In dicotyledonous plants, comparative mapping studies demonstrated the complexity of establishing shared macrosynteny among distant species [ 19, 43].
Epitope mapping studies demonstrated that pathogenic anti-BP180 antibodies recognize a 9 12 amino acid stretch within the murine BP180 NC14A region of the antigen [ 31].
Fate mapping studies demonstrated that, following elongation and formation of the primitive axial skeleton, the notochord gives rise to the nucleus pulposus in fully formed intervertebral discs.
The availability of broad genetic resources together with the use of grape genomic tools [ 31] helped refine our QTL detection through gene expression and association mapping studies, demonstrating the likely involvement of both cis-regulation and newly characterized allelic variation in shaping a complex phenotype.
Molecular mapping study demonstrated that Rcn4 is located between simple sequence repeat markers RM6581 and RM6608 on the long arm of rice chromosome 1.
The Fos mapping study demonstrated that circuitry including hypothalamic areas, the ventral tegmental area, the substantia nigra and the nucleus of the solitary tract was recruited by stimulation of mu receptors within the nucleus accumbens.
The microinjection mapping study demonstrated a broad anatomical gradient within the striatum, with sensitivity highest in relatively more lateral and ventral regions of the striatum (ventrolateral striatum, lateral shell and core).
Recent evidence from fate-mapping studies demonstrates that otherwise slowly proliferating SCs are the cells within the IFE that make the major contribution to wound repair (Mascré et al., 2012).
Recognizing that neighborhood contextual factors increase risk of heat-related morbidity and mortality, mapping studies have demonstrated spatial variability in heat vulnerability.
Genetic mapping studies have demonstrated that this phenomenon occurs in many species, including maize [ 57, 58], rice [ 59, 60] and cherimoya [ 61]; however, the cause of this marker skewing is still debated.
Previous empirical QTL mapping studies have demonstrated that joint-family mapping methods are generally better than single-family mapping in terms of the number of QTL detected, the likelihood statistics for QTL, the precision of QTL position estimates and the proportion of variation accounted for by the QTL (Blanc et al., 2006; Coles et al., 2010; Steinhoff et al., 2011).
