A study on the mapping of quantitative trait loci in advanced populations derived from two inbred lines

Abstract

In genetic and biological studies, the F2 population is one of the most popular and commonly used experimental populations mainly because it can be readily produced and its genome structure possesses several niceties that allow for productive investigation. These niceties include the equivalence between the proportion of recombinants and recombination rates, the capability of providing a complete set of three genotypes for every locus and an analytically attractive first-order Markovian property. Recently, there has been growing interest in using the progeny populations from F2 (advanced populations) because their genomes can be managed to meet specific purposes or can be used to enhance investigative studies. These advanced populations include recombinant inbred populations, advanced intercrossed populations, intermated recombinant inbred populations and immortalized F2 populations. Due to an increased number of meiosis cycles, the genomes of these advanced populations no longer possess the Markovian property and are relatively more complicated and different from the F2 genomes. Although issues related to quantitative trait locus (QTL) mapping using advanced populations have been well documented, still these advanced populations are often investigated in a manner similar to the way F2 populations are studied using a first-order Markovian assumption. Therefore, more efforts are needed to address the complexities of these advanced populations in more details. In this article, we attempt to tackle these issues by first modifying current methods developed under this Markovian assumption to propose an ad hoc method (the Markovian method) and explore its possible problems. We then consider the specific genome structures present in the advanced populations without invoking this assumption to propose a more adequate method (the non-Markovian method) for QTL mapping. Further, some QTL mapping properties related to the confounding problems that result from ignoring epistasis and to mapping closely linked QTL are derived and investigated across the different populations. Simulations show that the non-Markovian method outperforms the Markovian method, especially in the advanced populations subject to selfing. The results presented here may give some clues to the use of advanced populations for more powerful and precise QTL mapping.

Genetic architecture of voluntary exercise in an advanced intercross line of mice

“…The production of AILs provides an effective approach to map loci, but because of the complex breeding history the assumption of independence among individuals has been conclusively shown to be false, and several additional methods currently exist to account for family structure (33,48,68). Our multigenerational breeding protocol expanded the final generation by producing multiple litters from the same set of crosses.…”

An investigation of the power for separating closely linked QTL in experimental populations

“…Then, these advanced populations would provide smaller VIF and reduce correlations for the QTL parameters to facilitate QTL detection. Nevertheless, we should know that the sizes of marker intervals localizing QTLs may expand (relative to that in the backcross or F 2 population) in the more advanced AI populations (Lynch & Walsh, 1998;Kao & Zeng, 2009) so that the benefit may be offset. Greatly improving separation in the AI populations requires denser markers around the detected QTL (QTL located in narrow intervals), and the improvement would be limited if QTLs are in the sparse marker region (wide intervals).…”

“…Note that the derivation of p k1 and p k3 is not straightforward as has been done in BC and F 2 populations, and it involves using the genotypic distributions of three genes (Kao & Zeng, 2009). If the event of double recombinations is ignored within a marker interval, equation (3) can be explicitly formulated as…”

“…For the advanced populations beyond F 2 , trigenic and tetragenic genotypic distributions cannot be obtained from the direct product of pairwise gene distributions. We use special devises outlined in Kao & Zeng (2009) and in the Appendix to obtain the genotypic distributions of three and four genes. Although the covariance in equation (5) does not have a simple form as in equation (4) for variance, it can be easily written into a computer programme to obtain the covariances under different situations in different populations.…”

“…The genetic variance can be decomposed into components of genotypic frequencies and QTL effects. For two QTLs with additive effects only, V G =var(x ij )ra j 2 + var(x ik )ra k 2 +2rcov(x ij , x ik )ra j ra k , where x ij and x ik denote the coded variables of the two fully observed QTLs (see Kao & Zeng, 2009 for the components of V G with complete effects and contributed by more QTLs). As var(x ij )=var(x ik )= 2(C+D+E) and cov(x ij , x ik )=2(CxD) depend on the genotypic distribution of experimental populations, given specific QTL effects, V G is population dependent.…”