Wednesday, February 13, 2019

Precision Genomic Medicine and the UK




I just returned from the UK, where I attended a Ditchley Foundation Conference on machine learning and genetic engineering. The attendees included scientists, government officials, venture capitalists, ethicists, and medical professionals.

The UK could become the world leader in genomic research by combining population-level genotyping with NHS health records. The application of AI to datasets of this kind has already led to the creation of genomic predictors that can identify individuals at high risk for common disease conditions such as breast cancer, heart disease, diabetes, hypothyroidism, etc. Such breakthroughs provide insight into the genetics of disease, and allow more efficient allocation of resources for prevention and early detection to the individuals who would most benefit. This saves both lives and money.

The US private health insurance system produces the wrong incentives for this kind of innovation: payers are reluctant to fund prevention or early treatment because it is unclear who will capture the ROI. Consider a cost-effective intervention that, e.g., prevents a patient from developing diabetes. This produces an obvious health benefit, but if the patient later changes insurer the financial benefits are lost to the one that paid for the intervention. Not a problem, though, in a single-payer system.

The NHS has the right incentives, the necessary scale, and access to a deep pool of scientific talent. The UK can lead the world into a new era of precision genomic medicine.

NHS has already announced an out-of-pocket genotyping service which allows individuals to pay for their own genotyping and to contribute their health + DNA data to scientific research. In recent years NHS has built an impressive infrastructure for whole genome sequencing (cost ~$1k per individual) that is used to treat cancer and diagnose rare genetic diseases. The NHS subsidiary Genomics England recently announced they had reached the milestone of 100k whole genomes.

For common conditions such as heart disease, diabetes, breast cancer, etc., most of the recent advances in risk prediction have come from even larger datasets (many hundreds of thousands of individuals) with genotypes from inexpensive arrays (~$50; like those used by 23andMe) that sample the genome at the roughly million or so most informative locations. By contrast, a whole genome sequence measures all 3 billion base pairs. This provides more raw data per individual, but we do not currently know how to use most of that information.

At the meeting, I emphasized the following:

1. NHS should offer both inexpensive (~$50) genotyping (sufficient for risk prediction of common diseases) along with the more expensive $1k whole genome sequencing. This will alleviate some of the negative reaction concerning a "two-tier" NHS, as many more people can afford the former.

2. An in-depth analysis of cost-benefit for population wide inexpensive genotyping would likely show a large net cost savings: the risk predictors are good enough already to guide early interventions that save lives and money. Recognition of this net benefit would allow NHS to replace the $50 out-of-pocket cost with free standard of care.

More on genomic precision medicine.

Slides I presented at Ditchley.

A 10 minute video covering the slides.

Thursday, February 07, 2019

Manifold Show, episode 3: Noor Siddiqui on Stanford and Silicon Valley



Show Page    YouTube Channel

Noor Siddiqui, Thiel Fellow, on Stanford and Silicon Valley – Episode #3
Corey and Steve interview Noor Siddiqui, a student at Stanford studying AI, Machine Learning, and Genomics. She was previously a Thiel Fellow, and founded a medical collaboration technology startup after high school. The conversation covers topics like college admissions, Tiger parenting, Millennials, Stanford, Silicon Valley startup culture, innovation in the US healthcare industry, and Simplicity and Genius.


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.

Wednesday, February 06, 2019

Brexit, the movie: Benedict Cumberbatch as Dominic Cummings



The Brexit movie, starring Benedict Cumberbatch as Dominic Cummings, is really good.

I was able to watch it online for free: Channel 4 in the UK. (Perhaps you can do that as well if you don't get HBO.)

I had to see it this afternoon before heading over to Dom's for dinner tonight :-)

See Dom's blog On the referendum #20: the campaign, physics and data science
We created new software. This was a gamble but the whole campaign was a huge gamble and we had to take many calculated risks. One of our central ideas was that the campaign had to do things in the field of data that have never been done before. This included a) integrating data from social media, online advertising, websites, apps, canvassing, direct mail, polls, online fundraising, activist feedback, and some new things we tried such as a new way to do polling (about which I will write another time) and b) having experts in physics and machine learning do proper data science in the way only they can – i.e. far beyond the normal skills applied in political campaigns. We were the first campaign in the UK to put almost all our money into digital communication then have it partly controlled by people whose normal work was subjects like quantum information ...

If you want to make big improvements in communication, my advice is – hire physicists, not communications people from normal companies and never believe what advertising companies tell you about ‘data’ unless you can independently verify it. Physics, mathematics, and computer science are domains in which there are real experts, unlike macro-economic forecasting which satisfies neither of the necessary conditions – 1) enough structure in the information to enable good predictions, 2) conditions for good fast feedback and learning. Physicists and mathematicians regularly invade other fields but other fields do not invade theirs so we can see which fields are hardest for very talented people. It is no surprise that they can successfully invade politics and devise things that rout those who wrongly think they know what they are doing. Vote Leave paid very close attention to real experts. (The theoretical physicist Steve Hsu has a great blog HERE which often has stuff on this theme, e.g. HERE.)

More important than technology is the mindset – the hard discipline of obeying Richard Feynman’s advice: ‘The most important thing is not to fool yourself and you are the easiest person to fool.’ They were a hard floor on ‘fooling yourself’ and I empowered them to challenge everybody including me. They saved me from many bad decisions even though they had zero experience in politics and they forced me to change how I made important decisions like what got what money. We either operated scientifically or knew we were not, which is itself very useful knowledge.
See also these old posts Brexit in the Multiverse: Dominic Cummings on the Vote Leave campaign and Brexit: victory over the Hollow Men.

From Dom himself (physicists appear at 13min40 ;-)

Thursday, January 31, 2019

Manifold Show, episode 2: Bobby Kasthuri and Brain Mapping




Show Page    YouTube Channel

Our plan is to release new episodes on Thursdays, at a rate of one every week or two.

We've tried to keep the shows at roughly one hour length -- is this necessary, or should we just let them go long?
Corey and Steve are joined by Bobby Kasthuri, a Neuroscientist at Argonne National Laboratory and the University of Chicago. Bobby specializes in nanoscale mapping of brains using automated fine slicing followed by electron microscopy. Among the topics covered: Brain mapping, the nature of scientific progress (philosophy of science), Biology vs Physics, Is the brain too complex to be understood by our brains? AlphaGo, the Turing Test, and wiring diagrams, Are scientists underpaid? The future of Neuroscience.

Bobby Kasthuri Bio
https://microbiome.uchicago.edu/directory/bobby-kasthuri 

The Physicist and the Neuroscientist: A Tale of Two Connectomes
http://infoproc.blogspot.com/2017/10/the-physicist-and-neuroscientist-tale.html

COMPUTING MACHINERY AND INTELLIGENCE, A. M. Turing https://www.csee.umbc.edu/courses/471/papers/turing.pdf


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.

Wednesday, January 30, 2019

The Future of Genomic Precision Medicine

As I mentioned in this earlier post, I'll be in the UK next week for a Ditchley Foundation conference on the intersection of machine learning and genetic engineering.

I'll present these slides at the meeting.

The slides review the rapidly evolving situation in genomic prediction, focusing on disease risk predicted using inexpensive genotyping. There are now 10-20 disease conditions for which we can identify, e.g., the top 1% outliers with 5-10x normal risk for the disease. The papers reporting these results have almost all appeared within the last year or so!

On the last slide I give a simple cost-benefit analysis of population wide genotyping and conclude that the net benefit is already positive given the tools we have. The numbers used are per capita. The UK NHS is already headed in this direction.

I use breast cancer as the example on the slide, but since the same genotype can be used for 10+ disease risks (including diabetes, atrial fibrillation, hypothyroidism, etc.) the net benefit is potentially much larger than what is obtained from breast cancer alone. The point is that G is really small compared to the potential benefit.



Details of breast cancer calculation below. I am sure one can do much better, but it provides a quick back of the envelope estimate of the numbers.

Spend $100 per person to genotype all women in the population. Identify those with top 10% risk score. About 33% of these individuals will get breast cancer. Treat the risk outliers by giving them, e.g., regular mammograms starting a decade earlier than usual (~$100 annual mammogram x 10y = $1k). In the slide I assume the average cost of the intervention / treatment is $1k and the average benefit is $12k. All of the high risk women (10%) get the intervention, but only the 33% percent that get breast cancer (or some subset of that group) benefit from early detection. This paper estimates that early detection of breast cancer saves typically tens of thousands of dollars per individual, so my numbers are conservative. If one uses multiple tens of thousands as the benefit amount, one could spend much more on early treatment and still have a positive net benefit.

Thursday, January 24, 2019

On with the Show


Our YouTube / podcast show is live!

Show Page

YouTube Channel

Podcast version available on iTunes and Spotify.

Our plan is to record a new one every 1-2 weeks. We're in the process of scheduling now, so if you have contacted me to be on the show, or have suggested a guest, please bear with us as we get going.
Manifold man·i·fold /ˈmanəˌfōld/ many and various

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

Steve and Corey 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.




Sunday, January 20, 2019

Ditchley Foundation Conference: The intersection of machine learning and genetic engineering


I'll be back in the UK soon for the meeting described below. Above, Ditchley House.
The intersection of machine learning and genetic engineering: what should be our check list for society and state as we blast off?

07 - 09 FEB 2019

Advances in machine learning and genetic engineering are combining to produce rapid advances in medicine, development of materials and genetic engineering. Parallel advances in robotics and automation have made the practical process of gene editing scalable. The possibility exists that advances in quantum computing could further accelerate progress on machine learning, bringing a second boost to this technological rocket.

This Ditchley conference will bring together an unusual mix of deep expertise and scientific renown in the disciplines; thinkers on religion, ethics and law; investors fueling innovation; and political leaders looking to shape the approach of society and state to fast emerging possibilities. We will attempt to establish sufficient common understanding of what the science promises and what it doesn’t and then explore the opportunities and risks that are likely to unfold at speed. This will be a first pass at preparation for potential blast off – what should be our moral, legal, economic and national security checklist as we wait on the launch pad of a new age?

The progress on machine learning is quite narrow in scope – deep learning using neural networks and other techniques on large data sets that now exist that didn’t previously and that are store-able and computable in a way that was not possible previously. But whereas progress towards general AI is often overstated, full general AI is not required to radically accelerate gene sequencing, editing and programming, with costs falling all the time and scale and speed increasing.

We will examine and try to come to preliminary conclusions on questions such as the following:

How should the most aggressive genetic engineering technologies be regulated?

How can societies best assess the ethical issues raised by these technologies to find an optimal balance between fostering genetic technologies for the common good while preventing abuse?

What are the implications for the global economy and economic cooperation and competition between states? Are we entering a period of bio-nationalism as well as AI nationalism? Should this be compared to the space race of the Cold War? How can we avoid competition between states driving abandonment of norms and moral standards? What will be the impact on the labour force of the new combined technologies of AI and bio-engineering? Within countries, will potential applications of the new technologies further intensify the concentration of wealth and power in a few hands?

What are the implications of rapid combined advances in AI and bioengineering for defence and national security? Will countries be tempted to pursue military applications either through bio-weapons or through the genetic improvement of military forces? What new materials will emerge and how will they affect the balance of power in warfare?

What are the implications for medicine and public health? If we are able to find targeted genetic cures for diseases like cancer then what will the impact be on the population? What are the implications for ageing or declining populations?

How should we handle the implications of deeper knowledge about the influence of our genes on our characteristics and on the characteristics of groups? How do we chart a course between remaining scientifically objective and providing material that could be misused to support racist conclusions by those tending to that view?

What opportunities and threats are there in the potential of these combined technologies for democracies and the equal value put on the view point of each citizen in the electoral system and the rule of law? More philosophically, how can we make sure the development of these technologies contributes to a positive sense of human progress and meaning, rather than to a sense of alienation and loss of purpose? How can we manage the tension between science and religion as human capability to shape the genetic world increases?
I'm only briefly in London on my way there, but might be able to squeeze in a few meetings :-)

See also:

The Future of IVF and Gene-Editing (Psychology Today interview)

The Future is Here: Genomic Prediction in MIT Technology Review

Genomic Prediction of Complex Disease Risk (bioRxiv)

Thursday, January 17, 2019

Babylon Health (BBC documentary)



On my last trip to London I learned that the startup Bablyon Health is causing a huge stir. Babylon received some initial funding from the founders of DeepMind (leading AI company acquired by Google; I was visiting them to give a talk).

Babylon created an AI phone app (a chatbot) to gather information from patients. The AI does triage and directs the patient (depending on severity of situation) to a GP (General Practitioner). The GP interaction takes place over video chat. The AI also suggests diagnoses to the GP who sees the recommendations on their computer screen. In tests the AI performed similarly to an average human GP and better than the worst GPs.

Already ~500k Londoners (including the UK Health Minister!) have chosen Bablyon as their NHS GP.

The BBC documentary above is really good.

Thursday, January 10, 2019

A Grand Experiment

I've been making secret preparations for a YouTube / Podcast show :-)

We've taped a first episode and are working on a few more. The whole thing is an experiment -- no telling whether it will work out, and no promises I'll have time to really do it right.

Please give me suggestions for people you'd like to see interviewed on the show. Or volunteer yourself if you have something to share. I think we'll probably allow pseudonymous guests, so your identity can be kept private.

I'm especially interested in knowledgeable people who could give us insight on

Silicon Valley (Big Tech and startups and VC)
Financial Markets
Academia (Good, Bad, and Ugly)
The View from Europe
The View from Asia (Life in PRC? Fear and Loathing of PRC?)
Frontiers of Science (AI, Genomics, Physics, ...)
Frontiers of Rationality
The Billionaire Life
MMA / UFC
What Millennials think us old folks don't understand
True things that you are not allowed to say
Bubbles that are ready to pop?
Under-appreciated Genius?
Overrated Crap and Frauds?


Here are two old videos I'm in that are already on YouTube:



Sunday, January 06, 2019

Slate Star Codex Meetup -- Berkeley

I will be at this meetup later today:
BAY MEETUP 1/6 UPDATE

POSTED ON JANUARY 4, 2019 BY SCOTT ALEXANDER
Due to rain, we’re switching to holding the meetup indoors at 3045 Shattuck Ave, Berkeley, 94705. There will be several floors of space available for overflow, so hopefully it won’t be too crowded. Thanks to Claire, REACH, and Event Horizon for setting this up.

Time is still 3:30 PM on Sunday, 1/6. There’s also a Facebook event here.
For the unfamiliar, Slate Star Codex is one of the best blogs on the planet, with a large devoted following of rationalists. Scott is an incredibly talented writer and thinker, and I envy him his readership and commentariat :-)

Serotonin: Houellebecq and Gilets Jaunes


In Houellebecq on Tocqueville, Democracy, and Nietzsche (2015) I pointed out that most intellectuals and elites have been so strongly conditioned by the existing cultural hegemony that they cannot understand obvious realities about the world. In that case I referred specifically to Houellebecq's previous novel Soumission.

Events since 2015 -- Trump's election and populist movements in Europe -- have stimulated a vague (but distorted) understanding in the minds of brainwashed elites as to populist discontent, its causes and origins. The reaction of our "thought leaders" is to decry the (previously sacred) democratic process by which the masses exercise their limited influence on society.

Individuals who told me confidently before the election that Trump had no chance of winning now forget how wrong they were then. They continue to express great confidence in their understanding of world events and political/economic processes.

So few are capable of updating prior beliefs in the face of new information. So many are overconfident in their powers of rationality.

Houellebecq has shown again that he understands reality much better than his critics.
Guardian: Serotonin, the story of a lovesick agricultural engineer who writes trade reports for the French agriculture ministry and loathes the EU, has been hailed by the French media as scathing and visionary. The novel rails against politicians who “do not fight for the interests of their people but are ready to die to defend free trade”.

Written before the current gilets jaunes anti-government movement began blockading roundabouts and tollbooths across France, it features desperate farmers in Normandy who stage an armed blockade of roads amid police clashes.

... In a recent article for Harpers, Houellebecq lauded Donald Trump for his protectionist policies, calling him “one of the best American presidents I’ve ever seen”, and praised Brexit: “The British get on my nerves, but their courage cannot be denied.” Serotonin, which will be published in English in September, viciously criticises free trade.
In Houellebecq on Tocqueville, Democracy, and Nietzsche, Hoellebecq discusses Tocqueville's insight concerning the manner in which democracy is likely to be subverted: by a soft tyranny that
Tocqueville (Democracy in America, chapter 6) ... covers the surface of society with a network of small complicated rules, minute and uniform, through which the most original minds and the most energetic characters cannot penetrate, to rise above the crowd. The will of man is not shattered, but softened, bent, and guided; men are seldom forced by it to act, but they are constantly restrained from acting. Such a power does not destroy, but it prevents existence; it does not tyrannize, but it compresses, enervates, extinguishes, and stupefies a people, till each nation is reduced to nothing better than a flock of timid and industrious animals, of which the government is the shepherd.
Soma, Serotonin, soft censorship of dangerous ideas -- call it what you will.

See also Paris 2018: Global Capital and Its Discontents.

Mama Said Choke You Out



If you do Judo, MMA, or BJJ you've probably seen someone choked all the way out. In this video CrossFit athlete Brooke Ence learns how to do hadaka jime (naked choke) and goes out herself. Very interesting if you've never seen it before :-)

Judo/BJJ chokes block blood flow to the brain, not air flow to the lungs. Deprived of blood (hence, oxygen), the brain transitions to unconsciousness quickly and rather abruptly, with interesting effects on memory and awareness.

I trained for years with a former Navy SEAL who would fight a submission to the end, so I choked him out on a number of occasions. Sometimes he would wake up afterward and ask me what happened. He also made the same little gurgling noise that Ence makes in the video.

See also Mama said knock you out and here for the LL Cool J reference ;-)

Friday, January 04, 2019

The Golden State

Apologies for the lack of posts. I've been enjoying some time with the family in CA :-) Kids have the MI middle school state swimming championships coming up and we got some good training done in a beautiful outdoor pool. I'm using USRPT methods, which seem to work well for my kids. I wish we had it when I was competing.

We had really good luck with the weather, sunny and 60s every day on the central coast.




Now I'm in the bay area for this meeting:

37th Annual J.P. Morgan HEALTHCARE CONFERENCE
January 7 - 10, 2019
Westin St. Francis Hotel | San Francisco, California


Thursday, December 27, 2018

Genomic Prediction of Complex Disease Risk (bioRxiv)


Our new paper describes over a dozen genomic predictors for common disease risk, constructed via machine learning on hundreds of thousands of genotypes. The predictors use anywhere from a few tens (e.g., 20 or 50) to thousands of SNPs to compute the risk PGS (Poly-Genic Score) for a specific disease.

The figure above (Atrial Fibrillation) shows out-of-sample testing of risk prediction (black dots with error bars) compared to theoretical prediction (red line). The theoretical prediction uses the empirical fact that cases and controls are normally-distributed in PGS score, with the two distributions shifted relative to each other. Cases have, on average, higher risk scores, and come to dominate in high PGS percentile bins. So, conditional on a high PGS risk score (e.g., 99th percentile PGS), the probability of the condition can be significantly elevated (e.g., ~8 times typical probability of developing atrial fibrillation).

We can identify, from SNP genotype alone, a subset of the population with unusual risk for conditions like Atrial Fibrillation or Diabetes or Breast Cancer or Prostate Cancer.

Just a year or two ago this would have seemed like science fiction to biomedical researchers...

Empirical validation of risk is limited by availability of out-of-sample populations for whom we have genotype and disease status. However, it is clear from the results that the theoretical models do a good job of predicting odds ratios once the properties of the case and control normal distributions (mean and standard deviation of PGS) are known.

These predictors only require data from an inexpensive ~$50 SNP array. Once the ~1 million SNPs on the array are measured *all* of the disease risks can be computed for an individual patient. It is only a matter of time before genotyping of this kind becomes Standard of Care in health systems around the world.

In the paper we also analyze the rate of improvement of prediction AUC as training sample size increases. With more data these predictors will become significantly more accurate -- the relevant timescale is just a few years!

Genomic Prediction of Complex Disease Risk

Louis Lello, Timothy Raben, Soke Yuen Yong, Laurent CAM Tellier, Stephen D. H. Hsu

We construct risk predictors using polygenic scores (PGS) computed from common Single Nucleotide Polymorphisms (SNPs) for a number of complex disease conditions, using L1-penalized regression (also known as LASSO) on case-control data from UK Biobank. Among the disease conditions studied are Hypothyroidism, (Resistive) Hypertension, Type 1 and 2 Diabetes, Breast Cancer, Prostate Cancer, Testicular Cancer, Gallstones, Glaucoma, Gout, Atrial Fibrillation, High Cholesterol, Asthma, Basal Cell Carcinoma, Malignant Melanoma, and Heart Attack. We obtain values for the area under the receiver operating characteristic curves (AUC) in the range  0.58 - 0.71 using SNP data alone. Substantially higher predictor AUCs are obtained when incorporating additional variables such as age and sex. Some SNP predictors alone are sufficient to identify outliers (e.g., in the 99th percentile of PGS) with 3-8 times higher risk than typical individuals. We validate predictors out-of-sample using the eMERGE dataset, and also with different ancestry subgroups within the UK Biobank population. Our results indicate that substantial improvements in predictive power are attainable using training sets with larger case populations. We anticipate rapid improvement in genomic prediction as more case-control data become available for analysis.

https://www.biorxiv.org/content/early/2018/12/27/506600

Wednesday, December 26, 2018

Ghosts and Hybrids: Ancient DNA and Human Origins



Take a break from your holiday Netflix binge and learn something about recent breakthroughs in our understanding of human evolution from ancient DNA.

John Hawks (UW Madison) is an excellent speaker and this talk is for non-experts. Get the whole family together to watch -- it's a treat to learn from one of the leading researchers!

For more video of lectures at MSU, by our faculty and visitors, see this YouTube channel: https://www.youtube.com/user/msuresearch

Dr. John Hawks delivers a lecture on Ancient DNA & Human Origins at Michigan State University on October 4, 2018.

The rapidly changing field of ancient DNA has settled into a kind of normal science, as several teams of researchers have coalesced around a set of approaches to discover the genetic relationships among ancient peoples.

Hawks is the Vilas-Borghesi Distinguished Achievement Professor of Anthropology at the University of Wisconsin - Madison. He is an anthropologist and studies the bones and genes of ancient humans. He's worked on almost every part of our evolutionary story, from the very origin of our lineage among the apes up to the last 10,000 years of our history.

Tuesday, December 25, 2018

Peace on Earth, Good Will to Men 2018



For years, when asked what I wanted for Christmas, I've been replying: Peace On Earth, Good Will To All Men :-)

No one ever seems to recognize that this comes from the bible, Luke 2.14 to be precise!

Linus said it best in A Charlie Brown Christmas:
And there were in the same country shepherds abiding in the field, keeping watch over their flock by night.

And, lo, the angel of the Lord came upon them, and the glory of the Lord shone round about them: and they were sore afraid.

And the angel said unto them, Fear not: for, behold, I bring you good tidings of great joy, which shall be to all people.

For unto you is born this day in the city of David a Saviour, which is Christ the Lord.

And this shall be a sign unto you; Ye shall find the babe wrapped in swaddling clothes, lying in a manger.

And suddenly there was with the angel a multitude of the heavenly host praising God, and saying,

Glory to God in the highest, and on earth peace, good will toward men.

Merry Christmas!

Three years ago today I shared the following story on this blog: Nativity 2050

For an update, see

The Future of IVF and Gene-Editing (Psychology Today interview)

Validation of simultaneous preimplantation genetic testing (PGT) for aneuploidy, monogenic, and polygenic disorders

The Future is Here: Genomic Prediction in MIT Technology Review



And the angel said unto them, Fear not: for, behold, I bring you good tidings of great joy, which shall be to all people.
Mary was born in the twenties, when the tests were new and still primitive. Her mother had frozen a dozen eggs, from which came Mary and her sister Elizabeth. Mary had her father's long frame, brown eyes, and friendly demeanor. She was clever, but Elizabeth was the really brainy one. Both were healthy and strong and free from inherited disease. All this her parents knew from the tests -- performed on DNA taken from a few cells of each embryo. The reports came via email, from GP Inc., by way of the fertility doctor. Dad used to joke that Mary and Elizabeth were the pick of the litter, but never mentioned what happened to the other fertilized eggs.

Now Mary and Joe were ready for their first child. The choices were dizzying. Fortunately, Elizabeth had been through the same process just the year before, and referred them to her genetic engineer, a friend from Harvard. Joe was a bit reluctant about bleeding edge edits, but Mary had a feeling the GP engineer was right -- their son had the potential to be truly special, with just the right tweaks ...
See also [1], [2], and [3].

Wednesday, December 19, 2018

IceCube: neutrino astronomy in Antarctica



Tyce DeYoung (MSU Department of Physics and Astronomy) colloquium on high-energy astrophysics and exploration of the high-energy universe with the IceCube neutrino detector at the South Pole. Several MSU professors are part of the IceCube collaboration.

I predict very exciting developments in neutrino astronomy in the coming decade ;-)

The situation is similar to that for LIGO a few years ago. Events of significant scientific interest have already been seen with the detector at small (here small means instrumenting a cubic kilometer of ice!) fiducial volume. At a higher volume (10x or more scale up in IceCube anticipated upgrade), we therefore expect a robust new kind of astronomy to emerge, using a never before available probe of the universe -- for IceCube, high energy neutrinos, for LIGO, gravity waves. In both cases new insights into astrophysical black holes (and perhaps other very exotic objects) are likely to emerge.

Note the scale of the experiment in the image below -- in units of Eiffel Towers :-)

Tuesday, December 18, 2018

A Realist Appraisal of US Foreign Policy



An evenhanded realist appraisal of US foreign policy going back to the end of the Cold War.

Topics addressed: Should we have extended NATO to the east of Germany, despite promises made to Gorbachev by GHWB? Should we have supported PRC WTO accession? Should we have invaded Iraq after 9/11? Hasn't Obama openly admitted that what we did in Libya and Syria (thanks, Hillary!) recently was a tragic disaster? Trump's trade war, Populism and Democracy, Why are our foreign policy elites so stupid -- is there no penalty for being wrong again and again? (He doesn't really answer the last question -- it's mine.)
Wikipedia: Stephen Martin Walt (born July 2, 1955) is an American professor of international affairs at Harvard University's John F. Kennedy School of Government. He belongs to the realist school of international relations.

Walt was born in Los Alamos, New Mexico, where his father, a physicist, worked at Los Alamos National Laboratory. ... Walt pursued his undergraduate studies at Stanford University.  ... After attaining his B.A., Walt began graduate work at UC Berkeley, graduating with a M.A. in Political Science in 1978, and a Ph.D. in Political Science in 1983.
Walt and University of Chicago political scientist Stephen Mearsheimer endured significant blowback for their too-realistic 2007 book The Israel Lobby and U.S. Foreign Policy.

Monday, December 17, 2018

Advances in Genomic Prediction: Breast Cancer Risk


This is a new paper on polygenic prediction for breast cancer by a large collaboration that has been working for many years on GWAS and, more recently, genomic risk prediction.

"The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%" !
Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes
https://www.cell.com/ajhg/fulltext/S0002-9297(18)30405-1

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
Note 10-25x (ER-positive and -negative) range of risk between lowest and highest percentile PRS score.

One of the senior authors (Paul Pharoah of Cambridge) details the history of his work on genetics of breast cancer in a tweet thread. He describes historical progress from simple GWAS associations to full-blown genomic prediction:
1/n This paper has been many years in the making both conceptually and in terms of the time to generate the data. It has been part of almost all my scientific life (or at least since I started my PhD).

...

11/n This PRS is now being used in an EU funded trial of risk stratified screening. It is the culmination of many years of many people working together on samples donated by hundreds of thousands of patients.

https://twitter.com/paulpharoah/status/1073677455372759041
My small team of physicists has constructed a breast cancer predictor of similar power using UKBB data and our own automated ML pipeline :-)

See earlier post Advances in Genomic Prediction.

Friday, December 14, 2018

The Future of the U.S. Aircraft Carrier: Fearsome Warship or Expensive Target? (Heritage Panel; video)



This is a Heritage Foundation panel on the future of the aircraft carrier. The discussion addresses, in part, the possibility that in a peer-competitor conflict aircraft carriers will have to operate 1000 miles offshore (range of the PRC DF21 anti-ship ballistic missile; the DF26 may have twice the reach), requiring a new class of (perhaps unmanned) aircraft with greater range than, e.g., the F35 fighter. The focus on ASBM like the DF21 might have been too narrow, as both PRC and Russia have equally dangerous ASCM (anti-ship cruise missle) capability.

The basic problem is that aircraft carriers are easy to detect (e.g., satellite imaging via optical or radar sensors) and missiles (especially those with maneuver capability) are very difficult to stop. Advances in AI / machine learning tend to favor missile targeting, not defense of carriers.

For previous discussion of these issues, see these posts.
The Future of the U.S. Aircraft Carrier: Fearsome Warship or Expensive Target?

Over 70 years ago, U.S. Navy aircraft carriers supplanted battleships as preeminent warship with their ability to strike enemy warships or land targets hundreds of miles away. Since World War II, U.S. aircraft carriers and the carrier air wing have operated relatively unthreatened, providing unrivaled air support and power projection capability in every U.S. conflict. Recently, an increasing number of critics are predicting the end of the aircraft carrier era. They cite the growing threats from anti-ship missiles, such as China’s DF-21D “carrier killer”; the proliferation of increasingly quieter attack submarines; and advanced integrated air and missile defense capabilities. They also argue that current carrier strike fighter aircraft and their weapons lack sufficient range to engage targets in a denied/degraded environment. Aircraft carrier proponents argue that a modern U.S. supercarrier uniquely provides a globally deployable U.S. airfield that can rapidly respond to emergent crises and does not depend the approval of any host nation. While they acknowledge the increased threats to the carrier strike group and its air wing, they argue that introduction of the fifth generation F-35, long-range unmanned carrier-based tankers, advanced weapons and electronic warfare systems, and the employment of new operational tactics will enable the aircraft carrier to remain relevant for the foreseeable future. Can the new USS FORD-class aircraft carrier and a modernized carrier air wing provide effective sea-based power projection against near-peer competitors like Russia and China, should the U.S. Navy develop smaller aircraft carriers with new weapons systems and carrier aircraft to meet these 21st Century threats, or should the U.S. move on from the aircraft carrier?

Monday, December 10, 2018

Music and Mathematics: Noam Elkies


Dinner with two old Harvard friends -- mathematician Noam Elkies and MSU physicist Dean Lee. Noam is in town this week to give a lecture, a colloquium, and perform a piano recital.

At 26 Noam became the youngest full professor in Harvard history, and the youngest to ever receive tenure. He has an amazing Wikipedia entry :-)
In 1981, at age 14, he was awarded a gold medal at the 22nd International Mathematical Olympiad, receiving a perfect score of 42 and becoming one of just 26 participants to attain this score,[3] and one of the youngest ever to do so. Elkies graduated from Stuyvesant High School in 1982[4][5] and went on to Columbia University, where he won the Putnam competition at the age of sixteen years and four months, making him one of the youngest Putnam Fellows in history.[6] He was a Putnam Fellow two more times during his undergraduate years. After graduating as valedictorian at age 18 with a summa cum laude in Mathematics and Music, he earned his Ph.D. at the age 20 under the supervision of Benedict Gross and Barry Mazur at Harvard University.[7]

From 1987 to 1990 he was a junior fellow of the Harvard Society of Fellows.[8]

In 1987, he proved that an elliptic curve over the rational numbers is supersingular at infinitely many primes. In 1988, he found a counterexample to Euler's sum of powers conjecture for fourth powers.[9] His work on these and other problems won him recognition and a position as an associate professor at Harvard in 1990.[4] In 1993, he was made a full, tenured professor at the age of 26. This made him the youngest full professor in the history of Harvard.[10] Along with A. O. L. Atkin he extended Schoof's algorithm to create the Schoof–Elkies–Atkin algorithm.
Noam, Dean, and I are all veterans of the Malkin Athletic Center weight room, when it was old-school and gritty :-)

Here's an earlier version of the talk Noam gave tonight. Video should start with him constructing a canon from thin air!

Sunday, December 09, 2018

Paris 2018: Global Capital and Its Discontents



Is she shooting video of the riot outside, or is she video chatting with a friend, oblivious? Did she just have the Royale with Cheese? :-)

My suggested title is Global Capital and Its Discontents.

Amazing work by this photographer.








Paris in happier times.
“If you are lucky enough to have lived in Paris as a young man, then wherever you go for the rest of your life, it stays with you, for Paris is a moveable feast.” ― Ernest Hemingway, A Moveable Feast

A meal by the Seine.



Les Deux Magots.



Le Louvre.







View from Sacre Coeur.

Friday, December 07, 2018

Crude Awakening: The Yuan, the Dollar, and the Battle for Global Supremacy



Yuan-Dollar-Oil discussion starts about 20min in. Any professionals want to weigh in?

In the past the main drivers of oil prices were supply-demand and dollar confidence (all transactions in dollars). Now you can add dollar-yuan fx factors... even gold.

Have idiots screaming about Khashoggi thought about what happens if Saudi starts accepting Yuan for oil, as Russia, Iran, and Venezuela do now?

See also On the military balance of power in the Western Pacific.

Wednesday, December 05, 2018

The Quantum Theory of Fields


Excerpt from Sidney Coleman's Erice lectures. The period he describes just predates my entry into physics.
This was a great time to be a high-energy theorist, the period of the famous triumph of quantum field theory. And what a triumph it was, in the old sense of the word: a glorious victory parade, full of wonderful things brought back from far places to make the spectator gasp with awe and laugh with joy. I hope some of that awe and joy has been captured here.
Physics students learn quantum mechanics and special relativity as undergraduates, but typically do not encounter a synthesis of the two until graduate school, in a course on quantum field theory. Undergraduate quantum mechanics focuses on non-relativistic particles, moving at much less than the speed of light (e.g., the electrons in atomic systems or ordinary matter). Special relativity, as first encountered by students, is a modification of Newtonian (classical) mechanics, and ignores quantum effects.

In quantum field theory (QFT), the wave function of quantum mechanics Ψ(x) becomes a wave functional Ψ[ Φ(x) ], valued over field configurations Φ(x) which are themselves functions of spacetime coordinates. Individual particles are excitations ("quanta") of quantum fields. I think it is fair to say that almost no student really gets a deep understanding of quantum field theory when they take it for the first time. It is simply too complex to digest quickly. QFT introduces new intuitive pictures, novel calculational tricks, strange physical and mathematical constructs.

And how could it be otherwise? All of these tools are necessary to make sense of the generalization of ordinary quantum mechanics (of a finite number of degrees of freedom) to a physical system with an infinite number of degrees of freedom.

I first took quantum field theory (Physics 205) in my last year at Caltech, taught by Fredrik Zachariasen. Zachariasen used Bjorken and Drell I and II and Ramond as the main textbooks. He was what Russian theorists sometimes refer to as a "strong calculator" -- he would fill the blackboard with equations as fast as we could note them down. However, I would say his approach to the subject was rather old-fashioned by that time, and while I learned a good bit about the Dirac equation, spinors, how to compute Feynman diagrams, and even about path integrals, my overall understanding of the subject was still lacking. If I had been there the following year I would have enjoyed John Preskill's version of 205 (see below), but alas I was already in graduate school by then.

I remember that I also studied Feynman's short volume (in the Frontiers in Physics series; not to be confused with his later popular book) Quantum Electrodynamics. I was very confused at the time about the relationship between particles and fields and about so-called Second Quantization.  Also, what happened to the Schrodinger equation? At no point did Zachariasen (nor, I think, do Bjorken and Drell) clarify that while Dirac deduced his equation via relativistic generalization of Schrodinger's, the two are not on the same logical footing.

It was only some years later that I realized that Feynman himself had been confused about these things when he wrote his early papers on the subject. (Feynman, when someone explained a creation operator and Fock space to him: "How can you create an electron? It disagrees with conservation of charge!") Do Feynman diagrams describe spacetime trajectories of particles? Or are they simply graphical representations of terms in a perturbative expansion that happen to correspond, intuitively but not exactly, to physical processes?

As a first year graduate student at Berkeley I took Physics 230 from Stanley Mandelstam, a true master of the subject. This course was far more theoretical than the one I had taken the previous year. Amazingly, Stanley taught without notes. The only day he brought a single page of paper to class was when he covered the BPHZ proof of renormalizability. (Or was it the day he derived the beta function for non-Abelian gauge theories? I might be conflating two different instances.) His lectures followed no specific textbook, although the recommended one was probably Itzykson and Zuber.

My final student encounter with a QFT course was as the grader for Physics 230, taught by Martin Halpern. (I am sad to discover, in finding this link, that Marty passed away earlier this year.) Marty was a high strung chain smoker, and I recall many hours in his office going over solutions to his homework problems. He was especially on edge that fall because Vaughan Jones from the math department (who was about to share the Fields Medal with Ed Witten!) had decided to learn QFT and was sitting in on the class. As might be expected, the mathematician's insistence on clarity and precision slowed Marty down significantly. This wasn't Marty's fault -- QFT has not, even today, been placed on a completely rigorous footing (at least, not to the satisfaction of mathematicians), even though it is (in the form of Quantum Electrodynamics and the Standard Model) the most precisely tested theoretical construct in science.

This post is long enough. Perhaps I will revisit the topic in the future with a discussion of Sidney Coleman's lectures on QFT at Harvard, where I went after graduate school. It's nice to see that these lectures have been rendered into a book by his former students. For many years one could check out videotapes (Sony Betamax!) of his lectures from the physics library at Harvard. This made me think, even then, that the future of many professors might someday be as glorified teaching assistants, helping to explain and clarify recorded or streamed lectures by the true masters.

If I have kindled your interest in the subject, I recommend my friend Tony Zee's book: Quantum Field Theory in a Nutshell. Also, John Preskill's fantastic lecture notes, covering basic as well as advanced topics. It took me some time to learn to decipher his handwriting, but it was worth it!

Let me end by noting that the physics students who took these classes with me are quite a remarkable group. Among them are a number of well-known theoretical physicists, as well as the odd startup founder, AI researcher, or hedge fund billionaire. You could do worse in this life than get to know some students of quantum field theory :-)



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