![\"\"](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/08\/In_Text_Genomic_data_on-iPAD_27493073484_6f412da96a_h-515x343.jpg\")
Physician and patient look at genomic data. NHGRI<\/p>\n\tPhysician and patient look at genomic data. NHGRI<\/p>\n
A collaboration including Rice University<\/a>, Baylor College of Medicine<\/a> and Texas Children\u2019s Hospital\u2019s Jan and Dan Duncan Neurological Research Institute<\/a> (NRI) has produced a breakthrough in how to study and classify complex diseases. The new method, called the Causal Pivot, gives researchers a powerful way to detect hidden genetic drivers and subgroup patients by true biological causes of their illnesses using new computational tools. The findings, published in the American Journal of Human Genetics,\u00a0<\/a>pave the way for major advances in personalized genetic medicine.<\/p>\n Complex diseases like Parkinson\u2019s, breast cancer and high cholesterol are not the same for everyone who has them. While patients may share the same diagnosis, the path that led them there can differ dramatically; some cases may be caused by the combined effect of thousands of common genetic variations, while others may be driven largely by a single rare, harmful mutation in an important gene. Until now, large-scale genetic studies have tended to blur these differences, averaging genetic effects across all patients.<\/p>\n \u201cNot everyone with a complex disease gets there the same way,\u201d said lead author Dr. Chad Shaw<\/a>, a professor of molecular and human genetics<\/a> at Baylor with a joint appointment at Rice University. He also is a faculty member at the NRI and director of Rice\u2019s Data to Knowledge Lab<\/a> (D2K). \u201cThe Causal Pivot is designed to detect those differences and sort patients into more precise, biologically meaningful subgroups. This is a foundational step toward truly personalized genetic medicine.\u201d<\/p>\n The method is a novel extension in the methodology of Causal Analysis, which is an area of statistical science that Shaw teaches at Rice.<\/p>\n How does it work?<\/p>\n The Causal Pivot leverages the polygenic risk score (PRS), which is a summary of the combined effect of many common genetic variants and something researchers often already know about many diseases. The PRS acts as a \u201cpivot\u201d against which the method tests other potential causes, such as rare, harmful DNA changes known as rare variants.<\/p>\n If a rare variant truly drives disease in some people, then among patients with the disease, those who carry the variant tend to have lower polygenic risk scores than those who don\u2019t (because the rare variant itself provides the push into illness). The Causal Pivot formalizes this concept into a rigorous statistical test that can identify these rare variant\u2013driven subgroups and estimate their effect size.<\/p>\n Unlike traditional genome-wide association studies, which require large sets of both patient and control data, the Causal Pivot can work in case-only designs \u2013 an advantage in real-world settings where healthy control samples may be unavailable, such as in rare subtypes, certain clinical trials or studies of adverse drug reactions. The team also developed safeguards against confounding factors like ancestry, ensuring the method\u2019s reliability in diverse populations.<\/p>\n Validating the method on three well-known diseases<\/p>\n Using data from the UK Biobank, which includes genetic and health information from more than half a million volunteers, the researchers validated the method on three well-studied gene\u2013disease pairs: LDLR variants in people with very high LDL cholesterol (hypercholesterolemia); BRCA1 variants in breast cancer; and GBA1 variants in Parkinson\u2019s disease.<\/p>\n In each case, the Causal Pivot detected clear signals consistent with known biology \u2013 strong evidence that the method works as intended. Cross-disease tests (such as looking for GBA1\u2019s effect in breast cancer) showed no signals, and harmless \u2018synonymous\u2019 variants produced no false positives.<\/p>\n The team went further in Parkinson\u2019s testing, applying the approach to a lysosomal storage pathway, which includes a group of genes involved in recycling cellular waste. They found that patients with a heavier burden of rare variants in this pathway tended to have lower polygenic risk scores, suggesting multiple rare hits can combine to create an alternative route into disease.<\/p>\n A game changer for treatments<\/p>\n This kind of subgroup detection changes the game,\u201d Shaw said. \u201cIt opens the door to clinical testing and tools that can match patients to therapies based on the actual mechanism driving their disease, not just the name of the condition.\u201d<\/p>\n The implications for personalized medicine are far-reaching. By revealing the genetic \u2018routes\u2019 into a disease, the Causal Pivot could improve genetic testing strategies, helping doctors decide who should be tested for specific rare mutations. It could also enable more targeted clinical trials, focusing on patients most likely to respond to a given therapy. Even more, it provides a framework for classifying patients by mechanism, not just symptoms, paving the way for mechanism-specific treatments.<\/p>\n \u201cPersonalized medicine will be grounded in comprehensive genome sequencing for each patient, but it\u2019s hard for health care providers to understand because there are more than 5 million variants in each person\u2019s genome,\u201d said Dr. John Belmont<\/a>, adjunct professor of molecular and human genetics at Baylor and one of the authors of the paper. \u201cCurrent interpretation workflows are rule-based, uncalibrated and don\u2019t produce patient-level causal probabilities. Causal\u00a0analysis, by placing the emphasis on the\u00a0effects of interventions, is ideal for medical decision\u00a0making. Shaw\u2019s new work is a step along the road to formally integrating information from both clinical and population cohorts. It introduces a new framework for modeling the competing factors in disease and will help doctors accurately weigh all the possibilities.\u201d<\/p>\n \u201cMost genetic studies of human disease have focused exclusively on either common or rare genetic variations \u2013 changes in the DNA code,\u201d \u00a0said co-author Dr. Joshua Shulman<\/a>, professor of neurology<\/a>, neuroscience<\/a> and molecular and human genetics at Baylor. He also is the co-director of the Duncan NRI. \u201cShaw\u2019s team has created an elegant statistical model that helps unify these approaches, allowing for a more powerful, integrated analysis. We are excited to use this strategy to discover new genetic risk factors for Alzheimer\u2019s and Parkinson\u2019s disease.\u201d<\/p>\n The researchers envision future applications that go beyond genetics. In place of the polygenic risk score, the pivot could be any established disease driver \u2013 an environmental exposure, a biomarker or even an imaging feature, for example \u2013 making the framework a versatile discovery engine for multiple branches of medicine.<\/p>\n Personalized medicine needs structure,\u201d Shaw said. \u201cWe\u2019ve created a clear, testable way to account for genetic diversity within a disease. It\u2019s a tool we can use on large datasets today, and one we can adapt to the clinic tomorrow.\u201d<\/p>\n This collaborative study included C.J. Williams at Genetics & Genomics Services, Inc., Tao Tao Tan at Baylor College of Medicine and Daniel Illera and Nicholas Di at Rice University.<\/p>\n This research was supported by funding from the Jan and Dan Duncan Neurological Research Institute, the Ting Tsung and\u00a0Wei\u00a0Fong\u00a0Chao Foundation, the\u00a0Huffington Foundation and in part by a grant from Genetics & Genomics Services, Inc.<\/p>\n Original article by Rice University.<\/a><\/p>\n \u00a0<\/p>\n Follow From the Labs on X<\/a>, BlueSky<\/a> and Instagram<\/a>!<\/p>\n \u00a0<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/08\/1400014654185-Chad-A.-Shaw-PhD.jpeg\")
<\/a>Dr. Chad A. Shaw<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/08\/belmont-john-ffedb1880eba-515x515.jpeg\")
<\/a>Dr. John Belmont<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/08\/joshua-shulman-md-phd-1447862676545.jpeg\")
Dr. Joshua Shulman<\/p>\n