In the case of height, the first genetic association of any kind came out only a few months ago, with the publication of a SNP in the gene HMGA2. Though the SNPs mentioned in this post explain only a small percentage of variation in height population-wide, in particular cases they may account for a lot more than that. For example, two siblings may find that a substantial fraction of their height difference can be explained by these SNPs.

It’s also important to make a distinction between using SNPs as research tools as opposed to probes of a particular person’s genetics. It’s true that researchers have a limited ability to discover associations involving CNVs, rare variants, deletions, etc. using chips (though CNVs and deletions are within reach to some extent). But once those associations are found through other means 23andMe may be able to detect some of them with our custom chip.

These SNPs capture such a small proportion of the total variance that they provide no real useful data that might help you explain, for instance, which side of the family your tall stature came from, or how tall your kids are likely to be.

As for the brute force approach being the best way forward: researchers are likely to capture a little more of this variance by pulling together samples from existing studies and adding in new ones, but this approach will have rapidly diminishing returns.

That’s because current chip-based approaches can only capture common variants associated with height (or other complex traits, or common diseases). As I explained in the post linked to by Andrew, there are entire classes of genetic variation – such as rare variants, or copy-number variation – that probably underlie a major chunk of the variation in these traits but are essentially completely invisible to existing chips. They simply won’t be captured by chip-based approaches, regardless of how large the sample sizes are. (For the same reasons, they also won’t be tagged by the SNPs on 23andMe’s existing platform.)

That doesn’t completely negate the use of SNP chips by researchers or by personal genomics companies – they’re the best we have at the moment, unless you have a spare $350,000 to pay for whole-genome sequencing! But customers should be aware that these chips do (and always will) provide a seriously incomplete picture of the total genetic variation contributing to human traits and common disease risk.

Daniel.

But, as you’ve noticed, none of them is very big.

As Daniel MacArthur noted, genome-wide association studies have trouble finding SNPs that have really small effects. This was exactly the problem height researchers were facing. Height seems to be influenced by a huge number of variants that each only contribute a tiny effect.

As MacArthur notes, the solution to this problem is brute force – using really large numbers of subjects in the studies. And that’s exactly what the authors of the papers reported on here did. Two of the papers looked at more than 30,000 people!