The answer to the question seems obvious; how else would offspring get genetic contributions from both parents? This answer just raises another question; why is it important that offspring be constructed from the genetic contribution of two, usually unrelated parents? The accepted answer to this second question is that combining the genes of two unrelated parents promotes genetic diversity, which is recognized to be health and fitness enhancing both in the individual and the population.
The findings of the Human Genome Project make this answer a little more complex.
Decoding the entire genome of a few selected individuals would be an epic achievement. This is not, however, what the Genome Project attempted to do. The Project’s mission was to sequence the entire DNA strand, and to turn that knowledge over to geneticists who would tackle the immense task of understanding the codes recorded there. Much of the scientific interest in the DNA sequences documented by the project has focused on the segments known as genes, the sections whose functions we want to understand in order to tackle genetic disease. These sections work as analog codes for the construction of proteins and make up less than 5% of our DNA. I call them analog genes.
The focus on these codes raises an obvious question. We are each of us so different, unique in so many physical ways. Shouldn’t the genes that create us be equally different, equally unique? And if each of our DNA strands are unique, how would decoding your DNA help to understand mine?
The analog genome revealed by project has turned out to be largely identical for all humans, at least in function. Given this deep similarity, decoding any individual’s analog genome opens a window into ALL human analog genes. Sequencing the analog genome however leaves much unanswered about those myriad physical differences, the unique features that allow us to recognize each other at a glance.
The genome project expected to find that the human genome contained at least twice as many analog genes as did those of relatives like chimpanzees. Assuming that we were far more complex than chimps they looked to find far more genes to create that complexity. What they found however was that we have the same rough number of genes as do chimps, or most animals. Further research has shown that we share nearly 99% of our analog genes with our chimpanzee cousins, and that only about 7% of known analog genes are specific to vertebrate life. This shows that decoding the human analog genome tells you a lot about snakes and cows as well.
There was evidence suggesting that our genes should be similar to those of other mammals. If our analog genes had been markedly different then animal medical studies would have made little sense. In practice, our metabolic similarities to our animal test subjects spring from our shared analog genes, genes which create metabolisms similar enough to make these studies relevant.
So while the project did catalogue the roughly 4% of our genome serving as analog codes for the production of the proteins in our bodies, the function of much of the remaining 96% of the DNA strand remains terra incognito. Once thought of as “junk DNA”, much evidence now suggests that it contains many important regulatory codes.
The proteins produced by our analog genes are “mission critical” to our bodies. They assemble the cells and tissues which make up our physical structures and run the metabolic operations that make us go. When these genes are mutated out of function we suffer or die.
The mission critical nature of protein coding makes the pairing of chromosomes mission critical to the individual. When individuals receive a flawed copy of an analog gene on the chromosome donated by one parent, they usually get an functional copy of that gene on the chromosome from the other, a copy that can fulfill the gene’s function and so save them from genetic disaster.
I call this buffering sexual masking; the capability for one parent’s genetic contribution to cover a flaw in the other’s. This genetic “work around” for a flawed analog gene is the obvious benefit to the individual of the sexual masking accomplished by paired chromosomes. The benefit to the population is a little less clear.
The existence of an escape hatch for bad genes poses a problem for analog gene driven models of evolution, models such as the “selfish gene” perspective.
These models see evolution as a contest between good and bad genes and are gene elimination models. They posit that the holders of “good” genes out compete, out reproduce, and eventually replace the holders of “bad” genes. In this competition between genes it would seem then to make little sense however to give a flawed and dangerous gene a reprieve through the action of paired chromosome. Masking the effect of a bad gene would allow its possessor to escape from metabolic or selective judgement, and allow it to pass that bad gene on to another generation.
This reality I suspect is explained by the Genome Project’s findings, which showed that the analog gene set is largely common and universal throughout a population. This finding suggests that the analog gene elimination model of evolution is flawed, suggests even that analog genes might play a relatively minor role in selection and adaptation.
On the other hand I think that the unexplained 96% of our chromosomes is the home for the algorithmic genetic sequences I have been writing about in my Medium pieces, DNA and the Bell Curve, and Lamarck Wasn’t All Wrong. I think that these hypothetical sequences are at the heart of natural selection. Unique to the individual and creating the range of physical differences on which natural selection works, the range that we readily see and on which facial recognition programs rely, I suspect that these algorithmic sequences largely drive evolution and the origin of species.
The information in these sequences would not be stored in the order of their nucleotides, as is the information in the analog genome, but instead stored numerically, in their relative lengths. When examined as sets, many of these relative lengths are always unique to the individual, and a numeric nature of their coding would make the information stored there uniquely malleable.
Change the order of nucleotides in an analog gene sequence and you risk destroying its function. Change the length of an algorithmic section on the other hand, and you would just incrementally change the shape or size of the physical feature recorded there. All regions of all chromosomes are regularly mutated, but mutated analog sections and algorithmic sequences would have different fates. Mutated analog genes are generally masked by their correct counterparts in the other member of the chromosome pair. Only when they are paired with another non-functional analog gene do they cause what we think of as genetic disease, taking down their unlucky owner and dropping out of the population genome.
Unlike analog sections, mutated algorithmic sequences wouldn’t lose functionality but instead would just incrementally change in the outcome of their action. The information recorded in them would be the timing and duration of developmental events. Changing the duration and timing of these events would change the the size and shape of the body parts constructed under their direction, making those parts larger or smaller, more or less curved, and in the process would move those parts closer to the population norm or further away from it.
As the lengths of these algorithmic sequences were regularly mutated and the morphological sizes and shapes recorded in them were incrementally changed, those changes would accumulate in the population, becoming over time a genetic savings account of physical variation.
This account would hold the physical options available to the population when selective conditions change or niche opportunities presented themselves. Preserving them would seem to be as mission critical to the population as the possession of one correct copy of each analog gene is to the individual. The existence of a genetic mechanism to preserve an analog gene time bomb in the population suggests that losing the unique algorithmic genome of the individual is more costly than discarding the time bomb.
This is a gene preservation and accumulation model, serving the reality that the selective features of an environment always change, sometimes with stark rapidity. And given the adaptive options that a fund of algorithms would grant a population to respond to selective changes, I think it’s likely that sexual masking works on the algorithmic level as well as the analog level.
As discussed earlier, algorithmic sequences seem to work in concert in the individual. Offspring don’t present as exact copies of either parent’s physical features, but rather as a blending or averaging of both parents attributes. This averaging should often work to reduce the physical effect of more outlier algorithms, should tend to move their outlier effects back towards the population norm in the offspring.
For example, the pairing of tall parent with an average height parent will usually produce offspring with height somewhere between the two. The taller parent’s algorithms for height will most often be averaged down by the shorter parent’s, and the child usually won’t be a copy of either.
When outlier traits are thus averaged back towards the norm, their owners can pass selective tests more easily. The center of the bell curve distribution of physical traits represents the currently most fit version of those traits, and algorithmic sexual masking can hold possessors of variant traits closer to that norm, keeping them selectively viable. On occasion however, outlier algorithms held by both parents will hypothetically match up in what I call a genetic rogue wave.
Surfers know about rogue waves, giant swells that dwarf the normal crests they are riding. Surfers ride waves that come in sets; you can see the riders lined up on their boards outside the break, rising over smaller swells as they wait for the the next set of six or eight good waves to come in. On rare occasions however, two or more sets will overlap, doubling or tripling the size of the waves and giving the ready surfer either a great ride or a real scare.
In similar fashion, parents with outlier physical traits that have been pulled back towards the norm by algorithmic averaging will sometimes pass overlapping outlier algorithms to an offspring, resulting in a genetic rogue wave that creates a much taller, shorter, stronger or smarter individual than either parent.
You can picture how some of the early ancestors of giraffes, who were just starting to reach up to browse the lower leaves of trees, passed reinforcing outlier neck algorithms to their offspring that created unusually long necks. In similar fashion, reinforcing outlier front leg algorithms would occasionally lengthened the bones, or make them more stout. This early start on adaption to the new food source would be limited only by the range and frequency of outlier algorithms held in their population. In a classic selection cascade, the animals themselves would carry out selective breeding on the basis of the success that longer necks and front legs were granting. Tall browsers who received overlapping outlier traits from their parents would pair up and produced offspring who were taller still.
Pairing taller individuals would further reinforce the algorithms for height, which are thought to be complex and made up from many different sequences, but other adaptations would be needed to support the changing body shape. Pumping blood to a higher head would require a stronger heart, heart shapes and sizes that are variable and hypothetically determined by algorithmic instructions. Once again a cascade of selection would begin, favoring heart algorithms that increased pumping power, and with animals exhibiting greater fitness granted by right sized circulatory equipment having more breeding success. Once again the re-pairing of these outlier circulatory traits would work to reinforce them further, creating rogue heart algorithm waves with greater frequency.
Stories of prodigy children are common, and such early talents often appear in the offspring of parents who are not similarly gifted, parents who haven’t given their prodigy offspring early training in the areas of the unique gift. There should be a physical, genetic explanation for this kind of inborn aptitude, the kind of talent that far exceeds that of the parents who contributed the genes.
Using myself as an example, I was the middle born of three children. Neither sibling was a great reader and my brother needed remedial help. I entered first grade at the youngest in my class, knowing little about reading. Parents back then were told not to teach their kids to read and my parents obeyed.
Within 8 weeks of starting first grade I was reading at an eighth grade level, mostly self taught, and the question arises; why was my brain so ready to master this skill? Both my parents were smart, though my dad, like my brother, was slow to pick up reading. My mom was musically gifted from an early age, but no family member on either side had a history similar to mine.
Reading requires interactive participation from many different areas of the brain, from the visual cortex to the language centers. Each of these areas is hypothetically the physical product of algorithmic instructions, which themselves are somehow a mixture of the contributions of each parent.
With inherited algorithms determining the size and organization of each brain area, those determinations would in turn effect its ability to decode letter symbols, to translate them into language, and to build images and and logical sequences from them. Both of my parents had talents related to language such as pattern recognition and logical sequencing. They seem to have passed on to me reinforcing algorithms in these and other areas that enhanced my reading potential. I could just as easily have gotten brain construction algorithms similar to my brother’s however, and have had similar difficulty with reading.
While algorithmic sexual masking could create the potential for physical breakaways to exploit new resources like that of the giraffes, it could also maintain a wider physical oscillation in a population to match oscillating environmental challenges. Darwin’s Finches on the Galapagos islands may well be an example.
As evolutionary researchers Rosemary and Peter Grant documented in the 1970’s and 80’s (The Beak of the Finch, Johnathon Weiner) the Galapagos finches seemed have to divided into species along relatively small divergences in beak size, differences that allowed each species to focus on a discrete set of seeds. The Grants spent long periods of time on a single island and with the aid of mist nets were able to capture, measure and band nearly every finch on that island, Daphne Major. Arriving at the onset of a major drought, and documenting the rapid change in species population as seed resources changed and diminished, they hoped that they were observing a selective event leading to a new species.
The island climate refused to cooperate, dramatically shifting back towards ample rainfall, and they were able then to record the population shifts back to suit this new norm. They came to realize that the El Nino/La Nina oscillations in the surrounding ocean put the islands through a cycle of drought, flood, and temperate climates, each with its own characteristic menu of food sources for the birds.
They then observed what they thought of as hybridization events, with birds considered to be separate species interbreeding, stout billed birds with finer billed birds, and further, chronicled much success for the resulting “hybrids”. Sexual selection during climatically stable times seemed to sort the beak algorithms back into the supposed discrete species but I suspect a more complex game was being played.
This reshuffling of beak size algorithms guaranteed the population would possess birds well suited to whatever new wrinkle their inherently unstable island environment threw at them. It also assured that they didn’t separate into distinct species that wouldn’t interbreed. All the various beak sizes were more fit than the others at particular times, but committing as a species to any one of them was a recipe for disaster.
The island environment was too resource poor for the finches to use a large population size to bank their algorithmic diversity. With a limited population they found a different and clever way to do so, a constant sorting and remixing of beak traits that guaranteed all degrees of variation simultaneously, and gave the highest probability of genetic accumulation and the lowest probability of genetic elimination.
All of these alternative adaptation strategies, from physical breakaways that exploit new resources to maintaining broad sweeps of diversity to cope with cycling environments, they are all made possible by the paired chromosomes of sexual reproduction. The species who seem to be static while in the equilibrium phase of punctuated equilibrium are actually doing the invisible work of preparing for the next punctuation, the accumulation of variation concealed to some degree by sexual masking in the algorithmic codes. Like ducks paddling along a glassy smooth pond, most of the work is going on under the surface.