Daily Archives: December 1, 2021

The last decade has seen genetics and evolution grapple with its history; one composed of figures who laid the foundations of their field while also promoting vile racist, sexist, and eugenicist beliefs.

In her new book, The Genetic Lottery, Kathryn Paige Harden, professor of psychology at University of Texas at Austin, attempts the seemingly impossible task of showing that, despite a history of abuse, behavioral genetics is not only scientifically valuable but is an asset to the social justice movement.

In this attempt, she fails twice. For the first half of the book, Harden tries to transform the disappointment of behavioral genetics in the years following the Human Genome Project into a success that proves that genes are a major and important cause of social inequality, like educational attainment or income levels. In the second half, she tries to show that this information is not a justification for inequality, rather it is a tool to use in our efforts to make society more equitable and cannot be ignored if we wish to be successful. To say the least, this section too falls short. Harden refuses to engage with the history and trajectory of her field, and ultimately the science fails to uphold the idea that not considering genetic differences hinders our attempts to create a more equitable world.

In the book Misbehaving Science, sociologist Aaron Panofsky documents the history and progression of behavioral genetics, from its formal inception in the 1960s. Throughout its history behavioral genetics has responded to criticism in a variety of ways.

In 1969, the educational psychologist Arthur Jensen used behavioral genetics methods to argue that IQ gaps between white and Black Americans had genetic origins and, therefore, could not be remedied by educators or social policy. As criticism from mainstream geneticists and evolutionary biologists tied Jensen and behavioral geneticists to each other, the field attempted to hold a middle ground between Jensens racist conclusions and the belief that human behavioral genetics was fundamentally flawed. However, in this attempt to preserve their field from criticism, behavioral geneticists progressively defended the importance of race science research and adopted some core premises about the influence of genetic differences on the racial IQ gap.

In the following decades, Jensen and like-minded researchers like J. Philippe Rushton, Richard Lynn, and Linda Gottfredson received funding from the Pioneer Fund, an organization explicitly dedicated to race betterment. All the while, they were integrated into editorial boards of journals that published behavioral genetics work and treated as colleagues. Even mainstream behavioral genetics work like the Minnesota Study of Twins Reared Apart and the Texas Adoption Project would receive funding from the noxious Fund.

In attempts to justify their field against continued criticism, behavioral geneticists themselves used twin study results to argue social interventions would be ineffective. As Panofsky wrote:

This history, including behavioral genetics' own role in generating, promoting, and defending scientific racism and determinist views of genetics is completely absent from Harden's book. This history matters; it is the source of the isolation of behavioral genetics from mainstream genetics research. This isolation has produced the intellectual and ideologically stagnant lineage that Harden operates in.

These biases are most pronounced in the early chapters walking readers through the science, which often leads to an incomplete, misleading, or mistaken account of genetic research and behavior. Harden presents an argument about the major causal role of genetic differences. These results span decades, including twin studies, and recent developments like genome-wide association studies (GWAS), polygenic scores (a single value combining individual estimated effects of genome-wide variations on a phenotype), and genomic analyses of siblings. Unfortunately, Harden often gives these results in such a misleading way that it obscures how damaging they actually are to her own core thesis.

For example, Harden extols sibling analyses as unassailable evidence of independent, direct genetic causation free of biases found in other methods. While its true that polygenic scores from sibling analyses resolve substantial problems that sometimes create inaccurate associations between DNA and a phenotype, Harden fails to mention several key differences between these sibling-based methods and other genomic or twin-based methods. It is rarely stated clearly that these family methods produce much smaller estimates of genetic effect, often nearly half the size as population-based methods, making the 13% variance explained by current education polygenic scores a likely overestimate. Harden also fails to mention that a commonly used method employed does not fully eliminate the problems from population structure or that estimates from siblings can still include confounding effects that create correlations between genes and environment.

Even worse, Harden moves between the less biased, but smaller, results from sibling methods to the more biased but larger estimates from population-based polygenic scores without being clear this is what she is doing. This happens frequently when discussing research claiming that educational polygenic scores substantially explain differences in income. The result is Harden obscures the fact that more reliable techniques result in lower predicted genetic effects. Readers may be wrongfully led to believe genetic effects are both large and reliable when in reality they are more often one or the other.

Hardens failure to engage with critics of behavioral genetics, often from the political left, veers between simple omissions and outright misrepresentation. This treatment is in stark contrast to how she treats biological determinists on the political right. The work of Charles Murray, the co-author of The Bell Curve, which claimed that differences in IQ scores between the rich and poor were genetic, and whose research aligns neatly with Hardens, is described as mostly true and his political implications are lightly challenged. The most prominent critic of behavioral genetics, Richard Lewontin, gets much rougher treatment.

In one of the three cases in which Harden bothers to mention Lewontins decades-long engagement with behavioral genetics, she gets it wrong, claiming that Lewontin merely said that heritability is useless because it is specific to a particular population at a particular time. In reality, Lewontin showed why the statistical foundation of heritability analyses means it is unable to truly separate genetic and environmental effects. Contra Hardens characterization of her opponents, Lewontin recognized genetic factors as a cause of phenotypes; however, he stressed their effects cannot be independent of environmental factors and the dynamics of development.

Harden implies that giving people access to equal resources increases inequality and genetic influence. Lewontin explained why the outcome of equalizing environments precisely depends on which environment you equalize. As a toy example, a cactus and a rose bush respond differently to varying amounts of water. Giving both plants the same, small, volume of water is good for the cactuss health and bad for the rose, giving both a larger volume of water is bad for the cactus and good for the rose. Equalized environments regardless of quality can reduce or increase inequality and can reduce or increase the impact of genotypic differences depending on the environment and the norm of reaction for a trait and set of genotypes. Heritability analyses cannot provide insight on this distribution or nature of genotype and environment interactions. These detailed, quantitative, and analytic arguments are entirely ignored by Harden.

In her story, people on the political left are ideologically driven to oppose behavioral genetics because they believe it invalidates their desire to ameliorate inequality. In the powerful book-length criticism of behavioral genetics, Not in Our Genes, Lewontin, with neuroscientist Steven Rose and psychologist Leon Kamin, all socialists, defy Hardens characterization of her critics from the left, writing:

They further write:

Not in Our Genes criticizes biological determinism for oversimplifying the processes that create diversity in the natural world. And the ways that biological determinism is employed for political and ideological reasons by people like Arthur Jensen, Daniel Patrick Moynihan, or Hans Eysenck, to undermine movements for social and economic equality on the basis of biological data. Lewontin, Kamin, and Rose did not oppose biological determinism simply on ideological grounds. They knew there was no true threat to egalitarian beliefs posed by biological data if one properly understands biology in a non-determinist way. Instead, they wanted to move beyond just a scientific critique and provide a social analysis of why the mistakes of biological determinism are made, persist, and gain in popularity. They write:

This lack of meaningful engagement with critics is not just poor scholarship, it weakens Hardens case. Problems arise with Hardens discussion of heritability, for example, which would be remedied with a genuine engagement with critics from mainstream genetics and evolutionary biology. Harden takes a hardline position that heritability is a measure of genetic causation within a sampled population; however, despite her attempt over two chapters to build this case, she is still fundamentally mistaken about the concept.

Early work in plant breeding and genetics can help shed light on the source of this confusion. The pre-eminent statistical geneticist, Oscar Kempthorne, in a 1978 critique of behavioral genetics, wrote that the methods employed by the field can tell us nothing about causation because all they really represent is simply a linear association between genetics and phenotypes, without any further ability to connect the two to each other.

The extent to which correlations can be interpreted as causation depends on properly controlling for confounding variables. In the context of heritability, this means that genetics and environment need to be independent of each other, but this cannot be the case without direct experimental manipulation. In fields like plant breeding, it is possible to experimentally randomize which environments a plant genotype experiences, and genetically identical plants can be put in different environments for extra control, so these inferences are safer to make. In human genetics, however, this is not possible even with the sibling and twin methods Harden focuses on. These processes that complicate causal interpretation of heritability estimates have been discussed ad nauseum by other behavioral geneticists, which is why Harden is one of the few who comes to her conclusions.

One final glaring omission worth noting occurs in Hardens chapter on race and findings of behavioral genetics. Here, Harden does an admirable job trying to prevent the misapplication of behavioral genetics to questions of racial differences. Surprisingly absent though is the fact that across a variety of studies, genetic variation is much larger within races compared to between races. This finding undermines core perceptions about the biological nature and significance of race. It also has important implications for our assumptions about the role of genetics in phenotypic differences between races, namely that they will be small to nonexistent. One could speculate the omission is because the finding was from none other than Richard Lewontin. This case is particularly problematic because in randomized control trials, biology classes emphasizing Lewontins findings have shown very strong evidence of reducing racial essentialism, prejudice, and stereotyping. Few science education interventions against racism and prejudice have such strong evidence in their favor.

Above all, Harden desperately wants to impart one idea in the first part of the book: genes cause social inequality. Here she argues for causation as differences makers in counterfactual scenarios. In other words, X causes Y if the probability of Y occurring is different were X not to happen. As Harden notes, experimental science adopts a similar and in ways stronger, interventionist theory of causation, based around experimental interventions. Here X is said to cause Y if there is a regular response of Y to an intervention on X.

Under the interventionist theory, Hardens account of genetic causation runs into trouble. First, it requires us to be able to isolate a specific property on which we can intervene. This is possible in cases of simple genetic disorders with clear biological mechanisms and short pathways from gene to trait, like sickle cell anemia or Tay-Sachs. However, this doesnt work for behaviorally- and culturally-mediated traits involving large numbers of genes, with small effects and diffuse associations between genetic and non-genetic factors. There is simply no method to isolate and intervene on the effects of specific genetic variants that holds environmental factors constant in a way we would normally recognize as an experimental intervention. This applies still to the sibling analyses that Harden tries to portray as randomization experiments. Contrary to one of Hardens more bizarre claims, meiosis does not approximate a randomized experiment. All it does is randomize genotypes with respect to siblings, it does not randomize environments experienced by genotypes. Our broad array of social and cultural institutions still acts in a confounding way. Instead, we just have a polygenic score, which is more a statistical construct than a tangible property in the world.

Second, for Hardens causal claims to hold weight, genetic and environmental factors must be distinct components that are independently disruptable. This reflects what the philosopher John Stuart Mill called the principle of the composition of causes, which states that the joint effect of several causes is identical with the sum of their separate effects. At the core, Harden assumes that genetic and environmental influences on human behavior are independent and separable. To say the absolute least, this is a highly dubious assumption. Based on the arguments from critics like Lewontin and the work from research programs like developmental systems theory, there is very good reason to think that biological systems are not modular, especially in the case of educational attainment. Genetic and environmental influences interact throughout development, the interactions are dynamic, reciprocal, and highly contingent. It simply isnt plausible to estimate the independent effect of one or the other because they directly influence each other.

A further weakness of Hardens book is that just because genes make a difference in phenotype, it does not mean that genes are even relevant to the analysis of these phenotypes. In reality, Lewiss account of causation, that X is a cause if a different outcome would have occurred in the absence of X, can be a pretty low bar, and the causes it identified may not be very relevant. An obviously absurd example is that the argument could be made that the sun caused me to wake up this morning since it is the origin of the trophic cascade that nourished my body enough to continue necessary biological functions. Under Lewis account, the sun is a cause of my waking up, but its hardly a relevant or informative cause compared to my alarm clock or to the bus I need to catch at 8:35am.

In Biology as Ideology, Lewontin discusses the causes of the disease tuberculosis. He notes that in medical textbooks the tubercle bacillus, which gives people the disease when infected, is the cause of tuberculosis. Lewontin writes that this biological explanation is focused on the individual level and treats the biological sphere as independent from external causes related to the environment or social structure. While we can surely talk about the role of the tubercle bacillus in causing the disease we can also talk about the social conditions of unregulated industrial capitalism and its role in causing outbreaks and deaths by tuberculosis and can gain far more insight by analyzing the causes of tuberculosis in that way.

This distinction of whether a cause is relevant for particular social and scientific issues becomes a problem for Harden in the climax of her book where she tries to convince the reader that genetic information is a crucial tool for addressing social inequality.

One example given by Harden is that children who perform well but are in poor schools are able to achieve less, and that poor people with higher education end up making less money than rich people in the same fields. These findings are neither novel nor do they require the use of potentially misleading genetic data. While Harden tries to defuse right-wing arguments about shortcomings of social science research, this isnt a given. As research Harden herself presents shows, results from behavioral genetics bolster the far right and they regularly share this research to promote their beliefs and challenge egalitarian policies. Instead of engaging with this bad-faith criticism from the right, we can simply disregard them, just as Harden disregards their co-option of her field of research.

Finally, Harden expresses a general concern that social science and psychological studies are plagued by genetic confounding, that is the correlations they observe are actually due to unconsidered genetic forces that relate an individual to their outcome (i.e. low income doesnt cause poor health, genes cause both low income and poor health). For this example, Harden is hard on these complaints, equating research that does not include genetic information as tantamount to robbing taxpayers, but light on evidence that this genetic confounding is a widespread problem, or that it can only be addressed with behavioral genetic research.

Surprisingly, all these examples abandon the earlier bluster about genes being crucial causal factors in our life and instead opt for genetic data as one of many methods for causal inference of environmental interventions. We no longer care about heritability estimates; instead, we use twins as an experimental design. In some cases this is fine, however using individuals who have similar genotype, environmental characteristics, and phenotype does not mean that genes are significant causes, its just a good experimental design. Here, some of Hardens arguments about social science research are accurate. Observational and correlation-based studies are weak for a number of reasons, not simply because they ignore genetic differences. The goal should be strengthening causal inference in the social sciences, and we have some idea of how to do that from other fields. To strengthen the ability to identify causes, epidemiologists employ direct experiments, like randomized control trials, exploit natural experiments that can approximate experimental randomization, such as studies that observe changes in outcome shortly after changes in government policy are enacted, or designs that use statistical methods to match people based on background demographic information like income, neighborhood quality, family education, etc.

In fact, there are principled reasons to think genetic data has little to no benefit above and beyond the kinds of data we can collect from non-genetic social science experiments. Eric Turkheimer, Hardens doctoral advisor, has articulated the phenotypic null hypothesis which states that for many behavioral traits the genetic variance identified from behavioral genetics studies is not an independent mechanism of individual differences and instead reflects deeply intertwined developmental processes that are best understood and studied at the level of the phenotype. This certainly appears to hold for the traits Harden talks about. Even with GWAS and polygenic scores, we are given no coherent biological mechanism beyond...something to do with the brain, they interact with and are correlated with the environment, and they are contextual and modifiable. Harden laments focus on mechanisms, but identifying specific causal mechanisms would be precisely how education polygenic scores could be actually helpful. For example, in medicine, GWAS have helped identify potential drug targets by identifying biological mechanisms of disease, and can double the likelihood of a drug making it through clinical trials.

However, this situation doesnt exist for things like education. Instead, we can understand the role of correlated traits like ADHD, or the effect of interventions purely at the phenotypic level by seeing how educational performance and attainment itself change upon interventions from well-designed experiments. In fact, several polygenic scores, from educational attainment to schizophrenia, and even diseases like cardiovascular disease have been shown to have virtually no predictive power beyond common clinical or phenotypic measures, meaning we do not more accurately predict the outcome of those particular phenotypes even with robust polygenic scores. So why not focus our efforts on phenotypes instead of genotypes in cases like education, income, and health where we have some ability to do randomized experiments and a wealth of quasi-natural experiments?

There are existing studies that attempt some kind of true experimental manipulation related to education. Despite what Harden or the charter-school supporting billionaire John Arnold says, we do have some idea on what can improve schools. Research indicates that de-tracking education, that is ending the separation of students by academic ability and having all students engage in challenging curriculum, regularly improves student performance for those with lower ability and does not hinder students with higher ability.

Experiments have shown large benefits to those passing classes and the grades they receive when courses are structured around a more pedagogically informed curriculum that actively engages students. Detracking and active learning have the added advantage of greatly affecting racial gaps in educational performance. To achieve these goals it is likely that teachers will need to be better trained and compensated, and student-pupil ratios would need to change. These changes would likely be related to school funding, teacher salary and quality, and school resources even if those factors are not sufficient to improve educational outcomes in every situation.

Simply identifying that other methods can improve social sciences doesnt mean we shouldnt use every tool in our toolbox, as Harden says. However, there are convincing reasons we ought not to rely on genetic data for this kind of research. One reason is that polygenic scores are not very good as controls for experiments testing the effect of environmental intervention. Research has found that the pervasive interplay of genes and environment weakens their ability to control for genetic confounding or identify the efficacy of environmental interventions. Since polygenic scores can reflect contingent social biases without us knowing, it is possible, and likely, that by relying on them to identify effective interventions we are in fact reifying ingrained social and economic biases further in our systems.

One final concern is how this research is interpreted by people, were it to be widely adopted. Researchers found in online experiments that the very act of classifying someone based on their educational polygenic score led to stigmas and self-fulfilling prophecies. Those with high scores were perceived to have more potential and competence while those with low scores were perceived in the opposite way. Not only does this research suggest genetic data leads to essentialist beliefs that can re-entrench existing inequalities, but this kind of dependency can also create even more confounding influences that complicate the application of genetic data for social science questions.

Finally, we reach the last issue with The Genetic Lottery: we dont need the concept of genetic luck to pursue egalitarian policies. Harden regularly remarks that the alternative is to perceive peoples outcomes as their individual responsibility. Either something is the result of genes they have no control over, or it is their fault for not working hard enough. However, progressive politics revolves around structural and systemic factors that are outside of peoples control and contribute to their outcomes. There is already a recognition of moral luck, or that peoples outcomes are not their fault, but due to the situations they find themselves in. This engagement with progressive motivations and philosophy is absent in Hardens analysis.

In Hardens penultimate chapter she contrasts eugenic, genome-blind, and anti-eugenic approaches to policy. What ultimately occurs is a strawman of genome-blind policy approaches and often anti-eugenic policies that are hard to distinguish from eugenic policies. For example, what is the difference between Hardens description of the eugenic policy Classify people into social roles or positions based on their genetics and the anti-eugenic policy Use genetic data to maximize the real capabilities of people to achieve social roles and positions? While the genome-blind position is described as Pretend that all people have an equal likelihood of achieving all social roles or positions after taking into account their environment., all we really need to do to achieve our progressive goals is ensure that peoples ability to succeed and thrive in life is not conditioned upon their origin, preferences, or abilities. Theres simply no need to use genetic data on people at all.

In another case involving healthcare Harden suggests the genome-blind approach is to keep our system the same while prohibiting the use of genetic information, while the anti-eugenic approach is creating systems where everyone is included, regardless of the outcome of the genetic lottery. However, the system Harden describes is not universal social programs that ensure healthcare, housing, or education regardless of economic situations. Rather it is a system that resembles means-testing social welfare with genetic data. Of course, universal social programs do achieve exactly the anti-eugenic goal while still being genome-blind! Hardens complete disregard for actual rationale and form of progressive policies when crafting the genome-blind caricatures is inexcusable from someone who claims to be progressive.

For a progressive that supports universal healthcare, a living wage for all, housing as a human right, or free education, it does not matter that people are different and it does not matter the cause for that difference. The fact that some people need healthcare to survive is the reason why it should be available for free, whether the need is from an inherited or acquired disease. It is acknowledged that people have different preferences and strengths, which ultimately results in them living different lives. The fact that for some people this means the difference between a living wage and poverty is what progressives take issue with, and it doesnt matter what the cause of these differences are, simply that we address them.

Ultimately, Harden tries to sell us on research that we dont need, based on faulty premises, and that is incapable of delivering on what she promises. Her failure to engage with the history of her own field, her scientific critics, or the actual content of progressive political goals leaves this book in a very poor place. In a way, The Genetic Lottery represents the fact that behavioral genetics no longer has a place to go after the tenets of genetic determinism and biological reductionism were shown to be untenable. If one wants to gain an understanding of modern genetics, or to learn how we may strengthen progressive causes, they should look elsewhere.

See the article here:
The Genetic Lottery is a bust for both genetics and policy - Massive Science

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3 Market Overview 3.1 Market Participants Play Different Roles3.2 Whole Genome Sequencing - Markets, Examples and Discussion 3.3 Industry Structure

4 Market Trends4.1 Factors Driving Growth4.1.1 Diagnostic Factors4.1.2 Interpreting the Code Otherwise4.1.3 Changes in Agriculture4.1.4 Fertility Technology Comes of Age4.1.5 Pathogen Challenges 4.2 Factors Limiting Growth4.2.1 Increased Competition Lowers Price 4.2.2 Lower Costs4.2.3 Healthcare Cost Concerns Curtail Growth4.2.4 Wellness has a downside4.2.5 GMO Opposition Movement 4.3 Sequencing Instrumentation4.3.1 Instrumentation Tenacity4.3.2 Declining Cost Changes Industry Structure4.3.3 LISTING of CURRENT NGS INSTRUMENT SPECIFICATIONS4.3.4 llumina 4.3.5 ION4.3.6 Pacific Biosystems4.3.7 Roche 4544.3.8 SOLiD4.3.9 Oxford Nanopore4.3.9.1 What is Oxford Nanopore Sequencing?4.3.9.2 What can Oxford Nanopore Sequencingt be used for? 4.3.9.3 Oxford Nanopore Products4.3.10 Long Reads - Further Segmentation4.3.11 Linked Reads4.3.12 Targeted Sequencing Adopts CRISPR4.3.13 New Sequencing Technologies 4.3.13.1 RNAP sequencing4.3.13.2 In vitro virus high-throughput sequencing 4.3.13.3 Tunnelling currents DNA sequencing 4.3.13.4 Sequencing by hybridization4.3.13.5 Sequencing with mass spectrometry 4.3.13.6 Microfluidic Sanger sequencing 4.3.13.7 Microscopy-based techniques

5 WGES Recent Developments 5.1 Recent Developments - Importance and How to Use This Section5.1.1 Importance of These Developments 5.1.2 How to Use This Section5.2 GenomSys Gains CE Mark for New Genomic Analysis Software5.3 WGS Finds Lung Cancers Fall Into Molecular Subtypes5.4 Testing Distinguishes Benign Tumors From Precancerous Condition5.5 Plan to Sequence All Newborns in UK5.6 Clear Labs Raises $60M for Nanopore Sequencing 5.7 Variantyx Expands Into Prenatal, Cancer Testing5.8 Whole-Genome Sequencing Aids Diagnosis in Stockholm 5.9 Variantyx Raises $20M5.10 Nonacus WGS Service for SARS-CoV-2 Laboratories5.11 Center to Report Risk Scores in Clinical WGS5.12 Stanford Launches WGS for Cardiovascular Testing5.13 Illumina and NY Healthcare Partner on Clinical WGS5.14 Increased Adoption of WGS Needs Acceptance by Payors, Providers 5.15 Veritas Intercontinental Completes 5M Series B Financing Round5.16 M2GEN and Discovery Life Sciences in Bioinformatics Agreement5.17 Genomics England Adopts Quantum ActiveScale Object Storage5.18 GenomiQa, Icon Group to Validate Genomic Analysis Platform CapeDx5.19 NHS Wales Introduces WGS for Critically Ill Newborns5.20 Illumina Achieves EAU for NGS-Based SARS-CoV-2 Test5.21 C2i Genomics to Launch Trials for MRD Detection Tech 5.22 Roche Acquires Sequencing Company Stratos Genomics5.23 UK COVID-19 Sequencing Consortium Launches5.24 Invitae Acquires Three Companies: YouScript, Genelex, Diploid5.25 Experience From Centralized Genomic Medicine Lab5.26 MGI to Enable $100 Human Genome5.27 Nebula Genomics offers $299 WGS5.28 Team to Study Campylobacter Omics 5.29 Veritas Genetics Restarts US Business 5.30 NEOGEN, Gencove partner to advance animal genomics5.31 UK Whole-Genome Sequencing Project Obtains 200M5.32 WGS may help with disease outbreaks 5.33 Veritas Cuts WGS Price by 40%5.34 Dante Labs Launches GenomeL, Long Reads Human Whole Genome Sequencing5.35 Machine-learning system used to diagnose genetic diseases5.36 Whole Genome Sequencing for healthy creates controversy5.37 Nebula Genomics Offers FREE Whole Genome Sequencing

6 Profiles of Key Companies6.1 10x Genomics, Inc.6.2 23andME Inc.6.3 Abbott Diagnostics 6.4 AccuraGen Inc.6.5 Adaptive Biotechnologies6.6 Admera Health, LLC6.7 Agena Bioscience, Inc.6.8 Agilent6.9 Akonni Biosystems6.10 Ancestry.com LLC6.11 Anchor Dx6.12 ArcherDx, Inc.6.13 ARUP Laboratories6.14 Asuragen6.15 Baylor Miraca Genetics Laboratories6.16 Beckman Coulter Diagnostics6.17 Becton, Dickinson and Company 6.18 BGI Genomics Co. Ltd6.19 Bioarray Genetics6.20 Biocept, Inc. 6.21 Biodesix Inc. 6.22 BioFluidica6.23 BioGenex 6.24 Biolidics Ltd6.25 bioMerieux Diagnostics6.26 Bioneer Corporation6.27 Bio-Rad Laboratories, Inc6.28 Bio-Techne6.29 C2i Genomics6.30 Cancer Genetics 6.31 Caris Molecular Diagnostics6.32 CellMax Life 6.33 Centogene6.34 Chronix Biomedical 6.35 Circulogene 6.36 Clear Labs6.37 Clinical Genomics6.38 Complete Genomics, Inc. - A BGI Company6.39 Cynvenio6.40 Dante Labs6.41 Datar Cancer Genetics Limited 6.42 Day Zero Diagnostics6.43 Diasorin S.p.A.6.44 Epic Sciences6.45 Epigenomics AG.6.46 Eurofins Scientific 6.47 Excellerate Bioscience6.48 Exosome Diagnostics6.49 Fabric Genomics 6.50 Fluidigm Corp6.51 Freenome 6.52 FUJIFILM Wako Diagnostics 6.53 Fujirebio6.54 Fulgent Genetics 6.55 GE Global Research6.56 GE Healthcare Life Sciences6.57 Gencove6.58 Genedrive6.59 GeneFirst Ltd.6.60 Genetron Health (Beijing) Co., Ltd.6.61 Genewiz6.62 Genomic Health6.63 Genomics England 6.64 Genomics Personalized Health (GPH)6.65 GenomOncology6.66 Genzyme Corporation 6.67 Grail, Inc.6.68 Grifols6.69 Guardant Health 6.70 Guardiome6.71 HeiScreen 6.72 Helix6.73 Helomics6.74 Hologic 6.75 Horizon Discovery6.76 HTG Molecular Diagnostics6.77 Human Longevity, Inc.6.78 iCellate 6.79 Illumina 6.80 Incell Dx6.81 Inivata6.82 Invitae Corporation 6.83 Invivoscribe6.84 Karius6.85 Macrogen6.86 MDNA Life SCIENCES, Inc.6.87 MDx Health6.88 Medgenome6.89 Meridian Bioscience6.90 Mesa Biotech6.91 MIODx6.92 miR Scientific6.93 MNG Labs6.94 Molecular MD6.95 NantHealth, Inc.6.96 Natera6.97 Nebula Genomics6.98 NeoGenomics 6.99 New Oncology6.100 Novogene Bioinformatics Technology Co., Ltd.6.101 Omega Bioservices 6.102 OncoDNA6.103 OpGen 6.104 ORIG3N, Inc.6.105 Origene Technologies 6.106 Oxford Nanopore Technologies6.107 Panagene6.108 Perkin Elmer6.109 Personal Genome Diagnostics6.110 Personalis6.111 Precipio6.112 PrecisionMed6.113 Promega6.114 Protagen Diagnostics6.115 Qiagen Gmbh 6.116 Quantumdx 6.117 Regeneron6.118 Roche Molecular Diagnostics6.119 Roswell Biotechnologies 6.120 Seegene6.121 Sequencing.com 6.122 Siemens Healthineers6.123 simfo GmbH 6.124 Singlera Genomics Inc.6.125 SkylineDx6.126 Stratos Genomics6.127 Sure Genomics, Inc.6.128 Sysmex6.129 Sysmex Inostics6.130 Tempus Labs, Inc.6.131 Thermo Fisher Scientific Inc.6.132 Veritas Genetics6.133 Volition

7 The Global Market for Whole Genome Sequencing7.1 Global Market Overview by Country7.1.1 Table - Global Market by Country 7.1.2 Chart - Global Market by Country 7.2 Global Market by Application - Overview7.2.1 Table - Global Market by Application7.2.2 Chart - Global Market by Application - Base/Final Year Comparison7.2.3 Chart - Global Market by Application - Base Year 7.2.4 Chart - Global Market by Application - Final Year7.2.5 Chart - Global Market by Application - Share by Year7.2.6 Chart - Global Market by Application - Segment Growth 7.3 Global Market by Organism - Overview7.3.1 Table - Global Market by Organism7.3.2 Chart - Global Market by Organism - Base/Final Year Comparison7.3.3 Chart - Global Market by Organism - Base Year 7.3.4 Chart - Global Market by Organism - Final Year 7.3.5 Chart - Global Market by Organism - Share by Year7.3.6 Chart - Global Market by Organism - Segment Growth 7.4 Global Market by Product - Overview7.4.1 Table - Global Market by Product 7.4.2 Chart - Global Market by Product - Base/Final Year Comparison 7.4.3 Chart - Global Market by Product - Base Year7.4.4 Chart - Global Market by Product - Final Year7.4.5 Chart - Global Market by Product - Share by Year 7.4.6 Chart - Global Market by Product - Segment Growth

8 Global Whole Genome Sequencing Markets - by Application 8.1 Research8.1.1 Table Research - by Country 8.1.2 Chart - Research Growth8.2 Clinical Human8.2.1 Table Clinical Human - by Country8.2.2 Chart - Clinical Human Growth8.3 Clinical Tumor8.3.1 Table Clinical Tumor - by Country 8.3.2 Chart - Clinical Tumor Growth8.4 Clinical Pathogen8.4.1 Table Clinical Pathogen - by Country8.4.2 Chart - Clinical Pathogen Growth 8.5 Direct to Consumer 8.5.1 Table Direct to Consumer - by Country8.5.2 Chart - Direct to Consumer Growth8.6 Agriculture/Other8.6.1 Table Agriculture/Other - by Country8.6.2 Chart - Agriculture/Other Growth

9 Global Whole Genome Sequencing Markets - by Organism 9.1 Human 9.1.1 Table Human - by Country 9.1.2 Chart - Human Growth9.2 Pathogen 9.2.1 Table Pathogen - by Country9.2.2 Chart - Pathogen Growth9.3 Other Organism9.3.1 Table Other Organism - by Country9.3.2 Chart - Other Organism Growth

10 Global Whole Genome Sequencing Markets - by Product10.1 Instruments10.1.1 Table Instruments - by Country10.1.2 Chart - Instruments Growth 10.2 Reagents10.2.1 Table Reagents - by Country 10.2.2 Chart - Reagent Growth 10.3 Analysis 10.3.1 Table Analysis - by Country10.3.2 Chart - Analysis Growth10.4 Software & Other 10.4.1 Table Software & Other - by Country10.4.2 Chart - Software & Other Growth

11 Vision of the Future of Whole Genome Sequencing

12 Appendices

For more information about this report visit https://www.researchandmarkets.com/r/luzpqe

Read more:
Outlook on the Whole Genome and Exome Sequencing Global - GlobeNewswire

Curiosity is a great motivator, but my curiosity about Rascals breed mix would have come to nothing had I not submitted to a DNA test myself shortly before we adopted him. I wasnt looking for my own genetic ancestry; Im wary of what such tests reveal and warier still of how their results might be used. Commercial DNA testing has revealed family secrets, solved crimes long consigned to the cold-case files, even affected census results.

From human genetics research particularly studies involving identical twins we know that DNA influences much of what we consider to be our most human traits: our personality, our preferences, our I.Q. Lay people, even many researchers themselves, tend to find such research troubling, hinting at a kind of genetic determination.

Despite the unresolved ethical and cultural issues raised by DNA testing, its potential medical benefits are remarkable. I have a rare inherited syndrome that almost certainly killed my paternal grandmother at 51 and accounted for my fathers cancer diagnosis in middle age. When I submitted a saliva sample to a medical lab for genetic testing, I was contributing to research that might identify the gene that causes the condition, saving future patients from the expensive and disruptive cancer screenings that I undergo every year.

All of which primed me to reconsider DNA testing when we adopted Rascal; canine DNA tests can also reveal certain inherited medical conditions. In January, our rescue dog Millie died of complications of epilepsy. If Rascal carries a genetic risk for something terrible but treatable, too, I wanted to know about it.

Following a recommendation from Wirecutter, which evaluated 17 DNA tests on the commercial market, I ordered one from Embark and sent in a sample of Rascals saliva. A couple of weeks later, I got his results: 35.9 percent Chihuahua, 34.4 percent poodle, 6.9 percent bichon fris and 22.8 percent supermutt, Embarks catchall term for trace amounts of DNA from distant ancestors. Rascals ancestors apparently include a collie, a Pekingese, a Shih Tzu and a Maltese terrier.

The test also revealed that Rascal carries two copies of a gene variant associated with disk disease. Even before the breed results arrived, I got an email from one of Embarks veterinary geneticists explaining the risks associated with this variant and recommending some mitigation strategies. Some of them, like using a harness on walks, were easy to do. Others, like discouraging jumping, were less so. Keeping this buoyant little dog earthbound is a fools errand, but I was extremely grateful for the detailed advice.

Breed mix remains a matter of indifference to me. What does it mean that my gentle granddog has a wolf somewhere deep within her lineage? Apparently nothing. Thats the mystery of individuality, even in dogs.

Read more from the original source:
Opinion | What I Learned Testing My Dogs DNA - The New York Times

DNA methylation is one of the earliest epigenetic modifications to be discovered in human beings. It involves the transfer of methyl (CH3) groups to the C5 position of cytosine bases that comprise deoxyribonucleic acid (DNA) to produce 5-methylcytosine (5mC) the reaction is catalyzed by a family of enzymes called DNA methyltransferases (DNMTs). Typically, the altered cytosine bases reside immediately adjacent to guanine bases. This leads to two 5mC bases sitting diagonally to each other on complementary DNA strands.DNMTs have several distinct roles, for instance, they may function asde novoDNMTs, which involves establishing the initial pattern of methyl groups on a DNA molecule. While other DNMTs adopt maintenance roles, copying the methylation pattern from an existing DNA strand to its new partner after replication has occurred.

Several studies in the 1980s revealed that DNA methylation played a major part in both gene regulation and cell differentiation. Since then, further research has confirmed the role of abnormal methylation in the development and progression of various diseases. According to Manel Esteller, director of the Josep Carreras Leukaemia Research Institute and professor of genetics at the University of Barcelona, DNA methylation is one of the main controllers for specific-tissue expression allowing the correct expression of a gene in the right organ or cell type. He further added, DNA methylation acts as a buffer to stabilize our genome and silence repetitive chromosomic regions. Many diseases show an alteration of DNA methylation that disrupts cellular activity. Estellers research mainly focuses on alterations in DNA methylation, histone modifications and chromatin in human cancer. At present, he is working on establishing epigenome and epitranscriptome maps for normal and transformed cells.

In mammals, methylation is mostly sparse but is globally distributed in specific CpG or CG (cytosineguanine) sequences. In certain regions of the genome, CpG is abundantly found (e.g., CpG islands). In healthy cells, CpG islands associated with gene promotors are typically free from methylation, whereas islands found within gene bodies tend to become methylated during development. Researchers have pointed out that methylation of CpG islands at promotor regions can cause inappropriate downregulation of specific genes (e.g., silencing of tumor suppressor genes in cancer cells).

Most early detection methods, such as mammograms and colonoscopies, are unpleasant and, in some cases, invasive. Alternative, minimally invasive methods are needed to improve patient compliance and improve early detection rates. Download this whitepaper to discover a next-generation sequencing-based assay that can detect cancer from a single blood sample and targets DNAmethylation sequences.

DNA methylation plays an important role in many biological processes, for example, genomic imprinting, stem cell differentiation and chromosomal stability,and is considered an essential modification that regulates cell growth and proliferation. DNA methylation patterns are mutable and inheritable and in the case of abnormal DNA methylation in the parental allele, various serious diseases, such as cancer, aging disorders, metabolic ailments, psychological disorders and genetic diseases, may occur.

Systemic lupus erythematosus (SLE) is an autoimmune disease in which the bodys immune system incorrectly attacks its own healthy tissue. A genome-wide assessment of DNA methylation demonstrated differential DNA methylation in the genes of SLE patients, associated with autoantibody production. Abnormal DNA methylation was observed in the promoter region of the IL-6 gene.

In cancer, we observe a general global DNA hypomethylation of the genome and more focal DNA hypermethylation that affects CpG-rich sequences (so-called CpG islands) often found at the promoter, explained Professor Gerd Pfeifer, from the Center for Epigenetics, Van Andel Institute. Pfeifers laboratory investigates the underlying mechanisms of cancer and other diseases, specifically focusing on DNA mutations, DNA methylation and the role of 5mC oxidation. According to Pfeifer, most of the DNA hypermethylation events in cancer are inconsequential because the genes are already silent. However, some methylation events can be considered tumor drivers, when, for example, they silence genes encoding anti-proliferative factors, DNA repair genes, or genes essential for normal cell differentiation.

Paula Esteller-Cucala is a doctoral researcher in the Comparative Genomics Group at the Institut de Biologia Evolutiva (IBE), her work focuses on epigenetics and transcriptomics of non-human primates. She said,Methylation patterns are very heterogeneous. They might differ from one cancer type to another and also from one cell type to another cell type. Understanding the role of these modifications and their effect in different cancer types is essential to target potential treatments and therapies. Identifying cancer-specific DNA methylation markers (regions of the genome that are specifically methylated or unmethylated in one or more cancer types or subtypes), can be used to detect and monitor cancer with a view to developing therapeutic strategies.

Gliomas are a common type of brain cancer,which originate in the glial cells that support neurons in the brain. Recently, researchers have used a single-cell multiomics approach to identify methylation marks within individual tumor cells obtained from patients with glioma. They were able to confirm distinct patterns of DNA methylation responsible for shifting the cells from one state to another (e.g., stem-cell-like states to mature states) and developed a map of cell states from the sampled tumors. The insights gained from the study could help to develop better ways to detect, stage, monitor and treat the disease.

A lowered methylation level of catechol-O-methyl transferase in peripheral blood was observed in patients with schizophrenia.

An epigenome-wide association study compared the methylation patterns of tissues from three different mammalian species to determine if Huntingtons disease is accompanied by altered DNA methylation. The researchers found that the disease was associated with profound changes to the level of DNA methylation.

A systemic review of DNA methylation in Alzheimers disease found that the APP gene encoding a protein called amyloid precursor protein which has been associated with the formation of amyloid plaques is consistently hypermethylated in brain and peripheral blood.

Looking beyond traditional methods, recent advances in sequencing and array technologies have enabled researchers to conduct detailed DNA methylation profiling, providing a comprehensive picture of its role in disease. In Esteller-Cucalas opinion, the latest methodology used to study DNA methylation is by means of long-read sequencing these technologies allow much longer sequences to be read (> 10000 bp).

Esteller, also provided his thoughts, The technology most widely used to study, in a cost-effective manner, human DNA methylation is based on DNA methylation microarrays that interrogate 850K CpG sites of our genome.

Some techniques usedto determine DNA methylation are discussed in more detail below.

An advanced sequencing-based technique known as methylation-specific PCR (MS-PCR) has been developed which avoids the complex sequencing process.

Some examples of methylation-sensitive restriction enzymes (MREs) include HpaII, BstUI, NotI and SmaI. These enzymes only cut the nonmethylated target regions and keep the methylated DNA intact. These MRE cuttings are subsequently sequenced to predict the DNA methylation levels at the genomic level. Recently, scientists have developed an advanced enzymatic digestion technique, called methylation-sensitive restriction endonuclease-PCR/southern (MS-RE-PCR).

Pfeifer pointed out some additional considerations, "One challenge is to achieve coverage of the whole mammalian genome, which has over 25 million CpG sequences that can be methylated. To perform a quantitative analysis of the methylation state of each one of these CpGs, deep sequencing coverage is required, which is still expensive. There are more affordable methods available that can be used to analyze subsets of CpGs, but these methods may miss some critical methylation changes.

For comprehensive DNA methylation studies, a large amount of DNA may be required and therefore, analysis becomes challenging when the tissue samples are scarce. Sometimes it is difficult to distinguish 5mC from 5hmC, said Esteller.

Esteller explained that from knowledge of the DNA methylation landscape of tumoral cells, three translation uses have emerged in the oncology field: The discovery of new biomarkers of the disease that can even be detected in biological fluids and allow its pathological classification; the use of DNA hypermethylation events in certain genes as predictors of response to therapies, helping cancer precision medicine; and the use of DNA methylation as a target for epigenetic drugs such as inhibitors of DNA methylation that are being used in the treatment of hematological malignancies.

More:
The Role of DNA Methylation in Human Disease - Technology Networks

Beverly Hills , Dec. 01, 2021 (GLOBE NEWSWIRE) -- Drawing on his steep experience in business strategy, operations, manufacturing, and supply chain management across the life sciences spectrum, Todd Applebaum talks with Mission Matters about the pharmaceutical, biologics, and medical device industries. In addition to leading Converge Consulting as Founder and CEO, he also provides senior leadership for many of its projects.

Listen to the complete interview of Todd Applebaum with Adam Torres on Mission Matters Innovation Podcast.

What mission matters to you?

Applebaum says quality and value in healthcare are essential. With governments and payers under greater pressure than ever in the wake of the COVID-19 pandemic, quality is being measured as improvements in patient outcomes.

At Converge Consulting, the mission is to help pharmaceutical and biotech manufacturers understand the new environment and focus on patients and patient outcomes as they bring their new technologies and innovative science to market, ultimately improving or even saving lives.

Tell us more about how you started Converge Consulting.

Applebaum has many years of experience in manufacturing, starting with the automotive and high-tech electronics industries and ultimately moving into consulting for the life sciences. He recognized how manufacturing and operations can help companies address customer needs. He eventually led technical operations at a biotech startup developing cell therapy treatments and witnessed the importance of focusing on patient outcomes in producing and delivering these new treatments. This motivated him to found Converge Consulting to help biotech companies of all sizes build strategic advantage through best practices in manufacturing and operations.

What kind of trends do you see now in the biotech Industry?

The healthcare industry is changing dramatically, Applebaum notes, and points to the increased focus on patient outcomes. He sees two trends leading the way:

These trends are working together to focus biopharma companies on the patient, and specifically on improving patient outcomes. Along with innovative science, they must provide new services, including education, monitoring and financial assistance, as well as bundled diagnostics, devices and ancillary materials. Success now depends on building closer operating relationships with and managing more direct integrations between drug manufacturers, distributors, logistics providers, physicians, and patients to ensure patients are identified, enrolled smoothly, stay on treatment, and ultimately realize the benefits.

How is Converge Consulting helping biotech companies grow?

Applebaum says Converge Consulting helps biotech companies, big and small, understand the new landscape of patient outcomes-focused medicine and then to develop and execute the strategies and operating processes necessary to deliver their innovative treatments to patients.

Biotech companies deal with a variety of challenges and risks at each business stage. Converge helps them manage business risk without compromising essential operations.

Whats next for Converge Consulting?

Applebaum believes that, as our knowledge of biology and the genetic origins of disease continues to grow, developing new approaches for treating diseases and critical health conditions will only accelerate. Cost pressure and the risks inherent in drug development will also remain high, requiring pharmaceutical and biotech companies to focus scarce resources on their science. Converge will continue to operate at the intersection of these new scientific breakthroughs and innovation in patient care models, so that clients deliver treatments that ultimately improve patient outcomes.

To learn more, visit Converge Consulting online.

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Todd Applebaum Talks About Helping the Biotech Industry Bring Crucial Healthcare Innovations to Market - GlobeNewswire