New Findings Illustrate Possible Rationale for Some High-Elevation Diseases in Humans

By Henry Lemersal, SDSC REHS Program and Kimberly Mann Bruch, SDSC Communications

Photo by Alexas Fotos

UC Berkeley scientists Michael Nachman and Elizabeth Beckman have studied environmental adaptation of house mice for many years. Their latest project, which utilized XSEDE allocations on San Diego Supercomputer Center (SDSC) resources, looked at how house mice colonize and adapt to high elevations in Ecuador and Bolivia. They discovered that several hypoxia-associated genes were different in the mice at the higher elevations and that some of these genes exhibited a threshold effect – a large shift in how frequently certain forms occurred at only the highest elevations. Their findings were recently published in Genetics.

"This study is a continuation of earlier work on the genetic basis of environmental adaptation in house mice, and in a paper that we published last year we identified genes underlying adaptation to cold weather," said Nachman, a professor of integrative biology at UC Berkeley and director of the campus's Museum of Vertebrate Zoology. "In our earlier study we found that there was some predictability to evolution, both at the organismal level and at the genetic level.  However, in the present study, we found mostly distinct evolutionary responses to high elevation in mice from Ecuador and mice from Bolivia, showing that adaptation to high elevations can occur through changes in many different genes and pathways."

For their latest study, titled The Genomic Basis of High-Elevation Adaptation in Wild House Mice (Mus musculus domesticus) from South America, the researchers used XSEDE allocations on the Comet supercomputer at SDSC. Specifically, they utilized the processing software FastP to clean raw sequence reads and contrasted them with the house mice reference genome (GRCm38) using BWA (v 0.7.13), a tool for genetic similarity comparison. 

Why It's Important

"We were able to complete our large-scale bioinformatic analyses in a timely and robust way thanks to our XSEDE allocation and SDSC resources." – Elizabeth Beckman, postdoctoral researcher at UC Berkeley

The team's research has plenty of prospective applications for humans. An example is how the study could provide more clarity in the functionality of human traits.

"Humans have a lot of complex traits," noted Beckman, a postdoctoral researcher at Berkeley. "Every time we dive into the details of complex traits, including in a model system like house mice, it helps us understand more about how complex traits work, and it offers clues about the details of complex traits in humans."

She further explained that human disease associated with elevation increases around 2500 meters and occurrence increases the higher you go. Based on these similar patterns in humans and mice, a similar process may be occuring to both species as they get near 3000 meters. Studying this pattern in house mice could help the researchers better understand human diseases associated with high elevation.

How XSEDE Helped

The scientists used XSEDE allocations on Comet to index collected data in SAMtools and then used the Genome Analysis Toolkit (v 4.0.11.0) to successfully clean up and work with the aggregated information. 

"We were able to complete our large-scale bioinformatic analyses in a timely and robust way thanks to our XSEDE allocation and SDSC resources," Beckman said. "Now, we are using Expanse to continue our study of environmental adaptation across North and South America in house mice, as well as high-elevation adaptation in South American finches."

This research was supported by an NIH grant to Nachman (R01 GM127468). Computations on SDSC resources were allocated by XSEDE (TG-MCB130109).

At A Glance:

• UC Berkeley researchers used XSEDE allocations to examine at how house mice colonize and adapt to high elevations in Ecuador and Bolivia.
• They used a package called FastP to clean raw sequence reads and then aligned them to the M. musculus reference genome (GRCm38) with BWA (v 0.7.13) using the BWA-MEM algorithm. 
• The study was recently published in Genetics.