Houston Chronicle: Midlander creates algorithm to predict likelihood of infection

Determination would be made using person’s genetic make-up, medical history

By Caitlin Randle, MRT.com/Midland Reporter-Telegram Published 9:11 pm CDT, Thursday, April 16, 2020

A Midland data scientist and his two partners have created an algorithm that uses a person’s genetic markers and medical history to predict someone’s likelihood of becoming infected with the coronavirus and suffering complications from it.

Midlander A.J. Rosenthal and his partners, Dan Brue of Oklahoma and Warren Gieck of Alberta, Canada, filed patents this week related to the algorithm.

Rosenthal said it could use a person’s genetic make-up in combination with various factors, such as their medical history and types of exposure they’ve had (i.e. a miner exposed to coal dust), to determine someone’s risk factor and assign them a correlating score.

“We’re describing potentially where a person would fall, give them a score, and that score allows them to either start going back to the workplace because they’re not going to succumb to the disease, or they won’t even be susceptible to it,” he said.

The algorithm would use the medical histories of those who have been hospitalized with COVID-19 to determine what markers could put a person at risk, Rosenthal said. He described inputting the data from past patients as “training the algorithm.”

The goal of this project is for the information to be widely accessible, Rosenthal said. He said the algorithm could potentially be on a website where a person could enter their medical information after signing a HIPPA privacy release.

“What we’re trying to do is if people want this – and we’re hoping they do – is to make it easier for them to feel comfortable and safe going back out,” he said. “Because they’ve now been locked in their houses for weeks … they don’t know if they’re going to get sick. They don’t know if they’re even susceptible to it.”

The algorithm could also be applied to other viruses and diseases, Rosenthal said, but the trio has chosen to focus on COVID-19 because there’s an immediate need.

The project’s success is contingent on partnerships with other entities – primarily, with medical providers who would give access to the medical histories of past COVID-19 patients. HIPPA laws prevent that data from being publicly available.

Rosenthal pointed to studies linking ACE2 receptors in the lungs to COVID-19 as evidence that a person’s DNA could be used to predict their risk of being infected. Some studies have found the coronavirus uses these receptors to infiltrate cells in the body.

“When the coronavirus attaches, it has a certain type of envelope that it attaches to,” Rosenthal said. “Your receptor on your lung, a lot of the coronavirus sticks to it … and from there, it propagates an infection.”

Some health entities worldwide have advised against using ibuprofen to treat COVID-19 because it’s thought to increase the number of ACE2 receptors in the body, but there’s no clear consensus among the scientific community about whether more of these receptors create a higher risk of contracting or having complications from the coronavirus.

Rosenthal said the algorithm could determine if certain combinations of medications and genetics were frequently present in those infected with the virus and serve as a guide to those with similar DNA who are also on those medications.

A former multi-disciplinary engineer in the U.S. Navy and at General Electric, Rosenthal currently works for an oil and gas company in Midland. He said he and his partners, who met working at GE, were inspired to take up this enterprise by their kids, who want to “go back to school and go to the mall and play baseball.”

“We’re just three dads. We just want our kids to have a normal life again,” Rosenthal said.

“Maybe these three dads can help the world,” he said. “The only thing we’ve got left to lose are our jobs or the economy.”

https://www.chron.com/news/article/Midlander-creates-algorithm-to-predict-likelihood-15206787.php

Paper: Relationship between the ABO Blood Group and the COVID-19 Susceptibility

Abstract

The novel coronavirus disease-2019 (COVID-19) has been spreading around the world rapidly and declared as a pandemic by WHO. Here, we compared the ABO blood group distribution in 2,173 patients with COVID-19 confirmed by SARS-CoV-2 test from three hospitals in Wuhan and Shenzhen, China with that in normal people from the corresponding regions. The results showed that blood group A was associated with a higher risk for acquiring COVID-19 compared with non-A blood groups, whereas blood group O was associated with a lower risk for the infection compared with non-O blood groups. This is the first observation of an association between the ABO blood type and COVID-19. It should be emphasized, however, that this is an early study with limitations. It would be premature to use this study to guide clinical practice at this time, but it should encourage further investigation of the relationship between the ABO blood group and the COVID-19 susceptibility.

https://doi.org/10.1101/2020.03.11.20031096

Paper: Genome-wide association and HLA region fine-mapping studies identify susceptibility loci for multiple common infections

Abstract

Infectious diseases have a profound impact on our health and many studies suggest that host genetics play a major role in the pathogenesis of most of them. We perform 23 genome-wide association studies for common infections and infection-associated procedures, including chickenpox, shingles, cold sores, mononucleosis, mumps, hepatitis B, plantar warts, positive tuberculosis test results, strep throat, scarlet fever, pneumonia, bacterial meningitis, yeast infections, urinary tract infections, tonsillectomy, childhood ear infections, myringotomy, measles, hepatitis A, rheumatic fever, common colds, rubella and chronic sinus infection, in over 200,000 individuals of European ancestry. We detect 59 genome-wide significant (P < 5 × 10-8) associations in genes with key roles in immunity and embryonic development. We apply fine-mapping analysis to dissect associations in the human leukocyte antigen region, which suggests important roles of specific amino acid polymorphisms in the antigen-binding clefts. Our findings provide an important step toward dissecting the host genetic architecture of response to common infections. Susceptibility to infectious diseases is, among others, influenced by the genetic landscape of the host. Here, Tian and colleagues perform genome-wide association studies for 23 common infections and find 59 risk loci for 17 of these, both within the HLA region and non-HLA loci.

https://pubmed.ncbi.nlm.nih.gov/28928442/

Paper: Human genetic susceptibility to infectious disease

Abstract

Recent genome-wide studies have reported novel associations between common polymorphisms and susceptibility to many major infectious diseases in humans. In parallel, an increasing number of rare mutations underlying susceptibility to specific phenotypes of infectious disease have been described. Together, these developments have highlighted a key role for host genetic variation in determining the susceptibility to infectious disease. They have also provided insights into the genetic architecture of infectious disease susceptibility and identified immune molecules and pathways that are directly relevant to the human host defence.

https://pubmed.ncbi.nlm.nih.gov/22310894/