Scientists have long sought to uncover the genetic roots of sleep patterns, which are known to have a hereditary component. In a 2016 GWAS, research using advanced technology has begun shedding light on this complex relationship.Â
In a groundbreaking study involving nearly 1000 middle- to older-aged adults, researchers analyzed data collected through the SenseWear Pro 3 Armband to measure various aspects of sleep and rest. This included parameters like sleep timing, duration, and quality, as well as measures of rest during daytime hours.
According to the LIFE Adult Study, over 7 million genetic variants were scrutinized, revealing several notable findings. The study highlighted the strongest association of the CSNK2A1 with night sleep latency or the time an individual takes to fall asleep at night. SNP rs74448913 in the CSNK2A1 gene is involved in regulating circadian rhythms, influencing how our bodies synchronize with day and night cycles.
However, circadian parameters like sleep onset and offset were not significantly associated with rs74448913 according to the data presented, suggesting that the effect on sleep latency is likely not due to changes in the sleep rhythm. CSNK2A1 codes for the alpha subunit of casein kinase 2 (CKII) which influences the mammalian molecular clock mechanism. It also modulates various cellular processes such as cell cycle control, transcription, and apoptosis. This explains that other pathways involved could be plausible.
This finding represents a critical step forward in understanding the genetic underpinnings of sleep behaviors. While further research is needed to confirm and expand upon this discovery, they offer valuable insights into how genetics may influence sleep patterns. This knowledge could pave the way for future studies and personalized approaches to improving sleep quality and overall health.
In another 2013 GWAS, researchers aimed to uncover genetic influences on various sleep traits through a study involving 2,323 individuals from the Australian Twin Registry. They utilized genotyping on multiple Illumina arrays and imputed additional genetic variants to examine over 2 million common polymorphisms across the genome.Â
Although no single nucleotide polymorphisms (SNPs) reached the stringent genome-wide significance threshold, the study identified promising associations within plausible candidate genes. Notably, a cluster of SNPs (rs7304986,rs16929275, rs2051990, etc.) located in the third intron of the CACNA1C gene showed the most significant association with sleep latency.
SNP rs7304986 appears in the meta-analysis of the results of Australian and 4 cohorts in Chronogen. The meta-analysis provided a p-value of 0.01 (β = 0.12, S.E. = 0.05). The study also identified other gene variations that show potential associations that require validation in future independent samples.Â
Interestingly, the findings did not replicate previous genome-wide analyses based on self-reported sleep behaviors, highlighting the importance of objective measures in genetic sleep studies. These results provide valuable insights into the genetic architecture of sleep traits and emphasize the need for further research to confirm and extend these initial observations.