Exploration of Genetic Variation

Recently, a study published in Nature Immunology caught my eye. In it, the authors undertook an extensive study that charts generic variations influencing the tumour microenvironment (TME). The TME plays a crucial role in tumour progression and response to treatment. Understanding the genetic underpinnings of the TME could help pave the way for novel therapeutic approaches and enhanced treatment targeting. One of the study's most interesting aspects is its use of machine learning methods and advanced bioinformatic approaches to analyze and integrate large-scale datasets. The advanced computational methods used enabled identification of genetic variations that may have otherwise been overlooked, highlighting the power of computational biology in advancing our understanding of cancer. Leveraging these techniques, the researchers created a detailed atlas of genetic factors impacting the TME, which they refer to as immunity quantitative trait loci (immunQTLs), and showed that many of these genetic factors were likely co-localized with previously known expression quantitative trait loci. This observation suggests that the immunQTLs may contribute to the cellular heterogeneity observed within the TME by influencing the expression of genes modulating immune infiltration. Going beyond their initial discovery-driven computational work to further validate their findings, they mapped immunQTLs across >1,600 genes and 23 cancers that are associated with cancer pathogenesis and immune regulation. Diving even deeper, they went on to experimentally validate that one of the identified genes, CCL2, which is implicated in promoting colorectal carcinoma (CRC) progression by allowing tumour cells to evade immunity, may be a promising therapeutic target. This finding demonstrates the potential of the depth of the data set and how it might be used to identify and validate targets. This publication presents a significant amount of work that I have only scratched the surface of here. It offers new insights into the complexity of genetic factors influencing the TME, providing a comprehensive genetic map of the TME and its implications for cancer therapy. The authors have made their data available through a publicly accessible database to help propel further work by the research community. To me, an exciting aspect of this work is that it may help open the door to future combination therapeutic approaches that target both the tumour cells and their microenvironment. https://lnkd.in/ezRckvFh

Data science musings around genomics of India: Interesting new genomics study sheds light on India's evolutionary history and health implications: A recent study led by Elise Kerdoncuff, Laurits Skov, Nick Patterson, Priya Moorjani, and collaborators delved into "50,000 years of evolutionary history of India: Impact on health and disease variation." Whole genomes from 2,762 individuals across India were sequenced, revealing fascinating insights. Key Findings: - Indian ancestry traces back to a single migration from Africa 50,000 years ago, with 1-2% gene flow from Neanderthals and Denisovans. - Indians exhibit the highest diversity of Neanderthal DNA among non-Africans, contributing significantly to reconstructing Neanderthal and Denisovan genomes. - A three-way admixture model—South Asian hunter‑gatherers (AASI), Iranian‑related Neolithic farmers (Sarazm_EN), and Eurasian Steppe pastoralists—explains most ancestry in present-day India. Health & Genetic Variation: - Founder events and endogamy in the last 3,500 years increased homozygosity, particularly in South India, leading to a higher prevalence of recessive genetic disorders. - The study identified around 73 million variants, including 24 million novel ones not found in major databases, with over 40% being extremely rare or unique to the cohort. - Numerous pathogenic variants were discovered, including community-specific risk alleles and rare variants associated with various disorders. Implications: - Addresses a significant gap in global genomics by including diverse Indian populations often overlooked in studies. - Enhances knowledge of ancient migrations, archaic admixture, and cultural influences on genetic diversity and disease susceptibility. - Emphasizes the need for ancestry-informed models and homozygosity mapping in precision medicine and genetic research in India. Hope similar such studies are performed more if possible as it offers a profound narrative of India's past and informs future health strategies tailored to its unique genetic makeup. For more check the paper in the comments:

NEW INSIGHT INTO GENETIC DISEASE VARIABILITY New research reveals that certain cells inactivate one parent’s copy of a gene, leading to a bias in gene activity that may explain why some individuals with disease-causing mutations remain symptom-free. This selective gene inactivation, known as monoallelic expression, affects about 1 in 20 genes and varies between cell types. The study shows that in families with genetic disorders, the active copy of a gene often determines disease severity. These findings challenge traditional genetic paradigms and suggest new approaches to diagnosing and treating inherited diseases. 3 Key Facts: 1. Gene Inactivation: Cells can selectively inactivate one parent’s gene copy, influencing disease outcomes. 2. Disease Variability: Active copies of genes determine the severity or absence of symptoms in genetic disorders. 3. Treatment Potential: Understanding this phenomenon could lead to therapies that adjust gene expression patterns. Source: https://lnkd.in/gWRukaQ6

🎉 Exciting News in Genomic Foundation Models! 🎉 Genetic variants (GVs) are key in diagnosing and treating genetic diseases. With the plummeting costs of next-generation sequencing, we now have a treasure trove of GV data. But this surge presents a challenge for clinicians: how to prioritize patient-specific GVs and integrate them with existing databases for better patient care. 🔍 Deep learning models, and more recently foundation models, have shown promise in variant effect prediction (VEP), but they often oversimplify the problem into binary classifications: pathogenic or benign. These models also lack standardized performance assessments and fail to consider the complexities of genetic expression, such as penetrance and expressivity, across different biological contexts. 💡 Enter: Representation Learning! We believe it’s the key to effectively classifying and aligning unknown GVs with clinically-verified ones. Introducing our large-scale dataset: 🌟 GV-Rep 🌟 Designed for foundation models, it features: --Comprehensive Dataset: 7 million records, including data from 3,166,541 MAVEs, 17,548 gene knockout tests across 1,107 cell lines, QTLs across 14 tissue types, 1,808 oligenic variant combinations, and 156 clinically verified GVs. --Detailed Analysis: Exploring the structure and properties of the dataset. --Experimentation with Genomic Foundation Models (GFMs): Revealing the gap between current GFM capabilities and accurate GV representation, especially for cell- and tissue-level tasks. We hope GV-Rep will advance genomic foundation models and bridge this gap. 📚 Preprint: https://lnkd.in/gy722qZi 💻 Code: https://lnkd.in/ghKgrzMt Huge shoutout to Vallijah Subasri, an ML scientist in our team, and Zehui Li, an intern Vector Institute, for their leadership in this project! Stay tuned for our full paper with detailed experiments and more clinically meaningful use cases! 🚀

Your DNA could decide your dose, but what happens if your DNA isn't in the database? In the Netherlands, patients carry something called a DNA medication passport. A pharmacist scans it, and based on your genetics, they adjust your medication dosage to avoid harm. If you’re one gene variant too fast, you might metabolise the drug before it has a chance to help you. Too slow? It might overwhelm your body. This is called pharmacogenetics, and it's already saving lives. But the concern here is that most gene variants used to guide dosing today are derived from white populations. In one UK study, researchers found a variant affecting Africans that wasn't even in the standard testing panel. The consequences being that people of African ancestry (people like me, like you) could be misdiagnosed, mistreated, or missed altogether. As someone working to expand diagnostic access across Nigeria, this hits home. The work we're doing is not only about access. We seek excellence in everything we do, and that also means: accuracy, context, and dignity. Precision medicine must include African precision. We need: → Our data represented in global genomic panels → Our patients protected from "one-size-fits-all" assumptions → Our systems ready to regulate and implement innovations safely We cannot copy-paste models built elsewhere and expect them to fit. We must validate locally, innovate responsibly, and lead courageously. There’s inspiring work already happening at Syndicate Bio, led by Abasi Ene-Obong, PhD. Let's talk: How can we ensure pharmacogenetics works for Africa?

The FDA's Oncologic Drugs Advisory Committee recently voted overwhelmingly against expanding the use of a major pharma company's drug, emphasizing the need for drug sponsors to make "deliberate decisions" to ensure their clinical trial data truly applies to the U.S. population, specifically citing concerns about genetic polymorphisms between patient populations. You see, while humans are remarkably similar at the genetic level (approximately 99.6% identical), the remaining small percentage of variation, largely due to genetic polymorphisms, is incredibly important. These variations are not random "mutations" but rather common differences that account for much of the diversity among individuals and patient populations, influencing their health, disease susceptibility, and response to medical treatments. For instance, a significant number of people of African descent have specific polymorphisms in a gene called CYP2D6. This gene is responsible for metabolizing many common medications, including some antidepressants, certain pain relievers (such as codeine), and heart medications. The core message from regulatory bodies like the FDA is unequivocally clear: when trials predominantly focus on a limited segment of the population, we risk approving drugs that may not work optimally, or could even be harmful, for other groups due to these underlying genetic differences.

#Genomic research has historically been limited by population bias, with most large-scale studies disproportionately representing individuals of European descent. This lack of diversity creates significant gaps in variant interpretation, #Pharmacogenomics, and drug efficacy. Ultimately, this limits the effectiveness of precision medicine on a global scale. However, new studies emphasize how the genetic diversity of African populations can drive breakthroughs in rare variant discovery, polygenic risk scores, and treatment optimization across all ancestries.   Expanding genomic datasets to include underrepresented populations isn’t just about equity—it’s about scientific rigor. At Velsera, we are committed to enhancing population-scale genomic analysis by integrating cloud-native bioinformatics platforms that accurately process high-throughput sequencing data across diverse cohorts. Our #pangenome analytics solutions and context-specific variant interpretation #knowledgebase are designed to adapt to a broader spectrum of genetic backgrounds, improving the accuracy of disease risk assessments and clinical management predictions. By focusing on multi-ancestry data integration, we help researchers uncover previously undetected genetic associations, ultimately improving precision medicine for all populations.   This shift toward inclusive #Genomics is not only necessary but inevitable. The insights gained from African genetic diversity will inform global healthcare strategies, making precision medicine more effective and accessible. The future of genomics depends on removing bias, expanding datasets, and leveraging advanced bioinformatics to translate diversity into discovery. Read more on the impact of this research: https://bit.ly/3QhRGLq.

Pangenome represents the complete genetic diversity of a species, beyond the limitations of a single reference genome. The original human genome reference was largely based on a small number of individuals, predominantly of European ancestry, overlooking extensive variations present across global populations. By integrating multiple genome sequences, the pangenome captures structural variations such as insertions, deletions, duplications, inversions, and single nucleotide variants (SNVs), providing a richer and more accurate representation of human diversity. Transitioning from a single reference to a pangenome framework marks a fundamental advancement in genomics, evolutionary biology, and precision medicine, ensuring that discoveries are inclusive and representative of all populations. #Pangenome #HumanGenomics #GenomicDiversity #PrecisionMedicine #PopulationGenomics #Bioinformatics #EvolutionaryGenomics #Genomes_gov

Researchers have discovered over 5,000 genetic variants in the BAP1 gene that significantly elevate the risk of various cancers, including those of the eye, lung lining, brain, skin, and kidney. Approximately 20% of these variants are pathogenic, meaning they disrupt the tumor-suppressing function of the BAP1 protein, increasing a person's lifetime cancer risk by up to 50%. The study used a technique called "saturation genome editing" to examine all possible DNA changes in the BAP1 gene, identifying 5,665 harmful variants. Additionally, these harmful variants were associated with elevated levels of IGF-1, a growth factor hormone linked to cancer growth, indicating potential new therapeutic targets. #cancer #gene #geneticvariants