Final answer:
GWAS is less effective at identifying oligogenic or complex-combined genes because it is designed to find high-frequency variants with small individual effects, while oligogenic traits are influenced by fewer genes with larger effects that might not be detected in the noise of GWAS data.
Step-by-step explanation:
Genome-wide association studies (GWAS) are powerful tools for identifying single nucleotide polymorphisms (SNPs) related to diseases, particularly those caused by multiple genetic variations throughout the genome. However, GWAS are not as effective at identifying genes that work oligogenically or have complex combined effects because these studies typically look for associations with high-frequency variants that have individual, small effect sizes. Oligogenic traits, which involve a few genes with large effects, and complex-combined genetic interactions can be subtle and involve low-frequency variants, making them hard to detect amidst the noise of high-frequency genetic variation. Moreover, GWAS often require large sample sizes to detect the small effects of individual SNPs, but oligogenic and complex-combined traits may require even larger or more specifically designed studies to identify the interplay between multiple genes.
Researchers may also utilize techniques such as massively parallel sequencing and digital gene expression for more in-depth analysis. Such tools can offer insights into molecular networks and gene regulatory mechanisms, but they may not single-handedly clarify the relationships between multiple genes and phenotypes. Furthermore, in cases of oligenic inheritance or complex-combined effects, the involved genes might impact the phenotype without direct interaction, adding another layer of complexity to the detection and analysis.