple pedigree. The evaluation considered different scenarios of allele frequencies and different numbers of marker loci. The method of moments estimators were Similarity, Queller-Goodknight, Lynch-Ritland and Wang. The maximum likelihood estimators were Thompson, Anderson-Weir and Jacquard. The pedigree-based coancestry coefficients were taken as the point of reference in calculating correlations and root mean square error (RMSE). The marker data comprised 86 multi-allelic SSR markers on 17 linkage groups, covering 11 Morgans. Additionally, we simulated 10 datasets conditional on the real pedigree to support the results on the real dataset. None of the estimators outperformed the others. Knowledge of allele frequencies appeared to be the most influential, i.e., the highest correlations and lowest RMSE were found when frequencies from the founder population were available. When equal allele frequencies were used, all estimators resulted in very similar, but on average lower, correlations. The use of allele frequencies estimated from the set of 33 individuals gave, on average, the poorest results. The maximum likelihood estimators and the Lynch-Ritland estimator were the most sensitive to allele frequencies. The results from the simulation study fully supported the trends in results of the real dataset. This study indicated that high correlations (up to 0.90) and small RMSE (below 0.03), may be obtained when population allelic frequencies are available. In this scenario, the performances of the various estimators were similar, but seemed to favor the maximum likelihood estimators. In the absence of reliable allele frequencies the method of moments estimators were shown to be more robust. The number of marker loci influenced the average performance of the estimators; however, the ranking was not affected. Correlations up to 0.80 were obtained when two markers per chromosome and appropriate allele frequencies were available. Adding more markers to the current dataset may lead to marginal improvements.

show abstract

Section: Influence Of Inbreedingmentioning

confidence: 99%

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

et al. 2008

View full text Add to dashboard Cite

show abstract

“…Meuwissen and Goddard [9] described a multi QTL method combining linkage and linkage disequilibrium mapping (MP-LDLA) using a Bayesian framework for finding QTL. Basically, both these methods use a matrix of IBD probabilities (G i ), calculated as described by Meuwissen and Goddard [8], but use different test statistics for locating the QTL and make different assumptions about the QTL. LDLA tests whether there is a QTL at the putative QTL position or no QTL on the chromosome.…”

Section: Discussionmentioning

confidence: 99%

“…The second step calculates the G i matrix, which consists of IBD probabilities (at the putative QTL position) between the haplotypes. This matrix of IBD probabilities (G i ) is calculated from the marker haplotypes as described by Meuwissen and Goddard [8]. Identity by descent (IBD) probabilities between animals in the pedigree are calculated through linkage analysis (LA), while IBD probabilities between founder animals are estimated by linkage disequilibrium (LD) analysis.…”

Section: Modelsmentioning

confidence: 99%

Fine mapping of multiple QTL using combined linkage and linkage disequilibrium mapping – A comparison of single QTL and multi QTL methods

Uleberg

Meuwissen

2007

Genet. Sel. Evol.

View full text Add to dashboard Cite

-Two previously described QTL mapping methods, which combine linkage analysis (LA) and linkage disequilibrium analysis (LD), were compared for their ability to detect and map multiple QTL. The methods were tested on five different simulated data sets in which the exact QTL positions were known. Every simulated data set contained two QTL, but the distances between these QTL were varied from 15 to 150 cM. The results show that the single QTL mapping method (LDLA) gave good results as long as the distance between the QTL was large (> 90 cM). When the distance between the QTL was reduced, the single QTL method had problems positioning the two QTL and tended to position only one QTL, i.e. a "ghost" QTL, in between the two real QTL positions. The multi QTL mapping method (MP-LDLA) gave good results for all evaluated distances between the QTL. For the large distances between the QTL (> 90 cM) the single QTL method more often positioned the QTL in the correct marker bracket, but considering the broader likelihood peaks of the single point method it could be argued that the multi QTL method was more precise. Since the distances were reduced the multi QTL method was clearly more accurate than the single QTL method. The two methods combine well, and together provide a good tool to position single or multiple QTL in practical situations, where the number of QTL and their positions are unknown. fine mapping / multiple QTL / simulations

show abstract

“…Meuwissen and Goddard [13] inferred pair-wise IBD probabilities between haplotypes using the coalescent theory, which is an approximate representation of genealogies [7,8]. The coalescent recreates the phylogenetic tree among current population members proceeding backwards in time, until a single expected common ancestor is reached, and all lineages have merged.…”

Section: A Coalescent-based Methodsmentioning

confidence: 99%

“…Meuwissen and Goddard [13,14] proposed a new estimator of relationship based on multiple linked markers and population history information. This method capitalises on the expected built-up of linkage disequilibrium (LD) over generations, accounting for the correlations between loci due to both linkage and genetic drift.…”

Section: Introductionmentioning

confidence: 99%

Prediction of IBD based on population history for fine gene mapping

2006

View full text Add to dashboard Cite

-A novel multiple regression method (RM) is developed to predict identity-bydescent probabilities at a locus L (IBD L ), among individuals without pedigree, given information on surrounding markers and population history. These IBD L probabilities are a function of the increase in linkage disequilibrium (LD) generated by drift in a homogeneous population over generations. Three parameters are sufficient to describe population history: effective population size (Ne), number of generations since foundation (T ), and marker allele frequencies among founders (p). IBD L are used in a simulation study to map a quantitative trait locus (QTL) via variance component estimation. RM is compared to a coalescent method (CM) in terms of power and robustness of QTL detection. Differences between RM and CM are small but significant. For example, RM is more powerful than CM in dioecious populations, but not in monoecious populations. Moreover, RM is more robust than CM when marker phases are unknown or when there is complete LD among founders or Ne is wrong, and less robust when p is wrong. CM utilises all marker haplotype information, whereas RM utilises information contained in each individual marker and all possible marker pairs but not in higher order interactions. RM consists of a family of models encompassing four different population structures, and two ways of using marker information, which contrasts with the single model that must cater for all possible evolutionary scenarios in CM. QTL fine mapping/ identity-by-descent

show abstract

Prediction of identity by descent probabilities from marker-haplotypes

Cited by 199 publications

References 20 publications

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

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

Fine mapping of multiple QTL using combined linkage and linkage disequilibrium mapping – A comparison of single QTL and multi QTL methods

Prediction of IBD based on population history for fine gene mapping

Contact Info

Product

Resources

About