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Are mutations accumulating far faster than selected out?

Are mutations accumulating far faster than selected out?


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I just want to see the mutation accumulation rate in human populations. Versus the rate at which mutations are selected out. Just wanted to check if the genome is deteriorating


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Is the rate at which new mutations occurring higher than the rate at which alleles fix today in humans?

Human populations are exploding! Genetic diversity within a population is a function of the effective size of its population. As the population expansion in human is extremely recent, we ought to expect that our genetic diversity is much lower than what would be expected from our population size and structure. Therefore, yes, genetic diversity is currently increasing, or in other words, more mutations appear than there are alleles that fixed.

Is the genome deteriorating?

I am not sure what you mean by "the genome is deteriorating" but the answer to "Is the genome deteriorating?" is likely no!

Actually, larger populations tend to suffer from less drift load and as such our mean fitness would rather be increasing. However, given the population size, I doubt that would make such an important difference.

On the other hand, medicine is greatly affecting our environment and is reducing the strength of purifying selection. As a consequence, modern populations would eventually be less fit than our ancestors if placed in the environment experienced by our ancestor (that is an environment with little to no medicine). But this will not be true in our current environment.

Prerequisite to understand the answer

This answer felt quite introductory but it actually contains all the following concepts:

  • Allele
  • Allele fixation
  • Genetic drift
  • Effective population size
  • Population structure
  • Drift load
  • Purifying selection
  • Genetic diversity (typically defined as the expected heterozygosity)
  • Effective population size
  • Environment (and even niche construction)

If any of the above concept is unclear to you (and I very much suspect that at least the term "drift load" will be new to you), then you might want to start with an intro course to evolutionary biology and population genetics.


Yes it is of course deteriorating, as can be proven with simple math, see for example Lynch (2009) or this later paper by Lynch (2016).

Thus, although there is considerable uncertainty in the preceding numbers, it is difficult to escape the conclusion that the per-generation reduction in fitness due to recurrent mutation is at least 1% in humans and quite possibly as high as 5%. Although such a mutational buildup would be unnoticeable on a generation timescale, over the course of a couple of centuries (approximately six generations), the consequences are likely to become serious, particularly if human activities cause an increase in the mutation rate itself (by increasing levels of environmental mutagens). A doubling in the mutation rate would imply a 2% to 10% decline in fitness per generation, and by extension, a 12% to 60% decline in 200 years.


Mutation rate

In genetics, the mutation rate is the frequency of new mutations in a single gene or organism over time. [2] Mutation rates are not constant and are not limited to a single type of mutation, therefore there are many different types of mutations. Mutation rates are given for specific classes of mutations. Point mutations are a class of mutations which are changes to a single base. Missense and Nonsense mutations are two subtypes of point mutations. The rate of these types of substitutions can be further subdivided into a mutation spectrum which describes the influence of the genetic context on the mutation rate. [3]

There are several natural units of time for each of these rates, with rates being characterized either as mutations per base pair per cell division, per gene per generation, or per genome per generation. The mutation rate of an organism is an evolved characteristic and is strongly influenced by the genetics of each organism, in addition to strong influence from the environment. The upper and lower limits to which mutation rates can evolve is the subject of ongoing investigation. However, the mutation rate does vary over the genome. Over DNA, RNA or a single gene, mutation rates are changing.

When the mutation rate in humans increases certain health risks can occur, for example, cancer and other hereditary diseases. Having knowledge of mutation rates is vital to understanding the future of cancers and many hereditary diseases. [4]


Why Males Are Biology's Riskier Sex

Robert D. Martin is emeritus curator of biological anthropology at the Field Museum in Chicago, a member of the Committee on Evolutionary Biology at the University of Chicago, and academic guest at the Institute of Evolutionary Medicine at the University of Zürich. His most recent book is How We Do It: The Evolution and Future of Human Reproduction.

This may be surprising to some: A woman's age is not alone in affecting pregnancy and birth, despite the impression often given.

Reviewing Paul Raeburn's book Do Fathers Matter?, Tabitha Powledge wrote:

"Everybody knows that older mothers run higher risks of a baby with birth defects — Down syndrome being the most common and best-known. By comparison, hardly anybody knows that the older Dad gets, the riskier it is for him to conceive a child."

Partners age together, so a fetus or baby with an older mother will mostly have an older father, too. Logic demands exploration of age effects in both sexes. Though few and far between, such studies do indeed reveal that both men and women contribute.

With Down syndrome, age effects for fathers and mothers are roughly balanced. But new data clearly show that, when it comes to inherited defects, fathers actually carry greater risks than mothers. Random changes in DNA — mutations — accumulate four times faster in sperms than in eggs.

Charles Darwin and Alfred Russel Wallace realized that variety is not just the spice of life it is the very essence. Inherited differences between individuals are the raw material for natural selection. And the prime source of natural variation in genes is new mutations. These have been studied intensively, notably regarding rates of change. Yet mutation also has a dark side because it can produce adverse effects along with variety. Hence, the mutation rate has fundamental implications for medical genetics as well as for evolutionary biology.

Yet researchers rarely distinguish male and female contributions when calculating mutation rates. One reason for this is that accurate measurement demands large samples. This became feasible with sperms once suitable methods were developed. After all, an average human ejaculate contains 250 million sperms. But a woman can produce only a few hundred eggs over her entire lifetime, so large samples are virtually impossible. On the female side, therefore, analyses have so far relied heavily on indirect evidence.

But a key paper published in Nature by Hákon Jónsson and colleagues, including impressively large samples of both women and men, has dramatically confirmed mounting indications of major differences in mutation rate between the sexes — between sperms and eggs. Analysis of entire nuclear genome sequences from a large database for thousands of Icelanders clearly showed that mutations accumulate at significantly different rates in sperms and eggs.

Comparisons homed in on more than 1,500 "three-generation families," each including a couple, their parents and at least one child, permitting the researchers to tell whether mutations stemmed from either the father or the mother. Across all individuals, they identified more than 100,000 independent mutations, tracing the parent of origin for almost half of them. Estimated mutation rates increased steadily with parental age, but the average yearly increase for fathers was consistently four times greater than for mothers. One clear take-home message is that, genetically speaking, women are the conservative sex, while men are the risk-takers.

As Hákon Jónsson and colleagues noted, their results neatly fit starkly different development patterns for eggs and sperms. Because of DNA copying errors, mutations are far more likely when cells divide. A fourfold higher mutation rate for sperms in fathers than for eggs of mothers, at all ages, matches the fact that in a man's testes sperm starter cells (spermatogonia) divide continuously throughout life. Geneticist James Crow calculated numbers of cell divisions needed to form a sperm at any given age. He estimated that some three dozen divisions occur between conception and puberty, when sperm production begins. For every year after puberty, he counted another two dozen cell divisions, yielding a total of more than 1,000 by age 60.

Eggs, by contrast, develop a few at a time from starter cells (oogonia) in the ovary. Female mammals typically start out with a basic stock that is gradually depleted throughout life. Human ovaries begin to develop in the fetus during the eighth week of pregnancy and halfway through fetal development numbers of starter cells peak at around seven million. But by birth that supply has already shrunk to around two million. Crow estimated that a human egg starter cell is generated by only two dozen cell divisions during fetal life. Numbers of starter cells steadily decline until around 38 years of age, when some 25,000 are left. Loss then accelerates until menopause begins, when only 1,000 remain. Between puberty and menopause only a few hundred starter cells develop into mature eggs for release. The number of sperms in a single human ejaculate is half a million times greater than the total number of mature eggs that a woman's ovaries release during her lifetime!

The bottom line from the findings reported by Jónsson and colleagues is that children inherit many more mutations from their dads than from their moms.

These findings also have far wider implications that will resonate for some time. Take, for example, a long-standing puzzle with mitochondria. These tiny power houses of the cell are derived from once free-living bacteria that became residents in early organisms with a cell nucleus more than 1.5 billion years ago. Reflecting this origin, each mitochondrion carries a few copies of its own genome, a stripped-down circular strand of DNA. Both eggs and sperms have mitochondria, yet surprisingly those borne by sperms are eliminated after fertilization. This is seemingly counterproductive, as it removes a potential source of variability.

Current theory regarding the main genome in the cell nucleus interprets sexual reproduction as a mechanism that evolved to generate much-needed variability. Mitochondria lack sexual reproduction and replicate by simple clonal division. But one might, at the very least, expect mitochondria from both sperm and egg to persist after conception to maximize variation. Now that we know that mutations accumulate more rapidly in the male, we may have an answer to the puzzling elimination of mitochondria carried by sperms. Because the mutation rate is far higher for mitochondrial DNA than for DNA in the nucleus, sperm mitochondria may simply have too many mutations by the time fertilization occurs. The embryo may be far better off with mitochondria exclusively derived from the mother. This could explain why the male himself labels mitochondria in his sperms, marking them for execution after conception. Biologically speaking, it seems that dads have recognized that they are the risky sex.

The new study convincingly demonstrates that sperms pass along more mutations than eggs. Risks of fathers transmitting congenital defects are accordingly greater. Other research has, in fact, revealed that paternal age contributes more than maternal age to the incidence of certain psychiatric disorders, notably schizophrenia and autism. These disorders reportedly have a genetic basis, so the higher load of mutations carried by sperms may be a causal factor.

Regardless of all else, however, one thing is abundantly clear: Age effects must be studied in both sexes, not just in women.


Mutation accumulation in finite outbreeding and inbreeding populations

We have carried out an investigation of the effects of various parameters on the accumulation of deleterious mutant alleles in finite diploid populations. Two different processes contribute to mutation accumulation. In random-mating populations of very small size and with tight linkage, fixation of mutant alleles occurs at a high rate, but decreases with extremely tight linkage. With very restricted recombination, the numbers of low-frequency mutant alleles per genome in randommating populations increase over time independently of fixation (Muller's ratchet). Increased population size affects the ratchet less than the fixation process, and the decline in population fitness is dominated by the ratchet in populations of size greater than about 100, especially with high mutation rates. The effects of differences in the selection parameters (strength of selection, dominance coefficient), of multiplicative versus synergistic selection, and of different amounts of inbreeding, are complex, but can be interpreted in terms of opposing effects of selection on individual loci and associations between loci. Stronger selection slows the accumulation of mutations, though a faster decline in mean fitness sometimes results. Increasing dominance tends to have a similar effect to greater strength of selection. High inbreeding slows the ratchet, because the increased homozygous expression of mutant alleles in inbred populations has effects similar to stronger selection, and because with inbreeding there is a higher initial frequency of the least loaded class. Fixation of deleterious mutations is accelerated in highly inbred populations. Even with inbreeding, sexual populations larger than 100 will probably rarely experience mutation accumulation to the point that their survival is endangered because neither fixation nor the ratchet has effects of the magnitude seen in asexual populations. The effects of breeding system and rate of recombination on the rate of molecular evolution by the fixation of slightly deleterious alleles are discussed.


Discussion

Origin of Freshwater Populations in the Central Part of the Japanese Mainland

Our data showed that freshwater colonization in central Honshu, Japan, likely occurred at least twice. Two independent freshwater colonization events may reflect two different waves of southward dispersal of marine threespine stickleback during different glacial periods. Coldwater organisms expanded their distributions southward during glacial periods, and some populations have remained as relic populations during the interglacial periods ( Watanabe and Takahashi 2009 Hannah 2015). Because the contemporary ambient temperature of central Honshu is too high for sticklebacks ( Mori 1997 Kitano and Mori 2016), they are only able to survive in spring-fed habitats where colder temperatures are maintained by groundwater flow. Clearly the Gifu and Shiga “Hariyo” populations split from the main G. aculeatus lineage further in the past than the Nasu, Aizu, and Ono populations. Previous estimates of divergence for the Hariyo populations have suggested a split from the marine ancestor around 0.37–0.43 Ma based on the analysis of partial sequences of the mitochondrial cytochrome b gene ( Watanabe et al. 2003). Although our phylogenetic analysis is consistent with a split after the divergence between G. aculeatus and G. nipponicus which occurred 0.89 Ma ( Ravinet et al. 2018), more precise divergence time estimation is now necessary using genome-wide sequences of multiple individuals.

Our PSMC analysis showed that the Gifu and Shiga populations showed expansion of population sizes during the last glacial periods (10,000–70,000 years ago). This is consistent with the presence of a large freshwater fluvial environment in the Nobi Plain ( Watanabe et al. 2003 Watanabe and Mori 2008), where the Gifu population is distributed today. In contrast, the habitats of Ono, Nasu, and Aizu populations are small basins located at relatively high elevations (>100–200 m above the present sea levels) and surrounded by high mountains, suggesting that their distributions might have been relatively restricted to small areas even during the glacial periods compared with the Gifu and Shiga populations. Consistent with this, the Ono, Nasu, and Aizu populations substantially declined in effective population size during the last glacial period.

Reduction in Genetic Diversity in Landlocked Populations

A reduction in overall genetic diversity in the Japanese freshwater stickleback populations compared with the marine populations is consistent with previous studies on threespine sticklebacks in other geographical regions ( Withler and McPhail 1985 Mäkinen et al. 2008 Hohenlohe et al. 2010 DeFaveri et al. 2011 Jones, Chan, et al. 2012 Cassidy et al. 2013 DeFaveri and Merilä 2015 Ferchaud and Hansen 2016). Overall reduction in genetic diversity will reduce standing genetic variation, a source for adaptive evolution ( Barrett and Schluter 2008), and therefore can increase the risk of extinction of a population when it is faced with environmental change ( Frankham et al. 2010).

Furthermore, we observed variation in heterozygosity among genomic regions. First, X chromosomes showed a larger reduction in heterozygosity compared with autosomes ( fig. 3B). This pattern can be explained by the smaller effective population size of the X chromosome compared with autosomes ( Vicoso and Charlesworth 2009 Mank et al. 2010). Second, within each chromosome, chromosome centers tend to have lower genetic diversity compared with the peripheries. This can be explained by the fact that the stickleback exhibits lower recombination rates at chromosomal centers ( fig. 3C). An unexpected result was a difference in the patterns of proportion of heterozygosity near the extreme chromosomal end between the marine and freshwater populations. The moderate reduction of heterozygosity at the further end of chromosome in the marine populations is consistent with the recombination rate map, which shows reduction in recombination rates at the further end of chromosome ( Glazer et al. 2015 Sardell et al. 2018 Shanfelter et al. 2019). In contrast, freshwater populations showed a trend of gradual increase in the heterozygosity toward the chromosomal ends ( fig. 3C and supplementary fig. S4 , Supplementary Material online), although we are unsure what might have caused this difference.

Accumulation of Deleterious Mutations in Freshwater Populations

We also showed that isolated freshwater populations accumulated deleterious mutations, despite the possible purging effects of inbreeding. The Aizu, Nasu, and Ono populations not only have lower genetic diversity but also carry more putatively deleterious mutations than the Gifu and Shiga populations. Aizu, Nasu, and Ono populations belong to a phylogenetic clade different from that of the Gifu and Shiga populations ( fig. 2A) and have several unique phenotypic characteristics, such as the predominance of the partially plated morph in the Nasu population ( Yamasaki et al. 2019). Our present genomic data indicate that these unique freshwater stickleback populations are severely endangered, and any efforts to prevent further reduction in effective population size would be necessary for conserving these populations.

Comparison of deleterious mutations among chromosomes within individuals showed that X chromosomes have higher proportions of deleterious mutations than autosomes in the majority of populations examined. This is consistent with the theoretical prediction that X chromosomes have lower effective population sizes than autosomes ( Vicoso and Charlesworth 2009 Mank et al. 2010). In contrast, we found no clear intragenomic patterns of mutation loads despite the fact that the recombination rates considerably vary among chromosomal regions with the lowest levels at the center within chromosomes ( Roesti et al. 2013 Glazer et al. 2015 Sardell et al. 2018 Shanfelter et al. 2019) this is also confirmed by our result of the proportion of heterozygous sites. Several previous studies in Drosophila and primates have also shown that with the exception of regions with no recombination at all, there is no significant correlation between recombination rate and nonsynonymous mutation rate ( Haddrill et al. 2007 Bullaughey et al. 2008). Considering the inconsistency between the pattern of recombination and mutation loads, heterogeneity in deleterious mutation accumulation is likely influenced by confounding factors, such as variation in background mutation rates and the genomic location of adaptive alleles that might drive an increase in frequency of deleterious mutations via hitchhiking. Further investigation of background mutation rates and genomic location of adaptive alleles is necessary and possible using genomic tools and experimental approaches ( Makova and Hardison 2015 Lynch et al. 2016 Peichel and Marques 2017). However, it should also be noted that current recombination rate maps in sticklebacks are based on a small number of individuals and not very precise, which may be a reason why we could not find a significant correlation.

Here, we used only bioinformatic methods for predicting deleterious mutations, so we cannot exclude the possibility that nonsynonymous mutations predicted to be deleterious are actually neutral or even adaptive for freshwater populations. For example, we found that deleterious mutations occurred on the two genes (ENSGACG00000007767 and ENSGACG00000016736) in different freshwater populations but not in the marine population. Mutations of these genes may be adaptive for freshwater residency, or these genes are not important for freshwater residency and therefore under relaxed selection. However, because we showed that deleterious mutations are less likely shared among freshwater populations than nonsynonymous mutations that are predicted to be neutral ( fig. 5), we suggest that the majority of mutations predicted to be deleterious likely cause reductions in fitness and have been purged to some extent in each population. Furthermore, we have shown that dN/dS is higher in genes with putatively deleterious mutations ( supplementary fig. S7 , Supplementary Material online), supporting the idea that relaxed negative selection increases the accumulation of deleterious mutations. Interestingly, many individual deleterious mutations are not shared but the genes containing mutations have a higher probability of being shared among freshwater populations ( fig. 5C and supplementary fig. S8 , Supplementary Material online). Because negative selection is likely to independently purge deleterious mutations on the same genes, genes under relaxed negative selection in freshwater environment may carry independent deleterious mutations that have escaped purging.

In conclusion, we showed that whole-genome sequencing of endangered populations can inform us of the accumulation of deleterious mutations. This information will help to infer which populations are the most severely endangered. Furthermore, information on the regional variations in deleterious mutation loads across the genome can give insight into not only sex chromosome evolution but also genomics of adaptation and speciation. In genomic analysis of adaptation and speciation, regions with low genetic diversity within a population and/or high genetic differentiation between populations are often identified as candidate regions contributing to local adaptation and reproductive isolation ( Nosil 2012 Ravinet et al. 2017 Hahn 2019). However, negative selection against deleterious mutations can also reduce within population genetic diversity (i.e., via background selection) and also inflate statistics of genetic differentiation such as FST ( Noor and Bennett 2009 Cruickshank and Hahn 2014 Ravinet et al. 2017). The chromosomal distribution of deleterious mutations has been relatively underrepresented in genomic analysis of natural populations thus far but could act as a proxy for quantifying the strength of negative background selection across the genome. An important future direction is to investigate how well the distribution of the bioinformatically identified deleterious mutations reflect the strength of negative selection and the level of association with regions that are identified as targets of selection.


Conclusions

The evolution of aging ultimately requires genetic variants with deleterious late-life acting effects. If these mutations primarily have beneficial or neutral effects early in life has been vividly debated, while mutations already expressing smaller deleterious effects early in life have only rarely been considered [30, 33, 44,45,46]. Our study does however demonstrate that deleterious mutations indeed can have negative effects that amplify with age and hence that increasing negative effect size could be a common property of deleterious mutations. Expressing a deleterious effect early in life exposes these mutations to negative selection to a much higher extent than those having a neutral effect early in life, keeping them at a considerably lower frequency, on a per locus basis, at mutation-selection-drift balance. The genome-wide influx of this type of aging mutation may however largely exceed those suggested by established aging theories, since logic suggests that most deleterious mutations should already manifest their harmful effect early in life, as most genes are expressed throughout life and show little expression dynamics during adulthood [76, 77]. If future studies on a wider diversity of mutations corroborate our findings, the pool of mutations we know to be contributing to senescence will be substantially expanded.


5. Conclusions

We have provided an overview of the nature of mutations and theories that describe their fate once they have entered a population. Much work remains to be done, however, in order to integrate existing theories more fully and to better understand their implications. We have shown that such work is important for questions of practical interest, such as how fast species can adapt to new environments, how genetic factors can contribute to their extinction and what consequences follow from man-made technology driven increases in mutation rates that may unintentionally increase genetic diseases in humans as well as threaten the survival of endangered species. Mutations, however, also provide the raw material for the improvement of plants and animals for food production, and we need to know how best to use them. The population genetics of mutations is undoubtedly central to many theoretical and applied questions in biology.


Worrisome New Coronavirus Strains Are Emerging. Why Now?

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Toward the end of last year, doctors in Nelson Mandela Bay, a city of about 1 million people in the Eastern Cape of South Africa, started to see something alarming. The city had been hit by a tsunami of Covid-19 cases in June and July, swamping hospitals and leading to thousands of deaths. That wave began to subside as winter turned to spring in the southern hemisphere. But starting in November, hospitals in the city and its surrounding province began to fill up with Covid-19 patients again—this time twice as fast they had during the first surge.

To figure out what was going on with the steep uptick in new cases, doctors at those hospitals enlisted the help of Tulio de Oliveira, a geneticist and bioinformatician at the University of KwaZulu-Natal in Durban who leads a national network of sequencing labs. His team began piecing together the genomes of the coronavirus that had caused each person’s infection. For months, these researchers had been periodically doing similar genomic surveillance work to keep tabs on the dozens of strains of SARS-CoV-2 that were circulating around the country, looking for any problematic mutations in the virus’s spike protein. Eight months into the pandemic, in 99 percent of the more than 1,500 genomes they’d sequenced, they’d only found one such mutation. De Oliveira was in the process of submitting those findings to a journal.

Then, on December 1, the first results came back from Nelson Mandela Bay.

In each of the 16 samples gathered from 15 clinics around the city, the viruses all possessed a near identical constellation of mutations unlike any that had ever before been seen in South Africa. And eight of those mutations were in the spike protein. “Literally the day before I had written, ‘The spike genome in South Africa is very stable,’” de Oliveira told WIRED in an interview. “Then I saw this new cluster and I thought, ‘Wow, that has changed.’”

Everything You Need to Know About the Coronavirus

He walked upstairs, to the office of South Africa’s corollary to Anthony Fauci, an epidemiologist named Salim Abdool Karim, to tell him the news. Days later, they alerted the World Health Organization. Now on the lookout, scientists in the United Kingdom soon discovered one of those mutations spreading in the southeast part of Britain. A few weeks later, an eerily similar cluster of genetic changes surfaced among travelers from Brazil. But neither was a case of jet-setters seeding a single new strain around the world. Analyses of global coronavirus genome databases showed that these were in fact three distinct versions of the virus—three distantly related branches of the SARS-CoV-2 family tree that had independently acquired some of the same mutations despite emerging on three different continents.

That pattern is what scientists refer to as “convergent evolution,” and it’s a sign of trouble ahead.

All viruses mutate. They are, after all, just autonomous bits of protein-encased, self-replicating strings of code equipped with imperfect internal spell-checkers. Make enough copies and there are bound to be mistakes. Coronaviruses actually make fewer mistakes than most. This one, SARS-CoV-2, evolves at a rate of about 1,100 changes per location in the genome annually—or about one substitution every 11 days.

The predictable pace at which the coronavirus’s genetic building blocks shift around can be detected by genomic sequencing, which allows scientists to identify new strains and follow them as they spread through a population or fade away. For most of 2020, those random changes didn’t have much of an effect on the way the virus behaves. But recently, three notable mutations have begun to show up alone or in combination with each other. And everywhere they do, these versions of the virus tend to quickly outcompete other circulating strains.

“That suggests there’s an advantage to these mutations,” says Stephen Golstein, an evolutionary virologist who studies coronaviruses at the University of Utah. “Every SARS-CoV-2 variant ‘wants to be more transmissible,’ in a sense. So the fact that so many of them are landing on these mutations suggest there could be a real benefit for doing so. These different lineages are essentially arriving at the same solution for how to interact more efficiently with the human receptor, ACE2.”

Like any virologist, Goldstein is hesitant to anthropomorphize his subjects. Viruses don’t have dreams and desires. They’re intelligent micromachines programmed to make as many copies of themselves as possible. But one way to do that is to increase their odds of invading new hosts. SARS-CoV-2 does that by guiding the array of spike proteins that coat its exterior toward a protein called ACE2 that sits on the outside of some human cells. The spike is encrusted in sugars which camouflage the virus from the human immune system, except for the very tip, known as the receptor binding domain, or RBD for short. This exposed section is the part that latches onto ACE2, changing the receptor’s shape—like a key rearranging the tumblers inside a lock—and allowing the virus to enter the cell and start replicating.

The mutations that have scientists so worried all occur in that little exposed bit of spike. And now researchers are racing to figure out how each of them might be giving SARS-CoV-2 some new tricks.

There’s N501Y, a mutation that occurs in all three variants, which replaces the coronavirus’s 501st amino acid, asparagine, with tyrosine. Studies in cells and animal models suggest that the change makes it easier for SARS-CoV-2 to grab onto ACE2, which is one hypothesis for why the variant has been, at this point, pretty convincingly associated with increased transmission. The best evidence for that so far has come out of the UK, which is doing more genomic sequencing than any other country in the world. Scientists there estimate that the UK variant, alternatively known as B.1.1.7, is between 30 and 50 percent more infectious than other circulating strains.

In Ireland, it became the dominant version of the virus in just a few weeks, and it has since spread to more than 60 countries, including the US. As of Tuesday, the US had detected 293 cases of the UK variant, according to data from the US Centers for Disease Control and Prevention. The agency estimates it will become dominant in the US by March.

A Brazilian variant, also called P1, and the South African one, sometimes called B.1.351, also have a second and third mutation in common: K417T and E484K. At this moment, scientists know more about the latter. It changes an amino acid that was negatively charged to one that’s positively charged. In variants without this mutation, that section of the RBD sits across from a negatively charged stretch of ACE2, so they repel away from each other. But the E484K mutation reverses that charge, making them snap tightly together instead.

On Monday, Minnesota reported the US’ first case of the Brazil variant, but so far no cases of the South African variant have yet been confirmed in the US.

Scientists at the Fred Hutchinson Cancer Research Center found that E484K might be the most important alteration when it comes to enhancing the virus’s ability to evade immune defenses. In lab experiments, they observed that antibodies in the blood of recovered Covid-19 patients were 10 times less effective at neutralizing variants possessing the E484K mutation. In a separate study, some of De Oliveira’s colleagues tested the blood from Covid-19 patients who fell ill in South Africa’s first wave, and they found that 90 percent of them had some reduced immunity to the new E484K-containing variant. In nearly half of the samples, the new variant escaped the preexisting antibodies completely. Another study by another South African colleague, this time using live virus, found similar results. (All are being shared as preprints—neither has yet been peer-reviewed, as has become common in the age of Covid.)

“All the evidence is starting to point in the same direction,” says de Oliveira. “We have a virus that is much less neutralized by convalescent plasma.” It’s still too soon to tell what that means in the real world. True reinfections are notoriously difficult to pin down. Scientists have to sequence samples taken from the first bout of illness and the second, and then compare the genetic signatures to determine if a different viral variant is responsible for each infection. De Oliveira says his group is in the process of doing that right now, and they’re finding many instances of what appear to be real reinfections with the South African variant. That data is not yet published. And until they sequence more samples, they can’t say whether B.1.351 is causing more reinfections than previous versions of the virus, which would be a sign that herd immunity might be much farther off than previously thought.

Researchers in Brazil have also found evidence of at least one reinfection with the new P1 lineage, but data there is even sparser. Some reinfections are to be expected, says William Hanage, an infectious disease epidemiologist at the Harvard T. H. Chan School of Public Health. The important thing is whether there are more reinfections with the new variant than the models would expect.

Still, that these worrying mutations are all cropping up in the same region of the spike protein is not a coincidence, says Goldstein. Of all the places in the coronavirus’s genome, the RBD is the least stable. “That’s because, historically, it’s been under the most evolutionary pressure to change,” he says. It may feel like the Covid-19 pandemic has been happening forever. But in evolutionary terms, it’s been but a blink.

Before SARS-CoV-2 crossed into humans, it had been circulating inside bats for millions of years. And when scientists began taking a closer look at the bat version of ACE2, they found a staggering diversity of the gene that codes for that protein. What they were seeing were the genetic scars of an evolutionary arms race. Bat populations had lived with SARS-CoV-2 for long enough that their ACE2 receptors had started changing—morphing in shape so that they became harder for the virus to grab onto. And in turn, SARS-CoV-2 had evolved to try to fit into those new shapes. Eventually, one of those descendants looked enough like the human ACE2 receptor that it could make the cross-species leap (with perhaps an intermediary host in there somewhere).

There are two major evolutionary forces driving diversification of the spike protein: interacting with ACE2, and getting clobbered by neutralizing antibodies. In the human population, a year isn’t long enough for new versions of ACE2 to crop up and be passed on to a new generation of people. And ACE2 plays a key role in regulating blood pressure, wound healing, and other essential functions, so any genetic changes that impair its ability to do those things would likely not get very far, even if they made it more difficult for the coronavirus to start an infection.

So if the evolution of the ACE2 receptor can’t rescue us in the short term, that leaves the body’s immune system, and the armies of cells that orchestrate ejecting any unwanted visitors from it. Many pathogens mutate their proteins toward new shapes to avoid being recognized by the antibodies that would normally adhere to them, blocking their entry into cells. That’s called antigenic drift. And that’s what some scientists think drove the emergence of the Brazil and South African variants.


"Genetic Entropy" is BS: A Summary

The idea of “genetic entropy” is one of a very few “scientific” ideas to come from creationists. It’s the idea that humanity must be very young because harmful mutations are accumulating at a rate that will ultimately lead to our extinction, and so we, as a species, can’t be any older than a few thousand years. Therefore, creation. John Sanford proposed and tried to support this concept in his book “Genetic Entropy & The Mystery of the Genome,” which is…wow it’s bad. EDIT: If you want to read "Genetic Entropy," you can find it here (pdf). It's a quick read, and probably worth the time if you want to be familiar with the argument. Might as well get it from the source.

Everything about the genetic entropy argument is wrong, including the term itself. But it comes up over and over and over, including here, repeatedly, I think because it’s one of the few sciencey-sounding creationist arguments out there. So join me as we quickly cover each reason why "genetic entropy" is BS.

I’m going to do this in two parts. First we’ll have a bunch of quick points, and after, I’ll elaborate on the ones that merit a longer explanation. Each point will be labeled “P1”, “P2”, etc., as will each longer explanation. So if you want to find the long version, just control-f the P# for that point.

P1: “Genetic entropy” is a made-up term invented by creationists to describe a concept that already existed: Error catastrophe. Even before it’s a vaguely scientific idea, the term “genetic entropy” is an attempt at branding, to make a process seem more dangerous or inevitable through changing the name. I’m going to use the term “error catastrophe” from here on when we’re talking about the actual population genetics phenomenon, and “genetic entropy” when talking about the silly creationist idea.

P2: Error catastrophe has never been observed or documented in nature or experimentally. In order to conclusively demonstrate error catastrophe, you must show these two things: That harmful mutations accumulate in a population over generations, and that these mutations cause a terminal decline in fitness, meaning that they cause the average reproductive output to fall below 1, meaning the population is shrinking, and will ultimately go extinct.

This has never been demonstrated. There have been attempts to induce error catastrophe experimentally, and Sanford claims that H1N1 experienced error catastrophe during the 20th century, but all of these attempts have been unsuccessful and Sanford is wrong about H1N1 in every way possible.

P3: The process through which genetic entropy supposedly occur is inherently contradictory. Either neutral mutations are not selected against and therefore accumulate, or harmful mutations are selected against, and therefore don’t accumulate. Mutations cannot simultaneously hurt fitness and not be selected against.

P4: As deleterious mutations build up, the percentage of possible subsequent mutations that are harmful decreases, and the percentage of possible beneficial mutations increases. The simplest illustration is to look at a single site. Say a C mutates to a T and that this is harmful. Well now that harmful C-->T mutation is off the table, and a new beneficial T-->C mutation is possible. So over time, as harmful mutations accumulate, beneficial mutations become more likely.

P5: (Somewhat related to P4) A higher mutation rate provides more chances to find beneficial mutations, so even though more harmful mutations will occur, they are more likely to be selected out by novel beneficial genotypes that are found and selected for. This is slightly different from P4, which was about the proportion of mutations this is just raw numbers. More mutations means more beneficial mutations.

P6: Sanford is dishonest. His work surrounding “genetic entropy” is riddled with glaring inaccuracies that are either deliberate misrepresentations, or the result of such egregious ignorance that it qualifies as dishonesty.

Two of the most glaring examples are his misrepresentation of a distribution of fitness effects produced by Motoo Kimura, and his portrayal of H1N1 fitness over time.

Below this point you’ll find more details for some of the above points.

P2: Error catastrophe has never been observed, experimentally nor in nature. There have been a number of attempts at inducing error catastrophe experimentally, but none have been successful. Some work from Crotty et al. is notable in that they claimed to have induced error catastrophe, but actually only maybe documented lethal mutagenesis, a broader term that refers to any situation in which a large number of mutations cause death or extinction. Their single round of mutagenic treatment of infectious genomes necessarily could not involve mutation accumulation over generations, and so while mutations my have caused the fitness decline, it isn’t wasn’t through error catastrophe. It’s also possible the observed fitness costs were due to something else entirely, since the mutagen they used has many effects.

J.J. Bull and his team have also worked extensively on this question, and outline their work and the associated challenges here. In short, they were not able to demonstrate terminal fitness decline due to mutation accumulation over generations, and in one series of experiments actually observed fitness gains during mutagenic treatment of bacteriophages.

You’ll notice that all of that work involves bacteriophages and mutagenic treatment. What about humans? Well, phages are the ideal targets for lethal mutagenesis, especially RNA and single-stranded DNA (ssDNA) phages. These organisms have mutation and substitution rates orders of magnitude higher than double-stranded DNA viruses and cellular organisms (pdf). They also have small, dense genome, meaning that there are very few intergenic regions, most of which contain regulatory elements, and even some of the reading frames are overlapping and offset, which means there are regions with no wobble sites.

This means that deleterious mutations should be a higher percentage of the mutation spectrum compared to, say, the human genome. So mutations happening faster plus more likely to be harmful equals ideal targets for error catastrophe.

In contrast, the human genome is only about 10% functional (<2% exons, 1% regulatory, some RNA genes, a few percent structural and spacers stuff with documented functions adds up to a bit south of 10%). It’s possible up to 15% or so has a selected function, but given what we know about the rest, any more than that is very unlikely. So the percentage of possible mutations that are harmful is far lower in the human genome compared to the viral genomes. And we have lower mutation and substitution rates.

All of that just means we’re very unlikely to experience error catastrophe, while the viruses are the ideal candidates. And if the viruses aren’t susceptible to it, then the human genome sure as hell isn’t.

But what of H1N1? Isn’t that a documented case of error catastrophe. That’s what Sanford claims, after all.

Except yeah wow that H1N1 paper is terrible. Like, it’s my favorite bad paper, because they manage to get everything wrong. Here’s a short list of the errors the authors commit:

They ignored neutral mutations.

They claimed H1N1 went extinct. It didn’t. Strains cycle in frequency. It’s called strain replacement.

They conflated intra- and inter-host selection, and in doing so categorize a bunch of mutations as harmful when they were probably adaptive.

They treated codon bias as a strong indicator of fitness. It isn’t. Translational selection (i.e. selection to match host codon preferences) doesn’t seem to do much in RNA viruses.

They ignored host-specific constraints based on immune response, specifically how mammals use CpG dinucleotides to recognize foreign DNA/RNA and trigger an immune response. In doing so, they categorized changes in codon bias as deleterious when they were almost certainly adaptive.

They conflated virulence (how sick a virus makes you) with fitness (viral reproductive success). Not the same thing. And sometimes inversely correlated.

Related, in using virulence as a proxy for fitness, they ignored the major advances in medicine from 1918 to the 2000s, including the introduction of antibiotics, which is kind of a big deal, since back then and still today, most serious influenza cases and deaths are due to secondary pneumonia infections.

So no, we’ve never documented an instance of error catastrophe. Not in the lab. Not in H1N1.

P3: “Genetic entropy” supposedly works like this: Mutations that are only a little bit harmful (dubbed “very slightly deleterious mutations” or VSDMs) occur, and because they are only a teensy bit bad, they cannot be selected out of the population. So they accumulate, and at some point, they build up to the point where they are harmful, and at that point it’s too late everybody is burdened by the harmful mutations, has low fitness, and the population ultimately goes extinct.

Here are all of the options for how this doesn’t work.

One, you could have a bunch of neutral mutations. Neutral because they have no effect on reproductive output. That’s what neutral means. They accumulate, but there are no fitness effects. So the population doesn’t go extinct – no error catastrophe.

Or you could have a bunch of harmful mutations. Individually, each with have a small effect on fitness. Individuals who by chance have these mutations have lower fitness, meaning these mutations experience negative selection. Maybe they are selected out of the population. Maybe they persist at low frequency. Either way, the population doesn’t go extinct, since there are always more fit individuals (who don’t have any of the bad mutations) present to outcompete those who do. So no error catastrophe.

Or, option three, everyone experiences a bunch of mutations all at once. All in one generation, every member of a population gets slammed with a bunch of harmful mutations, and fitness declines precipitously. The average reproductive output falls below 1, and the population goes extinct. This is also not error catastrophe. Error catastrophe requires mutations to accumulate over generations. This all happened in a single generation. It’s lethal mutagenesis, a broader process in which a bunch of mutations cause death or extinction, but it isn’t the more specific error catastrophe.

But we can do a better job making the creationist case for them. Here’s the strongest version of this argument that creationists can make. It’s not that the mutations are neutral, having no fitness effect, and then at some threshold become harmful, and now cause a fitness decline population-wide. It’s that they are neutral alone, but together, they experience epistasis, which just means that two or more mutations interact to have an effect that is different from any of them alone.

So you can’t select out individual mutations (since they’re neutral), which accumulate in every member of the population over many generations. But subsequent mutations interact (that’s the epistasis), reducing fitness across the board.

But that still doesn’t work. It just pushed back the threshold for when selection happens. Instead of having some optimal baseline that can tolerate a bunch of mutations, we have a much more fragile baseline, wherein any one of a number of mutations causes a fitness decline.

But as soon as that happens in an individual, those mutations are selected against (because they hurt fitness due to the epistatic effects). So like above, you’d need everyone to get hit all in a single generation. And a one-generation fitness decline isn’t error catastrophe.

So even the best version of this argument fails.

P4 and P5: I’m going to cover these together, since they’re pretty similar and generally work the same way.

Basically, when you have bunch of mutations, two things operate that make error catastrophe less likely than you would expect.

First, the distribution of fitness effects changes as mutations occur. When a deleterious mutation occurs, at least one deleterious mutation (the one that just occurred) is removed from the universe of possible deleterious mutations, and at least one beneficial mutation is added (the back mutation). But there are also additional beneficial mutations that may be possible now, but weren’t before, due to epistasis with that new harmful mutation. These can recover the fitness cost of that mutation, or even work together with it to recover fitness above the initial baseline. These types of mutations are called compensatory mutations, and while Sanford discusses epistasis causing harmful mutations to stack, he does not adequately weigh the effects in the other direction, as I’ve described here.

Related, when you have a ton of mutations, you’re just more likely to find the good ones. We actually have evidence that a number of organisms have been selected to maintain higher-than-expected mutations rates, probably due to the advantage this provides. My favorite example is a ssDNA bacteriophage called phiX174. It infects E. coli, but lacks the “check me” sequences that its host uses to correct errors in its own genome. By artificially inserting those sequences into the phage genome, its mutation rate can be substantially decreased. Available evidence says that selection maintains the higher mutation rate. We also see that during mutagenic treatment, viruses can actually become more fit, contrary to expectations.

So as mutations occur, beneficial mutations become more likely, and more beneficial mutations will be found. Both processes undercut the notion of “genetic entropy”.

P6: John Sanford is a liar. There’s really isn’t a diplomatic way to say it. He’s a dishonest hack who misrepresents ideas and data. I’ve covered this before, but I’ll do it again here, for completeness.

I’m only going to cover one particularly egregious example here, but see here for another I’m going to stick to the use of a distribution of mutation fitness effects from Motoo Kimura’s work, which Sanford modifies in “Genetic Entropy,” and uses to argue that beneficial mutations are too rare to undo the inevitable buildup of harmful mutations.

Now first, Sanford claims to show a “corrected” distribution, since Kimura omitted beneficial mutations entirely from his. Except this “corrected” distribution is based on nothing. No data. No experiments. Nothing. It’s literally “I think this looks about right”. Ta-da! “Corrected”. Sure.

Second, Sanford justifies his distribution by claiming that Kimura omitted beneficial mutations because he knew they are so rare they don’t really matter anyway. He wrote:

In Kimura’s figure, he does not show any mutations to the right of zero – i.e. there are zero beneficial mutations shown. He obviously considered beneficial mutations so rare as to be outside of consideration.

Kimura’s rationale was the exact opposite of this. His distribution represents the parameters for a model demonstrating genetic drift (random changes in allele frequency). He wrote:

The situation becomes quite different if slightly advantageous mutations occur at a constant rate independent of environmental conditions. In this case, the evolutionary rate can become enormously higher in a species with a very large population size than in a species with a small population size, contrary to the observed pattern of evolution at the molecular level.

In other words, if you include beneficial mutations, they are selected for and take over the simulation, completely obscuring the role genetic drift plays. So because they occur too frequently and have too great an effect, they were omitted from consideration.

Okay, let’s give Sanford the benefit of the doubt on the first go. Maybe, despite writing a book that leans heavily on Kimura’s work, and using one of Kimura’s figures, Sanford never actually read Kimura’s work, and honestly didn’t realize hat Kimura’s rationale was the exact opposite of what Sanford claims. Seems improbable, but let’s say it was an honest mistake.

The above passage (and the broader context) were specifically pointed out to Sanford, but he persisted in his claim that he was accurately representing Kimura’s work. He wrote:

Kimura himself, were he alive, would gladly attest to the fact that beneficial mutations are the rarest type

The interesting thing with that line is that it’s a slight hedge compared to the earlier statement. This indicates two things. First, that Sanford knows he’s wrong about Kimura’s rationale, and second, that he wants to continue to portray Kimura as agreeing with him, even though he clearly knows better.

There’s more in the link at the top of this section, but this is sufficient to establish that Sanford is a liar.

So that’s…I won’t say everything, because this is a deep well, but that’s a reasonable rundown of why nobody should take “genetic entropy” seriously.

Creationists, if you want to beat the genetic entropy drum, you need to deal with each one of these points. (Okay maybe not P6, unless you want to defend Sanford.) So if and when you respond, specifically state which point you dispute and why. Be specific. Cite evidence.


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