I don't buy any of the LessWrong-Personality-Theory stuff. People who are old and retired have accepted the inevitability of their death because doing otherwise would be very difficult emotionally. They are following ancient wisdom embodied in writings like the Serenity Prayer or the Enchiridion.

Based on your more intimate knowledge and access to knowledge in the area, what kind of time frame (even an order of magnitude estimate would suffice, if the former is intractable) would we be looking at if an amount of resources, proportional to the potential humanitarian impact relative to mosquito transmitted diseases, where to be spent to develop a gene drive ready for use in the Tsetse fly, a species regarded as responsible for preventing an African 'green revolution' like was seen in Asia and thus part of the whole fable of African starvation? Any way to incorporate resource investment into mitigating relevant risks?. It seems like an academic has independently started thinking along the same lines.

Based on your more intimate knowledge and access to knowledge in the area, what kind of $USD investment (even an order of magnitude estimate would suffice, if the former is intractable) would we be looking at if an amount of resources, proportional to the potential humanitarian impact relative to mosquito transmitted diseases, where to be spent to develop a gene drive ready for use in the Tsetse fly, a species regarded as responsible for preventing an African 'green revolution' like was seen in Asia and thus part of the whole fable of African starvation? Any way to incorporate resource investment into mitigating relevant risks?. It seems like an academic has independently started thinking along the same lines.

I'm totally in favor of chlorinating that pool, but just bear in mind that the 'registry of standard parts', and other biological tools in general, like CRISPR, are nowwhere near as easy to use and reliable as it says on the packaging. I'm always amused by the contrast between articles about CRISPR which make it sound like you can just jam a thumbdrive into a mouse, and the people I know trying to get CRISPR to work on a new cell line or organism, who are up all night for months in a row muttering schizophrenically in the cell culture room. You need a lot of time and human capitol for these things.

Another genomics PhD here. It's a complex topic. We know that combinatorial effects (epistasis in genetics lingo) matter, from population genetics studies in model organisms. This is despite the fact that simple linear models perform well in the human population - provided they are against some reasonably constant genetic background, low allele frequencies mean that the combinatorial effects are well captured by linear ones.

The problem is that even if you only care about pairwise combinations, there are far too many of them, given a uniform prior. Even if we sequence everyone on earth we wouldn't have anywhere near enough info, sequencing additional individuals has diminishing returns because there's only so much genetic variation in the human population (and ~23000^2 possible pairwise combinations).

What we need are good priors over combinations of mutations. To do that we'll need detailed info about which genes function together to produce which phenotypes. Such models exist already and are seeing moderate success, but we need new ideas and more data than any one startup could provide. Which is exactly what molecular biologists are working on.

Short answer - no, this is a hard, ongoing problem.

I think you're looking for the concept of 'mutational variance'. This is the amount of variation in a trait that is generated by random mutation. The variance in a trait is going to be determined by the balance of mutational variance and selective effects. Things with lots of genes effecting them will have a large 'mutational target size'. So for instance intellectual disability has a large mutational target size because there are so many different ways to break a brain, while some kinds of haemophilia have a large mutational target size because the particular sequences of DNA involved mutate a lot.

In general mutation variance is very difficult to measure outside of single celled organisms, although good approximations have been done in e.g. fruit flies. The problem is that it's very difficult to stop evolution from exerting it's filtering effect on your mutations before you can measure them.

So in the absence of direct measures, It's difficult to guess at how many genes might be involved in something like homosexuality, and what the mutational variance could be. On the surface, we can imagine it's just a simple trait that should have few genes effecting it. Such is the case in fruit flies. But actually, we just don't know enough about how evolution has created the human mind. Without knowing how genes produce a brain, we don't know enough to say that homosexuality isn't just a particularly common "failure mode" of the brain, like autism and ID. Maybe something about the way the human brain has evolved makes it turn out gay a lot.

Myself I don't really buy the 'gayness is selected for' explanations. My own opinion is that exclusive homosexuality might be more due to our own present society than anything else, and it's need to cordon off homosexual behaviour from normal, straight behaviour. If that's the case most of the mystery disappears.

WGS is going to get cheaper and cheaper as time goes on, presumably in the future we'll have developed a process for analysing the results properly. In the intervening time, there isn't much to be gained from it. SNP genotyping gives you most of the info about common variants, because the things it doesn't catch (deletions, insertions, etc.) will generally have some SNP in linkage to them. The rare variants are what you miss, and right now we don't really know what to do with them.

In general I wouldn't overestimate how much genotyping will tell you. Your family history is likely to be more informative.

I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence are going to make human-run biology irrelevant before long.

I suspect that's the issue, and I suspect AI will not be the Panacea you expect it to; or rather, if AI gets to the point of making Human-run research in any field irrelevant - it may well do so in all fields shortly thereafter, so you're right back where you started.

I rather doubt it will happen that way at all; it seems to me in the forseeable future, the most likely scenario of computers and biology are as a force multiplier, allowing processes that are traditionally slow or tedious to be done rapidly and reliably, freeing humans to do that weird pattern-recognition and forecasting thing we do so well.

I'm just midway through a masters in bioinformatics, and am currently applying for jobs at deep learning startups, so I'm fairly familiar with AI and genomics.

A few suggestions: - Have you considered cognitive genomics? This is very relevant to the future of intelligence in the absence of radically superhuman AI. Plomin and Steve Hsu (BGI) are the main relevant researchers in this area. - Have you considered identifying pathogenic sequences? Companies that allow biological sequences to be ordered need to be able to accurately identify pathogenic sequences to reduce probabilities of disasters. Don't know much more about this but it's pretty future-relevant and close to your area, possibly even closer than biosecurity risks at large, which is also a good suggestion by Luke. - If by AI, you're interested in machine learning research, then it would make more sense to start with Numpy (or Matlab if you have it), you can transition easily from R, and then C/C++/CUDA for the lower levels and Caffe or Torch for higher-level programming. Java and Hadoop seem more useful for scaling existing algorithms than for researching AI and AI safety.

nobody has the vision to see that the initial problems in automating basic laboratory tasks would be more than compensated for in the long run

That's not true. Peter Thiel's Founders Fund for example backs http://emeraldcloudlab.com/ which automate basic laboratory tasks. They seem to have enough customers to have a waiting list.

I don't see a "Jobs" page on their homepage at first glance, but I would expect that they are a company that can make use of your skills.

It might very well be possible to get a meaningful happiness score out of high resolution GSR data and heart rate data. A few years ago I heard that they manage to do emotional detection with 80% accuracy. Having an objective way to measure happiness on a daily basis would be huge for treating depression and measuring which drug works.

Today's heart rate monitors might not be high accuracy enough, so there's a need to provide applications for higher resolution monitoring to incentivise Apple, Samsung and Microsoft to develop the tech for smartwatches. GSR data is very useless if you don't have an algorithm interpreting it but once you interpret it you can pick up bodily events.

Besides developing the actual hardware developing statistics like that, that provide meaningful insights seems to be important to me. Developing better scales for obesity isn't only about producing new technology but also about analysis of data.

The fact that we have an open access uniprot is also not just about technology. If you look at chemistry where the American Chemical Association claims ownership of CAS numbers, the state of affairs is worse. Thinking about getting more knowledge into a format like uniprot, isn't just about technology but requires thinking about ontology. I like Barry Smiths work in that area.