That was kind of my initial point... there are far too many permutations that alter what you want to maximise to consider this as a good option right now. Environment, gene-environment interactions, epigenetics, polygenecity, pleiotropy, etc, etc.
902 karma · joined April 30, 2019
So, it's a bit context dependent, and definitely dependent on other mutations that have occurred in other parts of your DNA. Most complex diseases are "polygenic", meaning it's a culmination of quite a few factors that would contribute to a specific good or bad outcome.
So, yes, it could be modeled as a sort very context dependent with a lot of highly correlated non-independent covariates. We do use quite a lot of statistical and ML methods to understand the genome (I work in statistical genetics), but the complexity of biology has so far proved a tough nut to crack.
This is a common, but poor framing of evolution. Remember: the only real rule with evolution is "did this evolutionary change mean they survived / reproduced more"? Evolution is not a local or global maxima solving function. I find this kind of attempt at interpreting selection common with engineers, programmers, etc.
Hardin did not argue that private industry was more efficient. His paper described that with an unmanaged, private, unregulated open pasture that has no property rights, individuals will exploit it until it collapses. It wasn't used used as justification for privatization, if anything it was the opposite.
Ostrom did not argue that unmanaged resources don't collapse. Instead, she showed with data a third way of organising which was more involved with self-governing, communal rules to manage shared resources without resorting to either a private corporation or government control.
Otherwise it's a really fun idea! Can I suggest you also scrape from https://www.medrxiv.org/? This is where a lot of medical research preprints, not arxiv
So, a specific SNP mutation being predictive of a gene expression / protein is basically a p-value of 0.
Can't speak for physics experiments, but this is almost certainly not a statistical error
That is the minimum threshold. This study found that peak was at p < 1e-37 or so. But that is where the biological analysis begins. Unlike social scientists, we don't stop with the statistical correlation, we then go on to look at what we know about that gene, the type of mutation, if it's a loss or gain of function, what role that gene has in various tissues, etc. And mendelian randomization is another way to unpick the causal direction of effect.
Not to say this is the truth or causal, but it's a lot closer to causal than what you are implying.
https://www.nytimes.com/2024/10/22/us/politics/john-kelly-tr...
I'm very interested in my research at the moment in pleiotropy, namely mapping pleiotropic effects in as many *omics/QTL measurements and complex traits as possible. This is really helpful for determining which genes / proteins to focus on for drug development.
The problem with drugs is in fact pleiotropy! A single protein can do quite a lot of things in your body, either through a causal downstream mechanism (vertical pleiotropy), or seemingly independent processes (horizontal). This limits a lot of possible drug target as the side-effect / detrimental effect may be too large.
So, if these tools can create ultra specific protein structures that somehow only bind in the areas of interest, then that would be a truly massive breakthrough.
He should be grilling his assistant profs / research fellows to get their act together and raise the bar, but this doesn't show malfeasance.
Wait, this is the social scientist who wrote The Skeptical Environmentalist, whom a Danish Science committee found to be "scientifically dishonest through misrepresentation of scientific facts, but Lomborg himself not guilty due to his lack of expertise in the fields in question." [1]
And now he's written a book about another topic he doesn't have a deep understanding of. Forgive me if I give it a miss.
[1] https://en.wikipedia.org/wiki/Bj%C3%B8rn_Lomborg#The_Skeptic...
I do genetic epidemiology (which is considerably more compute intensive than regular epidemiology), and R is still the most common language, with the most libraries and packages being used for it, compared to python for example.
I think maybe you should consider being less forthcoming with your opinions on topics which you are not well informed on.
That being said, I usually just stick to one notebook per thing.
It's also interesting, how if school didn't play a factor, putting your kid into a under-performing inner city school wouldn't matter (or conversely, elite private schools wouldn't either)
Glad they've come a long way since then.
Thinking that aborting female embryos because they will be considered less important in society is wrong, AND think that removing a women's right to choose entirely based on religious teachings is wrong, AND thinking that forced sterilization of a whole cultural/ethnic group is wrong, are not incompatible.
Where are you getting these opinions from? I'd honestly have a hard look at where you got these thoughts from, they are pretty backwards, and not morally considered at all.
Let's say someone has calculated the polygenic scores (PGS) of Heteronormativity, meaning that a model, can predict with a decent level of accuracy that someone will or will not be straight from their DNA.
This, in an ideal world, would be good knowledge to have. You can raise you child knowing and accepting this reality.
In the world we live in, this would be used to abort babies that don't pass the PGS to the vast majority of people who have this information.
So, in this case, where we have an oppressed group that can be oppressed further, is knowledge better than ignorance? It seems that many in the comments would say yes, and that the pursuit of knowledge is the clear winner, and anything else is merely the price of progress. Which I might ask, you would say the same thing if you were gay?
I want to make one thing clear, this is not a silly thought experiment. This is very possible right now with the advent of biobanks, GWAS tooling, and machine learning. Nature is thinking about these things when writing that up.
I presume that most of you would agree that releasing this information for anyone to know would have negative consequences, and should maybe be controlled. So, then, you fundamentally agree with Nature's stance, do you not? We're merely talking about where the line of publishing exists, not if one should exist at all? Are you not being a bit overzealous with your declarations of orthodoxy?
That is distinctly not causal.
1. The journal article didn't suggest it was causal. But such a correlation with such a large population warrants publication and further research into causation.
2. literally the first thing that any epidemiologist would consider is potential confounders. There is a big list of covariates they included into their model here: https://content.iospress.com/articles/journal-of-alzheimers-...
There are quite a few things that can be done to alleviate potential false correlations: DAGs, prior literature, removing confounders, and including covariates are all things at disposal.
3. Such a large sample size + previously reported findings + an inclusion of enough covariates still doesn't == causation, BUT it's important to publish and shout about so we can then look into the potential biological underpinnings that may cause this. Which by the way, those experiments may still use data science techniques.
4. If you are actually interested, there is a whole topic of this called 'causal inference' with one famous criteria list called the 'Bradford Hill Criteria': https://en.wikipedia.org/wiki/Bradford_Hill_criteria. This list is often argued about.
5. If all of this information was new to you, please stop spouting 'correlation != causation'. You probably don't know as much as you think
I don't think the paper or the journal article suggested it was causal either. But such a correlation with such a large population warrants publication and further research into causation.
You unfortunately chose literally the first thing that any epidemiologist would consider, which is considering potential confounders. Maybe read up a bit before commenting.