Abstract

BackgroundIdentity by descent (IBD) matrix estimation is a central component in mapping of Quantitative Trait Loci (QTL) using variance component models. A large number of algorithms have been developed for estimation of IBD between individuals in populations at discrete locations in the genome for use in genome scans to detect QTL affecting various traits of interest in experimental animal, human and agricultural pedigrees. Here, we propose a new approach to estimate IBD as continuous functions rather than as discrete values.ResultsEstimation of IBD functions improved the computational efficiency and memory usage in genome scanning for QTL. We have explored two approaches to obtain continuous marker-bracket IBD-functions. By re-implementing an existing and fast deterministic IBD-estimation method, we show that this approach results in IBD functions that produces the exact same IBD as the original algorithm, but with a greater than 2-fold improvement of the computational efficiency and a considerably lower memory requirement for storing the resulting genome-wide IBD. By developing a general IBD function approximation algorithm, we show that it is possible to estimate marker-bracket IBD functions from IBD matrices estimated at marker locations by any existing IBD estimation algorithm. The general algorithm provides approximations that lead to QTL variance component estimates that even in worst-case scenarios are very similar to the true values. The approach of storing IBD as polynomial IBD-function was also shown to reduce the amount of memory required in genome scans for QTL.ConclusionIn addition to direct improvements in computational and memory efficiency, estimation of IBD-functions is a fundamental step needed to develop and implement new efficient optimization algorithms for high precision localization of QTL. Here, we discuss and test two approaches for estimating IBD functions based on existing IBD estimation algorithms. Our approaches provide immediately useful techniques for use in single QTL analyses in the variance component QTL mapping framework. They will, however, be particularly useful in genome scans for multiple interacting QTL, where the improvements in both computational and memory efficiency are the key for successful development of efficient optimization algorithms to allow widespread use of this methodology.

Highlights

  • Identity by descent (IBD) matrix estimation is a central component in mapping of Quantitative Trait Loci (QTL) using variance component models

  • A new and general algorithm for estimating IBD functions We developed a general algorithm for approximating IBDfunctions in marker brackets (CF-IBD – Continuous Function IBD) from a limited set of IBD matrices from any existing IBD estimation algorithm and evaluated how well it approximates the IBD matrices between markers that were obtained using the software LOKI [3]

  • This particular software was chosen because it is a stochastic algorithm based on Monte Carlo Markov Chain (MCMC) iteration procedure, which means that the computation strategy of this algorithm is completely different from the one of Ricardo Pong-Wong's method

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Summary

Introduction

Identity by descent (IBD) matrix estimation is a central component in mapping of Quantitative Trait Loci (QTL) using variance component models. In QTL mapping, these methods are used to compute IBD matrices at pre-defined locations, a grid, in the genome where variance components for QTL will subsequently be estimated. Global optimization algorithms [6,7] have been shown to be a computationally efficient approach to search the genomic grid where the genetic relationships have been estimated in a least square based QTL mapping framework [8] In the least squares framework, the cost of computing and storing QTL genotype probabilities at high resolution is negligible compared to the statistical estimation of genetic effects. In variance component QTL mapping on the other hand, the high computational cost both in estimating variance components and computing and storing IBD matrices indicate that new and efficient algorithms for computing and storing IBD as well as for estimation of variance components are needed to facilitate more indepth exploratory analyses of experimental data using variance component models

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