Polycomb epigenetic silencing is mediated by specific DNA-binding recruiters

Polycomb group (PcG) proteins are essential for regulation of cell identity and development in plants and animals. PcG proteins are organized into diverse Polycomb repressive complexes (PRCs), among which PRC2 is probably the best described and most evolutionarily con­served. The catalytic subunit of PRC2 is able to add three methyl groups to lysine 27 of his­tone H3 (H3K27me3) –– which modifies chromatin structure and, as a result, represses gene expression. The H3K27me3 mark deco­rates thousands of genes in different cell types at distinct developmental stages in plants and animals. The genome-wide distribu­tion of this mark shows that PRC2 activity is targeted to euchromatic genes that are dynamically regulated.

Although the composition and activity of PRC2 is well known, specificity of its tar­geting and regulation is complicated and less clear. PRC2 subunits, as well as the mechanisms for the recruitment of this complex to specific targets, were first identified in the fruit fly, Drosophila melanogaster. In the fly, PRC2 chromatin binding occurs through interactions with Polycomb repres­sive elements (PREs) –– DNA stretches of vari­able length formed by an array of short motifs, which can be located far from target genes and depend on formation of DNA loops to reg­ulate them. The presence and dis­tribution of these motifs within PREs seem to depend on the target gene. The existence of mammalian PREs is more ambiguous, and their definition and the com­ponents that integrate them are more contro­versial. Current models in mammals indicate a complex interplay between cis elements (motifs close to the target gene), DNA-binding factors, CpG islands (cytosine base, followed by a guanine base, in the DNA), and noncoding RNAs in PRC2 recruitment.

There are also open questions on how PRC2 proteins are anchored to chromatin (everything making up chromosomes) in plants. In the attached article and editorial, authors have now confirmed the genetic general action of tandem PRE–transcription factor complexes in anchoring PRC2 to spe­cific loci in the tiny mustand plant, Arabidopsis thaliana, indicating evolution­ary conservation of the mechanisms between insects and plants.

Authors identified transcription factor families that bind to these PREs, co-localize with PRC2 on chromatin, physically interact with and recruit PRC2, and are required for PRC2-mediated gene silencing. Two of the cis sequence motifs enriched in the PREs are cognate binding sites for the identified transcription factors and are necessary and sufficient for PRE activity. These intriguing findings indicate that PRC2 recruitment in the plant Arabidopsis relies, in large part, on binding of trans-acting factors (motifs distant from the target gene), to cis-localized DNA sequence motifs.

Nature Genetics Oct 2o17; 49: 1546–1552 [article] and pp 1416–1417 [editorial]

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NIH “completes” the atlas of human DNA differences that influence gene expression

This news is a little bit misleading –– because scientists will continue to uncover increasingly more information, with each passing month and year.

Wednesday, October 11, 2017

Sections of the genome, known as expression Quantitative Trait Loci (eQTL) work to control how genes are turned off and on.

Researchers funded by the National Institutes of Health have completed a detailed atlas documenting the stretches of human DNA that influence gene expression – a key way in which a person’s genome gives rise to an observable trait, like hair color or disease risk. This atlas is a critical resource for the scientific community interested in how individual genomic variation leads to biological differences, like healthy and diseased states, across human tissues and cell types.

The atlas is the culmination of work from the Genotype-Tissue Expression (GTEx) Consortium, established to catalog how genomic variation influences how genes are turned off and on.

“GTEx was unique because its researchers explored how genomic variation affects the expression of genes in individual tissues, across many individuals, and even within an individual,” said Simona Volpi, Ph.D, program director for GTEx at the National Human Genome Research Institute (NHGRI), who oversaw various parts of the project.

According to Dr. Volpi, there was previously no resource at the scale used by GTEx that enabled researchers to study how gene expression in the liver might be different than in the lung or heart, for example, and how those differences relate to the inherited genomic variation in an individual.

Researchers involved in the GTEx Consortium collected data from more than 53 different tissue types (including brain, liver and lung) from autopsy, organ donations and tissue transplant programs. These tissues came from a approximately 960 donors in total.

“GTEx depended entirely on families choosing to donate biosamples for research after the death of a loved one,” said Susan Koester, Ph.D., deputy director for the Division of Neuroscience and Basic Behavioral Science and GTEx program director at the National Institute of Mental Health (NIMH). “GTEx researchers are deeply grateful for this priceless gift.”

The project continues to house a biobank of collected tissue samples, as well as extracted DNA and RNA for future studies by independent researchers. The summary-level data are available to the public through the GTEx Portal(link is external), and the most recent release of the raw data has been submitted to the Database of Genotypes and Phenotypes (dbGaP), an archive of results from studies that investigate the genomic contributions to phenotypes (physical characteristics or disease states).

GTEx launched in 2010 and concluded in the summer of 2017. It was supported by the NIH Common Fund and administered by NHGRI, NIMH and the National Cancer Institute, all part of NIH.

As one example of how the atlas can be used, a new study published online in the journal Nature, describes the results of expression quantitative trait locus (eQTL) mapping in 44 different human tissues from 449 individuals. An eQTL is a small section of the genome that contributes to the differences in gene expression between genes and between individuals. Typically, eQTLs are identified by sequencing the genomes of genetically different individuals to determine the variation in the genome between those individuals. This is followed by determining how much each gene is being expressed. Lastly, the eQTLs are identified by establishing which specific variants are associated with differences in gene expression levels.

The authors of the study used GTEx data to catalog all known eQTLs in the human genome for the first time. As in the Nature study, GTEx data will help researchers understand the mechanisms of how genes are expressed in a variety of tissues, which will ultimately better inform our knowledge of how genes are mis-regulated in the context of disease. GTEx data can also be used to better understand the variations in gene expression that underlie differences among healthy individuals.

Although the GTEx project has officially wrapped up, plans for future work are already underway. An endeavor known as the Enhancing GTEx (eGTEx) project, which began in 2013, extends GTEx’s efforts by combining gene expression studies with additional measurements, such as protein expression. This work is being conducted on the same tissues as in the GTEx project, providing a richer resource that integrates the complexity of how our genomes function in biologically meaningful ways.

NIH completes atlas of human DNA differences that influence gene expression

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The Genotype-Tissue Expression (GTEx) Project: Associations between genetic variation and gene expression in healthy tissues — Part I

These articles represent the follow-up (the scientific data) to the GEITP email of October 16th [see far below]. How does the same DNA sequence, present in virtually every cell in the body, give rise to diverse tissues that have distinct functions? The Genotype-Tissue Expression (GTEx) Consortium aims to answer this question by using a strategy called expression quantitative trait loci (eQTL) mapping. This technique allows the researchers to generate a comprehensive catalog of associations between genetic variation and gene expression across many tissues in many individuals.

In the attached articles in this email and Part II in a second email (because the total size of all the articles is too large for some colleagues’ server to accept), the consortium presents the second phase of their project, and the largest survey of this type to date. Over the past two decades, considerable progress has been made toward understanding the molecular mechanisms that underlie dynamic gene-regulatory programs that control development, differentiation, and function in specific cell types. The outstanding challenge is to understand, and ultimately to predict, how genetic differences between individuals contribute to specific multifactorial traits –– including susceptibility to disease and drug efficacy and toxicity.

A large body of work has shown that genetic variants that drive inter-individual differences in complex traits are often found in non-protein-coding regions of the genome that might determine how and when genes are expressed. As a result, biologists have set out to catalog and understand how genetic variation in both coding and non-coding regions affects dynamic and tissue-specific gene-expression programs. The GTEx project, initiated in 2010, represents a coordinated attempt to begin to achieve this goal.

Nature 12 Oct 2o17; 550: 204–213 & 239–243 & 244–248 & 249–254 plus pp. 190–191 [editorial]

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The Genotype-Tissue Expression (GTEx) Project: Associations between genetic variation and gene expression in healthy tissues — Part II

These articles represent the follow-up (the scientific data) to the GEITP email of October 16th [see far below]. How does the same DNA sequence, present in virtually every cell in the body, give rise to diverse tissues that have distinct functions? The Genotype-Tissue Expression (GTEx) Consortium aims to answer this question by using a strategy called expression quantitative trait loci (eQTL) mapping. This technique allows the researchers to generate a comprehensive catalog of associations between genetic variation and gene expression across many tissues in many individuals.

In the attached articles in the first email and Part II in this email (because the total size of all the articles is too large for some colleagues’ server to accept), the consortium presents the second phase of their project, and the largest survey of this type to date. Over the past two decades, considerable progress has been made toward understanding the molecular mechanisms that underlie dynamic gene-regulatory programs that control development, differentiation, and function in specific cell types. The outstanding challenge is to understand, and ultimately to predict, how genetic differences between individuals contribute to specific multifactorial traits –– including susceptibility to disease and drug efficacy and toxicity.

A large body of work has shown that genetic variants that drive inter-individual differences in complex traits are often found in non-protein-coding regions of the genome that might determine how and when genes are expressed. As a result, biologists have set out to catalog and understand how genetic variation in both coding and non-coding regions affects dynamic and tissue-specific gene-expression programs. The GTEx project, initiated in 2010, represents a coordinated attempt to begin to achieve this goal.

Nature 12 Oct 2o17; 550: 204–213 & 239–243 & 244–248 & 249–254 plus pp. 190–191 [editorial]

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Meta-analysis of GWAS identifies 17 new Parkinson disease (PD) risk loci: key role for autophagy and lysosomal biology ??!!??

Parkinson disease (PD) is the second most common neurodegenerative disorder (Alzheimer disease being first). The prevalence of PD is 3–4% in individuals over 80 years of age. PD is characterized by loss of dopaminergic neurons in the substantia nigra and presence of Lewy bodies. These neuropathologies in affected individuals lead primarily to motor-related symptoms (e.g. finger dexterity, walking). Early-onset, familial PD (beginning at age 60 years or earlier) accounts for a small fraction of cases, but the identified associated genes, including LRRK2, GBA, and SNCA, provide insight into disease pathogenesis. For the later-onset, common form of PD, at least 24 loci have been associated at a genome-wide significant level with disease risk in individuals of European ancestry. The narrow-sense heritability (h2) explained by the confirmed PD risk loci is low (0.033); for those who do not know, h2 denotes “heritability index” –– where a trait that is 100% inherited is 1.0 and 100% environmental is 0.0%.

However, the heritability [i,e. the proportion of known variation among individuals in a given population due to genetic variation] explained by the 24 common variant loci is estimated at less than 23%, which suggests that additional loci with smaller effect-sizes remain to be discovered. Therefore, to search for additional loci, authors [see attached report] carried out a GWAS, comparing 6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of more than 13,000 PD cases and 95,000 controls at 9,830 overlapping variants. Authors then tested 35 loci (P <1 × 10–6) in a replication cohort of 5,851 cases and 5,866 controls [replication cohorts are mandated in order to publish in Nature Genetics and certain other high-end journals]. Authors identified 17 novel risk loci (P <5.0 x 10–8) in the combined analysis of 26,035 cases and 403,190 controls. Authors used a neurocentric strategy to assign candidate risk genes to the loci. In 29 of the 41 PD loci (i.e. 24 + 17 new variants), they identified protein-altering or cis–expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index variant. Their exciting findings strongly suggested a key role for autophagy and lysosomal biology [normal physiological processes that are involved in destruction of cells in the body; autophagy maintains normal functioning by protein degradation, as well as turnover of destroyed cell organelles for new cell formation –– seen especially during cellular stress] in PD risk, which suggests potential new drug targets for PD..! Nature Genetics Oct 2o17; 49: 1511–1516

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Breast cancer genetics revealed: 72 new mutations discovered by a large consortium comprising ~300 institutions around the world

This is a layman’s report on several published papers by researchers from ~300 institutions worldwide (the scientific reports will follow later). As GEITP has shared often on these pages, breast cancer is just another of many multifactorial traits –– contributed by hundreds (perhaps thousands of) genes (DNA variants) plus epigenetic factors plus environmental effects. The genetics field “has wised up considerably” over the past 25 years, i.e. when BRCA1 was discovered and touted as “THE breast cancer gene,” everyone later realized how naive that original thinking was at the time…

Breast cancer genetics revealed: 72 new mutations discovered in global study
By Susan Scutti, CNN
Mon October 23, 2017

Story highlights
The total number of known gene variants associated with breast cancer is now nearly 180
The new variants may identify a small proportion of women at 3-times increased risk of breast cancer

(CNN) The genetic causes of breast cancer just got clearer. Or maybe more complicated. Researchers from 300 institutions around the world combined forces to discover 72 previously unknown gene mutations that lead to the development of breast cancer. Two studies describing their work published Monday in the journals Nature and Nature Genetics.

The teams found that 65 of the newly identified genetic variants are common among women with breast cancer. The remaining seven mutations predispose women to developing a type of breast cancer known as estrogen-receptor-negative breast cancer, which doesn’t respond to hormonal therapies, such as the drug tamoxifen. The new discoveries add to previous research bringing the total number of known variants associated with breast cancer to nearly 180.

Beyond BRCA1 and BRCA2

The international team of 550 researchers across six continents, known as the OncoArray Consortium, included Professor Doug Easton of the University of Cambridge, who led the investigation. “Essentially, we used blood samples from a very large number of women (nearly 300,000), about half of whom had had breast cancer,” Easton explained in an email. Next, the researchers used the DNA from the samples to look for genetic mutations.

“Think of a gene as a very long strand of DNA,” said Dr. Otis Brawley, chief medical officer of the American Cancer Society, who was not involved in the research. DNA is made up of nucleic acids, and when a nucleic acid is incorrectly placed along the strand, this is referred to as a genetic mutation, noted Brawley. Take BRCA1 and BRCA2, two well-known genes that confer a high risk of breast cancer when they contain mutations.

There are 125,950 base pairs in the BRCA 1 mutation, noted Brawley. “Think of it as a 125,950 letter word,” said Brawley. “A mutation is a misspelling such that the gene cannot code the proper protein.” A gene that cannot code the proper protein leads to disease. According to the National Cancer Institute, 55% to 65% of women who inherit a BRCA1 mutation and around 45% of women who inherit a BRCA2 mutation will develop breast cancer by age 70.

However, the BRCA1 and BRCA2 risk mutations, which are present in less than 1% of women, explain only a fraction of all inherited breast cancers. The consortium came together, then, to discover the other causes of breast cancer susceptibility — the additional genetic mutations that can lead to this form of cancer.

Finding the other mutations

The researchers measured DNA at over 10 million sites across the genome, said professor Peter Kraft of Harvard T.H. Chan School of Public Health, a study author.

“At each of these sites, we asked whether the DNA sequence in women with breast cancer was different than that in women without,” said Kraft. “Because our study was so large, we could detect subtle differences between these two groups of women and be sure these differences were not due to chance.”

According to Jacques Simard, a study author and professor and researcher at Université Laval, Quebec City, the newly discovered mutations only slightly — by anywhere from 5% to 10% — increase a woman’s risk of developing breast cancer. But even though, individually, these mutations don’t have as big as an effect as BRCA1 and BRCA2 defects, there are many of them, so their “overall contribution is larger,” said Easton. An individual woman, then, may have two or more of these common smaller risk gene mutations, and so her risk for developing breast cancer increases due to their combined effects.

Kraft noted that “taken together, these risk variants may identify a small proportion of women who are at 3-times increased risk of breast cancer.” Women found to have a number of these smaller risk genetic mutations, then, would likely benefit from earlier mammography screening. Simard agreed, noting that it may be time to “adapt” breast cancer screening guidelines based on this information instead of basing mammography guidelines on age alone. By doing so, Simard said, “we will detect a higher number of breast cancers.”

Quantifying cancer risk

Brawley described the new research as “not exactly earth-shattering.” It is “most important for us nerds,” he said, but less so for the general public. These types of studies help experts identify mutations that “help us quantify the risk,” said Brawley. “It helps us figure out that a non-patient, often a relative of a cancer patient, is at risk and helps us quantify that risk.” Normal lifetime risk of breast cancer is 12.5% for women in the US, said Brawley.

Lisa Schlager, vice president of community affairs & public policy for the nonprofit FORCE(Facing Our Risk of Cancer Empowered), said past studies and evidence indicate that about 10% of breast cancers are hereditary. “This new information may mean that that estimate is low,” said Schlager. It is important for patients to know whether their cancer is due to an inherited genetic mutation because they may be at increased risk of other cancers or their treatment recommendations may differ based on that fact, said Schlager. “And their family members may be affected with the same mutation,” said Schlager.

Enabling personalized medicine

For the promise of personalized medicine to be realized, our government and health system need to “embrace the ability to use genetic information to tailor health care by providing affordable access to the needed screening and preventive interventions,” said Schlager. As it stands now, men with BRCA mutations as well as some women may not be covered for screening by their insurance in the US.

Brawley said “this type of genome-wide association study (GWAS) has, and is, being used to identify genes that are associated with increased risk of a number of diseases, including type-2 diabetes, schizophrenia, Alzheimer disease, stroke and heart disease.” “The same methodology can be used for other cancers,” said Easton. The screening method used by the consortium, the OncoArray, was designed to be used in many other cancer types, including prostate, ovary, colorectal and lung cancer, he said.

Simard added that the cost of the genetic screen is “quite cheap,” at less than $50 per individual. “We can use just a blood sample or saliva sample. It’s not difficult to obtain the material for a genetic analysis,” he added. Kraft said it was important to keep in mind that the study was conducted primarily among women of European ancestry. “For sure we have missed some variants associated with cancers that are common in some non-European populations but rare in Europeans,” said Kraft. To find these, cancer genetic studies in Africans, African Americans, Latinas, Chinese and other populations are ongoing, he added.

Easton commented that most of the newly identified variants “are in regions of the genome that regulate nearby genes.” These may someday serve as targets for new therapies or drugs to cure the disease. In the end, the most important lesson here is the fact that this research has been a collaborative effort, said Simard. “Scientists are not in competition against each other,” he said. “We are really working together to expedite and to accelerate the discovery.”

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GWAS identifies 112 new loci for body mass index (BMI), and suggesting that lymphocytes are relevant to body weight regulation and obesity …??

Obesity, which appears to be at least in part heritable, is a risk factor for various diseases. Numerous genome-wide association studies (GWAS) have identified more than 100 DNA loci associated with body mass index (BMI) –– the most commonly used measurement for obesity. However, these loci can explain only a fraction (~20%) of heritability [defined as the proportion of total variation among individuals in a given population due to genetic causes; this number can range from 0% (no genetic contribution) to 100% (all differences of a trait reflect genetic variation).] in Europeans. Therefore, most of the genetic components of BMI have not yet been discovered.

Interestingly, whereas most previous studies were individuals of European ancestry, GWAS in non-Europeans have identified different loci. Moreover, epidemiological surveys reported differences in BMI across ethnicities (e.g. the prevalence of obesity is lower in Asians than in Europeans and North Americans. In addition, Asians tend to develop diabetes with a lower BMI than Europeans. These differences suggest that analyzing different populations could yield further insight into the etiology of obesity. In my personal experience of having visited Japan during the past 35+ years, however, the incidence of obesity has exploded during this time period –– as anyone can observe, just by viewing the younger generation walking around the subway stations of Tokyo or a similar city. This suggests to me that –– in addition to DNA-sequence variants –– obviously epigenetic and environmental factors (e.g. Western diet and lifestyle) have helped immensely in causing this dramatic rise in BMI among the Japanese population.

To identify genetic loci associated with obesity and to gain more insight into body weight regulation, authors [see attached study] conducted a GWAS that included >173,430 Japanese volunteers. They then conducted a comprehensive integration with the previous GWAS of Europeans, functional annotations of the associated loci, and genetic correlation analyses between BMI and other complex diseases in humans. Authors found 85 loci significantly associated with obesity (P <5.0 x 10–8), of which 51 were previously unknown. They conducted trans-ancestral meta-analyses –– by integrating these results with results from a GWAS of Europeans and identified 61 additional new loci. In total, this study herein identified 112 novel loci, doubling the number of previously-known BMI-associated loci. By annotating associated variants with cell-type-specific regulatory markers, authors found enrichment of variants in CD19-positive cells (a marker on the surface of B-lymphocytes; a type of white blood cell). They also found significant genetic correlations between BMI and lymphocyte count (P = 6.46 × 10–8; rg = 0.18) and between BMI and multiple complex diseases. These intriguing findings provide genetic evidence –– suggesting that lymphocytes are relevant to body weight regulation, and these data might offer insights into the pathogenesis of obesity. Nature Genetics Oct 2o17; 49: 1458–1467

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Kidney-specific genetic control module (in mouse) governs endocrine regulation of the cytochrome P450 gene Cyp27b1 essential for vitamin D3 activation

The vitamin D endocrine system in mammals is very complex –– regulating mineral homeostasis via enzyme activities in the intestine, kidney, and bone. Metabolic activation of vitamin D3 to its hormonal form, 1a,25-dihydroxyvitamin D3 [1,25(OH)2D3], i.e. most potent ligand for the vit D receptor (VDR), occurs in kidney via the cytochrome P450 enzyme CYP27B1. Despite its importance in vitamin D metabolism, molecular mechanisms underlying regulation of the Cyp27b1 gene are have remained elusive during the past 3-4 decades. That is why we at GEITP believe the present study [see the attached fascinating full paper and editorial] represents a landmark breakthrough.

Authors identified a kidney-specific control module –– governed by a renal cell-specific chromatin structure located distal to the Cyp27b1 transcribed gene –– that mediates unique basal as well as parathyroid hormone (PTH)-, fibroblast growth factor-23 (FGF23)-, and 1,25(OH)2D3-mediated regulation of Cyp27b1 gene expression. Genomics researchers typically look PROXIMAL to any gene to see if regulatory modules can be identified; this finding [herein] is therefore rather unique because the regulatory module is distal to the transcribed gene.

Selective genomic deletion of key components within this regulatory module in mice results in loss of either PTH induction, or FGF23- and 1,25(OH)2D3-mediated suppression of Cyp27b1 gene expression; loss of PTH induction causes a debilitating skeletal phenotype, whereas loss of FGF23- and 1,25(OH)2D3-mediated suppression confers a quasi-normal bone mineral phenotype –– through compensatory homeostatic mechanisms involving the Cyp24a1 gene activity. Authors found that Cyp27b1 is also expressed at low levels in non-renal cells, in which transcription was modulated exclusively by inflammatory factors via a process that was unaffected by deletion of the kidney-specific module.

These results reveal that differential regulation of Cyp27b1 expression represents a mechanism whereby 1,25(OH)2D3 can fulfill separate functional roles –– first in the kidney to control mineral homeostasis, and second in cells outside the kidney to regulate target genes linked to specific biological responses. Authors conclude, and rightly so, that these mouse findings open new avenues for approaching studies of vitamin D metabolism and its involvement in therapeutic strategies for human health and disease. For example, it is long been known that mutations in the human CYP27B1 gene are responsible for vitamin-D-dependent rickets.

J Biol Chem 2o17; 292: 17541-17558 and pp. 17559–17560 [editorial]

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Linkage disequilibrium (LD)-dependent architecture of human complex traits curiously shows a mechanism of negative selection

This report summarizes a project of trying to further understand human complex diseases. WHY should this topic be of interest to gene-environment interactions? As we’ve often discussed in these GEITP pages, multifactorial traits include height, body mass index, type-2 diabetes, dementia, cancer, and even phenotypes such as drug toxicity and drug efficacy. Why do two workers respond differently when expsed to the same amount of an occupational hazardous chemical? Why do two patients respond differently to the same dose of a particular drug?

Linkage disequilibrium (LD) is defined as “the occurrence of some genes (or alleles, or DNA segment) near one another on the same chromosome, more often than would be expected by chance alone.” This “nonrandom association of alleles at different loci” is a sensitive indicator of population-genetic forces that structure a genome, that build the genetic architecture. Thus, the older the population, the less likely it will be –– that genes in the same neighborhood (along the same chromosome) will remain “linked” –– because of chromosomal cross-over events during meiosis of germ cells, i.e. which occur with each generation of offspring; two genes near one another in an older population would more likely show “low levels of LD,” and two genes near one another in a younger population would more likely show “high levels of LD.” Finally, “heritability” is defined as “an estimate of how much variation in a trait in a population is due to genetic variation between individuals in that population.”

Recent studies have implied LD-dependent genetic architecture of human complex traits, in which single-nucleotide variants (SNVs) of DNA with lower levels of LD have larger per-SNV heritability. Authors [see attached report] analyzed 56 complex traits (average N = 101,401) by extending stratified LD-score regression to continuous annotations. They determined that SNVs with Lower Levels of LD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations that are negatively correlated with Low LD, such as DNase I hypersensitivity sites. The remaining signal is largely driven by the authors’ finding that more recent common variants tend to have Lower Levels of LD and tend to explain more heritability (P = 2.4 × 10–104). Using the model of extreme discordance, authors found that the youngest 20% of common SNVs explain 3.9 times more heritability than the oldest 20% –– which is consistent with a mechanism of negative selection.

Nature Genetics Oct 2o17; 49: 1421–1427

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The Mistaken Birth and Adoption of the Linear Non-Threshold (LNT) Model: An Abridged Version

As these GEITP pages have discussed a number of times, the detective work of Ed Calabrese has uncovered, and expanded upon, one of the top deceptions of all time: the purported Linear No-Threshold (LNT) Model, based on incomplete and dishonest scientific data. Thus, the LNT Model has been used for at least six decades –– costing the taxpayers of Western Society countries billions of dollars to construct data based on fundamental errors in the original basic research. This has led to risk assessment policy that is fundamentally flawed, and has been so, since the 1950s.

In 1927, Hermann Joseph Muller incorrectly asserted that he had used x-rays to induce “gene” mutations in the fruit fly, Drosophila melanogaster. Although his original experiments were flawed and unverified, this interpretation was well received and widely accepted by the scientific community. In fact, Muller proceeded to win the 1946 Nobel Prize in Physiology or Medicine for his so-called “breakthrough” research finding. Both the 1930 “Proportionality Rule” of Muller, and the 1935 “LNT Single-Hit dose–response model” of Timofeeff-Ressovsky and colleagues, were offspring to Muller’s misguided conception of x-ray-induced “gene mutations”. Critics from the genetics community –– including Lewis J. Stadler, Barbara McClintock, and others –– argued convincingly that Muller’s idea lacked scientific proof and could be explained alternatively by mechanisms involving gross chromosomal deletions and aberrations, rather than mutations within specific genes.

Responding to such criticisms, Muller quickly conducted further research to support the accuracy of his conclusions on x-ray-induced “gene” mutations –– specifically that mutational responses were cumulative (i.e. that the total dose — and not dose rate — was important), irreversible, and linear with respect to dose. However, more experimentally rigorous studies were performed under the oversight of the Manhattan Project and produced results that seriously challenged Muller’s concept of “total dose”. Unfortunately, influential leaders of the U.S. radiation and genetics communities, including Curt Stern and Muller, chose to misrepresent and thereby marginalize the more carefully performed studies. The LNT Model was subsequently recommended by the U.S. National Academy of Sciences, the Biological Effects of Atomic Radiation I (BEAR I), the Genetics Panel in 1956, and subsequent adoption by regulatory agencies worldwide. The attached article summarizes substantial recent historical revelations of this history, which should profoundly challenge the standard and widely acceptable history of cancer risk assessment, showing multiple significant scientific errors and incorrect interpretations, mixed with deliberate misrepresentation of the scientific record by a subset of leading ideologically motivated radiation geneticists. These novel historical findings demonstrate that the scientific foundations of the LNT Single-Hit Model were seriously flawed, from the beginning, and should not have been adopted for cancer risk assessment.

Dose-Response: An International Journal Oct-Dec 2o17; pp 1–3

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