Category Archives: Human Genetics

In biopharma, we often frame our genetics as a Goliathan adversary that must be compromised with or circumvented rather than overcome. We focus on using our genes the same way our body does: as a source of information concerning how our system operates and as a means of identifying and predicting the potential one has to develop certain conditions or diseases.

Our genes arent just a source of information, though; theyre also a source of creative inspiration and ingenious solutions for predestined problems. Instead of looking to our genes for a culprit that we can subject to interrogatory treatments, we could also look at our genes as a potential source from which to derive therapeutics.

Old Order Amish Variant Protects Against Heart Disease

In early December 2021, the University of Marylands School of Medicine (UMSOM) announced the discovery of a recently uncovered genetic variation linked to lower levels of fibrinogen and LDL cholesterol, suggesting that this may be behind the significantly lower risk of heart disease exhibited by those who express it.

While its unclear how exactly this mutation is influencing the amount of LDL cholesterol and fibrinogen in a given patients bloodstream, a general population study of over 500,000 individuals showed that anyone carrying a unique version of the B4GALT1 gene was 35% less likely to develop cardiovascular diseases.

However, this particular genetic variant is only present in less than a hundred people for about every million. Its highest prevalence by population size anywhere in the United States by far is found within the Old Order Amish community of Lancaster, Pennsylvania, where about 12% of the population possesses the B4GALT1 variation.

Through their longstanding partnership with members of the Amish community and collaboration with the Regeneron Genetics Center (RGC), the University of Marylands School of Medicine has collected and sequenced almost 7,000 samples from Amish research participants dating back to 1995.

This allowed them to pinpoint the exact variant and replicate its expression in mice. The mouse model, encoding for this gene mutation, also showed decreased levels of LDL cholesterol and fibrinogen, confirming the effect of this variant, said Giusy Della Gatta, Ph.D. RGC senior staff scientist and study senior co-author This model represents an invaluable tool to unravel the molecular mechanisms that help protect against cardiovascular disease.

This marks the first time that a genetic variant with the potential to decrease a patients risk for heart disease has been uncovered. While links between genetic mutations and increased risk have already been established, this discovery opens a new door into preventative cardiovascular care possibilities that may produce novel therapeutic drugs.

The genetic variant appears to either control the synthesis of cholesterol and fibrinogen or accelerate their clearance from the blood, which protects the heart. This finding could lead to targeted drugs that mimic the action of this variant to keep arteries free of plaque and clots, said study leader May Montasser, Ph.D., a member of UMSOMs program for personalized and genomic medicine and assistant professor of medicine.

It will be a long time, if ever, before this research produces an in-hand pharmaceutical product; but the newfound knowledge that there could be more variants with similar pharmacologic potential means it will only be a matter of time before one makes a real difference.

CCR5-32 Mutation Repurposed for COVID-19 Treatment

Originally discovered and described in the mid-1990s, the CCR5-32 mutation made waves for the resistance to HIV infection and AIDS development it apparently bestowed upon its host. Although it isnt exceptionally common, this mutation is present in about 10% of the population on average from Europe to western Asia.

Just a single copy of this mutation would protect a person against HIV infection, and slow the progression of the disease to AIDS if they did contract it, while a second copy would make a person almost completely immune to infection. However, this mutation only endows resistance in the case of HIV-1, and not for any other HIV variants.

Thats because the CCR5-32 mutation only disrupts the function of a singular variety of immune cell receptor, one that HIV-1 happens to be dependent on in order to infect the macrophage cells of their host. The less common strains of HIV are less dependent on the CCR5 receptor, making this mutation less effective at conferring resistance.

Now, creative minds at the late-stage biotechnology company CytoDyn, Inc. are crafting new tricks out of old hat research, announcing in late December that the U.S. Food and Drug Administration had given it the affirmative to commence with a Phase III trial to evaluate the efficacy and safety of CCR5 receptor antagonist leronlimab against COVID-19 in the critically ill population.

As it turns out, the same advantage against HIV-1 possessed by a person with the inactive CCR5 variant potentially doubles as resistance to any virus that capitalizes on or manipulates that receptor an umbrella that might include COVID-19. By artificially deactivating the CCR5 receptors, leronlimab also appears to encourage a stronger and more balanced immune response in patients critically afflicted with COVID-19.

The submission of the trials protocol was announced less than two weeks prior to the aforementioned press release and outlines a four-week treatment period, so its safe to say that it might be another month or two before the results are finalized and the analysis is made available for the public.

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The Unique Therapeutic Possibilities Posed by Rare Human Genes - BioSpace

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How are physicians and scientists including underrepresented groups in the promise of precision medicine? Baylor College of Medicine will host a panel of experts to discuss this topic and the intersection of identity and genetics at an upcoming Evenings with Genetics virtual seminar on Tuesday, Feb. 15, at 7 p.m.

The webinar, titled Race and Genetics: Perspectives on Precision Medicine, will discuss the complexity of racial identity and its impact on health and disease. Panelists will address specific examples of factors affecting diseases in genetically and culturally diverse populations and the foundations needed to deliver equitable precision medicine to communities of color.

The evenings panelists are Vence L. Bonham, Jr., acting deputy director of the National Human Genome Research Institute (NHGRI) and associate investigator of the NHGRI social and behavioral research branch, and Dr. Fatimah Jackson, professor of biology and former director of the Cobb Research Laboratory at Howard University. Dr. Cherilynn Shadding will join the panel as a guest parent speaker.

Evenings with Genetics is a regular speaker series hosted by Baylor and Texas Childrens Hospital that offers the most current information on care and research advances for many genetic conditions. This event is co-sponsored by the Baylor Office of Diversity, Equity and Inclusion.The program is free and open to the public, but registration is required. A Zoom link will be sent to all registered participants the day before the seminar. For more information, call 713-798-3148 or visit the event registration page.

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Evenings with Genetics: race and precision medicine - Baylor College of Medicine News

ORIGINA Genetic History of the AmericasBy Jennifer Raff

Its Anthropology 101. At the end of the last ice age, around 13,000 years ago, retreating glaciers created an inland corridor connecting Siberia to the Americas. People from northeast Asia crossed the Bering Strait land bridge and entered a new world. From there, these people often given the name Clovis, after a New Mexico site that was rich with the distinctive stone tools they made rapidly spread and successfully adapted to the various ecologies they encountered. All Native Americans can trace their ancestry back to these First Peoples.

But, according to the University of Kansas anthropological geneticist Jennifer Raff, thats not quite how it happened.

In her new book, Origin: A Genetic History of the Americas, Raff beautifully integrates new data from different sciences (archaeology, genetics, linguistics) and different ways of knowing, including Indigenous oral traditions, in a masterly retelling of the story of how, and when, people reached the Americas. While admittedly not an archaeologist herself, Raff skillfully reveals how well-dated archaeological sites, including recently announced 22,000-year-old human footprints from White Sands, N.M., are at odds with the Clovis first hypothesis. She builds a persuasive case with both archaeological and genetic evidence that the path to the Americas was coastal (the Kelp Highway hypothesis) rather than inland, and that Beringia was not a bridge but a homeland twice the size of Texas inhabited for millenniums by the ancestors of the First Peoples of the Americas.

Throughout, Raff effectively models how science is done, how hypotheses are tested, and how new data are used to refute old ideas and generate new ones.

As a paleoanthropologist who works on fossils of ancient human ancestors living millions of years ago, Ive never fully grasped why my colleagues who study the peopling of the Americas so fervently argue over a few thousand years. But Raff helps the reader understand why those several thousand years matter in terms of identifying source populations for the First Peoples of the Americas, the route they took (coastal versus inland) and the ecological challenges they faced. An informed and enthusiastic guide throughout, Raff takes the reader from underground caverns in Belize to a clean lab at the University of Kansas where ancient DNA is tediously teased from old bones. She explains difficult to understand concepts geoarchaeology, coalescence times, biodistance with well-placed sidebars. The book is richly referenced, and informative footnotes and endnotes give readers an opportunity to take a deeper dive if they wish.

Our job as anthropologists is to breathe life into the past, to retell the stories of our ancestors and extinct relatives. We do not work with lifeless old bones or inert molecules but with the precious, fragmentary remains of once living, breathing, thinking individuals who laughed, cried, lived and died.

As Raff explains, We have promised to treat the small scraps of bone and teeth with respect and mindfulness that they are cherished ancestors, not specimens. Sprinkled through Origin are lovely vignettes of life thousands of years ago. Raff playfully imagines how the Yana River boys lost their deciduous teeth in a Siberian river 31,000 years ago. She poignantly fills a page with the sorrow a family must have felt as they placed the limp body of their 2-year-old boy into the earth in south-central Nevada 12,600 years ago. Through a combination of rigorous science and a universal humanity, Raff gives ancient people a voice.

The first few chapters of Origin detail the long history of archaeology in the Americas. Here, we meet the usual characters Thomas Jefferson, Ales Hrdlicka, Franz Boas but also folks who were new to me. People like Jos de Acosta, a Jesuit priest who long ago predicted that the Indigenous peoples of the Americas were related to northeast Asian populations. And George McJunkin, a formerly enslaved man who made one of the most important archaeological discoveries of the 20th century at Folsom, N.M.

Throughout Origin, Raff takes on pseudoscientific nonsense rooted in bigotry and colonial thinking. She eviscerates claims of lost civilizations founded on the racist assumption that Indigenous people werent sophisticated enough to construct large, animal-shaped or pyramidal mounds and therefore couldnt have been the first people on the continent. She convincingly disposes of the Solutrean hypothesis of ancient Europeans in the Americas with logic and evidence. It puzzles her (and me) that people interested in this topic watch ill-informed documentaries on the History Channel when the true histories, evident in genetics, oral traditions and archaeology, are exciting enough.

Given the fast and furious pace of discovery in this field, Raff is clear that not everyone will agree with her interpretations of the data. All scientists must hold themselves open to the possibility that we could be wrong, and it may very well be that in five, 10 or 20 years, this book will be as out of date as any other, she writes. That possibility is what makes working in this field so rewarding. That, she explains, is how science is done.

While science is the most objective way of understanding the natural world that humans have ever devised, it is still done by an emotional, subjective primate us. Raff celebrates science, but also calls attention to the many ways science has harmed Indigenous communities. Origin details mistrust between some Native communities and helicopter scientists who have swooped into their lands and exploited them for their DNA without inviting input and participation from all stakeholders. The hashtag #decolonizescience looks good on Twitter and the concept sounds good in grants, but it rings hollow unless it is put into practice. Raff provides a road map for how to do this and convincingly argues why this must be the future of our science. In fact, Origin opens with the discovery of 10,000-year-old human bones from Shuk Ka Cave, Alaska, and details the constructive partnerships, built on transparency and trust, that emerged between scientists, Indigenous communities and federal agencies.

My only quibble with this outstanding book is that we dont learn who Raff herself is and how she personally has contributed to this work through her scholarship until halfway through Origin. At the end of the book, she describes herself as one obscure researcher from a small lab. To be sure, there are much bigger labs than hers, but I think shes being too modest. Jennifer Raff is a well-published scholar and accomplished scientific communicator who has contributed important insights into the genetic history and movement patterns of Indigenous Americans. She is at the forefront of a culture change in our science. And now she has written the book anyone interested in the peopling of the Americas must read.

Originally posted here:
Did the First Americans Arrive via Land Bridge? This Geneticist Says No. - The New York Times

Scientists identified an odor receptor that detects a synthetic musk used in fragrances, and another that detects underarm odor. Maskot via Getty Images

Humans may be slowly losing their sense of smell, according to new study published in PLoS Genetics last week..

When scientists tested individuals' perceptions of various smells,they found evidence thathumans' sense of smell is declining over evolutionary time. The team also discovered two new receptors in the nose that help distinguish between certain pleasant andrepulsiveodors.

When odor molecules in the air stimulate specialized nerve cells that line the nose, the brain interprets it as a scent, or combination of scents. Humans have around 800 olfactory receptor genes that can have minor variations, which change how an odor is perceived. The new resultshelpexplain why the fragranceof a specific perfume, for example, may seem pleasant to some and overpowering to others.

Were still, I would say, surprisingly ignorant about what all the olfactory receptors do and how they interact with each other to encode olfactory percepts, says Joel Mainland, a neuroscientist at Monell Chemical Senses Center and author of the research, to the Guardians Nicola Davis.

In a collaborative study between scientists in the United States and China,the team first looked at thegenesof 1,000 Han Chinese people to see howgeneticsplayed a role in scent perception. They exposed the study participants to ten common odors and asked them how they perceived each smell. The researcher then repeated the experiment for six odors in an ethnically diverse population of 364 participants. Eachperson rated the intensity and pleasantness of a given odor on a 100-point scale, which the scientists then compared their genome.

The study revealedtwo new receptors: one that detects a synthetic musk used in fragrances, and another that detects underarm odor. Because each participant had different versions of the musk and underarm odor receptor genes, those genetic variations affected how the person perceived the scents. Almost a quarter of participants couldnt smell the musk scent, for example, Catherine Schuster-Bruce reports for Business Insider.

Its really rare to find an effect thats as large as what we saw for this one receptor on the perception of the musk odor, says study author Marissa Kamarck, a neuroscientist at the University of Pennsylvania, to Sam Jones for the New York Times.

Kamarck and her colleagues say their results support the controversial hypothesis that primates smelling ability has slowly declined over time due to genetic changes. When the team looked at their results in combination with previously published studies on genes and scent, they found that participants with the ancestral versions of the scent receptorsthose shared with non-human primatestended to rate the corresponding odor as more intense.

While the results suggest ourability to detect smells is degrading, more studies are needed to better understand the evolution of human scent receptors.

It sheds light on a long debate in human and primate evolutionthe extent to which sight has tended to replace smell over the last few million years, says Matthew Cobb of the University of Manchester and author of Smell: A Very Short Introduction, to the Guardian. There are another 400 or so receptors to study, and the vast majority of our responses to odors remain a mystery.

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Humans' Sense of Smell May Be Worse Than Our Primate Ancestors' - Smithsonian

Chicago, Feb. 07, 2022 (GLOBE NEWSWIRE) -- The artificial intelligence in genomics market is expected to grow at a CAGR of over 48.44% during the period 20212027.

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Artificial Intelligence in Genomics Market Dynamics

More recently, the formation of DNA biobanks, which are collaborative repositories of genome sequences, and the growth of direct-to-consumer genetics testing companies such as 23andMe have increased the explosion of genomic data. Top healthcare investors, such as Sequoia Capital and Deerfield Management, acknowledge that data has unlocked considerable commercial opportunities across healthcare verticals. In 2017, liquid biopsy company GRAIL raised USD 914 million in its Series B round led by Smart Money VC ARCH Venture Partners and including Johnson & Johnson to continue product development and validation for its early-stage cancer detection blood tests. A number of genomic-focused companies have shown favorable returns. This can be exemplified by the MSCI ACWI Genomic Innovation Index, which has overtaken the standard by nearly 50% since 2013.

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Artificial Intelligence in Genomics Market Geography

North America accounted for a share of 45.19% in the global AI in genomics market in 2021. Post the human genome project, and multiple initiatives have been made across countries such as the US to sequence numerous patients with new targeted diseases. Also, with technological advances the cost of sequencing has been reduced in the market. This has increased patient interest in personal genomic sequencing for future personalized treatments, lifestyle, nutritional study, and other genomics studies. North America is one of the largest AI markets across the globe and is leading the way for other countries to increase the use of AI in the field of genomics and diagnosis in the medical sector. Countries such as Canada and the US are the major revenue contributors in North America. The AI in genomics market is expected to increase in North America due to the growing adoption of AI in genome sequencing and rising awareness among the regional pharma and biotech companies.

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Artificial Intelligence in Genomics Market Size to Reach Revenues of USD 5724.45 Million by 2027 - GlobeNewswire

DetailsCategory: DNA RNA and CellsPublished on Tuesday, 08 February 2022 16:28Hits: 278

- Data presented at the 18th Annual WORLDSymposiumTM 2022 -

TOKYO, Japan I February 7, 2022 I Astellas Pharma Inc. (TSE: 4503, President and CEO: Kenji Yasukawa, Ph.D., "Astellas") today announced positive interim safety data from FORTIS, the Phase I/II clinical trial evaluating AT845, an investigational adeno-associated virus (AAV) gene replacement therapy to deliver a functional alpha-glucosidase (GAA) gene to express acid alpha-glucosidase (GAA) directly in muscle cells in adults with Late-Onset Pompe Disease (LOPD) (Presentation & Poster: 206).

Pompe disease, a rare, severe, autosomal recessive metabolic disease characterized by progressive muscular degeneration, results from a mutation in the GAA gene that interferes with the production or function of the GAA protein. GAA is responsible for metabolizing glycogen, and dysfunction or absence of this protein results in the accumulation of glycogen, primarily in the skeletal and cardiac muscles, where it causes damage to tissue structure and function. Currently, the only approved treatment for Pompe disease is enzyme replacement therapy (ERT), which is delivered via chronic intravenous infusions every two weeks and relies solely on tissue uptake of GAA from plasma.

"There is significant unmet need for patients with Pompe diseasedue to the short half-life, inefficient uptake in the key tissues affected by the disease and the immunogenicity of ERT," said Tahseen Mozaffar, M.D., Professor of Neurology at UC Irvine. "AT845 has the potential to be a best-in-class approach as a muscle-directed gene therapy using an AAV8 capsid serotype. It is being investigated to determine whether it can deliver a functional GAA gene that is efficiently transduced to express GAA directly in tissues affected by the disease, including skeletal and cardiac muscle."

FORTIS is an ongoing multicenter, open-label, ascending dose Phase I/II first-in-human clinical trial to determine if AT845 is safe and tolerable in adults with LOPD. Enrolled participants receive a one-time peripheral intravenous infusion of AT845, followed by one year of frequent monitoring of safety, clinical and biochemical endpoints including GAA activity and protein level in muscle and four additional years of long-term safety monitoring. The primary endpoints of the trial are safety and tolerability, as well as efficacy measures, including change in muscle GAA protein expression and enzyme activity from baseline. Secondary endpoints evaluate improvements in respiratory, endurance and quality of life measures.

As of the December 3, 2021 data cut-off date, four participants have been enrolled in FORTIS, with two participants dosed at 3 x 1013 vg/kg (Cohort 1) and two participants dosed at 6 x 1013 vg/kg (Cohort 2). The reported data includes interim safety and tolerability assessments, as well as up to 24 weeks of follow-up for the two participants in Cohort 1 and preliminary data from the two participants in Cohort 2.

"We are pleased that AT845 has been well-tolerated so far in the four adults with LOPD who have received treatment," said Weston Miller, M.D., Senior Medical Director, Clinical Development at AstellasGene Therapies. "In the two participants in Cohort 1 with follow-up duration through week 24 after dosing, AT845 demonstrated an encouraging safety profile. Importantly, there have been no serious adverse events reported following dosing in any of the four participants as of the time of the data cut. One participant experienced elevated transaminases, which is considered a common immune-mediated treatment response based on the time of onset after dosing, its presentation during steroid taper initiation and its reversal with steroid re-initiation. These safety data are encouraging, and the program continues to enroll participants."

With the establishment of the Astellas Gene Therapies Center of Excellence following the 2020 acquisition of Audentes Therapeutics Inc., Astellas is a leader in genetic medicines, working alongside its world-renowned partners to build a portfolio of potentially life-changing gene therapies. Astellas strives to identify, develop and deliver transformative therapies for patients with genetic diseases who currently have few or no effective treatment options.

About Pompe DiseasePompe disease is a rare, severe, autosomal recessive metabolic disease characterized by progressive muscular degeneration. The overall incidence is estimated to be approximately 1 in 40,000 births,ialthough frequency and disease progression varies with age of onset, ethnicity and geography.iiThe disease is caused by mutations in the alpha-glucosidase (GAA) genethat prevent the production and function of a protein called acid alpha-glucosidase (GAA). GAA is responsible for metabolizing glycogen, and dysfunction or absence of this protein results in the accumulation of glycogen in tissues, primarily in the skeletal and cardiac muscles, where it causes damage to tissue structure and function. Currently, the only approved treatment for Pompe is enzyme replacement therapy (ERT), which is a chronic treatment delivered in bi-weekly infusions and relies solely on tissue uptake of GAA from plasma.

About AT845 for the treatment of Late-Onset Pompe Disease (LOPD)Astellas is developing AT845, a novel gene replacement therapy using an AAV8 vector, under a cardiac- and skeletal muscle-specific promotor, to deliver a functional copy of the GAA gene, for the treatment of Late-Onset Pompe Disease (LOPD). AT845 is being investigated to determine whether it can deliver a functional GAA gene that is efficiently transduced to express GAA directly in tissues affected by the disease, including skeletal and cardiac muscle.

About FORTISFORTIS is an ongoing multicenter, open-label, ascending dose Phase I/II first-in-human clinical trial to determine if AT845 is safe and tolerable in adults with Late-Onset Pompe Disease (LOPD). The primary endpoints of the trial are safety and tolerability, as well as efficacy measures, including change in muscle GAA protein expression and enzyme activity from baseline. Secondary endpoints evaluate improvements in respiratory, endurance and quality of life measures.

About AstellasAstellas Pharma Inc. is a pharmaceutical company conducting business in more than 70 countries around the world. We are promoting the Focus Area Approach that is designed to identify opportunities for the continuous creation of new drugs to address diseases with high unmet medical needs by focusing on Biology and Modality. Furthermore, we are also looking beyond our foundational Rx focus to create Rx+ healthcare solutions that combine our expertise and knowledge with cutting-edge technology in different fields of external partners. Through these efforts, Astellas stands on the forefront of healthcare change to turn innovative science into value for patients. For more information, please visit our website at https://www.astellas.com/en.

About Astellas Gene TherapiesAstellas integrated its wholly owned subsidiary, Audentes Therapeutics, Inc., as of April 1, 2021 and established "Astellas Gene Therapies" within the organization as an Astellas Center of Excellence to develop genetic medicines with the potential to deliver transformative value for patients. Based on an innovative scientific approach and industry leading internal manufacturing capability and expertise, we are currently exploring three gene therapy modalities: gene replacement, exon skipping gene therapy, and vectorized RNA knockdown and hope to also advance additional Astellas gene therapy programs toward clinical investigation. We are based in San Francisco, with manufacturing and laboratory facilities in South San Francisco and Sanford, North Carolina.

iKishnani, PS, et al. Pompe disease diagnosis and management guideline. Genetics in medicine: official journal of the American College of Medical Genetics, 2006. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110959/ii Ausems MG, et al. Frequency of glycogen storage disease type II in The Netherlands: implications for diagnosis and genetic counselling. European Journal of Human Genetics, 1999. Available from: https://www.nature.com/articles/5200367.pdf?origin=ppub; Lin CY, et al. Pompe's disease in Chinese and prenatal diagnosis by determination of alpha-glucosidase activity. Journal of Inherited Metabolic Disease, 1987. Available from: https://pubmed.ncbi.nlm.nih.gov/3106710/; Hirschhorn R, et al. Pediatric Research, 2004; Bashan N, et al. Glycogen storage disease type II in Israel. Israel Journal of Medical Sciences, 1988. Available from: https://europepmc.org/article/med/3132435; Meikle PJ, et al. Prevalence of Lysosomal Storage Disorders. JAMA, 1999. Available from: https://jamanetwork.com/journals/jama/article-abstract/188380

SOURCE: Astellas Pharma

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Astellas Announces Positive Safety Data from the FORTIS Study of AT845 in Adults with Late-Onset Pompe Disease | DNA RNA and Cells | News Channels -...

GENETICS Correction for Chromosome 10q26driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal pigment epithelium, by Brandi L. Williams, Nathan A. Seager, Jamie D. Gardiner, Chris M. Pappas, Monica C. Cronin, Cristina Amat di San Filippo, Robert A. Anstadt, Jin Liu, Marc A. Toso, Lisa Nichols, Timothy J. Parnell, Jacqueline R. Eve, Paul L. Bartel, Moussa A. Zouache, Burt T. Richards, and Gregory S. Hageman, which published July 22, 2021; 10.1073/pnas.2103617118 (Proc. Natl. Acad. Sci. U.S.A. 118, e2103617118).

The authors note that Sven Heinz and Michael G. B. Hayes should be added to the author list between Jacquelin R. Eve and Paul L. Bartel. These two authors developed and trained the other coauthors in the techniques used to perform the epigenetic profiling of the chromatin upstream of the HTRA1 gene. The chromatin data were invaluable to the paper and could not have been performed without the training and assistance from Michael Hayes and Sven Heinz. Both Sven Heinz and Michael G. B. Hayes should be credited with Contributed new reagents/analytic tools to this research. The corrected author line, affiliation line, and author contributions appear below. The online version has been corrected.

Brandi L. Williamsa,1, Nathan A. Seagera, Jamie D. Gardinera, Chris M. Pappasa, Monica C. Cronina,2, Cristina Amat di San Filippoa,3, Robert A. Anstadta, Jin Liua, Marc A. Tosoa, Lisa Nicholsa, Timothy J. Parnellb, Jacqueline R. Evea,4, Sven Heinzc, Michael G. B. Hayesc, Paul L. Bartela,6, Moussa A. Zouachea, Burt T. Richardsa, and Gregory S. Hagemana,1

aSteele Center for Translational Medicine, John A. Moran Eye Center, University of Utah, Salt Lake City, UT 84132; bBioinformatics Analysis, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132; and cDepartment of Medicine, University of California San Diego, La Jolla, CA 92093

Author contributions: B.L.W., P.L.B., B.T.R., and G.S.H. designed research; N.A.S., J.D.G., C.M.P., M.C.C., C.A.d.S.F., R.A.A., J.L., M.A.T., and J.R.E. performed research; L.N., J.R.E., S.H., M.G.B.H., and M.A.Z. contributed new reagents/analytic tools; B.L.W., T.J.P., and M.A.Z. analyzed data; B.L.W. wrote the paper; L.N. coordinated donor eye collection and processing; and G.S.H. provided donor eye tissue, graded AMD status, critically reviewed work, and provided all funding.

Excerpt from:
Correction for Williams et al., Chromosome 10q26driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal...

Denmark, like other Nordic countries (14), has maintained for more than half a century population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system (5, 6), including familial information via parental links (7, 8). This has allowed population-based studies of the causes and consequences of disease at an unprecedented scale and detail (9).

Several studies in the Nordic countries have leveraged diagnostic information from cross-referenced civil and health care registers on pairs of close relatives for quantitative genetic studiesthat is, co-occurrence and familial coaggregation, heritability and genetic correlation, and nonrandom mating (1014). However, the dynamics of a population (e.g., changes in mating patterns and family structure, health care provision, clinical practice, and diagnostic systems) may compromise such initiatives and bias quantitative genetic estimates and inference on human behavior. Thus, realizing the potential of Nordic population and health care registers depends on insights into the structure and network properties of the entire genealogy and accounting for underlying changes in the frequencies of human traits, notably in population demographics and disease frequencies.

Here, we reconstruct the Danish genealogy using the population-wide Danish Civil Registration System that holds information on family relationships for all individuals with at least 1 d of legal residence in Denmark since 1968 (7, 8). We describe the size, structure, and network properties of the genealogy along 116 y. We leverage the cross-reference to the nationwide, public, and health care registers to estimate occurrence, heritability, and genetic correlations for 10 broad categories of medical conditions across eight consecutive demographic cohorts.

The Danish Civil Registration System (7, 8) has been registering all people legally residing in Denmark since 1968, and it includes information about sex, date of birth, parental links, and life events (e.g., migration or death). By April 2017 (time of data freeze for this report), 9,851,330 individuals were registered in the Danish Civil Registration System. The system is linked via anonymized identification numbers to the Danish National Patient Register (6) and the Danish Psychiatric Central Research Register (5) that include all diagnostic information regarding general medical conditions and specific psychiatric conditions, respectively, including all inpatient and outpatient contacts.

By use of the parental links, we reconstructed the Danish genealogy, which we then leveraged together with the diagnostic information from the Danish National Patient Register to study the genetic architecture of medical conditions as defined in the 8th and 10th Revisions of the International Classification of Diseases (ICD-8 and ICD-10, respectively). Inspired by the extensive comorbidity (15) between disorders affecting the same organ or characterized by the same pathology, we analyzed 10 broad diagnostic categories of medical conditionsthat is, nine somatic and one mental (SI Appendix, Table 1).

To study changes in genetic architecture in time and identify possible epidemiological biases such as truncation and censoring, we carried out the analyses in eight consecutive demographic cohorts with characteristic cultural, political, and economic features of Western societies (SI Appendix, Table 2; https://www.careerplanner.com/Career-Articles/Generations.cfm). The eight cohorts are the Interbellum Generation (birth year: 1901 to 1913), the Greatest Generation (1910 to 1924), the Silent Generation (1925 to 1945), the Baby Boomers (1946 to 1964), Generation X (1965 to 1979), Millennials (1980 to 1994), Generation Z (1995 to 2012), and Generation Alpha (2013 to 2025). We note that there is a 4-y overlap between the Interbellum and Greatest Generation. Individuals born outside the eight cohorts were not considered in the analyses. In addition, we discarded individuals that died before January 1, 1977 (date on which the Danish National Patient Register was established).

To inform downstream heritability and genetic correlation analyses, we initially determined the size and structure of the Danish genealogy by examining its network properties (Fig. 1).

Size and structure of the Danish genealogy. (A) Membership of the 9,851,330 registered participants in the identified network components. The vast majority of the participants (69.04%) had at least one known relative (pink, blue, and green). (B) Frequency of network components ordered by pedigree size. One component with size n = 5,396,661 (pink) includes 79.35% of the connected individuals. (C) Frequency of number of an individuals relatives. The Danish genealogy is dominated by individuals with few relatives. (D) Frequency of familial relationships and relative position to Self. Color-coding corresponds to degree of relationship. FS, full siblings; HS, half siblings; 1C, first cousins, etc.; PO, parentoffspring; 1G, grandparentgrandchild, etc.; Av, avuncular; 1GAv, grandavuncular, etc.; 1R, once removed, etc. The structure is enriched for close familial relationships (outlined by the dotted line).

Of the 9,851,330 registered individuals, 6,801,107 (69.04%) had at least one registered relative, while 3,050,223 (30.96%) were unconnected singletons and were therefore excluded from further analysis (Fig. 1A). The single largest pedigree includes 5,396,661 individualsthat is, 54.78% of all registered individuals and 79.35% of the individuals with at least one relative (Fig. 1A). The genealogy also includes 251,513 smaller unconnected pedigrees (n = 1,404,446), among which there are 100,400 trios and 58,804 quartets (Fig. 1 A and B).

The 6,801,107 connected individuals span only six generations and include 2,377,043 foundersthat is, individuals with no parental links. It is expected that some of the founders are closely related (e.g., siblings or cousins), but, in the lack of parental links or genetic information, we are unable to consider this in our analyses. The narrow generation span combined with the high number of founders has implications in the ascertainment of distant relative pairs (Fig. 1C). As a result, 29,739,188 out of 41,798,152 annotated relative pairs (71.15%) are concentrated within a radius of three meioses, encompassing parentoffspring, full siblings, half siblings, grandparentgrandchild, avuncular, half avuncular, and great grandparentgreat grandchild pairs (Fig. 1D).

The two oldest (Interbellum and Greatest) and the one youngest (Generation Alpha) demographic cohort had considerably fewer individuals (N45,103 to 325,066) and were therefore expected to be less informative than the five larger cohorts (N1,020,953 to 1,489,329) (SI Appendix, Table 2).

Disease prevalence for all 10 diagnostic categories peaks in the Greatest and the Silent Generation and declines to a minimum in Generation Alpha (SI Appendix, Table 3). Circulatory conditions constitute the most frequent category, affecting 61.9% of the individuals in the Greatest Generation, whereas hematological and musculoskeletal conditions are the least frequent categories, affecting at their peak 12.11 and 12.71% of individuals in the Greatest and Silent Generations, respectively (SI Appendix, Table 3 and Fig. 1).

While the decline in relative frequency is very similar across diagnostic categories, consistent with a uniform age-dependent effect on the age of onset of disease, mental and pulmonary conditions are characterized by distinct profiles, remaining at elevated frequency until the two youngest cohortsthat is, Generation Z and Generation Alpha (SI Appendix, Fig. 2).

We estimated heritability (h2) of the 10 diagnostic categories (15) by applying the latent correlation of relative pairs to Falconers method (16). In our analysis, we considered all family relations within a radius of three meioses (i.e., all up to second degree and great grandparents/great grandchildren) because these were abundant enough to yield accurate estimates.

For all 10 diagnostic categories, heritability increases across demographic cohorts and peaks in Generation Z. Due to truncation, censoring, and data scarcity that characterize the oldest and youngest generations, we consider estimates from the four midmost and largest cohortsthat is, Silent Generation, Baby Boomers, Generation X, and Millennialsto be a priori more reliable (Figs. 2 and 3).

Heritability estimates of 10 broad diagnostic categories by demographic cohort. Most estimates correspond to average values from all available relative pairs weighted by sampling variance. Least-squares estimates are reported for the hematological category in Generation Z and Generation Alpha. Both tile size and shade intensity are proportional to heritability values. All estimates were significantly different from zero.

Heritability estimates (and 95% CIs) of 10 broad diagnostic categories by demographic cohort. Estimates were derived from parentoffspring pairs alone (empty circles), averages from all available relative pairs weighted by sampling variance (filled squares), or least-squares regression (filled circles). The gray line corresponds to cross-category weighted average estimates.

Estimates of heritability varied notably between diagnostic categories (Figs. 2 and 3) and consistently across demographic cohorts as reflected in their cross-cohort weighted average estimates (Table 1). Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers and neurological conditions were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134] and average h2 = 0.154, 95% CI = [0.151, 0.157], respectively).

Heritability of 10 broad diagnostic categories across eight demographic cohorts

Heritability could not be estimated for some diagnostic categories in the two oldest and the two youngest demographic cohorts due to data scarcity (Figs. 2 and 3 and Table 1). In addition, most heritability estimates were similar when analyses were restricted to full sib pairs only, although the consideration of multiple sib pairs from the same family resulted in wider CIs (SI Appendix, Figs. 3 and 4 and Table 4). A similar trend was observed when analyses were restricted only to individuals born in Denmark (n = 6,017,195) as reflected in the high correlation between measures (r = 0.94; SI Appendix, Fig. 5).

Finally, we note that with the notable exception of cancers and conditions of the hematological system, no single disease seems to dominate the 10 diagnostic categories under study (SI Appendix, Fig. 6)and consequently, the corresponding heritability estimates.

To understand the mutual relationships between the 10 broad diagnostic categories (15), we estimated their genetic correlations (rg) by combining within- and between-category estimates of the latent correlation into Falconers method (16). We considered all family relations within a radius of three meioses and restricted the analyses to the four most data-rich demographic cohorts mentioned in Genealogy Network Structure (Fig. 4 and Dataset S1).

Genetic correlations of each of 10 broad diagnostic categories with the remaining nine by demographic cohort. Only the four most data-rich cohortsSilent Generation, Baby Boomers, Generation X, and Millennialswere considered. Estimates were based on averages from all available relative pairs within a radius of three meioses weighted by sampling variance. Blank cells correspond to correlations not significantly different from zero.

All rg except two were positive, and all of them except one were also significantly different from zero. Overall, rg were highly consistent between consecutive cohorts, thus further boosting confidence in the estimates (SI Appendix, Fig. 7). This trend was more marked for certain diagnostic categories such as mental, pulmonary, and neurological than others. In all 10 diagnostic categories, younger cohorts showed lower rg than older generations, whereas the opposite trend was observed for heritability that consistently increased in younger cohorts (Fig. 4 and SI Appendix, Dataset S1). The average rg of each of the 10 diagnostic categories with the other nine categories was highest for gastrointestinal conditions (0.567; SE = 0.0005) and lowest for urogenital conditions (0.386; SE = 0.0008).

We further used 45 cross-generation weighted average genetic correlations to hierarchically cluster the 10 diagnostic categories (Fig. 5). We observed three major clusters in the dendrogram: one including mental, pulmonary, gastrointestinal, and neurological conditions; another one involving musculoskeletal conditions and cancers; and a third one involving urogenital, hematological, circulatory, and endocrine conditions.

Dendrogram of 10 broad diagnostic categories based on cross-cohort weighted average genetic correlations. Only the four most data-rich cohortsSilent Generation, Baby Boomers, Generation X, and Millennialswere considered. Estimates were based on averages from all available relative pairs within a radius of three meioses weighted by sampling variance.

Finally, genetic correlation estimates based on full sib pairs alone, in which most pairings are not intergenerational, are shown in SI Appendix, Figs. 810 as well as Dataset S2 and were generally consistent with analyses based on all family relations.

In this study, we present the Danish genealogy constructed from the Danish Civil Registration System, which holds information on all individuals born or with residence in Denmark since 1968. The genealogy extends back up to six generations, with the oldest connected individuals being born in 1872 and the youngest in 2017. We partitioned 6,691,426 Danish citizens into eight demographic cohorts based on year of birth. Notably, by cross-linking the Danish Civil Registration System with hospital discharge diagnoses from the public and egalitarian Danish health care system, we were able estimate heritability and genetic correlations for 10 broad diagnostic categories encompassing all major organ systems and most ICD-8/ICD-10 codes while describing the epidemiological biases of truncation and right censoring in the oldest and youngest demographic cohorts, respectively.

The heritability of single diseases and genetic correlations between them have been studied extensively not only in family data but also thanks to the development and application of genome-wide association studies to thousands of human traits (17). In a few instances (e.g., for mental disorders), genetic risk variants shared across diagnoses with clearly distinct clinical characteristics and age of onset have been identified (18). However, neither the heritability nor the genetic correlations have been systematically studied for organ-defined disease categories as grouped by 10 chapters of ICD-10. In addition, such studies have never been carried out within a single population such as the Danish, serviced and monitored uniformly for decades by an egalitarian health care system.

We estimate the heritability to be high for several of the 10 disease categories. This is consistent with high genetic correlation between individual diagnoses within each category as reported for mental disorders (18) and more broadly for brain disorders (19) as well as with the broader notion that genetic liability is generally organ specific. For mental conditions in particular, heritability point estimates reach 0.7, which is higher than reported for the common and less heritable mental disorders such as depression (0.4) (20) and anxiety (0.30.4) (21) and similar to those for highly heritable, rare illness, such as schizophrenia (0.81) (22) and bipolar disorder (0.60.8) (23).

Moreover, the lower heritability estimates in older cohorts and the higher heritability estimates in younger cohorts might be because disease risk is generally plateauing in the former, whereas accumulation of diagnoses in the latter is an ongoing process, interrupted by right censoring. Younger cohorts are therefore enriched for younger ages of onset, which in many instances go along with stronger genetic signals and higher heritability estimates as known for mental disorders in which early onset disorders such as autism and attention-deficit/hyperactivity disorder are commonplace. It could also be posited that the accumulation of environmental exposures throughout life renders nongenetic factors more important in aging-related conditions, thus resulting in overall lower heritability estimates in older cohorts. On the other hand, stronger genetic correlations in older cohorts might be due to the accumulation of comorbidities in older cohorts compared to younger cohorts.

The fact that genetic correlations were almost exclusively positive across all cohorts probably reflects how diseases, at least in the broad composite definitions we use in this work, are problems of the normal functioning of organs and systems, whereby the disorganization of one or more of them should be detrimental for others, ultimately resulting in further pathology. The positive genetic correlations match comorbidity observations in the clinical domain.

Notably, we observe that the ranking of heritability and average genetic correlation estimates compare for most of the 10 diagnostic groups, although there are also marked exceptions. Mental, gastrointestinal, and circulatory conditions rank high both for heritability and average genetic correlation, whereas neurological conditions, despite showing the lowest heritability estimates, are genetically highly correlated with the other diagnostic groups, implicating broadly the etiology of disease affecting the nervous system in disorders of most other organ systems. Contrary, other low-heritability groups, such as cancer and musculoskeletal conditions, have low genetic correlations suggestive of their etiologies being dominated by disease-specific, environmental exposures and somatic mutations for the former and accidents for the latter. Similarly, endocrine conditions, dominated by type 2 diabetes, have relatively low heritability, possibly reflecting behavioral causes.

While circulatory and gastrointestinal conditions are the most heritable and genetically correlated diagnostic categories, their patterns of genetic correlation with other diagnostic categories are nonetheless highly diverse. In fact, gastrointestinal conditions were clustered with neurological and mental disorders, and while the clustering of the two latter disease categories of the nervous system could be anticipated and possibly reflects organ-specific components of their heritability, their proximity to gastrointestinal conditions is notable and may stem from the extensive innervation that underlies the gutbrain axis and the proposed relation between gut microbiota for brain functioning and mental health (24). Contrary to the proximal clustering of brain and gut disorders reflecting shared organ specificity or functionalities, that of endocrine with circulatory conditions as well as that of cancers with hematological illnesses more likely reflects sequelae in which one illness is a consequence or complication of a prior and otherwise, unrelated condition, in case, diabetes leading to circulatory complications and cancers to anemia because of bleeding from internal organs.

Although the reconstructed Danish genealogy is limited to six generations and thus dates back in time considerably less than the genealogy of Iceland (25), we note that most diagnostic categories include between a quarter- and a-half-million individuals, making this genealogy dataset highly apt for studies of heritability, genetic correlations, and the impact of behavioral and environmental changes over time. Also in comparison with Iceland, the relative shallowness of the reconstructed Danish genealogy, compared to, for instance, the much deeper Icelandic pedigree dating back to the 11th century (25), renders linking distant relatives a challenging task and supports the use of classical relative pair-based methods rather than linear mixed models. Furthermore, truncation and censoring biases in the oldest and youngest cohorts, as well as changes in the environment and clinical practices over time, favor the use of horizontal over vertical familial relationships and justify the stratification of the analysis by demographic cohort rather as opposed to a single analysis across the entire genealogy.

While this dataset is ideally poised for quantitative genetic analyses, it also presents limitations. As already discussed, truncation in the older demographic cohorts and right censoring in the younger ones can introduce bias to heritability and genetic correlation estimates. In order to explore the effects and biases of time, we split the available data into eight demographic cohorts and show that the four midmost cohortsthat is, the ones least affected by truncation and censoringyield consistent estimates.

In addition, given the lack of genetic data, we have no means to safeguard our analysis from false paternities and adoptions. As a result, a small portion of the ascertained familial relationships may be overstated, affecting our heritability and genetic correlation estimates. Nevertheless, given the high abundance of relative pairs, we believe that the effect of these biases is limited. Similarly, the lack of parental links before the timeframe of the registries will lead to understating distant familial relationships, which could bias heritability estimates based on frameworks that utilize the entire relationship matrix such as linear mixed models. However, because our estimates are based on known family pairs, we believe that issues coming from an underestimation of familial relationships are limited in our analysis.

Furthermore, modifications in the diagnostic classification system, which changed from ICD-8 to ICD-10 in 1995, and the registration of outpatient contacts that began in the same year (9) may complicate precise tracking across demographic cohorts, although the focus on broad diagnostic categories in this study is expected to reduce this bias.

Finally, our analyses make no attempt to distinguish a priori between genetic correlation resulting from pleiotropy and co-occurrence of disease in relatives because of sequelae as discussed in the seventh paragraph of Discussion for cancers and anemia.

For mental disorders, the relatively high frequency in the younger cohorts coincides with the introduction of novel child and adolescent disorders in ICD-10that is, attention-deficit/hyperactivity disorder and autism. Similarly, pulmonary conditions show increasing frequencies in younger generations consistent with increasing worldwide prevalence of smoking and asthma in young people (26). While potentially biasing our findings, changes in disease frequency across time also constitutes an entirely novel research field opening for the identification of nongenetic factors independently or through gene-environment interactions influencing risk of disease. In fact, as the habit of smoking spreads and increases during the middle of the 20th century (26) and the prevalence of pulmonary and circulatory conditions increases correspondingly, the heritability is expected to decrease; thus, modeling a shared environment in households will allow for studies seeking to identify nongenetic factors that impact disease. Such analyses can be empowered by the knowledge of geographical (co)location of the residence of Danish citizens from cradle to grave as a proxy for shared environment.

In conclusion, here we presented the Danish genealogy as a resource that, in combination with the National Health Registers, allows whole-population, quantitative genetic analysis with applications to health sciences. The presented resource and analytical framework will contribute to the advancement of precision medicine, allowing the systematic mapping of heritabilities and genetic correlations of comorbidity patterns and sub-diagnostic traits such as age of onset and treatment response and to inform on clinically relevant phenomena such as assortative mating, nonadditive genetics, and shared environment. While this and similar genealogies from the Nordic countries represent unique resources (14), the changes in biases, environment, and clinical practices necessitate the integration of time-dependent and survival analysis frameworks. Explicit modeling of biases is warranted to fully exploit the oldest and youngest generations.

The Danish Civil Registration System was established in 1968, registering all people alive and living in Denmark since then (7, 8). The Danish Civil Registration System includes personal identification number, sex, date of birth, and continuously updated information on vital status (e.g., migration or death). The personal identification number is virtually immutable, thus enabling accurate links across different registers. As of April 2017, the system contained 9,851,330 individuals born between January 1, 1858, and April 21, 2017.

The Danish National Patient Register (6) includes the medical records of all patients treated in Danish general hospital inpatient departments since January 1, 1977, as well as in outpatient clinics since 1 January 1994 (or occasionally since 1995). Since 2002, the Register also includes Danish patients treated in hospitals outside Denmark and activities in specialist medical practices not paid by the health insurance agreement. As of April 2017, the register contained 287,593,154 records with diagnostic information for the 135,070,194 patient contacts available in the dataset.

The Danish Psychiatric Central Research Register (5) was first computerized in 1969 and includes admissions to psychiatric inpatient facilities up to and including 1994. Since 1995, the Register also contains outpatient contacts to psychiatric departments. As of April 2017, the register contains 7,298,910 records with diagnostic information for the 4,826,984 psychiatric hospital contacts.

This study was approved by the Danish Health Data Authority (project no. FSEID-00003339) and the Danish Data Protection Agency. By Danish law, informed consent is not required for register-based studies.

The most important requirement for accurately establishing pairwise familial relationships is that any given individual has either no register links to their parentsthat is, they are a founderor both register links to their parents. This is to guarantee that familial relationships are not underestimated (e.g., incorrectly ascertaining half siblings instead of full siblings). Bearing this in mind, the 2017 Danish Civil Registration System data freeze includes 1) 198,892 individuals with only one parental link, 2) five individuals with two identical parental personal identification numbers, 3) 880 individuals that are adopted, 4) 3,000 individuals with same-sex parents, and 5) 123,331 individuals belonging to twin pairs/multiple births. There is overlap in the above five categories. In order to yield as many pairwise relationships as possible, instead of eliminating the aforementioned individuals, we converted them into foundersthat is, we eliminated their parental links. Thus, if said individuals have descendants that meet our two-parent criterion, we can include their pairwise familial relationships in our analyses.

Genealogies can be analyzed as graphsthat is, a set of nodes (individuals) that are joined by edges representing parentoffspring relationships (27). Bearing this in mind, we used the networkx module in Python (28) to explore network connectivity in our data.

After eliminating invalid parental links, we converted the data into an edge list and loaded it as an undirected graph. Each edge in the graph represents a parentoffspring relationship between two nodes. If the parents of an individual are known, then two edges are added to the list (one for each parent). If no parental information is availablethat is, in the case of foundersno edge is added to the list. Individuals can be entirely unconnected (singletons)that is, they present no parental or offspring links.

The list consisted of 8,848,128 edges involving 6,801,107 individualsthat is, 69.04% of all available individuals in the Register. The remaining 3,050,223 individuals (30.96%) were singletons. A bit over half of those singletons (1,753,057 or 57.47%) were born in Denmark or Greenland, whereas the rest were born elsewhere. The distribution of the singletons by demographic cohort is shown in SI Appendix, Fig. 11. Overall, singletons born in Denmark belong to older demographic cohorts and represent childless individuals with no parental links, whereas singletons born outside of Denmark belong to younger demographic cohorts and represent immigrants without familial links in Denmark.

networkx computes the number and size of componentsthat is, the network subsets that are completely unconnected from all other subsets. This process returned one large component (n = 5,396,661) and 251,513 significantly smaller ones (n = 1,404,446), among which there were 100,400 trios and 58,804 quartets (Fig. 1 A and B). The single largest network component includes 54.78% of all registered individuals and 79.35% of the individuals with at least one relative (Fig. 1A). The overwhelming majority of the connected individuals (88.47%) were born in Denmark or Greenland. The distribution of the connected individuals by demographic cohort is shown in SI Appendix, Fig. 11.

Graph topology also indicated that the 6,801,107 connected individuals span only six generations; of those individuals, 2,377,043 (34.95%) are foundersthat is, they have no parental links. The narrow generation span combined with the high number of founders has implications in the ascertainment of distant relative pairs.

We used the pydigree module in Python (29) in order to estimate all nonzero pairwise coefficients of expected relatedness ^ for the 6,801,107 connected individuals. pydigree reads a file in pedigree (PED) format as a directed acyclic graph and enumerates all legitimate paths connecting a given pair of individuals. From any given starting point, only paths toward previous generations are allowed as well as one change of direction at most. The lengths gG of the paths connecting a pair of individuals are used to estimate their kinship coefficient (30, 31):=gG12g+1.

We note that ^ is twice the kinship coefficient .

To avoid looping over unconnected individuals, we applied the procedure only to each of the 2,377,043 founders with their corresponding descendants (easily identified with pydigree). Because different founders can share descendants, we removed duplicate estimates with a Python script. Kinship coefficients for unreported pairs were assumed to be 0.

As a result of the above procedure, we obtained 44,099,130 pairs of familial relationships from the large pedigree and 4,522,710 from the rest of the smaller pedigrees, totaling 48,621,840.

Apart from the value of ^ for any given pair of individuals, we registered the number of all possible connecting paths and their corresponding length as well as node depth of each individual in the path. Combined with ^, this additional topological information allowed us to annotate the familial relationships with great accuracy (SI Appendix, Table 5).

The distribution of number of an individuals relatives is heavily right skewed with a long tail (mean = 12.3; median = 9; mode = 6; Fig. 1C). Moreover, the distribution of number of meioses between connected individuals, considering the shortest path per pair, is also right skewed with mean = 2.7, median = 3, and mode = 2. This implies that the ascertained relative pairs in the Danish genealogy are dominated by close familial relationships.

Only a negligible fraction (0.03%) of the annotated familial relationships were connected by more than two paths, consistent with very few consanguineous relationships or marriage loops in the population, and these pairs were discarded from further analyses.

In this work, we focused on 10 broad diagnostic categories that correspond to the definitions used in a recent publication (15). These were conditions of the 1) circulatory system, 2) endocrine system, 3) pulmonary system including allergies, 4) gastrointestinal system, 5) urogenital system, 6) musculoskeletal system, 7) hematological system, and 8) neurological system as well as 9) cancers and 10) mental conditions. Each of these broad diagnostic categories is a composite measure of presence or absence of any disease falling within the specific diagnostic category (SI Appendix, Table 1).

In general, if an individual has an in- or outpatient hospital admission or contact for one of the above medical conditions in the Danish National Patient Register and/or the Danish Psychiatric Central Research Register, we ascertain said individual as a case for said condition, with no respect to contact frequency or comorbiditiesthat is, diagnostic categories were not mutually exclusive. We considered both ICD-8 and ICD-10 codes for the ascertainment of a given phenotype, even though it is important to note that there is not always a 1-to-1 correspondence between the two coding systems. Only diagnoses coded as main or auxiliary were considered for the phenotyping (as opposed to basic, referral, temporary, and complication).

In general, this study considered all diagnoses assigned in relation to an in- or outpatient hospital admission or contact as recorded systematically in the Danish National Patient Register and/or the Danish Psychiatric Central Research Register.

Individuals with no entries for a given condition were treated as controls for said condition. However, this strategy is vulnerable to truncation and censoring biases because health records are not quantitatively or qualitatively homogeneous across demographic cohorts. To minimize the risk of including too many false controls in the control group, we only studied individuals who were alive and living in Denmark after January 1, 1977 (date on which the Danish National Patient Register was established) or born in the interval (January 1, 1977, to January 1, 2017). As a result, we ended up with a subset of 6,691,426 individuals for all our quantitative analyses.

We used a classical approach for the estimation of total narrow-sense heritability and genetic correlations (16). For phenotype xand given a familial relationship R (e.g., parentoffspring, full siblings, etc.)if rx1,x2 is the correlation coefficient between two paired variables (x1, x2) holding the phenotypic observations for pairs of related individuals, the corresponding heritability is:hx2=rx1,x22R.

Similarly, the genetic correlation between phenotypes x and y, for a given familial relationship R, is:rg,xy=rx1,y2+ry1,x22rx1,x2ry1,y2.

Because disease phenotypes are binarythat is, case controlwe applied the latent correlation coefficient (also known as tetrachoric correlation coefficient), which measures agreement between two raters. In its simplest form, latent trait modeling assumes that the observed binary variables result from the discretization (at a given threshold) of unobserved (latent) variables that are normally distributed. The correspondence to the liability threshold model (32, 33) is apparent. In the case-control context, raters are vectors of binary phenotypes corresponding either to within- [(x1, x2) and (y1, y2)] or between-phenotype [(x1, y2) and (y1, x2)] paired data. We note that one rater corresponds to the genealogically older member of a familial relationship (e.g., father), whereas the other rater corresponds to the genealogically younger one (e.g., daughter). In the case of genealogically contemporary relationships such as siblings or cousins, relatives in the raters are sorted by age.

For the estimation of latent correlation coefficients, we used a standard maximum likelihood procedure from the polycor package in R.

In the case of heritability, valid estimates were those 1) with a positive value and 2) significantly different from zero. Moreover, when heritability estimates from multiple familial relationships were available, we combined them by computing their weighted average and weighted SE.

We computed average heritability values (h2) and SE (s) weighted by sampling variance:h2=i=1nhi2si2i=1n1si2,s=1i=1n1si2.

We also used weighted least squares to estimate the slope (corresponding to h2) of the modelR=+2+,where R is a vector of correlation coefficients, is a vector of kinship coefficients, is the intercept vector, and is the error vector with 2() = W1. W is a diagonal matrix of weights used in the regression.

We carried out the analysis for all available pairs with no regard to sex. For estimates from horizontal familial relationshipsthat is, siblings and cousinsboth individuals had to be from the same generation. For estimates from the rest of the relationships, only relatives from previous generations were considered. We did not consider heritability estimates when the correlation coefficient was negative or when the CIs fell outside [0, 1].

In the case of genetic correlations, valid estimates were those whose 95% CIs were contained within [1, 1]. When genetic correlation estimates from multiple familial relationships were available, we combined them by computing their weighted average and weighted SE as above.

We note that estimates of heritability and genetic correlations depend on the definitions of the traits under study and that heritability of broadly defined traits will also reflect genetic correlations between the narrowly defined traits included in each broad trait category.

Read more from the original source:
A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy - pnas.org

I get up in the morning and I read the obits. If Im not in them, I have breakfast.Carl Reiner

When were young, we say hello to our friends and share our daily highs and lows. When were old(er), we read the obits to say goodbye and remember.

So recently, before my breakfast, I saw a familiar name in the paper. The wife of a friend had died. After more than 50 years of marriage, there would be no more Hi honey, Im home or How was your day? No more conversations, deep or frivolous. No more shared worries about the state of our planet, economy, or children. No more date nights on the couch with a bowl of popcorn and a movie on demand. No more hugs and kisses. No more. No more. Such is the stuff of heart-rending grief.

As it so happens, a few years earlier the same friend described his wife with the following: She is my heart and soul. She has vastly broadened my world view, changed me for the better, and I am indebted to her in endless ways. Without her, I am nothing.

The combination of his loss and his tribute got me thinking about the notion of immortality. Carl Reiner hit on one level of immortality: The ability to eat breakfast cheats death for at least for one more day. On the other end of the spectrum, billions of people around the world believe that when their time for breakfast runs out, they will be united for all eternity with their family and friends, and their God, in heaven. Bookends to the notion of immortality. But what else lies on the shelf of our lives between those bookends?

After thinking about it, there are in fact other forms of immortality between breakfast and the belief of billions. The pharaohs have their pyramids, Shakespeare his plays, and every family their scrapbooks (or, today, iPhones) filled with hundreds if not thousands of photos of us full color immortality.

On a more serious note, psychologists debate how much of who we are is due to nature (genetics) or nurture (family, cultural, and environmental factors). While they debate the percentages of those two factors, the real point lies in the continuation (or immortality) of their effects.

So I went back to my friends tribute to his wife: She has vastly broadened my world view, changed me for the better. On the nurture side of the argument, we enter relationships as blind dates, looking for commonalities, ways we can connect, future roads we might travel together. After any long-term relationship, each partner has transformed, contributed to, and changed each other in ways both significant and subtle. And those changes endure even after the death of a spouse or friend. We are not human etch-a-sketches instantly reverting to blank slates upon the loss of the artist. In this way, the best traits of the people important to us, the traits that change us for the better, are immortal.

On the nature side of the equation, half (more or less) of our childrens DNA is me; the other half, my wife. I look at our grandchildren and realize that the half in each parent is now a quarter in them, and yet to be an eighth in a great-grandchild. Just as I am half of each of my parents and an eighth of each great-grandparent. This genetic immortality is something we share only with our immediate family, something of a seed bank to continue the lineage of a family.

The quest for immortality is a strictly human pursuit. We search for the fountain of youth, have our bodies frozen to near absolute zero waiting for a cure to the disease that put us on ice, have countless amazing life-extending surgeries, and take billions of dollars of life-extending medications. We resist death at all costs, and continually look forward to a tomorrow. Its the human condition.

So when someone changes us for the better, changes how we behave because of their influence be they spouse, child, parent, friend, mentor, or personal hero do they not live on in that sense? And when we inherit the DNA of our parents, and their parents, and pass that on to our children, and grandchildren, do they, and we, not also live on in that way? After all, without these influences, none of us would be who we are.

Without her, I am nothing. No. You were you until you met her and then the two of you created personalities entwined like two strands of psychological DNA, built around the molecular DNA you each inherited, and which, along with psychological traits, you pass on to your children. Is that not a way in which partners find immortality in others lives, and in the lives of all who will follow them?

Mexicos annual celebration of Die de los Muertos (captured so beautifully in the movie Coco), reminds people to remember their ancestors and to thank them for their lives and contributions. In reality, they live on not for just one day, but every day through the genetic and psychological contributions each of us exhibits every day of the year.

More than breakfast, less than heaven; not as enduring as a pyramid nor as fleeting as a digital photograph. Immortality lies in the play we write with each other with a debt to the past and hope for the future. I am enjoying writing that play and looking forward to fewer goodbyes and a lot more breakfasts.

Its the human condition.

See the article here:
On immortality and the human condition - Wednesday Journal

A recent study conducted by researchers at Baylor College of Medicine and Texas Childrens Hospital is the first to identify an underlying mechanism and possible drug targets for a new severe neurological disorder in children called NEDAMSS.

The disorder, called NEDAMSS for neurodevelopmental disorder with regression, abnormal movements, loss of speech and seizures, is caused by spontaneous mutations in the Interferon Regulatory Factor 2 Binding Protein Like (IRF2BPL) gene. In this study, the team led by Drs. Hugo Bellen, Paul Marcogliese and Debdeep Dutta at Baylor and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Childrens employed fruit flies to dissect the biological function of IRF2BPL.

In 2018, Marcogliese and Bellen, along with a team of researchers affiliated with the Undiagnosed Diseases Network a National Institutes of Health-funded network whose goal is to find genetic variants responsible for previously unidentified disorders discovered the NEDAMSS disorder. In collaboration with Drs. Shinya Yamamoto, Michael Wangler and others at the Duncan NRI and Baylor, as well as Drs. Loren Pena and Vandana Shashi at the UDNs Duke clinical site, they identified mutations in IRF2BPL as the cause of severe steady regression of motor and language skills in the initial cohort of five patients.

Today, there are 31 patients identified with mutations in IRF2BPL, said Marcogliese, co-corresponding author of the study and postdoctoral associate in the Bellen lab. IRF2BPL has been named as one of the top 100 autism candidate genes and its variants have been implicated in early-onset Parkinsonism. However, until now, very little was known about the biological function of this gene and how its loss results in neuronal dysfunction.

Interestingly, overexpression of Pits (the fly version of IRF2BPL) or human IRF2BPL in flies resulted in serrated wings and bristle loss, both of which are common when the Wnt/Wingless signaling pathway is perturbed during development. Reducing Pits in neurons, however, led to an age-dependent neurological defect in flies, which the team later found was associated with an increase in the activity of the Wnt/Wingless pathway.

In collaboration with Dr. Nan Cher Yeo at the University of Alabama Birmingham and Dr. Kathrin Meyer at Nationwide Childrens Hospital, they confirmed these findings in zebrafish and in patient-derived cells as well.

Numerous experimental observations led the researchers to conclude that Pits and Wnt/Wg act in an antagonistic manner. In agreement with these observations in animal models, they found that Wnt signaling is increased in NEDAMSS patients, whose cells are known to have reduced levels of IRF2BPL.

To understand how Pits/IRF2BPL regulates Wg/Wnt pathway, the researchers conducted biochemical and mass spectrometry assays, which revealed a Wnt antagonist, Casein kinaseI (CkI), as one of the top binding partners of Pits. Further, they also confirmed genetic interactions between IRF2BPL and CkI.

In summary, the study shows an antagonistic relationship between IRF2BPL/Pits and the Wnt/Wg pathway in the fruit fly model. It also shows that under normal circumstances, IRF2BPL/Pits regulates neural function by inhibiting the Wnt/Wg pathway.

In NEDAMSS patients, loss of IRF2BPL is accompanied by increased levels of Wnt in astrocytes a prominent type of cell in the brain. The Wnt pathway, the human counterpart of the Wingless pathway in flies, is known to be important for the proper development and function of neurons and other organs, and its disruption results in several types of cancers. The researchers also showed that they can suppress characteristics associated with the loss of IRF2BPL/Pits using Wnt inhibitors, which have proven to be safe and effective in treating many cancers.

It used to take decades from the initial discovery of a novel gene to dissecting its biological mechanism to finding a therapy, said Bellen, Distinguished Service Professor of Molecular and Human Genetics at Baylor and the corresponding author of the work. However, with the UDNs unique approach that fosters close collaborations between clinicians and researchers working on various model systems, we were able to move from the initial identification of this mutation to a potential therapy in just three years.

Furthermore, we were able to progress rapidly thanks to the continued support from the iDREAM For a Cure, as well as supplemental support from the NIH for which we are truly grateful. We hope to translate these promising discoveries into a viable therapy in the future.

The researchers report their findings in the journal Science Advances.

Other researchers involved in the study include Shrestha Sinha, Nghi Dang, Zhongyuan Zuo, Yuchun Wang, Di Lu, Fatima Fazal, Thomas Ravenscroft, Hyunglok Chung, Oguz Kanca, JiJuWan, Emilie Douine, Stanley Nelson, Matthew Might, Kathrin Meyer and Nan Cher Yeo. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Texas Childrens Hospital, the Research Institute at Nationwide Childrens Hospital, University of Alabama, David Geffen School of Medicine, Cincinnati Childrens Hospital and Ohio State University.

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Wnt signaling is identified as a target in NEDAMSS disorder - Baylor College of Medicine News