NIH grant funds research to pinpoint natural selection’s influence on genomesPosted on May 1, 2019
Story originally published on Penn State News
UNIVERSITY PARK, Pa. — The human genome — roughly 20,000 genes embedded in our DNA — is the result of millions of years of evolutionary forces as humans and humans’ ancestors struggled to survive. But scientists are unsure exactly how much influence natural selection, one of those evolutionary forces, has had on our genes. Now, through a $1.7 million grant through the National Institutes of Health, researchers led by Michael DeGiorgio, assistant professor of biology and Institute for Computational and Data Sciences co-hire at Penn State, will begin to tease apart individual forces to understand how much influence natural selection has had on our evolutionary path.
DeGiorgio and his team will develop sophisticated statistical models that can help identify the various forces acting on human genes. Different versions of genes, known as mutations, are passed from generation to generation in a population for a variety of reasons, including randomness. One of these forces, natural selection, is the process of “choosing” genes based on their increased benefit to individuals in a specific environment. For example, around many speedy predators, the combination of genes that helps make prey fast is more likely to survive naturally.
DeGiorgio will use statistical learning to figure out which forces in the past contributed to the current set of genes and genetic mutations that remain in humans today. Their approach is analogous to taking a meal in a restaurant and using your knowledge of cooking processes and the appearance of individual molecules on your plate to determine whether the food was cooked in a frying pan or in the oven, and at what temperature. In DeGiorgio’s team’s case, they will be pinpointing whether mutations were new or had existed for generations, how quickly beneficial mutations spread through populations, and other important biological criteria.
It’s a complicated challenge because little is known about the environmental conditions of our early human ancestors. DeGiorgio and other computational biologists have shown that supercomputers can run through thousands of possible scenarios until they find one that matches patterns observed in humans’ genes today. DeGiorgio’s work will use the Institute for Computational and Data Sciences’ Advanced CyberInfrastructure, Penn State’s high-performance research cluster.
“It’s only because we have sophisticated computing now that we can do thousands or hundreds of thousands of simulations to actually get this distribution of genetic diversity we should expect under different processes,” DeGiorgio said.
Their work will improve the understanding of which genes are important because of natural selection, which could have medical implications, said DeGiorgio. For example, in past work, DeGiorgio compared the genetic influences of Ethiopians living at high altitudes to those at low altitudes.
“These studies identified genes that are highly relevant medically; for example, genes involved in hypoxia response at high altitudes where there are low oxygen levels,” he said.
- Featured Researcher: Nick Tusay
- Multi-institutional team to use AI to evaluate social, behavioral science claims
- NSF invests in cyberinfrastructure institute to harness cosmic data
- Center for Immersive Experiences set to debut, serving researchers and students
- Distant Suns, Distant Worlds
- CyberScience Seminar: Researcher to discuss how AI can help people avoid adverse drug interactions
- AI could offer warnings about serious side effects of drug-drug interactions
- Taking RTKI drugs during radiotherapy may not aid survival, worsens side effects
- Cost-effective cloud research computing options now available for researchers
- Costs of natural disasters are increasing at the high end
- Model helps choose wind farm locations, predicts output
- Virus may jump species through ‘rock-and-roll’ motion with receptors
- Researchers seek to revolutionize catalyst design with machine learning
- Resilient Resumes team places third in Nittany AI Challenge
- ‘AI in Action’: Machine learning may help scientists explore deep sleep
- Clickbait Secrets Exposed! Humans and AI team up to improve clickbait detection
- Focusing computational power for more accurate, efficient weather forecasts
- How many Earth-like planets are around sun-like stars?
- Professor receives NSF grant to model cell disorder in heart
- SMH! Brains trained on e-devices may struggle to understand scientific info
- Whole genome sequencing may help officials get a handle on disease outbreaks
- New tool could reduce security analysts’ workloads by automating data triage
- Careful analysis of volcano’s plumbing system may give tips on pending eruptions
- Reducing farm greenhouse gas emissions may plant the seed for a cooler planet
- Using artificial intelligence to detect discrimination
- Four ways scholars say we can cut the chances of nasty satellite data surprises
- Game theory shows why stigmatization may not make sense in modern society
- Older adults can serve communities as engines of everyday innovation
- Pig-Pen effect: Mixing skin oil and ozone can produce a personal pollution cloud
- Researchers find genes that could help create more resilient chickens
- Despite dire predictions, levels of social support remain steady in the U.S.
- For many, friends and family, not doctors, serve as a gateway to opioid misuse
- New algorithm may help people store more pictures, share videos faster
- Head named for Ken and Mary Alice Lindquist Department of Nuclear Engineering
- Scientific evidence boosts action for activists, decreases action for scientists
- People explore options, then selectively represent good options to make difficult decisions
- Map reveals that lynching extended far beyond the deep South
- Gravitational forces in protoplanetary disks push super-Earths close to stars
- Supercomputer cluster donation helps turn high school class into climate science research lab
- Believing machines can out-do people may fuel acceptance of self-driving cars
- People more likely to trust machines than humans with their private info
- IBM donates system to Penn State to advance AI research
- ICS Seed Grants to power projects that use AI, machine learning for common good
- Penn State Berks team advances to MVP Phase of Nittany AI Challenge
- Creepy computers or people partners? Working to make AI that enhances humanity
- Sky is clearing for using AI to probe weather variability
- ‘AI will see you now’: Panel to discuss the AI revolution in health and medicine
- Privacy law scholars must address potential for nasty satellite data surprises
- Researchers take aim at hackers trying to attack high-value AI models
- Girls, economically disadvantaged less likely to get parental urging to study computers
- Seed grants awarded to projects using Twitter data
- Researchers find features that shape mechanical force during protein synthesis
- A peek at living room decor suggests how decorations vary around the world
- Interactive websites may cause antismoking messages to backfire
- Changing how government assesses risk may ease fallout from extreme financial events
- Algorithm aims to alert consumers before they use illicit online pharmacies
- Scientists tap AI betting agents to help solve research reproducibility concerns
- Deep learning may help doctors choose better lung cancer treatments
- Lessons on nutrition easy to digest in virtual reality spaces