What next for human germline editing?

The issues are somewhat clearly identified. The lack of concrete proposals is deafening.

 

Yesterday I attended an event at Harvard, “Editorial Humility: A Moratorium on Human Germline Editing?”, sparked by the recently published call for a moratorium on human germline editing that Eric Lander co-authored with 17 others.

Back in 2015 there was a clear call for a moratorium, with a focus on whether this is a road we want to go down at all. In 2017, the National Academies of Science and Medicine published a report that watered down this call, focusing on questions of safety and efficacy (I argued at the time against this watering down). He Jiankui pointed to this NASAM report in claiming that there was no clear writ against his decision to pursue the human germline editing that lead to the birth of Lulu and Nana. In other words, the absence of a clearly called for moratorium likely had a role in the actual use of the technology.

Is a moratorium the right approach? Eric Lander, speaking first, explained that the main aim of the new call was to seed the debate. He is backed by the NIH, represented at the event by Carrie Wolinetz. In the US there is a ban against germline editing already in place, but the world’s largest funder of biomedical research nonetheless thought using their “bully pulpit” position in support of a moratorium was the right thing to do.

A moratorium is a lightweight solution. It would be time limited from the get go. And it would leave eventual decisions with each sovereign power on a country by country basis. For panellists Betsy Bartholet and Sheila Jasanoff, this does not go far enough, and we should be aspiring to an International Treaty. Betsy Bartholet called on Eric Lander and the concerned scientific community to start the work to get a treaty in place. Eric Lander called on Bartholet and the lawyers to instead do this work. Both claimed a lack of expertise. This was concerning. An example of how things can truly fall between the cracks.

Playing devil’s advocate, I Glenn Cohen argued that moratoriums can be “sticky”, even if they have a sunset clause. Moreover, bioethicists have been talking about this for years; we’ve done all the thinking we need to. He also argued that we need to de-exceptionalize genetic modification as a technology. Many technologies, e.g. smartphones, have disturbing ethical implications. I agree with Lander’s response that Yes, we have issues across the board when it comes to new technologies, but that’s a reason to engage with all of them, not to disengage with this one.

Steve Hyman, ex-provost of Harvard, said that he was much more concerned about the use of cognitive polygenic scores for selecting embryos. Given my research interests, no surprise that I strongly agree. I’ve also published (joint with Sarah Polcz) that although scientists are making a big distinction between heritable/non-heritable, I think the bigger distinction is therapy/enhancement.  

I took two main things away from the panel.

First, everybody was horrified that the scientific community so thoroughly dropped the ball. Many others knew what He Jiankui was up to, and no-one raised the alarm. (Note that the scientific community is divided about whether blame should fall entirely on He or not.) What should be done? Eric Lander made the case that you can’t expect scientists to self-regulate because they have an inherent conflict of interest. When you work on anything you have to be the biggest believers in the upsides, the optimists. Jasanoff reminded Lander that the metro between Harvard and Kendall “runs both ways”, and that he should come to the Kennedy School more often to explore the issues from a different point of view. The question of the role of scientists seems prescient in light of the debate over the extent to which the social media giants can and should self-regulate. In this arena, bioethicists are given heat for being too conservative, for overplaying the risks of a technology and failing to see the potential benefits. So what is the right balance of roles?   

Second, as ever, there were broad calls for public debate. But when it came to concrete proposals for “deliberative explorations”, nothing. There was reference to learning about how other countries have done this. The UK is always held up as the shining example. (And the inevitable “How do they manage to achieve consensus on the use of reproductive technologies, but end up in such a mess over Brexit?”). I’ve added to my To Do list a comparative approach to the various approaches to public engagement taken around the world with respect to mitochondrial replacement therapy. Approaches that have caught my eye include the Moral Machines work, where the public were invited to state who they thought a self-driving car could kill. And something like what the folks at World Wide Views are up to. I’m very interested to hear of deliberation and public engagement that others are enthusiastic about.

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Round-up March 13 – April 25

Three topics have dominated genomics happenings. First, polygenic scores: the science continues to mature, there are calls for their rapid clinical integration, there are concerns about their use, and there are commercially available products. Second, how to regulate human germline modification. Third, use of genomics in forensics.

 

Polygenic scores

  • Polygenic scores for cognitive traits like IQ and Educational Attainment are confounded by gene-environment effects, especially socioeconomic status. Preprint from Plomin and team. Within family predictions were ~60% lower than between family predictions for these traits, but not for traits like BMI and height. The difference disappeared after accounting for SES, suggesting that SES is part of “passive gene environment correlation” or “genetic nurture”. All genetic influences operate via the environment. Three genotype-environment correlation (rGE) mechanisms:

Passive rGE: “Parents generate family environments consistent with their own genotypes, which in turn facilitate the development of the offspring trait, thus inducing a correlation between offspring genotype and family environment ”  

Active rGE: children select, modify and create experiences

Evocative rGE: children evoke responses in their environment (correlated to their genetic propensities.)

Within family genetic differences can include active and evocative rGE effects, but not passive rGE effects, which are shared within the family.

If aiming just to maximize trait prediction, using between family based scores (i.e. calculated from unrelated individuals) is legitimate. But for causal analysis, including the use of Mendelian Randomization, within family designs are necessary.

  • Another pre-print examining the “nature of nurture”, gene-environment correlations in the context of educational attainment which also had access to the polygenic scores for the mothers. They show that both mothers’ and children’s polygenic scores are predictive of parenting style. And also that mothers genetics predicted childhood educational attainment beyond direct transmission, mostly via providing a stimulating cognitive environment.

 

Germline genetic modification: moratorium or no?

 

Forensics

  • Rwanda is proposing a DNA database of all its citizens for fighting crime purposes. The plan is in its earliest stages, no legislation has yet been passed. In 2015 Kuwait proposed a similar database for fighting terrorism, but it was later struck down by the constitutional court.
  • FamilyTreeDNA let the FBU access genetic data without telling its customers, and faced a backlash for it. Now customers can opt out from law enforcement access to their data. But the company doesn’t want its customers to do that, and has launched Ad campaigns on that premise, stating it feels it has a “moral responsibility” to help solve cases.
  • Meanwhile, the growth of GEDMatch, the platform that consumers can choose to upload their genetic test results to in full knowledge that law enforcement have access to it, coupled with the ability that up to 4th cousins can be identified from these uploads, is leading to a “National DNA database by default”. So states Natalie Ram in Slate, who calls for ending familial searching, and points to a Bill in Maryland that hopes to do just that.
  • An example of DNA being used for more than an ID. In a murder case, police sequenced DNA found on the victim and found that it belonged to a black man, which changed their search strategy. They then asked nearly 400 black men who had been taken into custody in the region for DNA samples, as part of a “Race-biased dragnet”.
  • An apartment complex on Long Island is setting up a registry of the DNA of residents’ dogs, and will test dog poop to punish those dog owners that do to clean up after their pets.

 

Science

 

Applications

 

Regulation

Round-up Feb 19 – March 12

Two truly ginormous releases of data

  • The Biobank, ~500,000 individuals with extensive phenotypes, has released the first ~50,000 whole exome sequences (complementing the Chip data that has been around for longer).
  • The National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program has a data set of ~50,000 whole genomes (of a planned 145,000). An exciting fact about this data set is that ~30% are from individuals with African ancestry. The individuals are extensively phenotyped. Much of the genetic data (I’m unclear how much) is available on dbGap.

The size of NGS data has truly exploded. Here’s hoping that this sort of size dataset will allow us to peel back the curtain on the clinical relevance of rare variation.

Controversy — mostly China

Science

Applications

  • 23andMe have launched a Type 2 Diabetes score. Using a freshly developed polygenic score based on their 2.5 million customers, it adjusts the score based on ethnicity and age to give not just a relative odds, but a percentage chance of developing the condition in the next x years. I was unable to confirm this as it doesn’t work on my report — perhaps because it only works for the latest chip.
  • The PeopleSeq consortium has partnered with the major projects that offer genome sequencing to healthy individuals (“predispositional screening”). They send out surveys to participants before and after screening. In their first published results covering several hundred people, they found that while most individuals discussed the results with their doctor, only 13.5% made an appointment specifically for that purpose. About 40% reported that they learnt something new about their health, but fewer than 10% made any changes. More than half were disappointed that they did not receive more actionable information. One message the authors want us to take home: patients felt empowered rather than distressed by their results.
  • A group of 8 institutions wants to see whole genome sequencing in the clinic, and have formed the Medical Genome Initiative to help establish best practices etc to make this happen.

Regulation

And in other interesting things, here is a nice write-up of the extent to which humans are innately violent — tracing the debates, and pointing to the question, do we need an answer to this question? Also, whether our views on this question affect our beliefs about peace-keeping efforts.

Round-up Feb 1 – Feb 18

Controversy

  • An opinion piece in STAT by Michael Joyner and Nigel Paneth against the genetic reductionism of precision medicine: “We are calling for an open debate, in all centers of biomedical research, about the best way forward, and about whether precision medicine is really the most promising avenue for progress. It is time for precision medicine supporters to engage in debate — to go beyond asserting the truism that all individuals are unique, and that the increase in the volume of health data and measurements combined with the decline in the cost of studying the genome constitute sufficient argument for the adoption of the precision medicine program.” Their piece references a 1999 lecture from Francis Collins. He explains the background to the Human Genome Project (“a public science initiative focused so sharply on the molecular essence of humankind was too intriguing and too promising to forgo”) and then lays out his vision for what we now call Personalized Medicine, including an imagined 2010 encounter where a young soker learns of his increased risk of lung cancer, which “provides the key motivation for him to join a support group of persons at genetically high risk for serious complications of smoking, and he successfully kicks the habit.”

Science

  • Race as a biological variable. A paper that finds differences in Alzheimer biomarkers between African-Americans and non-Hispanic whites: lower cerebrospinal fluid concentrations of tau, with results varying by APOE status. In addition to a race-by-biomarker interaction, this is a race-by-genotype interaction. They argue that these uncovered links mean that a) any attempts to use biomarkers in diagnosis should adjust for race, and b) hope for better treatment based on incorporating “race dependent biological mechanisms”.
  • A review of polygenic risk scores in psychiatry. Reviews use of GWAS for “increasingly informative individual-level genetic risk prediction” for psychiatric disorders, which are not yet ready for the clinic. Does a nice job of showing that the idea of summing up the effects of many genetic variants to explain a continuous phenotype goes back to the very beginning of the study of hereditary. “ To understand how to incorporate PRS into clinical practice for patients with heritable psychiatric disorders, studies will need to assess health outcomes for various behavioral interventions, treatment regimens, and/or differential diagnoses” They make an interesting observation as regards height: “prediction accuracy is not distributed evenly; it performs particularly poorly at the extreme short end of the height distribution, indicating a larger contribution of environmental factors, large-effect rare variants, and/or other factors in these individuals”
  • Polygenic scores are trained on cohorts of diagnosed individuals. A Danish study shows that polygenic scores for depression are also predictive in a general cohort. A score of one standard deviation above the mean gives a 30% increased chance of a diagnosis of depression.

Applications

  • A look at how consumer genetic testing companies market testing for Native American ancestry, focusing on the claimed links to identity. They wonder, whilst acknowledging it is beyond the scope of their paper, “Are companies changing consumer behavior, or are consumers already coming in with certain expectations of verifying tribal ancestry and using the results as a means to accomplish this goal?” (See this paper for qualitative interviews with 100 people looking at this question). The paper references that the US government required tribes in 1934 to have a minimum “blood quantum” for enrollment. There is clear, and problematic, precedent for the use of genetics/blood to define identities. Rewind the clock to 2013, and in giving the majority opinion against a Native American father in a complicated court case, Justice Alito choose to start with “This case is about a little girl (Baby Girl) who is classified as an Indian because she is 1.2% (3/256) Cherokee.”, seemingly drawing attention to the genetic component of her Indian-ness, and in particular that it was only 1.2% (I recommend the More Perfect podcast episode about this case). Reading more about these considerations convinces me of just how counterproductive it was for Elizabeth Warren to act upon Trump’s goad to be genetically tested to proof her claims of Native ancestry.
  • A piece on SpectrumNews, about the benefits of genetic testing. I was struck by this quote from the mother of a child who received a pathogenic finding when her daughter was an adolescent: “Instead of trying to change her behaviors, we’re modifying how we take care of her… It has given me a lot of relief knowing where her autism comes from, and that there was nothing I could have done differently.” Her daughter was the “way she is because of biology”. Routine genetic testing, if it had been available at the time could have saved her ”years of worry and guilt”.  
  • Antonio Regalado reports on a start-up working towards designer babies. One of the founders “is skeptical of the role regulations can play—a lesson he says he learned working with Bitcoin, a digital currency outside the control of any central bank.” I was alarmed at this: “Bishop told me none of the ethicists he e-mailed had ever gotten back to him.”
  • The FBI can now send a sample to FamilyTree, and they will sequence it and see if it matches anything in their database:
  • Speaking of polygenic scores, did you see that the Scripps has an App for that?
  • Some lovely visual explainers of the difference between NGS and genotyping from NYT.

Regulation/Ethics

  • Designing babies with high IQs may be far off, but selecting between embryos based on polygenic scores for IQ is more or less upon us (witness Genomic Prediction). Erik Parens, Paul Appelbaum, and Wendy Chung argue that profiling embryos for IQ would be unethical. Parents have two competing ethical obligations: to accept their children as they are, and to shape their children. The market is producing a “grotesque” pull towards the latter. “Placing limits on the genetic selection of embryos is one small way for our society to affirm the importance of achieving a balance between the ethical obligations to shape our children and to accept them as they are — and the importance of closing, rather than widening, the gap between the rich and the poor.” They argue for regulation, pointing out that the UK manages to do just that.
  • Ethics dumping? The Economist asks whether Deem — the American scientist who was He’s thesis advisor and who had same role in the notorious experiments — is guilty.

The Backstreet Boys have a new album called DNA. Why? Watch this cringeworthy explainer.

Round-up Jan 16th- 31st

Updates to the human germline editing saga

Controversy

  • In a long piece in the New York Times Magazine on paleogenomics, takehomes here,  Gideon Lewis-Kraus points to the field’s major and recent successes, but also highlights how some archeologists fear that it traffics in “grand intellectual narratives” that history warns us against. This is an indication of culture wars between geneticists and others.

Science

  • A study that doubled the number of microbial genomes available (150,000) by producing data from metagenomic studies including those covering non-Westernized countries. They grouped their ~150,000 new sequences in to 5000 “species bins”, 77% of which were novel. The new sequences dramatically increase the mappability of samples to 87%.
  • A fine grained (682bp) map of where crossovers occur on chromosomes, from Decode. The CEO of Decode, and author, Dr Stefansson (source): “The classic premise of evolution is that it is powered first by random genetic change. But we see here in great detail how this process is in fact systematically regulated – by the genome itself and by the fact that recombination and de novo mutation are linked. We have identified 35 sequence variants affecting recombination rate and location, and show that de novo mutations are more than fifty times more likely at recombination sites than elsewhere in the genome. Furthermore, women contribute far more to recombination and men to de novo mutation, and it is the latter that comprise a major source of rare diseases of childhood. What we see here is that the genome is an engine for generating diversity within certain bounds. This is clearly beneficial to the success of our species but at great cost to some individuals with rare diseases, which are therefore a collective responsibility we must strive to address”
  • Polygenic score for lifespan, explaining 1% of the phenotypic variance, which is 5% of the heritability. Those in the top 10% of the score can expect, on average, to live 5 years longer than those in the bottom 10%.
  • GWAS for risk tolerance and risky behaviors, with genetic overlaps found between different “risky” phenotypes, and with various personality traits.

Applications

Regulation

  • More powers for DNA forensics. As of the beginning of January, the Rapid DNA Act comes into force. Rapid DNA machines sitting inside police stations allow police officers to obtain sequence results in 90 minutes. The Act allows for police to upload this data to CODIS, the National DNA database, and look for matches to e.g. previous crime scenes.

In other news: The preprint server for biology BioRxiv, just turned five. In 2018, about 1711 preprints were posted per month, and in October there were over 1 million downloads. A project called the Rxvist allows users to see which preprints are generating the most twitter attention. I have added this to my bookmarks, and will be using it to help inform this round-up from henceforth!

Round-up Dec 22 2018 – Jan 15 2019

Focus on polygenic risk scores

First, let’s look at a paradigmatic example of a polygenic score publication. Inouye et al constructed a Polygenic Risk Score for CAD, gaining a hazard ratio of 4.17 for those in the top 20% compared to bottom 20% of their score. It is better as a single predictor (based on Area Under ROC curve, also known as C-statistic) than any one of smoking, diabetes, hypertension, body mass index, self-reported high cholesterol, and family history (it does not do as well as all of them put together). They conclude that their score “strengthens the concept of using genomic information to stratify individuals for CAD risk in general populations and demonstrates the potential for genomic screening in early life to complement conventional risk prediction.”

In reaction to articles such as this, several clear lines of criticism have emerged

  1. The scores are only applicable in the ancestral population that they were developed in. Combine this with the well publicised fact that almost all studies are on Caucasian populations (reviewed here), and that the assays used are SNP chips whose genetic variants were chosen based on frequencies of variants within European populations, and several issues are immediately apparent. As an alternative to producing scores separately for each ancestral population, a suggestion that studies based on African populations would be less biased and more generalizable to other populations. It is  based on the simple fact that non-African populations have been subject to more genetic drift – i.e. change in genetic variant frequency because of small population sizes. It is also the case that there are hazards aplenty in using differences between populations to infer anything about genetics, and particularly about natural selection (see g.g. this article).
  2. Hazard ratios have to be very high to be useful as screening tools. In an article that has been well circulated on twitter, “The illusion of polygenic disease risk prediction”, the authors point out that “the paradox is largely explained by the fact that odds ratios or hazard ratios typically compare risks in the tails of a single risk distribution, but these ratios ignore the proportions of individuals who will or will not develop the disease that fall in the region between the tails of the distribution”. The first author, Nicholas Wald, has been pointing this out for a long time. Note that this does not just apply to genomics, in his 1999 paper Wald takes as its case study cholesterol levels for heart disease, and shows how poor a screening test this is. (They state that no future polygenic risk score will produce a high enough relative risk — it would be good to check this, based on a score that captured the full heritability estimates for a given trait.) This argument ought not to be news. I enjoyed this slide deck by epidemiologist Cecile Janssens that traces the history of the prospect of predictive genomic tests, and some of the known pitfalls.
  3. The role of the environment means that genetics is often not as useful as these scores would suggest. If the environment changes, e.g. all people stop smoking, then the polygenic scores change too. If the genetics is mediated by an independently measurable and modifiable intermediate phenotype, e.g. cholesterol levels, then it is much less useful to know the genetics. Though see the Inouye paper showing that their score is relatively independent of other known risk factors.

These skeptical voices are not preventing a full-scale rush to applications. Color announced a 100,000 person initiative to use low throughput whole genome data to provide individuals with polygenic scores.

 

Controversy

Science and Applications

  • Antonio Regalado has summed up the top advances in Genetics from 2018 — seeing it all in one place is definitely impressive.
  • The latest chapter on heritability, from a study of Aetna’s database of insurance claims covering about 45m individuals. Their dataset has over 56,000 pairs of twins born since 1985, and over 700,000 sibling pairs. They connect zipcodes to environmental factors of interest — SES, air pollution and weather/climate. They found that variance from these measures was much lower than from genetics and shared environment, with obesity being the phenotype with the strongest link to SES (var=0.027). Monthly cost of data was estimated at 29% heritable and 30% due to shared environment. The respective figures for co-morbidities were 43% and 24%.
  • There is often concern that receiving ambiguous results can lead to increased worry for individuals. But a new study based on a sample of over 5000 women receiving HBOC genetic risk testing fond that receiving uncertain results did not increase worry among women compared to a negative result.
  • The BabySeq project reports on results of exome sequencing of 159 newborns (127 healthy and 32 in the NICU). Of these 15 (9.4%) had genetic variants associated with a disease that could be managed in childhood. Genomic sequencing for newborns remains a contentious area.
  • An AP poll found 70% of Americans supportive of genetic editing “to prevent an incurable or fatal disease a child otherwise would inherit, such as cystic fibrosis or Huntington’s disease”, about two thirds to “prevent a child from inheriting a non-fatal condition such as blindness, and even to reduce the risk of diseases that might develop later in life, such as cancers”, and about 70% oppose “using gene editing to alter capabilities such as intelligence or athletic talent, and to alter physical features such as eye color or height.” I can’t find any original data, just reports e.g. here.
  • I thought this was an interesting story about how much of an impact the classification of a disease can make — in this case, the efforts to have schizophrenia classified as a brain disease so that it was covered by a new CDC program. Why does this matter? Mental conditions receive less funding and health insurance is often less generous. Strong echoes of dualism here.

Regulation

Round-up Nov 11 – Dec 21

Controversy

  • The major news was the report of the birth of the first children genetically modified as embryos. I report on that separately here.
  • The question of whether those who receive a genetic diagnosis have a duty to tell their family members is working its way through the UK courts. A man who received a genetic diagnosis of Huntington’s asked the hospital not to tell his daughter, who was pregnant at the time, because he feared she would abort the baby. The daughter has since been found to have the genetic variant associated with Huntington’s is now suing the hospital. Bioethicist Anna Middleton told the Guardian “This could really change the way we do medicine, because it is about the duty that doctors have to share genetic test results with relatives and whether the duty exists in law.”
  • UCL launched an inquiry into their past ties with eugenics, including to Galton. This was motivated by a series of secretive eugenics meetings that an academic of theirs was recently involved with.

Science

  • PsychENCODE, a large consortium looking at functional genomics in over 2000 developing and adult brains, has published 10 papers. To focus on one paper, they found that differences in gene expression between individuals are mostly explained by differences in fractions of cell types, and that some disorders and aging are associated with changes in these proportions. For schizophrenia, they found 321 genes associated with GWAS loci, and then did some fancy machine learning to predict phenotype from genotypes and from expression. This model did much better than a genotype polygenic score alone, and still did better when the expression data was imputed (i.e. not actually experimentally measured), “highlighting the value of having just a small amount of transcriptome data for disease prediction.”
  • Large study of the genetics of ADHD finds reproducible loci. A polygenic score predicts 5.5% of the variance (for an odds ratio of 1.56). The study presents evidence that ADHD should be considered the end of a distribution: “Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.”
  • A much smaller fraction of developmental disorders (DDs) are explained by recessive variation in protein-coding genes than previously thought: ~3.6% in non-consanguineous populations, compared to 31% in consanguineous populations. Compare to 50% of DDs caused by de novo mutations. This work, from Hilary Martin et al appearing in Science, is based on a burden analysis approach in over 6000 exomes from the Deciphering Developmental Disorders study. “The high proportion of unexplained patients even amongst those with affected siblings or high consanguinity suggests that future studies should investigate a wide range of modes of inheritance including oligogenic and polygenic inheritance as well as noncoding recessive variants.”
  • “Just thinking you have poor endurance genes changes your body” – individuals were told a test had revealed they had one or another version of the gene CREB1, which affects how easily one tires, and were then set to run on a treadmill. Those who were told they had the version would meant they would tire more easily did indeed tire more easily. In fact, the participants had been randomized. Likewise for FTO, which can affect how full you feel, participants who were told they had the “less hungry” version of the gene reported feeling less hungry, and had higher levels of a hormone associated with feeling full. This would be a type of placebo effect for genetic information: “The results suggest that if a person just thinks they are at high risk for, say, obesity, it could change their physiology in a way that makes them more prone to the condition, Turnwald says.” (Paper here). “If simply conveying genetic risk information can alter actual risk, clinicians and ethicists should wrestle with appropriate thresholds for when revealing genetic risk is warranted.”
  • Calico and Ancestry.com have teamed up to show that longevity is <10% genetic. Using a single pedigree of over 400 million individuals, they were able to show that previous estimates (about 15-30%) overestimated genetic inheritance because they were confounded by non-genetic inheritance showing up via the effects of assortative mating.
  • Mitochondria can be inherited from both parents in humans. The inheritance appears to be autosomal.
  • We’ve known for a long time that there is a lot of undiscovered genetic diversity in African populations, and that use of the reference genome is rife with problems. Sequencing of 910 African genomes has showed just how large the problem is: at least 10% of reads failed to align to the reference genome, but were alignable to constructed pan-African contigs.
  • A polygenic score for schizophrenia explained some of the variance in response to antipsychotics.
  • In a severe reminder that polygenic scores cannot be used in ancestral populations not included in their construction, also using the polygenic score for schizophrenia from the Psychiatric Genetics Consortium, it was shown that the mean difference between Europeans and Africans was ten times as great as the mean difference between the European cases and controls.
  • I missed this in my last round-up. At ASHG researchers in GeneRisk, from Finland, presented data on over 7000 individuals who were given cardiac risk information, some including their polygenic cardiac risk score. Those identified at higher risk, particularly if genetic, did well at making lifestyle changes.
  • Mendelian Randomization is the idea that because genetics at birth is randomized and not altered by environmental confounders, considering some gene X, one can see whether gene X is subject to Loss or Gain of Function variants in the disease of interest. If it is, then Gene X is s good drug candidate – many high cost drug trials could have been avoided if MR had been performed. The technique can also be used to distinguish between correlation and causation, for example in showing that the correlation between obesity and depression is at least partially explained by obesity causing depression (depression odds ratio of 1.18 for 1 SD higher genetic risk score for high BMI). There are many improvements to the most basic MR model, see e.g. correcting for genetic correlations with shared etiology.
  • As an alternative to Mendelian Randomization posted to bioarxix, BADGERS (Biobank-wide Association Discovery using GEnetic Risk Scores), designed to identify associations between a disease and hundreds to thousands genetically-predicted complex traits (using polygenic scores). Variants can be reclassified.
  • A study has shown that over 30% of patients had their variants reclassified within a five year time period. “The findings of this study suggest that pediatric patients with epilepsy and previous genomic test results should have their test results reinterpreted at least every 2 years and before further genetic testing.”
  • A nice review of the genetic variation relevant to immunotherapy, written by one of my former colleagues, Eric Kofman.

Applications

Regulation

  • The UN’s Convention on Biological Diversity considered, and rejected, a ban on gene drives. Both those working on gene drive technology and those campaigning against it called the result a win. They state the need for informed consent, but it is not clear what this means: “who gets to decide when the African people have consented — and how unanimous a decision you need when millions of lives are on the line”
  • China has cracked down on companies that have sequenced Chinese individuals and then exported the data. Adam Minter argues that this view of chinese genetic data as a national security matter is in keeping with other chinese policies on e.g. the internet. Ultimately, a lack of openness will damage science, and chinese science in particular.
  • In April 2018 the FDA issued guidance on the use of databases in genomic testing. Applying that guidance, they have approved use of ClinGen’s Expert Curated Human Genetic Data as clinical evidence in approval submissions for tests. The data covers over 10,000 variants (ClinVar review status (“reviewed by expert panel”or “practice guideline”).
  • LunaDNA received SEC approval to sell shares to customers, part of its business plan to incentivize individuals to share their genomic data. It currently values a whole genome at $21.