Population Histories of the United States Revealed through Fine-Scale Migration and Haplotype Analysis

These GEITP pages, from time-to-time, have discussed issues of human evolution and global migration; this topic represents one of the latter group. The U.S. population represents tremendous diversity of global ancestries — shaped by migration from distant continents, plus admixture of recent migrants and Native Americans. Admixture (i.e. interbreeding) between groups, which have historically been genetically and geographically distinct — has resulted in individuals with complex ancestries; in addition, within-country migration has led to genetic differentiation (accumulation of differences in allelic frequencies between completely, or partially, isolated populations — due to evolutionary forces such as selection or genetic drift). An in-depth understanding of population history is important for learning about human evolution and demographic history, as well as for adequate study design when associating genotypes (DNA sequence; genetic make-up) with phenotypes (traits). 😊

Earlier population-genetic studies in the U.S. broadly characterized this structure, usually using a limited set of ancestry-informative markers or uni-parental mtDNA (mitochondrial DNA) and Y-chromosome DNA data. Because the cost of genetic technologies has dropped, more recent studies have inferred population history with more complete genome-wide data — typically using >100,000 single-nucleotide variants (SNVs) ascertained via sequencing or genotyping. Also, previous genetic studies of the U.S. population have sought to infer genetic ancestry and population history primarily in European Americans, African Americans, and/or Hispanics/Latinos. European-American ancestry is characterized by substantial mixing between different ancestral European populations and, to a lesser extent, admixture with non-European populations.

Isolation among certain European populations (e.g. Ashkenazi-Jewish, French-Canadian, and Finnish populations) have also resulted in founder effects (i.e. decreased genetic diversity resulting from a population derived from a small number of colonizing ancestors). The mixing of European settlers with Native Americans has contributed to large variations in the admixture proportions of different Hispanic/Latino populations. Among Hispanics/Latinos — Mexicans and Central Americans have more Native-American ancestry; Puerto Ricans and Dominicans have more African ancestry; and Cubans have more European ancestry. In African Americans — proportions of African, European, and Native-American ancestry were shown to vary across the country — reflecting migration routes, slavery, and patterns of segregation between states.

Although much effort has been made to understand genetic diversity in the U.S. — fine-scale patterns of demography,

migration, isolation, and founder effects are still being uncovered with the growing scale of genetic data — particularly for Latin-American and African descendants having complex admixture histories. At the same time, there has been little research on the population structure of individuals with East-Asian, South-Asian, and Mid-Eastern ancestry in the U.S.

Authors [see attached article] studied the ancestry and population structure of >32,000 individuals in the U.S. — using genetic, ancestral birth origin, and geographic data from the National Geographic Genographic Project (NGGP). Authors could identify migration routes, and barriers, that reflect historical demographic events. They also uncovered spatial patterns of relatedness in subpopulations — through the combination of haplotype clustering (recall that ‘haplotype’ describes the combination of alleles, or a set of SNVs found on the same chromosome — indicating they arose from one parent or more distant ancestor), ancestral birth origin analysis, and local ancestry inference. Examples of these patterns included substantial substructure and heterogeneity in Hispanics/Latinos, isolation-by-distance in African Americans, higher levels of relatedness and homozygosity in Asian immigrants, and fine-scale structure in European descents. Taken together, these data offer detailed insights into the genetic structure and demographic history of the diverse U.S. population.


Am J Hum Genet 5 Mar 2020; 106: 371–388

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Parallel and nonparallel genomic responses contribute to herbicide resistance in a common agricultural weed

Here is a made-to-order topic of gene-environment interactions: the environmental signal is an “herbicide”, and genetic networks must quickly “adapt” [i.e. find a means of combatting this toxicant (the “response”)] — or else the plant will not survive. Pesticide resistance in plants is a great example of rapid evolution in response to strong, human-mediated selection. Due to widespread use of insecticides and herbicides in agriculture, multiple resistant pest populations often arise. These repeated examples of resistance allow for questions about the level at which parallel adaptation occurs (e.g. are parallel resistant phenotypes in separate lineages due to parallel changes at the developmental, physiological, or genetic level?).

Herbicide-resistant weeds represent fantastic examples of evolutionary parallelism — because the same nucleotide change can lead to resistance among separate lineages — and even separate species; this is an example of genomic constraint (i.e. parallel evolution of a trait occurs because of a finite number of genetic solutions to the same, but often novel, environmental pressure). Among herbicide-resistant weeds, data that support the genomic constraint hypothesis stem from sequence analysis of genes that are theoretically known to produce the protein targeted by the herbicide (i.e. cases of target-site resistance; TSR) — rather than genome-wide sequence surveys — (e.g. population genomics scans or gene-mapping studies). As a result, very little is understood about the potential for parallel genetic responses (that may occur, across the genome), beyond the potential for changes within the (most often) single genes responsible for TSR.

This can become a problem, because many weed species exhibit non-target-site resistance (NTSR; caused by any mechanism not due to TSR). NTSR can include a range of mechanisms — (from herbicide detoxication, to transport alterations to vacuole sequestration). Currently, it is unclear if cases of herbicide resistance via NTSR support the idea of extreme genetic parallelism. Previous research on the genetic basis of resistance to the herbicide glyphosate (active ingredient in the widely-used herbicide RoundUp) has focused largely on changes at the target-site (the enzyme 5-enolpyruvylshikimate-3-phosphate synthase; EPSPS). Conformational changes in EPSPS (due to mutations at the EPSPS locus) result in TSR.

Authors [see attached article] performed a population genomics screen, plus targeted exome re-sequencing, to uncover potential genetic mechanisms of glyphosate resistance in the common morning glory; they wished to determine if genetic parallelism underlies the (repeated) evolution of resistance across numerous resistant populations. Authors found no evidence for changes in the EPSPS gene, but instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxication — (cyt. P450s, ABC transporters, and glycosyltransferases) —were enriched and exhibited signs of selective sweeps. One region under selection showed parallel changes across all resistant populations studied; other regions exhibited signs of divergence. Therefore, whereas it appears that the mechanism of resistance in this species is likely the same among resistant populations, authors found patterns of both similar, and divergent, selection — across separate resistant populations at particular loci. 😊


PLoS Genet Feb 2020; 16: e1008593

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AKI in SARS-CoV-2 infection

The letter below is a request to any GEITP clinicians (pulmonologists, cardiologists or GI specialists) who know of someone who is, or who are, treating a substantial number of COVID-19 patients. Professor Soleimani ( Univ of New Mexico in Albuquerque.) is looking for potential collaborators with whom his group might interact.

Dear Colleagues,

The attached article is a commentary and emphasizes the role of kidney proximal tubule-specific priming proteases, and the fact that they are different from those in alveolar cells in mediating acute kidney injury (AKI) in SARS-CoV-2 infections. Trying to exploit the proximal tubule cell milieu and molecular machinery to combat SARS-CoV-2 invasion of kidney tubular cells — is a challenging task. 😊

We are currently doing research on some of these pathways in cultured kidney proximal tubule cells and are looking to interact with clinicians (pulmonologists, cardiologists or GI specialists) who are treating a substantial number of COVID 19 patients. Any suggestions would be appreciated.

Warm regards,


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Estimating excess mortality in people with cancer and multimorbidity in the COVID-19 emergency

The attached manuscript by a British group was embargoed until this morning. Appearing today on ResearchGate, this is a COVID-19 rapid communication, a preprint (Fred’s favorite kind, not peer-reviewed). Conclusions are that the COVID-19 emergency in England could result in at least 20% more deaths — over the next 12 months — in those who have been newly diagnosed with cancer. As these GEITP pages have mentioned before, while focusing on prevention of transmission of the virus, patients with underlying conditions are not receiving sufficient priority attention concerning their underlying serious medical conditions.

Authors [see attached preprint] used data from the health records (of more than 3.5 million patients in England). The study estimates that — pre-COVID-19 — ~31,354 newly diagnosed cancer patients would die within a year in England. As a result of this pandemic, authors estimate that there could be at least 6,270 additional deaths in newly diagnosed cancer patients alone, and this number could rise to an estimated 17,915 additional deaths if all people currently living with cancer are considered. The researchers analyzed recent weekly data from major cancer centers in the UK and found a 76% decrease in urgent referrals from general practitioners for people with suspected cancers and a 60% decrease in chemotherapy appointments for cancer patients, as compared to pre-COVID-19 levels.

The paper also models publicly available US data and shows an additional 33,890 deaths in the US in newly diagnosed cancer patients over the next year. The study estimates that pre-COVID-19, ~169,433 newly diagnosed cancer patients would die within a year in the US. The senior author added: “The overall impact of the COVID-19 emergency on deaths in cancer patients could be substantial. There are many factors operating here — including rapid changes to diagnosis and treatment protocols, social distancing measures, changes in people’s behavior in seeking medical attention, and the economic impact of COVID-19 — as well as deaths directly due to COVID-19 infection.”

Professor Mark Lawler said: “We applied our model to new cancers in the UK and the US, using publicly available data. The results are concerning. We believe countries need to rapidly understand how this pandemic is affecting cancer outcomes; otherwise, we risk adding cancer and other underlying health conditions to the escalating death toll of the COVID-19 pandemic.” This research provides a comprehensive picture of how patients living with a range of different cancers are affected by other (often treatable) long-term conditions — (including cardiovascular disease, hypertension, obesity and diabetes). Nearly eight out of ten of the additional deaths in people with cancer are estimated to occur in people with one or more of these long-term conditions.

The lead author stated: “Our findings demonstrate the serious potential for unintended consequences of the response to the COVID-19 pandemic, which may have a negative impact on patients with cancer and other underlying health conditions. It is vital that these patients are recognized as being vulnerable and their care is managed appropriately.” This research demonstrates the value to cancer patients, the wider public, and decision-makers —when trusted professionals use our patient data to help decide the best course of action. It also highlights the urgent need to be able to analyze these data quickly and accurately — to inform and influence current events.


ResearchGate 28 Apr 2020; Lai et al. | Excess deaths in cancer and multimorbidity | pp 1–10

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The race for coronavirus vaccines: a graphical guide

These diagrams came out today in Nature Rev Drug Disc http://doi.org/ggrnbr (2020). These are great diagrams. 😊 Contributed by a fellow GEITP-er, for everyone to share. If anyone wants a pdf of the entire paper, please ask.

The race for coronavirus vaccines: a graphical guide

Eight ways in which scientists hope to provide immunity to SARS-CoV-2 .
Ewen Callaway

More than 90 vaccines are being developed against SARS-CoV-2 by research teams in companies and universities across the world. Researchers are trialling different technologies, some of which haven’t been used in a licensed vaccine before. At least six groups have already begun injecting formulations into volunteers in safety trials; others have started testing in animals. Nature’s graphical guide explains each vaccine design.

A graphic that shows how the body develops immunity to coronavirus.

Graphics: Nik Spencer/Nature
SARS-CoV-2 vaccines: a variety of approaches

All vaccines aim to expose the body to an antigen that won’t cause disease, but will provoke an immune response that can block or kill the virus if a person becomes infected. There are at least eight types being tried against the coronavirus, and they rely on different viruses or viral parts.

A graph that shows the number of coronavirus vaccines in development.

Sources: Nature analysis based on: WHO COVID-19 Vaccine Landscape/Milken Institute COVID-19 Treatment and Vaccine Tracker/T. Thanh Le et al. Nature Rev. Drug. Disc. http://doi.org/ggrnbr (2020)/F. Amanat & F. Krammer Immunity 52, 583–589 (2020)/W. Shang et al. npj Vaccines 5, 18 (2020).
Virus vaccines

At least seven teams are developing vaccines using the virus itself, in a weakened or inactivated form. Many existing vaccines are made in this way, such as those against measles and polio, but they require extensive safety testing. Sinovac Biotech in Beijing has started to test an inactivated version of SARS-CoV-2 in humans.

A graphic that shows how weakened or inactivated coronavirus can be used in a vaccine.
Viral-vector vaccines

Around 25 groups say they are working on viral-vector vaccines. A virus such as measles or adenovirus is genetically engineered so that it can produce coronavirus proteins in the body. These viruses are weakened so they cannot cause disease. There are two types: those that can still replicate within cells and those that cannot because key genes have been disabled.

A graphic that shows how viral vectors containing coronavirus genes can be used in a vaccine.
Nucleic-acid vaccines

At least 20 teams are aiming to use genetic instructions (in the form of DNA or RNA) for a coronavirus protein that prompts an immune response. The nucleic acid is inserted into human cells, which then churn out copies of the virus protein; most of these vaccines encode the virus’s spike protein.

A graphic that shows how coronavirus genetic material can be used in a vaccine.
Protein-based vaccines

Many researchers want to inject coronavirus proteins directly into the body. Fragments of proteins or protein shells that mimic the coronavirus’s outer coat can also be used.

A graphic that shows how coronavirus proteins can be used in a vaccine.
Industry trials

More than 70% of the groups leading vaccine research efforts are from industrial or private firms. Clinical trials start with small safety studies in animals and people, followed by much larger trials to determine whether a vaccine generates an immune response. Researchers are accelerating these steps and hope to have a vaccine ready in 18 months.

A graph that shows the types of developers from different regions of the world that are developing a coronavirus vaccine.

Source: Fig. 2 in T. Thanh Le et al. Nature Rev. Drug. Disc. http://doi.org/ggrnbr (2020).

28 APRIL 2020

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Personalized Prognostic Prediction of Treatment Outcome for Depressed Patients in a Realistic Psychiatric Hospital Setting: A Comparison of Machine-Learning Approaches

This topic follows our gene-environment interactions theme. The “environmental signal” is the type of treatment, and the “response” (undoubtedly reflected in the expressivity of hundreds of genes) is “clinical outcome” in the patient. Research on “how to predict treatment outcome” for major depression disorder (MDD) patients has largely been carried out in randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of study clinicians. The extent — to which findings from such studies can be generalized to realistic psychiatric settings — is unclear. Authors [see attached article] sought to predict MDD outcomes for patients seeking treatment, within an intensive psychiatric-hospital setting, while comparing the performances of a range of 13 machine-learning approaches.

MDD patients (N = 484; ages 18–72; 89% Caucasian) — receiving treatment within a psychiatric-hospital program delivering pharmacotherapy and cognitive-behavioral therapy — were divided into a “training sample” and a “holdout sample”. Within the training sample, 51 pretreatment variables were submitted to 13 machine-learning algorithms to predict (via cross-validation) posttreatment (Patient Health Questionnaire–9) depression scores. The best performing modeling-approach (lowest mean-squared error; MSE) from the training sample was then selected to predict outcome in the holdout sample.

The best performing model in the training sample was discovered to be “elastic net regularization” (ENR; MSE = 20.5, R2 = 0.28) — which then was found to have a comparable performance in the holdout sample (MSE = 11.26; R2 = 0.38). Authors found 14 pretreatment variables that predicted outcome. To demonstrate “translation of the ENR model” to personalized prediction of treatment outcome, a patient-specific prognosis calculator is presented [see article for details]. Informed by pretreatment patient characteristics, such predictive models could be used to communicate prognosis to clinicians and to guide treatment planning. Identified predictors of poor prognosis might suggest important targets for drug intervention.

Knowledge of which patients are likely to exhibit a poor outcome may have important clinical implications regarding treatment recommendations (e.g. a more intensive, alternative or combination treatment) and can inform more careful symptom and treatment progress monitoring. In the present study, authors {see attached article] used machine-learning to develop predictions of treatment outcome for MDD patients seeking treatment in a “real-world” psychiatric hospital clinic. A prognosis calculator was shown to be successfully developed, which generates personalized predictions of treatment outcome for each individual MDD patient. 😊


J Consult Clin Psychol Jan 2020; 88: 25-38

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To interject a bit of levity into these ongoing discussions of the SARS-CoV-2 virus causing COVID-19 disease in some patients — this short youtube video was offered by one of our GEITP-ers. [The language of the video is Australianese, so THANKFULLY there are closed captions at the bottom, so that those of us in the rest of the world can understand him.] 😉


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Physicians now hesitant to treat COVID-19 as a high-altitude pulmonary edema (HAPE) syndrome

To follow-up on the previous claims that COVID-19 “might be more closely related to a high-altitude pulmonary edema (HAPE) — than an acute respiratory distress syndrome (ARDS) presentation” — knowledgeable experts [see article from Medscape below] are saying that each patient is heterogeneous (i.e. there exist genetic differences in response to this virus, just as there is for every other response to an environmental signal) and must be treated individually. The anxiety and frustration of the pandemic is pushing many to exploit social media with way too many scientifically-inaccurate anecdotal stories — which are simply confusing the entire population (including those in Washington DC, trying desperately to create “government policy based on scientific facts).

The social media wants answers and they want them immediately, but this is not possible. We’ve seen this for 35+ years with the global warming hysteria. The same has occurred with the Linear No-Threshold (LNT) policy for cancer-risk assessment, which has been going on without solid scientific facts since the 1960s. If a problem is extremely complex, then it requires much further scientific experimentation before solid facts can be teased out; to create government policy — before all facts are in — is simply irrational, as well as unnecessarily expensive to the taxpayer.

The same problem (worldwide) is being seen in “the urgent testing dilemma.” We want to know who is, and who is not, contagious (and therefore able to return to the workplace), and — if once a person has been infected with SARS-Cov-2 — does that create “immunity” (from being infected a second time), and, if there is immunity, how long does it last? There is no vaccine; an effective vaccine <> available by mid-2021, but there is no guarantee a vaccine will ever be successful for this virus.

There are ~90 “tests on the market” worldwide, I think only four approved by the FDA, but what can the tests tell us? We know SARS-CoV-2 infection causes an early rise in IgM antibodies, which lasts a few days; then a rise in IgG antibodies lasts for maybe 10-25 days. However, many individuals are exhibiting no sero-positive response, even after being infected with the virus…!! Elevated IgA levels sometimes interfere with increases in the IgM and IgG levels. Therefore — what do these “antibody tests” tell us — about who is contagious and when or for how long, and who is immune to future SARS-Cov-2 infection? Answer: Nothing yet — for certain… ☹ If Jonathan Bernstein, or any other knowledgeable expert, has scientific facts to refute anything stated here, all such comments are welcome. 😊

Physicians Push Back on Treating COVID-19 as HAPE

Sharon Worcester

April 21, 2020

· For Luanne Freer, MD, an expert in high-altitude pulmonary edema (HAPE) and founder and director of Everest ER, a nonprofit seasonal clinic at the Mt. Everest base camp in Nepal (elevation, 17,600 ft), a sudden flurry of messages and questions she received about a possible COVID-19/HAPE link was startling.

“That’s why it kind of poked me in the eye,” she said, referencing her extensive experience treating HAPE, which she described as a pressure-related phenomenon. “My goodness, they are so completely different.”

Dr Luanne Freer

Dr. Freer, an emergency physician, reached out to several pulmonary intensivists with experience treating both HAPE and COVID-19 to gauge their reactions, and within 36 hours, they had drafted their response. In the commentary, published in High Altitude Medicine & Biology, the clinicians note that the comparison between HAPE and COVID-19 is potentially risky.

“As a group of physicians who have in some cases cared for patients with COVID-19 and in all cases cared for patients with HAPE and studied its pathophysiology and management, we feel it important to correct this misconception, as continued amplification of this message could have adverse effects on management of these patients,” they wrote.

The suggestion that COVID-19 lung injury sometimes looks more like HAPE than like acute respiratory distress syndrome (ARDS) appeared in a journal review article in late March and was put forth by medical professionals on social media where it gained traction in recent weeks and was amplified in multiple media outlets, including this one.

“With COVID-19, we don’t understand everything that’s going on, but we know for sure it’s an inflammatory process – not a pressure-related problem,” Dr. Freer said. “I thought … this could be so dangerous to load the medicines that we use when we’re treating HAPE onto patients with COVID-19.”

The pathophysiological mechanisms in HAPE are different than those in other respiratory syndromes, including those associated with COVID-19, said Andrew M. Luks, MD, of the UW Medicine, Seattle, and the first author on the commentary.

“HAPE is a noncardiogenic form of pulmonary edema, as are ARDS due to bacteria or viral pneumonia, re-expansion pulmonary edema, immersion pulmonary edema, negative pressure pulmonary edema, and neurogenic pulmonary edema,” Dr. Luks, Dr. Freer, and colleagues wrote in the commentary, explaining that all of these entities cause varying degrees of hypoxemia and diffuse bilateral opacities on chest imaging. “Importantly, in all of these cases, edema accumulates in the interstitial and alveolar spaces of the lung as a result of imbalance in Starling forces.”

A difference between these entities, however, is “the mechanism by which that imbalance develops,” they noted.

The excessive and uneven hypoxic pulmonary vasoconstriction that leads to a marked increase in pulmonary artery pressure, subsequent lung overperfusion, increased pulmonary capillary hydrostatic pressure, and leakage of fluid from the vascular space into the alveolar space as seen in HAPE, is a “fundamentally different phenomenon than what is seen in COVID-19-related ARDS, which involves viral-mediated inflammatory responses as the primary pathophysiological mechanism,” they added.

The authors described several other differences between the conditions, ultimately noting that “understanding the distinction between the pathophysiological mechanisms of these entities is critical for patient management.”

In HAPE, supplemental oxygen alone may be sufficient; in COVID-19, supplemental oxygen may improve hypoxemia but won’t resolve the underlying inflammation or injury, they explained, adding that “only good supportive care including mechanical ventilation, quite often for long periods of time, allows some patients to survive until their disease resolves.”

Further, HAPE can be prevented or treated with pulmonary vasodilators such a nifedipine or sildenafil, which decrease pulmonary artery pressure and, as a result lower pulmonary capillary hydrostatic pressure, they said.

Use of such medications for COVID-19 might decrease pulmonary artery pressure and improve right ventricular function in COVID-19, but “by releasing hypoxic pulmonary vasoconstriction and increasing perfusion to nonventilated regions of the lung, they could also worsen ventilation-perfusion mismatch” and thereby worsen hypoxemia, they explained, adding that the treatments can also cause or worsen hypotension.

Efforts to share observations and experience are important in medicine, but sometimes, as in this circumstance, “they get out there, spread around by social media – like a brushfire almost – and get [unwarranted] face validity,” Dr. Luks said, noting that in response to information circulating about COVID-19 and HAPE, he has already heard medical professionals floating the idea of treating COVID-19 with treatments used for HAPE.

It’s true that some COVID-19 lung injury cases are behaving differently than typical ARDS, he said, adding that presentation can vary.

“But trying to equate HAPE and COVID-19 is just wrong,” he said. “HAPE and COVID-19 may share several features …but those are features that are shared by a lot of different forms of respiratory failure.”

In a recent video interview, WebMD’s chief medical officer John Whyte, MD, spoke with a New York City physician trained in critical care and emergency medicine, Cameron Kyle-Sidell, MD, who raised the need to consider different respiratory protocols for COVID-19, noting that standard protocols were falling short in many cases.

“What we’re seeing … is something unusual, it’s something that we are not used to,” Dr. Kyle-Sidell of Maimonides Medical Center said in that interview, stressing that the presentation differed from that seen in typical ARDS. “The patterns I was seeing did not make sense.”

Like others, he noted that COVID-19 patients were presenting with illness that clinically looked more like HAPE, but that the pathophysiology is not necessary similar to HAPE.

At around the same time, Luciano Gattinoni, MD, of the Medical University of Göttingen in Germany and colleagues, published a letter to the editor in the American Journal of Respiratory and Critical Care Medicine stressing that the ARDS presentation in COVID-19 patients is atypical and requires a patient physiology–driven treatment approach, rather than a standard protocol–driven approach. Dr. Gattinoni and colleagues suggested that instead of high positive end-expiratory pressure (PEEP), physicians should consider the lowest possible PEEP and gentle ventilation.

Dr. Luks agreed that “some patients with COVID-19 do not have the same physiologic derangements that we see in a lot of other people with ARDS.”

“[Dr. Gattinoni] is making the point that we need to treat these people differently … and I think that’s a valid point, and honestly, that’s a point that applied even before COVID-19,” he said. “Most of the things that we see in clinical practice – there’s a lot of heterogeneity between patients, and you have to be prepared to tailor your therapy in light of the differences that you’re picking up from your observations at the bedside and other data that you’re getting on the patient.”

The main concern Dr. Luks and his coauthors wanted to convey, they said, is making sure that the anecdotal experiences and observations of clinicians struggling to find answers don’t spiral out of control without proper vetting, thereby leading to patient harm.

“In this challenging time, we must identify the best means to care for these critically ill patients. That approach should be grounded in sound pulmonary physiology, clinical experience and, when available, evidence from clinical studies,” they concluded.

Dr. Luks and Dr. Freer reported having no financial disclosures.

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We are all related to Charlemagne ??

These GEITP pages have often discussed evolution, and quite often evolution of Homo sapiens during the Great Human Diaspora (migrations out of Africa, multiple times, and repeated admixture of sublines). Today, even seemingly unrelated humans are distant cousins to each other — because all members of a species are related to each other through a vastly subdivided family tree (their pedigree). We can follow traces of these relationships in genetic data, when individuals inherit shared genetic material (stretches of DNA) from a common ancestor; much interest has been generated recently by advances in genealogy companies (e.g. ancestry.com, 23andMe.com, heritage.com, etc.). Traditionally, population genetics has studied the distant bulk of these genetic relationships, which in humans typically date from hundreds of thousands of years ago.

Although most such genetic relationships among individuals are very old, some individuals can be shown to be related on far shorter time scales. Indeed, given that each individual has 2n ancestors from n generations ago, theoretical considerations suggest that all humans are related genealogically to each other over surprisingly short time scales (e.g. aren’t Meryl Streep, Kim Jong il, Robert De Niro, and Michelle Obama all ‘second cousins, once-removed’?). We are usually unaware of these close genealogical ties, because few of us have knowledge of family histories more than a few generations back; these ancestors often do not contribute any genetic material to us. However, in sufficiently large samples, we can hope to identify genetic evidence of more recent relatedness, and so obtain insight into the population history of the past tens of generations. Authors [see attached article & editorial] investigated such patterns of recent relatedness in a large European dataset (therefore, this study is about Caucasians, not the other four main groups)

Events that likely had significant impact on modern European relatedness include — the Neolithic expansion of farming, the Roman Empire, and the more recent expansions of the Slavs and Vikings; our current understanding of these events is deduced from archaeological, linguistic, cultural, historical, and genetic evidence — with widely varying degrees of certainty. However, the demographic and genealogical impact of these events is still uncertain. Genetic data describing the breadth of genealogical relationships can therefore add another dimension to our understanding of these historical events. Authors used genomic data from 2,257 Europeans [in the Population Reference Sample (POPRES) dataset] to conduct a survey of recent genealogical ancestry — over the past 3,000 years at a continental scale. Authors identified 1.9 million shared long genomic segments, and used the lengths of these to infer the distribution of shared ancestors across

time and geography.

Authors [see attached article] show that a pair of modern Europeans, living in neighboring populations, share between 2 and 12 genetically common ancestors from the last 1,500 years — and upwards of 100 genetic ancestors from the previous 1,000 years. These numbers drop off exponentially. with geographic distance; however, since these genetic ancestors are a tiny fraction of common genealogical ancestors, individuals from opposite ends of Europe are still expected to share millions of common genealogical ancestors over the last 1,000 years.

There is also substantial regional variation in the number of shared genetic ancestors. For example, there are especially high numbers of common ancestors shared between many eastern European populations that date roughly to the Migration Period (which includes the Slavic and Hun expansions into that region). Some of the lowest levels of common ancestry are seen in the Italian and Iberian peninsulas, which may indicate different effects of historical population expansions and wars in these areas and/or more stably structured populations. Population genomic datasets have considerable power to uncover recent demographic history, and will allow a much fuller picture of the close genealogical kinship of individuals across the world. 😊


PLoS Biol Nov 2013; 11: e1001555 & editorial doi:10.1038/nature.2013.12950

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Paternal Δ9-Tetrahydrocannabinol Exposure in Rats Prior to Mating Elicits Deficits in Cholinergic Synaptic Function in Offspring

This topic involves gene-environment interactions. The environmental signal is Δ9-tetrahydrocannabinol exposure (THC) to the male rat prior to mating; response in the offspring’s brain to the signal represents changes in phenotype, probably reflecting epigenetic effects caused by the THC. Authors [see attached article] exposed males for 28 days to THC at “doses commensurate with moderate cannabis use in humans,” mated them to drug-naïve females, and then assessed biomarkers of acetylcholine (ACh) synaptic function in the offspring; evaluations were conducted longitudinally from adolescence through adulthood — so as to capture early and late stages of brain development and function; assessments were carried out in brain regions comprising all the major ACh projections and their corresponding cell bodies. Authors specifically focused on ACh systems — which provide essential inputs for learning, memory, reward, and mood.

Authors measured presynaptic high-affinity choline transporter SLC5A7 (hemicholinium-3, or HC3-binding), choline acetyl-transferase (ChAT) activity, and α4β2 nicotinic ACh receptor (nAChR) concentrations. SLC5A7 and ChAT are both constitutive components of ACh nerve terminals, but they differ in regulatory mechanisms and hence in their functional significance. ChAT is the enzyme that synthesizes ACh, but it is not regulated by nerve impulse activity, so that its presence provides an index of the density of ACh innervation. In contrast, HC3-binding to SLC5A7 directly affects neuronal activity. Thus, comparative effects on HC3-binding and ChAT enable characterization of both the concentration of ACh terminals and presynaptic impulse activity.

Authors then calculated the HC3/ChAT ratio as an index of presynaptic activity, relative to the number of cholinergic nerve terminals. Finally, the α4β2 nAChR is the most abundant subtype in mammalian brain and regulates the ability of ACh systems to release other neurotransmitters involved in reward, cognition, and mood. These indices have been used successfully to characterize the impact of diverse neurotoxicants and diseases on ACh systems: neuroactive pesticides, nicotine or tobacco smoke, polycyclic aromatic hydrocarbons and glucocorticoids — as well as terminal stages of Alzheimer disease.

Authors [see attached article] found that THC produces a dose-dependent deficit in HC3-binding (index of presynaptic ACh activity), superimposed on regionally-selective increases in ChAT activity (biomarker for numbers of ACh terminals). The combined effects produce persistent decreases in the HC3/ChAT ratio (index of impulse activity per nerve terminal). At the low THC dose (2 mg/kg/day), decreased presynaptic activity was partially compensated by up-regulation of nAChRs, whereas at the high THC dose (4 mg/kg/day), nAChRs were subnormal, an effect that exacerbates the presynaptic defect. Overlaid on these effects, either dose of THC also accelerated the age-related decline in nAChRs. These data provide evidence for adverse effects of paternal THC administration on neurodevelopment in subsequent offspring and further demonstrate that adverse impacts of drug exposure on brain development are not limited to effects mediated by the in utero chemical environment, but rather that vulnerability can even be created by exposures occurring prior to conception, and involving the father as well as the mother [wa-a-a-ay too cool, man]. 😉


Toxicol Sci Apr 2020; 174: 210-217

Posted in Center for Environmental Genetics | Comments Off on Paternal Δ9-Tetrahydrocannabinol Exposure in Rats Prior to Mating Elicits Deficits in Cholinergic Synaptic Function in Offspring