Discussion Several GWAS studies have been performed to look at genetic variation associated with diseases or traits particularly look at common variants. Through GWAS studies only small portion of heritability have been explained. Heritability refers to the proportion of phenotype variation that can be explained by genetic component. This problem co-called missing heritability problem. Common variants cannot explain missing heritability so, rare variants can provide explanation of trait variability and disease risk. The advancement in sequencing technologies and their cost-effective enables a large collection of variants where we are probably going to have some polygenic variants. However, it is challenge to identify the rare variant associated with diseases and traits because of it is rarity.
The case-control analysis Genome-wide comparison are underpowered. The same pattern of genetic risk that we see for common variants with respect to complex traits such as autism, diabetes, schizophrenia, taking the pick is exactly the same for rare variants which is there a lot of polygenicity. A lot of genes that potentially involved in risk to disease and it’s going to require extremely large sample sizes to unequivocally identify those genes against this kind of background of polygenic inheritance. We have to consider many things in analysis, for example what variant we should select for testing associations as not all variants affecting phenotype. So separate rare variants into classes, non-functional (synonymous) and functional.
For example, if a study includes neutral variation, it will dilute out the signal that is in the data to detect the real associations. The bioinformatics tools have been established to predict functional roles of the variants. We have to consider which statistical test to use.
If we have prior information, we can choose the association test by take into account this information. In addition, population stratification with a study sample causes two factors, different frequencies of alleles and different frequencies of disease, and these leads to confounding. To control this issue, we have to make well-matched designs, adjustment for population stratification in statistical test (calculate principle components and add them to the regression model as covariates), use family-based designs that test association within families. Regarding to missing heritability problem, many mechanisms for this problem have been suggested including epistasis, epigenetic, small effect sizes, gene interaction, GWAS studies limitation, and other causes.
Still no definite explanation for missing heritability problem. Sandoval-Motta et al., (2017) have suggested that to understand missing heritability we have to take into account the compositional and functional human microbiome. And they states reasons for their hypothesis includes, many human traits such obesity, cancer, etc are associated with the composition of human microbiome. And as our microbiome have a larger genome than ours, it could be a good source of the variation and phenotypic plasticity. Moreover, our genotype interact with the composition and the structure of our microbiome.
In addition, the genetic structure of the microbiome can be influenced by the host environment or by the transmission from other hosts. Thus, familial studies might overestimates the genetic similarity. More enhancement might be needed to test and translate the findings of the GWAS studies into clinical practice. The declining in sequencing cost and the advances of sequencing approaches promise to generate a great signal to discover an informative low-frequency and rare variants through whole exome sequencing and whole genome sequencing. Thus, it might be possible for developing therapeutic targets.
