Literary Insights

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Here is a summary of the key points from the introduction of THE END OF GENETICS:

The book discusses the future of human reproduction and designing the DNA of our children.
While genetics research has made rapid progress in identifying genetic causes of disease, developing targeted treatments has lagged behind. There has been recurring optimism about precision/personalized medicine followed by setbacks.
Early gene therapy trials in the 1990s raised hopes but also showed challenges, like Jesse Gelsinger’s death in a trial for a liver disorder due to an unexpected immune reaction. It took over a decade for trust in gene therapy to recover.
Even if safe gene replacement therapies are developed, they may not cure diseases with developmental components, as the defective gene may have already altered development.
The author argues that many devastating genetic diseases will ultimately be “treated” through careful determination of children’s genomes, not precision medicine alone.
Parents will face complex choices about adjusting their children’s genomes and need to understand the science, limitations, and potential consequences to make informed decisions that could impact humanity’s future.

So in summary, the introduction sets up the discussion of how advancing genetics is leading to the potential for purposefully designing children’s genomes, which will have significant ethical implications.

The author argues that advances in genetics now make it possible to determine a person’s full genetic makeup in a matter of days, raising questions about selecting or engineering traits in future children.
Most people are comfortable selecting against severe genetic diseases in children. But there are open questions around less severe later-onset conditions, non-disease traits, and parental autonomy over such decisions.
The author presents a thought experiment where parents give their children only genetic variants common in the human population, removing rare variants. Geneticists’ views on the outcome varied widely, showing limits to predicting consequences of genetic changes.
The ability to select or edit children’s genomes will constitute a new kind of human genetics beyond natural genetic lotteries of the past. But what changes are desired or appropriate, and what can safely be achieved, are open questions society must grapple with to guide this emerging field.
The author’s goal is to inform non-experts to develop their own views on this issue impacting humanity’s future, not to claim any definitive answers themselves.
In the early days of personal genomics, the main attractions were genetic ancestry testing and genome-wide association studies (GWAS). Ancestry testing provided information about geographic origins, while GWAS identified common gene variants linked to diseases and traits.
However, the results from these tests had little clinical value. At most, they indicated slightly increased or decreased risks for common diseases, but did not change medical recommendations. One geneticist described this field as “recreational genomics.”
Some personal genomics companies exaggerated or misrepresented genetic findings. One former CEO claimed dubious discoveries like identifying descendants of biblical figures from DNA. This was criticized as “genetic astrology.”
Advances in genome sequencing are now identifying mutations with strong effects on disease risks. This raises new concerns about accuracy and oversight, as incorrect information could have serious consequences.
Cases are described where genome sequencing successfully diagnosed previously undiagnosed genetic diseases in children, highlighting its growing clinical utility when used properly. However, regulation of personal genomics companies is still limited.

In summary, while personal genomics initially offered little medical value, advances now mean genetic information could be clinically significant. But accuracy and oversight of such companies remains a concern given past overpromising of results.

The passage describes two case studies where whole genome sequencing led to accurate diagnoses and effective treatments for rare genetic diseases.
The first case was Bertrand “Buddy” Might, who was diagnosed with a defect in the NGLY1 gene based on analysis of his genome. This helped confirm the underlying cause of his condition.
The family’s efforts to find other similar cases helped validate the diagnosis. Bertrand’s story raised awareness and progress towards treatments for NGLY1 deficiency.
The second case was an 18-month-old girl with a progressive neurological condition. Genome sequencing revealed she had Brown-Vialetto-Van Laere syndrome caused by a vitamin B2 transporter defect. Supplementation with vitamin B2 almost immediately improved her symptoms.
The passage discusses how genome sequencing is increasingly used for diagnosis but access remains a challenge, as shown by the example of Dr. Liz Cirulli’s daughter where insurance initially denied testing despite a strong genetic basis for suspicion of Gitelman syndrome.
Overall it highlights how genome sequencing can provide accurate diagnoses and direct effective treatments for rare genetic diseases, but notes barriers still exist to ensure broader access to this diagnostic tool.
Addressing limitations in clinical genomics like incomplete genetic answers and lack of treatments is a priority. While genetics can identify single-gene causes for many diseases, few have effective precision medicine treatments like vitamin B2 for Brown-Vialetto-Van Laere syndrome.
Most people carry several recessive disease-causing mutations. A study found on average each person carries 2.8 mutations in 437 genes alone. For these genes, 2% of couples would have mutations in the same gene, meaning 1 in 4 of their children would be affected.
These inherited recessive diseases could be prevented by sequencing potential parents’ genomes to check for shared mutations before conceiving. Couples could then choose partners, use preimplantation screening, or avoid having children together to prevent affected births.
Genome screening will likely become routine for prevention, starting with clear recessive diseases but expanding to risk factors for complex diseases. Some parents may choose to alter genetic risks they transmit to children.
Widespread personal genomics will transition human genetics from treatment to selecting healthy genetics in future generations through screening and potential genome engineering of children’s genes. This raises ethical questions about genetic selection and engineering.
The author discusses the history and connotations of eugenics, which aimed to promote certain genes or traits over others. It was often linked to racist ideologies and pseudo-science.
While modern genetics may involve considering genetic traits in reproduction, the author rejects use of the term “eugenics” due to its problematic history.
The author proposes using the term “reproductive genome design” instead to describe efforts to reduce disease-causing mutations in children’s genomes.
However, the author acknowledges the potential for biases and sees this topic as inherently involving value judgments. Scientists are still human and may selectively present evidence to support their preexisting views.
The author notes having advocated in the past for the view that relatively rare genetic variations play an outsized role in human diseases. This perspective forms the basis for the book’s thesis explored in later chapters.
In summary, the author discusses the loaded history of eugenics, proposes an alternative term, but notes the unavoidable role of biases and value judgments in this complex topic area.

Gregor Mendel, an Augustinian friar, invented the science of genetics through his experiments with pea plants in the 1850s and 1860s. He observed consistent traits that were inherited, such as seed shape and color. The prevailing view at the time was “blending inheritance,” where offspring traits are an average of the parents’. However, Mendel found that traits did not blend but were inherited distinctly from one parental form or the other.

In his crosses between pea varieties with different traits (P1 and P2), the F1 hybrid generation always expressed the trait of one parent, which Mendel called the “dominant” trait. The trait of the other parent was “hidden.” When he self-fertilized the F1 hybrids, the F2 generation resurrected both parental traits in a predictable ratio.

This discovery refuted blending inheritance and established Mendel’s laws of inheritance - the laws of dominance/uniformity and segregation. His experiments with pea plants laid the foundation for modern genetics by demonstrating traits are inherited in discrete particles (genes) that assort and segregate independently according to probabilistic rules.

Mendel observed that his F1 generation plants looked like only one parent, and he recovered exact parental strains in the F2 generation in fixed ratios, which is inconsistent with blending inheritance.
He needed a new model of inheritance, which became the modern science of genetics. He postulated that traits are determined by underlying “elements” (genes) that come in different forms (alleles).
His naming of elements and particles as dominant/recessive with uppercase/lowercase allowed a mathematical analysis to explain the 3:1 ratio in F2 generations.
He considered hybrid crosses with plants differing in multiple traits. Assuming independent assortment of alleles, he derived expected proportions in the F2 generation, arriving at his law of independent assortment.
Some debate that Mendel’s results were “too good” and matched expectations too closely, raising questions about whether he manipulated results to fit his theory. However, his laws are still valid regardless of how he arrived at them.
The most common genetic mutation that causes cystic fibrosis is called deltaF508. It occurs in 3-5% of people of European ancestry.
If a couple both carrying the deltaF508 mutation has children, on average 25% of children will have normal copies of the gene, 50% will be carriers with one normal and one mutant copy, and 25% will have two mutant copies and thus cystic fibrosis (a recessive disease). This follows Mendel’s laws of inheritance.
All known genetic diseases that cause severe inherited conditions follow Mendel’s laws precisely. These are called Mendelian diseases. Complex diseases like heart disease and cancer do not clearly follow this pattern of inheritance.
While Mendel’s laws describe inheritance at the genetic level, additional work was needed to understand how genetic information is encoded, transmitted between generations, and translated into proteins that build organisms.
The passage then provides a brief overview of some of the key discoveries that helped elucidate these processes, starting from the rediscovery of Mendel’s work through establishing that genes reside on chromosomes, developing genetic maps, and identifying DNA as the physical material of heredity.
Griffith worked with two forms of the bacterium Streptococcus pneumoniae: a rough (R) form that was non-virulent, and a smooth (S) form that caused pneumonia.
When mice were infected with the R form, they did not get sick. Infection with the S form caused pneumonia.
Griffith heat-killed both forms and injected mice. As expected, neither form caused disease on their own.
Surprisingly, when Griffith injected mice with both heat-killed S bacteria and live R bacteria, the mice developed pneumonia and the bacteria cultured from them were live S bacteria.
This led Griffith to hypothesize that the R strain took up a “transforming principle” from the dead S strain, transforming into the virulent S form.
Avery and colleagues isolated the transforming principle through enzymatic treatments and found that degradation of DNA prevented transformation, while degradation of proteins did not. This supported that DNA was the hereditary material.
Watson and Crick combined existing data like X-ray images and Chargaff’s rules to propose the double helix structure of DNA in 1953, explaining how DNA could store and replicate genetic information through complementary base pairing.
Meselson-Stahl experiments confirmed Watson and Crick’s semi-conservative model of DNA replication. Kornberg later discovered DNA polymerase, the enzyme that carries out replication.
While proteins were initially thought to be the hereditary material, the genetic code transfers information from DNA to build proteins via RNA, linking DNA’s information to the cell’s machinery.
George Gamow was one of the first to formulate the question of how DNA encodes information to specify amino acids mathematically. He noted that if DNA words used only 2 bases, there would be too few codes to represent all amino acids.
Gamow reasoned that if DNA words used 3 bases, there would be 64 possible codons (4 x 4 x 4), comfortably allowing representation of all amino acids with some redundancy. This turned out to be correct.
Francis Crick proposed the “Central Dogma” framework - that DNA in the nucleus contains genetic information that is transcribed into messenger RNA, which is transported to the cytoplasm where it directs protein synthesis. This established the flow of genetic information.
Experiments confirmed that DNA words are triplets (codons) of 3 bases. Scientists then determined the genetic code by constructing artificial RNA sequences and identifying the resulting proteins, mapping each codon to an amino acid. This “cracked the code of life” by providing a dictionary to translate DNA sequences into protein sequences.
The genetic code can be represented as a table mapping the 64 possible codons to their corresponding amino acids. By studying this code, one can read how any gene encodes for specific amino acids and proteins. This allowed the flow of information described by Crick’s Central Dogma to be precisely defined.

In summary, Gamow, Crick and others established key conceptual frameworks, and experiments ultimately determined the genetic code by identifying the codon-amino acid mapping - thus enabling the flow of genetic information from DNA to proteins.

Here is a summary of molecular evolution controversies:

The shifting balance theory proposed by Sewall Wright argued that populations evolve new adaptations by having partially isolated subpopulations that can randomly explore new genetic combinations through genetic drift. This theory is largely ignored today.
J.B.S. Haldane argued that the rate of evolution is constrained by the “cost of selection” - positive selection comes at the expense of selective mortality against the unfavored allele. However, others showed that if selection acts against groups of alleles together, evolution can proceed faster at the same cost.
Ronald Fisher viewed evolution as simple - new mutations introduce variation and advantageous alleles increase in frequency through natural selection. But Sewall Wright argued this cannot explain complex adaptations that require coordinated changes across multiple genes.
In general, these pioneering evolutionary geneticists like Wright, Fisher and Haldane debated fundamental issues around how genetic variation arises and spreads through populations, the constraints on evolutionary rates, and how complex adaptations evolve - questions that are still not fully resolved today.

Here is a summary of the key points of contention between the classical and balancing selection hypotheses:

The classical hypothesis views most genetic variation as neutral or slightly deleterious. Variation is maintained by mutation-drift equilibrium, with new deleterious mutations regularly introduced by mutation and removed by purifying selection.
The balancing selection hypothesis posits that much genetic variation is selectively advantageous and maintained in populations by forms of balancing selection like overdominance or heterozygote advantage. Both allelic forms are beneficial under certain conditions.
A central question is what proportion of genetic variation is selectively neutral/slightly deleterious versus maintained by balancing selection. The classical view says most, balancing selection says a significant proportion.
Population genetics models show even a small proportion under balancing selection could explain observed levels of variation, so the theories are not necessarily mutually exclusive.
Direct evidence for or against either theory is limited. The sickle cell example demonstrates balancing selection/overdominance, but such clear examples are rare.
Reproductive genomic engineering faces high uncertainty because we don’t know how common balancing selection vs neutral/deleterious variation is in the human genome. Engineering could inadvertently remove selectively advantageous variation.

In summary, the debate comes down to disagreements over the relative importance and prevalence of neutral/deleterious versus selectively balanced genetic variation, with implications for how genomes might respond to artificial manipulation or selection pressures. More evidence is needed to conclusively validate one theory over the other.

Theodosius Dobzhansky argued that heterozygotes (having two different alleles of a gene) are often more fit than homozygotes (having two identical alleles) in natural populations. He believed this “balanced selection” was common.
Dobzhansky studied genetic variation in fruit flies in the wild and how allele frequencies changed over time due to natural selection. This convinced him that selection often favors maintaining two allelic forms of genes.
Hermann Muller disagreed, based on his studies of mutations induced by radiation in fruit flies. He believed most genetic variation involved a “good” wild-type allele and harmful mutant alleles introduced by mutation.
Both views - that heterozygote advantage is common vs most variation involves good vs bad alleles - have some validity. It depends on the gene and environmental factors. For things like malaria resistance and immune genes, heterozygote advantage seems to apply. For genes causing diseases, the good vs bad allele view often fits better.
The major point of debate was how common balanced selection vs deleterious mutations were as drivers of genetic variation within and among populations. There is evidence to support both views, and the relative importance is still unknown for more complex traits.

So in summary, Dobzhansky and Muller represented two influential but opposing views on the nature and origins of genetic variation, with evidence existing for both mechanisms playing a role depending on the context.

The Human Genome Project developed detailed physical and genetic maps of the human genome by identifying landmark sequences spaced regularly across chromosomes.
In the 1980s, geneticist David Botstein envisioned how these maps could be used to systematically track down the precise genetic causes of Mendelian (monogenic) diseases that run in families.
Prior to this, around the late 1800s, doctor Archibald Garrod observed families affected by inborn errors of metabolism and hypothesized they were due to inherited genetic traits, establishing the foundations of medical genetics.
In the late 1980s to 2000s, researchers used linkage analysis - identifying co-inheritance of genetic markers and diseases within families - to pinpoint the chromosomal regions and eventually the specific mutations responsible for thousands of Mendelian diseases.
This marked the first phase of systematically identifying disease-causing genes and mutations following completion of the Human Genome Project mapping efforts. It revolutionized understanding of Mendelian diseases at a molecular level.
William Bateson and Archibald Garrod studied the inheritance patterns of alkaptonuria, which Garrod had identified. They recognized it followed a recessive pattern, inspiring Garrod’s concept of human chemical individuality and the study of inborn errors of metabolism.
Garrod proposed the terminology of “inborn errors of metabolism” which is still used today to describe Mendelian diseases caused by genetic defects in metabolism.
Along with Bateson, Garrod developed an important philosophy in medical genetics of treasuring exceptions rather than just studying disease. Focusing on rare exceptions has driven advances in the field.
It took over a century after Garrod first described alkaptonuria for the responsible gene to be identified, due to lack of scientific tools.
In 1980, David Botstein proposed a roadmap for systematically identifying disease-causing genes through linkage mapping using genetic polymorphisms. This kickstarted progress in human gene mapping.
A milestone was identifying the cystic fibrosis gene in 1989, validating Botstein’s approach, though treatments targeting the underlying cause took much longer to develop.
Recessive diseases and de novo mutations were more difficult to map using this approach due to lack of informative families, requiring next-generation sequencing methods.

So in summary, it traces the early philosophy and concepts in medical genetics developed by Garrod and Bateson, the technical challenges overcome by Botstein’s linkage mapping proposal, and remaining challenges like recessive and de novo diseases.

Here is a summary of the key points about the “thrifty gene” hypothesis:

The hypothesis was proposed by geneticist James Neel to explain population differences in diabetes risk.
It suggests that in low-nutrient environments, natural selection would favor “thrifty” alleles that conserve calories and energy. These alleles would be advantageous for survival.
However, in modern high-nutrient environments with abundant food availability, these same thrifty alleles would become disadvantageous and increase risk of obesity and related diseases like diabetes.
The alleles were once beneficial for surviving famines but predispose to disease in an environment of calorie abundance.
In essence, certain genetic variants may have increased diabetes risk today due to a mismatch between our ancient genetic adaptations and the modern obesogenic environment with abundant highly palatable food.

So in summary, the thrifty gene hypothesis proposes that genes selected for calorie conservation in unstable ancient environments may underlie higher risk of obesity and diabetes in populations today due to changes in the food environment. The same alleles have different effects depending on nutritional conditions.

The passage discusses how whole genome sequencing has advanced our understanding of human genetic diseases. Two key developments enabled this: the Human Genome Project mapped the entire human genome sequence, and next-generation sequencing (NGS) made sequencing much faster and cheaper.
NGS allows researchers to now sequence entire human genomes to identify all variants present. They can start with a patient with a serious undiagnosed disease and scan their genome to find the likely causal mutation.
This has helped uncover genes responsible for diseases that could not be found through previous linkage studies. It has been especially useful for rare, de novo mutations causing diseases that impact reproduction.
Sequencing entire genomes provides an unprecedented level of genetic information, but also technical and analytical challenges in interpreting all the identified variants. Overall, whole genome sequencing has transformed our ability to diagnose rare genetic diseases and uncover new disease genes.
Genetic markers and family histories alone are not enough to track down disease-causing mutations, as some mutations occur de novo (spontaneously).
Next-generation sequencing allows identifying disease genes by sequencing unrelated affected individuals and finding disruptive mutations in the same gene. The gene with the most mutations across individuals is likely disease-causing.
This approach identified ATP1A3 as the gene causing alternating hemiplegia of childhood after sequencing just 10 patients. It has led to identifying hundreds of new disease genes.
Diagnostic sequencing without prior knowledge of the disease gene is now possible by sequencing whole exomes/genomes to search everywhere for mutations.
Genetic diseases can present unexpectedly, showing the need for sequencing all suspected genetic disease patients. Many “complex” diseases turn out to have genetic causes.
Population genomics using large sequencing databases allows learning what parts of the genome are tolerant/intolerant of functional variation. Intolerant regions where disease mutations tend to occur can be identified.
This helps interpret patient genomes and identified genetic causes of stillbirth that would otherwise be difficult to find.
Advances in sequencing are facilitating a systematic study of genetic variation and its relationship to human differences through large-scale population studies.
The author argues that as it becomes easier to determine genetic causes of disease, the focus should shift from treating patients to preventing the transmission of disease-causing genetic mutations.
Gene editing technologies like CRISPR will allow prospective parents to ensure their children do not inherit undesirable genetic variants. This could largely eliminate certain genetic diseases.
However, gene editing also risks reducing genetic diversity and individuality. Deciding what mutations to target involves complex ethical questions.
The author outlines the genetic mutations that could initially be targeted for non-transmission via gene editing, including recessive diseases caused by two mutations in one gene.
Current technologies like preimplantation genetic diagnosis could already eliminate some recessive and dominant genetic diseases.
Emerging gene editing tools like CRISPR will allow more advanced genetic manipulation beyond what is currently possible.
Predicting what variants parents may choose to edit in the near future, and how to avoid unintended consequences, will be challenges as gene editing capabilities advance. Informed discussion is needed.
Many genetic diseases follow recessive inheritance patterns, meaning two copies of a mutated gene are required to cause the disease. These are like Mendel’s wrinkled peas - a plant needs two copies of the wrinkled allele to have wrinkled seeds.
Around 2000 genes are known to cause autosomal recessive diseases when mutated. These diseases account for about 1 in 1000 live births in outbred populations. Globe burden is higher in areas with more inbreeding.
Some genetic diseases are X-linked, meaning they are on the X chromosome. Most affected individuals are males who inherit the mutation from their mother. Hemophilia is a famous example.
Around 1500 genes are known to cause dominant diseases when mutated. These are like Mendel’s smooth peas - only one copy is needed. Many dominant disease mutations occur de novo rather than being inherited.
Recessive and dominant genetic diseases together account for a large global burden of mortality and morbidity. Many are quite common in some populations. New technologies may help address these diseases.

So in summary, it outlines the major modes of genetic inheritance for diseases, estimates burdens, and notes some examples to illustrate different inheritance patterns and issues like de novo mutations.

The ACMG has recommended that patients always be informed of genetic findings from sequencing, even if unrelated to the initial reason for sequencing. This includes 59 genes linked to serious diseases where interventions can reduce risk.
Two notable genes are BRCA1 and BRCA2, which greatly increase cancer risk. Angelina Jolie publicly discussed her choice to have preventative surgery after learning she carried a BRCA1 mutation.
It’s estimated 2 in 100 people carry a mutation in one of the 59 “actionable” genes. This means over 6 million Americans could learn of increased disease risk from sequencing.
Choices about reproduction are also relevant, as carriers may wish to avoid transmitting genetic risks to children using technologies like IVF with genetic testing of embryos.
Other risk factors include common genetic variants associated with polygenic risk scores, and variants influencing infectious disease susceptibility, which future technologies may be able to address.
Reducing genetic variation in populations, as occurred with the Irish potato famine, can increase susceptibility to future pathogens we cannot predict.
The passage will now discuss genetic risks that can be addressed today using existing technologies to reduce the global burden of genetic disease.
Historically, 50+ Jewish children were born with Tay-Sachs disease each year in the US due to certain mutations being more common in Ashkenazi Jewish populations.
This was reduced through community-wide genetic testing programs like Dor Yeshorim that screens couples for risk of passing on genetic diseases before marriage.
Today, carrier screening identifies individuals with risk mutations. At-risk couples have two options - find a new partner, or do IVF with preimplantation genetic testing (PGT).
PGT works by fertilizing eggs in vitro, letting embryos develop to 8 cells, testing one cell from each embryo, and selecting embryos without two disease mutations for implantation.
This procedure, enabled by the development of IVF starting with Louise Brown in 1978, allows selection of embryos that will not pass on genetic diseases, reducing incidence in affected communities.

So in summary, widespread genetic testing and screening programs, combined with the ability to do IVF/PGT enabled by the development of IVF techniques, has dramatically reduced rates of genetic diseases like Tay-Sachs in at-risk Jewish communities.

Preimplantation genetic testing (PGT) is often done later in pregnancy because that is when many people learn it is an option from their doctor. But awareness will likely increase over time so testing can be done sooner.
In the future, it may be common for people to have their genome data on their smartphones. This would allow couples to check genomic compatibility for recessive disease genes even on a first date. They could avoid disease risks through embryo selection or finding a different partner.
Similar embryo selection could apply to dominant disease genes that a parent carries, like BRCA1 mutations. Once carriers are aware, some may choose not to pass on the mutation. This could significantly reduce disease burden over time.
While testing can address recessive and dominant genes, it cannot currently detect de novo mutations or comprehensively sequence embryos due to technical limitations. The small number of eggs also constrains how many genes can be targeted.
Increasing awareness and testing capabilities could help more people benefit from preimplantation genetic testing to have healthy children free from certain genetic diseases. But universal access may require addressing constraints on technologies.

Here are the key points:

Genome editing today is limited to selecting amongst a small number of pre-embryos from IVF. This restricts what can be targeted to usually just one or two sites to ensure transmission of a desired allele.
It’s very difficult to safely edit pre-embryos given the small number and high risk of mistakes. So genome editing of pre-embryos is not really possible now.
The main barrier is the limited number of pre-embryos from IVF. But this number can likely be overcome through technological advances.
Cloning is not a viable way to overcome this, as cloning faces its own limitations like limited egg sources.
However, technologies for reprogramming differentiated cells back to a stem cell-like state could eventually overcome this. Experiments in the 1950s showed nuclei from differentiated cells could direct embryonic development.
In the 2000s, Yamanaka discovered transcription factors that could reprogram differentiated cells into induced pluripotent stem cells, opening up new possibilities without needing eggs.
These advances suggest that with more incremental technology, we could one day have greater ability to design children’s genomes beyond just selecting from a few pre-embryos. But we are not there yet.
Yamanaka identified a set of four transcription factors that can transform skin cells into pluripotent stem cells capable of developing into any cell type. This work won Yamanaka the Nobel Prize and revolutionized stem cell research.
His discovery of induced pluripotent stem cells (iPSCs) showed that cell fate and development can be reprogrammed through genetic factors. iPSCs opened up new possibilities for modeling diseases and regenerative medicine.
The ability to derive functional sperm and egg cells from iPSCs in mice showed that in vitro gamete generation may be possible. This has implications for infertility treatment and potentially designing the human genome.
CRISPR-Cas9 gene editing technology allows precise genomic changes and, combined with iPSC-derived gametes, could enable selective changes to the human reproductive genome at a large scale.
However, the first known attempt to edit human embryos in China using CRISPR raised safety and ethical concerns, as it’s still difficult to detect off-target mutations and there was no compelling medical need or proper consent/oversight.
While genomic design holds promise, more research is needed to ensure it can be done safely and ethically before considering clinical applications like heritable human genome editing.
CRISPR gene editing technology may enable extensive reproductive genome design within 10-30 years, allowing selection of genetic traits for embryos or gametes.
Like the Manhattan Project addressed atomic weapons, we should recognize this technology is coming and prepare for its wise and ethical use through serious public discussion now.
Initial applications could protect against heritable genetic diseases, including de novo mutations. However, parents may also wish to edit out less severe genetic variations they carry.
Analysis of genomic data from thousands of individuals indicates people typically carry many rare genetic variants that are likely damaging and under negative selection. Extensive editing could remove all these variants.
While protecting against disease is reasonable, editing out less severe or ambiguous variations raises deeper questions about human genetic diversity, enhancement, and natural selection that require consideration well before this technology is applied to reproduction. Advance planning is needed for its responsible oversight.
The passage discusses randomly occurring genetic variants that are rare in the population (seen in less than 2% of individuals) and that change protein sequences or prevent protein production.
On average, individuals carry about 19 of these rare functional variants, but the number ranges from 0 to 48 variants per person. If less rare variants (seen in 1/10,000 individuals) are included, the average is 58 variants per person.
It is unknown what phenotypic effects these rare variants have. While some may be neutral, many are likely under negative selection and have slightly deleterious effects on traits.
These rare variants persist due to a balance between new mutations constantly introducing variants and natural selection slowly removing deleterious ones.
The author asked several geneticists what a “common variant human” without any rare variants would be like. Answers varied significantly, from being dead to having average intelligence.
The author’s guess is the common variant human would resemble Chris Hemsworth physically, having avoided slightly deleterious effects of rare variants on traits like height, health, and intelligence. However, this is highly uncertain.
While rare variant removal may initially target severe genetic diseases, the author predicts interest will grow in making more aggressive heritable genome edits once the technology is sufficiently advanced. Strict regulation of such edits will be difficult to maintain.
The report avoids challenging questions around what should be considered a disease. The boundaries are less clear than implying focus on solely “single-gene disease.”
Homosexuality was previously classified as a psychiatric disease, showing definitions are influenced by social views. This cautions against restricting to recognized diseases alone.
Many traits are influenced by a range of genetic variants operating in a “gray zone” between single-gene and complex diseases, making restrictions difficult.
If gene editing becomes widely available, some parents will use it to engineer features like intelligence, appearance, longevity in their children. This raises questions about harm versus benefit.
Allowing editing of polygenic traits also poses risks, as the effects of combined variants are largely unknown. Risk scores also vary by ancestry but are still derived mostly from European populations.
Widespread genomic data access raises concerns around potential discrimination or stigmatization based on genetic information. History shows eugenic views still influence scientists’ thinking on these issues. Strict definitions and views cannot capture the complex realities.
The chapter discusses the history of genetics and how our understanding of genetics has evolved from Mendel’s work in the 19th century to the discovery of DNA’s structure in the 1950s. This set the stage for learning to read the human genome.
It emphasizes that while we have made progress, there is still much to understand about how genetic variation shapes human differences. Early 20th century eugenics programs showed genetic information could be misused, even by distinguished scientists like Pauling.
Modern genetics has helped dispel some cruel and harmful ideas from that era, but issues like claims of genetic differences in intelligence based on race still exist today.
To guide decisions around new technologies like genetic design of children, we need a much richer understanding of how genetics influences human traits and health outcomes. Simply waiting is not an option, as experiments will be performed once technologies become possible.
A key step is incorporating complete genomic sequencing into healthcare for all to systematically study genetic variation and its effects at large scale. This could help us understand diseases like COVID-19 and make economies more resilient to future pandemics. It is one of the challenges we must address before technologies like genetic design are widely used.

Here is a summary of the key points about the development of coalescent theory in the 1980s:

Coalescent theory approached questions in population genetics from the opposite perspective of classical population genetics. Rather than focusing on populations evolving forward in time, it looked backward in time.
It focused only on the allelic forms present in the current generation and developed models to calculate expected ancestral relationships among those alleles based on population characteristics.
This backward-looking perspective allowed coalescent theory to provide a more efficient framework for deriving population genetic results compared to the traditional forward-looking approach.
Coalescent theory provides mathematical models to infer how alleles in a sample are related to each other and how far back their shared ancestral lineage extends based on the size and structure of the ancestral population.
It revolutionized population genetics by offering new insights and more powerful and computationally feasible methods for problems like estimating divergence times and effective population sizes.
Coalescent theory is now a critical tool in population genetics for deriving results and making inferences about population history and evolutionary processes from genetic data.

Here is a summary of the New York Times article “Meghan Markle Shares Her Grief After a Miscarriage”:

Meghan Markle, the Duchess of Sussex, wrote an opinion article published in the New York Times in November 2020 where she shared her experience suffering a miscarriage in July 2020.
She wrote about feeling a sharp cramp and dropping to the floor with her young son cradled in her arms, realizing something was not right. She was taken to the hospital where doctors confirmed she had miscarried.
Markle described the experience as an almost unbearable grief while experiencing the loss of her unborn second child. She wrote that “loss and pain have plagued every one of us in 2020.”
Markle hoped that by sharing her experience, she could help break the cycle of suffering in silence and normalize conversation about the often taboo topic of miscarriage. She said that by publicly acknowledging this loss, others may feel less alone in their own private suffering.
The op-ed provided an intimate look into Markle’s private grief and loss, as she struggled with the physical and emotional pain of miscarriage while continuing her public duties. She hoped it could bring light to those experiencing similar loss and grief.
Gregor Mendel conducted experiments with pea plants in the 1850s and discovered the laws of inheritance, including dominance, recessive traits, segregation of traits. His work was later rediscovered.
Francis Crick, James Watson and others discovered the structure of DNA in the 1950s, establishing DNA as the genetic material and explaining Mendelian inheritance molecularly.
Technological advancements like next-generation sequencing have made sequencing the human genome cheaper and more accessible.
Common diseases are polygenic, influenced by many variants each with small effects. Large cohort studies and risk scores can help identify variants and predict risks.
Reproductive genome design and editing aim to prevent diseases by modifying genetics before or at conception. This raises debates around germline editing, constraints, and consequences.
Future technologies like stem cell reprogramming and generating many pre-implantation embryos could enhance options for reproductive genome design to optimize health outcomes. But risks and ethics must be considered.
Understanding of human genetics continues improving with advancements, but treatment and prevention of diseases also depend on developing new therapies and ensuring access. Balancing individual interests with societal implications is important.

#book-summary

The End of Genetics - David B. Goldstein