Abstract

QTL (quantitative trait loci) mapping is commonly used to identify genetic regions responsible to important phenotype variation. A common strategy of QTL mapping is to use recombinant inbred lines (RILs), which are usually established by several generations of inbreeding of an F1 population (usually up to F6 or F7 populations). As this inbreeding process involves a large amount of labor, we are particularly interested in the effect of the number of inbreeding generations on the power of QTL mapping; a part of the labor could be saved if a smaller number of inbreeding provides sufficient power. By using simulations, we investigated the performance of QTL mapping with recombinant inbred lines (RILs). As expected, we found that the power of F4 population could be almost comparable to that of F6 and F7 populations. A potential problem in using F4 population is that a large proportion of RILs are heterozygotes. We here introduced a new method to partly relax this problem. The performance of this method was verified by simulations with a wide range of parameters including the size of the segregation population, recombination rate, genome size and the density of markers. We found our method works better than the commonly used standard method especially when there are a number of heterozygous markers. Our results imply that in most cases, QTL mapping does not necessarily require RILs at F6 or F7 generations; rather, F4 (or even F3) populations would be almost as useful as F6 or F7 populations. Because the cost to establish a number of RILs for many generations is enormous, this finding will cause a reduction in the cost of QTL mapping, thereby accelerating gene mapping in many species.

Highlights

  • Mapping quantitative trait loci (QTL) plays crucial roles in a number of research fields in biology

  • Simulation results We designed simulations to quantitatively evaluated the effect of the number of generations on the performance of QTL mapping

  • We assume a simple model, in which the simulated genome consists of L~12 chromosomes with equal length G~30 Mb, so that the genome size (360 Mb) is similar to that of rice, a species to which QTL mapping is frequently applied

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Summary

Introduction

Mapping quantitative trait loci (QTL) plays crucial roles in a number of research fields in biology. With the advent of molecular biology techniques such as sequencing, DNA microarray and primer extension assay [8,9,10], it became feasible to distribute a large number of markers across the genome and genotype those markers for a large sample of individuals. This revolutionary change in molecular biology further facilitated QTL mapping in many species

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