Category Archives: Transhuman News

The genetics and genomics revolution has at its core information and techniques that can be used to change humanness itself as well as the concepts of what it means to be human. The age-old human fantasies of the mythical chimeras of the ancients, supernatural intelligence, wiping disease from human inheritance, designing a better human being, the fountain of youth, and even immortality now have biotechnical credence in the theoretical promises of genetics and genetic engineering. Not only can humanity's collective genetic inheritance be shaped by selecting which embryos are allowed to develop via pre-implantation genetic diagnosis, but genetic engineering, the availability of the human embryo for experimentation, and combining genes from many species require only sufficient imagination to catalyze the designing of a new humanity.

To talk about some of the implications of these technologies, Wake Forest University School of Medicine held a conference entitled Genetics, Biotechnology and the Future: Medical, Scientific and Religious Perspectives on January 24, 2004 in Winston-Salem, North Carolina in partnership with The Center for Bioethics and Human Dignity. The conference was co-sponsored by the Bioethics Task Force of Wake Forest University, Christian Medical and Dental Associations, Piedmont Bioethics Network, and Trinity International University.

The conference brought together leaders from medicine, science, law, ethics, religion, and patient advocacy to examine how genetics and biotechnology should be used to shape our future. The overall goal of the conference was to spur in-depth deliberation across spheres of influence during the formative stages of genetic and biotechnological disciplines. The conference, promoted through Bioethics.com and other international venues, was a stimulating and rewarding experience featuring insightful exchange among the various fields.

In addition to discussing the genetic revolutions, competing conceptions of the human embryo's moral status were also debated at the conference. Greater support was voiced for a view in which "respect" entails more than just insisting that the benefits of killing be great enough. An embryo is a human being--genetically human and a being who will develop through a lifelong cycle, like other human beings, as long as suitable nurture and environment are provided. To diminish that being's status, because of the stage of development at the moment, appeared arbitrary to many--though some supported doing so.

While science is billed as morally neutral, there are many fallacies with this oversimplification. Science lacks moral neutrality not only in the priorities set but also in the hypotheses proposed and the questions asked, because the prevailing philosophical values of our culture influence all of these. The swaying of scientific aims by philosophical values is more fundamental to science's impact on our future than the actual gains of explorations themselves.

The conference noted that the medical profession--countering the narrowly focused, specific question-answering capabilities of science--humanizes scientific activity. The patient advocacy role of a physician takes the empirical-pragmatic scientific "logical way" of medicine into account, but guides patients to act consistently with their whole persons, not just their physical bodies. Medicine at its best never advocates a cure at the expense of denigrating a patient's soul. Medicine begins the ethical reflection on the "should we" questions. Recently, though, medicine has increasingly been preoccupied with patient autonomy and utility, and the need for the valuable counterbalance that can be provided by religious influences has become more apparent. Autonomy and utility should not trump all other ethical concerns.

To read current justifications of human cloning, embryonic stem cell research, and genetic intervention, though, one would think that constraining any scientific freedom is the ultimate evil. On the contrary, the greater evil arguably lies in allowing scientific development to proceed without ethical moorings. One would also think from current discussions that great medical benefits constitute their own justification; whereas common sense tells us otherwise. We don't remove all of the vital organs from a single healthy person just because a larger number of people can be enabled to live as a result.

Religious perspectives have a significant role to play in the ethical use of genetics and biotechnology--to connect autonomous choices with larger communal concerns. Religious views help ensure that scientific advances not only expand choices and produce benefits but do so without undermining our humanity and dignity in the process. This conference shattered the oft-quoted misconception that those who hold strong religious opinions are antagonistic to scientific investigation. Rather, all spheres of influence agreed on the high value of scientific and medical investigation with an aim to restore human health and alleviate disease and suffering. The consensus was that society should no longer allow these spheres of influence to remain separate and isolated in theoretical blindness. Rather society must prioritize cross-disciplinary examination to ensure that the future of human genetics and biotechnology is not only scientifically sophisticated and medically productive but also truly humane.

It is a cultural necessity today to have bioethics dialogs among informed citizens representing all spheres of influence. More opportunities like this are needed that bring together people of differing views to discuss and assess some of the most crucial issues of our time.

Editor's Note: The above text has been adapted from an article appearing in the Journal of International Biotechnology Law 1:2 (March, 2004): 53-55. The journal invited the authors to write the article, which discusses the most important ideas that emerged at the Center's latest regional conference, for its March 2004 issue.

To inquire about holding a CBHD conference in your area, please email the Center at info@cbhd.org.

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June 9, 2020

The genome is the complete set of your DNA, including all of your genes. The human genome was first decoded nearly two decades ago. The genetic sequencing of thousands of genomes has allowed researchers to begin to understand how the human body is built and maintained.

But each persons genome is unique. Not enough genomes have been sequenced to understand all the ways that genetic variation can contribute to disease. To better understand the genetic diversity of the human genome, the Genome Aggregation Database (gnomAD) Consortium was formed over eight years ago to collect and study the genomes of people around the world.

The international gnomAD team of over 100 scientists released its first set of discoveries in a collection of seven papers published on May 27, 2020 in Nature, Nature Communications, and Nature Medicine. The work was funded in part by several NIH institutes (see Funding section below for full list).

The flagship paper cataloged the genetic variation in both the protein coding and non-coding regions of human DNA. Included were more than 125,000 exomes (which include only the parts that code for proteins) and 15,000 whole genomes, from populations in Europe, East and South Asia, Africa, and more. The researchers identified a total of 241 million variants that were either small single point mutations (changes in a single DNA building block, called a nucleotide) or insertions or deletions of short pieces of DNA.

The team explored how likely certain variants are to cause a loss of function in the proteins produced from the gene. Protein-coding genes were categorized based on their ability to tolerate genetic variations without being disrupted or inactivated by them. This analysis found more than 443,000 genetic variants that were likely to cause a loss of protein function.

The second paper explored why mutations identified as likely to cause a loss of function dont always cause the problems that might be expected. The team found that such variants are within segments of DNA that are often spliced out of the final mRNA copies of the gene used to produce proteins.

A third paper detailed the analysis of more than 433,000 structural variants in the human genome. Structural variants are changes that span long stretches of DNA, of at least 50 nucleotides. Structural variants were less likely to appear in protein coding regions than in non-protein coding regions. The team estimated that only about 0.13% of people carry a structural variant with any clinical significance.

The fourth paper explored how loss of function variations could be used to identify new drug targets. The fifth paper provided an example of how gnomAD could be used to validate drug targets. It analyzed the effects of loss of function variants in a gene called LRRK2, which has been associated with Parkinsons disease. The results suggestthat therapies to inhibit the LRRK2 protein would be unlikely to cause severe side effects.

The sixth paper described the impacts of variants in the region that sits immediately before the protein coding region of genes, called the 5 untranslated region. The researchers identified specific genes where variants in this region could lead to disease. One novel variant they uncovered was tied to neurofibromatosis. Finally, the last paper showed how gnomAD could be used to analyze multi-nucleotide variantsclusters of two or more variants that are often inherited together.

The wide-ranging impact this resource has already had on medical research and clinical practice is a testament to the incredible value of genomic data sharing and aggregation, says Dr. Daniel MacArthur at the Broad Institute of MIT and Harvard, who is a lead author on the papers. More than 350 independent studies have already made use of gnomAD for research on cancer predisposition, cardiovascular disease, rare genetic disorders, and more since we made the data available.

The consortiums next steps are to expand gnomAD to increase the number of genomes and diversity of populations included. We are very far from saturating discoveries or solving variant interpretation, MacArthur says. The next steps for the consortium will be focused on increasing the size and population diversity of these resources, and linking the resulting massive-scale genetic data sets with clinical information.

by Tianna Hicklin, Ph.D.

References:

The mutational constraint spectrum quantified from variation in 141,456 humans. Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, Walters RK, Tashman K, Farjoun Y, Banks E, Poterba T, Wang A, Seed C, Whiffin N, Chong JX, Samocha KE, Pierce-Hoffman E, Zappala Z, O'Donnell-Luria AH, Minikel EV, Weisburd B, Lek M, Ware JS, Vittal C, Armean IM, Bergelson L, Cibulskis K, Connolly KM, Covarrubias M, Donnelly S, Ferriera S, Gabriel S, Gentry J, Gupta N, Jeandet T, Kaplan D, Llanwarne C, Munshi R, Novod S, Petrillo N, Roazen D, Ruano-Rubio V, Saltzman A, Schleicher M, Soto J, Tibbetts K, Tolonen C, Wade G, Talkowski ME; Genome Aggregation Database Consortium, Neale BM, Daly MJ,MacArthur DG. Nature. 2020 May;581(7809):434-443. doi: 10.1038/s41586-020-2308-7. Epub 2020 May 27. PMID:32461654.

Transcript expression-aware annotation improves rare variant interpretation. Cummings BB, Karczewski KJ, Kosmicki JA, Seaby EG, Watts NA, Singer-Berk M, Mudge JM, Karjalainen J, Satterstrom FK, O'Donnell-Luria AH, Poterba T, Seed C, Solomonson M, Alfldi J; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Daly MJ,MacArthur DG. Nature. 2020 May;581(7809):452-458. doi: 10.1038/s41586-020-2329-2. Epub 2020 May 27. PMID:32461655.

A Structural Variation Reference for Medical and Population Genetics Collins RL, Brand H, Karczewski KJ, Zhao X, Alfldi J, Francioli LC, Khera AV, Lowther C, Gauthier LD, Wang H, Watts NA, Solomonson M, O'Donnell-Luria A, Baumann A, Munshi R, Walker M, Whelan CW, Huang Y, Brookings T, Sharpe T, Stone MR, Valkanas E, Fu J, Tiao G, Laricchia KM, Ruano-Rubio V, Stevens C, Gupta N, Cusick C, Margolin L; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Taylor KD, Lin HJ, Rich SS, Post WS, Chen YI, Rotter JI, Nusbaum C, Philippakis A, Lander E, Gabriel S, Neale BM, Kathiresan S, Daly MJ, Banks E, MacArthur DG, Talkowski ME. Nature. 2020 May;581(7809):444-451. doi: 10.1038/s41586-020-2287-8. Epub 2020 May 27. PMID:32461652.

Evaluatingdrugtargetsthroughhumanloss-of-functiongeneticvariation. Minikel EV, Karczewski KJ, Martin HC, Cummings BB, Whiffin N, Rhodes D, Alfldi J, Trembath RC, van Heel DA, Daly MJ; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Schreiber SL, MacArthur DG. Nature. 2020 May;581(7809):459-464. doi: 10.1038/s41586-020-2267-z. Epub 2020 May 27. PMID:32461653.

The effect of LRRK2 loss-of-function variants in humans. Whiffin N, Armean IM, Kleinman A, Marshall JL, Minikel EV, Goodrich JK, Quaife NM, Cole JB, Wang Q, Karczewski KJ, Cummings BB, Francioli L, Laricchia K, Guan A, Alipanahi B, Morrison P, Baptista MAS, Merchant KM; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Ware JS, Havulinna AS, Iliadou B, Lee JJ, Nadkarni GN, Whiteman C; 23andMe Research Team, Daly M, Esko T, Hultman C, Loos RJF, Milani L, Palotie A, Pato C, Pato M, Saleheen D, Sullivan PF, Alfldi J, Cannon P,MacArthur DG. Nat Med. 2020 May 27. doi: 10.1038/s41591-020-0893-5. Online ahead of print. PMID:32461697.

Characterising the loss-of-function impact of 5' untranslated region variants in 15,708 individuals. Whiffin N, Karczewski KJ, Zhang X, Chothani S, Smith MJ, Evans DG, Roberts AM, Quaife NM, Schafer S, Rackham O, Alfldi J, O'Donnell-Luria AH, Francioli LC; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Cook SA, Barton PJR,MacArthur DG, Ware JS. Nat Commun. 2020 May 27;11(1):2523. doi: 10.1038/s41467-019-10717-9.PMID:32461616.

Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes. Wang Q, Pierce-Hoffman E, Cummings BB, Alfldi J, Francioli LC, Gauthier LD, Hill AJ, O'Donnell-Luria AH; Genome Aggregation Database Production Team; Genome Aggregation Database Consortium, Karczewski KJ,MacArthur DG. Nat Commun. 2020 May 27;11(1):2539. doi: 10.1038/s41467-019-12438-5. PMID:32461613.

Funding:NIHs National Institute of General Medical Sciences (NIGMS), National Human Genome Research Institute (NHGRI), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institute of Mental Health (NIMH), and National Heart, Lung, and Blood Institute (NHLBI), National Institute of Allergy and Infectious Diseases (NIAID), National Center for Advancing Translational Sciences (NCATS), National Institute of Dental and Craniofacial Research (NIDCR), and National Center for Research Resources (NCRR); Swiss National Science Foundation; BioMarin Pharmaceutical Inc.; Sanofi Genzyme Inc.; Broad Institute; Wellcome Trust; Medical Research Council (UK); University of Sheffield; Barts Charity; Health Data Research UK; NHS National Institute for Health Research; Rosetrees/Stoneygate Imperial College; Simons Foundation; National Science Foundation; Desmond and Ann Heathwood; Southern California Diabetes Endocrinology Research Center; Michael J. Fox Foundation; Estonian Research Council; Royal Brompton and Harefield NHS Foundation; Imperial College London; Fondation Leducq; Department of Health, UK; Swiss National Science Foundation; Imperial College Academic Health Science Centre; Nakajima Foundation Scholarship.

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Clemson Universitys Center for Human Genetics opened July 1, 2018, and is dedicated to advancing knowledge of the fundamental principles by which genetic and environmental factors determine and predict both healthy traits and susceptibility to disease.The Center for Human Genetics, which is part of theCollege of Science, is housed inSelf Regional Hall, a 17,000-square-foot building that opened in February 2017. The sparkling facility is nestled within the sprawling campus of theGreenwood (S.C.) Genetic Center, which has a long history of clinical and research excellence in the field of medical genetics and caring for families impacted by genetic disease and birth defects.

MEDIA RELEASE

Clemson University has further enhanced its standing as a pioneer in the field of human genomics by hiring a renowned scientist to lead the way.Groundbreaking geneticist Trudy Mackay has been named director of Clemsons Center for Human Genetics and has been tasked with building a team of researchers whose goal will be to significantly advance our understanding of genetic disorders.

Read more about the Center for Human Genetics

Oct. 25, 2018:Mackay to be honored at Trinity College Dublin

Oct. 8, 2018:CHG receives $1.87 million from NIH to advance research

Aug. 8, 2018:CHG opens its doors to the world

Feb. 15, 2017:CHG unveils new facility on Greenwood Genetic Center campus

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Center for Human Genetics | College of Science, Clemson ...

Almost a quarter of people who suffer severe forms of Covid-19 have a genetic or immune anomaly, a major Paris hospital group has said, citing two new French studies.

In a statement, the Assistance publique Hpitaux de Paris (AP-HP) highlighted two new studies on the subject. Both were published in scientific journal Science Immunology.

They are the result of international collaboration, including researchers from national medical institute lInstitut national de la sant et de la recherche mdicale (Inserm), the University of Paris, and the human genetics lab of infectious diseases at the AP-HP.

In the first study, researchers focused on men, as they are more likely to suffer from severe forms of Covid. Researchers sequenced the X chromosome of 1,202 male patients who had had a severe form of the virus.

Of these, 16 patients were found to have a genetic variation on the TLR7 gene, dubbed a loss of function, which led to the development of severe forms of the virus.

This is because this gene plays a major role in the production and mechanism of IFN 1, which is a protein that is produced in response to a viral threat, and which inhibits the replication of the virus in infected cells, the AP-HP said.

It summarised: The 16 patients who presented a deficit in IFN 1 stopped their cells from being able to fight against the SARS-CoV-2 infection, which explains the severe forms.

The study recruited patients from all over the world, involving 400 research centres in 38 different countries the hospital group said, which enabled researchers to gather a representative sample of people and avoid excess ethnicity bias.

This means that the results can be used to make predictions and conclusions about the general population.

Overall, the study concluded: It appears that 1.3% of several forms of Covid-19 can be explained by a genetic abnormality of the TLR7 gene in men. This deficit is more frequent (1.8%) in patients under 60.

The second study showed that 15-20% of severe forms of Covid are due to the patients blood having antibodies that specifically target the IFN 1.

The study looked at 3,595 patients who had had a severe form of Covid, 1,639 who had an asymptomatic form, and 34,159 people in good health. The participants were from 38 different countries.

In its statement, AP-HP said: They showed that these antibodies block the protecting effects of IFN 1 on the virus replication. The SARS-CoV-2 virus penetrates into the cells without meeting any resistance and replicates uncontrollably.

The study also showed that these antibodies against IFN 1 increase with age. They are very rare before the age of 65 (0.2-0.5%), and increase exponentially as you age, reaching 4% between the ages of 70-79, and 7% between the ages of 80-85.

Researchers do not yet know why this is, but the study does partly help to explain why age is a risk factor in the development of severe forms of Covid.

France is still recording relatively high numbers of cases of the virus, and of hospital admissions.

The most recent figures to August 21 from Sant publique France show that there were 22,636 confirmed cases in the previous 24 hours, and 81 deaths. There were 6,008 new hospitalisations in the past seven days, and 1,316 critical care admissions in the same time, including 969 into intensive care units.

Record 6million Covid tests taken in France after health pass extended200 anti-health pass protests to take place in France this SaturdayFrance hits 40 million goal for full vaccinations against Covid-19

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French study: 20% severe Covid patients have genetic or immune issue - The Connexion

A collaborative team of researchers have developed a novel system known as SEND that harnesses human proteins to deliver molecular therapies.

Proteins are often referred to as the "workhorses" of the cell. There are many different types of proteins expressed in the human body, such as enzymes, receptors and signaling molecules. Proteins are encoded by DNA. The central dogma of molecular biology specifies that DNA is transcribed to RNA, which is then translated to proteins. This is a highly simplified summary but enables you to understand where proteins come from. If there is a mutation or an error that occurs during this process, it can result in a faulty or absent protein, which can lead to human disease. By developing therapeutics that target the molecular processes that result in protein production, we can work to treat the cause of a disease, rather than just the symptoms. To learn more about transcription and translation, visit our summary piece.

Examples of such therapeutics include gene therapies and RNA-based therapies. The COVID-19 global pandemic has cast a spotlight on RNA, as the first vaccines to receive authorization for human use were mRNA-based. However, using RNA in a therapeutic context is not a novel idea. The authorization of mRNA-based COVID-19 vaccines is a culmination of many decades of research effort from groups across the world. Ultimately, there have been many barriers to overcome in the process of developing RNA therapeutics, and many challenges remain.

Studies that knock out the PEG10 gene have demonstrated that the subsequent protein plays a role in embryonic development, binding to cellular RNAs including Hbegf (Heparin-binding EGF-like growth factor), a type of RNA that is important in placentation (the forming of the placenta inside the uterus).Previous research had shown that another retrotransposon-derived protein known as ARC could form structures that resemble viruses and were able to transfer RNA between cells. Would it therefore be possible to engineer retrotransposon proteins to become a "courier" for genetic material? It was considered but had not yet been proven.

"Working with Eugene Koonin and his team at NCBI, we identified a number of retroelement- derived proteins in the human genome that were predicted to form capsids, including PEG10. We screened these proteins to find one that not only formed capsids, but also exhibited specificity for what mRNA was packaged inside the capsids. PEG10 fit the bill," Blake Lash, graduate student in the Zhang lab, and co-first author of the study, told Technology Networks. "It mostly had its own mRNA inside the capsids, which told us that there was a specific mechanism guiding the packaging process, and we hoped we would be able to take advantage of that to reprogram PEG10 packaging."

The engineering involved a number of steps. First, the researchers had to search for molecular sequences within the PEG10 mRNA that it is able to identify and package. These signals were utilized to modify PEG10 so that it would selectively package specific types of RNA. Fusogens were then attached to the surface of the PEG10 capsules. These are proteins that are found naturally on the surface of cells, and act like a "binding glue". The fusogens help SEND to target a particular cell, tissue or organ. Zhang said that mixing and matching different components within the system will open the door for developing therapeutics for different diseases.

"To test if our cargo was being delivered, we used assays to see if the cargo was functional in the recipient cell. For example, we delivered the mRNA encoding a fluorescent protein, and we could read out the delivery of that cargo by looking to see if the receiving cells started to fluoresce (this can be done visually with a microscope)," Segel said. "We also delivered the mRNA encoding the CRISPR gene editing protein Cas9 and the guide RNA that directs Cas9 to its targets. In that case, we tested to see if SEND worked by looking for gene editing at the target site in the genome of the receiving cells." These testing processes occurred in both mouse and human cells, where SEND was successful across both types of cells.

Both a limitation and a feature of the delivery system is that it does not deliver DNA, it delivers RNA. RNA is rapidly degraded, while DNA persists for longer. This is a typical feature of RNA delivery vectors and it is a property that has been harnessed to create therapeutics that can make reversible changes to human physiology. Ultimately, the therapy can be readministered as needed to ensure the intended therapeutic effect is maintained.

Zhang concluded, "The realization that we can use PEG10, and most likely other proteins, to engineer a delivery pathway in the human body to package and deliver new RNA and other potential therapies is a really powerful concept."

Feng Zhang, Michael Segel and Blake Lash were speaking to Molly Campbell, Science Writer for Technology Networks.References:1.Segel M, Lash B, et al. Mammalian retrovirus-like protein PEG10 packages its own mRNA and can be pseudotyped for intercellular mRNA delivery. Science. 2021. doi: 10.1126/science.abg6155.

2.Kaczmarek JC, Kowalski PS, Anderson DG. Advances in the delivery of RNA therapeutics: from concept to clinical reality. Genome Medicine. 2017;9(1):60. doi: 10.1186/s13073-017-0450-0.

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Human Protein Used To Deliver Molecular Therapies - Technology Networks

Abstract

Culture evolves in ways that are analogous to, but distinct from, genomes. Previous studies examined similarities between cultural variation and genetic variation (population history) at small scales within language families, but few studies have empirically investigated these parallels across language families using diverse cultural data. We report an analysis comparing culture and genomes from in and around northeast Asia spanning 11 language families. We extract and summarize the variation in language (grammar, phonology, lexicon), music (song structure, performance style), and genomes (genome-wide SNPs) and test for correlations. We find that grammatical structure correlates with population history (genetic history). Recent contact and shared descent fail to explain the signal, suggesting relationships that arose before the formation of current families. Our results suggest that grammar might be a cultural indicator of population history while also demonstrating differences among cultural and genetic relationships that highlight the complex nature of human history.

The history of our species has involved many examples of large-scale migrations and other movements of people. These processes have helped shape both our genetic and cultural diversity (1). While humans are relatively homogeneous genetically, compared to other species, there are subtle population-level differences in genetic variation that can be observed at different geographical scales (2). Furthermore, while there are universal features of human behavior [e.g., all known societies have language and music (3)], our cultural diversity is immense. For example, we speak or sign more than 7000 mutually unintelligible languages (4), and for each ethno-linguistic group, there tend to be many different musical styles (5). Researchers have long been interested in reconstructing the history of global migrations and diversification by combining historical and archeological data with patterns of present-day biological and cultural diversity. Going back as far as Darwin, many researchers have argued that cultural evolutionary histories will tend to mirror biological evolutionary histories (69). However, differences in the ways that cultural traits and genomes are transmitted mean that genetic and cultural variation may be explained by different historical processes (1015). Major advances in both population genetics and cultural evolution since the second half of the 20th century now allow us to test these ideas more readily by matching genetic and cultural data (10, 16).

The cultural evolution of language has proven particularly fruitful for understanding past population history (genetic history statistically inferred from genetic variations) (1719). A classic approach involves identifying and analyzing sets of homologous (cognate) words among languages. This lexical approach allows the reconstruction of evolutionary lineages and relationships within a single language family, such as Austronesian (20) or Indo-European (17, 18). However, lexical methods cannot usually be applied to multiple language families (19), as they do not share robustly identifiable cognates due to a time limit of approximately 10,000 years, after which phylogenetic signals are generally lost (20, 21). An alternative approach is to study the distribution of features of grammar and phonology, such as the relative order of word classes in sentences or the presence of nasal consonants. Structural data in language tend to evolve too fast to preserve phylogenetic signals of language families (22, 23), and the history of lexica and structure might be partially independent as, for example, in the emergence of creole languages (12). However, the geographical distribution of language structure often points to contact-induced parallels in the evolution of entire sets of language families beyond their individual time depths (24, 25).

Yet language is only one out of many complex cultural traits that could serve as a proxy for deep history. It has been proposed that music may preserve even deeper cultural history than language (2629). Standardized musical classification schemes (based on features such as rhythm, pitch, and singing style) can be used to quantify patterns of musical diversity among populations for the sake of comparison with genetic and linguistic differences (26, 27, 29). Among indigenous Taiwanese populations speaking Austronesian languages, these analyses revealed significant correlations between music, mitochondrial DNA, and the lexicon (27), suggesting that music may preserve population history. However, whether these relationships extend beyond the level of language families remains unknown.

To address this gap, we focus on populations in and around northeast Asia (Fig. 1). Northeast Asia provides a useful test region because it contains high levels of genetic and cultural diversity, including a large number of small language families or linguistic isolates (e.g., Tungusic, Chukuto-Kamchatkan, Eskimo-Aleut, Yukagir, Ainu, Nivkh, Korean, and Japanese). Crucially, while genetic and linguistic data throughout much of the world have been published, northeast Asia is the only region for which published musical data allow direct matched comparison of musical, genetic, and linguistic diversity (30, 31).

Because some of the areas overlap in space, they are plotted in two separate maps.

We here use these matched comparisons to test competing hypotheses about the extent to which different forms of cultural data reflect population history at a level beyond the limits of language families. Specifically, we aim to test whether patterns of cultural evolution are significantly correlated with patterns of genetic evolution (population history), and if so, whether music or language [lexicon (32), grammar (33, 34), or phonology (3436)] would show the highest correlation with patterns of genetic diversity, after controlling for the influence of recent contact between languages (spatial autocorrelation) and shared inheritance within individual language families.

We selected all available populations from in and around northeast Asia (14 populations, encompassing 11 language families/isolates) for which all four sources of data [genome-wide single-nucleotide polymorphisms (SNPs), grammars, phonology, and music] were available (Fig. 1; Materials and Methods) (29). For genetic data, we newly genotyped 22 Nivkh individuals from Sakhalin Island in Russia using the Illumina Human Omni 2.5-8 BeadChip array (Materials and Methods). First, we investigated the similarity between populations in each of the dimensions of inquiry. For this purpose, we used split networks (37), which display multiple sources of similarity in a consistent manner (Fig. 2, figs. S12 to S16, and tables S2 to S6). Distance analysis of lexical data resulted in a network topology with an overall star-shaped structure (Fig. 2C). Exceptions are given by the three pairs of languages that are related to one another and that stand out as proximate (Even and Evenki both belong to the Tungusic family, Chukchi and Koryak both belong to the Chukotko-Kamchatkan family, and Selkup and Nganasan both belong to the Uralic family) (4). The results of this distance analysis are consistent with the fact that lexical material is able to detect relationships within language families, but cannot resolve historical relations between families.

Colors indicate language families: Selkup and Nganasan belong both to Uralic; Even and Evenki to Tungusic; and Koryak and Chukchi to Chukotko-Kamchatkan.

Distance analyses of grammatical, phonological, genetic, and musical distances reveal potentially more informative structure. In agreement with the claim that language structure does not identify family relationships (20, 22), the clustering emerging from the distances does not generally coincide with language families, except for Chukotko-Kamchatkan (Chukchi and Koryak) in genetics and phonology (where the within-family distance dfam is smaller than the distance dnun to the next unrelated neighbor, relative to the total distance range: genetics dfam = 0.15 < dnun = 0.26; phonology dfam = 0.28 < dnun = 0.36 (Supporting Information 1, section 4.1), and marginally for Tungusic (Even and Evenki) in grammar (dfam = 0.22 < dnun = 0.28). Most of the clustering instead points to interfamily relations: for example, Korean and Japanese are neighbors in the networks based on grammar, SNPs, and music, but not phonology (38). Buryat and Yakut are close together in SNPs (39), grammar, and phonology, but not in music. The music-based network is consistent with a previous study showing the uniqueness of Ainu music and a distinction of East Asian music from circumpolar music based on cluster analysis of musical components (29). Nivkh shows different patterns for each factor. For example, Nivkh is genetically closer to Korean, Japanese, and Buryat than the others and shows the second highest affinity with Ainu in all populations in the distance matrix (table S3), reflecting the trees branch position. However, music, grammar, and phonology do not follow these relationships in Nivkh.

Together, these results suggest that neither the population history nor the cultural features (other than the lexicon) evolved by simple vertical descent along language families. Instead, apart from the possible case of Chukotko-Kamchatkan, they might have each followed independent trajectories. While this challenges the idea of a unified phylogeny, it leaves open the possibility that some of the features are associated with each other because they trace back to a prehistoric maze of horizontal and vertical transmission. In other words, features might still be associated with each other because they were present in the same period(s) and places in which people were in contact and/or were genetically related. To find out whether any such association is still detectable today, we implemented a redundancy analysis (RDA) on the principal components (or coordinates) of the data (Materials and Methods and Supporting Information 1). RDA summarizes the variation in a response variable that can be explained by an explanatory variable and finds directed associations. The RDA analysis reveals two associations that are significant under a permutation test (Fig. 3): Grammatical similarity predicts genetic similarity (grammar genetics, adjusted R2 = 0.64), and genetic similarity predicts grammatical similarity (genetics grammar, adjusted R2 = 0.54).

Variance in the response explained by each explanatory variable; * indicates a significant association (P 0.05).

While both associations possibly reflect deep-time correspondences, dating back to before the formation of current language families (as identifiably by cognate words), spatial proximity and contact between societies might lead to similar patterns of association that are relatively recent and shallow. To find out, we evaluated three possible scenarios to explain the signal in the data: (i) Recent contact scenario: The associations reflect recent and current contact and, hence, can be explained by spatial autocorrelation in the current data; that is, societies that are currently close to each other tend to have similar grammars and population history. (ii) Inheritance scenario: The associations reflect common ancestry. The associations result from vertical descent within the remaining linguistic families for which our sample contains more than one member (Tungusic, Chukotko-Kamchatkan, and Uralic). (iii) Deep-time correspondence scenario: The associations reflect a nonshallow correspondence between grammar and genetics that cannot be explained by recent contact or phylogenetic inheritance within known families.

To distinguish between the three scenarios, we treated spatial proximity and inheritance as potential confounds and carried out a partial RDA to control their effect (Supporting Information 1, section 5). As societies and languages placed far from the equator tend to display larger spatial ranges (40), we represented the territory of each society with areas rather than points and sampled random spatial locations from within these areas. The partial RDA reveals strong evidence against the recent contact scenario: Spatial proximity fails to explain both associations (figs. S18 to S20). When controlling for spatial autocorrelation (1000 random samples allowing the uncertainty of peoples locations), the observed explained variance is still greater than that of random permutations [normalized differences between observed and permuted explained variance z > 1 SD in more than 99% of spatial samples; Kullback-Leibler divergence (KLD) > 3; fig. S20 and table S7]. When controlling for both recent contact and phylogenetic inheritance of language in partial RDA, still both associations show stronger evidence than the other relationships (z > 1 SD in 90% of samples, KLD 1.5; Fig. 4, figs. S21 to S23, and table S8). Our analysis reveals no other associations at comparable strengths; there are a few weak signals (e.g., grammar, music, and phonology; Fig. 2), but they all disappear once we control for both spatial autocorrelation and genealogy (Fig. 4 and table S8), suggesting that any patterns here are likely to stem from recent contact and family-specific lines of inheritance.

Numbers right to the dashed line show the proportion of samples with a difference of at least one SD. Gray shading reflects the KLD between the observed and permuted adjusted R2. The KLD is transparent for associations where the z-normalized difference is negative for more than 50% of the samples.

Given the relatively small sample of only 14 groups, we evaluate the robustness of the grammar/genetics associations through three types of sensitivity analyses. First, we varied the number of principal components (or coordinates) passed to the RDA and, thus, the amount of variance in both the response and the predictor. Different thresholds of how much variance a component needs to explain to be included (10%, 15%, and 18%) show little effect on the results (z > 1 SD in at least 84%, KLD > 1.2; figs. S24 and S25 and table S9). Second, we varied the language sample passed to the RDA. While most languages have little to no effect on the signal, this is not true for Ainu, as removing Ainu from the analysis weakens the support for the associations of grammar and genetics (z > 1 SD in only 14 to 31%, KLD 0.2, when controlling for spatial proximity and inheritance; figs. S26 to S29 and tables S10 and S11). Third, in the partial RDA, some spatial samples happen to explain the variance in the response better than others (lower tail of observed adjusted R2 in figs. S21 and S22). Spatial clusters of locations with low adjusted R2 might indicate recent language contact (see section 5.4, Supporting Information 1), and clusters with high adjusted R2 might indicate that systematic outliers influence the signal. We mapped locations in the 0.2 (figs. S30 and S31) and 0.8 percentile (figs. S32 and S33). We find only weak and partial clustering in the high percentile, and none in the low percentile. This suggests that neither recent contact nor systematic outliers explain the signal.

To summarize, we found significant correlations between genetics and grammar by the basic RDA using the complete set of genomes, music, and language in northeast Asia. The partial RDA controlling for geography and linguistic inheritance as well as sensitivity analyses suggest that the relationships may trace back to earlier relationships between languages before the recent contacts and inheritance.

We have simultaneously explored the relations among genetic, linguistic, and musical data beyond the level of language families. We find remarkable evidence for the relationships between population history and grammatical similarity, while genomes and grammar might be influenced by different evolutionary forces, such as a difference between mating systems and cultural transmission (13).

A possible interpretation of our findings is that the relationship between grammar and population history was exceptionally well preserved over the recent contact beyond language families, regardless of whether or not the evolutionary mechanisms of grammar are the same as those of genomes. Population genetics detect gene flows between populations beyond phylogenetic relationships. Our dataset covers a phylogenetically broad range of populations: three lineages to the present-day East Eurasian (Ainu, East Asian, and northeast Asian) and one to North American (Greenlandic Inuit) (41), including gene flows beyond the lineages, such as Japanese-Ainu (38) and Buryat-Yakut (39). While the evolutionary forces that influence population history are fairly well understood, determining to what extent the genetic relationships of particular populations reflect shared ancestry versus prehistoric contact in culture is still challenging. Moreover, the evolutionary processes that influence culture and language are under debate (14) but can obviously be very different from those influencing genomes. For example, cultural replacement and language shift can occur even within a single generation due to colonization or other sociopolitical factors, like warfare and cultural expansion (15, 42). Our results removing the influence of the proximity in cultural similarities give support to the notion that these different data reveal different historical patterns, yet show that some cultural features can still preserve relationships extending even beyond the boundaries of language families. The similarities in grammar do not arise from simply following the genetic phylogeny (see Fig. 2D, which lacks the Korean-Japanese-Nivkhh-Ainu and Koryak-Chukchi-West Geenlandic clusters in Fig. 2A). Instead, they are likely to reflect a complex interplay of partially independent vertical and horizontal transmission in prehistory.

This pattern is markedly different for the lexicon that traces language families but does not reveal higher-level relationships in our dataset (Fig. 2). This contrasts with expectations from historical linguistics (22) and also from recent findings that suggest that grammar evolves faster than the lexicon in Austronesian (23) and also shows rapid evolution in Indo-European (43); for example, while English and Hindi preserve many cognate words (name versus nm, hand versus hth, etc.), they differ substantially in word order (verb-medial versus verb-final) and case-marking (invariable nouns versus complex case system). However, these findings bear on grammatical evolution within families, while our approach seeks to unravel a shared history that allows early contact between families. Therefore, our findings are compatible with a scenario where specific traits (e.g., word order) evolved rapidly within families but were repeatedly copied and readapted, yielding a relatively uniform profile over a prehistoric period (44) that mirrors the genetic network of the same period.

The statistical power to detect a signal is weakened when Ainu was removed in the sensitivity analysis (figs. S26 to S29 and table S10). While this might suggest a special position of Ainu in the northeast Asian context (45), we need larger samples of languages and populations inside and outside of the region to resolve this question.

Our results are qualitatively different from the only previous study to quantitatively compare genetic, linguistic, and musical relationships (27). Among indigenous Austronesian-speaking populations in Taiwan, music was significantly correlated with genetics but not language, while we find here that music is not robustly associated with either language or genetics. However, there are several methodological differences that might underlie these differences. In particular, the two studies looked at different types of data (genome-wide SNPs, structural linguistic features, and both group and solo songs here versus mitochondrial DNA, lexical data, and only group songs previously). Further research with larger samples and different types of data may help to elucidate general relationships among language, music, and genetics.

The recent studies highlight northeast Asian populations as one of major genetic components of basal East Eurasians (46). The high linguistic diversity in northeast Asia may reflect prehistorical relationships with less influence from agricultural populations by geographic barriers, as hypothesized in the previous studies (24, 47). However, our knowledge about relationships between culture and local population history is limited in northeast Asia. In addition to revealing an association between genetic and grammatical patterns, our results also reveal complex dissociations in which these data reflect different local histories, potentially including cultural shift. For example, while previous studies suggest specific genetic and cultural relationships between Korean and mainland Japanese populations (38) or posit a shared origin (48, 49), our findings support similarities in SNPs, music, and grammar, but not in lexicon and phonology (Fig. 2 and Supporting Information 1) (50). Although the Ainu show particular genetic similarity to the Japanese, their music clusters more closely with that of the Koryak (Fig. 2 and tables S3 and S4). This may reflect different levels of genetic, linguistic, and musical exchange at different points of history. Musical patterns may reflect more recent cultural diffusion and gene flow from the Okhotsk and other circumpolar populations that interacted with the Ainu from the north within the past 1500 years (51), as we previously proposed in our triple structure model of Japanese archipelago history (29). Newly genotyped Nivkh samples showed the closeness to Ainu in SNPs but not in others (Fig. 2A), suggesting historical relationships in the coastal region of northeast Asia. Nivkh might be a key population connecting Ainu and other northeast Asians; however, the population history of Nivkh is not well understood. Thus, Neighbornet trees might reflect the relationships linking populations, but further analyses are necessary to investigate, in more detail, the local population history and cultural relationships in northeast Asia including Nivkh. Most pressingly, future research will need a larger sample of societies and a richer coding of their cultural traits.

In conclusion, we have demonstrated a relationship between grammar and genome-wide SNPs across a variety of diverse northeast Asian language families. Our results suggest that grammatical structure may reflect population history more closely than other cultural (including lexical) data, but we also find that different aspects of genetic and cultural data reveal different aspects of our complex human histories. In other words, cultural relationships cannot be completely predicted by human population histories. Alternative interpretations of these mismatches would be historical events (e.g., language shift in local history) or culture-specific evolution independent from genetic evolution. Future analyses of these relationships at broader scales using more explicit models should help improve our understanding of the complex nature of human cultural and genetic evolution.

Selection of populations in this study. We selected 14 populations for which matching musical (Cantometrics/CantoCore), genetic (genome-wide SNP), and linguistic (grammatical/phonological features) data were available (tables S1 and S13 and Fig. 1). These represented a subset of 35 northeast Asian populations whose musical relationships were previously published and analyzed in detail (29). Linguistically, these 14 populations fall into 11 language families/isolates (4). Korean, Ainu, Nivkh, and Yukaghir are language isolates. Buryat, Japanese, Yakut, and West Greenland Inuit are the sole representatives in our sample of the Mongolic, Japonic, Turkic, and Eskimo-Aleut language families, respectively. The remaining languages are classified into three language families: Koryak and Chukchi are Chukotko-Kamchatkan languages; Even and Evenki are Tungusic languages; and Selkup and Nganasan are Uralic languages. Note that the need to assemble matching genetic, linguistic, and musical data meant that some important populations could not be included (e.g., we had matching musical and genetic data for multiple Ryukyuan populations, but no corresponding grammatical data were available, while for the Aleut genetic and linguistic data were available but not musical). Future research should attempt to collect new data to allow more complete comparisons within and between language families.

Music data. All music data and metadata are detailed in our previous report of circumpolar music (29). For the present analysis, we used a subset of 14 of the original 35 populations with matching genetic and linguistic data; these 14 populations are represented by 264 audio recordings of traditional songs. Each song was analyzed manually by P.E.S. using the same 41 classification characters used in (30) [from Cantometrics (29) and CantoCore (52)].

We used the DNAs of Nivkh maintained by the Asian DNA Repository Consortium (ADRC). The DNA samples were originally collected in Sakhalin, Russia by S. Horai in the 1990s (53) and were kept at 4C in Sokendai. We genotyped 32 Nivkh individuals (14 females and 18 males) with the Illumina Omni 2.5-8 BeadChip Array at the National Center for Global Health and Medicine (table_S16_SampleID_Nivkh.xlsx). Two DNA samples were removed because of their poor quality. We selected 2,246,124 sites for SNPs with a call rate greater than 95%. Using PLINK (54), we performed a Hardy-Weinberg equilibrium test to exclude sites with P < 106, resulting in 2,246,123 sites. Then, we calculated inbreeding coefficients using sites with minor allele frequency (MAF) > 0.01, confirming that none of the cousin equivalents exceeded F = 0.0625. Using the same threshold of MAF, we found kinship between 12 pairs (involving 14 individuals) with PI_HAT >0.125 (third-degree relative or closer). Eight samples were removed; 22 individuals thereby passed the quality control and kinship tests. Then, we carried out strand checks between the Illumina Human Omni 2.5-8 BeadChip SNPs and JPT + CHB in 1000 Genomes using BEAGLE 4.0 (55). In the Nivkh data, 2,041,779 sites passed the strand check and 114,077 sites were flipped using PLINK. After the strand check, all sites that did not have an allele match were removed. We converted the Illumina unique IDs to rsIDs.

Publicly available genome-wide SNP array data for 14 populations, including three Nivkh individuals (table S1) (38, 5659), were obtained and curated as follows. As several genotyping platforms were used, to avoid discordancy of alleles on +/ strands, we used the strand check utility in BEAGLE for a dataset of Ainu against JPT and CHB in 1000 Genomes. To obtain shared SNPs among different platforms, genotype datasets including our Nivkh data were merged into a single dataset in PLINK file format by PLINK.

We manually removed outlier individuals from the merged dataset based on results of principal components analysis (PCA) and ADMIXTURE (6062). Last, we used 15 individuals of Nivkh (13 individuals from our data and 2 individuals from public data) in the population genetics analysis (tables S1 and S16). The final merged genotype dataset included 245 individuals and 37,093 SNPs (total genotyping rate was 0.999). The merged dataset in PLINK format was converted to Genepop format using PGDSpider (63).

We measured lexical distances between those words in the ASJP (Automated Similarity Judgment Program) database v. 19 (32) that have best coverage in our sample, corresponding to 40 concepts that are attested in at least 74% of all word lists. These correspond to the concepts commonly thought to be most stable over time (64) and to best reflect language relatedness, at least as a first approximation (Supporting Information 3) (65).

We combined data on grammatical and phonological traits from AUTOTYP (34, 66), WALS (33), the ANU Phonotactics database (35), and PHOIBLE (36) and extracted a set of 25 grammar and 87 phonological features with coverage more than 80% in each language, and in most cases 100% (Supporting Information 2 and table S13).

In contrast to population history, standardized methods for modeling cultural evolution across different types of data are not yet established. Therefore, we matched population history to cultural similarities to analyze both genetic and cultural data in a common framework. We obtained distance matrices representing differences between populations/languages for a subsequent comparative analysis using the following procedures for music and language, because musical and linguistic (grammatical and phonological) data have different data structures.

Genetic analysis. To estimate population differentiations, pairwise Fst values between populations were calculated with Genepop version 4.2 (67). Pairwise Fst is the proportion of the total genetic variance due to between-population differences, and is a convenient measure because it does not depend on the actual magnitude of the genetic variance. In other words, genetic markers that evolve slowly are expected to have the same Fst value as markers that evolve more rapidly, because the total variance is decomposed into within-population and between-population components.

Music analysis. A previously published matrix of pairwise distances among all 283 songs was calculated using normalized Hamming distances (68) to calculate the weighted average similarity across all 41 musical features (29). This distance matrix was then used to compute a distance matrix of pairwise musical st values among the 14 populations using Arlequin (69) and the lingos function of the ade4 package in R (70). st is analogous to Fst but takes into account distances between individual items, making it more appropriate for analysis of cultural diversity (68, 70). Further details concerning the calculations can be found elsewhere (70).

For the main analysis, we compute distances in ASJP word alignments weighted by sound correspondence probabilities, a method that provides good first approximations of language relatedness (Supporting Information 3, table S14, and fig. S34) (65). For comparability with other ASJP-based work, we also report normalized Levenshtein distances (Supporting Information 3, table S15, and fig. S35).

In contrast to songs and individual genotypes, language data do not represent individuals for each population. In view of the fact that the data are partly numerical and partly categorical, we used a balanced mix of PCA and multiple correspondence analysis (MCA) to calculate differences between languages (Supporting Information 1, section 3) (71). Empty values were imputed using the R package missMDA (72).

We performed a principal coordinate analysis (PCoA) on the distance matrices of pairwise Fst for SNPs and pairwise st for music (Fst and st matrices are available from github; Supporting Information 1, section 3) (73). Similar to a PCA, a PCoA produces a set of orthogonal axes whose importance is measured by eigenvalues (figs. S2 to S6). However, in contrast to the PCA, non-Euclidean distance matrices can be used. Heat plots of PCo and PC were visualized by ggplot2 in R (figs. S7 to S11) (74).

Distances were visualized using the SplitsTree neighbornet algorithm [version 4; (37)] and are reported in detail in Supporting Information 1, tables S2 to S6, and figs. S12 to S16. To control for multicollinearity, we used PCA/MCAs and PCoAs as input rather than the raw data.

The geographical polygons were taken from the Ethnologue (75) via the World Language Mapping System (76), supplemented by a hand-drawn polygon estimate for Ainu.

In view of the mobility of speakers over time, we sampled 1000 random locations from within the polygons and used these for assessing correlations. Location samples were always taken from geometries (i.e., polygons on a sphere) and not from a potentially distorted image of these geometries on a map. Location samples were generated in PostGIS https://postgis.net/ (Supporting Information 1, section 2.4). For each of the 1000 samples, we computed the spherical distance between all random locations, which we store in a distance matrix. Then, we perform a distance-based Morans eigenvector map analysis (dbMEM) to decompose the spatial structure of each of the resulting 1000 distance matrices (Supporting Information 1, section 3.3) (77). Similar to a PCoA, dbMEM reveals the principal coordinates of the spatial locations from which the distance matrix was generated. We only return those eigenfunctions that correspond to positive spatial autocorrelation.

RDA was carried out to explore the linear relationship between SNPs, grammar, phonology, and music. Partial RDA was used to control for spatial dependence (Supporting Information 1, section 5) (78). (Partial) RDA is an alternative to the traditionally used Mantel test, which was found to yield severely underdispersed correlation coefficients and a high false-positive rate in the presence of spatially correlated data (79). RDA performs a regression of multiple response variables on multiple predictor variables (80), while partial RDA also allows to control for the influence of confounders. RDA yields an adjusted coefficient of determination (adjusted R2), which captures the variation in the response that can be explained by the predictors. We compare the observed adjusted R2 values against a distribution under random permutations (Fig. 4 and figs. S18 to S23). To assess robustness, we z-normalize the difference between observed and permuted adjusted R2 and report the proportion of samples for which the observed adjusted R2 is one SD larger than the permuted (z > 1 SD). Moreover, we compute the KLD between the distribution of observed adjusted R2 and permuted adjusted R2. The KLD allows to assess the overall divergence of the two distributions; z > 1 SD reports the proportion of samples with a strong positive difference. (p)RDA and subsequent analyses were performed in R using the vegan package (65).

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Exploring correlations in genetic and cultural variation across language families in northeast Asia - Science Advances

Being overweight causes depression and lowers wellbeing, a largescale new study has proved. It further indicated that both social and physical factors might play a role in the effect.

The findings of the study were published in the journal 'Human Molecular Genetics'.

With one in four adults estimated to be obese in the UK and growing numbers of children affected, obesity is a global health challenge. While the dangers of being obese on physical health are well known, researchers are now discovering that being overweight can also have a significant impact on mental health.

The study sought to investigate why a body of evidence now indicates that higher BMI causes depression. The team used genetic analysis, known as Mendelian Randomisation, to examine whether the causal link is the result of psychosocial pathways, such as societal influences and social stigma, or physical pathways, such as metabolic conditions linked to higher BMI. Such conditions include high blood pressure, type 2 diabetes and cardiovascular disease.

In research led by the University of Exeter and funded by the Academy of Medical Sciences, the team examined genetic data from more than 145,000 participants from the UK Biobank with detailed mental health data available.

In a multifaceted study, the researchers analysed genetic variants linked to higher BMI, as well as outcomes from a clinically relevant mental health questionnaire designed to assess levels of depression, anxiety and wellbeing.

To examine which pathways may be active in causing depression in people with higher BMI, the team also interrogated two sets of previously discovered genetic variants.

One set of genes makes people fatter, yet metabolically healthier, meaning they were less likely to develop conditions linked to higher BMI, such as high blood pressure and type 2 diabetes. The second set of genes analysed make people fatter and metabolically unhealthy, or more prone to such conditions.

The team found little difference between the two sets of genetic variants, indicating that both physical and social factors play a role in higher rates of depression and poorer wellbeing.

Lead author Jess O'Loughlin, at the University of Exeter Medical School, said, "Obesity and depression are both major global health challenges, and our study provides the most robust evidence to date that higher BMI causes depression. Understanding whether physical or social factors are responsible for this relationship can help inform effective strategies to improve mental health and wellbeing."

O'Loughlin added, "Our research suggests that being fatter leads to a higher risk of depression, regardless of the role of metabolic health. This suggests that both physical health and social factors, such as social stigma, both play a role in the relationship between obesity and depression."

Lead author Dr Francesco Casanova, of the University of Exeter Medical School, said, "This is a robust study, made possible by the quality of UK Biobank data. Our research adds to a body of evidence that being overweight causes depression. Finding ways to support people to lose weight could benefit their mental health as well as their physical health."

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Study suggests being overweight might cause depression and lower well-being - Hindustan Times

When Joseph Dalton Hooker, director of the Kew Royal Botanical Gardens in London between 1865 and 1885, first cast his gaze on an example of Welwitschia he could not contain himself: It is without question the most wonderful plant ever brought to this country, and one of the ugliest. This species, Welwitschia mirabilis, was first formally described in 1863 and has been the subject of debate ever since it was first discovered. It has been established that it can survive for thousands of years in the harshest environments, making it the longest-living plant on the planet. But a recent genetic analysis published in Nature Communications has revealed new data about this curious plant species. Welwitschias duplicated genome means that some of its genes can dedicate themselves to tasks that are not part of their original functions. Furthermore, this species can activate certain proteins to protect itself from the extreme conditions in which it lives and it grows slowly but continuously throughout its entire life.

Welwitschia is found in Namibias northwest and southeastern Angola, an area dominated by the Kaokoveld Desert. Despite being geographically near to the coast, this region is arid and annual rainfall is less than five cubic centimeters. The plants appearance is also distinctive, consisting of two foliage leaves that can grow by 10 to 13 centimeters each year. As they grow, the tips of the leaves dry out and curl together, which sometimes lends the plant an appearance similar to an octopus.

Genome analysis of Welwitschia has shown that all of its genes are duplicated, what experts describe as genetic redundancy. Andrew Leitch, a researcher at the Queen Mary University of London and one of the authors of the study, explains how this duplicity, over the course of millions of years, has altered the functioning of these genes: The duplicated copies can take on new functions and do new things that would be impossible if there was only one version of the gene. These adaptations have driven the evolution of the plants. For example, the researchers believe that the leaves are capable of absorbing some of the humidity from clouds of mist that form in the plants natural habitat when dawn breaks.

Welwitschias genetic duplication began around 86 million years ago and was prompted by the stress placed on the plants by being constantly exposed to some of the harshest environmental conditions on the planet (high temperatures, ultraviolet radiation, salinity and so forth). In the face of this constant assault, Welwitschia always maintains a variety of proteins overactivated that allow the plant to keep these environmental stress factors at bay. Leitch explains it with a culinary example: When you put an egg in boiling water, the proteins in the egg are denatured and the white of the egg hardens. This denaturalization is a problem for the plants and animals that live in conditions of extreme heat and Welwitschia activates certain genes to prevent this from happening.

Identifying genes that allow for survival in hostile conditions will be useful when we are looking to grow crops in ever more marginal areas of the planet

Furthermore, unlike other plants, Welwitschias growth does not occur at the tips of the leaves but at the base. This area of the plant is heavily protected by two lips consisting of a woody fiber that cover the basal meristem, the part of the plant that supplies new cells. This type of bulb is formed of a practically embryonic tissue, still poorly defined, that gradually transforms into leaf tissue at a very slow pace. While this bulb lives, the plant will never stop growing. As such, the name given to it in Afrikaans is tweeblaarkanniedood, which literally translates astwo leaves that cannot die. The plants can live to such an age that the researchers had to use carbon-dating technology usually reserved for fossils to determine how old their subjects were. The results confirmed that some individuals were more than 1,500 years old.

Leitch believes that this discovery could prove to be key in the medium- to long-term for the survival of the human race. Identifying genes that allow for survival in hostile conditions will be useful when we are looking to grow crops in ever more marginal areas of the planet, something that we will have to do to be able to feed the nine billion people that we will be within the next 50 years with a high-level diet, as well as finding space for bio-combustibles. And all of that has to be achieved in a context of climate change and alterations in rainfall and temperature.

Alfonso Blzquez, a professor and researcher at the Autonomous University of Madrid who did not take part in the study, harbors doubt over the viability of this potential application. Overexpressing one or two genes in commercial crops is unlikely to achieve the same effect, because this plant has a battery of protective genes activated at the same time, but they may obtain some kind of greater resistance to heat or a lack of humidity. This could be an intermediate application that should be investigated.

English version by Rob Train.

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Welwitschia: genetics unveil the secrets of the immortal plant - EL PAS in English