Identification of genomic regions under selection for complex traits requires approaches more sensitive to detect subtle changes in allele frequencies over time and with greater flexibility to detect selection on segregating variants.
This approach is more sensitive than the Blast approach described below, but also much slower.
The normalised within-subjects approach was more sensitive than the conventional between-subjects approach for defining LSMDs, identifying more than double the number of segments with LSMDs, particularly at the lower lumbar segments where LBP is thought to more commonly arise.
The GEE approach is more sensitive to outliers than non-parametric, rank-based approaches and both groupings in this study had outliers.
In contrast, DeLi-seq clearly detected more antisense transcripts than the HybMap approach (Supplementary information file, Figure S9B), suggesting that the sequencing-based approach is more sensitive for detecting low-abundance transcripts than hybridization-based platforms.
In general, we found that our RNA-seq approach was more sensitive in identifying differentially expressed genes than our previous expression array approach with pbx2/4-MO embryos (Maves et al., 2007).
In addition, a comparison with a vibration-based SHM approach was also carried out, and the results indicated that the temperature-based approach was more sensitive for the scenarios examined.
This confirms that the qPCR approach is more sensitive and has a larger dynamic range in fold-change than the microarray-based approach.
In addition, this approach is more sensitive to the model specification which may explain the large variance in estimates from the 1970s and 1980s.
In this context, simulation tests have been conducted showing that the best match compensation approach is more sensitive to noise in fast or complex motion locations.
