Thanks, this is exciting and inspiring stuff to learn about!
I guess another thing I’m wondering about, is how we could tell apart genes that impact a trait via their ongoing metabolic activities (maybe metabolic is not the right term… what I mean is that the gene is being expressed, creating proteins, etc, on an ongoing basis), versus genes that impact a trait via being important for early embryonic / childhood development, but which aren’t very relevant in adulthood. Genes related to intelligence, for instance, seem like they might show up with positive scores in a GWAS, but their function is confined to helping unfold the proper neuron connection structures during fetal development, and then they turn off, so editing them now won’t do anything. Versus other genes that affect, say, what kinds of cholesterol the body produces, seem more likely to have direct impact via their day-to-day operation (which could be changed using a CRISPR-like tool).
Do we have any way of distinguishing the one type of genes from the other? (Maybe we can just look at living tissue and examine what genes are expressed vs turned off? This sounds hard to do for the entire genome...) Or perhaps we have reason to believe something like “only 20% of genes are related to early development, 80% handle ongoing metabolism, so the GWAS --> gene therapy pipeline won’t be affected too badly by the dilution of editing useless early-development genes”?
I guess another thing I’m wondering about, is how we could tell apart genes that impact a trait via their ongoing metabolic activities (maybe metabolic is not the right term… what I mean is that the gene is being expressed, creating proteins, etc, on an ongoing basis), versus genes that impact a trait via being important for early embryonic / childhood development, but which aren’t very relevant in adulthood.
Yes, this is an excellent question. And I think it’s likely we could (at least for the brain) thanks to some data from this study that took brain biopsies from individuals of varying stages of life and looked at the transcriptome of cells from different parts of the brain.
My basic prior is that the effect of editing is likely to be close to the same as if you edited the same gene in an embryo iff the peak protein expression occurs in adulthood. Though there aren’t really any animal experiments that I know of yet which look at how the distribution of effect sizes vary by trait and organ.
Thanks, this is exciting and inspiring stuff to learn about!
I guess another thing I’m wondering about, is how we could tell apart genes that impact a trait via their ongoing metabolic activities (maybe metabolic is not the right term… what I mean is that the gene is being expressed, creating proteins, etc, on an ongoing basis), versus genes that impact a trait via being important for early embryonic / childhood development, but which aren’t very relevant in adulthood. Genes related to intelligence, for instance, seem like they might show up with positive scores in a GWAS, but their function is confined to helping unfold the proper neuron connection structures during fetal development, and then they turn off, so editing them now won’t do anything. Versus other genes that affect, say, what kinds of cholesterol the body produces, seem more likely to have direct impact via their day-to-day operation (which could be changed using a CRISPR-like tool).
Do we have any way of distinguishing the one type of genes from the other? (Maybe we can just look at living tissue and examine what genes are expressed vs turned off? This sounds hard to do for the entire genome...) Or perhaps we have reason to believe something like “only 20% of genes are related to early development, 80% handle ongoing metabolism, so the GWAS --> gene therapy pipeline won’t be affected too badly by the dilution of editing useless early-development genes”?
Yes, this is an excellent question. And I think it’s likely we could (at least for the brain) thanks to some data from this study that took brain biopsies from individuals of varying stages of life and looked at the transcriptome of cells from different parts of the brain.
My basic prior is that the effect of editing is likely to be close to the same as if you edited the same gene in an embryo iff the peak protein expression occurs in adulthood. Though there aren’t really any animal experiments that I know of yet which look at how the distribution of effect sizes vary by trait and organ.