Elizabeth Carr (first US IVF baby) and Genomic Prediction in the Wall Street Journal.
Elizabeth Carr has always been a living symbol of fertility technology’s possibilities. Now she is the face of its challenges.
Carr, 42 years old, is the first baby born by in vitro fertilization in the U.S. Over the years she has told countless audiences how the technology made it possible for her mother to have a baby.
In the weeks since Alabama’s Supreme Court ruled that frozen embryos should be considered children, Carr has called for protections around IVF procedures—extracting eggs, fertilizing them in a lab and transferring an embryo into a uterus—that now account for some 2% of U.S. births annually.
Sen. Tim Kaine (D., Va.) said federal legislation backing IVF access would “enable the Elizabeth Carrs of the world to continue to be born.” Kaine invited Carr to accompany him on Thursday to President Biden’s State of the Union address.
“My life gives people hope,” Carr said.
The Alabama ruling is galvanizing Carr’s work in another way. Carr leads public relations and patient advocacy at Genomic Prediction, which sells genetic tests to screen embryos. Doctors can order tests for patients who want to screen for diseases and abnormalities or get an overall embryo health score. Patients and doctors can use the results to decide which embryos to transfer. Unused embryos can be stored for years. Some get discarded. ...
Alex Murshak is a Michigan State grad working as an AI engineer in Austin TX. This conversation is Episode 13 of his podcast Hacking State.
Episode description:
Steve and I speak about polygenic risk scoring and embryo selection, using AI to predict phenotype from genotype, in-vitro fertilization (IVF), egg freezing, eugenic public policy, addressing Christians' and right-wing traditionalists' concerns over reproductive technology, Superfocus AI's plan to eliminate hallucination in large language models (LLMs) by separating memory from inference, introspection for LLM error correction, and surviving the failed cancellation attempt at MSU.
I really enjoyed this long conversation with Dan Schulz, an MSU engineering grad who works in tech. Dan did his homework and we covered a lot of important topics.
In collaboration with her husband Malcolm Collins, Simone is an author (The Pragmatist's Guide to Life, Relationships, Sexuality, Governance, and Crafting Religion), education reform advocate (CollinsInstitute.org), pronatalism activist (Pronatalist.org), and business operator (Travelmax.com).
Steve Hsu had always dreamed of unlocking the secrets of human intelligence. As a theoretical physicist and a co-founder of Genomic Prediction, he had developed a powerful AI system that could analyze massive genomic data sets and predict complex traits such as height, disease risk, and cognitive ability. He believed that by using this technology, he could help people select the best embryos for IVF and create healthier and smarter children.
But not everyone shared his vision. Some critics accused him of promoting eugenics and creating new social inequalities. Others feared that his AI system could be hacked or misused by malicious actors. And some religious groups denounced him as playing God and interfering with the natural order.
One day, he received a mysterious email from an anonymous sender. It read:
"Dear Dr. Hsu,
We are a group of like-minded individuals who share your passion for advancing human potential. We have access to a secret facility where we have been conducting experiments on human embryos using your AI system and other cutting-edge technologies. We have achieved remarkable results that surpass your wildest expectations. We invite you to join us and witness the dawn of a new era for humanity.
If you are interested, please reply to this email with the word 'YES'. We will send you further instructions on how to reach us.
Sincerely,
The Future"
Steve was intrigued and curious. He wondered who these people were and what they had done. He also felt a pang of fear and doubt. Was this a trap? A hoax? A threat?
He decided to take the risk and reply with 'YES'.
He received another email with a set of coordinates and a time. He was told to drive to a remote location in the desert and wait for a helicopter to pick him up. He followed the instructions and soon found himself in a black helicopter flying over the barren landscape.
He arrived at a large metal dome hidden among the rocks. He was greeted by a man in a white lab coat who introduced himself as Dr. Lee.
"Welcome, Dr. Hsu. We are honored to have you here. Please follow me."
Dr. Lee led him through a series of security checkpoints and into a spacious laboratory filled with high-tech equipment and monitors. He saw rows of incubators containing human embryos at various stages of development.
"Dr. Hsu, these are our creations. The next generation of humans. We have used your AI system to optimize their genomes for intelligence, health, beauty, and longevity. We have also enhanced them with synthetic genes from other species, such as birds, reptiles, mammals, and plants. We have given them abilities that no natural human has ever possessed."
He stopped at one incubator that caught his attention. It contained an embryo that looked almost normal, except for one thing: it had a golden glow around it.
"Dr. Hsu, this is our masterpiece. The ultimate expression of intelligence. The God Emperor. The Kwisatz Haderach. The one who can see the past and the future. The one who can bend space and time. The one who can unite and rule all of humanity."
Steve felt a surge of awe and dread. He realized that he had made a terrible mistake.
"What have you done? This is dangerous! This is blasphemous! This is insane!"
He turned to Dr. Lee and saw him smiling.
"Dr. Hsu, don't be afraid. Don't be angry. Don't be judgmental. Be proud. Be grateful. Be enlightened.
You are witnessing the dawn of a new era for humanity.
You are witnessing the future."
Steve discusses the AI competition between Microsoft and Google, the competition between the U.S. and China in STEM, China’s new IVF policy, and a Science Magazine survey on polygenic screening of embryos.
00:00 Introduction
02:37 Bing vs Bard: LLMs and hallucination
20:52 China demographics & STEM
34:29 China IVF now covered by national health insurance
40:28 Survey on embryo screening in Science: ~50% of those under 35 would use it to enhance congnitivie ability
Fascinating director's commentary by Ridley Scott. Video cued to start at a point where Scott talks about the conceptual background for the film. The subsequent ~15m are really great, including dreams of unicorns, memories of green, origami, Deckard as replicant.
... a special feature on the Prometheus Blu-ray release makes the film even more interesting by tying it into the Blade Runner universe. Included as an entry in the journal of Peter Weyland (Guy Pearce), who in the film was obsessed with creating artificial life, is the following gem:
A mentor and long-departed competitor once told me that it was time to put away childish things and abandon my “toys.” He encouraged me to come work for him and together we would take over the world and become the new Gods. That’s how he ran his corporation, like a God on top of a pyramid overlooking a city of angels. Of course, he chose to replicate the power of creation in an unoriginal way, by simply copying God. And look how that turned out for the poor bastard. Literally blew up in the old man’s face. I always suggested he stick with simple robotics instead of those genetic abominations he enslaved and sold off-world, although his idea to implant them with false memories was, well… “amusing,” is how I would put it politely.
Bonus: at 1h18m in the video, Tyrell cryogenically preserved at the heart of his great pyramid!
Raghu Parthasarathy is the Alec and Kay Keith Professor of Physics at the University of Oregon. His research focuses on biophysics, exploring systems in which the complex interactions between individual components, such as biomolecules or cells, can give rise to simple and robust physical patterns.
Raghu is the author of a recent popular science book, So Simple a Beginning: How Four Physical Principles Shape Our Living World.
Steve and Raghu discuss:
0:00 Introduction
1:34 Early life, transition from Physics to Biophysics
20:15 So Simple a Beginning: discussion of the Four Physical Principles in the title, which govern biological systems
26:06 DNA prediction
37:46 Machine learning / causality in science
46:23 Scaling (the fourth physical principle)
54:12 Who the book is for and what high schoolers are learning in their bio and physics classes
1:05:41 Science funding, grants, running a research lab
1:09:12 Scientific careers and radical sub-optimality of the existing system
It was a fascinating episode, and I immediately went out and ordered the book! One question that came to mind: given how much of the human genome is dedicated to complex regulatory mechanisms and not proteins as such, it seems unintuitive to me that so much of heritability seems to be additive. I would have thought that in a system with lots of complicated,messy on/off switches, small genetic differences would often lead to large phenotype differences -- but if what I've heard about polygenic prediction is right, then, empirically, assuming everything is linear seems to work just fine (outside of rare variants, maybe). Is there a clear explanation for how complex feedback patterns give rise to linearity in the end? Is it just another manifestation of the central limit theorem...?
steve hsu
This is an active area of research. It is somewhat surprising even to me how well linearity / additivity holds in human genetics. Searches for non-linear effects on complex traits have been largely unsuccessful -- i.e., in the sense that most of the variance seems to be controlled by additive effects. By now this has been investigated for large numbers of traits including major diseases, quantitive traits such as blood biomarkers, height, cognitive ability, etc.
One possible explanation is that because humans are so similar to each other, and have passed through tight evolutionary bottlenecks, *individual differences* between humans are mainly due to small additive effects, located both in regulatory and coding regions.
To genetically edit a human into a frog presumably requires many changes in loci with big nonlinear effects. However, it may be the case that almost all such genetic variants are *fixed* in the human population: what makes two individuals different from each other is mainly small additive effects.
Zooming out slightly, the implications for human genetic engineering are very positive. Vast pools of additive variance means that multiplex gene editing will not be impossibly hard...
My research group contributed a chapter to this new book on Complex Trait Prediction (see below). The book is somewhat unique, covering applications to humans, plants, and animals all in a single volume.
This volume explores the conceptual framework and the practical issues related to genomic prediction of complex traits in human medicine and in animal and plant breeding. The book is organized into five parts. Part One reminds molecular genetics approaches intending to predict phenotypic variations. Part Two presents the principles of genomic prediction of complex traits, and reviews factors that affect its reliability. Part Three describes genomic prediction methods, including machine-learning approaches, accounting for different degree of biological complexity, and reviews the associated computer-packages. Part Four reports on emerging trends such as phenomic prediction and incorporation into genomic prediction models of “omics” data and crop growth models. Part Five is dedicated to lessons learned from case studies in the fields of human health and animal and plant breeding, and to methods for analysis of the economic effectiveness of genomic prediction.
Written in the highly successful Methods in Molecular Biology series format, the book provides theoretical bases and practical guidelines for an informed decision making of practitioners and identifies pertinent routes for further methodological researches.
Cutting-edge and thorough, Complex Trait Predictions: Methods and Protocols is a valuable resource for scientists and researchers who are interested in learning more about this important and developing field.
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.
The Sunday Times’ tech correspondent Danny Fortson brings on Stephen Hsu, co-founder of Genomic Prediction, to talk about the plummeting price of genomic sequencing (5:00), predicting height and cancer (9:10), mining biobanks (14:25), scoring embryos (19:00), why investors are staying anonymous (28:00), the need for a society-wide discussion (32:30), when he was accused of being a eugenicist (37:25), how powerful genetic prediction can be (43:15), genetic engineering (49:45), and why Denmark is the future (59:30).
1. Shai's educational background. From statistical physics and network theory to genomics.
2. Shai's paper on embryo selection: Schizophrenia risk. Modeling synthetic sibling genomes. Variance among sibs vs general population. RRR vs ARR, family history and elevated polygenic risk.
The impact of genetic variation on overall disease burden has not been comprehensively evaluated. Here we introduce an approach to estimate the effect of different types of genetic risk factors on disease burden quantified through disability-adjusted life years (DALYs, “lost healthy life years”). We use genetic information from 735,748 individuals with registry-based follow-up of up to 48 years. At the individual level, rare variants had higher effects on DALYs than common variants, while common variants were more relevant for population-level disease burden. Among common variants, rs3798220 (LPA) had the strongest effect, with 1.18 DALYs attributable to carrying 1 vs 0 copies of the minor allele. Belonging to top 10% vs bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Carrying a deleterious rare variant in LDLR, MYBPC3, or BRCA1/2 had an effect of around 4.1-13.1 DALYs. The population-level disease burden attributable to some common variants is comparable to the burden from modifiable risk factors such as high sodium intake and low physical activity. Genetic risk factors can explain a sizeable number of healthy life years lost both at the individual and population level, highlighting the importance of incorporating genetic information into public health efforts.
The figure below shows DALYs attributable to being in the top 10% vs
bottom 90% of each PGS. (So, roughly, top 10% vs average individuals.)
The Shorter Lifespan Polygenic Score is a kind of index similar to the Embryo Health Score used by Genomic Prediction. Note that the difference between 90+ percentile and average individuals is roughly 4 DALYs!
In our 2019 sibling validation paper we showed that disease risk polygenic scores have roughly as much predictive power to differentiate high and low risk sibs as when applied to pairs of unrelated individuals.
I don't have an embed link so click here to listen to the podcast.
Genomic Prediction’s Elizabeth Carr: “Scoring embryos”
The Sunday Times’ tech correspondent Danny Fortson brings on Elizabeth Carr, America’s first baby conceived by in-vitro fertilization and patient advocate at Genomic Prediction, to talk about the new era of pre-natal screening (5:45), the dawn of in-vitro fertilization (8:40), the technology’s acceptance (12:10), what Genomic Prediction does (13:40), scoring embryos (16:30), the slippery slope (19:20), selecting for smarts (24:15), the cost (25:00), and the future of conception (28:30). PLUS Dan Benjamin, bio economist at UCLA, comes on to talk about why he and others raised the alarm about polygenic scoring (30:20), drawing the line between prevention and enhancement (34:15), limits of the tech (37:15), what else we can select for (40:00), and unexpected consequences (42:00). DEC 3, 2021
This is an earlier podcast I did with Elizabeth and IVF physician Serena Chen (IRMS and Rutgers University Medical School).
It is a great honor to co-author a paper with Simon Fishel, the last surviving member of the team that produced the first IVF baby (Louise Brown) in 1978. His mentors and collaborators were Robert Edwards (Nobel Prize 2010) and Patrick Steptoe (passed before 2010).
...
Today millions of babies are produced through IVF. In most developed countries roughly 3-5 percent of all births are through IVF, and in Denmark the fraction is about 10 percent! But when the technology was first introduced with the birth of Louise Brown in 1978, the pioneering scientists had to overcome significant resistance.
There may be an alternate universe in which IVF was not allowed to develop, and those millions of children were never born.
Wikipedia: ...During these controversial early years of IVF, Fishel and his colleagues received extensive opposition from critics both outside of and within the medical and scientific communities, including a civil writ for murder.[16] Fishel has since stated that "the whole establishment was outraged" by their early work and that people thought that he was "potentially a mad scientist".[17]
I predict that within 5 years the use of polygenic risk scores will become common in some health systems (i.e., for adults) and in IVF. Reasonable people will wonder why the technology was ever controversial at all, just as in the case of IVF.
I recommend the first article. Philip Ball is an accomplished science writer and former scientist. He touches on many of the most important aspects of the topic, not easy given the length restriction he was working with.
However I'd like to cover an aspect of embryo selection which is often missed, for example by the bioethicists quoted in Ball's article.
Several independent labs have published results on risk reduction from embryo selection, and all find that the technique is effective. But some people who are not following the field closely (or are not quantitative) still characterize the benefits -- incorrectly, in my view -- as modest. I honestly think they lack understanding of the actual numbers.
Some examples:
Carmi et al. find a ~50% risk reduction for schizophrenia from selecting the lowest risk embryo from a set of 5. For a selection among 2 embryos the risk reduction is ~30%. (We obtain a very similar result using empirical data: real adult siblings with known phenotype.)
Visscher et al. find the following results, see Table 1 and Figure 2 in their paper. To their credit they compute results for a range of ancestries (European, E. Asian, African). We have performed similar calculations using siblings but have not yet published the results for all ancestries.
Relative Risk Reduction (RRR):
Hypertension: 9-18% (ranges depend on specific ancestry)
Type 2 Diabetes: 7-16%
Coronary Artery Disease: 8-17%
Absolute Risk Reduction (ARR):
Hypertension: 4-8.5% (ranges depend on specific ancestry)
Type 2 Diabetes: 2.6-5.5%
Coronary Artery Disease: 0.55-1.1%
I don't view these risk reductions as modest. Given that an IVF family is already going to make a selection they clearly benefit from the additional information that comes with genotyping each embryo. The cost is a small fraction of the overall cost of an IVF cycle.
But here is the important mathematical point which many people miss: We buy risk insurance even when the expected return is negative, in order to ameliorate the worst possible outcomes.
Consider the example of home insurance. A typical family will spend tens of thousands of dollars over the years on home insurance, which protects against risks like fire or earthquake. However, very few homeowners (e.g., ~1 percent) ever suffer a really large loss! At the end of their lives, looking back, most families might conclude that the insurance was "a waste of money"!
So why buy the insurance? To avoid ruin in the event you are unlucky and your house does burn down. It is tail risk insurance.
Now consider an "unlucky" IVF family. At, say, the 1 percent level of "bad luck" they might have some embryos which are true outliers (e.g., at 10 times normal risk, which could mean over 50% absolute risk) for a serious condition like schizophrenia or breast cancer. This is especially likely if they have a family history.
What is the benefit to this specific subgroup of families? It is enormous -- using the embryo risk score they can avoid having a child with very high likelihood of serious health condition. This benefit is many many times (> 100x!) larger than the cost of the genetic screening, and it is not characterized by the average risk reductions given above.
The situation is very similar to that of aneuploidy testing (screening against Down syndrome), which is widespread, not just in IVF. The prevalence of trisomy 21 (extra copy of chromosome 21) is only ~1 percent, so almost all families doing aneuploidy screening are "wasting their money" if one uses faulty logic! Nevertheless, the families in the affected category are typically very happy to have paid for the test, and even families with no trisomy warning understand that it was worthwhile.
The point is that no one knows ahead of time whether their house will burn down, or that one or more of their embryos has an important genetic risk. The calculus of average return is misleading -- i.e., it says that home insurance is a "rip off" when in fact it serves an important social purpose of pooling risk and helping the unfortunate.
The same can be said for embryo screening in IVF -- one should focus on the benefit to "unlucky" families to determine the value. We can't identify the "unlucky" in advance, unless we do genetic screening!
Excellent interview with David Liu of Harvard, which gives an overview of key innovations in gene editing since the discovery of CRISPR.
Labs all around the world are busy building new tools and libraries for gene editing, with dramatic progress since CRISPR was first discovered less than 10 years ago.
Liu is optimistic about clinical applications over the next 10 years. He does not discuss germline editing (i.e., of embryos) but one can readily imagine how these advances in technology might be applied there.
In episode 14 of the Psychology Is podcast, we have the special opportunity to talk to Dr. Steve Hsu, a physicist, professor at MSU, and founder of Genomic Prediction. We discuss the newest innovations related to genetic testing and editing, including Genomic Prediction and CRISPR. We also discuss what these innovations may make possible (for better or worse), and how we can proceed carefully as we learn to harness this new power.
