. 2022 Oct 18;13:1019347. doi: 10.3389/fpls.2022.1019347

Table 2.

A comparison of GWAS benefits and limitations.

BENEFITS	LIMITATIONS
No need to have prior knowledge of biological pathways of the traits under study. Possibility of discovering novel candidate genes that have not been identified using previous methods. Encourages the formation of collaborative consortia to recruit sufficient numbers of participants for analysis, which tend to continue their collaboration for subsequent analyses. Rules out specific genetic associations. Provides data on the ancestry of each subject, which assists in matching case subjects with control subjects. Data on two types of structural variants, sequence and copy number variations, is provided, resulting in more robust data. Identify genetic contributors to common, complex traits for which each gene may only have a small effect.	The findings must be replicated in independent samples from diverse populations. It is necessary to have a large study population. GWAS studies look for correlation rather than causation. GWAS pinpoints a specific site rather than entire genes. Many of the variations (SNPs) found in GWAS aren’t near a protein-coding gene The combined impact of many SNPs typically only explain a modest fraction for any given characteristic. Finding related variations doesn’t always reveal the trait’s underlying biology.

BENEFITS

LIMITATIONS

No need to have prior knowledge of biological pathways of the traits under study.
Possibility of discovering novel candidate genes that have not been identified using previous methods.
Encourages the formation of collaborative consortia to recruit sufficient numbers of participants for analysis, which tend to continue their collaboration for subsequent analyses.
Rules out specific genetic associations.
Provides data on the ancestry of each subject, which assists in matching case subjects with control subjects.
Data on two types of structural variants, sequence and copy number variations, is provided, resulting in more robust data.
Identify genetic contributors to common, complex traits for which each gene may only have a small effect.

The findings must be replicated in independent samples from diverse populations.
It is necessary to have a large study population.
GWAS studies look for correlation rather than causation.
GWAS pinpoints a specific site rather than entire genes. Many of the variations (SNPs) found in GWAS aren’t near a protein-coding gene
The combined impact of many SNPs typically only explain a modest fraction for any given characteristic.
Finding related variations doesn’t always reveal the trait’s underlying biology.