Baylor center receives $10M NIH grant to continue rare disease research

NIH funding

BCM's Center for Precision Medicine Models has received funding that will allow it to study more complex diseases. Photo via Getty Images

Baylor College of Medicine’s Center for Precision Medicine Models received a $10 million, five-year grant from the National Institutes of Health last month that will allow it to continue its work studying rare genetic diseases.

The Center for Precision Medicine Models creates customized cell, fly and mouse models that mimic specific genetic variations found in patients, helping scientists to better understand how genetic changes cause disease and explore potential treatments.

The center was originally funded by an NIH grant, and its models have contributed to the discovery of several new rare disease genes and new symptoms caused by known disease genes. It hosts an online portal that allows physicians, families and advocacy groups to nominate genetic variants or rare diseases that need further investigation or new treatments.

Since its founding in 2020, it has received 156 disease/variant nominations, accepted 63 for modeling and produced more than 200 precision models, according to Baylor.

The center plans to use the latest round of funding to bring together more experts in rare disease research, animal modeling and bioinformatics, and to expand its focus and model more complex diseases.

Dr. Jason Heaney, associate professor in the Department of Molecular and Human Genetics at BCM, serves as the lead principal investigator of the center.

“The Department of Molecular and Human Genetics is uniquely equipped to bring together the diverse expertise needed to connect clinical human genetics, animal research and advanced bioinformatics tools,” Heaney added in the release. “This integration allows us to drive personalized medicine forward using precision animal models and to turn those discoveries into better care for patients.”

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A new AI tool from a Baylor College of Medicine Lab could help better diagnose specific types of autism spectrum disorder, epilepsy and developmental delay disorders. Photo via Getty Images.

Houston lab develops AI tool to improve neurodevelopmental diagnoses

developing news

One of the hardest parts of any medical condition is waiting for answers. Speeding up an accurate diagnosis can be a doctor’s greatest mercy to a family. A team at Baylor College of Medicine has created technology that may do exactly that.

Led by Dr. Ryan S. Dhindsa, assistant professor of pathology and immunology at Baylor and principal investigator at the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, the scientists have developed an artificial intelligence-based approach that will help doctors to identify genes tied to neurodevelopmental disorders. Their research was recently published the American Journal of Human Genetics.

According to its website, Dhindsa Lab uses “human genomics, human stem cell models, and computational biology to advance precision medicine.” The diagnoses that stem from the new computational tool could include specific types of autism spectrum disorder, epilepsy and developmental delay, disorders that often don’t come with a genetic diagnosis.

“Although researchers have made major strides identifying different genes associated with neurodevelopmental disorders, many patients with these conditions still do not receive a genetic diagnosis, indicating that there are many more genes waiting to be discovered,” Dhindsa said in a news release.

Typically, scientists must sequence the genes of many people with a diagnosis, as well as people not affected by the disorder, to find new genes associated with a particular disease or disorder. That takes time, money, and a little bit of luck. AI minimizes the need for all three, explains Dhindsa: “We used AI to find patterns among genes already linked to neurodevelopmental diseases and predict additional genes that might also be involved in these disorders.”

The models, made using patterns expressed at the single-cell level, are augmented with north of 300 additional biological features, including data on how intolerant genes are to mutations, whether they interact with other known disease-associated genes, and their functional roles in different biological pathways.

Dhindsa says that these models have exceptionally high predictive value.

“Top-ranked genes were up to two-fold or six-fold, depending on the mode of inheritance, more enriched for high-confidence neurodevelopmental disorder risk genes compared to genic intolerance metrics alone,” he said in the release. “Additionally, some top-ranking genes were 45 to 500 times more likely to be supported by the literature than lower-ranking genes.”

That means that the models may actually validate genes that haven’t yet been proven to be involved in neurodevelopmental conditions. Gene discovery done with the help of AI could possibly become the new normal for families seeking answers beyond umbrella terms like “autism spectrum disorder.”

“We hope that our models will accelerate gene discovery and patient diagnoses, and future studies will assess this possibility,” Dhindsa added.

Baylor Genetics has paired with Baylor’s department of molecular and human genetics to launch the Medical Genetics Multiomics Laboratory with a goal for the collaboration is to turn research into clinical diagnostics. Photo via Getty Images

This new Houston lab is translating genetics research into clinical diagnostics

DNA innovation

A new lab at Baylor College of Medicine is primed to do groundbreaking work in the field of genetics.

Baylor Genetics has paired with Baylor’s department of molecular and human genetics to launch the Medical Genetics Multiomics Laboratory (MGML). The goal for the collaboration is to turn research into clinical diagnostics.

MGML’s freshly launched first clinical test is Whole Transcriptomic RNA Sequencing (WT RNAseq). The new test builds upon the success of existing tests like whole exome sequencing (WES) and whole genome sequencing (WGS) currently on offer from Baylor Genetics by focusing on additional variants that could be missed by the other tests.

Baylor Genetics is offering WT RNAseq to the Undiagnosed Diseases Network (UDN) and its affiliated institutions. For more than a decade, the NIH-funded UDN has united clinical and research experts from across many fields and institutions to give answers to patients with rare genetic diseases. Since it became one of the first institutions to join the UDN in 2014, Baylor Genetics has been the UDN’s sequencing core, using WES, WGS and RNA sequencing to help diagnose patients. The additional offering of WT RNAseq could improve the diagnostic yield by as much as 17 percent.

“This agreement, and the MGML lab, bring to life our vision of innovation, allowing us to co-develop new tests, evaluate in terms of clinical utility, and offer commercially in either a research or clinical setting,” says Dr. Brendan Lee, professor, chair and Robert and Janice McNair Endowed Chair of Molecular and Human Genetics at Baylor College of Medicine, and scientific advisory and board of directors member at Baylor Genetics. “Baylor Genetics is turning around critical high-volume testing, but the challenge is also maintaining our innovative edge and our position as leaders in discovery and genomic health implementation. This agreement is a realization of the vision when Baylor Genetics was founded 10 years ago.”

The lab’s product offerings will continue to expand as it becomes commercially feasible to do so, and the new tests will be used both commercially and clinically.

Baylor Genetics combines the powers of Baylor College of Medicine, which has the NIH’s best-funded department of molecular and human genetics, and Japanese clinical diagnostic testing company H.U. Group Holdings.

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Rice student startup lands $1.85M to launch medical drone network

critical cargo

Students at Rice University have developed a medical cargo drone transport system to help deliver sensitive medical supplies and improve mobile healthcare efforts.

Haast Autonomous is the brainchild of graduating seniors Ege Halac, Jason Chen and Santiago Brent, who got their venture idea off the ground with help from the Liu Idea Lab for Innovation and Entrepreneurship (Lilie) Summer Venture Studio. The founders have developed the prototype at Rice’s Oshman Engineering Design Kitchen (OEDK) with fellow Rice researchers Felix Hasson, Ethan Javedan, Kenna Sanders and Caden Schmidt.

The startup has raised $1.85 million in pre-seed funding, according to Rice. The founders plan to focus on Haast full-time following graduation. They said they aim to launch pilot trials in 2027 and head to market later that year.

“We need better alternatives for a fast, safe and on-demand system of transport for life-critical cargo,” Halac said in a news release from Rice.

The Haast team has developed a custom aircraft with software that manages dispatch, routes, and chain of custody to assist in how materials move between sites in centralized medical systems. Generally, the transportation of medical supplies and materials between facilities and points of care relies on ground shipping or expensive air transport.

Haast Autonomous’ aircraft can take off and land vertically, and is designed around a mission profile of 50 to 62 miles. It can carry a payload of at least 5 pounds, with future versions intended to scale up in size. It also includes a built-in payload bay that regulates temperature, pressure, vibration and tilt to protect sensitive contents such as patient samples, antivenom or poisoning kits and radioligands or other therapies, according to Rice.

At first, the company envisioned the mission to be centered around transplants, but saw the product being best suited for a variety of operations.

“What we realized is that the platform we are building is suited for medicine, but it really underlies a much larger problem of mission-critical transport across industries,” Brent added in the news release. “We are building the fastest, most secure logistics chain for the world’s most sensitive cargo.”

Haast Autonomous was recognized at the 2026 Oshman Engineering Design Showcase and Competition, where it won Best Aerospace or Transportation Technology. It also performed well in the 2026 Napier Rice Launch Challenge.

In the future, Haast Autonomous plans to deploy a fleet of aircraft. The software will be designed to assist hospitals in requesting flights and tracking deliveries in real time.

“The drone is only part of the solution,” Chen also added in the release. “What matters is moving something from point A to point B in a way that fits into how hospitals already operate.”

Houston scientist wins prestigious Pew Scholar award for brain cancer research

standout scholar

Christina Tringides, an assistant professor of materials science and nanoengineering at Rice University, is one of 21 scientists to win a prestigious Pew Biomedical Scholar award.

She is the first faculty member from Rice to win the distinction, which provides $300,000 over four years for advances in biomedicine, according to the university. The awards are granted to researchers who are in the first few years at the assistant professor level.

In Tringides’ case, the funding will support her innovative new method of modeling glioblastoma, a common and extremely aggressive form of brain cancer. Thanks to producing its own blood supply, glioblastoma spreads quickly, weaving tendrils of blighted tissue throughout the brain. Because of this, surgery is difficult and conventional therapies ineffective.

Understanding the way glioblastoma spreads is crucial to the search for a cure. Tringides is using hydrogels that mimic the brain’s extracellular matrix. Using cultures and a microscopic labyrinth, her team can see how the cancer spreads, bonds with neurons and changes cell wall activity. Essentially, Tringides has devised an intelligence test for tumors in hopes of learning how to outsmart them.

“As cancer crawls through the maze, we can look at how it is interacting with the neurons more and more, and measure how electrical activity is changing as a result,” she said in a news release from Rice.

Examining how cancer cells grow can reveal which conditional changes slow them down. Finding ways to alter the structure of brain matter in a way that makes it inhospitable to the cancer could lead to therapies that would impede growth or even reverse it. Using her custom-made ersatz brain maze makes it easier to observe changes than it would be in a patient’s brain.

“Imaging synapses is time-intensive ⎯ it can involve large data files that are hard to visualize, but if we know that the only place where we might have a synapse is this tiny 1-by-4-by-10 micron channel, it makes it much faster and reliable to image them,” Tringides said.

Born in Ames, Iowa, Tringides received her doctorate in biophysics from Harvard before joining Rice in 2024 through a Cancer Prevention and Research Institute of Texas (CPRIT) recruitment award.

Her research was also one of the first four projects to receive research awards through the Rice Brain Institute and TMC Neuro Collaboration Seed Grant Program.

Texas residents earn 11th highest income in U.S., says 2026 study

Money Matters

A new WalletHub study comparing income disparities across America has ranked Texas residents No. 11 on the list of states with the highest earning residents in the nation.

The report, "States Where People Have the Highest Income (2026)," analyzed U.S. Census Bureau income data in all 50 states and the District of Columbia. The report evaluated the average annual income of the top five percent, the median annual household income, and the average annual income of the bottom 20 percent of residents in every state, all adjusted for the cost of living.

The report's data revealed the top five percent of Texans, the highest earners, make $520,378 on average yearly after adjusting for the cost of living. That's the seventh-highest income among the top five percent of earners nationwide.

Meanwhile, the median annual income of a Texas household is just under $76,000. The bottom 20 percent of Texas residents make $17,651 a year, the report found.

For additional context, the latest data from the Federal Reserve shows an American household's median yearly income is about $83,700. WalletHub analyst Chip Lupo also found that the highest earning 10 percent of individuals in the U.S. earn over 12 times more than those in the lowest-earning 10 percent, based on the latest Census data.

"By measuring the income of various percentiles against a state's median income, we can better identify where income disparities are more prevalent, which could help us better understand why residents of certain states struggle more to make ends meet," said Lupo.

Virginia is the state where residents earn the highest income in the U.S., WalletHub said. Based on the report's findings, the top five percent of Virginians make $545,097 on average per year after adjusting for the cost of living. The median annual income of a Virginia household comes out to $95,339, and the bottom 20 percent of residents make $19,671 annually on average.

Conversely, West Virginia is the state where people have the lowest income in the U.S. A West Virginia household makes a median annual income of $56,610, the third-lowest nationally, and the bottom 20 percent of residents make $13,260 on average per year, which is the fifth-lowest in the nation. The top five percent of West Virginians make $372,218 on average per year.

The top 10 states where residents have the highest income are:

  • No. 1 – Virginia
  • No. 2 – New York
  • No. 3 – New Jersey
  • No. 4 – Washington
  • No. 5 – Connecticut
  • No. 6 – Utah
  • No. 7 – Colorado
  • No. 8 – Minnesota
  • No. 9 – Illinois
  • No. 10 – Massachusetts

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This article originally appeared on CultureMap.com.

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