You know... I'm not so sure I completely agree. I mean, yes, the analysis is the bottleneck. Since about 2012 it takes more time to perform the analysis than it does to generate the data. And even though all of the raw data -> BAM -> VCF or counts steps is automated, nothing after that is (or can be, since that's where the science happens).
But, the $1k genome really is significant. Costs matter.
Researchers will only sequence what they can afford. So, when sequencing a genome was $10k, there weren't too many 30X whole genomes sequenced. Instead we had exome sequencing of individuals or low-depth sequencing of populations.
Fast forward a few years, and now the costs have gone down significantly. Because of this, we are now starting to see more whole-genomes. Now, the costs of to sequence a whole-genome at 30X is about the same as targeted exome sequencing (due to the extra capture costs). If it's almost cheaper to just sequence the whole-genome and skip any extra capture probe biases, why not just sequence the whole thing? This is particularly true when looking at complex diseases where the underlying cause(s) may not be in the coding part of a gene.
So, in that regard, cost really was a bottleneck. With all of the new whole-genomes available, we can start to ask even tougher questions. And with more data available from more people, we can start to get some real confidence behind our predictions.
But, you are right in that it does all lead back to the data-analysis bottleneck. Before we had as more data than we could deal with. Now, we can process data faster, so that's good. But we also have significantly more data to process, so in the end, it's about even.
The other issue then becomes data storage. What do you do when you have a few hundred terabytes or a petabyte of sequencing data? S3 can get pretty expensive at that scale. Instead, it's probably just cheaper to store the DNA in a freezer, and if you ever need the data - just resequence it.
Edit: reworded a bit