IMR Press / FBE / Volume 4 / Issue 8 / DOI: 10.2741/E582

Frontiers in Bioscience-Elite (FBE) is published by IMR Press from Volume 13 Issue 2 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Review

A score-statistic approach for determining threshold values in QTL mapping

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1 Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, R.O.C.

*Author to whom correspondence should be addressed.

Academic Editor: Rongling Wu

Front. Biosci. (Elite Ed) 2012, 4(8), 2670–2682; https://doi.org/10.2741/E582
Published: 1 June 2012
(This article belongs to the Special Issue Dynamic genetics and genomics)
Abstract

Issues in determining the threshold values of QTL mapping are often investigated for the backcross and F2 populations with relatively simple genome structures so far. The investigations of these issues in the progeny populations after F2 (advanced populations) with relatively more complicated genomes are generally inadequate. As these advanced populations have been well implemented in QTL mapping, it is important to address these issues for them in more details. Due to an increasing number of meiosis cycle, the genomes of the advanced populations can be very different from the backcross and F2 genomes. Therefore, special devices that consider the specific genome structures present in the advanced populations are required to resolve these issues. By considering the differences in genome structure between populations, we formulate more general score test statistics and Gaussian processes to evaluate their threshold values. In general, we found that, given a significance level and a genome size, threshold values for QTL detection are higher in the denser marker maps and in the more advanced populations. Simulations were performed to validate our approach.

Keywords
Advanced populations
Gaussian process
QTL mapping
Score statistics
Threshold values
Interval mapping
Genotypic distribution
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