Myriad Genetics, Inc.'s (NASDAQ:MYGN) Intrinsic Value Is Potentially 221% Above Its Share Price Yahoo Finance
Category Archives: Genetics
Educational attainment of East Asians are linked to genetics – KBR
400 Bad Request The server cannot or will not process the request due to an apparent client error (e.g., malformed request syntax, invalid request message framing, or deceptive request routing). If you have any questions, please contact the administrator with the following code. 40212FB0ED82403D3EBB7AD583E3DD64
Read this article:
Educational attainment of East Asians are linked to genetics - KBR
MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association … – Nature.com
Willer, C. J. et al. Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nat. Genet 40, 161169 (2008).
Article CAS PubMed PubMed Central Google Scholar
Nielsen, J. B. et al. Genome-wide study of atrial fibrillation identifies seven risk loci and highlights biological pathways and regulatory elements involved in cardiac development. Am. J. Hum. Genet 102, 103115 (2018).
Article CAS PubMed Google Scholar
Schaid, D. J. et al. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491504 (2018).
Article CAS PubMed PubMed Central Google Scholar
Chen, W. et al. Fine mapping causal variants with an approximate Bayesian method using marginal test statistics. Genetics 200, 719736 (2015).
Article PubMed PubMed Central Google Scholar
Yang, J., Fritsche, L. G., Zhou, X. & Abecasis, G. A scalable Bayesian method for integrating functional information in genome-wide association studies. Am. J. Hum. Genet 101, 404416 (2017).
Article CAS PubMed Central Google Scholar
Wen, X., Lee, Y., Luca, F. & Pique-Regi, R. Efficient integrative multi-SNP association analysis via deterministic approximation of posteriors. Am. J. Hum. Genet 98, 11141129 (2016).
Article CAS PubMed Google Scholar
Newcombe, P. J., Conti, D. V. & Richardson, S. JAM: a scalable Bayesian framework for joint analysis of marginal SNP effects. Genet. Epidemiol. 40, 188201 (2016).
Article PubMed PubMed Central Google Scholar
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 14931501 (2016).
Article CAS PubMed PubMed Central Google Scholar
Zhang, W., Najafabadi, H. S. & Li, Y. SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463133 (2021).
Zou, Y., Carbonetto, P., Wang, G. & Stephens, M. Fine-mapping from summary data with the Sum of Single Effects model. PLoS Genet. 18, e1010299 (2022).
Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B Stat. Methodol. 82, 12731300 (2020).
Article Google Scholar
Kichaev, G. et al. Integrating functional data to prioritize causal variants in statistical fine-mapping studies. PLoS Genet 10, e1004722 (2014).
Article PubMed PubMed Central Google Scholar
Yang, Z. et al. CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses. Nat. Genet 55, 10571065 (2023).
Article CAS PubMed Google Scholar
Shi, H. et al. Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data. Am. J. Hum. Genet 106, 805817 (2020).
Article CAS PubMed PubMed Central Google Scholar
Shi, H. et al. Population-specific causal disease effect sizes in functionally important regions impacted by selection. Nat. Commun. 12, 115 (2021).
Google Scholar
Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675679 (2021).
Article CAS PubMed PubMed Central Google Scholar
Mahajan, A. et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat. Genet 46, 234244 (2014).
Article CAS PubMed Google Scholar
Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514518 (2019).
Article CAS PubMed PubMed Central Google Scholar
Peterson, R. E. et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell 179, 589603 (2019).
Article CAS PubMed PubMed Central Google Scholar
LaPierre, N. et al. Identifying causal variants by fine mapping across multiple studies. PLoS Genet 17, e1009733 (2021).
Article CAS PubMed PubMed Central Google Scholar
Mgi, R. et al. Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum. Mol. Genet 26, 36393650 (2017).
Article PubMed PubMed Central Google Scholar
Kichaev, G. & Pasaniuc, B. Leveraging functional-annotation data in trans-ethnic fine-mapping studies. Am. J. Hum. Genet 97, 260271 (2015).
Article CAS PubMed PubMed Central Google Scholar
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203209 (2018).
Article CAS PubMed PubMed Central Google Scholar
Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 12, e10017779 (2015).
Article Google Scholar
Benjamin, M. UK Biobank Neale lab. http://www.nealelab.is/uk-biobank (2018).
Yuan, K. et al. Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases. Preprint at medRxiv https://doi.org/10.1101/2023.01.07.23284293 (2023).
Zou, Y., Carbonetto, P., Xie, D., Wang, G. & Stephens, M. Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model. Preprint at bioRxiv https://doi.org/10.1101/2023.04.14.536893 (2023).
Weissbrod, O. et al. Functionally informed fine-mapping and polygenic localization of complex trait heritability. Nat. Genet 52, 13551363 (2020).
Article CAS PubMed PubMed Central Google Scholar
Kanai, M. et al. Insights from complex trait fine-mapping across diverse populations. Preprint at medRxiv https://doi.org/10.1101/2021.09.03.21262975 (2021).
Liang, Y. et al. A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction. Nat. Commun. 12, 1424 (2021).
Article CAS PubMed PubMed Central Google Scholar
Wang, Q. S. et al. Leveraging supervised learning for functionally informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs. Nat. Commun. 12, 3394 (2021).
Article CAS PubMed PubMed Central Google Scholar
Qiao, J., Shao, Z., Wu, Y., Zeng, P. & Wang, T. Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing. J. Transl. Med 20, 424 (2022).
Article CAS PubMed PubMed Central Google Scholar
Shang, L. et al. Genetic architecture of gene expression in European and African Americans: an eQTL mapping study in GENOA. Am. J. Hum. Genet 106, 496512 (2020).
Article CAS PubMed PubMed Central Google Scholar
Nakamura, M. T. & Nara, T. Y. Structure, function, and dietary regulation of delta6, delta5, and delta9 desaturases. Annu Rev. Nutr. 24, 345376 (2004).
Article CAS PubMed Google Scholar
Stoffel, W. et al. Obesity resistance and deregulation of lipogenesis in 6-fatty acid desaturase (FADS2) deficiency. EMBO Rep. 15, 110120 (2014).
Article CAS PubMed Google Scholar
Nakaya, Y., Schaefer, E. J. & Brewer, H. B. Activation of human post heparin lipoprotein lipase by apolipoprotein H (2-glycoprotein I). Top. Catal. 95, 11681172 (1980).
CAS Google Scholar
Choudhury, A. et al. Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits. Nat. Commun. 13, 2578 (2022).
Article CAS PubMed PubMed Central Google Scholar
Cavalcante, L. N. et al. African genetic ancestry is associated with lower frequency of PNPLA3 G allele in non-alcoholic fatty liver in an admixed population. Ann. Hepatol. 27, 100728 (2022).
Article CAS PubMed Google Scholar
Goffredo, M. et al. Role of TM6SF2 rs58542926 in the pathogenesis of nonalcoholic pediatric fatty liver disease: a multiethnic study. Hepatology 63, 117125 (2016).
Article CAS Google Scholar
Fan, Y. et al. Hepatic transmembrane 6 superfamily member 2 regulates cholesterol metabolism in mice. Gastroenterology 150, 12081218 (2016).
Article CAS Google Scholar
OHare, E. A. et al. TM6SF2 rs58542926 impacts lipid processing in liver and small intestine. Hepatology 65, 15261542 (2017).
Article PubMed Google Scholar
Zeng, P., Hao, X. & Zhou, X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics 34, 27972807 (2018).
Article CAS PubMed PubMed Central Google Scholar
Zhou, G., Chen, T. & Zhao, H. SDPRX: a statistical method for cross-population prediction of complex traits. Am. J. Hum. Genet 110, 1322 (2023).
Article CAS PubMed Google Scholar
Thompson, W. D. et al. Association of maternal circulating 25(OH)D and calcium with birth weight: a mendelian randomisation analysis. PLoS Med 16, e1002828 (2019).
Article PubMed PubMed Central Google Scholar
Burns, A. C. et al. Genome-wide gene by environment study of time spent in daylight and chronotype identifies emerging genetic architecture underlying light sensitivity. Sleep 46, zsac287 (2023).
Article PubMed Google Scholar
Gharahkhani, P. et al. Effect of increased body mass index on risk of diagnosis or death from cancer. Br. J. Cancer 120, 565570 (2019).
Article PubMed PubMed Central Google Scholar
Bovijn, J. et al. GWAS identifies risk locus for erectile dysfunction and implicates hypothalamic neurobiology and diabetes in etiology. Am. J. Hum. Genet 104, 157163 (2019).
Article CAS PubMed Google Scholar
Yang, S. & Zhou, X. PGS-server: accuracy, robustness and transferability of polygenic score methods for biobank scale studies. Brief. Bioinform. 10, bbac039 (2022).
Article Google Scholar
Horton, R. et al. Gene map of the extended human MHC. Nat. Genet. 5, 889899 (2004).
Article CAS Google Scholar
Beck, S. et al. Complete sequence and gene map of a human major histocompatibility complex. Nature 401, 921923 (1999).
Article Google Scholar
Lonsdale, J. et al. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580585 (2013).
Article CAS Google Scholar
Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 13181330 (2020).
Article CAS Google Scholar
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 114 (2016).
Article Google Scholar
Read the original:
MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association ... - Nature.com
Novel Genetic Priority Score Unveiled to Enhance Target Prioritization in Drug Development – Mount Sinai
Driven by the need for a better way to prioritize targets for drug development, the Icahn School of Medicine at Mount Sinai has led the development of a novel genetic priority score (GPS) that will integrate various types of human genetic data into a single easy-to-interpret score.
The findings were described in the January 3 online issue of Nature Genetics [DOI: 10.1038/s41588-023-01609-2].
Studies have shown that drugs have an increased likelihood of success in clinical trials when the genes they target have been demonstrated to have genetic support. The new tool integrates multiple lines of genetic evidence to prioritize these drug targets.
The score measures the general ability of a gene to be targeted by drugs; genes with a high score in the new tool are more likely to succeed as a drug target. The score identifies both known drug gene targets as well as potential novel therapeutic targets.
We built a genetic priority score that was inspired by the realization that diverse human genetic data provides insights into drug targets, yet there was an absence of a cohesive strategy for integrating these various data types into an easily interpretable score. So we developed a computational score to prioritize drug targets for enhanced drug discovery," says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at Icahn Mount Sinai. Remarkably, several genes with high GPS were already known to be targeted by approved drugs, providing validation for the new tool.
The GPS, with its potential to streamline target prioritization, is positioned to have a significant impact on drug development. It offers a valuable resource for researchers seeking to optimize the selection of drug gene targets for enhanced efficiency in the drug development process, say the investigators.
The rising cost of developing therapeutics, in the billions, is primarily due to high clinical trial failures, underscoring inefficiencies in drug development pipelines. Improving early-stage target prioritization is critical. Studies consistently show that drug indications with human genetic support are more likely to succeed in trials and gain approval, says study first author Aine Duffy, a PhD candidate in the lab of Dr. Do.
The researchers are encouraged by the developments but emphasize this represents only a first step for prioritization and the need for careful follow-up and further investigation of gene targets with high scores. Next, the investigators plan to refine the model by incorporating additional genetic features and evaluating more sophisticated algorithms for constructing the GPS.
The paper is titled Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications. Please see [DOI:10.1038/s41588-023-01609-2] to view competing interests.
The remainingauthors, all with Icahn Mount Sinai except where indicated, are Ben Omega Petrazzini (Associate Bioinformatician); David Stein (PhD candidate); Joshua K. Park (MD/PhD candidate); Iain S. Forrest, PhD (and MD candidate); Kyle Gibson (MD candidate); Ha My Vy, PhD; Robert Chen Park (MD/PhD candidate); Carla Marquez-Luna, PhD; Matthew Mort, PhD (Cardiff University, UK); Marie Verbanck, PhD (Universite Paris Cite and Icahn Mount Sinai); Avner Schlessinger, PhD; Yuval Itan, PhD; David N. Cooper, PhD (Cardiff University, UK), Ghislain Rocheleau, PhD; and Daniel M. Jordan, PhD.
The study was funded by the following: the NIH T32 Postdoctoral Research Award (5T32HL00782424); the National Institute of General Medical Sciences of the NIH (R35-GM124836); the National Heart, Lung and Blood Institute of the NIH (R01-HL139865) and (R01-HL155915); the French National Research Agency (ANR) (ANR-21-CE45-0023-01); the Leducq Foundation (21CVD01); The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai; and the Helmsley Foundation Award (2209-05535).
About the Icahn School of Medicine at Mount Sinai
The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the eight- member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to a large and diverse patient population.
Ranked 14th nationwide in National Institutes of Health (NIH) funding and among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges, Icahn Mount Sinai has a talented, productive, and successful faculty.More than 3,000 full-time scientists, educators, and clinicians work within and across 44 academic departments and 36 multidisciplinary institutes, a structure that facilitates tremendous collaboration and synergy.Our emphasis on translational research and therapeutics is evident in such diverse areas as genomics/big data, virology, neuroscience, cardiology, geriatrics, as well as gastrointestinal and liver diseases.
Icahn Mount Sinai offers highly competitive MD, PhD, and Masters degree programs, with current enrollment of approximately 1,300 students.It has the largest graduate medical education program in the country, with more than 2,000 clinical residents and fellows training throughout the Health System.In addition, more than 550 postdoctoral research fellows are in training within the Health System.
A culture of innovation and discovery permeates every Icahn Mount Sinai program.Mount Sinais technology transfer office, one of the largest in the country, partners with faculty and trainees to pursue optimal commercialization of intellectual property to ensure that Mount Sinai discoveries and innovations translate into healthcare products and services that benefit the public.
Icahn Mount Sinais commitment to breakthrough science and clinical care is enhanced by academic affiliations that supplement and complement the Schools programs.
Through the Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. Additionally, MSIP develops research partnerships with industry leaders such as Merck & Co., AstraZeneca, Novo Nordisk, and others.
The Icahn School of Medicine at Mount Sinai is located in New York City on the border between the Upper East Side and East Harlem, and classroom teaching takes place on a campus facing Central Park. Icahn Mount Sinais location offers many opportunities to interact with and care for diverse communities.Learning extends well beyond the borders of our physical campus, to the eight hospitals of the Mount Sinai Health System, our academic affiliates, and globally.
-------------------------------------------------------
*Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Beth Israel; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai.
Biotechnology and Genomics: Illuminating the Path to Scientific Breakthroughs – Reliable Plant Magazine
Biotechnology and genomics, the intersection of biology and technology, have been at the forefront of scientific advancements for decades. However, recent years have witnessed an unprecedented surge in breakthroughs and innovations in these fields, revolutionizing how we understand, diagnose, and treat diseases, engineer organisms, and explore the fundamental building blocks of life.
In this article, we will explore some of the latest advancements in biotechnology and genomics that are reshaping the landscape of science and medicine, and how AI and machine learning, in particular, are impacted by machine reliability and a proper lubrication program.
CRISPR-Cas9 gene editing stands as one of the most remarkable and groundbreaking advancements in the realm of biotechnology in recent memory. This revolutionary system has upended the traditional methods of gene modification, offering scientists an unprecedented level of precision, efficiency, and versatility when it comes to manipulating DNA.
The CRISPR-Cas9 system, often likened to a genetic "scissor" and "tape," enables researchers to precisely target specific DNA sequences and either delete or insert genetic material with unparalleled accuracy. This precision has far-reaching implications across various fields of science and medicine, unlocking new possibilities for addressing genetic disorders, engineering organisms, and deepening our understanding of the intricate world of genetics.
The potential applications of CRISPR-Cas9 are nothing short of staggering. In the realm of medicine, this technology holds the promise of revolutionizing the treatment of genetic disorders that were once considered incurable. By correcting faulty genes responsible for conditions like cystic fibrosis or sickle cell anemia, CRISPR-based therapies offer newfound hope to patients and their families.
Furthermore, the system's versatility extends beyond human health, with implications for agriculture, as it allows for the development of genetically modified organisms designed for improved crop yields, disease resistance, and environmental adaptability. Additionally, CRISPR-Cas9 has become an indispensable tool for researchers seeking to uncover the mysteries of genetics, facilitating the exploration of gene functions and interactions, paving the way for scientific breakthroughs that were previously unimaginable.
CRISPR-Cas9's precision and versatility have opened doors to genome editing therapies that were once the stuff of science fiction. Recent breakthroughs, such as the successful treatment of sickle cell anemia and beta-thalassemia using CRISPR-based approaches, demonstrate the transformative potential of this technology. These therapies offer hope to individuals afflicted with previously untreatable genetic conditions, potentially providing them with a cure or significantly improved quality of life.
As scientists refine and expand the applications of CRISPR-Cas9, the prospects for addressing a wide array of genetic diseases continue to grow.
Single-cell genomics is a cutting-edge technique that has unlocked the ability to analyze individual cells within complex mixtures, shedding light on the cellular diversity that underpins life. This technology has provided profound insights into the heterogeneity of tissues and organisms, offering a better understanding of diseases like cancer and neurodegenerative disorders. Researchers can now explore genetic variations at the most granular level, unraveling the intricate mosaic of genetic signatures within a single organism.
Single-cell genomics holds significant implications for precision medicine, as it allows for the identification of unique cellular profiles, paving the way for tailored treatments and a deeper comprehension of complex biological systems.
Metagenomics has emerged as a groundbreaking field that is redefining our comprehension of the microbial world. It involves the study of the collective genetic material of entire microbial communities, transcending the limitations of traditional microbiology, which often focused on culturing individual microorganisms.
Through metagenomics, scientists can explore the intricate genetic diversity of these complex microbial ecosystems, gaining unprecedented insights into their functions and interactions. This transformative approach has profound implications for various domains, including human health, agriculture, and environmental science.
In the realm of human health, metagenomics has unveiled the crucial role of microbiomes in maintaining our well-being. These microbial communities residing in and on our bodies influence everything from digestion and immunity to mental health. By deciphering the genetic makeup of these microbiomes, researchers are uncovering links between microbial composition and various diseases, paving the way for personalized medicine approaches that leverage the microbiome to enhance health outcomes.
Additionally, in agriculture, metagenomics is driving sustainable practices by helping to optimize soil microbiomes, enhance crop health, and reduce the need for harmful chemical interventions. This holistic understanding of microbial ecosystems is reshaping our approach to both health and agriculture, with metagenomics as the guiding light toward a more harmonious coexistence with the microbial world.
Synthetic biology represents the fusion of biology and engineering, offering a dynamic platform to design and build biological components and systems with unprecedented precision. Recent advancements in this field have yielded remarkable creations, including synthetic organisms engineered with custom-designed genomes tailored for specific purposes. Notably, these synthetic organisms have been harnessed for environmentally impactful applications, such as the biodegradation of plastics, offering innovative solutions to address pollution and the production of biofuels, contributing to the quest for sustainable energy sources.
Synthetic biology's ability to engineer organisms for tailored functions extends beyond environmental applications. It holds promise in diverse domains, from medicine to industry, enabling the development of novel drugs, biosensors, and bio-based materials. As this interdisciplinary field continues to evolve, synthetic biology stands at the forefront of scientific innovation, redefining our capabilities to engineer life itself for the betterment of society and the environment.
AI and machine learning have become pivotal in the realm of genomics, reshaping the landscape of genetic research and its applications. These advanced technologies are adept at handling vast datasets, a characteristic especially crucial in the genomics field where genetic information is abundant and intricate. Through sophisticated algorithms, AI and machine learning are capable of identifying subtle patterns and associations within this data, making them invaluable tools in predicting disease risks and unraveling the genetic underpinnings of complex disorders.
In the realm of diagnostics and treatment planning, AI and machine learning offer an unprecedented level of precision. By analyzing an individual's genetic makeup alongside other clinical data, these technologies can identify genetic markers and biomarkers associated with specific diseases.
This not only aids in early disease detection but also informs healthcare professionals about potential treatment strategies tailored to a patient's unique genetic profile. The result is a more personalized and effective approach to healthcare, where treatments are optimized based on a patient's genetic susceptibilities and therapeutic responses.
AI and machine learning are at the forefront of genomics, providing researchers and healthcare professionals with powerful tools to analyze genetic data, predict disease risks, and identify critical genetic markers. This data-driven approach not only enhances our understanding of genetics but also transforms diagnostics and treatment planning by offering personalized, precise, and informed medical interventions.
As these technologies continue to evolve, they hold the promise of further revolutionizing the field of genomics and improving patient outcomes in the realm of healthcare.
Maintenance and reliability play a pivotal role in genomics' AI and machine-learning applications. Consistent upkeep of equipment ensures uninterrupted functionality, reducing downtime that can impede crucial data analysis and interpretation. Reliable systems and regular maintenance foster accurate genetic sequencing, allowing AI algorithms to derive precise insights that impact planning & scheduling, along with preventive maintenance procedures. This reliability also enables advancements in disease understanding, personalized medicine, and efficient drug development by providing consistent and dependable genomic data for AI-driven analyses.
A proper machinery lubrication program is also paramount in genomics for AI and machine-learning applications in a few remarkable ways. Firstly, lubrication obviously ensures the smooth and longterm operation of the intricate equipment and technologies utilized in genetic sequencing. Along with the intended benefits of preventing friction-induced damage and maintaining optimal performance, lubrication also safeguards sensitive machinery components, thereby reducing the risk of inaccuracies in interpreting results or research and ensuring consistent data output.
Practicing proper standards and safeguards for longterm asset reliability contributes to precise genomic analyses, aiding AI algorithms in deciphering genetic patterns that are crucial for timely disease identification, safe and responsible drug development, and advancing genomic research with enhanced accuracy and efficiency.
Epigenetics explores changes in gene expression that are not caused by alterations in the DNA sequence itself. Recent studies have revealed the critical role of epigenetics in cancer, aging, and various diseases. Understanding epigenetic modifications may lead to new therapeutic strategies and personalized medicine approaches.
With the exponential growth in genomic data availability, concerns regarding the privacy and security of this sensitive information have understandably risen. Genomic data contains highly personal and potentially sensitive details about an individual's genetic makeup and susceptibility to diseases, making it paramount to safeguard this data against unauthorized access or misuse.
To address these challenges, the field of genomics has seen remarkable innovations in secure data sharing and encryption. These advancements ensure that researchers can collaborate effectively while upholding the utmost protection of individuals' sensitive genetic information.
Secure data-sharing protocols in genomics involve robust encryption techniques that render genomic data unintelligible to anyone without the proper decryption keys. This ensures that even in the event of data breaches or unauthorized access attempts, the genomic information remains protected.
Furthermore, secure data sharing frameworks often include strict access controls and authorization mechanisms, allowing only authorized individuals or entities to access and utilize the data.
These privacy-enhancing measures strike a balance between enabling scientific collaboration and respecting individuals' rights to keep their genetic information confidential. As genomics continues to advance, the development and implementation of state-of-the-art privacy and security measures remain integral to maintaining the trust of individuals who contribute their genetic data for scientific research, ultimately fostering a secure and ethical genomic research environment.
Drug discovery and targeted therapies have entered a new era with the integration of genomics. This transformative approach enables researchers to delve into the genetic intricacies of diseases, unveiling potential drug targets and biomarkers that drive the development of highly precise, personalized treatment strategies. By analyzing the genomes of affected individuals, researchers can pinpoint specific genes or genetic mutations at the root of disease, paving the way for innovative therapeutic interventions.
The emergence of precision medicine, guided by genomics, tailors treatments to individual patients based on their unique genetic makeup and disease characteristics. This tailored approach not only maximizes treatment effectiveness but also minimizes side effects, improving patient outcomes and quality of life. Targeted therapies, designed to disrupt specific disease-related molecules or pathways, exemplify the power of genomics in minimizing collateral damage to healthy cells, leading to more efficient and safer treatments.
With genomics shaping the drug discovery process, clinical trials becoming more tailored, and adverse events reduced, patients are benefiting from treatments that are not only more effective but also less intrusive, marking a significant stride toward the future of medicine.
Environmental genomics is a cutting-edge field that leverages genomic technologies to investigate the intricate relationship between the environment, biodiversity, and genetic diversity within ecosystems. It plays a vital role in understanding how environmental changes, particularly those linked to climate change, influence the genetics and adaptability of species. This knowledge is pivotal for biodiversity conservation and offers insights into species' resilience in the face of changing environments.
Key aspects of environmental genomics include assessing biodiversity comprehensively, recognizing the significance of genetic diversity for adaptability, identifying keystone species' genetic makeup, monitoring genetic responses to climate change, informing conservation strategies, predicting species vulnerability, and assessing ecosystem health. In essence, environmental genomics illuminates the genetic underpinnings of ecosystems and their responses to environmental shifts, emphasizing the importance of genetic diversity in environmental adaptation and sustainability.
Biotechnology and genomics are propelling us into an era of unprecedented scientific discovery and medical breakthroughs. The recent advancements mentioned above are just a glimpse of the incredible potential these fields hold. As technology continues to evolve, biotechnology and genomics will play an increasingly central role in shaping the future of science and medicine.
With responsible and ethical applications, these innovations have the power to improve human health, address environmental challenges, and deepen our understanding of the biological world. As researchers and innovators continue to push the boundaries of what is possible, we can look forward to a brighter and healthier future.
Read the original post:
Biotechnology and Genomics: Illuminating the Path to Scientific Breakthroughs - Reliable Plant Magazine
Genetic Influence on COVID-19 Vaccine Response Revealed – Medriva
Unveiling the Genetic Influence on COVID-19 Vaccine Response
A recent genome-wide association study published in The American Journal of Human Genetics has shed light on the genetic factors that play a pivotal role in how individuals respond to COVID-19 vaccination. By using the comprehensive data available from the U.K. Biobank, researchers have identified significant genes, particularly the alleles from the HLA class II, that are linked to the IgG serostatus against the SARS-CoV-2 spike protein. This groundbreaking revelation is expected to shape future vaccination strategies, taking into account these genetic variations.
Among the identified alleles, the HLA-DRB1*13:02 allele displayed the most significant effect in protecting against seronegativity of IgG antibodies. In simpler terms, individuals possessing this particular allele have a higher likelihood of developing a positive IgG antibody response following COVID-19 vaccination. The alleles protective effect against IgG seronegativity has significant implications for understanding individual immune responses to the vaccine and devising effective vaccination strategies.
Another intriguing finding from the study was the shared genetic predisposition between the IgG serotype status and increased susceptibility to or severity of COVID-19. This implies that the same genetic factors that determine an individuals antibody response to the vaccine might also influence their susceptibility to the virus and the severity of the disease if infected.
The impact of HLA alleles on IgG responses was found to be cell type-specific. This implies that the influence of these genetic factors differs based on the cell type, adding another layer of complexity to the understanding of immune responses to COVID-19 vaccines. This finding necessitates further research to unravel the intricate relationship between these genetic factors and the cell types they affect.
The studys findings underscore the pressing need to consider the influence of constitutive genetics when designing vaccination strategies for diverse populations. As the HLA class II alleles significantly impact the bodys immune response to the vaccine, individualized vaccination strategies might be necessary to ensure optimal vaccine effectiveness for different individuals.
In a separate study, researchers investigated the time-dependent changes in SARS-CoV-2 antibody responses in infected and/or vaccinated and unvaccinated individuals. It was found that antibody levels declined after infection but persisted for at least 68 months. Individuals who had received only CoronaVac had higher anti-nucleocapsid antibody levels in the early months than those who received mixed vaccination. However, anti-spike antibodies persisted longer and at higher levels in individuals who had received mixed vaccinations. This finding suggests that combining two different vaccine platforms may provide a synergistic effect, informing future vaccination strategies.
In conclusion, understanding the genetic determinants of the IgG antibody response to COVID-19 vaccination is crucial to enhancing vaccine effectiveness and developing personalized vaccination strategies. The role of the HLA-DRB1*13:02 allele and the impact of HLA alleles on various cell types provide pivotal insights into the biological mechanisms contributing to variations in immune responses elicited by COVID-19 vaccines. Furthermore, the dynamics of antibody responses to COVID-19 infection and vaccination further underline the importance of considering individual differences in designing effective vaccination strategies.
View original post here:
Genetic Influence on COVID-19 Vaccine Response Revealed - Medriva
Straight Men With ‘Bisexual Genes’ Have More Kids, Study Finds – ScienceAlert
For the first time, scientists have identified genetic variations associated with human bisexual behavior and found these markers are linked to risk-taking and having more offspring when they are carried by heterosexual men.
Jianzhi "George" Zhang, a professor at the University of Michigan and senior author of the new research, told AFP it helped answer the long-standing evolutionary puzzle of why natural selection has not eliminated the genetics underpinning attraction within the same sex.
The study, published Wednesday in Science Advances, was based on data from more than 450,000 people of European descent who signed up for the UK Biobank, a long-term genomics project that has proven a major boon for health research.
It builds on growing research including a seminal 2019 paper in Science that found genetic variants influenced to some extent whether a person engaged in same-sex behavior, though environmental factors were more important.
"We realized that in the past, people lumped together all homosexual behavior but actually there's a spectrum," Zhang said, explaining part of the motivation for the new work.
By studying participants' complete sets of DNA, or genomes, and combining that information with survey responses, Zhang and his co-author Siliang Song were able to confirm the signatures associated with same-sex behavior and bisexual behavior were in fact distinct.
This meant they could be analyzed separately which in turn revealed that male heterosexuals carrying the markers, which they called bisexual behavior (BSB)-associated alleles, father more children than average and thus carry those genes forward.
What's more, men who describe themselves as risk-takers tended to have more children and were more likely to carry BSB-associated alleles.
"Our results suggest that male BSBassociated alleles are likely reproductively advantageous, which may explain their past persistence and predict their future maintenance," the authors wrote.
Although the UK Biobank's survey simply asked respondents whether they considered themselves risk-takers or not, it is likely risk-taking behavior involves more unprotected sex and more partners.
"Nature is complicated," said Zhang, reflecting on the fact that a single gene can influence multiple traits a phenomenon known as "pleiotropy."
"Here we're talking about three traits: number of children, risk taking, and bisexual behavior: they all share some genetic underpinnings."
On the other hand, exclusive same-sex behavior (eSSB) associated alleles were correlated with having fewer children when carried by heterosexual men suggesting that over time these traits will fade away.
However, the UK Biobank data also revealed the proportion of people reporting both bisexual and homosexual behavior has been rising for decades, which is probably due to growing societal openness.
The authors estimated, for instance, that whether a person is bisexual or not in their behavior is 40 percent influenced by genetics, and 60 percent by the environment.
"We want to make it clear that our results predominantly contribute to the diversity, richness, and better understanding of human sexuality," they stressed.
"They are not, in any way, intended to suggest or endorse discrimination on the basis of sexual behavior."
Agence France-Presse
See the original post here:
Straight Men With 'Bisexual Genes' Have More Kids, Study Finds - ScienceAlert
Cancer Genetics – Cancer Genetics – St George’s University Hospitals NHS Foundation Trust
On this page:
The Cancer Genetics team at the Southwest Thames Centre for Genomics serves a population of approximately three and a half million people across South West London, Surrey and West Sussex. The aim of the service is to identify people at increased risk of developing cancer because of underlying genetic factors. If we find that a person has an increased chance of getting cancer due to genetic factors we can any screening options to pick up cancers early at a treatable stage and other treatments to reduce the risk of cancer.
We see people who already have a cancer diagnosis to try to identify if the cancer may have been due to an underlying genetic cause. Identifying a genetic cause for a cancer may give an explanation for a cancer diagnosis and guide cancer treatment. It may also allow us to advise people with cancer, and their relatives, on their future cancer risk and offer a personalised plan to reduce this risk.
We also assess people who are concerned about their cancer risk because of their family history. In many cases we will not need to offer telephone, video or face to face appointments but we will write with our screening recommendations and other advice to reduce the chance of developing cancer.
If you are a healthcare professional who wishes to make a referral to our service or request a genetic test for inherited cancer risk please read our guidelines for healthcare professionals.
If you are a patient who has been referred to us or is thinking of requesting a referral to our service, please read our information for patients .
We can offer telephone and video appointments for patients who would prefer these, or those who do not need a physical examination.
We hold regular face to face clinics at St Georges Hospital in Tooting, London, and also offer face to face appointments in other hospitals across our region:
We also run the following specialised clinics:
See the article here:
Cancer Genetics - Cancer Genetics - St George's University Hospitals NHS Foundation Trust
The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders – Nature.com
Mller, H. L. et al. Hypothalamic syndrome. Nat. Rev. Dis. Prim. 8, 24 (2022).
Article PubMed Google Scholar
Yang, T. et al. Social control of hypothalamus-mediated male aggression. Neuron 95, 955970.e4 (2017).
Article CAS PubMed PubMed Central Google Scholar
Hu, R. K. et al. An amygdala-to-hypothalamus circuit for social reward. Nat. Neurosci. 24, 831842 (2021).
Article CAS PubMed PubMed Central Google Scholar
Riecher-Rssler, A. Oestrogens, prolactin, hypothalamicpituitarygonadal axis, and schizophrenic psychoses. Lancet Psychiatry 4, 6372 (2017).
Article PubMed Google Scholar
Zheng, Z. et al. Hypothalamushabenula potentiation encodes chronic stress experience and drives depression onset. Neuron 110, 14001415.e6 (2022).
Article CAS PubMed Google Scholar
Ishii, M. & Iadecola, C. Metabolic and non-cognitive manifestations of Alzheimers disease: the hypothalamus as both culprit and target of pathology. Cell Metab. 22, 761776 (2015).
Article CAS PubMed PubMed Central Google Scholar
Breen, D. P. et al. Hypothalamic volume loss is associated with reduced melatonin output in Parkinsons disease. Mov. Disord. 31, 10621066 (2016).
Article CAS PubMed PubMed Central Google Scholar
van Erp, T. G. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry 21, 547553 (2016).
Article PubMed Google Scholar
Hibar, D. P. et al. Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Mol. Psychiatry 23, 932942 (2018).
Article CAS PubMed Google Scholar
Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).
Article CAS PubMed PubMed Central Google Scholar
Satizabal, C. L. et al. Genetic architecture of subcortical brain structures in 38,851 individuals. Nat. Genet. 51, 16241636 (2019).
Article CAS PubMed PubMed Central Google Scholar
Bocchetta, M. et al. Detailed volumetric analysis of the hypothalamus in behavioral variant frontotemporal dementia. J. Neurol. 262, 26352642 (2015).
Article PubMed PubMed Central Google Scholar
Makris, N. et al. Volumetric parcellation methodology of the human hypothalamus in neuroimaging: normative data and sex differences. NeuroImage 69, 110 (2013).
Article PubMed Google Scholar
Baroncini, M. et al. MRI atlas of the human hypothalamus. NeuroImage 59, 168180 (2012).
Article PubMed Google Scholar
Billot, B. et al. Automated segmentation of the hypothalamus and associated subunits in brain MRI. NeuroImage 223, 117287 (2020).
Article PubMed Google Scholar
van der Meer, D. et al. Understanding the genetic determinants of the brain with MOSTest. Nat. Commun. 11, 3512 (2020).
Article PubMed PubMed Central Google Scholar
Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8, 1826 (2017).
Article PubMed PubMed Central Google Scholar
Bahrami, S. et al. Distributed genetic architecture across the hippocampal formation implies common neuropathology across brain disorders. Nat. Commun. 13, 3436 (2022).
Article CAS PubMed PubMed Central Google Scholar
Ou, Y. N. et al. The genetic architecture of human amygdala volumes and their overlap with common brain disorders. Transl. Psychiatry 13, 90 (2023).
Article CAS PubMed PubMed Central Google Scholar
de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
Article PubMed PubMed Central Google Scholar
Wain, L. V. et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat. Genet. 43, 10051011 (2011).
Article CAS PubMed PubMed Central Google Scholar
Wang, X. D. et al. Nectin-3 links CRHR1 signaling to stress-induced memory deficits and spine loss. Nat. Neurosci. 16, 706713 (2013).
Article CAS PubMed Google Scholar
He, L. et al. Exome-wide age-of-onset analysis reveals exonic variants in ERN1 and SPPL2C associated with Alzheimers disease. Transl. Psychiatry 11, 146 (2021).
Article CAS PubMed PubMed Central Google Scholar
Allen, M. et al. Gene expression, methylation and neuropathology correlations at progressive supranuclear palsy risk loci. Acta Neuropathol. 132, 197211 (2016).
Article CAS PubMed PubMed Central Google Scholar
Mignot, E., Taheri, S. & Nishino, S. Sleeping with the hypothalamus: emerging therapeutic targets for sleep disorders. Nat. Neurosci. 5, 10711075 (2002).
Article CAS PubMed Google Scholar
Jones, S. E. et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat. Commun. 10, 343 (2019).
Article CAS PubMed PubMed Central Google Scholar
Aydogan, G. et al. Genetic underpinnings of risky behaviour relate to altered neuroanatomy. Nat. Hum. Behav. 5, 787794 (2021).
Article PubMed PubMed Central Google Scholar
Floresco, S. B. Learning is a matter of history and relevance for lateral hypothalamus. Nat. Neurosci. 24, 295296 (2021).
Article CAS PubMed Google Scholar
Averbeck, B. B. & Murray, E. A. Hypothalamic interactions with large-scale neural circuits underlying reinforcement learning and motivated behavior. Trends Neurosci. 43, 681694 (2020).
Article CAS PubMed PubMed Central Google Scholar
Li, M. M. et al. The paraventricular hypothalamus regulates satiety and prevents obesity via two genetically distinct circuits. Neuron 102, 653667.e6 (2019).
Article CAS PubMed PubMed Central Google Scholar
Horvath, T. L. The hardship of obesity: a soft-wired hypothalamus. Nat. Neurosci. 8, 561565 (2005).
Article CAS PubMed Google Scholar
Zhou, J. J., Shao, J. Y., Chen, S. R. & Pan, H. L. Calcineurin controls hypothalamic NMDA receptor activity and sympathetic outflow. Circ. Res. 131, 345360 (2022).
Article CAS PubMed PubMed Central Google Scholar
Gruber, T. et al. Obesity-associated hyperleptinemia alters the gliovascular interface of the hypothalamus to promote hypertension. Cell Metab. 33, 11551170.e10 (2021).
Article CAS PubMed PubMed Central Google Scholar
Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32, 4354 (2018).
Article CAS PubMed PubMed Central Google Scholar
Mascarell Marii, L. et al. The IMAGEN study: a decade of imaging genetics in adolescents. Mol. Psychiatry 25, 26482671 (2020).
Article PubMed PubMed Central Google Scholar
Zhou, W. et al. SAIGE-GENE+ improves the efficiency and accuracy of set-based rare variant association tests. Nat. Genet. 54, 14661469 (2022).
Article CAS PubMed PubMed Central Google Scholar
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 13391348 (2019).
Article CAS PubMed Google Scholar
Hall, J. E. & Hall, M. E. Guyton and Hall Textbook of Medical Physiology (Elsevier, 2020).
Yamagata, T. et al. The hypothalamic link between arousal and sleep homeostasis in mice. Proc. Natl Acad. Sci. USA 118, e2101580118 (2021).
Article CAS PubMed PubMed Central Google Scholar
Omura, J. et al. ADAMTS8 promotes the development of pulmonary arterial hypertension and right ventricular failure: a possible novel therapeutic target. Circ. Res. 125, 884906 (2019).
Article CAS PubMed Google Scholar
Morihara, T. et al. Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid- accumulation modifier. Proc. Natl Acad. Sci. USA 111, 26382643 (2014).
Article CAS PubMed PubMed Central Google Scholar
Alsabban, A. H., Morikawa, M., Tanaka, Y., Takei, Y. & Hirokawa, N. Kinesin Kif3b mutation reduces NMDAR subunit NR2A trafficking and causes schizophrenia-like phenotypes in mice. EMBO J. 39, e101090 (2020).
Article CAS PubMed Google Scholar
Yin, X., Takei, Y., Kido, M. A. & Hirokawa, N. Molecular motor KIF17 is fundamental for memory and learning via differential support of synaptic NR2A/2B levels. Neuron 70, 310325 (2011).
Article CAS PubMed Google Scholar
Du, J. et al. A kinesin signaling complex mediates the ability of GSK-3 to affect mood-associated behaviors. Proc. Natl Acad. Sci. USA 107, 1157311578 (2010).
Article CAS PubMed PubMed Central Google Scholar
Gabery, S. et al. Volumetric analysis of the hypothalamus in Huntington disease using 3T MRI: the IMAGE-HD Study. PLoS ONE 10, e0117593 (2015).
Article PubMed PubMed Central Google Scholar
Goldstein, J. M. et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol. Psychiatry 61, 935945 (2007).
Article CAS PubMed Google Scholar
Sha, Z. et al. The genetic architecture of structural leftright asymmetry of the human brain. Nat. Hum. Behav. 5, 12261239 (2021).
Article PubMed PubMed Central Google Scholar
Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597608 (2016).
Article PubMed PubMed Central Google Scholar
LeBlanc, M. et al. A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics 19, 494 (2018).
Article PubMed PubMed Central Google Scholar
Schumann, G. et al. The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Mol. Psychiatry 15, 11281139 (2010).
Article CAS PubMed Google Scholar
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203209 (2018).
View original post here:
The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders - Nature.com
Raha Kapoor’s blue eyes remind fans of her great-grandfather, Raj Kapoor; here’s what genetics says – IndiaTimes
It's already been a day since Raha Kapoor's pap debut, and the internet can not stop gushing over the youngest member of the Kapoor clan. Born on November 6, 2022, to actors Ranbir Kapoor and Alia Bhatt, Raha was always kept away from the limelight by her parents. On Christmas Day, the family of three posed before the media and everyone got the first look of the youngest Kapoor. Raha can also be seen in the Christmas celebration pics of the Kapoors. Fans remember Rishi Kapoor, Ranbir's dad after seeing her. Social media is flooded with pictures comparing Raha with Raj Kapoor, Rishi Kapoor and Shashi Kapoor. But what has caught the attention of all is her eyes. Seems she has acquired the most distinguishable and desired genes of the Kapoor lineage. Yes, blue eyes are an inherited trait, and the color of a person's eyes is determined by the interplay of genetics. Eye color is primarily influenced by the amount and type of pigments in the front part of the iris, the colored part of the eye. Ultimate tips for moms only from Twinkle Khanna The color of the eyes is determined by multiple genes, with the two main types of pigments being melanin (responsible for brown, black, and some shades of green eyes) and lipochrome (responsible for green, blue, and gray eyes). The genetics of eye color inheritance is complex, involving multiple genes with varying degrees of influence. Worried about your teen's group chats? Here's how to reactQuestions every parent must ask in PTM The OCA2 and HERC2 genes, located on chromosome 15, play a significant role in eye color determination. These genes regulate the production and storage of melanin, affecting the amount and distribution of pigments in the iris. Variations in these genes can lead to different eye colors. Blue eyes result from the scattering of light in the iris, rather than the presence of blue pigments. It's the same phenomenon that makes the sky appear blue. The scattering of light occurs when there is little or no melanin in the front part of the iris, allowing light to be scattered rather than absorbed. Motivational quotes for new parents The inheritance of blue eyes follows a recessive pattern. Both parents must carry and pass on the recessive allele for blue eyes to their offspring. If a person inherits one brown-eyed allele and one blue-eyed allele, the brown eye trait is usually dominant, resulting in brown or hazel eyes.
It's important to note that eye color is a polygenic trait influenced by various factors, and predicting the exact eye color of an individual can be challenging. Additionally, environmental factors, such as lighting conditions and the reflection of clothing colors, can create the illusion of different eye colors. In summary, blue eyes are inherited, and the genetic interplay between multiple genes determines eye color. While genetics significantly influence eye color, the precise outcome can vary, contributing to the diversity of eye colors observed in the human population. Raha's striking blue eyes have delighted people around the country and we can only observe if her eyes stay this unique colour or turn darker as she gets older! Only time will tell!
Periods and pregnancy: Why regular cycles are not a guarantee
See the original post:
Raha Kapoor's blue eyes remind fans of her great-grandfather, Raj Kapoor; here's what genetics says - IndiaTimes