Genomic Prediction of Complex Traits and Disease Risks (video of talk at IGI and OpenAI)

Seminar at the Innovative Genomics Institute (IGI, Berkeley and UCSF) July 17 2019. I gave a similar talk the following day at OpenAI. Jennifer Doudna, one of the co-discoverers of CRISPR-Cas9 gene editing, is the Executive Director of IGI. You might recognize her voice if you can hear the audience questions.

IGI began in 2014 through the Li Ka Shing Center for Genetic Engineering, which was created thanks to a generous donation from the Li Ka Shing Foundation. The Innovative Genomics Initiative formed as a partnership between the University of California, Berkeley and the University of California, San Francisco. Combining the fundamental research expertise and the biomedical talent at UCB and UCSF, the Innovative Genomics Initiative focused on unraveling the mechanisms underlying CRISPR-based genome editing and applying this technology to improve human health.

Slides -- slightly updated from the ones I used in the talk.

Title: Genomic Prediction of Complex Traits and Disease Risks via AI/ML and Large Genomic Datasets

Abstract: The talk is divided into two parts. The first gives an overview of the rapidly advancing area of genomic prediction of disease risks using polygenic scores. We can now identify risk outliers (e.g., with 5 or 10 times normal risk) for about 20 common disease conditions, ranging from diabetes to heart diseases to breast cancer, using inexpensive SNP genotypes (i.e., as offered by 23andMe). We can also predict some complex quantitative traits (e.g., adult height with accuracy of few cm, using ~20k SNPs). I discuss application of these results in precision medicine as well as embryo selection in IVF, and give some details about genetic architectures. The second part covers the AI/ML used to build these predictors, with an emphasis on "sparse learning" and phase transitions in high dimensional statistics.

College quality measures highly correlated to student SAT scores

Research by Brown, Chabris, and Wai shows that quality of students is strongly correlated to other quality measures of a college. (Thanks to a reader for sending the link!) See also this global analysis of university quality rankings.

Of course, causality is complex: schools with strong reputations, large endowments, etc. can attract top applicants. But how did those schools acquire those reputations and endowments in the first place?

Salon: ... Though there is often public controversy over the value of standardized tests, research shows that these tests are quite robust measures to predict academic performance, career potential, creativity and job performance.

Critics of the SAT say it tests for students’ wealth, not caliber. While it is true that wealthier parents tend to have students with higher test scores, it turns out the research robustly shows that test scores, even when you consider socioeconomic status, are predictive of later outcomes.

We first found high correlations between our test score rankings and U.S. News national university rank – 0.892 – and liberal arts college rank – 0.890 – even though U.S. News weights these scores only about 8% in their formula. ...

See also Universities Ranked By SAT Score (2013):

Schools with the strongest students (e.g., as measured by SAT) produce graduates who make outstanding contributions at per capita rates easily 10x or 100x higher than others: see Where Nobel winners get their start (Nature) and Colleges ranked by Nobel, Fields, Turing and National Academies output.

Dept. of Physicists Can Do Stuff: Brexit!

Dominic Cummings on how Vote Leave won the Brexit campaign. (Video should start at 13m30s.)

Dom hired a team of physicists and data scientists who

1. Studied the literature on elections (i.e., entered a well-established subject, studying it de novo, applying real horsepower), figured out which results/beliefs in that field were likely correct (much found was incorrect)

2. Adapted results to the job at hand (Brexit referendum) and invented new techniques for applying them

3. Built a new platform, wrote new code, executed in real time, and won a huge electoral victory against all odds.

Of course, this is the age old story of physicists invading/creating other fields: early computing, electrical engineering, molecular biology, computational biology, quantitative finance, high frequency trading, etc.

This victory will have historical reverberations that are still playing out.

13m35s: ... we had to take risks and we had to do things in a slightly new way so one of the basic things that I did was I brought in a team of physicists who essentially looked at campaigning from complete first principles and what they did was they went they simply scanned around the world and they said what studies have been done on issues of turnout and persuasion that actually have good maths behind them to support and have been replicated and we can actually have confidence in and they basically filtered all when through filtered them all out and came back to me in the team and said here is a small selection of things actually high quality or reasonable quality work which you can rely upon and here are the principles that you can see in these studies that have been replicated with randomised controlled trials and whatnot in the States

we basically created a checklist of what these things were and we built the communications team around trying to exploit each of these elements which the physicists found they also constructed models to help direct resources on the ground campaigns to wedge where to send your activists and the digital campaign how do you actually do that in a in a scientific way and essentially you had streams of data coming in from all sorts of different ways the website email on the ground canvassing a social media blah blah all of this stuff could be traditional polling all of the stuff coming in and you had the data science people sitting at the heart of the operation and essentially taking our core messages and just running experimentally a whole bunch of different things on Facebook and elsewhere and then figuring out what what things and what things don't work and we started off with relatively small amounts of money just to run this experimental process

another thing which which I'll go into a little bit of detail because it's from perhaps of interest regarding this election is we did a new kind of polling so I'm sure all of you know the polling methodology used throughout the world essentially the same system that was invented in the late 1930s and the idea of it is yo you take roughly speaking a thousand person sample and if it's random a representative then you can rely on the mathematics of the normal distribution and the famous bell curve and you that should give you a pretty accurate picture of what people think for various reasons that is becoming harder and harder to do happy to answer questions about why that is but leaving that aside what the physicists said was this is actually not the way that you would invent polling if you were going to invent polling now the way actually to do it is take massive samples of hundreds of thousands of people ideally actually millions of people but say hundreds thousands people and then use machine learning and you will actually have a system which is faster cheaper more accurate and never has another great advantage which we exploited which is that if you do these very large sample surveys you then have sub sample you can define the demographics that you interrogate yourself and what we did was we basically use the exact same categories infer demographics that Facebook uses for its digital advertising platform so we sucked in data on the precise same basis that Facebook marketing allows and then we had therefore large sub samples of the overall polling samples which you could actually rely on and then you could take that data and plug it straight back into Facebook so you could say for example we will target women between 35 and 45 who live in these particular geographical entities who don't have a degree or who do have a degree or whatever it's after cetera

and because you've got very large samples you can actually get useful information on those kind of relatively small breakdowns so we did all this and we as I said we essentially ran a whole series of experiments based on what we found at the conventional polling in the focus groups out in the digital world and then filtered what worked and then we held back almost all of our budget and then we basically dumped the entire budget or in the last ten days...

See also Dept. of Physicists Can Do Stuff: Gene Sequencing, Harold Brown, Ashton Carter.

More Dom.

How Brexit was won, and the unreasonable effectiveness of physicists:

The scale of ... triumph cannot be exaggerated. He ... had brought about a complete transformation of the European international order. He had told those who would listen what he intended to do, how he intended to do it, and he did it. He achieved this incredible feat without commanding an army, and without the ability to give an order to the humblest common soldier, without control of a large party, without public support, indeed, in the face of almost universal hostility, without a majority in parliament, without control of his cabinet, and without a loyal following in the bureaucracy.

...

On the eve and day of Brexit I happened to be staying at the estate of a billionaire hedge fund manager, which hosted a meeting of elite capital allocators. At breakfast, more than half of these titans of capital were in shock ... Markets were down 8% or more and my host asked for my view. It will play out over years, I said. No one knows where this is going to go. The market is oversold and it's a buying opportunity. So it was.

Manifold #17: Mark Moffett on the Life and Death of Human Societies

Steve and Corey talk with Mark Moffett, Photographer and Research Fellow at the Smithsonian Institute, about his new book The Human Swarm: How our Societies Arise, Thrive and Fall. They discuss Mark’s view that being able walk into a cafe filled with others and not be attacked illustrates what makes human societies distinct and so successful. Mark explains why he is far more interested in questions about when war and other events occur than with traditional issues such as the genetic origins of human behavior. The three discuss Dehumanization and its Chimp equivalent, Dechimpanizeeization, and how they lead to the division of societies, friend turning against friend, and genocide. They discuss the conditions under which foreigners are embraced and whether the US might ever enter into a post-racial society where group differences don’t matter and immigrants are more easily accepted.

Mark Moffett's Bio

Mark Moffett's Photography

The Human Swarm: How Our Societies Arise, Thrive, and Fall

Transcript

man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.

MSU New Faculty Welcome 2019

These are excerpts from remarks I gave yesterday at a reception for new faculty.

Good afternoon and Welcome!

We are so pleased that you are here at Michigan State University. You have joined a great research university, at a very exciting time.

I’m told I only have 10 minutes in which to say something about the deep and varied research enterprise here at MSU. That’s only enough time for a high level overview, so let me start with some big picture numbers. Each year the National Science Foundation publishes its Higher Education Research and Development (or HERD) report on the total research expenditures of all US universities. MSU’s total HERD number has grown from about $500M to $700M in the last seven years. We’ve advanced faster than any other Big Ten university, and now rank 32nd in the US among all universities.

MSU is ahead of Rutgers, UT Austin, Illinois (UIUC), Purdue, Arizona, Maryland, Indiana, Iowa, ASU, Colorado (Boulder), and UVA.

Based on the HERD comparison data, MSU ranks 1st in the nation in combined Department of Energy and National Science Foundation research expenditures.

Almost all of the schools ranked above us (and many below) have major research hospitals. In those cases, the medical research component of the HERD total often exceeds the rest of campus combined. At MSU, about ~$100M of our total comes from NIH. We still have significant room to advance.

There are only a few schools without a major medical complex that rank above us -- let me mention two: UC Berkeley $771M (top public university in the US; home of Lawrence Berkeley National Lab) and MIT $952M (home of Lincoln Laboratory, a major defense research lab).

MSU, UC Berkeley, and MIT are all research powerhouses. But we are similar in another important way: all three are land-grant universities. As land-grant universities, we pride ourselves on making breakthroughs in basic research, and applying those breakthroughs to make life better for the entire world.

... MSU is home to the Facility for Rare Isotope Beams, a scientific user facility for the Office of Nuclear Physics in the Office of Science of the U.S. Department of Energy.

FRIB will be operational in 2021 and will deliver the highest intensity beams of rare isotopes available anywhere in the world. Estimates of the total investment in this project are roughly $1 billion dollars. Operated by MSU, FRIB will enable scientists to make discoveries about the properties of rare isotopes (which are unusual forms of the elements) in order to better understand the physics of nuclei, nuclear astrophysics, and the fundamental interactions of nature. It will also produce practical applications for society, including in medicine, homeland security, and industry.

... Another recent development is a new department called Computational Mathematics, Science, and Engineering or CMSE. This department was planned, authorized, and operational in only three years—quite a feat in academia. I often compare “startup time” (the fast pace at which things are accomplished in Silicon Valley) to “academic time” (i.e., nothing gets done, other than committee meetings, and a no-brainer project takes a decade to complete), but with CMSE this was a case of something on campus getting done in startup time. CMSE is one of very few such departments in the country -- it is focused on data science, machine learning, advanced computation and related applications, but is not a traditional CS department. It supports many of the new efforts on campus that require the analysis of large data sets and development of new tools and algorithms. Researchers in this department utilize datasets drawn from astrophysics, business analytics, mobile data, materials science, human and plant genomics, and many other areas. The department was conceived as fundamentally interdisciplinary -- bringing together experts in computation with subject matter experts in areas of science which are becoming increasingly reliant on data.

I can’t help mentioning a couple of big data examples related to my own research: we’ve created a compute resource with more than 500k human genomes, open to interested investigators on campus. All of the data is stored at our High Performance Computing Center or HPCC. Using this data, our collaboration demonstrated for the first time that machine learning applied to large genomic datasets could produce accurate predictors for complex human traits. We can now predict adult human height from genome alone, with accuracy roughly 1 inch. The predictor uses ~20k genetic variants distributed throughout the genome. Predictors of complex disease risk, for conditions such as heart disease, diabetes, schizophrenia, and breast cancer, have been developed and broadly replicated in out-of-sample tests. I recently participated in a meeting at No 10 Downing Street in the UK, to plan a project which will genotype 5 million individuals through their National Healthcare System. This is only the beginning for genomic Precision Medicine.

... If there is a problem -- tell us about it! -- whether it has to do with grant submissions, or startup incubation, or child care, food options on campus, your functional or dysfunctional department. We’re here to fix things, and to provide the best possible environment for your teaching, research, and creative activity.

Only one in a thousand people in our society have the privilege to engage full time in discovery -- in curiosity driven research -- for the benefit of humankind. You are part of that lucky one in a thousand, and we are here to help you succeed.

The bar has been set very high, but with the resources and new opportunities here at MSU, your potential is limitless.

My very best wishes to you all :-)

Polygenic Architecture and Risk Prediction for 14 Cancers and Schizophrenia

Two recent papers on polygenic risk prediction. As I've emphasized before, these predictors already have real clinical utility but they will get significantly better with more training data.

Assessment of Polygenic Architecture and Risk Prediction based on Common Variants Across Fourteen Cancers

Yan Zhang et al.

We analyzed summary-level data from genome-wide association studies (GWAS) of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) contributing to risk, as well as the distribution of their associated effect sizes. All cancers evaluated showed polygenicity, involving at a minimum thousands of independent susceptibility variants. For some malignancies, particularly chronic lymphoid leukemia (CLL) and testicular cancer, there are a larger proportion of variants with larger effect sizes than those for other cancers. In contrast, most variants for lung and breast cancers have very small associated effect sizes. For different cancer sites, we estimate a wide range of GWAS sample sizes, required to explain 80% of GWAS heritability, varying from 60,000 cases for CLL to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores, compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that polygenic risk scores have substantial potential for risk stratification for relatively common cancers such as breast, prostate and colon, but limited potential for other cancer sites because of modest heritability and lower disease incidence.

Some people are surprised that a mental disorder might be strongly controlled by genetics -- why? However, it has been known for some time that schizophrenia is highly heritable. I anticipate that good predictors for Autism and Alzheimer's disease will be available soon.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems

Amanda B. Zheutlin et al.

Objective:
Individuals at high risk for schizophrenia may benefit from early intervention, but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts. The authors sought to test the utility of this approach in clinical settings and to evaluate the broader health consequences of high genetic risk for schizophrenia.

Methods:
The authors used electronic health records for 106,160 patients from four health care systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1,359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.

Results:
PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS, 1.55; 95% CI=1.4, 1.7), and patients in the highest risk decile of the PRS distribution had up to 4.6-fold higher odds of schizophrenia compared with those in the bottom decile (95% CI=2.9, 7.3). PRSs were also positively associated with other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.

Conclusions:
The study demonstrates that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in health care settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. The results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in health care systems.

Bruno Maçães on The Power Game in a Connected World

Bruno Maçães in Singapore at IRAHSS Geopolitics Reimagined, 22 July 2019.

Maçães is author of Belt and Road - A Chinese World Order and former Europe Minister of Portugal. He discusses the trade war, his recent visit to a Huawei factory, and the idea of hybrid warfare or weaponized interdependence.

I met with Bruno in Beijing last month. He is among the most insightful geopolitical thinkers today.

02:35
I was shown the assembly line for the P30 smartphone [~$1k flagship using Huawei chipset] and told that this assembly line just two or three years ago was operated by 140 operators people it is now down to 17 by the end of this year we'll be down to 15 it's a very long assembly line perhaps 200 250 meters takes about 30 minutes more important than the time it takes to assemble a P30 is the time between each unit and that's now down to 29 seconds so every 29 seconds a fully produced P30 comes out at the end 17 people operate now this this assembly line but the remarkable thing is that I actually looked very carefully at what the 17 were doing and it's very obvious they're not doing anything of significance they left there more in order to keep a certain control over the process...

07:17
this is not a new Cold War and I see no indications that were moving in that direction China and the United States continue to be turned towards to each other continue to be very interested in learning from each other and I think this is an important point their way of life their ideology the way they look at the world is not predicated on a negation of the other side the Soviet Union was from the very start a revolutionary movement whose whole identity was the negation of capitalist Western Way of life and organizing society now China and the United States in a way are much less connected they are not part of the same history and their dispute is not a dispute about who is fundamentally right about questions that involved both...

08:39
they're not necessarily involved in a death and life struggle between them the world we live in is I'll sum it up this way a world where and this is I think the puzzling element of it we are neither at war nor at peace we are somewhere in the middle conflict takes below takes place below the threshold of kinetic war and other forms of direct confrontation but it is no less intense because of that...

11:21
the tactics might include the purchase of infrastructure in other states the corruption or blackmail of foreign officials important elements of this new world that is not often talked about [CALLING EPSTEIN AND GHISLAINE MAXWELL] manipulation of energy flows or energy prices all of these elements are magnified in an integrated global economy the networks that bring us together are used as tools or instruments of conflict...

More Bruno, on the Belt and Road initiative.

Epstein and the Big Lie

The biggest Epstein conspiracy mystery is not how he died. The more important mystery is how he managed to operate out in the open for 15-20 years. Rumors concerning Epstein and leading figures like Bill Clinton have been around for at least that long. I have been following his activities, at least casually, for well over a decade.

In the 1990s I was a Bill Clinton supporter. I voted for him twice and supported his efforts to move the democratic party in a centrist, pro-business direction. But my brother is a Republican. He fed me a steady stream of anti-Clinton information that I (at the time) dismissed as crazy right-wing conspiracy theories. However, with the advent of the internet in the mid 90s it became easier to obtain information that was not filtered by corrupt mainstream media outlets. I gradually realized that at least some of my brother's claims were correct. For example, Clinton's first presidential bid was almost derailed by charges of adultery by women like Gennifer Flowers. Supporters like myself dismissed these charges as a right-wing smear. However, years later, Clinton admitted under oath that he had indeed had sex with Flowers.

My first exposure to Hillary Clinton was her appearance on 60 Minutes after the SuperBowl in 1992. This was widely regarded to be the emotive performance ("stand by your man") that saved Bill Clinton's presidential candidacy. Hillary affects a fake southern accent and (I believe) lies boldly and convincingly about Flowers to an estimated 50 million Americans. Quite a display of talent.

A side-effect of my history as a Clinton supporter (and gradual enlightenment thanks to my brother!) is that I became quite interested in the tendency of the media to hide obvious truths from the general public. We Americans accept that foreign governments (e.g., the Soviets and "ChiComs") successfully brainwash their people to believe all sorts of crazy and false things. But we can't accept that the same might be true here. (The big difference is that people in the PRC and former Soviet states  -- especially intellectuals -- know propaganda when they see it, whereas most Americans do not...)

It was natural for me to become aware of Epstein once he was linked to Bill Clinton at the very birth of the Clinton Foundation. It was easy to uncover very disturbing aspects of the Epstein story -- including details of his private island, traffic in young women, connections to the rich, the powerful, and even to leading scientists, academics (many of whom I know), and Harvard University. Almost anyone with access to the internet (let alone an actual journalist) could have discovered these things at any point in the last decade.

But just 6 months ago I could mention Epstein to highly educated "politically aware" acquaintances with absolutely no recognition on their part.

Some obvious, and still unanswered, questions:

Former Federal prosecutor and Labor Secretary R. Alexander Acosta said he was told to lay off Epstein, as he "belongs to intelligence" -- why no media followup on this? (Still don't believe in a Deep State?)

Clinton said he only flew on Epstein's plane 4 times (but 26 is also commonly reported) and never visited the island (despite many eyewitness claims to the contrary). No investigative reporting on this by mainstream media?

Epstein's partner Ghislaine Maxwell is the daughter of Robert Maxwell, a billionaire with possible Mossad connections. What were Epstein's links to Israeli intelligence and national interests? (Robert Maxwell's death is at least as mysterious as Epstein's ...)

Why did it take the FBI so long to get to Epstein's island? What have they found in Epstein's house and on his island? How much blackmail material is there and who is implicated?

Were it not for the possibility that the Epstein scandal might be damaging to Trump, would there be anything close to this level of mainstream media interest?

Why was there almost zero MSM interest in Epstein in the previous 15-20 years?

Someone was protecting Epstein (someone with influence on the DOJ, FBI, perhaps US intelligence) long before Donald Trump had political power of any kind. Why?

What other obvious scandals are hidden in plain sight? Iraq WMD? Spygate? Compromised politicians and national leaders? Blackmail by national intelligence services? Ideology-driven Social Media and Search filtering of information? Ivy League discrimination against Asian Americans? ...

Update

Manifold Episode #16: John Schulman of OpenAI

John Schulman is a research scientist at OpenAI. He co-leads the Reinforcement Learning group and works on agent learning in virtual game worlds (e.g., Dota) as well as in robotics. John, Corey, and Steve talk about AI, AGI (Artificial General Intelligence), the Singularity (self-reinforcing advances in AI which lead to runaway behavior that is incomprehensible to humans), and the creation and goals of OpenAI. They discuss recent advances in language models (GPT-2) and whether these results raise doubts about the usefulness of linguistic research over the past 60 years. Does GPT-2 imply that neural networks trained using large amounts of human-generated text can encode "common sense" knowledge about the world? They also discuss what humans are better at than current AI systems, and near term examples of what is already feasible: for example, using AI drones to kill people.

John Schulman

OpenAI

Better Language Models and Their Implications (GPT-2)

Transcript of show


man·i·fold /ˈmanəˌfōld/ many and various.

In mathematics, a manifold is a topological space that locally resembles Euclidean space near each point.

Steve Hsu and Corey Washington have been friends for almost 30 years, and between them hold PhDs in Neuroscience, Philosophy, and Theoretical Physics. Join them for wide ranging and unfiltered conversations with leading writers, scientists, technologists, academics, entrepreneurs, investors, and more.

Steve Hsu is VP for Research and Professor of Theoretical Physics at Michigan State University. He is also a researcher in computational genomics and founder of several Silicon Valley startups, ranging from information security to biotech. Educated at Caltech and Berkeley, he was a Harvard Junior Fellow and held faculty positions at Yale and the University of Oregon before joining MSU.

Corey Washington is Director of Analytics in the Office of Research and Innovation at Michigan State University. He was educated at Amherst College and MIT before receiving a PhD in Philosophy from Stanford and a PhD in a Neuroscience from Columbia. He held faculty positions at the University Washington and the University of Maryland. Prior to MSU, Corey worked as a biotech consultant and is founder of a medical diagnostics startup.


Sporting my OpenAI t-shirt. Wish I had worn this at Number 10 Downing Street earlier this week ;-)

Different Class Altogether

BBC Radio 4 profile of Dominic Cummings. (Sorry, didn't see any embed code.)

Some interesting comments from Dom's Oxford tutor, Robin James Lane Fox: (@4m50s)

He was extremely sharp, very sure of his own abilities, but had every reason to be... not narrow minded in any way...

BBC: Who is cleverer, Boris Johnson or Dominic Cummings?

Oh Dominic, by a long way.

BBC: A long way?

Different class altogether.

Robin James Lane Fox (Wikipedia), FRSL (born 5 October 1946)[1] is an English classicist, ancient historian and gardening writer known for his works on Alexander the Great.[2] Lane Fox is an Emeritus Fellow of New College, Oxford and Reader in Ancient History, University of Oxford. Fellow and Tutor in Ancient History at New College from 1977 to 2014...

See The Differences are Enormous, Creators and Rulers, and The Gulf is Deep.

How Brexit was won, and the unreasonable effectiveness of physicists.