When I decided to have my genome sequenced, I went with Illumina's Understand Your Genome. This was a project they had to basically get rich execs to have their genome sequenced, learn their risks, and then invest in VCs investing in genomic startups (although it was just marketed as a product, Illumina had a larger goal).
I had my blood taken and a whole genome sequence- a 50GB file of reads off the machine, along with variant call files that should show how I differ from the reference genome, and another variant file that called out risky variants.
You can download the files (https://my.pgp-hms.org/profile/hu80855C).
When you did UYG you'd go to this fancy spa in La Jolla and they give you an iPad with the files and you also talk to some genetic counselors.
I made a number of interesting observations when talking ot the counselors. The first is that they said they were confused by my report because it said I had absolutely no known risk variants (apoe, bcl, etc) and they had never seen that before. They also said, when they see some rare v ariants that they would just google for the variant and read random papers. What they said convinced me that genetic counselors, and genome tests in general, have limited applicability- there are a few genes where variants are clearly associated with negative disease outcomes, and the tests for those are very valuable (This is especially true for cancer, but other diseases as well) because they are clinically actionable.
But it also showed to me that counselors are making up garbage, because scanning the raw literature for variants and assuming that because a person has that variant they will be at risk, is not a good assumption. In my mind, the variations on polygenic risks scores have convinced me that we need to build large-scale (whole-genome) models of disease that use nonlinear functions trained on extremely large-scale datasets (like UKBB) to build up wholistic predictive models that do a better job of encapsulating the complexity of biology and its relation to disease, to the point where we can actually start making useful treatments and cures for a wide-range of genetically determined diseases.
>In my mind, the variations on polygenic risks scores have convinced me that we need to build large-scale (whole-genome) models of disease that use nonlinear functions trained on extremely large-scale datasets (like UKBB) to build up wholistic predictive models that do a better job of encapsulating the complexity of biology and its relation to disease, to the point where we can actually start making useful treatments and cures for a wide-range of genetically determined diseases.
This is exactly where AI is going in the biomedical space. However, there's more than just the genome, you need to integrate multi-omics and some of the necessary tech hasn't been invented yet.
I had my blood taken and a whole genome sequence- a 50GB file of reads off the machine, along with variant call files that should show how I differ from the reference genome, and another variant file that called out risky variants. You can download the files (https://my.pgp-hms.org/profile/hu80855C).
When you did UYG you'd go to this fancy spa in La Jolla and they give you an iPad with the files and you also talk to some genetic counselors.
I made a number of interesting observations when talking ot the counselors. The first is that they said they were confused by my report because it said I had absolutely no known risk variants (apoe, bcl, etc) and they had never seen that before. They also said, when they see some rare v ariants that they would just google for the variant and read random papers. What they said convinced me that genetic counselors, and genome tests in general, have limited applicability- there are a few genes where variants are clearly associated with negative disease outcomes, and the tests for those are very valuable (This is especially true for cancer, but other diseases as well) because they are clinically actionable.
But it also showed to me that counselors are making up garbage, because scanning the raw literature for variants and assuming that because a person has that variant they will be at risk, is not a good assumption. In my mind, the variations on polygenic risks scores have convinced me that we need to build large-scale (whole-genome) models of disease that use nonlinear functions trained on extremely large-scale datasets (like UKBB) to build up wholistic predictive models that do a better job of encapsulating the complexity of biology and its relation to disease, to the point where we can actually start making useful treatments and cures for a wide-range of genetically determined diseases.