Lifestyle behaviors such as eating well and exercising can be significant factors in one’s overall health. But the risk of developing cancer is predominantly at the whim of an individual’s genetics. Our bodies are constantly making copies of our genes to produce new cells. However, there are occasional mistakes in those copies, a phenomenon geneticists call mutation. In some cases, these mistakes can alter proteins, fuse genes and change how much a gene gets copied, ultimately impacting a person’s risk of developing cancer. Scientists can better understand the impact of mutations by developing predictive models for tumor activity.

Christopher Plaisier, an assistant professor of biomedical engineering in the Ira A. Fulton Schools of Engineering at Arizona State University, is developing a software tool called OncoMerge that uses genetic data to improve cancer modeling technology. OncoMerge is a platform that detects abnormal gene fusions as well as mutations that affect protein expression and how many times a gene is copied. The software then analyzes the network behind the mutations to reveal connections and develop a model to predict future changes caused by the mutations.

“We are able to look at the gene expression patterns using correlation,” says Plaisier, who is also an associate faculty member in the ASU Biodesign Center for Biocomputing, Security and Society. “Then we can see what is being activated or repressed, which allows us to look at the deeper functions behind that.”

Plaisier has been reflecting on the idea of OncoMerge since his postdoctoral work, during which he first noticed a need for a platform that could process the network behind mutations. The effort combines his expertise as a human geneticist, computational biologist and cancer biologist into a single project. His most recent research tackles gene mutation detection challenges by designing a database that uses genetic data to analyze linked activity within the networks. The results are published today in the journal Cell Reports Methods.

The information derived from examining genetics has countless potential health care applications but is especially valuable for understanding cancer. Due to the significant variation among forms of cancers, Plaisier is enhancing prediction models that can offer insight into specific cancer environments.

Source: https://www.cell.com/
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https://www.asu.edu/

Biologists don’t understand the link between genes and behavior, so why should economists? Many outside critics of economics complain that it’s not a science. In response, most economists have steadily improved the quality of their empirical methods. But a few economists are taking a different tack by borrowing from natural science. Neuroeconomists, for example, have put experimental subjects in MRI machines to measure how their brains behave when they’re making economic decisions, in order to search for clues to the mechanisms behind everyday behavior. Recently, a few economists have sought to use genetics to augment their understanding of economic outcomes. This has become possible thanks to the advent of cheap genome sequencing and widely available databases of human genetic information. But there are a number of reasons this line of research is likely to do more harm than good, at least until biologists better understand the ways that genes affect human development.

One major foray into the field of geno-economics came from Quamrul Ashraf of Williams College and Oded Galor of Brown University. In a 2013 paper published in the American Economic Review — arguably the most prestigious journal in economics — Ashraf and Galor argue that genetic diversity exerts a big influence on economic development. Too much diversity, they argue, and people don’t trust each other. Too little diversity, and original ideas are hard to come by. Thus, the optional amount of diversity is a happy medium — a population homogeneous enough to cooperate, but diverse enough to have originality. Looking at genetic data, they found that Europe and East Asia tend to have a medium range of genetic diversity, with Africa on the high end and the indigenous populations of the Americas and Oceania on the low end. Since Europe and East Asia contain the most industrialized nations, Ashraf and Galor concluded that the data supported their hypothesis. Another geno-economics paper was recently published in the Journal of Public Economics — also a top journal — by economists Daniel Barth, Nicholas Papageorge and Kevin Thom. Rather than tackling the broad sweep of international development as Ashraf and Galor did, Barth et al. tried to use genetics to explain differences in individual wealth, using the Health and Retirement Study, which measures wealth and various other financial information. For each individual, they obtained a polygenic score — a number that represents statistical differences in a large set of genes — that tends to be correlated with educational attainment. Restricting their analysis only to people of European descent, Barth et al. then showed that this genetic statistic is correlated with more success in investing, even after controlling for things like income and education. They concluded that genetic endowments help some people invest more successfully, leading them to build up wealth over time.

Source: https://www.bloomberg.com/