NEWSLETTER January 2024

Coming Soon: National DNA Day

National DNA Day is celebrated annually on April 25, but you are not limited to only celebrating on that day. DNA Day can be celebrated in your classroom and community anytime between January and May.


Learn about how DNA Day began, some of its key players, the mission and how you can celebrate by scrolling through the National DNA Day interactive!

View Interactive


Already have your DNA Day activity ready to host?



Register your event and get listed on our National DNA Day Events map! 

Register Here

Say NO to Plastics!

Earth Day's theme this year is Planet vs. Plastics and the goal is to reduce the use of plastics by 60% by 2040. How do we do this? Share your ideas through your art.


Create a digital poster illustrating the need to say NO to plastics now!


The art of winners may be featured on the Earth Day website, social media accounts, and retail items.


Student submissions due by January 22, 2024

Learn More

ASHG DNA Day Essay Contest Open Now for Students!

The 2024 American Society of Human Genetics DNA Day essay contest is now open to students in grades 9-12! First place prize is $1000 for the student essay writer and a $1000 genetics materials grant.  


To enter, students examine, question and reflect on how the interplay of genetics and environment can shape human health.  


Submissions will be due in early March 2024

Learn more

Keep Your Eye on the Future Video Contest

The National Institutes of Health’s National Eye Institute wants you to tell us why science is so important by entering the Eye on the Future Teen Video Contest. First place wins $2000 and a visit to the NIH campus!


Make a video in one of the three categories:


  • What science means to you
  • A demo of some science you’ve learned
  • How science can affect the future and its role in our lives


Submissions due by April 14, 2024

Learn more


This month's featured article in

Genomics: Insights


"Improving Accuracy of Coronary Artery Disease Diagnosis with Biomarker-based Machine Learning Models"


Authors: K Chan, A Kim and J Liu



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