Fascinating post, decimates all those idealistic myths and exposes the limits of understanding our own genome (so far...).
We’ve reached a stage where the mapping from genotype to phenotype is getting a bit on the baroque side. We have come to confront and wrestle with ‘genetic architecture.
Old fashioned quantitative genetics using statistical techniques based on family relationships is still a better bet for many traits and diseases (e.g., I have a family history of type 2 diabetes, but 23andMe gives me no greater risk). A group last year suggested a solution to the conundrum of why GWAS wasn’t picking most of the genetic variation: synthetic associations. Let me jump to their author summary:
It has long been assumed that common genetic variants of modest effect make an important contribution to common human diseases, such as most forms of cardiovascular disease, asthma, and neuropsychiatric disease. Genome-wide scans evaluating the role of common variation have now been completed for all common disease using technology that claims to capture greater than 90% of common variants in major human populations. Surprisingly, the proportion of variation explained by common variation appears to be very modest, and moreover, there are very few examples of the actual variant being identified. At the same time, rare variants have been found with very large effects. Now it is demonstrated in a simulation study that even those signals that have been detected for common variants could, in principle, come from the effect of rare ones. This has important implications for our understanding of the genetic architecture of human disease and in the design of future studies to detect causal genetic variants.
Why does it seems so synonymous to our economic theories sans those black Swans?
We’ve reached a stage where the mapping from genotype to phenotype is getting a bit on the baroque side. We have come to confront and wrestle with ‘genetic architecture.
Old fashioned quantitative genetics using statistical techniques based on family relationships is still a better bet for many traits and diseases (e.g., I have a family history of type 2 diabetes, but 23andMe gives me no greater risk). A group last year suggested a solution to the conundrum of why GWAS wasn’t picking most of the genetic variation: synthetic associations. Let me jump to their author summary:
It has long been assumed that common genetic variants of modest effect make an important contribution to common human diseases, such as most forms of cardiovascular disease, asthma, and neuropsychiatric disease. Genome-wide scans evaluating the role of common variation have now been completed for all common disease using technology that claims to capture greater than 90% of common variants in major human populations. Surprisingly, the proportion of variation explained by common variation appears to be very modest, and moreover, there are very few examples of the actual variant being identified. At the same time, rare variants have been found with very large effects. Now it is demonstrated in a simulation study that even those signals that have been detected for common variants could, in principle, come from the effect of rare ones. This has important implications for our understanding of the genetic architecture of human disease and in the design of future studies to detect causal genetic variants.
Why does it seems so synonymous to our economic theories sans those black Swans?
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