Chicken Heads! I’m not sure where we’re all headed, but I’ve been feeling on edge for the longest time. I’m constantly trapped in rage as I watch so many things not working out in favor of the majority of us women. Being too aware is a curse no one warns you about.
Curiosity opens the door to an abundance of knowledge the world has to offer — but then it leaves you there. You’re left needing to channel it in a self-reliant way so it can benefit you and others just as easily as it can overwhelm you.
People admire smart people.
I admire people with common sense — those who can read the room, understand context, and see the bigger picture, rather than living in tunneled, linear ways of thinking.
If science holds all the answers, then we are all questions to that science. We are sample-size cohort populations — living experiments trying to understand cause and effect.
Working in bioscience makes you wander and wonder differently. It changes how you think and function — not through loud aha moments, but by subtly guiding you toward things that are bigger than us. And I love that part of me very much. I’m a personality that thrives wherever I go, because there is always something bigger and better I can do. It’s easy for me to stay consistent and self-reliant — nothing tames me.
That’s why I want to be positioned somewhere I can truly contribute, rather than falling into a mundane, one-way life.
So, in continuation of my last article, let’s dive into today’s brainstorming session — with sass.
Locus → Gene Mapping (and why it keeps falling short)
Understanding how people currently try to solve locus → gene mapping — and why every approach is incomplete.
I started by asking a simpler question:
How do scientists currently connect a locus to a gene?
There isn’t one answer. There are many partial ones.
Each works sometimes. None works always.
That’s the problem.
The naïve baseline: nearest-gene mapping
The most straightforward approach is also the laziest one.
You take a genetic signal — an SNP that pops up in a GWAS — and assume the closest gene is the causal one.
It’s simple. It’s fast. It’s still widely used. And it’s often wrong.
Why?
Because DNA doesn’t behave like a straight line on a PowerPoint slide.
It folds, loops, bends, and reorganizes itself in three dimensions inside the nucleus.
What looks “close” on paper might be worlds apart biologically.
And what looks far away might be in constant physical conversation.
Distance on a genome browser is not distance in real life.
Expression-based mapping: following the volume knob
A more biologically informed approach asks a different question:
Does this SNP change how much a gene is expressed?
If a variant affects gene expression — an eQTL — maybe that gene is the real actor.
This feels smarter. More grounded. More alive.
But expression is fickle.
An SNP might affect expression: in one tissue, at one age, under one environmental condition
Miss the context, and you miss the gene entirely.
Silence in the data doesn’t mean irrelevance. Sometimes it just means you weren’t looking at the right moment.
Regulatory and chromatin interaction models: entering 3D space
Then come the models that try to respect biology’s spatial complexity.
Hi-C. ATAC-seq. Promoter–enhancer maps.
The idea is elegant: if DNA physically loops so that a regulatory region touches a gene, that contact might explain the signal.
This captures something real. Something beautiful. But it’s still fragile.
These interactions are: cell-type specific, expensive to measure, probabilistic, not causal
They tell you what might be interacting — not what must be responsible.
Network and pathway thinking: zooming out
Another strategy steps back entirely.
Instead of obsessing over a single gene, it asks:
Do multiple signals point to the same pathway?
If several genes in one biological process light up, maybe that process matters more than any individual gene.
This is systems thinking. It feels closer to reality.
But it’s also correlation-heavy and limited by what we already think we know. Pathways aren’t laws of nature — they’re human summaries of incomplete knowledge.
The insight that kept looping back to me
Every method is asking a different version of the same question.
Not: “Which gene is correct?”
But: “Under what conditions does this gene make sense?”
Genes aren’t static. Disease isn’t static. Context is everything.
And this is where my thinking softens — and sharpens — at the same time.
I’m not here to invent a new biological truth.
I’m here to understand how uncertainty is handled.
How quickly do we collapse ambiguity just to feel certain?
And what do we lose when we do?
Where I see myself in this picture
What excites me isn’t declaring the answer.
It’s learning how to hold multiple plausible explanations without losing rigor.
Reading GWAS-to-gene mapping papers.
Studying how models integrate distance, expression, chromatin, and pathways.
Watching where they break — and where they hesitate.
And asking a quieter, more honest question:
What if AI didn’t force one answer — but ranked possibilities?
What if it helped us reason across uncertainty instead of erasing it?
That’s not hype.
That’s humility — with computation.
And honestly?
That feels like common sense to me.