The full-sib intercross line (FSIL): a QTL mapping design for outcrossing species

Abstract

A full-sib intercross line (FSIL) is constructed in an outcrossing species by mating two parents and intercrossing their progeny to form a large intercross line. For given statistical power, a FSIL design requires only slightly more individuals than an F2 design derived from inbred line cross, but 6-to 10-fold fewer than a half-sib or full-sib design. Due to population-wide linkage disequilibrium, a FSIL is amenable to analysis by selective DNA pooling. In addition, a FSIL is maintained by continued intercrossing so that DNA samples and phenotypic information are accumulated across generations. Continued intercrossing also leads to map expansion and thus to increased mapping accuracy in the later generations. A FSIL can thus provide a bridge to positional cloning of quantitative trait loci (QTL) and marker-assisted selection in outcrossers ; and is particularly effective in exploiting the QTL mapping potential of crosses between selection lines or phenotypically differentiated populations that differ in frequency, but are not at fixation, for alternative QTL alleles. In the course of the power analyses, it is shown that for F2 and FSIL designs, power is a function of Nd# alone, where N is the total size of the mapping population and d is the standardized gene effect ; while for half-sib and full-sib populations, power is a function of Nd# and of the number of families included in the mapping population. This provides a convenient means of estimating power for a wide variety of mapping designs.

Identification of chromosomal regions associated with growth and carcass traits in an F3 full sib intercross line originating from a cross of chicken lines divergently selected on body weight

“…2002). In chickens, two experimental designs, the advanced intercross line (Darvasi & Soller 1995) and the full sib intercross line (FSIL) (Song et al. 1999), are suitable for detection as well as fine‐mapping of QTL.…”

An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design

“…Assuming codominance at the QTL, the contrast values will be δ D =d for the BC design and δ D =2d for the F 2 and AIL designs. The effect for the FSIL designs approaches δ D =2d, depending on the specific configuration of the marker and the QTL alleles (Song et al 1999). The contrast value for the RIL design depends on the number of individuals phenotyped in each line, and will be δ D =2d/σ f , where σ f 2 is the variance among means of inbred lines.…”

“…In the F 2 and AIL designs one quarter of the population are in each of the two contrasted marker genotype groups, so that k D =0.25. In the FSIL population, at the optimal configuration of marker and QTL alleles, the proportions will be the equivalent of k D =0.25 in each marker genotype group (Song et al 1999). Table 1 also shows the general expressions for CI (1-α) as a function of Z α/2 , δ D , t D , k D and N, and specific values for CI (0.95) .…”

“…More complex mapping designs that accumulate recombination events, such as advanced intercross lines (AIL, Darvasi and Soller 1995), full-sib intercross lines (FSIL, Song et al 1999), and recombinant inbred lines (RIL, Soller and Beckmann 1990) differ from the BC and F 2 designs in the proportion of recombination per cM. To take this into account, Eq.…”

Engineering Advantages, Challenges and Status of Grass Energy Crops

“…Different ploidy levels in Miscanthus, self incompatibility, and the prospect of highly repetitive sequences, as commonly observed in other grasses, will pose challenges for the mapping process of Miscanthus. However, a preliminary M. sinensis map has already been created based on restriction fragment length polymorphisms (Atienza et al 2002), following a crossing design suggested for outcrossing species (Song et al 1999). Whereas single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) de novo discovery will take some effort, markers from close relatives of Miscanthus like sorghum, sugarcane and corn could be readily exploited for the identification of trait-marker associations.…”