About This Project

What I’m Learning (Technical Overview)

This section explains the concepts I’m studying in plain language, with an emphasis on how scientists think about evidence and uncertainty.

Genomics vs. genetics

Genetics often looks at individual genes and how traits are inherited. It is usually focused on specific questions like, “How does this gene affect this trait?” or “What mutation causes this disease?”

Genomics zooms out. It looks at DNA more broadly, including patterns across many genes or even the entire genome. Genomics is also more data-heavy. It involves comparing many sequences, identifying patterns, and asking questions like, Which genetic differences show up more often in people with a certain condition? or How do multiple genes work together? A key idea is that genomics is not just about finding differences, but understanding what those differences mean in context.

DNA sequencing and what a variant means

DNA sequencing can identify differences in DNA called variants. But finding a variant is only step one. The harder step is interpretation.

A variant might be:

• harmless: common variation that many healthy people have

• uncertain: not enough evidence to know what it does

• more likely to matter: evidence suggests it affects how a gene works

Scientists do not decide this based on one result. They use multiple lines of evidence, such as:

• Population data: Is the variant common in healthy populations? If so, it is less likely to cause severe disease.

• Predicted impact: Does it change a protein-coding region, disrupt splicing, or affect a regulatory region?

• Lab evidence: Do experiments show changes in protein function or cell behavior?

• Biological reasoning: Does the variant make sense given what is already known about that gene and pathway?

One of the biggest lessons I’ve learned is that sequencing produces a lot of information, but interpreting it responsibly requires caution. “We found something” is not the same as “we know what it means.”

CRISPR basics (what it is, and what it is not)

CRISPR is often explained as “genetic scissors,” but the key idea is control and precision.

A simplified view:

• Cas9 is a protein that can cut DNA.

• A guide RNA (gRNA) helps Cas9 find a specific DNA sequence.

• After the cut, the cell repairs the DNA using pathways such as:

• NHEJ: fast repair that can introduce small insertions or deletions, which can disrupt a gene

• HDR: more precise repair if a donor template is available, but harder to achieve reliably

Two major challenges are:

• Off-target effects: cuts in the wrong place, which could cause unintended changes

• Unpredictable outcomes: even on-target edits can produce different repair results depending on the cell type and conditions

What CRISPR is not: it is not a simple “find and replace” tool that guarantees a clean, perfect edit every time. A responsible approach always includes careful design, testing, and clear reporting of limitations.

Delivery and real-world constraints

Even if an edit idea looks strong on paper, delivery is a major hurdle. Scientists study ways to deliver CRISPR components into the right cells:

• delivering CRISPR as an RNP complex (protein + guide RNA)

• using viral delivery methods

• using non-viral delivery methods (like lipid-based approaches)

Each method involves tradeoffs, including:

• efficiency: how many target cells get edited

• control: how long the editing tool stays active

• safety: immune response, unintended effects, and dose limits

• scalability: whether it can be produced and used consistently

Case study: beta thalassemia and gene editing

During a Rice University precollege capstone, I studied beta thalassemia as a case study to learn how researchers think through genetic disease strategies. My assignment was not to “create a real therapy,” but to design a responsible, evidence-based proposal and explain it clearly, including limitations and risks.

What I learned from this case study:

• Why target selection matters: the strategy depends on which gene or regulatory region you choose and what outcome you are trying to achieve

• How repair pathways affect results: NHEJ and HDR can lead to very different outcomes, and the “best” option depends on the goal

• Why delivery and off-target risk are major constraints: these are not side details; they can determine whether an approach is realistic or not

• How scientists evaluate tradeoffs: effectiveness, safety, feasibility, scalability, and ethics all matter, not just whether an idea sounds promising

• How to communicate responsibly: stating what is supported, what is uncertain, and what would need to be tested next

This experience was a turning point for me because it made science feel more real. I realized that the most impressive part of research is not big claims but careful reasoning and clear evidence.