Nanopore sequencing very much has the potential to deliver this personalized treatment, without looking at any human genes or panels. If we could rapidly sequence bacteria in the bloodstream and predict their antimicrobial susceptibilities, we can make a difference.
What I'm saying is that nobody has delivered on any of the huge claims about the genome which genomicists made for the last 20 years, specifically in terms of actionable human health.
it's time to start calling the bluff.
I mean. Sure, sequencing the human genome didn't solve our problem overnight, and you can't sequence a genome at a vending machine for a nickel to tell your future, but I think there has been an avalanche of medical data derived from the genome and that is only continue to get bigger.
Now that we are really starting to figure out the polygenic risks and the single deleterious variants and their links with phenotype, people will have a much better picture of what their future might hold (and how to prevent it).
I don't think it was ever a bluff. The problem just turned out harder than we thought it was going to be.
I had my genome sequenced a few years ago by Illumina. They had a big slick presentation, blah blah blah, ApoE1, etc. When the genetic counsellors came to my genome they said "huh. you don't have any risk factors". I checked and each of their risks was from an existing gene panel, so the WGS wasn't valuable (it's on PGP, if you want to work with it https://my.pgp-hms.org/profile/hu80855C).
I talked in more detail with the counsellors. Turns out, whenever they saw a novel variant that wasn't covered by a gene panel they were googling the variant and skimming the abstracts of papers.
It was at that point I realized the difference between research, PR, and actionable medical data.
Fair.
I've done my as well. Most of the "company" sites don't tell you much, which I think is a legal thing. They aren't cleared to release clinical predictions from genotypes, so they just... don't. I ended up running my through promethease (which mines SNPepedia) and found quite a bit more than what was reported.
I work with some certified clinical geneticists and yeah they do take a much closer look, but at the end of the day its all just sequencing and interpretation. I think its mostly just safeguards to keep bad actors at bay.
PGP looks interesting. I see that you submitted phenotype data. I didn't know they had a questionnaire with that. That's actually really interesting. I need to see what kind of questions they ask.
The following have been revolutionized by the human genome project and subsequent technological innovation in sequencing:
-Non-invasive prenatal diagnostics
-Screening for cancer with cell-free DNA
-Rapid and accurate diagnostics for children with suspected genetic disorders
-Targeted cancer therapeutics
Many of these are already in routine clinical use in high income countries and result in significant improvement in human health.
I worked in genomics for 20 years. I have deep knowledge of biology and medicine. And the reality is, for the amount of money invested, the actionable medical returns have been relatively tiny and industry continues to not invest in sequencers for a good reason.
I agree with this, but I disagree with the following:
> most of the progress did NOT come from HGP data.
Without HGP (Human Genome Project), many biological discoveries in the past two decades would have become much more difficult.
> it's a huge waste of investment until we understand the multigenicity of diseases better
If you don't invest, you will never approach a solution. Applied science goes nowhere without a solid foundation in basic science.
NIPT uses low-coverage sequencing to identify aneuploidies for chromosomes 13,18,21 and some larger microdeletion syndromes - this is not WGS.
Cell free cancer screening is panel based and assays specific, known driver mutations.
Rare disease diagnostics can be WGS based (and some of the rapid 48h WGS studies of NICU babies are compelling from a technical standpoint) but most diagnoses identified via WGS can also be found via WES + chromosomal microarray.
Targeted cancer therapeutic target identification is panel based for most patients, as WGS doesn't identify too many targets for FDA-approved therapies that a panel + IHC + FISH + fusion testing won't.
But agreed, it is about time we start to understand regulatory regions better. But that will require gathering more WGS data, and indeed most data is Whole Exome or Panel.
Source: Am MD and practice laboratory medicine.
The other thing you have to realize is that because of the regulatory burden, it takes a while for these tools to make it into practice. Many of the successful genetic tests today were approved 20 years ago. Look up Oncotype Dx which is used in a huge % of breast cancer surgery, for example. WGS and WES will undoubtedly be far superior but it takes a while to get these things into practice.
HLA associations with autoimmune disorders are extraordinarily strong. Same applies to infectious diseases, vaccine efficiency and checkpoint inhibitor efficiency.
While you can type HLA with classical techniques, the only really reliable way is really to use long reads.
Same applies to CYP enzyme superfamily, where variation is linked to some rare drug toxicity events for example.
We should all know our HLA and our CYP genotypes. Why 23andme does not even attempt to impute HLA is beyond my understanding.
I have consulted to National Marrow Donor Program/Be The Match [0] off and on for several years. There are typing labs using long reads but most reporting/matching/analysis is still performed at the nomenclature level [1].
I hope in the near future we'll be able to simply assemble the entire MHC for each sample, as messy as it might be, see e.g., "A diploid assembly-based benchmark for variants in the major histocompatibility complex" [2].
[0] https://bethematch.org [1] https://www.ebi.ac.uk/ipd/imgt/hla [2] https://www.nature.com/articles/s41467-020-18564-9
But we know less associations about them. Same applies to TCR genes. A chicken-and-egg problem, we need good massive GWAS to find out.
My background is in CS, AI and statistics. But I've done lots of graduate research in genetics and epigenetics. I'm very interested in understanding the interactions between HLA and commensal / pathogen epitopes in health & disease. Also in vaccine design.
How about you? I can see from your posts you are with the Big Data Genomics team at UC Berkeley AMPLab.