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> The most recent estimate for how many genes are involved in complex traits like height or intelligence is approximately “all of them” – by the latest count, a

> The most recent estimate for how many genes are involved in complex traits like height or intelligence is approximately “all of them” – by the latest count, about twenty thousand. From this side of the veil, it all seems so obvious. It’s hard to remember back a mere twenty or thirty years ago, when people earnestly awaited “the gene for depression”. I wonder how much that's just a technicality, in the same way you could say the inverse square law for gravitation is wrong because really every massive particle has some influence on every other particle, etc. So maybe it's the case that every gene is involved in every trait, but maybe there's a handful that account for 99% of what we care about in that trait? (Then again, I can imagine for something like intelligence that most of the genome is really involved—height though?) EDIT: I had some remarks about the term 'gene' here that were incorrect and turned into a useless diversion.

dragonwriter 8y ago
> I'm using 'gene' as it's used in the quote, but from my understanding of the term it seems like it becomes meaningless if the author's statement were true It's not; the authors statement seems to use what I udnerstand to be the standard definition used in the field, where a gene is sepcifically a sequence which codes for the synthesis of a particular protein or nucleic acid sequence, which is essentially the lowest level “simple” trait. This is perfectly consistent with large and overlapping sets of genes being involved in determining each of the complex traits that humans mostly care about, and does not make the term “gene” meaningless.
- westoncb 8y ago
  I see. That make sense. I had thought we just used it to refer to genome sections related to 'macro traits' (not sure of a better term) we're interested in.
  - dragonwriter 8y ago
    That is a common use, which shouldn't be surprising because it's how we built a theory around genetics even before we had any idea about DNA. It just turns out in practice that the idea of single distinct unit of heredity, while it works as a close enough approximation for the traits that were observed to establish genetic theory, doesn't work for lots of traits of interest.
deleted 8y ago
[deleted]
lacker 8y ago
It's more like, early on we found some traits like "blood type" that really did correspond closely to a small number of genes. So we theorized they all worked like that. Now, we know that however height works, it doesn't work like blue eyes do.
- deleted 8y ago
  [deleted]
skybrian 8y ago
There is apparently a single gene variant that some short Peruvians have that (with two copies) can make a 4 cm difference. [1] But that's the extreme case. The next 700 genes explain 7% of their height. Generally speaking, most genes individually make such a small difference that it takes a huge dataset of genomes to find them. [1] http://www.sciencemag.org/news/2018/05/study-short-peruvians-reveals-new-gene-major-impact-height http://www.sciencemag.org/news/2018/05/study-short-peruvians...
- jon_richards 8y ago
  They are probably only applying the 700 genes as single factor interactions. I doubt all genes affect height. It only takes about 33 gene variants to have more genetic possibilities than there are people on earth. If they interact non-linearly, they are almost impossible to find by genetic surveys. A fundamental problem with genetic analysis is that gene variants are categorical variables that often have non-linear effects, but predictive power goes down drastically as soon as you start looking for multi-factor effects. You have to narrow down the multi-factor search space, possibly by narrowing it down to genes that have a linear effect, but that could still be too large and you could easily miss genes that on average have no effect. I wrote a critique of a paper using this method. I proposed studying the distribution of variant data's residual distance from a single-factor linear fit. Consider a variant that has a positive effect on height when combined with another variant, but a negative effect otherwise. The single-factor linear fit will assign it 0 effect, but residuals involving the variant will be unusually large, positive when the second variant is present and negative when it is not. My critique found several variants fitting this description (after a bonferroni correction), but i didnt have the compute power or time to rerun the 2nd order interactions with my findings. But hey, I got a B on that paper, so maybe it's a terrible idea.
  - waserwill 8y ago
    This is indeed a massive problem with GWA (genome-wide association), since the studies only see additive variation. Lot of interesting techniques around to look at non-additive variation. Had acquaintances working on drug-drug interactions and the genetics of skin colour; had to find some cool workarounds. On the other hand, given that height is closely linked to metabolic conditions, and both the transcription/translation rate and functioning of every cellular component relies on metabolism, it doesn't take long for changes to propagate to simple traits like height.

dragonwriter 8y ago

> I'm using 'gene' as it's used in the quote, but from my understanding of the term it seems like it becomes meaningless if the author's statement were true It's not; the authors statement seems to use what I udnerstand to be the standard definition used in the field, where a gene is sepcifically a sequence which codes for the synthesis of a particular protein or nucleic acid sequence, which is essentially the lowest level “simple” trait. This is perfectly consistent with large and overlapping sets of genes being involved in determining each of the complex traits that humans mostly care about, and does not make the term “gene” meaningless.

westoncb 8y ago
I see. That make sense. I had thought we just used it to refer to genome sections related to 'macro traits' (not sure of a better term) we're interested in.
- dragonwriter 8y ago
  That is a common use, which shouldn't be surprising because it's how we built a theory around genetics even before we had any idea about DNA. It just turns out in practice that the idea of single distinct unit of heredity, while it works as a close enough approximation for the traits that were observed to establish genetic theory, doesn't work for lots of traits of interest.

deleted 8y ago

[deleted]

lacker 8y ago

It's more like, early on we found some traits like "blood type" that really did correspond closely to a small number of genes. So we theorized they all worked like that. Now, we know that however height works, it doesn't work like blue eyes do.

deleted 8y ago
[deleted]

skybrian 8y ago

There is apparently a single gene variant that some short Peruvians have that (with two copies) can make a 4 cm difference. [1] But that's the extreme case. The next 700 genes explain 7% of their height. Generally speaking, most genes individually make such a small difference that it takes a huge dataset of genomes to find them. [1] http://www.sciencemag.org/news/2018/05/study-short-peruvians-reveals-new-gene-major-impact-height http://www.sciencemag.org/news/2018/05/study-short-peruvians...

jon_richards 8y ago
They are probably only applying the 700 genes as single factor interactions. I doubt all genes affect height. It only takes about 33 gene variants to have more genetic possibilities than there are people on earth. If they interact non-linearly, they are almost impossible to find by genetic surveys. A fundamental problem with genetic analysis is that gene variants are categorical variables that often have non-linear effects, but predictive power goes down drastically as soon as you start looking for multi-factor effects. You have to narrow down the multi-factor search space, possibly by narrowing it down to genes that have a linear effect, but that could still be too large and you could easily miss genes that on average have no effect. I wrote a critique of a paper using this method. I proposed studying the distribution of variant data's residual distance from a single-factor linear fit. Consider a variant that has a positive effect on height when combined with another variant, but a negative effect otherwise. The single-factor linear fit will assign it 0 effect, but residuals involving the variant will be unusually large, positive when the second variant is present and negative when it is not. My critique found several variants fitting this description (after a bonferroni correction), but i didnt have the compute power or time to rerun the 2nd order interactions with my findings. But hey, I got a B on that paper, so maybe it's a terrible idea.
- waserwill 8y ago
  This is indeed a massive problem with GWA (genome-wide association), since the studies only see additive variation. Lot of interesting techniques around to look at non-additive variation. Had acquaintances working on drug-drug interactions and the genetics of skin colour; had to find some cool workarounds. On the other hand, given that height is closely linked to metabolic conditions, and both the transcription/translation rate and functioning of every cellular component relies on metabolism, it doesn't take long for changes to propagate to simple traits like height.