I initially had a few small bits of critique on the facts provided alongside their argument. However, as it went on, I realized it was just an ungrounded tirade based on a loose understanding of a (admittedly slightly overhyped) field. Especially telling, was the launch into inconsistent criticisms of liberals, "big pharma", and genetics for...wtf?

scientific explanations for rape and patriarchy

The author seems to have an impression of the field of genetics that is simply not reflective of what those scientists do or teach. Let's start with the basics:

For a trait such as height, there are about 180 SNPs known to contribute to human height variation

As the author partially pointed out, for each of these associations, you have: Quantitative association (i.e. 0.3-0.5 cm explainable difference), speculation on gene function, speculation on difference between alleles, and analysis of how the findings fits in to the larger picture (i.e. The known mutations explain 10-20% of the difference in height between these groups).

Scientists have observed approximately 12 million SNPs in human populations

Not just that, they look at Tag SNPs, which are SNPs located within separate segments of DNA. Scientists noticed that genetic recombination, the process by which DNA fragments as swapped between chromosomes, occurs at hot spots. Between these hot spots, you have continuous fragments of DNA that are passed down without being scrambled. The Tag SNPs are selected and conserved SNPs that are spread out evenly across these fragments. So, rather than sequencing your entire genome, or every SNP, you pick spots ones that serve as proximity detectors to mutants in nearby genes.

Once you have a suspect region, you knuckle down and sequence it fully. Now, there's a million ways to knock-out or knock-down a protein. You see there are many different mutations in the promoter, frameshifts in the coding region, a missense on top of the terminator. Then you form a model relating these mutants to phenotype (No BRAC1? Cancer risk increased X%. 40+ Q repeats? High risk of Huntington's.

Find an ill-defined trait (like political preference). Find a gene that is statistically overrepresented in the sub-population that “possesses” that trait. Declare victory.

Author missed a few steps: Fail to be published in a journal. Be ignored by field. See no change to the world.

All this is to say that though heritability is a useful concept, it is an abstraction — one that depends entirely on the statistical models (with all their assumptions and prejudices) we use to define it.

It's not just an abstraction. Heritability is defined in both a broad sense (Variation between twins) and a narrow sense (Variation between parent and child). Certain genetically identical crops will grow to nearly the exact same height, under the same environmental conditions. Different strains may grow to different heights in the same soil / sun / air. Those differences can be attributed to genetics, and with some work, you can break down which genes have what quantitative effect.

Currently, GWAS are able to explain only about 6% of this heritability, with no loci (genes) particularly predictive for whether an individual will develop diabetes

It attempts to explain the heritable DNA mutations, to say nothing of mutations in regulatory genes and epigenetic markers. Note how that whole "abstract heritability" thing becomes useful here in establishing the limitations of the results. Scientists studying genetics and genomics know there is still plenty of work to be done.

Putting the validity of IQ tests aside for a moment, studies show a long and sustained increase in IQ scores over the course of the twentieth century (the Flynn Effect), pointing to the importance of environment rather than genetics in determining IQ.

This is literally a textbook example on what happens when you don't properly control your test. Not only do controls eliminate IQ differences between races, but adopted children, raised in environments independent of their parents show no correlation in IQ:

These results convey a cautionary message for whether, how, and how soon molecular genetic data can contribute to, and potentially transform, research in social science. We propose some constructive responses to the inferential challenges posed by the small explanatory power of individual SNPS.

The sheer hubris speaks for itself

"Cautionary message", "constructive responses", how the hell is that hubris?

The author is greatly exaggerating the danger of GWAS. The results of association carry with them the quantification of effect. And often they serve as amazing springboards for investigation into the molecular biology underlying disease.

The issue isn't with the scientists, it's with the media and amateur bloggers who regularly misinterpret their results. Obligatory