I don't really know too much about the amount of clinical data 23andme collects and I don't know what SNP data they collect, so I can't say with certainty that they're less valuable than RGC, but I'd guess they are

My argument: If you are exploring genomic datasets to find new potential drug targets, then what you really want as the output are single genes that are very strongly associated with dramatic phenotypes. Drugs generally only hit one molecule, so you want a monogenic vs polygenic signal, and drugs can't hit every molecule of a given type in the body, so you want a large effect size (if you only hit 10% of the target you still want a meaningful clinical effect)

If you don't have clinical data, your genetic data is Not super valuable for target discovery. You can't correlate genetics to clinical outcomes. The more clinical data you have, the more valuable your dataset, because you can uncover more gene-phenotype correlations. So you need high quality medical records, lab data, etc. if you just have self reported data on a few diseases, you'd miss all sorts of potentially useful signals

The type of genetic analysis is also super important. 23 and me does "genotyping": they have a chip with like 500k-1M molecular probes. Each probe is designed to detect a specific "SNP" mutation, i.e. A mutation where one DNA "letter" is different. So this doesn't pick up other non-SNP mutations but that's not as important. What's more important is that there are like 10M+ (edit: prev said 360M) known SNPs and prob a lot more unknown ones. So with 23andme you are only exploring a small part of the genome

And this part of the genome is fairly well explored. While next gen sequencing is a newer tech, gene chips (what 23andme uses) have been around longer. Most common mutations have been studied. And 23andme is just studying those common mutations but at a larger scale

However larger scale isn't necessarily that great for target discovery. With larger database you can pick up 1) more relationships with small effect size and 2) more rare relationships with large effect size. Except 23andme is using a gene chip that detects mostly common mutations, and bc they have limited clinical data they will mostly have common clinical condistion in their dataset. So you can really just pick up a lot of common mutations with significant but not meaningful relationship w outcomes

If you use exome sequencing like RGC, you get much richer coverage of the protein coding genome than 23andme. So you can pick up rare mutations. And you have more clinical data (arguably having more clinical data per genome is more valuable than having more genomes), so you can pick up more gene-phenotype relationships. You need to scale your sample set so you can detect rare mutations, but do you need 5M people? The more the better but RGC has already yielded some promising targets w it's smaller dataset

RGC is also smart and targeted about the kind of patients they recruit. So there is less noise and more signal, so you don't need as many patients. For example they look at fairly genetically homogenous "founder populations" that have less background genetic variability. Like the Icelandic population -- PCSK9 was discerned by analyzing this pop

PCSK9 gene is a classic example: mutations in this gene are very strongly associated with extreme levels of LDL cholesterol. And the relationship works for both gain of function mutations and loss of function mutations, and the causality can be validated experimentally.

The effect is dramatic: patients with loss of function mutations in PSCK9 have like 10% of the normal level of LDL cholesterol

And it's a monogenic trait: you can get these extreme LDL levels just by modifying PCSK9

So this is a great target assuming you can design a molecule to "block" it (you can). You can create an antibody that can basically have the same effect as the mutation (keeping PCSK9 from doing its job) but on a smaller and less durable scale.

The drug worked at lowering LDL cholesterol. It has had mixed commercial success for a variety of reasons unrelated to its effectiveness of lowering cholesterol

So this was really the first drug discovered based on large scale genomics. Regeneron developed one of the two PCSK9 inhibitors on the market. They purpose-built RGC to find more of these