Genetic correlation of social outcomes between relatives (Fisher 1918) tested using lineage of 400k English individuals

Greg Clark (UC Davis and London School of Economics) deserves enormous credit for producing a large multi-generational dataset which is relevant to some of the most fundamental issues in social science: inequality, economic development, social policy, wealth formation, meritocracy, and even recent human evolution. If you have even a casual interest in the dynamics of human society you should study these results carefully...

See previous discussion on this blog. 

Clark recently posted this preprint on his web page. A book covering similar topics is forthcoming.

For Whom the Bell Curve Tolls: A Lineage of 400,000 English Individuals 1750-2020 shows Genetics Determines most Social Outcomes 

Gregory Clark, University of California, Davis and LSE (March 1, 2021) 

Economics, Sociology, and Anthropology are dominated by the belief that social outcomes depend mainly on parental investment and community socialization. Using a lineage of 402,000 English people 1750-2020 we test whether such mechanisms better predict outcomes than a simple additive genetics model. The genetics model predicts better in all cases except for the transmission of wealth. The high persistence of status over multiple generations, however, would require in a genetic mechanism strong genetic assortative in mating. This has been until recently believed impossible. There is however, also strong evidence consistent with just such sorting, all the way from 1837 to 2020. Thus the outcomes here are actually the product of an interesting genetics-culture combination.

The correlational results in the table below were originally deduced by Fisher under the assumption of additive genetic inheritance: h2 is heritability, m is assortativity by genotype, r assortativity by phenotype. (Assortative mating describes the tendency of husband and wife to resemble each other more than randomly chosen M-F pairs in the general population.)

Fisher, R. A. 1918. “The Correlation between Relatives on the Supposition of Mendelian Inheritance.” Transactions of the Royal Society of Edinburgh, 52: 399-433

Thanks to Clark the predictions of Fisher's models, applied to social outcomes, can now be compared directly to data through many generations and across many branches of English family trees. (Figures below from the paper.)

The additive model fits the data well, but requires high heritabilities h2 and a high level m of assortative mating. Most analysts, including myself, thought that the required values of m were implausibly large. However, using modern genomic datasets one can estimate the level of assortative mating by simply looking at the genotypes of married couples. 

From the paper:

(p.26) a recent study from the UK Biobank, which has a collection of genotypes of individuals together with measures of their social characteristics, supports the idea that there is strong genetic assortment in mating. Robinson et al. (2017) look at the phenotype and genotype correlations for a variety of traits – height, BMI, blood pressure, years of education - using data from the biobank. For most traits they find as expected that the genotype correlation between the parties is less than the phenotype correlation. But there is one notable exception. For years of education, the phenotype correlation across spouses is 0.41 (0.011 SE). However, the correlation across the same couples for the genetic predictor of educational attainment is significantly higher at 0.654 (0.014 SE) (Robinson et al., 2017, 4). Thus couples in marriage in recent years in England were sorting on the genotype as opposed to the phenotype when it comes to educational status. 

It is not mysterious how this happens. The phenotype measure here is just the number of years of education. But when couples interact they will have a much more refined sense of what the intellectual abilities of their partner are: what is their general knowledge, ability to reason about the world, and general intellectual ability. Somehow in the process of matching modern couples in England are combining based on the weighted sum of a set of variations at several hundred locations on the genome, to the point where their correlation on this measure is 0.65.

Correction: Height, Educational Attainment (EA), and cognitive ability predictors are controlled by many thousands of genetic loci, not hundreds! 

This is a 2018 talk by Clark which covers most of what is in the paper.

For out of sample validation of the Educational Attainment (EA) polygenic score, see Game Over: Genomic Prediction of Social Mobility.

 

Infinity and Solipsism, Physicists and Science Fiction

The excerpt below is from Roger Zelazny's Creatures of Light and Darkness (1969), an experimental novel which is somewhat obscure, even to fans of Zelazny. 

Positing infinity, the rest is easy. 

The Prince Who Was A Thousand is ... a teleportationist, among other things ... the only one of his kind. He can transport himself, in no time at all, to any place that he can visualize. And he has a very vivid imagination. 

Granting that any place you can think of exists somewhere in infinity, if the Prince can think of it too, he is able to visit it. Now, a few theorists claim that the Prince’s visualizing a place and willing himself into it is actually an act of creation. No one knew about the place before, and if the Prince can find it, then perhaps what he really did was make it happen. However, positing infinity, the rest is easy.

This contains already the central idea that is expressed more fully in Nine Princes in Amber and subsequent books in that series.

While traveling (shifting) between Shadows, [the prince] can alter reality or create a new reality by choosing which elements of which Shadows to keep or add, and which to subtract.

Creatures of Light and Darkness also has obvious similarities to Lord of Light, which many regard as Zelazny's best book and even one of the greatest science fiction novels ever written. Both have been among my favorites since I read them as a kid.

Infinity, probability measures, and solipsism have received serious analysis by theoretical physicists: see, e.g.,  Boltzmann brains. (Which is less improbable: the existence of the universe around you, or the existence of a single brain whose memory records encode that universe?) Perhaps this means theorists have too much time on their hands, due to lack of experimental progress in fundamental physics. 

Science fiction is popular amongst physicists, but I've always been surprised that the level of interest isn't even higher. Two examples I know well: the late Sidney Coleman and my collaborator Bob Scherrer at Vanderbilt were/are scholars and creators of the genre. See these stories by Bob, and Greg Benford's Remembing Sid: 

... Sid and some others created a fannish publishing house, Advent Publishers, in 1956. He was a teenager when he helped publish Advent’s first book, Damon Knight’s In Search of Wonder. ... 

[Sid] loved SF whereas Einstein deplored it. Lest SF distort pure science and give people the false illusion of scientific understanding, Einstein recommended complete abstinence from any type of science fiction. “I never think of the future. It comes soon enough,” he said.

While I've never written science fiction, occasionally my research comes close -- it has at times addressed questions of the form: 

Do the Laws of Nature as we know them allow ... 

... time travel?

... the construction of a stable wormhole? 

... the creation of a baby universe in the laboratory? 

... the creator of a baby universe to send a message to those inside? 

... genetic engineering of superhumans?

This research might be considered as the ultimate in hard SF ;-) 

Wikipedia: Hard science fiction is a category of science fiction characterized by concern for scientific accuracy and logic.

Note Added: Bob Scherrer writes: In my experience, about 1/3 of research physicists are SF fans, about 1/3 have absolutely no interest in SF, and the remaining 1/3 were avid readers of science fiction in middle school/early high school but then "outgrew" it.

Here is a recent story by Bob which I really enjoyed -- based on many worlds quantum mechanics :-) 

It was ranked #2 in the 2019 Analog Magazine reader poll!

Note Added 2: Kazuo Ishiguro (2017 Nobel Prize in Literature) has been evolving into an SF/fantasy writer over time. And why not? For where else can one work with genuinely new ideas? See Never Let Me Go (clones), The Buried Giant (post-Arthurian England), and his latest book Klara and the Sun.

NYTimes: ... we slowly discover (and those wishing to avoid spoilers should now skip to the start of the next paragraph), the cause of Josie’s mysterious illness is a gene-editing surgery to enhance her intellectual faculties. The procedure carries high risks as well as potential high rewards — the main one being membership in a professional superelite. Those who forgo or simply can’t afford it are essentially consigning themselves to economic serfdom.

WSJ: ... Automation has created a kind of technological apartheid state, which is reinforced by a dangerous “genetic editing” procedure that separates “lifted,” intellectually enhanced children from the abandoned masses of the “unlifted.” Josie is lifted, but the procedure is the cause of her illness, which is often terminal. Her oldest friend and love interest, Rick, is unlifted and so has few prospects despite his obvious brilliance. Her absentee father is an engineer who was outsourced by machines and has since joined a Community, one of the closed groups formed by those lacking social rank. In a conversational aside it is suggested that the Communities have self-sorted along racial lines and are heavily armed.

Othram: Appalachian hiker found dead in tent identified via DNA forensics

 

Othram helps solve another mystery: the identity of a dead Appalachian hiker. 

There are ~50k unidentified deceased individuals in the US, with ~1k new cases each year.

CBS Sunday Morning: He was a mystery who intrigued thousands: Who was the hiker who walked almost the entire length of the Appalachian Trail, living completely off the grid, only to be found dead in a tent in Florida? It took years, and the persistence of amateur sleuths, to crack the case. Nicholas Thompson of The Atlantic Magazine tells the tale of the man who went by the name "Mostly Harmless," and about the efforts stirred by the mystery of his identity to give names to nameless missing persons.

See also Othram: the future of DNA forensics.

David Reich: Prehistory of Europe and S. Asia from Ancient DNA

 

In case you have not followed the adventures of the Yamnaya (proto Indo-Europeans from the Steppe), I recommend this recent Harvard lecture by David Reich. It summarizes advances in our understanding of deep human history in Europe and South Asia resulting from analysis of ancient DNA. 

The new technology of ancient DNA has highlighted a remarkable parallel in the prehistory of Europe and South Asia. In both cases, the arrival of agriculture from southwest Asia after 9,000 years ago catalyzed profound population mixtures of groups related to Southwest Asian farmers and local hunter-gatherers. In both cases, the spread of ancestry ultimately deriving from Steppe pastoralists had a further major impact after 5,000 years ago and almost certainly brought Indo-European languages. Mixtures of these three source populations form the primary gradients of ancestry in both regions today. 

In this lecture, Prof. Reich will discuss his new book, Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past. 

There seems to be a strange glitch at 16:19 and again at 27:55 -- what did he say?

See also Reich's 2018 NYTimes editorial.

The Post-American World: Crooke, Escobar, Blumenthal, and Marandi

 

Even if you disagree violently with the viewpoints expressed in this discussion, it will inform you as to how the rest of the world thinks about the decline of US empire. 

The group is very diverse: a former UK diplomat, an Iranian professor educated in the West but now at University of Tehran, a progressive author and journalist (son of Clinton advisor Sidney Blumenthal) who spent 5 years reporting from Israel, and a Brazilian geopolitical analyst who writes for Asia Times (if I recall correctly, lives in Thailand).

Thirty years ago, the United States dominated the world politically, economically, and scientifically. But today? 

Watch this in-depth discussion with distinguished guests: 

Alastair Crooke - Former British Diplomat, Founder and Director of the Conflicts Forum 

Pepe Escobar - Brazilian Political Analyst and Author 

Max Blumenthal - American Journalist and Author from Grayzone 

Chaired by Dr. Mohammad Marandi - Professor at University of Tehran

See also two Escobar articles linked here. Related: Foreign Observers of US Empire.  

Physics and AI: some recent papers

Three AI paper recommendations from a theoretical physicist (former collaborator) who now runs an AI lab in SV. Less than 5 years after leaving physics research, he and his team have shipped AI products that are used by millions of people. (Figure above is from the third paper below.)

This paper elucidates the relationship between symmetry principles (familiar from physics) and specific mathematical structures like convolutions used in DL.

Covariance in Physics and CNN  

https://arxiv.org/abs/1906.02481 

Cheng, et al.  (Amsterdam)

In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs). We study the similarities and differences between the use of covariance in theoretical physics and in the CNN context. Additionally, we demonstrate that the simple assumption of covariance, together with the required properties of locality, linearity and weight sharing, is sufficient to uniquely determine the form of the convolution.

The following two papers explore connections between AI/ML and statistical physics, including renormalization group (RG) flow. 

Theoretical Connections between Statistical Physics and RL 

https://arxiv.org/abs/1906.10228 

Rahme and Adams  (Princeton)

Sequential decision making in the presence of uncertainty and stochastic dynamics gives rise to distributions over state/action trajectories in reinforcement learning (RL) and optimal control problems. This observation has led to a variety of connections between RL and inference in probabilistic graphical models (PGMs). Here we explore a different dimension to this relationship, examining reinforcement learning using the tools and abstractions of statistical physics. The central object in the statistical physics abstraction is the idea of a partition function Z, and here we construct a partition function from the ensemble of possible trajectories that an agent might take in a Markov decision process. Although value functions and Q-functions can be derived from this partition function and interpreted via average energies, the Z-function provides an object with its own Bellman equation that can form the basis of alternative dynamic programming approaches. Moreover, when the MDP dynamics are deterministic, the Bellman equation for Z is linear, allowing direct solutions that are unavailable for the nonlinear equations associated with traditional value functions. The policies learned via these Z-based Bellman updates are tightly linked to Boltzmann-like policy parameterizations. In addition to sampling actions proportionally to the exponential of the expected cumulative reward as Boltzmann policies would, these policies take entropy into account favoring states from which many outcomes are possible.

 

RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior

ttps://arxiv.org/abs/2010.00029 

Hu et al.   (UCSD and Berkeley AI Lab) 

Flow-based generative models have become an important class of unsupervised learning approaches. In this work, we incorporate the key idea of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, called RG-Flow, which can separate information at different scales of images with disentangled representations at each scale. We demonstrate our method mainly on the CelebA dataset and show that the disentangled representations at different scales enable semantic manipulation and style mixing of the images. To visualize the latent representations, we introduce receptive fields for flow-based models and find that the receptive fields learned by RG-Flow are similar to those in convolutional neural networks. In addition, we replace the widely adopted Gaussian prior distribution by a sparse prior distribution to further enhance the disentanglement of representations. From a theoretical perspective, the proposed method has O(logL) complexity for image inpainting compared to previous generative models with O(L^2) complexity.

See related remarks: ICML notes (2018).

It may turn out that the problems on which DL works well are precisely those in which the training data (and underlying generative processes) have a hierarchical structure which is sparse, level by level. Layered networks perform a kind of coarse graining (renormalization group flow): first layers filter by feature, subsequent layers by combinations of features, etc. But the whole thing can be understood as products of sparse filters, and the performance under training is described by sparse performance guarantees (ReLU = thresholded penalization?). Given the inherent locality of physics (atoms, molecules, cells, tissue; atoms, words, sentences, ...) it is not surprising that natural phenomena generate data with this kind of hierarchical structure.

Gradient Descent Models Are Kernel Machines (Deep Learning)

This paper shows that models which result from gradient descent training (e.g., deep neural nets) can be expressed as a weighted sum of similarity functions (kernels) which measure the similarity of a given instance to the examples used in training. The kernels are defined by the inner product of model gradients in the parameter space, integrated over the descent (learning) path.

Roughly speaking, two data points x and x' are similar, i.e., have large kernel function K(x,x'), if they have similar effects on the model parameters in the gradient descent. With respect to the learning algorithm, x and x' have similar information content. The learned model y = f(x) matches x to similar data points x_i: the resulting value y is simply a weighted (linear) sum of kernel values K(x,x_i).

This result makes it very clear that without regularity imposed by the ground truth mechanism which generates the actual data (e.g., some natural process), a neural net is unlikely to perform well on an example which deviates strongly (as defined by the kernel) from all training examples. See note added at bottom for more on this point, re: AGI, etc. Given the complexity (e.g., dimensionality) of the ground truth model, one can place bounds on the amount of data required for successful training.

This formulation locates the nonlinearity of deep learning models in the kernel function. The superposition of kernels is entirely linear as long as the loss function is additive over training data.
 

Every Model Learned by Gradient Descent Is Approximately a Kernel Machine  

P. Domingos      

https://arxiv.org/pdf/2012.00152.pdf

Deep learning’s successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other learning methods. We show, however, that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines, a learning method that simply memorizes the data and uses it directly for prediction via a similarity function (the kernel). This greatly enhances the interpretability of deep network weights, by elucidating that they are effectively a superposition of the training examples. The network architecture incorporates knowledge of the target function into the kernel. This improved understanding should lead to better learning algorithms.

From the paper:

... Here we show that every model learned by this method, regardless of architecture, is approximately equivalent to a kernel machine with a particular type of kernel. This kernel measures the similarity of the model at two data points in the neighborhood of the path taken by the model parameters during learning. Kernel machines store a subset of the training data points and match them to the query using the kernel. Deep network weights can thus be seen as a superposition of the training data points in the kernel’s feature space, enabling their efficient storage and matching. This contrasts with the standard view of deep learning as a method for discovering representations from data. ... 

... the weights of a deep network have a straightforward interpretation as a superposition of the training examples in gradient space, where each example is represented by the corresponding gradient of the model. Fig. 2 illustrates this. One well-studied approach to interpreting the output of deep networks involves looking for training instances that are close to the query in Euclidean or some other simple space (Ribeiro et al., 2016). Path kernels tell us what the exact space for these comparisons should be, and how it relates to the model’s predictions. ...

See also this video which discusses the paper. 

You can almost grasp the result from the figure and definitions below.

Note Added: I was asked to elaborate further on this sentence, especially regarding AGI and human cognition: 

... without regularity imposed by the ground truth mechanism which generates the actual data (e.g., some natural process), a neural net is unlikely to perform well on an example which deviates strongly (as defined by the kernel) from all training examples.

It should not be taken as a suggestion that gradient descent models can't achieve AGI, or that our minds can't be (effectively) models of this kernel type. 

1. The universe is highly compressible: it is governed by very simple effective models. These models can be learned, which allows for prediction beyond specific examples.

2. A sufficiently complex neural net can incorporate layers of abstraction. Thus a new instance and a previously seen example might be similar in an abstract (non-explicit) sense, but that similarity is still incorporated into the kernel. When Einstein invented Special Relativity he was not exactly aping another physical theory he had seen before, but at an abstract level the physical constraint (speed of light constant in all reference frames) and algebraic incorporation of this fact into a description of spacetime (Lorentz symmetry) may have been "similar" to examples he had seen already in simple geometry / algebra. (See Poincare and Einstein for more.)

Ulam: Banach once told me, "Good mathematicians see analogies between theorems or theories, the very best ones see analogies between analogies." Gamow possessed this ability to see analogies between models for physical theories to an almost uncanny degree... 

See also Analogies Between Analogies.

Enter the Finns: FinnGen and FINRISK polygenic prediction of cardiometabolic diseases, common cancers, alcohol use, and cognition

In 2018 Dr. Aarno Palotie visited MSU (video of talk) to give an overview of the FinnGen research project. FinnGen aims to collect the genomic data of 500k citizens in Finland in order to study the origins of diseases and their treatment. Finland is well suited for this kind of study because it is relatively homogenous and has a good national healthcare system.

Professor Aarno Palotie, M.D., Ph.D. is the research director of the Human Genomics program at FIMM. He is also a faculty member at the Center for Human Genome Research at the Massachusetts General Hospital in Boston and associate member of the Broad Institute of MIT and Harvard. He has a long track record in human disease genetics. He has held professorships and group leader positions at the University of Helsinki, UCLA, and the Wellcome Trust Sanger Institute. He has also been the director of the Finnish Genome Center and Laboratory of Molecular Genetics in the Helsinki University Hospital.

FinnGen is now producing very interesting results in polygenic risk prediction and clinical / public health applications of genomics. Below are a few recent papers.

1. This paper studies the use of PRS in prediction of five common diseases, with an eye towards clinical utility.

Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers 

Nature Medicine volume 26, 549–557(2020) 

Polygenic risk scores (PRSs) have shown promise in predicting susceptibility to common diseases1,2,3. We estimated their added value in clinical risk prediction of five common diseases, using large-scale biobank data (FinnGen; n = 135,300) and the FINRISK study with clinical risk factors to test genome-wide PRSs for coronary heart disease, type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer. We evaluated the lifetime risk at different PRS levels, and the impact on disease onset and on prediction together with clinical risk scores. Compared to having an average PRS, having a high PRS contributed 21% to 38% higher lifetime risk, and 4 to 9 years earlier disease onset. PRSs improved model discrimination over age and sex in type 2 diabetes, atrial fibrillation, breast cancer and prostate cancer, and over clinical risk in type 2 diabetes, breast cancer and prostate cancer. In all diseases, PRSs improved reclassification over clinical thresholds, with the largest net reclassification improvements for early-onset coronary heart disease, atrial fibrillation and prostate cancer. This study provides evidence for the additional value of PRSs in clinical disease prediction. The practical applications of polygenic risk information for stratified screening or for guiding lifestyle and medical interventions in the clinical setting remain to be defined in further studies.

2. This paper is a well-powered study of genetic influence on alcohol use and effects on mortality.

Genomic prediction of alcohol-related morbidity and mortality 

Nature Translational Psychiatry volume 10, Article number: 23 (2020) 

While polygenic risk scores (PRS) have been shown to predict many diseases and risk factors, the potential of genomic prediction in harm caused by alcohol use has not yet been extensively studied. Here, we built a novel polygenic risk score of 1.1 million variants for alcohol consumption and studied its predictive capacity in 96,499 participants from the FinnGen study and 39,695 participants from prospective cohorts with detailed baseline data and up to 25 years of follow-up time. A 1 SD increase in the PRS was associated with 11.2 g (=0.93 drinks) higher weekly alcohol consumption (CI = 9.85–12.58 g, p = 2.3 × 10–58). The PRS was associated with alcohol-related morbidity (4785 incident events) and the risk estimate between the highest and lowest quintiles of the PRS was 1.83 (95% CI = 1.66–2.01, p = 1.6 × 10–36). When adjusted for self-reported alcohol consumption, education, marital status, and gamma-glutamyl transferase blood levels in 28,639 participants with comprehensive baseline data from prospective cohorts, the risk estimate between the highest and lowest quintiles of the PRS was 1.58 (CI = 1.26–1.99, p = 8.2 × 10–5). The PRS was also associated with all-cause mortality with a risk estimate of 1.33 between the highest and lowest quintiles (CI = 1.20–1.47, p = 4.5 × 10–8) in the adjusted model. In conclusion, the PRS for alcohol consumption independently associates for both alcohol-related morbidity and all-cause mortality. Together, these findings underline the importance of heritable factors in alcohol-related health burden while highlighting how measured genetic risk for an important behavioral risk factor can be used to predict related health outcomes.

3. This paper examines rare CNVs (Copy Number Variants) and PRS (Polygenic Risk Score) prediction using a combined Finnish sample of ~30k for whom education, income, and health outcomes are known. The study finds that low polygenic scores for Educational Attainment (EA) and intelligence predict worse outcomes in education, income, and health.

Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants 

https://www.nature.com/articles/s41380-021-01026-z 

Abstract Copy number variants (CNVs) are associated with syndromic and severe neurological and psychiatric disorders (SNPDs), such as intellectual disability, epilepsy, schizophrenia, and bipolar disorder. Although considered high-impact, CNVs are also observed in the general population. This presents a diagnostic challenge in evaluating their clinical significance. To estimate the phenotypic differences between CNV carriers and non-carriers regarding general health and well-being, we compared the impact of SNPD-associated CNVs on health, cognition, and socioeconomic phenotypes to the impact of three genome-wide polygenic risk score (PRS) in two Finnish cohorts (FINRISK, n = 23,053 and NFBC1966, n = 4895). The focus was on CNV carriers and PRS extremes who do not have an SNPD diagnosis. We identified high-risk CNVs (DECIPHER CNVs, risk gene deletions, or large [>1 Mb] CNVs) in 744 study participants (2.66%), 36 (4.8%) of whom had a diagnosed SNPD. In the remaining 708 unaffected carriers, we observed lower educational attainment (EA; OR = 0.77 [95% CI 0.66–0.89]) and lower household income (OR = 0.77 [0.66–0.89]). Income-associated CNVs also lowered household income (OR = 0.50 [0.38–0.66]), and CNVs with medical consequences lowered subjective health (OR = 0.48 [0.32–0.72]). The impact of PRSs was broader. At the lowest extreme of PRS for EA, we observed lower EA (OR = 0.31 [0.26–0.37]), lower-income (OR = 0.66 [0.57–0.77]), lower subjective health (OR = 0.72 [0.61–0.83]), and increased mortality (Cox’s HR = 1.55 [1.21–1.98]). PRS for intelligence had a similar impact, whereas PRS for schizophrenia did not affect these traits. We conclude that the majority of working-age individuals carrying high-risk CNVs without SNPD diagnosis have a modest impact on morbidity and mortality, as well as the limited impact on income and educational attainment, compared to individuals at the extreme end of common genetic variation. Our findings highlight that the contribution of traditional high-risk variants such as CNVs should be analyzed in a broader genetic context, rather than evaluated in isolation. 

From the paper:

 ... we compared the impact of CNVs to the impact of the PRSs for educational attainment [24], schizophrenia [25], and general intelligence [26] on general health, morbidity, mortality, and socioeconomic burden. We analyzed these effects in two cohorts: one sampled at random from the Finnish working-age population (FINRISK), the other a Finnish birth cohort (Northern Finland Birth Cohort 1966; NFBC1966). Both cohorts link to national health records, enabling analysis of longitudinal health data and socioeconomic status data over several decades. 

... we observed a clear polygenic effect on socioeconomic outcome with educational attainment and IQ PRS scores. Belonging to the matched lowest PRS extremes (lowest 2.66%) of educational attainment or IQ had an overall stronger impact on the socioeconomic outcome than belonging to most high-risk CNV groups, and a generally stronger impact on health and survival, with the exception of household income-associated CNVs. 

... odds for subsequent level of education were even lower at the matched lowest extreme of PRSEA (OR = 0.31 [0.26–0.37]) and PRSIQ (OR = 0.51 [0.44–0.60]).

... Rare deleterious variants, including CNVs, can have a major impact on health outcomes for an individual and are thus under strong negative selection. However, such variants might not always have a strong phenotypic impact (incomplete penetrance), and as observed here, can have a very modest—if any—effect on well-being. The reason for this wide spectrum of outcomes remains speculative. From a genetic perspective, one hypothesis is that additional variants, both rare and common, modify the phenotypic outcome of a CNV carrier (Supplementary Figs. 11 and 12). This type of effect is observable in analyzes of hereditary breast and ovarian cancer in the UK Biobank [40] and in FinnGen [41], where strong-impacting variants’ penetrance is modified by compensatory polygenic effects. 

... As stated above, the observed effect of polygenic scores was broader than that of structural variants. We observed strong effects in PRSs for intelligence and educational attainment on education, income and socioeconomic status. 

Gerald Feinberg and The Prometheus Project

Gerald Feinberg (1933-1992) was a theoretical physicist at Columbia, perhaps best known for positing the tachyon -- a particle that travels faster than light. He also predicted the existence of the mu neutrino. 

Feinberg attended Bronx Science with Glashow and Weinberg. Interesting stories abound concerning how the three young theorists were regarded by their seniors at the start of their careers. 

I became aware of Feinberg when Pierre Sikivie and I worked out the long range force resulting from two neutrino exchange. Although we came to the idea independently and derived, for the first time, the correct result, we learned later that it had been studied before by Feinberg and Sucher. Sadly, Feinberg died of cancer shortly before Pierre and I wrote our paper. 

Recently I came across Feinberg's 1969 book The Prometheus Project, which is one of the first serious examinations (outside of science fiction) of world-changing technologies such as genetic engineering and AI. See reviews in Science, Physics Today, and H+ Magazine. A scanned copy of the book can be found at Libgen.

Feinberg had the courage to engage with ideas that were much more speculative in the late 60s than they are today. He foresaw correctly, I believe, that technologies like AI and genetic engineering will alter not just human society but the nature of the human species itself. In the final chapter, he outlines a proposal for the eponymous Prometheus Project -- a global democratic process by which the human species can set long term goals in order to guide our approach to what today would be called the Singularity.

See From Genotype to Phenotype: polygenic prediction of complex human traits.

   

All Men Are Brothers -- 3 AM Edition

Afu Thomas is a German internet personality, known for his fluent Chinese, who lives in Shanghai. His videos are extremely popular in China and people often recognize him in public. 

These are a series of street interviews shot at 3 AM, in which he elicits sometimes moving and philosophical responses from ordinary people about hopes, dreams, family, money, happiness. These individuals, ranging from teen boys and girls to middle aged men, answer the questions simply but with insight and sincerity.  

The subtitled translations are very good. 

Yuri Milner interviews Donaldson, Kontsevich, Lurie, Tao, and Taylor (2015 Breakthrough Prize)

 

I came across this panel discussion recently, with Yuri Milner (former theoretical physicist, internet billionaire, and sponsor of the Breakthrough Prize) as interlocutor and panelists Simon Donaldson, Maxim Kontsevich, Jacob Lurie, Terence Tao, and Richard Taylor. 

Among the topics covered: the nature of mathematics, the simulation question, AGI and automated proof, human-machine collaboration in mathematics. Kontsevich marvels at the crystalline form of quantum mechanics: why linearity? why a vector space structure? 

Highly recommended!

See also 

The Quantum Simulation Hypothesis: Do we live in a quantum multiverse simulation? 

Locality and Nonlinear Quantum Mechanics

Clinical Applications of Polygenic Risk Scores

Last week we posted a new paper (see bottom), prepared for the book Genomic Prediction of Complex Traits, Springer Nature series Methods in Molecular Biology. Someone asked me to comment more on clinical applications of polygenic risk scores. Here's what we say in the paper, using the specific example of breast cancer (emphasis added):

There is already signifcant interest in the application of PRS in a clinical setting, for example to identify high risk individuals who might receive early screening or preventative care [2, 13–24]. As a concrete example, women with high PRS scores for breast cancer can be offered early screening: already standard of care for those with BRCA risk variants [25, 26]. However, BRCA mutations affect no more than a few women per thousand in the general population [27–29]. Importantly, the number of (BRCA negative) women who are at high risk for breast cancer due to polygenic effects is an order of magnitude larger than the population of BRCA carriers [2, 10, 30–34]. From this one example it is clear that significant medical, public health, and cost benefits could result from PRS (e.g. [35]). It is well known that patients with atherosclerotic diseases, coronary artery disease (CAD), and lung diseases can benefit from early intervention [36–38]. ... Precision genetics is already used in identification of candidates for early intervention, and will become widespread in the near future (cf. Myriad’s riskScore test and other examples [33, 34]). In figure 4, we illustrate the predicted risk of breast cancer and coronary artery disease as function of age for high, medium and low risk groups, respectively.

We have verified in sibling studies that among two sisters the outlier with high risk score is much more likely to have breast cancer than the one with normal range score. The excerpt below is from the section on sibling validation: 

... We tested a variety of polygenic predictors using tens of thousands of genetic siblings for whom we have SNP genotypes, health status, and phenotype information in late adulthood. Siblings have typically experienced similar environments during childhood, and exhibit negligible population stratification relative to each other. Therefore, the ability to predict differences in disease risk or complex trait values between siblings is a strong test of genomic prediction in humans. We compare validation results obtained using non-sibling subjects to those obtained among siblings and find that typically most of the predictive power persists in within-family designs. Given 1 sibling with normal-range PRS score (less than 84th percentile) and 1 sibling with high PRS score (top few percentiles), the predictors identify the affected sibling about 70-90 percent of the time across a variety of disease conditions, including breast cancer, heart attack, diabetes, etc. For height, the predictor correctly identities the taller sibling roughly 80 percent of the time when the (male) height difference is 2 inches or more.

The evidence is strong that PRS outliers are at unusual absolute risk. In fact, the likelihood that an individual in the high PRS tail will eventually have the disease can approach 100% for some conditions -- see figure below. This is a concrete realization of precision medicine, at least for these individuals.

In addition to commercial products like Myriad's riskScore (which extends their BRCA panel to additional polygenic factors, and is already widely available), I am aware of many healthcare systems (including some national healthcare systems) that are seriously investigating the use of PRS in standard clinical care. 

Another example: a relative of mine had a prostate cancer diagnosis and took a (standard of care) genetic risk test which, like the pre-riskScore Myriad product, is simply a panel of rare monogenic risk variants. We and other groups have developed prostate cancer polygenic predictors which could be easily incorporated into standard of care and would likely be much more useful than the existing panel. I haven't looked carefully at the prostate cancer numbers but I strongly suspect that, as in the breast cancer example, many more men are at high risk due to high PRS than are carriers of the rare variants.

It's only a matter of time before these improvements in diagnostic screening become widespread.

Here is what we say about IVF applications:

In the past, parents with more viable embryos than they intended to use made a selection based on very little information — typically nothing more than the appearance or morphology of each blastocyst. With modern technology it has become common to genotype embryos before selection, in order to detect potential genetic issues such as trisomy 21 (Down Syndrome). Parents who are carriers of a single gene variant linked to a Mendelian condition can use genetic screening to avoid passing the risk variant on to their child. Millions of embryos are now genetically tested each year. With polygenic risk prediction, it is possible now to screen against outlier risk for many common disease conditions, not just rare single gene conditions. For example, the overwhelming majority of families with breast cancer history are not carriers of a BRCA risk variant, but rather have elevated polygenic risk. It is now possible for these families to select an embryo with average or even below average breast cancer risk if they so wish.

 Here is the paper:

From Genotype to Phenotype: polygenic prediction of complex human traits 

arXiv.org > q-bio > arXiv:2101.05870 33 pages, 7 figures, 1 table 

Timothy G. Raben, Louis Lello, Erik Widen, Stephen D.H. Hsu 

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

From Genotype to Phenotype: polygenic prediction of complex human traits

New paper, prepared for the book Genomic Prediction of Complex Traits, Springer Nature series Methods in Molecular Biology.

From Genotype to Phenotype: polygenic prediction of complex human traits  

arXiv.org > q-bio > arXiv:2101.05870   33 pages, 7 figures, 1 table

Timothy G. Raben, Louis Lello, Erik Widen, Stephen D.H. Hsu 

Decoding the genome confers the capability to predict characteristics of the organism (phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (e.g., top few percent) have been shown to have 5 or even 10 times higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

From the introduction:

I, on the other hand, knew nothing, except ... physics and mathematics and an ability to turn my hand to new things. — Francis Crick 

The challenge of decoding the genome has loomed large over biology since the time of Watson and Crick. Initially, decoding referred to the relationship between DNA and specific proteins or molecular mechanisms, but the ultimate goal is to deduce the relationship between DNA and phenotype — the character of the organism itself. How does Nature encode the traits of the organism in DNA? In this review we describe recent advances toward this goal, which have resulted from the application of machine learning (ML) to large genomic data sets. Genomic prediction is the real decoding of the genome: the creation of mathematical models which map genotypes to complex traits. 

It is a peculiarity of ML and artificial intelligence (AI) applied to complex systems that these methods can often “solve” a problem without explicating, in a manner that humans can absorb, the intricate mechanisms that lie intermediate between input and output. For example, AlphaGo [1] achieved superhuman mastery of an ancient game that had been under serious study for thousands of years. Yet nowhere in the resulting neural network with millions of connection strengths is there a human-comprehensible guide to Go strategy or game dynamics. Similarly, genomic prediction has produced mathematical functions which predict quantitative human traits with surprising accuracy — e.g., height, bone density, and cholesterol or lipoprotein A levels in blood (see Table 1); using typically thousands of genetic variants as input (see next section for details) — but without explicitly revealing the role of these variants in actual biochemical mechanisms. Characterizing these mechanisms — which are involved in phenomena such as bone growth, lipid metabolism, hormonal regulation, protein interactions — will be a project which takes much longer to complete. 

If recent trends persist, in particular the continued growth of large genotype | phenotype data sets, we will likely have good genomic predictors for a host of human traits within the next decade. ...

Harvard CMSA talks (video)

I recently came across this channel on YouTube, produced by CMSA at Harvard.

The new Center for Mathematical Sciences and Applications in the Faculty of Arts and Sciences will serve as a fusion point for mathematics, statistics, physics, and related sciences. Evergrande will support new professorships, research, and core programming. 

Shing-Tung Yau, Harvard’s William Caspar Graustein Professor of Mathematics, will serve as the center’s first director. 

“The Center for Mathematical Sciences and Applications will establish applied mathematics at Harvard as a first-class, interdisciplinary field of study, relating mathematics with many other important fields,” Yau said. “The center will not only carry out the most innovative research but also train young researchers from all over the world, especially those from China. The center marks a new chapter in the development of mathematical science.”

If I'm not mistaken Evergrande is a big real estate developer in China. It's nice to see them supporting mathematics and science in the US :-) 

In 2010 I accompanied S.T. Yau and a number of other US academics and technologists to visit Alibaba, which wanted to establish a center for data science in China. Unfortunately this never really got off the ground, but CMSA looks like it is off to a good start. 

Here are some talks I found interesting. There are quite a few more.

The talk on Atiyah, Geometry, and Physics led me to this poem which I like very much. Sadly, Atiyah passed in 2019. I believe we met once at a dinner at the Society of Fellows, but I hardly knew him.

In the broad light of day mathematicians check their equations and their proofs, leaving no stone unturned in their search for rigour. 

But, at night, under the full moon, they dream, they float among the stars and wonder at the mystery of the heavens: they are inspired. 

Without dreams there is no art, no mathematics, no life. 

—Michael Atiyah

Global AI Talent Flows

The illustration above describes a global population of ~5k researchers whose papers were accepted to the leading 2019 conference in deep neural nets. To be precise they looked at ~700 authors of a randomly chosen subset of papers. There is also a more select population of individuals who gave presentations at the meeting. This is certainly not the entire field of AI, but a reasonable proxy for it.

Global AI talent tracker:

For its December 2019 conference, NeurIPS saw a record-breaking 15,920 researchers submit 6,614 papers, with a paper acceptance rate of 21.6%, making it one of the largest, most popular, and most selective AI conferences on record. 

Key Takeaways 

1. The United States has a large lead over all other countries in top-tier AI research, with nearly 60% of top-tier researchers working for American universities and companies. The US lead is built on attracting international talent, with more than two-thirds of the top-tier AI researchers working in the United States having received undergraduate degrees in other countries.   

2. China is the largest source of top-tier researchers, with 29% of these researchers having received undergraduate degrees in China. But the majority of those Chinese researchers (56%) go on to study, work, and live in the United States. 

3. Over half (53%) of all the top-tier AI researchers are immigrants or foreign nationals currently working in a different country from where they received their undergraduate degrees.

Prediction: PRC share in all 3 categories will increase in coming decades as their K12, undergraduate, and graduate schools continue to improve, and their high-tech economy grows much larger. See Ditchley Foundation meeting: World Order today. 

Using conference papers as the filter probably misses a lot of world class work (especially implementation at scale) that is going on in PRC at tech companies. Note in the list below the only Chinese institutions are Tsinghua and Beijing universities. But I would be surprised if those were the main accumulation of top AI talent in China, compared to large tech companies.

 

Spengler (Asia Times): American Democracy died on Capitol Hill

Note although this appears in the Asia Times column Spengler, the byline is Paul Muir, not David Goldman.

American democracy died on Capitol Hill 

No Russian cyberspooks, no Chinese spies, no jihadi terrorists – no external enemies of any kind could have brought as much harm to the United States as its own self-inflicted wounds. 

I spent last evening taking calls from friends around the world, including a senior diplomat of an American ally who asked me what I thought of the first evacuation of Capitol Hill since the British invaded in 1812. “I’m horrified,” I said. “So is the entire free world,” the diplomat replied. 

There are belly-laughs in Beijing this morning. The Chinese government daily Global Times taunted: 

... The world is watching ... the country that they used to admire descend into a huge mess. Chinese observers said this is a “Waterloo to US international image,” and the US has totally lost legitimacy and qualification to interfere in other countries’ domestic affairs with the excuse of “democracy” in the future.  

[[ When protestors in HK occupied their legislature, US propaganda hailed it as a victory for democracy... when the same thing happens here it is declared domestic terrorism. ]]

It’s actually worse than the Global Times editors think. 

If it were only a matter of Trump’s misbehavior, this disaster would be survivable. The trouble is that the popular belief in a vast and nefarious conspiracy has a foundation in fact: Starting before Trump’s term in office his political opponents abused the surveillance powers of the intelligence community to concoct a black legend of Russian collusion on the part of his campaign. The mainstream media, staffed overwhelmingly by Trump’s enemies, slavishly repeated this black legend until large parts of the population refused to believe anything it read in the newspapers or saw on television. 

The leadership of the Democratic Party, its allied media, and the Bush-Romney wing of the Republican Party decided to play dirty to expunge an obstreperous, incalculable outsider from the political system. And in doing so, this combination, America’s establishment, destroyed public trust in the Congress and the media. It’s no surprise that two out of five Americans now believe that a vast conspiracy rigged the 2020 presidential elections. 

The spectacle of a serving president inciting a mob against the US Congress to stop the certification of his successor held the world in morbid fascination. But the biggest problem isn’t Trump’s misbehavior, egregious as it is, but the eruption of popular rancor against the constitutional system that has made America a model of governance for the world. Leftist mobs last spring burned police stations and destroyed shopping districts in a rampage against supposed systemic racism, and Trump supporters desecrated the Holy of Holies of American democracy, the chamber of the United States Senate. 

Behind the minority of violent actors is a majority that believes the system is rigged against them – whoever “them” might be. The Democrats say that the system is rigged against African-Americans, women, and other minorities, and the Republicans say that a global elite has rigged the system against middle-income Americans. “Rigged elections” has the same resonance as “systemic racism.” These by-words imply that disagreement is prima facie proof of villainy: To deny that there is systemic racism is to be a racist, and to deny that elections are rigged is evidence of complicity in a vast plot. 

A quarter of Americans believe that Covid-19 was a planned conspiracy of one kind or another, according to the Pew Survey; just under half of Americans with a high school education or less believe this. One out of three believes that a “deep state” is trying to undermine Trump. I reject the first and believe the second: my colleagues at Asia Times and I have regular access to virologists in a number of countries with scientific credentials and no political agenda to pursue, and can sift scientific evidence and opinion. By contrast, I know personally enough of the actors in the so-called “deep state” to conclude that they are acting in concert to wreck the Trump Administration. I also know many of the writers who have exposed the “deep state,” including Andrew McCarthy and Lee Smith, to trust their bona fides. I denounced this conspiracy repeatedly in these pages, most recently in an essay entitled “The Treason of the Spooks” (Dec. 4, 2020). For details, see Andrew McCarthy’s 2019 book Ball of Collusion, which I reviewed in Asia Times, or Lee Smith’s The Plot against the President. 

Sometimes there is a conspiracy and sometimes there isn’t. But Trump’s political supporters, bombarded daily by fake news about Russian collusion and other alleged misbehavior, have come to distrust any criticism of their president. 

If Trump was right that the whole impeachment business was an extra-legal conspiracy on the part of his enemies, why shouldn’t they believe that the election was rigged? This is a lose-lose proposition. Assume that Trump is right, and the election was rigged. In that case the United States has become a banana republic and American democracy a twisted joke. Assume that he is wrong, and that nonetheless – as Sen. Ted Cruz (R-Texas) intoned to justify his refusal to accept the election outcome – 39% of Americans nonetheless believe that election has rigged, because their president told them it was rigged. In that case the public trust that makes democracy possible has collapsed. The people, as Bertolt Brecht observed after demonstrations against the Soviet puppet government in East Germany, have lost the confidence of the government, and the simplest course of action would be for the government to dissolve and for the people to elect a new one. 

... Americans are frightened for their future, with good reason. They see enormous rewards accrue to a handful of tech companies, and stagnation and decay in large parts of the rest of the country. Donald Trump gave them a frisson of hope, and the Establishment reaction against Trump confirms the popular suspicion that a malevolent global elite has seized control of their country. Trump shamefully exploited this suspicion to direct a popular storm against the Congress. 

The US is living off borrowing from the rest of the world. Its net international investment position fell by about $12 trillion during the past 10 years. And the federal deficit is now 15% of gross domestic product, the highest since World War II. What can’t go on forever, won’t (in the late Herb Stein’s famous formulation).

YouGov on storming of capitol

I misread the last line of this when I first saw it... I thought 56% of all voters polled believed the election was stolen. Probably what it actually says is that 56% of people who believe the election was stolen think storming the capitol is OK.

These results suggest 30-40% of all voters think the election was stolen -- i.e., roughly 2x the number who approve of the storming:  0.21 / 0.56 ~ 38%

YouGov poll of 1,397 registered voters.

The Last Emperor

1987 seems so long ago. Watch this movie! 

Nine Academy Awards, including Best Picture, Best Director, and Best Score.

See also 

From Emperor to Citizen: the autobiography of PuYi

Twilight in the Forbidden City  (account of Sir Reginald Fleming Johnston, tutor to the last emperor of China)

Two from Spengler (David Goldman at AsiaTimes)

David Goldman, a former banker, writes the Spengler column for AsiaTimes, where he is business editor. 

Huawei 5G in Germany, Japan, and S. Korea? 

1. When America’s religion goes wrong 

Book Review: American Awakening: Identity Politics and Other Afflictions of Our Time, by Joshua Mitchell (Georgetown University) 

2. AsiaTimes tells Le Figaro why China is winning the tech war (interview)

LM: Germany just announced that it will allow Huawei 5G to be installed. What conclusions do you draw from this decision? Is this short-term logic, that will hand the control of big data to China? 

DG: To my knowledge, Germany has made no announcement, but the German media have leaked the draft law that the government will present to the Bundestag, which allows Huawei 5G. Trump’s defeat in the US election probably tipped the balance in favor of Huawei. Huawei always has viewed 5G as the core of an “ecosystem” of new technologies that 5G makes possible. ... 

LM: Obama had launched the Trans-Pacific Partnership. Now there is a China-led trade zone, the RCEP. Have Australians, South Koreans and others decided to go back to China in a realpolitik move, because they see America as a declining power, engulfed in internal wars and not to be trusted? 

DG: The Regional Comprehensive Economic Partnership will cut tariffs dramatically – by about 90% in the case of Japanese exports to China – and now China is trying to negotiate free trade areas with South Korea and Japan. Asian trade is now as concentrated within Asia as European trade is concentrated within Europe. 

The logic of the development of an Asian internal market is similar to that of the European Community, and it is not surprising that the Asians are creating a giant free trade zone. Australia is in a nasty fight with China, but it now sells a higher proportion of its exports to China than ever before. It could not afford to stay out of the RCEP. 

The American consumer for decades was the main source of demand in the world economy. Now the internal Asian market is far more important. South Korea, for example, exports twice as much to China as to the US. I am sure that the Japanese and South Koreans like the United States much better than they like China, but the economic logic behind an Asian free trade zone is overwhelming. 

An Asian free trade zone certainly is compatible with America’s role as the leading superpower, just as the European Community originally was formed with American sponsorship during the Cold War. 

The difference, of course, is that China’s economic strength makes it a magnet for all the Asian economies. In this context, it is noteworthy that Japan and South Korea politely rejected American demands to exclude Huawei from their 5G networks. 

...

To restore high-tech manufacturing, we would need the sort of tax credits and subsidies for capital-intensive industry that Asian governments provide; we would need the sort of support from the Defense Department that led to every important technology of the digital age, from microprocessors to the Internet; and we would need a greater emphasis on mathematics and science at every level of education. 

Above all, we would need the sense of national purpose that John Kennedy evoked with the space program or Reagan with the Strategic Defense Initiative. Considering that we have just spent several trillion dollars subsidizing incomes and supporting capital markets, another trillion dollars to support technological superiority doesn’t seem extravagant. ...

China CDC Director interview: vaccine progress, viroid sequencing, transmission via food/packaging

 

This is a recent interview with the PRC CDC head, which includes: 

1. Discussion of various vaccines. He confirms that their vaccine(s) are using the standard method (inactivated viruses), which a priori one might consider safer than the new mRNA type. Efficacy remains to be seen but he seemed to hint that they would be releasing some data/results in the next few days. 

2. He notes (at ~7m) that PRC is sequencing every new case of covid. They see all the mutant versions, and find that infections are coming both from visitors to PRC as well as from imported food/packaging! So the latter really happens. 

If anyone can find primary sources related to these topics I would be very interested.

Here is some discussion of the different vaccines: costs, ongoing validations, etc.

Peace on Earth, Good Will to Men 2020

When asked what I want for Christmas, I reply: Peace On Earth, Good Will To Men :-)

No one ever seems to recognize that this comes from the Bible (Luke 2.14).

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!

This has been a difficult year for many people. Please accept my best wishes and hopes for a wonderful 2021. Be of good cheer, for we shall prevail! :-) 

The first baby conceived from an embryo screened with Genomic Prediction preimplantation genetic testing for polygenic risk scores (PGT-P) was born in mid-2020.  

Genomic Prediction has now performed embryo genetic tests for almost 200 IVF clinics in many countries. Millions of embryos are screened each year, worldwide.

Five years ago on Christmas day I shared the Nativity 2050 story below. See also The Economist on Polygenic Risk Scores and Embryo Selection.

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 ...

Lianda

The videos below are about Lianda, a wartime university located in Kunming that was formed by the merger of Peking University, Tsinghua University, and Nankai University.

Lianda: A Chinese University in War and Revolution 

In the summer of 1937, Japanese troops occupied the campuses of Beijing’s two leading universities, Beida and Qinghua, and reduced Nankai, in Tianjin, to rubble. These were China's leading institutions of higher learning, run by men educated in the West and committed to modern liberal education. The three universities first moved to Changsha, 900 miles southwest of Beijing, where they joined forces. But with the fall of Nanjing in mid-December, many students left to fight the Japanese, who soon began bombing Changsha. 

In February 1938, the 800 remaining students and faculty made the thousand-mile trek to Kunming, in China’s remote, mountainous southwest, where they formed the National Southwest Associated University (Lianda). In makeshift quarters, subject to sporadic bombing by the Japanese and shortages of food, books, and clothing, students and professors did their best to conduct a modern university. In the next eight years, many of China’s most prominent intellectuals taught or studied at Lianda. ... 

Lianda’s wartime saga crystallized the experience of a generation of Chinese intellectuals, beginning with epic journeys, followed by years of privation and endurance, and concluding with politicization, polarization, and radicalization, as China moved from a war of resistance against a foreign foe to a civil war pitting brother against brother. The Lianda community, which had entered the war fiercely loyal to the government of Chiang Kai-shek, emerged in 1946 as a bastion of criticism of China’s ruling Guomindang party. Within three years, the majority of the Lianda community, now returned to its north China campuses in Beijing and Tianjin, was prepared to accept Communist rule. ...

My father attended this university at age 16, admitted via Tsinghua. Among its most famous alumni are the Nobel Prize winning theoretical physicists C.N. Yang and T.D. Lee. As the university only existed for 8 years, there are very few alumni still living.

 

I came across the 一条 Yit channel because I recently bought an Android Smart TV and it caused an increase in consumption of YouTube, etc. I got the TV to use as a big monitor but it's great for content as well. One of the most enjoyable things I do with it is watch seminars (e.g., in theoretical physics or AI)!

In case you are wondering I bought a 70inch HiSense on sale for under $400: good 4k picture and sound -- highly recommended!