Table of Contents

1: Abstract

2: Introduction

3: The Problem Space

• 3.1 The Medical Industry
• 3.2 The Biohacking Industry

4: Background & Existing Science

• 4.1 Quantum Entanglement and Quantum Biology
• 4.2 Where Quantum Entanglement is Shown in Biology
• 4.3 Current Gene Editing Tools
• 4.4 Existing Genetic Biohacking

5: The Hypothesis

• 5.1 Justification
• 5.2 Assumptions

6: Applications

• 6.1 Medical Applications
• 6.2 Biohacking Applications

7: Proposed Methodology

• 7.1 Cellular Entanglement Procedure
• 7.2 Medical Experiment
• 7.3 Biohacking Experiment

8: Challenges

9: Vision & Impact

10: Conclusion

About Me

References

1: Abstract

Quantum biology is an emerging field that continues to unlock new therapeutic possibilities. In quantum physics, quantum entanglement occurs when two particles share a correlated quantum state. Entanglement can be induced through various methods: cascade transitions, parametric downconversion, photon absorption, or direct particle interaction. Quantum biology applies quantum mechanical principles to explain biological processes that classical physics alone cannot account for, for example consciousness. This article proposes a theoretical framework in which quantum entanglement is established between all cells of an organism. If sustained, such cellular entanglement could theoretically allow a targeted genetic edit introduced to a single cell to propagate universally throughout the organism. We propose that this may occur through the synergy of protein-based qubits (Enhanced Yellow Fluorescent Proteins), ordered water lattices within cells, and decoherence shields that protect quantum coherence from thermal disruption. The Fröhlich mechanism — a process by which biological systems harness ambient thermal energy to maintain macroscopic quantum coherence — would sustain this entanglement network. While significant technical and experimental challenges remain, this concept could revolutionize both medical genetics (enabling systemic treatment of late-stage diseases) and biohacking (permitting organism-wide phenotypic enhancement). This article outlines the theoretical foundation, proposed mechanisms, experimental design, and current limitations of cellular entanglement.

2. Introduction

The primary bottleneck in modern genetic engineering is no longer the precision of the edit itself, but the logistics of delivery. Our current “shipping” methods — such as lentiviral vectors and lipid nanoparticles — are constrained by classical physics. They are too slow, too localized, and frequently trigger immune responses. To achieve systemic gene editing — the ability to edit genes across trillions of cells simultaneously — we must move beyond the physical transport of particles and toward the transfer of genetic information.

Classically, biological systems were thought too “warm and noisy” to support quantum effects. However, quantum biology is challenging this assumption. Nature has already evolved to exploit quantum coherence and entanglement for extraordinary efficiency, for example photosynthesis achieves near-perfect energy transfer through quantum tunneling & energy-efficient coherence and emerging evidence suggests human consciousness may depend on quantum processes in neural microtubules. These examples demonstrate that biological systems are not imcompatible to quantum mechanics, but simply require specialized structures to protect quantum states from thermal disruption.

This raises a profound question: what if the human body functions not as a collection of isolated cellular “bags,” but as a synchronized quantum network? If so, the delivery problem might fundamentally be a synchronization problem, rather than a vector problem. Rather than shuttling gene-editing tools to every affected cell, we could entangle cells quantum mechanically and allow a single targeted edit to propagate instantaneously throughout the entangled system.

This article explores the theoretical framework for cellular entanglement. By examining the roles of protein-based qubits, ordered water lattices, and decoherence shields — and how the Fröhlich mechanism could sustain quantum coherence in living tissue — we can begin to model how quantum states might persist within the cellular environment. If we can bridge the gap between quantum mechanics and molecular biology, we move one step closer to overcoming the delivery bottleneck that has long limited genetic medicine. The implications could be transformative: late-stage cancers could be cured in a single intervention, genetic disorders treated systematically, and human physiology enhanced in ways currently impossible.

3. The Problem Space

Genetic engineering via cellular entanglement could advance the medical industry and the biohacking innovations.

3.1: The Medical Industry

In the medical field, a current gap that exists in treating and curing diseases are disease development. Some disorders are uncurable, and often untreatable at their later stages, for example cancer or diabetes. The current genetic therapies involve a select type of edited cell, for example with cancer, Chimeric Antigen Receptor (CAR) T-cells are implemented. CAR T-cells are specialized and personalized cells to bind to and attack cancer cells.

With diabetes, stem cells edited by CRISPR-Cas9 are designed to produce insulin, to make up for the lack of these insulin-producing beta cells. However, by the time the diseases are in the late stages, it has systemically affected almost the entire body, making the damage widespread. Most therapies involving genetics for these diseases are too localized and too slow, making the treatment inefficient. This makes the one of the key factors of someone succumbing to their illness not only the things related to the disease themselves, but also the treatment. In fact, studies show that a four-week delay of treatment can raise the risk of mortality by approximately 6% to 13% (Hanna et al., 2020). If the genetic treatment itself also takes too long to take into effect, especially if it’s too localized, the mortality can increase in the similar way.

3.2: The Biohacking Industry

Most biohacking advancements that use genetics aren’t always bodily enhancements. The reality of our current capabilities in biohacking is that they are mainly therapies for genetic restoration, rather than true enhancement. For example, biohacking is mostly done with by not actually changing the genotype (the makeup of DNA), but rather the expression of their genes, otherwise known as epigenetics. An example of the deliberate alteration of the body’s epigenetics is using compounds (like HDAC inhibitors) to alter the expression of genes associated with Brain-Derived Neurotrophic Factor (BDNF) to increase cognitive plasticity (Vecsey et al., 2007). This “soft edit” is one of the safest and most accepted versions of biohacking because current technologies aren’t able to scale massively while also enhancing phenotypes (the genetic trait being expressed) safely.

Another example is that vision is restored by the use of a one-time FDA approved gene therapy called Luxturna. The way lexturna works is by using a viral vector to deliver a functional copy of the RPE65 gene into the retina. Even though Luxturna is used to treat blindness, it’s a proven “upgrade” to human vision. The reason biohacking isn’t able to create significantly enhanced phenotypes is mainly due to the delivery. Even if a CRISPR-edited vector was injected into an organism, the maximum amount of cells it could reach would be 1% of the total number of cells in the body, which results in little to no noticeable change in phenotype. Additionally, systemic genetic engineerng relies on either viral vectors (AAVs) or lipid nanoparticles (LNPs). They are bulky, trigger the immune system, and usually get stuck in the liver.

4. Background & Existing Science

4.1: Quantum Entanglement and Quantum Biology

Quantum entanglement is a fundamental concept in quantum physics that happens when two particles are entangled together. Quantum entanglement can happen in a multitude of ways:

• Entanglement by Birth: This involves putting atoms into a highly excited energy level where they cannot return to the ground state by emitting just one photon. Instead, they emit two photons in quick succession. When these photons are emitted in opposite directions, conservation of angular momentum requires their polarizations to be perfectly correlated, creating an entangled pair (Orzel 2017). If a single atom or particle with a known angular momentum decays and releases two photons, the total angular momentum of those two photons combined must equal the original amount. Because of this strict balancing act, the two photons are “perfectly correlated,” meaning their properties are not independent. Therefore, knowing the state of one automatically tells you the state of the other, regardless of how far apart they travel. They become a state of superposition since, until a measurement is made, neither photon has a definite polarization.
• Parametric Downconversion: This modern method uses non-linear optical crystals such as beta barium borate. Non-linear optical crystals are different from normal prisms, where instead of bending light normally, they’re able to split a single photon in two due to it’s atomic structure. Scientists shine a high-energy laser, typically violet or blue into the crystal. Most of the time, the light just passes through. But occasionally, a single high-energy violet photon will interact with the crystal and split into two lower-energy near-infrared photons. Now, the child photons must have about half the energy of the original parent, which is what makes them go from violet (around 7.5 × ¹⁰¹⁴ Hz) to near-infrared (around 3.75 × ¹⁰¹⁴ Hz). Due to the conservation of momentum and polarization within the crystal, the resulting pair is naturally entangled.

• Entanglement by Interaction: Quantum entanglement can happen through interaction. This happens when two particles are brought close enough to interact so that their final states depend on one another. A Rydberg blockade can be used for example. A Rydberg blockade is a mechanism used to create quantum entanglement by taking advantage of how atoms in a Rydberg state interact one another. A Rydberg state is where an atom or molecule has one of its electrons excited to a very high energy level. This places the electron into an orbit whose dimensions are massive compared to the remaining ion nucleus. Because the electron is spread out so far, atoms in a Rydberg state become extremely sensitive to external influences, interacting strongly over unusually long ranges. Exciting one atom to a high-energy state shifts the energy levels of a nearby second atom, preventing it from being excited by the same laser and forcing the two into an entangled superposition.
• Entanglement By Accident: This method involves two separated atoms that each emit a photon. If those photons are brought together and measured in a way that makes it impossible to know which atom emitted which photon, the original atoms become entangled. In practice, this is done with holding ions in seperate ion traps and vaccuum chambers, then highly exciting them. As the ions are highly excited, they begin to decay, which releases photons. These photons have one of two possible polarizations. Photons emitted by both of the separated ions are collected and guided together into an optical device called a beamsplitter, which has two photodetectors placed at its outputs. About 25% of the time, the setup results in one photon hitting each of the two detectors simultaneously. When this specific outcome occurs, the laws of quantum optics dictate that the two photons must have had opposite polarizations, which means the two original ions must have ended up in different states. Once the photons mix at the beamsplitter, it becomes fundamentally impossible to know which ion emitted which photon. Because that information is entirely erased, the two parent ions become intrinsically linked, effectively making them entangled.

4.2: Where Quantum Entanglement is Shown in Biology

Research on quantum biology has shown that quantum entanglement potentially plays a role in biological functions. Nature appears to utilize specialized biological structures that act as decoherence shields — areas that prevent the loss of superposition of particles — to support these non-local correlations on a macroscopic scale. Natural applications of quantum entanglement is shown in the human consciousness, which relies on the synchronized activity of millions of neurons across the human brain. Recent research demonstrates that the myelin sheath — the fatty, insulating membrane wrapping around a neuron’s axon — can act as a “chamber” that amplifies infrared photons. Within this chamber, the vibrations of carbon-hydrogen (CH) bonds in the lipid molecules undergo cascade emission, spontaneously generating large numbers of highly correlated, entangled photon pairs (biphotons).

These entangled photons are proposed to travel through the brain’s biochemical ionic tides — fluctuation of ions within cellular environments driven by metabolic activity — and link widespread cellular components, such as potassium (K+) ion channels. Because the channels are quantumly linked, when a neuron activates one channel, it can instantaneously influence the state of others, providing a mechanism for over-distance neuronal synchronization and quantum information transfer throughout the brain (Liu et al., 2024). As these channels and tides provide the parts of the fundamentals of brain activity, thereby enabling conscious processes.

4.3: Current Gene Editing Tools

Gene editing technologies advancements have significantly accelerated since CRISPR-Cas9. However, a consistent hurdle that is shown is accuracy. While driving for perfection is futile, aiming for improvement produces effective results. A more recent example of genetic advancements are Prime Editing and CRISPR-Cas13.

• Prime Editing: Prime editing is a versatile and highly precise gene-editing technology introduced in 2019. It allows for small insertions, deletions, and all 12 possible base-to-base conversions. It is often described as a “search-and-replace” tool for the genome because it can target specific DNA sites and encode the exact genetic modification desired. It primarily uses a Prime Editor and Prime Editing Guide RNA (pegRNA). A Prime Editor is a fusion protein combining a Cas9 nickase (an enzyme that cuts only one strand of DNA rather than both) and a reverse transcriptase enzyme — A process where DNA is synthesized from an RNA template. A pegRNA is a specialized guide that is longer and more complex than standard CRISPR-Cas9 guides. It contains the target sequence to find the DNA, a Primer-Binding Site (PBS) to initiate synthesis, and a reverse transcription template that carries the desired edit. After the edit, natural cellular DNA repair mechanisms kick in and convert the old, unedited DNA segment and replace it with the new sequence. In advanced versions like PE3, a second guide RNA is used to nick the unedited strand, prompting the cell to use the newly edited strand as a template to complete the repair on both sides of the DNA duplex.

• CRISPR-Cas13: Cas13 is a specialized class 2 type VI gene-editing system that uniquely targets and cleaves RNA rather than DNA (Abudayyeh et al., 2016). Because it modifies RNA transcripts, it provides a major safety advantage over DNA-targeting systems like Cas9: it avoids introducing double-strand breaks into the genome, thereby preventing unintended genomic instability. Additionally, Cas13 does not require a standard protospacer adjacent motif (PAM) for targeting. A PAM is a type of “permissory slip” for editing a specific sequence of DNA to ensure the enzyme. which significantly broadens the range of genetic sequences it can access.

4.4: Existing Genetic Biohacking

Genetic biohacking to alter or enhance traits is already being successfully demonstrated in animal models, and researchers believe the same techniques could be applied to humans. Beyond vision, advanced gene-editing tools are also being used to “biohack” and correct specific genetic conditions, for example tetrachromia. In one study, scientists used virally-mediated gene therapy via retinal injections to transform two adult squirrel monkeys, named Dalton and Sam, into trichromats. After five months, the monkeys successfully developed functional L cones (which detect long wavelengths of light) and woke up able to distinguish between red and green stimuli — colors that were previously invisible to them (Newall 2025). Because the visual system of the squirrel monkeys were able to adapt to process the new stimuli within 5 months, researchers believed it was possible to adjust this treatment to biohack a normal human trichromat into a tetrachromat as well. By introducing a fourth type of cone cell into the retina of an adult human, their visual system could potentially adapt to the new input, allowing them to see entirely different colours that are currently unimaginable to the human eye, simlar to already naturally occuring tetrachromats.

Beyond injecting new cone cells, modern biohacking can also use precision gene-editing tools like CRISPR-Cas13 and Prime Editing to alter specific traits and cure diseases. For example, to prevent axutosomal-dominant hearing loss and recessive hearing loss, RNA base editors have been utilized to target the Tmc1 and Otoferlin genes. This effectively resulted in substantial improvements in hearing impairments (Zhu et al., 2024).

5. The Hypothesis

If quantum entanglement is established between all cells of an organism such, then a targeted genetic edit to one cell can propagate universally to the other cells of an organism.

5.1: Justification

Despite how the biological environment is not an ideal place for a particle to maintain quantum coherence, that does not mean it is entirely impossible. To begin, there are components in cells that can act as “pockets” for the “warm, wet, and noisy” environment of the cell. Some enzymes, such as type II restriction endonucleases — a bacterial enzyme that cuts double-stranded DNA at highly specific recognition sequences — act as “decoherence shields”. Upon binding to DNA, these enzymes undergo conformational changes that squeeze water molecules and ions away from the DNA surface. This creates a decoherence-free space where quantum entanglement and coherent energy transfer can occur without disruption, which can be described as a “local quasi-vacuum.” Another of naturally occuring quantum coherence pockets is water. While cell interiors contain water, the water isn’t always in its standard “aqueous” state. For example, surface charges (accumulation of excess electric charge on the outer boundary) on cytoskeletal structures, such as actin and microtubules, bind and order polar water molecules. This transforms the cytoplasm from a simple liquid into a crystal-like structure that provides a more stable environment for quantum states (Hameroff, 2004). Additionally, During cell division, the entire mitotic complex (centrioles, pericentrin, and spindles) is embedded in protein gel and ordered water, which may allow it to function as a pumped quantum system — a quantum system supplied with external energy to manipulate its physical properties. As for what particle would be in superposition, proteins have been utilized to create qubits, which make this theory plausible.

To expand, scientists successfully converted Enhanced Yellow Fluorescent Protein (EYFP) into a functional qubit that demonstrates measurable spin coherence even within a living cell’s noisy environment. These protein qubits may be genetically encoded directly into the cell, allowing them to participate in biological processes like protein folding while maintaining their quantum behavior (Feder et al., 2025). Additionally, protons in DNA can act as qubits. These qubits are thought to be formed by fermions (subatomic particles that make up physical matter) within hydrogen bonds that connect the nitrogenous base pairs (Hubac et al., 2017). Rather than just being passive structural components, these coherent protons are theorized to actively participate in the processing of genetic information. Recent analysis of human molecular genetic data suggests entanglement patterns in DNA-transcriptase interactions (Shirmovsky & Chizhov, 2025).

In theory, if DNA maintains quantum coherence, it means it is possible to quantum entangle more than one qubit as well. Since the measured state of one cell’s DNA would be the same for another entangled cell’s DNA, that would mean editing or “measuring” the state of one qubit effects another qubit — the genome. With enough cells in entanglement, editing one cell can can cause influential changes within the body, allowing for systemic gene editing with greater effects than lentiviral vectors.

5.2: Assumptions

Since the technology required for this theory to be realized has not been fully created yet, the theory is based off some assumptions, which should be addressed.

• Organism-Wide Bose-Einstein Condensation: We would have to assume that the DNA (or cellular organizing structures like centrioles) across all cells could be brought into a coherent state where they surrender their individual identities, similar to a Bose-Einstein condensate — a macroscopic quantum coherent system where a group of atoms or molecules are brought into a state where they surrender their individual identities and behave like one unified quantum system (Hameroff, 2004). In such a state, the DNA across the entire body would be governed by a single quantum wave function, ensuring that if one component is perturbed, all other components react accordingly.

• Systemic Decoherence Shielding: We would have to assume the organism being edited possesses flawless, systemic decoherence shields to protect organism-wide entanglement from thermal disruption. An example of a decoherence shield can be the ordered lattice structures of water within a cell, creating pockets or a pumped quantum system.
• The Fröhlich mechanism: Another assumption would be the organism utilizes active coherent pumping energy, or the Fröhlich mechanism. The Fröhlich mechanism is a theoretical process by which biological systems can harness ambient, non-specific thermal energy to generate and sustain macroscopic coherence. Starting with the “laser-like” coherent pumping, in biological structures with periodic organization, such as the tubulin proteins making up microtubules, random thermal energy from the environment is utilized for laser-like coherent pumping. This energy causes the molecules to vibrate perfectly in phase, generating what is known as synchronized quantum dipole oscillations. By vibrating synchronously, these biological structures can achieve a highly ordered state referred to as a Fröhlich Bose-Einstein condensate, which is a state where massive complexes — such as the entire mitotic spindle and centriole network during cell division — surrender their individual identities and are unified by quantum coherence to function as a single pumped system. By constantly pumping structures into synchronized vibration, it acts as a mechanism for laser retrieval, which is the continuous trapping, reading, and retrieval of photons and their quantum states. This laser retrieval allows spatially separated components (like paired centrioles) to remain entangled and perfectly mirror each other’s activities despite constant thermal disruption (Hameroff, 2004). Beyond structural organization, Fröhlich coherence is believed to play a critical role in maximizing the efficiency of energy transfer and signal transduction.
• Continuous Re-entanglement: Standard quantum measurement permanently destroys the entangled connection between particles. To allow for an edit to take hold without permanently severing the quantum link between all the cells, we would have to assume the organism has a natural mechanism for continuous “re-entanglement”. This might be mediated by persistent quantum optical coherence or the exchange of biophotons (photons that appear from biological chemical reactions) to constantly resynchronize the cells. It’s important to maintain entanglement since coherence is fragile and goes into decoherence over time.

• Non-Local Transfer of Complex Information: The quantum connection being capable of transmitting highly complex, specific biological data is another assumption that needs to be made. We assume that biological structures, such as microtubule networks, can function as an internal quantum communication system capable of non-locally distributing the exact genetic instructions of the edit throughout the entire body.

6. Applications

6.1: Medical Applications

Cellular entanglement can be applied in the medical industry by treating hard-to-cure such as cancer, diabetes, or any other genetic disorder. As cancer is the rapid, uncontrolled replication of cell caused by genetic mutations, it can be difficult to control if it becomes too developed. In late-stage cancer, malignant cells have spread throughout the body, making it physically impossible to deliver a gene-editing tool to every single cancerous cell. Theoretical models propose that malignancy is fundamentally linked to a breakdown in quantum mechanics during cell division. In a normal cell, microtubule-based centrioles use quantum entanglement to ensure precise, mirror-like separation of chromosomes into two daughter cells (Hameroff, 2004).

When this entanglement fails, it results in aneuploidy (abnormal, unequal chromosome distribution), which is a primary driver of cancer and genomic instability. Furthermore, some theorists hypothesize that cancer cells may maintain their own abnormal communication networks via the continuous exchange of entangled biophotons (Boretti, 2025). Another example of a medical application of cellular entanglment is diabetes. Treating diabetes requires updating the metabolic system across the body. Cellular entanglement would allow a localized edit to target insulin-related gene expression. The entire body would instantly cascade the recalibration of the glucose metabolism.

6.2: Biohacking Applications

Systemic editing via cellular entanglement represents a theoretical leap in biohacking that would allow scientists to execute massive, organism-wide transformations that are impossible with classical gene-editing tools. A biohacking application of this can be put in with plant-animal cell hybrid (also known as planimal cells). Because mammals cannot produce their own oxygen, highly oxygen-dependent organs like the brain and heart are extremely vulnerable to conditions with low amounts of oxygen. By integrating chloroplasts, these planimal cells can utilize light energy to independently convert carbon dioxide and water into essential oxygen and glucose (Mubarik et al., 2025).

Creating a multicellular planimal without editing it from birth requires systemic editing since the host’s genome would need to be modified to manage the new metabolic byproducts, regulate the foreign chloroplasts, and prevent immune rejection across all tissues. Localized editing could only create a small patch of photosynthetic cells. Cellular entanglement would allow a single genetic update to rewrite the metabolic pathways of every cell in the heart and brain simultaneously, ensuring the entire organism can seamlessly survive on light and endogenous oxygen. In addition to planimals, cellular editing can be used for sensory enhancement. Biohacking the brain to expand sensory perception — such as editing the brain’s filtering mechanisms to allow previously suppressed information the actual perception — would require an extreme rewiring of the nervous system. The brain’s sensory filtering mechanisms are not localized to one specific spot. If the goal is to edit the brain to stop filtering out certain sensory inputs, a localized CRISPR edit to a few neurons would have no effect to rest of the brain. However, by leveraging cellular entanglement, an edit could be introduced into the genome of a single neuron, simultaneously updating the genetic instructions for filtering mechanisms.

7. Proposed Methodology / Experimental Design

7.1: Cellular Entanglement Procedure

1. Genetically Encoded Spin Qubits (Active Carriers): Both the Prime Editor and the Cas13 effector will be genetically fused to EYFP. EYFP possesses a metastable triplet state, which is as a highly stable, optically addressable spin qubit directly inside the complex environment of living cells.

1. Organism-Wide Fröhlich Coherence: To protect the quantum states of both genomic and transcriptomic targets, the system will utilize the cells’ Fröhlich mechanism. Ambient thermal energy will actively pump the cells’ microtubule networks and pericentrin matrices (which are compontents of the cytoskeleton) into a synchronized vibration, creating a Fröhlich Bose-Einstein condensate. This constant vibration acts as a laser retrieval mechanism that “refreshes” the entangled links across the entire cell.
2. Simultaneous Entanglement Initiation: A calibrated near-infrared optical and microwave pulse is applied to the EYFP qubits. Near-infrared wavelengths are applied over other wavelengths because it has the unique ability to pass through mammalian skin and penetrate deep into tissues. This acts as a simultaneous quantum measurement, forcing the separated Prime Editing and Cas13 complexes to surrender their individual identities and condense into a synchronized quantum entangled network.

7.2: Medical Experiment

This experimental design is in a controlled cell culture stage (in vitro).

• Preparation and Editing: A tissue culture with a known genetic mutation (for example, the HBB mutation in sickle cell disease) will have its genome recorded. Later, that same tissue culture will be pumped into the entangled state, then a Prime Editor would be introduced to a single cell.
• The Quantum Measurement: The reverse transcriptase of the Prime Editor will act as a quantum information processor that reads the coherent proton qubits in the DNA (Shirmovsky & Chizhov, 2025). The act of rewriting the HBB sequence in the localized cell acts as the measurement that forces the quantum collapse, including for all other cells.
• Positive Result Indicator: The unedited “bystander” cells spatially isolated in the culture will be continuously monitored. After the edits, the cells will be observed as they go through mitosis and meiosis. A positive result would be the unedited cells instantaneously exhibiting the exact same corrected DNA base sequence and none show the previously recorded genome sequence and the daughter cells that come from the meiosis and mitosis exhibiting the same edits, proving the Prime Edit propagated throughout the entangled genomic network.

7.3: Biohacking Experiment

This experiment will be in the cell culture (in vitro) and animal model (in vivo) stages of testing.

• Applying the Edit: A mammalian tissue model engineered to express chloroplast machinery from Synechococcus elongatus cyanobacteria. A Cas13 base editor (such as REPAIR or RESCUE which perform A-to-I or C-to-U RNA base conversions), is introduced to a single cell. The tool is programmed to target and edit the RNA transcripts responsible for the host’s immune rejection pathways, temporarily altering protein expression to tolerate the foreign algae.
• Introducing an Unrecognized Stimulus: The tissue is subjected to severe low-oxygen conditions. Near-infrared light is introduced deep into the tissue to stimulate the chloroplasts.

• Positive Result Indicator: A positive result is confirmed if the entire unedited tissue produces oxygen from within the cells and can survive the hypoxic environment for 15 minutes, since that’s generally, at most, how long mammals can survive without oxygen. The survival of the culture proves that the biohack successfully propagated across the culture’s transcriptome network, updating protein expression system-wide without destroying the quantum coherence.

8. Challenges & Open Questions

While cellular entanglement can solve a lot of society’s problems today, it is important to note that the required technologies and techniques are likely years away from realization. The reason is simply due to the fact that the cells’ environment are extremely likely to cause quantum decoherence, despite the decoherence shields, often described as “trying to preserve a snowflake in a hot kitchen.” Additionally, while theoretical models exist for quantum communication (such as biphoton emission in myelin sheaths), there is currently no direct evidence proving that sustained quantum entanglement operates across entire living cells. Despite these hurdles, there are still discoveries for quantum biology that advance society to a future where cellular entanglement is possible, for example EYFPs as qubits within a cell. Because the field of quantum biology is still relatively new, bridging the massive gap between microscopic quantum events and macroscopic biological functions remains a significant hurdle. To continue, researchers must figure out how to scale entanglement from a single trapped photon or a single protein qubit up to the entire DNA network or microtubule without the state collapsing.

Ethics are also something to consider with cellular entanglement. Systemic editing via cellular entanglement creates the critical vulnerability of any error being able to propagate organism-wide. If a Prime Editor or Cas13 tool makes an off-target edit in the initial cell, the quantum collapse could force all entangled cells and daughter cells to inherit that exact same error, resulting in instantaneous, whole-body genomic damage. Furthermore, the long-term, constitutive expression of editing tools like Cas13 in vivo raises significant clinical concerns regarding potential immunotoxicity and the risk of off-target effects across the entire edited population (Zhu et al., 2024).

9. Vision & Impact

If cellular entanglement were successfully harnessed, it would completely shatter the bottleneck of modern genetic medicine, which is physical delivery. Traditional gene editing tools, such as CRISPR-Cas9, CRISPR-Cas13, or Prime Editing, rely on physical delivery mechanisms like lentiviral vectors. However, this means traditional gene therapy must be efficiently allocated to a certain amount of cells or areas for it to be effective. Cellular entanglement could open up many categories of biological capabilties, for example sensory expansion to allow perception of certain sensory inputs such as novel colours, frequencies, etc. It can also be used for creating ethical stem cell factories since researchers have proposed that non-thermal laser therapy could be used to intentionally retrodifferentiate mature, adult cells, providing an abundant, highly ethical source of stem cells for regenerative medicine (Hameroff, 2004).

Without the current systemic limitations, late-stage diseases could be cured in a single intervention. For example, with late-stage cancer, since malignant cells spread throughout the body, it is almost impossible to make an effective delivery to every affected part of the body. However, prime editing propagated through the use of cellular entanglement would broadcast the genomic correction, eliminating the cancerous cells. Another example can be demonstrated with diabetes. With cellular entanglement, a localized edit targeting insulin-related genes would be expressed across trillions of relevant cells.

10. Conclusion

To summarize, the hypothesis is that quantum entanglement and coherence can be harnessed within living tissues to overcome the delivery limitations of traditional gene therapy. This allows a localized edit to act as a quantum measurement, forcing all other entangled cells to express the newly edited state. The entanglement procedure relies the entanglement of both DNA and RNA. Prime Editors and Cas-13 Editors are fused to EYFP. The qubit carriers are pulsed with near-infrared frequencies to initiate the entanglement. After the entanglement is in place, the cells’ Fröhlich coherence would sustain the quantum network. Not only would this aid in medical practices, but boost biohacking innovations.

Quantum biology would explode as an industry if quantum entanglement could be harnessed for cells. This wouldn’t just effect the medical and biohacking industry, but also the lives of billions of people.

About Me

My name is Ankit Chandna, a 16 year-old researcher in gene editing and how control on it can be further enhanced. If this article was interesting to you, consider checking out my other articles!

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Table of Contents

1: The Shadow of the Shoreline

2: A Hole in the Ground

3: From Mobility to Lockdown

4: The Convergence

5: Architecture of the Framework

6: The RNA Operating System: Survival via Cas13

7: The Proxy Strategy

8: The Hard Truths

9: A Legacy Resolved

Thirty kilometers east of downtown Toronto, on the shore of Lake Ontario, there is a pile of nuclear waste that no one has figured out what to do with. The Pickering Nuclear Generating Station has been generating power since 1971. It has also been generating spent nuclear fuel — fuel rods containing Uranium, Plutonium, and Neptunium that remain dangerously radioactive for tens of thousands of years.

Right now, that fuel sits in concrete casks on the lakeshore. Lake Ontario is the drinking water source for 9 million people. The federal government distributes potassium iodide pills to every home within 10 kilometers of the station as a precautionary measure. That radius includes parts of the City of Toronto. Ontario Power Generation is currently extending Pickering’s operational life into the 2030s. More power is good. But the waste problem isn’t getting smaller.

I’m a member of Quorum, and our team has worked on trying to figure out whether there’s a way to actually solve this — not manage it, not move it somewhere more remote, but fundamentally change the physical form of the waste so it stops being the threat it currently is. Here’s what we came up with.

1. The Shadow of the Shoreline

There are roughly 390,000 metric tonnes of radioactive waste stored globally right now. Of that, about 3% is classified as high-level waste — spent nuclear fuel and weapons byproducts that contain 95% of all the radioactivity in the entire inventory.

The global strategy for dealing with it can be summarized in one word: storage. Concrete rooms at reactor sites. Deep geological repositories buried hundreds of meters underground.

2. A Hole in the Ground

That is the state of the art. A hole in the ground.

The U.S. Department of Energy alone faces a $44.5 billion liability for failing to dispose of commercial spent nuclear fuel. The waste isn’t going away. It’s accumulating. And as nuclear energy expands globally in response to climate commitments, it will keep accumulating.

The reason no one has chemically neutralized radioactive waste is that radioactive decay is a nuclear phenomenon, not a chemical one. You can’t change how quickly Plutonium-239 decays through any chemistry. Its half-life is 24,100 years and there’s nothing you can do about it. But here’s the thing: you don’t need to change the decay rate. You just need to change the form.

3. From Mobility to Lockdown

A dissolved Plutonium ion in liquid waste is mobile, diffusible, and dangerous — it can migrate through soil, contaminate groundwater, and enter food chains. A Plutonium atom locked inside a dense, insoluble crystal lattice is none of those things. It’s still radioactive, but it’s not going anywhere. It’s compact. It can be collected. It can be stored in a fraction of the volume of liquid waste with essentially no leaching risk.

Converting radioactive ions from mobile liquid form into immobile solid crystals through biological action — that’s what bioremediation means. And certain bacteria already do versions of this. Deinococcus radiodurans can precipitate uranium and reduce ionic mercury. Additionally, they can survive up to 1.5 million rads of radiation through a DNA repair system that reassembles fragmented chromosomes faster than the damage accumulates.

The problem is that these organisms evolved these capabilities incidentally. Their binding proteins were never optimized. They were never built to work at the scale or precision that nuclear waste remediation actually requires. Designing an organism for this purpose from scratch has always been the goal. The barrier has been computational — designing a protein that binds to a specific atom requires understanding that atom’s electron structure with precision that classical computers simply can’t deliver for heavy elements like actinides.

4. The Convergence

Actinide atoms — Uranium, Plutonium, Neptunium — have huge, complex electron clouds. Plutonium has 94 electrons, all influencing each other simultaneously. Calculating all those interactions on a classical computer requires exponentially increasing resources. For Plutonium, the calculation would take longer than the age of the universe.

Our quantum computer model called Halos approaches this differently. It represents and processes quantum states natively — it operates in the same physical language as the atom itself. The algorithm used for this is called the Variational Quantum Eigensolver (VQE). It finds the lowest-energy state of a quantum system, which tells you the atom’s most stable configuration and, critically, where a protein could form a strong, stable chemical bond with it.

IBM’s 1,121-qubit Condor processor and Google’s Willow chip, both announced in 2024, represent the first generation of quantum hardware capable of running meaningful molecular simulations. Prime editing, the second technology we need, was published in 2019 by David Liu’s lab at the Broad Institute. Both of these technologies are new. Neither existed in usable form five years ago.

5. The Architecture of the Framework

The approach my team is building works in four stages:

• Stage 1: A quantum processor running VQE simulates the electron cloud of a target actinide atom. The output is a precise map of every location on that atom’s surface where a protein could bond stably.
• Stage 2: That binding site map goes into a hybrid quantum-classical search pipeline. Classical AI tools generate candidate protein sequences, and a quantum algorithm evaluates the binding energy each candidate would produce.
• Stage 3: Top candidates are validated via AlphaFold2. Then, the sequence gets prime-edited into Deinococcus radiodurans along with a Cas13-based RNA regulatory circuit to manage cellular stress in real-time.
• Stage 4: Engineered bacteria are introduced into nuclear waste. They survive the radiation, produce the quantum-designed binding protein, and trigger biomineralization — locking the ions into stable crystal structures.

6. The RNA Operating System: Survival via Cas13

While Prime Editing allows us to “write” new instructions into the bacterial DNA, we need a way to manage those instructions in the middle of a radioactive storm. This is where Cas13 comes in.

In the extreme environment of nuclear waste, a cell’s internal systems are under constant attack. Without regulation, the bacteria might trigger “self-destruct” sequences or stop producing the binding proteins we need to stay alive. We use Cas13 as an RNA-level “operating system.” Unlike DNA editing, Cas13 targets RNA, acting as a dynamic dimmer switch that can silence stress signals and optimize protein production. It ensures that even when the radiation is hitting 1 million rads, the bacterium’s “focus” remains on building crystals, not shutting down.

7. The Proxy Strategy

Current quantum hardware can’t yet simulate actinide-scale atoms with full precision. That requires fault-tolerant qubits, which are 3 to 5 years out. So what we’re doing now is building and validating the entire pipeline on small proxy atoms — Hydrogen, Lithium Hydride — so that when fault-tolerant hardware arrives, we swap in the actinide target and run.

On the biology side, we’re developing the full prime editing and Cas13 regulatory protocols for D. radiodurans using non-radioactive metal proxies — Cerium and Lanthanum. The bacterial engineering protocol, the biomineralization assay, the crystal characterization workflow — all of it gets validated now on safe proxies.

8. The Hard Truths

The highest-risk blocker isn’t the science. It’s regulation. Deploying genetically engineered organisms into nuclear waste sites requires simultaneous approval from nuclear regulators, environmental agencies, and biosafety bodies. No GMO has ever been approved for consistent, commercial deployment within a lot of nuclear stations.

The simulation-to-biology gap is the other real technical challenge. A protein that is computationally optimal still has to fold correctly inside a living cell. Multi-stressor survival is genuinely hard. D. radiodurans is extraordinary, but it hasn’t been validated under the full combination of radiation and chemical toxicity present in high-level nuclear waste. That’s a real gap that needs to be acknowledged.

9. A Legacy Resolved

We are not proposing to do something that hasn’t been attempted. We’re proposing to do something that couldn’t be attempted, because the tools required didn’t exist simultaneously and won’t for 5 to 10 years.

The waste is here. Ontario Power Generation is extending the plant’s life. The waste will grow. The current approach (concrete chambers, deep geological repositories, perpetual managed risk) is not going to simply get better with time.

We’re not claiming this is solved. But none of what we’re describing requires a new law of physics. It requires engineering on a known trajectory, and the trajectory is real. The ten-year goal is to have engineered strains validated for deployment. The longer-term goal is to have a technology that works at every nuclear site on Earth.

But it starts here.
One calculation at a time. With Quorum.

Table of Contents

1: Abstract

2: Introduction

3: Polygenic Architecture: The Scale of the Problem

• 3.1 Many SNPs, Tiny Effects
• 3.2 The Infinitesimal Model
• 3.3 Missing Heritability

4: Statistical Barriers to Prediction

• 4.1 Linkage Disequilibrium
• 4.2 Sampling Noise
• 4.3 Overfitting and Shrinkage
• 4.4 Summary Statistics vs Individual-Level Data

5: Biological Complexity Beyond SNP Effects

• 5.1 Gene to Gene Interactions (Epistasis)
• 5.2 Regulatory Architecture
• 5.3 Environmental Effects

6: Prediction vs Causal Understanding

7: Functional Interpretation Challenges

8: Current Improvements and Future Directions

9: Conclusion

About Me

References

1: Abstract

While media such as GATTACA suggests that traits can be directly predicted from DNA, this is far from reality. Although the genotype (the genetic composition of an organism) is able to be read, interpreting it is much more difficult. It can be compared to reading a book in a language that is not fully understandable. Most phenotypes (the traits expressed in an organism) are multifactorial, meaning they are influenced by both genetic and environmental factors, making prediction difficult. To study the relationship between genotype and phenotype, Genome-Wide Association Studies (GWAS) are used, which analyze Single Nucleotide Polymorphisms (SNPs), or single-base changes in DNA. These studies identify statistical associations between genetic variants and traits, rather than direct cause-and-effect relationships. Researchers also use Linkage Disequilibrium (LD) to measure how often certain genetic variants are inherited together. However, LD reflects correlation between SNPs and alleles (variations of a gene), not necessarily which variant is biologically responsible for a trait. Early predictive models assumed that all genetic variants contributed equally to a phenotype, but newer models recognize that some regions of the genome have larger effects than others. While these improvements have increased accuracy, prediction remains limited. Models that perform well on known datasets often fail to generalize to new individuals, with accuracy heavily influenced by factors such as ancestry and sample composition. Although ongoing improvements — such as larger datasets, better statistical models, and multi-trait analysis — continue to advance the field, the complexity of genetic architecture and environmental influence makes precise phenotype prediction a significant challenge.

2: Introduction

The primary challenge in modern genomics is not our ability to edit DNA, but our inability to predict the resulting phenotypes. Scientists have reached a paradox in biotechnology: tools like CRISPR-Cas9 allow us to rewrite the genetic code with extreme precision, yet our capacity to forecast the effects of those edits has stalled. This gap in prediction is most evident in polygenic traits, where researchers can identify statistical associations through GWAS but struggle to establish direct biological causality. A GWAS is a systemic genomic scan of an organism’s DNA, specifically identifying SNPs that correlate with specific traits (Ma & Zhou, 2021). While these studies have successfully connected genomic regions to phenotypes, they often fail to explain exactly how it contributes to the phenotype. GWAS leverages Linkage Disequilibrium (LD), which is the non-random association of alleles at different loci. In simpler terms, LD means that certain genetic variants are inherited together more often than expected by chance, allowing researchers to use ‘tag SNPs’ to represent entire blocks of the genome. To improve these predictions, researchers rely on LD to identify which SNPs are inherited together. By leveraging LD, scientists can build models to estimate phenotypic outcomes more accurately. Despite these advancements, the “instruction manual” for complex human biology remains difficult to read.

3: Polygenic Architecture: The Scale of the Problem

3.1 Many SNPs, Tiny Effects

Most complex traits such as height, eye colour, and lifespan are not decided by one gene, it is usually the result of polygenic architecture. Polygenic architecture is essentially the biological blueprint that explains how traits are influenced by several genetic variants. The genotype of these genes vary from person to person. In other words, there are a multitude of SNPs within a genome for each person which have a multitude of negligible effect sizes contributing to the final trait. SNPs contribute small phenotypic variance because they only change one nucleobase from the entire genome, but since there are multiple SNPs within multiple people, it becomes much more difficult to associate genes and traits. In other words, each SNP has an infinitesimal (very small) effect on the final outcome. This limits prediction simply because there are too many SNPs to keep track of. It becomes difficult to organize and connect the SNPs to a trait, especially if they’re not in LD with other SNPs, as it requires aggregating massive amounts of data points, increasing statistical noise.

3.2 The Infinitesimal Model

To combat the difficulty of connecting SNPs to traits, researchers developed a model that is used to explain how complex traits are inherited, known as the infinitesimal model. The model states that a trait is influenced by thousands of tiny genetic variations, each having an infinitesimal effect. When scientists use mathematical tools to predict traits, they often use what they call a “pseudo-infinitesimal” model. In this math model, they assume every single variation in the genome has some effect on the trait and that all the effects are roughly the same magnitude. An example of this is if someone has 1 million variations, the model might assume each one only explains one-millionth of the trait (Gouddard et al., 2016).

3.3 Missing Heritability

For a long time, researchers were confused because they could only find a few genes for traits like height, which only explained about 5% of the variation (Gouddard et al., 2016). For a trait like human height, twin studies suggest a heritability of ~80%, yet early GWAS results only accounted for ~10 to 20% (Meuwissen & Goddard, 2010). This discrepancy is known as ‘Missing Heritability,’ caused by rare variants, structural variations, and epistasis that common SNP arrays fail to capture.. This was called the missing heritability problem. Missing heritability is the lack of connection between high trait estimates from family studies and the much lower estimates from GWAS. It suggests identified common DNA variants do not fully explain inherited phenotypic variance. The potential causes for missing heritability includes total vs SNP heritability, rare variants, gene to gene interactions (epistasis), and non-coding and/or epigenetic factors — anything influencing the amount of expression of genes. Total heritability is the proportion of the overall variation in a trait that is caused by all genetic factors and it is generally estimated by looking at families or twins. However, SNP heritability is a narrower measure that refers specifically to the proportion of variation that can be explained by common Single Nucleotide Polymorphisms (SNPs).

This matters because of how much each type explains: height has a total heritability of ~80%, yet initial studies found that common SNPs only explained about 45% of the variance. This gap (A.K.A. missing heritability) exists because common SNPs don’t capture every type of genetic variation, and many individual genetic effects are too small to be detected by most mathematical models. Rare variants are genetic differences that occur very infrequently in the population, often defined as being present in less than 0.1% or 1% of people. Unlike common variations, which usually have tiny effects, rare variants can be “highly penetrant,” meaning a single rare variant can have a very large impact on an individual’s risk for a disease. Scientists believe rare variants are a major reason for “missing heritability” because standard SNP tests often miss them. While they don’t affect many people across the whole population, discovering them is highly valuable for the specific individuals who carry them, as they provide a much clearer “meaning” for that person’s health. With structural variants, they involve large-scale changes to the DNA sequence, rather than just a single nucleobase change. Like rare variants, structural variants are considered a primary suspect in the “missing heritability” mystery because they are often undetected by standard tools designed to look for SNPs. Scientists try to combat this with mathematical tools to create more accurate and complete prediction models. Despite all this, even perfect measurement of common SNPs explains limited variance (Campos et al., 2018).

4: Statistical Barriers to Prediction

4.1 Linkage Disequilibrium

Linkage disequilibrium indicates that specific alleles are correlated, often because they are physically close on the same chromosome, restricting rearrangement of genes for different alleles. When scientists run a study and see that a specific SNP is linked to a trait, they don’t immediately know if that SNP is the cause or if it’s just in an inherited “block” and in LD with a group of other SNPs. The actual cause SNP is known as the causal variant and they are the actual mutations that change how a protein is made or how a gene is turned on or off, directly affecting a trait or disease risk. The SNPs that don’t change anything within the proteins are called tagging SNPs and they are more like bookmarks/proxies to nearby causal variants. Often, they are able to “tag” a causal variant because they are correlated with a causal variant in a similar location. Standard genetic tests usually use a set of common “tagging” SNPs to scan the genome. They can tell that a “culprit” is in a certain genetic neighborhood, but they don’t always point to the exact house. However, it’s not as simple as using the tagging variant to find the causal variant. There are several reasons causal SNPs can’t be found easily. An example of this is there being too many suspects for causal variants. If five SNPs are always inherited together, they are statistically indistinguishable. A computer model can tell the causal variant is one of those five, but it has no mathematical way to pick the single biological cause without further experiments. Another reason is that there are too many causal SNPs. For most complex traits, there isn’t one “big” causal SNP. Instead, there are thousands of tiny ones. Many of these individual variants explain less than 1% of the trait. It is very hard to distinguish such a miniscule biological target from random, unrelated statistical data or sampling error. Furthermore, the SNP can be in a locus that is not common within the genome. Scientists used to look mainly at the parts of DNA that code for proteins. However, research shows that the majority of causal mutations for complex traits actually live in regulatory regions, which control how much certain genes are expressed. Researchers are still learning how to comprehend how these regulatory regions work, which makes identifying the causal SNPs much harder.

4.2 Sampling Noise

Sampling noise is like the “static” on a radio. Because scientists usually study a small group of people rather than every person on Earth, the results can be slightly different every time just by chance. If a study is too small, it is very hard to tell if a genetic variation is actually important or if it just looks important because of random luck. To filter the noise, researchers need massive sample sizes. A way to measure unrelated data, is by something called standard errors. This is a measure of how much “noise” is in a specific measurement. A smaller standard error (often shown as a narrow confidence interval) means the scientists have a more precise and trustworthy estimate of a gene’s effect.

Other measurements that are used are Z-scores and P-values. Z-scores are the number of deviations above or below an average value. Z-scores are used to compare different genetic variations. P-values tell the probability that a result happened purely by chance. Because geneticists test millions of DNA variations at once, they have to use extremely strict P-values (usually less than 0.00000005 or 5 × 10^-8) to make sure they aren’t being fooled by random patterns. Sampling noise can also occur with something known “winner’s curse.” Winner’s curse happens when scientists usually only report the SNPs that had the most impressive, significant results in their study. Because of random sampling noise, the “winners” in a specific study often have their effect sizes overestimated. They look much more powerful than they actually are because their “natural” effect was randomly boosted in that specific group of people. To beat the Winner’s Curse, scientists must test those same “winning” SNPs in a completely independent dataset to see if the effect is still as strong when the “luck” is removed.

4.3 Overfitting & Shrinkage

Moving into the more advanced mathematical side of genetics, it helps to think of models as “filters” that try to separate the real genetic signals from the random noise. In most genetic studies, scientists face a massive math problem: they have millions of SNPs (p) but only a few thousand people (n). Because there are way more “instructions” than there are people to study, a standard computer model will try to be “too perfect.” This can lead to overfitting, which is when a model “memorizes” the specific people and noise in the original study, rather than learning the general rules of biology. An overfitted model looks 95% accurate on the people it already knows, but it fails miserably (dropping to 15% to 35% accuracy) when trying to predict a new person. In an attempt to move away from overfitting, shrinkage is utilized to regulate the “noise” that comes from statistical data. Shrinkage is when a math model is told to “pull” the estimated effect of every SNP toward zero, in other words shrinking its value. The logic is if a SNP has a very tiny, weak link to a trait, the model assumes it’s just noise and “shrinks” its effect to zero. Only the SNPs with signals strong enough to “resist” this pull are kept as part of the prediction. A commonly used model today is the Genomic Best Linear Unbiased Prediction (GBLUP). It works by using a “Genomic Relationship Matrix” to see how similar two people are based on their entire DNA. However, this model’s flaw is that it assumes every SNP has an effect and that all these effects are pseudo-infinitesimal. Because it treats every part of the DNA as equally important, it can’t properly account for the specific regions that might be way more important than others. In contrast, smarter models called Bayesian models are being utilized for predictions. They are often more accurate because they don’t assume every SNP is equal. They work by starting with a “prior,” which is a set of biological assumptions. The main difference is instead of assuming every SNP does something, a Bayesian model like BayesR might assume that 95% of DNA does absolutely nothing for a specific trait, while the other 5% has varying levels of impact. By putting a weight on some genes, these models are much better at mapping where the real biological action is happening. Despite the advancement in models, it still doesn’t mean prediction equals to causal identification. This means that knowing that something will happen is not the same as knowing why it happens. A predictive model only needs to find correlation patterns. For example, a tagging SNP might be 100% accurate at predicting a disease, even if that specific SNP is biologically useless. It works simply because it is in LD with the real causal mutation. A model can be built that is great at predicting who might get Type 2 Diabetes without actually understanding the mutation that causes it. To find the causal variant, scientists have to go beyond math and do functional studies to see which mutation actually changes a protein or influences a gene’s expression.

4.4 Summary Statistics vs Individual-Level Data

Summary statistics are the average statistics done from studies whereas the individual-level data is specific to one person only. To understand the difference between summary statistics and individual-level data, imagine the difference between a high school’s final report card for the whole class versus having every student’s actual exam papers. Individual-level data includes the specific genotype and trait values for every single person in a study. It is very high-standards for research because it allows for the most detailed math, but it is often hard to get because of privacy concerns and the massive computer power needed to process it. Summary statistics however are simplified “per-allele” results. Instead of seeing every person, scientists just see the average effect of each SNP and the standard error. These are much easier to share and cheaper to analyze because the math model difficulty doesn’t increase as add more people are added to the study. While summary statistics are great for sharing, they have specific limits. Unlike individual data, summary statistics cannot capture “non-linear” relationships between SNPs. They see the SNPs one by one rather than seeing the complex “blocks” of DNA (haplotypes) that individuals actually carry.

Additionally, For certain tasks, like studying very rare variants, summary statistics aren’t enough. Scientists often need the actual “in-sample” data (the original genotypes) to avoid getting false-positive results. Because summary statistics don’t tell how different SNPs are linked together, scientists have to “guess” that linkage using an LD Reference Panel. The way it works is with scientists using a separate group of people as a template for what typical human DNA linkage looks like. With this approach, the risk is if the people in the template study have a different ancestry than the people in current study, the estimates will be wrong. Because these reference templates are usually small (usually only a few hundred or thousand people), scientists have to use a trick called regularization. Regularization involves cleaning up the math, such as by assuming SNPs very far apart have zero connection to each other, to make the results more reliable. In continuation, one of the biggest problems with using summary statistics for advanced predictions is noise accumulation. There is always a tiny difference between the DNA linkage in a reference panel and the real linkage in the current study group. As one tries to calculate the effects of millions of SNPs across the entire genome, these tiny errors start to add up, eventually making the final prediction totally inaccurate. To stop this noise from building up, newer tools (like MegaPRS) use a “sliding window” approach. Instead of looking at the whole genome at once, the computer only looks at a tiny chunk (about 3 million base pairs) at a time, makes the calculation, and then moves to the next chunk. This prevents the errors from one part of the genome from ruining the math for the rest of the DNA.

5: Biological Complexity Beyond SNP Effects

5.1 Gene to Gene Interactions (Epistasis)

Understanding epistasis can be done with an analogy of DNA as team workers. In a simple model, each worker does their job independently, and the individual work is simply added to get the final result. Epistasis is when the workers have to interact or use teamwork to get the job done. One worker might be useless unless another specific worker is also present to help them. Epistasis is a common non-additive effect. In genetics, additive means that every DNA variation has a fixed “value” that it adds to a trait, no matter what other genes someone has. Non-additive effects, however, happen when that value changes depending on the genetic background. Epistasis being a non-additive effect means the effect of one gene is hidden or modified by another gene (Schrodi et al., 2014). Another example of a non-additive effect is dominance, which is when 2 versions of a gene inherited from both parents interact with each other at the same location. The reason most linear models struggle is because of these non-additive effects. Most of the common mathematical tools used today, such as GBLUP or standard regression, are linear models. These models are designed to find the “average” effect of each gene across a whole population. They struggle with epistasis because of several reasons such as the assumption of independence, the “search space” problem, need for massive data, and missing heritability. Starting with the assumption of independence, linear models assume that each SNP works alone. They can be thought as a calculator that only has a plus button, they are great at adding up thousands of tiny independent effects, but they cannot easily calculate “if A and B are both present, then multiply the result.” Moving on to the “search space” problem, there are millions of SNPs in the human genome. While it’s easy for a computer to check them one by one, it is computationally inefficient to check every possible pair or triplet of SNPs to see if they interact. This is why mathematical models involve matrices and vectors: because they handle a massive amount of SNPs and data that matrices and vectors are necessary to organize them. The number of combinations is simply too massive for standard computers to handle easily. Furthermore to the need for massive data, to prove the epistasis occurance isn’t a fluke, scientists need much larger sample sizes than they do to find simple additive genes. Without enough people in a study, the signal of an interaction gets lost in statistical noise. Finally to missing heritability, because linear models don’t consider complex interactions, they often fail to explain the full genetic cause of a trait. This is a major reason why scientists believe some heritability remains missing — it is because it is hidden in interactions that current math can’t see. To attempt to move away from these flaws, researchers are trying to develop new tools. Some use machine learning (like Random Forests or Neural Networks) to find patterns that a simple linear model would miss. Others use tools like MultiBLUP, which can be told to look for specific categories of genes or pathways where interactions are biologically likely to happen.

5.2 Regulatory Architecture

The regulatory architecture of the genome refers to the system that controls that influence when, where, and how much of a protein a gene produces. Research shows that the majority of mutations affecting complex traits are located in these regulatory elements rather than in the genes themselves. Several concepts can be used to study regulatory architecture, for example eQTLs. An expression Quantitative Trait Locus (eQTL) is a specific genetic variation that is associated with the expression level of a gene. Most eQTLs studied are cis-acting, meaning they are located very close to the gene they regulate and affect the expression of the gene on that same chromosome. Scientists use Transcriptome-Wide Association Studies (TWAS) to integrate eQTL data with GWAS results (Kerimov et al., 2021).

This allows them to predict whether a person’s genetic risk for a disease is actually being driven by changes in how their genes are being regulated. Using eQTLs can be more powerful than standard genetic tests because the gene expression is often much stronger and clearer than the final trait. The regulatory architecture is highly tissue-specific, meaning a genetic regulations might be active in the brain but completely silent in the liver. By overlapping genetic risk signals with maps of active regulatory regions, researchers can identify the critical cell types where a disease actually starts. An example of this is polygenic signals for platelet count are specifically enriched in regulatory regions active in the cell lineage that leads to platelets. This concept also applies to alleles where one version of a gene is more active than the other in a specific tissue. Studies show that about 89% of genes in some species exhibit this kind of specific imbalance in at least one tissue. Timing of when genes are active is vital to understanding the genetic architecture of traits. In human studies, accounting for developmental stages (like teenagers who are still growing) is necessary to avoid adding “noise” to genetic models, as the regulatory systems in a growing child differ from those in an adult. Using biobanks linked to medical records allows scientists to perform historical prospective studies. They can track how a person’s regulatory architecture correlates with the actual timing of a disease’s onset or how a trait changes over their lifetime. In evolution, traits under strong natural selection often involve regulatory changes that drive alleles to low frequencies in a population. This suggests that the timing and precision of gene regulation are essential for an organism’s survival and fitness.

5.3 Environmental Effects

To understand why DNA isn’t “destiny,” it helps to think of genetic code as the blueprints for a house, while the environment is the construction crew, the weather, and the neighborhood. No matter how perfect the blueprint is, the final house depends on who builds it and what happens to it over time. In other words, traits can be affected by more than DNA, it can also be affected by the environment through different interactions. Gene to environment interactions happen when a specific genetic variation only matters if in a specific environment, known as interaction co-conspirators. The context matters though; a gene can give a negative effect under one environment, but that same gene might have no effect (or even a positive one) in another environment. For example, some genetic risks for a major depressive disorder are only “activated” or made worse by specific stressful life events or childhood trauma. A similar concept is phenotypic plasticity. This is the idea that one genotype can produce different phenotypes based on the environment. For example, in height studies, researchers have to account for the fact that teenagers are still growing; this creates “noise” in the data because their current height isn’t their “final” genetic height yet. However, some factors classified as the “environment” might actually just be stochastic events, which are purely random biological accidents that DNA cannot predict. Examples of the environment that don’t relate to immediate surroundings are socioeconomic and lifestyle factors. These are external forces that act on biology regardless of the “blueprints.” Researchers use tools like the Townsend Deprivation Index to measure how wealth and neighborhood quality affect health outcomes (Zhang et al., 2021). A person might have healthy genes, but living in a high-pollution or high-stress area can devalue those genetic advantages. Additionally, Factors like smoking, diet, and exercise are massive external variables (Schrodi et al., 2014). For a trait like age-related macular degeneration (an eye disease), adding info about a person’s smoking habits to their genetic data makes the prediction much more accurate than using DNA alone (Wray et al., 2013).

The main difference between gene to environment and phenotypic plasticity is gene to environment happens when the effect of a specific genetic variation depends on the environment, meaning a genetic variant might be a high-risk factor in one environment but have zero effect in another. However, phenotypic plasticity means one genotype can produce more than one phenotype, which means the final result changes even if the genotype stays exactly the same. It includes purely random biological accidents that DNA simply cannot predict. An analogy that can be used to demonstrate gene to environment interaction is a seed that can only grow if it’s planted in a specific soil, but for phenotypic plasticity, that plant grows crooked, regardless of species. This means that gene to environment interactions are hard to predict as both the DNA and specific environment need to be known, whereas phenotypic plasticity is impossible to predict because one can’t predict the stochastic events (Bernal Rubio et al., 2015).

In short, a genetic predictor is a probabilistic tool, not a crystal ball. It can tell one the baseline risk, but it cannot see the infections that can be caught, the food one will eat, or the random accidents that will shape someone’s health.

6: Prediction vs Causal Understanding

The goal of a predictive model is statistical accuracy, but this does not always lead to biological understanding. In many cases, it’s possible to predict a phenotype successfully without ever identifying the causal variants responsible for it. This happens because models prioritize statistical optimization — they look for any signal, even if that signal comes from a tagging SNP that is simply located near the actual mutation. For a researcher focused on prediction, a high correlation is a success. However, for a researcher focused on gene editing, a model that predicts well but lacks causal discovery is functionally useless, as editing a non-causal SNP will not result in a phenotypic change. Models are able to be accurate without knowing biological cause because of SNPs in LD. If a tagging SNP is in LD with a causal SNP, it is often sufficient to predict who is at risk for a disease since a model only needs to find a general correlation. It does not need to know which chemical pathway is broken or which protein is shaped incorrectly to give a useful risk score. While it is easy for a computer to see that, for example, an SNP is linked to height it is very difficult for scientists to prove that the same SNP influences height. If five SNPs are always inherited together in a tight block, they are statistically indistinguishable, meaning it’s not possible to find the SNP that is the biological cause for the trait. The math model might pick one of them as a great predictor, but that doesn’t mean it’s the biological cause, it might just be pure chance that it’s in LD with the cause SNP (Gouddard et al., 2016). To optimize with the intent to find causal SNPs, scientists can look for eQTLs by doing a TWAS. By aggregating GWAS data, researchers develop Polygenic Risk Scores (PRS). These models act as a statistical summary of an individual’s genetic liability. However, a high PRS can predict a trait without identifying its biological etiology. By doing this, researchers can test whether a person’s risk for a disease is actually driven by a specific gene. If a DNA variant matches the pattern of a gene’s expression and the final disease trait, it is a very strong candidate for a biological cause. However, a high PRS can predict a trait without identifying its biological causes. For gene editing technologies like CRISPR-Cas9, this is a critical bottleneck since a model may predict who is at risk for a disease, but it cannot tell the editor exactly which base pair must be modified to fix it (Pasaniuc & Price, 2016).

Additionally, because different ethnic groups have different SNPs in LD with each other, a tagging SNP that works in South Asians might not work in East Asians. However, the true biological cause should be consistent across all humans. By comparing data from multiple ancestries, scientists can find the average of the total signals from random tagging SNPs and pinpoint the one variant that remains linked to the trait across all groups. A biological cause is often pleiotropic, meaning it affects more than one trait. For example, a variant that changes how a specific phosphorus-transporting protein works might affect both milk volume and the chemical concentration of that milk (Gouddard et al., 2016). When scientists see a single SNP having a strong impact on multiple biologically related traits, they gain much higher confidence that they have found a true causal mechanism.

For the intention of optimizing prediction, Bayesian models such as LDAK-Bolt-Predict or the BayesR are severely beneficial for this goal (Zhang et al., 2021). Additionally, optimization with consideration of gene to environment interactions, phenotypic plasticity, epistasis, or epigenetics can enhance phenotype prediction accuracy. This distinction is vital for the future of therapeutics. While a predictive model might tell us who is at risk, only causal discovery tells a researcher which exact nucleotide to target with CRISPR-Cas9 to reverse a disease phenotype.

7: Functional Interpretation Challenges

Attempting to understand the function of certain SNPs or how it translates into its final trait is one of the greatest challenges of modern genetics. While studies have identified tens of thousands of associated variants, actually proving why they affect health is difficult for several reasons. Most genetic variations linked to diseases do not actually change the amino acids of proteins. In fact, approximately 39% of disease-associated variants are intergenic (found between genes) and 36% are intronic (found inside non-coding parts of genes) (Schrodi et al., 2014), where the rest are found within the amino acids of proteins. Because these variants don’t change a protein’s structure, they often have no obvious biological function, making them much harder to study than mutations that directly break a gene. Additionally, Finding the exact “culprit” mutation is difficult because of LD. When a study finds a strong signal, there are often dozens of nearby SNPs that are also associated with the trait simply because they are essentially “physically stuck” to the real causal variant. If several variants are in high LD, mathematical models often cannot determine which one is the true cause and which are tagging SNPs. Even with large datasets, the variant with the highest statistical score may not be the actual causative mutation due to random sampling noise.

To continue, making sense of thousands of tiny genetic effects requires scientists to use something known as pathway enrichment to see if associated SNPs fit within specific biological classifications or pathways. Pathway enrichment is a process that identifies biological pathways or functions, such as metabolic pathways or gene ontology terms — standardized vocabulary to describe gene product functions — that are overrepresented in a set of genes (Bass et al., 2013). By fusing SNP data with known biological pathways, researchers can identify which systems (like the immune system or metabolism) are likely being disrupted. However, for many traits, variants are clustered across hundreds of different loci and pathways, making it hard to pinpoint to a single dysfunctional process. Scientists use colocalization — a statistical method used to determine if two or more traits share the same causal genetic variant at a specific genomic location — to see if a genetic signal for a disease overlaps perfectly with a signal for gene expression (which leads to the actual protein being made). The “instructions” for DNA is incredibly complex because regulatory elements. Evidence suggests that the majority of mutations affecting complex traits live in these regulatory switches rather than in the genes themselves (Gouddard et al., 2016). A genetic switch might be broken in the brain but work perfectly in the heart, meaning scientists have to test the DNA in many different cell types to find the mechanism (Gouddard et al., 2016).

Furthermore, allele-specific expression can occur, which is when one inherits more than one version of a regulatory element, but one is more active than the other. Research shows that 89% of genes show this kind of specific imbalance in at least one tissue, adding a massive layer of complexity to finding the true biological cause (Makowsky et al., 2011).

8: Current Improvements & Future Directions

To summarize the steps scientists can take, it’s important to consider that most of the solutions apply with specific goals in mind, for example there can be a goal either have the goal to improve their prediction of complex traits or finding causal genetic variations, but some of these can apply with both cases as well. To begin, scientists can try to get as large of a sample size as possible, especially from family members. By doing this, it is possible to not only understand the true effect of a genetic variation’s effect, but also filter out any irrelevent noise and be able to focus more on the biological signals from the causal SNP. This helps in both cases since it ensures the results from are more accurate, meaning it will help predict by looking for the causal SNP through the tagging SNPs, and it will help look for the cause by creating more precise results from looking for eQTLs through a TWAS. In continuation, multi-omics integration is way to find biological causes. Multi-omics integration is the process of combining different layers of biological information to get a complete picture of how a disease or trait works. While a standard genetic study looks only at DNA coding, multi-omics adds other layers. For example, with transcriptomics scientists study eQTLs to see how much DNA variations affect how much of its protein product a gene makes. Additionally, with epigenomics, chemical markers on DNA like histone marks are used to regulate gene expression. Furthermore, proteomics and metabolomics are used to measure current level of proteins and metabolites in blood. These are called “dynamic markers” because, unlike static DNA, they reflect current status of DNA in one’s body. Multi-omics integration is necessary to consider because most disease-linked DNA variations are non-coding, making their function more ambiguous. By utilizing both these layers together, researchers can identify the specific biological pathways that are mutated. Moving on, fine mapping is also a major factor to enhance. Fine-mapping is the statistical process of pinpointing the exact causal variant from a group of nearby candidates in LD (Pasaniuc & Price, 2016). Fine-mapping aims to move from simple correlation (knowing a neighbor is linked) to causal identification (proving which DNA letter is the cause) to enable further laboratory experiments. Additionally, machine learning can be utilized to find patterns that are difficult to spot. As stated before, standard models assume each gene works independently. However, biology is often about teamwork (shown through epistasis), where one gene only matters if another gene is also present. Scientists are using ML tools like Random Forests, Neural Networks, and Support Vector Machines to detect these complex interactions (Bass et al., 2013). These tools can look at thousands of genetic variations in parallel to see how they interact, which is computationally too hard for traditional math. Moving forward, current medicine often groups diseases by their visible symptoms like stomach pain, but that pain could easily be caused by five different biological mutations. Machine learning aids by looking at the molecular makeup of the DNA to determine classification, instead of using just their symptoms. This approach allows for precision medicine, where a doctor can treat the exact biological cause of a disease rather than just the symptoms. Furthermore, despite large samples being optimal, it can be a strain computationally. Advanced algorithmic techniques like Variational Bayes and Coordinate Descent are being integrated into new tools (like MegaPRS and LDAK) (Speed & Balding, 2014). These ML-inspired methods allow scientists to build highly accurate prediction models in a few hours on a standard desktop computer, whereas older methods might have taken weeks or required a supercomputer. Finally, a major challenge in current genetic research is that some models built using one population (often individuals of European descent) typically perform poorly when applied to other groups, such as individuals of African, East Asian, or South Asian ancestry. This is because LD varies significantly across different ethnic groups. A SNP that is a great “proxy” or “tag” for a disease in Europeans might not be linked to that disease in another group (Meuwissen & Goddard, 2010). This is the reason sample sizes must increase so that more versatile models are developed that are applicable to all ancestries. While population diversity makes prediction harder, it is actually a powerful tool for fine-mapping. Because different populations have different DNA groups, scientists can compare them to see which genetic variation remains linked to a trait across all groups. The true biological cause should be consistent across all humans, while the random tagging SNPs will vary.

Models like the Bayes RC or MultiBLUP can help move away from models that assume everyone’s DNA works the same. This approach has been shown to significantly reduce the number of potential suspects in a genomic region, helping scientists pinpoint the real biological source (Bernal Rubio et al., 2015). Many modern genetic tools require LD Reference Panels to account for the gap of missing heritability, however if the template used doesn’t match the same ethnicity of the individual who’s DNA is being studied, it can be inaccurate and leads to false-positive results. Scientists need to create larger and more diverse reference templates to ensure that techniques like estimating missing DNA nucleotides work correctly for everyone. Finally, it is emphasized that a “one size fits all” approach to genetic medicine won’t work. Applying predictive models to diverse clinical populations is essential to understand their limitations and ensure they provide improved health outcomes for everyone, not just those from the groups that have been studied the most.

9: Conclusion

To summarize, phenotype prediction remains one of the most challenging aspects of modern genomics. The relationship between genotype and phenotype is not a simple linear map, but a vast, interconnected network of polygenic architecture. With millions of variations producing infinitesimal effects, the task of organizing these patterns would be impossible without the integration of Bayesian frameworks and machine learning. However, statistical success does not always equal biological understanding. The presence of LD continues to obscure true causal variants, leaving researchers with a sea of candidates that “ride along” actual causal variants and complicate functional discovery. Furthermore, it’s important to contend with “undetectable” part of biology: the environmental interactions, epistatic networks, and epigenetic regulations that introduce non-genetic variance. Moreover, the future of personalized medicine and CRISPR-Cas9 depends on what researchers can do now with new advancements. Scientists currently possess a “molecular scalpel” capable of rewriting the code of life, but they are still learning how to read the instructions. The full potential of gene editing will remain hindered by our inability to predict the consequences of a single edit unless the gap between statistical correlation and biological causality is birdged. The difficulty of phenotype prediction is not a failure of genetics, but rather, it is a profound reflection of the staggering complexity of life itself. Realizing that the genome is not a simple, static blueprint, but a dynamic network that simply has not been fully explored yet will significantly aid scientists comprehend the true nature of the relationship between the genotype and phenotype.

About Me

My name is Ankit Chandna, a 16 year-old researcher in gene editing and how control on it can be further enhanced. If this article was interesting to you, consider checking out my other articles!

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References

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Bernal Rubio, Y. L., Gualdró, J. L., Bates, R. O., Ernst, C. W., Nonneman, D., Rohrer, G. A., King, A., Shackelford, S. D., Wheeler, T. L., Cantet, R. J. C., & Steibel, J. P. (2015, November 26). Meta-analysis of genome-wide association from genomic prediction models. Wiley Online Library. Retrieved March 19, 2026, from https://onlinelibrary.wiley.com/doi/full/10.1111/age.12378

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Gouddard, M. E., Kemper, K. E., MacLeod, I. M., Chamberlain, A. J., & Hayes, B. J. (2016, July 1). Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture. The Royal Society Publishing. Retrieved March 21, 2026, from https://royalsocietypublishing.org/rspb/article/283/1835/20160569/84471/Genetics-of-complex-traits-prediction-of-phenotype

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All life must eventually come to an end. However, even though every organism dies at some point, some creatures seem like they don’t die due to their extreme lifespans. Some humans even live past 100 with fewer age-related diseases, partly due to their genetics. One gene that consistently appears in longevity studies is FOXO3, a gene conserved across species that diverged hundreds of millions of years ago.

Genetics and Longevity

Genetics plays a crucial role in longevity because it regulates cellular processes involved in survival and maintenance. Various genes have various purposes, for example the MC4R gene controls appetite by telling the brain how much fat is in the body. The gene that I will analyze is the FOXO3 gene. FOXO-family gene regulators are conserved across many animal species, reflecting their deep evolutionary importance.

FOXO3 Function

The function of FOXO3 is to respond to internal or external stress with activities strongly associated with longevity. It does this by acting as a regulator, turning networks of genes on and off in response to cellular stress to promote cell survival, apoptosis (controlled death of cells), or autophagy (reducing waste of cells). According to the National Institutes of Health, these responses to stress support lifespan extension and are controlled by nutrient and stress sensors. Essentially, if a cell was stimulated in a negative way (either through low/too much energy, oxygen, or glucose), the FOXO3 gene would control the intensity of the other genes’ expression in the cell. It’s important to note that these actions do not directly lead to extended lifespan because the gene regulates the cell’s other genes to protect it from internal or external threats.

Evolution and FOXO3

FOXO-family gene regulators are conserved across several animal species, reflecting their deep evolutionary importance. This is because organisms have retained this gene for millions of years to ensure their survival. In fact, there are some regions in the FOXO3 gene that are almost identical in many animal species, indicating these sequences are ancient, otherwise known as “highly conserved.” However, it is these ancient sections of the gene that are important to analyze, since they have been selected by evolution for a reason. Highly conserved regions tend to indicate main function, protein structure, binding sites, etc. This is because evolution has changed portions of the sequence as they contribute to the main function. Learning about the functions of the different regions can be difficult, but that is where comparative genomics comes into play.

My Analysis: Comparative Genomics

After researching longevity, I was curious about how to identify conserved vs variable regions. To understand which sequences are the most important, I aligned the DNA and proteins together to find conserved or variable regions.

Species Selection

For this experiment I had to carefully select the species whose genomes I’m comparing, but how did I decide which species I was analyzing? To do this, I needed to consider two major things: what resources I had and what measurement I would use.

Resources
The resources I had were Benchling, Ensembl — A library for genomes — , YouTube, and. I used Benchling to simulate the alignment, Ensembl to load the gene from the genome of the species, and I used YouTube to understand how I can use Benchling since the user interface is complex and takes a while to learn.

Measurement
For my measurement, I decided to use lifespan, since longevity in a cell contributes to the lifespan of an organism. Considering my resources, this was the most optimal since Ensembl had a variety of animals that differed in lifespans.

Variable Groups
After considering the experiment, I realized I needed the genomes of 3 different species: one with a low lifespan, a high lifespan, and a control/reference group. I used a mouse, Abingdon island giant tortoise, and the human genome. I used these groups because a mouse has an average lifespan of 12–18 months, an Abingdon island giant tortoise has an average lifespan of 80–120 years, and I needed a control group, which were humans, having an average lifespan of 70–80 years, according to Our World in Data.

My Hypothesis

My hypothesis was that the human DNA and Abingdon island giant tortoise would share relatively similar DNA since the tortoise has a lifespan almost similar to that of humans. I was right for the most part, however I still didn’t understand why we can’t just take inspiration from animals with greater lifespans and copy their DNA. Additionally, I saw more similarities than I thought I would.

Alignment Observations

Before going into the observations, it is important to understand how to read the alignment. Let’s start with the format. On the left hand side, I’ve labeled the species and their FOXO3 gene. I decided to align the sequences together relative to the Abingdon island giant tortoise since I was trying to see how the DNA of a longer life span creature compares to the DNA of organisms with shorter lifespans. Next, the coloured letters and numbers. The colours’ purpose is to serve as a method of distinction between amino acids, which are essentially groups of DNA, and the numbers are meant to show the amino acid position relative to a region in the sequence. The grey bars on top of the sequences are known as votes, which mean how many sequences have the same nucleotide, i.e. if there are 2 votes, then 2 out of 3 sequences have a common base. The annotations in the bottom mean various things, from depicting the full gene sequence, to exons — which are the regions that are shown in the final protein. Finally, the blank spaces in the alignments mean a difference in amino acids. The alignment has blank spaces to ensure functionally equivalent genes are lined up. After carefully analyzing the alignments, I compared it to my hypothesis. I was right with my hypothesis when I was saying humans and Abingdon island giant tortoises share similar DNA for FOXO3, but something didn’t sit right with me: why did the mouse FOXO3 sequence have a greater pairwise identity?

The results were unexpected.

Findings for DNA Sequences:

When aligning the sequences, there were almost no similarities, or when there were similarities, they were very small, especially due to the gaps. However at certain points, there were times where similarities occurred within all 3 sequences. Generally these regions would occur at long amino acid points, at points with an asterisk amino acid symbol, or at exons. This could indicate highly conserved and functional regions. These regions most likely date back hundreds of millions of years ago when species have begun to diverge and evolve into different organisms. However, despite massive differences in lifespan, the FOXO3 exon regions for each sequence have a decent amount of similarity, especially in exon 2. This could mean that exon 2 contains conserved regions, indicating a tightly constrained function. This suggests that exon 2 contains functionally constrained regions likely important for FOXO3 activity.”

While overall protein length varies across species, conservation of key functional domains suggests that core FOXO3 activity is maintained. Here are pictures of my findings in the alignments.

Here are screenshots of my DNA sequence alignment:

Proteins Findings

For quick clarification, the grey bars on top of the sequences represent identity, meaning the number of sequences that have the same amino acids. Something I noticed for proteins is that they were a lot shorter and there were significantly less gaps. I later realized this was because proteins get affected less by change in DNA and rather change in amino acid. DNA can mutate, but if it’s a silent mutation, the end protein doesn’t get affected, meaning most of the changes in the other sequences were silent and were almost meaningless. I noticed there were a lot more similarities and they were exactly the same when they were under a certain annotation: the compositionally biased region. This is a region where it has been significantly enhanced with a type of amino acid, meaning it uses it more than others, and it deviates from its “average” composition. These regions often play regulatory or interaction-related roles rather than structural roles. These important regions are made from a small region of amino acids, so they have certain classifications depending on these amino acids, called residues. They’re called residues because they are what remain after the protein forms. Residues describe the chemical properties of amino acids at specific positions within the protein. They can only tolerate a certain type of chemistry as there aren’t a diverse amount of amino acids. Here are some important types of residues to know about when reading this article:

• Nonpolar residues: hydrophobic amino acids
• Polar residues: hydrophilic/uncharged amino acids
• Acidic residues: negatively charged amino acids
• Basic residues: positively charged amino acids
• Cysteine residues: forms disulfide bonds with other cysteines (amino acid that contains sulfur), reinforcing stability of proteins
• Glycine residues: amino acid that is flexible and allows tight turns/folds in protein structure
• Proline residues: protein structure amino acid that’s used with collagen (primary protein for connective tissue)
• Low complexity: regions where the amino acids are simple and often repeating, for example ARARARAR, OOXOOX, etc.

When analyzing the compositionally biased regions in the FOXO3 gene, I noticed two things: the first thing was that the regions were almost always in the exact same position with the similar lengths. The second thing was that there were mainly polar residues, indicating water must play a crucial role in the FOXO3 protein. However, there were more compositionally biased regions with different types of residues. I’ll list the types of amino acids that were common in the regions, depending on the residue.

Polar residue: S (Serine)

Acidic residue: E (Glutamic Acid) and D (Aspartic Acid)

Basic residue: K (Lysine)

Gly residue: G (Glycine)

Low complexity: A (Alanine) and G (Glycine), and H (Histidine) and Q (Glutamine)

Below, I will show the screenshots of my protein alignment:

Conclusions drawn from the experiment

In the end, I understood why my hypothesis wasn’t completely correct: it was due to evolution. Yes, there were points where parts of the some sequences had more votes with the human and Abingdon island giant tortoise and not the mouse, but there were also points where they were all nearly identical or points where the mouse DNA had more in common with the Abingdon island giant island tortoise than the human one did, especially in the regions where they all relatively matched and that is worth mentioning. These nearly identical regions are most definitely regions that have been around for eons. Because the regions have existed for so long, — especially before species have begun to diverge due to evolution — they likely correlate to FOXO3’s actual function of gene expression regulation. When understanding the true meaning of the alignment, not only is it important to look at the DNA alignment, but also the protein alignment. This is because mutations (evolution changing the DNA due to environmental factors) in the DNA can change the sequences, but they can also be completely harmless to the amino acids. However, if the amino acids chain, the protein’s function, structure, and folds change completely.

Limitations

While FOXO3 is an important gene for longevity, it isn’t a direct factor to it. It’s impossible to directly control lifespan by changing sequence DNA, however we can try to control what aspects build together to determine lifespan. Longevity is multi-genic, meaning it involves more than one gene. An example of another gene relating to lifespan is the APOE gene, — which provides instructions for packaging and transporting cholesterol fats in the bloodstream and brain — according to Medline Plus. FOXO3 This is due to the fact that the actual FOXO3 gene function is to regulate the expression of the genes around it in response to some sort of stress. This proves longevity cannot be reduced to a single factor, because it’s multi-genic.

Understanding DNA and proteins on a fundamental level is important to exploring longevity more and trying to enhance our lifespan. But, is it even worth it to try?

Ethical reflection

Just because we can edit genes, especially longevity ones, does that necessarily mean we should? Most people jump at the chance of extra time, but is it even worth it? If we lived in a world where we fully comprehended how protein or DNA sequences can be manipulated to enhance longevity, our lives could be changed significantly. Let’s start with the positives.

Pros:

• People will be able to spend more time with loved ones or enjoy the things they love the most
• There is more time to create positive contributions to society
• There is reduced disability and illness in the majority one’s life due to healthier cells
• More wisdom in life due to greater experience

However, there are also as many negatives as there are positives and it is very important to focus on them too.

Cons

• There can be a lot of strain on mental health due to seeing many deaths of close ones or for living unnaturally longer
• There is more time to create negative contributions to society
• Lack of resources due to larger populations
• Right before death, life declines at an extreme rate. This can lead to excess suffering right before death.

If longevity were to be commercial, it could solve and cause a lot of problems. The pros and cons prove that utilizing genetic editing tools requires a lot of caution because of its impact. Biological technologies can change the code of life itself and how it’s used, meaning if it is used negatively, the damage could be immense. This is why understanding must come before application. By properly understanding not only the current effects — how the treatment will affect everyone or what procedures to follow — but also the future effects, it sets us up in a position to understand all perspectives of genetic tools and longevity. Examples of using these perspectives include keeping longevity technologies commercial or limited.

So in the end, is it even worth it? Right now, it is hard to say if longevity is something we should abandon or leave because of the new advances in the genetic industry. Despite all the implications, genetic technologies should still continue to develop. Even though the industry has potential to cause problems in the world, it can also solve a considerable amount too. The better decisions we make due to understanding almost completely cancel out the negative implications of genetic tools. That’s why if we want to be ethical and use genetic technologies properly, we must understand as much as possible, from our current tools to our final effects.

Conclusion: What FOXO3 teaches us

In the end, FOXO3 is more than a gene, it’s a lesson. It taught me that evolution is incredibly complex and highly optimized because of the natural changes it makes to the DNA. It takes into account everything from diet to external environments to natural selection (the cycle of surviving, reproducing, and passing on favourable traits because of organisms with traits better suited to their environment). When I compared DNA or protein sequences together, the alignment didn’t simply say which code gives the better lifespan, it said what is the most optimal because of the circumstances the species lived in. Whether it’s a mouse, human, or an Abingdon island giant tortoise, evolution teaches us so much about genetics and its future. Using comparative genomics can put us in a state of clarity by understanding how evolution selects organisms their desirable traits to survive. We can use this knowledge to properly and controllably apply it to ourselves. However, before doing that, we must take a step back and analyze the entire situation. Properly analyzing where we are as a society, we will be in a much better position to make decisions. While this seems obvious, it unfortunately isn’t because of how scientists can blindly work towards a goal that can harm others if not managed properly. This proves FOXO3 is a symbol of evolution and how complex the process of natural selection is.

What is Genetic Engineering?
Genetic engineering is the modification of a living organism’s DNA with the goal of changing its traits. Scientists do this by inserting, removing, or altering specific genes. This makes the process of evolution to get desired traits significantly faster. Common examples include crops engineered to resist insects or survive droughts and bacteria modified to produce human insulin. The capabilities of gene editing make it one of the most influential advancements in modern science.

The Timeline Of Gene Editing
Genetic engineering has significantly impacted our lives more than you can think. An example of genetic engineering in your life is in the food you eat. According to the US Food and Drug Administration, agriculture has been modified to protect itself from pests in farms or to be healthier since 1922. These modified plants are known as Genetically Modified Organisms (GMOs). These GMOs started with creating a hybrid corn breed with cross-pollination. Breeders would take the pollen of a male from plant and transfer it to a female of the other plant, which would birth a hybrid of the 2 plants.

1940: scientists noticed that the genome of an organism changes when exposed to radiation. This discovery motivated biochemists like Herbert Boyer and Stanley Cohen to explore gene editing further.

1973: Herbert Boyer and Stanley Cohen discovered they can insert DNA from bacteria into another organism. An example is genetically modifying corn to create insect-resistant corn. Scientists identified a gene in a soil bacterium called Bacillus thuringiensis, which produces a natural insecticide. This modification of the corn reduced the need to spray insecticide on the corn and allowed corn to fully grow and be healthy.

1982: GMOs finally become legally recognized and the US government’s FDA had approved the first GMO product, which was human insulin to treat diabetes. This medicine showed that GMOs were not only for plants, but also for medicinal purposes.

1983: A technique to rapidly duplicate DNA called Polymerase Chain Reaction (PCR) was created by American biochemist Kary Mullis at Cetus Corporation. This allowed a more detailed study of genetics and unlocked a path to more advanced gene editing advancements.

1990: The first federally approved gene editing surgery was successfully performed on a girl who suffered from an immune disorder. This surgery singlehandedly showed the world the power of gene editing and taught everyone to trust it too.

1994: British scientists, led by Ian Wilmut from the Roslin Institute, were able to clone Dolly the sheep. They way they did this was with a technique called somatic cell nuclear transfer where the cell nucleus from an adult male Finn-Dorset cell is transferred to an empty, developing egg cell of a female Scottish Blackface. After, this cell gets stimulated using electric shocks, then when it’s developed enough the cell gets implanted into another surrogate Scottish Blackface mother. This is one of the most well known genetic engineering advances since it is one of the first step humans took to cloning complex organisms.

2000: The Human Genome project, which is a global scientific research to determine the genome sequence of our DNA, had released a “rough draft” of the human genome, signifying progress in understanding not only other organisms, but also ourselves.

2003: The Human Genome Project is completed and officially provides information on the sequences and locations of human genes on all 46 chromosomes.

2010: TALENs were created with a collaborative effort from scientists from the Minnesota and Iowa State University. TALEN was one of the first known biotechnologies for changing genome sequences and became the motivation to other people like Emmanuelle Charpentier and Jennifer Doudna.

2012: CRISPR-Cas9 was created for the first time by Emmanuelle Charpentier and Jennifer Doudna. Their groundbreaking research paper in 2012 showed how possible it was to effectively edit an organism’s genome with minimal error

2013: CRISPR-Cas9 was used to edit the genome of a zebrafish and human. This biotechnology became recognized almost immediately after its use due to it’s effectiveness and cost-efficiency. However, to understand how impactful CRISPR-Cas9 truly is, we need to take a closer look to how it works.

What is CRISPR-Cas9?
Clustered Regularly Interspaced Short Palindromic Repeats-Cas9 (CRISPR-Cas9) is a genetic editing tool that cuts off specific parts of DNA of an organism. It uses messenger RNA (mRNA) to guide a protein called Cas9, which are molecular scissors, to edit and cut the desired genes. Here’s a breakdown of the process:

1. Finding the target sequence: First, scientists begin by looking for a specific section in the genome needs to be edited. Reasons for editing the genes include removing undesired gene mutations, cutting harmful DNA, or conducting genetic research.
2. Designing guide RNA (gRNA): Next, scientists must design their own gRNA to guide the Cas9 protein to cut the DNA sequence. The way they do this is by finding an appropriate PAM sequence (signature nucleotides next to target DNA that allows a cut), then the 20 nucleotides (A, T, C, or G) towards the beginning of the sequence (excluding the PAM). This sequence becomes put into the gRNA that gets implanted in the cell.
3. Cas9 protein cutting: After the gRNA has been created and implanted into the cell, the Cas9 protein activates and cuts the targeted sequence that the gRNA guides it to cut.
4. Repair: Finally, the cell’s DNA sequence begin to mend the cut part of the gene sequence. It either gets naturally mended or replaced with a set sequence of DNA.

When understanding the process of CRISPR-Cas9, it’s important to understand it’s backstory.

CRISPR-Cas9 Origins
For an effective and powerful technology like this, it’s important to know it’s backstory. CRISPR-Cas9’s origin began in 2012 when scientists Jennifer Doudna and Emmanuelle Charpentier published a groundbreaking paper that introduced CRISPR-Cas9 as a new method for gene editing. Their report explained how a natural defense system found in bacteria — which used RNA to recognize and cut the DNA of viruses — could be reprogrammed to edit genes in any organism. They created this report to show that CRISPR-Cas9 could serve as a simple, efficient, and precise tool for modifying DNA.

What’s so good about it?
This technology is known to be revolutionary because it’s the first of its kind to be not only significantly effective, but also efficient and cheap. It’s effective because it’s a process that many bacteria have been using for millions of years. If a virus attacks a bacteria, it remembers the virus and wrap it into it’s own bacterial code (known as guide RNA or gRNA), so if the virus ever attacks them again, they’ll know which DNA to cut using enzymes (the Cas9 protein). Before CRISPR-Cas9, editing genes was complicated and expensive, but Doudma and Charpentier’s research proved that genetic editing could be done faster and more accurately than ever before, opening the door for a new era in biotechnology. A great example of a previous gene editing biotechnology is Transcription-Activator-Like Effector Nuclease (TALEN).

What are TALENs?
TALENs or Transcription-Activator-Like Effector Nucleases are a protein-based system with two main parts:

• A “finder” part, which uses arranged TALE proteins to decide which part of the DNA gets cut.
• A “cutter” part, which uses an enzyme called FokI to cut DNA, but only works when paired with another FokI enzyme. The enzymes bind by outside the DNA located with the TALE proteins, then the enzymes cut between it.

Process

1. Designing the TALENs: First, scientists begin by designing the first two proteins. Both of these proteins are made up of a customized DNA-binding region (TALE protein) and a cutting enzyme (FokI). The TALE region is built from nucleotides that can be arranged to resemble specific DNA sequences.
2. Targeting the DNA: Next, the two proteins are then programmed to bind to opposite sides of the target DNA sequence. Each TALEN attaches to the an end to the desired DNA strand.
3. DNA Binding: Then, the proteins are in the ends of the DNA sequence, the Fok1 enzymes bind together and cut the DNA in the middle of it. This process is also known as dimerization.
4. Repair: Finally, once the DNA is cut, it follows the same process as CRISPR-Cas9 where it either naturally mends or is replaced with a prepared DNA sequence.

TALENs Origin
According to Science Direct, TALENs were developed around 2010 by scientists who discovered that certain bacteria, known as Xanthomonas, could attach to plant cells and use special proteins to interact with their DNA. Researchers realized they could reprogram TALEs to target specific DNA sequences in any organism. By combining TALEs with a cutting enzyme called FokI, they created a new gene-editing tool — TALEN. The purpose of developing TALENs was to make gene editing more precise and flexible than earlier tools like Zinc Finger Nucleases (ZFNs). Although, creating TALENs is more complex because each one must be customized for a specific DNA target. Despite the complexity of TALENs, it played a major role in advancing biotechnology.

What’s so good about it?
Although CRISPR-Cas9 has become the most widely used gene-editing tool, TALENs still currently play an important role in biotechnology. Because TALENs are extremely precise, they are often used in medical research and gene therapy, where accuracy is critical. For example, TALENs have been successfully used to develop CAR-T cell therapies that help treat certain types of cancer by reprogramming a patient’s immune cells to attack tumors. They are also used in cases where CRISPR-Cas9 cannot target a DNA sequence due to a lack of a PAM sequence near the desired DNA strand. Additionally, TALENs continue to be used for agricultural genetic engineering, such as creating disease-resistant crops or improving livestock genetics. While CRISPR is the most well-known biotechnology, TALENs remain a valuable and reliable alternative for high-precision editing and clinical applications where safety and accuracy matter most.

How does CRISPR-Cas9 compare to TALENs?
Although CRISPR-Cas9 and TALEN are both efficient, their efficiency varies dependent on the application, species, and cell type. However, generally CRISPR-Cas9 can edit DNA more efficiently than TALENs. According to Vector Builder, CRISPR-Cas9 targets around 20 base pairs (A-T and C-G), while TALEN targets around 36 base pairs. However, CRISPR-Cas9 mismatch sequences more than TALENs, meaning TALENs usually provide better specificity — how accurately a gene tool hits the target DNA sequence — whereas CRISPR is more prone to off-target effects — unintended changes that happen in places other than the intended DNA target. With this in mind, it’s safe to say CRISPR-Cas9 is inferior to TALENs in the context of off-target effects.

Final verdict: why CRISPR-Cas9 is superior
Overall, CRISPR-Cas9 stands out for its simplicity, speed, and cost-effectiveness, despite how powerful TALENs are. Designing a guide RNA for CRISPR-Cas9 is quick, and delivering it into the cell is also relatively easy, making it ideal for general gene cuts. On the other hand, TALENs offer higher specificity and flexibility because it doesn’t rely on a PAM sequence, however it’s a double-edged sword because it can cause unwanted mutations if there are similar sequences in the cell’s DNA. Additionally, the FokI proteins that are the foundation of TALENs take time and are complex to make, as each TALEN must be custom-made for specific targets. Even though CRISPR-Cas9 is the more practical better genetic technology, TALENs’ performance make it valuable for experiments requiring exceptional precision or targeting regions CRISPR cannot reach.