Daily Archives: June 4, 2021

Mission: The Division of Human Genetics at Cincinnati Childrens leverages genomic technology to provide world-class clinical care, train the next generation of caregivers and researchers, and develop new therapies for our patients and others like them around the world.

We work at the forefront of genomic medicine and research to diagnosis even the rarest diseases and provide outstanding care, as well as discover and implement therapies to improve the health of all children. As a leader in pediatric health, our approach is to integrate a culture of genomics through institution-wide collaboration in both research and clinical initiatives that cross every subspecialty at Cincinnati Childrens.

One focus of our division is the Genetics of Time. This allows us to concentrate on fetal development, premature birth, circadian rhythms related to sleep or taking medication, and mitochondrial disorders. These areas impact the clinical care we provide, as well as areas of research.

The Division of Human Genetics features three key areas:

In our clinics, we use a precision-medicine approach so that our geneticists, genetic counselors, advanced practice and registered nurses diagnose, manage and treat genetic diseases in children and adults taking into account individual variations and responses to treatment.

In addition, we provide comprehensive services through our Genetics and Genomics Diagnostic Laboratory, whichoffers leading-edge technology for biochemical, cytogenetic and molecular genetic testing for a variety of disorders.

The world of genomics is evolving rapidly and our investigators are at the front line of basic science discovery, as well as translational and clinical trial research. Our investigators ultimate goal is to bring genomics into medical care through discovery of better diagnoses, therapeutics and disease prevention. To do this, we use innovative methods to discover new genes and genetic mutations, diagnose and treat rare diseases, and gain deeper understanding of basic biological events, such as preterm birth or craniofacial development. Learn more about some of our key programs.

The Division of Human Genetics is a leader in genetics education. Cincinnati Childrens offers a genetics specialty residency and a genetics fellowship training program. And the Genetic Counseling Graduate Program is one of the oldest and largest such programs in the United States.

We also welcome graduate students and postdocs from other areas such as Molecular and Developmental Biology, Biomedical Informatics and Immunology who are interested in learning more about genetics and genomics.

In addition, we have a Grassroots Genomics Speaker Series a series of videos explaining genomics and how it is used in pediatric medicine and our Genomic Discovery & Translational Series of talks from outside speakers.

To learn more about genomics in the news, as well as about published research from our faculty, follow us on Twitter @CincyKidsGenomX.

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Human Genetics | Cincinnati Children's

In a new study, University of Maine researchers found that culture helps humans adapt to their environment and overcome challenges better and faster than genetics.

After conducting an extensive review of the literature and evidence of long-term human evolution, scientists Tim Waring and Zach Wood concluded that humans are experiencing a special evolutionary transition in which the importance of culture, such as learned knowledge, practices and skills, is surpassing the value of genes as the primary driver of human evolution.

Culture is an under-appreciated factor in human evolution, Waring says. Like genes, culture helps people adjust to their environment and meet the challenges of survival and reproduction. Culture, however, does so more effectively than genes because the transfer of knowledge is faster and more flexible than the inheritance of genes, according to Waring and Wood.

Culture is a stronger mechanism of adaptation for a couple of reasons, Waring says. Its faster: gene transfer occurs only once a generation, while cultural practices can be rapidly learned and frequently updated. Culture is also more flexible than genes: gene transfer is rigid and limited to the genetic information of two parents, while cultural transmission is based on flexible human learning and effectively unlimited with the ability to make use of information from peers and experts far beyond parents. As a result, cultural evolution is a stronger type of adaptation than old genetics.

Waring, an associate professor of social-ecological systems modeling, and Wood, a postdoctoral research associate with the School of Biology and Ecology, have just published their findings in a literature review in the Proceedings of the Royal Society B, the flagship biological research journal of The Royal Society in London.

This research explains why humans are such a unique species. We evolve both genetically and culturally over time, but we are slowly becoming ever more cultural and ever less genetic, Waring says.

Culture has influenced how humans survive and evolve for millenia. According to Waring and Wood, the combination of both culture and genes has fueled several key adaptations in humans such as reduced aggression, cooperative inclinations, collaborative abilities and the capacity for social learning. Increasingly, the researchers suggest, human adaptations are steered by culture, and require genes to accommodate.

Waring and Wood say culture is also special in one important way: it is strongly group-oriented. Factors like conformity, social identity and shared norms and institutions factors that have no genetic equivalent make cultural evolution very group-oriented, according to researchers. Therefore, competition between culturally organized groups propels adaptations such as new cooperative norms and social systems that help groups survive better together.

According to researchers, culturally organized groups appear to solve adaptive problems more readily than individuals, through the compounding value of social learning and cultural transmission in groups. Cultural adaptations may also occur faster in larger groups than in small ones.

With groups primarily driving culture and culture now fueling human evolution more than genetics, Waring and Wood found that evolution itself has become more group-oriented.

In the very long term, we suggest that humans are evolving from individual genetic organisms to cultural groups which function as superorganisms, similar to ant colonies and beehives, Waring says. The society as organism metaphor is not so metaphorical after all. This insight can help society better understand how individuals can fit into a well-organized and mutually beneficial system. Take the coronavirus pandemic, for example. An effective national epidemic response program is truly a national immune system, and we can therefore learn directly from how immune systems work to improve our COVID response.

Waring is a member of the Cultural Evolution Society, an international research network that studies the evolution of culture in all species. He applies cultural evolution to the study of sustainability in social-ecological systems and cooperation in organizational evolution.

Wood works in the UMaine Evolutionary Applications Laboratory managed by Michael Kinnison, a professor of evolutionary applications. His research focuses on eco-evolutionary dynamics, particularly rapid evolution during trophic cascades.

Contact: Marcus Wolf, 207.581.3721; marcus.wolf@maine.edu

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UMaine researchers: Culture drives human evolution more than genetics - UMaine News - University of Maine - University of Maine

SAN DIEGO, June 2, 2021 /PRNewswire/ -- Primordial Genetics ("Primordial"), a synthetic biology company developing enzymatic production systems for nucleic acids, today announced granting Arcturus Therapeutics Holdings Inc.("Arcturus",Nasdaq: ARCT), a clinical-stage messenger RNA (mRNA) medicines company, an exclusive license of an enzyme for an RNA polymerase (RNApol). The enzyme was discovered by Primordial to meet the challenge of manufacturing high-quality, long RNAs for therapeutic applications.

mRNA based medicines represent a promising new approach to drug and vaccine development. Primordial owns proprietary technology relating to RNA manufacturing, specifically its collection of RNA polymerase genes, promoters, and proteins that may be used to synthesize RNA. Primordial is focused on developing improved RNApols for higher efficiency and lower cost manufacturing of mRNAs used in therapeutics and vaccines.

"We look forward to the meaningful achievements Arcturus can make with this licensed Primordial Genetics RNA polymerase, from clinical trials to a marketable RNA product that can be used in pharmaceuticals to improve or save lives," said Helge Zieler, PhD, founder and President of Primordial Genetics. "We are thrilled for this Primordial and Arcturus collaboration that meets the core of our mission to connect innovation with social needs via new, biologically-based alternatives to traditional therapeutics.

About Primordial Genetics

Primordial Genetics is a synthetic biology company founded in 2013 and based in San Diego, California. The company is the world leader in constructive biology; a revolutionary new way of practicing biotechnology that creates novel genes from genomic building blocks to accelerate the evolution of highly efficient enzymes and microbes. Primordial's product focus is to develop efficient production processes for DNA and RNA manufacturing to enable biologically-based alternatives to traditional therapeutics, nutritional products, agriculture and fuels. For more information, visit: http://www.primordialgenetics.com

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http://www.primordialgenetics.com

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Primordial Genetics Grants Arcturus Therapeutics Exclusive License of an RNA Polymerase for Human and Animal Therapeutics - PRNewswire

SUNNYVALE, Calif., June 2, 2021 /PRNewswire/ -- 23andMe Inc., a leading consumer genetics and research company, today announced that D. Wade Walke, Ph.D. will join the company as Vice President of Investor Relations. Earlier this year, 23andMe entered into a definitive merger agreement with VG Acquisition Corp. (NYSE: VGAC), a special purpose acquisition company sponsored by Virgin Group, to become a publicly-traded company. Walke will be responsible for demonstrating 23andMe's vision and long-term value with its shareholders and the financial community as the company transitions to the public markets.

"With his extensive background developing and implementing strategic investor relations programs within the biotech space, we are confident that Wade is the ideal fit to help build and shape 23andMe's future IR program," said Steve Schoch, Chief Financial Officer of 23andMe. "Studying genetics early in his career, Wade brings a deep scientific background and he will undoubtedly help the investment community appreciate the significant value-building opportunity that lies ahead in both 23andMe's consumer and biotechnology segments. He also brings a strong network of relationships with buy-side and sell-side healthcare analysts and institutional investors."

"I am excited to join 23andMe at this unique time of transition. I have been following the company closely for many years now, and it is evident to me that the team at 23andMe has worked diligently to create innovative, impactful and actionable insights for its entire customer base," said Dr. Walke. "In particular, I've been impressed with the recent work 23andMe has done to build a personalized health and wellness experience that brings so much value to the individual customer. I am very much looking forward to working closely with Anne, Steve, the entire team and, most importantly, our global shareholder base as 23andMe begins its next phase of growth."

Prior to joining 23andMe, Walke spent nine years at Ionis Pharmaceuticals, a leading company in RNA-targeted drug discovery and development, where he most recently served as Vice President of Investor Relations. During his time there, he was ranked as one of the top IR professionals three years in a row (2017 - 2019) by Institutional Investor magazine's "All-American Executive Team" (Biotechnology) rankings.

Prior to joining Ionis Pharmaceuticals, Walke also spent 14 years at Lexicon Pharmaceuticals, where he began as a Scientist and worked his way up to Associate Director of Bioinformatics in the Department of Functional Genomics. From there, he pivoted to lead Communications and Investor Relations for the company, where he oversaw the implementation of a targeted program of IR activities. Walke holds a Bachelor of Science degree from Brigham Young University and a Ph.D. and Master of Science degree from the University of Michigan.

About 23andMe23andMe, Inc., headquartered in Sunnyvale, CA, is a leading consumer genetics and research company. Founded in 2006, the company's mission is to help people access, understand, and benefit from the human genome. 23andMe has pioneered direct access to genetic information as the only company with multiple FDA authorizations for genetic health risk reports. The company has created the world's largest crowdsourced platform for genetic research, with 80 percent of its customers electing to participate. The 23andMe research platform has generated more than 180 publications on the genetic underpinnings of a wide range of diseases, conditions and traits. The platform also powers the 23andMe Therapeutics group, currently pursuing drug discovery programs rooted in human genetics across a spectrum of disease areas, including oncology, respiratory, and cardiovascular diseases, in addition to other therapeutic areas. More information is available at http://www.23andMe.com.

Forward-Looking StatementsThis communication contains certain "forward-looking statements" including statements regarding the Company's ability to timely prepare and file the Quarterly Report. The words "anticipate," "believe," "continue," "could," "estimate," "expect," "intends," "may," "might," "plan," "possible," "potential," "predict," "project," "should," "would," and similar expressions may identify forward-looking statements, but the absence of these words does not mean that a statement is not forward-looking. The forward-looking statements contained herein are based on the Company's current expectations and beliefs concerning future developments and their potential effects, but there can be no assurance that these will be as anticipated. These forward-looking statements involve a number of risks, uncertainties (some of which are beyond the control of the Company), or other assumptions that may cause actual results or performance to be materially different from those expressed or implied by these forward-looking statements. These factors include, among others: the inability to complete the proposed business combination with VGAC (the "Business Combination"), including due to the failure to receive required security holder approvals, or the failure of other closing conditions. Except as required by law, the Company does not undertake any obligation to update or revise any forward-looking statements whether as a result of new information, future events, or otherwise.

Additional InformationVGAC has filed with the Securities and Exchange Commission (the "SEC") a Registration Statement on Form S-4, as amended (the "Form S-4"), which included the definitive proxy statement of VGAC, a prospectus, and 23andMe's consent solicitation statement. The Form S-4 was declared effective on May 14, 2021. The definitive proxy statement/prospectus and other proxy materials were mailed to VGAC's shareholders of record as of the close of business on May 5, 2021. Shareholders of VGAC and other interested persons are advised to read the Form S-4, the definitive proxy statement/prospectus included in the Form S-4, and documents incorporated by reference therein filed in connection with the proposed Business Combination because these documents contain important information about VGAC, 23andMe, and the Business Combination. Shareholders will also be able to obtain copies of the Form S-4 and the proxy statement/prospectus, without charge, by directing a request to: VG Acquisition Corp. 65 Bleecker Street, 6th Floor, New York NY 10012. These documents and VGAC's annual and other reports filed with the SEC can also be obtained, without charge, at the SEC's internet site (https://www.sec.gov).

The date of VGAC's extraordinary general meeting of shareholders to vote on the proposed Business Combination has been set for June 10, 2021. VGAC's shareholders of record as of the close of business on May 5, 2021 are entitled to vote on matters that come before the extraordinary general meeting, including the proposed Business Combination. The Business Combination, if approved by VGAC's shareholders, is expected to close as soon as practicable following the extraordinary general meeting.

This communication does not constitute an offer to sell or the solicitation of an offer to buy any securities, or a solicitation of any vote or approval, nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation, or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction.

Participants in the SolicitationVGAC, 23andMe, and their respective directors, executive officers, other members of management, and employees may be deemed to be participants in the solicitation of proxies from VGAC's shareholders in connection with the Business Combination. Information regarding the names and interests in the proposed Business Combination of VGAC's directors and officers is contained in VGAC's filings with the SEC. Additional information regarding the interests of such potential participants in the solicitation process is included in the Form S-4 (and the definitive proxy statement/prospectus) and other relevant documents filed with the SEC.

SOURCE 23andMe Inc.

http://www.23andMe.com

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D. Wade Walke, Ph.D. Joins 23andMe as Vice President of Investor Relations - PRNewswire

Variations in the human gut microbiome have been linked to cancer an exciting prospect for cancer prevention, but teasing apart causation from correlation is no easy task says Dr Kaitlin Wade

Cancer remains one of the leading causes of death worldwide as well as one of the greatest economical burdens on health care systems. And yet, evidence indicates that over 40% of all cancers are likely explained by preventable causes.

One of the main challenges is identifying so-called modifiable risk factors for cancer aspects of our environment that we can change to reduce the incidence of disease. One very promising avenue of research has been the gut microbiome. There is growing evidence from human and predominantly mouse models supporting the relationship between the human gut microbiome and cancer aetiology.

Human studies have largely been observational, and investigations have so far been unable to offer convincing causal evidence

We know the gut microbiome can have a substantial impact on host metabolism, inflammation, and immune response to external infections, so there are many plausible biological mechanisms by which it could influence cancer development and progression. However, findings have been inconsistent, or even contradictory, and very few hypotheses have been reliably supported with data from multiple model organisms.

Human studies have largely been observational, and investigations have so far been unable to offer convincing causal evidence. This isnt helped by several important limitations in the design of studies and analyses of data linking the gut microbiome and cancer aetiology. Common causes of gut microbiome variation and cancer (confounding), the ability for cancer to influence the gut microbiome (reverse causation) and various biases can distort results. This, of course, affects our ability to find out what variation in the human gut microbiome, if any, may cause cancer. Distinguishing correlation from causation therefore requires very precise data analysis.

The gut microbiome is a complex system of microorganisms aiding digestion, providing protection against pathogens and creating essential metabolites. Variation in the gut microbiome has been linked to many common cancers.

Taking colorectal cancer (CRC) as an example, there is compelling in vivo and in vitro evidence that modifying gut microbiota may reduce the incidence of the disease. Alongside this, epidemiological studies suggest a lower microbiota diversity in people with CRC. There is also research showing lower levels of some bacteria, such as Bifidobacterium and Roseburia, as well as higher levels of others, such as Fusobacterium and Porphyromonas, in those with CRC.

Despite a lack of causal evidence, there is still a growing market for commercial products targeting the microbiome

Despite a lack of causal evidence, there is still a growing market for commercial products targeting the microbiome several companies now offer sequencing of faecal samples and prescribe personalised nutritional information. There is controversy around this given the uncertainty of the likely impact, which is not helped by a lack of consistency between observational studies and large-scale randomised controlled trials. Something which is clearly a barrier when trying to harness the gut microbiome to tackle disease. What this does highlight though, is the public demand for such information which could suggest an untapped opportunity to make important population-based health interventions. Therefore, we need alternative approaches to interrogate causality and tease apart the links between the gut microbiome and cancer aetiology.

One way of improving causal inference has been the integration of human genetic variation within population health sciences. With the growth in genome-wide association studies (GWASs), we now know thousands of genetic variants across the genome that influence almost every aspect of human physiology and even elements of behaviour.

We need alternative approaches to interrogate causality and tease apart the links between the gut microbiome and cancer aetiology

Within the last few years, GWAS has been used to understand the relationships between human genetic variation and the gut microbiome. These studies have provided evidence for the contributions of human genetics on features of the gut microbiome such as diversity, abundance and enterotype. While this is not in itself causal evidence, knowledge of the relationships between human genetic variation and various characteristics has provided an opportunity to tease out causality from observational epidemiological associations.

Established in the early 2000s and applied mainly to understand the links between modifiable risk factors and cardiometabolic diseases, Mendelian randomisation (MR) is a method that enables the interrogation of causality.

MR utilises human germline genetic variation usually single nucleotide polymorphisms to help investigate whether the gut microbiome changes the risk of cancer or whether cancer changes the gut microbiome. Genetic variation cant be influenced by the gut microbiome or disease. Therefore, if people who are genetically predisposed to having a higher abundance of certain bacteria within their gut also have a lower risk of cancer, this would strongly suggest a causal and protective role of those bacteria in cancer aetiology.

With the recent growth in GWASs focusing on the gut microbiome, there have been a handful of studies applying MR to assess the impacts of gut microbiome variation on several cardiometabolic, inflammatory and auto-immune diseases. As yet however, there are no studies that focus on the appropriate application of these methodologies to cancer something I aim to change as part of my Cancer Research UK fellowship. I plan to use human genetic information to shed light on the relationship between the gut microbiome and cancer aetiology.

Knowledge of the relationships between human genetic variation and various characteristics has provided an opportunity to tease out causality from observational epidemiological associations

Using MR should largely avoid the limitations of observational epidemiological studies however, the specifics of the way the method is applied is very important and is something that can be hard to get right. Clearly, I want to ensure my results give a reliable indication of causality. So, first order of business for this study will be to apply a more robust way to tease apart correlation from causation. To be able to do this, there are a number of caveats to the current use of MR in the field that need to be considered carefully.

These caveats centre around the core assumptions of the MR framework. First, that human genetic variation must be associated with the gut microbiome. Second, that there must be no confounding that is to say common causes of the gut microbiome and cancer and, third, there must be no relationship between microbiome-related genetic variation and cancer independent of the gut microbiome. However, the current applications of MR to try and understand the role of the gut microbiome on health outcomes rarely consider these caveats carefully enough.

The appropriate application of MR to interrogate causality of the gut microbiome in cancer has begun to show promise. However, early work has also highlighted the importance of inter-disciplinary collaboration between population health, genetic and basic sciences. We really do need a triangulation of evidence to unpick causation from correlation. Any research conducted within one discipline cannot provide concrete evidence to support or challenge the role of the gut microbiome in cancer aetiology.

I am hopeful with this Fellowship, and the support from my team of experts in microbiology, basic sciences and population health sciences, we can take a new and important step towards refining the current applications of complex integrative methodologies in cancer research. And it is this which will in turn allow more accurate evaluation of potential treatments or protective factors for cancer prevention.

About the author

Dr Kaitlin Wade is a lecturer in epidemiology and Co-Director of the MSc in Epidemiology in the MRC Integrative Epidemiology Unit based at the University of Bristol. She was awarded a Cancer Research UK Population Research Postdoctoral Fellowship in 2020.

AcknowledgementsThe research conducted as part of my CRUK Population Research Postdoctoral Fellowship will be supported by the following collaborators: Nicholas Timpson, Caroline Relton, Jeroen Raes, Trevor Lawley, Lindsay Hall and Marc Gunter. Additional thanks to Chloe Russell, who supplied the image for this piece.

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A bug in the system the difficulties of linking the microbiome to cancer aetiology - Cancer Research UK

LOS ANGELES, United States: QY Research offers an overarching research and analysis-based study on, Global Human Genetics Market Report, History and Forecast 2016-2027, Breakdown Data by Companies, Key Regions, Types and Application. This report offers an insightful take on the drivers and restraints present in the market. Human Genetics data reports also provide a 5 year pre-historic and forecast for the sector and include data on socio-economic data of global. Key stakeholders can consider statistics, tables & figures mentioned in this report for strategic planning which lead to success of the organization. It sheds light on strategic production, revenue, and consumption trends for players to improve sales and growth in the global Human Genetics Market. Here, it focuses on the recent developments, sales, market value, production, gross margin, and other significant factors of the business of the major players operating in the global Human Genetics Market. Players can use the accurate market facts and figures and statistical studies provided in the report to understand the current and future growth of the global Human Genetics market.

This report includes assessment of various drivers, government policies, technological innovations, upcoming technologies, opportunities, market risks, restrains, market barriers, challenges, trends, competitive landscape, and segments which gives an exact picture of the growth of the global Human Genetics market.

Competitive Landscape

Competitor analysis is one of the best sections of the report that compares the progress of leading players based on crucial parameters, including market share, new developments, global reach, local competition, price, and production. From the nature of competition to future changes in the vendor landscape, the report provides in-depth analysis of the competition in the global Human Genetics market.

Key questions answered in the report:

Table of Contents

1 Market Overview of Human Genetics1.1 Human Genetics Market Overview1.1.1 Human Genetics Product Scope1.1.2 Human Genetics Market Status and Outlook1.2 Global Human Genetics Market Size Overview by Region 2016 VS 2021VS 20271.3 Global Human Genetics Market Size by Region (2016-2027)1.4 Global Human Genetics Historic Market Size by Region (2016-2021)1.5 Global Human Genetics Market Size Forecast by Region (2022-2027)1.6 Key Regions, Human Genetics Market Size (2016-2027)1.6.1 North America Human Genetics Market Size (2016-2027)1.6.2 Europe Human Genetics Market Size (2016-2027)1.6.3 Asia-Pacific Human Genetics Market Size (2016-2027)1.6.4 Latin America Human Genetics Market Size (2016-2027)1.6.5 Middle East & Africa Human Genetics Market Size (2016-2027) 2 Human Genetics Market Overview by Type2.1 Global Human Genetics Market Size by Type: 2016 VS 2021 VS 20272.2 Global Human Genetics Historic Market Size by Type (2016-2021)2.3 Global Human Genetics Forecasted Market Size by Type (2022-2027)2.4 Cytogenetics2.5 Prenatal Genetics2.6 Molecular Genetics2.7 Symptom Genetics 3 Human Genetics Market Overview by Application3.1 Global Human Genetics Market Size by Application: 2016 VS 2021 VS 20273.2 Global Human Genetics Historic Market Size by Application (2016-2021)3.3 Global Human Genetics Forecasted Market Size by Application (2022-2027)3.4 Research Center3.5 Hospital3.6 Forensic Laboratories 4 Human Genetics Competition Analysis by Players4.1 Global Human Genetics Market Size by Players (2016-2021)4.2 Global Top Players by Company Type (Tier 1, Tier 2 and Tier 3) & (based on the Revenue in Human Genetics as of 2020)4.3 Date of Key Players Enter into Human Genetics Market4.4 Global Top Players Human Genetics Headquarters and Area Served4.5 Key Players Human Genetics Product Solution and Service4.6 Competitive Status4.6.1 Human Genetics Market Concentration Rate4.6.2 Mergers & Acquisitions, Expansion Plans 5 Company (Top Players) Profiles and Key Data5.1 QIAGEN5.1.1 QIAGEN Profile5.1.2 QIAGEN Main Business5.1.3 QIAGEN Human Genetics Products, Services and Solutions5.1.4 QIAGEN Human Genetics Revenue (US$ Million) & (2016-2021)5.1.5 QIAGEN Recent Developments5.2 Agilent Technologies5.2.1 Agilent Technologies Profile5.2.2 Agilent Technologies Main Business5.2.3 Agilent Technologies Human Genetics Products, Services and Solutions5.2.4 Agilent Technologies Human Genetics Revenue (US$ Million) & (2016-2021)5.2.5 Agilent Technologies Recent Developments5.3 Thermo Fisher Scientific5.5.1 Thermo Fisher Scientific Profile5.3.2 Thermo Fisher Scientific Main Business5.3.3 Thermo Fisher Scientific Human Genetics Products, Services and Solutions5.3.4 Thermo Fisher Scientific Human Genetics Revenue (US$ Million) & (2016-2021)5.3.5 Illumina Recent Developments5.4 Illumina5.4.1 Illumina Profile5.4.2 Illumina Main Business5.4.3 Illumina Human Genetics Products, Services and Solutions5.4.4 Illumina Human Genetics Revenue (US$ Million) & (2016-2021)5.4.5 Illumina Recent Developments5.5 Promega5.5.1 Promega Profile5.5.2 Promega Main Business5.5.3 Promega Human Genetics Products, Services and Solutions5.5.4 Promega Human Genetics Revenue (US$ Million) & (2016-2021)5.5.5 Promega Recent Developments5.6 LabCorp5.6.1 LabCorp Profile5.6.2 LabCorp Main Business5.6.3 LabCorp Human Genetics Products, Services and Solutions5.6.4 LabCorp Human Genetics Revenue (US$ Million) & (2016-2021)5.6.5 LabCorp Recent Developments5.7 GE5.7.1 GE Profile5.7.2 GE Main Business5.7.3 GE Human Genetics Products, Services and Solutions5.7.4 GE Human Genetics Revenue (US$ Million) & (2016-2021)5.7.5 GE Recent Developments 6 North America6.1 North America Human Genetics Market Size by Country (2016-2027)6.2 United States6.3 Canada 7 Europe7.1 Europe Human Genetics Market Size by Country (2016-2027)7.2 Germany7.3 France7.4 U.K.7.5 Italy7.6 Russia7.7 Nordic7.8 Rest of Europe 8 Asia-Pacific8.1 Asia-Pacific Human Genetics Market Size by Region (2016-2027)8.2 China8.3 Japan8.4 South Korea8.5 Southeast Asia8.6 India8.7 Australia8.8 Rest of Asia-Pacific 9 Latin America9.1 Latin America Human Genetics Market Size by Country (2016-2027)9.2 Mexico9.3 Brazil9.4 Rest of Latin America 10 Middle East & Africa10.1 Middle East & Africa Human Genetics Market Size by Country (2016-2027)10.2 Turkey10.3 Saudi Arabia10.4 UAE10.5 Rest of Middle East & Africa 11 Human Genetics Market Dynamics11.1 Human Genetics Industry Trends11.2 Human Genetics Market Drivers11.3 Human Genetics Market Challenges11.4 Human Genetics Market Restraints 12 Research Finding /Conclusion 13 Methodology and Data Source13.1 Methodology/Research Approach13.1.1 Research Programs/Design13.1.2 Market Size Estimation13.1.3 Market Breakdown and Data Triangulation13.2 Data Source13.2.1 Secondary Sources13.2.2 Primary Sources13.3 Disclaimer13.4 Author List

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Human Genetics Market is Thriving Worldwide with Top Growing Companies QIAGEN, Agilent Technologies, Thermo Fisher Scientific The Almanian - The...

Introduction

Osteoarthritis, a common disease of the musculoskeletal system, is an important cause of pain and disability in the elderly.1 Although joint replacement is effective for treating end-stage osteoarthritis, problems such as poor joint function recovery and limited lifetime of artificial joint hinder further improvement in prognosis. Therefore, the management of the disease is shifting to the prevention and early treatment of osteoarthritis.1,2 Overweight and obesity have been identified as risk factors of the occurrence and progression of osteoarthritis,35 and relief of symptoms was observed in patients with osteoarthritis who had undergone diet and exercise therapy.5,6 However, a causal effect of overweight or obesity on osteoarthritis cannot be convincingly established with evidence from observational studies, in which there is a lack of randomization of exposure factors and therefore confounding and reverse causality cannot be ruled out in the observed association. Traditional randomized clinical controlled trials are suitable from the viewpoint of methodology, but are not practicable due to ethical concern.

Mendelian randomization (MR) is a method that used genetic variations as instrumental variables of exposure factors to infer the causal relationship between exposure factors and outcomes. Because genetic variations follow the law of Mendelian and are randomly distributed in the population, the influence of confounding factors are largely controlled.7 With the popularity of genome-wide association study (GWAS) studies and GWAS meta-analysis, MR becomes an efficient and practicable method to investigate causal effect.8 The two-sample MR is a method to estimate the causal effect of an exposure on an outcome using only summary statistics from GWAS, in which genetic variation-exposure factor association data and genetic variation-disease outcome association data from two independent samples with similar distribution characteristics were used. In the study, we used the two-sample MR method based on GWAS data to analyze whether there is a causal effect of body mass index (BMI) on the risk of osteoarthritis.

Publicly accessible data for genetic variants associated with BMI were obtained from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium.9 The detail of studies and datasets was presented in Table 1. The consortium included 3,339,224 participants and the number of included single nucleotide polymorphism (SNP) was 2,555,511. To minimize the impact caused by linkage disequilibrium (LD), we set the threshold of statistical significance as P <5108; LD r2 <0.1 to identify the SNPs associated with BMI. In total, there were 79 SNPs included in this study (rs1000940, rs10132280, rs1016287, rs10182181, rs10733682, rs10840100, rs11030104, rs11057405, rs11165643, rs11672660, rs1167827, rs11727676, rs12286929, rs12429545, rs12448257, rs12940622, rs12986742, rs13021737, rs13078960, rs13107325, rs13130484, rs13191362, rs13201877, rs13329567, rs1421085, rs1441264, rs1460676, rs14810, rs1516725, rs1528435, rs16851483, rs17001654, rs17066856, rs17094222, rs17203016, rs17381664, rs17724992, rs1928295, rs2033529, rs2060604, rs2112347, rs2176598, rs2183825, rs2365389, rs2820292, rs2836754, rs2890652, rs3736485, rs3800229, rs3817334, rs3849570, rs3888190, rs4740619, rs4889606, rs543874, rs6091540, rs6457796, rs6477694, rs6567160, rs657452, rs6713510, rs6804842, rs7138803, rs7144011, rs7531118, rs7550711, rs7599312, rs7715256, rs7899106, rs7903146, rs879620, rs891389, rs9304665, rs9374842, rs943005, rs9540493, rs9579083, rs977747, and rs9926784). The variation in the included SNPs was 2.7%. The F value was 5,529, which was larger than 10 and suggested the strength of the instrumental variable was not weak.10 As a result, all these SNPs were included into the study.

Table 1 Details of Studies and Datasets Used in the Study

The GWAS summary data for osteoarthritis were obtained from MRC Integrative Epidemiology Unit (MRC-IEU) consortium, which was published in 2018 and available through the UK Biobank.11 The sample size was 462,933, with 38,472 cases and 424,461 participants in the control group. The number of SNP included in the study was 9,851,867. All the above SNPs associated with BMI were found in MRC-IEU consortium.

After data from the GWAS study or GWAS meta-analysis associated with BMI or osteoarthritis were obtained via MR-Base platform,12 MR analysis was further carried out using the package TwoSampleMR of the R program (version 3.4.2). Three statistical methods including inverse-variance weighted (IVW) method, weighted median estimator, and MR-Egger regression were used to investigate the causal relationship between BMI and osteoarthritis.1215 The IVW method is the method to assess the causal relationship by the meta-analysis of every Wald ratio for the included SNPs.12,13 Significantly, there is a premise for the IVW method that all the included SNPs must be valid variables. Unlike the IVW method, the MR-Egger regression can still function when all the SNPs are invalid.15 The slope of MR-Egger indicates the effect of BMI on osteoarthritis when the intercept term is zero or without statistical significance.12,15 The weighted median estimator was intermediate and the valid variables must be no less than 50%.14 The result of the weighted median estimator was the median when the effect estimations of each single SNP are sorted in the order of weight values. The estimation of the causal relationship between BMI and osteoarthritis was expressed as odds ratio (OR) and its 95% confidence interval (CI). A P value less than 0.05 indicates that the difference is statistically significant.

The method of leave-one-out method was utilized to investigate the sensitivity of the results. Similar to the meta-analysis, we removed the single SNP one by one and calculated the effect of the remaining SNPs by the IVW method.16 In this way, we examined the effect of individual SNP on the causal inference.

The details of each SNP were presented in Table 2, including the chromosome location, genes, effect allele (EA), and effect allele frequency (EAF). Estimations of the associations of each SNP with BMI and osteoarthritis including beta value, standard error (SE) and P value were also presented in Table 2. Among them, 17 SNPs, namely rs13107325 ( 0.0096; SE 0.0011; P<0.001), rs6457796 ( 0.0025; SE 0.0006; P<0.001), rs3736485 ( 0.0017; SE 0.0006; P<0.001), rs2836754 ( 0.0016; SE 0.0006; P 0.01), rs2820292 ( 0.0014; SE 0.0006; P 0.02), rs6477694 ( 0.0012; SE 0.0006; P 0.04), rs11030104 ( 0.0029; SE 0.0007; P <0.001), rs3849570 ( 0.0012; SE 0.0006; P 0.04), rs16851483 ( 0.0029; SE 0.0012; P 0.01), rs891389 ( 0.0012; SE 0.0006; P 0.05), rs1516725 ( 0.0023; SE 0.0008; P 0.01), rs10182181 ( 0.0013; SE 0.0006; P 0.02), rs13021737 ( 0.0026; SE 0.0008; P <0.001), rs7138803 ( 0.0013; SE 0.0006; P 0.03), rs7531118 ( 0.0012; SE 0.0006; P 0.04), rs6567160 ( 0.0017; SE 0.0007; P 0.01), and rs1421085 ( 0.0013; SE 0.0006; P 0.03), were significantly associated with both BMI and osteoarthritis.

As presented in Table 3, result of the IVW method suggested that there was a positive association between BMI with higher genetic predictability for the risk of osteoarthritis (OR 1.028, 95% CI 1.0211.036). The weighted median estimator and MR-Egger method showed consistent results (the weighted median estimator: OR 1.028, 95% CI 1.0191.037; MR-Egger Method: OR 1.028, 95% CI 1.0091.046). These results could also be observed in the forest plot (Figure 1) and the scatter diagram (Figure 2).

Table 3 Causal Associations Between Genetically Determined BMI and Osteoarthritis

Figure 1 Forest plot of single nucleotide polymorphisms (SNPs) associated with body mass index (BMI) and the risk of osteoarthritis. Black points represent the log odds ratio (OR) for osteoarthritis per standard deviation (SD) increase in BMI, which is produced by using each SNP selected as a separate instrument (rs1000940, rs10132280, rs1016287, rs10182181, rs10733682, rs10840100, rs11030104, rs11057405, rs11165643, rs11672660, rs1167827, rs11727676, rs12286929, rs12429545, rs12448257, rs12940622, rs12986742, rs13021737, rs13078960, rs13107325, rs13130484, rs13191362, rs13201877, rs13329567, rs1421085, rs1441264, rs1460676, rs14810, rs1516725, rs1528435, rs16851483, rs17001654, rs17066856, rs17094222, rs17203016, rs17381664, rs17724992, rs1928295, rs2033529, rs2060604, rs2112347, rs2176598, rs2183825, rs2365389, rs2820292, rs2836754, rs2890652, rs3736485, rs3800229, rs3817334, rs3849570, rs3888190, rs4740619, rs4889606, rs543874, rs6091540, rs6457796, rs6477694, rs6567160, rs657452, rs6713510, rs6804842, rs7138803, rs7144011, rs7531118, rs7550711, rs7599312, rs7715256, rs7899106, rs7903146, rs879620, rs891389, rs9304665, rs9374842, rs943005, rs9540493, rs9579083, rs977747, and rs9926784). Red points show the combined causal estimate using all SNPs together as a single instrument, using the three different methods (the inversevariance weighted (IVW) method, weighted median estimator, and MREgger). Horizontal line segments denote 95% confidence intervals of the estimate.

Figure 2 Scatter plot of SNPs associated with BMI and the risk of osteoarthritis. The plot presents the effect sizes of the SNP-BMI association (x-axis, SD units) and the SNP-osteoarthritis association (y-axis, log (OR)) with 95% confidence intervals. The regression slopes of the lines correspond to causal estimates using the three Mendelian randomization (MR) methods (the IVW method, weighted median estimator, and MREgger).

There was no evidence that the result was affected by genetic pleiotropy (MR-Egger regression intercept=1.3105, SE=0.00025, P=0.959). From the result of the Leave-one-out method, there was no single SNP playing a decisive role in the causal inference (Figure 3).

Figure 3 Leave-one-out of SNPs associated with BMI and their risk of osteoarthritis. Each black point represents result of the IVW MR method applied to estimate the causal effect of BMI on osteoarthritis excluding particular SNP (rs1000940, rs10132280, rs1016287, rs10182181, rs10733682, rs10840100, rs11030104, rs11057405, rs11165643, rs11672660, rs1167827, rs11727676, rs12286929, rs12429545, rs12448257, rs12940622, rs12986742, rs13021737, rs13078960, rs13107325, rs13130484, rs13191362, rs13201877, rs13329567, rs1421085, rs1441264, rs1460676, rs14810, rs1516725, rs1528435, rs16851483, rs17001654, rs17066856, rs17094222, rs17203016, rs17381664, rs17724992, rs1928295, rs2033529, rs2060604, rs2112347, rs2176598, rs2183825, rs2365389, rs2820292, rs2836754, rs2890652, rs3736485, rs3800229, rs3817334, rs3849570, rs3888190, rs4740619, rs4889606, rs543874, rs6091540, rs6457796, rs6477694, rs6567160, rs657452, rs6713510, rs6804842, rs7138803, rs7144011, rs7531118, rs7550711, rs7599312, rs7715256, rs7899106, rs7903146, rs879620, rs891389, rs9304665, rs9374842, rs943005, rs9540493, rs9579083, rs977747, and rs9926784) from the analysis. Each red point depicts the IVW estimate using all SNPs. No single SNP is strongly driving the overall effect of BMI on osteoarthritis in this leave-one-out sensitivity analysis.

Osteoarthritis is a chronic joint inflammatory disease with joint swelling, pain and dysfunction as the main clinical manifestations, which seriously affects the quality of life of patients and increases the economic burden of the family and society.2 In recent years, with the gradual intensification of the aging trend in the population, the prevalence rate of osteoarthritis is also increasing year by year.1,2 Age, sex, joint injury and obesity are important risk factors for osteoarthritis, among which overweight and obesity are easier to control and prevent than other risk factors. However, due to the influence of confounding factors, it is difficult for classical epidemiological studies to explain the causal sequence of exposure factors and disease results. The purpose of this study is to explore the relationship between BMI and the risk of osteoarthritis through a two-sample Mendelian randomized study based on GWAS. The result indicated that the causal relationship between BMI with the increased risk of osteoarthritis.

In this study, we explored the relationship between BMI and the risk of osteoarthritis through a two-sample MR study based on GWAS. 79 SNPs significantly related to BMI were selected as tool variables. When using the IVW method, weighted median estimator, and MR-Egger MR method with data from the GWAS study of osteoarthritis, it was found that there was a causal relationship between high BMI and the increased risk of osteoarthritis. The main implication of our findings is that it supports weight control as a intervention for the prevention and management of osteoarthritis.

Overweight and obesity are modifiable and easier to manage than other risk factors of osteoarthritis (such as age, sex, and joint injury), and therefore it becomes a promising target for management of osteoarthritis if overweight and obesity do casually increase the risk of the occurrence and/or progress of osteoarthritis. The association between overweight and osteoarthritis has been reported in several studies. Raud et al17 found that there was a dose-response relation between BMI and the knee of osteoarthritis. A Spanish research demonstrated that the osteoarthritis risk both in hip and knee was increased in being overweight.18 However, due to the potential influence of confounding factors and reverse causality, it is difficult for conventional observational studies to establish the causal relationship between BMI and osteoarthritis, while the MR method is a promising tool for such an investigation, since it uses genetic variations as instrumental variables of exposure factors to infer the causal relationship between exposure factors and outcomes. There were a few available studies that used the MR method to investigate the relationship between BMI and osteoarthritis.19,20 Hindy et al19 used methods including independent sample traditional and multivariate MR, and two sample traditional and multivariate MR methods to explore the causal relationship between BMI and osteoarthritis. Their results showed that there was a causal relationship between high BMI and low LDL and osteoarthritis. However, the study population they investigated was mainly from Malm Diet and Cancer Study, which contained 4,226 cases and 23,456 controls in 1991 to 1996. Instead, we used the GWAS data released in 2018, with 38,472 cases and 424,461 controls. The updated data and increased sample sizes increased the strengths of our study, which had more practical significance and statistical power to confirm the investigated causal relationship. FunckBrentano et al20 also found a causal relationship between high BMI and knee arthritis and hip arthritis through MR method, but not with hand arthritis. The conclusion of our study was similar to these two studies, but in addition to the use of updated data and a larger sample size, the methods we used for investigation were more rigorous since a sensitivity analysis using the method of leave-one-out was conducted and consistent results were observed.

The etiology of the causal effect of BMI on osteoarthritis is not completely clear. Some studies have suggested that individuals with high BMI might be with increased joint load and therefore it accelerates the aging of the articular surface, leading to the occurrence and aggravation of osteoarthritis.21 In addition, obesity can induce the development of inflammation by increasing the intermediates produced by lipid metabolism, leading to osteoarthritis.22 However, more researches are need for further study.

Our study had some strengths. First, the MR method was used in the study, and therefore confounding factors and reverse causality were well controlled, at least to a great extent. Second, the study was based on data from published GWAS researches and GWAS meta-analyses, with a large sample size and genetic variations. However, the study also had some limitations. First, genetic polymorphisms are difficult to validate, and even if we used the MR-Egger method, misclassification in genetic polymorphisms cannot be completely ruled out. Second, the GWAS dataset of BMI used in this study was based on a mixed population, while the population from which the data of osteoarthritis was derived was European. This might lead to the bias from population stratification, and it remains unknown whether the result can be directly applied to other populations, which warrants further investigations. Third, there might be over-identification in the two-sample MR study, which may overestimate the association between SNP and exposure.23

This study indicated that high BMI might be causally associated with increased risk of osteoarthritis, which supports the importance of weight control for the prevention and treatment of osteoarthritis. Further researches are needed to explore the underlying mechanisms of this causal relationship.

This article does not contain any studies with human participants or animals performed by any of the authors.

The authors declare that they have no competing interests.

1. Geyer M, Schnfeld C. Novel insights into the pathogenesis of osteoarthritis. Curr Rheumatol Rev. 2018;14(2):98107. doi:10.2174/1573397113666170807122312

2. Glyn-Jones S, Palmer AJR, Agricola R, et al. Osteoarthritis. Lancet. 2015;386(9991):376387. doi:10.1016/S0140-6736(14)60802-3

3. Blagojevic M, Jinks C, Jeffery A, et al. Risk factors for onset of osteoarthritis of the knee in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2010;18(1):2433. doi:10.1016/j.joca.2009.08.010

4. Butterworth PA, Landorf KB, Smith SE, et al. The association between body mass index and musculoskeletal foot disorders: a systematic review. Obes Rev. 2012;13(7):630642. doi:10.1111/j.1467-789X.2012.00996.x

5. Kolasinski SL, Neogi T, Hochberg MC, et al. 2019 American College of Rheumatology/Arthritis Foundation guideline for the management of osteoarthritis of the hand, hip, and knee. Arthritis Rheumatol. 2020;72(2):220233. doi:10.1002/acr.24131

6. Messier SP, Mihalko SL, Legault C, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013;310(12):12631273. doi:10.1001/jama.2013.277669

7. Ference BA, Ray KK, Catapano AL, et al. Mendelian randomization study of ACLY and cardiovascular disease. N Engl J Med. 2019;380(11):10331042. doi:10.1056/NEJMoa1806747

8. Li MJ, Liu Z, Wang P, et al. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2016;44(D1):D869D876. doi:10.1093/nar/gkv1317

9. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197206. doi:10.1038/nature14177

10. Burgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755764. doi:10.1093/ije/dyr036

11. Elsworth BL, Lyon MS, Alexander T, et al. The MRC IEU OpenGWAS data infrastructure. bioRxiv. 2020.

12. Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. doi:10.7554/eLife.34408

13. Bowden J, Del Greco MF, Minelli C, et al. A framework for the investigation of pleiotropy in twosample summary data Mendelian randomization. Stat Med. 2017;36(11):17831802. doi:10.1002/sim.7221

14. Bowden J, Davey smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304314. doi:10.1002/gepi.21965

15. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):19851998. doi:10.1093/ije/dyx102

16. Mikshowsky AA, Gianola D, Weigel KA. Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation. J Dairy Sci. 2017;100(1):453464. doi:10.3168/jds.2016-11496

17. Raud B, Gay C, Guiguet-Auclair C, et al. Level of obesity is directly associated with the clinical and functional consequences of knee osteoarthritis. Sci Rep. 2020;10(1):17. doi:10.1038/s41598-020-60587-1

18. Reyes C, Leyland KM, Peat G, et al. Association between overweight and obesity and risk of clinically diagnosed knee, hip, and hand osteoarthritis: a populationbased cohort study. Arthritis Rheumatol. 2016;68(8):18691875. doi:10.1002/art.39707

19. Hindy G, kesson KE, Melander O, et al. Cardiometabolic polygenic risk scores and osteoarthritis outcomes: a Mendelian randomization study using data from the Malm Diet and Cancer Study and the UK Biobank. Arthritis Rheumatol. 2019;71(6):925934. doi:10.1002/art.40812

20. FunckBrentano T, Nethander M, MovrareSkrtic S, et al. Causal factors for knee, hip, and hand osteoarthritis: a Mendelian randomization study in the UK biobank. Arthritis Rheumatol. 2019;71(10):16341641. doi:10.1002/art.40928

21. Ibounig T, Simons T, Launonen A, et al. Glenohumeral osteoarthritis: an overview of etiology and diagnostics. Scand J Surg. 2020:1457496920935018. doi:10.1177/1457496920935018

22. Francisco V, Prez T, Pino J, et al. Biomechanics, obesity, and osteoarthritis. The role of adipokines: when the levee breaks. J Orthop Res. 2018;36(2):594604. doi:10.1002/jor.23788

23. Bowden J, Dudbridge F. Unbiased estimation of odds ratios: combining genomewide association scans with replication studies. Genetic Epidemiol. 2009;33(5):406418. doi:10.1002/gepi.20394

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