I’ve been enjoying your evolution posts and wanted to toss in my own thoughts and see what I can learn.
“Our first lemma is a rule sometimes paraphrased as “one mutation, one death”.”
Imagine that having a working copy of gene “E” is essential. Now suppose a mutation creates a broken gene “Ex”. Animals that are heterozygous with “E” and “Ex” are fine and pass on their genes. Only homozygous “Ex” “Ex” result in a “death” that removes 2 mutations.
Now imagine that a duplication event gives four copies of “E”. In this example an animal would only need one working gene out of the four possible copies. When the rare “Ex” “Ex” “Ex” “Ex” combination arises then the resulting “death” removes four mutations.
In fruit fly knock-out experiments, breaking one development gene often had no visible affect. Backup genes worked well enough. The backup gene could have multiple roles: First, it has a special function that improves the animal fitness. Second, it works as a backup when the primary gene is disabled. The resulting system is robust since the animal can thrive with many broken copies and evolution is efficient since a single “death” can remove four harmful mutations.
I’ve focussed on protein-coding genes, but this concept also applies to short DNA segments that code for elements such as miRNA’s. Imagine that the DNA segment is duplicated. Being short, it is rarely deactivated by a mutation. Over time a genome may acquire many working copies that code for that miRNA. Rarely an animal would inherit no working copies and so a “death” would remove multiple chromosomes that “lacked” that DNA segment. On the other hand, too many copies might also be fatal. Chromosomes with too few or too many active copies would suffer a fitness penalty.
On a different note, imagine two stags. The first stag has lucked-out and inherited many alleles that improve its fitness. The second stag wasn’t so lucky and inherited many bad alleles. The first stag successfully mates and the second doesn’t. One “death” removed many inferior alleles.
Animals may have evolved sexual attraction based on traits that depend on the proper combined functioning of many genes. An unattractive mate might have many slightly harmful mutations. Thus one “death” based on sexual selection might remove many harmful mutations.
Evolution might be a little better than the “one mutation, one death” lemma implies. (I agree that evolution is an inefficient process.)
“This 1 bit per generation has to be divided up among all the genetic variants being selected on, for the whole population. It’s not 1 bit per organism per generation, it’s 1 bit per gene pool per generation.”
Suppose new allele “A” has fitness advantage 1.03 compared to the wild allele “a” and that another allele “B” on the same type chromosome has fitness advantage 1.02. Eventually the “A” and “B” alleles will be sufficiently common that a crossover creating a new chromosome “AB” with “A” and “B” alleles is likely (This crossover probability depends on the population sizes of “Ab” and “aB” chromosomes and the distance between the alleles). The new chromosome “AB” should have a fitness of 1.05 compared to the chromosome “ab”. Both “A” and “B” should then see an accelerated spread until the “ab” chromosomes are largely displaced. The rate would then diminish as “AB” displaced “Ab” and “aB” chromosomes. Thus multiple beneficial mutations of the same type chromosome should spread faster than the “single mutation” formula would indicate.
Due to crossover, good “bits” would tend to accumulate on good chromosomes thereby increasing the fitness of the entire chromosome as described above. The highly fit good chromosome thus displaces chromosome with many bad “bits”. The good “bits” are no longer inherited independently and each “death” can now select multiple information “bits”.
We seem to view evolution from a similar perspective.
Information requires selection in order to be preserved. The DNA information in an animal genome could be ranked in “fitness” value and the resulting graph would likely follow a power law. I.e., some DNA information is extremely important and likely to be preserved while most of the DNA is relatively free to drift. In a species such as fruit flies with many offspring selection can drive the species high up a local fitness peak. Much of the animal genome will be optimized. In a species such as humans with few offspring there is much less selection pressure and the specie gene pool wanders further from local peaks. More of the human genome drifts. (E.g., human regulatory elements are less conserved than rodent regulatory elements.)
I’ve been enjoying your evolution posts and wanted to toss in my own thoughts and see what I can learn.
“Our first lemma is a rule sometimes paraphrased as “one mutation, one death”.”
Imagine that having a working copy of gene “E” is essential. Now suppose a mutation creates a broken gene “Ex”. Animals that are heterozygous with “E” and “Ex” are fine and pass on their genes. Only homozygous “Ex” “Ex” result in a “death” that removes 2 mutations.
Now imagine that a duplication event gives four copies of “E”. In this example an animal would only need one working gene out of the four possible copies. When the rare “Ex” “Ex” “Ex” “Ex” combination arises then the resulting “death” removes four mutations.
In fruit fly knock-out experiments, breaking one development gene often had no visible affect. Backup genes worked well enough. The backup gene could have multiple roles: First, it has a special function that improves the animal fitness. Second, it works as a backup when the primary gene is disabled. The resulting system is robust since the animal can thrive with many broken copies and evolution is efficient since a single “death” can remove four harmful mutations.
I’ve focussed on protein-coding genes, but this concept also applies to short DNA segments that code for elements such as miRNA’s. Imagine that the DNA segment is duplicated. Being short, it is rarely deactivated by a mutation. Over time a genome may acquire many working copies that code for that miRNA. Rarely an animal would inherit no working copies and so a “death” would remove multiple chromosomes that “lacked” that DNA segment. On the other hand, too many copies might also be fatal. Chromosomes with too few or too many active copies would suffer a fitness penalty.
On a different note, imagine two stags. The first stag has lucked-out and inherited many alleles that improve its fitness. The second stag wasn’t so lucky and inherited many bad alleles. The first stag successfully mates and the second doesn’t. One “death” removed many inferior alleles.
Animals may have evolved sexual attraction based on traits that depend on the proper combined functioning of many genes. An unattractive mate might have many slightly harmful mutations. Thus one “death” based on sexual selection might remove many harmful mutations.
Evolution might be a little better than the “one mutation, one death” lemma implies. (I agree that evolution is an inefficient process.)
“This 1 bit per generation has to be divided up among all the genetic variants being selected on, for the whole population. It’s not 1 bit per organism per generation, it’s 1 bit per gene pool per generation.”
Suppose new allele “A” has fitness advantage 1.03 compared to the wild allele “a” and that another allele “B” on the same type chromosome has fitness advantage 1.02. Eventually the “A” and “B” alleles will be sufficiently common that a crossover creating a new chromosome “AB” with “A” and “B” alleles is likely (This crossover probability depends on the population sizes of “Ab” and “aB” chromosomes and the distance between the alleles). The new chromosome “AB” should have a fitness of 1.05 compared to the chromosome “ab”. Both “A” and “B” should then see an accelerated spread until the “ab” chromosomes are largely displaced. The rate would then diminish as “AB” displaced “Ab” and “aB” chromosomes. Thus multiple beneficial mutations of the same type chromosome should spread faster than the “single mutation” formula would indicate.
Due to crossover, good “bits” would tend to accumulate on good chromosomes thereby increasing the fitness of the entire chromosome as described above. The highly fit good chromosome thus displaces chromosome with many bad “bits”. The good “bits” are no longer inherited independently and each “death” can now select multiple information “bits”.
We seem to view evolution from a similar perspective.
Information requires selection in order to be preserved. The DNA information in an animal genome could be ranked in “fitness” value and the resulting graph would likely follow a power law. I.e., some DNA information is extremely important and likely to be preserved while most of the DNA is relatively free to drift. In a species such as fruit flies with many offspring selection can drive the species high up a local fitness peak. Much of the animal genome will be optimized. In a species such as humans with few offspring there is much less selection pressure and the specie gene pool wanders further from local peaks. More of the human genome drifts. (E.g., human regulatory elements are less conserved than rodent regulatory elements.)