Updated: Jun 14
By Eddie Jiang
If you were to unravel the roughly two meters of DNA found in one cell, you might find a segment of genes that gives you your freckles. Those traits are expressed by a specific gene, which makes locating them relatively easy. Disorders and illnesses caused by a single gene are referred to as monogenic disorders. While these disorders, like Huntington’s Disease and Sickle Cell Disease, are oftentimes incurable, their known location and sequence within our genome offers treatment plans available via gene therapy. However, everything changes when the disease is caused by multiple genes.
Polygenic disorders are expressed due to the influence of two or more genes. For disorders like depression, heart disease, and diabetes, the genetic factors that are involved in the expression of these diseases are not limited to one specific segment. Due to the possibility of small fragments of dispersed genes all contributing to the onset of polygenic disorders, locating where those small segments genes are is a difficult task. So how can we attempt to find them?
One method scientists have used to uncover the hidden dispersion of these elusive genes lies in genome-wide association studies, or GWAS. In the case of attempting to find a genetic correlation to depression, researchers at the University of Edinburgh employed this method by comparing 807,553 individuals from three different cohorts of studies all focusing on depression or depressive symptoms. By first analyzing genetic variants of all datasets, single nucleotide polymorphisms (SNPs), which will act as markers, can be compared to examine the associations between these genetic variants and expressions of depression Then, statistical analysis can be used to measure the association of a specific SNP across all datasets.
They found 102 independent genetic variants associated with depression. The genes NEGR1, a gene encoding for neuronal growth regulators, TCF4, and RAB27B were all determined to be positively associated with depression.
Similar studies were done with other polygenic disorders. In 2018, researchers at The University of Queensland, Brisbane conducted a GWAS on diabetes and found 143 risk variants after genotyping 5,053,015 sets of genomes. Just last year, researchers of another study focusing on heart disease found 15 loci of genome-wide significance, which are general locations of where risk variants could be, for clinical coronary artery disease.
Using genome-wide association studies