Despite the numerous available markers, the whole genome association studies in pepper are not possible as the marker density is not enough to target candidate genes underlying a QTL region and conduct association mapping for complex traits (Livingstone et al. 1999; Minamiyama et al. 2006; Barchi et al. 2007; Wu et al. 2009; Truong et al. 2010; Kong et al. 2012).
Whether haplotypes would provide any advantage when marker density is much higher remains to be determined.
One must then conclude that SIs perform poorly when marker density is high (Figure 2).
Some good reasons to perform CIM-NPCI are that marker density is high and accuracy is critically important.
This decision is best made on a locus by locus basis, as marker density is not typically uniform on a map.
We show that CIM-NPCIs consistently capture the simulated QTL across all sets of conditions, and that the slightly narrower SIs and NPCIs fail to do so at unacceptably high rates, especially when marker density is high.
In cases where marker density is as low as it is surrounding Q3, CIM-NPCI confers no advantage in coverage probability and incurs a moderate penalty for its wider interval widths (Tables 1 and 2; Figure 2).
Thus, we do not recommend using the CIM-NPCI method for cases when marker density is low, and conclude that the SI method actually is appropriate for these cases.
CIM-NPCIs consistently capture the simulated QTL across these conditions while slightly narrower SIs and NPCIs fail at unacceptably high rates, especially in genomic regions where marker density is high, which is increasingly common for real studies.
However, in most foodfish species, marker density is still low.
However, the marker density is ample for most mapping purposes.
