As such, GWAS has largely not succeeded in identifying disease genes with large effects, raising the concept of the "missing heritability" of common disease (Manolio et al. 2009).
Moreover, a gene expression meta-analysis reveals that genes with large fitness changes during stress have low transcriptional variation across hundreds of other conditions, and vice versa.
It can be concluded that fewer genomic regions affect the environmental variance of SCS than the mean of SCS, but genes with large effects seem to be absent for both traits.
So far, the examples presented are all major genes with large effects.
For example, it is possible to override the liability scale established by small effects of a number of genes with large effects on the disease phenotype [24, 71].
QTL mapping by GWAS in rice germplasm can be used as a complementary strategy for classical biparental cross-mapping of dissecting complex traits, but it seems to be effective only for genes with large effects.
Noticeably, this correlation is mainly due to genes with large expression differences between species.
Genes with large absolute standard deviations between replicate spots (≥0.5 in log2 scale) were also eliminated from further analysis.
First, genetic networks present a number of genes with large connectivity, or hubs, particularly enriched with chromating remodeling functions [13], [26].
It tends to down-weight noise genes (genes with little variation across all samples) while depending more on differentially expressed genes (genes with large variation between the classes).
Genes with large number of replication have more degree of freedom so they have more statistical power when testing for differential expression.
