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Embryo biopsies are taken and shipped to the lab, where the DNA is genotyped and analyzed. The company provides predictive reports on various traits based on the genetic data. The company works with IVF clinics globally and has established channel relationships in the market. The first-mover advantage and expertise in the field give the company an edge, but competition from companies with larger data sets, like 23andMe, is a potential threat. The future potential of genomic prediction and the ability to navigate high-dimensional trait spaces is substantial, allowing for personalized selection and optimization of various traits.
The predictive models used in genomic predictions are often simple and adactive, summing over the effect sizes of relevant genetic variants. The simplicity of the models contrasts with the complexity of the underlying genetic architecture and the substantial number of independent genetic variants that influence traits. Evolutionary and genetic arguments support the idea that there is a multitude of unexplored variation available for selection or editing. Longevity predictions, facial reconstruction, and identification of individuals from DNA samples are some potential applications of genomic predictions.
The genomic prediction company primarily focuses on health-related traits, but there are broader implications for other traits as well. The technology is likely to revolutionize the field of assisted reproduction, with the ability to select embryos based on various traits. However, the long-term advantages of such companies will depend on various factors, including business channel connections and scientific advancements. Questions regarding regulation, privacy concerns, and societal implications need to be taken into consideration.
As technology advances, genomic prediction may become less dependent on specific labs and more accessible globally. The ability to select and optimize traits based on genetic predictions may lead to longer lifespans, among other improvements. The vast amount of unexplored genetic variation provides a well of opportunity for selection or editing. However, the long-term effects, limitations, and ethical considerations of such advancements will require careful evaluation.
Embryo selection technology has the potential for significant advancements in choosing embryos with desirable traits. The ability to upload genetic information and match individuals based on their genome is being explored by dating apps companies. There are discussions about incorporating verified height to prevent misrepresentation. Additionally, selecting embryos based on complementary regions of intelligence-related genes is being considered, potentially leading to offspring with higher intelligence levels.
The role of environment in shaping traits like height is discussed. While genetic factors play a significant role in determining height, environmental conditions, such as nutrition, can also affect one's height potential. The height prediction for individuals raised in a favorable environment becomes highly heritable due to reduced environmental variation. In comparison, older generations may be shorter due to environmental and nutritional differences.
The quality of prediction decreases when applying predictors from one ancestral population to another. The correlation and tagging quality of certain genetic markers vary between populations, impacting the accuracy of predictions across different ancestral groups. Research is actively exploring the causes and potential solutions to enhance prediction accuracy in diverse populations.
The ability to select embryos with desired traits raises ethical and social concerns. The possibility of differential selection based on characteristics such as intelligence or physical appearance sparks debates about potential disparities and the future impact on society. Additionally, uncertainties surrounding the role of environmental factors and the underlying mechanisms involved in trait determination further complicate the discussion around genetic selection.
The podcast episode discusses the application of mathematical concepts to finance, emphasizing the importance of mathematicians entering the field. It highlights examples of mathematicians using pricing theory and calculus in finance, demonstrating their ability to approach financial problems from a unique perspective. The episode also contrasts physicists' experience with dealing with noisy data to mathematicians' acceptance of data, stating that physicists are adept at analyzing and connecting observational data to theoretical models. It suggests that physicists and computer scientists, who have experience working with imperfect data and complex systems, can bring valuable skills to finance and other fields.
The podcast delves into the concept of intelligence and its application in various domains. It challenges the idea of a unified measure of intelligence, asserting that different areas of expertise demand different skill sets. It presents examples of individuals who excel in technical problem-solving, despite lacking extensive background knowledge, emphasizing their ability to provide valuable insights. The discussion also touches on the impact of education and social networks, suggesting that exposure to ambitious and influential individuals can shape one's understanding of opportunity and success. The episode raises questions about the long-term effects of talent migration and highlights the importance of nurturing and utilizing native talent within a country.
Steve Hsu is a Professor of Theoretical Physics at Michigan State University and cofounder of the company Genomic Prediction.
We go deep into the weeds on how embryo selection can make babies healthier and smarter.
Watch on YouTube. Listen on Apple Podcasts, Spotify, or any other podcast platform.
Read the full transcript here.
Follow Steve on Twitter. Follow me on Twitter for updates on future episodes.
Timestamps
(0:00:14) - Feynman’s advice on picking up women
(0:11:46) - Embryo selection
(0:24:19) - Why hasn't natural selection already optimized humans?
(0:34:13) - Aging
(0:43:18) - First Mover Advantage
(0:53:38) - Genomics in dating
(0:59:20) - Ancestral populations
(1:07:07) - Is this eugenics?
(1:15:08) - Tradeoffs to intelligence
(1:24:25) - Consumer preferences
(1:29:34) - Gwern
(1:33:55) - Will parents matter?
(1:44:45) - Wordcels and shape rotators
(1:56:45) - Bezos and brilliant physicists
(2:09:35) - Elite education
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