Predicting Phenotype Of Gene Variation Towards Improved Understanding Of Human Health

Pixabay License | Source: Gordon Johnson , no changes made.

Many rare diseases are causally related to rare genetic variants and a plethora of research surrounds the genetic basis and treatment of these, however less is known about the effect rare variants could have on subclinical conditions and non-clinical phenotypes. This is mainly due to the power or sample sizes needed for such associations, however using techniques such as collapsing rare variants it is possible to group the data at the gene level in such a way that statistically significant associations can be extracted from UK biobank exome sequences (~50,000) supplemented with other exome sequence data sources.

Helix of San Mateo, California in collaboration with the Renown Institute of Health Innovation based in Reno, Nevada combined the UK biobank exome dataset with the Healthy Nevada Project exome dataset of 21,866 participants to identify rare variants associated with phenotypes by taking the gene based collapsing rare variants approach. The study was led by Nicole L. Washington and published in the journal Nature Communications.

Using the combined datasets, 47 significant associations between genes and phenotypes were made. An additional 30 associations could only be made in the UK biobank. In a mixed ancestry cohort, an additional 24 gene phenotype relationships were uncovered. Few gene-phenotype associations could be fully explained by a single rare coding variant indicating a plurality of phenotypic variants per gene.

The authors have provided an interactive browser of their results as a resource for the human genetics community.

The study replicated many known associations including rare variants in PCSK9 and APOB associated with low density lipoprotein (LDL) levels, and rare loss of function variants in TUBB1 associated with platelet count. A significant association was observed between variants in ASGR1 and alkaline phosphatase levels. Loss of function mutations in ASGR1 had previously been associated with increased levels of alkaline phosphatase. They also confirmed that rare coding variants in TYRP1 were associated with blonde hair in a broader population ancestry than noted before.

They also found novel associations such as loss of function in SMAD6 and eye measurements. Another example was rare coding variants in STAB1 that associated with changes in several brain structures, with the strongest association in the putamen. The authors note that these new associations provide avenues for future experimental work.

Limitations of the study include that an intentionally simple model was used to provide proof of concept which could be enhanced in future iterations. Only genes were considered, excluding non-coding regions.

The authors concluded, “This method [reported in the paper] brings us closer to a future where a single comprehensive calculation (incorporating both common and rare variation) is able to more accurately predict phenotypic outcomes of polygenic variation towards an improvement in our understanding of human health.”